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Enormous gene expression data generated through next-generation sequencing (NGS) technologies are accessible to the scientific community via public repositories. The data harboured in these repositories are foundational for data integrative studies enabling large-scale data analysis whose potential is yet to be fully realized. Prudent integration of individual gene expression data i.e. RNA-Seq datasets is remarkably challenging as it encompasses an assortment and series of data analysis steps that requires to be accomplished before arriving at meaningful insights on biological interrogations. These insights are at all times latent within the data and are not usually revealed from the modest individual data analysis owing to the limited number of biological samples in individual studies. Nevertheless, a sensibly designed meta-analysis of select individual studies would not only maximize the sample size of the analysis but also significantly improves the statistical power of analysis thereby revealing the latent insights. In the present study, a custom-built meta-analysis pipeline is presented for the integration of multiple datasets from different origins. As a case study, we have tested with the integration of two relevant datasets pertaining to diabetic vasculopathy retrieved from the open source domain. We report the meta-analysis ameliorated distinctive and latent gene regulators of diabetic vasculopathy and uncovered a total of 975 i.e. 930 up-regulated and 45 down-regulated gene signatures. Further investigation revealed a subset of 14 DEGs including CTLA4, CALR, G0S2, CALCR, OMA1, and DNAJC3 as latent i.e. novel as these signatures have not been reported earlier. Moreover, downstream investigations including enrichment analysis, and protein-protein interaction (PPI) network analysis of DEGs revealed durable disease association signifying their potential as novel transcriptomic biomarkers of diabetic vasculopathy. While the meta-analysis of individual whole transcriptomic datasets for diabetic vasculopathy is exclusive to our comprehension, however, the novel meta-analysis pipeline could very well be extended to study the mechanistic links of DEGs in other disease conditions.

The dramatic increase in the number of single-cell RNA-sequence (scRNA-seq) investigations is indeed an endorsement of the new-fangled proficiencies of next generation sequencing technologies that facilitate the accurate measurement of tens of thousands of RNA expression levels at the cellular resolution. Nevertheless, missing values of RNA amplification persist and remain as a significant computational challenge, as these data omission induce further noise in their respective cellular data and ultimately impede downstream functional analysis of scRNA-seq data. Consequently, it turns imperative to develop robust and efficient scRNA-seq data imputation methods for improved downstream functional analysis outcomes. To overcome this adversity, we have designed an imputation framework namely deep generative autoencoder network [DGAN]. In essence, DGAN is an evolved variational autoencoder designed to robustly impute data dropouts in scRNA-seq data manifested as a sparse gene expression matrix. DGAN principally reckons count distribution, besides data sparsity utilizing a gaussian model whereby, cell dependencies are capitalized to detect and exclude outlier cells via imputation. When tested on five publicly available scRNA-seq data, DGAN outperformed every single baseline method paralleled, with respect to downstream functional analysis including cell data visualization, clustering, classification and differential expression analysis. DGAN is executed in Python and is accessible at https://github.com/dikshap11/DGAN .

In the last decade, transcriptome research adopting next-generation sequencing (NGS) technologies has gathered incredible momentum amongst functional genomics scientists, particularly amongst clinical/biomedical research groups. The progressive enfoldment/adoption of NGS technologies has incited an abundance of next-generation transcriptomic data harbouring an opulence of new knowledge in public databases. Nevertheless, knowledge discovery from these next-generation RNA-Seq. data analysis necessitates extensive bioinformatics know-how besides elaborate data analysis software packages consistent with the type and context of data analysis. Several reliability and reproducibility concerns continue to impede RNA-Seq. data analysis. Characteristic challenges comprise of data quality, hardware and networking provisions, selection and prioritisation of data analysis tools, and yet significantly implementing of robust machine learning algorithms for maximised exploitation of these experimental transcriptomic data. Over the years, numerous machine learning algorithms have been implemented for improved transcriptomic data analysis executing predominantly shallow learning approaches. More recently, deep learning algorithms are becoming more mainstream, and enactment for next-generation RNA-Seq. data analysis could be revolutionary in the coming years in the biomedical domain. In this scoping review, we attempt to determine the existing literature’s size and potential nature in deep learning and NGS RNA-Seq. data analysis. An analysis of the contemporary topics of next-generation RNA-Seq. data analysis based on deep learning algorithms is critically reviewed, emphasising open-source resources.

