**3. Exploring new perspectives in essential oil use**

In this section, we embark on a journey to discover the captivating role of essential oils in enhancing mental well-being, their potential applications in environmental and agricultural practices, and the exciting emerging trends and opportunities within the essential oil industry.

### **3.1 The role of essential oils in promoting mental well-being and stress reduction**

The enchanting scents of essential oils have long been celebrated for their profound impact on mental health and emotional balance. Numerous studies have shown that inhaling certain essential oils can activate the olfactory system, triggering the

*Essential Oils and Their Bioactive Molecules: Recent Advances and New Applications DOI: http://dx.doi.org/10.5772/intechopen.113406*

release of neurochemicals that influence our mood and emotions [20, 21]. Lavender, for example, has been found to reduce anxiety and promote relaxation [22], while citrus-based oils like bergamot and sweet orange have uplifting effects on mood [23, 24].

Moreover, essential oils can be used in aromatherapy, a holistic practice that involves inhaling or applying oils to the skin to improve psychological and physical well-being [25]. Aromatherapy has gained popularity as a complementary therapy in various healthcare settings, such as hospitals, hospices, and wellness centers [26]. Its stress-reducing and mood-enhancing properties have also been beneficial for managing stress-related conditions like depression and anxiety [27].


*Article* **Robust Classification and Detection of Big Medical Data Using Advanced Parallel** *K***-Means Clustering, YOLOv4, and Logistic Regression**

**Fouad H. Awad 1,\* , Murtadha M. Hamad 1 and Laith Alzubaidi 2,3,4,\***

**Abstract:** Big-medical-data classification and image detection are crucial tasks in the field of healthcare, as they can assist with diagnosis, treatment planning, and disease monitoring. Logistic regression and YOLOv4 are popular algorithms that can be used for these tasks. However, these techniques have limitations and performance issue with big medical data. In this study, we presented a robust approach for big-medical-data classification and image detection using logistic regression and YOLOv4, respectively. To improve the performance of these algorithms, we proposed the use of advanced parallel *k*-means pre-processing, a clustering technique that identified patterns and structures in the data. Additionally, we leveraged the acceleration capabilities of a neural engine processor to further enhance the speed and efficiency of our approach. We evaluated our approach on several large medical datasets and showed that it could accurately classify large amounts of medical data and detect medical images. Our results demonstrated that the combination of advanced parallel *k*-means pre-processing, and the neural engine processor resulted in a significant improvement in the performance of logistic regression and YOLOv4, making them more reliable for use in medical applications. This new approach offers a promising solution for medical data classification and image detection and may have significant implications for the field of healthcare.

**Keywords:** medical data; medical imaging; data classification; image detection; YOLOv4; logistic regression; machine learning; AI; deep learning

**1. Introduction**

The advancement of digital medical technology, coupled with the exponential growth of medical data, has led to biomedical research becoming a data-intensive science, resulting in the emergence of the "big-data" phenomenon, as reported in the literature, such as in [1]. Data have become a strategic resource and a key driver of innovation in the era of big data, transforming not only the way biomedical research has been conducted, but also the ways in which people live and think, which has been highlighted in studies such as [2]. To capitalize on this, the relevant departments in the medical industry should focus on collecting and managing medical health data and use this information as a foundation for later developments through the integration, analysis, and application requirements required to employ big data in the medical field [3].

Big medical data and image detection is an essential element of healthcare that plays a critical role in the storage, organization, and analysis of medical information [4]. the effective classification of medical data enables the efficient retrieval and examination of patient records, which can aid in the diagnosis and treatment of illnesses. It can also assist in identifying trends and patterns in patient health data, enabling healthcare professionals to

**Citation:** Awad, F.H.; Hamad, M.M.; Alzubaidi, L. Robust Classification and Detection of Big Medical Data Using Advanced Parallel *K*-Means Clustering, YOLOv4, and Logistic Regression. *Life* **2023**, *13*, 691. https://doi.org/10.3390/ life13030691

Academic Editor: Daniele Giansanti

Received: 30 January 2023 Revised: 24 February 2023 Accepted: 28 February 2023 Published: 3 March 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

recognize potential risk factors and take preventative measures. Furthermore, medical data classification has facilitated the advancement of new treatments and therapies by allowing researchers to analyze large datasets and uncover potential correlations and trends [5].

COVID-19 data classification has involved organizing and labeling data related to the coronavirus pandemic, such as information about confirmed cases, deaths, and vaccination rates. These types of data have often been used to track the spread of the virus and inform public health decisions. Image detection techniques have been used to identify COVID-19 related images, such as X-ray scans showing lung abnormalities associated with the virus. These techniques have assisted healthcare professionals and researchers better understand and track the spread of the virus.

However, there have been several challenges and problems associated with COVID-19 data classification and image detection. One major challenge has been ensuring the accuracy and reliability of the data being used. There have been errors and biases in the data that affected the results. Additionally, there have been privacy concerns related to collecting and using personal health data. There have also been technical challenges in developing and implementing image detection algorithms, such as difficulties in obtaining a sufficiently large dataset for training. Overall, addressing these challenges is crucial in order to effectively use data and image detection techniques to understand and combat the COVID-19 pandemic.

In this study, an efficient and high-performance solution to enhance the accuracy of medical data classification and image detection was proposed. Advanced *k*-means clustering was merged with both classification and detection techniques to elevate the performance and accuracy of these techniques [6]. To evaluate the performance of medical data classification, a large medical dataset was used. Furthermore, to evaluate the effectiveness of the detection technique, a dataset comprising X-ray COVID-19 and CT images was utilized. The results indicated that the proposed models significantly improved the performances of classification and detection. The proposed model's contributions were the following:

This paper is divided into seven sections. The introduction addresses the significance of medical data classification and medical image detection. Section 2 discusses various data classification and image detection algorithms, including their advantages and limitations. Section 3 addresses the current challenges and features of solutions for processing large amounts of medical data and images. Section 4 presents the proposed solution. Section 5 outlines the methodology and performance metrics used to evaluate the proposed solution. The implementation and results of the proposed solution are presented in Section 6. Section 7 concludes the paper.


**3.3 Drug administration**

The test drugs were reconstituted into appropriate concentrations as follows: 500 mg of CdCl2 was dissolved in 10,000 ml of distilled water; (50 ppm). 400 mg of vitamin E was dissolved in 5 ml of castor oil. 100 mg of zinc was dissolved in 69 ml of normal saline.

The CdCl2 was administered orally with the aid of an orogastric cannula to the mice in group B for a period of 14 days (short-term exposure), while to the mice in group C, it was administered for 28 days (long-term exposure). Mice in groups D and E received same dose of CdCl2 for 14 days but in addition they were given zinc and vitamin E, respectively. Following the Light and Dark Transition Box (LDTB) and Elevated Plus Maze (EPM) paradigms, anxiety-like behaviors were assessed at the conclusion of the treatment session.


