Human Olfactory Receptor Comparative Sequence Assessments from the Genome

We present a review of the results of research to identify olfactory receptor gene sequences from the Human Genome. This effort began soon after the publication of the first draft of the genome. Subsequent publications of the genome have allowed the evolution of OR gene identification with improved accuracy. We performed a comprehensive sequence analysis to identify 100% sequence matches, as well as partial matches by performing the same sequence matching step against random partial regions of sequences. Only sequences supplied to us from the groups that mined the olfactory receptor sequences from the genome or were available online were used in the analysis. The results are publicly available at https:// ordb.biotech.ttu.edu/ORDB/info/humanorseqanal. We also performed a similar assessment of mouse olfactory receptor sequences from two groups that assessed the mouse genome, publicly available at https://ordb.biotech.ttu.edu/ORDB/info/mouseorseqanal. Both resources are linked through a list of potential orthologs between the two species’ receptor sequences. This review also contains a discussion of nomenclatures of these genes and the resources as information dissemination portals.

Read More About This Article: 10.31031/OABB.2024.03.000571

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Enhancing Learning with ChatGPT: A Transformative Educational Companion

In recent years, the integration of Artificial Intelligence (AI) technologies into educational settings has opened up new possibilities for personalized and interactive learning experiences. Among these technologies, ChatGPT, an advanced language model developed by OpenAI, has emerged as a promising educational companion. ChatGPT utilizes Natural Language Processing (NLP) algorithms to engage in conversational interactions, provide feedback, and deliver personalized learning materials. This academic review explores the potential of leveraging ChatGPT as a transformative educational companion, examining its applications, benefits, challenges, and implications for teaching and learning.

Applications of ChatGPT in Education

ChatGPT offers a wide range of applications in education, ranging from providing instant feedback on student responses to facilitating interactive learning experiences. As a conversational agent, ChatGPT can engage students in dialogues on various topics, clarify concepts, and answer questions in real-time. Additionally, ChatGPT can generate personalized learning materials, such as summaries, quizzes, and study guides, tailored to individual students’ needs and preferences. Furthermore, ChatGPT can assist educators in tasks such as lesson planning, content creation, and assessment design, enhancing instructional efficiency and effectiveness.

 Read More About This Article: 10.31031/OABB.2024.03.000570

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Managing Pandemic Disasters: A Resource-Based Analysis

Healthcare crises are well-known phenomena that occur and are highly disruptive. Consider, for example, Covid19 and other pandemics, wars, multi-casualty natural disasters, industrial disasters, etc. Healthcare systems in Brazil, Spain, Italy, and more around the world collapsed due to the lack of a clear methodology for handling such crises. Following a largescale value creation project with a large medical center we developed a resource-based methodology to cope with three healthcare crisis scenarios: “Peace”, “War” and “Tsunami”.

We identified three load/capacity scenarios for hospitals: “Peace”, “War” and “Tsunami”. The Peacetime scenario is the normal overload situation in a hospital. Figure 1 during “Peace” times, hospitals are usually 10%-20% short in resources. To overcome bottlenecks, managers apply evolutionary methods such as constraint management [1], the complete kit concept, etc. This is the normal condition in hospitals where there are fluctuations in supply and demand. Bottlenecks in this situation are typically medical crews: senior physicians and experienced nurses.

Read More About This Article: 10.31031/OABB.2024.03.000567

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Artificial Cell Membranes as Bioinformation Hubs: Unraveling Therapeutic Networks through Nano-Informatics

In this brief the innovative realm of artificial cell membranes as bioinformation hubs, specifically focusing on their role in creating therapeutic networks, is proposed. The integration of advanced nanotechnologies, bioinformatics, cheminformatics, and medical informatics has paved the way for the development of in silico tools that facilitate the simulation of interactions and mechanisms of toxicity in therapeutic products, particularly in drug delivery nanosystems. Various specialized cloud platforms incorporate libraries of diverse nanomaterials with comprehensive morphological and biological data, enabling correlation with potential adverse effects. Such tools prove invaluable in guiding the pharmaceutical industry in the development of innovative therapeutical formulations and aiding regulatory agencies in evaluating decision-making nanoplatforms. Finally, in this opinion-article we support the idea the cell membranes could be considered as bioinformation hubs and artificial pharmaceutical nanoplatforms.

