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Abstract To overcome the limitation of a single classification model’s inability to simultaneously identify multiple lesion targets within periapical radiographs, This study proposes YoCNET (Yolov5 + ConvNeXt), a novel deep learning integrated model. YoCNET leverages the target detection capability of Yolov5 and the image classification capability of ConvNeXt to achieve automatic segmentation of individual teeth and concurrent detection of periapical lesions across multiple teeth. A dataset of 1,305 periapical radiographs was used to train and validate the ConvNeXt and ResNet34 models, with an 8:2 split for training and validation. Deciduous teeth were excluded from the dataset. Furthermore, 717 individual teeth images were extracted from 200 previously unused periapical radiographs for integrated model validation. Evaluation metrics included accuracy, precision, sensitivity, F1 score, AUC (Area Under Curve), and a confusion matrix.The YoCNET integrated model demonstrated values of 90.93%, 98.88%, 85.30%, 0.9159, and 0.9757 for accuracy, precision, sensitivity, F1 score, and AUC, respectively. These metrics were superior to those achieved by the YoRNET (Yolov5 + ResNet34) integrated model, which recorded 80.47%, 83.78%, 82.16%, 0.8296, and 0.8822. The integrated model achieved high accuracy and efficiency in automatic teeh segmentation by Yolov5 and in automatically detecting multiple periapical lesions by ConvNeXt. YoCNET exhibited superior overall data performance, making it a more suitable deep learning integrated model for clinical applications.

Background: The cross-sectional area of skeletal muscles at the level of the third lumbar vertebra (L3) measured from computed tomography (CT) images is an established imaging biomarker used to assess patients’ nutritional status. With the increasing prevalence of low-dose CT scans in clinical practice, accurate and automated skeletal muscle segmentation at the L3 level in low-dose CT images has become an issue to address. This study proposed a lightweight algorithm for automated segmentation of skeletal muscles at the L3 level in low-dose CT images. Methods: This study included 57 patients with rectal cancer, with both low-dose plain and contrast-enhanced pelvic CT image series acquired using a radiotherapy CT scanner. A training set of 30 randomly selected patients was used to develop a lightweight segmentation algorithm, and the other 27 patients were used as the test set. A radiologist selected the most representative axial CT image at the L3 level for both the image series for all the patients, and three groups of observers manually annotated the skeletal muscles in the 54 CT images of the test set as the gold standard. The performance of the proposed algorithm was evaluated in terms of the Dice similarity coefficient (DSC), precision, recall, 95th percentile of the Hausdorff distance (HD95), and average surface distance (ASD). The running time of the proposed algorithm was recorded. An open source deep learning-based AutoMATICA algorithm was compared with the proposed algorithm. The inter-observer variations were also used as the reference. Results: The DSC, precision, recall, HD95, ASD, and running time were 93.2 ± 1.9% (mean ± standard deviation), 96.7 ± 2.9%, 90.0 ± 2.9%, 4.8 ± 1.3 mm, 0.8 ± 0.2 mm, and 303 ± 43 ms (on CPU) for the proposed algorithm, and 94.1 ± 4.1%, 92.7 ± 5.5%, 95.7 ± 4.0%, 7.4 ± 5.7 mm, 0.9 ± 0.6 mm, and 448 ± 40 ms (on GPU) for AutoMATICA, respectively. The differences between the proposed algorithm and the inter-observer reference were 4.7%, 1.2%, 7.9%, 3.2 mm, and 0.6 mm, respectively, for the averaged DSC, precision, recall, HD95, and ASD. Conclusion: The proposed algorithm can be used to segment skeletal muscles at the L3 level in either the plain or enhanced low-dose CT images.

Abstract A crucial aim in neuroscience is to understand how the human brain adapts to varying cognitive demands. This study investigates network reconfiguration during controlled semantic retrieval in differing contexts. We analyze brain responses to two semantic tasks of varying difficulty – global association and feature matching judgments – which are contrasted with non-semantic tasks on the cortical surface and within a whole-brain state space. Demanding semantic association tasks elicit activation in anterior prefrontal and temporal regions, while challenging semantic feature matching and non-semantic tasks predominantly activate posterior regions. Task difficulty also modulates activation along different dimensions of functional organization, suggesting different mechanisms of cognitive control. More demanding semantic association judgments engage cognitive control and default mode networks together, while feature matching and non-semantic tasks are skewed towards cognitive control networks. These findings highlight the brain’s dynamic ability to tailor its networks to support diverse neurocognitive states, enriching our understanding of controlled cognition.

Abstract Background and purpose Various deep learning auto-segmentation (DLAS) models have been proposed, some of which have been commercialized. However, the issue of performance degradation is notable when pretrained models are deployed in the clinic. This study aims to enhance precision of a popular commercial DLAS product in rectal cancer radiotherapy by localized fine-tuning, addressing challenges in practicality and generalizability in real-world clinical settings. Materials and methods A total of 120 Stage II/III mid-low rectal cancer patients were retrospectively enrolled and divided into three datasets: training (n = 60), external validation (ExVal, n = 30), and generalizability evaluation (GenEva, n = 30) datasets respectively. The patients in the training and ExVal dataset were acquired on the same CT simulator, while those in GenEva were on a different CT simulator. The commercial DLAS software was first localized fine-tuned (LFT) for clinical target volume (CTV) and organs-at-risk (OAR) using the training data, and then validated on ExVal and GenEva respectively. Performance evaluation involved comparing the LFT model with the vendor-provided pretrained model (VPM) against ground truth contours, using metrics like Dice similarity coefficient (DSC), 95th Hausdorff distance (95HD), sensitivity and specificity. Results LFT significantly improved CTV delineation accuracy (p

Objective To observe the effect of deglycerolized red blood cells suspended in MAP on preservation and explore the most effective preservation method. Methods Concentrated red blood cells were prepared by centrifuging 400 mL of whole blood on the third day after collection. 40% compound glycerol solution was added using the ACP 215 automatic blood cell analyzer, and the resulting mixture was stored in an ultra-low temperature refrigerator at -65℃ for 30 days. After thawing and washing, it was equally separated into two bags. The control group received 0.9% sodium chloride solution, while the experimental group received MAP. Both groups were stored at 2-6℃. Hematological parameters, hemolysis indexes and cell metabolism indexes were measured on day 0, 1, 3, 5, 7 and 14 after storage. The quality changes of both groups were observed during the 14-day storage period. Results The quality of red blood cells in both groups was assessed through a panel of quality tests, including volume, hemoglobin content, free hemoglobin content, white blood cell residue, glycerin residue and sterility. These results met the Quality Requirements outlined in the "Quality Requirements of Whole Blood and Component Blood" (GB18469-2012), Hematocrit, red blood cell count, Hb recovery rate after washing and MCV meet the detection limit outlined in the "Expert Consensus on Quality Evaluation Indicators for Frozen Red Blood Cells", and the residual amount of platelets exceeds the detection limit(≤1%). There were no significant differences in RBC, Hct, MCV and hemoglobin between the two groups during the 14 day storage period. The level of free hemoglobin, hemolysis rate and K+ value increased in both groups over time. Significant differences in free hemoglobin were found on day 3, 5, 7 and 14 between the two groups(P

Radio frequency (RF) energy harvester as an efficient tool for capturing and converting the flourishing ambient RF energy provides a promising solution for long-term powering the wireless sensor networks and the Internet of things (IoTs). However, the actual distribution of the environmental RF signals is dynamically frequency-dependent due to the diverse wireless terminals only interacting with specified frequencies. To take full advantage of the RF energy carrying this characteristic, an intelligent RF energy harvester is in demand to automatically sense the frequency information of an incident signal and conduct the corresponding RF-to-direct current transformation process. Here, to the best of my knowledge, a frequency-self-adaptive RF harvester is first presented with the help of the shape-reconfigurable liquid metal, which can precisely identify and efficiently convert an arbitrary signal from the frequency span of 1.8 to 2.6 GHz. Companied with a microcontroller unit and a tensile system, the dynamic functionality of the entire system is comprehensively demonstrated, showing promising potential to significantly advance various fields, including sustainable IoT applications, green wearable technologies, and self-powered devices.

