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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.

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.

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.

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.

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.

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...

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

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.

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.

Criddle, K. R. 2012. Adaptation and maladaptation: factors that influence the resilience of four Alaskan fisheries governed by durable entitlements. – ICES Journal of Marine Science, 69: . Sustainability of fisheries and fishery-dependent communities depends largely on the intrinsic characteristics of social, economic, and legal systems that determine who is allowed to fish and how fishing takes place. That is, fisheries sustainability is not a biological problem, it is a social problem. Social factors that contribute to or detract from sustainability are illustrated in four Alaskan fisheries as they have evolved over time. Each fishery has come to be managed under durable entitlements (DEs) in terms of their participation. DE programmes, such as limited entry permits and individual or community fishing quotas, can increase profitability and help fishers adapt to modest adverse changes in stock abundance, ex-vessel prices, or input costs, but the design characteristics of some DE programmes makes them vulnerable to larger perturbations. Moreover, although DE programmes increase choice and therefore resilience from the perspective of individuals, they can increase or decrease the resilience of fishery-dependent communities.

Tunnel enlargement after anterior cruciate ligament (ACL) reconstruction may compromise revision surgery. The cause of this tunnel enlargement is not yet fully understood, but it is thought to be multifactorial, with biomechanical and biological factors playing a role. Tunnel enlargement has been described particularly in patients who underwent ACL reconstruction with hamstring tendons with extracortical fixation devices. The purpose of our study was to evaluate prospectively with magnetic resonance imaging (MRI) the changes in femoral tunnel diameter following arthroscopic anatomic ACL reconstruction with hamstring tendons. At 3-month post-op, all tunnels had enlarged compared to the diameter of the drill and most tunnels enlarged more in the midsection than at the aperture. In the posterolateral tunnels, the entrance increased 16% in diameter and the middle of the tunnel increased 30% in diameter. In the anteromedial femoral tunnels, the tunnels enlarged 14% at the aperture and 35% in the midsection. All femoral tunnels enlarged and most of them enlarged in a fusiform manner. The biological factors explain better our findings than the mechanical theory, although mechanical factors may play a role and the cortical bone at the entrance of the tunnel may modify the way tunnels respond to mechanical stress.

Microscopy, especially fluorescence microscopy, has proven to be a powerful method for studying biological processes. Unfortunately, some of the same features that make biological membranes powerful (for example, all of the action taking place across a narrow 4nm film) also make it difficult to visualize by fluorescence. Over the past 30 years, numerous tricks have been developed to narrow the plane over which data is collected. One approach, total internal reflection (TIR) fluorescence microscopy, is particularly well suited for studying membrane events. A key issue to address when using TIR to tackle a new biological problem is: how can one judge whether the signals being observed are actually the biological phenomena that one wishes to study?

Hair aging comprises weathering of the hair shaft and aging of the hair follicle. Aging of the follicle manifests as a decrease of melanocyte function or graying, and decrease in hair production in androgenetic and senescent alopecia. The study of hair aging focuses on two main streams of interest: the esthetic problem of aging hair and its management and also the biological problem of aging hair in terms of microscopic, biochemical and molecular changes. The scalp is subject to intrinsic or physiologic aging and extrinsic aging due to external factors. Intrinsic factors are related to individual genetic and epigenetic mechanisms with interindividual variation. Examples of genetic factors are familial premature graying and androgenetic alopecia. Extrinsic factors include UV radiation and smoking. The appearance of hair plays an important role in people’s overall physical appearance and self-perception. With today’s increasing life-expectancy, the desire to look youthful plays a bigger role than ever. The ...

Coronary artery disease remains a significant health threat afflicting millions of individuals worldwide. Despite the development of a variety of technologies and catheter based interventions, post-procedure restenosis is still a significant concern. Gene therapy has emerged as a promising approach aimed at modification of cellular processes that give rise to restenosis. When juxtaposed alongside the failure of traditional pharmacotherapeutics to eliminate restenosis, gene therapy has engendered great expectations for cubing coronary restenosis. In this review we have discussed an overview of gene therapy approaches that hve been utilized to reduce restenosis in preclinical and clinical studies, current status of anti-restenosis gene therapy and perspectives on its future application. For brevity, we have limited our discussion on anti-restenosis gene therapy to the introduction of a nucleic acid to the cell, tissue, organ or organism in order to give rise to the expression of a protein, the function of which will confer therapeutic effect. For the purpose of this review, we have focused ou discussion on two relevant anti-restenosis strategies, anti-proliferative and pro-endothelialization.

