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This work addresses the COVID-19 pandemic on two fronts: proposing a system of ordinary differential equations to model it and fitting this model to Brazilian and Portuguese data. It presents estimations to important parameters for the infection dynamics, such as the percentage of asymptomatic individuals, and it stresses out that non-biological human aspects, for example, cultural, social, and economic, are not only impacted by the pandemic but also impact the pandemic dynamics itself. We state that, despite significant variations in the parameters, due to those human elements present in the contemporary pandemic, and despite the strong nonlinearities of the problem, wise human intervention is possible and able to minimize human losses. We show that the mortality rate does not behave as one would expect for a biological problem, independent of cultural aspects, and we also point to possible dates for the peaks of infection in both countries depending on the control of the transmissibility.

The paper deals with the problem of the distribution of the medicine (enzyme) in the damaged biological tissue where the reaction enzyme—substrat takes place. The biological problem is reduced to a singular degenerate initial-boundary value problem for two coupled ordinary differential equations. Analytical solution of the singular degenerated IBV-problem was obtained by power series. The solution demonstrates the real situation and found suitable to depict the degeneration of singular system, caused by low concentration of the enzyme.

Subtle changes in Raman spectral line shape have been observed from malignant human brain cells and its possibility for being used in detection and grading of Glioma has been explored here. The lat...

MotivationThe universal expressibility assumption of Deep Neural Networks (DNNs) is the key motivation behind recent worksin the systems biology community to employDNNs to solve important problems in functional genomics and moleculargenetics. Typically, such investigations have taken a ‘black box’ approach in which the internal structure of themodel used is set purely by machine learning considerations with little consideration of representing the internalstructure of the biological system by the mathematical structure of the DNN. DNNs have not yet been applied to thedetailed modeling of transcriptional control in which mRNA production is controlled by the binding of specific transcriptionfactors to DNA, in part because such models are in part formulated in terms of specific chemical equationsthat appear different in form from those used in neural networks.ResultsIn this paper, we give an example of a DNN whichcan model the detailed control of transcription in a precise and predictive manner. Its internal structure is fully interpretableand is faithful to underlying chemistry of transcription factor binding to DNA. We derive our DNN from asystems biology model that was not previously recognized as having a DNN structure. Although we apply our DNNto data from the early embryo of the fruit fly Drosophila, this system serves as a test bed for analysis of much larger datasets obtained by systems biology studies on a genomic scale. .Availability and implementationThe implementation and data for the models used in this paper are in a zip file in the supplementary material.Supplementary informationSupplementary data are available at Bioinformatics online.

Despite the recent advances in the standardization of untargeted metabolomics workflows, there is still a lack of attention to specific data treatment strategies that require deep knowledge of the biological problem and need to be applied after a well-thought out process to understand the effect of the practice. One of those strategies is data normalization. Data-driven assumptions are critical especially addressing unwanted variation present in the biological model as it can be the case in heterogeneous tissues, cells with different sizes or biofluids with different concentrations. Chronic kidney disease (CKD) is a widespread disorder affecting kidney structure and function. Animal models are being developed to be able to get valuable insights into the etiopathogenesis of the condition and effect of the treatments. Moreover, diagnosis and disease staging still require defining appropriate biomarkers. Untargeted metabolomics has the potential to deal with those challenges. Renal fibrosis is one of the consequences of kidney injury which greatly affects the concentration of metabolites in the same quantity of sample. To overcome this challenge, several data normalization strategies have been applied, following a multilevel normalization method with the overall aim of focussing on the relevant biological information and reducing the influence of disturbing factors. A comprehensive evaluation of the performance of the normalization strategies, both on methods assessing the intragroup variation and on the impact on differential analysis, is provided. Finally, we present evidence of the importance of biological-model-driven guided normalization methods and discuss multiple criteria that need to be taken into consideration to obtain robust and reliable data. Special concern is transmitted on the misleading conclusions that might be the consequence of inappropriate data pre-treatment solutions applied for untargeted methods.

The paper considers modeling of technical problems and problems of applied mathematics, their algorithms and programming. The characteristics of the numerical modeling of technical problems and applied mathematics are given: physical and technical experiments, energy, ballistic and seismic methods of I.V. Kurchatov, starting with mathematical methods of the 17-20th centuries, the first computers and computers. The analysis of the first technical problems and problems of applied mathematics, their modeling, algorithmization and programming using the A.A. Lyapunov graph-schematic language, address language and programming languages is given. Numerical methods are presented, implemented under the guidance of A.A. Dorodnitsyn, A.A. Samarsky, O.M. Belotserkovsky and other scientists on modern supercomputers. Examples of mathematical modeling of the biological problem of eye treatment and the subject of «Computational geometry» on the Internet are given.

