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Laboratory automation technologies have revolutionized biomedical research. However, the availability of automation solutions at the single-cell level remains scarce, primarily owing to the inherent challenges of handling cells with such small dimensions in a precise, biocompatible manner. Here, we present a single-cell-level laboratory automation solution that configures various experiments onto standardized, microscale test-tube matrices via our precise ultrasonic liquid sample ejection technology, known as PULSE. PULSE enables the transformation of titer plates into microdroplet arrays by printing nanodrops and single cells acoustically in a programmable, scalable, and biocompatible manner. Unlike pipetting robots, PULSE enables researchers to conduct biological experiments using single cells as anchoring points (e.g., 1 cell vs. 1000 cells per "tube"), achieving higher resolution and potentially more relevant data for modeling and downstream analyses. We demonstrate the ability of PULSE to perform biofabrication, precision gating, and deterministic array barcoding via preallocated droplet-addressable primers. Single cells can be gently printed at a speed range of 5–20 cell⋅s−1 with an accuracy of 90.5–97.7%, which can then adhere to the substrate and grow for up to 72 h while preserving cell integrity. In the deterministic barcoding experiment, 95.6% barcoding accuracy and 2.7% barcode hopping were observed by comparing the phenotypic data with known genotypic data from two types of single cells. Our PULSE platform allows for precise and dynamic analyses by automating experiments at the single-cell level, offering researchers a powerful tool in biomedical research. [ABSTRACT FROM AUTHOR]

Recent technological advancements in single-cell genomics have enabled joint profiling of gene expression and alternative modalities at unprecedented scale. Consequently, the complexity of multi-omics data sets is increasing massively. Existing models for multi-modal data are typically limited in functionality or scalability, making data integration and downstream analysis cumbersome. We present multiDGD, a scalable deep generative model providing a probabilistic framework to learn shared representations of transcriptome and chromatin accessibility. It shows outstanding performance on data reconstruction without feature selection. We demonstrate on several data sets from human and mouse that multiDGD learns well-clustered joint representations. We further find that probabilistic modeling of sample covariates enables post-hoc data integration without the need for fine-tuning. Additionally, we show that multiDGD can detect statistical associations between genes and regulatory regions conditioned on the learned representations. multiDGD is available as an scverse-compatible package on GitHub. Understanding single-cell multi-omics data requires powerful solutions. Here, authors present a data-efficient machine learning approach for paired data. It enables integration from unseen covariates and can link distal regulatory elements to promoters, presenting a computational version of HiC. [ABSTRACT FROM AUTHOR]

Patient-derived xenografts (PDXs) are tumour fragments engrafted into mice for preclinical studies. PDXs offer clear advantages over simpler in vitro cancer models - such as cancer cell lines (CCLs) and organoids - in terms of structural complexity, heterogeneity, and stromal interactions. Here, we characterise 231 colorectal cancer PDXs at the genomic, transcriptomic, and epigenetic levels, along with their response to cetuximab, an EGFR inhibitor used clinically for metastatic colorectal cancer. After evaluating the PDXs' quality, stability, and molecular concordance with publicly available patient cohorts, we present results from training, interpreting, and validating the integrative ensemble classifier CeSta. This model takes in input the PDXs' multi-omic characterisation and predicts their sensitivity to cetuximab treatment, achieving an area under the receiver operating characteristics curve > 0.88. Our study demonstrates that large PDX collections can be leveraged to train accurate, interpretable drug sensitivity models that: (1) better capture patient-derived therapeutic biomarkers compared to models trained on CCL data, (2) can be robustly validated across independent PDX cohorts, and (3) could contribute to the development of future therapeutic biomarkers. Patient-derived xenografts (PDX) could contribute to understanding how colorectal cancer (CRC) responds to targeted therapies like cetuximab. Here, the authors characterise the response to cetuximab in 231 CRC PDXs using multiomics and develop an integrative ensemble classifier - CeSta - to predict sensitivity to cetuximab. [ABSTRACT FROM AUTHOR]

Integrating single-cell datasets produced by multiple omics technologies is essential for defining cellular heterogeneity. Mosaic integration, in which different datasets share only some of the measured modalities, poses major challenges, particularly regarding modality alignment and batch effect removal. Here, we present a deep probabilistic framework for the mosaic integration and knowledge transfer (MIDAS) of single-cell multimodal data. MIDAS simultaneously achieves dimensionality reduction, imputation and batch correction of mosaic data by using self-supervised modality alignment and information-theoretic latent disentanglement. We demonstrate its superiority to 19 other methods and reliability by evaluating its performance in trimodal and mosaic integration tasks. We also constructed a single-cell trimodal atlas of human peripheral blood mononuclear cells and tailored transfer learning and reciprocal reference mapping schemes to enable flexible and accurate knowledge transfer from the atlas to new data. Applications in mosaic integration, pseudotime analysis and cross-tissue knowledge transfer on bone marrow mosaic datasets demonstrate the versatility and superiority of MIDAS. MIDAS is available at https://github.com/labomics/midas. Single-cell, multiomic datasets are integrated using dimensionality reduction, imputation and batch correction. [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing (scRNA-seq) technologies have become essential tools for characterizing cellular landscapes within complex tissues. Large-scale single-cell transcriptomics holds great potential for identifying rare cell types critical to the pathogenesis of diseases and biological processes. Existing methods for identifying rare cell types often rely on one-time clustering using partial or global gene expression. However, these rare cell types may be overlooked during the clustering phase, posing challenges for their accurate identification. In this paper, we propose a Cluster decomposition-based Anomaly Detection method (scCAD), which iteratively decomposes clusters based on the most differential signals in each cluster to effectively separate rare cell types and achieve accurate identification. We benchmark scCAD on 25 real-world scRNA-seq datasets, demonstrating its superior performance compared to 10 state-of-the-art methods. In-depth case studies across diverse datasets, including mouse airway, brain, intestine, human pancreas, immunology data, and clear cell renal cell carcinoma, showcase scCAD's efficiency in identifying rare cell types in complex biological scenarios. Furthermore, scCAD can correct the annotation of rare cell types and identify immune cell subtypes associated with disease, thereby offering valuable insights into disease progression. Identifying rare cells is essential for advancing our understanding of complex biological systems and disease mechanisms. Here, authors propose scCAD, a method that combines cluster decomposition and anomaly detection to effectively identify rare cell types across diverse biological scenarios. [ABSTRACT FROM AUTHOR]

