**1. Introduction**

Mozambique (Figure 1) is a country located in southeastern Africa (10◦30 –26◦52 S and 40◦50 –30◦31 E) covering a total land area of 800,000 km2. It is bathed by the Indian Ocean in the east and makes borders with Tanzania in the north; Malawi and Zambia in the northwest; Zimbabwe in the west and Swaziland and South Africa in the southwest.

**Figure 1.** Map of Mozambique. Red points or lines indicate the sites where Microcystin (MC) or MC producers were detected in Mozambique, and in near sites or in the shared rivers with Mozambique, Green points indicate the water sources or water treatment centers.

According to IV populational census carried out in 2017, this country has 29.67 million habitants distributed in 11 provinces [1]. The climate of Mozambique varies from subtropical climates (north and center) to dry arid (south) [2]. Like many African countries, Mozambique is highly vulnerable to climate variability and extreme weather events (droughts, floods, and tropical cyclones) [3]. Droughts are the most frequent natural disaster that have a negative impact on the population that reside in these rural areas [4]. The location of Mozambique in the coastal area makes it vulnerable to floods since many transnational river basins end [2]. Unfortunately, only 50% of the population has access to "safe drinking water". Urban areas are the most favored, with 80%, while rural and most of the population have only 35% coverage and consume untreated water daily from rivers, lakes, and small puddles that form after or during the rain [5–7], putting at risk public health.

Eutrophication of freshwater resources may lead to the occurrence of cyanobacterial blooms and the presence of cyanotoxins, being microcystins (MC), the most common toxins worldwide [8]. The presence of MC in untreated drinking water is a major threat to public health because this potent cyanotoxin causes hepatotoxicity in humans. Thus, this review evaluates the incidence of MC and its producers in drinking water bodies of Mozambique, based on reported and available data and the estimated human illness case numbers and associated economic damage caused by Microcystin both overall globally, and in Mozambique—Africa. Recommendations for routine control and monitoring of MC will also be done since this hepatotoxin is not included in water control tests data in Mozambique.

#### **2. Microcystin-Producing Species and Toxicology**

#### *2.1. Microcystin-Producing Species*

MCs are secondary metabolites produced by cyanobacteria species that occur naturally (but it can be increased severely by human activities) in freshwater environments. The most reported cyanobacteria species, which produce MCs are listed in Table 1 and include species of the families *Microcystaceae, Nostocaceae, Microcoleaceae, Oscillatoriaceae, Pseudanabaenaceae* (Table 1). The occurrence and development of a particular genus and species of cyanobacteria and cyanotoxins production worldwide seem to be conditioned to water chemistry and climate conditions [8]. In a temperate climate, *Microcystis* and *Anabaena* blooms occur widely while *Cylindrospermopsis* develops in tropical regions [9]. There are toxic and non-toxic cyanobacteria of the same species, which may be found together [8,10,11]. Toxic cyanobacteria can produce several toxins with different toxicity making it uncertain to assess the overall toxicity of bloom due to the variations of toxins concentration spatially and seasonally [12]. To distinguish toxic and non-toxic cyanobacteria species is very complicated, and consequently, the methods used are also complex. It implicates that the prevention of cyanobacteria bloom development is a suitable way to control toxic blooms [13,14].

**Table 1.** Microcystin-producing species detected in freshwater bodies.

The factors that promote the MC synthesis are not yet clearly understood, however, the optimal growth of MC-producing species and toxicity seem influenced by light intensity, nutrients, and temperature, among other factors. For example, the higher toxicity of *M. aeruginosa* extracts was verified in extreme pH values [39,40], and heavy metals such as Zinc and Iron did not influence the *M. aeruginosa* toxicity [41]. The content of nitrogen and phosphorus influenced the toxicity of *M. aeruginosa* extracts. Low nitrogen content reduces the *M. aeruginosa* toxicity, while low phosphorous increased the toxicity in the natural population [42,43] and reduced in lab experiments [16,21,44,45]. Another lab conclusion was the correlation of colony size and content of toxic cyclic heptapeptide of the non-axenic strain of *M. viridis* and axenic *M. viridis* was also verified [20,46,47]. In general, the optimal temperature for which MC-producing species produce MC ranged from 20 to 25 ◦C [21,40,48,49]. This range of optimal temperature suggests that cyanobacteria blooms are most toxic during periods with warm weather and in areas with warm climates [8].

#### *2.2. Toxicology*

Microcystins (Figure 2) are the largest diverse group of cyanobacterial toxins, and to date, more than 240 MCs analogs are known, and they vary structurally in terms of the degree of methylation, hydroxylation, epimerization, peptide sequence, and consequently in their toxic effects [50–52]. Chemically, MC is a group of monocyclic heptapeptides (numbered in Figure 2) containing both D- and L-amino acids plus N-methyldehydroalanine (Mdha) and a unique β-amino acid side-group, 3-amino-9-methoxy-2-6,8-trymethyl-10-phenyldeca-4,6-dienoic acid (Adda) and their analogs differ among them, at the two L-amino acids and on the methyl groups on D-erythro-β-methylaspartic acid (D-MeAsp) and Mdha with molecular weight varying from 900 to 1100 Daltons. MC-LR, MC-RR, and MC-YR are common MC variants, the letters L, R, and Y represent the aminoacids leucine, arginine, and tyrosine, which appear on the MC molecule in different combinations [50,53–58] being MC-LR the most studied. The biosynthesis of this group of cyanotoxin is regulated by non-ribosomal peptide synthetase and polyketide synthase domains, being *MCyS* the gene cluster, which has been sequenced and partially characterized in several cyanobacterial species of the family *Microcystaceae, Nostocaceae, Microcoleaceae, Oscillatoriaceae, Merismopediacea,* and *Pseudanabaenacea* [16–38,59,60]

**Figure 2.** General chemical structure of microcystins. The common MC variant is MC-LR when X and Y correspond to L-Leu and L-Arg.

The mechanism of MCs toxicity seems to be well understood. They bind to serine/threonine-specific protein phosphatases (PPs) such as PP1 and PP2A, inhibiting their activity [61–63]. Adda moiety (Figure 1) plays an important role in the MC toxicity group since its isomerization and/or oxidation reduces the toxicity [64,65]. The inhibition of PP1and PP2A as a result of MC acute exposure causes excessive protein phosphorylation, alterations in the cytoskeleton, loss of cell shape, and consequently destruction of liver cells leading to intrahepatic hemorrhage or hepatic insufficiency [58]. Oxidative stress increasing in cells and consequent apoptosis, which can cause tumor promotion, is another mechanism of MC toxicity [66–68].

#### **3. E**ff**ects of Microcystin in Humans, Symptoms, and Treatment**

Microcystin effects in humans depend on the time of exposure and concentration ingested [69], and the studies are based on epidemiologic data but the reported studies on laboratory animals. Human health problems are mostly caused by chronic exposure by consumption of contaminated water or food, dermal exposure, or inhalation [57]. MC human poisoning episodes were reported in different parts of the world after the consumption of contaminated water or during sport or recreational activities [59,70–72]. Some examples of MC human poisoning cases are described; America—in 1996, an episode of human intoxications by MC was reported in Brazil with more than 76 deaths of patients at two dialysis centers in Caruaru. The municipal water supplied to the dialysis centers was the source of MC [71,73–75]. In Argentina, a human poisoning caused by MC involving a young man after immersion in an intense bloom *Microcystis* sp lake during sport and recreational activities were recorded. Four hours after exposure, the patient showed nausea, abdominal pain, and fever, and 48.6 μg·L−1 of microcystin-LR was detected in the water samples [76]. Other cases were recorded in Uruguay (January 2015) involving a 20-month-old child and her family during recreational activities. These victims were admitted to the hospital with diarrhea, vomiting, fatigue, and jaundice and the analysis confirmed the presence of MC-LR (2.4 ng·g−1 tissue) and [D-Leu1]MC-LR (75.4 ng·g−1 tissue) explanted liver [77]. Africa—toxic cyanobacteria suspected intoxication cases were reported in

