Chemical and Geological Properties of Shale Gas: In Situ Desorption of Lower Cambrian Niutitang Shale in the Micangshan Tectonic Zone of South Shaanxi, China.

Shale gas was recently found in the Lower Cambrian Niutitang Formation (LCNF) of the Micangshan tectonic zone of south Shaanxi (MTZSS), but not in commercial quantities. To determine the laws governing the generation, enrichment, and desorption of shale gases in overmatured shale strata in the LCNF of MTZSS, we carried out in situ desorption experiments on nine shale core samples and got 168 desorbed gas samples at different phases of desorption. Also measured were the chemical and carbon isotopic compositions of these desorbed gas samples and the geochemical parameters of the shale core samples. CH4 was the predominant hydrocarbon shale gas identified in the 82.06-98.48% range, suggesting that the gases were mainly dry. The nonhydrocarbon gases found were CO2 and H2. The CH4 content of the desorbed gas samples dropped continuously during desorption, lowering the dryness index to 98.48 and 92.26% of the first and last desorbed shale gas, respectively. The change in the gas ratio during shale gas desorption proved that the adsorbability of the LCNF to the various gases follows the trend H2 > CO2 > C2H6 > CH4 > He. Further, δ13C2H6 and δ13CH4 become heavier during desorption, showing isotopic fractionation arising from the desorption-diffusion coeffect. As the desorption temperature increases, the value of δ13CH4 increases because 12CH4 is more sensitive to temperature than 13CH4, so it is with the ethane. Similar to the LCNF shale gas in other areas of China, the desorbed shale gases are characteristic of carbon isotope reversal (CIR) (δ13CH4 > δ13C2H6). The cracking of the residual soluble organic matter at the high overmaturity stage mixed with the cracking of kerogen at the early stage of maturation, causing CIR. Furthermore, the desorbed gas content was proportionally and inversely related to the CIR degree and final dryness index of the desorbed gas, respectively. Moreover, the carbon isotope fractionation degree of CH4 and δ13C1 of the last desorbed gas correlated positively with the desorbed gas content and the desorbed time of the gas. In conclusion, the four parameters are effective parameters for identifying shale gas sweet spots.

The role of microbiota in gastric cancer: A comprehensive review.

BACKGROUND: Gastric cancer (GC) continues to pose a significant global threat in terms of cancer-related fatalities. Despite notable advancements in medical research and therapies, further investigation is warranted to elucidate its underlying etiology and risk factors. Recent times have witnessed an escalated emphasis on comprehending the role of the microbiota in cancer development. METHODS: This review briefly delves into recent developments in microbiome-related research pertaining to gastric cancer. RESULTS: According to studies, the microbiota can influence GC growth by inciting inflammation, disrupting immunological processes, and generating harmful microbial metabolites. Furthermore, there is ongoing research into how the microbiome can impact a patient's response to chemotherapy and immunotherapy. CONCLUSION: The utilization of the microbiome for detecting, preventing, and managing stomach cancer remains an active area of exploration.

3D Multimodal Fusion Network with Disease-induced Joint Learning for Early Alzheimer's Disease Diagnosis.

Multimodal neuroimaging provides complementary information critical for accurate early diagnosis of Alzheimer's disease (AD). However, the inherent variability between multimodal neuroimages hinders the effective fusion of multimodal features. Moreover, achieving reliable and interpretable diagnoses in the field of multimodal fusion remains challenging. To address them, we propose a novel multimodal diagnosis network based on multi-fusion and disease-induced learning (MDL-Net) to enhance early AD diagnosis by efficiently fusing multimodal data. Specifically, MDL-Net proposes a multi-fusion joint learning (MJL) module, which effectively fuses multimodal features and enhances the feature representation from global, local, and latent learning perspectives. MJL consists of three modules, global-aware learning (GAL), local-aware learning (LAL), and outer latent-space learning (LSL) modules. GAL via a self-adaptive Transformer (SAT) learns the global relationships among the modalities. LAL constructs local-aware convolution to learn the local associations. LSL module introduces latent information through outer product operation to further enhance feature representation. MDL-Net integrates the disease-induced region-aware learning (DRL) module via gradient weight to enhance interpretability, which iteratively learns weight matrices to identify AD-related brain regions. We conduct the extensive experiments on public datasets and the results confirm the superiority of our proposed method. Our code will be available at: https://github.com/qzf0320/MDL-Net.

