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A theoretical study on the coupled electron-nuclear dynamics of HD+ molecular ions under ultrashort, intense laser pulses is performed by employing a well-established quasi-classical model. The influence of the laser carrier-envelope phase on various channel (H + D+, D + H+, and H+ + D+) probabilities is investigated at different laser field intensities. The carrier-envelope phase is found to govern the dissociation (H + D+ and D + H+) and Coulomb explosion (H+ + D+) channel probabilities. The kinetic energy release distributions of the fragments are also found to be sensitive to the carrier-envelope phase of the laser pulse. Our results are in agreement with the previously reported quantum dynamics studies and experiments.

Summary Background The COVID-19 pandemic has affected millions of individuals and caused hundreds of thousands of deaths worldwide. Predicting mortality among patients with COVID-19 who present with a spectrum of complications is very difficult, hindering the prognostication and management of the disease. We aimed to develop an accurate prediction model of COVID-19 mortality using unbiased computational methods, and identify the clinical features most predictive of this outcome. Methods In this prediction model development and validation study, we applied machine learning techniques to clinical data from a large cohort of patients with COVID-19 treated at the Mount Sinai Health System in New York City, NY, USA, to predict mortality. We analysed patient-level data captured in the Mount Sinai Data Warehouse database for individuals with a confirmed diagnosis of COVID-19 who had a health system encounter between March 9 and April 6, 2020. For initial analyses, we used patient data from March 9 to April 5, and randomly assigned (80:20) the patients to the development dataset or test dataset 1 (retrospective). Patient data for those with encounters on April 6, 2020, were used in test dataset 2 (prospective). We designed prediction models based on clinical features and patient characteristics during health system encounters to predict mortality using the development dataset. We assessed the resultant models in terms of the area under the receiver operating characteristic curve (AUC) score in the test datasets. Findings Using the development dataset (n=3841) and a systematic machine learning framework, we developed a COVID-19 mortality prediction model that showed high accuracy (AUC=0·91) when applied to test datasets of retrospective (n=961) and prospective (n=249) patients. This model was based on three clinical features: patient's age, minimum oxygen saturation over the course of their medical encounter, and type of patient encounter (inpatient vs outpatient and telehealth visits). Interpretation An accurate and parsimonious COVID-19 mortality prediction model based on three features might have utility in clinical settings to guide the management and prognostication of patients affected by this disease. External validation of this prediction model in other populations is needed. Funding National Institutes of Health.

The COVID-19 pandemic has become a major threat to the whole world. Analysis of this disease requires major attention by the government in all countries to take necessary steps in reducing the effect of this global pandemic. In this study, outbreak of this disease has been analysed and trained for Indian region till 10th May, 2020, and testing has been done for the number of cases for the next three weeks. Machine learning models such as SEIR model and Regression model have been used for predictions based on the data collected from the official portal of the Government of India in the time period of 30th January, 2020, to 10th May, 2020. The performance of the models was evaluated using RMSLE and achieved 1.52 for SEIR model and 1.75 for the regression model. The RMSLE error rate between SEIR model and Regression model was found to be 2.01. Also, the value of R0, which is the spread of the disease, was calculated to be 2.84. Expected cases are predicted around 175K--200K in the three-week time period of test data, which is very close to the actual numbers. This study will help the government and doctors in preparing their plans for the future.

This document provides an itemized investigation of the Phototherapy Air bed designed and curated to maximize the comfort level of phototherapy treatment of new born babies. Phototherapy bed is normally hard for infants and opaque, which restrict the ultra violet light from floor side to hit the babies back side. Thus doesn’t give comfort to the babies which causes them unrest during the treatment and decreases the efficiency of the breakdown of bilirubin by bili-lights and thus results in bad or inefficient treatment. Beds had been designed like gel-pads and bubble wrap to give them comfort and increase the exposure of lights on neonatal but hadn’t been that much successful due to their difficulty in application. Thus we had designed a very comfortable and easily equitable air bed which smoothly changes the pressure points of babies while they lay on it and gives them maximum comfort like a home bed which eliminates their random motion on the bed surface and also enhance the body surface area to exposure of Ultra- Violet Rays which maximizes efficiency of neonatal intensive care unit (NICU) and eases treatment of neonatal jaundice.

This work proposes a novel motion guided method for target-less self-calibration of a LiDAR and camera and use the re-projection of LiDAR points onto the image reference frame for real-time depth upsampling. The calibration parameters are estimated by optimizing an objective function that penalizes distances between 2D and re-projected 3D motion vectors obtained from time-synchronized image and point cloud sequences. For upsampling, a simple, yet effective and time efficient formulation that minimizes depth gradients subject to an equality constraint involving the LiDAR measurements is proposed. Validation is performed on recorded real data from urban environments and demonstrations that our two methods are effective and suitable to mobile robotics and autonomous vehicle applications imposing real-time requirements is shown.