Zimbabwe involving children that were hospitalized in the Hospital of Harare with gastroenteritis symptoms [78]. In Europe—121 people presented abdominal pain, nausea, vomiting, diarrhea, fever, headaches, and muscle pain after consumption of untreated water from the River Kavlingean in Sweden. In this case a bloom of MC—producing such as *Planktothrix agardhii* and *Microcystis* spp. was observed in the river [79]. The most affected human organ is the liver [57]. However, in vivo and in vitro studies indicated that the kidney and colon are also affected [80–85]. The symptoms generally reported in humans due to the MC intoxication include gastroenteritis and related diseases, allergic and irritation reactions, liver diseases, tumors, and primary liver cancer and colorectal cancers, and massive hepatic hemorrhage. MC human poisoning treatment is very complicated due to the rapid, irreversible, and severe liver damage [86], however, gastric lavage [87], administration of monoclonal antibodies against MC-LR [88], immunosupressant Cyclosporine A, antibiotic rifampin [89], and membrane-active antioxidant vitamin E, taken as a dietary supplement [90] are recommended.


**11.9 Improving the internalization of the radiolabeled mAb**

One significant factor that influences the absorbed radiation dose to the tumor is the fate of the radiolabel after internalization of the radiolabeled mAb into the tumor cells. In case of internalisation, the radionuclides will come closer to the critical radiation target, i.e. nuclear DNA. Internalization of the antibody depends on various factors including the antibody, the targeted antigen and the tumor cells. On the other hand, internalisation might be disadvantageous, if it leads to quick degradation of the targeting agent followed by diffusion and elimination of the radionuclide (Boswell et al., 2007). Most antibodies including those that target antigens located on the surface of the tumor cells such as anti-CEA antibodies, are eventually catabolized. Intracellular degradation of the targeting agent can be prevented by e.g. dextranation (Bue et al., 2000). Radiolabeling of antibodies with 90Y or 177Lu is performed by linking these radionuclides to chelators (DTPA or DOTA) which are chemical moieties that complex free metal ions. These chelators are conjugated to the antibodies and subsequently to the radiolabel. After catabolization of mAbs labeled with 90Y- or 177Lu-DTPA/-DOTA, the catabolic products are conjugated to, in most cases, lysine (e.g. 90Y- or 177Lu-DOTA-lysine). Whereas radioiodinated tyrosine is excreted by the cell, the 90Y- or 177Lu-DTPA/DOTA-lysine metabolites are trapped within the lysosomes, thereby increasing the tumor retention time of these radiolabels (Koppe et al., 2005). Cellular excretion can also be limited if the radionuclides are of metal type, e.g. indium or yttrium, and this is due to intracellular retention of metal containing catabolic products (Press et al., 1996).


**2. Definition and theories of stigma**

Stigma refers to the situation of being exposed to an accusatory, exclusionary approach and attitude due to the existence of a situation that should be ashamed for a person or group, being different from normal or different from other individuals with any feature. Stigma as a word means a scar, a stain, a sign of shame and humiliation that marks the person [8]. Etymologically, the concept of stigmatization was used for the first time in Ancient Greek with the meanings of hole, puncture, wound, scar, and today it is used in the sense of humiliation and loss of reputation, similar to this definition. Sociologist E. Goffman defined stigma as social rejection resulting from negatively perceived characteristics [9]. According to Goffman, the term stamp goes back to the Greeks who cut or burned the skins of criminals, slaves, and traitors to describe them as tainted or immoral people to be avoided [9]. Stigma is a term that refers to social disapproval, not just a physical marking. Link and Phelan extended Goffman's conceptualization by identifying four attributes of stigma; (1) individual differences are noticed, (2) these differences are perceived negatively by society, (3) the stigmatized group is viewed as an outgroup, (4) the end result is loss of opportunity, power, or status [10].

While explaining the reasons for stigma, 4 terms were used. These are as follows:

a.*Stereotypes:* This concept represents the common general view of societies. It is a concept adopted by the general public. When a "sick person" is mentioned,

the person first perceives and evaluates this situation with stereotypes. Some of these definitions share commonalities, but each may also contain unique aspects that may contradict the others. Stereotypes are grouped into positive and negative.


*2.3.2. Ultrathin nanosheets of MoS2 through lithium-intercalated exfoliation*

Recently, Chhowalla's group successfully prepared monolayer MoS2 through lithiumintercalated and exfoliation [18]. In detail, they immersed 3 g bulk MoS2 crystals in certain concentration of butyllithium solution in hexane for 2 days in a flask filled with argon gas to obtain lithium intercalation compound. Exfoliation is achieved immediately after this process (within 30 min to avoid deintercalation) by ultra-sonicating LixMoS2 in water for 1 h. The mixture is centrifuged several times to remove excess lithium in the form of LiOH and unexfoliated material.

Commercial MoS2 powder (**Figure 3e**) was used to prepare highly monodisperse monolayer MoS2 nanosheets through Li intercalation and exfoliation. As shown in **Figure 3f** and **g**, the lateral size of product is 300∼800 nm and the average thickness is about 1∼1.2 nm, which is larger than the dimension of 0.65∼0.7 nm reported for mechanically exfoliated MoS2 mono‐ layers. This discrepancy may be explained by surface corrugation due to the distortions and the presence of adsorbed or trapped molecules. The absence of any sheets below the thickness values and no evidence of step edges on the nanosheets surface suggest that they consist of monolayers. The selected area electron diffraction (SAED) patterns indicate hexagonal symmetry of the atomic arrangement and that individual sheets consist of a single-crystal domain (**Figure 3h**). All of the results undoubtedly confirm that MoS2 with monolayer thickness has been successfully synthesized.

#### **2.4. Ion-change exfoliation**

layers. The black phosphorus nanosheets show excellent photodegradation of organic

Ultrasonic exfoliation is incapable of peeling off a single layer of 2D nanostructure; herein, lithium (Li) intercalation process is introduced for synthesizing single sheets. The scheme of lithium-intercalated exfoliation strategy is shown in **Figure 3a** [15]. The formation of LixXS2 compound is a key step in lithium intercalation process, and this reaction can be tuned to control the yield of single layers [16, 17]. The yield of this strategy for obtaining single-layer transition metal dichalcogenide is nearly 100%, while some challenges still remain. The first one is that the experiment is carried out at high temperature for long durations. Also, the lithium intercalation must be controlled carefully to obtain single-layer nanosheets, while preventing the formation of metal nanoparticles and precipitation of Li2S. To make readers understand this method more in-depth, in the following, we would take MoS2 as an example

**Figure 3.** (a) Electrochemical lithiation process for the fabrication of 2D nanosheets from the layered bulk material. (b– d) Morphology characterization of MoS2 exfoliated by electrochemical lithium-intercalated and exfoliation process [15].

(e–h) Morphology characterization of MoS2 exfoliated by lithium-intercalated exfoliation process. [18].

components such as DPBF and MO.

to introduce this strategy.

**2.3. Lithium-intercalated and exfoliation**

6 Two-dimensional Materials - Synthesis, Characterization and Potential Applications

Although the exfoliation strategy mentioned previously is convenient to delaminate van der Waals solids into ultrathin nanostructure, it is hard to exfoliate layered ionic solids such as LiCoO2 or LDHs. This is because these ionic solids have strong ionic bonds in the layers. Ionchange exfoliation is a normal method to get this kind of 2D materials [19–23]. The scheme of ion-change exfoliation is shown in **Figure 4a** [19]. In the next section, we will take LiCoO2 and (Co2+-Co3+)-LDHs as examples to illuminate this method [23, 24].