Characterization and integration of the genome, epigenome, transcriptome, proteome and metabolome of different datasets is difficult owing to a lack of ground truth. Here we develop and characterize suites of publicly available multi-omics reference materials of matched DNA, RNA, protein and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters. These references provide built-in truth defined by relationships among the family members and the information flow from DNA to RNA to protein. We demonstrate how using a ratio-based profiling approach that scales the absolute feature values of a study sample relative to those of a concurrently measured common reference sample produces reproducible and comparable data suitable for integration across batches, labs, platforms and omics types. Our study identifies reference-free 'absolute' feature quantification as the root cause of irreproducibility in multi-omics measurement and data integration and establishes the advantages of ratio-based multi-omics profiling with common reference materials. Multi-omics data are readily integrated using quartet reference materials. [ABSTRACT FROM AUTHOR]

Although single-cell and spatial sequencing methods enable simultaneous measurement of more than one biological modality, no technology can capture all modalities within the same cell. For current data integration methods, the feasibility of cross-modal integration relies on the existence of highly correlated, a priori 'linked' features. We describe matching X-modality via fuzzy smoothed embedding (MaxFuse), a cross-modal data integration method that, through iterative coembedding, data smoothing and cell matching, uses all information in each modality to obtain high-quality integration even when features are weakly linked. MaxFuse is modality-agnostic and demonstrates high robustness and accuracy in the weak linkage scenario, achieving 20~70% relative improvement over existing methods under key evaluation metrics on benchmarking datasets. A prototypical example of weak linkage is the integration of spatial proteomic data with single-cell sequencing data. On two example analyses of this type, MaxFuse enabled the spatial consolidation of proteomic, transcriptomic and epigenomic information at single-cell resolution on the same tissue section. MaxFuse enables data integration between modalities even when features are weakly correlated. [ABSTRACT FROM AUTHOR]

Certified RNA reference materials are indispensable for assessing the reliability of RNA sequencing to detect intrinsically small biological differences in clinical settings, such as molecular subtyping of diseases. As part of the Quartet Project for quality control and data integration of multi-omics profiling, we established four RNA reference materials derived from immortalized B-lymphoblastoid cell lines from four members of a monozygotic twin family. Additionally, we constructed ratio-based transcriptome-wide reference datasets between two samples, providing cross-platform and cross-laboratory 'ground truth'. Investigation of the intrinsically subtle biological differences among the Quartet samples enables sensitive assessment of cross-batch integration of transcriptomic measurements at the ratio level. The Quartet RNA reference materials, combined with the ratio-based reference datasets, can serve as unique resources for assessing and improving the quality of transcriptomic data in clinical and biological settings. A new RNA reference set improves detection of differential expression in clinical settings. [ABSTRACT FROM AUTHOR]

The Human Cell Atlas is yielding detailed maps of human tissues and systems throughout life, along with methods to handle single-cell data. Four scientists reflect on how the project is transforming our understanding of human biology.Scientists reflect on insights from the Human Cell Atlas. [ABSTRACT FROM AUTHOR]

Despite growing concern in the social innovation (SI) literature about the tackling of grand challenges, our understanding of the role of multinational enterprises (MNEs) remains in its infancy. This article examines foreign MNE subsidiaries' SI investments focusing on United Nations' Sustainable Development Goals (UNSDGs) in host countries. Using financial data from large, listed subsidiaries of foreign MNEs operating in India, along with hand-collected data from firms' disclosures of corporate social responsibility (CSR) activity for five years starting in 2015, we utilise the externalities framework propounded by Montiel et al. (2021). This neatly translates the 17 UNSDGS into actionable goals to examine the efforts of foreign MNE subsidiaries in increasing positive externalities as opposed to reducing negative externalities via SI-related investment in host countries. The study also evaluates the effects of the local embeddedness of the foreign MNE subsidiaries on SI investment. We find that MNE subsidiaries tend to favour increasing positive externalities as compared to reducing negative externalities through their SI investments. Also, older subsidiaries tend to prioritize greater investments in SI projects related to reducing negative externalities and subsidiaries with higher MNE ownership tend to reduce investments in SI projects related to increasing positive externalities. We discuss possible interpretations of the exploratory results using the institutional logics perspective and conclude with implications for policy and future research. [ABSTRACT FROM AUTHOR]

Cannabinoids, such as ∆9-tetrahydrocannabinol (THC) and cannabidiol (CBD), are effective bioactive compounds that improve the quality of life of patients with certain chronic conditions. The copolymer poly(lactic-co-glycolic acid) (PLGA) has been used to encapsulate such compounds separately, providing pharmaceutical grade edible products with unique features. In this work, a variety of PLGA based nanoformulations that maintain the natural cannabinoid profile found in the plant (known as full-spectrum) are proposed and evaluated. Three different cannabis sources were used, representing the three most relevant cannabis chemotypes. PLGA nanocapsules loaded with different amounts of cannabinoids were prepared by nanoemulsion, and were then functionalized with three of the most common coating polymers: pectin, alginate and chitosan. In order to evaluate the suitability of the proposed formulations, all the synthesized nanocapsules were characterized, and their cannabinoid content, size, zeta-potential, morphology and in vitro bioaccessibility was determined. Regardless of the employed cannabis source, its load and the functionalization, high cannabinoid content PLGA nanocapsules with suitable particle size and zeta-potential were obtained. Study of nanocapsules' morphology and in vitro release assays in gastro-intestinal media suggested that high cannabis source load may compromise the structure of nanocapsules and their release properties, and hence, the use of lower content of cannabis source is recommended. [ABSTRACT FROM AUTHOR]

Background: Internet addiction is increasing among adolescents worldwide. There is a lack of research assessing internet addiction and factors contributing to it among adolescents in Jeddah city. The current study aimed to assess the rate of internet addiction among adolescents in Jeddah, Saudi Arabia, and the potential factors associated with it. Methods: A cross-sectional online survey, hosted by SurveyMonkey, was used to capture data on internet use from 462 adolescents aged 12–18 years between March and May 2022. Young's Internet Addiction Scale was used to assess the degree of internet addiction as perceived by adolescents. Multiple linear regression analysis was used to identify possible predictors of internet addiction among adolescents in Jeddah. Results: The mean age of the participants was 15.5 ± 1.9 years. The majority were females (75%) from public schools (63%), spent an average of six hours on the internet daily, owned smartphones (98%), accessed the internet via a smartphone (94%), and used the internet for socializing (82%). Internet addiction mean score was 39.20 ± 15.20 out of 100. More than two-thirds of the participants had mild to moderate levels of internet addiction (68%). Significant predictors contributing to internet addiction were using the internet for socialization and playing online games. The more hours spent on the internet daily, the more the internet addiction was (p ≤ 0.05). Conclusions: The internet addiction rate is high among school adolescents in Jeddah. The majority of high school adolescents had mild to moderate levels of internet addiction. Interventional multidisciplinary programs are needed to mitigate the factors that influence internet addiction. [ABSTRACT FROM AUTHOR]

