IDENTIFICATION OF NOVEL SLEEP RELATED GENES FROM LARGE SCALE PHENOTYPING EXPERIMENTS IN MICE

Humans spend a third of their lives sleeping but very little is known about the physiological and genetic mechanisms controlling sleep. Increased data from sleep phenotyping studies in mouse and other species, genetic crosses, and gene expression databases can all help improve our understanding of the process. Here, we present analysis of our own sleep data from the large-scale phenotyping program at The Jackson Laboratory (JAX), to identify the best gene candidates and phenotype predictors for influencing sleep traits. The original knockout mouse project (KOMP) was a worldwide collaborative effort to produce embryonic stem (ES) cell lines with one of mouse’s 21,000 protein coding genes knocked out. The objective of KOMP2 is to phenotype as many as of these lines as feasible, with each mouse studied over a ten-week period (www.mousephenotype.org). The phenotyping for sleep behavior is done using our non-invasive Piezo system for mouse activity monitoring. Thus far, sleep behavior has been recorded in more than 6000 mice representing 343 knockout lines and nearly 2000 control mice. Control and KO mice have been compared using multivariate statistical approaches to identify genes that exhibit significant effects on sleep variables from Piezo data. Using these statistical approaches, significant genes affecting sleep have been identified. Genes affecting sleep in a specific sex and that specifically affect sleep during daytime and/or night have also been identified and reported. The KOMP2 consists of a broad-based phenotyping pipeline that consists of collection of physiological and biochemical parameters through a variety of assays. Mice enter the pipeline at 4 weeks of age and leave at 18 weeks. Currently, the IMPC (International Mouse Phenotyping Consortium) database consists of more than 33 million observations. Our final dataset prepared by extracting biological sample data for whom sleep recordings are available consists of nearly 1.5 million observations from multitude of phenotyping assays. Through big data analytics and sophisticated machine learning approaches, we have been able to identify predictor phenotypes that affect sleep in mice. The phenotypes thus identified can play a key role in developing our understanding of mechanism of sleep regulation

Synthesis of Hybrid Inorganic-Organic Microparticles

The self-assembly of isotropic and anisotropic colloidal particles into higher-ordered structures has been of great interest recently due to the promise of creating metamaterials with novel macroscopic properties. The physicochemical properties of these metamaterials can be tailored to achieve composites with tunable functionalities. The formation of these metamaterials can be used as a pathway to emulating advanced biological systems. In particular, synthetically mimicking the surface of a moth’s eye, which consists of arrays of ellipsoidal protuberances, can be used as a strategy for fabricating antireflective coatings. To enable this technology, it is necessary to design a synthesis scheme that produces micron-sized composite particles with tunable refractive index. In the future, the resulting composite microparticles can then undergo geometric and spatial modifications to form self-assemblies that have unique macroscopic material properties. This research work delineates a strategy of developing microparticles with a hybrid configuration that constitutes an inorganic and an organic part. The inorganic part comprises ~30 nm diameter titania (TiO2) nanoparticles, which are embedded within an organic polymer particle comprised of diethyl methylene malonate polymer [p(DEMM)]. Anionic polymerization is modified to controllably incorporate TiO2 nanoparticles into the polymer matrix. A design of experiments was identified and carried out to identify the major process variables that influence the final particle size. In particular, since DEMM polymerization may be initiated entirely by the presence of hydroxyl anions, pH was found to control the final overall particle diameter between 300 nm and 1 micrometer. The overall inorganic particle loading can be readily modified and is confirmed by thermogravimetric analysis, allowing for the desired macroscopic refractive index to be controlled. Light scattering, scanning electron microscopy and zeta potential analysis reveals that the colloidal stability of the hybrid microparticles is dependent on the ligand coating the inorganic constituent. In addition, this synthetic scheme is applied to different inorganic constituents that have interesting functionalities, such as fluorescent CdTe quantum dots, in order to show the methods versatility method to produce composite particles for a wide spectrum of applications. These initial investigations provide a the synthetic groundwork to evaluating the coating properties of the microparticles and their self-assembly into novel materials in the future

GENERAL TREATMENT PROTOCOL OF POISONING AND TWENTY FOUR TREATMENT MODALITIES BY ACHARYA CHARAK - A REVIEW

Ayurveda has its own way of approach towards the management of Visha (Poison), Agadtantra is a special branch of Ashtang Ayurveda having its own importance in Visha Chikitsa. A general principle of treatment of poisoning is explained in Ayurvedic Samhitas (Treatises) like Charaka, Sushruta and Vagbhata. Acharya Charak has explained Twenty four modalities in the treatment of poisoning irrespective of animate or inanimate poisoning. Other scholars have stated specific treatment in the poisoning cases but still 24 modalities retains its importance. Toxicology also explains general treatment protocol for management of poisoning. The Protocol consists of vital establishment, removal of poison, use of antidote, general and psychiatric care of patient. But due to complexity of poisoning cases; General Treatment protocol becomes the guideline for the further management. When we compare both Ayurveda and modern medicine with respect to this aspect, we find that all these principles which are suggested by modern medicine are already described in Ayurvedic Samhitas before thousands of years. Chaturvimshati Upakrama (24 modalities) are like the treatment principle which directs the actual treatment regime in individual cases. General treatment protocol of poisoning is the modern replica of Acharya Charak’s Chaturvimshati Upakarama. A comparison of twenty four modalities with the general treatment protocol of poisoning is attempted in this review article

