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Multi-omics studies have enabled us to understand the mechanistic drivers behind complex disease states and progressions, thereby providing novel and actionable biological insights into health status. However, integrating data from multiple modalities is challenging due to high dimensionality and diverse nature of data, and noise associated with each platform. Sparsity in data, non-overlapping features and technical batch effects make the task of learning more complicated. Conventional machine learning (ML) tools are not quite effective against such data integration hazards due to their simplistic nature with less capacity. In addition, existing methods for single cell multi-omics integration are computationally expensive. Therefore, in this work, we have introduced a novel Unsupervised neural network for single cell Multi-omics INTegration (UMINT). UMINT serves as a promising model for integrating variable number of single cell omics layers with high dimensions. It has a light-weight architecture with substantially reduced number of parameters. The proposed model is capable of learning a latent low-dimensional embedding that can extract useful features from the data facilitating further downstream analyses. UMINT has been applied to integrate healthy and disease CITE-seq (paired RNA and surface proteins) datasets including a rare disease Mucosa-Associated Lymphoid Tissue (MALT) tumor. It has been benchmarked against existing state-of-the-art methods for single cell multi-omics integration. Furthermore, UMINT is capable of integrating paired single cell gene expression and ATAC-seq (Transposase-Accessible Chromatin) assays as well.

Multi-omics studies have enabled us to understand the mechanistic drivers behind complex disease states and progressions, thereby providing novel and actionable biological insights into health status. However, integrating data from multiple modalities is challenging due to the high dimensionality of data and noise associated with each platform. Non-overlapping features and technical batch effects in the data make the task of learning more complicated. Conventional machine learning (ML) tools are not quite effective against such data integration hazards. In addition, existing methods for single cell multi-omics integration are computationally expensive. This has encouraged the development of a novel architecture that produces a robust model for integration of high-dimensional multi-omics data, which would be capable of learning meaningful features for further downstream analysis. In this work, we have introduced a novel Unsupervised neural network for single cell Multi-omics INTegration (UMINT). UMINT serves as a promising model for integrating variable number of single cell omics layers with high dimensions, and provides substantial reduction in the number of parameters. It is capable of learning a latent low-dimensional embedding that can capture useful data characteristics. The effectiveness of UMINT has been evaluated on benchmark CITE-seq (paired RNA and surface proteins) datasets. It has outperformed existing state-of-the-art methods for multi-omics integration.

Gene expression analysis plays a significant role for providing molecular insights in cancer. Various genetic and epigenetic factors (being dealt under multi-omics) affect gene expression giving rise to cancer phenotypes. A recent growth in understanding of multi-omics seems to provide a resource for integration in interdisciplinary biology since they altogether can draw the comprehensive picture of an organism's developmental and disease biology in cancers. Such large scale multi-omics data can be obtained from public consortium like The Cancer Genome Atlas (TCGA) and several other platforms. Integrating these multi-omics data from varied platforms is still challenging due to high noise and sensitivity of the platforms used. Currently, a robust integrative predictive model to estimate gene expression from these genetic and epigenetic data is lacking. In this study, we have developed a deep learning-based predictive model using Deep Denoising Auto-encoder (DDAE) and Multi-layer Perceptron (MLP) that can quantitatively capture how genetic and epigenetic alterations correlate with directionality of gene expression for liver hepatocellular carcinoma (LIHC). The DDAE used in the study has been trained to extract significant features from the input omics data to estimate the gene expression. These features have then been used for back-propagation learning by the multilayer perceptron for the task of regression and classification. We have benchmarked the proposed model against state-of-the-art regression models. Finally, the deep learning-based integration model has been evaluated for its disease classification capability, where an accuracy of 95.1% has been obtained.

