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Background: Research is needed to understand the impact of social determinants of health on health literacy throughout the life course. This study examined how racial composition of multiple past and current social environments was related to adults' health literacy. Methods: In this study, 546 adult patients at a primary care clinic in St. Louis, Missouri, completed a self-administered written questionnaire that assessed demographic characteristics and a verbally administered component that assessed health literacy with the Rapid Estimate of Adult Literacy in Medicine – Revised (REALM-R) and Newest Vital Sign (NVS), and self-reported racial composition of six past and four current social environments. Multilevel logistic regression models were built to examine the relationships between racial composition of past and current social environments and health literacy. Results: Most participants identified as Black or multiracial (61%), had a high school diploma or less (54%), and household income

Abstract Metal–organic frameworks (MOFs) are a class of promising nanomaterials for atmospheric water harvesting (AWH), especially in arid remote areas. However, several challenges are still faced for practical applications because of the dissatisfied water adsorption/desorption properties in terms of the capability, kinetics, and stability. Herein, we report the facile synthesis of a nano‐sized octahedral nitrogen‐modified MOF‐801 that exhibits superior solar‐powered AWH performance using a custom‐made device, with a state‐of‐the‐art water harvesting ability up to 4.64LH2OkgMOFs−1 from air upon 12‐h test under a relative humidity (RH) of 30% and simulated sunlight irradiation. The nitrogen‐modified MOF‐801 with rapid sorption–desorption kinetics, uptakes 0.29gH2OgMOFs‐1 of water at 30% RH within 30 min and releases 90% of the captured water within 10 min under 1‐sun illumination. The success relies on N‐doping‐induced mixed‐linkers in the form of 2,3‐diaminobutanedioic acid and fumaric acid in the unique pore structures of the MOFs for rapid and high‐capacity water capture. The N‐doped MOF‐801 with water uptake capacity, fast adsorption kinetics, and cycle stability sheds light on the practical use of MOFs for effective solar‐powered water harvesting from droughty air.

Background: This study aimed to assess the effectiveness of surgery in the management of vertical compensatory head posture in patients with congenital nystagmus (CN) inherited in an X-linked manner in a Chinese family and determine the molecular pathogenesis of this disease. Methods: We studied 18 members belonging to four generations in a family with congenital nystagmus. Parks shift of neutral zone surgeries were performed on 7 patients with vertical compensatory head posture from the family. In addition, head posture, visual acuity, and stereopsis of the 7 patients were evaluated before and 2-years after the displacement surgeries. Gene alternations of the disease were researched by sequencing a candidate gene (FRMD7). From each generation of the family, one patient (including the proband) and one normal control were sampled for Sanger sequencing. Results: Over a median follow-up period of 2 years, the anomalous head posture, visual acuity, and stereopsis significantly improved postoperatively (P T (p.D196Y) in exon 7 of FRMD7 was co-segregated with the disease in this family. Conclusions: Parks shift of neutral zone surgeries relieved the vertical compensatory head posture and improved visual acuity and stereopsis in the primary position of CN patients. In this study, it was concluded that a missense mutation in exon 7 (c.586G > 7, p.D196Y) in FRMD7 was possibly responsible for the disease in this family.

The rich and unique energy level structure arising from 4fn inner shell configuration of trivalent lanthanide ions (Ln3+) renders them highly attractive for light‐emitting applications. Currently, research primarily focuses on Ln3+ doping in either traditional garnets or the recently developed perovskite phosphors. However, there have been few reports on stable phosphors crystallized with pure lanthanide elements. Herein, a universal solution‐based route to eight Ln3+‐based metal halides from the organic‐inorganic A4LnX7 family is described, where A+ = 4,4‐difluoropiperidinium (DFPD+), Ln3+ = Nd3+, Eu3+, Ho3+, Sm3+, Tm3+, Tb3+, Yb3+, Er3+, and X− = Cl−, Br−. Visible photoluminescence (PL) is observed from Tb3+‐, Eu3+‐, Ho3+‐, and Sm3+‐based compounds with Tb and Eu compositions exhibiting high PL quantum yields of 90–100%; Nd3+‐, Tm3+‐, Yb3+‐, and Er3+‐based crystals show fascinating near‐infrared emission. Light‐emitting diodes (LEDs) fabricated with (DFPD)4TbCl7 yield characteristic emission of Tb3+, representing the first demonstration of electroluminescence from these organic‐inorganic Ln3+‐based metal halides. Moreover, these materials exhibit distinct excitation wavelength‐dependent emission after alloying with different Ln3+ ions, making them interesting for multicolor display and multilevel information encryption applications. It is foreseen that this study will open up the way to a possible design of robust optoelectronic devices based on lanthanide metal halides.