Fluorescent pH biosensors have gained importance owing to their low cost utilization in real time monitoring of biological and food samples in comparison to conventional pH meters. The research reports a novel method of ultrasonic atomization for developing a fluorescent pH sensor for real-time analysis made of Fluorescein isothiocyanate (FITC)-dextran/FITC-dextran-Tris (2, 2′-bipyridyl) dichlororuthenium (II) hexahydrate as indicator and reference fluorophores, respectively. The process of ultrasonic atomization ensures formation of monodisperse dye immobilized alginate microspheres ensuring efficient pH sensing. The developed biosensor was tested on milk samples, which has a short life span and shows a significant fall in pH with time due to microbial spoilage. The proposed biosensor showed a linear range of pH 4–8 (R2 between 0.96–0.99 for different single/dual fluorophore biosensors) which suitably cover the pH of milk during the entire storage period and spoilage. The % recovery for predicted pH falls between 90–110% compared against standard pH meter, indicating a good accuracy of estimation and low turnaround time (10 min). Thus, real-time monitoring using fluorescent pH biosensor for milk samples may profoundly improve the economics of losses occurring in processing and storage with capability of in-package continuous quality assessment.

This paper proposes a framework for automatic classification of mobile laser scanner (MLS) point cloud using multi-faceted multi-object convolutional neural network (MMCN). The proposed method take...

Underwater imaging has long been focused on dehazing and color correction to address severe degradation in the water medium. In this paper, we propose a learning-based image restoration method that uses Generative Adversarial Networks (GAN). For network generality and learning flexibility, we constituted unpaired image translation frameworks into image restoration. The proposed method utilizes multiple cyclic consistency losses that capture image characteristics and details of underwater images. To prepare unpaired images of clean and degraded scenes, we collected images from Flickr and filtered out false images using image characteristics. For validation, we extensively evaluated the proposed network on simulated and real underwater hazy images. Also, we tested our method on conventional computer vision algorithms, such as the level of edges and feature matching results.

Introduction: Fast-dissolving oral delivery system is a solid dosage form, which is used as a novel approach, as it dissolves rapidly in the mouth and directly reaches the systemic circulation. This facilitates rapid absorption and reduces first-pass effects. These films provide better utilization without the need to chew and do not require the need of water for administration. Fast-dissolving oral films might be useful in special patients like those with swallowing difficulties and in that in chemotherapy. This study was aimed to formulate and evaluate the fast dissolving oral films of Cetirizine Hydrochloride. Methods: Oral films of Cetirizine Hydrochloride were prepared using Hydroxy Propyl Methyl Cellulose (HPMC), Polyvinyl alcohol, glycerol, aspartame in different concentrations by the solvent casting method. Films were evaluated for different physical Films were subjected to USP dissolution studies. Results: The drug content of the nine formulations was found to be in the range as per Indian Pharmacopeia. In vitro release studies indicated 60-91% release within 30 min in the formulated oral films. The best release was found to be 91.58% of formulation F7, having a minimum concentration of HPMC alone as a polymer along with other common ingredients and minimum release was found to be 60.01% of formulation F1. Surface pH was found to be near to neutral (pH 7) which was good for buccal mucosa. Conclusion: Increasing the concentration of polymer (PVA and HPMC) while keeping the plasticizer ratio constant showed enhanced folding endurance. The dissolution rates were also increased with the increase in the concentration of the polymers. The high concentration of polymers decreased the release rate of cetirizine while a low concentration of polymers causes an increment in the release rate of cetirizine.

Summary: PDGFRα+ mesenchymal progenitor cells are associated with pathological fibro-adipogenic processes. Conversely, a beneficial role for these cells during homeostasis or in response to revascularization and regeneration stimuli is suggested, but remains to be defined. We studied the molecular profile and function of PDGFRα+ cells in order to understand the mechanisms underlying their role in fibrosis versus regeneration. We show that PDGFRα+ cells are essential for tissue revascularization and restructuring through injury-stimulated remodeling of stromal and vascular components, context-dependent clonal expansion, and ultimate removal of pro-fibrotic PDGFRα+-derived cells. Tissue ischemia modulates the PDGFRα+ phenotype toward cells capable of remodeling the extracellular matrix and inducing cell-cell and cell-matrix adhesion, likely favoring tissue repair. Conversely, pathological healing occurs if PDGFRα+-derived cells persist as terminally differentiated mesenchymal cells. These studies support a context-dependent “yin-yang” biology of tissue-resident mesenchymal progenitor cells, which possess an innate ability to limit injury expansion while also promoting fibrosis in an unfavorable environment. : Santini et al. show that progenitor PDGFRα+ cells residing in skeletal muscle are mesenchymal stromal cells with a dual function, which on the one hand can stabilize newly formed blood vessels and limit injury expansion after ischemia, but on the other hand are also capable of promoting fibrosis in an unfavorable environment. Keywords: platelet-derived growth factor receptor α, revascularization, regeneration, fibrosis, hindlimb ischemia, RNA sequencing, Brainbow

