Different Gene Networks Are Disturbed by Zika Virus Infection in A Mouse Microcephaly Model

The association of Zika virus (ZIKV) infection with microcephaly has raised alarm worldwide. Their causal link has been confirmed in different animal models infected by ZIKV. However, the molecular mechanisms underlying ZIKV pathogenesis are far from clear. Hence, we performed global gene expression analysis of ZIKV-infected mouse brains to unveil the biological and molecular networks underpinning microcephaly. We found significant dysregulation of the sub-networks associated with brain development, immune response, cell death, microglial cell activation, and autophagy amongst others. We provided detailed analysis of the related complicated gene networks and the links between them. Additionally, we analyzed the signaling pathways that were likely to be involved. This report provides systemic insights into not only the pathogenesis, but also a path to the development of prophylactic and therapeutic strategies against ZIKV infection.     

Brain Banks Spur New Frontiers in Neuropsychiatric Research and Strategies for Analysis and Validation

Neuropsychiatric disorders affect hundreds of millions of patients and families worldwide. To decode the molecular framework of these diseases, many studies use human postmortem brain samples. These studies reveal brain-specific genetic and epigenetic patterns via high-throughput sequencing technologies. Identifying best practices for the collection of postmortem brain samples, analyzing such large amounts of sequencing data, and interpreting these results are critical to advance neuropsychiatry. We provide an overview of human brain banks worldwide, including progress in China, highlighting some well-known projects using human postmortem brain samples to understand molecular regulation in both normal brains and those with neuropsychiatric disorders. Finally, we discuss future research strategies, as well as state-of-the-art statistical and experimental methods that are drawn upon brain bank resources to improve our understanding of the agents of neuropsychiatric disorders.     

Paravirtualization effect on single- and multi-threaded memory-intensive linear algebra software

Previous studies have revealed that paravirtualization imposes minimal performance overhead on High Performance Computing (HPC) workloads, while exposing numerous benefits for this field. In this study, we are investigating the impact of paravirtualization on the performance of automatically-tuned software systems. We compare peak performance, performance degradation in constrained memory situations, performance degradation in multi-threaded applications, and inter-VM shared memory performance. For comparison purposes, we examine the proficiency of ATLAS, a quintessential example of an autotuning software system, in tuning the BLAS library routines for paravirtualized systems. Our results show that the combination of ATLAS and Xen paravirtualization delivers native execution performance and nearly identical memory hierarchy performance profiles in both single and multi-threaded scenarios. Furthermore, we show that it is possible to achieve memory sharing among OS instances at native speeds. These results expose new benefits to memory-intensive applications arising from the ability to slim down the guest OS without influencing the system performance. In addition, our findings support a novel and very attractive deployment scenario for computational science and engineering codes on virtual clusters and computational clouds.

An empirical EEG analysis in brain death diagnosis for adults

Electroencephalogram (EEG) is often used in the confirmatory test for brain death diagnosis in clinical practice. Because EEG recording and monitoring is relatively safe for the patients in deep coma, it is believed to be valuable for either reducing the risk of brain death diagnosis (while comparing other tests such as the apnea) or preventing mistaken diagnosis. The objective of this paper is to study several statistical methods for quantitative EEG analysis in order to help bedside or ambulatory monitoring or diagnosis. We apply signal processing and quantitative statistical analysis for the EEG recordings of 32 adult patients. For EEG signal processing, independent component analysis (ICA) was applied to separate the independent source components, followed by Fourier and time-frequency analysis. For quantitative EEG analysis, we apply several statistical complexity measures to the EEG signals and evaluate the differences between two groups of patients: the subjects in deep coma, and the subjects who were categorized as brain death. We report statistically significant differences of quantitative statistics with real-life EEG recordings in such a clinical study, and we also present interpretation and discussions on the preliminary experimental results.

Synthesis,SAR study,and biological evaluation of novel 2,3-dihydro-1H-imidazo[1,2-a]benzimidazole derivatives as phosphodiesterase 10A inhibitors

Phosphodiesterase 10A (PDE10A) inhibitors were designed and synthesized based on the dihydro-imidazobenzimidazole scaffold. Compound 5a showed moderate inhibitory activity and good permeability, but unfavorable high P-glycoprotein (P-gp) liability for brain penetration. We performed an optimization study to improve both the P-gp efflux ratio and PDE10A inhibitory activity. As a result, 6d was identified with improved P-gp liability and high PDE10A inhibitory activity. Compound 6d also showed satisfactory brain penetration, suppressed phencyclidine-induced hyperlocomotion and improved MK-801-induced working memory deficit.     

Vegetation science in the age of big data

The age of big data is poised to revolutionize vegetation science. As online resources continue to grow, vegetation ecologists will need a growing set of computational skills to advance vegetation science in the digital age. Two papers in this issue of the Journal of Vegetation Science (Wiser 2016, Sandel et al. 2016) illustrate the resources available and use of big data to explore challenging ecological questions.     