A meaningful distinction can be made between functions and mere effects in biological systems without resorting to teleological arguments: (i) biological systems must cope with a multitude of problems or they will cease to exist; (ii) the solutions to these problems invariably depend on circular causal chains (“feedback loops”); and (iii) biological functions are attributes of elements in biological systems that have an effect which, by contributing to the correcting behavior of a feedback control system, assists in solving a biological problem. The analysis is applied to several biological systems. The proposed solution is discussed primarily in its relation to two popular approaches to the concept of biological function, i.e., the “causal role accounts” and the “selected effect accounts”.

Metabolomics is a rapidly emerging multidisciplinary field that combines analytical biochemistry for the identification and quantification of metabolites in biological systems in a high-throughput manner, with the application of sophisticated bioinformatics tools for data extraction, analysis, and statistics to support biological interpretation. In today‘s biological sciences, the utilization of ‘omics‘-type analyses of biological molecules including genes (genomics), transcripts (transcriptomics), proteins (proteomics) and metabolites (metabolomics) are essential for the comprehensive investigation of a particular biological problem. These approaches enhance our understanding of the biology of an organism and its response to environmental stimuli or genetic perturbation. In this chapter we review current and emerging applications of metabolomics in the agriculture industry, food production, security, nutrition and wine production, including a discussion on the use of metabolomics to assess genetically modified organisms.

Die Anti-Aging Medizin hat sich die Anwendung wissenschaftlicher Erkenntnisse und medizinischer Technologien im Dienste der Prävention, Früherkennung, Umkehr und Behandlung altersabhängiger Funktionseinbußen des Organismus und alterstypischer Veränderungen zur Aufgabe gemacht. Die Dermatologie liefert gute Fundamente für rationale präventive und therapeutische Anti-Aging Strategien. Der Stellenwert der Haut liegt nicht nur darin begründet, dass sich die Alterungsprozesse an ihr am augenfälligsten abspielen, sondern dass die Haut durch ihre Zugänglichkeit auch ein Substrat für die Altersforschung liefert. Vermutlich sind die molekularen Vorgänge der Alterung von Haut und Haaren, einschließlich des Pigmentsystems, zumindest teilweise auch für die altersabhängige Entwicklung degenerativer Veränderungen der inneren Organe und des Gesamtorganismus relevant. Der physiologische Alterungsprozess der Haut und ihrer Anhangsgebilde setzt bereits um 30 Jahre ein und schreitet mit individuell unterschiedlicher Geschwindigkeit fort. Der chronologisch bedingten, physiologischen oder intrinsischen Alterung von Haut und Haaren steht die extrinsische Hautalterung gegenüber, die hauptsächlich durch chronische Exposition der Haut gegenüber UV-Strahlung und anderen Noxen induziert wird. Bei den Maßnahmen, die den Alterungsprozess zu verhindern, zu verlangsamen oder umzukehren haben, werden Primärpräventionsmaßnahmen, die ergriffen werden, bevor der Alterungsprozess beginnt, Sekundärpräventionsmaßnahmen, wenn die ersten Anzeichen der Alterung sichtbar sind, und Tertiärmaßnahmen bei sichtbar fortgeschrittener Alterung unterschieden. Die Vorsorge beginnt mit der frühzeitigen Vermeidung schädigender Einflüsse wie UV-Strahlen und Tabakrauchen. Zur Gesunderhaltung der Haut und Prävention spielen in der dermatologischen Praxis Lichtschutzmittel, dermokosmetische Wirkstoffe und Anti-Aging-Wirkstoffe eine immer größere Rolle. Zur Sekundärprävention zählen die optimale medizinische Gesundheitsversorgung, spezifische Anti-Aging Protokolle und pharmakologische Maßnahmen gegen Haarausfall. Sichtbar fortgeschrittene Alterung ist die Domäne der Tertiärmaßnahmen. Dazu gehören minimal-invasive dermatologisch-ästhetische Verfahren wie chemische Peels, Mikrodermabrasion, Fillers, nicht-ablative Laser-Rejuvenation, Radiofrequenztechniken und Botulinumtoxin, sowie Laserresurfacing und kosmetisch-chirurgische Eingriffe, inkl. der autologen Haartransplantation.

