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Abstract Using (27.12 ± 0.14) × 108 ψ(3686) events collected by the BESIII detector at the center-of-mass energy of s $$ \sqrt{s} $$ = 3.686 GeV, we search for the first time for two nonleptonic hyperon decays that change strangeness by two units, Ω − → Σ0 π − and Ω − → nK − . No significant signal is observed. The upper limits on their decay branching fractions are determined to be B $$ \mathcal{B} $$ (Ω − → Σ0 π − ) < 5.4 × 10 −4 and B $$ \mathcal{B} $$ (Ω − → nK − ) < 2.4 × 10 −4 at the 90% confidence level.

Abstract Using 24.1 fb −1 of e + e − collision data collected with the BESIII detector at the BEPCII collider, the Born cross sections and effective form factors of the e + e − → Σ + Σ ¯ − $$ {e}^{+}{e}^{-}\to {\Sigma}^{+}{\overline{\Sigma}}^{-} $$ reaction are measured. The measurements are performed at center-of-mass energies ranging from 3.510 to 4.951 GeV. No significant evidence for the decay of the charmonium(-like) states, ψ(3770), ψ(4040), ψ(4160), Y(4230), Y(4360), ψ(4415), and Y(4660), into a Σ + Σ ¯ − $$ {\Sigma}^{+}{\overline{\Sigma}}^{-} $$ final state is observed. Consequently, upper limits for the products of the branching fractions and the electronic partial widths at the 90% confidence level are reported for these decays.

Nickel-based single-crystal superalloys are designed for extreme conditions due to their superior corrosion and creep resistance properties. However, these pose challenges in the subsequent recycling after reaching their end-of-life. Molten magnesium (Mg) can rapidly corrode the stable spent nickel-based superalloys and selectively dissolve nickel (Ni). This waste-free process represents an effective method for recycling spent superalloys and accomplishing metal regeneration. This study investigates the mechanism of selectively dissolving Ni from DD5, a nickel-based single-crystal superalloy, by optimizing process temperature, time, and Mg content in an inert atmosphere. Vacuum distillation was employed to separate the resulting Mg, residual superalloy (i.e., the material left post-extraction), and Ni-rich alloy (i.e., the metal product selectively extracted). The findings revealed that the residual superalloy after selective Ni dissolution is characterized by a porous skeleton structure with pore sizes predominantly ranging from 2 to 30 nm and a low compressive strength which is 1/10 of the original DD5 superalloy.

As major forming elements of γ′ strengthening phase, Al and Ti are critical for achieving superior properties of Ni-based superalloy. However, Al + Ti over 6 wt% leads to poor printability and cracking during additive manufacturing. In this work, the printability and properties of a nickel-based superalloy with Al + Ti reaching 7 wt% is synchronously improved via alloying minor Sc, the mechanism behind that is revealed by investigating the effects of Sc on the grain size, precipitation, and tensile fracture behavior of that nickel-based superalloy. This work provides a promising alternative method for optimizing the performance of superalloys prepared by additive manufacturing.

Abstract The lethality, chemoresistance and metastatic characteristics of cancers are associated with phenotypically plastic cancer stem cells (CSCs). How the non-cell autonomous signalling pathways and cell-autonomous transcriptional machinery orchestrate the stem cell-like characteristics of CSCs is still poorly understood. Here we use a quantitative proteomic approach for identifying secreted proteins of CSCs in pancreatic cancer. We uncover that the cell-autonomous E2F1/4-pRb/RBL2 axis balances non-cell-autonomous signalling in healthy ductal cells but becomes deregulated upon KRAS mutation. E2F1 and E2F4 induce whereas pRb/RBL2 reduce WNT ligand expression (e.g. WNT7A, WNT7B, WNT10A, WNT4) thereby regulating self-renewal, chemoresistance and invasiveness of CSCs in both PDAC and breast cancer, and fibroblast proliferation. Screening for epigenetic enzymes identifies GCN5 as a regulator of CSCs that deposits H3K9ac onto WNT promoters and enhancers. Collectively, paracrine signalling pathways are controlled by the E2F-GCN5-RB axis in diverse cancers and this could be a therapeutic target for eliminating CSCs.

Abstract Using user-generated content (UGC) is of utmost importance for e-commerce platforms to extract valuable commercial information. In this paper, we propose an explainable multimodal learning approach named the visual–semantic embedding model with a self-attention mechanism for multimodal interaction (VSEM-SAMMI) to predict user-generated image (UGI) helpfulness and product sales. Focusing on SHEIN (i.e. a fast-fashion retailer), we collect the images posted by consumers, along with product and portrait characteristics. Moreover, we use VSEM-SAMMI, which adopts a self-attention mechanism to enforce attention weights between image and text, to extract features from UGI then use machine learning algorithms to predict UGI helpfulness and product sales. We explain features using a caption generation model and test the predictive power of embeddings and portrait characteristics. The results indicate that when predicting commercial information, embeddings are more informative than product and portrait characteristics. Combining VSEM-SAMMI with light gradient boosting (LightGBM) yields a mean squared error (MSE) of 0.208 for UGI helpfulness prediction and 0.184 for product sales prediction. Our study offers valuable insights for e-commerce platforms, enhances feature extraction from UGI through image–text joint embeddings for UGI helpfulness and product sales prediction, and pioneers a caption generation model for interpreting image embeddings in the e-commerce domain.

Abstract Cisplatin (DDP)-based chemoradiotherapy is one of the standard treatments for nasopharyngeal carcinoma (NPC). However, the sensitivity and side effects of DDP to patients remain major obstacles for NPC treatment. This research aimed to study DDP sensitivity regulated by cancer-associated fibroblasts (CAFs) through modulating ferroptosis. We demonstrated that DDP triggered ferroptosis in NPC cells, and it inhibited tumor growth via inducing ferroptosis in xenograft model. CAFs secreted high level of FGF5, thus inhibiting DDP-induced ferroptosis in NPC cells. Mechanistically, FGF5 secreted by CAFs directly bound to FGFR2 in NPC cells, leading to the activation of Keap1/Nrf2/HO-1 signaling. Rescued experiments indicated that FGFR2 overexpression inhibited DDP-induced ferroptosis, and CAFs protected against DDP-induced ferroptosis via FGF5/FGFR2 axis in NPC cells. In vivo data further showed the protective effects of FGF5 on DDP-triggered ferroptosis in NPC xenograft model. In conclusion, CAFs inhibited ferroptosis to decrease DDP sensitivity in NPC through secreting FGF5 and activating downstream FGFR2/Nrf2 signaling. The therapeutic strategy targeting FGF5/FGFR2 axis from CAFs might augment DDP sensitivity, thus decreasing the side effects of DDP in NPC treatment.

Abstract Using (27.12 ± 0.14) × 108 ψ(3686) events collected with the BESIII detector at BEPCII, the decay of ψ(3686) → Ω − K + Ξ ¯ 0 $$ {\varOmega}^{-}{K}^{+}{\overline{\Xi}}^0 $$ + c.c. is observed for the first time. The branching fraction of this decay is measured to be B ψ 3686 → Ω − K + Ξ ¯ 0 + c . c . $$ {\mathcal{B}}_{\psi (3686)\to {\varOmega}^{-}{K}^{+}{\overline{\Xi}}^0+\textrm{c}.\textrm{c}.} $$ = (2.78 ± 0.40 ± 0.18) × 10 −6, where the first uncertainty is statistical and the second is systematic. Possible baryon excited states are searched for in this decay, but no evident intermediate state is observed with the current sample size.