Signaling pathway analysis has become a routine task after differentially expressed gene (DEG) studies across disease conditions, drug treatments, or developmental stages. A signaling pathway can be represented by a graph that consists of Genes and interactions (the genetic regulation) between them. However, existing signaling pathway analysis methods ignore the strength variations of interactions in signaling pathways under different conditions. Here, we developed a novel method named SPACI (Signaling Pathway Analysis Combined with the strength variations of Interactions between genes in signaling pathways under different conditions) to improve signaling pathway analysis after DEG studies. To further evaluate the performance of SPACI, we compared SPACI with nine other methods by using a benchmark of 28 gene expression datasets in two standard measures: sensitivity and prioritization. The False positive rate (FPR) of SPACI was also compared with five methods. The results show that SPACI is the second-ranked method in terms of prioritization and the third-ranked method in terms of sensitivity. SPACI is the top method when compared in terms of the sum value of the two ranks. Also, the FPR of SPACI is modest compared with the classic methods. Furthermore, the strength variation of the interaction is demonstrated as coherent with the biological problem. The interactions with high strength variations under different conditions can help improve the discovery of the underlying biological information. The R package of SPACI can be accessed at https://github.com/ZhenshenBao/SPACI .

Low back pain is the leading cause of disability worldwide and in many patients the source of pain can be attributed to pathological changes within the intervertebral disc (IVD). As present treatment options fail to address the underlying biological problem, novel therapies are currently subject to intense research. The physiologic IVD microenvironment features a highly complex interaction of biochemical and mechanical factors influencing cell metabolism and extracellular matrix turnover and is therefore difficult to simulate for research purposes on IVD pathology. The first whole organ culture models were not able to sufficiently replicate human in vivo conditions as mechanical loading, the predominant way of IVD nutrient supply and waste exchange, remained disregarded. To mimic the unique IVD niche more realistically, whole organ culture bioreactors have been developed, allowing for dynamic loading of IVDs and nutrient exchange. Recent advancements on bioreactor systems have facilitated whole organ culture of various IVDs for extended periods. IVD organ culture bioreactors have the potential to bridge the gap between in vitro and in vivo systems and thus may give valuable insights on IVD pathology and/or potential novel treatment approaches if the respective model is adjusted according to a well-defined research question. In this review, we outline the potential of currently utilized IVD bioreactor systems and present suggestions for further developments to more reliably investigate IVD biology and novel treatment approaches.

The prediction of drug candidates for given tissues of organisms based on expression data is a critical biological problem. By correctly predicting drug candidates for given tissues, biologists can (1) avoid an experimental process of high-throughput screening that requires excessive time and costly equipment and (2) accelerate the drug discovery process by automatically assigning drug candidates. Although high throughput screening for therapeutic compounds lead to the generation of expression data, the process of correctly assigning candidate drugs based on such data remains a rigorous task. Hence, the design of high-performance machine learning (ML) algorithms is crucial for data analysts who work with clinicians. Clinicians incorporate advanced ML tools into expert and intelligent systems to improve the drug discovery process by accurately identifying drug candidates. The transfer learning approaches that are necessary to improve the prediction performance of several tasks that are involved in identifying drug candidates are presented in this paper. The performances of machine learning algorithms are compared in the transfer learning setting by employing several evaluation measures on real data that are obtained from experiments conducted on rats to identify drug candidates. The experimental results show that the proposed transfer learning approaches outperform baseline approaches in terms of prediction performance and statistical significance.

Despite recent advances in understanding brain function, consciousness – specifically, how the brain gives rise to conscious experiences – remains ‘the hard problem.’ In humans, there are often multiple routes to the same actions, some of them involving conscious experience, others not. Furthermore, differences in brain circuitry make analogies between humans and other animals more difficult than is generally acknowledged. In this essay, I argue that both the study of consciousness itself and the science of animal welfare benefit from facing up to to these difficulties rather than glossing over them. Animal welfare science, although often defining good welfare in term of what animals feel, does not have to be based on assumptions about which species have conscious experiences. Animal welfare (well-being) can be defined objectively in terms of animal health and what animals want. Such a conscious-free definition is readily understandable by people with very different views about animals and yet is practical enough to point to what factual scientific information is needed in any given case. While not precluding conscious awareness in other species, it allows animal welfare science to move forward without having solved the hardest biological problem of all.

