bluuebunny/crossref_metadata_2025_split huggingface dataset:
Aged blood inhibits hippocampal neurogenesis and activates microglia through VCAM1 at the blood-brain barrier
Authors: Yousef Hanadie , Czupalla Cathrin J , Lee Davis , Burke Ashley , Chen Michelle , Zandstra Judith , Berber Elisabeth , Lehallier Benoit , Mathur Vidhu , Nair Ramesh V , Bonanno Liana , Merkel Taylor , Schwaninger Markus , Quake Stephen , Butcher Eugene C , Wyss-Coray Tony 
Published: January 2018

AbstractAn aged circulatory environment can promote brain dysfunction and we hypothesized that the blood-brain barrier (BBB) mediates at least some of these effects. We observe brain endothelial cells (BECs) in the aged mouse hippocampus express an inflammatory transcriptional profile with focal upregulation of Vascular Cell Adhesion Molecule 1 (VCAM1), a protein that facilitates vascular-immune cell interactions. Concomitantly, the shed, soluble form of VCAM1 is prominently increased in the aged circulation of humans and mice, and aged plasma is sufficient to increase VCAM1 expression in cultured BECs and young mouse hippocampi. Systemic anti-VCAM1 antibody or genetic ablation of VCAM1 in BECs counteracts the detrimental effects of aged plasma on young brains and reverses aging aspects in old mouse brains. Thus, VCAM1 is a negative regulator of adult neurogenesis and inducer of microglial reactivity, establishing VCAM1 and the luminal side of the BBB as possible targets to treat age-related neurodegeneration.
Algebraic Shortcuts for Leave-One-Out Cross-Validation in Supervised Network Inference
Authors: Stock Michiel , Pahikkala Tapio , Airola Antti , Waegeman Willem , De Baets Bernard 
Published: January 2018

AbstractMotivationSupervised machine learning techniques have traditionally been very successful at reconstructing biological networks, such as protein-ligand interaction, protein-protein interaction and gene regulatory networks. Recently, much emphasis has been placed on the correct evaluation of such supervised models. It is vital to distinguish between using the model to either predict new interactions in a given network or to predict interactions for a new vertex not present in the original network. Specific cross-validation schemes need to be used to assess the performance in such different prediction settings.ResultsWe present a series of leave-one-out cross-validation shortcuts to rapidly estimate the performance of state-of-the-art kernel-based network inference techniques.AvailabilityThe machine learning techniques with the algebraic shortcuts are implemented in the RLScore software package.
Power spectrum slope and motor function recovery after focal cerebral ischemia in the rat
Authors: Leemburg Susan , Gao Bo , Cam Ertugrul , Sarnthein Johannes , Bassetti Claudio L. 
Published: January 2018

AbstractEEG changes across vigilance states have been observed after ischemic stroke in patients and experimental stroke models, but their relation to functional recovery remains unclear. Here, we evaluate motor function, as measured by single pellet reaching (SPR), as well as local EEG changes in NREM, REM and wakefulness during a 30-day recovery period after middle cerebral artery occlusion (MCAO) or sham surgery in rats. Small cortical infarcts resulted in poor SPR performance and induced widespread changes in EEG spectra in the ipsilesional hemisphere in all vigilance states, without causing major changes in sleep-wake architecture. Ipsilesional 1–4 Hz power was increased after stroke, whereas power in higher frequencies was reduced, resulting in a steeper slope of the power spectrum. Multielectrode array analysis of ipsilesional M1 showed that these spectral changes were present on the microelectrode level throughout M1 and were not related to increased synchronization between electrodes. Spectrum slope was significantly correlated with post-stroke motor function.
Reproductive phasiRNAs in grasses are compositionally distinct from other classes of small RNAs
Authors: Patel Parth , Mathioni Sandra , Kakrana Atul , Shatkay Hagit , Meyers Blake C. 
Published: January 2018