ObjectiveAdverse events associated with dexmedetomidine were analyzed using data from the FDA’s FAERS database, spanning from 2004 to the third quarter of 2023. This analysis serves as a foundation for monitoring dexmedetomidine’s safety in clinical applications.MethodsData on adverse events associated with dexmedetomidine were standardized and analyzed to identify clinical adverse events closely linked to its use. This analysis employed various signal quantification analysis algorithms, including Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-Item Gamma Poisson Shrinker (MGPS).ResultsIn the FAERS database, dexmedetomidine was identified as the primary suspect in 1,910 adverse events. Our analysis encompassed 26 organ system levels, from which we selected 346 relevant Preferred Terms (PTs) for further examination. Notably, adverse drug reactions such as diabetes insipidus, abnormal transcranial electrical motor evoked potential monitoring, acute motor axonal neuropathy, and trigeminal cardiac reflex were identified. These reactions are not explicitly mentioned in the drug’s specification, indicating the emergence of new signals for adverse drug reactions.ConclusionData mining in the FAERS database has elucidated the characteristics of dexmedetomidine-related adverse drug reactions. This analysis enhances our understanding of dexmedetomidine’s drug safety, aids in the clinical management of pharmacovigilance studies, and offers valuable insights for refining drug-use protocols.

Major depressive disorder (MDD) is a debilitating mental health condition that poses significant risks and burdens. Resting-state functional magnetic resonance imaging (fMRI) has emerged as a promising tool in investigating the neural mechanisms underlying MDD. However, a comprehensive bibliometric analysis of resting-state fMRI in MDD is currently lacking. Here, we aimed to thoroughly explore the trends and frontiers of resting-state fMRI in MDD research. The relevant publications were retrieved from the Web of Science database for the period between 1998 and 2022, and the CiteSpace software was employed to identify the influence of authors, institutions, countries/regions, and the latest research trends. A total of 1501 publications met the search criteria, revealing a gradual increase in the number of annual publications over the years. China contributed the largest publication output, accounting for the highest percentage among all countries. Particularly, the University of Electronic Science and Technology of China, Capital Medical University, and Harvard Medical School were identified as key institutions that have made substantial contributions to this growth. Neuroimage, Biological Psychiatry, Journal of Affective Disorders, and Proceedings of the National Academy of Sciences of the United States of America are among the influential journals in the field of resting-state fMRI research in MDD. Burst keywords analysis suggest the emerging research frontiers in this field are characterized by prominent keywords such as dynamic functional connectivity, cognitive control network, transcranial brain stimulation, and childhood trauma. Overall, our study provides a systematic overview into the historical development, current status, and future trends of resting-state fMRI in MDD, thus offering a useful guide for researchers to plan their future research.

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.

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.

Sleep Apnea (SA) is a prevalent sleep disorder with multifaceted etiologies that can have severe consequences for patients. Diagnosing SA traditionally relies on the in-laboratory polysomnogram (PSG), which records various human physiological activities overnight. SA diagnosis involves manual scoring by qualified physicians. Traditional machine learning methods for SA detection depend on hand-crafted features, making feature selection pivotal for downstream classification tasks. In recent years, deep learning has gained popularity in SA detection due to its capability for automatic feature extraction and superior classification accuracy. This study introduces a Deep Attention Network with Multi-Temporal Information Fusion (DAN-MTIF) for SA detection using single-lead electrocardiogram (ECG) signals. This framework utilizes three 1D convolutional neural network (CNN) blocks to extract features from R-R intervals and R-peak amplitudes using segments of varying lengths. Recognizing that features derived from different temporal scales vary in their contribution to classification, we integrate a multi-head attention module with a self-attention mechanism to learn the weights for each feature vector. Comprehensive experiments and comparisons between two paradigms of classical machine learning approaches and deep learning approaches are conducted. Our experiment results demonstrate that (1) compared with benchmark methods, the proposed DAN-MTIF exhibits excellent performance with 0.9106 accuracy, 0.9396 precision, 0.8470 sensitivity, 0.9588 specificity, and 0.8909 $F_{1}$ score at per-segment level; (2) DAN-MTIF can effectively extract features with a higher degree of discrimination from ECG segments of multiple timescales than those with a single time scale, ensuring a better SA detection performance; (3) the overall performance of deep learning methods is better than the classical machine learning algorithms, highlighting the superior performance of deep learning approaches for SA detection.

