title: Acute encephalopathy with elevated CSF inflammatory markers as the initial presentation of COVID-19.

text: BACKGROUND: COVID-19 is caused by the severe acute respiratory syndrome virus SARS-CoV-2. It is widely recognized as a respiratory pathogen, but neurologic complications can be the presenting manifestation in a subset of infected patients. CASE PRESENTATION: We describe a 78-year old immunocompromised woman who presented with altered mental status after witnessed seizure-like activity at home. She was found to have SARS-CoV-2 infection and associated neuroinflammation. In this case, we undertake the first detailed analysis of cerebrospinal fluid (CSF) cytokines during COVID-19 infection and find a unique pattern of inflammation in CSF, but no evidence of viral neuroinvasion. CONCLUSION: Our findings suggest that neurologic symptoms such as encephalopathy and seizures may be the initial presentation of COVID-19. Central nervous system inflammation may associate with neurologic manifestations of disease.


title: E-cigarette-Induced Pulmonary Inflammation and Dysregulated Repair are Mediated by nAChR α7 Receptor: Role of nAChR α7 in ACE2 Covid-19 receptor regulation

text: Electronic cigarette (e-cig) vaping is increasing rapidly in the United States, as e-cigs are considered less harmful than combustible cigarettes. However, limited research has been conducted to understand the possible mechanism that mediate, toxicity and pulmonary health effects of e-cigs. We hypothesized that sub-chronic e-cig exposure induces inflammatory response and dysregulated repair/extracellular matrix (ECM) remodeling, which occur through the α7 nicotinic acetylcholine receptor (nAChR α7). Adult wild-type (WT), nAChRα7 knockout (KO), and lung epithelial cell-specific KO (nAChRα7 CreCC10) mice were exposed to e-cig aerosol containing propylene glycol (PG) with or without nicotine. Bronchoalveolar lavage fluids (BALF) and lungs tissues were collected to determine e-cig induced inflammatory response and ECM remodeling, respectively. Sub-chronic e-cig exposure with nicotine increased the inflammatory cellular influx of macrophages and T-lymphocytes including increased pro-inflammatory cytokines in BALF and increased ACE2 Covid-19 receptor, whereas nAChR α7 KO mice show reduced inflammatory responses associated with decreased ACE2 receptor. Interestingly, matrix metalloproteinases (MMPs), such as MMP2, MMP8, and MMP9 were altered both at the protein and mRNA transcript levels in female and male, but WT mice exposed to PG alone showed a sex-dependent phenotype. Moreover, MMP12 was increased significantly in male mice exposed to PG with or without nicotine in a nAChR α7-dependent manner. Additionally, sub-chronic e-cig exposure with or without nicotine altered the abundance of ECM proteins, such as collagen and fibronectin significantly in a sex-dependent manner, but without the direct role of nAChR α7 gene. Overall, sub-chronic e-cig exposure with or without nicotine affected lung inflammation and repair responses/ECM remodeling, which were mediated by nAChR α7 in a sex-dependent manner.


title: Pre-Clinical Remote Undergraduate Medical Education During the COVID-19 Pandemic: A Survey Study

text: Background : The COVID-19 pandemic has necessitated a sudden transition to remote learning in medical schools. We aimed to assess student perceptions of remote learning during the pre-clinical curricular training phase. Methods: A survey was distributed to first- and second-year medical students enrolled at the University of California San Diego School of Medicine in March 2020. Frequencies of responses to structured multiple-choice questions were compared regarding impacts of remote learning on quality of instruction and ability to participate, value of various remote learning resources, living environment, and preparedness for subsequent stages of training. Responses to open-ended questions about strengths and weaknesses of the remote curriculum and overall reflections were coded for thematic content. Results: Of 268 students enrolled, 104 responded (53.7% of first-year students and 23.9% of second-year students). Overall, students felt the quality of instruction and their ability to participate had been negatively affected. Most (64.1%) preferred the flexibility of learning material at their own pace. Only 25.5% of respondents still felt connected to the medical school or classmates. Most second-year students (56.7%) felt their preparation for the USMLE Step 1 exam was negatively affected and 43.3% felt unprepared to begin clerkships. In narrative responses, most appreciated the increased flexibility of remote learning but recognized that digital fatigue, decreased ability to participate, and lack of clinical skills and hands-on lab learning were notable deficits. Conclusions: Videocasted lectures uploaded in advance, electronic health record and telehealth training for students, and training for teaching faculty to increase technological fluency may be considered to optimize remote learning curricula.


title: Evolution of effective serial interval of SARS-CoV-2 by non-pharmaceutical interventions

text: Studies of novel coronavirus disease (COVID-19) have reported varying estimates of epidemiological parameters such as serial intervals and reproduction numbers. By compiling a unique line-list database of transmission pairs in mainland China, we demonstrated that serial intervals of COVID-19 have shortened substantially from a mean of 7.8 days to 2.6 days within a month. This change is driven by enhanced non-pharmaceutical interventions, in particular case isolation. We also demonstrated that using real-time estimation of serial intervals allowing for variation over time would provide more accurate estimates of reproduction numbers, than by using conventional definition of fixed serial interval distributions. These findings are essential to improve the assessment of transmission dynamics, forecasting future incidence, and estimating the impact of control measures.


title: Epidemiologic, Clinical, and Laboratory Findings of the COVID-19 in the current pandemic

text: BACKGROUND: The COVID-19 caused the pandemic affected the world deeply, with more than 3,000,000 people infected and nearly 200,000 deaths. This article aimed to summarize the epidemiologic traits, clinical spectrum, CT results and laboratory findings of COVID-19 pandemic. METHODS: We scoped for relevant literatures published during 1st Dec 2019 to 23rd Apr 2020 based on four databases by using English and Chinese. The evidence was synthesized narratively. RESULTS: The COVID-19 pandemic was found to have a higher transmission rate compared to SARS and MERS, and involved 4 stages of evolution. The basic reproduction number (R0) is 3.32 (95% CI:3.24–3.39) and the incubation period was 5.24 days (95% CI:3.97–6.50, 5 studies) on average, and the average time for symptoms onset varied by countries. Common clinical spectrums identified included fever (38.1–39.0°C), cough and fatigue, with Acute Respiratory Distress Syndrome (ARDS) being the most common complication reported. Body temperatures above 39.0 °C, dyspnea, and anorexia were more common symptoms in severe patients. Aged over 60 years old, having co-morbidities, and developing complications were the commonest high-risk factors associated with severe conditions. Leucopenia and lymphopenia were the most common signs of infection while liver and kidney damage were rare but may cause bad outcomes for patients. The bilateral, multifocal Ground-Glass Opacification (GGO) on peripheral, and the consolidative pulmonary opacity were the most frequent CT results and the tendency of mortality rates differed by region. CONCLUSIONS: We provided a bird’s-eye view of the COVID-19 during the current pandemic, which will help better understanding the key traits of the disease. The findings could be used for disease’s future research, control and prevention.


title: The COVID-19 Gene and Drug Set Library

text: The coronavirus (CoV) severe acute respiratory syndrome (SARS)-CoV-2 (COVID-19) pandemic has received rapid response by the research community to offer suggestions for repurposing of approved drugs as well as to improve our understanding of the COVID-19 viral life cycle molecular mechanisms. In a short period, tens of thousands of research preprints and other publications have emerged including those that report lists of experimentally validated drugs and compounds as potential COVID-19 therapies. In addition, gene sets from interacting COVID-19 virus-host proteins and differentially expressed genes when comparing infected to uninfected cells are being published at a fast rate. To organize this rapidly accumulating knowledge, we developed the COVID-19 Gene and Drug Set Library (https://amp.pharm.mssm.edu/covid19/), a collection of gene and drug sets related to COVID-19 research from multiple sources. The COVID-19 Gene and Drug Set Library is delivered as a web-based interface that enables users to view, download, analyze, visualize, and contribute gene and drug sets related to COVID-19 research. To evaluate the content of the library, we performed several analyses including comparing the results from 6 in-vitro drug screens for COVID-19 repurposing candidates. Surprisingly, we observe little overlap across these initial screens. The most common and unique hit across these screen is mefloquine, a malaria drug that should receive more attention as a potential therapeutic for COVID-19. Overall, the library of gene and drug sets can be used to identify community consensus, make researchers and clinicians aware of the development of new potential therapies, as well as allow the research community to work together towards a cure for COVID-19.


title: Genetic correlates of psychological responses to the COVID-19 crisis in young adult twins in Great Britain

text: We investigated how the COVID-19 crisis and the extraordinary experience of lockdown affected young adults in England and Wales psychologically. One month after lockdown commenced (T2), we assessed 30 psychological and behavioural traits in 4,000 twins in their mid-twenties and compared their responses to the same traits assessed in 2018 (T1). Mean changes from T1 to T2 were modest and inconsistent: just as many changes were in a positive as negative direction. Twin analyses revealed that genetics accounted for about half of the reliable variance at T1 and T2. Genetic factors correlated on average .86 between T1 and T2 and accounted for over half of the phenotypic stability. Systematic environmental influences had negligible impact on T1, T2 or T2 change. Rather than the crisis fundamentally changing people psychologically, our results suggest that genetic differences between individuals play a fundamental role in shaping psychological and behavioural responses to the COVID-19 crisis.