Summary and keywordsLittle is known about the characteristics and function of reproductive phased, secondary, small interfering RNAs (phasiRNAs) in the Poaceae, despite the availability of significant genomic resources, experimental data, and a growing number of computational tools. We utilized machine-learning methods to identify sequence-based and structural features that distinguish phasiRNAs in rice and maize from other small RNAs (sRNAs).We developed Random Forest classifiers that can distinguish reproductive phasiRNAs from other sRNAs in complex sets of sequencing data, utilizing sequence-based (k-mers) and features describing position-specific sequence biases.The classification performance attained is >80% in accuracy, sensitivity, specificity, and positive predicted value. Feature selection identified important features in both ends of phasiRNAs. We demonstrated that phasiRNAs have strand specificity and position-specific nucleotide biases potentially influencing AGO sorting; we also predicted targets to infer functions of phasiRNAs, and computationally-assessed their sequence characteristics relative to other sRNAs.Our results demonstrate that machine-learning methods effectively identify phasiRNAs despite the lack of characteristic features typically present in precursor loci of other small RNAs, such as sequence conservation or structural motifs. The 5’-end features we identified provide insights into AGO-phasiRNA interactions; we describe a hypothetical model of competition for AGO loading between phasiRNAs of different nucleotide compositions.
Genome-wide signals of drift and local adaptation during rapid lineage divergence in a songbird
Authors: Friis Guillermo , Fandos Guillermo , Zellmer Amanda , McCormack John , Faircloth Brant , Milá Borja 
Published: January 2018

AbstractThe formation of independent evolutionary lineages involves neutral and selective factors, and understanding their relative roles in population divergence is a fundamental goal of speciation research. Correlations between allele frequencies and environmental variability can reveal the role of selection, yet the relative contribution of drift can be difficult to establish. Recently diversified systems such as that of the Oregon junco (Aves: Emberizidae) of western North America provide ideal scenarios to apply genetic-environment association analyses (GEA) while controlling for population structure. Genome-wide SNP loci analyses revealed marked genetic structure consisting of differentiated populations in isolated, dry southern mountain ranges, and more admixed recently expanded populations in humid northern latitudes. We used correlations between genomic and environmental variance to test for three specific modes of evolutionary divergence: (i) drift in geographic isolation, (ii) differentiation along continuous selective gradients, and (iii) isolation by adaptation. We found evidence of strong drift in southern mountains, but also signals of local adaptation in several populations, driven by temperature, precipitation, elevation and vegetation, especially when controlling for population history. We identified numerous variants under selection scattered across the genome, suggesting that local adaptation can promote rapid differentiation over short periods when acting over multiple independent loci.
Cis-directed cleavage and nonstoichiometric abundances of 21-nt reproductive phasiRNAs in grasses
Authors: Tamim Saleh , Cai Zhaoxia , Mathioni Sandra , Zhai Jixian , Teng Chong , Zhang Qifa , Meyers Blake C. 
Published: January 2018

SummaryPost-transcriptional gene silencing in plants results from independent activities of diverse small RNA types. In anthers of grasses, hundreds of loci yield non-coding RNAs that are processed into 21- and 24-nt phased small interfering RNAs (phasiRNAs); these are triggered by miR2118 and miR2275.We characterized these “reproductive phasiRNAs” from rice panicles and anthers across seven developmental stages. Our computational analysis identified characteristics of the 21-nt reproductive phasiRNAs that impact their biogenesis, stability, and potential functions.We demonstrate that 21-nt reproductive phasiRNAs can function in cis to target their own precursors. We observed evidence of this cis regulatory activity in both rice (Oryza sativa) and maize (Zea mays). We validated this activity with evidence of cleavage and a resulting shift in the pattern of phasiRNA production.We characterize biases in phasiRNA biogenesis, demonstrating that the Pol II-derived “top” strand phasiRNAs are consistently higher abundance than the bottom strand. The first phasiRNA from each precursor overlaps the miR2118 target site, and this impacts phasiRNA accumulation or stability, evident in the weak accumulation of this phasiRNA position. Additional influences on this first phasiRNA duplex include the sequence composition and length, and we show that these factors impact Argonaute loading.
Enhancer-gene maps in the human and zebrafish genomes using evolutionary linkage conservation
Authors: Clément Yves , Torbey Patrick , Gilardi-Hebenstreit Pascale , Crollius Hugues Roest 
Published: January 2018