**2. Data and methodology**

#### **2.1. Dataset description**

**1. Introduction**

26 Recent Developments in Tropical Cyclone Dynamics, Prediction, and Detection

inadequate.

developed in [7].

Modern statistical and dynamic forecast models continue to demonstrate low forecast skill in identifying the onset of rapid intensification (hereafter known as RI) within tropical cyclones (hereafter known as TCs). Even though storms which rapidly intensify can cost governments billions of dollars in damage upon landfall (e.g. by destroying property through flooding–as with Katrina in 2005) and RI forecasting is considered one of the top priorities for the National Hurricane Center [1], little advancement has been made in improvement of probabilistic tropical cyclone RI forecasting. While previous studies have examined the intensification patterns between RI and non-RI TCs [2, 3], the technological impairments, coupled with the complexity of these systems, have left gaps in understanding the large-scale structures associated with RI storms [1–4]. These gaps result in poor statistical forecast model accuracy, which requires prior knowledge of relevant RI variables. While recent research shows modest improvements in RI forecasts, and global models have steadily improved in their ability to predict the large-scale environmental conditions of TCs [3], forecast skill scores still remain

Current statistical forecast models blend both thermodynamic and kinematic variables in attempts to increase the skill, emphasizing meteorological processes deemed more crucial to RI prediction [1, 4–6]. Improvements to the Statistical Hurricane Intensity Prediction Scheme Rapid Intensification Index (SHIPS-RII) continue to be added regularly since the original implementation by the National Hurricane Center (NHC) in 2004 for the North Atlantic [1]. The latest enhanced SHIPS-RII consists of 10 predictors, including previous 12-hour intensity change, vertical shear, divergence at 200 hPa, total precipitable water, GOES-IR imagery, potential intensity, oceanic heat content, max sustained wind, and an inner-core dry air predictor [1]. Despite the addition of new predictors, Brier skill scores (BSS) relative to climatology for Atlantic RI forecasts remain below 20% [1]. Additionally, verification of all operational consensus intensity forecast models for the NHC, including their official intensity forecast, showed only limited improvement as Peirce skill scores remained below 0.2 [1]. Other studies have included predictors that resolve the inner-core environment more effectively, utilizing microwave passive imagery predictors in a probabilistic logistic regression (LR) model. Despite this effort, BSS values only improved to roughly 22% with either simulated real-time LR models or LR models utilizing reanalysis data [4]. Additionally, using a baseline peak wind speed of 25 knot intensity (at all RI thresholds) severely reduces skill to below 15% when compared to a probabilistic LR model utilizing current SHIPS parameters previously

In order to improve statistical model prediction of the onset of TC RI, the ability to identify distinguishing meteorological characteristics of the storm structure between RI and non-RI TCs with 24 hours lead time is crucial. While research of this nature is not new [2, 3], the approaches have differed (e.g. data selection, data reduction, meteorological variables chosen, compositing approaches). For example, Kaplan and DeMaria [2] and Kaplan et al. [8] noted that RI was more likely to occur for TCs that were situated over regions of higher than average sea surface temperature (SST), strong upper-level divergence, large low- to mid-tropospheric While the NHC defines RI as an increase in wind of 30 knots (kt) in 24 hours, several RI definitions are usually considered during the research phase of model development [1, 4]. This study examined three separate definitions of RI (following [1]), including the operational definition of a 30kt increase of wind speed in 24 hours and two experimental definitions of 25kt and 40kt increases. All Atlantic tropical and subtropical systems, from 1985–2009 from the NHC Atlantic best track data (HURDAT–[9]) were considered. For the three different RI definitions, the full database of 298 TC events were divided into RI and non-RI groups, yielding 152 RI and 146 non-RI cases with the 25kt definition, 119 RI and 179 non-RI for the 30kt definition, and 46 RI and 252 non-RI for the 40kt definition (**Figure 1** breaks these down by Saffir-Simpson scale category). Since a forecast proxy was desired, base-state meteorological fields from the National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS–[10]) reforecast database were retained 24 hours prior to the period of greatest intensification for all storms (RI and non-RI). GEFS reforecast data are provided at a 1° resolution at 3-hour forecast intervals from 0 to 72-hours. Three-dimensional base-state meteorological fields at eight vertical levels (1000–100 hPa) were utilized, including: geopotential height, temperature, *u* and *v* wind components, and specific humidity. Additionally, single-layer variables were considered, including mean sea level pressure (MSLP), skin temperature (a proxy for SST), latent heat flux, sensible heat flux, convective available potential energy (CAPE), convective inhibition (CIN), and vertical velocity at 850 hPa were evaluated.

**Figure 1.** Distribution of category per TC event type per RI definition using 25kt/24-hours, 30kt/24-hours, and 40kt/24 hours definitions.

As a primary goal was to diagnose RI using TC structure relative to the storm center, stormcentric GEFS reforecast domains for each cyclone were obtained. Storm centers were identified by determining the local minimum in GEFS MSLP nearest the NHC-defined TC center, 24 hours prior to the timestep associated with the greatest intensification. Each variable was retained on a 15° × 11° latitude/longitude grid centered on this domain. In the event multiple occurrences of peak intensification occurred for an individual TC (which occurred 28 times when using 25kt/24-hours, 13 times for 30kt/24-hours, and once for 40kt/24-hours), the first was chosen. Thus, the results presented herein deal with the first instance of peak intensification regardless of the frequency of peak intensification for a given TC.

#### **2.2. RPCA**

As the primary goal of this research was the identification of variables and spatial locations most favorable for distinguishing RI and non-RI storms, discriminatory statistical methods were needed. One method, rotated principal component analysis (RPCA), has been shown to be useful in discriminating meteorological environments of different types [11–14]. These studies also used permutation testing to evaluate magnitude differences in diagnostic variables for each environment. Both of these techniques were utilized in the current study so that both spatial configuration and magnitude difference could be assessed.

#### *2.2.1. S-mode RPCA*

single-layer variables were considered, including mean sea level pressure (MSLP), skin temperature (a proxy for SST), latent heat flux, sensible heat flux, convective available potential energy (CAPE), convective inhibition (CIN), and vertical velocity at 850 hPa were evaluated.