Abstract Brainbow is a genetic cell-labeling technique that allows random colorization of multiple cells and real-time visualization of cell fate within a tissue, providing valuable insights into understanding complex biological processes. However, fluorescent proteins (FPs) in Brainbow have distinct excitation spectra with peak difference greater than 35 nm, which requires sequential imaging under multiple excitations and thus leads to long acquisition times. In addition, they are not easily used together with other fluorophores due to severe spectral bleed-through. Here, we report the development of a single-wavelength excitable Brainbow, UFObow, incorporating three newly developed blue-excitable FPs. We have demonstrated that UFObow enables not only tracking the growth dynamics of tumor cells in vivo but also mapping spatial distribution of immune cells within a sub-cubic centimeter tissue, revealing cell heterogeneity. This provides a powerful means to explore complex biology in a simultaneous imaging manner at a single-cell resolution in organs or in vivo.

Abstract Artificial intelligence has made substantial progress in many medical application scenarios. The quantity and complexity of pathology images are enormous, but conventional visual screening techniques are labor-intensive, time-consuming, and subject to some degree of subjectivity. Complex pathological data can be converted into mineable image features using artificial intelligence image analysis technology, enabling medical professionals to quickly and quantitatively identify regions of interest and extract information about cellular tissue. In this study, we designed a medical information assistance system for segmenting pathology images and quantifying statistical results, including data enhancement, cell nucleus segmentation, model tumor, and quantitative analysis. In cell nucleus segmentation, to address the problem of uneven healthcare resources, we designed a high-precision teacher model (HRMED_T) and a lightweight student model (HRMED_S). The HRMED_T model is based on visual Transformer and high-resolution representation learning. It achieves accurate segmentation by parallel low-resolution convolution and high-scaled image iterative fusion, while also maintaining the high-resolution representation. The HRMED_S model is based on the Channel-wise Knowledge Distillation approach to simplify the structure, achieve faster convergence, and refine the segmentation results by using conditional random fields instead of fully connected structures. The experimental results show that our system has better performance than other methods. The Intersection over the Union (IoU) of HRMED_T model reaches 0.756. The IoU of HRMED_S model also reaches 0.710 and params is only 3.99 M.

The coal industry is the ballast stone and stabilizer that supports the rapid development of China’s economy and society and ensures national energy security. It also undertakes the mission of ensuring energy supply and supporting the stable development of new energy. At present, the high-quality development of China’s coal industry has achieved some staged results, with remarkable achievements in energy supply, the continuous optimization of industrial structure, the significant improvement of scientific and technological innovation capabilities, and the continuous advance of green development and clean and efficient utilization. However, the current development level of the coal industry is far from meeting the national needs of Chinese-style modernization. The sustainable development is restricted by multiple factors. The digital technology and coal development and utilization have not yet been deeply integrated. The intelligent construction is still in the demonstration and cultivation stage. The green development is unbalanced and contradictory and prominent. The transformation and upgrading of the coal industry still has a long way to go. In view of the current development status and challenges of the coal industry, this paper proposes a synergistic development mode of digitalization, intelligentization and greenization (three -izations), clarifies the core connotation of new-quality coal productivity, constructs the overall framework of the synergistic development of new-quality coal productivity, and expounds the technical path for the transformation of the innovative allocation of production factors by digitalization, the breakthrough of key technologies led by intelligentization, and the deep transformation of traditional industries by greenization, i.e., by these three factors to generate new-quality coal productivity. Six major technical systems are proposed: through the research and development of the technical system of efficient data collection and in-depth governance of the whole industry chain, the data barriers among coal production, transportation, storage, marketing and use of the whole process are removed. Through the research and development of the knowledge map and intelligent decision-making and control technological system of the coal industry, the correlation relationship and value of the upstream and downstream business scenarios of the coal industry are deeply explored, so as to lay the foundation for the independent decision-making and intelligent control of the whole process of coal production and utilization. Also, the research should be carried out in terms of the intelligent-green-efficient development technological system of coal resources, the technical system of collaborative guarantee of safety in the coal industry, the technical system of ecological intelligent monitoring-early warning-restoration in mining areas, and the technical system of flexible development and clean and low-carbon utilization of coal, so as to realize the synergistic development of the whole industrial chain and the whole process of coal development and utilization, cultivate and form a new-quality coal productivity, and bring some fundamental changes and improvements to the overall image of the coal industry.

The Yellow River Basin is an energy basin that has the dual responsibility of ecological environment governance and economic and social development. The precise and intelligent recognition of the categories, numbers and characteristics of coal-related industrial sites is a key basic issue for energy resources-low carbon development-ecological protection in the basin. This study integrated the multi-source data and deep learning algorithms to precisely analyze the characteristics of coal-based sites in 13 large-scale coal-fired power bases in the Yellow River Basin from the basin-base-site scale, obtained the high-precision and high-quality background information of coal-power bases, and proposed a new method of real-time real-scene intelligent recognition of spatial characteristics of coal-related industries. In this study, ① Multi-source data such as Google image, GF-6 image, Sentinel-2 image, etc. were collected as coal-based site samples from 13 large-scale coal-fired power bases to build four datasets of coal mine sites (open-pit), coal mine sites (underground), coal-power sites, and coal chemical sites, covering 21 categories of samples. According to each type of sample, 6×10 samples were set for each hexagonal cell, totaling 1260 site samples. The confidence interval of the optimal sample number-highest recognition efficiency-optimal recognition model was 80%−86%. ② A coal-based site classification quantitative model (CSCQM) and a coal-based site range characteristic model (CSRCM) were established. The average accuracy of the models was 0.837. The background information of coal-related industrial sites in the Yellow River Basin were clarified, and a high-precision site intelligent recognition method based on Google image base map overlaying site intelligent recognition model calculation results was proposed. ③ The precise background data of the Shendong coal-power industrial agglomeration area in the basin were analyzed. Analyzed by remote sensing based ecological index (RSEI), the surface ecological quality of the 2 km core area of coal-based sites was significantly affected by coal mine and coal-power industries, while the 5 km buffer zone was not significantly affected, and the 8 km control zone was basically not affected by coal mine and coal power industries. Thus, the low-carbon pathways such as dynamic remediation and key management by region and stage were proposed. ④ The precise background data of the Ningdong coal-power-chemical industrial agglomeration area in the basin were analyzed. In 2022, the area of coal mine sites covered an area of 17.81 km2, accounting for 34.1% of the total area, the area of coal chemical sites covered an area of 22.3 km2, accounting for 42.6% of the total area, and the area of coal-power sites covered an area of 12.2 km2, accounting for 23.3% of the total area. The area ratio was coal chemical sites > coal mine sites > coal-power sites. Then, using the PSR (Pressure-State-Response) model, the comprehensive score of risk management was obtained as 53.93 points, which was 27.2% higher than that in 2003. A zoning management mode of ecological maintenance zone, production monitoring and early warning zone, damage repair and reconstruction zone, and other regulation zone were implemented. The study provided some technical methods and practical support for the potential pollution control, site management and regional ecological restoration of coal-related industrial sites.