title: Age-dependent assessment of genes involved in cellular senescence, telomere and mitochondrial pathways in human lung tissue of smokers, COPD and IPF: Associations with SARS-CoV-2 COVID-19 ACE2-TMPRSS2-Furin-DPP4 axis

text: Aging is one of the key contributing factors for chronic obstructive pulmonary diseases (COPD) and other chronic inflammatory lung diseases. Cigarette smoke is a major etiological risk factor that has been shown to alter cellular processes involving mitochondrial function, cellular senescence and telomeric length. Here we determined how aging contribute to the alteration in the gene expression of above mentioned cellular processes that play an important role in the progression of COPD and IPF. We hypothesized that aging may differentially alter the expression of mitochondrial, cellular senescence and telomere genes in smokers and patients with COPD and IPF compared to non-smokers. Total RNA from human lung tissues from non-smokers, smokers, and patients with COPD and IPF were processed and analyzed based on their ages (younger: <55 yrs and older: >55 yrs). NanoString nCounter panel was used to analyze the gene expression profiles using a custom designed codeset containing 112 genes including 6 housekeeping controls (mitochondrial biogenesis and function, cellular senescence, telomere replication and maintenance). mRNA counts were normalized, log2 transformed for differential expression analysis using linear models in the limma package (R/Bioconductor). Data from non-smokers, smokers and patients with COPD and IPF were analyzed based on the age groups (pairwise comparisons between younger vs. older groups). Several genes were differentially expressed in younger and older smokers, and patients with COPD and IPF compared to non-smokers which were part of the mitochondrial biogenesis/function (HSPD1, FEN1, COX18, COX10, UCP2 & 3), cellular senescence (PCNA, PTEN, KLOTHO, CDKN1C, TNKS2, NFATC1 & 2, GADD45A) and telomere replication/maintenance (PARP1, SIRT6, NBN, TERT, RAD17, SLX4, HAT1) target genes. Interestingly, NOX4 and TNKS2 were increased in the young IPF as compared to the young COPD patients. Genes in the mitochondrial dynamics and other quality control mechanisms like FIS1 and RHOT2 were decreased in young IPF compared to their age matched COPD subjects. ERCC1 (Excision Repair Cross-Complementation Group 1) and GADD45B were higher in young COPD as compared to IPF. Aging plays an important role in various infectious diseases. Elderly patients with chronic lung disease and smokers were found to have high incidence and mortality rates in the current pandemic of SARS-CoV-2 infection. Immunoblot analysis in the lung homogenates of smokers, COPD and IPF subjects revealed increased protein abundance of important proteases and spike proteins like TMPRSS2, furin and DPP4 in association with a slight increase in SARS-CoV-2 receptor ACE2 levels. This may further strengthen the observation that smokers, COPD and IPF subjects are more prone to COVID-19 infection. Overall, these findings suggest that altered transcription of target genes that regulate mitochondrial function, cellular senescence, and telomere attrition add to the pathobiology of lung aging in COPD and IPF and other smoking-related chronic lung disease in associated with alterations in SARS-CoV-2 ACE2-TMPRSS2-Furin-DPP4 axis for COVID-19 infection.


title: COVID-19: Famotidine, Histamine, Mast Cells, and Mechanisms

text: SARS-CoV-2 infection is required for COVID-19, but many signs and symptoms of COVID-19 differ from common acute viral diseases. Currently, there are no pre- or post-exposure prophylactic COVID-19 medical countermeasures. Clinical data suggest that famotidine may mitigate COVID-19 disease, but both mechanism of action and rationale for dose selection remain obscure. We explore several plausible avenues of activity including antiviral and host-mediated actions. We propose that the principal famotidine mechanism of action for COVID-19 involves on-target histamine receptor H (2) activity, and that development of clinical COVID-19 involves dysfunctional mast cell activation and histamine release.


title: SARS-CoV2 drives JAK1/2-dependent local and systemic complement hyper-activation

text: Patients with coronavirus disease 2019 (COVID-19) present with a range of devastating acute clinical manifestations affecting the lungs, liver, kidneys and gut. The best-characterized entry receptor for the disease-causing virus SARS-CoV2, angiotensin converting enzyme (ACE) 2, is highly expressed in these tissues. However, the pathways that underlie the disease are still poorly understood. Here we show that the complement system is unexpectedly one of the intracellular pathways most highly induced by SARS-CoV2 infection in lung epithelial and liver cells. Within cells of the bronchoalveolar lavage of patients, distinct signatures of complement activation in myeloid, lymphoid and epithelial cells tracked with disease severity. Modelling the regulome of host genes induced by COVID-19 and the drugs that could normalize these genes both implicated the JAK1/2-STAT1 signaling system downstream of type I interferon receptors, and NF-kB. Ruxolitinib, a JAK1/2 inhibitor and the top predicted pharmaceutical candidate, normalized interferon signature genes, IL-6 (the best characterized severity marker in COVID-19) and all complement genes induced by SARS-CoV2, but did not affect NF-kB-regulated genes. We predict that combination therapy with JAK inhibitors and other agents with the potential to normalize NF-kB-signaling, such as anti-viral agents, may serve as an effective clinical strategy.


title: Prognostic value of Cardiac Biomarkers in COVID-19 Infection: A Meta-analysis

text: Background Multiple Biomarkers have recently been shown to be elevated in COVID-19, a respiratory infection with multi-organ dysfunction; however, information regarding the prognostic value of cardiac biomarkers as it relates to disease severity and cardiac injury are inconsistent. Research Question The goal of this meta-analysis was to summarize the evidence regarding the prognostic relevance of cardiac biomarkers from data available in published reports. Study Design and Methods PubMed was searched from inception through April 2020 for studies comparing median values of cardiac biomarkers in critically ill versus non-critically ill COVID-19 patients, or patients who died versus those who survived. The weighted mean differences (WMD) and 95% confidence interval (CI) between the groups were calculated for each study and combined using a random effects meta-analysis model. The odds ratio (OR) for mortality based on cardiac injury was combined from studies reporting it. Results Troponin levels were significantly higher in COVID-19 patients who died or were critically ill versus those who were alive or not critically ill (WMD 0.58, 95% CI 0.42-0.71, p<0.001). Cardiac injury was independently associated with significantly increased odds of mortality (OR 6.641, 95% CI 1.26 - 35.1, p=0.03). No difference in BNP was seen between the two groups. A significant difference in levels of D-dimer was seen in those who died or were critically ill. CK levels were only significantly higher in those who died versus those who were alive (WMD 0.47 95% CI 0.09-0.84, p=0.014). Interpretation Cardiac biomarkers add prognostic value to the determination of the severity of COVID-19 and can predict mortality.


title: B cell clonal expansion and convergent antibody responses to SARS-CoV-2

text: During virus infection B cells are critical for the production of antibodies and protective immunity. Establishment of a diverse antibody repertoire occurs by rearrangement of germline DNA at the immunoglobulin heavy and light chain loci to encode the membrane-bound form of antibodies, the B cell antigen receptor. Little is known about the B cells and antigen receptors stimulated by the novel human coronavirus SARS-CoV-2. Here we show that the human B cell compartment in patients with diagnostically confirmed SARS-CoV-2 and clinical COVID-19 is rapidly altered with the early recruitment of B cells expressing a limited subset of V genes, and extensive activation of IgG and IgA subclasses without significant somatic mutation. We detect expansion of B cell clones as well as convergent antibodies with highly similar sequences across SARS-CoV-2 patients, highlighting stereotyped naïve responses to this virus. A shared convergent B cell clonotype in SARS-CoV-2 infected patients was previously seen in patients with SARS. These findings offer molecular insights into shared features of human B cell responses to SARS-CoV-2 and other zoonotic spillover coronaviruses.


title: A Novel RP-HPLC-DAD Method Development for Anti-Malarial and COVID-19 Hydroxy Chloroquine Sulfate Tablets and Profiling of In-Vitro Dissolution in Multimedia.

text: Hydroxychloroquine sulfate is one of a large series of 4-aminoquinolines with antimalarial activity. Moreover, it is used for the treatment of rheumatoid arthritis. Sometimes Hydroxychloroquine sulfate is very effective for the treatment of autoimmune diseases. Based on the recent clinical experiments it is exploiting for the treatment of COVID-19, corona virus across the globe. A Reverse phase RP-HPLC method have been developed and validated as per the current ICH guidelines for estimation of Hydroxychloroquine sulfate tablets. As part of method validation specificity, linearity, precision and recovery parameters were verified. The concentration and area relationships were linear (R (2) > 0.999), over the concentration range of 25 to 300 µg mL (-1) for HCQ. The relative standard deviations for precision and intermediate precision were less than 1.5%. The proposed RP-HPLC generic method was applied successfully for evaluation of invitro dissolution profile with different pH conditions like 0.1N HCl, pH 4.5 Acetate buffer and pH 6.8 Phosphate buffers of US marketed reference product.


title: Can Hyperperfusion of Nonaerated Lung Explain COVID-19 Hypoxia?

text: Early stages of the novel coronavirus disease (COVID-19) have been associated with ‘silent hypoxia’ and poor oxygenation despite relatively small fractions of afflicted lung. Although it has been speculated that such paradoxical findings may be explained by impairment of hypoxic pulmonary vasoconstriction in infected lungs regions, no studies have confirmed this hypothesis nor determined whether such extreme degrees of perfusion redistribution are physiologically plausible. Here, we present a mathematical model which provides evidence that the extreme amount of pulmonary shunt observed in patients with early COVID-19 is not plausible without hyperperfusion of the relatively small fraction of injured lung, with three-fold increases in regional perfusion to afflicted regions. Although underlying perfusion heterogeneity (e.g., due to gravity or pulmonary emboli) exacerbated existing shunt in the model, the reported severity of hypoxia in early COVID-19 patients could not be replicated without considerable reduction of vascular resistance in nonoxygenated regions.


title: Potentially repurposable drugs for COVID-19 identified from SARS-CoV-2 Host Protein Interactome

text: We previously presented the protein-protein interaction network - the ‘HoP’ or the host protein interactome - of 332 host proteins that were identified to interact with 27 nCoV19 viral proteins by Gordon et al. Here, we studied drugs targeting the proteins in this interactome to identify whether any of them may potentially be repurposable against SARS-CoV-2. We studied each of the drugs using the BaseSpace Correlation Engine and identified those that induce gene expression profiles negatively correlated with SARS-associated expression profile. This analysis resulted in 20 drugs whose differential gene expression (drug versus normal) had an anti-correlation with differential expression for SARS (viral infection versus normal). These included drugs that were already being tested for their clinical activity against SARS-CoV-2, those with proven activity against SARS-CoV/MERS-CoV, broad-spectrum antiviral drugs, and those identified/prioritized by other computational re-purposing studies. In summary, our integrated computational analysis of the HoP interactome in conjunction with drug-induced transcriptomic data resulted in drugs that may be repurposable for COVID-19.