Abstract Humans produce two forms of cognitively complex vocalizations: speech and song. It is debated whether these differ based primarily on culturally specific, learned features, or if acoustical features can reliably distinguish them. We study the spectro-temporal modulation patterns of vocalizations produced by 369 people living in 21 urban, rural, and small-scale societies across six continents. Specific ranges of spectral and temporal modulations, overlapping within categories and across societies, significantly differentiate speech from song. Machine-learning classification shows that this effect is cross-culturally robust, vocalizations being reliably classified solely from their spectro-temporal features across all 21 societies. Listeners unfamiliar with the cultures classify these vocalizations using similar spectro-temporal cues as the machine learning algorithm. Finally, spectro-temporal features are better able to discriminate song from speech than a broad range of other acoustical variables, suggesting that spectro-temporal modulation—a key feature of auditory neuronal tuning—accounts for a fundamental difference between these categories.

Abstract The sensory–neuromorphic interface is key to the application of neuromorphic electronics. Artificial spiking neurons and artificial sensory nerves have been created, and a few studies showed a complete neuromorphic system through cointegration with synaptic electronics. However, artificial synaptic devices and systems often do not work in real environments, which limits their ability to provide realistic neural simulations and interface with biological nerves. We report a sensory–neuromorphic interface that uses a fiber synapse to emulate a biological afferent nerve. For the first time, a sensing–neuromorphic interface is connected to a living organism for peripheral nerve stimulation, allowing the organism to establish a connection with its surrounding environment. The interface converts perceived environmental information into analog electrical signals and then into frequency‐dependent pulse signals, which simplify the information interface between the sensor and the pulse‐data processing center. The frequency of the interface shows a sublinear dependence on strain amplitude at different stimulus intensities, and can deliver increased frequency spikes at potentially damaging stimulus intensities, similar to the response of biological afferent nerves. To verify the application of this interface, a system that monitors strain and provides an overstrain alarm was constructed based on this afferent neural circuit. The system has a response time of

Abstract Flexible electronic devices extended abilities of humans to perceive their environment conveniently and comfortably. Among them, flexible magnetic field sensors are crucial to detect changes in the external magnetic field. State‐of‐the‐art flexible magnetoelectronics do not exhibit low detection limit and large working range simultaneously, which limits their application potential. Herein, a flexible magnetic field sensor possessing a low detection limit of 22 nT and wide sensing range from 22 nT up to 400 mT is reported. With the detection range of seven orders of magnitude in magnetic field sensor constitutes at least one order of magnitude improvement over current flexible magnetic field sensor technologies. The sensor is designed as a cantilever beam structure accommodating a flexible permanent magnetic composite and an amorphous magnetic wire enabling sensitivity to low magnetic fields. To detect high fields, the anisotropy of the giant magnetoimpedance effect of amorphous magnetic wires to the magnetic field direction is explored. Benefiting from mechanical flexibility of sensor and its broad detection range, its application potential for smart wearables targeting geomagnetic navigation, touchless interactivity, rehabilitation appliances, and safety interfaces providing warnings of exposure to high magnetic fields are explored.

Abstract Dispersionless flat bands (FBs) in momentum space, given rise to electron destructive interference in frustrated lattices, offer opportunities to enhance electronic correlations and host exotic many‐body phenomena, such as Wigner crystal, fractional quantum hall state, and superconductivity. Despite successes in theory, great challenges remain in experimentally realizing FBs in frustrated lattices due to thermodynamically structural instability. Here, the observation of electronic FB in a potassium distorted colouring triangle (DCT) lattice is reported, which is supported on a blue phosphorene‐gold network. It is verified that the interaction between potassium and the underlayer dominates and stabilizes the frustrated structures. Two‐dimensional electron gas is modulated by the DCT lattice, and in turn results in a FB dispersion due to destructive quantum interferences. The FB exhibits suppressed bandwidth with high density of states, which is directly observed by scanning tunneling microscopy and confirmed by the first‐principles calculation. This work demonstrates that DCT lattice is a promising platform to study FB physics and explore exotic phenomena of correlation and topological matters.

Abstract Flexible pressure sensors are crucial force‐sensitive devices in wearable electronics, robotics, and other fields due to their stretchability, high sensitivity, and easy integration. However, a limitation of existing pressure sensors is their reduced sensing accuracy when subjected to stretching. This study addresses this issue by adopting finite element simulation optimization, using digital light processing (DLP) 3D printing technology to design and fabricate the force‐sensitive structure of flexible pressure sensors. This is the first systematic study of how force‐sensitive structures enhance tensile strain stability of flexible resistive pressure sensors. 18 types of force‐sensitive structures have been investigated by finite element design, simultaneously, the modulus of the force‐sensitive structure is also a critical consideration as it exerts a significant influence on the overall tensile stability of the sensor. Based on simulation results, a well‐designed and highly stretch‐stable flexible resistive pressure sensor has been fabricated which exhibits a resistance change rate of 0.76% and pressure sensitivity change rate of 0.22% when subjected to strains ranging from no tensile strain to 20% tensile strain, demonstrating extremely low stretching response characteristics. This study presents innovative solutions for designing and fabricating flexible resistive pressure sensors that maintain stable sensing performance even under stretch conditions.

Deciphering the functional architecture that underpins diverse cognitive functions is fundamental quest in neuroscience. In this study, we employed an innovative machine learning framework that integrated cognitive ontology with functional connectivity analysis to identify brain networks essential for cognition. We identified a core assembly of functional connectomes, primarily located within the association cortex, which showed superior predictive performance compared to two conventional methods widely employed in previous research across various cognitive domains. Our approach achieved a mean prediction accuracy of 0.13 across 16 cognitive tasks, including working memory, reading comprehension, and sustained attention, outperforming the traditional methods' accuracy of 0.08. In contrast, our method showed limited predictive power for sensory, motor, and emotional functions, with a mean prediction accuracy of 0.03 across 9 relevant tasks, slightly lower than the traditional methods' accuracy of 0.04. These cognitive connectomes were further characterized by distinctive patterns of resting-state functional connectivity, structural connectivity via white matter tracts, and gene expression, highlighting their neurogenetic underpinnings. Our findings reveal a domain-general functional network fingerprint that pivotal to cognition, offering a novel computational approach to explore the neural foundations of cognitive abilities.