The role of intestinal-barrier in acute pancreatitis(AP) is poorly understood. We aimed to assess structural and functional changes in the intestinal-barrier in patients with early AP (time from onset2 weeks) and the effect of enteral nutrition on them.In this prospective observational study, patients with early AP not on enteral nutrition were compared with controls for baseline intestinal-permeability(lactulose: mannitol ratio(L:M)), endotoxinemia(serum IgM/IgG anti-endotoxin antibodies), bacterial-translocation(serum bacterial 16S rRNA) and duodenal epithelial tight-junction structure by immunohistochemistry(IHC) for tight-junction proteins(claudin-2,-3,-4, zonula occludens-1(ZO1), junctional adhesion molecule(JAM) and occludin) and electron microscopy. These parameters were reassessed after 2 weeks enteral feeding in a AP patients subset.96 patients with AP(age: 38.0 ± 14.5 years; etiology: biliary[46.8%]/alcohol[39.6%]; severe:53.2%, mortality:11.4%) and 40 matched controls were recruited. Patients with AP had higher baseline intestinal permeability(median L:M 0.176(IQR 0.073-0.376) vs 0.049(0.024-0.075) in controls; p 0.001) and more frequent bacteraemia(positive bacterial 16S rRNA in 24/48 AP vs 0/21 controls; p 0.001) with trend towards higher serum endotoxinemia(median IgG anti-endotoxin 78(51.2-171.6) GMU/ml vs 51.2(26.16-79.2) in controls; p = 0.061). Claudin-2, claudin-3, ZO1 were downregulated in both duodenal crypts and villi while claudin-4 and JAM were downregulated in duodenal villi and crypts respectively. 22 AP patients reassessed after initiation of enteral nutrition showed trend towards improving intestinal permeability, serum endotoxinemia and bacteraemia, with significant improvement in claudin-2,-3 in duodenal villi.Patients with AP have significant disturbances in intestinal barrier structure and function in first 2 weeks from onset that persist despite institution of enteral nutrition.

Audio and speech have several implicit characteristics that have the potential for the identification and quantification of clinical disorders. This PRISMA-guided review is designed to provide an overview of the landscape of automated clinical audio processing to build data-driven predictive models and infer phenotypes of a variety of neuropsychiatric, cardiac, respiratory and other disorders. We detail the important components of this processing workflow, specifically data acquisition and processing, algorithms used and their customization for clinical applications, commonly used tools and software, and benchmarking and evaluation methodologies. Finally, we discuss important open challenges for the field, and potential strategies for addressing them.

Abstract: Nanomaterials due to their small size have high surface area and catalytic activity and therefore find applications in forensic sample analysis. Surface engineering of these nanomaterials enhance their stability, chemical reactivity, specificity and sensitivity towards other analytes. Covalent bonds, hydrogen bonds and electrostatic interaction play a major role in attaching functional groups on the surface of various nanomaterials. Over the years, several SENMs have been developed for the analysis of forensic science samples such as fingerprints, unlawful drugs, explosives, doping agents, chemical and biological warfare agents. These SENMs have been functionalized with a myriad of functionalization agents such as polymers, organosilanes, acids or alkalis and compounds of biological origin with different strategies. In this paper, the different surface engineering strategies of nanomaterials, the applications of these nanomaterials in forensic science and the mechanism behind their detection of forensic analytes have been discussed. The challenges for using SENMs for forensic applications have also been elaborated.

Cannabis is one of the most widely used substances in the world, with usage trending upward in recent years. However, although the psychiatric burden associated with maladaptive cannabis use has been well established, reliable and interpretable biomarkers associated with chronic use remain elusive. In this study, we combine large-scale functional magnetic resonance imaging with machine learning and network analysis and develop an interpretable decoding model that offers both accurate prediction and novel insights into chronic cannabis use.Chronic cannabis users (n = 166) and nonusing healthy control subjects (n = 124) completed a cue-elicited craving task during functional magnetic resonance imaging. Linear machine learning methods were used to classify individuals into chronic users and nonusers based on whole-brain functional connectivity. Network analysis was used to identify the most predictive regions and communities.We obtained high (∼80% out-of-sample) accuracy across 4 different classification models, demonstrating that task-evoked connectivity can successfully differentiate chronic cannabis users from nonusers. We also identified key predictive regions implicating motor, sensory, attention, and craving-related areas, as well as a core set of brain networks that contributed to successful classification. The most predictive networks also strongly correlated with cannabis craving within the chronic user group.This novel approach produced a neural signature of chronic cannabis use that is both accurate in terms of out-of-sample prediction and interpretable in terms of predictive networks and their relation to cannabis craving.