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This paper proposes a blended teaching design model on WeChat Platform-based SPOC and carries out an empirical study of its applicability and effectiveness in lower-secondary school science teaching. Participants were from two eighth grade classes with different academic performance, and after conducting the same blended teaching, it was found that pre-class knowledge transmission on the WeChat platform considerably enhanced students' classroom participation, which was also highly related to their improved post-class test scores. The improvement in the performance of the weak groups is more obvious. In addition, backend data from the WeChat platform revealed that the number of resource clicks was significantly higher than the total number of students, indicating that the sharing function of the WeChat platform facilitated the sharing of educational resources. Seven in-service science teachers were also interviewed to qualitatively evaluate and analyze the teaching model from the perspective of in-service teachers. The comprehensive results indicate that the teaching model constructed in this study achieves good results, facilitates the development of students' independent learning ability, and allows for the integration of this blended teaching model into lower-secondary curriculum.

Delineating the gene-regulatory programs underlying complex cell types is fundamental for understanding brain function in health and disease. Here, we comprehensively examined human brain cell epigenomes by probing DNA methylation and chromatin conformation at single-cell resolution in 517 thousand cells (399 thousand neurons and 118 thousand non-neurons) from 46 regions of three adult male brains. We identified 188 cell types and characterized their molecular signatures. Integrative analyses revealed concordant changes in DNA methylation, chromatin accessibility, chromatin organization, and gene expression across cell types, cortical areas, and basal ganglia structures. We further developed single-cell methylation barcodes that reliably predict brain cell types using the methylation status of select genomic sites. This multimodal epigenomic brain cell atlas provides new insights into the complexity of cell-type-specific gene regulation in adult human brains.

Ozone‐depleting substances (ODSs) are well known as primary emission from the production and consumption of traditional industrial sectors. Here, we reported the unintentional emission of ODSs from iron and steel plants as a new source, basing on real‐world measurements of flue gases emitted from their major processes. The sintering was found to be the major emission process of ODSs, including chlorofluorocarbons (CFCs), hydrochlorofluorocarbons (HCFCs), halons, methyl halide (CH3Cl), methyl chloroform, carbon tetrachloride, methyl bromide and halogenated very short‐lived substances. The median emission factors of CFC‐113, CFC‐115, HCFC‐22, and CH3Cl for typical sintering processes are 1.7, 0.7, 44.5 and 237.0 mg/t, respectively. Quantum chemical calculation figures out that the ODS species are mainly formed in the low efficiency combustion process of halogenated materials. Annual amounts of ODS and CFC‐11‐equivalent emissions were estimated to be 1,785 tons and 78 tons in 2019 over mainland China, respectively. Given these findings, this study provides a new prospective on searching for ODS emission sources, especially unintentional sources such as iron and steel industry and other combustion related activities. Plain Language Summary: Ozone‐depletion substances (ODSs) are halocarbons that can damage the ozone layer. Although the tropospheric chlorine has been continuously declined, some halogenated organic gaseous species are still found to be increasingly emitted. This study firstly reports the unintentional emission of ODSs from iron and steel plants via real‐world measurements. ODS emission from the sintering process shows larger ozone depleting effect than that from ironmaking and steelmaking processes. The halogen‐containing iron ore, coke and fluxing agent provide mineral halogens for the generation of halogenated compounds, while the high temperature of combustion atmosphere in the sinter machine benefits for the chemical combination of halogens and hydrocarbons. Current air pollution control devices cannot effectively remove ODSs. This study aims at attracting attention to the identification and control of unintentional ODS emission sources in the future. Key Points: CFCs, HCFCs and halons were observed to be unintendedly emitted from iron and steel industryThe total CFC‐11‐eq emission from iron and steel industry was 78 tons over mainland China in 2019Homogenous gas‐phase formation routes of ODS species in flue gas were proposed and calculated [ABSTRACT FROM AUTHOR]