Abstract Enhancing the device electroluminescence quantum efficiency (EQE EL ) is a critical factor in mitigating non-radiative voltage losses (V NR ) and further improving the performance of organic solar cells (OSCs). While the common understanding attributes EQE EL in OSCs to the dynamics of charge transfer (CT) states, persistent efforts to manipulate these decay dynamics have yielded limited results, with the EQE EL of high-efficiency OSCs typically remaining below 10−2%. This value is considerably lower than that observed in high efficiency inorganic photovoltaic devices. Here, we report that EQE EL is also influenced by the dissociation rate constant of singlet states (k DS ). Importantly, in contrast to the traditional belief that advocates maximizing k DS for superior photovoltaic quantum efficiency (EQE PV ), a controlled reduction in k DS is shown to enhance EQE EL without compromising EQE PV . Consequently, a promising experimental approach to address the V NR challenge is proposed, resulting in a significant improvement in the performance of OSCs.

Rock thermal physical properties play a crucial role in understanding deep thermal conditions, modeling the thermal structure of the lithosphere, and discovering the evolutionary history of sedimentary basins. Recent advancements in geothermal exploration, particularly the identification of high-temperature geothermal resources in Datong Basin, Shanxi, China, have opened new possibilities. This study aims to characterize the thermal properties of rocks and explore factors influencing thermal conductivity in basins hosting high-temperature geothermal resources. A total of 70 groups of rock samples were collected from outcrops in and around Datong Basin, Shanxi Province. Thermal property tests were carried out to analyze the rock properties, and the influencing factors of thermal conductivity were studied through experiments at different temperature and water-filled states. The results indicate that the thermal conductivity of rocks in Datong, Shanxi Province, typically ranges from 0.690 W/(m·K) to 6.460 W/(m·K), the thermal diffusion coefficient ranges from 0.441 mm2/s to 2.023 mm2/s, and the specific heat capacity of the rocks ranges from 0.569 KJ/(kg·°C) to 1.117 KJ/(kg·°C). Experimental results reveal the impact of temperature and water saturation on the thermal conductivity of the rock. The thermal conductivity decreases with increasing temperature and rises with high water saturation. A temperature correction formula for the thermal conductivity of different lithologies in the area is proposed through linear fitting. The findings from this study provide essential parameters for the assessment and prediction, development, and utilization of geothermal resources in the region and other basins with typical high-temperature geothermal resource.

Abstract Schizophrenia is a mental health disorder characterized by functional dysconnectivity. Eigenvector centrality mapping (ECM) has been employed to investigate alterations in functional connectivity in schizophrenia, yet the results lack consistency, and the genetic mechanisms underlying these changes remain unclear. In this study, whole-brain voxel-wise ECM analyses were conducted on resting-state functional magnetic resonance imaging data. A cohort of 91 patients with schizophrenia and 91 matched healthy controls were included during the discovery stage. Additionally, in the replication stage, 153 individuals with schizophrenia and 182 healthy individuals participated. Subsequently, a comprehensive analysis was performed using an independent transcriptional database derived from six postmortem healthy adult brains to explore potential genetic factors influencing the observed functional dysconnectivity, and to investigate the roles of identified genes in neural processes and pathways. The results revealed significant and reliable alterations in the ECM across multiple brain regions in schizophrenia. Specifically, there was a significant decrease in ECM in the bilateral superior and middle temporal gyrus, and an increase in the bilateral thalamus in both the discovery and replication stages. Furthermore, transcriptional analysis revealed 420 genes whose expression patterns were related to changes in ECM, and these genes were enriched mainly in biological processes associated with synaptic signaling and transmission. Together, this study enhances our knowledge of the neural processes and pathways involved in schizophrenia, shedding light on the genetic factors that may be linked to functional dysconnectivity in this disorder.

Abstract Schizophrenia, a multifaceted mental disorder characterized by disturbances in thought, perception, and emotion, has been extensively investigated through resting-state fMRI, uncovering changes in spontaneous brain activity among those affected. However, a bibliometric examination regarding publication trends in resting-state fMRI studies related to schizophrenia is lacking. This study obtained relevant publications from the Web of Science Core Collection spanning the period from 1998 to 2022. Data extracted from these publications included information on countries/regions, institutions, authors, journals, and keywords. The collected data underwent analysis and visualization using VOSviewer software. The primary analyses included examination of international and institutional collaborations, authorship patterns, co-citation analyses of authors and journals, as well as exploration of keyword co-occurrence and temporal trend networks. A total of 859 publications were retrieved, indicating an overall growth trend from 1998 to 2022. China and the United States emerged as the leading contributors in both publication outputs and citations, with Central South University and the University of New Mexico being identified as the most productive institutions. Vince D. Calhoun had the highest number of publications and citation counts, while Karl J. Friston was recognized as the most influential author based on co-citations. Key journals such as Neuroimage, Schizophrenia Research, Schizophrenia Bulletin, and Biological Psychiatry played pivotal roles in advancing this field. Recent popular keywords included support vector machine, antipsychotic medication, transcranial magnetic stimulation, and related terms. This study systematically synthesizes the historical development, current status, and future trends in resting-state fMRI research in schizophrenia, offering valuable insights for future research directions.

Abstract EEG/MEG source imaging (ESI) aims to find the underlying brain sources to explain the observed EEG or MEG measurement. Multiple classical approaches have been proposed to solve the ESI problem based on different neurophysiological assumptions. To support clinical decision-making, it is important to estimate not only the exact location of the source signal but also the extended source activation regions. Existing methods may render over-diffuse or sparse solutions, which limit the source extent estimation accuracy. In this work, we leverage the graph structures defined in the 3D mesh of the brain and the spatial graph Fourier transform (GFT) to decompose the spatial graph structure into sub-spaces of low-, medium-, and high-frequency basis. We propose to use the low-frequency basis of spatial graph filters to approximate the extended areas of brain activation and embed the GFT into the classical ESI methods. We validated the classical source localization methods with the corresponding improved version using GFT in both synthetic data and real data. We found the proposed method can effectively reconstruct focal source patterns and significantly improve the performance compared to the classical algorithms.

Abstract Schizophrenia (SCZ), a highly heritable mental disorder, is characterized by cognitive impairment, yet the extent of the shared genetic basis between schizophrenia and cognitive performance (CP) remains poorly understood. Therefore, we aimed to explore the polygenic overlap between SCZ and CP. Specifically, the bivariate causal mixture model (MiXeR) was employed to estimate the extent of genetic overlap between SCZ (n = 130,644) and CP (n = 257,841), and conjunctional false discovery rate (conjFDR) approach was used to identify shared genetic loci. Subsequently, functional annotation and enrichment analysis were carried out on the identified genomic loci. The MiXeR analyses revealed that 9.6 K genetic variants are associated with SCZ and 10.9 K genetic variants for CP, of which 9.5 K variants are shared between these two traits (Dice coefficient = 92.8%). By employing conjFDR, 236 loci were identified jointly associated with SCZ and CP, of which 139 were novel for the two traits. Within these shared loci, 60 exhibited consistent effect directions, while 176 had opposite effect directions. Functional annotation analysis indicated that the shared genetic loci were mainly located in intronic and intergenic regions, and were found to be involved in relevant biological processes such as nervous system development, multicellular organism development, and generation of neurons. Together, our findings provide insights into the shared genetic architecture between SCZ and CP, suggesting common pathways and mechanisms contributing to both traits.