28 Recent Developments in Tropical Cyclone Dynamics, Prediction, and Detection

**Figure 1.** Distribution of category per TC event type per RI definition using 25kt/24-hours, 30kt/24-hours, and 40kt/24-

As a primary goal was to diagnose RI using TC structure relative to the storm center, stormcentric GEFS reforecast domains for each cyclone were obtained. Storm centers were identified by determining the local minimum in GEFS MSLP nearest the NHC-defined TC center, 24 hours prior to the timestep associated with the greatest intensification. Each variable was retained on a 15° × 11° latitude/longitude grid centered on this domain. In the event multiple occurrences of peak intensification occurred for an individual TC (which occurred 28 times when using 25kt/24-hours, 13 times for 30kt/24-hours, and once for 40kt/24-hours), the first was chosen. Thus, the results presented herein deal with the first instance of peak intensifica-

As the primary goal of this research was the identification of variables and spatial locations most favorable for distinguishing RI and non-RI storms, discriminatory statistical methods were needed. One method, rotated principal component analysis (RPCA), has been shown to be useful in discriminating meteorological environments of different types [11–14]. These

tion regardless of the frequency of peak intensification for a given TC.

hours definitions.

**2.2. RPCA**

The first approach to RPCA, S-mode analysis [13], provided a diagnosis of the spatial relationship among gridpoints for all cases. For S-mode, the similarity matrix is computed on the individual spatial locations and is eigenanalyzed to identify particular locations that group together. The S-mode rotated principal component (RPC) loadings are maps that demonstrate these spatial relationships (known as modes of variability), with the RPC scores revealing the similarity between the individual cases and the resulting S-mode loading maps. To reduce the dimensions of the eigenvector matrix, truncation of RPCs was completed by evaluating a scree plot, as well as using a congruence test. A congruence test is a way to measure pattern and magnitude similarity of a dataset, corresponding to the cosine of the angular separation between the loadings, by maximizing the dissimilarity of the two loading patterns [15]. The congruence coefficient presenting a strong relationship for any absolute value greater than 0.81 was marked as the truncation point. RI and non-RI datasets (consisting only of base-state variables for all 298 cases) were combined, where the analysis of both RI and non-RI event

**Figure 2.** Pairwise scatterplots of all six PC score vectors. RI PC scores are redpoints, while non-RI points are blue. The significant overlap among the groups demonstrates the challenges in linearly separating these types of TCs.

deviations and the loading patterns provided information on how the systems are grouping together (e.g. cooler SSTs versus warmer, upper level trough/ridge patterns, and influence of land at the surface 24-hours prior). To demonstrate the lack of linear separability in the resulting RPCs, a pairwise scatterplot of all six PC score vectors was formulated (**Figure 2**). There is significant overlap among the RI and non-RI PC scores, rendering separation via classification very difficult, motivating the need to consider additional analysis techniques.

**Table 1.** Rotated principal components variance explained for S-mode, as well as T-mode, for each event type and each RI definition. Dashes simply mean that this number of RPCs was not retained based on the previously described testing methodologies.

#### *2.2.2. T-mode RPCA*

While S-mode helped reveal the difficulties in identifying relevant RI/non-RI distinguishing characteristics, the results did not provide the necessary discrimination capability of interest in this work. Recent work has shown the value of composite analysis with T-mode RPCA in identifying discriminating characteristics for different meteorological event types [11, 12]. Following the methodology of [11, 12], a T-mode varimax-rotated RPCA [11, 16], conducted simultaneously on all GEFS reforecast fields, was completed on all RI events and all non-RI events separately. T-mode contrasts S-mode in that in T-mode, the relationships between events, as opposed to spatial locations, are of interest, and thus the correlation matrix is computed on the event dimension of the data. Following methods established in [11, 12], the resulting uncorrelated eigenvector matrix and associated eigenvalues reduced to a subset of RPCs for each event type and each RI definition (**Table 1**). Similar to the S-mode RPCA approach, the truncation point was determined through utilization of a scree plot and the congruence test. The resulting RPC loadings maintain the same dimension as the event dimension, so events were clustered by RPC loading magnitude using hierarchical clustering with Ward's minimum variance method [16]. To assess cluster quality, a cluster verification statistic (silhouette coefficient [17]) was found that includes two components:

**1.** a measure of intra-cluster spread (cluster cohesion–should be small) and

#### **2.** a measure of inter-cluster spread (cluster separation–should be large) [11].

In this study, the mean of the silhouette coefficient values for all events considered in the cluster analysis was retained as a measure of cluster analysis performance. With the silhouette coefficient, values approaching 1 suggest a minimization of cluster cohesion and a maximization of cluster separation. Negative values suggest a particular event was misclustered. The cluster analysis revealed six clusters each for RI and non-RI storm types using the 25kt/24 hours definition, seven non-RI and six RI using the 30kt/24-hours definition, and seven non-RI and five RI using the 40kt/24-hours definition (**Table 2** provides the number of events per cluster, as well as silhouette coefficient values). Events within each cluster were averaged together, yielding map types that retained unique synoptic-scale structures and provided more detailed map types of RI and non-RI TC environments than simply averaging all events together. The resulting composites allowed for the identification of spatial structure among RI/non-RI events.

**Table 2.** Silhouette coefficients and number of events per cluster through Ward's method on T-mode RPC loadings.

#### **2.3. Permutation testing**

deviations and the loading patterns provided information on how the systems are grouping together (e.g. cooler SSTs versus warmer, upper level trough/ridge patterns, and influence of land at the surface 24-hours prior). To demonstrate the lack of linear separability in the resulting RPCs, a pairwise scatterplot of all six PC score vectors was formulated (**Figure 2**). There is significant overlap among the RI and non-RI PC scores, rendering separation via classification very difficult, motivating the need to consider additional analysis techniques.

30 Recent Developments in Tropical Cyclone Dynamics, Prediction, and Detection

**S-mode T-mode T-mode T-mode RPCs Combined RI non-RI RI non-RI RI non-RI** 24 18 13 14 12 16 11 12 11 12.8 11 13 11 14 7.4 7.6 4.8 7.7 5.0 7.3 6.3 3.6 6.6 7.7 7.2 7.8 7.0 8.4 4.5 4.7 4.7 4.9 5.8 5.4 4.8 3.3 — 4.5 — — — —

**Table 1.** Rotated principal components variance explained for S-mode, as well as T-mode, for each event type and each RI definition. Dashes simply mean that this number of RPCs was not retained based on the previously described

While S-mode helped reveal the difficulties in identifying relevant RI/non-RI distinguishing characteristics, the results did not provide the necessary discrimination capability of interest in this work. Recent work has shown the value of composite analysis with T-mode RPCA in identifying discriminating characteristics for different meteorological event types [11, 12]. Following the methodology of [11, 12], a T-mode varimax-rotated RPCA [11, 16], conducted simultaneously on all GEFS reforecast fields, was completed on all RI events and all non-RI events separately. T-mode contrasts S-mode in that in T-mode, the relationships between events, as opposed to spatial locations, are of interest, and thus the correlation matrix is computed on the event dimension of the data. Following methods established in [11, 12], the resulting uncorrelated eigenvector matrix and associated eigenvalues reduced to a subset of RPCs for each event type and each RI definition (**Table 1**). Similar to the S-mode RPCA approach, the truncation point was determined through utilization of a scree plot and the congruence test. The resulting RPC loadings maintain the same dimension as the event dimension, so events were clustered by RPC loading magnitude using hierarchical clustering with Ward's minimum variance method [16]. To assess cluster quality, a cluster verification

statistic (silhouette coefficient [17]) was found that includes two components: **1.** a measure of intra-cluster spread (cluster cohesion–should be small) and

testing methodologies.