Dual-space high-frequency learning for transformer-based MRI super-resolution.

BACKGROUND AND OBJECTIVE: Magnetic resonance imaging (MRI) can provide rich and detailed high-contrast information of soft tissues, while the scanning of MRI is time-consuming. To accelerate MR imaging, a variety of Transformer-based single image super-resolution methods are proposed in recent years, achieving promising results thanks to their superior capability of capturing long-range dependencies. Nevertheless, most existing works prioritize the design of transformer attention blocks to capture global information. The local high-frequency details, which are pivotal to faithful MRI restoration, are unfortunately neglected. METHODS: In this work, we propose a high-frequency enhanced learning scheme to effectively improve the awareness of high frequency information in current Transformer-based MRI single image super-resolution methods. Specifically, we present two entirely plug-and-play modules designed to equip Transformer-based networks with the ability to recover high-frequency details from dual spaces: 1) in the feature space, we design a high-frequency block (Hi-Fe block) paralleled with Transformer-based attention layers to extract rich high-frequency features; while 2) in the image intensity space, we tailor a high-frequency amplification module (HFA) to further refine the high-frequency details. By fully exploiting the merits of the two modules, our framework can recover abundant and diverse high-frequency information, rendering faithful MRI super-resolved results with fine details. RESULTS: We integrated our modules with six Transformer-based models and conducted experiments across three datasets. The results indicate that our plug-and-play modules can enhance the super-resolution performance of all foundational models to varying degrees, surpassing the capabilities of existing state-of-the-art single image super-resolution networks. CONCLUSION: Comprehensive comparison of super-resolution images and high-frequency maps from various methods, clearly demonstrating that our module possesses the capability to restore high-frequency information, showing huge potential in clinical practice for accelerated MRI reconstruction.

Breast Cancer Classification from Digital Pathology Images via Connectivity-aware Graph Transformer.

Automated classification of breast cancer subtypes from digital pathology images has been an extremely challenging task due to the complicated spatial patterns of cells in the tissue micro-environment. While newly proposed graph transformers are able to capture more long-range dependencies to enhance accuracy, they largely ignore the topological connectivity between graph nodes, which is nevertheless critical to extract more representative features to address this difficult task. In this paper, we propose a novel connectivity-aware graph transformer (CGT) for phenotyping the topology connectivity of the tissue graph constructed from digital pathology images for breast cancer classification. Our CGT seamlessly integrates connectivity embedding to node feature at every graph transformer layer by using local connectivity aggregation, in order to yield more comprehensive graph representations to distinguish different breast cancer subtypes. In light of the realistic intercellular communication mode, we then encode the spatial distance between two arbitrary nodes as connectivity bias in self-attention calculation, thereby allowing the CGT to distinctively harness the connectivity embedding based on the distance of two nodes. We extensively evaluate the proposed CGT on a large cohort of breast carcinoma digital pathology images stained by Haematoxylin & Eosin. Experimental results demonstrate the effectiveness of our CGT, which outperforms state-of-the-art methods by a large margin. Codes are released on https://github.com/wang-kang-6/CGT.

MHD-Net: Memory-aware Hetero-modal Distillation Network for Thymic Epithelial Tumor Typing with Missing Pathology Modality.

