Synthesis, crystal structure analysis, computational modelling and evaluation of anti-cervical cancer activity of novel 1,5-dicyclooctyl thiocarbohydrazone.

Thiocarbazones are widely used as bioactive and pharmaceutical intermediates in medicinal chemistry and have been shown to exhibit diverse biological and pharmacological activities such as antimicrobial, anticancer, anti-viral, anti-convulsant and anti-inflammatory In continuation of our interest in biologically active heterocycles and in an attempt to synthesize a spiro derivative, 1,2,4,5-tetraazaspiro[5.7]tridecane-3-thione, herein, the synthesis of 1,5-dicyclooctyl thiocarbohydrazone (3) has been reported reaction of the cyclooctanone and thiocarbohydrazide. The structure was assigned on the basis of detailed spectral analysis and also confirmed by X-ray crystal studies.

Spectroscopic characterization, molecular docking and machine learning studies of sulphur containing hydrazide derivatives.

Machine learning applied in chemistry is a growing field of research. For assessing structure-property variations, this paper describes studies of the hydrazide derivatives of thiosemicarbazide (TSCZ) and thiocarbohydrazide (TCHZ). The structures of TSCZ and TCHZ have been elucidated using modern spectroscopic techniques.

Conv-CapsNet: capsule based network for COVID-19 detection through X-Ray scans.

Coronavirus, a virus that spread worldwide rapidly and was eventually declared a pandemic. The rapid spread made it essential to detect Coronavirus infected people to control the further spread. Recent studies show that radiological images such as X-Rays and CT scans provide essential information in detecting infection using deep learning models.

Incremental Trainable Parameter Selection in Deep Neural Networks.

This article explores the utilization of the effective degree-of-freedom (DoF) of a deep learning model to regularize its stochastic gradient descent (SGD)-based training. The effective DoF of a deep learning model is defined only by a subset of its total parameters. This subset is highly responsive or sensitive toward the training loss, and its cardinality can be used to govern the effective DoF of a model during training.

Dynamic Neural Graphs Based Federated Reptile for Semi-Supervised Multi-Tasking in Healthcare Applications.

AI healthcare applications rely on sensitive electronic healthcare records (EHRs) that are scarcely labelled and are often distributed across a network of the symbiont institutions. It is challenging to train the effective machine learning models on such data. In this work, we propose dynamic neural graphs based federated learning framework to address these challenges.

Data Pre-Processing Using Neural Processes for Modeling Personalized Vital-Sign Time-Series Data.

Clinical time-series data retrieved from electronic medical records are widely used to build predictive models of adverse events to support resource management. Such data is often sparse and irregularly-sampled, which makes it challenging to use many common machine learning methods. Missing values may be interpolated by carrying the last value forward, or through linear regression.

Deep Interpretable Early Warning System for the Detection of Clinical Deterioration.

Assessment of physiological instability preceding adverse events on hospital wards has been previously investigated through clinical early warning score systems. Early warning scores are simple to use yet they consider data as independent and identically distributed random variables. Deep learning applications are able to learn from sequential data, however they lack interpretability and are thus difficult to deploy in clinical settings.

Local compressed convex spectral embedding for bird species identification.

This paper proposes a multi-layer alternating sparse-dense framework for bird species identification. The framework takes audio recordings of bird vocalizations and produces compressed convex spectral embeddings (CCSE). Temporal and frequency modulations in bird vocalizations are ensnared by concatenating frames of the spectrogram, resulting in a high dimensional and highly sparse super-frame-based representation.

A simple and novel DNA combing methodology for Fiber-FISH and optical mapping.

Single molecule analysis can help us study genomics efficiently. It involves studying single DNA molecules for genomic studies. DNA combing is one of such techniques which allowed us to study single DNA molecules for multiple uses.

Enhanced post wash retention of combed DNA molecules by varying multiple combing parameters.

Recent advances in genomics have created a need for efficient techniques for deciphering information hidden in various genomes. Single molecule analysis is one such technique to understand molecular processes at single molecule level. Fiber- FISH performed with the help of DNA combing can help us in understanding genetic rearrangements and changes in genome at single DNA molecule level.

Diagnostic utility of serum and pleural fluid carcinoembryonic antigen, and cytokeratin 19 fragments in patients with effusion from nonsmall cell lung cancer.

Aims: To assess the diagnostic value of CEA and CYFRA 21-1 (cytokeratin 19 fragments) in serum and pleural fluid in non small cell lung cancer with malignant pleural effusion (MPE).

Settings And Design: Two subsets of patients were recruited with lymphocytic exudative effusion, one subset constituted diagnosed patients of NSCLC with malignant pleural effusion and the other subset of constituted with Tubercular pleural effusion.

Materials And Methods: CYFRA 21-1 and CEA levels were measured using Electrochemilumiscence Immunoassay (ECLIA).

Contemporary perspectives on spirituality and mental health.

The paper strives to elucidate the complex yet intimate relation between spirituality and mental health from contemporary perspectives. The diverse and constantly evolving views that spiritualists and mental health professionals have held toward each other over last century are discussed with special accent on the transpersonal spiritual framework within psychology. The role of spirituality in promoting mental health and alleviating mental illness is highlighted.