Recent advancements for simultaneously profiling multi-omics modalities within individual cells have enabled the interrogation of cellular heterogeneity and molecular hierarchy. However, technical limitations lead to highly noisy multi-modal data and substantial costs. Although computational methods have been proposed to translate single-cell data across modalities, broad applications of the methods still remain impeded by formidable challenges. Here, we propose scButterfly, a versatile single-cell cross-modality translation method based on dual-aligned variational autoencoders and data augmentation schemes. With comprehensive experiments on multiple datasets, we provide compelling evidence of scButterfly's superiority over baseline methods in preserving cellular heterogeneity while translating datasets of various contexts and in revealing cell type-specific biological insights. Besides, we demonstrate the extensive applications of scButterfly for integrative multi-omics analysis of single-modality data, data enhancement of poor-quality single-cell multi-omics, and automatic cell type annotation of scATAC-seq data. Moreover, scButterfly can be generalized to unpaired data training, perturbation-response analysis, and consecutive translation. Technical limitations of simultaneously multi-omics profiling lead to highly noisy multi-modal data and substantial costs. Here, authors proposed a versatile framework and data augmentation schemes, capable of single-cell cross-modality translation and multiple extensive applications. [ABSTRACT FROM AUTHOR]

Problematic internet use (PIU) is of treatment interest in adolescent clinical samples. Gender specific differences in terms of personality traits and psychopathological symptoms remain unclear. In an adolescent clinical sample (n = 104; 69 girls) PIU, psychopathology, temperament and character traits as well as emotional and behavioral problems were assessed. 62% of the sample showed subthreshold PIU and 34% full PIU (fPIU). Boys reported more gaming whereas girls social networking. Sex specific analyses revealed gender differences: Girls with fPIU scored significantly higher on internalizing/externalizing problems/behavior, novelty seeking and transcendence, and lower on persistence, self-directedness, and cooperativeness than girls without fPIU. Boys with fPIU scored significantly higher on internalizing problems and self-transcendence and lower on harm avoidance than boys without fPIU. Gender plays an important role in PIU. Gender specific differences in both application use as well as symptomatic, temperament and character traits call for a gender specific approach in prevention and treatment integration. [ABSTRACT FROM AUTHOR]

It is known that increasing internet use increases internet addiction levels in adolescents and this leads to many health problems, especially musculoskeletal pain. The purpose of this study was to investigate the prevalence of internet addiction in high school students as well as the impact on the musculoskeletal system. This cross-sectional study included 986 high school students from Diyarbakir, Turkey. The students were assessed using the Socio-demographic Information Form, Young Internet Addiction Scale, Nordic Musculoskeletal System Questionnaire, Visual Analog Scale, and Online Questionnaire method. It was found that 26.2% of the students are addicted to the internet. While neck pain was the leading musculoskeletal system problem of the students in the last 12 months with 61.3%, the low back was the region that had the most impact on adolescents ability to go about their daily lives with 31.2%. The internet addicts' musculoskeletal discomfort in the previous year in all body parts and pain in the last 7 days in all body parts except the elbows were statistically significantly higher than the non-addicted group (p<0.05). The prevalence of internet addiction among students has been found to be significant, and this addiction causes musculoskeletal pain and activity limitations in many body parts. [ABSTRACT FROM AUTHOR]

Tissues are spatially orchestrated ecosystems composed of heterogeneous cell populations and non-cellular elements. Tissue components' interactions shape the biological processes that govern homeostasis and disease, thus comprehensive insights into tissues' composition are crucial for understanding their biology. Recently, advancements in the spatial biology field enabled the in-depth analyses of tissue architecture at single-cell resolution, while preserving the structural context. The increasing number of biomarkers analyzed, together with whole tissue imaging, generate datasets approaching several hundreds of gigabytes in size, which are rich sources of valuable knowledge but require investments in infrastructure and resources for extracting quantitative information. The analysis of multiplex whole-tissue images requires extensive training and experience in data analysis. Here, we showcase how a set of open-source tools can allow semi-automated image data extraction to study the spatial composition of tissues with a focus on tumor microenvironment (TME). With the use of Lunaphore COMET platform, we interrogated lung cancer specimens where we examined the expression of 20 biomarkers. Subsequently, the tissue composition was interrogated using an in-house optimized nuclei detection algorithm followed by a newly developed image artifact exclusion approach. Thereafter, the data was processed using several publicly available tools, highlighting the compatibility of COMET-derived data with currently available image analysis frameworks. In summary, we showcased an innovative semi-automated workflow that highlights the ease of adoption of multiplex imaging to explore TME composition at single-cell resolution using a simple slide in, data out approach. Our workflow is easily transferrable to various cohorts of specimens to provide a toolset for spatial cellular dissection of the tissue composition. [ABSTRACT FROM AUTHOR]

Cannabidiol (CBD) is a promising neurological agent with potential beneficial effects on memory and cognitive function. The combination of CBD and topiramate in the treatment of some neurological diseases has been of great interest. Since Topiramate-induced memory loss is a major drawback of its clinical application and the overall effect of the combination of CBD and topiramate on memory is still unclear, here we investigated the effect of CBD on topiramate-induced memory loss and the underlying molecular mechanisms. A one trial step-through inhibitory test was used to evaluate memory consolidation in rats. Moreover, the role of N-methyl-D-aspartate receptors (NMDARs) in the combination of CBD and topiramate in memory consolidation was evaluated through the intra-CA1 administration of MK-801 and NMDA. Western blot analysis was used to evaluate variations in brain-derived neurotrophic factor (BDNF) and phosphorylated cyclic AMP response element-binding protein (pCREB)/CREB ratio in the prefrontal cortex (PFC) and hippocampus (HPC). While the intraperitoneal (i.p.) administration of topiramate (50, 75, and 100 mg/kg) significantly reduced inhibitory time latency, the i.p. administration of CBD (20 and 40 mg/kg) could effectively reverse these effects. Similarly, the sub-effective doses of NMDA plus CBD (10 mg/kg) could improve the topiramate-induced memory loss along with an enhancement in BDNF and pCREB expression in the PFC and HPC. Contrarily, the administration of sub-effective doses of the NMDAR antagonist (MK-801) diminished the protective effects of CBD (20 mg/kg) on topiramate-induced memory loss associated with decreased BDNF and pCREB levels in the PFC and HPC. These findings suggest that CBD can improve topiramate-induced memory impairment, partially by the NMDARs of the PFC and HPC, possibly regulated by the CREB/BDNF signaling pathway. Considering the neuroprotective effect of cannabidiol and the deteriorative effect of topiramate on memory function, we investigated the overall effect of the interesting and novel option of their co-administration. Our findings revealed that cannabidiol has ameliorative effects on topiramate-induced memory loss, partially via hippocampal and prefrontal cortical NMDA receptors and CREB/BDNF signaling pathways. [ABSTRACT FROM AUTHOR]