Abstract CO2 electroreduction has garnered significant attention from both the academic and industrial communities. Extracting crucial information related to catalysts from domain literature can help scientists find new and effective electrocatalysts. Herein, we used various advanced machine learning, natural language processing techniques and large language models (LLMs) approaches to extract relevant information about the CO2 electrocatalytic reduction process from scientific literature. By applying the extraction pipeline, we present an open-source corpus for electrocatalytic CO2 reduction. The database contains two types of corpus: (1) the benchmark corpus, which is a collection of 6,985 records extracted from 1,081 publications by catalysis postgraduates; and (2) the extended corpus, which consists of content extracted from 5,941 documents using traditional NLP techniques and LLMs techniques. The Extended Corpus I and II contain 77,016 and 30,283 records, respectively. Furthermore, several domain literature fine-tuned LLMs were developed. Overall, this work will contribute to the exploration of new and effective electrocatalysts by leveraging information from domain literature using cutting-edge computer techniques.

Abstract Heparin-induced thrombocytopenia (HIT) is an adverse reaction to heparin leading to a reduction in circulating platelets with an increased risk of thrombosis. It is precipitated by polymerized immune complexes consisting of pathogenic antibodies that recognize a small chemokine platelet factor 4 (PF4) bound to heparin. Characterization of these immune complexes is extremely challenging due to the enormous structural heterogeneity of such macromolecular assemblies and their constituents. Native mass spectrometry demonstrates that up to three PF4 tetramers can be assembled on a heparin chain, consistent with the molecular modeling studies showing facile polyanion wrapping along the polycationic belt on the PF4 surface. Although these assemblies can accommodate a maximum of only two antibodies, the resulting immune complexes are capable of platelet activation despite their modest size. Taken together, these studies provide further insight into molecular mechanisms of HIT and other immune disorders where anti-PF4 antibodies play a central role.

PurposeRepeated cone-beam CT (CBCT) scans for image-guided radiotherapy (IGRT) increase the health risk of radiation-induced malignancies. Patient-enrolled studies to optimize scan protocols are inadequate. We proposed a virtual clinical trial-based approach to evaluate projection-reduced low-dose CBCT for IGRT.Materials and methodsA total of 71 patients were virtually enrolled with 26 head, 23 thorax and 22 pelvis scans. Projection numbers of full-dose CBCT scans were reduced to 1/2, 1/4, and 1/8 of the original to simulate low-dose scans. Contrast-to-noise ratio (CNR) values in fat and muscle were measured in the full-dose and low-dose images. CBCT images were registered to planning CT to derive 6-degree-of-freedom couch shifts. Registration errors were statistically analyzed with the Wilcoxon paired signed-rank test.ResultsAs projection numbers were reduced, CNR values descended and the magnitude of registration errors increased. The mean CNR values of full-dose and half-dose CBCT were >3.0. For full-dose and low-dose CBCT (i.e. 1/2, 1/4 and 1/8 full-dose), the mean registration errors were< ± 0.4 mm in translational directions (LAT, LNG, VRT) and ±0.2 degree in rotational directions (Pitch, Roll, Yaw); the mean magnitude of registration errors were< 1 mm in translation and< 0.5 degree in rotation. The couch shift differences between full-dose and low-dose CBCT were not statistically significant (p>0.05) in all the directions.ConclusionThe results indicate that while the impact of dose-reduction on CBCT couch shifts is not significant, the impact on CNR values is significant. Further validation on optimizing CBCT imaging dose is required.

Abstract In Wireless Sensor Networks (WSN), there are three conflicting important issues, including extensive coverage, low node redundancy, and energy consumption for secondary deployment. To balance network coverage and longevity, a WSN coverage model with three objectives is developed. A novel multi‐strategy gold rush optimizer is proposed for the coverage optimization problem in WSN. First, the fusion search method becomes extensive and flexible by combining the migration strategy with the Levy flight mechanism and joining a follower group. Then, the dynamic opposite learning strategy is used to increase population diversity in the panning stage. In addition, a multi‐player collaborative strategy is utilized to improve search ability of the algorithm in the collaboration stage. Finally, the random differential mutation method is used to try to assist the algorithm in escaping local optima. The simulation results show that this algorithm compared with other algorithms performs better on all three objectives considered.

Abstract In this work, 2D ferromagnetic M3GeTe2 (MGT, M = Ni/Fe) nanosheets with rich atomic Te vacancies (2D‐MGTv) are demonstrated as efficient OER electrocatalyst via a general mechanical exfoliation strategy. X‐ray absorption spectra (XAS) and scanning transmission electron microscope (STEM) results validate the dominant presence of metal‐O moieties and rich Te vacancies, respectively. The formed Te vacancies are active for the adsorption of OH* and O* species while the metal‐O moieties promote the O* and OOH* adsorption, contributing synergistically to the faster oxygen evolution kinetics. Consequently, 2D‐Ni3GeTe2v exhibits superior OER activity with only 370 mV overpotential to reach the current density of 100 mA cm−2 and turnover frequency (TOF) value of 101.6 s−1 at the overpotential of 200 mV in alkaline media. Furthermore, a 2D‐Ni3GeTe2v‐based anion‐exchange membrane (AEM) water electrolysis cell (1 cm2) delivers a current density of 1.02 and 1.32 A cm−2 at the voltage of 3 V feeding with 0.1 and 1 m KOH solution, respectively. The demonstrated metal‐O coordination with abundant atomic vacancies for ferromagnetic M3GeTe2 and the easily extended preparation strategy would enlighten the rational design and fabrication of other ferromagnetic materials for wider electrocatalytic applications.

Robotic manipulators play a crucial role in providing support for automation and intelligence in various fields. The inverse kinematics problem becomes a significant challenge for modern robotic manipulator systems. This work proposes an enhanced Golden Jackal Optimization ( $\beta $ -FGJO) to solve the inverse kinematics problem in multi-degree-of-freedom (multi-DOF) robotic manipulators. In $\beta $ -FGJO, the Fuch chaotic map is utilized to generate an efficient initial population to enhance search efficiency. Individual behavior is regulated by the adaptive $\beta $ -distribution to improve both global exploration and local exploitation capabilities at different stages. Meanwhile, predators and prey in the population dynamically explore and exploit based on their energy level and hunger rate. Simulation results demonstrate that $\beta $ -FGJO has shorter computation time, higher numerical precision and greater robustness. Compared to the best-performing method on PUMA560, $\beta $ -FGJO improved time performance by 24.57%, while maintaining the same level of accuracy.