A scalable and facile regeneration route is utilized to recover the graphite from a spent lithium-ion battery (LIB). Eco-friendly organic acid is employed as a leaching-curing reagent for the present work. All the unwanted content of elements e.g. Ni, Co, Li, Cu and Al has been completely terminated from the graphite after the purification step without any additional calcination process. The optical, structural and electrochemical properties of as-reclaimed graphite have been studied by several analytical methods. Regenerated graphite is restored to its layered crystal structure along with expansion in the interlayer distance, and the same is confirmed from scanning electron microscopy and X-ray diffraction analysis respectively. Notably, high purity graphite is achieved and tested in its electrochemical storage property in supercapacitor (SC) applications. As an outcome, recreated graphite exhibits a maximum areal capacitance of 285 mF cm −2 at 5 mV s −1 . The fabricated symmetric SC demonstrates the superior energy storage performance in terms of durability and higher capacitance (131 mF cm −2 ) with better capacity retention over several cycles. It is worth mentioning that this curing process is a facile route, consumes lower energy and eco-friendly methodology and thereby may have futuristic extent for the bench scale reclamation of graphite from spent LIBs.

The present study aimed to evaluate the microplastic degradation efficiency of bacterial isolates collected from Vaigai River, Madurai, India. The isolates were processed with proper methods and incorporated in to the UV-treated polyethylene (PE) and polypropylene (PP) degradation. Based on preliminary screening, four bacterial isolates such as Bacillus sp. (BS-1), Bacillus cereus (BC), Bacillus sp. (BS-2), and Bacillus paramycoides (BP) were proceed to further degradation experiment for 21 days. The microplastics were filled with bacterial isolates which is use microplastic (PE, PP) as carbon source for their growth and proceed for shake flask experiment were carried out by two approaches with control. The microplastic degradation was confirmed through their weight loss, increasing fragmentations and changes of surface area against control experiments (microplastic without isolates) also confirms degrading efficiency of isolated bacterial strains through non-changes in their weight and surface area. The highest degradation of PP and PE were observed in BP (78.99 ± 0.005%), and BC (63.08 ± 0.009%) in single approach, while in combined approach BC & BP recorded the highest degradation in both PP (78.62 ± 2.16%), and PE (72.50 ± 20.53%). The formation of new functional groups is confirming the biofilm formation in the surface area of microplastics by isolates and proving their efficiency in degrade the microplastics. The degradation of microplastic experiments should be cost effective and zero waste which is helpful to save the environment and the present findings could reveal the way to degrade the microplastics and prevent the microplastic pollution in aquatic environment.

Safety is crucial in the railway industry because railways transport millions of passengers and employees daily, making it paramount to prevent injuries and fatalities. In order to guarantee passenger safety, computer vision, unmanned aerial vehicles (UAV), and artificial intelligence will be essential tools in the near future for routinely evaluating the railway environment. An unmanned aerial vehicle captured dataset for railroad segmentation and obstacle detection (UAV-RSOD) comprises high-resolution images captured by UAVs over various obstacles within railroad scenes, enabling automatic railroad extraction and obstacle detection. The dataset includes 315 raw images, along with 630 labeled and 630 masked images for railroad semantic segmentation. The dataset consists of 315 original images captured by the UAV for object detection and obstacle detection. To increase dataset diversity for training purposes, we applied data augmentation techniques, which expanded the dataset to 2002 augmented and annotated images for obstacle detection cover six different classes of obstacles on railroad lines. Additionally, we provide the original 315 images along with a script for augmentation, allowing users to generate their own augmented data as needed, offering a more sustainable and customizable option. Each image in the dataset is accurately annotated with bounding boxes and labeled under six categories, including person, boulder, barrel, branch, jerry can, and iron rod. This comprehensive classification and detailed annotation make the dataset an essential tool for researchers and developers working on computer vision applications in the railroad domain.

Purpose Bioprinting is a promising technology, which has gained a recent attention, for application in all aspects of human life and has specific advantages in different areas of medicines, especially in ophthalmology. The three-dimensional (3D) printing tools have been widely used in different applications, from surgical planning procedures to 3D models for certain highly delicate organs (such as: eye and heart). The purpose of this paper is to review the dedicated research efforts that so far have been made to highlight applications of 3D printing in the field of ophthalmology. Design/methodology/approach In this paper, the state-of-the-art review has been summarized for bioprinters, biomaterials and methodologies adopted to cure eye diseases. This paper starts with fundamental discussions and gradually leads toward the summary and future trends by covering almost all the research insights. For better understanding of the readers, various tables and figures have also been incorporated. Findings The usages of bioprinted surgical models have shown to be helpful in shortening the time of operation and decreasing the risk of donor, and hence, it could boost certain surgical effects. This demonstrates the wide use of bioprinting to design more precise biological research models for research in broader range of applications such as in generating blood vessels and cardiac tissue. Although bioprinting has not created a significant impact in ophthalmology, in recent times, these technologies could be helpful in treating several ocular disorders in the near future. Originality/value This review work emphasizes the understanding of 3D printing technologies, in the light of which these can be applied in ophthalmology to achieve successful treatment of eye diseases.