*2.2.2. T-mode RPCA*

**Variance explained (%)**

**25kt/24-hours 30kt/24-hours 40kt/24-hours**

While the composites resulting from the RPCA approach are useful for diagnosing spatial characteristics within RI and non-RI environments, magnitude differences are diagnosed more effectively using hypothesis testing. In this study, permutation tests [16] comparing magnitudes of diagnostic fields in RI and non-RI storms were utilized at each gridpoint from the study domains, yielding a spatial map of significance values associated with each variable tested. The resulting plots provided specific regions in the study domain where statistically significant magnitude differences between RI and non-RI storms existed for individual GEFS reforecast variables. These results provided insight not only into the scope of these magnitude differences but into the spatial locations of the differences, which complement the RPCA results well.


**1. Introduction**

When Schelling (1960) wrote *Strategy of Conflict*, it pivoted attention from zero-sum games to the more general behavior allowed by games with mutually beneficial outcomes (which was appropriate during this Cold War period) [1]. Specifically, Schelling made a case for coordination games, which Lewis (1969) used to discuss culture and convention [2]. This behavioral notion of mutually beneficial outcomes was further explored by Rosenthal (1973) with his development of the congestion game [3]. Monderer and Shapley (1996) built on the congestion game with "common interest" games; namely the potential games (which include coordination games) [4]. More recently, Young (2006, 2011) and Newton and Sercombe (2020) took this analysis a step further by modeling, with potential games, how populations on networks evolve from one convention to another [5–7]. The natural question in this work is to discover whether the status quo or an innovation will be accepted.

This issue of finding the "better outcome" (e.g., an innovation or the status quo), which is a theme of this paper, is a fundamental and general concern for game theory; answers require selecting a measure of comparison. A natural choice is to prefer those outcomes where the players receive larger payoffs. Rather than payoff dominance, another refinement of Nash equilibria offered by Harsanyi and Selton (1988) is risk dominance [8].1 The choice used by Young (2006, 2011) and later by Newton and Sercombe (2020) is to maximize social welfare. Still, other measures can be constructed [5–7].

With the social welfare measure, Young constructed a clever ad-hoc example where, although it is seemingly profitable to adopt the innovation, the innovation is worse than the status quo [5]. Young's observation underscores the important need to understand when and why a model's conclusions can change. This includes his concerns of identifying when and why a new cultural convention is "better". Is there a boundary between the quality of innovations?

1 A payoff dominant Nash cell is where each agent does at least as well as in any other Nash cell, and at least one does better. A risk-dominant Nash cell is less costly should coordination be mistakenly expected; see Section 3.1.

Conflicting conclusions must be anticipated because different measures emphasize different traits of games. Thus, answering the "better" question requires determining which aspects of a game a given measure ignores or accentuates. The approach used here to address this concern is new; it appeals to a recent decomposition (or coordinate system) created for the space of games [9–12] (in what follows, the [9–11] papers are designated by [JS1], and the book [12] by [JS2]). There are many ways to decompose games, where the emphases reflect different objectives. An early approach was to express a game in terms of its zero sum and identical play components, which plays a role in the more recent Kalai and Kalai bargaining solution [13]. Others include examining harmonic and potential games [14] and strategic behavior such as in [15,16]. While some overlap must be expected, the material in [JS1] and [JS2] appears to be the first to strictly separate and emphasize Nash and non-Nash structures.

Indeed, in [JS1], [JS2], and this paper, appropriate aspects of a game are used to extract all information, and only this information, needed for the game's Nash structures; this is the game's "Nash" component. Other coordinate directions (orthogonal to, and hence independent of the Nash structures) identify those features of a game that require interaction among the players, such as coordination, cooperation, externalities, and so forth. By isolating the attributes that induce behavior among players, these terms define the game's "Behavioral" component. The final component, the kernel, is akin to adding the same value to each of a player's payoffs. While this is a valuable variable with transferable assets, or to avoid having games with negative payoffs, it plays no role in the analysis of most settings. In [JS2], the [JS1] decomposition is extended to handle more player and strategies.2

One objective of this current paper is to develop a coordinate system that is more convenient to use with a wide variety of choices that include potential games (a later paper extends this to more players and strategies). An advantage of using these coordinates is that they intuitively organize the strategic and payoff structures of all games. This is achieved by extracting from each payoff entry the behavioral portions that capture ways in which players agree or disagree (e.g., in accepting or rejecting an innovation) and affect payoff values. Of interest is how this structure applies to all 2 × 2 normal form games. When placing the emphasis on potential games, these coordinates cover their full seven-dimensional space, so they subsume the lower dimensional models in the literature.

By being a change of basis of the original decomposition, this system still highlights the unexpected facts that Nash equilibria and similar solution concepts (e.g., solution notions based on "best response" such as standard Quantal Response Equilibria) ignore nontrivial aspects of a game's payoff structure; see [11]. In fact, this is the precise feature that answers certain concerns in the innovation diffusion literature. Young's example [5], for instance, turns out to combine disagreement between two natural measures of "good" outcomes: one measure depends on unilateral deviations; the other aggregates the collective payoff. Newton and Sercombe re-parametrize Young's model to further explore this disagreement [7]. As we show, Young's example and the Newton and Sercombe arguments stem from a game's tension between group cooperative behavior and individualistic forces.

Other contributions of this current paper are to

2 Experimental work has been done by Jessie and Kendall [17] by building on the decomposition in [JS1]. More precisely and as given in this paper, the separation aspect of the decomposition permits constructing large classes of games with an identical Nash component (or, the strategic component), but with wildly different externalities components (or, the behavioral component). As they showed, the choice of the behavioral term influenced an agent's selection. Section 2 discusses these components.

The paper begins with an overview of the coordinate system for all 2 × 2 normal form games. After identifying the source of all conflict with symmetric potential games, the full seven-dimensional class is described.


*5.2. NTV-SIMD Engine Results*

The SIMD permutation engine operates at a nominal supply voltage of 0.9 V and is implemented in a 22-nm tri-gate bulk CMOS technology featuring high-k metal-gate transistors and strained silicon technology.  The permutation engine with 2-dimensional shuffle results in 36% to 63% fewer register file reads, writes, and permutes compared to a conventional 256b shuffle-based implementation. The SIMD engine contains 439,000 transistors.

