Topographical analysis of the subependymal zone neurogenic niche

The emerging model for the adult subependymal zone (SEZ) cell population indicates that neuronal diversity is not generated from a uniform pool of stem cells but rather from diverse and spatially confined stem cell populations. Hence, when analysing SEZ proliferation, the topography along the anterior-posterior and dorsal-ventral axes must be taken into account. However, to date, no studies have assessed SEZ proliferation according to topographical specificities and, additionally, SEZ studies in animal models of neurological/psychiatric disorders often fail to clearly specify the SEZ coordinates. This may render difficult the comparison between studies and yield contradictory results. More so, by focusing in a single spatial dimension of the SEZ, relevant findings might pass unnoticed. In this study we characterized the neural stem cell/progenitor population and its proliferation rates throughout the rat SEZ anterior-posterior and dorsal-ventral axes. We found that SEZ proliferation decreases along the anterior-posterior axis and that proliferative rates vary considerably according to the position in the dorsal-ventral axis. These were associated with relevant gradients in the neuroblasts and in the neural stem cell populations throughout the dorsal-ventral axis. In addition, we observed spatially dependent differences in BrdU/Ki67 ratios that suggest a high variability in the proliferation rate and cell cycle length throughout the SEZ; in accordance, estimation of the cell cycle length of the neuroblasts revealed shorter cell cycles at the dorsolateral SEZ. These findings highlight the need to establish standardized procedures of SEZ analysis. Herein we propose an anatomical division of the SEZ that should be considered in future studies addressing proliferation in this neural stem cell niche.Fundação para a Ciência e a Tecnologia (FCT

Including cognitive aspects in multiple criteria decision analysis

"First Online: 21 December 2016"Many Multiple Criteria Decision Analysis (MCDA) methods have been proposed over the last decades. Some of the most known methods share some similarities in the way they are used and configured. However, we live in a time of change and nowadays the decision-making process (especially when done in group) is even more demanding and dynamic. In this work, we propose a Multiple Criteria Decision Analysis method that includes cognitive aspects (Cognitive Analytic Process). By taking advantage of aspects such as expertise level, credibility and behaviour style of the decision-makers, we propose a method that relates these aspects with problem configurations (alternatives and criteria preferences) done by each decision-maker. In this work, we evaluated the Cognitive Analytic Process (CAP) in terms of configuration costs and the capability to enhance the quality of the decision. We have used the satisfaction level as a metric to compare our method with other known MCDA methods in literature (Utility function, AHP and TOPSIS). Our method proved to be capable to achieve higher satisfaction levels compared to other MCDA methods, especially when the decision suggested by CAP is different from the one proposed by those methods.This work was supported by COMPETE Programme (operational programme for competitiveness) within project POCI-01-0145-FEDER-007043, by National Funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within the Projects UID/CEC/00319/2013, UID/EEA/00760/2013, and the João Carneiro PhD grant with the reference SFRH/BD/89697/2012.info:eu-repo/semantics/publishedVersio

Asymmetric Genome Organization in an RNA Virus Revealed via Graph-Theoretical Analysis of Tomographic Data

Cryo-electron microscopy permits 3-D structures of viral pathogens to be determined in remarkable detail. In particular, the protein containers encapsulating viral genomes have been determined to high resolution using symmetry averaging techniques that exploit the icosahedral architecture seen in many viruses. By contrast, structure determination of asymmetric components remains a challenge, and novel analysis methods are required to reveal such features and characterize their functional roles during infection. Motivated by the important, cooperative roles of viral genomes in the assembly of single-stranded RNA viruses, we have developed a new analysis method that reveals the asymmetric structural organization of viral genomes in proximity to the capsid in such viruses. The method uses geometric constraints on genome organization, formulated based on knowledge of icosahedrally-averaged reconstructions and the roles of the RNA-capsid protein contacts, to analyse cryo-electron tomographic data. We apply this method to the low-resolution tomographic data of a model virus and infer the unique asymmetric organization of its genome in contact with the protein shell of the capsid. This opens unprecedented opportunities to analyse viral genomes, revealing conserved structural features and mechanisms that can be targeted in antiviral drug desig

Multi-period Project Portfolio Selection under Risk considerations and Stochastic Income

This paper deals with multi-period project portfolio selection problem. In this problem, the available budget is invested on the best portfolio of projects in each period such that the net profit is maximized. We also consider more realistic assumptions to cover wider range of applications than those reported in previous studies. A novel mathematical model is presented to solve the problem, considering risks, stochastic incomes, and possibility of investing extra budget in each time period. Due to the complexity of the problem, an effective meta-heuristic method hybridized with a local search procedure is presented to solve the problem. The algorithm is based on genetic algorithm (GA), which is a prominent method to solve this type of problems. The GA is enhanced by a new solution representation and well selected operators. It also is hybridized with a local search mechanism to gain better solution in shorter time. The performance of the proposed algorithm is then compared with well-known algorithms, like basic genetic algorithm (GA), particle swarm optimization (PSO), and electromagnetism-like algorithm (EM-like) by means of some prominent indicators. The computation results show the superiority of the proposed algorithm in terms of accuracy, robustness and computation time. At last, the proposed algorithm is wisely combined with PSO to improve the computing time considerably

Genetic Variants of Cytochrome b-245, Alpha Polypeptide Gene and Premature Acute Myocardial Infarction Risk in An Iranian Population

