Characterization and prediction of clinical pathways of vulnerability to psychosis through graph signal processing

Causal interactions between specific psychiatric symptoms could contribute to the heterogenous clinical trajectories observed in early psychopathology. Current diagnostic approaches merge clinical manifestations that co-occur across subjects and could significantly hinder our understanding of clinical pathways connecting individual symptoms. Network analysis techniques have emerged as alternative approaches that could help shed light on the complex dynamics of early psychopathology. The present study attempts to address the two main limitations that have in our opinion hindered the application of network approaches in the clinical setting. Firstly, we show that a multi-layer network analysis approach, can move beyond a static view of psychopathology, by providing an intuitive characterization of the role of specific symptoms in contributing to clinical trajectories over time. Secondly, we show that a Graph-Signal-Processing approach, can exploit knowledge of longitudinal interactions between symptoms, to predict clinical trajectories at the level of the individual. We test our approaches in two independent samples of individuals with genetic and clinical vulnerability for developing psychosis. Novel network approaches can allow to embrace the dynamic complexity of early psychopathology and help pave the way towards a more a personalized approach to clinical care

Rational truncation of aptamer for cross-species application to detect krait envenomation

Abstract In majority of snakebite cases, the snake responsible for the bite remains unidentified. The traditional snakebite diagnostics method relies upon clinical symptoms and blood coagulation assays that do not provide accurate diagnosis which is important for epidemiological as well as diagnostics point of view. On the other hand, high batch-to-batch variations in antibody performance limit its application for diagnostic assays. In recent years, nucleic acid aptamers have emerged as a strong chemical rival of antibodies due to several obvious advantages, including but not limited to in vitro generation, synthetic nature, ease of functionalization, high stability and adaptability to various diagnostic formats. In the current study, we have rationally truncated an aptamer developed for α-Toxin of Bungarus multicinctus and demonstrated its utility for the detection of venom of Bungarus caeruleus. The truncated aptamer α-Tox-T2 (26mer) is found to have greater affinity than its 40-mer parent counterpart α-Tox-FL. The truncated aptamers are characterized and compared with parent aptamer for their binding, selectivity, affinity, alteration in secondary structure and limit of detection. Altogether, our findings establish the cross-species application of a DNA aptamer generated for α-Toxin of Bungarus multicinctus (a snake found in Taiwan and China) for the reliable detection of venom of Bungarus caeruleus (a snake found in the Indian subcontinent)

Access to Personal Transportation for People with Disabilities with Autonomous Vehicles

The objective of this paper was to explore the potential of emerging technology of autonomous vehicles in accessible transportation and incorporate these findings a standardized transportation solution that readily accommodates future travelers with disabilities based on careful study on current trends in accessible transportation and interviews and surveys that were conducted as a part of this effort. The suggested solution and design principles associated with it took in account, the popular opinions of people with disabilities as well as various experts in the field of accessible transportation. The presented solution is based on emerging technology that is being actively pursued by the automotive industry and research institutions and seriously being considered through current and pending state legislation as a viable product in the near future. This paper explores the legal, technical and safety obstacles that lay in the path to making this a reality

Mapping India's Energy Policy 2022

Carefully designed energy support measures—subsidies, public utilities' investments, and public finance institutions' lending—and government's energy revenues play a key role in India's transition to clean energy and reaching net-zero emissions by 2070. Looking at how the Government of India has supported different types of energy from FY 2014 to FY 2021, the study aims to improve transparency, create accountability, and encourage a responsible shift in support away from fossil fuels and toward clean energy.Mapping India's Energy Subsidies 2022 covers India's subsidies to fossil fuels, electricity transmission and distribution, renewable energy, and electric vehicles between fiscal year (FY) 2014 and FY 2021.We found that fossil fuels continue to receive far more subsidies than clean energy in India. This disparity became even more pronounced from FY 2020 to FY 2021, going from 7.3 times to 9 times the amount of subsidies to renewables

CRISPR-Cas9: Role in Processing of Modular Metabolic Engineered Bio-Based Products

Biogenetic engineering is a significant technology to sensibly manage microbial metabolic product factories. Genome modification methods for efficiently controlling and modifying genes at the genome level have progressed in biogenetic engineering during the last decade. CRISPR is genome editing technology that allows for the modification of organisms’ genomes. CRISPR and its related RNA-guided endonuclease are versatile advanced immune system frameworks for defending against foreign DNA and RNAs. CRISPR is efficient, accessible, and trustworthy genomic modification tool in unparalleled resolution. At present, CRISPR-Cas9 method is expanded to industrially manipulate cells. Metabolically modified organisms are quickly becoming interested in the production of different bio-based components. Here, chapter explore about the control productivity of targeted biomolecules in divergent cells based on the use of different CRISPR-related Cas9

Large-scale functional network dynamics in human callosal agenesis:Increased subcortical involvement and preserved laterality

