Non-viral delivery and optimized optogenetic stimulation of retinal ganglion cells led to behavioral restoration of vision

Stimulation of retinal neurons using optogenetics via use of chanelrhodopsin-2 (ChR2) has opened up a new direction for restoration of vision for treatment of retinitis pigmentosa (RP). Here, we report non-viral in-vivo electroporation of degenerated retina of adult RP-mice with ChR2-plasmids and subsequent in-vivo imaging of retina to confirm expression. Further, we demonstrate that in addition to efficient non-viral delivery of ChR2 to a specific retinal layer, threshold level of stimulation light needs to be delivered onto the retina for achieving successful behavioral outcome. Measurement of intensity of light reaching the retina of RP-mouse models along with geometrical optics simulation of light propagation in the eye is reported in order to determine the stimulating source position for optimal light delivery to the retina. The light-guided navigation of mice with ChR2 expressing retinal ganglion cells was found to be significantly improved over a long distance in correlation with stimulation intensity

Statistical approaches of gene set analysis with quantitative trait loci for high-throughput genomic studies.

Recently, gene set analysis has become the first choice for gaining insights into the underlying complex biology of diseases through high-throughput genomic studies, such as Microarrays, bulk RNA-Sequencing, single cell RNA-Sequencing, etc. It also reduces the complexity of statistical analysis and enhances the explanatory power of the obtained results. Further, the statistical structure and steps common to these approaches have not yet been comprehensively discussed, which limits their utility. Hence, a comprehensive overview of the available gene set analysis approaches used for different high-throughput genomic studies is provided. The analysis of gene sets is usually carried out based on gene ontology terms, known biological pathways, etc., which may not establish any formal relation between genotype and trait specific phenotype. Further, in plant biology and breeding, gene set analysis with trait specific Quantitative Trait Loci data are considered to be a great source for biological knowledge discovery. Therefore, innovative statistical approaches are developed for analyzing, and interpreting gene expression data from Microarrays, RNA-sequencing studies in the context of gene sets with trait specific Quantitative Trait Loci. The utility of the developed approaches is studied on multiple real gene expression datasets obtained from various Microarrays and RNA-sequencing studies. The selection of gene sets through differential expression analysis is the primary step of gene set analysis, and which can be achieved through using gene selection methods. The existing methods for such analysis in high-throughput studies, such as Microarrays, RNA-sequencing studies, suffer from serious limitations. For instance, in Microarrays, most of the available methods are either based on relevancy or redundancy measures. Through these methods, the ranking of genes is done on single Microarray expression data, which leads to the selection of spuriously associated, and redundant gene sets. Therefore, newer, and innovative differential expression analytical methods have been developed for Microarrays, and single-cell RNA-sequencing studies for identification of gene sets to successfully carry out the gene set and other downstream analyses. Furthermore, several methods specifically designed for single-cell data have been developed in the literature for the differential expression analysis. To provide guidance on choosing an appropriate tool or developing a new one, it is necessary to review the performance of the existing methods. Hence, a comprehensive overview, classification, and comparative study of the available single-cell methods is hereby undertaken to study their unique features, underlying statistical models and their shortcomings on real applications. Moreover, to address one of the shortcomings (i.e., higher dropout events due to lower cell capture rates), an improved statistical method for downstream analysis of single-cell data has been developed. From the users’ point of view, the different developed statistical methods are implemented in various software tools and made publicly available. These methods and tools will help the experimental biologists and genome researchers to analyze their experimental data more objectively and efficiently. Moreover, the limitations and shortcomings of the available methods are reported in this study, and these need to be addressed by statisticians and biologists collectively to develop efficient approaches. These new approaches will be able to analyze high-throughput genomic data more efficiently to better understand the biological systems and increase the specificity, sensitivity, utility, and relevance of high-throughput genomic studies