Fusing multi-modal radiology and pathology data with complementary information can improve the accuracy of tumor typing. However, collecting pathology data is difficult since it is high-cost and sometimes only obtainable after the surgery, which limits the application of multi-modal methods in diagnosis. To address this problem, we propose comprehensively learning multi-modal radiology-pathology data in training, and only using uni-modal radiology data in testing. Concretely, a Memory-aware Hetero-modal Distillation Network (MHD-Net) is proposed, which can distill well-learned multi-modal knowledge with the assistance of memory from the teacher to the student. In the teacher, to tackle the challenge in hetero-modal feature fusion, we propose a novel spatial-differentiated hetero-modal fusion module (SHFM) that models spatial-specific tumor information correlations across modalities. As only radiology data is accessible to the student, we store pathology features in the proposed contrast-boosted typing memory module (CTMM) that achieves type-wise memory updating and stage-wise contrastive memory boosting to ensure the effectiveness and generalization of memory items. In the student, to improve the cross-modal distillation, we propose a multi-stage memory-aware distillation (MMD) scheme that reads memory-aware pathology features from CTMM to remedy missing modal-specific information. Furthermore, we construct a Radiology-Pathology Thymic Epithelial Tumor (RPTET) dataset containing paired CT and WSI images with annotations. Experiments on the RPTET and CPTAC-LUAD datasets demonstrate that MHD-Net significantly improves tumor typing and outperforms existing multi-modal methods on missing modality situations.

Bone metastasis in non-small-cell lung cancer: genomic characterization and exploration of potential targets.

Background: Bone metastasis (BM) seriously affects the quality of life and reduces the survival time of patients with non-small-cell lung cancer (NSCLC). The genomic characteristics and potential targets of BMs are yet to be fully explored. Objective: To explore the genetic characteristics and potential targets of BM in NSCLC. Design: In all, 83 patients with NSCLC were retrospectively selected in this study. Genomic characterization of BMs was explored with the analysis of NGS results from primary tumors and BMs in 6 patients, then combined with NGS results of lung tumors in 16 patients with initial recurrence in bone to analyze mutations potentially associated with BMs, and finally, the correlation was further validated in 61 postoperative patients. Methods: The next generation sequencing (NGS) was performed to identify genomic differences between pulmonary primary tumors and BM. Fluorescence in situ hybridization and immunohistochemistry were performed in postoperative tumor tissues from patients who had undergone radical surgery to validate the predictive role of molecular targets for BM. The correlation between cyclin-dependent kinase 4 (CDK4) and BM was evaluated by Pearson's chi-square test. The university of alabama at birminghan cancer data analysis portal (UALCAN) was carried out for the detection of CDK4 expression in lung cancer and the relationship between CDK4 and clinicopathological parameters. The relationship between prognosis and CDK4 expression was analyzed by the Kaplan-Meier plotter. Results: The rate of gene amplification was increased (24% versus 36%) while gene substitution/indel was decreased (64% versus 52%) in BMs. The BM-specific mutations were analyzed in 16 recurrent patients which revealed the highest incidence of CDK4 amplification (18.8%). According to the Kaplan-Meier plotter database, the NSCLC patients with high CDK4 gene expression showed poor overall survival (OS) and recurrence-free survival (RFS) (p < 0.05). The incidence of CDK4 amplification tended to be higher in recurrent patients compared to the patients without BM (18.8% versus 4.7%, p = 0.118). Conclusion: Compared to the primary tumors of NSCLC, the genome of BMs showed an increased proportion of amplification and a decreased proportion of gene substitution/indel. Furthermore, the CDK4 amplification ratio seemed to be elevated in NSCLC patients with BM which may be associated with poor OS and RFS.

Genomic characterization and potential targets of bone metastasis in non-small cell lung cancer NGS was performed on the matched primary tumors and bone metastases to explore the differences in the genomes of bone metastases, and it was found that gene amplification increased in bone metastases. Combined with the results of NGS in NSCLC patients with the first postoperative recurrence site in the bone, it was found that CDK4 amplification expression increased in bone metastases. Finally, the correlation between bone metastasis and CDK4 amplification was verified by expanding the sample.

Synergizing economic growth and carbon emission reduction in China: A path to coupling the MFLP and PLUS models for optimizing the territorial spatial functional pattern.

