Background
Informed by the differential susceptibility to media effects model (DSMM), the current study aims to investigate associations of COVID‐19‐related social media use with mental health ...outcomes and to uncover potential mechanisms underlying the links.
Methods
A sample of 512 (62.5% women; Mage = 22.12 years, SD = 2.47) Chinese college students participated in this study from 24 March to 1 April 2020 via online questionnaire. They completed measures of social media use, the COVID‐19 stressor, negative affect, secondary traumatic stress (STS), depression, and anxiety as well as covariates.
Results
As expected, results from regression analyses indicated that a higher level of social media use was associated with worse mental health. More exposure to disaster news via social media was associated with greater depression for participants with high (but not low) levels of the disaster stressor. Moreover, path analysis showed negative affect mediated the relationship of social media use and mental health.
Conclusions
These findings suggest that the disaster stressor may be a risk factor that amplifies the deleterious impact of social media use on depression. In addition, excessive exposure to disaster on social media may trigger negative affect, which may in turn contribute to mental health problems. Future interventions to improve mental health should consider elements of both disaster stressor and negative affect.
Full text
Available for:
FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK
The ongoing COVID-19 pandemic is likely to enhance the risk of addictive social media use (SMU) as people spend more time online maintaining connectivity when face-to-face communication is limited. ...Stress is assumed to be a critical predictor of addictive SMU. However, the mechanisms underlying the association between stress and addictive SMU in crises like the current COVID-19 situation remain unclear. The present study aimed to understand the relationship between COVID-19 stress and addictive SMU by examining the mediating role of active use and social media flow (i.e., an intensive, enjoyable experience generated by SMU that perpetuates media use behaviors). A sample of 512 Chinese college students (
= 22.12 years,
= 2.47; 62.5% women) provided self-report data on COVID-19 stress and SMU variables (i.e., time, active use, flow, addictive behavior) via an online survey from March 24 to April 1, 2020. The results showed that COVID-19 stress was positively associated with tendencies toward addictive SMU. Path analyses revealed that this relationship was significantly serially mediated by active use and social media flow, with SMU time being controlled. Our findings suggest that individuals who experience more COVID-19 stress are at increased risk of addictive SMU that may be fostered by active use and flow experience. Specific attention should be paid to these high-risk populations and future interventions to reduce addictive SMU could consider targeting factors of both active use and social media flow.
The need to breathe links the mammalian olfactory system inextricably to the respiratory rhythms that draw air through the nose. In rodents and other small animals, slow oscillations of local field ...potential activity are driven at the rate of breathing (∼2-12 Hz) in olfactory bulb and cortex, and faster oscillatory bursts are coupled to specific phases of the respiratory cycle. These dynamic rhythms are thought to regulate cortical excitability and coordinate network interactions, helping to shape olfactory coding, memory, and behavior. However, while respiratory oscillations are a ubiquitous hallmark of olfactory system function in animals, direct evidence for such patterns is lacking in humans. In this study, we acquired intracranial EEG data from rare patients (Ps) with medically refractory epilepsy, enabling us to test the hypothesis that cortical oscillatory activity would be entrained to the human respiratory cycle, albeit at the much slower rhythm of ∼0.16-0.33 Hz. Our results reveal that natural breathing synchronizes electrical activity in human piriform (olfactory) cortex, as well as in limbic-related brain areas, including amygdala and hippocampus. Notably, oscillatory power peaked during inspiration and dissipated when breathing was diverted from nose to mouth. Parallel behavioral experiments showed that breathing phase enhances fear discrimination and memory retrieval. Our findings provide a unique framework for understanding the pivotal role of nasal breathing in coordinating neuronal oscillations to support stimulus processing and behavior.
Animal studies have long shown that olfactory oscillatory activity emerges in line with the natural rhythm of breathing, even in the absence of an odor stimulus. Whether the breathing cycle induces cortical oscillations in the human brain is poorly understood. In this study, we collected intracranial EEG data from rare patients with medically intractable epilepsy, and found evidence for respiratory entrainment of local field potential activity in human piriform cortex, amygdala, and hippocampus. These effects diminished when breathing was diverted to the mouth, highlighting the importance of nasal airflow for generating respiratory oscillations. Finally, behavioral data in healthy subjects suggest that breathing phase systematically influences cognitive tasks related to amygdala and hippocampal functions.