Solving relevant biological problems requires answering complex questions. Addressing such questions traditionally implied the design of time-consuming experimental procedures which most of the time are not accessible to average-sized laboratories. The current trend is to move towards a multidisciplinary approach integrating both theoretical knowledge and experimental work. This combination creates a powerful tool for shedding light on biological problems. To illustrate this concept, we present here a descriptive example of where computational methods were shown to be a key aspect in detecting crucial players in an important biological problem: the dimerization of chemokine receptors. Using evolutionary based sequence analysis in combination with structural predictions two CCR5 residues were selected as important for dimerization and further validated experimentally. The experimental validation of computational procedures demonstrated here provides a wealth of valuable information not obtainable by any of the individual approaches alone., Funding for the following projects: Temblor QLRT-2001-00015, Biosapiens BIO2004-00875, GeneFun LSHG-CT-2004-503567. D.I.O. is supported by CSIC and Pfizer.

Background: Confronted with the fact that the onset of an incisional hernia is due to a biological problem of stable scar tissue formation, the mesh techniques today are the methods of choice for incisional hernia repair.

et al., In this study the first results on evaluation and assessment of grafted bioengineered skin substitutes using an optical Diffuse Reflectance Spectroscopy (DRS) system with a remote optical probe are shown. The proposed system is able to detect early vascularization of skin substitutes expressing the Vascular Endothelial Growth Factor (VEGF) protein compared to normal grafts, even though devitalized skin is used to protect the grafts. Given the particularities of the biological problem, data analysis is performed using two Blind Signal Separation (BSS) methods: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). These preliminary results are the first step towards point-of-care diagnostics for skin implants early assessment.

S ubstantial improvements have been made over the past 15 years in the treatment of acute cardiac illnesses. Patients are often carried through 1 or more episodes using medical or surgical therapies. From 1980 to 1993, the death rate from acute myocardial infarction (MI) decreased by 30%.’ However, the natural history of ischemic heart disease is rclentlessly progressive despite palliative ellbrts. Consequently, a conversion occurs from acute episodic illness to chronic disease. The final common pathway of many chronic cardiac diseases leads to muscular left ventricular (Lv) failure, manifest primarily as congestive heart failure (CHF). As stated by Eugene Braunwald in his editorial, “Cardiovascular Care at the Turn of the Millennium,” CHF is “one of two emerging epidemics of cardiovascular disease.” Z

The problem of thyroid cancer (thyroid cancer) is far from new, but until recently it was a general biological problem, part of its purely oncological aspect, in recent years it is one of the most urgent problems of both oncology and endocrinology, especially the practical sections of these industries medicine.

The question mark in the title of this paper may look outdated and even artificial in view of the recent exponential increase in the use of stents. This almost frightening upswing occurred after a little less than one decade following their introduction into clinical medicine''. It was, however, only a few years ago that the role of stents in cardiology was in serious doubt''. In what follows, I shall try to review the story of stenting in the light of my personal experience against the background of diverse currents in the catheter treatment of human vascular disease.

Traumatic brain injury (TBI) is a significant biomedical problem among military personnel and civilians. There exists an urgent need to develop and refine biological measures of acute brain injury and chronic recovery after brain injury. Such measures “biomarkers” can assist clinicians in helping to define and refine the recovery process and developing treatment paradigms for the acutely injured to reduce secondary injury processes. Recent biomarker studies in the acute phase of TBI have highlighted the importance and feasibilities of identifying clinically useful biomarkers. However, much less is known about the subacute and chronic phases of TBI. We propose here that for a complex biological problem such as TBI, multiple biomarker types might be needed to harness the wide range of pathological and systemic perturbations following injuries, including acute neuronal death, neuroinflammation, neurodegeneration and neuroregeneration to systemic responses. In terms of biomarker types, they range from brain-specific proteins, microRNA, genetic polymorphism, inflammatory cytokines and autoimmune markers and neuro-endocrine hormones. Furthermore, systems biology-driven biomarkers integration can help present a holistic approach to understanding scenarios and complexity pathways involved in brain injury.