Carbon emissions caused by economic growth are the main cause of global warming, but controlling economic growth to reduce carbon emissions does not meet China's conditions. Therefore, how to synergize economic growth and carbon emission reduction is not only a sustainable development issue for China, but also significant for mitigating global warming. The territorial spatial functional pattern (TSFP) is the spatial carrier for coordinating economic development and carbon emissions, but how to establish the TSFP to synergize economic growth and carbon emission reduction remains unresolved. We propose a decision framework for optimizing TSFP coupled with the multi-objective fuzzy linear programming and the patch-generating land use simulation model, to provide a new path to synergize economic growth and carbon emission reduction in China. To confirm the reliability, we took Qionglai City as the demonstration. The results found a significant spatiotemporal coupling between TSFP and the synergistic effect between economic growth and carbon emission reduction (q ≥ 0.8220), which resolves the theoretical uncertainty about synergizing economic growth and carbon emission reduction through optimizing TSFP. The urban space of Qionglai City in 2025 and 2030 obtained by the decision framework was 6497.57 hm2 and 6628.72 hm2 respectively, distributed in the central and eastern regions; the rural space was 60,132.92 hm2 and 56,084.97 hm2, concentrated in the east, with a few located in the west; and the ecological space was 71,072.52 hm2 and 74,998.31 hm2, mainly located in the western and southeastern areas. Compared with 2020, the carbon emission intensity of the TSFP that realized the synergy (decoupling index was 0.25 and 0.21, respectively) was reduced by 0.7 and 4.7 tons/million yuan, respectively. Further confirming that optimizing TSFP is an effective way to synergize economic growth and carbon emission reduction, which can provide policy implications for coordinating economic growth and carbon emissions for China and even similar developing countries.

Intercorrelated Ferroelectricity and Bulk Photovoltaic Effect in Two-Dimensional Sn2P2S6 Semiconductor for Polarization-Sensitive Photodetection.

A two-dimensional (2D) ferroelectric semiconductor, which is coupled with photosensitivity and room-temperature ferroelectricity, provides the possibility of coordinated conductance modulation by both electric field and light illumination and is promising for triggering the revolution of optoelectronics for monolithic multifunctional integration. Here, we report that semiconducting Sn2P2S6 crystals can be achieved in a 2D morphology using a chemical vapor transport approach with the assistant of space confinement and experimentally demonstrate the robust ferroelectricity in atomic-thin Sn2P2S6 nanosheet at room temperature. The intercorrelated programming of ferroelectric order along out-of-plane (OOP) and in-plane (IP) directions enables a tunable bulk photovoltaic (BPV) effect through multidirectional electrical control. By combining the capability of anisotropic in-plane optical absorption, a highly integrated Sn2P2S6 optoelectronic device vertically sandwiched with graphene electrodes yields the polarization-dependent open-circuit photovoltage with a dichroic ratio of 2.0 under 405 nm light illumination. The reintroduction of ferroelectric Sn2P2S6 to the 2D asymmetric semiconductor family provides possibilities to hardware implement of the self-powered polarization-sensitive photodetection and spotlights the promising applications for next-generation photovoltaic devices.

Recent advances in metal oxide nanozyme-based optical biosensors for food safety assays.

Metal oxide nanozymes are emerging as promising materials for food safety detection, offering several advantages over natural enzymes, including superior stability, cost-effectiveness, large-scale production capability, customisable functionality, design options, and ease of modification. Optical biosensors based on metal oxide nanozymes have significantly accelerated the advancement of analytical research, facilitating the rapid, effortless, efficient, and precise detection and characterisation of contaminants in food. However, few reviews have focused on the application of optical biosensors based on metal oxide nanozymes for food safety detection. In this review, the catalytic mechanisms of the catalase, oxidase, peroxidase, and superoxide dismutase activities of metal oxide nanozymes are characterized. Research developments in optical biosensors based on metal oxide nanozymes, including colorimetric, fluorescent, chemiluminescent, and surface-enhanced Raman scattering biosensors, are comprehensively summarized. The application of metal oxide nanozyme-based biosensors for the detection of nitrites, sulphites, metal ions, pesticides, antibiotics, antioxidants, foodborne pathogens, toxins, and other food contaminants has been highlighted. Furthermore, the challenges and future development prospects of metal oxide nanozymes for sensing applications are discussed. This review offers insights and inspiration for further investigations on optical biosensors based on metal oxide nanozymes for food safety detection.