We present a statistical simulator, scDesign3, to generate realistic single-cell and spatial omics data, including various cell states, experimental designs and feature modalities, by learning interpretable parameters from real data. Using a unified probabilistic model for single-cell and spatial omics data, scDesign3 infers biologically meaningful parameters; assesses the goodness-of-fit of inferred cell clusters, trajectories and spatial locations; and generates in silico negative and positive controls for benchmarking computational tools. The challenge of simulating multiomic single-cell data is addressed by a probabilistic model. [ABSTRACT FROM AUTHOR]

Currently available single-cell omics technologies capture many unique features with different biological information content. Data integration aims to place cells, captured with different technologies, onto a common embedding to facilitate downstream analytical tasks. Current horizontal data integration techniques use a set of common features, thereby ignoring non-overlapping features and losing information. Here we introduce StabMap, a mosaic data integration technique that stabilizes mapping of single-cell data by exploiting the non-overlapping features. StabMap first infers a mosaic data topology based on shared features, then projects all cells onto supervised or unsupervised reference coordinates by traversing shortest paths along the topology. We show that StabMap performs well in various simulation contexts, facilitates 'multi-hop' mosaic data integration where some datasets do not share any features and enables the use of spatial gene expression features for mapping dissociated single-cell data onto a spatial transcriptomic reference. Mosaic data integration is improved by capturing features that do not overlap between datasets. [ABSTRACT FROM AUTHOR]

The scientific literature firmly states that bullying can affect adolescent mental health, however, it lacks longitudinal studies on potential psychological risk factors of bullying involvement, especially in less developed countries. This longitudinal study with 1,739 7th-grade students of 30 Brazilian schools aimed to analyze how psychiatric symptomatology can predict bullying patterns among adolescents. We conducted a latent class analysis to identify patterns of bullying victimization and perpetration. Multinomial logistic regressions were conducted to assess if baseline psychiatric symptoms affected bullying latent classes at nine-month follow-up. Analyses considered the complex multilevel data structure and were adjusted for sociodemographic variables. We found three bullying latent classes: minimal involvement, victims, and bully-victims. Internalizing psychiatric symptomatology predicted the probability of belonging to the "victims" class. Externalizing symptoms increased the future probability of belonging to the "bully-victims" class. These findings are independent of sociodemografic variables, such as age, socioeconomic status, and sex. Conduct problems were a risk factor for all bullying latent classes. Therefore, bullying prevention actions should target children and early adolescents with internalizing and externalizing symptoms. [ABSTRACT FROM AUTHOR]

This study aimed to validate and evaluate the psychometric properties of the Turkish version of the Generalized Problematic Internet Use Scale-2 (GPIUS2), to categorize Internet use patterns (IUP) that are academic, social, and recreational, and to elucidate the current state of the relationships between demographic characteristics, problematic Internet use (PIU), and IUP. To this end, two studies were conducted 1 year apart at two different public universities in Turkey. The first study tested the psychometric properties of the Turkish version of the GPIUS2 for young adults alongside piloting the Internet Use Patterns Questionnaire (IUPQ) with a total of 328 university students in the fall semester of 2017–2018. In a follow-up study, the Turkish version of the GPIUS2 was further validated alongside the examination of IUP with 479 university students in the fall semester of 2018–2019. Factor analyses were conducted in both studies to examine the psychometric properties of the Turkish version of the GPIUS2. Pearson correlation, independent samples T-test, and ANOVA were conducted in the follow-up study to examine the relationship between demographic information, PIU, and IUP. In both the initial and follow-up studies, the translated version of the GPIUS2 proved to be a reliable, valid, and acceptable measurement instrument with 14 items and three factors. Regarding the relationship between IUP and PIU, duration of use rather than purpose of use frequency was found to be related to PIU. An increase in the duration of academic use implies a decrease in PIU, and the opposite is true for social and recreational use. The surprising, nonsignificant relationship between grade point average and PIU is discussed in line with the literature. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the mediating mechanisms of a Brazilian school-based program to prevent drug use among adolescents. In this study, we enrolled 4,030 students in two cluster randomized controlled trials to evaluate two PROERD curricula (fifth and seventh grades) in 30 public schools. Mediation analysis was used to evaluate the program's indirect effects on drug use through communication skills, decision-making skills, attitudes toward drugs, and refusal skills. Evidence regarding differences in mediator variables and drug use between the intervention and control groups is lacking. A clear association was noted between drug use and mediators, independent of group allocation; therefore, program activities should be revised and improved to influence these mediators. [ABSTRACT FROM AUTHOR]

Problematic social media use (PSMU) refers to excessive uncontrolled use of social media which impacts upon daily functioning (Blackwell et al., 2017). Self-regulation is central to the development and experience of PSMU, and conceptually interrelates with individual usage motivations (Reinecke et al., 2022). While there is a growing body of research on social media use motivations, how usage motivations and self-regulation combined influence PSMU is not well understood. There are also persistent questions around the effectiveness of addiction-based measures of PSMU. The quantitative component of this nested mixed-methods study (N = 607) employed hierarchical regression and structural equation modelling, principally identifying that impulsive social media usage mediates the pathway between perceived executive/attentional functioning and the Bergen Social Media Addiction Scale (BSMAS, Andreassen et al., 2012, 2016), a popular tool used to measure PSMU. In contrast, social-engagement motivations had a negative influence on the BSMAS. The qualitative component, comprising interview/open-ended questionnaire, explored individual experiences self-regulating social media use. Participants (N = 24) were recruited from the survey study, based on meeting screening criteria for executive dysfunction (Adult Self-Report ADHD Scale, Kessler et al., 2005), with sub-groups defined by top and bottom quartile BSMAS scores (evenly grouped). Thematic analysis found that most individuals with attention dysregulation, regardless of their BSMAS category, perceive self-regulation of social media use as highly challenging and effortful, describing broadly problematic relationship with social media. They also described rich combination of motivations and context of using social media, and strategies for managing use. This research questions the effectiveness of the BSMAS as a measure of general PSMU (lacking a formed self-regulation component), especially in individuals with attentional dysregulation. Future research investigating self-regulation strategies and focusing on characteristics of positive social media use is needed. [ABSTRACT FROM AUTHOR]

In Mexico, productive activities related to poultry farming and the sugarcane industry generate large amounts of three byproducts, sewage sludge, chicken litter, and sugarcane wastes, which have high concentrations of organic matter and lignocellulosic material. Therefore, the objective of the present study was to evaluate the effect of acid pretreatment with different doses of acetic acid (2%, 3%, and 4% v/v) and exposure times of 30, 60, and 90 min on the solubilization of organic matter and biogas production in the anaerobic codigestion of a mixture of agroindustrial wastes. The biodegradability and biogas production were evaluated by anaerobic digestion for 30 days. Finally, for 90 days of semi-continuous operation, the increase in applied organic load of 1 KgVS/(m3.d) was considered with Δ1 KgVS/(m3.d) every 30 days until reaching 3 KgVS/(m3.d). Acid pretreatment with a dose of 4% acetic acid and an exposure time of 90 min improved the solubility and hydrolysis, considering the limiting stage of the anaerobic process, reducing the hydraulic retention time from 19 to 11 d to reach –Δ 38% VS, and increasing biogas yields (Ybio = 609 ± 11.7 to 1857.2 ± 7.5 Lbio/gVSrem) and methane yields (YCH4 = 426.9 ± 8.19 to 1392.9 ± 5.65 LCH4/gVSrem). [ABSTRACT FROM AUTHOR]