Abstract Background Systemic juvenile idiopathic arthritis (SJIA) is a form of childhood arthritis with clinical features such as fever, lymphadenopathy, arthritis, rash, and serositis. It seriously affects the growth and development of children and has a high rate of disability and mortality. SJIA may result from genetic, infectious, or autoimmune factors since the precise source of the disease is unknown. Our study aims to develop a genetic-based diagnostic model to explore the identification of SJIA at the genetic level. Methods The gene expression dataset of peripheral blood mononuclear cell (PBMC) samples from SJIA was collected from the Gene Expression Omnibus (GEO) database. Then, three GEO datasets (GSE11907-GPL96, GSE8650-GPL96 and GSE13501) were merged and used as a training dataset, which included 125 SJIA samples and 92 health samples. GSE7753 was used as a validation dataset. The limma method was used to screen differentially expressed genes (DEGs). Feature selection was performed using Lasso, random forest (RF)-recursive feature elimination (RFE) and RF classifier. Results We finally identified 4 key genes (ALDH1A1, CEACAM1, YBX3 and SLC6A8) that were essential to distinguish SJIA from healthy samples. And we combined the 4 key genes and performed a grid search as well as 10-fold cross-validation with 5 repetitions to finally identify the RF model with optimal mtry. The mean area under the curve (AUC) value for 5-fold cross-validation was greater than 0.95. The model’s performance was then assessed once more using the validation dataset, and an AUC value of 0.990 was obtained. All of the above AUC values demonstrated the strong robustness of the SJIA diagnostic model. Conclusions We successfully developed a new SJIA diagnostic model that can be used for a novel aid in the identification of SJIA. In addition, the identification of 4 key genes that may serve as potential biomarkers for SJIA provides new insights to further understand the mechanisms of SJIA.

Abstract The notion of topological insulators (TIs), characterized by an insulating bulk and conducting topological surface states, can be extended to higher-order topological insulators (HOTIs) hosting gapless modes localized at the boundaries of two or more dimensions lower than the insulating bulk. In this work, by performing high-resolution angle-resolved photoemission spectroscopy (ARPES) measurements with submicron spatial and spin resolution, we systematically investigate the electronic structure and spin texture of quasi-one-dimensional (1D) HOTI candidate Bi4Br4. In contrast to the bulk-state-dominant spectra on the (001) surface, we observe gapped surface states on the (100) surface, whose dispersion and spin-polarization agree well with our ab-initio calculations. Moreover, we reveal in-gap states connecting the surface valence and conduction bands, which is a signature of the hinge states inside the (100) surface gap. Our findings provide compelling evidence for the HOTI phase of Bi4Br4. The identification of the higher-order topological phase promises applications based on 1D spin-momentum locked current in electronic and spintronic devices.

The process and function that underlie the assembly of root-associated microbiomes may be strongly linked to the survival strategy of plants. However, the assembly and functional changes of root-associated microbial communities in different desert plants in natural desert ecosystems are still unclear. Thus, we studied the microbial communities and diversity of root endosphere (RE), rhizosphere soil (RS), and bulk soil (BS) among three representative desert plants (Alhagi sparsifolia, Tamarix ramosissima, and Calligonum caput-medusae) in three Xinjiang desert regions {Taklimakan (CL), Gurbantünggüt (MSW), and Kumtag (TLF)} in China. This study found that the soil properties {electrical conductivity (EC), soil organic carbon (SOC), total nitrogen (TN) and phosphorus (TP), available nitrogen (AN) and phosphorus (AP)} of C. caput-medusae were significantly lower than those of A. sparsifolia and T. ramosissima, while the root nutrients (TN and TP) of A. sparsifolia were significantly higher compared to C. caput-medusae and T. ramosissima. The beta diversity of bacteria and fungi (RE) among the three desert plants was significantly different. The common OTU numbers of bacteria and fungi in three compartments (RE, RS, and BS) of the three desert plants were ranked as RS > BS > RE. The bacterial and fungal (RE) Shannon and Simpson indexes of C. caput-medusae were significantly lower as compared to those of A. sparsifolia and T. ramosissima. Additionally, bacterial and fungal (RE and RS) node numbers and average degree of C. caput-medusae were lower than those found in A. sparsifolia and T. ramosissima. Root and soil nutrients collectively contributed to the composition of root-associated bacterial (RE, 12.4%; RS, 10.6%; BS, 16.6%) and fungal communities (RE, 34.3%; RS, 1.5%; BS, 17.7%). These findings demonstrate variations in the bacterial and fungal populations across different plant species with distinct compartments (RE, RS, and BS) in arid environments. More importantly, the study highlights how much soil and plant nutrients contribute to root-associated microbial communities.

BackgroundAcute hematologic toxicity (HT) is a prevalent adverse tissue reaction observed in cervical cancer patients undergoing chemoradiotherapy (CRT), which may lead to various negative effects such as compromised therapeutic efficacy and prolonged treatment duration. Accurate prediction of HT occurrence prior to CRT remains challenging.MethodsA discovery dataset comprising 478 continuous cervical cancer patients (140 HT patients) and a validation dataset consisting of 205 patients (52 HT patients) were retrospectively enrolled. Both datasets were categorized into the CRT group and radiotherapy (RT)-alone group based on the treatment regimen, i.e., whether chemotherapy was administered within the focused RT duration. Radiomics features were derived by contouring three regions of interest (ROIs)—bone marrow (BM), femoral head (FH), and clinical target volume (CTV)—on the treatment planning CT images before RT. A comprehensive model combining the radiomics features as well as the demographic, clinical, and dosimetric features was constructed to classify patients exhibiting acute HT symptoms in the CRT group, RT group, and combination group. Furthermore, the time-to-event analysis of the discriminative ROI was performed on all patients with acute HT to understand the HT temporal progression.ResultsAmong three ROIs, BM exhibited the best performance in classifying acute HT, which was verified across all patient groups in both discovery and validation datasets. Among different patient groups in the discovery dataset, acute HT was more precisely predicted in the CRT group [area under the curve (AUC) = 0.779, 95% CI: 0.657–0.874] than that in the RT-alone (AUC = 0.686, 95% CI: 0.529–0.817) or combination group (AUC = 0.748, 95% CI: 0.655–0.827). The predictive results in the validation dataset similarly coincided with those in the discovery dataset: CRT group (AUC = 0.802, 95% CI: 0.669–0.914), RT-alone group (AUC = 0.737, 95% CI: 0.612–0.862), and combination group (AUC = 0.793, 95% CI: 0.713–0.874). In addition, distinct feature sets were adopted for different patient groups. Moreover, the predicted HT risk of BM was also indicative of the HT temporal progression.ConclusionsHT prediction in cervical patients is dependent on both the treatment regimen and ROI selection, and BM is closely related to the occurrence and progression of HT, especially for CRT patients.