In silico models and computer simulation are invaluable tools to better understand complex biological processes such as cancer evolution. However, the complexity of the biological environment, with many cell mechanisms in response to changing physical and chemical external stimuli, makes the associated mathematical models highly non-linear and multiparametric. One of the main problems of these models is the determination of the parameters’ values, which are usually fitted for specific conditions, making the conclusions drawn difficult to generalise. We analyse here an important biological problem: the evolution of hypoxia-driven migratory structures in Glioblastoma Multiforme (GBM), the most aggressive and lethal primary brain tumour. We establish a mathematical model considering the interaction of the tumour cells with oxygen concentration in what is called the go or grow paradigm. We reproduce in this work three different experiments, showing the main GBM structures (pseudopalisade and necrotic core formation), only changing the initial and boundary conditions. We prove that it is possible to obtain versatile mathematical tools which, together with a sound parametric analysis, allow to explain complex biological phenomena. We show the utility of this hybrid “biomimetic in vitro-in silico” platform to help to elucidate the mechanisms involved in cancer processes, to better understand the role of the different phenomena, to test new scientific hypotheses and to design new data-driven experiments.

Recent advances in sequencing technologies, namely next-generation and third-generation sequencing, have had a major impact on biomedical research in many different fields, including epigenetics and immunological research. Although these technologies have a common goal, the experimental approaches and techniques used by the different sequencing platforms may differ significantly, leading to both specific advantages and characteristic shortcomings. To harness their full potential by selecting the appropriate technology for a biological problem at hand, and to perform an appropriate computational analysis of the generated sequencing data, it is imperative to have a basic understanding of how the technologies work and what individual strengths and weaknesses they have. This chapter provides an introductory, necessarily incomplete overview of these sequencing technologies, and the computational analysis of the data that they produce. A particular focus will lie on their application to study the epigenetic regulation of cellular functions in the context of the immune system, but most considerations hold for other research fields as well.

Novelty detection is a fundamental biological problem that organisms must solve to determine whether a given stimulus departs from those previously experienced. In computer science, this problem is solved efficiently using a data structure called a Bloom filter. We found that the fruit fly olfactory circuit evolved a variant of a Bloom filter to assess the novelty of odors. Compared with a traditional Bloom filter, the fly adjusts novelty responses based on two additional features: the similarity of an odor to previously experienced odors and the time elapsed since the odor was last experienced. We elaborate and validate a framework to predict novelty responses of fruit flies to given pairs of odors. We also translate insights from the fly circuit to develop a class of distance- and time-sensitive Bloom filters that outperform prior filters when evaluated on several biological and computational datasets. Overall, our work illuminates the algorithmic basis of an important neurobiological problem and offers strategies for novelty detection in computational systems.

In contrast to the skin, aging of the hair has seemingly only recently found the attention of dermatological meetings, mainly promoted by the cosmetic industry for marketing purposes. In fact, basic scientists interested in the biology of hair growth and pigmentation have for some time already exposed the hair follicle as a highly accessible model with unique opportunities for the study of age-related effects. As a result, the science of hair aging focuses on two main streams of interest: the esthetic problem of aging hair and its management, in terms of age-related effects on hair color, quantity, and quality; and the biological problem of aging hair, in terms of microscopic, biochemical, and molecular changes underlying the aging process. Ultimately, the aim of hair anti-aging is to delay, lessen, or reverse the effects of aging on hair. According to the complex nature of the aging process, the treatment for lifetime scalp and hair health has to be holistic to include the multitude of contributing factors in a polyhedral and patient-specific manner. It comprises both medical treatments and hair cosmetics. Accordingly, the discovery of pharmacological targets and the development of safe and effective drugs for treatment of hair loss indicate strategies of the drug industry for maintenance of hair growth and quantity, while the hair care industry has become capable of delivering active compounds directed toward meeting the consumer demand for maintenance of hair cosmesis and quality. "Where there's life, there's hope" (Ecclesiastes 9:3-5).