**Figure 4.** (a) Schematic illustration of the osmotic swelling to exfoliation process [19]. (b) AFM image and height profile of the exfoliated cobalt oxide adsorbed onto PEI-coated mica substrate. Photograph of the colloidal suspension of co‐ balt oxide. (c) The visible light is illuminated from the side of the beaker to demonstrate the Tyndall scattering effect [23]. Co2+–Co3+ LDH nanosheets: (d) AFM image; (e) TEM image [24].

*2.4.1. LiCoO2*

LiCoO2 is a kind of cation-exchange layered metal oxides. Seong-Ju's group delaminated LiCoO2 into monolayer recently [23]. The procedure of exfoliating could be described as follows: A proton-exchange reaction for the generation of layered LixH1-xCoO2 is carried out in an aqueous solution of HCl (1 M, 1 mg mL−1 of LiCoO2) at room temperature for 3 days. During the proton-exchange reaction, HCl solution is replaced with fresh solution every day. Exfoliation of the layered cobalt oxide is achieved by the intercalation of tetrabutylammonium (TBA) cation into the layers of LixH1-xCoO2. After the reaction, incompletely exfoliated particles were separated from the colloidal suspension through centrifugation (6000 rpm for 10 min), resulting in a pure colloidal suspension. The powdered samples of completely exfoliated nanosheets are collected from the pure colloidal suspension through high-speed centrifugation at 17,000 rpm for 30 min.

AFM of the as-obtained sample provides direct evidence for the exfoliation of layered cobalt oxide nanosheets (**Figure 4b**). The thickness of LiCoO2 nanosheets is about 1.2 nm, which is slightly thicker than the crystallographic thickness of individual cobalt oxide layers. The observation of Tyndall phenomenon from the pure suspension, characteristic of colloidal suspensions, provides strong evidence for the exfoliation of layered cobalt oxide nanosheets too (**Figure 4c**).


*3.2. Course of Chronic Milk-FPIES*

The children were observed from 2 to 6 years of age. The weight deficiency present at the time of chronic milk-FPIES diagnosis regressed in all children by the twelfth month of age. Most of them reached a weight and height range of 25–75 c (48 children, 87%). Hypoproteinemia resolved within 2 months, and the treatment of iron deficiency anemia was usually completed before 6 months of age. Blood count abnormalities resolved within 1–2 months.

The chronic milk-FPIES symptoms in the twelfth month of life were absent in 62% of children, in the nineteenth month—in 78% of children (Table 2). The median duration of treatment with a milk-free diet was 16.8 months (range: 9.3–34.6 mo). After 18 months of observation the three of patients discontinued their participation in the project (they did not report for control milk provocations). After 25 months of age, chronic milk-FPIES symptoms persisted only in one child. They disappeared at the thirty-seventh month of age.

During the 3. OFC and 4. OFC three children showed symptoms of IgE-CMA (Table 2). It was the transition of chronic-milk-FPIES to IgE-CMA.

In the next 2–4 years of follow-up, symptoms of IgE-mediated allergy were found following the ingestion of soya (1 child) and peanuts (2 children) (Table 3). All of them developed extensive urticaria.

At the time of diagnosis, 10 children (18%) had mild atopic dermatitis (AD) (Table 2). By the age of 2, it had resolved in 3 children and by the third year in another 2 children (50%). In the following years, 4 children (7%) developed hay fever and 3 children (5%) developed bronchial asthma (house dust mite allergy).


**2.1 For computing-experiment**

### **2.1.1 e-Science research environment**

We define a computing-experiment tool as an e-Science research environment. In order to study particle physics, we can access the environment anytime and anywhere even if we are not on-site an accelerator laboratory. A virtual laboratory enables us to perform research as if we were on-site (Cho, 2008).We apply e-Science components to the CDF experiment.

#### **<IP_ADDRESS> Data production**

The purpose of data production is to take both on-line shifts and off-line shifts anywhere. On-line shifts have been conducted through the use of a remote control room at KISTI and off-line shifts have been conducted via the sequential access through metadata (SAM) data handling (DH) system at KISTI. The remote control room is built to help non-US CDF members to fulfill their shift duties as a Consumer Operator (CO) part of the CDF data taking shift crew. The remote control room facilitates various monitoring applications that the CO has to monitor for a given eight hour shift. We have been operating the CDF remote control room at KISTI since July 22, 2008. A real Data Acquisition (DAQ) has been recorded at the remote control room at KISTI between August 1 and August 8, 2008. The CDF detector is an experimental apparatus for recording electrical events produced by the accelerator at an enormous rate. This apparatus is comprised of several components that perform different functions including a detector with millions of data channels transmitted to a corresponding number of electronic readout devices. The operation of an apparatus with this degree of complexity needs to be collaboratively controlled by researchers. In general, each shift crew takes an eight hour shift so that three shift crews will cover 24 hours. In the CDF experiment, the shift crew consists of three people with different missions. First, the Science Coordinator (SciCo) is responsible for the entire shift session and must have a lot of experience. The second person is the Ace shifter, who is an expert on the control of all detector components and electronic readout devices. The third person is the CO who has been trained in interpreting the meaning of the data being monitoring. UNIX processes intercept the on-line data transmitted from the front-end readout electronics and generate various plots that represent the quality of the data taken by the detector. These plots help the CO to determine whether or not the data collection is continuing as expected. Accordingly, the CO advises the Ace shifter to interrupt the detector operation in order to correct any problems.

Although the CO's monitoring task involves on-line data collection, this can be performed in a remote location due to its mostly monitoring-related nature. These remote control rooms are located at the Pisa University in Italy, the University of Tsukuba in Japan, and KISTI in Korea. In Korea, there are about 30 collaborators from six institutions, most of which have to fulfill CDF duties by taking detector operation shifts. All the plots that the consumers generate are accessible via web browsers where all the monitoring can be done. The CO has to not only monitor any plots generated by consumers but also must monitor

The e-Science Paradigm for Particle Physics 79

We perform another type of remote data handling shift at KISTI. Whereas the remote control room implements an on-line version of remote data handling, there is a second shift that implements an off-line version of remote data handling. This second type of shift is actually in the form of a SAM DH shift. This shift also occurs eight hours per day for seven days. These shifts do not need to cover the entire twenty four hours with three shifts per day since they are off-line. Furthermore, one can take the shift in the daytime of his or her time zone if participating in the shift schedule outside of the USA. The CDF SAM DH is called off-line since the data handled in this case includes data inbound to the tape from SAM stations in reconstruction farms and vice versa. The off-line data transfers in CDF are between SAM stations and mass storage system (MSS). In Fermilab, MSS consists of a Storage Resource Manager (SRM), dCache, and the Enstore system. The dCache software was the result of joint project between Fermilab in Batavia, USA and DESY (Deutches Elecktronen SYnchrotron laboratory) in Hamburg, Germany. dCache is a front-end for disk caching and provides end-users with the functionalities of reading cached files and writing files to and from Enstore indirectly via dCache. The Enstore system is a direct interface to files on tape for end-users. End-users can refer to SAM stations of CAF and farm machines. In the present context, the SAM stations in the CDF Analysis Farm (CAF) and farm clusters use an Application Programming Interface (API) provided by dCache to read files from and write files to the tapes via dCache and the Enstore systems. Thus, the mission of the CDF SAM shift includes monitoring the Enstore system, the dCache system, and SAM stations of

Data processing is accomplished using a High-Energy Physics (HEP) data grid. The objective of the high-energy physics data grid is to construct a system to manage and process high-