AbstractThe spatiotemporal expression of genes is controlled by enhancer sequences that bind transcription factors. Identifying the target genes of enhancers remains difficult because enhancers regulate gene expression over long genomic distances. To address this, we used an evolutionary approach to build two genome-wide maps of enhancer-gene associations in the human and zebrafish genomes. Enhancers were identified using sequence conservation, and linked to their predicted target genes using PEGASUS, a bioinformatics method that relies on evolutionary conservation of synteny. The analysis of these maps revealed that the number of enhancers linked to a gene correlate with its expression breadth. Comparison of both maps identified hundreds of vertebrate ancestral regulatory relationships from which we could determine that enhancer-gene distances scale with genome size despite strong positional conservation. The two maps represent a resource for further studies, including the prioritisation of sequence variants in whole genome sequence of patients affected by genetic diseases.
NanoAmpli-Seq: A workflow for amplicon sequencing for mixed microbial communities on the nanopore sequencing platform
Authors: Calus Szymon T , Ijaz Umer Z , Pinto Ameet J 
Published: January 2018

AbstractBackgroundAmplicon sequencing on Illumina sequencing platforms leverages their deep sequencing and multiplexing capacity, but is limited in genetic resolution due to short read lengths. While Oxford Nanopore or Pacific Biosciences platforms overcome this limitation, their application has been limited due to higher error rates or smaller data output.ResultsIn this study, we introduce an amplicon sequencing workflow, i.e., NanoAmpli-Seq, that builds on Intramolecular-ligated Nanopore Consensus Sequencing (INC-Seq) approach and demonstrate its application for full-length 16S rRNA gene sequencing. NanoAmpli-Seq includes vital improvements to the aforementioned protocol that reduces sample-processing time while significantly improving sequence accuracy. The developed protocol includes chopSeq software for fragmentation and read orientation correction of INC-Seq consensus reads while nanoClust algorithm was designed for read partitioning-based de novo clustering and within cluster consensus calling to obtain full-length 16S rRNA gene sequences.ConclusionsNanoAmpli-Seq accurately estimates the diversity of tested mock communities with average sequence accuracy of 99.5% for 2D and 1D2 sequencing on the nanopore sequencing platform. Nearly all residual errors in NanoAmpli-Seq sequences originate from deletions in homopolymer regions, indicating that homopolymer aware basecalling or error correction may allow for sequencing accuracy comparable to short-read sequencing platforms.
A simple assembly planner for the insertion of ring-shaped deformable objects
Authors: Ramirez-Alpizar Ixchel G. , Harada Kensuke , Yoshida Eiichi 
Published: January 2018

Purpose
The aim of this work is to develop a simple planner that is able to automatically plan the motion for a dual-arm manipulator that assembles a ring-shaped elastic object into a cylinder. Moreover, it is desirable to keep the amount of deformation as small as possible, because stretching the object can permanently change its size thus failing to perfectly fit in the cylindrical part and generating undesired gaps between the object and the cylinder.

Design/methodology/approach
The assembly task is divided in two parts: assembly task planning and assembly step planning. The first one computes key configurations of the robot’s end-effectors, and it is based on a simple heuristic method, whereas the latter computes the robot’s motion between key configurations using an optimization-based planner that includes a potential-energy-based cost function for minimizing the object’s deformation.

Findings
The optimization-based planner is shown to be effective for minimizing the deformation of the ring-shaped object. A simple heuristic approach is demonstrated to be valid for inserting deformable objects into a cylinder. Experimental results show that the object can be kept without deformation for the first part of the assembly task, thus reducing the time it is being stretched.