Purpose The purpose of this paper is to investigate the mediating role of psychological problems (stress, anxiety, depression) between hearing impairment (HI) and tinnitus in tinnitus patients. Sample comprised of 110 patients (male, n=70; female, n=40). Design/methodology/approach The purposive sampling technique was used based on correlational design. Two scales, tinnitus handicap inventory developed by Newman et al. (1996), and depression, anxiety and stress scale developed by Lovibond and Lovibond (1995), were employed to assess tinnitus, anxiety, stress and depression among tinnitus patients. Findings The study revealed that tinnitus was positively significant correlated with depression (r=0.55, pr=0.63, pr=0.54, pr=0.81, pβ=20.72, pβ=2.62, pβ=2.33, pβ=2.71, pβ=−0.51, pβ=−0.57, pβ=−0.62, p Research limitations/implications This study consisted on population from urban cities of Pakistan; therefore, the findings cannot be generalized on overall population. In future, descriptive as well as experimental studies will be beneficial to describe the mechanism of tinnitus across gender in detail. Native tool for measuring gender-related problems would be effective in future as it integrates cultural aspects as well. Originality/value There is a consensus across researchers that tinnitus is one of the most important biological problem which initiates various other psychological problem (Minen et al., 2014; Vogel et al., 2014). Numerous studies have been conducted to find out the relationship of tinnitus with various psychological and biological factors (Boi et al., 2012; Li et al., 2014; Marciano et al., 2003; McCormack et al., 2015; Møller et al., 2010; Udupi et al., 2013; Zöger et al., 2006).

Many naturally occurring protein systems function primarily as symmetric assemblies. Prediction of the quaternary structure of these assemblies is an important biological problem. This manuscript describes automated tools we have developed for predicting the structure of symmetric protein assemblies in the Robetta structure prediction server. We assess the performance of this pipeline on a set of targets from the recent CASP12/CAPRI blind quaternary structure prediction experiment. Our approach successfully predicted five of seven symmetric assemblies in this challenge, and was assessed as the best participating server group, and one of only two groups (human or server) with two predictions judged as high quality by the assessors. We also assess the method on a broader set of 22 natively symmetric CASP12 targets, where we show that oligomeric modeling can improve the accuracy of monomeric structure determination, particularly in highly intertwined oligomers.

Incorporation of the excluding features in fuzzy relational systems is proposed.Three alternatives ways of the incorporation are introduced.Their coincidence is investigated.Theoretical properties are studied.Application potential and impact is demonstrated on a real biological problem (Odonata identification). The aim of this paper is, first, to recall fuzzy relational compositions (products) and, to introduce an idea, how excluding features could be incorporated into the theoretical background. Apart from definitions, we provide readers with a theoretical investigation. This investigation addresses two natural questions. Firstly, under which conditions (in which underlying algebraic structures) the given three natural approaches to the incorporation of excluding symptoms coincide. And secondly, under which conditions, the proposed incorporation of excluding features preserves the same natural and desirable properties similar to those preserved by fuzzy relational compositions.The positive impact of the incorporation on reducing the suspicions provided by the basic circlet composition without losing the possibly correct suspicion is demonstrated on a real taxonomic identification (classification) of Odonata. Here, we demonstrate how the proposed concept may eliminate the weaknesses provided by the classical fuzzy relational compositions and, at the same time, compete with powerful machine learning methods. The aim of the demonstration is not to show that proposed concept outperforms classical approaches, but to show, that its potential is strong enough in order to complete them or in order to be combined with them and to use its different nature.

This chapter discusses the case of two spatial dimension to study the diffusion with an increasing quantity of substance. In addition to diffusion, there is also a growth of the quantity of the substance, and at each point and each moment this growth is occurring at a rate depending on the density then observed. The chapter derives the diffusion equation for the study with the help of theorems and their proofs. It also applies the theorems to the study of some biological problems.

Despite August Krogh's famous admonition that a 'convenient' organism exists for every biological problem, we argue that appeals to 'convenience' are not sufficient to capture reasoning about organism choice. Instead, we offer a detailed analysis based on empirical data and philosophical arguments for a working set of twenty criteria that interact with each other in the highly contextualized judgements that biologists make about organism choice. We propose to think of these decisions as a form of 'differential analysis' where researchers weigh multiple criteria for organismal choice against each other, and often utilize multidimensional refinement processes to finalize their choices. The specific details of any one case make it difficult to draw generalizations or to abstract away from specific research situations. However, this analysis of criteria for organismal choice and how these are related in practice allows us to reflect more generally on what makes a particular organism useful or 'good.'

We present a general theory of stochastic model reduction which is based on a normal form coordinate transform method of A.J. Roberts. This nonlinear, stochastic projection allows for the deterministic and stochastic dynamics to interact correctly on the lower-dimensional manifold so that the dynamics predicted by the reduced, stochastic system agrees well with the dynamics predicted by the original, high-dimensional stochastic system. The method may be applied to any system with well-separated time scales. In this article, we consider a physical problem that involves a singularly perturbed Duffing oscillator as well as a biological problem that involves the prediction of infectious disease outbreaks.