**Figure 15.** NTV-SIMD vector engine die micrographs and characteristics.

Frequency and power measurements for the SIMD engine components are presented in Figure 16, obtained by sweeping the supply voltage from 280 mV to 1.1 V in a temperature-stabilized environment of 50 ◦C. Chip measurements show that the register file and crossbar operate from 3 GHz (1.1 V) down to 10 MHz (280 mV). The register file dissipates 227 mW (1.1 V) and 108 μW (280 mV) respectively, while the permute crossbar consumes 69 mW–19 μW over the same VDD range. The maximum energy efficiency of 154 GOPS/W (1 OP = three 256b reads and one 256b write) is obtained at a supply voltage of 280 mV (VOPT) and is 9× higher than the efficiency at nominal voltage. The 256b byte-wise any-to-any permute crossbar executes horizontal shuffle operations down to supply voltages of 240 mV.

Peak energy efficiency of 585 GOPS/W (1 OP = one 32-way 256b permutation) is achieved at a supply voltage of 260 mV, also with 9× better energy efficiency.

**Figure 16.** Measured NTV-SIMD engine: (**a**) Maximum frequency and power vs. VDD (**b**) Energy efficiency vs. VDD.

## *5.3. NTV-NoC Measurement Results and Learnings*

The 2 × 2 2-D mesh-based resilient NoC prototype is fabricated in a 22 nm, 9-metal layer technology. Each router port features bidirectional, 36-bit wide, 1.5 mm long on-die links.  There are approximately 31,400 cells in each router. The experimental setup and key design characteristics are shown in Figure 17b,d.

**Figure 17.** NTV-NoC design: (**a**) Die photograph with supply noise injectors; (**b**) Experiment setup; (**c**) NoC layout with key IP blocks identified and 1.5 mm folded links (**d**) Die characteristics.

Silicon measurements are performed at 25 ◦C for a representative NoC traffic pattern with FLIT injection at each router port every clock cycle at 10% data activity. The 2 × 2 NoC is functional over a wide operating range (Figure 18) with maximum frequency (FMAX) of 1 GHz (0.85 V), 734 MHz (0.7 V), 151 MHz (400 mV), scaling down to 67 MHz (340 mV). A 3.3X improvement in energy-efficiency is achieved at a VOPT of 400mV with an aggregate router bandwidth (BW) of 3.6 GB/s.

**Figure 18.** Measured 2 × 2 NoC power, performance and energy characteristics across wide voltage range with EDS and replay mechanisms disabled.

The measured NoC silicon logic analyzer trace (Figure 19) shows a supply noise-induced timing failure on the control bits of the packet header FLIT, followed by two cycles of bubble (null) FLITS and persistent retransmission (replay) of the FLIT until successful recovery. As shown, timing error synchronization and roll-back incurs a 2-cycle delay between an error event and successful recovery.

**Figure 19.** Silicon logic analyzer trace showing successful recovery of FLITs from timing failures.

Figure 20 plots the measured BW for the resilient router at 400 mV, in the presence of a 10% VNoC droop induced by the on-die noise injectors. The number of erroneous FLIT increases exponentially with FCLK. To account for such droop, a non-resilient router must operate with 28% (700 mV) and 63% (400 mV) FCLK margins, respectively, thus limiting FMAX. The resilient router reclaims these margins and offers near-ideal BW improvement until higher error rates and FLIT replay overheads limit overall BW gains. Past the point-of-first failure (PoFF), both control and data bits are corrupted. While ECC can identify data bit failures, control bit failures can invalidate the entire FLIT, rendering any ECC scheme ineffective. If control paths are designed with enough timing margins such that the control bits do not fail, the FCLK gain from SECDED ECC is only 7% beyond PoFF, since several data bits fail simultaneously. In contrast, at 400 mV, the EDS scheme provides tolerance to multi-bit failures over a 9X wider FCLK range, past PoFF. Compared to a conventional router implementation, the resilient router offers 28% higher bandwidth for 5.7% energy overhead at 700 mV and 63% higher bandwidth with 14.6% energy improvement at 400 mV.

**Figure 20.** Improvement in resilient router bandwidth at VOPT (400 mV) over a non-resilient version.

Resilience to Inverse Temperature Dependence Effects

As the supply voltage approaches VT, elevated (lowered) silicon temperature results in increased (decreased) device currents. This phenomenon is generally known as Inverse Temperature Dependence (ITD) [25]. With process scaling and the introduction of high-κ/metal-gate, devices exhibit higher (negative) temperature coefficient along with weaker mobility temperature sensitivity [26]. This inverses the impact of temperature rise on delay, particularly as VDD is lowered, where a small change in VT results in a large current change—requiring large timing margins for NTV designs. As device and VDD scaling exacerbates ITD, the need for characterizing and understanding ITD, and incorporating adaptive architectures becomes even more imperative. Measurements on the 22-nm NoC prototype indicate that ITD effects are observed at NTV, with router timing failures increasing as the die temperature decreases. Data in Figure 21 shows at 400 mV operation, a 30 ◦C temperature decrease (from 40 ◦C → 10 ◦C) causes the percentage of failing FLITs to rapidly increase. However, the resilient router recovers from transient timing failures due to EDS circuit error detection and the FLIT replay mechanism. This improves BW and FCLK margins by 50% at 10 ◦C, when compared to a non-resilient router design.

**Figure 21.** Measured NTV-NoC resilience to temperature variations and ITD effects at 400 mV. Timing failures in a conventional router increases at a faster rate over an EDS-enhanced one. The percentage of failing FLITS is indicated in the secondary y-axis.

## *5.4. NTV-MCU Measurement Results and WSN Operation*

The MCU is fabricated using 14-nm tri-gate CMOS technology with nine metal interconnect layers (Figure 22). The MCU cell count is approximately 160 K and the die area is 0.79 mm2 (0.56 mm × 1.42 mm). The surface-mount ball grid array (BGA) package has 24 pins with an area of 4.08 mm2 (2.46 mm × 1.66 mm). The die photograph with key IP blocks identified and design characteristics are highlighted in Figure 23. The diminutive low-power MCU can serve as a key component for future autonomous, self-powered "smart dust" WSNs [27]—which can sense, compute, and wirelessly relay real-time information about the ambient.

**Figure 22.** The 32-b IA NTV MCU is packaged in a miniature 4.08-mm2 (2.46 mm × 1.63 mm) 24-pin BGA substrate.

**Figure 23.** NTV-MCU 14-nm design: (**a**) Die photograph with key IP blocks identified.

The IA MCU is functional over a wide operating range (Figure 24) from 297 MHz (1 V) scaling down to 0.5 MHz (308 mV) at 25 ◦C. While the entire MCU is functional down to 308 mV, SMEM functionality was validated down to 300 mV by independently writing and reading to it via the TAP debug interface. The ROM and the AHB logic are found to be functional down to 297 mV. With the MCU continuously executing a data encryption workload (AES-128), the minimum energy point is observed at 370 mV (VOPT) at T = 25 ◦C. At VOPT, the MCU operates at 3.5 MHz and dissipates 58 μW power, which translates to an energy-efficiency metric of 17.18 pJ/cycle. Compared to super-threshold operation at 1 V, NTV operation at VOPT achieves 4.8× improvement in energy efficiency.