For data processing, Taiwan has the only WLCG Tier-1 center and Regional Operation Center in Asia since 2005. ASGC has also been serving as the Asia Pacific Regional Operational Center to maximize grid service availability and to facilitate extension of e-Science (Lin & Yen, 2009). In Japan, a Tier-2 computing center supporting the A Toroidal LHC Apparatus (ATLAS) experiment has been running at the University of Tokyo. There is another Tier-2 center at Hiroshima University for the A Large Ion Collider Experiment (ALICE) (Matsunaga, 2009). At KEK, collaborating institutes operate a grid site as members of the WLCG. These institutes try to use their grid resources for the Belle and Belle II experiments. The Belle II experiment, which will start in 2015, will use distributed

We explain the history of data processing for the CDF experiment. The CDF is an experiment on the Tevatron, at Fermilab. The CDF group ran its Run II phase between 2001 and 2011. CDF computing needs include raw data reconstruction, data reduction, event simulation, and user analysis. Although very different in the amount of resources needed, they are all naturally parallel activities. The CDF computing model is based on the concept of a Central Analysis Farm. The increasing luminosity of the Tevatron collider has caused the computing requirement for data analysis and Monte Carlo production to grow larger than available dedicated CPU resources. In order to meet demand, CDF has examined the possibility of using shared computing resources. CDF is using several computing processing systems, such as CAF, Decentralized CDF Analysis Farm (DCAF), and grid systems. The

energy physics data and to support the high-energy physics community (Cho, 2007).

the CDF analysis farm (CAF) and the CDF experiment farm.

**<IP_ADDRESS> Data processing**

computing resources.

the consumers themselves. However, the policy imposed by the Department of Energy (DOE) in the United States prohibits any remote researcher outside of Fermilab from executing any control-related UNIX command. Instead, control-related execution must be initiated by a person on-site. At the same time, all transmissions of control commands have to be encrypted using Kerberos. Thus, we can solve this problem by having an on-site crew send a graphic user interface (GUI) named "consumer controller" to the remote monitor via the Kerberized secure shell port. The CDF II experiment has been taking data from June 30, 2001 to September 30, 2011. Fig. 2 shows the CDF main operation center and remote control room at KISTI. As shown in Fig. 3, we have taken remote shifts (24 days per year on average) successfully.

Fig. 2. The CDF main operation center and remote control room at KISTI.

Fig. 3. The CDF remote control used at KISTI.

78 Particle Physics

the consumers themselves. However, the policy imposed by the Department of Energy (DOE) in the United States prohibits any remote researcher outside of Fermilab from executing any control-related UNIX command. Instead, control-related execution must be initiated by a person on-site. At the same time, all transmissions of control commands have to be encrypted using Kerberos. Thus, we can solve this problem by having an on-site crew send a graphic user interface (GUI) named "consumer controller" to the remote monitor via the Kerberized secure shell port. The CDF II experiment has been taking data from June 30, 2001 to September 30, 2011. Fig. 2 shows the CDF main operation center and remote control room at KISTI. As shown in Fig. 3, we have taken remote shifts (24 days per year on

Fig. 2. The CDF main operation center and remote control room at KISTI.

Fig. 3. The CDF remote control used at KISTI.

average) successfully.

We perform another type of remote data handling shift at KISTI. Whereas the remote control room implements an on-line version of remote data handling, there is a second shift that implements an off-line version of remote data handling. This second type of shift is actually in the form of a SAM DH shift. This shift also occurs eight hours per day for seven days. These shifts do not need to cover the entire twenty four hours with three shifts per day since they are off-line. Furthermore, one can take the shift in the daytime of his or her time zone if participating in the shift schedule outside of the USA. The CDF SAM DH is called off-line since the data handled in this case includes data inbound to the tape from SAM stations in reconstruction farms and vice versa. The off-line data transfers in CDF are between SAM stations and mass storage system (MSS). In Fermilab, MSS consists of a Storage Resource Manager (SRM), dCache, and the Enstore system. The dCache software was the result of joint project between Fermilab in Batavia, USA and DESY (Deutches Elecktronen SYnchrotron laboratory) in Hamburg, Germany. dCache is a front-end for disk caching and provides end-users with the functionalities of reading cached files and writing files to and from Enstore indirectly via dCache. The Enstore system is a direct interface to files on tape for end-users. End-users can refer to SAM stations of CAF and farm machines. In the present context, the SAM stations in the CDF Analysis Farm (CAF) and farm clusters use an Application Programming Interface (API) provided by dCache to read files from and write files to the tapes via dCache and the Enstore systems. Thus, the mission of the CDF SAM shift includes monitoring the Enstore system, the dCache system, and SAM stations of the CDF analysis farm (CAF) and the CDF experiment farm.


**4.1. Dichorionic pregnancies**

Finberg et al. [46] suggested repeat scans every 4–6 weeks for noncomplicated dichorionic pregnancies. However, in current daily medical routine, surveillance is closer: follow-up ultrasound assessments are performed every 3–4 weeks [4, 47]. But, if a complication is suspected, and more specifically when CRL, estimated fetal weight or amniotic fluid volume are different between the two fetuses, routine scans have to be repeated every 2 weeks, or within a week [48].


**7. The role of autophagy in preventing terminal senescence**

Our further observations in the PA1 model showed the importance of autophagy in withstanding the proteotoxic stress following ETO treatment and its crucial role in maintaining viability; inhibition of autophagy culminated in chromatin fragmentation and nuclei disintegration [46, 57]. Stress-activated OCT4A mainly colocalized, and correlated in its nuclear concentration in individual cells, with activated AMPthr172 kinase (**Figure 5A, B**). AMP-activated protein kinase (AMPK) serves as a general energy depletion sensor and activator of autophagy. The energy stress-response of AMPK is also tightly linked to the DDR of p53 [58, 59] and can induce a p53-dependent glucose-sensitive metabolic checkpoint [51] precipitating apoptotic cell death from the G1/S and likely autophagic death from G2M checkpoint [60].

We found, in addition, that active autophagy in PA1-ETO cells sequestered p16INK4A aggresomes within the autophagic vacuoles (**Figure 5C** i–ii), while disability of autophagy enabled p16 diffuse distribution in the cell nuclei and caused terminal senescence with nuclear disintegration (**Figure 5** iii-iv) preventing survival of ETO-treated cells [46]. The p53-dependent role of AMPK, its relationship with stress-activated OCT4A, and the role of autophagy in the prevention of terminal senescence for TP53 wild-type CSC cells is schematically presented in **Figure 3**.

However, in *TP53* mutants, the cells find an additional pathway for potential recovery – they undergo mitotic slippage and employ polyploidy for repair and sorting the damaged DNA.

**Figure 4.** Population heterogeneity in expression of OCT4A and p21CIP1 in response of PA1 cells to ETO treatment. (**A**) Population heterogeneity in expression of OCT4A and p21CIP1 is extended for days 2–6 post ETO treatment, before recovery from day 7, when it reduces; (**B**) Population heterogeneity is accompanied by a degree of polyploidy as represented by DNA histograms on day 4, with underreplication of the cells in late S-phase of diploid and polyploid cell

cohorts; this phenomenon is enhanced after impediment of autophagic flux. Republished from Ref. [46].

**Figure 5.** Autophagic response of PA1 cells to ETO-mediated DNA damage: (**A**) Scatterplot of image cytometry of OCT4A and pAMPK in individual cells assessed on day 4 after ETO treatment. There is a clear correlation between enhanced expression of OCT4A and pAMPKThr172 of ETO-treated cells; (**B**) OCT4A and pAMPKThr172 immunofluorescence in PA-1 cells 4 days after ETO-treatment. (**C**) The relationship between autophagy and senescence. PA1-ETO cells were treated without or with Bafilomycin A1, prior to media being removed and replaced with fresh media; cells were harvested 48 h later (day 4). (i) imunofluorescent staining for p16ink4a, LAMP2 or DAPI as shown via the BRG optical filter, (ii) only LAMP2 demonstrating high level of functional autophagy sequestrating p16ink4a-containing aggresomes. (iii–iv) shows an example of autophagic failure, where sequestration of p16ink4a is partly lost, diffusing into the cytoplasm and nucleus, which is destroyed. Bars = 10 μm. Republished from Ref. [46].