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Adapting Hartigan & Wong K-Means for the Efficient Clustering of Sets1

Abstract

This paper proposes an algorithm, named HWK-Sets, based on K-Means, suited for clustering data which are variable-sized sets of elementary items. An example of such data occurs in the analysis of medical diagnosis, where the goal is to detect human subjects who share common diseases so as to predict future illnesses from previous medical history possibly. Clustering sets is difficult because data objects do not have numerical attributes and therefore it is not possible to use the classical Euclidean distance upon which K-Means is normally based. An adaptation of the Jaccard distance between sets is used, which exploits application-sensitive information. More in particular, the Hartigan and Wong variation of K-Means is adopted, which can favor the fast attainment of a careful solution. The HWK-Sets algorithm can flexibly use various stochastic seeding techniques. Since the difficulty of calculating a mean among the sets of a cluster, the concept of a medoid is employed as a cluster representative (centroid), which always remains a data object of the application. The paper describes the HWK-Sets clustering algorithm and outlines its current implementation in Java based on parallel streams. After that, the efficiency and accuracy of the proposed algorithm are demonstrated by applying it to 15 benchmark datasets.

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Binomial Population of Biological Objects

Opinion

Let the system consist of n of the same type and independent biological individuals with the same indicator p survival at a given interval [0,T] time [1]. Let us assume that a population is subject to an epidemic that leads to the death of some of the individuals.

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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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Profile, Pattern and Outcome of Shri Amaranth Ji Yatri Patients attending Sher-i-Kashmir Institute of Medical Sciences, Srinagar, India during Holy Yatra of 2017_Crimson Publishers

Profile, Pattern and Outcome of Shri Amaranth Ji Yatri Patients attending Sher-i-Kashmir Institute of Medical Sciences, Srinagar, India during Holy Yatra of 2017 by Mohammad Sarwar Mir in Open Access Biostatistics & Bioinformatics 

Introduction: Located deep in the Himalayas, the cave of Amarnath is one of the holiest pilgrimage site for Hindus in general and Shiva followers in particular. It is regarded to be the abode of Lord Shiva. Because of high altitude, rough terrain, harsh weather, pilgrims are prone to many illnesses which sometimes these prove fatal.

Objective: To study the profile, pattern and outcome, among Shri Amarnath Ji yatri patients attending SKIMS in year 2017.

https://crimsonpublishers.com/oabb/fulltext/OABB.000525.php

high impact journals in Crimson Publishers?

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Quinacrine and Berberine as Antiviral Agents against Dengue and Zika Fever: In Silico Approach- Crimson Publishers

Quinacrine and Berberine as Antiviral Agents against Dengue and Zika Fever: In Silico Approach by Vivek Srivastava in OpenAccess Biostatistics & Bioinformatics: Crimson Publishers- Open Access Biostatistics & Bioinformatics journals

TDengue and Zika fever are mosquito-borne viral diseases that have rapidly spread in all over world. Currently there are no specific drugs for DENV and ZIKV infection. The recent outbreak of these viruses realized that there are major health risks, demands an enhanced surveillance and a need to develop novel drugs against them. Non-structural proteins NS5 and NS3 are essential for the replication of the flavi-viral RNA genome. Therefore its inhibition could be considered as a useful strategy for treatment of DENV and ZIKV infection. Quinacrine and Berberine had been docked with NS5-methyltransferase of Dengue virus and NS3 protein of ZIKV using Auto dock 4.2 tools. Quinacrine and Berberine showed binding affinity -6.83kcal/mol and -6.22kcal/mol with NS5-methyltransferase and -7.32kcal/mol and -8.03kcal/mol with NS3 protease of ZIKV, respectively. Observations discussion in review will be useful in designing single drug against both virus infections.

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Untargeted UPLC-MS Downstream Data Processing and Statistical Analysis - Illustrated by a Pilot Study on Cognitive Impairment- Crimson Publishers

Untargeted UPLC-MS Downstream Data Processing and Statistical Analysis - Illustrated by a Pilot Study on Cognitive Impairment by TANG Xingyu in Open Access Biostatistics & Bioinformatics: Crimson Publishers- Open Access Biostatistics & Bioinformatics journals

This article is to introduce the procedure of untargeted ultra-performance liquid chromatography-mass spectrometry(UPLC-MS) downstream data processing and statistical analysis, developed and optimized in Singapore Phenome Centre (SPC). The procedure is illustrated by a pilot study on cognitive impairment.

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A New Lifetime Model: The Kumaraswamy Extension Exponential Distribution- Crimson Publishers

A New Lifetime Model: The Kumaraswamy Extension Exponential Distribution by Francisco Louzada in Open Access Biostatistics & Bioinformatics: Crimson Publishers- Open Access Biostatistics & Bioinformatics journals

Based on the Kumaraswamy distribution, we study the so called Kumaraswamy Extension Exponential Distribution (KEE). The new distribution has a number of well-known lifetime special sub-models such as a new exponential type distribution, extension exponential distribution Kumaraswamy generalized exponential distribution, among several others. We derive some mathematical properties of the (KEE) including quan tile function, moments, moment generating function and mean residual lifetime. In addition, the method of maximum likelihood and least squares and weighted least squares estimators are discussing for estimating the model parameters. By using the likelihood method a simulation study was made.

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