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Uncontrolled activation of c-Kit is closely related to the pathogenesis and progression of leukemia, gastrointestinal cancer, and other malignant diseases. Although there are several inhibitors available, due to the limitation of selectivity and the unfavorable side effects, designing and discovering highly selective inhibitors targeting c-Kit kinase, especially the gain of function mutation (for example c-Kit D816V), is still necessary. To identify novel c-Kit inhibitors, a metastable state-based virtual screening approach, which was successfully implemented in other kinase inhibitors, was employed in the current study. The results from our current study demonstrated the residues adjacent to the DFG motif within the activation loop could fold into short α-helices aside from the random coil, which was commonly found in the crystal structure. By expanding the conformation pool of the activation loop via PyRosetta-based ab initio folding protocol, we constructed a series of structural models of the c-Kit kinase intermediate between the inactive and active states. After evaluation of the thermal stability of the metastable state with molecular dynamics simulation, one structural model showed higher stability of α-helix, and the activation loop was retained. Considering the wild-type and D816V mutated KIT kinase shared similar metastable states during the kinase activation process, we developed a hypothesis that the identified intermediate might hold the potential to identify inhibitors targeting D816V mutations from the compound database. As expected, the intermediate structure showed higher selectivity to KIT D816V selective inhibitors, such as bezuclastinib, avapritinib, BLU-263, and elenestinib, than imatinib or masitinib. The virtual screening of the available KIT kinase inhibitor database further identified vorolanib, semaxanib, henatinib, and pexmetinib may possess potential inhibitory effects against wild type, as well as the mutated c-Kit kinase. The results from our current study not only proposed a novel structural model that could be used for the identification of selective c-Kit D816V inhibitors but also identified several potential inhibitors from available kinase inhibitors, which might shed new light on the design of new therapeutic approaches for c-Kit mutation-driven malignant diseases.

The thin film composite (TFC) membrane possesses the highest commercial application potential among all types of forward osmosis (FO) membranes. The support layer of the membrane plays a crucial role in both bearing mechanical loads and facilitating the active layer formation. Herein, we addressed the issues of low permeability and poor mechanical strength of the conventional TFC FO membrane by selecting suitable fabrics and optimizing membrane design and preparation. Polyester (PS) woven fabric, with its high strength, low thickness, and high porosity, served as an excellent material for reinforcing the strength of the FO membrane. We employed a novel double-blade casting method to address the defect issue associated with the conventional single-blade casting method. The influencing factors and quality control strategies of the fabric-reinforced substrate were systematically investigated. Furthermore, membrane characterization techniques, including scanning electron microscopy (SEM), liquid-liquid displacement porometer, and cross-flow FO system, were used to reveal the influences of different substrates, including polysulfone (PSU), polyacrylonitrile (PAN), and polyetherimide (PEI), on the structure and performance of the fabric-reinforced TFC-FO membrane. Compared to commercial FO membranes, the TFC/PSU membrane exhibited superior FO performance, with a water flux of 24.1 LMH, and outstanding mechanical strength that was more than 10-times higher than that of free-standing membranes. This study demonstrates a new pathway for engineering robust TFC-FO membranes with low structural parameters as well as excellent scalability of the membrane fabrication method.

Abstract The current challenge of wearable/implantable personal dosimeters for medical diagnosis and radiotherapy applications is lack of suitable detector materials possessing both excellent detection performance and biocompatibility. Here, we report a solution-grown biocompatible organic single crystalline semiconductor (OSCS), 4-Hydroxyphenylacetic acid (4HPA), achieving real-time spectral detection of charged particles with single-particle sensitivity. Along in-plane direction, two-dimensional anisotropic 4HPA exhibits a large electron drift velocity of 5 × 105 cm s−1 at “radiation-mode” while maintaining a high resistivity of (1.28 ± 0.003) × 1012 Ω·cm at “dark-mode” due to influence of dense π-π overlaps and high-energy L1 level. Therefore, 4HPA detectors exhibit the record spectra detection of charged particles among their organic counterparts, with energy resolution of 36%, (μt)e of (4.91 ± 0.07) × 10−5 cm2 V−1, and detection time down to 3 ms. These detectors also show high X-ray detection sensitivity of 16,612 μC Gyabs −1 cm−3, detection of limit of 20 nGyair s−1, and long-term stability after 690 Gyair irradiation.