The influence of Ag doping on the SnO2 sensor operating at room temperature is reported in this work. The SnO2 and Ag-doped SnO2 films are deposited by the nebulizer spray pyrolysis method with different Ag (1, 2, 3, and 4 wt%) concentrations. The characterization of doped thin films was done using multiple advanced techniques to understand their crystallization, morphology, optical and electrical properties to find the optimized Ag concentration on the SnO2 surface. It was found that the doping of different concentrations into the SnO2 lattice increases the preferential orientation of the peak along (211) plane up to 3wt% Ag and starts decreasing at 4 wt%. The estimated crystallite size of the 3wt% Ag-doped SnO2 thin films shows larger value of 98 nm with improved thickness and decreased strain. The morphologies of all the prepared thin films exhibits small leafy flakes structure with change in the particle size and improved agglomeration with increased Ag concentration. The optical studies showed better transparency in the visible and near-IR regions with decreased transmittance and increase in bandgap with increasing Ag concentration in SnO2. The PL results suggest that the 3wt% Ag-doped SnO2 has high-intensity emission peaks over the visible regions indicating the presence of more oxygen vacancies which acts as recombination centers to trap large amount of target gas. Finally, the gas sensing properties of the samples at different concentration of ammonia gas (i.e., 50, 100, 150, 200, and 250 ppm) are studied, which revealed that the 3wt% Ag-doped SnO2 showed better sensitivity/response of 120% with maximum response and recovery times of 32 and 17 s at 250 ppm of ammonia gas. Thus, the undoped SnO2 has been optimized with different doping amounts of Ag ions to obtain the best ammonia sensor.

Casting is a manufacturing process in which a fluid product is normally poured into a mold, which contains a hollow cavity of the preferred form, and afterwards allowed to solidify. A Casting defect is an undesirable abnormality in a metal casting procedure. There are lots of types of issues in casting like blow openings, pinholes, burr, shrinkage issues, product flaws. Defects are an undesirable thing in the casting Industry. In this job, we extracted different casting items features and then applied convolutional neural network-based models for the discovery of the casted item is great or otherwise. So, it observed that neural networks can record the colours as well as textures of casting particularly to respective, which looks like human decision-making. This design is to deploy the Django internet framework. We try out different surfaces as input to convolutional neural networks for the efficient classification of the surface defect.

The effect of microplastic adsorption on marine microalgae Tetraselmis suecica, Amphora subtropica, and copepod Pseudodiaptomus annandalei was investigated in the present study. Fluorescence microscopic images were used to evaluate MP interactions with algae and copepods. T. suecica growth rate decreased with effects of 0.1 µm polystyrene exposure to 75 µl/100 ml (0.899 to 0.601 abs), 50 µl/100 ml (0.996 to 0.632 abs) and 25 µl/100 ml (0.996 to 0.632 abs), respectively. On the other hand, at 10th day of experiment, the control T. suecica showed the highest growth rate (0.965 abs), chlorophyll concentration (Chl-‘a' = 21.36 µg/L; Chl-‘b' = 13.65 µg/L), and cell density (3.3 × 106 cells/ml). A marine diatom A. subtropica absorbed 2.0 μm microplastics, and the maximal inhibition rate increased at higher MP concentration until 10th day. The highest MPs (75 μl/100 ml) treatment resulted in decreased growth rate of A. subtropica from 0.163 to 0.096 abs. A. subtropica (without MPs) had the highest lipid concentration of 27.15%, whereas T. suecica had the lowest lipid concentration of 11.2% (without MP). The maximum survival (80%) of P. annandalei was found in control on 15th day whereas on 12th day, the microplastics ingested copepod had the lowest survival rate (0%). On 15th day, the maximum Nauplii Production Rate (NPR) (19.33) female−1 was observed in control, whereas the minimum (17.33) female−1 NPR was observed in copepod ingested with MPs. The maximum lipid production (17.33% without MPs) was reported in control, whereas MPs fed copepods had the lowest lipid production (16%). Long-term exposure to polystyrene microplastics significantly reduced algae growth and chlorophyll concentration and also NPR and lipid concentration rate of copepod. We inferred that microplastic exposure of algae and copepods might results in persistent decreases in ingested carbon biomass over time.