A New Type of Coincidence and Common Fixed-Point Theorems for Modified ð-Admissible ð©-Contraction Via Simulation Function

In this manuscript, we introduce the concept of modified α-admissible contraction with the help of a simulation function and use this concept to establish some coincidence and common fixed-point theorems in metric space. An illustrative example that yields the main result is given. Also, several existing results within the frame of metric space are established. The main theorem was applied to derive the coincidence and common fixed-point results for α-admissible ð’µ-contraction

Detection and Identification of Camouflaged Targets using Hyperspectral and LiDAR data

Camouflaging is the process of merging the target with the background with the aim to reduce/delay its detection. It can be done using different materials/methods such as camouflaging nets, paints. Defence applications often require quick detection of camouflaged targets in a dynamic battlefield scenario. Though HSI data may facilitate detection of camouflaged targets but detection gets complicated due to issues (spectral variability, dimensionality). This paper presents a framework for detection of camouflaged target that allows military analysts to coordinate and utilise the expert knowledge for resolving camouflaged targets using remotely sensed data. Desired camouflaged target (set of three chairs as a target under a camouflaging net) has been resolved in three steps: First, hyperspectral data processing helps to detect the locations of potential camouflaged targets. It narrows down the location of the potential camouflaged targets by detecting camouflaging net using Independent component analysis and spectral matching algorithms. Second, detection and identification have been performed using LiDAR point cloud classification and morphological analysis. HSI processing helps to discard the redundant majority of LiDAR point clouds and support detailed analysis of only the minute portion of the point cloud data the system deems relevant. This facilitates extraction of salient features of the potential camouflaged target. Lastly, the decisions obtained have been fused to infer the identity of the desired targets. The experimental results indicate that the proposed approach may be used to successfully resolve camouflaged target assuming some a priori knowledge about the morphology of targets likely to be present.

A retrospective cohort study to find out the association of hydroxychloroquine prophylaxis and COVID 19 infection prevention among health care workers in a tertiary care hospital of New Delhi

Background- With the high morbidity and mortality year 2020 will be remembered as Covid19 pandemic year. Occupational exposure to COVID 19 among health care workers poses a major risk to their lives. Hydroxychloroquine (HCQ) prophylaxis has been indicated for their use without much scientific evidence. Objective- to find if HCQ prophylaxis had association with Covid19 infection prevention among health care workers. Material &Method- A retrospective cohort study was conducted; through online by utilizing social media platform, among Health care workers of a tertiary care hospital from 1st June 2020 to 27 July 2020. Those HCWs who have taken HCQ (exposed) and who have not taken (nonexposed) and PCR tested Covid19 Positive were taken as diseased.  Results Out of 527 who were analyzed, study subjects who took HCQ prophylaxis had 30% less chance of having Covid19 test positive, {RR- 0.709(0.383-1.296)} as compared those who didn’t took it, but the results were not significant. Conclusion- Hydroxychloroquine prophylaxis does not prevent Covid 19 infection and more evidence may be required for use of HCQ prophylaxis for Covid19 infection. Keywords- Hydroxychloroquine prophylaxis, Covid19 infection, Health care workers, Retrospective cohort stud

Predicting prognosis in large cohort of decompensated cirrhosis of liver (DCLD)- a machine learning (ML) approach

Background and aims: Onset of decompensation in cirrhosis is associated with poor outcome. The current clinico-biochemical tools have limited accuracy in predicting outcomes reliably. Identifying the predictors with precision model on the big data using artificial intelligence may improve predictability. We aimed to develop a machine learning (ML) based prognostic model for predicting 90 day survival in patients of cirrhosis presenting with decompensation. Method: We analysed electronic medical records retrospectively of hospitalised cirrhosis patients at the ILBS, with a complete 90-day follow-up. Clinical data, laboratory parameters and organ involvement were serially noted. AI-modelling was done after appropriate mining, feature engineering, splitted randomly into train and testsets (20:80). The class imbalance problem was handled by random over-sampling technique, to make balanced 50:50 ratios. After 10- fold cross validation, 3 repetitions and grid search for optimal hyper parameters, the XGB-CV model was chosen. AUC was the primary selection criteria and confusion matrix was used to compare AUCs between AI-models and existing indices; CTP and MELD-score. Results: Total of 6326 patients [mean age 48.2 ± 11.5 years, 84% male, Mean CTP 10.4 ± 2.2 and MELD Na-30.4 ± 11.9, alcohol 49.4%] were included. Ninety day mortality was 29.2%. Acute insult was identified in 80% cases; of which extra-hepatic 49%, hepatic 46% and unknown 5% cases respectively. The XGB-CV model had the best accuracy for prediction of 90 days event in the train set 0.90 (0.90–0.93), validation set 0.80 (0.79–0.81) and for overall dataset 0.80 (0.79– 0.81). The AUC of the XGB-CV model was better than CTP and MELD Na-score by 16% and 15% respectively. The prediction model considered 43 variables; 18 of which predicted the outcome, and 10 maximum contributors are shown in concordance classifier. The most contributors to poor outcome included, index presentation as HE, diagnosis of AD/ACLF/ESLD, PT-INR, serum creatinine, total bilirubin, acute insult etiology, prior decompensation, acute hepatic or extrahepatic insult, leukocyte count and present duration of illness. In the Decision Tree Model, the presence of HE, PT-INR and syndromic diagnosis of AD or ACLF/ESLD was able to stratify the patients into low (22%), intermediate (23–46%) and high risk (\u3e75%) of mortality at 90 days. Conclusion: The AI based current model developed using a large data base of CLD patients presenting with decompensation immensely adds to the current indices of liver disease severity and can stratify patients at admission. Simple ML algorithms using HE and INR besides syndromic presentation, could help treatment decisions and prognostication