Multi-Label ECG Classification using Temporal Convolutional Neural Network

Automated analysis of 12-lead electrocardiogram (ECG) plays a crucial role in the early screening and management of cardiovascular diseases (CVDs). In practice, it is common to see multiple co-occurring cardiac disorders, i.e., multi-label or multimorbidity in patients with CVDs, which increases the risk for mortality. Most current research focuses on the single-label ECG classification, i.e., each ECG record corresponds to one cardiac disorder, ignoring ECG records with multi-label phenomenon. In this paper, we propose an ensemble of attention-based temporal convolutional neural network (ATCNN) models for the multi-label classification of 12-lead ECG records. Specifically, a set of ATCNN-based single-lead binary classifiers are trained one for each cardiac disorder, and the predictions from these classifiers with simple thresholding generate the final multi-label decisions. The ATCNN contains a stack of TCNN layers followed by temporal and spatial attention layers. The TCNN layers operate at different dilation rates to represent the multi-scaled pathological ECG features dynamics, and attention layers emphasize/reduce the diagnostically relevant/redundant 12-lead ECG information. The proposed framework is evaluated on the PTBXL-2020 dataset and achieved an average F1-score of 76.51%. Moreover, the spatial and temporal attention weights visualization provides the optimal ECG-lead subset selection for each disease and model interpretability results, respectively. The improved performance and interpretability of the proposed approach demonstrate its ability to screen multimorbidity patients and help clinicians initiate timely treatment.Comment: Under review for publication in the IEEE Journal (8 pages, 6 figures

Evaluation of rice–legume–rice cropping system on grain yield, nutrient uptake, nitrogen fixation, and chemical, physical, and biological properties of soil

To achieve higher yields and better soil quality under rice–legume–rice (RLR) rotation in a rainfed production system, we formulated integrated nutrient management (INM) comprised of Azospirillum (Azo), Rhizobium (Rh), and phosphate-solubilizing bacteria (PSB) with phosphate rock (PR), compost, and muriate of potash (MOP). Performance of bacterial bioinoculants was evaluated by determining grain yield, nitrogenase activity, uptake and balance of N, P, and Zn, changes in water stability and distribution of soil aggregates, soil organic C and pH, fungal/bacterial biomass C ratio, casting activities of earthworms, and bacterial community composition using denaturing gradient gel electrophoresis (DGGE) fingerprinting. The performance comparison was made against the prevailing farmers’ nutrient management practices [N/P2O5/K2O at 40:20:20 kg ha−1 for rice and 20:30:20 kg ha−1 for legume as urea/single super-phosphate/MOP (urea/SSP/MOP)]. Cumulative grain yields of crops increased by 7–16% per RLR rotation and removal of N and P by six crops of 2 years rotation increased significantly (P < 0.05) in bacterial bioinoculants-based INM plots over that in compost alone or urea/SSP/MOP plots. Apparent loss of soil total N and P at 0–15 cm soil depth was minimum and apparent N gain at 15–30 cm depth was maximum in Azo/Rh plus PSB dual INM plots. Zinc uptake by rice crop and diethylenetriaminepentaacetate-extractable Zn content in soil increased significantly (P < 0.05) in bacterial bioinoculants-based INM plots compared to other nutrient management plots. Total organic C content in soil declined at 0–15 cm depth and increased at 15–30 cm depth in all nutrient management plots after a 2-year crop cycle; however, bacterial bioinoculants-based INM plots showed minimum loss and maximum gain of total organic C content in the corresponding soil depths. Water-stable aggregation and distribution of soil aggregates in 53–250- and 250–2,000 μm classes increased significantly (P < 0.05) in bacterial bioinoculants-based INM plots compared to other nutrient management plots. Fungal/bacterial biomass C ratio seems to be a more reliable indicator of C and N dynamics in acidic soils than total microbial biomass C. Compost alone or Azo/Rh plus PSB dual INM plots showed significantly (P < 0.05) higher numbers of earthworms’ casts compared to urea/SSP/MOP alone and bacterial bioinoculants with urea or SSP-applied plots. Hierarchical cluster analysis based on similarity matrix of DGGE profiles revealed changes in bacterial community composition in soils due to differences in nutrient management, and these changes were seen to occur according to the states of C and N dynamics in acidic soil under RLR rotation

FEEDBACK EQUALIZER FOR VEHICULAR CHANNEL

In this fast moving world, the number of fatal accidents is increasing day by day and this leads to the requirement of the availability of the traffic condition and road conditions related data to the users. Therefore, to support Vehicle-to-vehicle (V2V) communication in high speed mobility condition, it is required to have reliable and secure of communication. Here, the performance of multiple input and multiple output (MIMO) system as a combination of nonlinear decision feedback receiver (DFE) have been investigated in V2V channel. In this paper, through the simulation, the results are presented to show the effect of the channel correlation coefficient and Doppler shift (Fd) (because of the relative velocity of the vehicle) over the performance of the MIMO system. As a counter measure of those problems non-linear receivers have been formulated and analyzed