Misunderstandings And Pitfalls of Gendered Dispositions in Educational Practice Abstract

The Special Action Plan for Comprehensively Strengthening and Improving Mental Health Work for Students in the New Era proposes that "mental health work in the new era should be comprehensively strengthened and improved to improve the quality of students' mental health", but in the context of the traditional binary gender temperament, The public has a one-sided understanding of male and female gender temperament, and the phenomenon of "mismatch between cognition and action" has caused many problems in the implementation of mental health work in schools, which not only deepens gender stereotypes but also harms the development of students' mental health

Deep Structured Feature Networks for Table Detection and Tabular Data Extraction from Scanned Financial Document Images

Automatic table detection in PDF documents has achieved a great success but tabular data extraction are still challenging due to the integrity and noise issues in detected table areas. The accurate data extraction is extremely crucial in finance area. Inspired by this, the aim of this research is proposing an automated table detection and tabular data extraction from financial PDF documents. We proposed a method that consists of three main processes, which are detecting table areas with a Faster R-CNN (Region-based Convolutional Neural Network) model with Feature Pyramid Network (FPN) on each page image, extracting contents and structures by a compounded layout segmentation technique based on optical character recognition (OCR) and formulating regular expression rules for table header separation. The tabular data extraction feature is embedded with rule-based filtering and restructuring functions that are highly scalable. We annotate a new Financial Documents dataset with table regions for the experiment. The excellent table detection performance of the detection model is obtained from our customized dataset. The main contributions of this paper are proposing the Financial Documents dataset with table-area annotations, the superior detection model and the rule-based layout segmentation technique for the tabular data extraction from PDF files

Iterative Reconstruction Based on Latent Diffusion Model for Sparse Data Reconstruction

Reconstructing Computed tomography (CT) images from sparse measurement is a well-known ill-posed inverse problem. The Iterative Reconstruction (IR) algorithm is a solution to inverse problems. However, recent IR methods require paired data and the approximation of the inverse projection matrix. To address those problems, we present Latent Diffusion Iterative Reconstruction (LDIR), a pioneering zero-shot method that extends IR with a pre-trained Latent Diffusion Model (LDM) as a accurate and efficient data prior. By approximating the prior distribution with an unconditional latent diffusion model, LDIR is the first method to successfully integrate iterative reconstruction and LDM in an unsupervised manner. LDIR makes the reconstruction of high-resolution images more efficient. Moreover, LDIR utilizes the gradient from the data-fidelity term to guide the sampling process of the LDM, therefore, LDIR does not need the approximation of the inverse projection matrix and can solve various CT reconstruction tasks with a single model. Additionally, for enhancing the sample consistency of the reconstruction, we introduce a novel approach that uses historical gradient information to guide the gradient. Our experiments on extremely sparse CT data reconstruction tasks show that LDIR outperforms other state-of-the-art unsupervised and even exceeds supervised methods, establishing it as a leading technique in terms of both quantity and quality. Furthermore, LDIR also achieves competitive performance on nature image tasks. It is worth noting that LDIR also exhibits significantly faster execution times and lower memory consumption compared to methods with similar network settings. Our code will be publicly available

Recent Update on the Pharmacological Effects and Mechanisms of Dihydromyricetin

As the most abundant natural flavonoid in rattan tea, dihydromyricetin (DMY) has shown a wide range of pharmacological effects. In addition to the general characteristics of flavonoids, DMY has the effects of cardioprotection, anti-diabetes, hepatoprotection, neuroprotection, anti-tumor, and dermatoprotection. DMY was also applied for the treatment of bacterial infection, osteoporosis, asthma, kidney injury, nephrotoxicity and so on. These effects to some extent enrich the understanding about the role of DMY in disease prevention and therapy. However, to date, we still have no outlined knowledge about the detailed mechanism of DMY, which might be related to anti-oxidation and anti-inflammation. And the detailed mechanisms may be associated with several different molecules involved in cellular apoptosis, oxidative stress, and inflammation, such as AMP-activated protein kinase (AMPK), mitogen-activated protein kinase (MAPK), protein kinase B (Akt), nuclear factor-κB (NF-κB), nuclear factor E2-related factor 2 (Nrf2), ATP-binding cassette transporter A1 (ABCA1), peroxisome proliferator-activated receptor-γ (PPARγ) and so on. Here, we summarized the current pharmacological developments of DMY as well as possible mechanisms, aiming to push the understanding about the protective role of DMY as well as its preclinical assessment of novel application

Successional change in species composition alters climate sensitivity of grassland productivity.