title: Interactome of SARS-CoV-2 / nCoV19 modulated host proteins with computationally predicted PPIs

text: World over, people are looking for solutions to tackle the pandemic coronavirus disease (COVID-19) caused by the virus SARS-CoV-2/nCoV-19. Notable contributions in biomedical field have been characterizing viral genomes, host transcriptomes and proteomes, repurposable drugs and vaccines. In one such study, 332 human proteins targeted by nCoV19 were identified. We expanded this set of host proteins by constructing their protein interactome, including in it not only the known protein-protein interactions (PPIs) but also novel, hitherto unknown PPIs predicted with our High-precision Protein-Protein Interaction Prediction (HiPPIP) model that was shown to be highly accurate. In fact, one of the earliest discoveries made possible by HiPPIP is related to activation of immunity upon viral infection. We found that several interactors of the host proteins are differentially expressed upon viral infection, are related to highly relevant pathways, and that the novel interaction of NUP98 with CHMP5 may activate an antiviral mechanism leading to disruption of viral budding. We are making the interactions available as downloadable files to facilitate future systems biology studies and also on a web-server at http://hagrid.dbmi.pitt.edu/corona that allows not only keyword search but also queries such as “PPIs where one protein is associated with ‘virus’ and the interactors with ‘pulmonary’”.


title: An in-silico approach to develop of a multi-epitope vaccine candidate against SARS-CoV-2 envelope (E) protein

text: Since the first appearance of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS- CoV-2) in China on December 2019, the world has now witnessed the emergence of the SARS- CoV-2 outbreak. Therefore, due to the high transmissibility rate of virus, there is an urgent need to design and develop vaccines against SARS-CoV-2 to prevent more cases affected by the virus. In this study, a computational approach is proposed for vaccine design against the envelope (E) protein of SARS-CoV-2, which contains a conserved sequence feature. First, we sought to gain potential B-cell and T-cell epitopes for vaccine designing against SARS-CoV-2. Second, we attempted to develop a multi-epitope vaccine. Immune targeting of such epitopes could theoretically provide defense against SARS-CoV-2. Finally, we evaluated the affinity of the vaccine to major histocompatibility complex (MHC) molecules to stimulate the immune system response to this vaccine. We also identified a collection of B-cell and T-cell epitopes derived from E proteins that correspond identically to SARS-CoV-2 E proteins. The in-silico design of our potential vaccine against E protein of SARS-CoV-2 demonstrated a high affinity to MHC molecules, and it can be a candidate to make a protection against this pandemic event.


title: Identification of new drug treatments to combat COVID19: A signature-based approach using iLINCS

text: The COVID-19 pandemic caused by the novel SARS-CoV-2 is more contagious than other coronaviruses and has higher rates of mortality than influenza. As no vaccine or drugs are currently approved to specifically treat COVID-19, identification of effective therapeutics is crucial to treat the afflicted and limit disease spread. We deployed a bioinformatics workflow to identify candidate drugs for the treatment of COVID-19. Using an “omics” repository, the Library of Integrated Network-Based Cellular Signatures (LINCS), we simultaneously probed transcriptomic signatures of putative COVID-19 drugs and signatures of coronavirus-infected cell lines to identify therapeutics with concordant signatures and discordant signatures, respectively. Our findings include three FDA approved drugs that have established antiviral activity, including protein kinase inhibitors, providing a promising new category of candidates for COVID-19 interventions.


title: Experimenting with At-Home General Chemistry Laboratories During the COVID-19 Pandemic

text: [Image: see text] During the COVID-19 pandemic, an at-home laboratory program was created and implemented for a section of the general chemistry course at the University of Southern California. The experiments were designed to only utilize safe household items and no special equipment. These laboratory activities, spanning over 4 weeks, focused on concepts usually covered in the final one-third of our second-semester chemistry laboratory, including pH, acid–base titrations, buffers, solubility, phase equilibria, and thermodynamics. In this article, we describe the design of the laboratories and our experience with this experiment, while also providing an assessment on how similar activities could be integrated profitably into a regular general chemistry course.


title: Treating a Global Health Crisis with a Dose of Synthetic Chemistry

text: [Image: see text] The SARS-CoV-2 pandemic has prompted scientists from many disciplines to work collaboratively toward an effective response. As academic synthetic chemists, we examine how best to contribute to this ongoing effort.


title: Pulmonary Embolism on CTPA in COVID-19 Patients

text: BACKGROUND: Understanding pulmonary embolism (PE) rate and contributing comorbid, clinical, laboratory, and imaging characteristics may aid in management of pro-thombotic events in COVID-19 (COVID+) patients. PURPOSE: To evaluate PE prevalence on computed tomography pulmonary angiogram (CTPA) in COVID+ patients and factors associated with PE severity. MATERIALS AND METHODS: A retrospective, single-center study evaluated 62 COVID+ patients who underwent CTPA between March 13 and April 5, 2020. A 62-patient cohort who underwent CTPA prior to the first reported local COVID-19 case was retrogradely selected. The relative rate of CTPA-positivity was recorded. For the COVID+ cohort, comorbidities, laboratory values, clinical outcome, and venous thrombosis were recorded. Two thoracic radiologists assessed embolic severity using the Mastora system and evaluated right heart strain. Statistical analysis evaluated factors associated with PE and arterial obstruction severity. A P-value<.05 was considered significant. RESULTS: 37.1% of COVID+ patients had PE, higher than 14.5% of pre-COVID patients (P=.007). D-dimer levels closest to CTPA date correlated with Mastora obstruction score. ROC analysis identified optimal sensitivity (95%) and specificity (71%) for PE diagnosis at 1394 ng/mL DDU. The mean D-dimer was 1774 ng/mL and 6432 ng/mL DDU in CTPA-negative and CTPA-positive subgroups, respectively (P<.001). One additional CTPA-negative patient had DVT, for a total 38.7% with PE/DVT, despite 40% receiving prophylactic anticoagulation. Other factors did not demonstrate significant PE association. CONCLUSION: 37.1% of COVID+ CTPA exams diagnosed PE. PE can be a cause of decompensation in COVID+, and D-dimer can be used to stratify patients regarding PE risk and severity.


Physiotherapists should consider joining an ethics review committee


COVID-19 Pandemic: the greatest challenge in the history of critical care()


Gender Differences, Hyperuricemia and Brain Natriuretic Factor


title: A Perspective on Yoga as a Preventive Strategy for Coronavirus Disease 2019

text: The pandemic outbreak of coronavirus disease 2019 (COVID-19) infection caused by severe acute respiratory syndrome-coronavirus 2 has led to profound public health crisis. In particular, individuals with preexisting conditions of heart disease, diabetes, cerebrovascular diseases and the elderly are most vulnerable to succumb to this infection. The current COVID-19 emergency calls for rapid development of potential prevention and management strategies against this virus-mediated disease. There is a plethora of evidence that supports the add-on benefits of yoga in stress management, as well as prevention and management of chronic noncommunicable diseases. There are some studies on the effect of yoga in communicable diseases as well but very few for acute conditions and almost none for the rapidly spreading infections resulting in pandemics. Based on the available scientific evidences on yoga in improving respiratory and immune functions, we have formulated very simple doable integrated yoga modules in the form of videos to be practiced for prevention of the disease by children, adults, and the elderly.


Yoga for COVID-19


title: Seizures and epilepsy in cats

text: Seizures are a common presenting complaint in cats, although causes and options for the treatment of seizures in this species have been historically poorly described in the veterinary literature. Seizure manifestation in cats may be different than what is typically seen in dogs, but the underlying causes of seizure activity are the same. These include primary epilepsies, structural epilepsies, and reactive seizures. Although primary epilepsy was once believed to be rare in cats, we now commonly appreciate this syndrome, albeit at a lower frequency than in dogs. Because of this, a complete diagnostic work-up is recommended for all cats presenting for initial evaluation of seizures. Symptomatic treatment of seizures in cats is similar to dogs, with only a few limitations related to species-specific antiepileptic drug toxicities. The goal of this review is to summarize the recent veterinary literature related to feline seizures, with a focus on seizure classification, clinical manifestation, diagnostic evaluation, and treatment options.


COVID-19 and risk management in a tissue bank


What You Gain and What You Lose in COVID-19: Perception of Medical Students on their Education


Trained innate immunity, COVID-19 therapeutic dilemma, and fake science


title: Trend Dynamics of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Transmission in 16 Cities of Hubei Province, China

text: OBJECTIVE: A severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was detected by researchers from a patient in Wuhan, Hubei province, China, in December 2019, and broke out in January 2020. Then, the pandemic was detected in countries around the world. Therefore, precise estimates of its current and future trends are highly required for future policy implications. METHODS: We retrieved data from the Health Commission of Hubei, China. Logistic-S curve model was used to estimate the current and future trends of SARS-CoV-2-infected cases among 16 cities of Hubei, China from Jan-11 to Feb-24, 2020. RESULTS: Out of 64,287 confirmed cases of SARS-CoV-2 infection in Hubei, higher percentage of cases were in Wuhan and Xiaogan. The highest death percentage was found in Wuhan and Qianjiang. A significant percentage of cures were found in Enshi Prefecture and Huanggang, while Wuhan showed the lowest percentage of cures. Rising trends in infected cases were observed throughout the study period, particularly in Wuhan, and a higher trend was observed after 12-Feb. Gradual decline trend of SARS-CoV-2 cases was observed during Feb-25 to Mar-15 in Hubei Province. Future forecast showed that the average number of SARS-CoV-2-infected cases might be decreased or stable in Hubei in the coming 20 days. CONCLUSION: The public must take precautionary measures in order to control and prevent disease spread and avoid extra travelling.