Engineering-approach accelerates computational understanding of V1–V2 neural properties

We present two computational models (i) long-range horizontal connections and the nonlinear effect in V1 and (ii) the filling-in process at the blind spot. Both models are obtained deductively from standard regularization theory to show that physiological evidence of V1 and V2 neural properties is essential for efficient image processing. We stress that the engineering approach should be imported to understand visual systems computationally, even though this approach usually ignores physiological evidence and the target is neither neurons nor the brain.

A field guide to cultivating computational biology

Evolving in sync with the computation revolution over the past 30 years, computational biology has emerged as a mature scientific field. While the field has made major contributions toward improving scientific knowledge and human health, individual computational biology practitioners at various institutions often languish in career development. As optimistic biologists passionate about the future of our field, we propose solutions for both eager and reluctant individual scientists, institutions, publishers, funding agencies, and educators to fully embrace computational biology. We believe that in order to pave the way for the next generation of discoveries, we need to improve recognition for computational biologists and better align pathways of career success with pathways of scientific progress. With 10 outlined steps, we call on all adjacent fields to move away from the traditional individual, single-discipline investigator research model and embrace multidisciplinary, data-driven, team science.

Do you want to attract computational biologists to your project or to your department? Despite the major contributions of computational biology, those attempting to bridge the interdisciplinary gap often languish in career advancement, publication, and grant review. Here, sixteen computational biologists around the globe present "A field guide to cultivating computational biology," focusing on solutions.

Biology in the digital era requires computation and collaboration. A modern research project may include multiple model systems, use multiple assay technologies, collect varying data types, and require complex computational strategies, which together make effective design and execution difficult or impossible for any individual scientist. While some labs, institutions, funding bodies, publishers, and other educators have already embraced a team science model in computational biology and thrived [1–7], others who have not yet fully adopted it risk severely lagging behind the cutting edge. We propose a general solution: “deep integration” between biology and the computational sciences. Many different collaborative models can yield deep integration, and different problems require different approaches (Fig 1).Open in a separate windowFig 1Supporting interdisciplinary team science will accelerate biological discoveries.Scientists who have little exposure to different fields build silos, in which they perform science without external input. To solve hard problems and to extend your impact, collaborate with diverse scientists, communicate effectively, recognize the importance of core facilities, and embrace research parasitism. In biologically focused parasitism, wet lab biologists use existing computational tools to solve problems; in computationally focused parasitism, primarily dry lab biologists analyze publicly available data. Both strategies maximize the use and societal benefit of scientific data.In this article, we define computational science extremely broadly to include all quantitative approaches such as computer science, statistics, machine learning, and mathematics. We also define biology broadly, including any scientific inquiry pertaining to life and its many complications. A harmonious deep integration between biology and computer science requires action—we outline 10 immediate calls to action in this article and aim our speech directly at individual scientists, institutions, funding agencies, and publishers in an attempt to shift perspectives and enable action toward accepting and embracing computational biology as a mature, necessary, and inevitable discipline (Box 1).Box 1. Ten calls to action for individual scientists, funding bodies, publishers, and institutions to cultivate computational biology. Many actions require increased funding support, while others require a perspective shift. For those actions that require funding, we believe convincing the community of need is the first step toward agencies and systems allocating sufficient support