Originality value
A simple assembly planner for inserting ring-shaped deformable objects was developed and validated through several experiments. The proposed planner is able to insert ring-shaped objects without using any sensor (visual and/or force) feedback. The only feedback used is the position of the robot’s end-effectors, which is usually available for any robot.
A document expansion framework for tag-based image retrieval
Authors: Lu Wei , Ding Heng , Jiang Jiepu 
Published: January 2018

Purpose
The purpose of this paper is to utilize document expansion techniques for improving image representation and retrieval. This paper proposes a concise framework for tag-based image retrieval (TBIR).

Design/methodology/approach
The proposed approach includes three core components: a strategy of selecting expansion (similar) images from the whole corpus (e.g. cluster-based or nearest neighbor-based); a technique for assessing image similarity, which is adopted for selecting expansion images (text, image, or mixed); and a model for matching the expanded image representation with the search query (merging or separate).

Findings
The results show that applying the proposed method yields significant improvements in effectiveness, and the method obtains better performance on the top of the rank and makes a great improvement on some topics with zero score in baseline. Moreover, nearest neighbor-based expansion strategy outperforms the cluster-based expansion strategy, and using image features for selecting expansion images is better than using text features in most cases, and the separate method for calculating the augmented probability P(q|RD) is able to erase the negative influences of error images in RD.

Research limitations/implications
Despite these methods only outperform on the top of the rank instead of the entire rank list, TBIR on mobile platforms still can benefit from this approach.

Originality/value
Unlike former studies addressing the sparsity, vocabulary mismatch, and tag relatedness in TBIR individually, the approach proposed by this paper addresses all these issues with a single document expansion framework. It is a comprehensive investigation of document expansion techniques in TBIR.
4P delays in project management
Authors: Ansah Richard Hannis , Sorooshian Shahryar 
Published: January 2018

Purpose
As a result of the increasing challenges of time overrun, several companies regularly set up delay assessment procedures for performance improvement and profit maximization. In the construction industry, projects are enormously complicated and involve significant budgets, and therefore optimizing project performance through the root cause analysis should be imperative for every project manager. Typically issues of delays in projects are generally discussed and problematized separately, yet a comprehensive framework for systematic analysis and grouping of delays is rarely contended. The purpose of this paper is to introduce the 4P concept (project related, practices, participants, and procurement) for identifying, analyzing, and classifying delays in the internal environment of the project development process.

Design/methodology/approach
The 4P conceptual framework was validated through the synthesis of the existing literature.

Findings
Interestingly, despite the increasing concerns about delays in the construction industry, most research on delays are project and/or country specific, thus no consensus about the sources of delays. The application of the proposed concept as a theoretical framework would provide an understanding of the available delay sources, their risks and use for project delay assessment and classification.

Research limitations/implications
This study grouped delays based on the shared characteristics and four main sources were identified and analyzed. There may be other sources or factors, yet would be dependent or aspect of the 4P.

Practical implications
The study informs project teams to reduce delays in the construction industry.

Originality/value
The paper is an application of risk assessment tool to conceptualize delays in the internal project environment.
Challenges in applying design research studies to assess benefits of BIM in infrastructure projects
Authors: Gerbov Alexander , Singh Vishal , Herva Maila 
Published: January 2018

Purpose
The purpose of this paper is to present a reflective assessment on a research project aimed at applying design research methodologies to understand the benefits of building information modelling (BIM) in infrastructure design projects, a context where design research has not been applied adequately.

Design/methodology/approach
The findings reported in this paper are based on a reflective diary and reporting by the researchers on the challenges and barriers they faced in a research project that involved four case studies. The data in the case studies were collected from project documentation, questionnaire surveys, interviews and workshops. Sentiment analysis was conducted to assess the research participants’ perceptions.

Findings
Several challenges faced during the research study forced changes to the research process as well as adjustments to the research objectives. From the original objective of quantitatively measuring the benefits of BIM in the design process, the objective was shifted to measuring qualitatively how the designers’ perceived the benefits. There was evident lack of shared understanding between the researchers and industry partners about the scope of design research and design in infrastructure projects.