**3.4 SOM-ir cells and fibers in the PeVN**

Total numbers of SOM-ir cells were roughly comparable between age groups (Figure 1). In the young animals, SOM-ir numbers were not significantly affected by time after E2 treatment, although they appeared to be consistently lower at ZT5 compared to ZT11 (Figure 1A). In middle-aged rats, total numbers of SOM-ir cells were significantly lower at ZT 5 on day 1 compared to day 2 (Figure 1B).

SOM-ir cells within the PeVN showed a clear rostro-caudal distribution pattern, with maximal numbers of cells appearing in the more caudal part of the PeVN. The distribution in young females varied slightly over the different time points after E2 treatment: maximal numbers of SOM-ir cells were found consistently in PeVN section 8 at ZT5, but in PeVN section 7 at ZT11 on both days (Figure 2A and B). In middle aged female, the rostro-caudal distribution pattern in the number of SOM-ir cells at ZT 5 on day 1 (Figure 2C) was absent, e.g. the number of SOM-ir cells was comparable between PeVN sections. Distribution

Plasma P levels increased gradually during proestrus, but did not show a distinct peak during the time window evaluated. Basal P levels defined by LH release characteristics showed no significant differences between groups and age did not affect P profiles (Table 1). Total P levels correlated significantly with basal P levels (r=0.876 with *P* < 0.001) and with P surge levels (r=0.992 with *P* < 0.001), in line with the observed elevation in P levels during

Administration of Ovalyse at ZT 5.5 resulted in a rapid and consistent increase in LH plasma levels irrespective of age (Table 2). Highest plasma LH levels were measured at 1 or 2 h after Ovalyse administration and decreased thereafter (defined as the 'early', induced LH surge). After ZT 8.5, LH levels increased again (defined as the 'late', endogenous LH

LH levels were of comparable magnitude at 1 and 2 h after Ovalyse injection between 4 and 8.5 months old females. Also, the second, endogenous LH surge was comparable in magnitude (peak height and total LH levels) between groups and accompanied by a gradual

peak height total peak height total total total total total 4 12 30.0 ± 1.6 66.9 ± 2.5 14.8 ± 1.7 48.9 ± 5.4 115.9 ± 6.0 172.9 ± 12.2 365.5 ± 30.3 538.3 ± 42.0 8.5 10 31.0 ± 3.1 63.4 ± 7.2 16.3 ± 2.2 55.0 ± 7.7 118.4 ± 12.6 157.8 ± 17.9 338.2 ± 45.6 496.0 ± 61.9

all samples <ZT9 >ZT9 all samples

P following Ovalyse®

Table 2. LH and P surge characteristics following Ovalyse® administration in young and middle-aged 4-day cyclic rats on proestrus. The surge was divided into a 'early' part (ZT<9; 'induced' surge) and a 'late' surge (ZT>9; 'endogenous' surge). Measured characteristics: peak height of the 'early' and 'late' LH surge (the highest concentration measured), and the total amount of LH or P released during the 'early' and 'late' and the entire sampling period (cumulative LH or P levels during the corresponding sampling periods). All data are

Total numbers of SOM-ir cells were roughly comparable between age groups (Figure 1). In the young animals, SOM-ir numbers were not significantly affected by time after E2 treatment, although they appeared to be consistently lower at ZT5 compared to ZT11 (Figure 1A). In middle-aged rats, total numbers of SOM-ir cells were significantly lower at

SOM-ir cells within the PeVN showed a clear rostro-caudal distribution pattern, with maximal numbers of cells appearing in the more caudal part of the PeVN. The distribution in young females varied slightly over the different time points after E2 treatment: maximal numbers of SOM-ir cells were found consistently in PeVN section 8 at ZT5, but in PeVN section 7 at ZT11 on both days (Figure 2A and B). In middle aged female, the rostro-caudal distribution pattern in the number of SOM-ir cells at ZT 5 on day 1 (Figure 2C) was absent, e.g. the number of SOM-ir cells was comparable between PeVN sections. Distribution

**3.2 Proestrous P levels**

the entire sampling period.

surge).

(mo) n

**3.3 Pituitary responsiveness**

increase in P levels comparable between ages.

<ZT9 >ZT9 age

expressed as group means SEM in ng/ml (concentrations).

LH following Ovalyse®

**3.4 SOM-ir cells and fibers in the PeVN**

ZT 5 on day 1 compared to day 2 (Figure 1B).

patterns at other time points were in general comparable with those found in the young rats, i.e. maximal numbers of SOM-ir cells in PeVN section 7 at ZT 11 and in PeVN section 8 at ZT 5 on day 2 (Figure 2D).

Fig. 1. Total number of SOM-ir cells (sum of PeVN sections 4-8) in young (4.5 months old) and middle aged (9 mo old) Wistar rats at different time points after E2 treatment. ZT 5: 2 (day 1) or 26 (day 2) h after E2 treatment, ZT 11: 8 (day 1) or 32 (day 2) h after E2 treatment. n=5 for each young age group, numbers within base of bars indicate the number of animals.

Fig. 2. Rostral to caudal distribution of SOM-ir cells in the PeVN of young (4.5 months old) (A and B) and middle aged (C and D) OVX females at different time points after E2 treatment. Numbers within base of bars indicate the number of animals.

Somatostatin in the Periventricular Nucleus of the Female Rat:

GnRH priming mechanism may be particularly affected.

**4.2 Reproductive aging and the ovary**

1984) and impaired GnRH priming (Brito et al,1994; Keizer et al, 2001).

explained by an increased responsiveness to LH stimulation.

**4.3 Reproductive aging and the hypothalamus**

**4.1 Reproductive aging and the pituitary gland**

Age Specific Effects of Estrogen and Onset of Reproductive Aging 79

The attenuation of the natural LH surge at 8.5-months old is in accordance with previous reports concerning other rat strains (Brito et al, 1984; DePaolo et al, 1986; Krieg et al, 1995; Nass et al, 1984). Some studies suggested that the decrease in proestrus LH levels with age may follow changes at the level of the pituitary gland, such as changes in LH storage and/or release capacity (Matt et al, 1998; Wise et al, 1984). The results of the present study, however, suggest that this is not the case. Although the timing of GnRH analog administration was early (i.e. 3 to 4 hours before the natural LH surge occurred), no age-related differences in total and peak LH levels of the 'induced' LH surge (until ZT9) were observed. This implies that LH responsiveness to a bolus of GnRH is comparable between 4- and 8.5-month-old rats. Others did show that the acutely releasable pool of LH was reduced at the age of 9-12 months in cyclic Sprague-Dawley rats (Brann and Mahesh, 2005; Wise et al, 1984). In addition, pituitary responsiveness to GnRH in vitro is decreased in 10-12 month-old Long-Evans rats that show attenuated LH surges (Brito et al, 1994), and in pituitaries from 9 compared to 4-month-old Wistar rats that were tested in a superfusion system in our lab (Keizer et al, 2001). Since the age-related reduction in LH release after GnRH stimulation was more evident during the second and third stimulus in all studies, this suggests that the

Yet, we found no age-related differences in total LH levels of the 'late', 'endogenous' LH surge that results from endogenous GnRH release. Since the LH surge requires repeated pulses of GnRH to induce full pituitary priming, the absence of these age-related changes in this study suggest that GnRH priming is not significantly affected in our 8.5-month-old rats. The time between GnRH stimuli, however, differs between endogenous GnRH release (~1 hour between pulses) and our stimulus with the long-acting GnRH-analog Ovalyse® (~3 hours). Altogether, these results indicate that in our 8.5-month-old females, the attenuation of the LH surge is not caused by a diminished responsiveness of LH to initial GnRH signaling, although reproductive aging may eventually result in a decrease in the releasable pool of LH (Wise et al,

