On Structure-Function Asymmetry

Abstract

This is a short opinion article pointing out the importance of examining more precisely the intimate relationship between structure and function that arises for all complex systems. It is claimed that this relationship is highly asymmetrical, which has many unforeseen consequences. The presented claims are made based on intuition and inductive reasoning. Although some claims may be only partially valid, for instance, under yet to be specified conditions, they can inspire new research directions to investigate in future

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Copulas in Gene’s Relation

Opinion

Genes are considered as segments of DNA that provide instructions for the production of specific proteins or RNA molecules. Genes can be related to one another in a variety of ways. These relationships can play important roles in the regulation of cellular processes and functions, as well as in the development and evolution of organisms. Gene relation refers to the relationship between two or more genes in terms of their function, location, or sequence similarity. In studying the expression and relationship of genes, researchers can get a sense of how gene-information is used to know cell functions and how changes in gene expression or regulation can lead to disease. As an example, co-expression analysis can help to determine gene-groups that are functionally related and may be involved in specific biological processes or diseases. Also, network analysis can be used to identify regulatory relationships between genes as well as to build gene regulation networks that control specific cellular processes. See also Allocco et al. [1], Elise et al. [2], Ruan et al. [3] & Song et al. [4], By interacting with each other, genes form a complicated network. The relationship between groups of genes with different functions can be represented as gene networks. Since the accessibility of the large gene expression data, several methods have been developed to analyze the gene-to-gene relationship. Copula approach is being used to study the relationship between the expression profiles of pairs of genes, since a copula is a multivariate model that can be used for explaining gene-gene dependence.

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Again, about the Rhythms of Biological Processes

Opinion

Biological rhythms are one of the components of the endogenous time of an organism as a set of all its temporal processes. Circadian, ultra- and intracardiac rhythms have such temporal parameters as latency, speed, period, duration and frequency, i.e., are temporary processes. They are superimposed on the directed time of ontogenesis and, together with monophasic processes, trends and cycles, determine the biochemical and physiological specifics of its certain periods. Numerous studies of the past three decades have contributed to the understanding of the types of molecular oscillators that shape cellular rhythms. These include: an oscillator of impulse activity in neurons and some endocrinocytes; an oscillator that reflects the circadian rhythms of the cAMP and Ca2+ content; redox oscillator and finally PER - oscillator, working on the basis of interactions of clock proteins [1-5]. The specificity of the interaction of these oscillators in cells of different tissues is gradually becoming clear: in endocrinocytes, in blood cells, in hepatocytes and adipocytes. However, the essence of the functions of the rhythms themselves as time processes is not clear enough.

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Bayesian Shape Invariant Model for Latent Growth Curve with Time-Invariant Covariates

Abstract

In the attention-deficit hyperactivity disorder (ADHD) study, children are prescribed different stimulant medications. The height measurements are recorded longitudinally along with the medication time. Differences among the patients are captured by the parameters suggested the Superimposition by Translation and Rotation (SITAR) model using three subject-specific parameters to estimate their deviation from the mean growth curve. In this paper, we generalize the SITAR model in a Bayesian way with time-invariant covariates. The time-invariant model allows us to predict latent growth factors. Since patients suffer from a common disease, they usually exhibit a similar pattern, and it is natural to build a nonlinear model that is shaped invariant. The model is semi-parametric, where the population time curve is modeled with a natural cubic spline. The original shape invariant growth curve model, motivated by epidemiological research on the evolution of pubertal heights over time, fits the underlying shape function for height over age and estimates subject-specific deviations from this curve in terms of size, tempo, and velocity using maximum likelihood. The usefulness of the model is illustrated in the attention deficit hyperactivity disorder (ADHD) study. Further, we demonstrated the effect of stimulant medications on pubertal growth by gender.

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Resolving Issues at Host Level Using Variable Reduction Methods: Case of Cornudiscoides Spp. (Platyhelminthes: Monogenoidea)

 

Abstract

Taxonomy deals with delineating and classifying organisms, and traditionally relies on morphological characters only. We have used morphological characters, and molecular biology to distinguish monogenoids at generic and specific levels. Principal Component Analysis provided a magnifying glass to resolve the taxonomic issues as well as their host levels.