In today's world, rising energy demands are a significant challenge, and the smart grid emerges as a solution for sustainable energy management. An essential view of advancing the Smart Grid (SG) capabilities is the collaborative scheduling of Wind Power Generation (WPG) and energy storage. It plays a significant role in elevating SG efficiency, reliability, and environmental sustainability. This kind of strategic planning is essential to increase coordination between WPG and flexible deployment of Energy Storage Systems (ESS). Efficient SG functions will be maintained, and energy sources can be regulated with demand variations. Putting an emphasis on assumptions and empirical data is vital in conventional techniques. When it comes to the continuously shifting environment of SG and RE resources, traditional approaches aren't highly reliable or adaptable. The present article uses a hybrid model that integrates Deep Reinforcement Learning (DRL) and Particle Swarm Optimization (PSO) to address those drawbacks. The primary purpose of it is to help with the joint scheduling of WP and ESS. This technique is what permits DRL to reach selections rapidly in convoluted, ever-changing environments. The proposed approach, when combined with PSO's effectiveness for variable optimization, will result in improved scheduling findings. The framework additionally exploits the finest use of ESS, but it also effectively addresses the challenging task of integrating dynamic WP with the SG. Reliable and cost-effective supply is ensured by the system's design. The accuracy, stability, and versatility of the suggested approach to the dynamic features of Wind Energy (WE) and storage management are incomparable to traditional approaches. The findings indicate the method's actual validity and its significance for improving SG functions. Applying state-of-the-art statistical techniques for holistic optimization of RE resources and storage systems is emphasized by the framework. Owing to minimizing Energy Consumption (EC) and lowering greenhouse gas emissions, this study provides a significant step towards achieving the goal of effective and eco-friendly SG functions.

The process of reconstructing underlying cortical and subcortical electrical activities from Electroencephalography (EEG) or Magnetoencephalography (MEG) recordings is called Electrophysiological Source Imaging (ESI). Given the complementarity between EEG and MEG in measuring radial and tangential cortical sources, combined EEG/MEG is considered beneficial in improving the reconstruction performance of ESI algorithms. Traditional algorithms mainly emphasize incorporating predesigned neurophysiological priors to solve the ESI problem. Deep learning frameworks aim to directly learn the mapping from scalp EEG/MEG measurements to the underlying brain source activities in a data-driven manner, demonstrating superior performance compared to traditional methods. However, most of the existing deep learning approaches for the ESI problem are performed on a single modality of EEG or MEG, meaning the complementarity of these two modalities has not been fully utilized. How to fuse the EEG and MEG in a more principled manner under the deep learning paradigm remains a challenging question. This study develops a Multi-Modal Deep Fusion (MMDF) framework using Attention Neural Networks (ANN) to fully leverage the complementary information between EEG and MEG for solving the ESI inverse problem, which is termed as MMDF-ANN. Specifically, our proposed brain source imaging approach consists of four phases, including feature extraction, weight generation, deep feature fusion, and source mapping. Our experimental results on both synthetic dataset and real dataset demonstrated that using a fusion of EEG and MEG can significantly improve the source localization accuracy compared to using a single-modality of EEG or MEG. Compared to the benchmark algorithms, MMDF-ANN demonstrated good stability when reconstructing sources with extended activation areas and situations of EEG/MEG measurements with a low signal-to-noise ratio.

Abstract Background Epigenetic alterations contribute greatly to the development and progression of colorectal cancer, and effect of aberrant miR-622 expression is still controversial. This study aimed to discover miR-622 regulation in CRC proliferation. Methods miR-622 expression and prognosis were analyzed in clinical CRC samples from Nanfang Hospital. miR-622 regulation on cell cycle and tumor proliferation was discovered, and FOLR2 was screened as functional target of miR-622 using bioinformatics analysis, which was validated via dual luciferase assay and gain-of-function and loss-of-function experiments both in vitro and in vivo. Results miR-622 overexpression in CRC indicated unfavorable prognosis and it regulated cell cycle to promote tumor growth both in vitro and in vivo. FOLR2 is a specific, functional target of miR-622, which negatively correlates with signature genes in cell cycle process to promote CRC proliferation. Conclusions miR-622 upregulates cell cycle process by targeting FOLR2 to promote CRC proliferation, proposing a novel mechanism and treatment target in CRC epigenetic regulation of miR-622.