Estimating musical vogue patterns is critical for the Chinese music industry, as it assists artists, producers, record labels, and investors in discerning public musical preferences. This knowledge facilitates market strategy optimization, recommendation system improvement, inventory management, and provides informed support for advertising and sponsorships. The benefits extend beyond enhancing user experience and contributing to cultural studies, as they are also invaluable for the discovery of emerging artists and the sustainable growth of the entire Chinese music ecosystem. Nonetheless, existing models for prolonged musical vogue pattern estimation have been challenged by the severe decay of past information. Inspired by the concept of incremental learning, we propose the Dynamic Incremental Network (DINet). DINet capitalizes on high-dimensional sequential information embedded in the memory states of trained sequence models. It dynamically integrates information from both previous and current time steps when constructing the model to estimate the next time step’s input sequence, enabling the incremental transmission of past information and addressing the decay problem in prolonged musical vogue pattern estimation. Leveraging an open-source dataset from a musical pattern estimation contest, ablation experiments and comparative analyses have shown that DINet not only surpasses traditional and baseline models but also outperforms existing musical vogue pattern estimation models, demonstrating its superior performance. In the future, we intend to employ the proposed Dynamic Incremental Method (DIM)—an Occam’s razor (simple yet effective) mechanism—to further enhance the performance of state-of-the-art musical and other sequential estimation tasks.

Abstract Background The aim of this study was to compare the efficacy of ultrasound-guided PENG (pericapsular nerve group) block and drug therapy with intravenous flurbiprofen for early analgesia in elderly patients with hip fractures after hospitalization. Methods This is a single-center, observer-blinded, prospective, randomized, controlled trial. A total of 41 elderly patients (aged 60 or older) with hip fractures were enrolled in the current study. Patients were randomly assigned to two groups: Group P (ultrasound-guided PENG block, 20 mL of 0.375% ropivacaine) and Group F (intravenous flurbiprofen 50 mg). The primary outcome measure was the dynamic (passive straight leg raising 15°) NRS (numerical rating scale 0 to 10) pain scores at different time points. The secondary outcomes were the static NRS scores at different time points, the number of rescue analgesia sessions, patient satisfaction, and the incidence of complications. Results Patients in the two groups had comparable baseline characteristics. The group P had lower dynamic and static NRS scores at 15 min, 30 min, 6 h, and 12 h after intervention (P0.05). The group P had higher satisfaction scores (Group P: 9 (9,9) vs. Group F: 8 (7,8), P

Small-modulus gears, which are essential for motion transmission in precision instruments, present a measurement challenge due to their minuscule gear gaps. A high-precision measurement method under the influence of positioning errors is proposed, enabling precise evaluation of the machining quality of small-modulus gears. Firstly, a compound measurement platform for small-modulus gears is developed. Using a 3D model of the measurement system, the mathematical relationships governing motion transmission between various components are analyzed. Secondly, the formation mechanism of gear positioning error is revealed and its important influence on measurement accuracy is discussed. An optimization method for spatial coordinate transformation matrices under positioning errors of gears is proposed. Thirdly, the study focuses on small-sized gears with a modulus of 0.1 mm and a six-level accuracy. Based on the aforementioned measurement system, the tooth profile measurement points are collected in the actual workpiece coordinate system. Then, gear error parameters are extracted based on the established models for tooth profile deviation and pitch deviation. Finally, the accuracy and effectiveness of the proposed measurement method are verified by comparing the measurement results of the P26 gear measuring center.