Abstract Video‐based emotion recognition has been a long‐standing research topic for computer scientists and psychiatrists. In contrast to traditional discrete emotional models, emotion recognition based on continuous emotional models can better describe the progression of emotions. Quantitative analysis of emotions will have crucial impacts on promoting the development of intelligent products. The current solutions to continuous emotion recognition still have many issues. The original continuous emotion dataset contains incomplete data annotations, and the existing methods often ignore temporal information between frames. The following measures are taken in response to the above problems. Initially, aiming at the problem of incomplete video labels, the correlation between discrete and continuous video emotion labels is used to complete the dataset labels. This correlation is used to propose a mathematical model to fill the missing labels of the original dataset without adding data. Moreover, this paper proposes a continuous emotion recognition network based on an optimized temporal convolutional network, which adds a feature extraction submodule and a residual module to retain shallow features while improving the feature extraction ability. Finally, validation experiments on the Aff‐wild2 dataset achieved accuracies of 0.5159 and 0.65611 on the valence and arousal dimensions, respectively, by adopting the above measures.

Abstract Signal transduction is crucial for many biological functions. However, it is still unclear how signaling systems function accurately under noisy situations. More specifically, such systems operate in a regime of low response noise, while maintaining high sensitivity to signals. To gain further insight on this regime, here we derive a fundamental trade-off between response sensitivity and precision in biological signaling processes under the static noise condition. We find that the optimal trade-off in signaling networks can be better characterized by a phase diagram structure rather than topological structures. We confirm that the patterning network of early Drosophila embryos agrees with our derived relationship, and apply the optimal phase diagram structure to quantitatively predict the patterning position shifts of the downstream genes, including hunchback, Krüppel, giant, knirps and even-skipped, upon the dosage perturbation of the morphogen Bicoid.

Abstract Single-cell and spatial transcriptome sequencing, two recently optimized transcriptome sequencing methods, are increasingly used to study cancer and related diseases. Cell annotation, particularly for malignant cell annotation, is essential and crucial for in-depth analyses in these studies. However, current algorithms lack accuracy and generalization, making it difficult to consistently and rapidly infer malignant cells from pan-cancer data. To address this issue, we present Cancer-Finder, a domain generalization-based deep-learning algorithm that can rapidly identify malignant cells in single-cell data with an average accuracy of 95.16%. More importantly, by replacing the single-cell training data with spatial transcriptomic datasets, Cancer-Finder can accurately identify malignant spots on spatial slides. Applying Cancer-Finder to 5 clear cell renal cell carcinoma spatial transcriptomic samples, Cancer-Finder demonstrates a good ability to identify malignant spots and identifies a gene signature consisting of 10 genes that are significantly co-localized and enriched at the tumor-normal interface and have a strong correlation with the prognosis of clear cell renal cell carcinoma patients. In conclusion, Cancer-Finder is an efficient and extensible tool for malignant cell annotation.

RuO2 has been considered a potential alternative to commercial IrO2 for the oxygen evolution reaction (OER) due to its superior intrinsic activity. However, its inherent structure dissolution in acidic environments restricts its commercial applications. In this study, we report a novel Pd-doped ruthenium oxide (Pd–RuO2) nanosheet catalyst that exhibits improved activity and stability through a synergistic effect of Pd modulation of Ru electronic structure and the two-dimensional structure. The catalyst exhibits excellent performance, achieving an overpotential of only 204 mV at a current density of 10 mA cm−2. Impressively, after undergoing 8000 cycles of cyclic voltammetry testing, the overpotential merely decreased by 5 mV. The PEM electrolyzer with Pd0.08Ru0.92O2 as an anode catalyst survived an almost 130 h operation at 200 mA cm−2. To elucidate the underlying mechanisms responsible for the enhanced stability, we conducted an X-ray photoelectron spectroscopy (XPS) analysis, which reveals that the electron transfer from Pd to Ru effectively circumvents the over-oxidation of Ru, thus playing a crucial role in enhancing the catalyst's stability. Furthermore, density functional theory (DFT) calculations provide compelling evidence that the introduction of Pd into RuO2 effectively modulates electron correlations and facilitates the electron transfer from Pd to Ru, thereby preventing the over-oxidation of Ru. Additionally, the application of the two-dimensional structure effectively inhibited the aggregation and growth of nanoparticles, further bolstering the structural integrity of the catalyst.

Solar photovoltaic (PV) electricity offers a promising solution for the increasing demand for clean and sustainable energy, particularly in developing countries like Ghana. Power utility operators maintain power quality as their utmost priority per benchmarks established by regulators. Some researchers argue that increasing variable renewable sources in the grid compromises its stability, leading to poor power quality. This paper aims to evaluate the suitability of reactive power management (technical aspect) in a solar PV microgrid in meeting regulators' power quality benchmarks. It also presents the economics associated with reactive power management. The study uses Python optimisation algorithms to achieve the technical objective. The net present value (NPV), profitability index (PI) and discounted payback period (DPP) are the econometrics used to assess the economic viability of reactive power management in this study. The results show that optimising reactive power in microgrids reduces power losses, increasing the utility's energy sales. An annual energy savings of 1,985,600 MWh was made due to the reactive power optimisation, leading to significant economic returns for the microgrid. The NPV, PI and DPP calculated from the study were GHS 12,528,583,512, 10,404.3, and one year, respectively. Therefore, it suggests reactive power optimisation to microgrid and grid operators as a viable solution to meet regulators' benchmarks.

Abstract Lip-to-Speech (LTS) generation is an emerging technology that is highly visible, widely supported, and rapidly evolving. LTS has a wide range of promising applications, including assisting speech impairment and improving speech interaction in virtual assistants and robots. However, the technique faces the following challenges: (1) Chinese lip-to-speech generation is poorly recognized. (2) The wide range of variation in lip-speaking is poorly aligned with lip movements. Addressing these challenges will contribute to advancing Lip-to-Speech (LTS) technology, enhancing the communication abilities, and improving the quality of life for individuals with disabilities. Currently, lip-to-speech generation techniques usually employ the GAN architecture but suffer from the following problems: The primary issue lies in the insufficient joint modeling of local and global lip movements, resulting in visual ambiguities and inadequate image representations. To solve these problems, we design Flash Attention GAN (FA-GAN) with the following features: (1) Vision and audio are separately coded, and lip motion is jointly modelled to improve speech recognition accuracy. (2) A multilevel Swin-transformer is introduced to improve image representation. (3) A hierarchical iterative generator is introduced to improve speech generation. (4) A flash attention mechanism is introduced to improve computational efficiency. Many experiments have indicated that FA-GAN can recognize Chinese and English datasets better than existing architectures, especially the recognition error rate of Chinese, which is only 43.19%, the lowest among the same type.

Abstract Message passing neural networks such as graph convolutional networks (GCN) can jointly consider various types of features for social bot detection. However, the expressive power of GCN is upper-bounded by the 1st-order Weisfeiler–Leman isomorphism test, which limits the detection performance for the social bots. In this paper, we propose a subgraph encoding based GCN model, SEGCN, with stronger expressive power for social bot detection. Each node representation of this model is computed as the encoding of a surrounding induced subgraph rather than encoding of immediate neighbors only. Extensive experimental results on two publicly available datasets, Twibot-20 and Twibot-22, showed that the proposed model improves the accuracy of the state-of-the-art social bot detection models by around 2.4%, 3.1%, respectively.