‘Cryptic’ species are an emerging biological problem that is broadly discussed in the present study. Recently, a cryptic species definition was suggested for those species which manifest low morphological, but considerable genetic, disparity. As a case study we present unique material from a charismatic group of nudibranch molluscs of the genus Trinchesia from European waters to reveal three new species and demonstrate that they show a dual nature: on one hand, they can be considered a ‘cryptic’ species complex due to their overall similarity, but on the other hand, stable morphological differences as well as molecular differences are demonstrated for every species in that complex. Thus, this species complex can equally be named ‘cryptic’, ‘pseudocryptic’ or ‘non-cryptic’. We also present evidence for an extremely rapid speciation rate in this species complex and link the species problem with epigenetics. Available metazoan-wide data, which are broadly discussed in the present study, show the unsuitability of a ‘cryptic’ species concept because the degree of crypticity represents a continuum when a finer multilevel morphological and molecular scale is applied to uncover more narrowly defined species making the ‘cryptic’ addition to ‘species’ redundant. Morphological and molecular methods should be applied in concordance to form a fine-scale multilevel taxonomic framework, and not necessarily implying only an a posteriori transformation of exclusively molecular-based ‘cryptic’ species into morphologically-defined ‘pseudocryptic’ ones. Implications of the present study have importance for many fields, including conservation biology and fine-scale biodiversity assessments.

Collaborative learning boosts student involvement and engagement to be responsible and accountable for the success of their own learning. However, developing critical collaborative skill is imperative for students to allow them to work in a collaborative environment. The proposed research followed an inter-institutional and inter-disciplinary collaborative approach where staff and students of Computing and Biotechnology Departments of two different Higher Education Institutions (HEIs) in Sultanate of Oman, collaboratively worked on solving a biological problem computationally. Ideas and information amongst students and staff were exchanged through social media platforms. Finally, the research outcome of the collaborative effort resulted in the development of a software which is freely accessible. The research paper describes the basis of setting up a collaborative research, implementation of the problem statement, final outcome of the collaboration, learner attributes achieved, challenges faced, and recommendations to the significance of inter-departmental collaborative efforts in HEIs.

This paper describes the use of ePortfolio as a platform for an interdisciplinary, collaborative course project for First Year Seminar (FYS) students in an Engineering and Computer Science FYS class, and in a Liberal Arts: Math and Science FYS class. This project was designed to enrich the integrative research experience of new-to-college students at LaGuardia Community College, and to engage them in hands-on inquiry and problem-solving practice, with a broader goal of increasing the retention rate in STEM majors by introducing students to the interdisciplinary nature of modern science and encouraging them to think of their future career paths in the same context. The major task in the project was to compare RNA sequences using computer programs, in order to facilitate the study of blood cancer. Throughout the project, students in both classes applied mathematical skills to perform algorithm analysis on a biological problem and were exposed to the application and basis of relevant technology in Computer Science. Collaborative work was conducted using ePortfolio as an online course platform which hosted all student work and allowed remote communication and collaboration to happen between two classes in different majors. Based on survey feedback, students were impressed by how they could contribute to the task using basic math and computer skills, and interest in continuing their STEM education was expressed unanimously.

The explosion in the amount of genomic data has changed how we educate future biologists. Students are inundated with information and need to develop skills in how to observe and understand this data. This chapter describes a teaching laboratory for undergraduate Genetics students that uses a combination of bioinformatics and wet lab exercises to teach skills in applying genomic data to a real scientific question. Students identify a target protein and search for the encoding RNA in newly available sea star transcriptome databases. The students design primers against specific regions or domains in their target RNA and amplify these by reverse transcription PCR. The PCR reactions are analyzed by agarose gel electrophoresis. The goal of this laboratory is to provide an example of how bioinformatics can be used to solve a real biological problem.

This paper investigates the mechanics of clearance of an embedded particle in a lubrication layer of viscoelastic fluid. We show theoretically that in a slider bearing domain containing a viscoelastic fluid, the oscillating shearing motion of a wall aids in transporting away any embedded particle towards the moving boundary. The impact of geometry and material properties of the fluid layer are explored by coupling theoretical and numerical methods. Our approach suggests a possible mechanism by which the human eye could clear out any debris beneath the eyelid, under responsive blinking. Our simplified analysis brings to bear interesting approaches from physics and engineering upon a very complex biological problem and could provide essential clues about the physiological design of the tear film.