**Figure 24.** Measured 14-nm NTV-MCU power, performance, and energy efficiency across wide VDD.

The MCU integrates 8 KB of Instruction cache (I\$) and 8 KB data tightly coupled memory (DTCM). DTCM functions as a local scratch-pad memory, offering low latency (single cycle) and deterministic access, particularly valuable for data-intensive workloads. For typical WSN workloads with code footprint ~16 KB, MCU energy can be further improved by enabling I\$ and DTCM. Enabling I\$ and DTCM helps to exploit any code and data locality present in the application, thereby reducing the active power consumed in AHB interconnect and large SMEM (64 KB) access. Our experiments show 40% energy improvement is achievable from enabling both I\$ and DTCM.

The WSN incorporating the NTV CPU operates continuously using the energy harvested by a 1 cm2 solar cell from indoor light (1000 lux), with sensor data transmitted over BLE radio. The measured WSN power profile in AOAS mode over a 4-min interval is shown in Figure 25. In the AOAS operating mode (with BLE advertising + sensor polling every four seconds), average power (PAVG) for the entire WSN is 360 μW, with the MCU contributing 290 μW (13 MHz, 0.45 V). The MCU power further drops to 120 μW in deep sleep state. In the deep sleep state, the core (IA + AHB) and CRO domains are power gated. The AON logic is still powered-ON and driven by RTC clock.

**Figure 25.** Measured WSN power profile in AOAS mode over a 4-min interval.

## **6. Conclusions and Future Work**

NTV computing with wide dynamic operational range o ffers the flexibility to provide the performance on demand for a variety of workloads while minimizing energy consumption. The technology has the potential to permeate the entire range of computing—from ultra energy-e fficient servers, personal and mobile computing to self-powered WSNs. It allows us to exploit the advantages of continued Moore's law to provide highest energy e fficiency for throughput-oriented parallel workloads without compromising performance. The overheads of NTV design techniques in complex SoCs must be carefully balanced against impacts on power-performance at the higher end of the operating regime. Adaptive designs with in-situ monitoring circuitry can help detect and fix timing errors dynamically, but at an added cost. Four case-studies highlighting novel resilient architecture and circuit techniques, multi-voltage designs, and variation-aware design methodologies are presented for realizing robust NTV SoCs in scaled CMOS process nodes. In general, designs can tradeo ff performance for reduced leakage power to realize better energy gains at NTV. The results demonstrate 3–9× energy benefits at NTV and the proposed design automation methodology can indeed help achieve greater energy reduction. As a future work project, we intend to build unified reliability models for NTC circuits and systems and validate the model against experimental data obtained across a wide voltage range.

**Author Contributions:** Conceptualization, S.V., S.P., S.H., A.A., R.K., J.T. and V.D.; methodology, S.V., S.H. and S.P.; software, S.P.; validation, S.V., S.P., A.A. and S.H.; investigation, S.P., A.A. and V.D.; resources, S.V.; data curation, S.P. and S.H.; writing—original draft preparation, S.V.; writing—review and editing, S.V., S.P., S.H., R.K. and V.D.; visualization, S.V.; supervision, S.V., R.K., J.T. and V.D.; project administration, S.V. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** The authors thank S. Jain, S. Khare, V. Honkote, M. Abbott, T. Majumder, P. Aseron, T. Nguyen, H. Kaul, M. Anders, M. Khellah, S. Mathew at Intel Labs, D. Mallik, and V. Grossnickle at Intel for technical contributions, encouragement, and support and the e fforts of Intel's ATTD team with chip package design and assembly.

**Conflicts of Interest:** The authors declare no conflict of interest.


*Article* **Flow Behavior and Hot Processing Map of GH4698 for Isothermal Compression Process**

**Rongchuang Chen 1,\*, Haifeng Xiao 1, Min Wang 1,\* and Jianjun Li 2**

Received: 1 July 2019; Accepted: 29 July 2019; Published: 1 August 2019

**Abstract:** An in-depth understanding of the flow behaviors of materials deformed at high temperatures is of paramount significance. However, insufficient research on the nickel-based GH4698 alloy has resulted in inaccurate material flow prediction or even cracking in the practical billet opening of GH4698 large forgings. In this study, hot compressions were performed at 950–1150 ◦C and 0.001–3 s−1. Single-peaked strain-stress curves were obtained under various conditions, owing to dislocation motions in dynamic recrystallizations. The Arrhenius model was formulated to accurately describe the flow stress evolutions and the mean prediction error of the flow stress was 5.90%. Processing maps were constructed at various hot working conditions. It was found that the hot working ability of GH4698 markedly decreased under lower temperatures (950–1080 ◦C) and higher strain rates (0.1–3 s−1). Optimal thermal processing parameters were suggested. In sum, this study systematically investigated the flow behaviors and hot working ability of GH4698 in isothermal compressions.

**Keywords:** processing maps; nickel-based alloy; flow behavior; arrhenius equation


**3.1 Classification of lymphoma**

The earliest classifications of lymphoma were based entirely on the morphological features of the neoplastic cells involved. Historically, lymphomas represented by large cells were known as *reticulosarcomas* and those by small cells were *lymphosarcomas* (Diebold, 2001). Later, lymphomas began to be distinguished according to their origin from B or T lymphocytes. With the development of immunophenotyping and cytogenetics, as well as an appreciation of differences in prognosis and patient stratification, more complex classification systems have developed. The World Health Organisation (WHO) Classification of Tumours of Haemopoietic and Lymphoid Tissues, updated in 2008 (see Table 1), is now widely accepted (Swerdlow et al., 2008) and categorises lymphoid neoplasms in to those derived from:

314 New Advances in the Basic and Clinical Gastroenterology

studies have proven their efficacy (Hanauer et al., 2006, Hanauer et al., 2002, Jarnerot et al., 2005). A third, pegylated anti-TNF drug, certolizumab has also been studied and appears to have equivalent clinical efficacy (Sandborn et al., 2007a). Despite medical therapy, up to 50- 70% of patients with CD will undergo surgery within 5 years of diagnosis and UC patients

The increased risk of colonic adenocarcinoma in patients with ulcerative colitis and Crohn's colitis is well documented (Rutter et al., 2006, Jess et al., 2006). One study has suggested a protective role for thiopurines in this context (Beaugerie et al., 2009b) though these patients were not corrected for co-administration of 5ASA preparations which may also have a