Chimeric antigen receptor (CAR) T cells have shown great promise in the treatment of hematological and solid malignancies. However, despite the success of this field, there remain some major ...challenges, including accelerated T cell exhaustion, potential toxicities, and insertional oncogenesis. To overcome these limitations, recent advances in CRISPR technology have enabled targetable interventions of endogenous genes in human CAR T cells. These CRISPR genome editing approaches have unleashed the therapeutic potential of CAR T cell therapy. Here, we summarize the potential benefits, safety concerns, and difficulties in the generation of gene-edited CAR T cells using CRISPR technology.
Abstract
Motivation
Sequence-based protein–protein interaction (PPI) prediction represents a fundamental computational biology problem. To address this problem, extensive research efforts have been ...made to extract predefined features from the sequences. Based on these features, statistical algorithms are learned to classify the PPIs. However, such explicit features are usually costly to extract, and typically have limited coverage on the PPI information.
Results
We present an end-to-end framework, PIPR (Protein–Protein Interaction Prediction Based on Siamese Residual RCNN), for PPI predictions using only the protein sequences. PIPR incorporates a deep residual recurrent convolutional neural network in the Siamese architecture, which leverages both robust local features and contextualized information, which are significant for capturing the mutual influence of proteins sequences. PIPR relieves the data pre-processing efforts that are required by other systems, and generalizes well to different application scenarios. Experimental evaluations show that PIPR outperforms various state-of-the-art systems on the binary PPI prediction problem. Moreover, it shows a promising performance on more challenging problems of interaction type prediction and binding affinity estimation, where existing approaches fall short.
Availability and implementation
The implementation is available at https://github.com/muhaochen/seq_ppi.git.
Supplementary information
Supplementary data are available at Bioinformatics online.
It is very difficult to detect multi-scale synthetic aperture radar (SAR) ships, especially under complex backgrounds. Traditional constant false alarm rate methods are cumbersome in manual design ...and weak in migration capabilities. Based on deep learning, researchers have introduced methods that have shown good performance in order to get better detection results. However, the majority of these methods have a huge network structure and many parameters which greatly restrict the application and promotion. In this paper, a fast and lightweight detection network, namely FASC-Net, is proposed for multi-scale SAR ship detection under complex backgrounds. The proposed FASC-Net is mainly composed of ASIR-Block, Focus-Block, SPP-Block, and CAPE-Block. Specifically, without losing information, Focus-Block is placed at the forefront of FASC-Net for the first down-sampling of input SAR images at first. Then, ASIR-Block continues to down-sample the feature maps and use a small number of parameters for feature extraction. After that, the receptive field of the feature maps is increased by SPP-Block, and then CAPE-Block is used to perform feature fusion and predict targets of different scales on different feature maps. Based on this, a novel loss function is designed in the present paper in order to train the FASC-Net. The detection performance and generalization ability of FASC-Net have been demonstrated by a series of comparative experiments on the SSDD dataset, SAR-Ship-Dataset, and HRSID dataset, from which it is obvious that FASC-Net has outstanding detection performance on the three datasets and is superior to the existing excellent ship detection methods.
Full text
Available for:
IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
To promote HIV-testing and offer optimal care for men who have sex with men (MSM), health-care providers (HCPs) must first be aware of their patients’ sexual behaviors. Otherwise, HCPs may overlook ...MSM’s risks for HIV infection and their special health-care needs. For MSM, reporting their same-sex behaviors to HCPs (disclosure to HCPs) may promote their linkage to HIV prevention and treatment cascade and improve their health outcomes. No literature review has been conducted to examine the relationship between disclosure to HCPs and uptake of HIV-testing among MSM. The current study reviewed and synthesized findings from 29 empirical studies published in English by 2016. We summarized the rates of MSM’s disclosure to HCPs, investigated the association between disclosure and HIV-testing among MSM, identified potential facilitators and barriers for disclosure, and discussed the implications of our findings in research and clinical practices. The disclosure rates varied across subgroups and study settings, ranging from 16% to 90% with a median of 61%. Disclosure to HCPs was positively associated with uptake of HIV-testing. African American MSM were less likely to disclose to HCPs. MSM who lived in urban settings with higher education attainment and higher income were more likely to disclose. MSM tended to perceive younger or gay-friendly doctors as safer targets of disclosure. Clinics with LGBT-friendly signs were viewed as safer contexts for disclosure. Having previous communications about substance use, sex, and HIV with HCPs could also facilitate disclosure. The main reasons for nondisclosure included lack of probing from HCPs, concerns on confidentiality breach and stigma, and perceived irrelevance with services. Providing appropriate trainings for HCPs and creating gay-friendly clinical settings can be effective strategies to facilitate disclosures of same-sex behaviors among MSM and meet their specific medical needs. Interventions to promote disclosure should give priorities to MSM from the most marginalized subgroups (e.g., MSM in rural areas, MSM of ethnic minorities).