The Polycomb Repressive Complex 2 (PRC2) plays important roles in the epigenetic regulation of cellular development and differentiation through H3K27me3-dependent transcriptional repression. Aberrant PRC2 activity has been associated with cancer and neurodevelopmental disorders, particularly with respect to the malfunction of sits catalytic subunit EZH2. Here, we investigated the role of the EZH2-mediated H3K27me3 apposition in neuronal differentiation. We made use of a transgenic mouse model harboring Ezh2 conditional KO alleles to derive embryonic stem cells and differentiate them into glutamatergic neurons. Time course transcriptomics and epigenomic analyses of H3K27me3 in absence of EZH2 revealed a significant dysregulation of molecular networks affecting the glutamatergic differentiation trajectory that resulted in: (i) the deregulation of transcriptional circuitries related to neuronal differentiation and synaptic plasticity, in particular LTD, as a direct effect of EZH2 loss and (ii) the appearance of a GABAergic gene expression signature during glutamatergic neuron differentiation. These results expand the knowledge about the molecular pathways targeted by Polycomb during glutamatergic neuron differentiation.

BackgroundDietary high fructose (HFr) is a known metabolic disruptor contributing to development of obesity and diabetes in Western societies. Initial molecular changes from exposure to HFr on liver metabolism may be essential to understand the perturbations leading to insulin resistance and abnormalities in lipid and carbohydrate metabolism. We studied vervet monkeys (Clorocebus aethiops sabaeus) fed a HFr (n=5) or chow diet (n=5) for 6 weeks, and obtained clinical measures of liver function, blood insulin, cholesterol and triglycerides. In addition, we performed untargeted global transcriptomics, proteomics, and metabolomics analyses on liver biopsies to determine the molecular impact of a HFr diet on coordinated pathways and networks that differed by diet.ResultsWe show that integration of omics data sets improved statistical significance for some pathways and networks, and decreased significance for others, suggesting that multiple omics datasets enhance confidence in relevant pathway and network identification. Specifically, we found that sirtuin signaling and a peroxisome proliferator activated receptor alpha (PPARA) regulatory network were significantly altered in hepatic response to HFr. Integration of metabolomics and miRNAs data further strengthened our findings.ConclusionsOur integrated analysis of three types of omics data with pathway and regulatory network analysis demonstrates the usefulness of this approach for discovery of molecular networks central to a biological response. In addition, metabolites aspartic acid and docosahexaenoic acid (DHA), protein ATG3, and genesATG7, HMGCS2link sirtuin signaling and the PPARA network suggesting molecular mechanisms for altered hepatic gluconeogenesis from consumption of a HFr diet.

Among many smart materials, piezoelectric materials have emerged as the most studied ones for practical applications. They owe their success to several factors, including low price, high bandwidth, availability in various formats, and ease of handling and implementation. The present study focused on the performance of piezoelectric laminated composite plate under various electromechanical loading conditions by utilizing the first-order shear deformation theory with the Newton–Raphson residual and iteration with Gauss integration point in Ansys. For the first time, the effects of electrical loading, circuit arrangement, voltage variation, and polynomial variable transverse loading are studied over piezoelectric composite plate (PCP). The effects of plate aspect ratio, thickness ratio, boundary conditions, ply orientations, nature of loading conditions, and voltage variation are presented. The study also utilized open and close circuit arrangements as sensors and actuators to gauge the performance of PCP in the form of static bending analysis. The maximum OC (open circuit) output voltage is generated with the N4 type of loading compared with N i ( i = 1–4); on the other hand, the OC output voltage is minimum with N5. The combined effect of external load and voltage presented in the study will be useful for analyzing the deflection variation, and it can further be implemented in reducing deflection or vibration. It is noted that, with a higher piezoelectric to laminate thickness (t/h) ratio, the maximum OC output voltage is observed. In addition, the rate of voltage generation observed is the highest under the N4 loading condition and the lowest under the N5 type of load.