Abstract Repeat vaccination with egg-based influenza vaccines could preferentially boost antibodies targeting the egg-adapted epitopes and reduce immunogenicity to circulating viruses. In this randomized trial (Clinicaltrials.gov: NCT03722589), sera pre- and post-vaccination with quadrivalent inactivated egg-based (IIV4), cell culture-based (ccIIV4), and recombinant (RIV4) influenza vaccines were collected from healthcare personnel (18-64 years) in 2018−19 (N = 723) and 2019−20 (N = 684) influenza seasons. We performed an exploratory analysis. Vaccine egg-adapted changes had the most impact on A(H3N2) immunogenicity. In year 1, RIV4 induced higher neutralizing and total HA head binding antibodies to cell- A(H3N2) virus than ccIIV4 and IIV4. In year 2, among the 7 repeat vaccination arms (IIV4-IIV4, IIV4-ccIIV4, IIV4-RIV4, RIV4-ccIIV4, RIV4-RIV4, ccIIV4-ccIIV4 and ccIIV4-RIV4), repeat vaccination with either RIV4 or ccIIV4 further improved antibody responses to circulating viruses with decreased neutralizing antibody egg/cell ratio. RIV4 also had higher post-vaccination A(H1N1)pdm09 and A(H3N2) HA stalk antibodies in year 1, but there was no significant difference in HA stalk antibody fold rise among vaccine groups in either year 1 or year 2. Multiple seasons of non-egg-based vaccination may be needed to redirect antibody responses from immune memory to egg-adapted epitopes and re-focus the immune responses towards epitopes on the circulating viruses to improve vaccine effectiveness.

Abstract Background Osteoarthritis (OA) is a common degenerative disease involving articular cartilage, in which ferroptosis of chondrocytes plays an important role. Baicalin (BAI) exerts regulatory effects in a wide range of orthopedic diseases including OA, but its effect on ferroptosis of chondrocytes (CHs) is still unclear. The purpose of this study was to determine the effect of BAI on ferroptosis in human OA chondrocytes (OACs), and to explore its possible mechanism. Methods CHs were treated with IL-1β (10 ng/mL) to simulate inflammation in vitro. Immunofluorescence, quantitative RT-PCR, Western blotting and cell viability assay were performed to evaluate the impacts of BAI on Fe2+ level, mitochondrial dysfunction, ferroptosis-related proteins, oxidative stress and cytotoxicity in CHs. Additionally, siRNA was made use of to knock out nuclear factor E2-related factor 2 (Nrf2) to analyze the role played by Nrf2 in BAI-induced CH ferroptosis. Results BAI eliminated IL-1β-induced Fe2+ accumulation, changes in mitochondrial membrane potential and ferroptosis-related protein GPX4, SLC7A11, P53 and ACSL4 levels, as well as reactive oxygen species (ROS), lipid peroxidation (LPO) and malondialdehyde (MDA) accumulation in CHs. Besides, BAI reversed IL-1β-induced decrease of Collagen II and increase of MMP13 in CHs. Meanwhile, BAI attenuated IL-1β-induced CH toxicity and promoted Nrf2 antioxidant system activation. When Nrf2 was knocked down by siRNA, the effects of BAI on IL-1β-induced ferroptosis-related proteins and antioxidant stress in CHs were significantly weakened. Conclusions This study demonstrates that IL-1β can induce CH ferroptosis. BAI is able to inhibit IL-1β-induced CH ferroptosis and ECM degradation, and the specific mechanism may be that it can inhibit IL-1β-induced CH ferroptosis by activating Nrf2 antioxidant system to attenuate the accumulation of intracellular ROS and lipid ROS.