1. Respect collaborators’ specific research interests and motivationsProblem: Researchers face conflicts when their goals do not align with collaborators. For example, projects with routine analyses provide little benefit for computational biologists.Solution: Explicit discussion about interests/expertise/goals at project onset.Opportunity: Clearly defined expectations identify gaps, provide commitment to mutual benefit.
2. Seek necessary input during project design and throughout the project life cycleProblem: Modern research projects require multiple experts spanning the project’s complexity.Solution: Engage complementary scientists with necessary expertise throughout the entire project life cycle.Opportunity: Better designed and controlled studies with higher likelihood for success.
3. Provide and preserve budgets for computational biologists’ workProblem: The perception that analysis is “free” leads to collaborator budget cuts.Solution: When budget cuts are necessary, ensure that they are spread evenly.Opportunity: More accurate, reproducible, and trustworthy computational analyses.
4. Downplay publication author order as an evaluation metric for computational biologistsProblem: Computational biologist roles on publications are poorly understood and undervalued.Solution: Journals provide more equitable opportunities, funding bodies and institutions improve understanding of the importance of team science, scientists educate each other.Opportunity: Engage more computational biologist collaborators, provide opportunities for more high-impact work.
5. Value software as an academic productProblem: Software is relatively undervalued and can end up poorly maintained and supported, wasting the time put into its creation.Solution: Scientists cite software, and funding bodies provide more software funding opportunities.Opportunity: More high-quality maintainable biology software will save time, reduce reimplementation, and increase analysis reproducibility.
6. Establish academic structures and review panels that specifically reward team scienceProblem: Current mechanisms do not consistently reward multidisciplinary work.Solution: Separate evaluation structures to better align peer review to reward indicators of team science.Opportunity: More collaboration to attack complex multidisciplinary problems.
7. Develop and reward cross-disciplinary training and mentoringProblem: Academic labs and institutions are often insufficiently equipped to provide training to tackle the next generation of biological problems, which require computational skills.Solution: Create better training programs aligned to necessary on-the-job skills with an emphasis on communication, encourage wet/dry co-mentorship, and engage younger students to pursue computational biology.Opportunity: Interdisciplinary students uncover important insights in their own data.
8. Support computing and experimental infrastructure to empower computational biologistsProblem: Individual computational labs often fund suboptimal cluster computing systems and lack access to data generation facilities.Solution: Institutions can support centralized compute and engage core facilities to provide data services.Opportunity: Time and cost savings for often overlooked administrative tasks.
9. Provide incentives and mechanisms to share open data to empower discovery through reanalysisProblem: Data are often siloed and have untapped potential.Solution: Provide institutional data storage with standardized identifiers and provide separate funding mechanisms and publishing venues for data reuse.Opportunity: Foster new breed of researchers, “research parasites,” who will integrate multimodal data and enhance mechanistic insights.
10. Consider infrastructural, ethical, and cultural barriers to clinical data accessProblem: Identifiable health data, which include sensitive information that must be kept hidden, are distributed and disorganized, and thus underutilized.Solution: Leadership must enforce policies to share deidentifiable data with interoperable metadata identifiers.Opportunity: Derive new insights from multimodal data integration and build datasets with increased power to make biological discoveries.

     

iGMDR:Integrated Pharmacogenetic Resource Guide to Cancer Therapy and Research

Current pharmacogenetic studies have obtained many genetic models that can predict the therapeutic efficacy of anticancer drugs. Although some of these models are of crucial importance and have been used in clinical practice, these very valuable models have not been well adopted into cancer research to promote the development of cancer therapies due to the lack of integration and standards for the existing data of the pharmacogenetic studies. For this purpose, we built a resource investigating genetic model of drug response (iGMDR), which integrates the models from in vitro and in vivo pharmacogenetic studies with different omics data from a variety of technical systems. In this study, we introduced a standardized process for all integrations, and described how users can utilize these models to gain insights into cancer. iGMDR is freely accessible at https://igmdr.modellab.cn.     

Technique and preliminary findings for in vivo quantification of brain motion during injurious head impacts

Computational models of the human brain are widely used in the evaluation and development of helmets and other protective equipment. These models are often attempted to be validated using cadaver tissue displacements despite studies showing neural tissue degrades quickly after death. Addressing this limitation, this study aimed to develop a technique for quantifying living brain motion in vivo using a closed head impact animal model of traumatic brain injury (TBI) called CHIMERA. We implanted radiopaque markers within the brain of three adult ferrets and resealed the skull while the animals were anesthetized. We affixed additional markers to the skull to track skull kinematics. The CHIMERA device delivered controlled, repeatable head impacts to the head of the animals while the impacts were fluoroscopically stereo-visualized. We observed that 1.5 mm stainless steel fiducials (∼8 times the density of the brain) migrated from their implanted positions while neutral density targets remained in their implanted position post-impact. Brain motion relative to the skull was quantified in neutral density target tests and showed increasing relative motion at higher head impact severities. We observed the motion of the brain lagged behind that of the skull, similar to previous studies. This technique can be used to obtain a comprehensive dataset of in vivo brain motion to validate computational models reflecting the mechanical properties of the living brain. The technique would also allow the mechanical response of in vivo brain tissue to be compared to cadaveric preparations for investigating the fidelity of current human computational brain models.     

Tertiary phosphines containing ortho-carborane as backbone. Some palladium(II) complexes

Coordination compounds of the type Pd(HPC)₂- X₂, Pd(DPC)X₂ and Pd(NPC)X₂, where X  Cl or Br and HPC = 1-diphenylphosphino-o-carborane; DPC = 1,2-bis(diphenylphoshino)-_o-carborane; and NPC= 1-bis(dimethylamminophosphino, 2-diphenylphosphino) -o-carborane, have been prepared and characterized by IR, Raman and electronic spectroscopy, magnetic and conductivity measurements, and elemental analyses. The compounds containing the monodentate HPC ligand possess a trans-planar structure, whereas for those containing the bidentate DPC and NPC ligands a cis-planar configuration was found. In all cases, the phosphorus atoms of the tertiary phosphine (HPC) and the ditertiary phosphines (DPC and NPC) are bonded to the palladium atom. The complex [Pd(HPC)Cl₂]₂ has also been prepared, and a halogen bridge dimeric structure is proposed on the basis of the IR and Raman spectra.     