The present study used untargeted metabolomics to investigate the short-term metabolic changes induced in wheat seedlings by the specialized metabolite umbelliferone, an allelochemical. We used 10 day-old wheat seedlings treated with 104 μM umbelliferone over a time course experiment covering 6 time points (0 h, 6 h, 12 h, 24 h, 48 h, and 96 h), and compared the metabolomic changes to control (mock-treated) plants. Using gas chromatography mass spectrometry (GCMS)-based metabolomics, we obtained quantitative data on 177 metabolites that were derivatized (either derivatized singly or multiple times) or not, representing 139 non-redundant (unique) metabolites. Of these 139 metabolites, 118 were associated with a unique Human Metabolome Database (HMDB) identifier, while 113 were associated with a Kyoto Encyclopedia of Genes and Genomes (KEGG) identifier. Relative quantification of these metabolites across the time-course of umbelliferone treatment revealed 22 compounds (sugars, fatty acids, secondary metabolites, organic acids, and amino acids) that changed significantly (repeated measures ANOVA, P-value < 0.05) over time. Using multivariate partial least squares discriminant analysis (PLS-DA), we showed the grouping of samples based on time-course across the control and umbelliferone-treated plants, whereas the metabolite-metabolite Pearson correlations revealed tightly formed clusters of umbelliferone-derived metabolites, fatty acids, amino acids, and carbohydrates. Also, the time-course umbelliferone treatment revealed that phospho-l-serine, maltose, and dehydroquinic acid were the top three metabolites showing highest importance in discrimination among the time-points. Overall, the biochemical changes converge towards a mechanistic explanation of the plant metabolic responses induced by umbelliferone. In particular, the perturbation of metabolites involved in tryptophan metabolism, as well as the imbalance of the shikimate pathways, which are strictly interconnected, were significantly altered by the treatment, suggesting a possible mechanism of action of this natural compound.

The research on heterometallic 3d-4f polyoxometalates has made immense progress over the last decade with a larger number of structures being reported every year until the present day. The formation of 3d-4f POMs has been considered a challenge as both the 3d and 4f ions compete for coordination to the lacunary POMs ligands. It was seen that mostly amorphous precipitate was obtained instead of crystalline material. Hence, as a result, the synthetic parameters such as pH, temperature, solvent, time of reaction has to be precisely controlled in order to isolate the desired product. This review focuses on the heterometallic 3d-4f polyoxometalates reported in the last decade mainly after 2011, when the field of 3d-4f heterometallic polyoxometalates was considered an incipient field. The structural variety exhibited by this kind is diverse with a large number of structures including dimers, trimers, tetramers which can be divided mainly into four types of polyanions. These are pure 3d-4f polyanions, 3d substituted anions with 4f linkers, 4f substituted anions with 3d linkers, and POMs with both 3d and 4f as linkers. The reason for the desire to create such complexes is mainly due to the interesting magnetic properties exhibited by them. Introduction of 4f ions into the paramagnetic 3d cluster enhances the already present magnetic properties and allows the formation of single-molecule/ion magnets (SMMs or SIMs). Many of the complexes show slow magnetic relaxation which is ideal for SMMs/SIMs. A few of the complexes displaying such properties are discussed in the review. Apart from that, the catalytic properties exhibited by such 3d-4f complexes have also been discussed.

This research paper presents path following two wheeled compact portable robot with arduino nano as cental driving functional unit with novel features of wireless control using wifi and bluetooth module with collision detection, avoidance and control features which provides the unique ability of danger avoidance, falling from a hieght with improved stablity and precision control. The extremely sophistcated design provides very good controlled movement on horizantal ground terrain surfaces with data collecting and processing capabilties. The design is integrated with infrared sensors, bluetooth module, wifi module control with dc gear motors which controls the speed of the vehicle of the robotic vehicle and avoid collision with any obstacle detected in the path of the robot. It has the unique abilty of running in maze with path following abilties controlled from any remote location using WiFi for long range control and bluetooth for short range control. In this research article a entire system is designed and implemented in which movement is stably controlled based on feedback from infrared transreciever module. A low cost robust portable design using GUI control has been implemented with advanced features which makes it very unique and attractive for commercial production.