Using Magnetic Levitation to Separate Mixtures of Crystal Polymorphs

Magnetische Levitation (MagLev) ist eine einfache Trennmethode für Kristallpolymorphe mit Dichteunterschieden (Δρ) von nur 0.001 g cm−3. Für vier organische Verbindungen wurden dichtebasierte Trennungen verschiedener kristalliner Formen gezeigt: 5-Methyl-2-[(2-nitrophenyl)amino]-3-thiophencarbonitril, Sulfathiazol, Carbamazepin und trans-Zimtsäure.Chemistry and Chemical Biolog

Automated Tool for Calibration Features Checking Engine Platform

Fuel economy and government emissions regulations and other compulsory features like ABS and Cruise Control are important for automotive engine manufacturers. New engine sensors and actuators are introduced to meet these requirements, which increases engine complexity and cost. Calibration is the process of achieving optimal settings by evaluating the behavior of an engine. This multistep process involves designing tests, collecting data, analyzing the data and calibrating lookup tables to model the engine. This process helps to identify the optimal balance of engine performance, emissions, and fuel economy. There are number of calibration parameters which control the engine performance and behavior of its accessories. These parameters needs to be calibrated and adjusted to arrive at an engine settings which are optimized for performance, fuel economy, emissions and cost. In this project, we have developed an automated tool which helps in this calibration tuning process to reduce time and efforts. In this project, the calibration process is to be automated

Comparative techniques for detecting mastitis disease in bovine milk samples

Objective-The objective of our study is to analyze various techniques that are applied for detecting mastitis disease in milk samples.Methods- In this study, we collect milk samples (n=100) from different dairy farms and diagnosed through various tests. In addition, flow cytometric analysis was also performed for milk samples in order to analyzed lymphocytes, monocytes and granulocytes count.Results- The results of these studies showed that California mastitis Test (CMT) showed much more positive results against mastitis disease as compared to other techniques whereas flow cytometric results revealed that during mastitis disease, there is enhancement in granulocytes count.Conclusion- Out of these techniques, CMT is the most reliable and cost effective method for detecting mastitis disease

eNavigate: A Survey On Effective and Efficient User Website Navigation

Web Structure Improvement has attracted much attention now days. The large websites such as E-commerce, universities etc. have lots of pages visited by users daily. The users needed to be navigated effectively and efficiently throughout the website. Each user has its own set of target pages where the stay time is larger than that of other pages. While making website structure improvements the web master must consider the set of target pages of the users. The users’ web log must be maintained at the server side which further has to be divided into session and mini sessions. These mini sessions provides the inputs to extract the target pages of a specific user. The improvement in website must be done under some circumstances. The newly added links must satisfy some criteria such as how many number of links can be added. Previous literature provides some meaningful considerations about static and dynamic websites. The effective website structure improvements along with the set of target pages can be efficiently designed with static and informative websites while it’s difficult to consider the target pages in dynamic one. The structure optimization can be done with minimization or maximization strategies. Here in this survey paper we studied some previously published journals to get better knowledge about the web structure improvements for effective user navigation

An MFS Transporter-Like ORF from MDR Acinetobacter baumannii AIIMS 7 Is Associated with Adherence and Biofilm Formation on Biotic/Abiotic Surface

A major facilitator superfamily (MFS) transporter-like open reading frame (ORF) of 453 bp was identified in a pathogenic strain Acinetobacter baumannii AIIMS 7, and its association with adherence and biofilm formation was investigated. Reverse transcription PCR (RT-PCR) showed differential expression in surface-attached biofilm cells than nonadherent cells. In vitro translation showed synthesis of a ~17 kDa protein, further confirmed by cloning and heterologous expression in E. coli DH5α. Up to 2.1-, 3.1-, and 4.1- fold biofilm augmentation was observed on abiotic (polystyrene) and biotic (S. cerevisiae/HeLa) surface, respectively. Scanning electron microscopy (SEM) and gfp-tagged fluorescence microscopy revealed increased adherence to abiotic (glass) and biotic (S. cerevisiae) surface. Extracellular DNA(eDNA) was found significantly during active growth; due to probable involvement of the protein in DNA export, strong sequence homology with MFS transporter proteins, and presence of transmembrane helices. In summary, our findings show that the putative MFS transporter-like ORF (pmt) is associated with adherence, biofilm formation, and probable eDNA release in A. baumannii AIIMS 7