Objective: Dysregulation of the immune system plays a vital role in the pathological process of vascular dementia, and this study aims to spot critical biomarkers and immune infiltrations in vascular dementia employing a bioinformatics approach. Methods: We acquired gene expression profiles from the Gene Expression Database. The gene expression data were analyzed using the bioinformatics method to identify candidate immune-related central genes for the diagnosis of vascular dementia. and the diagnostic value of nomograms and Receiver Operating Characteristic (ROC) curves were evaluated. We also examined the role of the VaD hub genes. Using the database and potential therapeutic drugs, we predicted the miRNA and lncRNA controlling the Hub genes. Immune cell infiltration was initiated to examine immune cell dysregulation in vascular dementia. Results: 1321 immune genes were included in the combined immune dataset, and 2816 DEGs were examined in GSE122063. Twenty potential genes were found using differential gene analysis and co-expression network analysis. PPI network design and functional enrichment analysis were also done using the immune system as the main subject. To create the nomogram for evaluating the diagnostic value, four potential core genes were chosen by machine learning. All four putative center genes and nomograms have a solid diagnostic value (AUC ranged from 0.81 to 0.92). Their high confidence level became unquestionable by validating each of the four biomarkers using a different dataset. According to GeneMANIA and GSEA enrichment investigations, the pathophysiology of VaD is strongly related to inflammatory responses, drug reactions, and central nervous system degeneration. The data and Hub genes were used to construct a ceRNA network that includes three miRNAs, 90 lncRNA, and potential VaD therapeutics. Immune cells with varying dysregulation were also found. Conclusion: Using bioinformatic techniques, our research identified four immune-related candidate core genes (HMOX1, EBI3, CYBB, and CCR5). Our study confirms the role of these Hub genes in the onset and progression of VaD at the level of immune infiltration. It predicts potential RNA regulatory pathways control VaD progression, which may provide ideas for treating clinical disease.

BackgroundSingle-cell RNA sequencing (scRNA-Seq) provides new perspectives and ideas to investigate the interactions between different cell types and organisms. By integrating scRNA-seq with new computational frameworks or specific technologies, better Alzheimer’s disease (AD) treatments may be developed.MethodsThe single-cell sequencing dataset GSE158234 was obtained from the GEO database. Preprocessing, quality control, dimensionality-reducing clustering, and annotation to identify cell types were performed on it. RNA-seq profiling dataset GSE238013 was used to determine the components of specific cell subpopulations in diverse samples. A set of genes included in the OMIM, Genecards, CTD, and DisGeNET databases were selected as highly plausible AD-related genes. Then, ROC curves were created to predict the diagnostic value using the significantly expressed genes in the KO group as hub genes. The genes mentioned above were mapped to the Coremine Medical database to forecast prospective therapeutic Chinese medicines, and a “Chinese medicine-ingredient-target” network was constructed to screen for potential therapeutic targets. The last step was to undertake Mendelian randomization research to determine the causal link between the critical gene IL1B and AD in the genome-wide association study.ResultsUsing the scRNA-seq dataset, five unique cell clusters were discovered. These clusters were further subdivided into four distinct cell types using marker genes. The KO group showed a more substantial differential subgroup of macrophages than the WT group. By using the available datasets and PPI network analysis, 54 common genes were discovered. Four clusters were identified using the MCODE approach, and correlation analysis showed that seven genes in those four clusters had a significantly negative correlation with macrophages. Six genes in four sets had a significantly positive correlation. Five genes had different levels of expression in the WT and KO groups. The String database was used to identify the regulatory relationships between the four genes (IL10, CX3CR1, IL1B, and IL6) that were finally selected as AD hub genes. Screening identified potential traditional Chinese medicine to intervene in the transformation process of AD, including Radix Salviae, ginseng, Ganoderma, licorice, Coptidis Rhizoma, and Scutellariae Radix, in addition to promising therapeutic targets, such as PTGS1, PTGS2, and RXRA. Finally, it was shown that IL1B directly correlated with immune cell infiltration in AD. In inverse variance weighting, we found that IL1B was associated with a higher risk of AD, with an OR of 1.003 (95% CI = 1.001–1.006, p = 0.038).ConclusionOur research combined network pharmacology and the scRNA-seq computational framework to uncover pertinent hub genes and prospective traditional Chinese medicine potential therapeutic targets for AD. These discoveries may aid in understanding the molecular processes behind AD genes and the development of novel medications to treat the condition.