In the present study we showed that proestrous P levels were comparable between 4- and 8.5-month-old rats, and thus do not underlie the observed attenuation of the LH surge. In contrast, another study (Miller and Riegle, 1980) showed that the attenuated preovulatory LH surge was accompanied by an attenuated P surge in 12-month-old cyclic Long-Evans rats. It has been suggested that attenuated P levels result from a decrease in proestrous LH levels, although increased responsiveness of the ovary to hCG stimulation in regular cyclic middle-aged compared has been reported for Long-Evans rats (Chern et al, 2000). Consequently, the lack of concurrent changes in P and LH release in our rats could be

Based on these data, we hypothesize that the initial attenuation of the LH surge is indeed initiated by alterations at the hypothalamic level (i.e. GnRH release), and not at the pituitary

A previous study by Rubin (Rubin, 1992) showed that the secretory capacity of the GnRH system is still intact in middle-aged rats, but that the LH secretion per GnRH burst during

gland (i.e. responsiveness to GnRH, GnRH priming) or the ovary (P levels).

In young animals, the area occupied by SOM-ir fibers in the PeVN region was significantly different at ZT5 on day 1 compared to ZT11 day 2 (Figure 3A). The total area of SOM-ir fibers, i.e. including the fibers projecting to the ME, was significantly different between ZT5 and ZT11 on both days (Figure 3B).

Fig. 3. Area of SOM-ir fibers in the PeVN region (A) or total SOM-ir area (B) in young (4.5 months old) OVX female Wistar rats at different time points after E2 treatment. a significantly different from b (p=0.047; Bonferroni); c significantly different from d (p ≤ 0.05; Tukey HSD). n=5 for each group.


**2.1 Graphical representations of impedance measurements**

As mentioned before, data obtained in electrochemical impedance spectroscopy tests are reported by the potentiostat equipment in the following two ways:

a. Real component of total impedance (Z real) and imaginary component of total impedance (Z imaginary), as shown in **Figure 9**.

Although the International Union of Pure and Applied Chemistry (IUPAC) conventions hold that the real part should be represented by Z0 and the imaginary part denoted by Z″, the use of this notation can also be found as Z with a subscript notation of "r" for the real part and "j" for the imaginary part.

b. Impedance modulus (|Z|) and phase shift (angle) (°) versus the frequency (*f*), as shown in **Figure 10**.

These two methods of describing impedance measurements are the basis for two common ways of presenting data, called Nyquist and Bode plots, respectively.

**Figure 9.** *Impedance data presented in -Zimaginary (Z″)* versus *Zreal (Z*0 *) representation.*

*Electrochemical Impedance Spectroscopy and Its Applications DOI: http://dx.doi.org/10.5772/intechopen.101636*

**Figure 10.** *Impedance data presented in phase shift and total impedance* versus f *representation.*

Nyquist plots show the correlation between the real and imaginary parts of the impedance when the frequency varies. The difficulty of Nyquist plots is that frequency information cannot be directly shown. However, Bode plots show how the magnitude and phase angle of impedance change as a function of the frequency [6]. Because of this, it is important to have a complete understanding of both Nyquist and Bode plots, when analyzing impedance data.

The impedance modulus |Z|, the phase shift, ∅ (°), and the real and imaginary components of the total impedance are related to each other according to the following expressions.

$$\left|\mathbf{Z}\right|^2 = \mathbf{Z}'^2 + \mathbf{Z}''^2. \tag{10}$$

$$\tan \mathfrak{Q} = \frac{Z''}{Z'}.\tag{11}$$

$$Z' = |Z|\cos\mathfrak{D}.\tag{12}$$

$$Z'' = |Z|\text{sen}\mathfrak{D}.\tag{13}$$

The previous analysis has not considered the fact that all electrodes show a capacitance, called "double layer capacitance" (Cdl), which is independent of Faradaic reactions. On the other hand, an electrical resistance, associated with the resistance of the electrolyte (Rsol), exists between the point at which the potential is measured (usually the tip of the Luggin capillary) and the working electrode. This resistance will also manifest itself in the total impedance of the system.

#### **2.2 EIS data manipulation and fitting**

All Kramers–Kronig-consistent impedance spectra may be fitted to an equivalent circuit, which is the point of using a measurement model. The effects of Cdl and Rsol can be considered in impedance analysis if their magnitudes are known. They can also be determined by measurements in the absence of the pair of electroactive species. However, determining the Cdl and Rsol values separately increases the complexity of experimentation and information analysis. An analysis method that avoids the need to separate measurements is derived from a process widely used in other areas, such as electrical engineering, and adapted for electrochemical applications. This method is called "complex plane impedance analysis."

Considering a simple series circuit of resistance and capacitance, the total impedance is equal to

$$Z = R + \frac{1}{j\alpha \mathbf{C}},\tag{14}$$

where the real part of Z is simply *R* and the corresponding imaginary part is 1 *jωC*.

If the behavior described by Eq. (14) is represented in a diagram of Z = Z0 + Z″ (Argand diagram), where Z0 = real component of total impedance and Z″ = imaginary component of total impedance, the graph of **Figure 11** will be obtained. In this case, the corresponding graph is a series of points at different values of *ω*, where the value of the imaginary impedance component (Z″) tends to zero as the frequency tends to infinity. Here, the capacitance can be considered as short-circuited. Additionally, it is necessary to mention that, in electrochemical studies, the imaginary component of the total impedance (Z0 ) is usually multiplied by �1. This is because, in strict mathematical rigor, in most of these systems, Z″ has negative a value (as shown in **Figure 9**).

The ohmic resistance-adjusted phase angle has an asymptotic value of �90° at high frequency when an electrode is considered "ideally polarizable." If there is a constant-phase element (CPE), the asymptotic value at high frequency would be lower than 90°. Thus, plots of the ohmic resistance-adjusted phase angle present a direct demonstration of a capacitive behavior or frequency dispersion behavior.

The constant-phase element (CPE) is frequently used to improve the fit of models of impedance data. Then, a capacitance may be obtained from a distribution of time constants along the electrode surface. However, not all time-constant distributions lead to a CPE. The ohmic resistance-corrected phase angle gives a useful method to establish whether a time-constant distribution is represented by a CPE or a Cdl.

Every graphical result can be described in an equivalent circuit. For the results shown in **Figure 11**, two main elements appear: (i) Rsol, which corresponds to the resistivity of the electrolyte, and (ii) a capacitor in series to the Rsol, which assumes

**Figure 11.** *Representation of Eq. (14).*

*Electrochemical Impedance Spectroscopy and Its Applications DOI: http://dx.doi.org/10.5772/intechopen.101636*

**Figure 12.** *Electrical equivalent circuit consisting of Rsol in series to a capacitor (Cdl).*

the formation of the double layer or the deposition of charged species over the electrode surface. The equivalent circuit is shown in **Figure 12**.

In other cases, the real and imaginary components of the total impedance can behave as parallel combinations of resistors and capacitors. Thus, the response is characterized by the presence of a semicircle. At low frequencies, the impedance is purely resistive because the reactance of the capacitor is very large. The diagram in **Figure 13** corresponds to the simplest analogy of a Faradaic reaction on an electrode with an interfacial capacitance, Cdl. Graphically, in a Nyquist plot, the initial point of the semicircle corresponds to the Rsol, while the final point that reaches the *x*-axis minus the value of the Rsol, corresponds to an Rct. Here, it is important to consider that, for a redox reaction to occur, the species must be close enough to allow the electron transfer. Then, a capacitor (Cdl) or CPE in parallel will always be used in this circuit model.