Phyllosphere microbes residing on plant leaf surfaces for maintaining plant health have gained increasing recognition. However, in desert ecosystems, knowledge about the variety, composition, and coexistence patterns of microbial communities in the phyllosphere remains limited. This study, conducted across three basins (Turpan-TLF, Tarim-CL, and Dzungaria-MSW) and three seasons (spring, summer, and autumn) in Xinjiang, China, aimed to explore the diversity and composition of microbial communities in the phyllosphere, encompassing both bacteria and fungi in Alhagi sparsifolia. We also investigated the co-occurrence patterns, influencing factors, and underlying mechanisms driving these dynamics. Results indicate that phyllosphere bacteria exhibited lower diversity indices (ACE, Shannon, Simpson, Fisher phylogenetic diversity, and Richness) in spring compared to summer and autumn, while the Goods Coverage Index (GCI) was higher in spring. Conversely, diversity indices and GCI of phyllosphere fungi showed an opposite trend. Interestingly, the lowest level of multi-functionality and niche width in phyllosphere bacteria occurred in spring, while the highest level was observed in phyllosphere fungi. Furthermore, the study revealed that no significant differences in multi-functionality were found among the regions (CL, MSW, and TLF). Network analysis highlighted that during spring, phyllosphere bacteria exhibited the lowest number of nodes, edges, and average degree, while phyllosphere fungi had the highest. Surprisingly, the multi-functionality of both phyllosphere bacteria and fungi showed no significant correlation with climatic and environmental factors but displayed a significant association with the morphological characteristics and physicochemical properties of leaves. Structural Equation Model indicated that the morphological characteristics of leaves significantly influenced the multi-functionality of phyllosphere bacteria and fungi. However, the indirect and total effects of climate on multi-functionality were greater than the effects of physicochemical properties and morphological characteristics of leaves. These findings offer new insights into leaf phyllosphere microbial community structure, laying a theoretical foundation for vegetation restoration and rational plant resource utilization in desert ecosystems.

This study evaluated the feasibilities and outcomes following four-dimensional magnetic resonance imaging (4D-MRI) assisted stereotactic body radiation therapy (SBRT) for unresectable colorectal liver metastases (CRLMs). From March 2018 to January 2022, we identified 76 unresectable CRLMs patients with 123 lesions who received 4D-MRI guided SBRT in our institution. 4D-MRI simulation with or without abdominal compression was conducted for all patients. The prescription dose was 50–65 Gy in 5–12 fractions. The image quality of computed tomography (CT) and MRI were compared using the Clarity Score. Clinical outcomes and toxicity profiles were evaluated. 4D-MRI improved the image quality compared with CT images (mean Clarity Score: 1.67 vs 2.88, P

Abstract Purpose Manual clinical target volume (CTV) and gross tumor volume (GTV) delineation for rectal cancer neoadjuvant radiotherapy is pivotal but labor-intensive. This study aims to propose a deep learning (DL)-based workflow towards fully automated clinical target volume (CTV) and gross tumor volume (GTV) delineation for rectal cancer neoadjuvant radiotherapy. Materials & methods We retrospectively included 141 patients with Stage II-III mid-low rectal cancer and randomly grouped them into training (n = 121) and testing (n = 20) cohorts. We adopted a divide-and-conquer strategy to address CTV and GTV segmentation using two separate DL models with DpuUnet as backend-one model for CTV segmentation in the CT domain, and the other for GTV in the MRI domain. The workflow was validated using a three-level multicenter-involved blind and randomized evaluation scheme. Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (95HD) metrics were calculated in Level 1, four-grade expert scoring was performed in Level 2, and head-to-head Turing test in Level 3. Results For the DL-based CTV contours over the testing cohort, the DSC and 95HD (mean ± SD) were 0.85 ± 0.06 and 7.75 ± 6.42 mm respectively, and 96.4% cases achieved clinical viable scores (≥ 2). The positive rate in the Turing test was 52.3%. For GTV, the DSC and 95HD were 0.87 ± 0.07 and 4.07 ± 1.67 mm respectively, and 100% of the DL-based contours achieved clinical viable scores (≥ 2). The positive rate in the Turing test was 52.0%. Conclusion The proposed DL-based workflow exhibited promising accuracy and excellent clinical viability towards automated CTV and GTV delineation for rectal cancer neoadjuvant radiotherapy.

Abstract The dental operative microscope has been widely employed in the field of dentistry, particularly in endodontics and operative dentistry, resulting in significant advancements in the effectiveness of root canal therapy, endodontic surgery, and dental restoration. However, the improper use of this microscope continues to be common in clinical settings, primarily due to operators’ insufficient understanding and proficiency in both the features and established operating procedures of this equipment. In October 2019, Professor Jingping Liang, Vice Chairman of the Society of Cariology and Endodontology, Chinese Stomatological Association, organized a consensus meeting with Chinese experts in endodontics and operative dentistry. The objective of this meeting was to establish a standard operation procedure for the dental operative microscope. Subsequently, a consensus was reached and officially issued. Over the span of about four years, the content of this consensus has been further developed and improved through practical experience.

The conflict between economic development and ecological protection continues to intensify, highlighting the necessity for constructing regional ecological security patterns (ESPs) to reconcile the relationship between development and protection effectively. This study used the GMOP and PLUS model to simulate future land use changes by 2030 under the following three scenarios: natural development (ND), ecological protection (EP), and economic development (ED). Employing the MSPA model and circuit theory, it identified ecological source areas and constructed the ESP for the Beijing–Tianjin–Hebei urban agglomeration (BTH) from 2000 to 2030. The results indicate that the proportion of ecological source areas increased from 22.24% in 2000 to 23.09% in 2020, the EP scenario showing the highest proportion of ecological source areas compared with the other two scenarios. These areas are densely distributed in the northern and western mountainous regions, with sparse distributions in the southern plains. The number of ecological corridors grew from 603 in 2000 to 616 in 2020, with the EP scenario having more corridors than the other two scenarios. From 2000 to 2030, corridors in the northern and western mountainous areas were denser, shorter, and more variable, while those in the southern plains were less dense, longer, and relatively stable. Over two decades, habitat areas for species in BTH increased, while landscape connectivity decreased. Compared with 2020 and the other two scenarios, the EP scenario saw an increase in habitat areas and improved landscape connectivity. The impact on ecological corridors and improvement areas primarily arose from a combination of socio-ecological drivers (e.g., elevation, slope, population), while the influence on restoration and key areas mainly stemmed from ecological factors (e.g., elevation, temperature, NDVI, precipitation). The findings demonstrate that distinguishing different geomorphological units to improve and restore the regional environment, while considering socio-ecological drivers, is crucial for restoring the overall ESP and landscape connectivity of BTH.

Abstract During the past decade, cognitive neuroscience has been calling for population diversity to address the challenge of validity and generalizability, ushering in a new era of population neuroscience. The developing Chinese Color Nest Project (devCCNP, 2013–2022), the first ten-year stage of the lifespan CCNP (2013–2032), is a two-stages project focusing on brain-mind development. The project aims to create and share a large-scale, longitudinal and multimodal dataset of typically developing children and adolescents (ages 6.0–17.9 at enrolment) in the Chinese population. The devCCNP houses not only phenotypes measured by demographic, biophysical, psychological and behavioural, cognitive, affective, and ocular-tracking assessments but also neurotypes measured with magnetic resonance imaging (MRI) of brain morphometry, resting-state function, naturalistic viewing function and diffusion structure. This Data Descriptor introduces the first data release of devCCNP including a total of 864 visits from 479 participants. Herein, we provided details of the experimental design, sampling strategies, and technical validation of the devCCNP resource. We demonstrate and discuss the potential of a multicohort longitudinal design to depict normative brain growth curves from the perspective of developmental population neuroscience. The devCCNP resource is shared as part of the “Chinese Data-sharing Warehouse for In-vivo Imaging Brain” in the Chinese Color Nest Project (CCNP) – Lifespan Brain-Mind Development Data Community ( https://ccnp.scidb.cn ) at the Science Data Bank.