Investigating the interactions of RNA-binding proteins (RBPs) with RNAs is a complex task for molecular and computational biologists. The molecular biology techniques and the computational approaches to understand RBP-RNA (or ribonucleoprotein, RNP) interactions have advanced considerably over the past few years and numerous and diverse software tools have been developed to analyze these data. Accordingly, laboratories interested in RNP biology face the challenge of choosing adequately among the available software tools those that best address the biological problem they are studying. Here, we focus on state-of-the-art molecular biology techniques that employ crosslinking and immunoprecipitation (CLIP) of an RBP to study and map RNP interactions. We review the different software tools and databases available to analyze the most widely used CLIP methods, HITS-CLIP, PAR-CLIP, and iCLIP. WIREs RNA 2017, 8:e1404. doi: 10.1002/wrna.1404 For further resources related to this article, please visit the WIREs website.

In its first 25 years JCAMD has been disseminating a large number of techniques aimed at finding better medicines faster. These include genetic algorithms, COMFA, QSAR, structure based techniques, homology modelling, high throughput screening, combichem, and dozens more that were a hype in their time and that now are just a useful addition to the drug-designers toolbox. Despite massive efforts throughout academic and industrial drug design research departments, the number of FDA-approved new molecular entities per year stagnates, and the pharmaceutical industry is reorganising accordingly. The recent spate of industrial consolidations and the concomitant move towards outsourcing of research activities requires better integration of all activities along the chain from bench to bedside. The next 25 years will undoubtedly show a series of translational science activities that are aimed at a better communication between all parties involved, from quantum chemistry to bedside and from academia to industry. This will above all include understanding the underlying biological problem and optimal use of all available data. Electronic supplementary material The online version of this article (doi:10.1007/s10822-011-9519-9) contains supplementary material, which is available to authorized users.

The most critical functions of the various proteomics organisations are the training of young scientists and the dissemination of information to the general scientific community. The education committees of the Human Proteome Organisation (HUPO) and the European Proteomics Association (EuPA) together with their national counterparts are therefore launching the International Proteomics Tutorial Programme to meet these needs. The programme is being led by Peter James (Sweden), Thierry Rabilloud (France) and Kazuyuki Nakamura (Japan). It involves collaboration between the leading proteomics journals: Journal of Proteome Research, Journal of Proteomics, Molecular and Cellular Proteomics, and Proteomics. The overall level is aimed at Masters/PhD level students who are starting out their research and who would benefit from a solid grounding in the techniques used in modern protein-based research. The tutorial program will cover core techniques and basics as an introduction to scientists new to the field. At a later stage the programme may be expanded with a series of more advanced topics focussing on the application of proteomics techniques to biological problem solving. The entire series of articles and slides will be made freely available for teaching use at the Journals and Organisations homepages and at a special website, www.proteomicstutorials.org.

Background The study of biological systems demands computational support. If targeting a biological problem, the reuse of existing computational models can save time and effort. Deciding for potentially suitable models, however, becomes more challenging with the increasing number of computational models available, and even more when considering the models' growing complexity. Firstly, among a set of potential model candidates it is difficult to decide for the model that best suits ones needs. Secondly, it is hard to grasp the nature of an unknown model listed in a search result set, and to judge how well it fits for the particular problem one has in mind. Results Here we present an improved search approach for computational models of biological processes. It is based on existing retrieval and ranking methods from Information Retrieval. The approach incorporates annotations suggested by MIRIAM, and additional meta-information. It is now part of the search engine of BioModels Database, a standard repository for computational models. Conclusions The introduced concept and implementation are, to our knowledge, the first application of Information Retrieval techniques on model search in Computational Systems Biology. Using the example of BioModels Database, it was shown that the approach is feasible and extends the current possibilities to search for relevant models. The advantages of our system over existing solutions are that we incorporate a rich set of meta-information, and that we provide the user with a relevance ranking of the models found for a query. Better search capabilities in model databases are expected to have a positive effect on the reuse of existing models.