There also appears to be an increased risk of certain non-colorectal malignancies amongst IBD patients. In the same cohort of patients from the CESAME study, prospective data suggested a 20-fold increased risk of small bowel adenocarcinoma and suggestion of an increased risk of skin and cervical malignancy (Beaugerie et al., 2009c). In a database of over 27,000 UC patients from Sweden, the standardised incidence ratio (SIR) for all cancers was 1.46 with increased risk of malignancy of the liver, small bowel (carcinoid), prostate and breast (Hemminki et al., 2008). In a recent review of malignancies associated with thiopurine therapy, Smith et al concluded that these drugs did not increase the risk of cervical dysplasia, colonic cancer or solid organ tumours in IBD patients (Smith et al., 2010). A retrospective cohort study of over 50,000 IBD patients from the US suggested an increased risk of non-melanomatous skin cancer and that this risk was highest in patients treated with thiopurines (Odds Ratio 4.27) and biological therapies (Odds Ratio 2.18) (Long et al., 2010). Many of these studies have also shown an increased risk of lymphoma and this will be

Lymphoma is a broad term used to describe a variety of neoplasms due to proliferation of lymphoid cells. Traditionally, lymphoid neoplasms that presented with bone marrow and blood involvement were referred to by the term *leukaemia* and those that presented with a mass would be called a *lymphoma*. However, it is now appreciated that any *lymphoma* can present with or evolve in to a leukaemic picture and occasionally, *leukaemia* can present with

The earliest classifications of lymphoma were based entirely on the morphological features of the neoplastic cells involved. Historically, lymphomas represented by large cells were known as *reticulosarcomas* and those by small cells were *lymphosarcomas* (Diebold, 2001). Later, lymphomas began to be distinguished according to their origin from B or T lymphocytes. With the development of immunophenotyping and cytogenetics, as well as an appreciation of differences in prognosis and patient stratification, more complex classification systems have developed. The World Health Organisation (WHO)

have a 20-30% lifetime risk of colectomy (Cosnes et al., 2005).

**2.4 Cancer risk in IBD**

protective role in this setting.

discussed further in this chapter.

**3.1 Classification of lymphoma**

**3. Lymphoma**

a mass lesion.

#### **Non-Hodgkin's Lymphoma**

Evaluating Lymphoma Risk in Inflammatory Bowel Disease 317

A B

C D

Fig. 2. Figures adapted from Cancer Research UK 2011. A: Incidence of non-Hodgkin's lymphoma by gender. B: Incidence of Hodgkin's lymphoma by gender. C: Age standardised incidence rates for non-Hodgkin's lymphoma. D: Age standardised incidence rates for

nodal NHL presents in the GI tract (Newton et al., 1997).

A number of risk factors for the development of NHL have been studied:

with an increase of 35% during the 20 year interval between 1988 and 2007 (see Figure 2C). This trend seems to be reflected throughout the world. Mortality in the UK is estimated at 6.9 per 100,000 from NHL and with improvements in treatment, it is estimated that over half of patients now survive for at least 10 years following diagnosis. Up to 15% of all extra-

 **Infectious agents** – It is thought that a proportion of the worldwide rise in incidence of NHL parallels, but is not completely explained by, the HIV epidemic. The risk of NHL in HIV and AIDS is well documented but only 3-5% of these patients will develop NHL (Serraino et al., 1992). Epstein-Barr virus has been linked to Burkitt's lymphoma and post-transplant lymphoma (Epstein et al., 1964). Other infections associated with an increased risk of NHL include *Helicobacter Pylori* (Xue et al., 2001), Hepatitis C (Dal Maso and Franceschi, 2006) and Human T-cell Lymphotropic Virus (HTLV-1) (Parkin,

 **Immunosuppression** – The use of immunosuppression following organ transplant has been shown to increase the risk of NHL and in a significant proportion of these, EBV

Hodgkin's lymphoma.

2006).

Table 1. WHO Classification of Non-Hodgkin's and Hodgkin's Lymphoma (Swerdlow et al, 2008). (MALT – mucosa-associated lymphoid tissue; HL – Hodgkin's Lymphoma; NK – Natural Killer).

Hodgkin's lymphoma (formerly known as Hodgkin's disease) arises from B-cells of germinal or post-germinal centres of peripheral lymph nodes. It is pathologically and clinically distinct from other lymphoid neoplasia and generally has a good prognosis. Hodgkin's lymphoma has a distinguishing cellular composition on biopsy of lymphomatous tissues with abundant inflammatory cells and only a minority of neoplastic cells, known as Reed-Sternberg cells. Reed-Sternberg cells are large, binucleated or multinucleated containing multiple eosinophilic nucleoli and have prominent cytoplasm. Hodgkin's lymphoma (HL) is classified in to *nodular lymphocyte predominant HL* and *classical HL*, which is further subdivided in to *nodular sclerosis*, *mixed cellularity*, *lymphocyte-rich* and *lymphocytedepleted* types (see Table 1).

The term Non-Hodgkin's lymphoma encompasses all other types of lymphoma. Although the WHO classification does not distinguish NHL on the basis of disease activity, it has classically been divided in to two subtypes:

### **3.2 Non-Hodgkin's lymphoma**

NHL can occur in children and adults but over two-thirds are diagnosed in people aged over 60 years. There is a male preponderance with a ratio of up to 1.5 in older age groups. NHL is the fifth most common cancer in the UK with over 10,000 people diagnosed with the condition in 2007 and an age-standardised rate of 14.2 per 100,000 population (Cancer-Research-UK, 2011) (see Figure 2A). In the United States, the Surveillance Epidemiology & End Results (SEER) registry provides an age-standardised rate of 19.6 per 100,000 between 2003 and 2007 (Altekruse et al., 2010). The incidence of NHL seems to be rising in the UK

**Hodgkin's Lymphoma**

**Classical Hodgkin's lymphoma**

Nodular sclerosis classical HL Mixed cellularity classical HL

Lymphocyte rich classical HL Lymphocyte depleted classical HL

**Nodular lymphocyte predominant HL**

Table 1. WHO Classification of Non-Hodgkin's and Hodgkin's Lymphoma (Swerdlow et al, 2008). (MALT – mucosa-associated lymphoid tissue; HL – Hodgkin's Lymphoma; NK –

Hodgkin's lymphoma (formerly known as Hodgkin's disease) arises from B-cells of germinal or post-germinal centres of peripheral lymph nodes. It is pathologically and clinically distinct from other lymphoid neoplasia and generally has a good prognosis. Hodgkin's lymphoma has a distinguishing cellular composition on biopsy of lymphomatous tissues with abundant inflammatory cells and only a minority of neoplastic cells, known as Reed-Sternberg cells. Reed-Sternberg cells are large, binucleated or multinucleated containing multiple eosinophilic nucleoli and have prominent cytoplasm. Hodgkin's lymphoma (HL) is classified in to *nodular lymphocyte predominant HL* and *classical HL*, which is further subdivided in to *nodular sclerosis*, *mixed cellularity*, *lymphocyte-rich* and *lymphocyte-*