The presence of natural ambient noise interferes with the system for locating and identifying underwater targets. This paper suggests that a Dual-Path Transformation Network (DPTN) reduces ambient noise in underwater acoustic signals. First, the input acoustic signals’ higher-order non-linear features are extracted using a multi-scale convolutional encoder neural network. Second, sub-vectors with the same length are created according to the time dimension from the higher-order non-linear features. The sub-vectors are stitched together to form a three-dimensional tensor. Third, a neural network transformer based on the feed-forward network is constructed. Further, to capture long-term series features and separate the target signal from the noisy signals, the three-dimensional tensor is used as the input of the transformer-based masking network. Finally, overlap-add and transpose are used to obtain discernible target signals. The experimental results verify the effectiveness of the proposed underwater acoustic signal denoising algorithm and demonstrate that the proposed DPRN method can obtain higher output signal-to-noise ratio (SNR) and the scale-invariant signal-to-noise ratio (SI-SNR) compared with the other classical algorithms.

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.

The envelope demarcates the boundary between bacterial cell and its environment, providing a place for bacteria to transport nutrients and excrete metabolic waste, while buffering external environmental stress. Envelope stress responses (ESRs) are important tools for bacteria to sense and repair envelope damage. In this review, we discussed evidence that indicates the important role of the Cpx ESR in pathogen-host interactions, including environmental stress sensing and responses, modulation of bacterial virulence, antimicrobial resistance, and inter-kingdom signaling.

The perfluorosulfonic acid (PFSA) ionomers are key materials for proton exchange membranes (PEMs) and catalyst layers (CLs). Their morphology is profoundly influenced by the chain assembly behavior of PFSA in dispersions. Hence, we combine the characterization techniques of dynamic light scattering (DLS), small-angle X-ray scattering (SAXS), and cryo-transmission electron microscopy (Cryo-TEM) to study the nanostructures of PFSA dispersions, and provide a structure model for diverse PFSA ionomers in water/ethanol solvent. It is seen that PFSA ionomers self-assembly into a rod-like particle in dilute dispersion. As the concentration increases, the primary rod aggregates gradually assemble into a swollen- or Gaussian-network structure. Beyond this feature, we see that different PFSA ionomers show different nanostructures in dispersion. For the long-side-chain (LSC) PFSA ionomers, the 800-LSC PFSA tends to form monodisperse rod-like aggregates that is in a highly ordered arrangement with a rod diameter of 3.16 nm and a length of 28.72 nm. As the equivalent weight (EW) increases to 960, the poor solubility of the main chains in water/ethanol solvents leads to the “end-to-end” assemblies of the primary rod particles and dendritic secondary aggregates. The short-side-chain (SSC) PFSA ionomer that shares the same backbone with 960-LSC PFSA exhibit remarkable mono-dispersity and ordered arrangement of rod-like aggregates in water/ethanol solvents due to the strong electrostatic repulsion.

Background CDK4 is highly expressed and associated with poor prognosis and decreased survival in advanced neuroblastoma (NB). Targeting CDK4 degradation presents a potentially promising therapeutic strategy compared to conventional CDK4 inhibitors. However, the autophagic degradation of the CDK4 protein and its anti-proliferation effect in NB cells has not been mentioned.Results We identified autophagy as a new pathway for the degradation of CDK4. Firstly, autophagic degradation of CDK4 is critical for NVP-BEZ235-induced G0/G1 arrest, as demonstrated by the overexpression of CDK4, autophagy inhibition, and blockade of autophagy-related genes. Secondly, we present the first evidence that p62 binds to CDK4 and then enters the autophagy-lysosome to degrade CDK4 in a CTSB-dependent manner in NVP-BEZ235 treated NB cells. Similar results regarding the interaction between p62 and CDK4 were observed in the NVP-BEZ235 treated NB xenograft mouse model.Conclusions Autophagic degradation of CDK4 plays a pivotal role in G0/G1 cell cycle arrest in NB cells treated with NVP-BEZ235.

ABSTRACTIn contrast to traditional intermetallics that are difficult to process and prone to cracking during additive manufacturing, our work demonstrates the successful fabrication of crack-free, high-performance CCIMAs via electron beam powder bed fusion (EBPBF). The microstructures of CCIMAs (NiCoFeAlTiB) fabricated by EBPBF are remarkably complex, exhibiting a multiphase composition where the disordered FCC phase forms the matrix interleaved with L12-ordered intermetallic phase. The unique combination of ordered lattices and high-entropy disordered phases, tuned by optimised EBPBF processing, imparts exceptional mechanical properties with a high tensile strength (∼1 GPa) and a sufficient ductility (∼11%). It is found that additional minor HCP and L21 precipitates can also effectively slow down and block the crack extension. This work demonstrates a pathway for crack-free fabrication of CCIMAs with tailored microstructures using EBPBF which may provide new insights and manufacturing strategies to unlock the potential of these advanced intermetallic alloys.