In the human brain, the corpus callosum is the major white-matter commissural tract enabling the transmission of sensory-motor, and higher level cognitive information between homotopic regions of the two cerebral hemispheres. Despite developmental absence (i.e., agenesis) of the corpus callosum (AgCC), functional connectivity is preserved, including interhemispheric connectivity. Subcortical structures have been hypothesised to provide alternative pathways to enable this preservation. To test this hypothesis, we used functional Magnetic Resonance Imaging (fMRI) recordings in children with AgCC and typically developing children, and a time-resolved approach to retrieve temporal characteristics of whole-brain functional networks. We observed an increased engagement of the cerebellum and amygdala/hippocampus networks in children with AgCC compared to typically developing children. There was little evidence that laterality of activation networks was affected in AgCC. Our findings support the hypothesis that subcortical structures play an essential role in the functional reconfiguration of the brain in the absence of a corpus callosum

Characterization and prediction of clinical pathways of vulnerability to psychosis through graph signal processing

Causal interactions between specific psychiatric symptoms could contribute to the heterogenous clinical trajectories observed in early psychopathology. Current diagnostic approaches merge clinical manifestations that co-occur across subjects and could significantly hinder our understanding of clinical pathways connecting individual symptoms. Network analysis techniques have emerged as alternative approaches that could help shed light on the complex dynamics of early psychopathology. The present study attempts to address the two main limitations that have in our opinion hindered the application of network approaches in the clinical setting. Firstly, we show that a multi-layer network analysis approach, can move beyond a static view of psychopathology, by providing an intuitive characterization of the role of specific symptoms in contributing to clinical trajectories over time. Secondly, we show that a Graph-Signal-Processing approach, can exploit knowledge of longitudinal interactions between symptoms, to predict clinical trajectories at the level of the individual. We test our approaches in two independent samples of individuals with genetic and clinical vulnerability for developing psychosis. Novel network approaches can allow to embrace the dynamic complexity of early psychopathology and help pave the way towards a more a personalized approach to clinical care

Exploring dynamic functional connectivity by incorporating prior knowledge of brain structure

The synchronized firing of distant neuronal populations gives rise to a wide array of functional brain networks that underlie human brain function. Given the enormous perception, learning, and cognition potential of the human brain, it is not surprising that network-level representations of brain function reveal intrinsically rich organizational structure. What remains puzzling is how the human brain maintains its vast repertoire of functional connectivity (FC) states despite being constrained by the underlying fixed anatomical substrate. Advances in modern neuroimaging technologies, such as diffusion-weighted magnetic resonance imaging (DW-MRI), have made it possible to map the brain's anatomical scaffold, while functional MRI (fMRI) provides complementary information on neural activity. In this thesis, we develop and apply methods for combining DW-MRI and fMRI data into an integrated framework to analyze the interplay between brain structure and function. We first explored the dynamics of brain function during wakefulness and across the different non-rapid eye movement (NREM) sleep stages. We applied the innovation-driven co-activation pattern (iCAP) analysis to uncover the spatial and temporal organization of overlapping large-scale brain networks. Our results reveal new spatial patterns covering regions that support the physiological organization of sleep and arousal. Contrary to the previously observed decreasing FC that accompanies increasing sleep depth, we instead observe a surge of network activity and cross-network interactions during NREM stage 2, followed by an abrupt decrease in NREM stage 3. In the next step of the thesis, we propose a new method that models all brain voxels as nodes of a high-resolution voxel-level brain graph. We provide two ways to construct the graph and we characterize their properties by performing a spectral analysis of the graph Laplacian operator. Our findings show that despite the huge dimensionality of the proposed brain graphs, the majority of the structural information is captured by the Laplacian's lowest frequency eigenmodes. The lower end of the Laplacian spectra also captures about 85% of the energy content of functional MRI, suggesting that functional patterns are overall smooth over the structure, thus providing, for the first time, a direct and quantitative measure of how much brain function is shaped by the anatomy. Going beyond a scalar measure of the SC-FC link, we introduce a new framework that interpolates gray matter signals onto the white matter using the structure embedded in the voxel-level brain grid to guide the process. This enables visualization of key white matter structures that link temporally coherent gray matter areas. We found whole-brain structure-function networks that extend currently known spatial patterns that are limited within the gray matter only. Finally, we assessed the collective mediation of white matter pathways by giving a quantitative measure of the overall anatomical range between temporally coherent gray matter areas. We utilized a canonical model of graph diffusion to extract the anatomical range of functional network interactions. We find that this measure meaningfully differentiates brain regions according to a behaviorally relevant macroscale gradient that divides the cortex between low-level primary sensory areas and high-level cognitive functions

Naphthalene derivatives as fluorescent probe

850-853The electronic and fluorescence spectral properties of several synthesized naphthalene derivatives have been examined in acetone and in an anionic micelle. The change in fluorescence emission process of several naphthalene compounds have been compared with their parent moiety after hydroxylation, acetylation and with the increase in their skeletal rigidity. This report supplements the first hand information to choose naphthalene compounds as probe molecules in organic solvents as well as organized medium