The term Non-Hodgkin's lymphoma encompasses all other types of lymphoma. Although the WHO classification does not distinguish NHL on the basis of disease activity, it has

Low-grade or indolent NHL develops slowly and there may be no symptoms for many

NHL can occur in children and adults but over two-thirds are diagnosed in people aged over 60 years. There is a male preponderance with a ratio of up to 1.5 in older age groups. NHL is the fifth most common cancer in the UK with over 10,000 people diagnosed with the condition in 2007 and an age-standardised rate of 14.2 per 100,000 population (Cancer-Research-UK, 2011) (see Figure 2A). In the United States, the Surveillance Epidemiology & End Results (SEER) registry provides an age-standardised rate of 19.6 per 100,000 between 2003 and 2007 (Altekruse et al., 2010). The incidence of NHL seems to be rising in the UK

Natural Killer).

*depleted* types (see Table 1).

**3.2 Non-Hodgkin's lymphoma**

years.

classically been divided in to two subtypes:

High-grade NHL develops quickly and aggressively.

Fig. 2. Figures adapted from Cancer Research UK 2011. A: Incidence of non-Hodgkin's lymphoma by gender. B: Incidence of Hodgkin's lymphoma by gender. C: Age standardised incidence rates for non-Hodgkin's lymphoma. D: Age standardised incidence rates for Hodgkin's lymphoma.

with an increase of 35% during the 20 year interval between 1988 and 2007 (see Figure 2C). This trend seems to be reflected throughout the world. Mortality in the UK is estimated at 6.9 per 100,000 from NHL and with improvements in treatment, it is estimated that over half of patients now survive for at least 10 years following diagnosis. Up to 15% of all extranodal NHL presents in the GI tract (Newton et al., 1997).

A number of risk factors for the development of NHL have been studied:

Evaluating Lymphoma Risk in Inflammatory Bowel Disease 319

 **Epstein-Barr virus** – EBV infection has long been implicated in the development of Hodgkin's lymphoma. EBV DNA can be found in 40% of cases with higher rates of association found in the paediatric population (Jarrett et al., 1996). EBV positivity is more commonly found in the Mixed Cellularity than the Nodular Sclerosis subtypes of classical HL. A previous history of infectious mononucleosis confers an increased risk

of HL with an SIR of 3.49 in patients aged 15 to 34 years (Hjalgrim et al., 2000). **Previous non-Hodgkin's lymphoma** – Studies suggest that patients who have previously been treated for NHL are at increased risk of subsequently developing HL with a magnitude in the order of four- to twelve-fold (Travis et al., 1991, Travis et al.,

Lymphoma is a clonal expansion of B- and T- lymphocytes caused by the accumulation of a series of genetic mutations affecting proto-oncogenes and tumour suppressor genes. This results in dysregulated proliferation, evasion of immune surveillance mechanisms and inhibition of apoptosis (Jaffe et al., 2001). Significant progress has been made in to the understanding of these mechanisms at a molecular level. The activation of oncogenes by aberrant chromosomal translocations as well as the inactivation of tumour suppressor genes by chromosomal deletion or mutation are both important mechanisms of lymphomagenesis (Kuppers et al., 1999). Oncogenic viruses such as EBV and HTLV1 can also introduce foreign genetic sequences into the lymphocyte genome causing disruption of normal function (Neri

There are a number of genetic, environmental, infectious and iatrogenic factors amongst patients with inflammatory bowel disease which can predispose to increased susceptibility

 **Chronic inflammation** – The pathogenesis of IBD is not completely understood but aberrations in the innate and adaptive immune response to luminal antigens has been the focus of much research. It can be postulated that the dysregulation of these immune systems seen in the chronic inflammation associated with IBD may lead to antigendriven lymphocyte proliferation and a relatively unhindered risk of genetic and chromosomal deviations (Sokol and Beaugerie, 2009). Another possibility is that the combination of metabolites, cytokines and chemokines seen in the mucosa of IBD patients promotes mutagenesis in bystander cells. These theories may help to explain the increased risk of lymphoma seen in a variety of different auto-immune conditions and their concordance to sites of inflammation (Smedby et al., 2006). EBV related lymphoma has been reported in longstanding pyothorax of over 20 years duration (Aozasa et al., 2005). This is a condition which is regarded to be due to chronic suppuration with no autoimmunity and it is suggested that any chronic inflammatory

 **Genetic susceptibility** – Linkage studies and genome wide association studies have identified a large array of susceptibility genes for IBD (Barrett et al., 2008). These genetic changes may also be involved in the pathogenesis of lymphoma in certain individuals. For example, the first susceptibility gene identified, IBD1, encodes for the protein

susceptibility with lower risk in blacks than whites (Glaser, 1991).

1993).

et al., 1991).

**4. Pathogenesis of lymphoma in IBD**

to these mechanisms for the development of lymphoma:

state may predispose to lymphoma development.

et al., 2005, Goldin et al., 2004). Studies from the USA suggest racial differences in

infection has been implicated (Kawashima et al., 1994). A recent meta-analysis suggests an 8-fold increased risk of NHL in post-transplant patients (Grulich et al., 2007b).


*Review* **Antiviral Compounds from Myxobacteria**

**Lucky S. Mulwa 1,2 and Marc Stadler 1,\***

Received: 25 May 2018; Accepted: 17 July 2018; Published: 19 July 2018

**Abstract:** Viral infections including human immunodeficiency virus (HIV), cytomegalovirus (CMV), hepatitis B virus (HBV), and hepatitis C virus (HCV) pose an ongoing threat to human health due to the lack of effective therapeutic agents. The re-emergence of old viral diseases such as the recent Ebola outbreaks in West Africa represents a global public health issue. Drug resistance and toxicity to target cells are the major challenges for the current antiviral agents. Therefore, there is a need for identifying agents with novel modes of action and improved efficacy. Viral-based illnesses are further aggravated by co-infections, such as an HIV patient co-infected with HBV or HCV. The drugs used to treat or manage HIV tend to increase the pathogenesis of HBV and HCV. Hence, novel antiviral drug candidates should ideally have broad-spectrum activity and no negative drug-drug interactions. Myxobacteria are in the focus of this review since they produce numerous structurally and functionally unique bioactive compounds, which have only recently been screened for antiviral effects. This research has already led to some interesting findings, including the discovery of several candidate compounds with broad-spectrum antiviral activity. The present review looks at myxobacteria-derived antiviral secondary metabolites.

**Keywords:** myxobacteria; antivirals; secondary metabolites; HIV; Ebola; hepatitis viruses