Monitoring of spatio‐temporal glaciers dynamics in Bhagirathi Basin, Gharhwal Himalayas using remote sensing data

Glacier retreat represents a highly sensitive indicator of climate change and global warming. Therefore, timely mapping and monitoring of glacier dynamics is strategic for water budget forecasting and sustainable management of water resources. In this study, Landsat satellite images of 2000 and 2015 have been used to estimate area extent variations in 29 glaciers of the Bhagirathi basin, Garhwali Himalayas. ASTER DEM has been used for extraction of glacier terrain features, such as elevation, slope, area, etc. It is observed from the analysis that Bhagirathi sub-basin has a maximum glaciated area of ~ 35% and Pilang has the least with ~ 3.2%, whereas Kaldi sub-basin has no glacier. In this region, out of 29 glaciers, 25 glaciers have shown retreat, while only 4 glaciers have shown advancement resulting in a total glacier area loss of ~ 0.5%, while the retreat rate varies from ~ 0.06 m/yr to ~ 19.4 m/yr. Dokarni glacier has maximum retreat rate (~ 19.4 m/yr), whereas Dehigad has maximum advancing rate (~ 10.1 m/yr). Glaciers retreat and advance have also been analyzed based on terrain parameters and observed that northern and southern orientations have shown retreat, whereas the area change is highly correlated with glacier length. The study covers more than 65% of the total glaciated area and based on the existing literature represents one of the most exhaustive studies to cover the highest number of glaciers in all sub-basins of the Bhagirathi basin

Improvement of the machining performance of the TW-ECDM process using magnetohydrodynamics (MHD) on quartz material

Many microslits are typically manufactured on quartz substrates and are used to improve their industrial performance. The fabrication of microslits on quartz is difficult and expensive to achieve using recent traditional machining processes due to its hardness, electrically insulating nature, and brittleness. The key objective of the current study was to demonstrate the fabrication of microslits on quartz material through a magnetohydrodynamics (MHD)-assisted traveling wire-electrochemical discharge micromachining process. Hydrogen gas bubbles were concentrated around the entire wire surface during electrolysis. This led to a less active dynamic region of the wire electrode, which decreased the adequacy of the electrolysis process and the machining effectiveness. The test results affirmed that the MHD convection approach evacuated the gas bubbles more rapidly and improved the void fraction in the gas bubble scattering layer. Furthermore, the improvements in the material removal rate and length of the cut were 85.28% and 48.86%, respectively, and the surface roughness was reduced by 30.39% using the MHD approach. A crossover methodology with a Taguchi design and ANOVA was utilized to study the machining performance. This exploratory investigation gives an unused strategy that shows a few advantages over the traditional TW-ECDM process

Cellular interaction of folic acid conjugated superparamagnetic iron oxide nanoparticles and its use as contrast agent for targeted magnetic imaging of tumor cells

The purpose of the study was to develop tumor specific, water dispersible superparamagnetic iron oxide nanoparticles (SPIONs) and evaluate their efficacy as a contrast agent in magnetic resonance imaging (MRI). We have developed SPIONs capped with citric acid/2-bromo-2-methylpropionic acid which are compact, water dispersible, biocompatible having narrow range of size dispersity (8–10 nm), and relatively high T2 relaxivity (R2 = 222L · mmol−1 · sec−l). The targeting efficacy of unconjugated and folic acid-conjugated SPIONs (FA-SPIONS) was evaluated in a folic acid receptor overexpressing and negative tumor cell lines. Folic acid receptor-positive cells incubated with FA-SPIONs showed much higher intracellular iron content without any cytotoxicity. Ultrastructurally, SPIONs were seen as clustered inside the various stages of endocytic pathways without damaging cellular organelles and possible mechanism for their entry is via receptor mediated endocytosis. In vitro MRI studies on tumor cells showed better T2-weighted images in FA-SPIONs. These findings indicate that FA-SPIONs possess high colloidal stability with excellent sensitivity of imaging and can be a useful MRI contrast agent for the detection of cancer

Hypoxia induced lactate acidosis modulates tumor microenvironment and lipid reprogramming to sustain the cancer cell survival

It is well known that solid hypoxic tumour cells oxidise glucose through glycolysis, and the end product of this pathway is fermented into lactate which accumulates in the tumour microenvironment (TME). Initially, it was proclaimed that cancer cells cannot use lactate; therefore, they dump it into the TME and subsequently augment the acidity of the tumour milieu. Furthermore, the TME acts as a lactate sink with stope variable amount of lactate in different pathophysiological condition. Regardless of the amount of lactate pumped out within TME, it disappears immediately which still remains an unresolved puzzle. Recent findings have paved pathway in exploring the main role of lactate acidosis in TME. Cancer cells utilise lactate in the de novo fatty acid synthesis pathway to initiate angiogenesis and invasiveness, and lactate also plays a crucial role in the suppression of immunity. Furthermore, lactate re-programme the lipid biosynthetic pathway to develop a metabolic symbiosis in normoxic, moderately hypoxic and severely hypoxic cancer cells. For instance: severely hypoxic cancer cells enable to synthesizing poly unsaturated fatty acids (PUFA) in oxygen scarcity secretes excess of lactate in TME. Lactate from TME is taken up by the normoxic cancer cells whereas it is converted back to PUFAs after a sequence of reactions and then liberated in the TME to be utilized in the severely hypoxic cancer cells. Although much is known about the role of lactate in these biological processes, the exact molecular pathways that are involved remain unclear. This review attempts to understand the molecular pathways exploited by lactate to initiate angiogenesis, invasiveness, suppression of immunity and cause re-programming of lipid synthesis. This review will help the researchers to develop proper understanding of lactate associated bimodal regulations of TME