From the Editor’s Desk: Teaching History in the Time of COVID-19


Entomology in the Time of COVID-19


Cartels in the time of COVID-19


title: Legislating in the Time of a Pandemic: Window of Opportunity or Invitation for Recklessness?

text: The COVID-19 epidemic has been exacerbated by failures in diagnostic testing for the virus in the United States. In response to these problems, two bills have been introduced in Congress to not only reform emergency use of diagnostic tests, but to fundamentally reform diagnostics regulation in non-emergencies. There has been a long-standing recognition that current U.S. regulation of diagnostics is outdated and problematic, and the history of public health legislation is that emergencies and crises have been the primary motivating factor to break Congressional inertia and to implement new legislation. Thus, the COVID-19 may create a useful “window of opportunity” to pass much-needed legislative reform of diagnostic regulation in both emergency and non-emergency contexts. At the same time, rushing radical legislative changes, especially if they have not been subject to careful stakeholder engagement and Congressional deliberation in advance, is precarious and could result in reckless and disruptive changes. We review and apply the historical lessons of legislating in response to a crisis and conclude the one but not both of the pending legislative proposals may satisfy the criteria for an appropriate opportunistic change for diagnostics regulation.


title: An “Infodemic”: Leveraging High-Volume Twitter Data to Understand Early Public Sentiment for the COVID-19 Outbreak

text: BACKGROUND: Twitter has been used to track trends and disseminate health information during viral epidemics. On January 21, 2020, the CDC activated its Emergency Operations Center and the WHO released its first situation report about Coronavirus Disease 2019 (COVID-19), sparking significant media attention. How Twitter content and sentiment evolved in the early stages of the COVID-19 pandemic has not been described. METHODS: We extracted tweets matching hashtags related to COVID-19 from January 14(th) to 28(th), 2020 using Twitter’s application programming interface. We measured themes and frequency of keywords related to infection prevention practices. We performed a sentiment analysis to identify the sentiment polarity and predominant emotions in tweets and conducted topic modeling to identify and explore discussion topics over time. We compared sentiment, emotion, and topics among the most popular tweets, defined by the number of retweets. RESULTS: We evaluated 126,049 tweets from 53,196 unique users. The hourly number of COVID-19 related tweets starkly increased from January 21, 2020 onward. Nearly half (49.5%) of all tweets expressed fear and nearly 30% expressed surprise. In the full cohort, the economic and political impact of COVID-19 was the most commonly discussed topic. When focusing on the most retweeted tweets, the incidence of fear decreased and topics focused on quarantine efforts, the outbreak and its transmission, as well as prevention. CONCLUSION: Twitter is a rich medium that can be leveraged to understand public sentiment in real-time and potentially target individualized public health messages based on user interest and emotion.


title: COVID-19: Insights from innovation economists

text: The present document provides the take of innovation economists on the COVID-19 pandemic. It targets the general public and focuses on questions related to the Science, Technology, and Innovation ecosystem. It provides a reading of current real-world developments using economic reasoning and relying on existing economic research.


title: Cholesterol-modifying drugs in COVID-19

text: Infection with SARS-CoV-2 is more likely to lead to poor outcomes in the elderly and those with cardiovascular disease, obesity or metabolic syndrome. Here we consider mechanisms by which dyslipidemia and the use of cholesterol-modifying drugs could influence the virus-host relationship. Cholesterol is essential for the assembly, replication and infectivity of enveloped virus particles; we highlight several cholesterol-modifying drugs with the potential to alter the SARS-CoV-2 life cycle that could be tested in in vitro and in vivo models. Although cholesterol is an essential component of immune cell membranes, excess levels can dysregulate protective immunity and promote exaggerated pulmonary and systemic inflammatory responses. Statins block the production of multiple sterols, oxysterols and isoprenoids, resulting in a pleotropic range of context-dependent effects on virus infectivity, immunity and inflammation. We highlight antiviral, immunomodulatory and anti-inflammatory effects of cholesterol-modifying drugs that merit further consideration in the management of SARS-CoV-2 infection.


The use of data from electronic health records in times of a pandemic—a legal and ethical assessment


title: Preference Refinement After a Budget Contraction

text: How does coping with a resource loss of time, space, or money change a consumer? In the current work, we argue that resource losses that give rise to budget contractions require a coping strategy that not only influences choice in the moment but also changes underlying consumer preferences. We show that the preference restructuring that occurs when coping with a budget loss also leads to stabilization of preferences. Specifically, a consumer who allocates a budget to a set of items prior to a budget contraction and allocates that same budget post-contraction when the budget is fully restored will allocate the restored budget to fewer options in the set. Coping with the contraction helps consumers prioritize what matters to them, leading to refinement of preference. This within-consumer preference refinement effect exists for budgets of time, space, and money. We identify boundary conditions (i.e., significant budget contractions and self-determined contraction allocations are necessary for prioritization to occur) and rule out non-prioritization explanations (e.g., anchoring and under-adjusting). These findings suggest that marketers should focus on capturing consumers who are dealing with budget contractions as this is one of the moments where individuals revisit and rediscover what matters most to them.


Occupational Health and Covid-19


title: The initial impact of COVID-19 and policy responses on household incomes

text: As soon as the scale of the coronavirus shock to the economy became clear, the UK government introduced three policies to protect directly household incomes: a Job Retention Scheme, to pay the wages of employees who were temporarily furloughed; a Self-Employment Income Support Scheme, to give grants to established self-employed people whose businesses had been affected; and a package of increases to entitlements to social security benefits, with Universal Credit at the core, that bolstered the UK’s means-tested ‘safety net’. This paper analyses the design and beneficiaries of these policies and, given the distributional pattern of the labour market shock, considers the emerging overall impact on living standards, particularly of low-income households.


The Rise and Regulation of Thermal Facial Recognition Technology during the COVID-19 Pandemic


ACC Advocacy: We Own the Problem, We Must Own the Solution


AANPconnect -- Reimaging a Conference


Earlier Breast Cancer Detection in Peru: Establishing a Comprehensive Program in an Underserved Region


Picking Up the Torch: Our Mission as the New FIT & EC Section Editors


China’s Belt and Road Initiative and the COVID-19 crisis


title: Expanding the international trade and investment policy agenda: The role of cities and services

text: We explore the public policy implications of two new, significant, and inter-related global phenomena. First, the rising share of services, particularly innovation-driven digital and knowledge-based services, in foreign trade and multinational enterprise activity; and second, the increasingly important role of global cities as home and hosts to these activities. Our framework distinguishes between national economic policies to promote trade and FDI, referred to as economic diplomacy, and comparable policies originating in cities, referred to as city diplomacy. National economic diplomacy has traditionally promoted trade and investment in goods, often through trade agreements and promotion agencies, and we explore the limitations of these tools as trade in services becomes more important. However, we also note that trade in services, particularly innovation-driven services, is concentrated in global cities, and traded between them, often within MNEs. We conclude that national policies on trade and investment cannot be divorced from innovation and knowledge strategies, and that these strategies cannot be divorced from cities. We emphasize that national economic diplomacy should be better aligned with city diplomacy. We also discuss how the transition to stronger city diplomacy may have consequences for firms and their strategies for corporate diplomacy.


title: How globalization became a thing that goes bump in the night

text: For almost 200 years, globalization has been seen as a positive development, albeit with costs and benefits, and as progress and modernization, a broadening of humanity’s scope from the local and parochial to the cosmopolitan and international. That changed dramatically with the Great Recession, the waves of migration of the last decade, and the global Coronavirus (COVID-19) pandemic of 2020. For many, globalization now connotes economic dislocation, increasing inequality, unwanted immigration, and a vehicle for the transmission of disease. The pandemic reminds us that most economic activity takes place within national borders. It has emphasized the dangers rather than the benefits of efficient linkages between markets, laying bare the dangers of complex global supply chains where any node can become a “choke point”, and the risks of overspecialization or the concentration of technological knowledge and/or production capacity in a single country or region. A more positive view of globalization will require restoring the balance between independence and integration, mitigation of its costs within and between countries, and dealing with redundancy and supply risk.


Uraemia disarms neutrophils against Candida


How did the U.S. stock market recover from the Covid-19 contagion?


title: JC Polyomavirus, progressive multifocal leukoencephalopathy and immune reconstitution inflammatory syndrome: a review

text: The human neurotropic virus JC Polyomavirus, a member of the Polyomaviridae family, is the opportunistic infectious agent causing progressive multifocal leukoencephalopathy, typically in immunocompromised individuals. The spectrum of underlying reasons for the systemic immunosuppression that permits JCV infection in the central nervous system has evolved over the past 2 decades, and therapeutic immunosuppression arousing JCV infection in the brain has become increasingly prominent as a trigger for PML. Effective immune restoration subsequent to human immunodeficiency virus-related suppression is now recognized as a cause for unexpected deterioration of symptoms in patients with PML, secondary to a rebound inflammatory phenomenon called immune reconstitution inflammatory syndrome, resulting in significantly increased morbidity and mortality in a disease already infamous for its lethality. This review addresses current knowledge regarding JC Polyomavirus, progressive multifocal leukoencephalopathy, progressive multifocal leukoencephalopathy-related immune reconstitution inflammatory syndrome, and the immunocompromised states that incite JC Polyomavirus central nervous system infection, and discusses prospects for the future management of these conditions.


title: Reliable cross-docking location problem under the risk of disruptions

text: This paper seeks to develop a reliable network of cross-docks by taking in to account disruption and reliability issues to hedge against heterogeneous risk of cross-docking failure. In real environments, applying a recovery policy can be a feasible strategy to handle disruptions. Hence, in this study, a recovery policy has been addressed in the form of reallocating suppliers to alternative cross-docks or altering the transportation strategy to move shipments. In addition to cross-dock location design, the optimum capacity of opened cross-docks will be determined considering the loads that will be served by each cross-docking center under regular and disruption conditions. A mixed integer nonlinear programming formulation is presented for the problem and is then linearized to present an efficient model. In order to solve it, two Lagrangian relaxation algorithms are designed and tested on 40 problem instances with different values of parameters. The results achieved by GAMS/CPLEX are compared with those of two algorithms and some analyses are performed on the solutions. Moreover, as the case study, the focus has been placed on logistic part of a car-manufacturing company with a vast supply chain network, containing more than 600 suppliers. The logistic strategies have been applied in order to reduce the transportation cost through the supply chain network and diminish the disruption subsequences in such a network. Based on the results, some managerial recommendations are presented.