AbstractIn this study, UV-B radiation of 96 kJ·m−2·d−1 exposed to the ‘Tainong 1’ mango tree, and trees as the control group under natural lighting. The relative conductivity and the contents of MDA, superoxide anion, H2O2 were lower in the treatment group than in the control group before 60 DAF, and the opposite occurred after 60 DAF. The total flavonoid content in the treatment group was higher than that in the control group, but the opposite occurred later. The 60 DAF serves as the critical point. Before 60 DAF, the treatment enhanced the activity of each enzyme by inducing the upregulated expression of genes such as CHS, R02446, and CYP98A so that gallochol, leucoside, kaempferoside, quercetin, isoquercetin and luteolin, and then removed ROS through the reduction of flavonoids. After 60 DAF, the treatment enhanced the activity of each enzyme by inducing the upregulated expression of HCT and R02446, but the synthesized flavonoids are consumed by ROS. Meanwhile, ROS also accumulate continuously because of the enhanced continuous exposure of UV-B radiation. The downregulated expression of CYP75A led to a decline in enzyme activity. The accumulation rate of flavonoid compounds was lower than that of ROS, which led to ROS damage in pulp.

Electroencephalography (EEG) or Magnetoencephalography (MEG) source imaging aims to estimate the underlying activated brain sources to explain the observed EEG/MEG recordings. Solving the inverse problem of EEG/MEG Source Imaging (ESI) is challenging due to its ill-posed nature. To achieve a unique solution, it is essential to apply sophisticated regularization constraints to restrict the solution space. Traditionally, the design of regularization terms is based on assumptions about the spatiotemporal structure of the underlying source dynamics. In this paper, we propose a novel paradigm for ESI via an Explainable Deep Learning framework, termed as XDL-ESI, which connects the iterative optimization algorithm with deep learning architecture by unfolding the iterative updates with neural network modules. The proposed framework has the advantages of (1) establishing a data-driven approach to model the source solution structure instead of using hand-crafted regularization terms; (2) improving the robustness of source solutions by introducing a topological loss that leverages the geometric spatial information applying varying penalties on distinct localization errors; (3) improving the reconstruction efficiency and interpretability as it inherits the advantages from both the iterative optimization algorithms (interpretability) and deep learning approaches (function approximation). The proposed XDL-ESI framework provides an efficient, accurate, and interpretable paradigm to solve the ESI inverse problem with satisfactory performance in both simulated data and real clinical data. Specially, this approach is further validated using simultaneous EEG and intracranial EEG (iEEG).

Object: Adipose-derived mesenchymal stem cells (ADSCs) have received significant attention in the field of cartilage tissue repair. Angelica sinensis polysaccharide (ASP) can enhance both the proliferation and differentiation of mesenchymal stem cells. Therefore, we intend to explore the effect of ASP on chondrogenic differentiation of ADSCs in vitro, and elucidate the underlying mechanisms. Method: ADSCs were treated with different concentrations of ASP to determine the optimal concentration. The chondrogenic differentiation of ADSCs was evaluated using Alcian blue staining, qRT-PCR, western blot, and IF staining. Transcriptome sequencing was performed to identify the expression profiles of ADSCs before and after ASP treatment, followed by bioinformatic analyses including differential expression analysis, enrichment analysis, and construction of PPI networks to identify differentially expressed genes (DEGs) associated with ASP and chondrogenic differentiation. Result: Surface markers of isolated rat-derived ADSCs were identified by CD44+CD90+CD45-CD106-, and exhibited the capacity for lipogenic, osteogenic, and chondrogenic differentiation. With increasing concentration of ASP treatment, there was an upregulation in the activity and acidic mucosubstance of ADSCs. The levels of Aggrecan, COL2A1, and Sox9 showed an increase in ADSCs after 28 days of 80 µg/ml ASP treatment. Transcriptome sequencing revealed that ASP-associated DEGs regulate extracellular matrix synthesis, immune response, inflammatory response, and cell cycle, and are involved in the NF-κB, AGE-RAGE, and calcium pathways. Moreover, Edn1, Frzb, Mdk, Nog, and Sulf1 are hub genes in DEGs. Notably, ASP upregulated MDK levels in ADSCs, while knockdown of MDK mitigated ASP-induced elevations in acidic mucosubstance, chondrogenic differentiation-related markers (Aggrecan, COL2A1, and Sox9), and the activity of the PI3K/AKT pathway. Conclusion: ASP enhances the proliferation and chondrogenic differentiation of ADSCs by activating the MDK-mediated PI3K/AKT pathway.