•The human microbiome plays a critical role in human health and disease.•Predicting disease status from metagenomic data is increasingly important.•We review new methods for this task, focusing ...mostly on deep learning.•We perform an in-depth analysis of challenging type 2 diabetes and obesity datasets.•We offer perspectives on study design concerns and potential future directions.
The human microbiome plays a number of critical roles, impacting almost every aspect of human health and well-being. Conditions in the microbiome have been linked to a number of significant diseases. Additionally, revolutions in sequencing technology have led to a rapid increase in publicly-available sequencing data. Consequently, there have been growing efforts to predict disease status from metagenomic sequencing data, with a proliferation of new approaches in the last few years. Some of these efforts have explored utilizing a powerful form of machine learning called deep learning, which has been applied successfully in several biological domains. Here, we review some of these methods and the algorithms that they are based on, with a particular focus on deep learning methods. We also perform a deeper analysis of Type 2 Diabetes and obesity datasets that have eluded improved results, using a variety of machine learning and feature extraction methods. We conclude by offering perspectives on study design considerations that may impact results and future directions the field can take to improve results and offer more valuable conclusions. The scripts and extracted features for the analyses conducted in this paper are available via GitHub:https://github.com/nlapier2/metapheno.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The short-chain fatty acids (SCFAs) accumulated in waste activated sludge (WAS) fermentation was adopted as an alternative extra carbon source for biohydrogen production in microbial electrolysis ...cells (MECs). WAS was pretreated by bi-frequency ultrasonic and the highest SCFAs were accumulated at 3rd day. Three groups of tests were conducted in single chamber MECs for H2 production under different SCOD concentrations. SCOD removals were up to 60% at diluted influent, but reduced to 50% at original concentration. Highest H2 yield was 1.2 mL H2/mg COD at 2-fold dilution with 155% energy efficiency. Results showed that >90% of acetate and ∼90% of propionate were effectively converted to hydrogen, and next were n-butyrate and n-valerate (at dilutions), but <20% of iso-butyrate and iso-valerate were converted. The overall biohydrogen recovery in this study was 120 ml H2/g VSS/d. This work shows a possibility of cascade utilization of WAS fermentation liquid and H2 generation in MEC.
► We studied bio-H2 production in MECs feeding with waste activated sludge. ► Hydrolysis and acidification were improved by bi-frequency ultrasonic pretreatment. ► Acetate and butyrate in fermentation liquid were most converted to hydrogen in MECs. ► Energy efficiency using sludge for H2 was much higher than that without pretreatment.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
The central processing pathways of the human olfactory system are not fully understood. The olfactory bulb projects directly to a number of cortical brain structures, but the distinct networks formed ...by projections from each of these structures to the rest of the brain have not been well-defined. Here, we used functional magnetic resonance imaging and k-means clustering to parcellate human primary olfactory cortex into clusters based on whole-brain functional connectivity patterns. Resulting clusters accurately corresponded to anterior olfactory nucleus, olfactory tubercle, and frontal and temporal piriform cortices, suggesting dissociable whole-brain networks formed by the subregions of primary olfactory cortex. This result was replicated in an independent data set. We then characterized the unique functional connectivity profiles of each subregion, producing a map of the large-scale processing pathways of the human olfactory system. These results provide insight into the functional and anatomical organization of the human olfactory system.
PDF