title: Effects of intraoperative individualized PEEP on postoperative atelectasis in obese patients: study protocol for a prospective randomized controlled trial

text: BACKGROUND: Obese patients undergoing general anesthesia and mechanical ventilation during laparoscopic abdominal surgery commonly have a higher incidence of postoperative pulmonary complications (PPCs), due to factors such as decreasing oxygen reserve, declining functional residual capacity, and reducing lung compliance. Pulmonary atelectasis caused by pneumoperitoneum and mechanical ventilation is further aggravated in obese patients. Recent studies demonstrated that individualized positive end-expiratory pressure (iPEEP) was one of effective lung-protective ventilation strategies. However, there is still no exact method to determine the best iPEEP, especially for obese patients. Here, we will use the best static lung compliance (Cstat) method to determine iPEEP, compared with regular PEEP, by observing the atelectasis area measured by electrical impedance tomography (EIT), and try to prove a better iPEEP setting method for obese patients. METHODS: This study is a single-center, two-arm, prospective, randomized control trial. A total number of 80 obese patients with body mass index ≥ 32.5 kg/m(2) scheduled for laparoscopic gastric volume reduction and at medium to high risk for PPCs will be enrolled. They will be randomly assigned to control group (PEEP5 group) and iPEEP group. A PEEP of 5 cmH(2)O will be used in PEEP5 group, whereas an individualized PEEP value determined by a Cstat-directed PEEP titration procedure will be applied in the iPEEP group. Standard lung-protective ventilation methods such as low tidal volumes (7 ml/kg, predicted body weight, PBW), a fraction of inspired oxygen ≥ 0.5, and recruitment maneuvers (RM) will be applied during and after operation in both groups. Primary endpoints will be postoperative atelectasis measured by chest electrical impedance tomography (EIT) and intraoperative oxygen index. Secondary endpoints will be serum IL-6, TNF-α, procalcitonin (PCT) kinetics during and after surgery, incidence of PPCs, organ dysfunction, length of in-hospital stay, and hospital expense. DISCUSSION: Although there are several studies about the effect of iPEEP titration on perioperative PPCs in obese patients recently, the iPEEP setting method they used was complex and was not always feasible in routine clinical practice. This trial will assess a possible simple method to determine individualized optimal PEEP in obese patients and try to demonstrate that individualized PEEP with lung-protective ventilation methods is necessary for obese patients undergoing general surgery. The results of this trial will support anesthesiologist a feasible Cstat-directed PEEP titration method during anesthesia for obese patients in attempt to prevent PPCs. TRIAL REGISTRATION: www.chictr.org.cn ChiCTR1900026466. Registered on 11 October 2019


BAST newsletter—June 2020


title: Marie Curie takes to the screen

text: Radioactive, a new biopic of Marie Curie, weaves together her personal story, her discoveries and the repercussions of her research into radioactivity.


Sarcopenia, Frailty, and Gero-Science: A Decade of Progress and a Bright Future of Discovery


title: Analysis of the bitcoin stock market indexes using comparative study of two models SV with MCMC algorithm

text: The purpose of this article is to find a better technique for estimating the volatility of the price of bitcoin on the one hand and to check if this special kind of asset called cryptocurrency behaves like other stock market indices. We include five stock market indexes for different countries such as Standard and Poor’s 500 composite Index (S&P), Nasdaq, Nikkei, Stoxx, and DowJones. Using daily data over the period 2010–2019. We examine two asymmetric stochastic volatility models used to describe the volatility dependencies found in most financial returns. Two models are compared, the first is the autoregressive stochastic volatility model with Student’s t-distribution (ARSV-t), and the second is the basic SVOL. To estimate these models, our analysis is based on the Markov Chain Monte-Carlo method. Therefore, the technique used is a Metropolis–Hastings (Hastings in Biometrika 57:97–109, 1970), and the Gibbs sampler (Casella and George in Am Stat 46:167–174, 1992; Gelfand and Smith in J Am Stat Assoc 85:398–409, 1990; Gilks and Wild in 41:337–348, 1992). Model comparisons illustrate that the ARSV-t model performs better performances. We conclude that this model is better than the SVOL model on the MSE and AIC function. This result concerns bitcoin as well as the other stock market indices. Without forgetting that our finding proves the efficiency of Markov Chain for our sample and the convergence and stability for all parameters to a certain level. On the whole, it seems that permanent shocks have an effect on the volatility of the price of the bitcoin and also on the other stock market. Our results will help investors better diversify their portfolio by adding this cryptocurrency.


title: The first nationwide multicenter study of Acinetobacter baumannii recovered in Serbia: emergence of OXA-72, OXA-23 and NDM-1-producing isolates

text: BACKGROUND: The worldwide emergence and clonal spread of carbapenem-resistant Acinetobacter baumannii (CRAB) is of great concern. The aim of this nationwide study was to investigate the prevalence of CRAB isolates in Serbia and to characterize underlying resistance mechanisms and their genetic relatedness. METHODS: Non-redundant clinical samples obtained from hospitalized patients throughout Serbia were included in the prospective, observational, multicenter study conducted from January to June 2018. Samples were initially screened for the presence of Acinetobacter baumannii-calcoaceticus (Acb) complex using conventional bacteriological techniques. Acb complexes recovered from clinical samples obtained from inpatients with confirmed bacterial infections were further evaluated for the presence of A. baumannii. Identification to the species level was done by the detection of the bla(OXA-51) gene and rpoB gene sequence analysis. Susceptibility testing was done by disk diffusion and broth microdilution method. CRAB isolates were tested for the presence of acquired carbapenemases (bla(OXA-24-like), bla(OXA-23-like,)bla(OXA-58-like), bla(OXA-143-like), bla(IMP), bla(VIM), bla(GIM), bla(SPM), bla(SIM), bla(NDM)) by PCR. Clonal relatedness was assessed by pulsed-field gel electrophoresis (PFGE) and multilocus sequence typing (MLST). RESULTS: Acb complex was isolated in 280 out of 2401 clinical samples (11.6%). Overall, A. baumannii was identified in 237 out of 280 Acb complex (84.6%). CRAB prevalence was found to be 93.7% (237/222). The MIC(50)/MIC(90) for imipenem and meropenem were 8/> 32 μg/mL and 16/> 32 μg/mL, respectively. Although susceptibility was high for colistin (95.7%; n = 227) and tigecycline (75.1%; n = 178), ten isolates (4.3%) were classified as pandrug-resistant. The following carbapenemases-encoding genes were found: 98 (44.2%) bla(OXA-24-like), 76 (34.5%) bla(OXA-23-like), and 7 (3.2%) bla(NDM-1). PFGE analysis revealed six different clusters. MLST analysis identified three STs: ST2 (n = 13), ST492 (n = 14), and ST636 (n = 10). Obtained results evaluated that circulating CRAB clones in Serbia were as follows: bla(OXA66)/bla(OXA23)/ST2 (32.4%), bla(OXA66)/bla(OXA23)/bla(OXA72)/ST2 (2.7%), bla(OXA66)/bla(OXA72)/ST492 (37.8%), and bla(OXA66)/bla(OXA72)/ST636 (27.1%). CONCLUSION: This study revealed extremely high proportions of carbapenem resistance among A. baumannii clinical isolates due to the emergence of bla(OXA-72), bla(OXA-23), and bla(NDM-1) genes among CRAB isolates in Serbia and their clonal propagation.


title: Crime and coronavirus: social distancing, lockdown, and the mobility elasticity of crime

text: Governments around the world restricted movement of people, using social distancing and lockdowns, to help stem the global coronavirus (COVID-19) pandemic. We examine crime effects for one UK police force area in comparison to 5-year averages. There is variation in the onset of change by crime type, some declining from the WHO ‘global pandemic’ announcement of 11 March, others later. By 1 week after the 23 March lockdown, all recorded crime had declined 41%, with variation: shoplifting (− 62%), theft (− 52%), domestic abuse (− 45%), theft from vehicle (− 43%), assault (− 36%), burglary dwelling (− 25%) and burglary non-dwelling (− 25%). We use Google Covid-19 Community Mobility Reports to calculate the mobility elasticity of crime for four crime types, finding shoplifting and other theft inelastic but responsive to reduced retail sector mobility (MEC = 0.84, 0.71 respectively), burglary dwelling elastic to increases in residential area mobility (− 1), with assault inelastic but responsive to reduced workplace mobility (0.56). We theorise that crime rate changes were primarily caused by those in mobility, suggesting a mobility theory of crime change in the pandemic. We identify implications for crime theory, policy and future research.


title: NICE’s rejection of pembrolizumab for platinum-refractory urothelial carcinoma: is there a greater good?

text: Anti-PD1 and anti-PDL1 immunotherapy has transformed urothelial carcinoma treatment. Pembrolizumab is the only immunotherapy agent shown to have survival benefit compared with standard chemotherapy after progression on platinum-containing chemotherapy. Initial National Institute for Health and Care Excellence (NICE) approval was based on efficacy data, but the final appraisal report rejected use of pembrolizumab owing to treatment cost.


