Effect of Double Decker Flyover Construction on Urban Fabric of Ashok Rajpath, Patna, India

Rapid urbanization and increase in number of vehicles has led to diminishing vehicular spaces on the street. This has led to construction of flyovers, which has dominated the streets. They reduce the “Sky-View” factor and has a put a serious threat to “Enclosure” widely used in Urban Design concepts. It has reduced the openness for the pedestrians and severely obstructed the views. This paper examines the effect of fly-over on the “Urban Fabric of an ancient street “Ashok Rajpath” of Patna, India

Alevin efficiently estimates accurate gene abundances from dscRNA-seq data.

We introduce alevin, a fast end-to-end pipeline to process droplet-based single-cell RNA sequencing data, performing cell barcode detection, read mapping, unique molecular identifier (UMI) deduplication, gene count estimation, and cell barcode whitelisting. Alevin's approach to UMI deduplication considers transcript-level constraints on the molecules from which UMIs may have arisen and accounts for both gene-unique reads and reads that multimap between genes. This addresses the inherent bias in existing tools which discard gene-ambiguous reads and improves the accuracy of gene abundance estimates. Alevin is considerably faster, typically eight times, than existing gene quantification approaches, while also using less memory

Systems Analysis Implicates WAVE2 Complex in the Pathogenesis of Developmental Left-Sided Obstructive Heart Defects.

Genetic variants are the primary driver of congenital heart disease (CHD) pathogenesis. However, our ability to identify causative variants is limited. To identify causal CHD genes that are associated with specific molecular functions, the study used prior knowledge to filter de novo variants from 2,881 probands with sporadic severe CHD. This approach enabled the authors to identify an association between left ventricular outflow tract obstruction lesions and genes associated with the WAVE2 complex and regulation of small GTPase-mediated signal transduction. Using CRISPR zebrafish knockdowns, the study confirmed that WAVE2 complex proteins brk1, nckap1, and wasf2 and the regulators of small GTPase signaling cul3a and racgap1 are critical to cardiac development

Particle-yield modification in jet-like azimuthal di-hadron correlations in Pb-Pb collisions at sNN\sqrt{s_{\rm NN}} = 2.76 TeV

The yield of charged particles associated with high-$p_{\rm T}$ trigger particles ($8 < p_{\rm T} < 15$ GeV/$c$) is measured with the ALICE detector in Pb-Pb collisions at $\sqrt{s_{\rm NN}}$ = 2.76 TeV relative to proton-proton collisions at the same energy. The conditional per-trigger yields are extracted from the narrow jet-like correlation peaks in azimuthal di-hadron correlations. In the 5% most central collisions, we observe that the yield of associated charged particles with transverse momenta $p_{\rm T}> 3$ GeV/$c$ on the away-side drops to about 60% of that observed in pp collisions, while on the near-side a moderate enhancement of 20-30% is found.Comment: 15 pages, 2 captioned figures, 1 table, authors from page 10, published version, figures at http://aliceinfo.cern.ch/ArtSubmission/node/350

Testing Shoal Quant files

<p>Testing files for shoal</p

Synthesis of Multimetal-Graphene Composite by Mechanical Milling

Multimetal-graphene composites were synthesized using the ball milling technique. To prepare the composite, graphite powder was mixed with Fe, Cr, Co, Cu and Mg powders. This mixture was then mechanically milled for 35 h in toluene medium. After milling, the multimetal-graphite mixture was mixed with sodium lauryl sulfate and sonicated for 2 h. Sonication led to the exfoliation of graphene sheets. Formation of graphene was confirmed from x-ray diffraction and Raman spectroscopy. Transmission electron microscopy-based analysis revealed the formation of multimetal deposits over the graphene surface. Compositional analysis of the multimetal deposits revealed fairly uniform distribution of all the five component metal atoms over the graphene sheet. The average composition of the multimetal deposit was determined to be 11.4 +/- A 4 at.% Mg, 33.8 +/- A 19 at.% Cr, 21.8 +/- A 16 at.% Fe, 9.4 +/- A 5.7 at.% Co and 23.6 +/- A 12 at.% Cu

Preprocessing choices affect RNA velocity results for droplet scRNA-seq data.

Experimental single-cell approaches are becoming widely used for many purposes, including investigation of the dynamic behaviour of developing biological systems. Consequently, a large number of computational methods for extracting dynamic information from such data have been developed. One example is RNA velocity analysis, in which spliced and unspliced RNA abundances are jointly modeled in order to infer a 'direction of change' and thereby a future state for each cell in the gene expression space. Naturally, the accuracy and interpretability of the inferred RNA velocities depend crucially on the correctness of the estimated abundances. Here, we systematically compare five widely used quantification tools, in total yielding thirteen different quantification approaches, in terms of their estimates of spliced and unspliced RNA abundances in five experimental droplet scRNA-seq data sets. We show that there are substantial differences between the quantifications obtained from different tools, and identify typical genes for which such discrepancies are observed. We further show that these abundance differences propagate to the downstream analysis, and can have a large effect on estimated velocities as well as the biological interpretation. Our results highlight that abundance quantification is a crucial aspect of the RNA velocity analysis workflow, and that both the definition of the genomic features of interest and the quantification algorithm itself require careful consideration

A Framework for Digital Health Policy: Insights from Virtual Primary Care Systems Across Five Nations.

Digital health technologies used in primary care, referred to as, virtual primary care, allow patients to interact with primary healthcare professionals remotely though the current iteration of virtual primary care may also come with several unintended consequences, such as accessibility barriers and cream skimming. The World Health Organization (WHO) has a well-established framework to understand the functional components of health systems. However, the existing building blocks framework does not sufficiently account for the disruptive and multi-modal impact of digital transformations. In this review, we aimed to develop the first iteration of this updated framework by reviewing the deployment of virtual primary care systems in five leading countries: Canada, Finland, Germany and Sweden and the United Kingdom (England). We found that all five countries have taken different approaches with the deployment of virtual primary care, yet seven common themes were highlighted across countries: (1) stated policy objectives, (2) regulation and governance, (3) financing and reimbursement, (4) delivery and integration, (5) workforce training and support, (6) IT systems and data sharing, and (7) the extent of patient involvement in the virtual primary care system. The conceptual framework that was derived from these findings offers a set of guiding principles that can facilitate the assessment of virtual primary care in health system settings

Airpart: interpretable statistical models for analyzing allelic imbalance in single-cell datasets.

MOTIVATION: Allelic expression analysis aids in detection of cis-regulatory mechanisms of genetic variation, which produce allelic imbalance (AI) in heterozygotes. Measuring AI in bulk data lacking time or spatial resolution has the limitation that cell-type-specific (CTS), spatial- or time-dependent AI signals may be dampened or not detected. RESULTS: We introduce a statistical method airpart for identifying differential CTS AI from single-cell RNA-sequencing data, or dynamics AI from other spatially or time-resolved datasets. airpart outputs discrete partitions of data, pointing to groups of genes and cells under common mechanisms of cis-genetic regulation. In order to account for low counts in single-cell data, our method uses a Generalized Fused Lasso with Binomial likelihood for partitioning groups of cells by AI signal, and a hierarchical Bayesian model for AI statistical inference. In simulation, airpart accurately detected partitions of cell types by their AI and had lower Root Mean Square Error (RMSE) of allelic ratio estimates than existing methods. In real data, airpart identified differential allelic imbalance patterns across cell states and could be used to define trends of AI signal over spatial or time axes. AVAILABILITY AND IMPLEMENTATION: The airpart package is available as an R/Bioconductor package at https://bioconductor.org/packages/airpart. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online