STADNet: Spatial-Temporal Attention-Guided Dual-Path Network for cardiac cine MRI super-resolution.

Cardiac cine magnetic resonance imaging (MRI) is a commonly used clinical tool for evaluating cardiac function and morphology. However, its diagnostic accuracy may be compromised by the low spatial resolution. Current methods for cine MRI super-resolution reconstruction still have limitations. They typically rely on 3D convolutional neural networks or recurrent neural networks, which may not effectively capture long-range or non-local features due to their limited receptive fields. Optical flow estimators are also commonly used to align neighboring frames, which may cause information loss and inaccurate motion estimation. Additionally, pre-warping strategies may involve interpolation, leading to potential loss of texture details and complicated anatomical structures. To overcome these challenges, we propose a novel Spatial-Temporal Attention-Guided Dual-Path Network (STADNet) for cardiac cine MRI super-resolution. We utilize transformers to model long-range dependencies in cardiac cine MR images and design a cross-frame attention module in the location-aware spatial path, which enhances the spatial details of the current frame by using complementary information from neighboring frames. We also introduce a recurrent flow-enhanced attention module in the motion-aware temporal path that exploits the correlation between cine MRI frames and extracts the motion information of the heart. Experimental results demonstrate that STADNet outperforms SOTA approaches and has significant potential for clinical practice.

Robustness of the synergistic partial-denitrification, anammox, and fermentation process for treating domestic and nitrate wastewaters under fluctuating C/N ratios.

The synergistic partial-denitrification, anammox, and fermentation (SPDAF) process presents a promising solution to treat domestic and nitrate wastewaters. However, its capability to handle fluctuating C/N ratios (the ratios of COD to total inorganic nitrogen) in practical applications remains uncertain. In this study, the SPDAF process was operated for 236 days with C/N ratios of 0.7-3.5, and a high and stable efficiency of nitrogen removal (84.9 ± 7.8%) was achieved. The denitrification and anammox contributions were 6.1 ± 7.1% and 93.9 ± 7.1%, respectively. Batch tests highlighted the pivotal role of in situ fermentation at low biodegradable chemical oxygen demand (BCOD)/NO3- ratios. As the BCOD/NO3- ratios increased from 0 to 6, the NH4+ and NO3- removal rates increased, while the anammox contribution decreased from 100% to 80.1% but remained the primary pathway of nitrogen removal. The cooperation and balanced growth of denitrifying bacteria, anammox bacteria, and fermentation bacteria contributed to the system's robustness under fluctuating C/N ratios.

Neuropathological characteristics of abnormal white matter functional signaling in adolescents with major depression.

BACKGROUND: Major depression disorder (MDD) constitutes a significant mental health concern. Epidemiological surveys indicate that the lifetime prevalence of depression in adolescents is much higher than that in adults, with a corresponding increased risk of suicide. In studying brain dysfunction associated with MDD in adole-scents, research on brain white matter (WM) is sparse. Some researchers even mistakenly regard the signals generated by the WM as noise points. In fact, studies have shown that WM exhibits similar blood oxygen level-dependent signal fluctuations. The alterations in WM signals and their relationship with disease severity in adolescents with MDD remain unclear. AIM: To explore potential abnormalities in WM functional signals in adolescents with MDD. METHODS: This study involved 48 adolescent patients with MDD and 31 healthy controls (HC). All participants were assessed using the Patient Health Questionnaire-9 Scale and the mini international neuropsychiatric interview (MINI) suicide inventory. In addition, a Siemens Skyra 3.0T magnetic resonance scanner was used to obtain the subjects' image data. The DPABI software was utilized to calculate the WM signal of the fractional amplitude of low frequency fluctuations (fALFF) and regional homogeneity, followed by a two-sample t-test between the MDD and HC groups. Independent component analysis (ICA) was also used to evaluate the WM functional signal. Pearson's correlation was performed to assess the relationship between statistical test results and clinical scales. RESULTS: Compared to HC, individuals with MDD demonstrated a decrease in the fALFF of WM in the corpus callosum body, left posterior limb of the internal capsule, right superior corona radiata, and bilateral posterior corona radiata [P < 0.001, family-wise error (FWE) voxel correction]. The regional homogeneity of WM increased in the right posterior limb of internal capsule and left superior corona radiata, and decreased in the left superior longitudinal fasciculus (P < 0.001, FWE voxel correction). The ICA results of WM overlapped with those of regional homo-geneity. The fALFF of WM signal in the left posterior limb of the internal capsule was negatively correlated with the MINI suicide scale (P = 0.026, r = -0.32), and the right posterior corona radiata was also negatively correlated with the MINI suicide scale (P = 0.047, r = -0.288). CONCLUSION: Adolescents with MDD involves changes in WM functional signals, and these differences in brain regions may increase the risk of suicide.