A series of K-shaped bolapolyphiles, consisting of a p-terphenyl core, two polar glycerol end-groups and a swallow-tailed alkyl side-chain were synthesized and investigated. By increasing the side-chain volume an astonishing variety of very different liquid crystalline (LC) phases was observed, ranging from a rectangular (Colrec/c2mm) and a square honeycomb (Colsqu/p4mm) via a highly complex zeolite-like octagon/pentagon honeycomb filled with additional strings of rod-bundles (ColrecZ/c2mm), a new 3D-hexagonal (R3‾c) double network phase, a double and even a single network cubic phase (double gyroid Cub/Ia3‾d and single diamond Cub/Fd3‾m, respectively) to a correlated lamellar phase (LamSm/c2mm). Though these LC structures are highly complex and there is a delicate balance of steric and geometric frustration determining the phase formation, there is only a small effect of permanent molecular chirality in the glycerol groups ((R,R)-configuration) on them, which is attributed to a slightly different packing density of uniformly chiral and racemic glycerols, but not to an effect of induced helicity. Compared to related T-shaped bolapolyphiles with a single linear n-alkyl side-chain, which form exclusively honeycomb phases, the complexity of self-assembly is enhanced for the K-shaped compounds due to a competition between the requirements of space filling, chain stretching and geometric frustration, and affected by the shape of the polar glycerol domains at the junctions.

Abstract Postoperative peritoneal adhesion (PPA) is the most frequent complication after abdominal surgery. Current anti‐adhesion strategies largely rely on the use of physical separating barriers creating an interface blocking peritoneal adhesion, which cannot reduce inflammation and suffers from limited anti‐adhesion efficacy with unwanted side effects. Here, by exploiting the alternative activated macrophages to alleviate inflammation in adhesion development, a flexible graphene‐composite‐film (F‐GCF) generating far‐infrared (FIR) irradiation that effectively modulates the macrophage phenotype toward the anti‐inflammatory M2 type, resulting in reduced PPA formation, is designed. The anti‐adhesion effect of the FIR generated by F‐GCF is determined in the rat abdominal wall abrasion‐cecum defect models, which exhibit reduced incidence and area of PPA by 67.0% and 92.1% after FIR treatment without skin damage, significantly superior to the clinically used chitosan hydrogel. Notably, within peritoneal macrophages, FIR reduces inflammation reaction and promotes tissue plasminogen activator (t‐PA) level via the polarization of peritoneal macrophages through upregulating Nr4a2 expression. To facilitate clinical use, a wirelessly controlled, wearable, F‐GCF‐based FIR therapy apparatus (GRAFT) is further developed and its remarkable anti‐adhesion ability in the porcine PPA model is revealed. Collectively, the physical, biochemical, and in vivo preclinical data provide compelling evidence demonstrating the clinical‐translational value of FIR in PPA prevention.

Bletilla striata is an endangered traditional medicinal herb in China. In May 2020, the emergence of white root rot severely impacted the quality and yield of B. striata, affecting about 5% of the plants at plant nurseries of the Chengdu Academy of Agricultural and Forestry Sciences. Through a series of experiments and evaluations, the pathogen was identified as Fusarium solani. This is the first report of B. striata white root rot caused by F. solani in Sichuan, China. To better understand this disease and provide data support for its control, a combination of morphological, molecular characterisation and pathogenicity determination was used in this study for assessment. Meanwhile, the effects of different carbon and nitrogen sources, culture medium, temperature, photoperiod and pH on mycelial growth and spore production of F. solani were investigated. In addition, effective fungicides were screened and the concentration ratios of fungicides were optimized using response surface methodology (RSM). The experimental results showed that sucrose was the optimum carbon source for the pathogen, and the optimum temperature and pH were 25°C and pH 7, respectively, while light did no significant effect. Effective fungicides were screened, among which difenoconazole showed the strongest inhibition with EC50 of 142.773 µg/mL. The optimum fungicide concentration scheme (difenoconazole, pyraclostrobin, and thiophanate-methyl at 395.42, 781.03, and 561.11 µg/mL, respectively) was obtained using response surface methodology (RSM) to improve the inhibition rate of 92.24 ± 0.34%. This study provides basic data for the pathogen characterization of B. striata white root rot and its potential fungicides in Sichuan, China. In addition, the optimal fungicide concentration ratios were obtained through response surface methodology (RSM) optimization, which significantly enhanced the fungicidal effect and provided a scientific basis for the future control of B. striata white root rot.