Abstract This study aimed to investigate the predictive factors of therapeutic efficacy for chronic subdural hematoma (CSDH) patients receiving atorvastatin combined with dexamethasone therapy by using clinical imaging characteristics in conjunction with computed tomography (CT) texture analysis (CTTA). Clinical imaging characteristics and CT texture parameters at admission were retrospectively investigated in 141 CSDH patients who received atorvastatin combined with dexamethasone therapy from June 2019 to December 2022. The patients were divided into a training set (n = 81) and a validation set (n = 60). Patients in the training data were divided into two groups based on the effectiveness of the treatment. Univariate and multivariate analyses were performed to assess the potential factors that could indicate the prognosis of CSDH patients in the training set. The receiver operating characteristic (ROC) curve was used to analyze the predictive efficacy of the significant factors in predicting the prognosis of CSDH patients and was validated using a validation set. The multivariate analysis showed that the hematoma density to brain parenchyma density ratio, singal min (minimum) and singal standard deviation of the pixel distribution histogram, and inhomogeneity were independent predictors for the prognosis of CSDH patients based on atorvastatin and dexamethasone therapy. The area under the ROC curve between the two groups was between 0.716 and 0.806. As determined by significant factors, the validation's accuracy range was 0.816 to 0.952. Clinical imaging characteristics in conjunction with CTTA could aid in distinguishing patients with CSDH who responded well to atorvastatin combined with dexamethasone.

Abstract Pancreatic ductal adenocarcinoma (PDAC) is recognized as the most aggressive and fatal malignancy. A previous study reported that PDAC patients who exhibit elevated levels of DDX3X have a poor prognosis and low overall survival rate. However, the underlying molecular mechanism remains unclear. This study aimed to investigate the specific roles of DDX3X in PDAC. Multiple bioinformatics analyses were used to evaluate DDX3X expression and its potential role in PDAC. In vitro and in vivo studies were performed to assess the effects of DDX3X on PDAC cell growth. Furthermore, Western blotting, quantitative PCR, immunohistochemistry, immunofluorescence, mass spectrometry, coimmunoprecipitation and multiplexed immunohistochemical staining were conducted to identify the specific regulatory mechanism in PDAC. The results verified that DDX3X expression is notably upregulated in the tumor tissue vs. normal tissue of PDAC patients. DDX3X knockdown markedly suppressed the proliferation, invasion and migration of PDAC cells in vitro and inhibited tumor growth in vivo. Conversely, overexpression of DDX3X induced the opposite effect. Further studies supported that the DDX3X protein can associate with sirtuin 7 (SIRT7) to stimulate PDAC carcinogenesis and progression. Furthermore, SIRT7 inhibition significantly impeded DDX3X-mediated tumor growth both ex vivo and in vivo. The results also revealed that programmed death ligand 1 (PD-L1) expression is positively correlated with DDX3X expression. These results reveal significant involvement of the DDX3X-SIRT7 axis in the initiation and advancement of PDAC and offer previously undiscovered therapeutic options for PDAC management.

Abstract Dynamic failure widely exists in rock engineering, such as excavation, blasting, and rockburst. However, the quantitative measurement of the dynamic damage process using experimental methods remains a challenge. In this study, a SHPB modeling technique is established based on Voronoi-based DDA to study the damage evolution of Fangshan granite under dynamic loading. The assessment of cracking along the artificial joints among Voronoi sub-blocks is conducted using the modified contact constitutive law. A calibration procedure has been implemented to investigate the rock dynamic properties quantitatively. The dispersion and damping effect can be effectively eliminated by regular discretization in SHPB bars, based on which the dynamic stress equilibrium can be satisfied. To reproduce the loading rate effect of the dynamic compressive strength, which has been observed in the experiment, a modification strategy considering the influence of the rate effect on the strength meso-parameters is proposed. Using this strategy, the peak stresses of the transmitted waves predicted by DDA match well with those obtained from experiments conducted at different loading rates. The simulation results show that more microcracks are generated and the proportion of tensile cracks decreases as the loading rate increases. Furthermore, the dynamic mechanical behavior and fracturing process have also been discussed and compared with the experiments. The results show that the established SHPB system is a powerful tool for quantitative analysis of rock dynamics problems and can handle more complex problems in the future.

The orb-weaver spider genus Gea C.L. Koch, 1843 from China is revised, and three species including one new species, are recognized: Gea jingdong Mi, Wang & Gan, sp. nov. (♂♀) from Yunnan; Gea spinipes C.L. Koch, 1843 (♂♀) from Guangdong, Guangxi, Guizhou, Hainan, Taiwan, and Yunnan; and Gea subarmata Thorell, 1890 (♂♀) from Guangxi and Hainan. Gea subarmata is newly recorded in China.

Abstract Background Increasing evidence has indicated that long non-coding RNAs (lncRNAs) have been proven to regulate esophageal cancer progression. The lncRNA protein disulfide isomerase family A member 3 pseudogene 1 (PDIA3P1) has been shown to promote cancer stem cell properties; however, its mechanism of action remains unclear. In this study, we investigated the regulation of esophageal cancer stem cell properties by the interaction of PDIA3P1 with proteins. Methods The GEPIA2 and Gene Expression Omnibus databases were used to analyze gene expression. PDIA3P1 expression in human esophageal squamous cell carcinoma (ESCC) tissues and cell lines was detected by quantitative real-time polymerase chain reaction (qRT-PCR). Loss-of-function experiments were performed to determine the effects of PDIA3P1 on ESCC cell proliferation, migration, and invasion. The sphere formation assay, number of side population cells, and CD271 + /CD44 + cells were detected by flow cytometry to identify the cancer stem cell properties. RNA immunoprecipitation (RIP), RNA pull-down, co-immunoprecipitation (co-IP), dual luciferase reporter, and cleavage under targets and tagmentation (CUT&Tag) assays were performed to elucidate the underlying molecular mechanisms. Results PDIA3P1 expression was upregulated in ESCC cell lines and tissues. Functionally, higher PDIA3P1 expression promoted cell proliferation, invasion, and metastasis and inhibited apoptosis in esophageal cancer. Importantly, PDIA3P1 promoted cancer stem cell properties in ESCC. Mechanistically, PDIA3P1 interacted with and stabilized octamer-binding transcription factor 4 (OCT4) by eliminating its ubiquitination by the ubiquitinating enzyme WW domain-containing protein 2 (WWP2). Moreover, as a transcription factor, OCT4 bound to the PDIA3P1 promoter and promoted its transcription. Conclusions Our research revealed a novel mechanism by which a positive feedback loop exists between PDIA3P1 and OCT4. It also demonstrated that the PDIA3P1-WWP2-OCT4 loop is beneficial for promoting the cancer stem cell properties of ESCC. Owing to this regulatory relationship, the PDIA3P1-WWP2-OCT4-positive feedback loop might be used in the diagnosis and prognosis, as well as in the development of novel therapeutics for esophageal cancer.

Abstract Based on e + e − collision data collected at center-of-mass energies from 2.000 to 3.080 GeV by the BESIII detector at the BEPCII collider, a partial wave analysis is performed for the process e + e − → K S 0 K L 0 $$ {K}_S^0{K}_L^0 $$ π 0. The results allow the Born cross sections of the process e + e − → K S 0 K L 0 $$ {K}_S^0{K}_L^0 $$ π 0, as well as its subprocesses e + e − → K ∗(892)0 K ¯ $$ \overline{K} $$ 0 and K 2 ∗ $$ {K}_2^{\ast } $$ (1430)0 K ¯ $$ \overline{K} $$ 0 to be measured. The Born cross sections for e + e − → K S 0 K L 0 $$ {K}_S^0{K}_L^0 $$ π 0 are consistent with previous measurements by BaBar, but with substantially improved precision. The Born cross section lineshape of the process e + e − K ∗(892)0 K ¯ $$ \overline{K} $$ 0 is consistent with a vector meson state around 2.2 GeV with a significance of 3.2σ. A Breit-Wigner fit determines its mass as M Y = (2164.7 ± 9.1 ± 3.1) MeV/c 2 and its width as Γ Y = (32.4 ± 21.0 ± 1.8) MeV.

