Abstract
Single-cell technologies are emerging fast due to their ability to unravel the heterogeneity of biological systems. While scRNA-seq is a powerful tool that measures whole-transcriptome ...expression of single cells, it lacks their spatial localization. Novel spatial transcriptomics methods do retain cells spatial information but some methods can only measure tens to hundreds of transcripts. To resolve this discrepancy, we developed SpaGE, a method that integrates spatial and scRNA-seq datasets to predict whole-transcriptome expressions in their spatial configuration. Using five dataset-pairs, SpaGE outperformed previously published methods and showed scalability to large datasets. Moreover, SpaGE predicted new spatial gene patterns that are confirmed independently using in situ hybridization data from the Allen Mouse Brain Atlas.
Single-cell transcriptomics is rapidly advancing our understanding of the cellular composition of complex tissues and organisms. A major limitation in most analysis pipelines is the reliance on ...manual annotations to determine cell identities, which are time-consuming and irreproducible. The exponential growth in the number of cells and samples has prompted the adaptation and development of supervised classification methods for automatic cell identification.
Here, we benchmarked 22 classification methods that automatically assign cell identities including single-cell-specific and general-purpose classifiers. The performance of the methods is evaluated using 27 publicly available single-cell RNA sequencing datasets of different sizes, technologies, species, and levels of complexity. We use 2 experimental setups to evaluate the performance of each method for within dataset predictions (intra-dataset) and across datasets (inter-dataset) based on accuracy, percentage of unclassified cells, and computation time. We further evaluate the methods' sensitivity to the input features, number of cells per population, and their performance across different annotation levels and datasets. We find that most classifiers perform well on a variety of datasets with decreased accuracy for complex datasets with overlapping classes or deep annotations. The general-purpose support vector machine classifier has overall the best performance across the different experiments.
We present a comprehensive evaluation of automatic cell identification methods for single-cell RNA sequencing data. All the code used for the evaluation is available on GitHub ( https://github.com/tabdelaal/scRNAseq_Benchmark ). Additionally, we provide a Snakemake workflow to facilitate the benchmarking and to support the extension of new methods and new datasets.
Addressing the need for innovative burner designs to optimize combustion characteristics and meet energy efficiency goals, this study introduces a novel concept rooted in swirl-induced flames. The ...proposed burner employs a unique approach to generate air and fuel swirl using four opposing slots in two sets, each set at distinct angles. Incorporating of a channel within the outer casing allows for fine-tuning of entry angles, optimizing the double swirling process. The objective of this research is to enhance combustion characteristics through the implementation of the swirl principle, introducing a tangential velocity component to the airflow or fuel-air mixture upon entry into the burner. Systematic measurements at various entry angles (0°, 15°, 30°, 45°, and 60°) are conducted to identify optimal or near-optimal conditions. The research covers a comparison between flow fields generated by traditional swirled vanes and swirl-induced injection ports at identical inlet conditions, revealing enhanced flame stability and combustion efficiency in the latter configuration. The formation of recirculation zones around the burner, facilitated by increased outer air jet velocities, promotes better mixing and stability. This research contributes valuable insights into burner design and optimization, with implications for improved combustion efficiency and environmental sustainability.
•Swirl induction was done by a tangential velocity component to the airflow or fuel-air mixture upon entry into the burner.•Numerical simulations of LPG diffusion flames at different equivalence ratios are conducted.•Thermal inflame contours measurements are agreed with numerical calculations.•The locations of the generated recirculation zones are shown via flow field simulation analysis.