Abstract Weak topological insulators, constructed by stacking quantum spin Hall insulators with weak interlayer coupling, offer promising quantum electronic applications through topologically non-trivial edge channels. However, the currently available weak topological insulators are stacks of the same quantum spin Hall layer with translational symmetry in the out-of-plane direction—leading to the absence of the channel degree of freedom for edge states. Here, we study a candidate weak topological insulator, Bi4Br2I2, which is alternately stacked by three different quantum spin Hall insulators, each with tunable topologically non-trivial edge states. Our angle-resolved photoemission spectroscopy and first-principles calculations show that an energy gap opens at the crossing points of different Dirac cones correlated with different layers due to the interlayer interaction. This is essential to achieve the tunability of topological edge states as controlled by varying the chemical potential. Our work offers a perspective for the construction of tunable quantized conductance devices for future spintronic applications.

Abstract Speech perception is believed to recruit the left motor cortex. However, the exact role of the laryngeal subregion and its right counterpart in speech perception, as well as their temporal patterns of involvement remain unclear. To address these questions, we conducted a hypothesis-driven study, utilizing transcranial magnetic stimulation on the left or right dorsal laryngeal motor cortex (dLMC) when participants performed perceptual decision on Mandarin lexical tone or consonant (voicing contrast) presented with or without noise. We used psychometric function and hierarchical drift-diffusion model to disentangle perceptual sensitivity and dynamic decision-making parameters. Results showed that bilateral dLMCs were engaged with effector specificity, and this engagement was left-lateralized with right upregulation in noise. Furthermore, the dLMC contributed to various decision stages depending on the hemisphere and task difficulty. These findings substantially advance our understanding of the hemispherical lateralization and temporal dynamics of bilateral dLMC in sensorimotor integration during speech perceptual decision-making.

In this paper, a pole-changing (PC) flux reversal permanent magnet (FRPM) motor is proposed for obtaining a wide speed range. According to the general air-gap field modulation theory, the permanent magnet (PM) magnetic field is modulated into a series of harmonics with different pole numbers by rotor teeth. By introducing an especial PM arrangement, two groups of working harmonics with different pole-pair numbers are generated in the proposed 12-7 PC-FRPM motor due to the modulation of rotor teeth. Based on the calculated slot pitch angles of these harmonics, two winding connections can be obtained for PC operation to achieve two operating modes. The electromagnetic performance of PC-FRPM motor before and after PC are analyzed using finite element analysis and verified by the experimental test results.

ObjectiveThe present study aimed to develop a prediction model for predicting developing debilities after optic neuritis.MethodsThe data for this research was obtained from the Optic Neuritis Treatment Trial (ONTT). The predictive model was built based on a Cox proportional hazards regression model. Model performance was assessed using Harrell’s C-index for discrimination, calibration plots for calibration, and stratification of patients into low-risk and high-risk groups for utility evaluation.ResultsA total of 416 patients participated. Among them, 101 patients (24.3%) experienced disability, which was defined as achieving or surpassing a score of 3 on the expanded disability status scale. The median follow-up duration was 15.5 years (interquartile range, 7.0 to 16.8). Two predictors in the final predictive model included the classification of multiple sclerosis at baseline and the condition of the optic disk in the affected eye at baseline. Upon incorporating these two factors into the model, the model’s C-index stood at 0.71 (95% CI, 0.66–0.76, with an optimism of 0.005) with a favorable alignment with the calibration curve. By utilizing this model, the ONTT cohort can be categorized into two risk categories, each having distinct rates of disability development within a 15-year timeframe (high-risk group, 41% [95% CI, 31–49%] and low-risk group, 13% [95% CI, 8.4–17%]; log-rank p-value of

Abstract Integrating nanomaterials into the polymer matrix is an effective strategy to optimize the performance of polymer‐based piezoelectric devices. Nevertheless, the trade‐off between the output enhancement and stability maintenance of piezoelectric composites usually leads to an unsatisfied overall performance for the high‐strength operation of devices. Here, by setting liquid metal (LM) nanodroplets as the nanofillers in a poly(vinylidene difluoride) (PVDF) matrix, the as‐formed liquid‐solid/conductive‐dielectric interfaces significantly promote the piezoelectric output and the reliability of this piezoelectric composite. A giant performance improvement featured is obtained with, nearly 1000% boosting on the output voltage (as high as 212 V), 270% increment on the piezoelectric coefficient (d33∼51.1 pC N−1) and long‐term reliability on both structure and output (over 36 000 cycles). The design of a novel heterogenous interface with both mechanical matching and electric coupling can be the new orientation for developing high performance piezoelectric composite‐based devices.

The stress background and rock fractures are essential factors affecting the stability of mines. In order to better understand the in situ stress background and rock fractures in the Guangdong Fankou Mine, we use ultrasonic borehole television scanning to measure rock fractures. The results indicate that rock fractures are intensively distributed at depths of −360 m to −450 m below the surface, suggesting the effect of intensive mining activities. The present maximum horizontal principal stress direction is NWW, which is consistent with the regional tectonic stress field direction. Systematic measurement of rock fractures is fundamental for further three-dimensional geological modeling and is significant for mining engineering.

Abstract Considering that the AC–DC hybrid microgrid (HMG) is connected to the low‐voltage distribution network (DN) where voltage faults occur most commonly, the fault ride‐through (FRT) capability and power quality performance of the HMG under abnormal DN conditions have become a critical issue. In this paper, a decentralized coordination control scheme based on an improved topology is proposed for the grid‐interactive HMG to guarantee uninterrupted and high power‐quality operation during grid faults. The method of it includes: (1) a new HMG topology is presented by adding a series transformer (ST), which is inserted between the DN and HMG; (2) an improved coordination control strategy that considers the interaction between converters of the enhanced HMG. Based on the proposed method, the operation modes of the system are introduced, and the specific design process and small‐signal model analysis of the proposed control scheme are presented. Finally, the simulated and experimental results show that the proposed method can keep both AC and DC bus voltage stable and improve the fault ride‐through capability and power quality of the HMG under abnormal DN conditions.

Background: Saccades are often observed on video head impulse tests (vHIT) in patients with Meniere's Disease (MD) and Vestibular Migraine (VM). However, their saccadic features are not fully described. Objective: This study aims to identify the saccades characteristics of MD and VM. Methods: 75 VM patients and 103 definite unilateral MD patients were enrolled in this study. First raw saccades were exported and analyzed. The VM patients were divided into left and right based on their ears, while the MD patients were separated into affected and unaffected subgroups based on their audiograms and symptoms. Results: The MD patients have more saccades on the affected side (85% vs. 69%), and saccade velocity is more consistent than the contralateral side (shown by the coefficient of variation). The saccades occurrence rates on both sides are similar in VM (77% vs. 76%), as are other saccadic parameters. The MD patients have more significant inter-aural differences than the VM patients, manifested in higher velocity (p-value 0.000), earlier arriving (p-value 0.010), and more time-domain gathered (p-value 0.003) on the affected side. Conclusions: Bilateral saccades are commonly observed in MD and VM. In contrast to MD, saccades on VM are subtle, scattered, and late-arrived. Furthermore, the MD patients showed inconsistent saccadic distribution with more velocity-uniform saccades on the affected side.