This representation allows obtaining a simile of an electrochemical reaction and increases the complexity of the analysis. Then, if the Rsol or Re value is high, the semicircle will be shifted to higher values of the *x*-axis of the graph. The corresponding electrical circuit is shown in **Figure 14**.

Another representation that is common to see in practice is a diffusional component in series to the Rct. This diffusional component is called "Warburg element" (denoted as W) and it is shown as a 45° linear response right after the semicircle

#### **Figure 13.**

*Representation of the real and imaginary components of the total impedance of a parallel combination of a resistor and a capacitor.* α*, a parameter associated with the CPE.*

#### **Figure 14.**

*Equivalent circuit in which a mechanistic interpretation of the system under study is used to extract a meaning for the faradaic impedance, Rct; double-layer constant-phase element, CPEdl; and ohmic resistance of the solution, Re.*

**Figure 15.** *Equivalent circuit corresponding to the Rsol in series with a Warburg element, and a capacitor in parallel.*

closes at the *x*-axis (**Figure 15**). Warburg semi-infinite diffusion is an impedance element, which describes the diffusion behavior of the electrolyte in the absence of convection with a diffusion layer that can spread to infinity.

When this electrical component is shown, the equivalent circuit is called the "Randles equivalent circuit" (**Figure 16**).

The circuit representation of the process model shown in **Figure 16** has a corresponding mathematical expression given as

$$Z = R\_{\epsilon} + \frac{R\_{\epsilon t} + W\_o}{1 + (j\omega)^a Q (R\_{\epsilon t} + W\_o)},\tag{15}$$

where Re is the ohmic resistance, Rct is the charge-transfer resistance, Wo is the diffusion impedance, and α and Q are parameters for a CPE, that is, Z = ((jω) α Q) CPE�1 . The diffusion impedance is expressed in terms of a diffusion resistance, Rd, and a dimensionless diffusion impedance, �1/θ0 (0), as:

*Electrochemical Impedance Spectroscopy and Its Applications DOI: http://dx.doi.org/10.5772/intechopen.101636*

#### **Figure 16.**

*Electrical circuit corresponding to a high-frequency constant-phase element (CPE) behavior, which is associated with the double layer. Re (Rsol) is the ohmic resistance; Rct is the charge-transfer resistance associated with electrode kinetics; Wo, diffusion impedance is associated with the transport of reactive species to the electrode surface.*

**Figure 17.** *Nyquist plot with more than one Rct denoted by semi-circles.*

$$\mathcal{W}\_o = R\_d \left( \frac{-1}{(\theta)'(0)} \right),\tag{16}$$

where θ is the dimensionless concentration phasor scaled to its value at the electrode surface, and θ0 (0) is the derivative with respect to the position evaluated at the electrode surface [7].

Once we obtain the measured data and the corresponding Nyquist and Bode plots, the data can be fitted with these corresponding electrical equivalent circuits. Most of the software that are in the potentiostats can fit the results by using common electrical circuits. Nevertheless, it is highly important to know what kind of materials we are using in practice to give the electrical circuit a physical meaning. The fitting software, such as ZView/ZPlot from Scribner Associates ®, can approach the fit of the results by graphing an electrical circuit and getting the "theoretical" graph out of the model, in addition to the precision of the electrical circuit. The quality of the fit can be defined using a graphical comparison of the impedance data or by the weighted χ2 statistic. Both simplex regression and Levenberg–Marquardt regression strategies are used [7, 8].

Finally, when two or more semicircles are shown, it is possible to modify the electrical equivalent circuit shown in **Figures 14** and **16**, by adding more Rcts in the fitting. Usually, the Rcts that are away from the Rsol are the charge transfers occurring on the proximities of the surface of the electrode and are in the low-frequency zone (**Figure 17**). Here, many possibilities appear as options for fitting the data as shown in **Figure 18**.

**Figure 18.**

*Different examples of equivalent circuits when two charge transfers are involved plus the resistance of the electrolyte (in boxes).*

**Figure 19.** *Bode plot from circuit presented in Figure 14.*

Regarding the Bode plots for these examples, **Figure 19** shows the expected diagram for the Randles equivalent circuit in **Figure 14**.

As it is observed in **Figure 19**, a typical Bode plot is shown for a simplified Randles circuit (without the diffusional component). In this case, the frequency values go from the lowest to the highest, which means that the first points correspond to the impedance of the electrolyte if we analyze the graph from the right to the left. When a charge transfer occurs, the |Z| *vs. f* graph shows a slope (red dashed line), while the phase shift *vs. f* shows a valley at the same frequency value. The flattened areas correspond to the rearrangements of the double layer until we observe another slope and a semi-valley. At these frequencies, the electrical behavior may be attributed to diffusional effects. In an ideal system, where no resistance or impedance is contributing to the electrochemical system, the phase shift tends to zero, but, as mentioned earlier, all electrodes present a resistance toward the charge transfer, as well as the electrolyte, where the reaction is taking place. For instance, neither of these parameters can be ignored.

#### **2.3 Applications**

Electrochemical impedance spectroscopy can be used in many areas, and its application is highly important when describing the interface electrode/solution. As the results are represented in equivalent circuits, it must be considered that this

electrical behavior occurs between the WE and theCE, where a differential in current is taking place.


**4. Analysis**

The out-of-sample period begins on 1st September 2016 and the forecast horizon ranges from *h* = 1 to *h* = 7 days ahead. The analysis compares the performances of two models: the first—M1—includes all the predictors: financial, commodity and crypto predictors, see Table 1. The second, M2, excludes crypto predictors. The benchmark model, denoted with M0, is an ARMA(1,1)-GARCH(1,1) model. M1 and M2 can suffer from massive model uncertainty due to the number of possible predictor's combination at each time point *t*. For example, M1 has 29 = 512 models at each point in time. To mitigate this fact, we use the DMA and DMS as described in Koop and Korobilis (2012) and reported in Supplementary Materials. As already mentioned, the methodology requires fixing three hyperparameters: the forgetting factor *λ* for the parameter variation; the decay factor *κ* for the EWMA; and, finally, the discount weight *α* that weights each model based on forecast performances.

The results reported in this section are based on *κ* = 0.94. This value suits daily data, see Riskmetrics (1996) and Prado and West (2010). The other parameters are set to *α* = 0.99 and *λ* = 0.99, coherently with Raftery et al. (2010). In Section 4.2 a robustness analysis for the forgetting factors *α* and *λ* is carried out. Moreover, we also tried to optimize at each time point *λt* using a standard data-driven approach minimizing the expected prediction error. Unfortunately, the optimized *λt* with crypto time series seems to be very unstable; we leave this issue as a topic of further research.

The analysis begins with the investigation of the posterior inclusion probabilities of each predictor: the higher the probability the higher the predictor's influence over the dependent variable. Figure 2 depicts the posterior probabilities of BTC (Panel (a)), and of BHL (Panel (b)). Time-varying posterior probabilities of inclusion for the other exogenous variables are reported in Supplementary Materials.

**Figure 2.** Posterior inclusion probabilities. Panel (**a**) shows posterior inclusion probability for

 BTC.

Panel (**b**) shows posterior inclusion probability for BHL.

The figures show that the importance of each predictor switches rapidly over time, with a high inclusion probability of BTC in some specific periods. One important change is in 2016 when the inclusion probability suddenly jumped from 0.5 to 0.9 increasing the correlation with the S&P500 and potentially its role as a leading indicator.

After a calm period during 2017, the BTC gained importance once again at the end of the same year with a steep rise in price. During this period, a lot of articles pointed out a correlation between BTC and financial markets. Bloomberg (2018) stated that "big investors may be dragging Bitcoin toward market correlation" and see BTC as an asset which guarantees the highest potential risk/return combination in the market. This may have attracted the interest of big investors able to move huge amounts of funds and consequently correlate BTC to the USA stock market. Another article by Cointelegraph (2018) asserted that BTC might be correlated with VIX, but there is no evidence that it may influence the

S&P500 index. An extensive analysis of the latter issue is carried out in the next sections using point and density forecast.