Abstract Background This study aimed to investigate the interactions among three core elements of respiratory infection—pathogen, lung microbiome, and host response—and their avocation with the severity and outcomes of Mycoplasma pneumoniae pneumonia (MPP) in children. Methods We prospectively collected bronchoalveolar lavage fluid from a cohort of 41 children with MPP, including general MPP (GMPP) and complicated MPP (CMPP), followed by microbiome and transcriptomic analyses to characterize the association among pathogen, lung microbiome, and host response and correlate it with the clinical features and outcomes. Results The lung microbiome of patients with CMPP had an increased relative abundance of Mycoplasma pneumoniae (MP) and reduced alpha diversity, with 76 differentially expressed species. Host gene analysis revealed a key module associated with neutrophil function and several inflammatory response pathways. Patients with a high relative abundance of MP, manifested by a specific lung microbiome and host response type, were more prone to CMPP and had a long imaging recovery time. Conclusion Patients with CMPP have a more disrupted lung microbiome than those with GMPP. MP, lung microbiome, and host response interacts with each other and are closely related to disease severity and outcomes in children with MPP. Graphical Abstract

Hypericum liboense M.T.An & T.R.Wu, sp. nov. (Hypericaceae) is a newly described species found in the Maolan National Nature Reserve of Guizhou Province, where it grows in rocky habitats without soil on karst mountain tops. In this study, key morphological characters were compared between the new species and the other known Hypericum species of Hypericaceae. DNA sequences were extracted from the leaves of the new species, with nuclear gene sequences (ITS) generated to reconstruct phylogenetic trees and describe its phylogenetic position in relation to other species of Hypericum. Our results show that the proposed new species has the typical characteristics of the genus Hypericum in morphology being similar to Hypericum monogynum, but differing in its sessile and semi-clasped leaves, long elliptical to long circular leaf blades, thickly papery to thinly leathery, with entire and wavy leaf margins. The abaxial side of the leaves is covered with white powder, giving them a grey-white appearance. The main lateral veins of the leaves are 8–15-paired, and the midvein on both sides is convex. The main lateral veins and midvein branch are conspicuous, with tertiary venation forming a network on the leaf surface and appearing prominently sunken. The inflorescences are 1–3-flowered, with a large calyx and conspicuous veins. The molecular phylogenetic analysis (PP = 1.00) provided substantial evidence for the proposition of H. liboense as a new species within Hypericum. Morphological and molecular evidence is presented, corroborating the proposition of the new species, including a comprehensive account of the distinctive morphological attributes of H. liboense, along with its key distinguishing features from similar species.

Tuberculosis is the leading cause of death due to a single infection prior to the COVID-19 pandemic. Screening of tuberculosis patients in a large population is of paramount importance for disease treatment and control, especially with an economical, accurate, and easy-to-operate method. Based on cough sounds, we proposed DMRNet to distinguish between patients with tuberculosis, other respiratory diseases, and healthy individuals. DMRNet comprises four convolutional blocks and six identification blocks and incorporates dynamic convolution into the first three convolutional blocks to promote feature extraction. After the second and third dynamic convolutions, a polarized self-attention mechanism was added to reduce the information loss caused by the dimensionality reduction. Finally, a multihead self-attention layer is added to the fourth convolutional block and the last three identification blocks to enhance the aggregation of global information. Using a dataset with 1323 cough sound fragments, the results achieved the accuracy, sensitivity, and specificity of tuberculosis screening were 94.32%, 97.73%, and 99.43%, respectively. Compared with reported studies, the proposed model demonstrated better accuracy and reliability. Cough sound-based DMRNet analysis is a promising method for tuberculosis screening, especially in densely populated areas. Owing to its convenience, low equipment requirements, and low cost, it is expected to become an effective tool for community tuberculosis screening.

Mode-locked microcombs with flat spectral profiles provide the high signal-to-noise ratio and are in high demand for wavelength division multiplexing (WDM)-based applications, particularly in future high-capacity communication and parallel optical computing. Here, we present two solutions to generate local relatively flat spectral profiles. One microcavity with ultra-flat integrated dispersion is pumped to generate one relatively flat single soliton source spanning over 150 nm. Besides, one extraordinary soliton crystal with single vacancy demonstrates the local relatively flat microcomb lines when the inner soliton spacings are slightly irregular. Our work paves a new way for soliton-based applications owing to the relatively flat spectral characteristics.

Abstract The D s + → K + K − μ + ν μ $$ {D}_s^{+}\to {K}^{+}{K}^{-}{\mu}^{+}{\nu}_{\mu } $$ decay is studied based on 7.33 fb −1 of e + e − collision data collected with the BESIII detector at center-of-mass energies in the range from 4.128 to 4.226 GeV. The absolute branching fraction is measured as B D s + → ϕ μ + ν μ = 2.25 ± 0.09 ± 0.07 × 10 − 2 $$ \mathcal{B}\left({D}_s^{+}\to \phi {\mu}^{+}{\nu}_{\mu}\right)=\left(2.25\pm 0.09\pm 0.07\right)\times {10}^{-2} $$ , the most precise measurement to date. Combining with the world average of B D s + → ϕ e + ν e $$ \mathcal{B}\left({D}_s^{+}\to \phi {e}^{+}{\nu}_e\right) $$ , the ratio of the branching fractions obtained is B D s + → ϕ μ + ν μ B D s + → ϕ e + ν e = 0.94 ± 0.08 $$ \frac{\mathcal{B}\left({D}_s^{+}\to \phi {\mu}^{+}{\nu}_{\mu}\right)}{\mathcal{B}\left({D}_s^{+}\to \phi {e}^{+}{\nu}_e\right)}=0.94\pm 0.08 $$ , in agreement with lepton universality. By performing a partial wave analysis, the hadronic form factor ratios at q 2 = 0 are extracted, finding r V = V 0 A 1 0 = 1.58 ± 0.17 ± 0.02 $$ {r}_V=\frac{V(0)}{A_1(0)}=1.58\pm 0.17\pm 0.02 $$ and r 2 = A 2 0 A 1 0 = 0.71 ± 0.14 ± 0.02 $$ {r}_2=\frac{A_2(0)}{A_1(0)}=0.71\pm 0.14\pm 0.02 $$ , where the first uncertainties are statistical and the second are systematic. No significant S-wave contribution from f 0(980) → K + K − is found. The upper limit B D s + → f 0 980 μ + ν μ ⋅ B f 0 980 → K + K − < 5.45 × 10 − 4 $$ \mathcal{B}\left({D}_s^{+}\to {f}_0(980){\mu}^{+}{\nu}_{\mu}\right)\cdot \mathcal{B}\left({f}_0(980)\to {K}^{+}{K}^{-}\right)

Abstract Using e + e − collision datasets corresponding to a total integrated luminosity of 21.7 fb −1 collected with the BESIII detector at the BEPCII collider at center-of-mass energies ranging from 4.009 GeV to 4.951 GeV, the energy-dependent cross sections of e + e − → p K − Λ ¯ + c . c . $$ {e}^{+}{e}^{-}\to p{K}^{-}\overline{\Lambda}+c.c. $$ are measured for the first time. By fitting these energy-dependent cross sections, we search for the excited ψ states ψ(4160) and ψ(4415), and the vector charmonium-like states ψ(4230), ψ(4360), and ψ(4660). No evidence for these is observed and the upper limits on the branching fractions of these states decaying into p K − Λ ¯ + c . c . $$ p{K}^{-}\overline{\Lambda}+c.c. $$ are set at the 90% confidence level.