Genetic drug target validation using Mendelian randomisation

Mendelian randomisation (MR) analysis is an important tool to elucidate the causal relevance of environmental and biological risk factors for disease. However, causal inference is undermined if genetic variants used to instrument a risk factor also influence alternative disease-pathways (horizontal pleiotropy). Here we report how the 'no horizontal pleiotropy assumption' is strengthened when proteins are the risk factors of interest. Proteins are typically the proximal effectors of biological processes encoded in the genome. Moreover, proteins are the targets of most medicines, so MR studies of drug targets are becoming a fundamental tool in drug development. To enable such studies, we introduce a mathematical framework that contrasts MR analysis of proteins with that of risk factors located more distally in the causal chain from gene to disease. We illustrate key model decisions and introduce an analytical framework for maximising power and evaluating the robustness of analyses

Genetic evidence for serum amyloid P component as a drug target in neurodegenerative disorders

The mechanisms responsible for neuronal death causing cognitive loss in Alzheimer's disease (AD) and many other dementias are not known. Serum amyloid P component (SAP) is a constitutive plasma protein, which is cytotoxic for cerebral neurones and also promotes formation and persistence of cerebral Aβ amyloid and neurofibrillary tangles. Circulating SAP, which is produced exclusively by the liver, is normally almost completely excluded from the brain. Conditions increasing brain exposure to SAP increase dementia risk, consistent with a causative role in neurodegeneration. Furthermore, neocortex content of SAP is strongly and independently associated with dementia at death. Here, seeking genomic evidence for a causal link of SAP with neurodegeneration, we meta-analysed three genome-wide association studies of 44 288 participants, then conducted cis-Mendelian randomization assessment of associations with neurodegenerative diseases. Higher genetically instrumented plasma SAP concentrations were associated with AD (odds ratio 1.07, 95% confidence interval (CI) 1.02; 1.11, p = 1.8 × 10-3), Lewy body dementia (odds ratio 1.37, 95%CI 1.19; 1.59, p = 1.5 × 10-5) and plasma tau concentration (0.06 log2(ng l-1) 95%CI 0.03; 0.08, p = 4.55 × 10-6). These genetic findings are consistent with neuropathogenicity of SAP. Depletion of SAP from the blood and the brain, by the safe, well tolerated, experimental drug miridesap may thus be neuroprotective

DTL-DephosSite: Deep transfer learning based approach to predict dephosphorylation sites

Copyright © 2021 Chaudhari, Thapa, Ismail, Chopade, Caragea, Köhn, Newman and KC. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.Phosphorylation, which is mediated by protein kinases and opposed by protein phosphatases, is an important post-translational modification that regulates many cellular processes, including cellular metabolism, cell migration, and cell division. Due to its essential role in cellular physiology, a great deal of attention has been devoted to identifying sites of phosphorylation on cellular proteins and understanding how modification of these sites affects their cellular functions. This has led to the development of several computational methods designed to predict sites of phosphorylation based on a protein’s primary amino acid sequence. In contrast, much less attention has been paid to dephosphorylation and its role in regulating the phosphorylation status of proteins inside cells. Indeed, to date, dephosphorylation site prediction tools have been restricted to a few tyrosine phosphatases. To fill this knowledge gap, we have employed a transfer learning strategy to develop a deep learning-based model to predict sites that are likely to be dephosphorylated. Based on independent test results, our model, which we termed DTL-DephosSite, achieved efficiency scores for phosphoserine/phosphothreonine residues of 84%, 84% and 0.68 with respect to sensitivity (SN), specificity (SP) and Matthew’s correlation coefficient (MCC). Similarly, DTL-DephosSite exhibited efficiency scores of 75%, 88% and 0.64 for phosphotyrosine residues with respect to SN, SP, and MCC. © Copyright © 2021 Chaudhari, Thapa, Ismail, Chopade, Caragea, Köhn, Newman and KC.This work was supported by National Science Foundation (NSF) grant nos. 1901793, 2003019, and 2021734 to DK. RN is supported by an HBCU-UP Excellence in Research Award from NSF (1901793) and an SC1 Award from the National Institutes of Health National Institute of General Medical Science (5SC1GM130545)