Identifying Candidate Genes for Variation in Sleep-Related Quantitative Traits

Background Humans spend approximately one third of their lives sleeping, but compared with other biological processes, most of the molecular and genetic aspects of sleep have not been elucidated. A non-existent gene ontology and lack of a dedicated database containing a comprehensive list of sleep-related genes and their function presents a hurdle for sleep researchers. Materials and methods Using a two-pronged approach to solve this problem, publicly available microarray data from NCBI GEO (National Center for Biotechnology Information – Gene Expression Omnibus) database was used to develop a list of sleep-related genes for traits of interest. The data were analyzed using R Bioconductor and custom Perl scripts. The genes from this list were then matched with the genes in QTL (Quantitative Trait Loci) for the trait. The genes within the QTL chromosomal region matching any in the list of sleep-related genes were considered as potential candidates for causing variations in the quantitative trait. Results Here we present the results for our study conducted for sleep deprivation (SD) using this approach. 227 genes were identified which showed significant differential expression after 3, 6, 9 and 12 hours of sleep deprivation in three mouse strains. We were able to identify 4 candidate genes in Dps1 QTL, 2 in Dps2, and 9 genes in Dps3. Dps loci are the QTL associated with delta power in slow wave sleep. The list also contains Homer1 which has already been established as a molecular correlate of sleep loss. The advantage with this approach is that it provides more information and cross support than a simple list of sleep-related candidate genes. The association of information about genes with their function and role in sleep can help in forming sleep-specific gene ontologies, which would be useful for sleep researchers

SD-Measure: A Social Distancing Detector

The practice of social distancing is imperative to curbing the spread of contagious diseases and has been globally adopted as a non-pharmaceutical prevention measure during the COVID-19 pandemic. This work proposes a novel framework named SD-Measure for detecting social distancing from video footages. The proposed framework leverages the Mask R-CNN deep neural network to detect people in a video frame. To consistently identify whether social distancing is practiced during the interaction between people, a centroid tracking algorithm is utilised to track the subjects over the course of the footage. With the aid of authentic algorithms for approximating the distance of people from the camera and between themselves, we determine whether the social distancing guidelines are being adhered to. The framework attained a high accuracy value in conjunction with a low false alarm rate when tested on Custom Video Footage Dataset (CVFD) and Custom Personal Images Dataset (CPID), where it manifested its effectiveness in determining whether social distancing guidelines were practiced.Comment: Contains 6 pages & 7 figures. Published in 12th CICN 202

Deep Learning Framework to Detect Face Masks from Video Footage

The use of facial masks in public spaces has become a social obligation since the wake of the COVID-19 global pandemic and the identification of facial masks can be imperative to ensure public safety. Detection of facial masks in video footages is a challenging task primarily due to the fact that the masks themselves behave as occlusions to face detection algorithms due to the absence of facial landmarks in the masked regions. In this work, we propose an approach for detecting facial masks in videos using deep learning. The proposed framework capitalizes on the MTCNN face detection model to identify the faces and their corresponding facial landmarks present in the video frame. These facial images and cues are then processed by a neoteric classifier that utilises the MobileNetV2 architecture as an object detector for identifying masked regions. The proposed framework was tested on a dataset which is a collection of videos capturing the movement of people in public spaces while complying with COVID-19 safety protocols. The proposed methodology demonstrated its effectiveness in detecting facial masks by achieving high precision, recall, and accuracy.Comment: Contains 6 pages and 6 figures. Published in 12th CICN 202

Air-Assisted Atomization at Constant Mass and Momentum Flow Rate: Investigation of the Ambient Pressure Influence with the SPH Method

A twin-fluid atomizer configuration is simulated by means of the 2D weakly-compressible Smooth Particle Hydrodynamics method, and compared to experiments. The Gas-to-Liquid-Ratio, the momentum flux ratio and the velocity ratio are set constant for different ambient pressures, which leads to different gaseous flow sections. The objectives of this study are to (i) investigate the effect of ambient pressure at constant global parameters, and (ii) to verify the capability of 2D SPH to qualitatively predict the proper disintegration mechanism and to recover the correct evolution of the spray characteristics. The setup consists of an axial liquid jet of water fragmented by a co-flowing high-speed air stream (Ug = 80 m/s) in a pressurized atmosphere up to 16 bar. The results are compared to the experiment, and presented in terms of (i) mean velocity profiles, (ii) drop size distributions and (iii) Sauter Mean Diameter of the spray. It is found that there exists an optimal pressure to minimize the mean size of the spray droplets. Finally, two new quantities related to atomization are presented: (i) the breakup activity that quantifies the number of breakup events per time and volume unit and (ii) the fragmentation spectrum of the whole breakup chain, which characterizes the cascade phenomenon in terms of probability. The breakup activity confirms the presence of the optimal pressure and the fragmentation spectrum gives information on the type of breakup, depending on the ambient pressure