Abstract Traditionally, the clinical evaluation of respiratory diseases was pulmonary function testing, which can be used for the detection of severity and prognosis through pulmonary function parameters. However, this method is limited by the complex process, which is impossible for patients to monitor daily. In order to evaluate pulmonary function parameters conveniently with less time and location restrictions, cough sound is the substitute parameter. In this paper, 371 cough sounds segments from 150 individuals were separated into 309 and 62 as the training and test samples. Short-time Fourier transform (STFT) was applied to transform cough sound into spectrogram, and ResNet50 model was used to extract 2048-dimensional features. Through support vector regression (SVR) model with biological attributes, the data were regressed with pulmonary function parameters, FEV1, FEV1%, FEV1/FVC, FVC, FVC%, and the performance of this models was evaluated with fivefold cross-validation. Combines with deep learning and machine learning technologies, the better results in the case of small samples were achieved. Using the coefficient of determination (R2), the ResNet50 + SVR model shows best performance in five basic pulmonary function parameters evaluation as FEV1(0.94), FEV1%(0.84), FEV1/FVC(0.68), FVC(0.92), and FVC%(0.72). This ResNet50 + SVR hybrid model shows excellent evaluation of pulmonary function parameters during coughing, making it possible to realize a simple and rapid evaluation for pneumonia patients. The technology implemented in this paper is beneficial in judge the patient's condition, realize early screening of respiratory diseases, evaluate postoperative disease changes and detect respiratory infectious diseases without time and location restrictions.

Abstract Neuroimaging studies have revealed that patients with schizophrenia exhibit disrupted resting-state functional connectivity. However, the inconsistent findings across these studies have hindered our comprehensive understanding of the functional connectivity changes associated with schizophrenia, and the molecular mechanisms associated with these alterations remain largely unclear. A quantitative meta-analysis was first conducted on 21 datasets, involving 1057 patients and 1186 healthy controls, to examine disrupted resting-state functional connectivity in schizophrenia, as measured by whole-brain voxel-wise functional network centrality (FNC). Subsequently, partial least squares regression analysis was employed to investigate the relationship between FNC changes and gene expression profiles obtained from the Allen Human Brain Atlas database. Finally, gene enrichment analysis was performed to unveil the biological significance of the altered FNC-related genes. Compared with healthy controls, patients with schizophrenia show consistently increased FNC in the right inferior parietal cortex extending to the supramarginal gyrus, angular gyrus, bilateral medial prefrontal cortex, and right dorsolateral prefrontal cortex, while decreased FNC in the bilateral insula, bilateral postcentral gyrus, and right inferior temporal gyrus. Meta-regression analysis revealed that increased FNC in the right inferior parietal cortex was positively correlated with clinical score. In addition, these observed functional connectivity changes were found to be spatially associated with the brain-wide expression of specific genes, which were enriched in diverse biological pathways and cell types. These findings highlight the aberrant functional connectivity observed in schizophrenia and its potential molecular underpinnings, providing valuable insights into the neuropathology of dysconnectivity associated with this disorder.

The COVID-19 pandemic has had a profound impact on public mental health, leading to a surge in loneliness, depression, and anxiety. And these public psychological issues increasingly become a factor affecting social order. As researchers explore ways to address these issues, artificial intelligence (AI) has emerged as a powerful tool for understanding and supporting mental health. In this paper, we provide a thorough literature review on the emotions(EMO) of loneliness, depression, and anxiety (EMO-LDA) before and during the COVID-19 pandemic. Additionally, we evaluate the application of AI in EMO-LDA research from 2018 to 2023(AI-LDA) using Latent Dirichlet Allocation (LDA) topic modeling. Our analysis reveals a significant increase in the proportion of literature on EMO-LDA and AI-LDA before and during the COVID-19 pandemic. We also observe changes in research hotspots and trends in both field. Moreover, our results suggest that the collaborative research of EMO-LDA and AI-LDA is a promising direction for future research. In conclusion, our review highlights the urgent need for effective interventions to address the mental health challenges posed by the COVID-19 pandemic. Our findings suggest that the integration of AI in EMO-LDA research has the potential to provide new insights and solutions to support individuals facing loneliness, depression, and anxiety. And we hope that our study will inspire further research in this vital and revelant domin.

Abstract Flexible pressure sensors employing porous polymer materials are renowned for their superior sensing capabilities, attributed to the inherently low stiffness of porous polymers. The practical utilization of such sensors hinges on the development of a straightforward, cost‐effective, and patternable method for preparing porous polymer materials. In this research, a novel laser thermoforming process is introduced to craft a porous Polydimethylsiloxane (PDMS) film, leveraging carbon black (CB) as an endothermic agent and glucose as a porogen. The resulting porous PDMS film serves as the foundation for a remarkably sensitive flexible piezoresistive sensor. Owing to the inherent flexibility endowed by the porous structure, the porous PDMS‐based pressure sensor achieves a remarkable sensitivity of 109.4 kPa−1 within 0–200 Pa, an effective measurement span of 0–100 kPa, rapid response and recovery times of 79 and 55 ms, and impressive stability over 5000 cycles. The sensor's utility extends to applications such as human pulse monitoring, Morse coding, and robot claw sensing, underscoring its promise in the realm of flexible electronics. In summary, the laser thermoforming process realizes the one‐step, economical, and patternable preparation of porous polymer materials, and introduces a novel avenue for the realization of high‐performance flexible sensors based on thermally cured porous polymers.

Multiagent Reinforcement Learning (MARL) has been well adopted due to its exceptional ability to solve multiagent decision-making problems. To further enhance learning efficiency, knowledge transfer algorithms have been developed, among which experience-sharing-based and action-advising-based transfer strategies share the mainstream. However, it is notable that, although there exist many successful applications of both strategies, they are not flawless. For the long-developed action-advising-based methods (namely KT-AA, short for knowledge transfer based on action advising), their data efficiency and scalability are not satisfactory. As for the newly proposed experience-sharing-based knowledge transfer methods (KT-ES), although the shortcomings of KT-AA have been partially overcome, they are incompetent to correct specific bad decisions in the later learning stage. To leverage the superiority of both KT-AA and KT-ES, this study proposes KT-Hybrid, a hybrid knowledge transfer approach. In the early learning phase, KT-ES methods are employed, expecting better data efficiency from KT-ES to enhance the policy to a basic level as soon as possible. Later, we focus on correcting specific errors made by the basic policy, trying to use KT-AA methods to further improve the performance. Simulations demonstrate that the proposed KT-Hybrid outperforms well-received action-advising- and experience-sharing-based methods.