The popularity of green building housing products as a mainstream environmentally friendly product has risen remarkably. But, are consumers in general receptive to this product? Are they willing to pay (WTP) more for it? Do consumers have consistent perceptions, attitudes, purchase intentions, and WTP? This paper is based on a questionnaire to house buyers in Kaohsiung City, Taiwan's third-largest city by population. The data were analyzed via structural equation modeling. The results revealed that consumers' perception and attitude toward green building housing products affect their purchase intention, and purchase intention affects WTP. Secondly, attitude also directly affects the WTP, which is also indirectly impacted by attitude through the mediating effect of purchase intention. It shows that the gap between the attitude and WTP of consumers in the consumption decision of green building housing products has been reduced. This result is related to the increasing education, promotion, and marketing of green building housing products, and a consistent consumption decision has gradually formed. In terms of the managerial implications, further education and public awareness messages should be used to narrow the consumption decision gap. Moreover, the importance of the health benefits of green building housing products should be emphasized. Furthermore, green building transactions in the housing market should be more transparent so that consistency in consumer decision-making can be attained. [ABSTRACT FROM AUTHOR]

This article reports the results of a novel perioperative treatment implementing the gut microbiota to prevent anastomotic fistula and leakage (AL) in patients undergoing laparoscopic colorectal resections for cancer and represents the continuation of our pilot study on 60 cases. A series of 131 patients underwent elective colorectal surgery at the S. Eugenio Hospital (Rome—Italy) between December 1, 2020, and November 30, 2022, and received a perioperative preparation following the Microbiota Implementation to Reduce Anastomotic Colorectal Leaks (MIRACLe) protocol comprising oral antibiotics, mechanical bowel preparation and perioperative probiotics. The results obtained in the MIRACLe group (MG) were compared to those registered in a Control group (CG) of 500 patients operated on between March 2015 and November 30, 2020, who received a standard ERAS protocol. Propensity score-matching (PSM) analysis was performed to overcome patients' selection bias. Patients were categorised according to perioperative preparation (MIRACLe protocol vs standard ERAS protocol) into two groups: 118 patients were in post-matched MIRACLe group (pmMG) and 356 were in post-matched Control group (pmCG). In the pmMG, only 2 anastomotic leaks were registered, and the incidence of AL was just 1.7% vs. 6.5% in the pmCG (p = 0.044). The incidence of surgical site infections (1.7% vs. 3.1%; p = 0.536), reoperations (0.8% vs. 4.2%; p = 0.136) and postoperative mortality (0% vs. 2.0%; p = 0.200) was lower in pmMG. Additionally, the postoperative outcomes were better: the times to first flatus, to first stool and to oral feeding were shorter (1 vs. 2, 2 vs. 3 and 2 vs. 3 days, respectively; p < 0.001). The postoperative recovery was faster, with a shorter time to discharge (5 vs. 6 days; p < 0.001). The MIRACLe protocol was confirmed to be safe and significantly able to reduce anastomotic leaks in patients receiving elective laparoscopic colorectal surgery for cancer. [ABSTRACT FROM AUTHOR]

Previous research investigated the role of social media use and perceived socio-cultural pressure as predictors of the endorsement of ideal body stereotypes. However, not much has been explored concerning cognitive fusion and its role within this framework. The current study investigated social media use as a predictor of ideal body stereotypes and how this relationship is mediated by perceived social pressure. Additionally, we explored the potential moderating roles of cognitive fusion within these relationships. Our sample consisted of 489 participants aged 18 to 53 (73.2% females). The findings suggested that the participants' reported social media use level significantly predicted both the ideal body stereotypes and the perceived social pressure. The overall effect of perceived social pressure on ideal body stereotypes was not significant. However, at low levels of cognitive fusion, the perceived social pressure significantly mediated the relationship between social media use and ideal body stereotype. We consider the current findings significant for their contribution to potential educational programs designed to address the adverse consequences of social media use on psychological and physical well-being. Highlights: Higher social media use (SMU) levels are associated with higher levels of ideal body stereotype (IBS) Perceived socio-cultural pressure (PSP) mediates the relationship between SMU and IBS Cognitive fusion (CF) moderates the pathway between SMU and perceived social pressure (PSP) PSP predicted IBS only at low levels of CF We found significant gender differences in cognitive fusion and IBS [ABSTRACT FROM AUTHOR]

Utilization of online social networking sites (SNSs) is often problematic in young people. However, studies seldom seek to understand personal differences and deep-seated reasons in its problematic utilization. This study aims to explore the longstanding and recent psychosocial predictors of problematic utilization of WeChat friend center (PUWF) longitudinally. A total of 433 college students (17–25 years old, male/female ratio: 389/44) were investigated over 2 successive years (T1: first year; T2: second year) using the Sixteen Personality Factor Questionnaire, Adolescent Self-Rating Life Events Checklist, Social Support Scale, Patient Health Questionnaire, Connor-Davidson Resilience Scale, and the problematic utilization scale of the WeChat friend center which was developed in this study. Correlation, regression, and structural equation analyses were conducted. A problematic utilization scale of the WeChat friend center was developed with Cronbach's alpha of.836. 21.02% of students reported WeChat PUWF. Males utilized the WeChat friend center less than females, and females were at higher risk of PUWF, which was correlated with worse mental health. In the longitudinal prediction, regression and modeling analyses showed that apprehension of personality predicted PUWF consistently and directly, and this was partially mediated by T1 depression and T2 negative life events. Resultys suggest that females are at higher risk for PUWF. Apprehension personality has a direct and indirect effect on PUWF through recent depression and life events. The findings help to recognize individuals at risk for PUWF as well as to better prevent it, and provide suggestions as to the functional design of SNSs according to different need of users. Core tips: Utilization of SNSs is often problematic in young people. However, personal differences and deep-seated reasons in its problematic utilization has been poorly revealed. Through a longitudinal investigation, this study confirms that females are at higher risk for PUWF. Apprehension personality has a direct and indirect effect on PUWF through recent depression and life events. The findings help to recognize individuals at risk for PUWF and give theoretical evidence to the functional design of SNSs for diferent users. [ABSTRACT FROM AUTHOR]

