Dataset on the effects of psychological care on depression and suicide ideation in underrepresented children.

Massive increases in the risks of depressive disorders and the ensuing suicide have become the overarching menace for children/adolescents. Despite global consensus to instigate psychological healthcare policy for these children/adolescents, their effects remain largely unclear neither from a small amount of official data nor from small-scale scientific studies. More importantly, in underprivileged children/adolescents in lower-middle-economic-status countries/areas, the data collection may not be as equally accessible as in developed countries/areas, thus resulting in underrepresented observations. To address these challenges, we released a large-scale and multi-center cohort dataset (n = 249,772) showing the effects of primary psychological healthcare on decreasing depression and suicidal ideation in these children/adolescents who were underrepresented in previous studies or current healthcare systems, including unattended children/adolescents, orphans, children/adolescents in especially difficult circumstances, and "left-behind" and "single-parenting" children/adolescents. We provided all individual data recording the depressive symptoms and suicide ideation that had been collected at baseline (Oct 2022) and half-year follow-up (May 2023) from practicing this psychological healthcare system.

Cortical gradient perturbation in attention deficit hyperactivity disorder correlates with neurotransmitter-, cell type-specific and chromosome- transcriptomic signatures.

AIMS: This study aimed to illuminate the neuropathological landscape of attention deficit hyperactivity disorder (ADHD) by a multiscale macro-micro-molecular perspective from in vivo neuroimaging data. METHODS: The "ADHD-200 initiative" repository provided multi-site high-quality resting-state functional connectivity (rsfc-) neuroimaging for ADHD children and matched typically developing (TD) cohort. Diffusion mapping embedding model to derive the functional connectome gradient detecting biologically plausible neural pattern was built, and the multivariate partial least square method to uncover the enrichment of neurotransmitomic, cellular and chromosomal gradient-transcriptional signatures of AHBA enrichment and meta-analytic decoding. RESULTS: Compared to TD, ADHD children presented connectopic cortical gradient perturbations in almost all the cognition-involved brain macroscale networks (all pBH <0.001), but not in the brain global topology. As an intermediate phenotypic variant, such gradient perturbation was spatially enriched into distributions of GABAA/BZ and 5-HT2A receptors (all pBH <0.01) and co-varied with genetic transcriptional expressions (e.g. DYDC2, ATOH7, all pBH <0.01), associated with phenotypic variants in episodic memory and emotional regulations. Enrichment models demonstrated such gradient-transcriptional variants indicated the risk of both cell-specific and chromosome- dysfunctions, especially in enriched expression of oligodendrocyte precursors and endothelial cells (all pperm <0.05) as well enrichment into chromosome 18, 19 and X (pperm <0.05). CONCLUSIONS: Our findings bridged brain macroscale neuropathological patterns to microscale/cellular biological architectures for ADHD children, demonstrating the neurobiologically pathological mechanism of ADHD into the genetic and molecular variants in GABA and 5-HT systems as well brain-derived enrichment of specific cellular/chromosomal expressions.

Neuromorphic Nanoionics for Human-Machine Interaction: From Materials to Applications.

Human-machine interaction (HMI) technology has undergone significant advancements in recent years, enabling seamless communication between humans and machines. Its expansion has extended into various emerging domains, including human healthcare, machine perception, and biointerfaces, thereby magnifying the demand for advanced intelligent technologies. Neuromorphic computing, a paradigm rooted in nanoionic devices that emulate the operations and architecture of the human brain, has emerged as a powerful tool for highly efficient information processing. This paper delivers a comprehensive review of recent developments in nanoionic device-based neuromorphic computing technologies and their pivotal role in shaping the next-generation of HMI. Through a detailed examination of fundamental mechanisms and behaviors, the paper explores the ability of nanoionic memristors and ion-gated transistors to emulate the intricate functions of neurons and synapses. Crucial performance metrics, such as reliability, energy efficiency, flexibility, and biocompatibility, are rigorously evaluated. Potential applications, challenges, and opportunities of using the neuromorphic computing technologies in emerging HMI technologies, are discussed and outlooked, shedding light on the fusion of humans with machines.