Abstract The electrocatalytic CO2 reduction process has gained enormous attention for both environmental protection and chemicals production. Thereinto, the design of new electrocatalysts with high activity and selectivity can draw inspiration from the abundant scientific literature. An annotated and verified corpus made from massive literature can assist the development of natural language processing (NLP) models, which can offer insight to help guide the understanding of these underlying mechanisms. To facilitate data mining in this direction, we present a benchmark corpus of 6,086 records manually extracted from 835 electrocatalytic publications, along with an extended corpus with 145,179 records in this article. In this corpus, nine types of knowledge such as material, regulation method, product, faradaic efficiency, cell setup, electrolyte, synthesis method, current density, and voltage are provided by either annotating or extracting. Machine learning algorithms can be applied to the corpus to help scientists find new and effective electrocatalysts. Furthermore, researchers familiar with NLP can use this corpus to design domain-specific named entity recognition (NER) models.

Background: The development of novel therapies holds significance in improving the prognosis of ovarian cancer (OC). This study aimed to prepare 177Lu-FA-DOTA-PEG-PLGA nanoparticles and evaluate their antitumor efficacy in OC. Methods: To obtain nanoparticles with both targeting and degradable properties, we employed folate receptor (FR) as the targeting molecule, the biodegradable material polyethyleneglycol-polylactic acid-co-glycolic acid (PEG-PLGA) as the carrier matrix, and diethylene triamine pentaacetic acid (DOTA) as the metal chelating agent to prepare 177Lu-FA-DOTA-PEG-PLGA nanoparticles. The labeling yield and radiochemical purity were determined. Healthy Institute of Cancer Research (ICR) mice and Bagg albino strain C (BALB/c) nude mice bearing subcutaneously transplanted SKOV3 human OC tumors were given 18.5 Mbq of 177Lu-FA-DOTA-PEG-PLGA nanoparticles for histological distribution analysis and micro-single-photon emission computed tomography/computed tomography (micro-SPECT/CT) imaging, respectively. Twelve BALB/c nude mice bearing subcutaneously transplanted tumors or 12 BALB/c nude mice bearing intraperitoneal metastatic tumors were assigned to control (received 0.1 mL saline solution), chemotherapy (received twice 3 mg/kg cisplatin per week), and nanoparticle groups (received 18.5 Mbq nanoparticles via tail vein or intraperitoneal injection) (n = 4 per group). Tumor growth inhibition (TGI) and ascitic fluid volume were calculated to investigate antitumor efficiency. Hematoxylin and eosin (HE) staining was performed to evaluate the safety of nanoparticles. Results: The 177Lu-FA-DOTA-PEG-PLGA nanoparticles (labeling yield: 97–98%; radiochemical purity: 96–98%) exhibited a long blood circulation time and a low renal radioactivity uptake (1.646 %ID/g). Micro-SPECT/CT imaging revealed the highest tumor-to-muscle uptake ratio of 2.81 at 24 h. After tail vein injection of nanoparticles, the tumor growth in the chemotherapy and nanoparticle groups was inhibited compared with the control group. Upon intraperitoneal injection, fluorescence intensities of intraperitoneal metastatic tumors in the control, chemotherapy and nanoparticle groups showed a statistical difference (F = 6.09, p = 0.029). Ascitic fluid volumes in the chemotherapy and nanoparticle groups were significantly lower than that in the control group (F = 13.43, p = 0.006). HE staining results showed no obvious abnormalities in the small intestine and colon tissues of the mice in the nanoparticle group compared to the control group. Conclusions: We successfully developed 177Lu-FA-DOTA-PEG-PLGA nanoparticles with a long blood circulation time and low renal radioactivity uptake. These nanoparticles could inhibit OC tumor growth and intraperitoneal metastasis, suggesting a potential novel therapy for OC patients.

Abstract Perovskite solar cells are an attractive technology for renewable energy production. However, stability issues with the electron transport layer (ETL), particularly the colloidal tin oxide (SnO2) solution, can impact cell efficiency. In this study, a novel acidization treatment is introduced to reactivate long‐time stored SnO2 solutions, which previously led to low‐efficiency perovskite solar cells. The acidization treatment results in enhanced conductivity of the SnO2 layer, improved perovskite film quality, and ultimately increased efficiency. These findings show that a 1‐month stored SnO2 solution treated with acetic acid produces a device with a photoelectric conversion efficiency (PCE) of 20.9%, compared to 13.5% efficiency without treatment. With the addition of PEAI, the champion efficiency of the acetic acid‐treated device is 22.3%. This study provides a simple and effective engineering approach to fabricating high‐performance and stable ETLs for perovskite solar cells.

ObjectiveAquaporin-4 (AQP4) antibody-seropositive optic neuritis (AQP4-ON) is one of the most common types of optic neuritis in China. However, the association between AQP4-ON and vision-related quality of life (QoL) and depression remains poorly understood.MethodsIn this cross-sectional study, 57 patients with optic neuritis were evaluated for their vision-related QoL using a Chinese version of the 25-item National Eye Institute Visual Function Questionnaire (VFQ-25) and assessed for depressive symptoms using a Chinese version of the Beck Depression Inventory-II (BDI-II). Data regarding participants’ age, sex, visual acuity, and the number of recurrence events were gathered. Linear regression analysis was employed to investigate the relationships between AQP4-ON and vision-related QoL, as well as depression.ResultsOf the 57 included patients, 28 were AQP4-ON, and 29 were idiopathic optic neuritis (ION). AQP4-ON demonstrated a significant correlation with a decreased VFQ-25 composite score (Mean difference, −11.65 [95% CI, −21.61 to −1.69]; p = 0.023) and an increased BDI-II score (Mean difference, 6.48 [95% CI, 0.25 to 12.71]; p = 0.042) when compared to ION. The BDI-II score was correlated with the VFQ-25 composite score (Spearman ρ = −0.469; p

BackgroundVertigo and hearing loss are both prevalent in the elderly. This study retrospectively analyzed hearing test results from elderly patients experiencing vertigo and dizziness at ENT outpatient over a 10-year period, in order to study the patterns of hearing loss in this patient population.MethodsNine thousand three hundred eighty four patients over 50 years old underwent retrospective collection and screening of outpatient diagnosis, pure tone audiometry, acoustic immittance measurement (tympanogram) and auditory brainstem response (ABR) test. The patient's audiograms are divided into 7 subtypes according to a set of fixed criteria. Meanwhile, K-Means clustering analysis method was used to classify the audiogram.ResultsThe Jerger classification of tympanogram in elderly patients with vertigo and dizziness showed the majority falling under type A. The leading audiogram shapes were flat (27.81% in right ear and 26.89% in left ear), high-frequency gently sloping (25.97% in right ear and 27.34% in left ear), and high-frequency steeply sloping (21.60% in right ear and 22.53% in left ear). Meniere's disease (MD; 30.87%), benign recurrent vertigo (BRV; 19.07%), and benign paroxysmal positional vertigo (BPPV; 15.66%) were the most common etiologies in elderly vestibular diseases. We observed statistically significant differences in hearing thresholds among these vestibular diseases (P < 0.001). K-Means clustering analysis suggested that the optimal number of clusters was three, with sample sizes for the three clusters being 2,747, 2,413, and 4,139, respectively. The ANOVA statistical results of each characteristic value showed P < 0.001.ConclusionThe elderly patients often have mild to moderate hearing loss as a concomitant symptom with vertigo. Female patients have better hearing thresholds than males. The dominant audiometric shapes in this patient population were flat, high-frequency gently sloping, and high-frequency steeply sloping according to a set of fixed criteria. This study highlights the need for tailored strategies in managing hearing loss in elderly patients with vertigo and dizziness.