Seafloor primary production in a changing Arctic Ocean.

Phytoplankton and sea ice algae are traditionally considered to be the main primary producers in the Arctic Ocean. In this Perspective, we explore the importance of benthic primary producers (BPPs) encompassing microalgae, macroalgae, and seagrasses, which represent a poorly quantified source of Arctic marine primary production. Despite scarce observations, models predict that BPPs are widespread, colonizing ~3 million km2 of the extensive Arctic coastal and shelf seas. Using a synthesis of published data and a novel model, we estimate that BPPs currently contribute ~77 Tg C y-1 of primary production to the Arctic, equivalent to ~20 to 35% of annual phytoplankton production. Macroalgae contribute ~43 Tg C y-1, seagrasses contribute ~23 Tg C y-1, and microalgae-dominated shelf habitats contribute ~11 to 16 Tg C y-1. Since 2003, the Arctic seafloor area exposed to sunlight has increased by ~47,000 km2 y-1, expanding the realm of BPPs in a warming Arctic. Increased macrophyte abundance and productivity is expected along Arctic coastlines with continued ocean warming and sea ice loss. However, microalgal benthic primary production has increased in only a few shelf regions despite substantial sea ice loss over the past 20 y, as higher solar irradiance in the ice-free ocean is counterbalanced by reduced water transparency. This suggests complex impacts of climate change on Arctic light availability and marine primary production. Despite significant knowledge gaps on Arctic BPPs, their widespread presence and obvious contribution to coastal and shelf ecosystem production call for further investigation and for their inclusion in Arctic ecosystem models and carbon budgets.

Mangiferin, a Potential Supplement to Improve Metabolic Syndrome: Current Status and Future Opportunities.

Metabolic syndrome (MetS) represents a considerable clinical and public health burden worldwide. Mangiferin (MF), a flavonoid compound present in diverse species such as mango (Mangifera indica L.), papaya (Pseudocydonia sinensis (Thouin) C. K. Schneid.), zhimu (Anemarrhena asphodeloides Bunge), and honeybush tea (Cyclopia genistoides), boasts a broad array of pharmacological effects. It holds promising uses in nutritionally and functionally targeted foods, particularly concerning MetS treatment. It is therefore pivotal to systematically investigate MF's therapeutic mechanism for MetS and its applications in food and pharmaceutical sectors. This review, with the aid of a network pharmacology approach complemented by this experimental studies, unravels possible mechanisms underlying MF's MetS treatment. Network pharmacology results suggest that MF treats MetS effectively through promoting insulin secretion, targeting obesity and inflammation, alleviating insulin resistance (IR), and mainly operating via the phosphatidylinositol 3 kinase (PI3K)/Akt, nuclear factor kappa-B (NF-[Formula: see text]B), microtubule-associated protein kinase (MAPK), and oxidative stress signaling pathways while repairing damaged insulin signaling. These insights provide a comprehensive framework to understand MF's potential mechanisms in treating MetS. These, however, warrant further experimental validation. Moreover, molecular docking techniques confirmed the plausibility of the predicted outcomes. Hereafter, these findings might form the theoretical bedrock for prospective research into MF's therapeutic potential in MetS therapy.