The Combination of Anti-CD47 Antibody with CTLA4 Blockade Enhances Anti-Tumor Immunity in Non-Small Cell Lung Cancer via Normalization of Tumor Vasculature and Reprogramming of the Immune Microenvironment.

In solid tumors, the formidable anti-tumor impact resulting from blocking the "don't eat me" signal, arising from CD47-SIRPα interaction, is constrained, especially compared to its efficacy in hematopoietic malignancies. Activating macrophage anti-tumor activity not only necessitates the inhibition of the "don't eat me" signal, but also the activation of the "eat me" (pre-phagocyte) signal. Intriguingly, the cytotoxic T-lymphocyte-associated antigen 4 (CTLA4) antibody (Ab) has been identified to stimulate Fc receptor-mediated active phagocytes in the tumor microenvironment, thereby generating "eat me" signals. This study postulates that concurrently targeting CD47 and CTLA4 could intensify the anti-tumor effects by simultaneously blocking the "don't eat me" signal while triggering the "eat me" signal. The experimental data from this investigation confirm that the combined targeting of CD47 and CTLA4 enhances immunity against solid tumors in LLC cell-transplanted tumor-bearing mice. This effect is achieved by reducing myeloid-derived suppressor cell infiltration while increasing the presence of effector memory CD8+ T cells, NK1.1+ CD8+ T cells, and activated natural killer T cells. Meanwhile, combination therapy also alleviated anemia. Mechanistically, the anti-CD47 Ab is shown to upregulate CTLA4 levels in NSCLC cells by regulating Foxp1. Furthermore, targeting CD47 is demonstrated to promote tumor vascular normalization through the heightened infiltration of CD4+ T cells. These findings suggest that the dual targeting of CD47 and CTLA4 exerts anti-tumor effects by orchestrating the "eat me" and "don't eat me" signals, reshaping the immune microenvironment, and fostering tumor vascular normalization. This combined therapeutic approach emerges as a potent strategy for effectively treating solid tumors.

Development and efficacy evaluation of a novel water-in-oil-in-water adjuvant for an inactivated foot-and-mouth disease vaccine.

To develop a novel water-in-oil-in-water (W/O/W) adjuvant and evaluate the effect on foot-and-mouth disease (FMD) inactivated vaccine, in this study, we prepared the novel nano-emulsion adjuvant based on QS-21 (BEA) which is composed of the mixture of mineral oil Marcol52, surfactant Tween80, oleate polyoxyethylene ether ester, polyoxyethylene palmitic acid ester and span80, cosurfactant polyethylene glycol and QS-21. The two-step emulsification method formed the W/O/W nano-emulsion with two films and three-phase structures. The effective particle diameter of the BEA was about 184 nm, and it has good thermal stability. Then, BEA was emulsified as an adjuvant to prepare for the inactivated FMDV vaccine, and BALB/c mice and pigs were immunized to evaluate its safety and immunization effect. The results showed that the inactivated BEA-FMDV vaccine significantly increased BALB/c mice and pigs' antibodies and cytokine IFN-γ in serum. Meanwhile, the pig-neutralizing antibodies were higher than control group. Safety tests found no symptoms of FMD or significant toxic reactions. After 28 days of immunization, the protection rate can reach 93.3%. The BEA vaccine had good stability at 4 °C, no stratification after 180 days, and the content of 146S in the vaccine did not decrease. In conclusion, the BEA prepared in this study is suitable for FMDV inactivated vaccine and is an effective adjuvant.

Unraveling the Nanoscale Segregation Mechanism in N-Doped Niobium for Enhanced SRF Performance.