Abstract Background Non-alcoholic steatohepatitis (NASH) had not yet been approved therapy. Electro-acupuncture (EA) has been reported to have potential efficacy. However, high-quality clinical evidence was still lacking. Methods NASH patients were randomized and allocated to either sham acupuncture (SA) or EA group in a 1:1 ratio, with the patient blinded. Each patient received 36 sessions of SA or EA treatment over 12 weeks, followed by additional 4 weeks. The primary outcome was the changes in relative liver fat content measured by magnetic resonance imaging proton density fat fraction (MRI-PDFF). Results A total of 60 patients were enrolled. From baseline to week 12, the reduction of relative liver fat content measured by MRI-PDFF in the EA group (− 33.6%, quantile range: − 52.9%, − 22.7%) was significantly more significant than that in the SA group (− 15.8%, quantile range: − 36.1%, − 2.7%) (p = 0.022). Furthermore, the EA group had more patients who achieved MRI-PDFF to 30% reduction at week 12 (53.3% vs. 25.9%, p = 0.035). EA treatment also significantly reduced body weight (− 3.0 vs. + 0.1 kg, p = 0.034) and BMI (− 1.5 vs. − 0.2 kg/m2, p = 0.013) at week 16. Except for AST (− 27.4 vs. − 16.2 U/L, p = 0.015), other biochemical varieties, including ALT, fasting-glucose, cholesterol, and triglyceride, showed no statistically significant difference. Both groups measured no significant changes in liver stiffness by magnetic resonance elastography (MRE). There were no serious adverse events in either group. Conclusions Twelve weeks of EA effectively and safely reduces relative liver fat content in NASH patients. Further multicenter randomized controlled studies are needed. Trial registration Chinese Clinical Trial Registry, ChiCTR2100046617. Registered 23 May 2021, http://www.chictr.org.cn/edit.aspx?pid=127023&htm=4

Methylprednisolone pulse treatment is currently used for optic neuritis. It can speed visual recovery, but does not improve the ultimate visual outcomes. Recent studies have reported that miR-125a-5p has immunomodulatory effects on autoimmune diseases. However, it remains unclear whether miR-125a-5p has effects on optic neuritis. In this study, we used adeno-associated virus to overexpress or silence miR-125a-5p in mice. We found that silencing miR-125a-5p increased the latency of visual evoked potential and aggravated inflammation of the optic nerve. Overexpression of miR-125a-5p suppressed inflammation of the optic nerve, protected retinal ganglion cells, and increased the percentage of Treg cells. Our findings show that miR-125a-5p exhibits anti-inflammatory effects through promoting the differentiation of Treg cells.

The deformation of slopes is comprehensively controlled by many factors, and the sensitivity and action rules of different factors are obviously different in the deformation process. This article takes the bedding landslide in Yangxin County, Hubei Province as the research object. Through orthogonal experimental design combined with 3DEC (distinct element method, DEM), the sensitivity of multiple influencing factors to the deformation of bedding landslides is studied, and the dominant factors are established. Then, based on the response surface method to fit the quantitative relationship between the dominant factors and the deformation degree of different parts of the landslide, the influence law of the interaction of dominant factors on the deformation and failure mode of landslides is revealed. The study of the slope in the Yangxin County area shows that the slope and the strata dip are the dominant and secondary dominant factors affecting the bedding landslide, and the interaction between the two has a significant impact on the slope deformation. In the medium-steep bedding landslide, when the slope is less than the angle of rock, the slope deformation mainly concentrates on the slope top, and the degree of deformation increases with increasing rock dip, showing the slip-bending deformation failure mode. In a gently dipping bedding slope, when the slope is larger than the angle of rock, the displacement of the slope toe is more significant than that of the slope top, and the deformation degree of the slope toe increases with increasing slope, showing the slip-rupture deformation failure mode. This study can provide guidances for the prevention and control of such landslides.

Potential application of Titanium carbide MXene in wearable devices is limited by the formation of voids during assembly. Here, the authors demonstrate a synergistic densification strategy by intercalating small flakes and interfacial bridging to obtain high-performance MXene films.

Objective: Saccades accompanied by normal gain in video head impulse tests (vHIT) are often observed in patients with vestibular migraine (VM). However, they are not considered as an independent indicator, reducing their utility in diagnosing VM. To better understand clinical features of VM, it is necessary to understand raw saccades data. Methods: Fourteen patients with confirmed VM, 45 patients with probable VM (p-VM) and 14 age-matched healthy volunteers were included in this study. Clinical findings related to spontaneous nystagmus (SN), positional nystagmus (PN), head-shaking nystagmus (HSN), caloric test and vHIT were recorded. Raw saccades data were exported and numbered by their sequences, and their features analyzed. Results: VM patients showed no SN, PN or HSN, and less than half of them showed unilateral weakness (UW) on caloric test. The first saccades from lateral semicircular canal stimulation were the most predominant for both left and right sides. Neither velocity nor time parameters were significantly different when compared between the two sides. Most VM patients (86%) exhibited small saccades, around 35% of the head peak velocity, with a latency of 200–400 ms. Characteristics of saccades were similar in patients with p-VM. Only four normal subjects showed saccades, all unilateral and seemingly random. Conclusions: Small saccades involving bilateral semicircular canals with a scattered distribution pattern are common in patients with VM and p-VM.

Abstract Sanggenon C (SC), a herbal flavonoid extracted from Cortex Mori, has been mentioned to possess more than one treasured organic properties. However, the molecular mechanism of its anti‐tumor impact in glioblastoma (GBM) remains unclear. In this study, we reported that SC displayed a GBM‐suppressing impact in vitro and in vivo with no apparent organ toxicity. SC dramatically suppressed cell proliferation‐induced cell apoptosis in GBM cells. Mechanistically, we unveiled that SC modulated the protein expression of death associated protain kinase 1 (DAPK1) by controlling the ubiquitination and degradation of DAPK1. Quantitative proteomic and Western blot analyses showed that SC improved DAPK1 protein degradation via decreasing the expression of E3 ubiquitin ligase Mindbomb 1 (MIB1). More importantly, the effects of SC on cell proliferation and apoptosis of GBM cells have been in part reversed through DAPK1 downregulation or MIB1 overexpression, respectively. These results indicated that SC might suppress cell proliferation and induce cell apoptosis by decreasing MIB1‐mediated DAPK1 degradation. Furthermore, we found that SC acted synergistically with temozolomide (TMZ), an anti‐cancer drug used in GBM, resulting in elevated chemotherapeutic sensitivity of GBM to TMZ. Collectively, our data suggest that SC might be a promising anti‐cancer agent for GBM therapy.