Succession theory predicts altered sensitivity of ecosystem functions to disturbance (i.e., climate change) due to the temporal shift in plant community composition. However, empirical evidence in global change experiments is lacking to support this prediction. Here, we present findings from an 8-year long-term global change experiment with warming and altered precipitation manipulation (double and halved amount). First, we observed a temporal shift in species composition over 8 years, resulting in a transition from an annual C3 -dominant plant community to a perennial C4 -dominant plant community. This successional transition was independent of any experimental treatments. During the successional transition, the response of aboveground net primary productivity (ANPP) to precipitation addition magnified from neutral to +45.3%, while the response to halved precipitation attenuated substantially from -17.6% to neutral. However, warming did not affect ANPP in either state. The findings further reveal that the time-dependent climate sensitivity may be regulated by successional change in species composition, highlighting the importance of vegetation dynamics in regulating the response of ecosystem productivity to precipitation change

Diversity Transfer Network for Few-Shot Learning

Few-shot learning is a challenging task that aims at training a classifier for unseen classes with only a few training examples. The main difficulty of few-shot learning lies in the lack of intra-class diversity within insufficient training samples. To alleviate this problem, we propose a novel generative framework, Diversity Transfer Network (DTN), that learns to transfer latent diversities from known categories and composite them with support features to generate diverse samples for novel categories in feature space. The learning problem of the sample generation (i.e., diversity transfer) is solved via minimizing an effective meta-classification loss in a single-stage network, instead of the generative loss in previous works. Besides, an organized auxiliary task co-training over known categories is proposed to stabilize the meta-training process of DTN. We perform extensive experiments and ablation studies on three datasets, i.e., \emph{mini}ImageNet, CIFAR100 and CUB. The results show that DTN, with single-stage training and faster convergence speed, obtains the state-of-the-art results among the feature generation based few-shot learning methods. Code and supplementary material are available at: \texttt{https://github.com/Yuxin-CV/DTN}Comment: 9 pages, 3 figures, AAAI 202

Transcriptomic profiling suggests candidate molecular responses to waterlogging in cassava

Owing to climate change impacts, waterlogging is a serious abiotic stress that affects crops, resulting in stunted growth and loss of productivity. Cassava (Manihot esculenta Grantz) is usually grown in areas that experience high amounts of rainfall; however, little research has been done on the waterlogging tolerance mechanism of this species. Therefore, we investigated the physiological responses of cassava plants to waterlogging stress and analyzed global gene transcription responses in the leaves and roots of waterlogged cassava plants. The results showed that waterlogging stress significantly decreased the leaf chlorophyll content, caused premature senescence, and increased the activities of superoxide dismutase (SOD), catalase (CAT) and peroxidase (POD) in the leaves and roots. In total, 2538 differentially expressed genes (DEGs) were detected in the leaves and 13364 in the roots, with 1523 genes shared between the two tissues. Comparative analysis revealed that the DEGs were related mainly to photosynthesis, amino metabolism, RNA transport and degradation. We also summarized the functions of the pathways that respond to waterlogging and are involved in photosynthesis, glycolysis and galactose metabolism. Additionally, many transcription factors (TFs), such as MYBs, AP2/ERFs, WRKYs and NACs, were identified, suggesting that they potentially function in the waterlogging response in cassava. The expression of 12 randomly selected genes evaluated via both quantitative real-time PCR (qRT-PCR) and RNA sequencing (RNA-seq) was highly correlated (R2 = 0.9077), validating the reliability of the RNA-seq results. The potential waterlogging stress-related transcripts identified in this study are representatives of candidate genes and molecular resources for further understanding the molecular mechanisms underlying the waterlogging response in cassava

Microbial functional diversity covaries with permafrost thaw-induced environmental heterogeneity in tundra soil.

Permafrost soil in high latitude tundra is one of the largest terrestrial carbon (C) stocks and is highly sensitive to climate warming. Understanding microbial responses to warming-induced environmental changes is critical to evaluating their influences on soil biogeochemical cycles. In this study, a functional gene array (i.e., geochip 4.2) was used to analyze the functional capacities of soil microbial communities collected from a naturally degrading permafrost region in Central Alaska. Varied thaw history was reported to be the main driver of soil and plant differences across a gradient of minimally, moderately, and extensively thawed sites. Compared with the minimally thawed site, the number of detected functional gene probes across the 15-65 cm depth profile at the moderately and extensively thawed sites decreased by 25% and 5%, while the community functional gene β-diversity increased by 34% and 45%, respectively, revealing decreased functional gene richness but increased community heterogeneity along the thaw progression. Particularly, the moderately thawed site contained microbial communities with the highest abundances of many genes involved in prokaryotic C degradation, ammonification, and nitrification processes, but lower abundances of fungal C decomposition and anaerobic-related genes. Significant correlations were observed between functional gene abundance and vascular plant primary productivity, suggesting that plant growth and species composition could be co-evolving traits together with microbial community composition. Altogether, this study reveals the complex responses of microbial functional potentials to thaw-related soil and plant changes and provides information on potential microbially mediated biogeochemical cycles in tundra ecosystems