Research limitations/implications
The findings are based on the studies in one country, and the reflective assessment could have researchers’ bias in terms of their interpretation and experience.

Practical implications
The presented findings and the lessons learnt from the research will be useful for others planning to conduct similar studies, because they are likely to encounter similar problems. The recommendations will benefit both the industry as well as the academia in bridging the gap between design theory and practice.

Originality/value
This paper primarily focusses on the challenges and barriers to conducting empirical research, especially from the viewpoints of a young researcher aiming to adopt design research methodology in infrastructure design projects. While the reported challenges may be widespread in similar research projects, they have not been the focus of research themselves, and are often sidelined. The primary contribution of this research is to identify and bring the key barriers and challenges to the forefront.
Efficient automatic discrete adjoint sensitivity computation for topology optimization – heat conduction applications
Authors: Vadakkepatt Ajay , Mathur Sanjay R. , Murthy Jayathi Y. 
Published: January 2018

Purpose
Topology optimization is a method used for developing optimized geometric designs by distributing material pixels in a given design space that maximizes a chosen quantity of interest (QoI) subject to constraints. The purpose of this study is to develop a problem-agnostic automatic differentiation (AD) framework to compute sensitivities of the QoI required for density distribution-based topology optimization in an unstructured co-located cell-centered finite volume framework. Using this AD framework, the authors develop and demonstrate the topology optimization procedure for multi-dimensional steady-state heat conduction problems.

Design/methodology/approach
Topology optimization is performed using the well-established solid isotropic material with penalization approach. The method of moving asymptotes, a gradient-based optimization algorithm, is used to perform the optimization. The sensitivities of the QoI with respect to design variables, required for optimization algorithm, are computed using a discrete adjoint method with a novel AD library named residual automatic partial differentiator (Rapid).

Findings
Topologies that maximize or minimize relevant quantities of interest in heat conduction applications are presented. The efficacy of the technique is demonstrated using a variety of realistic heat transfer applications in both two and three dimensions, in conjugate heat transfer problems with finite conductivity ratios and in non-rectangular/non-cuboidal domains.

Originality/value
In contrast to most published work which has either used finite element methods or Cartesian finite volume methods for transport applications, the topology optimization procedure is developed in a general unstructured finite volume framework. This permits topology optimization for flow and heat transfer applications in complex design domains such as those encountered in industry. In addition, the Rapid library is designed to provide a problem-agnostic pathway to automatically compute all required derivatives to machine accuracy. This obviates the necessity to write new code for finding sensitivities when new physics are added or new cost functions are considered and permits general-purpose implementations of topology optimization for complex industrial applications.
Three computational methods for analysing thermal airflow distributions in the cooling of data centres
Authors: de Boer Gregory Nicholas , Johns Adam , Delbosc Nicolas , Burdett Daniel , Tatchell-Evans Morgan , Summers Jonathan , Baudot Remi 
Published: January 2018

Purpose
This aim of this work is to investigate different modelling approaches for air-cooled data centres. The study employs three computational methods, which are based on finite element, finite volume and lattice Boltzmann methods and which are respectively implemented via commercial Multiphysics software, open-source computational fluid dynamics code and graphical processing unit-based code developed by the authors. The results focus on comparison of the three methods, all of which include models for turbulence, when applied to two rows of datacom racks with cool air supplied via an underfloor plenum.

Design/methodology/approach
This paper studies thermal airflows in a data centre by applying different numerical simulation techniques that are able to analyse the thermal airflow distribution for a simplified layout of datacom racks in the presence of a computer room air conditioner.

Findings
Good quantitative agreement between the three methods is seen in terms of the inlet temperatures to the datacom equipment. The computational methods are contrasted in terms of application to thermal management of data centres.

Originality/value
The work demonstrates how the different simulation techniques applied to thermal management of airflow in a data centre can provide valuable design and operational understanding. Basing the analysis on three very different computational approaches is new and would offer an informed understanding of their potential for a class of problems.