Background Coronary microvascular dysfunction as measured by myocardial flow reserve (MFR) is associated with increased cardiovascular risk in rheumatoid arthritis (RA). The objective of this study was to determine the association between reducing inflammation with MFR and other measures of cardiovascular risk. Methods and Results Patients with RA with active disease about to initiate a tumor necrosis factor inhibitor were enrolled (NCT02714881). All subjects underwent a cardiac perfusion positron emission tomography scan to quantify MFR at baseline before tumor necrosis factor inhibitor initiation, and after tumor necrosis factor inhibitor initiation at 24 weeks. MFR

Objective To evaluate the safety and preliminary efficacy of YSCH-01 (Recombinant L-IFN adenovirus) in subjects with advanced solid tumors.Methods In this single-center, open-label, investigator-initiated trial of YSCH-01, 14 patients with advanced solid tumors were enrolled. The study consisted of two distinct phases: (1) the dose escalation phase and (2) the dose expansion phase; with three dose groups in the dose escalation phase based on dose levels (5.0×109 viral particles (VP)/subject, 5.0×1010 VP/subject, and 5.0×1011 VP/subject). Subjects were administered YSCH-01 injection via intratumoral injections. The safety was assessed using National Cancer Institute Common Terminology Criteria for Adverse Events V.5.0, and the efficacy evaluation was performed using Response Evaluation Criteria in Solid Tumor V.1.1.Results 14 subjects were enrolled in the study, including 9 subjects in the dose escalation phase and 5 subjects in the dose expansion phase. Of the 13 subjects included in the full analysis set, 4 (30.8%) were men and 9 (69.2%) were women. The most common tumor type was lung cancer (38.5%, 5 subjects), followed by breast cancer (23.1%, 3 subjects) and melanoma (23.1%, 3 subjects). During the dose escalation phase, no subject experienced dose-limiting toxicities. The content of recombinant L-IFN adenovirus genome and recombinant L-IFN protein in blood showed no trend of significant intergroup changes. No significant change was observed in interleukin-6 and interferon-gamma. For 11 subjects evaluated for efficacy, the overall response rate with its 95% CI was 27.3% (6.02% to 60.97%) and the disease control rate with its 95% CI was 81.8% (48.22% to 97.72%). The median progression-free survival was 4.97 months, and the median overall survival was 8.62 months. In addition, a tendency of decrease in the sum of the diameters of target lesions was observed. For 13 subjects evaluated for safety, the overall incidence of adverse events (AEs) was 92.3%, the overall incidence of adverse drug reactions (ADRs) was 84.6%, and the overall incidence of >Grade 3 AEs was 7.7%, while no AEs/ADRs leading to death occurred. The most common AEs were fever (69.2%), nausea (30.8%), vomiting (30.8%), and hypophagia (23.1%).Conclusions The study shows that YSCH-01 injections were safe and well tolerated and exhibited preliminary efficacy in patients with advanced solid tumors, supporting further investigation to evaluate its efficacy and safety.Trial registration number NCT05180851.

A massless particle beyond the Standard Model is searched for in the two-body decay Σ+→p+invisible using (1.0087±0.0044)×1010 J/ψ events collected at a center-of-mass energy of s=3.097GeV with the BESIII detector at the BEPCII collider. No significant signal is observed, and the upper limit on the branching fraction B(Σ+→p+invisible) is determined to be 3.2×10−5 at the 90% confidence level. This is the first search for a flavor-changing neutral current process with missing energy in hyperon decays which plays an important role in constraining new physics models.

The data-driven approach of supervised learning methods has limited applicability in solving dipole inversion in Quantitative Susceptibility Mapping (QSM) with varying scan parameters across different objects. To address this generalization issue in supervised QSM methods, we propose a novel training-free model-based unsupervised method called MoDIP (Model-based Deep Image Prior). MoDIP comprises a small, untrained network and a Data Fidelity Optimization (DFO) module. The network converges to an interim state, acting as an implicit prior for image regularization, while the optimization process enforces the physical model of QSM dipole inversion. Experimental results demonstrate MoDIP's excellent generalizability in solving QSM dipole inversion across different scan parameters. It exhibits robustness against pathological brain QSM, achieving over 32 % accuracy improvement than supervised deep learning methods. It is also 33 % more computationally efficient and runs 4 times faster than conventional DIP-based approaches, enabling 3D high-resolution image reconstruction in under 4.5 min.

In this paper, a novel nano sensor structure is proposed, which consists of a metal-insulator-metal waveguide (MIM) with rectangular baffles and a water droplet cavity with rectangular stubs (WDCRS). The WDCRS structure optimizes the sensitivity of a single water droplet cavity and makes the transmission curve clearer and smoother. The transmission characteristics of WDCRS structure were simulated using finite element method (FEM). The transmission characteristics of the exported structure were analyzed in detail. In addition, the influence of structural geometric parameters on sensing performance was also studied, and it was found that the size of the water droplet cavity is a key factor in improving sensitivity. When applied to a refractive index sensor, the structure achieves a sensitivity of up to 2,300 nm/RIU with a corresponding figure of merit (FOM) of 60.5. These works provide some ideas for the design of high-performance nanostructures and multiple Fano resonance excitation structures.

Background Despite the encouraging outcome of chimeric antigen receptor T cell (CAR-T) targeting B cell maturation antigen (BCMA) in managing relapsed or refractory multiple myeloma (RRMM) patients, the therapeutic side effects and dysfunctions of CAR-T cells have limited the efficacy and clinical application of this promising approach.Methods In this study, we incorporated a short hairpin RNA cassette targeting PD-1 into a BCMA-CAR with an OX-40 costimulatory domain. The transduced PD-1KD BCMA CAR-T cells were evaluated for surface CAR expression, T-cell proliferation, cytotoxicity, cytokine production, and subsets when they were exposed to a single or repetitive antigen stimulation. Safety and efficacy were initially observed in a phase I clinical trial for RRMM patients.Results Compared with parental BCMA CAR-T cells, PD-1KD BCMA CAR-T cell therapy showed reduced T-cell exhaustion and increased percentage of memory T cells in vitro. Better antitumor activity in vivo was also observed in PD-1KD BCMA CAR-T group. In the phase I clinical trial of the CAR-T cell therapy for seven RRMM patients, safety and efficacy were initially observed in all seven patients, including four patients (4/7, 57.1%) with at least one extramedullary site and four patients (4/7, 57.1%) with high-risk cytogenetics. The overall response rate was 85.7% (6/7). Four patients had a stringent complete response (sCR), one patient had a CR, one patient had a partial response, and one patient had stable disease. Safety profile was also observed in these patients, with an incidence of manageable mild to moderate cytokine release syndrome and without the occurrence of neurological toxicity.Conclusions Our study demonstrates a design concept of CAR-T cells independent of antigen specificity and provides an alternative approach for improving the efficacy of CAR-T cell therapy.

Abstract γδ T cells are evolutionarily conserved T lymphocytes that manifest unique antitumor efficacy independent of tumor mutation burden (TMB) and conventional human leukocyte antigen (HLA) recognition. However, the dynamic changes in their T cell receptor (TCR) repertoire during cancer progression and treatment courses remain unclear. Here, a comprehensive characterization of γδTCR repertoires are performed in thyroid cancers with divergent differentiation states through cross‐sectional studies. The findings revealed a significant correlation between the differentiation states and TCR repertoire diversity. Notably, highly expanded clones are prominently enriched in γδ T cell compartment of dedifferentiated patients. Moreover, by longitudinal investigations of the γδ T cell response to various antitumor therapies, it is found that the emergence and expansion of the Vδ2neg subset may be potentially associated with favorable clinical outcomes after post‐radiotherapeutic immunotherapy. These findings are further validated at single‐cell resolution in both advanced thyroid cancer patients and a murine model, underlining the importance of further investigations into the role of γδTCR in cancer immunity and therapeutic strategies.