Digital technology use plays an essential role in adolescents' psychological adjustment, impacting their mental health and well-being. In this scenario, Problematic Internet Use (PIU) is a risky condition for developing behavioral addiction in adolescence. Most of the research on PIU in adolescence focus on dimensions that may amplify or buffer it, finding significant associations between PIU and interpersonal problems with peers, maladaptive personality traits, low self-esteem, emotion dysregulation, and increasing psychological difficulties. It has been suggested that PIU might represent a maladaptive coping strategy to tackle problematic psychosocial functioning. In this line, the current cross-sectional study focused on PIU's role in the association between personality dimensions and internalizing/externalizing problems. Two-hundred thirty-one middle and late adolescents (age range 15–19 years; 62% Female) attending public junior high schools in Italy completed the Internet Addiction Test (IAT), the Adolescent Personality Structure Questionnaire (APS-Q), and the Youth Self Report (YSR). Moderation analyses were used to test the hypothesis that higher PIU amplifies the relationship between maladaptive personality dimensions and psychological symptoms. Results indicated that only high PIU influenced the relationship between difficulties in building significant relationships with peers and internalizing problems. Conversely, PIU buffered the relationship between difficulties in adolescents' sense of self (identity) and internalizing problems and the association between aggression regulation and internalizing problems, supporting the role of PIU as a maladaptive coping strategy. These findings encourage accurately evaluating PIU as a risk factor in adolescence: (1) considering how high PIU's presence should impact the relationship between adolescent personality and the quality of their relationships with peers; (2) acknowledging the role of PIU as a regulation strategy for identity difficulties and aggression dysregulation. [ABSTRACT FROM AUTHOR]

Multiplexed imaging enables measurement of multiple proteins in situ, offering an unprecedented opportunity to chart various cell types and states in tissues. However, cell classification, the task of identifying the type of individual cells, remains challenging, labor-intensive, and limiting to throughput. Here, we present CellSighter, a deep-learning based pipeline to accelerate cell classification in multiplexed images. Given a small training set of expert-labeled images, CellSighter outputs the label probabilities for all cells in new images. CellSighter achieves over 80% accuracy for major cell types across imaging platforms, which approaches inter-observer concordance. Ablation studies and simulations show that CellSighter is able to generalize its training data and learn features of protein expression levels, as well as spatial features such as subcellular expression patterns. CellSighter's design reduces overfitting, and it can be trained with only thousands or even hundreds of labeled examples. CellSighter also outputs a prediction confidence, allowing downstream experts control over the results. Altogether, CellSighter drastically reduces hands-on time for cell classification in multiplexed images, while improving accuracy and consistency across datasets. Identification and classification of cells in multiplexed microscopy remain challenging. Here, the authors propose CellSighter, which uses neural networks to perform cell classification directly on multiplexed images, thus leveraging the spatial expression characteristics of proteins. [ABSTRACT FROM AUTHOR]

Internet addiction (IA) among adolescents has become a major problem nowadays. Previous studies have found the association between childhood trauma and Internet addiction. There is evidence that coping styles may play a mediating role in this association, but related studies are still limited. This study aims to investigate the association between childhood trauma and Internet addiction among Chinese adolescents and whether coping styles mediate this association. A cross-sectional study design was adopted. Data were drawn from the Longitudinal Study of Adolescents' Mental and Behavioral Well-being Research (Registration No. ChiCTR1900022032). A multi-stage random cluster sampling was adopted to recruit a total of 1, 956 first-grade middle school students from ten high schools in Guangzhou. Demographic characteristics, childhood trauma, Internet addiction, and coping styles were measured. After controlling for sex, grade, household socioeconomic status, one child or not, living arrangement, family relation, smoking and drinking, multivariable linear regression models showed that physical abuse (unstandardized β estimate = 0.846, 95% CI = 0.615 ~ 1.076), emotional abuse (unstandardized β estimate = 0.997, 95% CI = 0.843 ~ 1.151), emotional neglect (unstandardized β estimate = 0.218, 95% CI = 0.099 ~ 0.338) and sexual abuse (unstandardized β estimate = 1.454, 95% CI = 1.065 ~ 1.844) were positively associated with IA. Further analyses showed that negative coping styles partially mediated the association between physical abuse, emotional abuse, sexual abuse, and IA among all the participants. Adolescents with childhood traumatic experiences may have a higher risk for Internet addiction, and negative coping styles may play a mediating role. Attention should be paid to adolescents with childhood traumatic experiences, especially those possessing negative coping styles. [ABSTRACT FROM AUTHOR]

Purpose: Cannabidiol (CBD) has been gaining popularity in recent years. Knowing that CBD products can contain more tetrahydrocannabinol (THC) than expected, interpretation of cannabinoids concentration in urine can be tricky, especially when low amounts of THC and CBD are found. Moreover, interpretation can also be difficult due to interindividual variation in pharmacokinetics. The objective of this work was to take a critical look at the data from our daily practice as a toxicology laboratory. Methods: We have collected results obtained in a first batch of 1074 urine samples submitted to cannabinoids analysis, and results of cannabinoids content of a second batch of 719 seized materials. Results: CBD was detected in 163 urine specimens (15%). Its concentration was higher than the limit of quantification of 5 ng/mL in 108 samples only (10% of the sampling population). Most of CBD-positive samples were associated with a high THC-COOH concentration (> 500 ng/mL in 63.8% of CBD-positive samples) suggesting only a few CBD consumers in our population. Cannabinoids composition of seized plant materials (drug type at first glance) revealed CBD in 110 of them (15% of the sampling population), with a concentration mostly below 1%. All of the resin samples were CBD positive, and contained more THC compared to flowers. Conclusions: We can conclude that urine samples from drug-type cannabis users contained a low amount of CBD, what was not described previously. These findings are useful for the interpretation of cannabinoids results in daily practice. [ABSTRACT FROM AUTHOR]

The structure and function of the circulatory system, including the heart, have undergone substantial changes with the vertebrate evolution. Although the basic function of the heart is to pump blood through the body, its size, shape, speed, regeneration capacity, etc. vary considerably across species. Here, we address the differences among vertebrate hearts using a single-cell transcriptomics approach. Published datasets of macaque (Macaca fascicularis), mouse, and zebrafish hearts were integrated and compared to the human heart as a reference. While the three mammalian hearts integrated well, the zebrafish heart showed very little overlap with the other species. Our analysis revealed a mouse-specific cell subpopulation of ventricular cardiomyocytes (CM), represented by strikingly different expression patterns of specific genes related to high-energy metabolism. Interestingly, the observed differences between mouse and human CM coincided with actual biological differences between the two species. Smooth muscle and endothelial cells (EC) exhibited species-specific differences in clustering and gene expression, respectively, which we attribute to the tissues selected for sequencing, given different focuses of the original studies. Finally, we compared human and zebrafish heart-specific fibroblasts (FB) and identified a distinctively high expression of genes associated with heart regeneration following injury in zebrafish. Together, our results show that integration of numerous datasets of different species and different sequencing technologies is feasible and that this approach can identify species-specific differences and similarities in the heart. [ABSTRACT FROM AUTHOR]