The nitrogen doping (N-doping) treatment for niobium superconducting radio-frequency (SRF) cavities is one of the key enabling technologies that support the development of more efficient future large accelerators. However, the N-doping results have diverged due to a complex chemical profile under the nitrogen-doped surface. Particularly, under industrial-scale production conditions, it is difficult to understand the underlying mechanism thus hindering performance improvement. Herein, a combination of spatially resolved and surface-sensitive approaches is employed to establish the detailed near-surface phase composition of thermally processed niobium. The results show that intermediate phase segregations, particularly the nanometric carbon-rich phase, can impede the nitridation process and limit the interactions between nitrogen and the niobium sub-surface. In comparison, the removal of the carbon-rich layer at the Nb surface leads to enhanced nitrogen binding at the Nb surface. Combining the RF test results, it is shown that the complex uniformity and grain boundary penetrations of impurity elements have a direct correlation with the mid-field quench behavior in the N-doped Nb cavities. Therefore, proper control of the nanometric intermediate phase formation in discrete thermal steps is critical in improving the ultimate performance and production yield of the Nb cavities.

A Discolorable Flexible Synaptic Transistor for Wearable Health Monitoring.

Multifunctional intelligent wearable electronics, providing integrated physiological signal analysis, storage, and display for real-time and on-site health status diagnosis, have great potential to revolutionize health monitoring technologies. Advanced wearable systems combine isolated digital processor, memory, and display modules for function integration; however, they suffer from compatibility and reliability issues. Here, we introduce a flexible multifunctional electrolyte-gated transistor (EGT) that integrates synaptic learning, memory, and autonomous discoloration functionalities for intelligent wearable application. This device exhibits synergistic light absorption coefficient changes during voltage-gated ion doping that modulate the electrical conductance changes for synaptic function implementation. By adaptively changing color, the EGT can differentiate voltage pulse inputs with different frequency, amplitude, and duration parameters, exhibiting excellent reversibility and reliability. We developed a smart wearable monitoring system that incorporates EGT devices and sensors for respiratory and electrocardiogram signal analysis, providing health warnings through real-time and on-site discoloration. This study represents a significant step toward smart wearable technologies for health management, offering health evaluation through intelligent displays.

Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.

BACKGROUND: The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation. METHODS: Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses. RESULTS: A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (ß = - 2.75, p < .001, R2adj = 0.40; r = - .84, 95% CI: - .41 to - .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0-87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2-56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9-90.8%)/availability (80.88% of models, 95% CI: 77.3-84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF10 > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance. CONCLUSIONS: Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.

Measuring the effect of newborn screening on survival after haematopoietic cell transplantation for severe combined immunodeficiency: a 36-year longitudinal study from the Primary Immune Deficiency Treatment Consortium.

BACKGROUND: Severe combined immunodeficiency (SCID) is fatal unless durable adaptive immunity is established, most commonly through allogeneic haematopoietic cell transplantation (HCT). The Primary Immune Deficiency Treatment Consortium (PIDTC) explored factors affecting the survival of individuals with SCID over almost four decades, focusing on the effects of population-based newborn screening for SCID that was initiated in 2008 and expanded during 2010-18. METHODS: We analysed transplantation-related data from children with SCID treated at 34 PIDTC sites in the USA and Canada, using the calendar time intervals 1982-89, 1990-99, 2000-09, and 2010-18. Categorical variables were compared by χ2 test and continuous outcomes by the Kruskal-Wallis test. Overall survival was estimated by the Kaplan-Meier method. A multivariable analysis using Cox proportional hazards regression models examined risk factors for HCT outcomes, including the variables of time interval of HCT, infection status and age at HCT, trigger for diagnosis, SCID type and genotype, race and ethnicity of the patient, non-HLA-matched sibling donor type, graft type, GVHD prophylaxis, and conditioning intensity. FINDINGS: For 902 children with confirmed SCID, 5-year overall survival remained unchanged at 72%-73% for 28 years until 2010-18, when it increased to 87% (95% CI 82·1-90·6; n=268; p=0·0005). For children identified as having SCID by newborn screening since 2010, 5-year overall survival was 92·5% (95% CI 85·8-96·1), better than that of children identified by clinical illness or family history in the same interval (79·9% [69·5-87·0] and 85·4% [71·8-92·8], respectively [p=0·043]). Multivariable analysis demonstrated that the factors of active infection (hazard ratio [HR] 2·41, 95% CI 1·56-3·72; p<0·0001), age 3·5 months or older at HCT (2·12, 1·38-3·24; p=0·001), Black or African-American race (2·33, 1·56-3·46; p<0·0001), and certain SCID genotypes to be associated with lower overall survival during all time intervals. Moreover, after adjusting for several factors in this multivariable analysis, HCT after 2010 no longer conveyed a survival advantage over earlier time intervals studied (HR 0·73, 95% CI 0·43-1·26; p=0·097). This indicated that younger age and freedom from infections at HCT, both directly driven by newborn screening, were the main drivers for recent improvement in overall survival. INTERPRETATION: Population-based newborn screening has facilitated the identification of infants with SCID early in life, in turn leading to prompt HCT while avoiding infections. Public health programmes worldwide can benefit from this definitive demonstration of the value of newborn screening for SCID. FUNDING: National Institute of Allergy and Infectious Diseases, Office of Rare Diseases Research, and National Center for Advancing Translational Sciences.