Full-length RNA sequencing (RNA-Seq) has been applied to bulk tissue, cell lines and sorted cells to characterize transcriptomes, but applying this technology to single cells has proven to be ...difficult, with less than ten single-cell transcriptomes having been analyzed thus far. Although single splicing events have been described for ≤200 single cells with statistical confidence, full-length mRNA analyses for hundreds of cells have not been reported. Single-cell short-read 3' sequencing enables the identification of cellular subtypes, but full-length mRNA isoforms for these cell types cannot be profiled. We developed a method that starts with bulk tissue and identifies single-cell types and their full-length RNA isoforms without fluorescence-activated cell sorting. Using single-cell isoform RNA-Seq (ScISOr-Seq), we identified RNA isoforms in neurons, astrocytes, microglia, and cell subtypes such as Purkinje and Granule cells, and cell-type-specific combination patterns of distant splice sites. We used ScISOr-Seq to improve genome annotation in mouse Gencode version 10 by determining the cell-type-specific expression of 18,173 known and 16,872 novel isoforms.
Optic Disc (OD) localization is an important pre-processing step that significantly simplifies subsequent segmentation of the OD and other retinal structures. Current OD localization techniques ...suffer from impractically-high computation times (few minutes per image). In this work, we present a fast technique that requires less than a second to localize the OD. The technique is based upon obtaining two projections of certain image features that encode the x- and y- coordinates of the OD. The resulting 1-D projections are then searched to determine the location of the OD. This avoids searching the 2-D image space and, thus, enhances the speed of the OD localization process. Image features such as retinal vessels orientation and the OD brightness are used in the current method. Four publicly-available databases, including STARE and DRIVE, are used to evaluate the proposed technique. The OD was successfully located in 330 images out of 340 images (97%) with an average computation time of 0.65 s.
Due to the extensive use of computer networks, new risks have arisen, and improving the speed and accuracy of security mechanisms has become a critical need. Although new security tools have been ...developed, the fast growth of malicious activities continues to be a pressing issue that creates severe threats to network security. Classical security tools such as firewalls are used as a first-line defense against security problems. However, firewalls do not entirely or perfectly eliminate intrusions. Thus, network administrators rely heavily on intrusion detection systems (IDSs) to detect such network intrusion activities. Machine learning (ML) is a practical approach to intrusion detection that, based on data, learns how to differentiate between abnormal and regular traffic. This paper provides a comprehensive analysis of some existing ML classifiers for identifying intrusions in network traffic. It also produces a new reliable dataset called GTCS (Game Theory and Cyber Security) that matches real-world criteria and can be used to assess the performance of the ML classifiers in a detailed experimental evaluation. Finally, the paper proposes an ensemble and adaptive classifier model composed of multiple classifiers with different learning paradigms to address the issue of the accuracy and false alarm rate in IDSs. Our classifiers show high precision and recall rates and use a comprehensive set of features compared to previous work.
The purpose was to measure the prevalence and related risk factors of low back pain (LBP) among health care workers (HCWs) at different levels of health care in southwestern Saudi Arabia.
A ...cross-sectional study using a self-administered questionnaire was conducted among HCWs providing primary, secondary and tertiary health care services in the Aseer region, southwestern Saudi Arabia. The questionnaire collected data regarding having LBP in the past 12 months, socio-demographics, work conditions and history of chronic diseases, regular physical exercise and overexertional back trauma. Univariate and multivariable logistic regression analyses were performed.
Out of 740 participants, the overall prevalence of LBP in the past 12 months amounted to73.9% (95% CI: 70.7-77.0). The prevalence of LBP with neurological symptoms reached 50.0%. The prevalence of LBP necessitating medications and or physiotherapy was 40.5%, while the prevalence of LBP requiring medical consultation was 20%. Using multivariable logistic regression, the following risk factors were identified: working in secondary and tertiary hospitals (aOR = 1.32, 95% CI:1.01-1.76), increased BMI (aOR = 1.10, 95% CI:1.01-3.65), and positive history of overexertional back trauma (aOR = 11.52, 95% CI:4.14-32.08). On the other hand, practising regular physical exercise was a significant protective factor (aOR = 0.61, 95% CI: 0.42-0.89).