Current biotechnologies can simultaneously measure multiple high-dimensional modalities (e.g., RNA, DNA accessibility, and protein) from the same cells. A combination of different analytical tasks (e.g., multi-modal integration and cross-modal analysis) is required to comprehensively understand such data, inferring how gene regulation drives biological diversity and functions. However, current analytical methods are designed to perform a single task, only providing a partial picture of the multi-modal data. Here, we present UnitedNet, an explainable multi-task deep neural network capable of integrating different tasks to analyze single-cell multi-modality data. Applied to various multi-modality datasets (e.g., Patch-seq, multiome ATAC + gene expression, and spatial transcriptomics), UnitedNet demonstrates similar or better accuracy in multi-modal integration and cross-modal prediction compared with state-of-the-art methods. Moreover, by dissecting the trained UnitedNet with the explainable machine learning algorithm, we can directly quantify the relationship between gene expression and other modalities with cell-type specificity. UnitedNet is a comprehensive end-to-end framework that could be broadly applicable to single-cell multi-modality biology. This framework has the potential to facilitate the discovery of cell-type-specific regulation kinetics across transcriptomics and other modalities. Multimodal biological data is challenging to analyze. Here, the authors develop UnitedNet, an explainable deep neural network for analyzing single-cell multimodal biological data and estimating relationships between gene expression and other modalities with cell-type specificity. [ABSTRACT FROM AUTHOR]

Background: Prostate cancer is the second most frequently occurring carcinoma in males worldwide and one of the leading causes of death in men around the world. Recent studies estimate that over 1.4 million males are diagnosed with prostate cancer on an annual basis, with approximately 375,000 succumbing to the disease annually. With current treatments continuing to show severe side effects, there is a need for new treatments. In this study we looked at the effect of cannabis sativa extract, cannabidiol and cisplatin on prostate cancer cells, PC3. Methods: In addressing the above questions, we employed the MTT assay to measure the antiproliferative effect on PC3 cells following treatment with varying concentrations of Cannabis sativa extract, cisplatin and cannabidiol. xCELLigence was also used to confirm the IC50 activity in which cells were grown in a 16 well plate coated with gold and monitor cell attachment. Caspase 3/7 activity was also measured using 96 well-plate following treatment. Western-blot and qRT-PCR was also used to measure the gene expression of tumour suppressor genes, p53, Bax and Bcl2. Animal studies were employed to measure the growth of PC3-mouse derived cancer to evaluate the effect of compounds in vivo. Results: From the treatment with varying concentrations of Cannabis sativa extract, cannabidiol and cisplatin, we have observed that the three compounds induced antiproliferation of PC3 cancer cell lines through the activation of caspase 3/7 activity. We also observed induction of apoptosis in these cells following silencing of retinoblastoma binding protein 6 (RBBP6), with upregulation of p53 and bax mRNA expression, and a reduction in Bcl2 gene expression. The growth of tumours in the mouse models were reduced following treatment with cisplatin and cannabidiol. Conclusion: We demonstrated that cannabidiol is a viable therapy to treat prostate cancer cells, in combination with silencing of RBBP6. This suggests that cannabidiol rather Cannabis sativa extract may play an important role in reducing cancer progression. [ABSTRACT FROM AUTHOR]

Purpose of Review: Gastroenteropancreatic NEN (GEP-NEN) are group of malignancies with significant clinical, anatomical and molecular heterogeneity. High-grade GEP-NEN in particular present unique management challenges. Recent Findings: In the current era, multidisciplinary management with access to a combination of functional imaging and targeted molecular profiling can provide important disease characterisation, guide individualised management and improve patient outcome. Multiple treatment options are now available, and combination and novel therapies are being explored in clinical trials. Summary: Precision medicine is highly relevant for a heterogenous disease like NEN. The integration of dual-tracer functional PET/CT imaging, molecular histopathology and genomic data has the potential to be used to gain a more comprehensive understanding of an individual patient's disease biology for precision diagnosis, prognostication and optimal treatment allocation. [ABSTRACT FROM AUTHOR]

The use of cannabidiol (CBD) for treating brain disorders has gained increasing interest. While the mechanism of action of CBD in these conditions is still under investigation, CBD has been shown to affect numerous different drug targets in the brain that are involved in brain disorders. Here we review the preclinical and clinical evidence on the potential therapeutic use of CBD in treating various brain disorders. Moreover, we also examine various drug delivery approaches that have been applied to CBD. Due to the slow absorption and low bioavailability with the current oral CBD therapy, more efficient routes of administration to bypass hepatic metabolism, particularly pulmonary delivery, should be considered. Comparison of pharmacokinetic studies of different delivery routes highlight the advantages of intranasal and inhalation drug delivery over other routes of administration (oral, injection, sublingual, buccal, and transdermal) for treating brain disorders. These two routes of delivery, being non-invasive and able to achieve fast absorption and increase bioavailability, are attracting increasing interest for CBD applications, with more research and development expected in the near future. [ABSTRACT FROM AUTHOR]

The coffee pulp is the solid waste most generated during the coffee humid pulping. It contains an elevated concentration of organic matter and lignocellulosic materials. In addition, the low rate of biodegradability of the coffee pulp waste may cause severe environmental damage. Therefore, the aim of this study was to evaluate the effect of acid and thermal pre-treatments in the biodegradability rate, followed by an anaerobic digestion of the coffee pulp and collect biogas production. Thermal and acid pre-treatments (50, 70, and 90 °C, and concentration of 2.5, 5, and 10% V·V−1 of acetic acid, respectively) were applied, both during 1 h, evaluating the organic matter solubilization. Posteriorly, the biodegradability and biogas production using anaerobic digestion for 35 days were evaluated. Finally, during 90 days in semicontinuous, the increase of applied organic load of 1 kg VS m−3 day−1 with Δ 1 kg VS m−3 day−1 every 30 days until reaching 3 kg m−3 day−1 was evaluated. The thermal pretreatment to 90 °C and 1 h improved the solubility and hydrolysis considered limiting of the anaerobic process, reducing the hydraulic retention time from 21 to 15 days, and increasing the biogas yields (0.92 L g VSrem−1 year 79.8% CH4). [ABSTRACT FROM AUTHOR]