Optogenetics-inspired manipulation of synaptic memory using all-optically controlled memristors.

Memristive synapses compatible with optogenetic techniques allow for the fast and low-power manipulation of memory activities using light in artificial neural systems. However, most of the optoelectronic memristors operate in the hybrid optic-electric mode; the reversible regulation of memristive states solely using light for optogenetic emulation is difficult. In this work, an all-optical controlled optoelectronic memristor (Au/Cs2AgBiBr6/Au) is developed for mimicking optogenetics-tuned memory formation and erasing behaviors in biological synapses. We show that the memristor exhibits positive and negative persistent photoconductivity effects under different light wavelengths, attributed to light-regulated carrier de-trapping/trapping at the Au/Cs2AgBiBr6 interface. This device can emulate both excitatory and inhibitory synaptic plasticity and associated learning and memory effects under light illumination. We constructed a prototype optoelectronic synaptic array and implemented the all-optically controlled memory implantation, erasing, and modification, demonstrating the light-reconfigured cognition capabilities. Our findings will inspire the development of all-optically controlled artificial neural systems with good reconfigurability for efficient neuromorphic computing and machine vision.

Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.

Importance: Neuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated. Objective: To systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis. Evidence Review: PubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality. Findings: A total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%). Conclusions and Relevance: This systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.

Near-Sensor Reservoir Computing for Gait Recognition via a Multi-Gate Electrolyte-Gated Transistor.

The recent emergence of various smart wearable electronics has furnished the rapid development of human-computer interaction, medical health monitoring technologies, etc. Unfortunately, processing redundant motion and physiological data acquired by multiple wearable sensors using conventional off-site digital computers typically result in serious latency and energy consumption problems. In this work, a multi-gate electrolyte-gated transistor (EGT)-based reservoir device for efficient multi-channel near-sensor computing is reported. The EGT, exhibiting rich short-term dynamics under voltage modulation, can implement nonlinear parallel integration of the time-series signals thus extracting the temporal features such as the synchronization state and collective frequency in the inputs. The flexible EGT integrated with pressure sensors can perform on-site gait information analysis, enabling the identification of motion behaviors and Parkinson's disease. This near-sensor reservoir computing system offers a new route for rapid analysis of the motion and physiological signals with significantly improved efficiency and will lead to robust smart flexible wearable electronics.

Selective Dual-Ion Modulation in Solid-State Magnetoelectric Heterojunctions for In-Memory Encryption.

Nanoionic technologies are identified as a promising approach to modulating the physical properties of solid-state dielectrics, which have resulted in various emergent nanodevices, such as nanoionic resistive switching devices and magnetoionic devices for memory and computing applications. Previous studies are limited to single-type ion manipulation, and the investigation of multiple-type ion modulation on the coupled magnetoelectric effects, for developing information devices with multiple integrated functionalities, remains elusive. Here, a dual-ion solid-state magnetoelectric heterojunction based on Pt/HfO2- x /NiOy /Ni with reconfigurable magnetoresistance (MR) characteristics is reported for in-memory encryption. It is shown that the oxygen anions and nickel cations can be selectively driven by voltages with controlled polarity and intensity, which concurrently change the overall electrical resistance and the interfacial magnetic coupling, thus significantly modulate the MR symmetry. Based on this device, a magnetoelectric memory prototype array with in-memory encryption functionality is designed for the secure storage of image and digit information. Along with the advantages including simple structure, multistate encryption, good reversibility, and nonvolatile modulation capability, this proof-of-concept device opens new avenues toward next-generation compact electronics with integrated information functionalities.