title: Functional Outcomes in Patients Admitted to the Intensive Care Unit with Traumatic Brain Injury and Exposed to Hyperoxia: A Retrospective Multicentre Cohort Study

text: BACKGROUND: Supplemental oxygen administration to critically ill patients is ubiquitous in the intensive care unit (ICU). Uncertainty persists as to whether hyperoxia is benign in patients with traumatic brain injury (TBI), particularly in regard to their long-term functional neurological outcomes. METHODS: We conducted a retrospective multicenter cohort study of invasively ventilated patients with TBI admitted to the ICU. A database linkage between the Australian and New Zealand Intensive Care Society Adult Patient Database (ANZICS-APD) and the Victorian State Trauma Registry (VSTR) was utilized. The primary exposure variable was minimum acute physiology and chronic health evaluation (APACHE) III P(a)O(2) in the first 24 h of ICU. We defined hypoxia as P(a)O(2) < 60 mmHg, normoxia as 60–299 mmHg, and hyperoxia as ≥ 300 mmHg. The primary outcome was a Glasgow Outcome Scale-Extended (GOSE) < 5 at 6 months while secondary outcomes included 12 and 24 months GOSE and mortality at each of these timepoints. Additional sensitivity analyses were undertaken in the following subgroups: isolated head injury, patients with operative intervention, head injury severity, and P(a)O(2) either subcategorized by increments of 60 mmHg or treated as a continuous variable. RESULTS: A total of 3699 patients met the inclusion criteria. The mean age was 42.8 years, 77.7% were male and the mean acute physiology and chronic health evaluation (APACHE) III score was 60.1 (26.3). 2842 patients experienced normoxia, and 783 hyperoxia. The primary outcome occurred in 1470 (47.1%) of patients overall with 1123 (47.1%) from the normoxia group and 312 (45.9%) from the hyperoxia group—odds ratio 0.99 (0.78–1.25). No significant differences in outcomes between groups at 6, 12, and 24 months were observed. Sensitivity analyses did not identify subgroups that were adversely affected by exposure to hyperoxia. CONCLUSIONS: No associations were observed between hyperoxia in ICU during the first 24 h and adverse neurological outcome at 6 months in ventilated TBI patients. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s12028-020-01033-y) contains supplementary material, which is available to authorized users.


title: Secreted frizzled-related protein 2 prevents pressure-overload-induced cardiac hypertrophy by targeting the Wnt/β-catenin pathway

text: BACKGROUND AND AIM: Secreted frizzled-related protein 2 (sFRP2) has been reported to be involved in cardiovascular diseases. However, its role in cardiac hypertrophy induced by pressure overload is still elusive. We aimed to examine the role of sFRP2 in the development of cardiac hypertrophy in vivo and in vitro. METHODS AND RESULTS: Following cardiac hypertrophy stimulated by aortic banding (AB), the expression of sFRP2 was downregulated in the hypertrophic ventricle. Adeno-associated virus 9 (AAV9) was injected through the tail vein to overexpress sFRP2 in the mouse myocardium. Overexpression of sFRP2 alleviated cardiomyocyte hypertrophy and interstitial fibrosis, as identified by the reduced cardiomyocyte cross-sectional area, heart weight/body weight ratio, and left ventricular (LV) collagen ratio. Additionally, sFRP2 decreased cardiomyocyte apoptosis induced by pressure overload. Western blot showed that sFRP2 prevented the expression of active β-catenin. The Wnt/β-catenin agonist LiCl (1 mmol/kg) abolished the inhibitory effects of sFRP2 on cardiac hypertrophy and apoptosis, as evidenced by the increased cross-sectional area and LV collagen ratio and the deterioration of echocardiographic data. CONCLUSION: Our study indicated that decreased sFRP2 levels were observed in failing mouse hearts. Overexpression of sFRP2 attenuated myocyte hypertrophy and interstitial fibrosis induced by hypertrophic stimuli by inhibiting the Wnt/β-catenin pathway. We revealed that sFRP2 may be a promising therapeutic target for the development of cardiac remodeling. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s11010-020-03802-x) contains supplementary material, which is available to authorized users.


title: Comparison of a Continuous Noninvasive Temperature to Monitor Core Temperature Measures During Targeted Temperature Management

text: BACKGROUND: Temperature modulating devices (TMD) currently utilize core temperature measurements during targeted temperature management (TTM) that are currently limited to esophageal (Et), bladder (Bt), or rectal (Rt) temperatures. We assessed the ability of a continuous noninvasive temperature monitor to accurately approximate core temperature during TTM. METHODS: All patients undergoing TTM using a gel pad surface TMD and an existing core temperature monitoring device were eligible for this study. Core and continuous noninvasive temperature monitoring values were simultaneously recorded for up to 72 h of TTM. The two sets of temperature data were downloaded from a clinical data acquisition storage system at 1-min intervals. The Bland–Altman method assessed agreement between the core and continuous noninvasive temperature monitor values, by measuring the mean difference (± 2 SD) between these values. RESULTS: There were 20 subjects that underwent study between January 2018 and March 2018 (55% women, age: 57 ± 14 years old, BMI: 28.9 + 9.8 kg/m(2), 100% mechanically ventilated). The comparison patient temperature source was predominantly esophageal (n = 10) followed by bladder (n = 5) or rectal (n = 5). There were a total of 999 h of paired patient temperature data from esophageal (50%), bladder (25%), and rectal (25%) temperatures. Bland–Altman analysis demonstrated good agreement with the superficial temperature monitor and core temperature measures in all patients overall, with a difference mean of 0.06 ± 0.39 C (P = 0.99) and no proportional bias noted (β =0.002, P = 0.917). CONCLUSIONS: Continuous noninvasive temperature monitoring is a suitable alternative method for assessing core temperature during TTM. Future studies should focus on developing connectivity with a continuous noninvasive temperature monitor to approximate core temperature during TTM.


title: Has air quality improved in Ecuador during the COVID-19 pandemic? A parametric analysis

text: Many governments around the world have enforced quarantine policies to control the spread of the new coronavirus (SARS-CoV-2). These policies have had positive and negative effects on the environment. For example, the concentrations of certain harmful pollutants have decreased in some countries. In contrast, the concentrations of other pollutants have increased. This research analyzes the effect of quarantine policies on air quality in Quito, Ecuador. Using a parametric approach, it was found that NO(2) and PM2.5 concentrations have decreased significantly since the establishment of lockdown measures. However, O(3) concentrations have increased considerably in 2020.


title: An Invitation to Act on the Value of Self-Care: Being a Whole Person in All That You Do

text: The emergence of COVID-19 resulted in a sudden, unprecedented change in context that impacted the way behavior analysts live and work worldwide. Any rapidly shifting context requires behavioral flexibility, in addition to the acquisition of new skills and access to resources that foster resilience in the face of practical challenges and uncertainty about the future. Behavior analysts (particularly novice practitioners) may already be vulnerable to burnout (Plantiveau, Dounavi, & Virués-Ortega, 2018) and in need of greater support to adopt protective self-care practices. Such practices will enable them to continue providing effective services to distressed families, while navigating their own challenges. This article seeks to offer behavior analysts some tools and practices drawn from the work of contextual behavior scientists that can promote well-being and resilience. This includes strategies for clarifying and committing to an overarching value of self-care, acting congruently with personal and professional values across many domains of living, and practicing self-compassion in the process.


title: Processing of Complex Materials in the Copper Industry: Challenges and Opportunities Ahead

text: With the gradual decrease in the grade of copper ores being processed, copper concentrates have become more complex with higher impurity and gangue content. This trend has had a detrimental effect on smelters as they have to increase throughput to maintain copper metal production, while increasing operating costs due to processing the increased amounts of secondary products (slag, acid) and stabilizing waste streams. This paper discusses impacts from the increased complexity of resources from mine to smelters, highlighting the need for an integrated processing approach to achieve sustainable and competitive multi-metal recovery.


COVID-19 Fear, Mental Health, and Substance Misuse Conditions Among University Social Work Students in Israel and Russia


title: The interplay between knowledge-based competitiveness, people’s good health and well-being: new empirical evidence from Central and Eastern European countries

text: The enhancement of countries’ competitiveness represents an essential credential in current economic interventions, national policy frameworks and strategies, mainly in relation to sustainable economic development. This paper explores the drivers of competitiveness in Central and Eastern European Countries (CEECs) in the knowledge economy, and its further impact on economic welfare, with a particular focus on individuals’ perceived good health and well-being (a good predictor of people’s future use of the healthcare system and mortality risk and a benchmark for sustainable development). The dataset grasps 11 CEECs analyzed during the 2000–2018 lapse of time. The methodological endeavor is based on three econometric procedures encompassing macroeconometric models, structural equation modelling (SEM) and network analysis through Gaussian Graphical Models. Main findings entail that CEECs competitiveness is driven by the key coordinates of the knowledge economy, particularly tertiary education, employment in technology and knowledge-intensive sectors, and additional government financial support dedicated for research and development activities. Moreover, in a further complex setting, competitiveness increases under the impact of education, research, development and innovation leads to significant upwards in GDP per capita levels. However, the further linkages with individuals’ good health and well-being, when considered as a consequence of welfare increase following knowledge-driven competitiveness, or as a direct impact of competitiveness increases, entail a downsized outcome in terms of individuals’ perceived health. Our results provide solid grounds for the development of new tailored policies designed to enhance CEECs knowledge-driven competitiveness in a comprehensive framework, with benefit spillovers on sustainable economic development. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s11135-020-01015-4) contains supplementary material, which is available to authorized users.


title: How Indian agriculture should change after COVID-19

text: The COVID-19 crisis has exposed the vulnerability of India’s Agri food system and accentuated the need for agricultural market reforms and digital solutions to connect farmers to markets, to create safety nets and ensure reasonable working conditions, and to decentralize Agri food systems to make them more resilient.


title: Assuring food security in Singapore, a small island state facing COVID-19

text: Small island states have features in common which make it difficult for them to assure food security through self-production, notably limited land, fresh water and labour. As these island states grow economically, diet diversification by an increasingly affluent population demands a balance between food imports and self-production. Singapore, a wealthy, small island state has consistently been ranked high in food security in international comparisons, but only under conditions when trade is uninhibited and countries do not reduce food exports. The COVID-19 pandemic has shown vulnerabilities in the country’s “Resilience” strategy to maintain food security through importing over 90% of its food needs from over 170 countries. Leading up to and during the pandemic, strategic policy initiatives were announced by the government and new measures were taken to increase the stability of imports, ramp up production from existing farms, increase self-production by 300% by 2030 through increasing the number of high technology urban vegetable and fish farms, and factory-cultured food, and reducing food waste. Singapore offers lessons for other small island states in ways to improve their food security.