Species of Eucalyptus are some of the most planted trees in the world, providing fiber, cellulose, energy, and wood for construction and furniture in renewable fashion, with the added advantage of fixing large amounts of atmospheric carbon. The efficiency of eucalypts in forestry relies mostly on the clonal propagation of selected genotypes both as pure species and interspecific hybrids. The formation of new roots from cambium tissues at the base of cuttings, referred to as adventitious rooting (AR), is essential for accomplishing clonal propagation successfully. AR is a highly complex, multi-level regulated developmental process, affected by a number of endogenous and environmental factors. In several cases, highly desirable genotypes from an industrial point of view carry along the undesirable trait of difficulty-to-root (recalcitrance). Understanding the bases of this phenotype is needed to identify ways to overcome recalcitrance and allow efficient clonal propagation. Herein, an overview of the state-of-the-art on the basis of AR recalcitrance in eucalypts addressed at various levels of regulation (transcript, protein, metabolite and phenotype), and OMICs techniques is presented. In addition, a focus is also provided on the gaps that need to be filled in order to advance in this strategic biological problem for global forestry industry relying on eucalypts.

Understanding how neurons form, extend, and navigate their finger-like axonal and dendritic processes is crucial for developing therapeutics for the diseased and damaged brain. Although less well appreciated, many other types of cells also send out similar finger-like projections. Indeed, unlike neuronal specific phenomena such as synapse formation or synaptic transmission, an important issue for thought is that this critical long-standing question of how a cellular process like an axon or dendrite forms and extends is not primarily a neuroscience problem but a cell biological problem. In that case, the use of simple cellular processes — such as the bristle cell process of Drosophila — can aid in the fight to answer these critical questions. Specifically, determining how a model cellular process is generated can provide a framework for manipulations of all types of membranous process-containing cells, including different types of neurons.

Redox biology has become a major issue in numerous areas of physiology. Reactive oxygen species (ROS) have a broad range of roles from signal transduction to growth control and cell death. To understand the nature of these roles, accurate measurement of the reactive compounds is required. An increasing number of tools for ROS detection is available; however, the specificity and sensitivity of these tools are often insufficient. Furthermore, their specificity has been rarely evaluated in complex physiological conditions. Many ROS probes are sensitive to environmental conditions in particular pH, which may interfere with ROS detection and cause misleading results. Accurate detection of ROS in physiology and pathophysiology faces additional challenges concerning the precise localization of the ROS and the timing of their production and disappearance. Certain ROS are membrane permeable, and certain ROS probes move across cells and organelles. Targetable ROS probes such as fluorescent protein-based biosensors are required for accurate localization. Here we analyze these challenges in more detail, provide indications on the strength and weakness of current tools for ROS detection, and point out developments that will provide improved ROS detection methods in the future. There is no universal method that fits all situations in physiology and cell biology. A detailed knowledge of the ROS probes is required to choose the appropriate method for a given biological problem. The knowledge of the shortcomings of these probes should also guide the development of new sensors.

Our paper describes a solution we found to a still existing need to develop mathematical modeling courses for undergraduate biology majors. Some challenges of such courses are: (i) relatively limited exposure of biology students to higher-level mathematical and computational concepts; (ii) availability of texts that can give a flavor of how contemporary biological problems are approached with quantitative methods; (iii) expectations to cover specific mathematical (or biological) content rather than teach a problem-solving approach. We solved these challenges by designing a course where several modules were developed, each beginning with a presentation from a campus expert in a biological area of research and concluding with a seminar-style student-led paper presentation of an article that addresses the biological problem using mathematical and computational tools. The aim of the course was to expose students to the vast possibilities and potential in quantitative biology, and to give them a vocabu...

Sleep is a physiological process not only for the rest of the body but also for several brain functions such as mood, memory, and consciousness. Nevertheless, the nature and functions of sleep remain largely unknown due to its extremely complicated nature and lack of optimized technology for the experiments. Here we review the recent progress in the biology of the mammalian sleep, which covers a wide range of research areas: the basic knowledge about sleep, the physiology of cerebral cortex in sleeping animals, the detailed morphological features of thalamocortical networks, the mechanisms underlying fluctuating activity of autonomic nervous systems during rapid eye movement sleep, the cutting-edge technology of tissue clearing for visualization of the whole brain, the ketogenesis-mediated homeostatic regulation of sleep, and the forward genetic approach for identification of novel genes involved in sleep. We hope this multifaceted review will be helpful for researchers who are interested in the biology of sleep.