An informal poll of neurobiologists indicates the following widely-held misconceptions about cGMP: (1) we know very little about it; (2) it must not be very different from cAMP; and (3) no new biological principles are likely to emerge from studying it. In fact, despite these prejudices, our understanding of the cGMP second messenger cascade has increased dramatically in the last few years. We now know that it is very different from the cAMP system in almost every particular, and the differences reveal interesting and novel solutions to the biological problem of receptor effector coupling.

Pathophysiologically, nerve root pain production is a complex issue. For many years, mechanical compression has been regarded as the sole cause for such pain. There are indications, however, that other factors, such as intraneural inflammation, may be of importance in this context. In this article, four cases are presented in which nerve root related pain gradually resolved without any proven change in the mechanical deformation of the involved nerve root. The data presented here suggest that mechanical compression, per se, may not always be the sole cause of radicular pain and dysfunction. These findings suggest that improvement is needed in our understanding of the pathophysiology of nerve root compression and inflammation.

Recently, there has been great interest in bioinformatics among researches from various disciplines such as computer science, mathematics, statistics and artificial intelligence. Bioinformatics mainly deals with solving biological problems at molecular levels. One of the classic problems of bioinformatics which has gain a lot attention lately is haplotyping, the goal of which is categorizing SNP-fragments into two clusters and deducing a haplotype for each. Since the problem is proved to be NP-hard, several computational and heuristic methods have addressed the problem seeking feasible answers. In this work it is shown that using PCM to solve haplotyping problem in DALY dataset yields better results comparing to current available methods.

Flow injection electrospray mass spectrometry (FIE-MS) metabolite fingerprinting is widely used as a 'first pass' screen for compositional differences, where discrimination between samples can be achieved without any preconceptions. Powerful data analysis algorithms can be used to select and rank FIE-MS fingerprint variables highly explanatory of the biological problem under investigation. We describe how to create a species-specific FIE-MS/MS(n) metabolite database and how to then query the database to predict the identity of highly significant variables within FIE-MS fingerprints. The protocol details how to interpret m/z signals within the explanatory variable list based on a correlation analysis in conjunction with an investigation of mathematical relationships regarding (de)protonated molecular ions, salt adducts, neutral losses and dimeric associations routinely observed in FIE-MS fingerprints. Although designed for use by biologists/analytical chemists, collaboration with data-mining experts is generally advised. The protocol is applicable in any areas of bioscience research involving FIE-MS fingerprinting.

This paper explores a constructive systems approach to understand the immune system, starting from antibodies which are major units bearing specific recognition of the adaptive immune system. The exploration proceeds in stages: arrayed recognitions; networked recognition/actions; and diversified recognition/actions. System theoretic aspects of the immune system will be discussed with respect to possible application of immunity-based problem solving

A large volume of research in temporal data mining is focusing on discovering temporal rules from time-stamped data. The majority of the methods proposed so far have been mainly devoted to the mining of temporal rules which describe relationships between data sequences or instantaneous events and do not consider the presence of complex temporal patterns into the dataset. Such complex patterns, such as trends or up and down behaviors, are often very interesting for the users. In this paper we propose a new kind of temporal association rule and the related extraction algorithm; the learned rules involve complex temporal patterns in both their antecedent and consequent. Within our proposed approach, the user defines a set of complex patterns of interest that constitute the basis for the construction of the temporal rule; such complex patterns are represented and retrieved in the data through the formalism of knowledge-based Temporal Abstractions. An Apriori-like algorithm looks then for meaningful temporal relationships (in particular, precedence temporal relationships) among the complex patterns of interest. The paper presents the results obtained by the rule extraction algorithm on a simulated dataset and on two different datasets related to biomedical applications: the first one concerns the analysis of time series coming from the monitoring of different clinical variables during hemodialysis sessions, while the other one deals with the biological problem of inferring relationships between genes from DNA microarray data.

Collagens belong to the most abundant proteins in the body. After tissue injury, a coordinated regulation of collagen gene expression guides the formation of a provisional matrix that subsequently evolves into a mature scar with tensile strength. In the following, knowledge regarding collagen gene regulation that may provide insight into how to specifically address the biological problem of soft tissue weakness and recurrent hernia disease is summarized.