title: Learning in times of lockdown: how Covid-19 is affecting education and food security in India

text: A vast majority of the relief and rehabilitation packages announced in the months following the nationwide lockdown in India have focused on economic rehabilitation. However, the education sector has remained absent from this effort, including in India’s central government’s 250 billion dollar stimulus package. In this paper, we discuss the implications of lockdown-induced school and rural child-care center closures on education and health outcomes for the urban and rural poor. We especially focus on food and nutritional security of children who depend on school feeding and supplementary nutrition programs. We argue that the impacts are likely to be much more severe for girls as well as for children from already disadvantaged ethnic and caste groups. We also discuss ways in which existing social security programs can be leveraged and strengthened to ameliorate these impacts.


title: Discovering Fails in Software Projects Planning Based on Linguistic Summaries

text: Linguistic data summarization techniques help to discover complex relationships between variables and to present the information in natural language. There are some investigations associated to algorithms to build linguistic summaries. But the literature does no report investigations concerned with combination linguistic data summarization techniques and outliers’ mining applied to planning of software project. In particular, outliers’ mining is a datamining technique, useful in errors and fraud detection. In this work authors present new algorithms to build linguistic data summaries from outliers in software project planning context. Besides, authors compare different outliers’ detection algorithms in software project planning context. The main motivation of this work is to detect planning errors in projects, to avoid high cost and time delays. Authors consider that the combination of outliers’ mining and linguistic data summarization support project managers to decision-making process in the software project planning. Finally, authors present the interpretation of obtained summaries and comment about its impact.


title: Sec2graph: Network Attack Detection Based on Novelty Detection on Graph Structured Data

text: Being able to timely detect new kinds of attacks in highly distributed, heterogeneous and evolving networks without generating too many false alarms is especially challenging. Many researchers proposed various anomaly detection techniques to identify events that are inconsistent with past observations. While supervised learning is often used to that end, security experts generally do not have labeled datasets and labeling their data would be excessively expensive. Unsupervised learning, that does not require labeled data should then be used preferably, even if these approaches have led to less relevant results. We introduce in this paper a unified and unique graph representation called security objects’ graphs. This representation mixes and links events of different kinds and allows a rich description of the activities to be analyzed. To detect anomalies in these graphs, we propose an unsupervised learning approach based on auto-encoder. Our hypothesis is that as security objects’ graphs bring a rich vision of the normal situation, an auto-encoder is able to build a relevant model of this situation. To validate this hypothesis, we apply our approach to the CICIDS2017 dataset and show that although our approach is unsupervised, its detection results are as good, and even better than those obtained by many supervised approaches.


title: Agent EXPRI: Licence to Explain

text: Online social networks are known to lack adequate multi-user privacy support. In this paper we present EXPRI, an agent architecture that aims to assist users in managing multi-user privacy conflicts. By considering the personal utility of sharing content and the individually preferred moral values of each user involved in the conflict, EXPRI identifies the best collaborative solution by applying practical reasoning techniques. Such techniques provide the agent with the cognitive process that is necessary for explainability. Furthermore, the knowledge gathered during the practical reasoning process allows EXPRI to engage in contrastive explanations.


title: Explainable Agents as Static Web Pages: UAV Simulation Example

text: Motivated by the apparent societal need to design complex autonomous systems whose decisions and actions are humanly intelligible, the study of explainable artificial intelligence, and with it, research on explainable autonomous agents has gained increased attention from the research community. One important objective of research on explainable agents is the evaluation of explanation approaches in human-computer interaction studies. In this demonstration paper, we present a way to facilitate such studies by implementing explainable agents and multi-agent systems that i) can be deployed as static files, not requiring the execution of server-side code, which minimizes administration and operation overhead, and ii) can be embedded into web front ends and other JavaScript-enabled user interfaces, hence increasing the ability to reach a broad range of users. We then demonstrate the approach with the help of an application that was designed to assess the effect of different explainability approaches on the human intelligibility of an unmanned aerial vehicle simulation.


title: A Privacy-Preserving, Distributed and Cooperative FCM-Based Learning Approach for Cancer Research

text: Distributed Artificial Intelligence is attracting interest day by day. In this paper, the authors introduce an innovative methodology for distributed learning of Particle Swarm Optimization-based Fuzzy Cognitive Maps in a privacy-preserving way. The authors design a training scheme for collaborative FCM learning that offers data privacy compliant with the current regulation. This method is applied to a cancer detection problem, proving that the performance of the model is improved by the Federated Learning process, and obtaining similar results to the ones that can be found in the literature.


title: Approximate Reaction Systems Based on Rough Set Theory

text: In this work we investigate how Rough Set Theory could be employed to model uncertainty and information incompleteness about a Reaction System. The approach that we propose is inspired by the idea of an abstract scientific experiment: we define the notion of test, which defines an approximation space on the states of a Reaction System, and observation, to represent the interactive process of knowledge building that is typical of complex systems. We then define appropriate notions of reducts and study their characterization in terms of both computational complexity and relationships with standard definitions of reducts in terms of Information Tables.


title: Similarity Based Granules

text: In the authors’ previous research, a possible usage of the correlation clustering in rough set theory was investigated. Correlation clustering is based on a tolerance relation and its output is a partition. The system of granules can be derived from the partition and as a result, a new approximation space appears. This space focuses on the similarity (represented by a tolerance relation) itself and it is different from the covering type approximation space relying on a tolerance relation. In real-world applications, the number of objects is very high. So it can be effective only if a portion of the data points is used. Previously we provided a method that chooses the necessary number of objects that represent the data set. These members are called representatives and it can be useful to apply them in the approximation of an arbitrary set. A new approximation pair can be defined based on the representatives. In this paper, some very important properties are checked for this approximation pair and the system of granules.


title: A Graph-Based Keyphrase Extraction Model with Three-Way Decision

text: Keyphrase extraction has been a popular research topic in the field of natural language processing in recent years. But how to extract keyphrases precisely and effectively is still a challenge. The mainstream methods are supervised learning methods and graph-based methods. Generally, the effects of supervised methods are better than unsupervised methods. However, there are many problems in supervised methods such as the difficulty in obtaining training data, the cost of labeling and the limitation of the classification function trained by training data. In recent years, the development of the graph-based method has made great progress and its performance of extraction is getting closer and closer to the supervised method, so the graph-based method of keyphrase extraction has got a wide concern from researchers. In this paper, we propose a new model that applies the three-way decision theory to graph-based keyphrase extraction model. In our model, we propose algorithms dividing the set of candidate phrases into the positive domain, the boundary domain and the negative domain depending on graph-based attributes, and combining candidate phrases in the positive domain and the boundary domain qualified by graph-based attributes and non- graph-based attributes to get keyphrases. Experimental results show that our model can effectively improve the extraction precision compared with baseline methods.


title: Putting Attacks in Context: A Building Automation Testbed for Impact Assessment from the Victim’s Perspective

text: Cybersecurity research relies on the reproducibility and deep understanding of attacks to devise appropriate solutions. Different kinds of testbeds are typically used to systematically execute attacks and evaluate defenses. Testbeds are widely used to demonstrate Building Automation and Control System (BACS) attacks and defenses, considered too risky to be executed on real infrastructures. However, those testbeds implement arbitrary configurations of building services that do not resemble real-world deployments. In this work, we present the first BACS testbed specially designed to assess the impact of cyberattacks from the victim’s perspective. It features general purpose building services such as illumination, ventilation, and temperature control, whose configuration is easily adapted to emulate the requirements of real-world locations. In this way, the context added to our testbed allows us to better understand the impact of BACS attacks through concrete and realistic scenarios. Moreover, by analyzing different configurations of the BACS (i.e., contexts), we found out that identical attacks may have dramatically different impacts. Thus, reinforcing our view on the relevance of adding context to BACS testbeds.


title: On the Convergence of Input-Output Fuzzy Cognitive Maps

text: Fuzzy cognitive maps are recurrent neural networks, where the neurons have a well-defined meaning. In certain models, some neurons receive outer input, while other neurons produce the output of the system. According to this observation, some neurons are categorized as input neurons and the others are the state neurons and output neurons. The output of the system is provided as a limit of an iteration process, which may converge to an equilibrium point, but limit cycles or chaotic behaviour may also show up. In this paper, we examine the existence and uniqueness of fixed points for two types of input-output fuzzy cognitive maps. Moreover, we use network-based measures like in-degree, out-degree and connectivity, to express conditions for the convergence of the iteration process.


title: An Adjusted Apriori Algorithm to Itemsets Defined by Tables and an Improved Rule Generator with Three-Way Decisions

text: The NIS-Apriori algorithm, which is extended from the Apriori algorithm, was proposed for rule generation from non-deterministic information systems and implemented in SQL. The realized system handles the concept of certainty, possibility, and three-way decisions. This paper newly focuses on such a characteristic of table data sets that there is usually a fixed decision attribute. Therefore, it is enough for us to handle itemsets with one decision attribute, and we can see that one frequent itemset defines one implication. We make use of these characteristics and reduce the unnecessary itemsets for improving the performance of execution. Some experiments by the implemented software tool in Python clarify the improved performance.


title: Feature and Label Association Based on Granulation Entropy for Deep Neural Networks

text: Pooling layers help reduce redundancy and the number of parameters before building a multilayered neural network that performs the remaining processing operations. Usually, pooling operators in deep learning models use an explicit topological organization, which is not always possible to obtain on multi-label data. In a previous paper, we proposed a pooling architecture based on association to deal with this issue. The association was defined by means of Pearson’s correlation. However, features must exhibit a certain degree of correlation with each other, which might not hold in all situations. In this paper, we propose a new method that replaces the correlation measure with another one that computes the entropy in the information granules that are generated from two features or labels. Numerical simulations have shown that our proposal is superior in those datasets with low correlation. This means that it induces a significant reduction in the number of parameters of neural networks, without affecting their accuracy.


title: On the Hierarchy of Equivalence Classes Provided by Local Congruences

text: In this work, we consider a special kind of equivalence relations, which are called local congruences. Specifically, local congruences are equivalence relations defined on lattices, whose equivalence classes are convex sublattices of the original lattices. In the present paper, we introduce an initial study about how the set of equivalence classes provided by a local congruence can be ordered.