Emerging electrolyte-gated transistors for neuromorphic perception.

With the rapid development of intelligent robotics, the Internet of Things, and smart sensor technologies, great enthusiasm has been devoted to developing next-generation intelligent systems for the emulation of advanced perception functions of humans. Neuromorphic devices, capable of emulating the learning, memory, analysis, and recognition functions of biological neural systems, offer solutions to intelligently process sensory information. As one of the most important neuromorphic devices, Electrolyte-gated transistors (EGTs) have shown great promise in implementing various vital neural functions and good compatibility with sensors. This review introduces the materials, operating principle, and performances of EGTs, followed by discussing the recent progress of EGTs for synapse and neuron emulation. Integrating EGTs with sensors that faithfully emulate diverse perception functions of humans such as tactile and visual perception is discussed. The challenges of EGTs for further development are given.

A Positive Association between Working Memory Capacity and Human Creativity: A Meta-Analytic Evidence.

Creativity serves as a fountain for social and scientific development. As one of the most crucial human capabilities, creativity has been believed to be supported by the core component of higher cognitive functions­working memory capacity (WMC). However, the evidence supporting the association between WMC and creativity remains contradictory. Here, we conducted a meta-analysis using random-effects models to investigate the linear association between WMC and creativity by pooling the individual effect size from the previous literature. Further, a subgroup analysis was performed to examine whether such association is specific for different WMC categories (i.e., verbal WMC, visual−spatial WMC and dual-task WMC). The main meta-analytic results showed a significantly positive association between WMC and creativity (r = .083, 95% CI: .050−.115, p < .001, n = 3104, k = 28). The subgroup analysis demonstrated consistent results by showing a significantly positive association between them, irrespective of WMC category. We also found that cultural environments could moderate this association, and we identified a strong correlation in participants from an Asian cultural context. In conclusion, this study provides the evidence to clarify the positive association between WMC and creativity, and implies that the Asian cultural context may boost such an association.

Elevated Expression and Activation of GPR15 in Immune Cells in Graves' Disease.

GPR15 plays an important role in lymphocyte homing and is a key immune molecule to maintain organ immune homeostasis. Yet, no study on the association between GPR15 and Graves' disease (GD) is available. In this study, we systematically investigated the expression of GPR15 in different types of immune cells and different tissues of GD patients. We found that the expressions of GPR15 and GPR15L in peripheral blood of GD patients were increased compared with those in healthy controls. A flow cytometry analysis showed that GPR15 positive cells were mainly CD14+ monocytes and CD56+ natural killer cells (NK cells) of innate immunity, T helper cells and cytotoxic T cells of adaptive immunity. We also found that the expressions of GPR15 and GPR15L in the PBMC of GD patients were positively correlated with the Tfh-specific cytokines IL21 and IL4. In addition, immunohistochemistry showed that the level of GPR15 in thyroid tissue of GD patients was higher than that of the control group. Our results demonstrate for the first time that GPR15 is highly expressed in various immune cells in GD patients, suggesting that GPR15-GPR15L is associated with the activation and infiltration of proinflammatory immune cells in the thyroid tissue of GD patients.

Revision and psychometric properties of the negative cognitive processing bias scale.

Negative cognitive processing bias (NCPB) is a cognitive trait that makes individuals more inclined to prioritize negative external stimuli (cues) when processing information. Cognitive biases have long been observed in mood and anxiety disorders, improving validation of tools to measure this phenomenon will aid us to determine whether there is a robust relationship between NCPB and major depressive disorder, anxiety disorders and other clinical disorders. Despite the development of an initial measure of this trait, that is, the negative cognitive processing bias questionnaire (NCPBQ), the lack of psychometric examinations and applications in large-scale samples hinders the determination of its reliability and validity and further limits our understanding of how to measure the NCPB traits of individuals accurately. To address these issues, the current study evaluated the psychometric properties of the NCPBQ in a large-scale sample (n = 6,069), which was divided into two subsamples (Subsample 1, n = 3,035, serving as the exploratory subsample, and Subsample 2, n = 3,034, serving as the validation subsample), and further revised it into a standardized scale, that is the negative cognitive processing bias scale (NCPBS), based on psychometric constructs. The results show that NCPBS possesses good construct reliability, internally consistent reliability, and test-retest reliability. Furthermore, by removing two original items from NCPBQ, NCPBS was found to have good criterion-related validity. In conclusion, the present study provides a reliable and valid scale for assessing negative cognitive processing bias of individuals.