This paper presents a novel solution to evaluate the real impact of information and information technology (IT) in companies as fundamental tools to assess how valuable the company's customers are. The focus is on the concept of information and knowledge as an asset with intrinsic economic value and a variable to consider in order to achieve effective customer management. It is dual research. On the one hand, it is an exploratory-conceptual type based on a review of the literature and, on the other hand, it is descriptive-quantitative through the application of an empirical case study. The proposed new customer relationship management (CRM) information value model (VICRM) offers significantly different results from other traditional models. Its main advantages include the possibility of carrying out new segmentations of customers and estimations of the company's value. On the other hand, the model corrects some of the classic problems in customer evaluation models, such as their high dependence on the retention rate or being very regressive in the first years. The results of this research aim to serve any company whose business model is Business to Consumer (B2C) to calculate, in an effective way, the value of a large amount of information companies have about their customers. [ABSTRACT FROM AUTHOR]

Considering young adults' extensive use of social media since the outbreak of the COVID-19 pandemic, the present study examined the pattern of Facebook use by university students during the period of hygienic crisis. Specifically, it was investigated students' Facebook intensity use and self-disclosure to unknown online friends, as well as the role of sense of resilience and loneliness in the manifestation of the above Facebook behaviors. Overall, 792 undergraduate and postgraduate university students (48% women) completed online self-report questionnaires regarding the above variables. Undergraduate students, regardless of gender and Department of studies, made more intense Facebook use and self-disclosure to unknown online friends. Sense of loneliness positively predicted students' online self-disclosure not only directly but also indirectly through their Facebook intensity use. Students' resilience negatively moderated the relationship between sense of loneliness and Facebook behaviors. The findings propose a new explanatory model of emotional and behavioral mechanisms, which leads to a less safe pattern of Facebook use. This pattern possibly reflects youth's collective tendency to use this social media platform recklessly as a way out of crisis periods, such as the pandemic period. The emergence of this pattern could be useful for launching or enriching university counselling/prevention actions aimed at strengthening students' psycho-emotional skills, and subsequently their prudent use of social media. [ABSTRACT FROM AUTHOR]

Single-cell epigenomic data has been growing continuously at an unprecedented pace, but their characteristics such as high dimensionality and sparsity pose substantial challenges to downstream analysis. Although deep learning models-especially variational autoencoders-have been widely used to capture low-dimensional feature embeddings, the prevalent Gaussian assumption somewhat disagrees with real data, and these models tend to struggle to incorporate reference information from abundant cell atlases. Here we propose CASTLE, a deep generative model based on the vector-quantized variational autoencoder framework to extract discrete latent embeddings that interpretably characterize single-cell chromatin accessibility sequencing data. We validate the performance and robustness of CASTLE for accurate cell-type identification and reasonable visualization compared with state-of-the-art methods. We demonstrate the advantages of CASTLE for effective incorporation of existing massive reference datasets in a weakly supervised or supervised manner. We further demonstrate CASTLE's capacity for intuitively distilling cell-type-specific feature spectra that unveil cell heterogeneity and biological implications quantitatively., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Integration of single-cell RNA sequencing data between different samples has been a major challenge for analyzing cell populations. However, strategies to integrate differential expression analysis of single-cell data remain underinvestigated. Here, we benchmark 46 workflows for differential expression analysis of single-cell data with multiple batches. We show that batch effects, sequencing depth and data sparsity substantially impact their performances. Notably, we find that the use of batch-corrected data rarely improves the analysis for sparse data, whereas batch covariate modeling improves the analysis for substantial batch effects. We show that for low depth data, single-cell techniques based on zero-inflation model deteriorate the performance, whereas the analysis of uncorrected data using limmatrend, Wilcoxon test and fixed effects model performs well. We suggest several high-performance methods under different conditions based on various simulation and real data analyses. Additionally, we demonstrate that differential expression analysis for a specific cell type outperforms that of large-scale bulk sample data in prioritizing disease-related genes. Integration of single-cell RNA sequencing data between different samples has been a major challenge for analyzing cell populations. Here the authors benchmark 46 workflows for differential expression analysis of single-cell data with multiple batches and suggest several high-performance methods under different conditions based on simulation and real data analyses. [ABSTRACT FROM AUTHOR]

The past five decades have witnessed satellite remote sensing become one of the most efficient tools for fire detection and estimating total burned areas. Not all algorithms are appropriate for detecting high-temperature events on a global scale. It is difficult for traditional fire detection algorithms to capture small and low-intensity fires with significant accuracy. For this, we propose an improved fire detection algorithm by considering channel 4 and 11 µm of MODIS data with two different thresholds for hot and cold seasons. Moreover, false alarm rejections caused by the edges of clouds are designed for this algorithm. The validation against 231 reference fires showed good performance of our algorithm over the northern forests of Iran. This algorithm detected 72 fires, while the MODIS fire product, a widely used source for fire detection, detected only 26 fires. The results indicate an outperformance of 19.91%. The results show that our algorithm for the fire detection method overperforms the traditional methods and can be particularly useful for fire detection in the northern forests of Iran and can be applied in similar forests worldwide. [ABSTRACT FROM AUTHOR]

The central nervous system is the main target of MeHg toxicity and glial cells are the first line of defense; however, their true role remains unclear. This study aimed to identify the global map of human glial-like (U87) cells transcriptome after exposure to a non-toxic and non-lethal MeHg concentration and to investigate the related molecular changes. U87 cells were exposed upon 0.1, 0.5, and 1 µM MeHg for 4 and 24 h. Although no changes were observed in the percentage of viable cells, the metabolic viability was significantly decreased after exposure to 1 µM MeHg for 24 h; thus, the non-toxic concentration of 0.1 µM MeHg was chosen to perform microarray analysis. Significant changes in U87 cells transcriptome were observed only after 24 h. The expression of 392 genes was down regulated while 431 genes were up-regulated. Gene ontology showed alterations in biological processes (75%), cellular components (21%), and molecular functions (4%). The main pathways showed by KEGG and Reactome were cell cycle regulation and Rho GTPase signaling. The complex mechanism of U87 cells response against MeHg exposure indicates that even a low and non-toxic concentration is able to alter the gene expression profile. [ABSTRACT FROM AUTHOR]

Large compendia of gene expression data have proven valuable for the discovery of novel biological relationships. Historically, most available RNA assays were run on microarray, while RNA-seq is now the platform of choice for many new experiments. The data structure and distributions between the platforms differ, making it challenging to combine them directly. Here we perform supervised and unsupervised machine learning evaluations to assess which existing normalization methods are best suited for combining microarray and RNA-seq data. We find that quantile and Training Distribution Matching normalization allow for supervised and unsupervised model training on microarray and RNA-seq data simultaneously. Nonparanormal normalization and z-scores are also appropriate for some applications, including pathway analysis with Pathway-Level Information Extractor (PLIER). We demonstrate that it is possible to perform effective cross-platform normalization using existing methods to combine microarray and RNA-seq data for machine learning applications. An evaluation of normalization methods shows that it is possible to combine microarray and RNA-seq data for machine learning applications. [ABSTRACT FROM AUTHOR]