Yabi: An online research environment for grid,high performance and cloud computing

Background  

There is a significant demand for creating pipelines or workflows in the life science discipline that chain a number of discrete compute and data intensive analysis tasks into sophisticated analysis procedures. This need has led to the development of general as well as domain-specific workflow environments that are either complex desktop applications or Internet-based applications. Complexities can arise when configuring these applications in heterogeneous compute and storage environments if the execution and data access models are not designed appropriately. These complexities manifest themselves through limited access to available HPC resources, significant overhead required to configure tools and inability for users to simply manage files across heterogenous HPC storage infrastructure.     

Performance analysis of MPI collective operations

Previous studies of application usage show that the performance of collective communications are critical for high-performance computing. Despite active research in the field, both general and feasible solution to the optimization of collective communication problem is still missing. In this paper, we analyze and attempt to improve intra-cluster collective communication in the context of the widely deployed MPI programming paradigm by extending accepted models of point-to-point communication, such as Hockney, LogP/LogGP, and PLogP, to collective operations. We compare the predictions from models against the experimentally gathered data and using these results, construct optimal decision function for broadcast collective. We quantitatively compare the quality of the model-based decision functions to the experimentally-optimal one. Additionally, in this work, we also introduce a new form of an optimized tree-based broadcast algorithm, splitted-binary. Our results show that all of the models can provide useful insights into various aspects of the different algorithms as well as their relative performance. Still, based on our findings, we believe that the complete reliance on models would not yield optimal results. In addition, our experimental results have identified the gap parameter as being the most critical for accurate modeling of both the classical point-to-point-based pipeline and our extensions to fan-out topologies.

Expression, purification, and characterization of cytokinin signaling intermediates: Arabidopsis histidine phosphotransfer protein 1 (AHP1) and AHP2

Key message

We have expressed, purified, and biophysically characterized recombinant AHP1 and AHP2. Also, using computational homology models for AHP1, ARR7, and AHP1–ARR7 complex, we identified three-dimensional positioning of key amino acids.

Abstract

Cytokinin signaling involves activation of Arabidopsis Response Regulators (ARRs) by Arabidopsis Histidine Phosphotransfer Proteins (AHPs) by phosphorylation. Type-A ARRs are key regulators of several developmental pathways, but the mechanism underlying this phosphorylation and activation is not known in plants. In this study, we report the successful expression and purification of recombinant AHP1 and AHP2. Biophysical characterization shows that these two recombinant proteins were purified to homogeneity and possess well-defined secondary structures. Brief attempts to purify recombinant ARR7 posed problems during size-exclusion chromatography. Nevertheless, we generated computational homology models for AHP1, ARR7, and AHP1–ARR7 complex using crystal structures of homologous proteins from other organisms. The homology models helped to identify the three-dimensional positioning of the key conserved residues of AHP1 and ARR7 involved in phosphorylation. The similarity in positioning of these residues to other homologous proteins suggests that AHPs and type-A ARRs could be structurally conserved across kingdoms. Thus, our homology models can serve as valuable tools to gain structural insights into the phosphorylation and activation of cytokinin response regulators in plants.     

Design,synthesis, and biological evaluation of a series of piperazine ureas as fatty acid amide hydrolase inhibitors

A series of piperazine ureas were designed, synthesized, and evaluated for their potential as novel orally efficacious fatty acid amide hydrolase (FAAH) inhibitors for the treatment of neuropathic and inflammatory pain. We carried out an optimization study of compound 5 to improve its in vitro FAAH inhibitory activity, and identified the 2-pyrimidinylpiperazine derivative 21d with potent inhibitory activity, favorable DMPK profile and brain permeability. Compound 21d showed robust and dose-dependent analgesic efficacy in animal models of both neuropathic and inflammatory pain.     

Translational Informatics for Parkinson’s Disease: from Big Biomedical Data to Small Actionable Alterations

Parkinson's disease (PD) is a common neurological disease in elderly people, and its morbidity and mortality are increasing with the advent of global ageing. The traditional paradigm of moving from small data to big data in biomedical research is shifting toward big data-based identification of small actionable alterations. To highlight the use of big data for precision PD medicine, we review PD big data and informatics for the translation of basic PD research to clinical applications. We emphasize some key findings in clinically actionable changes, such as susceptibility genetic variations for PD risk population screening, biomarkers for the diagnosis and stratification of PD patients, risk factors for PD, and lifestyles for the prevention of PD. The challenges associated with the collection, storage, and modelling of diverse big data for PD precision medicine and healthcare are also summarized. Future perspectives on systems modelling and intelligent medicine for PD monitoring, diagnosis, treatment, and healthcare are discussed in the end.