LA-ViT: A Network with Transformers Constrained by Learned-Parameter-Free Attention for Interpretable Grading in a New Laryngeal Histopathology Image Dataset.

Grading laryngeal squamous cell carcinoma (LSCC) based on histopathological images is a clinically significant yet challenging task. However, more low-effect background semantic information appeared in the feature maps, feature channels, and class activation maps, which caused a serious impact on the accuracy and interpretability of LSCC grading. While the traditional transformer block makes extensive use of parameter attention, the model overlearns the low-effect background semantic information, resulting in ineffectively reducing the proportion of background semantics. Therefore, we propose an end-to-end network with transformers constrained by learned-parameter-free attention (LA-ViT), which improve the ability to learn high-effect target semantic information and reduce the proportion of background semantics. Firstly, according to generalized linear model and probabilistic, we demonstrate that learned-parameter-free attention (LA) has a stronger ability to learn highly effective target semantic information than parameter attention. Secondly, the first-type LA transformer block of LA-ViT utilizes the feature map position subspace to realize the query. Then, it uses the feature channel subspace to realize the key, and adopts the average convergence to obtain a value. And those construct the LA mechanism. Thus, it reduces the proportion of background semantics in the feature maps and feature channels. Thirdly, the second-type LA transformer block of LA-ViT uses the model probability matrix information and decision level weight information to realize key and query, respectively. And those realize the LA mechanism. So, it reduces the proportion of background semantics in class activation maps. Finally, we build a new complex semantic LSCC pathology image dataset to address the problem, which is less research on LSCC grading models because of lacking clinically meaningful datasets. After extensive experiments, the whole metrics of LA-ViT outperform those of other state-of-the-art methods, and the visualization maps match better with the regions of interest in the pathologists' decision-making. Moreover, the experimental results conducted on a public LSCC pathology image dataset show that LA-ViT has superior generalization performance to that of other state-of-the-art methods.

Combined Influence of Eight Lifestyle Factors on Metabolic Syndrome Incidence: A Prospective Cohort Study from the MECH-HK Study.

Although previous studies have shown significant associations between individual lifestyles and metabolic syndrome, limited studies have explored the combined effect of lifestyles. The purpose of this study was to investigate whether a combined lifestyle score was associated with metabolic syndrome incidence in Hong Kong Chinese women. This prospective cohort study included 1634 women (55.9 ± 8.6 years) without baseline metabolic syndrome, diabetes, myocardial infarction, or stroke. Eight lifestyle factors (smoking, physical activity, sedentary time, sleep, stress, fatigue, diet, and alcohol) were included by assigning 0 (unhealthy) or 1 point (healthy). The overall score was the sum of these points, ranging from 0 (the least healthy) to 8 points (the healthiest). Metabolic syndrome was diagnosed by the joint interim statement. During a 1.16-year follow-up, 179 (11.0%) new metabolic syndrome cases were identified. The incidences for the 0-3-point, 4-point, 5-point, and 6-8-point groups were 12.8% (79/618), 11.5% (42/366), 9.4% (29/309), and 8.5% (29/341), respectively. Compared to the lowest combined lifestyle score group, the highest group had a 47% reduced metabolic syndrome incidence, with an adjusted odds ratio and 95% confidence interval of 0.53 (0.33-0.86) (p = 0.010). These findings indicate that a higher combined lifestyle score was associated with a lower metabolic syndrome incidence in this population.

Nickel-Catalyzed Reductive Cross-Coupling of Propargylic Acetates with Chloro(vinyl)silanes: Access to Silylallenes.