IntroductionThis study aimed to investigate the effects of emotional themes and the COVID-19 pandemic context priming on the size and color of drawings.MethodsA 3 (emotion: peacefulness, gratitude, loneliness) × 2 (context: pandemic, regular) mixed design was used and 113 university students in Shanghai were recruited to draw human figures and trees using 10 marker colors.ResultsThe size of the drawings depicting loneliness was smaller than that of the those depicting peacefulness and gratitude. Drawings depicting loneliness used more cool and non-preferred colors; drawings depicting gratitude used more warm and preferred colors. Drawings in the pandemic context were larger, which may reflect the more significant threat perceived by individuals. Drawings in the pandemic context used more red colors, showing symbolic meanings such as danger.DiscussionThe drawing size and drawing color are influenced by emotional themes and the pandemic context priming.

Abstract Background Drawing can regulate emotions through venting or distraction. Distraction is more helpful for short‐term emotion recovery; however, the sustainability of this difference is yet to be clarified. This study used functional near‐infrared spectroscopy (fNIRS) to explore potential differences between venting and distraction. Methods A total of 44 college students participated in the experiment. After inducing fear by video, they were divided into two groups: The venting group drew their emotional experience, and the distraction group drew a house. Subsequently, the participants were instructed to relax by a brief video. Results Although the distraction group had a higher valence than the venting group at the end of the drawing activity, there was no difference between the two groups after a relaxation period. Additionally, the activation pattern of the prefrontal cortex differed between the two groups. Compared to the distraction group, the venting group had fewer channels with elevated prefrontal activity during drawing, suggesting less cognitive control, and had more channels with reduced prefrontal activity during relaxation, suggesting a higher level of relaxation. Drawing coding and fNIRS data were both associated with variations in valence. Conclusion The less the cognitive control over emotion and the more free the expression of emotion during drawing, the higher the increase in valence; inversely, the more the cognitive control over emotion and the less free the expression of emotion, the lower the increase in valence.

PM2.5 is directly related to the air quality and poses a threat to human health, thus high-precision monitoring of PM2.5 is necessary. The dispersion and accumulation of PM2.5 are affected by meteorological elements near the ground and in the upper air. Nevertheless, the current PM2.5 inversion models based on the deep neural network only consider ground elements. To further optimize the model and improve the inversion accuracy, a PM2.5 hourly inversion model integrating upper-air meteorological data was proposed, and a parametric rectified linear unit was used as the activation function of the model. The results showed that among the input elements, PM2.5 had the highest correlation with AOD, with a correlation coefficient of 0.33. The proposed model achieved the highest accuracy on the test set, with RMSE, MAE, and $\text{R}^{2}$ of $14.39 \mu \text{g}/\text{m}^{3}$ , $9.67 \mu \text{g}/\text{m}^{3}$ , and 0.83, respectively. Compared with the deep neural network models for surface meteorological data and surface+850hPa meteorological data, the RMSE of the proposed model on the test set was reduced by 23.13% and 17.05%, respectively. Meanwhile, the RMSE of the proposed model on the test set was reduced by 56.15%, 39.99%, 14.60% and 5.76%, respectively, compared with adaptive boosting, gradient boosting regression, random forest, and the integrated model of these three models. During the heating season in Shanxi Province, the high-value areas of PM2.5 were mainly distributed in the basin area, the PM2.5 concentration reached the highest in November and peaked at 11 a.m. during the day.