Background: Oxidative stress induced by superoxide anion plays critical roles in the pathogenesis of coronary artery disease (CAD) and hence acute myocardial infarction (AMI). The major source of superoxide production in vascular smooth muscle and endothelial cells is the NADPH oxidase complex. An essential component of this complex is p22phox, that is encoded by the cytochrome b-245, alpha polypeptide (CYBA) gene. The aim of this study was to investigate the association of CYBA variants (rs1049255 and rs4673) and premature acute myocardial infarction risk in an Iranian population. Methods: The study population consisted of 158 patients under the age of 50 years, with a diagnosis of premature AMI, and 168 age-matched controls with normal coronary angiograms. Genotyping of the polymorphisms was performed by the polymerase chain reaction and restriction fragment length polymorphism (PCR-RFLP). Results: There was no association between the genotypes and allele frequencies of rs4673 polymorphism and premature acute myocardial infarction (P>0.05). A significant statistical association was observed between the genotypes distribution of rs1049255 polymorphism and AMI risk (P=0.037). Furthermore, the distribution of AA+AG/GG genotypes was found to be statistically significant between the two groups (P=0.011). Conclusions: Our findings indicated that rs1049255 but not rs4673 polymorphism is associated with premature AMI

Accurate detection of spontaneous seizures using a generalized linear model with external validation

Objective Seizure detection is a major facet of electroencephalography (EEG) analysis in neurocritical care, epilepsy diagnosis and management, and the instantiation of novel therapies such as closed-loop stimulation or optogenetic control of seizures. It is also of increased importance in high-throughput, robust, and reproducible pre-clinical research. However, seizure detectors are not widely relied upon in either clinical or research settings due to limited validation. In this study, we create a high-performance seizure-detection approach, validated in multiple data sets, with the intention that such a system could be available to users for multiple purposes. Methods We introduce a generalized linear model trained on 141 EEG signal features for classification of seizures in continuous EEG for two data sets. In the first (Focal Epilepsy) data set consisting of 16 rats with focal epilepsy, we collected 1012 spontaneous seizures over 3 months of 24/7 recording. We trained a generalized linear model on the 141 features representing 20 feature classes, including univariate and multivariate, linear and nonlinear, time, and frequency domains. We tested performance on multiple hold-out test data sets. We then used the trained model in a second (Multifocal Epilepsy) data set consisting of 96 rats with 2883 spontaneous multifocal seizures. Results From the Focal Epilepsy data set, we built a pooled classifier with an Area Under the Receiver Operating Characteristic (AUROC) of 0.995 and leave-one-out classifiers with an AUROC of 0.962. We validated our method within the independently constructed Multifocal Epilepsy data set, resulting in a pooled AUROC of 0.963. We separately validated a model trained exclusively on the Focal Epilepsy data set and tested on the held-out Multifocal Epilepsy data set with an AUROC of 0.890. Latency to detection was under 5 seconds for over 80% of seizures and under 12 seconds for over 99% of seizures. Significance This method achieves the highest performance published for seizure detection on multiple independent data sets. This method of seizure detection can be applied to automated EEG analysis pipelines as well as closed loop interventional approaches, and can be especially useful in the setting of research using animals in which there is an increased need for standardization and high-throughput analysis of large number of seizures

Growth hormone responsive neural precursor cells reside within the adult mammalian brain

The detection of growth hormone (GH) and its receptor in germinal regions of the mammalian brain prompted our investigation of GH and its role in the regulation of endogenous neural precursor cell activity. Here we report that the addition of exogenous GH significantly increased the expansion rate in long-term neurosphere cultures derived from wild-type mice, while neurospheres derived from GH null mice exhibited a reduced expansion rate. We also detected a doubling in the frequency of large (i.e. stem cell-derived) colonies for up to 120 days following a 7-day intracerebroventricular infusion of GH suggesting the activation of endogenous stem cells. Moreover, gamma irradiation induced the ablation of normally quiescent stem cells in GH-infused mice, resulting in a decline in olfactory bulb neurogenesis. These results suggest that GH activates populations of resident stem and progenitor cells, and therefore may represent a novel therapeutic target for age-related neurodegeneration and associated cognitive decline

Mechanical and Assembly Units of Viral Capsids Identified via Quasi-Rigid Domain Decomposition

Key steps in a viral life-cycle, such as self-assembly of a protective protein container or in some cases also subsequent maturation events, are governed by the interplay of physico-chemical mechanisms involving various spatial and temporal scales. These salient aspects of a viral life cycle are hence well described and rationalised from a mesoscopic perspective. Accordingly, various experimental and computational efforts have been directed towards identifying the fundamental building blocks that are instrumental for the mechanical response, or constitute the assembly units, of a few specific viral shells. Motivated by these earlier studies we introduce and apply a general and efficient computational scheme for identifying the stable domains of a given viral capsid. The method is based on elastic network models and quasi-rigid domain decomposition. It is first applied to a heterogeneous set of well-characterized viruses (CCMV, MS2, STNV, STMV) for which the known mechanical or assembly domains are correctly identified. The validated method is next applied to other viral particles such as L-A, Pariacoto and polyoma viruses, whose fundamental functional domains are still unknown or debated and for which we formulate verifiable predictions. The numerical code implementing the domain decomposition strategy is made freely available