Because of their various reactivities, propargyl acetates are refined chemical intermediates that are extensively applied in pharmaceutical synthesis. Currently, reactions between propargyl acetates and chlorosilanes may be the most effective method for synthesizing silylallenes. Nevertheless, owing to the adaptability and selectivity of substrates, transition metal catalysis is difficult to achieve. Herein, nickel-catalyzed reductive cross-coupling reactions between propargyl acetates and substituted vinyl chlorosilanes for the synthesis of tetrasubstituted silylallenes are described. Therein, metallic zinc is a crucial reductant that effectively enables two electrophilic reagents to selectively construct C(sp2)-Si bonds. Additionally, a Ni-catalyzed reductive mechanism involving a radical process is proposed on the basis of deuteration-labeled experiments.

Potential added value of computed tomography radiomics to multimodal prediction models for benign and malignant breast tumors.

Background: Early diagnosis is crucial to the treatment of breast cancer, but conventional imaging detection is challenging. Radiomics has the potential to improve early diagnostic efficacy in a noninvasive manner. This study examined whether integrating computed tomography (CT) radiomics information based on ultrasound (US) models can improve the efficacy of breast cancer prediction. Methods: We retrospectively analyzed 420 patients with pathologically confirmed benign or malignant breast tumors. Clinical data and examination images were collected, and the population was divided into training (n=294) and validation (n=126) groups at a ratio of 7:3. The region of interest (ROI) was manually segmented along the tumor boundary using MaZda software, and the features of each ROI was extracted. After dimension reduction and screening, the best features were retained. Subsequently, random forest (RF), support vector machines, and K-nearest neighbor classifiers were used to establish prediction models in an US and combined-methods group. Results: Finally, 8 of the 379 features were retained in the US group. Random forest was found to be the best model, and the area under the curve (AUC) of the training and validation groups was 0.90 [95% confidence interval (CI): 0.852-0.942] and 0.85 (95% CI: 0.775-0.930), respectively. Meanwhile, 12 of the 750 features were retained in the combined group. In this regard, random forest proved to be the best model, and the AUC of the training and validation group was 0.95 (95% CI: 0.918-0.981) and 0.92 (95% CI: 0.866-0.969), respectively. The calibration curve showed a good fit of the model. The decision curve showed that the clinical net benefit of the combined group was far greater than that of any single examination, and the prediction model of the combined group exhibited a degree of practical clinical value. Conclusions: The combined model based on US and CT images has potential application value in the prognostic prediction of benign and malignant breast diseases.

Hybrid nanospheres of silica covalently containing yellow-emitting cationic iridium(III) complex: preparation and application in white light-emitting diodes.

A yellow-emitting cationic iridium(III) complex [(dfppy)2Ir(TBD)]PF6 (TBD: N4,N4'-bis(3-(triethoxysilyl)propyl)-[2,2'-bipyridine]-4,4'-dicarboxamide; dfppy: 2-(2,4-difluorophenyl)pyridine) containing hydrolysable alkoxysilanes was synthesized. Then, a series of silica-based hybrid nanospheres with diameters of around 400 nm was prepared via the hydrolysis of this complex together with tetraethyl orthosilicate (TEOS, a silica source). When the amount of the complex used was 5.0 wt%, hybrid nanospheres showed the best photoluminescence (PL) properties, relative to the PL quantum yield of pure solid [(dfppy)2Ir(TBD)]PF6 (12.7%), that of hybrid nanospheres increased to 26.2%. Moreover, the thermal decomposition temperature (Td) of pure solid [(dfppy)2Ir(TBD)]PF6 was 331 °C, the Td of the complex in hybrid nanospheres increased to 447 °C. However, the yellow light emission was almost unchanged and was still located at 500-750 nm with a maximum wavelength (λem,max) of 577 nm. Under the excitation of blue-emitting chips (λem,max ≈ 455 nm), cold/neutral/warm white light-emitting diodes (WLEDs) with good luminous quality can all be fabricated using these hybrid nanospheres as phosphors in epoxy resin at different blending concentrations. Compared with two or three iridium(III) complexes being contained in silica-based particles as phosphors as described in literatures, in this study, silica-based hybrid nanospheres covalently containing only one yellow-emitting cationic iridium(III) complex as phosphors provide a more effective and simpler method for preparation high-performance WLEDs.