From a review of some aspects of epididymal structure, function and research done largely in my research area over the last 50 years, I conclude that more is known than is understood of sperm maturation and storage in the epididymis. Highly qualified technicians have not always applied sophisticated modern techniques in well-considered experiments to physiologically relevant and properly-prepared samples, so that our understanding of the biological problem of the nature of the epididymal epithelial influence on maturing epididymal spermatozoa has not kept pace with the outpouring of data generated, much of which is difficult to interpret. We stand at a crossroads of where to aim our limited resources and personnel: should we continue new technology-led studies in many directions, backtrack to test hypotheses and fill in gaps in our knowledge, or consider more biological directions to our research?

With the increase in frequency and visibility of disasters in contemporary state societies, national governments have developed a collection of agencies to manage catastrophic events. These institutions invariably deal with human populations as a political, scientific, and biological problem, an approach Michel Foucault described as biopolitical. In this article, I discuss some aspects of disaster governance, focusing on the long-term recovery process. Specifically, I analyze the fundamental biopolitical assumptions of the discourses and practices on the part of governmental disaster response agencies in São Luiz do Paraitinga, Brazil. In this case of biopolitical response to disaster, the discourses and practices implemented by governmental agencies created the illusion that state agencies successfully responded to the disaster by saving biological lives. This article shows how these biopolitcal discourses and practices also had the unintended and unacknowledged effects of devaluing social lives and abandoning disaster-affected populations. By calling attention to the unintended and unacknowledged effects of biopolitical governance, this article demonstrates how disaster anthropology can document and address the shortcomings of governmental disaster recovery policy and practice.

It is unclear whether late THA periprosthetic femoral fractures are related to a mechanical mechanism that decreases strength of the femur (for example, loosening) or to a biological problem as osteolysis. It is also unknown if ceramic on ceramic bearing couples decrease the risk of late periprosthetic fractures as a result of the absence of wear and osteolysis. We therefore asked whether the cumulative long-term fractures were different according to the couple of friction ceramic on ceramic or ceramic on polyethylene in 327 patients (654 hips) with bilateral THA (one ceramic-ceramic, and the contralateral ceramic-polyethylene) who had THA with cemented stems performed between from 1978 to 2000 for osteonecrosis. There were two intra-operative fractures (0.3%). The median follow-up was 22 years (range, 15–40 years), and at the most recent follow-up, the cumulative number of late (after 7 years of follow-up) post-operative fractures was 32 (5% of 654 hips). Fractures were unilateral, which means for the 327 patients, a 10% rate of fractures. Periprosthetic fractures increased in number with follow-up: seven fractures (1% of 654 hips) occurred within ten years of THA implantation, 20 (3%) within 20 years, 26 (4%) within 30 years, and 32 (5%) within 40 years. The risk of fracture was influenced (p

The ACM SAC 2017 Bioinformatics Track aimed at promoting current advances in biological sciences relying on analytical methods that integrate mathematical, physical and computer sciences. The track is primary devoted to publish papers very focused on timely well-defined biological issues whose solution have benefited from the use of computational techniques or the implementation of new ones. The track solicited the submission of papers presenting a biological problem in a comprehensive way and (part of) its solution obtained through the application of computational methods including analysis, modeling and simulation.

Detecting and identifying biomolecules or microorganisms in aqueous solutions are often a complex task requiring precious amounts of time. Decreasing this time while reducing costs and minimizing complexity is crucial for several applications in the life sciences and other fields and is the subject of extensive work by biologists and biomedical engineers around the world. Optical sensors, more specifically dielectric microspheres, have been proposed as suitable sensors for viruses, bacteria, and other biological analytes. This paper reviews initial key publications as well as the latest progress regarding such microspheres and their potential use as biological sensors. We cover recent work on fluorescent microspheres and their integration in microfluidic devices, while addressing the limitations and practical requirements of such biodiagnostics. Our aim in this paper is to appeal to both biologists and physicists, even if new to this field. We conclude by briefly suggesting ways of integrating dielectric microspheres and biosensing into college and university courses in both physics and in biology.

Phylogenetic tree reconstruction is a fundamental biological problem. Quartet trees, trees over four species, are the minimal informational unit for phylogenetic classification. While every phylogenetic tree over $n$ species defines ${n \choose 4}$ quartets, not every set of quartets is compatible with some phylogenetic tree. Here we focus on the compatibility of quartet sets. We provide several results addressing the question of what can be inferred about the compatibility of a set from its subsets. Most of our results use probabilistic arguments to prove the sought characteristics. In particular we show that there are quartet sets $Q$ of size $m=c n \log n$ in which every subset of cardinality $c' n/ \log n$ is compatible, and yet no fraction of more than $1/3+\epsilon$ of $Q$ is compatible. On the other hand, in contrast to the classical result stating when $Q$ is the densest, i.e., $m={n \choose 4}$ and the compatibility of any set of three quartets implies full compatibility, we show that even for $m...