LBP is a common problem among HCWs. Many preventable risk factors have been identified, including exertional back trauma, increased BMI and lack of regular physical exercise. Occupational health and safety programmes to build ergonomically safe working conditions and encourage regular physical exercise are needed.
An overview of flow theory is presented from the literature across multiple disciplines, including information systems, ecommerce, marketing, digital gaming, user interface, management, and cultural ...contexts. Flow can play a pivotal role in the user experience and impact the user interaction with a site, computing device, or app. It is worthwhile to examine the effects of flow experience on users and incorporate these findings in designing engaging user experiences and interfaces in both web sites and mobile applications. To further understand these implications, the present study gave a questionnaire to 310 participants in a computer laboratory setting following an online shopping episode. The factor analysis revealed three dimensions of flow experience: control, attention focus, and cognitive enjoyment. All three dimensions had very low correlations. No gender effect on flow was found.
•A comprehensive review of flow in many disciplines and cultural contexts is detailed.•A laboratory study analyzed with factor analysis.•Three flow dimensions emerged: control, attention focus, and cognitive enjoyment.•Control, attention focus, and cognitive enjoyment had very low correlations.•No flow gender effect was found.
Splicing varies across brain regions, but the single-cell resolution of regional variation is unclear. We present a single-cell investigation of differential isoform expression (DIE) between brain ...regions using single-cell long-read sequencing in mouse hippocampus and prefrontal cortex in 45 cell types at postnatal day 7 ( www.isoformAtlas.com ). Isoform tests for DIE show better performance than exon tests. We detect hundreds of DIE events traceable to cell types, often corresponding to functionally distinct protein isoforms. Mostly, one cell type is responsible for brain-region specific DIE. However, for fewer genes, multiple cell types influence DIE. Thus, regional identity can, although rarely, override cell-type specificity. Cell types indigenous to one anatomic structure display distinctive DIE, e.g. the choroid plexus epithelium manifests distinct transcription-start-site usage. Spatial transcriptomics and long-read sequencing yield a spatially resolved splicing map. Our methods quantify isoform expression with cell-type and spatial resolution and it contributes to further our understanding of how the brain integrates molecular and cellular complexity.
Data about allergic sensitization to rhinitis among adults are limited. The objectives were to explore the prevalence of current rhinitis (CR) and associated specific allergen sensitizations in ...southwestern Saudi Arabia. A cross-sectional study was conducted on 969 adults in southwestern regions of Saudi Arabia, namely Aseer, Jazan, and Al Baha. From each region, 5 primary health care centers were chosen. The validated Arabic Version of the International Study of Asthma and Allergies in Childhood questionnaire was used. Total immunoglobulin E (IgE) enzyme-linked immunosorbent assay, cytokine enzyme-linked immunosorbent assay (interleukin IL-4, IL-10, IL-13, and interferon-γ), aeroallergen-specific IgE immunoassay (a panel of 30 common aeroallergens; 9 indoor and 21 outdoors), and eosinophilic count were assessed. A prevalence of CR of 35.8% (95% confidence interval: 32.8%–38.9%) was found. Regarding outdoor aeroallergens, Mesquite -positive IgE antibodies were higher among CR adults (odds ratio = 1.52, 95% confidence interval: 1.02–2.21) compared to those without CR. The same significant pattern was found with Chenopodium , Ragweed , Pigweed , Russian thistle , Bermuda grass , Timothy grass , and Rye . All indoor aeroallergens were not significantly associated with CR. Total IgE and eosinophil count were significantly higher among adults with CR. In conclusion, CR in southwestern regions of Saudi Arabia is common and of significant public health importance. Aeroallergens that associate with adult sensitization to CR tend to be of the outdoor variety particularly the herbaceous grass and their pollens. The magnitude of CR and its association with exposure to outdoor aeroallergens should be taken into account by health policy decision makers, clinicians, and medical practitioners when diagnosing and treating related conditions.
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