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A Behavioral Learning Theory Public Health Education and Promotion Campaign Plan for COVID-19

Abstract

The article includes a COVID-19 public health education and promotion campaign plan to prompt change by applying major behavioral change principles and procedures. Best practices research to motivate, support, and sustain health behavior change includes the application of Behavioral Learning Theory when educating the public regarding COVID-19 health challenges. Topics evaluated include:

1)Behavioral Learning Theory,

2)6-month timeline for accomplishing three COVID-19 public health communication objectives, and

3)SWOT analysis.

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Global Social and Economic Impact of SARS- CoV-2 Pandemic on Industry Sectors

Abstract

On December 31, 2019, the Chinese government formally proclaimed the identification of a new type of coronavirus (SARS-CoV-2) as the etiological source of a critical respiratory disease in Wuhan city, Hubei Province. Over the next few weeks, SARS-CoV-2 introduced a global pandemic as formally declared by the WHO on March 11th, 2020, with inveterate cases and deaths in more than 212 countries leading to exceptional social and economic consequences.

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Differential Mathematical Models of Intellectu- alization of Fast Flow Processes on Railway Elec- tricity Supply Networks

 

Abstract

The problem of innovative transformation of railway power supply networks Is Investigated and the direction of researchrelates to the organization of differential mathematical models of optimization of procedures by control of high-speed technological processes of power supply of railways. A graphis proposed, whose logical structure adequately reflects the architecture of the computerized network for managing the power supply system and developed its differential mathematical model. On the basis of the principle of minimax, the optimal strategy of intellectualization of the power supply processes in each of the nodes of the computer network was developed for the cases of the worst combination of the intensity of the requests and the intensity of their service. Hereis a way to ensure that the specified performance of the individual no desands egments of theintelligent computer control network by exploring the extremum off unctionality, implemented in the field of T-images using differential spectra.

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Maximum Entropy Risk Model for Investment Management

Abstract

In the present communication Markowitz’s method of mean- variance efficient frontier has been explained. Some introductory entropy models and concepts related to risk in investments have been discussed. Risk aversion index and Pareto-optimal sharing of risk have been defined. A new measure of risk based on maximum entropy principle has been studied in detail. Mathematics Subject Classification 2000: 91b24 and 94a15.

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Genomics of Newly Discovered Leprosy Pathogen: Mycobacterium lepromatosis and Its Geographic Isolation in Mexico

Overview

Leprosy is a chronic dermatologic infection that has plagued human populations for thousands of years. Mycobacterium leprae, the etiological agent for leprosy, has puzzled scientists since its identification by Hansen in 1873 [1]. Leprosy is one of the leading causes of treatable neuropathy [2]. It has been affecting mankind since the earliest historically recorded identification and far beyond (2000 B.C, in ancient Indus civilization) [3]. Leprosy is also one of the earliest recognized diseases which have a proven association with the bacterial pathogen, M. leprae [3]. In 2008, a new bacterium, M. lepromatosis was discovered in Mexico which was also reported to cause leprosy [4]. M. lepromatosis was found to be associated with a typical severe form of lepromatous leprosy (LL), called Diffuse Lepromatous Leprosy (DLL) which is usually seen in the Americas [5-7]. DLL is characterized by an unusual form of immune reaction against the pathogen, called Lucio’s phenomenon, characterized by diffuse, non-nodular cutaneous infiltration with sharply demarcated skin lesions, which often gets infected to result in life threatening situations [8,9].

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COVID-19 Epidemic: China and ex-China

 

Abstract

Here we report the current COVID-19 epidemic in terms of mortality and recovery compared to the confirmed patient population of provinces in China as well as countries outside of China. Data was obtained from the Center for System Science and Engineering (CSSE) by Johns Hopkins University (JHU) and was plotted in log-log charts. For China, mortality dropped as low as 0.08% but then converged to a band of 1%-5%, with the median value of 1.1% as of March 9th, 2020. For countries outside of China, mortality dropped to a low of 0.2% with a median value of 2.4% as of March 9th, 2020. This difference was statistically significant with p=0.0057.A bi-modal distribution in mortality was observed for both China and countries outside of China, which would concur with reports mentioning two possible strains of the SARS-CoV-2 virus. China exhibited a median recovery rate of 95.0% with the lowest being Hubei province with a recovery rate of 57%. Outside of China, the median recovery rate was 10.7% and was significantly lower than that of China, p <0.0001, t-test. Distribution-wise, both China and countries outside of China were observed to be similar. As of now, the spread of COVID-19 in countries outside of China are showing properties more similar to that of Hubei–the epicenter of COVID-19 epidemic in China.

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