title: Attribute Reduction from Closure Operators and Matroids in Rough Set Theory

text: In this paper, we present a new closure operator defined on the set of attributes of an information system that satisfies the conditions for defining a matroid. We establish some basic relationships between equivalence classes and approximation operators where different sets of attributes are used. It is shown that the reducts of an information system can be obtained from dependent sets of a matroid. Finally, we show that the closure operator can be defined at least in three different ways.


title: Quick Maximum Distribution Reduction in Inconsistent Decision Tables

text: Attribute reduction is a key issue in rough set theory, and this paper focuses on the maximum distribution reduction for complete inconsistent decision tables. It is quite inconvenient to judge the maximum distribution reduct directly according to its definition and the existing heuristic based judgment methods are inefficient due to the lack of acceleration mechanisms that mainstream heuristic judgment methods have. In this paper, we firstly point out the defect of judgment method proposed by Li et al. [15]. After analyzing the root cause of the defect, we proposed two novel heuristic attribute reduction algorithms for maximum distribution reduction. The experiments show that proposed algorithms are more efficient.


title: Overlapping Community Detection Using Multi-objective Approach and Rough Clustering

text: The detection of overlapping communities in Social Networks has been successfully applied in several contexts. Taking into account the high computational complexity of this problem as well as the drawbacks of single-objective approaches, community detection has been recently addressed as Multi-objective Optimization Evolutionary Algorithms (MOEAs). One of the challenges is to attain a final solution from the set of non-dominated solutions obtained by the MOEAs. In this paper, an algorithm to build a covering of the network based on the principles of the Rough Clustering is proposed. The experiments in a synthetic networks showed that our proposal is promising and effective for overlapping community detection in social networks.


title: Approximate Decision Tree Induction over Approximately Engineered Data Features

text: We propose a simple SQL-based decision tree induction algorithm which makes its heuristic choices how to split the data basing on the results of automatically generated analytical queries. We run this algorithm using standard SQL and the approximate SQL engine which works on granulated data summaries. We compare the accuracy of trees obtained in these two modes on the real-world dataset provided to participants of the Suspicious Network Event Recognition competition organized at IEEE BigData 2019. We investigate whether trees induced using approximate SQL queries – although execution of such queries is incomparably faster – may yield poorer accuracy than in the standard scenario. Next, we investigate features – inputs to the decision tree induction algorithm – derived using SQL from a bigger associated data table which was provided in the aforementioned competition too. As before, we run standard and approximate SQL, although again, that latter mode needs to be checked with respect to the accuracy of trees learnt over the data with approximately extracted features.


title: Fast and Furious: Outrunning Windows Kernel Notification Routines from User-Mode

text: Modern Operating Systems (OSs) enable user processes to obtain full access control over other processes initiated by the same user. In scenarios of sensitive security processes (e.g., antivirus software), protection schemes are enforced at the kernel level such as to confront arbitrary user processes overtaking with malicious intent. Within the Windows family of OSs, the kernel driver is notified via dedicated routines for user-mode processes that require protection. In such cases the kernel driver establishes a callback mechanism triggered whenever a handle request for the original user-mode process is initiated by a different user process. Subsequently, the kernel driver performs a selective permission removal process (e.g., read access to the process memory) prior to passing a handle to the requesting process. In this paper we are the first to demonstrate a fundamental user-mode process access control vulnerability, existing in Windows 7 up to the most recent Windows 10 OSs. We show that a user-mode process can indeed obtain a fully privileged access handle before the kernel driver is notified, thus prior to the callback mechanism establishment. Our study shows that this flaw can be exploited by a method to (i) disable the anti-malware suite Symantec Endpoint Protection; (ii) overtake VirtualBox protected processes; (iii) circumvent two major video game anti-cheat protection solutions, BattlEye and EasyAntiCheat. Finally we provide recommendations on how to address the discovered vulnerability.


title: Three-Way Decisions Community Detection Model Based on Weighted Graph Representation

text: Community detection is of great significance to the study of complex networks. Community detection algorithm based on three-way decisions (TWD) forms a multi-layered community structure by hierarchical clustering and then selects a suitable layer as the community detection result. However, this layer usually contains overlapping communities. Based on the idea of TWD, we define the overlapping part in the communities as boundary region (BND), and the non-overlapping part as positive region (POS) or negative region (NEG). How to correctly divide the nodes in the BND into the POS or NEG is a challenge for three-way decisions community detection. The general methods to deal with boundary region are modularity increment and similarity calculation. But these methods only take advantage of the local features of the network, without considering the information of the divided communities and the similarity of the global structure. Therefore, in this paper, we propose a method for three-way decisions community detection based on weighted graph representation (WGR-TWD). The weighted graph representation (WGR) can well transform the global structure into vector representation and make the two nodes in the boundary region more similar by using frequency of appearing in the same community as the weight. Firstly, the multi-layered community structure is constructed by hierarchical clustering. The target layer is selected according to the extended modularity value of each layer. Secondly, all nodes are converted into vectors by WGR. Finally, the nodes in the BND are divided into the POS or NEG based on cosine similarity. Experiments on real-world networks demonstrate that WGR-TWD is effective for community detection in networks compared with the state-of-the-art algorithms.


title: The Problem of Finding the Simplest Classifier Ensemble is NP-Hard – A Rough-Set-Inspired Formulation Based on Decision Bireducts

text: We investigate decision bireducts which extend the notion of a decision reduct developed in the theory of rough sets. For a decision table [Formula: see text], a decision bireduct is a pair (X, B), where [Formula: see text] is a subset of attributes which allows to distinguish between all pairs of objects in [Formula: see text] labeled with different values of decision attribute d, and where B and X cannot be made, respectively, smaller and bigger without losing this property. We refer to our earlier studies on deriving bireducts (X, B) from decision tables and utilizing them to construct families of rule-based classifiers, where [Formula: see text] is equal to total support of decision rules built using attributes in [Formula: see text]. We introduce the notion of a correct ensemble of decision bireducts [Formula: see text], where each [Formula: see text] must be validly classified by more than [Formula: see text] of the corresponding models. We show that the problem of finding a correct ensemble of bireducts with the lowest cardinalities of subsets [Formula: see text] is NP-hard.


title: Backstabber’s Knife Collection: A Review of Open Source Software Supply Chain Attacks

text: A software supply chain attack is characterized by the injection of malicious code into a software package in order to compromise dependent systems further down the chain. Recent years saw a number of supply chain attacks that leverage the increasing use of open source during software development, which is facilitated by dependency managers that automatically resolve, download and install hundreds of open source packages throughout the software life cycle. Even though many approaches for detection and discovery of vulnerable packages exist, no prior work has focused on malicious packages. This paper presents a dataset as well as analysis of 174 malicious software packages that were used in real-world attacks on open source software supply chains and which were distributed via the popular package repositories npm, PyPI, and RubyGems. Those packages, dating from November 2015 to November 2019, were manually collected and analyzed. This work is meant to facilitate the future development of preventive and detective safeguards by open source and research communities.


title: Modeling Use-Oriented Attribute Importance with the Three-Way Decision Theory

text: Ranking and measuring attribute importance is one of the key research topics in data mining and machine learning. Most of the existing attribute importance research relying on data-oriented approaches such as statistics and information theory perspectives. User preference, which involves a user specifying his or her preferential attitude towards a set of attributes, is another meaningful perspective. However, the research community has not paid much attention to this perspective. We adopt the three-way decision theory as a framework and concentrate on analyzing attribute importance based on user preference in this paper. In particular, we propose qualitative and quantitative analysis of attribute importance approaches that result a ranking list as well as a set of numerical weights of an attribute set. We then categorize attributes into different groups of importance using qualitative and quantitative analysis results. Finally, a unified model to analyze user-oriented attribute importance is constructed.


title: Rough Sets Meet Statistics - A New View on Rough Set Reasoning About Numerical Data

text: In this paper, we present a new view on how the concept of rough sets may be interpreted in terms of statistics and used for reasoning about numerical data. We show that under specific assumptions, neighborhood based rough approximations may be seen as statistical estimations of certain and possible events. We propose a way of choosing the optimal neighborhood size inspired by statistical theory. We also discuss possible directions for future research on the integration of rough sets and statistics.


title: Weighted Generalized Fuzzy Petri Nets and Rough Sets for Knowledge Representation and Reasoning

text: In this paper, we consider the decision tables provided by experts in the field. We construct an algorithm for executing a highly parallel program represented by a fuzzy Petri net from a given decision table. The constructed net allows objects to be identified in decision tables to the extent that appropriate decisions can be made. Conditional attribute values given by experts are propagated by the net at maximum speed. This is done by properly organizing the net’s work. Our approach is based on rough set theory and weighted generalized fuzzy Petri nets.


title: Representative Set of Objects in Rough Sets Based on Galois Connections

text: This paper introduces a novel definition, called representative set of objects of a decision class, in the framework of decision systems based on rough sets. The idea behind such a notion is to consider subsets of objects that characterize the different classes given by a decision system. Besides the formal definition of representative set of objects of a decision class, we present different mathematical properties of such sets and a relationship with classification tasks based on rough sets.


title: Towards Demystifying Subliminal Persuasiveness: Using XAI-Techniques to Highlight Persuasive Markers of Public Speeches

text: The literature provides evidence for the importance of non-verbal cues when it comes to persuading other people and developing persuasive robots. Mostly, people use these non-verbal cues subconsciously and, more importantly, are not aware of the subliminal impact of them. To raise awareness of subliminal persuasion and to explore a way for investigating persuasive cues for the development of persuasive robots and agents, we have analyzed videos of political public speeches and trained a neural network capable of predicting the degree of perceived convincingness based on visual input only. We then created visualizations of the predictions by making use of the explainable artificial intelligence methods Grad-CAM and layer-wise relevance propagation that highlight the most relevant image sections and markers. Our results show that the neural network learned to focus on the person, more specifically their posture and contours, as well as on their hands and face. These results are in line with existing literature and, thus, show the practical potential of our approach.