An important issue in multiobjective optimization is the study of the convergence speed of algorithms. An optimization problem must be defined as simple as possible to minimize the computational cost required to solve it. In this work, we study the convergence speed of seven multiobjective evolutionary algorithms: DEPT, MO-VNS, MOABC, MO-GSA, MO-FA, NSGA-II, and SPEA2; when solving an important biological problem: the motif discovery problem. We have used twelve instances of four different organisms as benchmark, analyzing the number of fitness function evaluations required by each algorithm to achieve reasonable quality solutions. We have used the hypervolume indicator to evaluate the solutions discovered by each algorithm, measuring its quality every 100 evaluations. This methodology also allows us to study the hit rates of the algorithms over 30 independent runs. Moreover, we have made a deeper study in the more complex instance of each organism. In this study, we observe the increase of the archive (number of non-dominated solutions) and the spread of the Pareto fronts obtained by the algorithm in the median execution. As we will see, our study reveals that DEPT, MOABC, and MO-FA provide the best convergence speeds and the highest hit rates.

Protein structures contain highly complex systems of voids, making up specific features such as surface clefts or grooves, pockets, protrusions, cavities, pores or channels, and tunnels. Many of them are essential for the migration of solvents, ions and small molecules through proteins, and their binding to the functional sites. Analysis of these structural features is very important for understanding of structure-function relationships, for the design of potential inhibitors or proteins with improved functional properties. Here we critically review existing software tools specialized in rapid identification, visualization, analysis and design of protein tunnels and channels. The strengths and weaknesses of individual tools are reported together with examples of their applications for the analysis and engineering of various biological systems. This review can assist users with selecting a proper software tool for study of their biological problem as well as highlighting possible avenues for further development of existing tools. Development of novel descriptors representing not only geometry, but also electrostatics, hydrophobicity or dynamics, is needed for reliable identification of biologically relevant tunnels and channels.

During mRNA translation, the ribosomal density profile along the coding region of the mRNA molecule affects various fundamental intracellular phenomena. Thus, steering this profile from a given to a desired density is an important biological problem. This paper studies this problem using a dynamical model for mRNA translation, called the ribosome flow model (RFM), in which one views the transition rates along the mRNA molecule as controls.

During the maturation the high pressure blood from the artery inflows directly to the vein, extends its diameter, and finally the blood flow rate in the vein is even 500-times greater than normal one. The changes of the wall shear stress (WSS) in the vein are thought to play a key role in the remodelling of its wall. However, this process is still not well understood. The aim of this paper is to show an innovative approach for modelling of the vein deformation during the maturation process of a-v fistulas. Dilation of the vein was modelled as two-step complex biomechanical process. The obtained results concerning final diameter of the vein are compared with average diameter obtained for large group of patients. Moreover, this study shows the changes in the flow rate and the WSS that occur after maturation in the patient-specific fistula.

The aim of the research is to describe the creative thinking process of abstract sequential and abstract random type-students in solving biological problem. The research conducted on two subjects that had differences in the type of the thinking that is abstract sequential type-student (STBSA) and abstract random type-student (STBAA) at Attaufiq Senior High School Jambi city. The data were selected according to the purpose of research. The data was collected by interviewing and modified think aloud method. Data was analyzed by process of creative thinking frame work based on Polya’s problem solving steps. The over all results of the study concluded that the process of STBSA’s creative thinking conducted sequentially from the first stage to the last stage. The data which obtained according to problem-solving strategies and the steps in solving problems. The indicators of creativity are notified in the form of fluency, flexibility, originality, and the detail of biological solving problems. While STBAA, the steps of creative thinking process is done randomly and not sequentially. The results of the problem solving which conducted is not be conviced because STBAA used more insight, imagination and logic. Then, in terms of creativity, the flexibility of STBAA is not appropriate to the indicator to solve problems because only applying one method of complishment, doesn’t have capability to produce a variety of ideas to implement the problem solving and not able to present a concept in different ways during biological problems solving. STBSA precisely solves the problem exactly, because it believes in the results of the thinking, as well as maintaining the criticality in the process of biological solving problems. While STBAA is less precise in solving problems due to the less of the self-confidence, less critical and contented easily in the process of solving biological problems.