Experimental Characterisation of Moreno Cross Slot Couplers for Blass Matrix Design

This paper presents the experimental characterisation of Moreno cross-slot coupler which is the basic building block of multiple beam forming network (Blass matrix). The lack of exact theory of such coupler requires extensive experimental evaluation. A novel test jig has been designed, fabricated and tested for this purpose. The experimental results for different scattering parameters are presented

Estimation of fish production from Hirakud reservoir

A suitable procedure based broadly on stratified random sampling for estimation of fish production from Hirakud reservoir is described. The total fish production for the years 1978 and 1979 from Hirakud reservoir, along with seasonal variation of different species is discussed

Fast and Compact Distributed Verification and Self-Stabilization of a DFS Tree

We present algorithms for distributed verification and silent-stabilization of a DFS(Depth First Search) spanning tree of a connected network. Computing and maintaining such a DFS tree is an important task, e.g., for constructing efficient routing schemes. Our algorithm improves upon previous work in various ways. Comparable previous work has space and time complexities of $O(n\log \Delta)$ bits per node and $O(nD)$ respectively, where $\Delta$ is the highest degree of a node, $n$ is the number of nodes and $D$ is the diameter of the network. In contrast, our algorithm has a space complexity of $O(\log n)$ bits per node, which is optimal for silent-stabilizing spanning trees and runs in $O(n)$ time. In addition, our solution is modular since it utilizes the distributed verification algorithm as an independent subtask of the overall solution. It is possible to use the verification algorithm as a stand alone task or as a subtask in another algorithm. To demonstrate the simplicity of constructing efficient DFS algorithms using the modular approach, We also present a (non-sielnt) self-stabilizing DFS token circulation algorithm for general networks based on our silent-stabilizing DFS tree. The complexities of this token circulation algorithm are comparable to the known ones

Corrigendum to �Tonelli M, Nkunu V, Varghese C, et al. Framework for establishing integrated kidney care programs in low- and middle-income countries� Kidney Int Suppl. 2020;10:e19�e23 (Kidney International Supplements (2020) 10(1) (e19�e23), (S2157171619300164), (10.1016/j.kisu.2019.11.002))

The authors regret that Bassam Bernieh was inadvertently left out as an author of the article. The authors and affiliations are as follows. The authors would like to apologize for any inconvenience caused. © 2020 International Society of Nephrolog

Novel Druggable Hot Spots in Avian Influenza Neuraminidase H5N1 Revealed by Computational Solvent Mapping of a Reduced and Representative Receptor Ensemble

The influenza virus subtype H5N1 has raised concerns of a possible human pandemic threat because of its high virulence and mutation rate. Although several approved anti-influenza drugs effectively target the neuraminidase, some strains have already acquired resistance to the currently available anti-influenza drugs. In this study, we present the synergistic application of extended explicit solvent molecular dynamics (MD) and computational solvent mapping (CS-Map) to identify putative ‘hot spots’ within flexible binding regions of N1 neuraminidase. Using representative conformations of the N1 binding region extracted from a clustering analysis of four concatenated 40-ns MD simulations, CS-Map was utilized to assess the ability of small, solvent-sized molecules to bind within close proximity to the sialic acid binding region. Mapping analyses of the dominant MD conformations reveal the presence of additional hot spot regions in the 150- and 430-loop regions. Our hot spot analysis provides further support for the feasibility of developing high-affinity inhibitors capable of binding these regions, which appear to be unique to the N1 strain

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

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Not AvailableFood security has become one of the major concerns of governments around the world as the human population rapidly expands. Food security is getting higher priority in this era of revolutionary information technology. Near saturated agriculture and livestock production sectors are struggling to meet the requirements of a growing population, especially for higher quality and cheaper sources of protein. Aquaculture is considered to be a promising potential production sector for high-protein food. In effect, aquaculture must share social responsibilities with agriculture because it is a rising sector in global food production. Blooming global aquaculture can go hand in hand with agriculture to aid in the mission of attaining food security. Aquaculture can be a food production sector for the poor because sh protein can be less expensive than other animal protein sources. However, the dependency of fed aquaculture on costly ingredients reduces its economic viability and limits demand from poorer members of society (Allison 2011). Also, the rise in the proportion of the population that consumes sh demands more from aquaculture than near-stagnant capture sheries.Feed is the greatest fraction of operating cost of fed aquaculture systems and the cost and availability of ingredients limit the growth of fed aquaculture. Thus, sh nutrition is important to the progress of global aquaculture production. However, there are a multitude of challenges associated with the growth of the aquafeed industry and nutrition research. Systematic and technological interventions are essential for making aquaculture operations economical. At present, the aquafeed industry is struggling to include cheaper plant ingredients to replace costly animal protein sources to manufacture cost-effective aquafeeds.Not Availabl