Biomedical literature and database annotations, available in electronic forms, contain a vast amount of knowledge resulting from global research. Users, attempting to utilize the current state-of-the-art research results are frequently overwhelmed by the volume of such information, making it difficult and time-consuming to locate the relevant knowledge. Literature mining, data mining, and domain specific knowledge integration techniques can be effectively used to provide a user-centric view of the information in a real-world biological problem setting. Bioinformatics tools that are based on real-world problems can provide varying levels of information content, bridging the gap between biomedical and bioinformatics research. We have developed a user-centric bioinformatics research tool, called BioMap, that can provide a customized, adaptive view of the information and knowledge space. BioMap was validated by using inflammatory diseases as a problem domain to identify and elucidate the associations among cells and cellular components involved in multiple sclerosis (MS) and its animal model, experimental allergic encephalomyelitis (EAE). The BioMap system was able to demonstrate the associations between cells directly excavated from biomedical literature for inflammation, EAE and MS. These association graphs followed the scale-free network behavior (average gamma = 2.1) that are commonly found in biological networks.

This chapter describes a specific case in which theory is extremely instructive and essential in illuminating a fundamental biological problem. The advent of gene microarray technology in the post-genomic era has sensitized many biologists to the need for new quantitative methods of all kinds as part of the analysis of complex biological systems in general, and neuroscience in specific. Thus, both the need for formal models to capture the dynamics of networks of molecules and neurons and the need for sophisticated statistical measures to extract meaning from multi-dimensional data sets have made experimental biologists more interested than ever before in the tools that can be provided by theory of all kinds. Neuroscience is today ideally poised to profit optimally from the influx of talented theorists. Today more than ever it is clear that theory is necessary to catalyze paradigm shifts in the way people pose problems about the nervous system. These paradigm shifts will occur when smart experimentalists and smart theorists find common language and common ground to reveal how the glorious richness and detailed idiosyncrasies of neurobiological systems contribute to their ability to be at the same time plastic and stable. It is not yet obvious how networks can learn and develop without losing their ability to function as they are changed.

High-throughput biotechnology has enabled genome-wide investigation of gene expression and has the potential to identify genes that have a role to play in focal cerebral ischemia, as well as many other interventions. The advent of this technology has also led to the generation of large amounts of expensive and complex expression data. One of the major problems with the generation of so much data is locating and extracting the relevant information to aid target identification and interpretation effectively and reliably. Statistical involvement is vital. Not only does it help to ensure effective extraction of information from the data, it also increases the likelihood that the data collected will embody the information about the differential expression of interest in the first place. The goal of this chapter is to recommend an effective process for investigating gene expression data. There are five stages in this process that we believe lead to reliable results when routinely applied to an expression dataset, once it has been appropriately generated and collected: (1) biological problem definition and design selection; (2) data examination, "preprocessing," and reexamination; (3) data analysis step I: screening for differentially expressed genes; (4) data analysis step II: verifying differential expression; and (5) biological verification, interpretation, and communication.

Summary form only given. We treat the linkage disequilibrium, used to discover haplotypes, candidate to explain multifactorial diseases such as diabetes or obesity, as an optimization problem where a given objective function has to be optimized. In order to determine what kind of algorithm is able to solve this problem, we first study the specificities and the structure of the problem. Results of this study show that exact algorithms are not adapted to this specific problem and lead us to the development of a parallel dedicated adaptive multipopulation genetic algorithm that is able to find several haplotypes of different sizes. After describing the biological problem, we present the dedicated genetic algorithm, its specificities, such as the use of several populations and its advanced mechanisms such as the adaptive choice of operators, random immigrants, and its parallel implementation. We give results on a real dataset.

Nuclear export of mRNA is a key transport process in eukaryotic cells. To investigate it, we labeled native mRNP particles in living Chironomus tentans salivary gland cells with fluorescent hrp36, the hnRNP A1 homolog, and the nuclear envelope by fluorescent NTF2. Using light sheet microscopy, we traced single native mRNA particles across the nuclear envelope. The particles were observed to often probe nuclear pore complexes (NPC) at their nuclear face, and in only 25% of the cases yielded actual export. The complete export process took between 65 ms up to several seconds. A rate-limiting step was observed, which could be assigned to the nuclear basket of the pore and might correspond to a repositioning and unfolding of mRNPs before the actual translocation. Analysis of single fluorescent Dbp5 molecules, the RNA helicase essential for mRNA export, revealed that Dbp5 most often approached the cytoplasmic face of the NPC, and exhibited a binding duration of approximately 55 ms. Our results have allowed a refinement of the current models for mRNA export.