Background: In clinical practice guidelines, there is no consensus about the medications that should be initially offered to patients with brucellosis. To provide informative evidence, we compared and ranked brucellosis medications based on their efficacy and safety. Methods: For this systematic review and network meta-analysis, we searched 4 English databases and 3 Chinese databases, from the date of database inception to December 13, 2023. We included randomized controlled trials (RCTs) involving children and adolescents with brucellosis, comparing different antibiotic regimens. We excluded studies explicitly targeting patients with spondylitis brucellosis, endocarditis brucellosis, and neuro-brucellosis. The primary outcomes were overall failure (efficacy) and side effects (safety). Secondary outcomes were relapse and therapeutic failure. Pairwise meta-analysis was first examined. Data were analyzed using random effects network meta-analysis, with subgroup and sensitivity analyses performed. The Confidence in Network Meta-Analysis (CINeMA) framework was used to assess the certainty of evidence. The protocol was preregistered in PROSPERO (CRD42023491331). Results: Of the 11,747 records identified through the database search, 43 RCTs were included in the network meta-analysis. Compared with standard therapy (Doxycycline + Rifampicin), Rifampicin + Tetracyclines (RR 4.96; 95% CI 1.47 to 16.70; very low certainty of evidence), Doxycycline + TMP/SMX (RR 0.18; 95% CI 0.06 to 0.52; low certainty of evidence), Doxycycline + Quinolones (RR 0.27; 95% CI 0.11 to 0.71; low certainty of evidence), Streptomycin + Tetracyclines (RR 0.04; 95% CI 0.01 to 0.16; low certainty of evidence), and Single (RR 0.05; 95% CI 0.02 to 0.16; moderate certainty of evidence) were less efficacious. Doxycycline + Gentamicin ranked the best in efficacy (SUCRA values: 0.94), the second is Triple (SUCRA values: 0.87), and the third is Doxycycline + Streptomycin (SUCRA values: 0.78). Conclusions: Brucellosis medications differ in efficacy and safety. Doxycycline + Gentamicin, Triple, and Doxycycline + Streptomycin have superior efficacy and safety. Treatment of brucellosis should strike a balance between efficacy, safety, and cost. Author summary: Brucellosis, with nearly 500,000 new cases outbreak in more than 170 countries or regions worldwide each year, is a zoonotic infectious disease caused by Brucella. Due to the lack of an effective human vaccine and the impracticality of eradicating brucellosis in animals globally, treatment remains the primary strategy for addressing human brucellosis. However, there is no consensus in clinical practice guidelines regarding the initial medications for brucellosis treatment. Therefore, we compared and ranked brucellosis medications based on their efficacy and safety to aid clinicians in matching risk/benefits of drugs to brucellosis treatment. In this systematic review and network meta-analysis of 43 randomized clinical trials with 4,283 patients, 6 weeks of doxycycline plus 1 to 2 weeks of gentamicin or plus 2 to 3 weeks of streptomycin is the best therapy for human brucellosis. Six weeks of rifampicin plus quinolones treatment is considered as an alternative therapy. If finances allow, consider triple therapy. Single, Streptomycin + Tetracyclines, Streptomycin + TMP/SMX, and Rifampicin + TMP/SMX therapies cannot currently be recommended. [ABSTRACT FROM AUTHOR]

The heliospheric current sheet (HCS) serves as the boundary that separates two sectors in which the interplanetary magnetic field lines diverge in opposite directions. In this study, we conducted a statistical analysis of 216 HCS events, utilizing data from the Wind spacecraft to examine the impact of stream interaction regions (SIRs) on the HCSs. Our findings revealed that out of all the HCS events, 153 (70.8%) were succeeded by a SIR within 36 h. Conversely, 63 HCS events (29.2%) were not followed by any SIR. The occurrence of HCSs accompanied by SIRs displayed a rough anti-correlation with solar sunspots. The statistical results further indicated that the presence of a SIR exerts some influence on the preceding HCS. Specifically, the thickness of the HCS is significantly reduced by the trailing SIR, and the subsequent SIR can cause the leading HCS to stand more vertically to the ecliptic plane and more aligned with the Sun–Earth line (a smaller angle between the normal of the HCS and the Earth's dawn–dusk line in the ecliptic plane). [ABSTRACT FROM AUTHOR]

Simple Summary: Electroencephalograms provide valuable insights into brain activity with diverse medical utility that encompasses diagnosis, monitoring, drug discovery, and treatment evaluation. We propose an artificial intelligence model that is uniquely optimized to analyze electroencephalogram signals by directly processing raw data. This model captures intricate spatial and temporal patterns in electroencephalograms through dedicated components like spatial channel attention and sparse transformer encoding. Through being evaluated extensively, our model demonstrates high accuracy in detecting brain diseases and classifying psychotropic drugs. By automatically learning the representations from raw electroencephalogram data, it adapts well across diseases, subjects, and tasks. The model's end-to-end learning aptitude and task versatility constitutes a robust and widely applicable automated electroencephalogram analytics solution. We believe it has the potential to significantly advance electroencephalogram-based diagnosis and personalized medicine. Electroencephalogram (EEG) analysis plays an indispensable role across contemporary medical applications, which encompasses diagnosis, monitoring, drug discovery, and therapeutic assessment. This work puts forth an end-to-end deep learning framework that is uniquely tailored for versatile EEG analysis tasks by directly operating on raw waveform inputs. It aims to address the challenges of manual feature engineering and the neglect of spatial interrelationships in existing methodologies. Specifically, a spatial channel attention module is introduced to emphasize the critical inter-channel dependencies in EEG signals through channel statistics aggregation and multi-layer perceptron operations. Furthermore, a sparse transformer encoder is used to leverage selective sparse attention in order to efficiently process long EEG sequences while reducing computational complexity. Distilling convolutional layers further concatenates the temporal features and retains only the salient patterns. As it was rigorously evaluated on key EEG datasets, our model consistently accomplished a superior performance over the current approaches in detection and classification assignments. By accounting for both spatial and temporal relationships in an end-to-end paradigm, this work facilitates a versatile, automated EEG understanding across diseases, subjects, and objectives through a singular yet customizable architecture. Extensive empirical validation and further architectural refinement may promote broader clinical adoption prospects. [ABSTRACT FROM AUTHOR]