#### *4.1. Forecast Metrics*

To assess the leading property of BTC we use point and density forecast. For the point forecasts, we use the mean absolute forecast error (MAFE) for each forecast horizon, *h* = 1, . . . , 7:

$$\text{MAFE}\_{h} = \frac{1}{T - R} \sum\_{t = R}^{T - h} \left| \hat{g}\_{i, t + h|t} - y\_{i, t + h} \right| \,\text{}\tag{2}$$

where *T* is the number of observations, *R* is the length of the rolling window, *y*ˆ*t*+*h*|*t* is the S&P500 forecast made at time *t* for horizon *h* and *yt*+*h* is the realization.

To evaluate the density forecasts, we use predictive log score (LS) that is commonly viewed as the broadest measure of density accuracy, see Geweke and Amisano (2010). As for the MAFE, we compute the LS for each horizon:

$$s\_h(y\_i) = \sum\_{t=R}^{T-h} \ln \left( f(y\_{t+h}|I\_t) \right),\tag{3}$$

where *f*(*yt*+*h*|*It*) is the predictive density for *yt*+*h* constructed using information up to time *t*.

We report the MAFEs and the LSs as a ratio of each model's with respect to the baseline. Entries smaller than 1 indicate that a given model yields forecasts that are more accurate than those from the baseline and differences in score relative to the baseline, such as a negative number, indicates a model that beats the baseline. In order to statistically assess the differences between alternative models, we apply the Diebold and Mariano (1995) test for equality of the average loss (with loss defined as squared error and negative log score) of each model versus the ARMA(1,1)-GARCH(1,1) benchmark and we also employ the Model Confidence Set procedure of Hansen et al. (2011) using the R package MCS detailed in Bernardi and Catania (2016) to jointly compare all predictions. Differences are tested separately for each forecast horizon.

#### *4.2. Point Forecast*

Point forecast is evaluated through MAFE for both DMA and DMS as well as for their special case, Bayesian Model Averaging (BMA). For each forecast horizon, the errors are calculated using the following combination of forgetting and discount factors: *λ* = *α* = 0.99, *λ* = *α* = 0.95, *λ* = 1 and *α* = 0.99, *λ* = 0.99 and *α* = 1, and finally *λ* = *α* = 1. In all the cases, the decay factor is fixed to *κ* = 0.94.

Table 2 compares point forecast for M1 and M2 as a ratio M0 (top) and against M0 (bottom). From the upper table, it emerges that the errors are increasing in accordance with the forecast horizon. Moreover, when *h* increases, the ratio increases, meaning that the benchmark model displays better results than DMA and DMS. Table S8 in Supplementary Materials B shows that increasing the forecasting horizon to *h* = 10 does not improve the forecasting performance of M1 and M2. However, Section 4.3, which analyses density forecasts results, reveals different outcomes.

Another peculiarity is that forecasts improve when *α* and *λ* tend to 1. When *α* = *λ* = 0.95 we get the worst forecast performance for DMA and DMS, while the best results are obtained with BMA. This may be due to the nature of the series: the presence of outliers and high peaks in BTC series may distort the point forecast.

To see if BTC improves predictability over the S&P 500 a DM test is performed with a level of significance equal to *α* = 95%. Results are reported in Supplementary Materials Table S2. There is no evidence of an improvement in prediction when the BTC is added to the set of predictors. Further results for different forecast horizons are reported in Supplementary Materials.

Using point forecast it seems that BTC does not improve predictability over the S&P 500 index. **Table 2.** Point forecast: M1 vs. M0 top table and M2 vs. M0 bottom table. Results are reported as the ratio between the model considered over the benchmark. From both the tables it emerges that the simplest model, the ARMA(1,1)-GARCH(1,1), forecasts better than both DMA and DMS.

*J. Risk Financial Manag.* **2019**, *12*, 93


**3.4 Black men**

As opposed to the literature detailing the coping mechanisms for African American women, there are no similar tendencies one can draw in the responses to racism for African American men. The findings specific to men were much more diverse. These strategies included seeking social support [32], active anger [38], substance use for instance alcohol and drugs [25, 32], planning [24], religion [32, 34], not responding [45], active coping and acceptance [24]. Consistent with the overall category of African Americans, for Black men, there seems to be a lack of common consensus on the frequency or efficacy of which responses are most prevalent. Therefore, it is difficult to say with certainty the most common or effective strategy utilized by American Black men to cope with racist incidents. However, as with African American women, African American men will also use a variety of coping strategies when responding to racism. These coping strategies are captured in **Figure 3**.


**4. Atherosclerosis and cholesterol hypothesis**

The hypothesis of cholesterol suggests that lipids play a major role in the development of atherosclerosis. The 4S trial (Scandinavian Simvastanin Survival Study) showed that while there was significant reduction in total cholesterol level, LDL cholesterol level, and decrease in major coronary events, HDL cholesterol level was elevated in simvastatin receiving group [4]. After 4S study, REVERSAL, ASTEROID, and SATURN studies revealed that parallel plaque regression was observed with aggressive lipid-lowering therapy and reduction in major cardiovascular events was achieved. These similar studies have proven the relationship between hyperlipidemia and atherosclerosis [2, 5, 6].

Statins reduce macrophages and extracellular lipid accumulation in atherosclerotic plaque region and increase the content of collagen in the extracellular matrix which result in intimal calcification. Statins also stabilize inflammation and coagulation cascade after plaque rupture.


**2. Materials and Methods**

#### *2.1. Cells and Reagents*

HEp-2 cells (BioWhittaker, Walkersville, MD, USA) were grown in minimum essential medium (MEM) supplemented with 10% of fetal calf serum (FCS), 1% of L-glutamine, 1% of nonessential amino acids, and 1% of penicillin and streptomycin. The human ductal cell line Panc-1 (ATCC) was cultured in Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% of FCS, 1% of L-glutamine, and 1% of penicillin and streptomycin.

Fluoxetine chlorhydrate (Lilly France, Fegersheim, France) was dissolved in dimethyl sulfoxide (DMSO) at a final concentration of 5.48 uM and was used in all experiments, as previously reported [12]. Guanidine hydrochloride (GuHCl) was purchased from Sigma-Aldrich (Saint-Quentin-Fallavier, France) and was used at a final concentration of 2 mM.

#### *2.2. Virus and Persistent Infection*

The diabetogenic CVB4 E2 strain, provided initially by Ji-Won Yoon (Julia McFarlane Diabetes Research Center, Calgary, Alberta, Canada), was propagated in HEp-2 cells and used to establish CVB4 persistent infections.

The model of persistent CVB4 infection of Panc-1 cells has been previously described [13,14]. Briefly, a 25 cm2 Nunc cell culture flask (Thermofisher Scientific, Villebon, France) containing an average of 106 cells in DMEM was inoculated with CVB4 at a multiplicity of infection (MOI) of 0.01. During the acute lytic infection, the culture medium was regularly changed, and finally a stable equilibrium was found between the viral replication and cell proliferation. The medium was changed twice a week, and cells were scraped and subcultured once a week. The supernatants were collected at different time points (1, 10, 20, 21, 24, 28, and 30 weeks post infection) during the persistent infection.

#### *2.3. Antiviral Activity Testing*

The antiviral activity of fluoxetine was evaluated using HEp-2 cells. Cells were seeded in a 96-well cell culture plate at 1.25 <sup>×</sup> 104 cells per well. Cells were inoculated with the virus at a MOI of 0.01, mixed with fluoxetine or DMSO. The plates were incubated at 37 ◦C, and the cell cultures were observed every day. The supernatants were collected when 100% cytopathic effect (CPE) was observed in DMSO-treated wells.