Determining how cells distinguish between adaptive and maladaptive signals when they appear to share the same molecular pathways has been a vexing biological problem. The ability to identify distinctive features of a pathophysiological response compared with a physiological response would allow for the rational design of approaches to eliminate or diminish undesirable consequences of pathophysiological responses while preserving the beneficial effects of physiological signals. These issues are especially pertinent in the heart and in understanding the development of heart failure, for which >500,000 new cases are diagnosed in the United States each year. Major factors contributing to worsening heart failure include a number of compensatory neurohormonal signals intended to counteract decreased cardiac output, such as hyperadrenergic stimulation (1). In a recent issue of PNAS, Balijepalli et al. (2) provide new insight into how adrenergic signaling pathways are organized in the heart.

Artificial neural networks have proven to be a useful technique for analyzing biological data and automatically producing accurate pattern recognizers. However, most applications of neural networks have not taken advantage of existing knowledge about the task at hand. This paper presents a method for using such problem-specific knowledge. The KBANN algorithm uses inference rules about the current biological problem, which need only be approximately correct, to initially configure a neural network. This network is then refined by analyzing sample examples and counter examples of the concept being learned. An application of KBANN to the prediction of E. coli transcriptional promoters demonstrates its superiority to alternative techniques, taken from both the machine-learning and molecular biology literatures. In addition, since KBANN uses a human comprehensible ``theory`` of the current problem to define the initial neural network topology, it is possible to extract a refined set of inference rules following training. A refined theory for promoter recognition is presented; the extracted rules are roughly as accurate on novel data as the trained neural network from which they came.

Endovascular aneurysm repair has matured significantly over the last 20 years and is becoming increasingly popular as a minimally invasive treatment option for patients with abdominal aortic aneurysms (AAA). Long-term durability of this fascinating treatment, however, is in doubt as continuing aneurysmal degeneration of the aortoiliac graft attachment zones is clearly associated with late adverse sequelae. In recent years, our growing understanding of the physiopathology of AAA formation has facilitated scrutiny of various potential drug treatment concepts. In this article we review the mechanical and biological challenges associated with endovascular treatment of infrarenal AAAs and discuss potential approaches to ongoing aneurysmal degeneration, which hampers long-term outcomes of this minimally invasive therapy.

Steve Iliffe, Linda Patterson, Mairi Gould BMJ Publishing Group, £17.95, pp 177 ISBN 0 7279 1192 9 Ed David Beales, Michael Denham, Alistair Tulloch Radcliffe Medical Press, £17.50, pp 224 ISBN 185775 032 2 Problems in the care of elderly people are often “messy.” That is to say they are not straightforward, unidimensional issues but involve complex interplays of factors, often with more unknown factors and irreversible elements than in other branches of medicine. “Clean” problems — does treatment x help condition y ? — are less commonly encountered and usually form only part of an overall scenario. This lack of clean problems is just one of the difficulties bedevilling the development of evidence based medicine in the care of elderly people. Just as the problems are messy so are the solutions. Professor Grimley Evans has emphasised the place of both “therapeutic” and “prosthetic” approaches — the former being an attempt to cure or modify the biological problem, the latter accepting that this may not be possible but seeking to modify the environment to help …

Forty cases of congenital pseudarthrosis of the tibia were reviewed. The results were assessed so as to emphasise function rather than simply the presence or absence of union. At the time of review, 14 patients had undergone amputation; eight others had non-union or tenuous union. No surgical procedure except the Farmer operation (a composite skin and bone pedicle graft from the other leg) showed any clear superiority. Among the factors associated with a poor result were considerable shortening, older children, and rapid resorption of the bone graft. It is felt that congenital pseudarthrosis of the tibia is a biological problem and not merely a mechanical one; consequently biological approaches to its treatment are needed.