JK5G postbiotics attenuate immune-related adverse events in NSCLC patients by regulating gut microbiota: a randomized controlled trial in China

ScopeThis study aimed to evaluate the effects of JK5G postbiotics to regulate imbalanced gut microbiota and its impacts on the efficacy and incidence rate of immune-related adverse events (irAEs) in non-small-cell lung cancer (NSCLC) patients treated with immune checkpoint inhibitors (ICIs).MethodsThis randomized, double-blind, placebo-controlled trial was conducted in China and included non-squamous or squamous NSCLC patients without EGFR, ROS1, and ALK alteration, treatment-naive, and stage IIIb-IV. Patients were randomly (1:1) divided into two groups to receive four cycles (three weeks for each cycle) of programmed cell death-1 (PD-1) plus chemotherapy plus placebo (control group, n = 30) or to receive PD-1 plus chemotherapy plus JK5G postbiotics (JK5G group, n = 30). The primary endpoint was objective response rate. The secondary endpoints were quality of life (QoL), adverse effects, and the 16S DNA sequencing of gut microbiota, blood inflammatory cytokines, and lymphocyte subsets. This study was registered at www.chictr.org.cn (ChiCTR2200064690).ResultsSixty patients were enrolled. The objective response rate was 36.67% (11/30) in the control group and 50.00% (15/30) in the JK5G group (p = 0.297). The JK5G group had better QoL and nutritional levels, as well as lower depression symptoms than the control group (all p < 0.05). Moreover, the JK5G group had a lower incidence of anemia (63.33% vs. 13.33%, p < 0.001), decreased lymphocyte count (20.00% vs. 0%, p = 0.010), decreased appetite (53.33% vs. 16.67%, p = 0.003), nausea (33.33% vs. 6.67%, p = 0.010), and asthenia (30.00% vs. 6.67%, p = 0.017) than the control group. Moreover, JK5G attenuated gut microbiota imbalance, accompanied by increased Faecalibacterium, Ruminococcaceae, and fecal butyrate concentration, and diminished Escherichia-Shigella. Furthermore, JK5G administration significantly decreased the levels of pro-inflammatory markers, including TNF-α, IL-2, and C-reactive protein (CRP) (all p < 0.05). Significant increases in CD3+CD4+ T cells and CD4/CD8 ratio were observed in the peripheral blood of JK5G group patients (all p < 0.05). The enterotype data showed that patients were clustered into Blautia (E1) and Escherichia-Shigella (E2) enterotypes, and JK5G postbiotics intervention might be related to enterotype modulations.ConclusionOur current findings indicated that JK5G postbiotics might attenuate irAEs, and enhance the QoL and nutrition levels of advanced NSCLC patients who received ICIs. JK5G postbiotics could also improve the gut microbiota structures and ameliorate the tumor microenvironment and inflammation.Clinical trial registrationwww.chictr.org.cn, identifier ChiCTR2200064690

The incidence of congenital hypothyroidism (CH) in Guangxi, China and the predictors of permanent and transient CH

Background: The incidence of congenital hypothyroidism (CH) differs significantly among different ethnicities and regions, and early differentiation of transient CH is important to avoid unnecessary prolonged treatment with L-T4. Objective: To investigate the incidence of CH based on the newborn screening program in Guangxi Zhuang Autonomous Region, China, and to analyze the predictors that might allow for an early differentiation between permanent (P) and transient (T) CH. Design and methods Data from newborn screening program over a seven-year period (January 2009 to January 2016) at Guangxi Maternal and Child Health Hospital are analyzed. Blood samples were collected on filter paper between 3 and 7 days after birth, and TSH level was measured by time-resolved fluorescence assay. Individuals with increased TSH (TSH ≥ 8 IU/L) levels detected by newborn screening were recalled for further evaluation. Serum TSH, FT3 and FT4 were determined by electrochemiluminescence assay using venous blood samples. Diagnosis of CH is based on elevated TSH levels (>10 IU/L) and decreased FT4 levels (30 μg/day) has the highest predictive value for PCH. Earlier differentiation of PCH and TCH helps to determine appropriate treatment course