Attitude and Performance for Online Learning during COVID-19 Pandemic: A Meta-Analytic Evidence.

The COVID-19 pandemic prominently hit almost all the aspects of our life, especially in routine education. For public health security, online learning has to be enforced to replace classroom learning. Thus, it is a priority to clarify how these changes impacted students. We built a random-effect model of a meta-analysis to pool individual effect sizes for published articles concerning the attitudes and performance towards online learning. Databases included Google Scholar, PubMed and (Chinese) CNKI repository. Further, a moderated analysis and meta-regression were further used to clarify potential heterogenous factors impacting this pooled effect. Forty published papers (n = 98,558) were screened that were eligible for formal analysis. Meta-analytic results demonstrated that 13.3% (95% CI: 10.0-17.5) of students possessed negative attitudes towards online learning during the COVID-19 pandemic. A total of 12.7% (95% CI: 9.6-16.8) students were found to report poor performance in online learning. Moderated analysis revealed poor performance in online learning in the early pandemic (p = 0.006). Results for the meta-regression analysis showed that negative attitudes could predict poor learning performance significantly (p = 0.026). In conclusion, online learning that is caused by COVID-19 pandemic may have brought about negative learning attitudes and poorer learning performance compared to classroom learning, especially in the early pandemic.

A flexible dual-gate hetero-synaptic transistor for spatiotemporal information processing.

Artificial synapses based on electrolyte gated transistors with conductance modulation characteristics have demonstrated their great potential in emulating the memory functions in the human brain for neuromorphic computing. While previous studies are mostly focused on the emulation of the basic memory functions of homo-synapses using single-gate transistors, multi-gate transistors offer opportunities for the mimicry of more complex and advanced memory formation behaviors in biological hetero-synapses. In this work, we demonstrate an artificial hetero-synapse based on a dual-gate electrolyte transistor that can implement in situ spatiotemporal information integration and storage. We show that electric pulses applied on a single gate or unsynchronized electric pulses applied on dual gates only induce volatile conductance modulation for short-term memory emulation. In contrast, the device integrates the electric pulses coincidently applied on the dual gates in a supralinear manner and exhibits nonvolatile conductance modulation, enabling long-term memory emulation. Further studies prove that artificial neural networks based on such hetero-synaptic transistors can autonomously filter the random noise signals in the dual-gate inputs during spatiotemporal integration, facilitating the formation of accurate and stable memory. Compared to the single-gate synaptic transistor, the classification accuracy of MNIST handwritten digits using the hetero-synaptic transistor is improved from 89.3% to 99.0%. These findings demonstrate the great potential of multi-gate hetero-synaptic transistors in simulating complex spatiotemporal information processing functions and provide new platforms for the design of advanced neuromorphic computing systems.

[Retracted] MicroRNA­337 inhibits colorectal cancer progression by directly targeting KRAS and suppressing the AKT and ERK pathways.

Following the publication of this paper, it was drawn to the Editors' attention by a concerned reader that various panels showing data from flow cytometric experiments in Figs. 2D and 5D were strikingly similar to data appearing in different form in other articles by different authors. Owing to the fact that the contentious data in the above article were already under consideration for publication, or had already been published, elsewhere when it was submitted to Oncology Reports, the Editor has decided that this paper should be retracted from the Journal. The authors were asked for an explanation to account for these concerns, but the Editorial Office did not receive a reply. The Editor apologizes to the readership for any inconvenience caused. [Oncology Reports 38: 3187­3196, 2017; DOI: 10.3892/or.2017.5997].