Understanding public emotion on social media about community wellness is crucial for enhancing health awareness and guiding policy-making. In order to more fully mine the deep contextual semantical information of short texts and further enhance the effectiveness of emotion prediction in social media, we propose the Deep Parallel Contextual Analysis Framework (DPCAF) in the community wellness domain, specifically addressing the challenges of limited text length and available semantical features in social media text. Specifically, at the embedding layer, we first utilize two different word embedding techniques to generate high-quality vector representations, aiming to achieve more comprehensive semantical capture, stronger generalization ability, and more robust model performance. Subsequently, in the deep contextual layer, the obtained representations are fused with POS and locational representations, and processed through a deep parallel layer composed of Convolutional Neural Networks and Bidirectional Long Short-Term Memory Network. An attention model is then used to further extract semantical features of social media texts. Finally, these deep parallel contextual representations are post-integrated for emotion prediction. Experiments on a dataset collected from social media regarding community wellness demonstrate that compared to benchmark models, DPCAF achieves at least a 4.81 % increase in Precision, a 3.44 % increase in Recall, and a 10.81 % increase in F1-score. Relative to the most advanced models, DPCAF shows a minimum improvement of 2.65 % in Precision, 3.02 % in Recall, and 2.53 % in F1-score.

Superconducting nanowire single-photon detectors (SNSPDs) have become a mainstream photon-counting technology that has been widely applied in various scenarios. So far, most multi-channel SNSPD systems, either reported in literature or commercially available, are polarization sensitive, that is, the system detection efficiency (SDE) of each channel is dependent on the state of polarization of the to-be-detected photons. Here, we reported an eight-channel system with fractal SNSPDs working in the wavelength range of 930 to 940 nm, which are all featured with low polarization sensitivity. In a close-cycled Gifford-McMahon cryocooler system with the base temperature of 2.2 K, we installed and compared the performance of two types of devices: (1) SNSPD, composed of a single, continuous nanowire and (2) superconducting nanowire avalanche photodetector (SNAP), composed of 16 cascaded units of two nanowires electrically connected in parallel. The highest SDE among the eight channels reaches 96−5+4%, with the polarization sensitivity of 1.02 and a dark-count rate of 13 counts per second. The average SDE for eight channels for all states of polarization is estimated to be 90 ± 5%. It is concluded that both the SNSPDs and the SNAPs can reach saturated, high SDE at the wavelength of interest, and the SNSPDs show lower dark-count (false-count) rates, whereas the SNAPs show better properties in the time domain. With the adoption of this system, we showcased the measurements of the second-order photon-correlation functions of light emission from a single-photon source based on a semiconductor quantum dot and from a pulsed laser. It is believed that this work will provide new choices of systems with single-photon detectors combining the merits of high SDE, low polarization sensitivity, and low noise that can be tailored for different applications.

Abstract Lead halide perovskites with superior optoelectrical properties are emerging as a class of excellent materials for applications in solar cells and light‐emitting devices. However, perovskite films often exhibit abundant intrinsic defects, which can limit the efficiency of perovskite‐based optoelectronic devices by acting as carrier recombination centers. Thus, an understanding of defect chemistry in lead halide perovskites assumes a prominent role in further advancing the exploitation of perovskites, which, to a large extent, is performed by relying on first‐principles calculations. However, the complex defect structure, strong anharmonicity, and soft lattice of lead halide perovskites pose challenges to defect studies. In this perspective, on the basis of briefly reviewing the current knowledge concerning computational studies on defects, this work concentrates on addressing the unsolved problems and proposing possible research directions in future. This perspective particularly emphasizes the indispensability of developing advanced approaches for deeply understanding the nature of defects and conducting data‐driven defect research for designing reasonable strategies to further improve the performance of perovskite applications. Finally, this work highlights that theoretical studies should pay more attention to establishing close and clear links with experimental investigations to provide useful insights to the scientific and industrial communities.