Real-time and accurate detection of flame and smoke is an important prerequisite to reduce the loss caused by fire. There are exists some problems in traditional detection algorithms of flame and smoke, such as low accuracy, high miss rate, low detection efficiency, and low detection rate of small targets. This paper proposes the detection algorithm of flame and smoke based on ODConvBS in YOLOv5s. Firstly, the ordinary convolutional blocks in the backbone network of YOLOv5s are replaced with ODConvBS to achieve the extraction of attentional features from the convolutional kernel. Secondly, Gnconv is introduced into Neck to improve the high-order spatial information extraction ability of the model. Then, the Shuffle Attention module is added at the end of the Neck to facilitate the fusion of different groups of features. Finally, the prediction part uses a SIOU loss function that can take into account the angle of the prediction frame vectors to accelerate the model convergence. When utilizing the self-made dataset of flame and smoke, the upgraded YOLOv5s model mAP grew by 9.3%. At the same time, the accuracy rate, the recall rate, and the detection speed increased to 83.5%, 83.7%, 33.3FPS respectively.

Rosai–Dorfman disease (RDD) is a rare, benign, non-Langerhans cell histiocytic proliferative disease. RDD with central nervous system involvement is extremely rare. Surgical excision is generally regarded as the appropriate treatment of choice for this disease, especially when the lesion causes neurological compression. RDD can be accompanied by systemic symptoms, such as malaise, fever, weight change, leukocytosis, anemia, and hormonal disturbance, which may be challenging during general management. Little is known regarding peri-anesthesia management of this rare disease. We report a case of a patient in his 20s who had recurrent RDD and had general anesthesia with perioperative management. He was obese and hepatic insufficiency. This case report adds to the literature regarding the perioperative anesthetic management of RDD with central nervous system involvement.

ABSTRACT The function of Seryl-tRNA synthetase in fungi during gene transcription regulation beyond translation has not been reported. Here, we report a seryl-tRNA synthetase, ThserRS, which can negatively regulate laccase lacA transcription in Trametes hirsuta AH28-2 under exposure to copper ion. ThserRS was obtained through yeast one-hybrid screening using a bait sequence of lacA promoter (−502 to −372 bp). ThserRS decreased while lacA increased at the transcription level in T. hirsuta AH28-2 in the first 36 h upon CuSO4 induction. Then, ThserRS was upregulated, and lacA was downregulated. ThserRS overexpression in T. hirsuta AH28-2 resulted in a decrement in lacA transcription and LacA activity. By comparison, ThserRS silencing led to increased LacA transcripts and activity. A minimum of a 32-bp DNA fragment containing two putative xenobiotic response elements could interact with ThserRS, with a dissociation constant of 919.9 nM. ThserRS localized in the cell cytoplasm and nucleus in T. hirsuta AH28-2 and was heterologously expressed in yeast. ThserRS overexpression also enhanced mycelial growth and oxidative stress resistance. The transcriptional level of several intracellular antioxidative enzymes in T. hirsuta AH28-2 was upregulated. Our results demonstrate a noncanonical activity of SerRS that acts as a transcriptional regulation factor to upregulate laccase expression at an early stage after exposure to copper ions. IMPORTANCE Seryl-tRNA synthetase is well known for the attachment of serine to the corresponding cognate tRNA during protein translation. In contrast, its functions beyond translation in microorganisms are underexplored. We performed in vitro and cell experiments to show that the seryl-tRNA synthetase in fungi with no UNE-S domain at the carboxyl terminus can enter the nucleus, directly interact with the promoter of the laccase gene, and negatively regulate the fungal laccase transcription early upon copper ion induction. Our study deepens our understanding of the Seryl-tRNA synthetase noncanonical activities in microorganisms. It also demonstrates a new transcription factor for fungal laccase transcription.

Computer viruses keep evolving with the development of society and progress of technologies, and they become more complex and hidden.The worm virus is the earliest computer virus, which has evolved to an industrial control worm virus and caused a great impact on the safety of the industrial system.Neither the single network isolation nor the patching immunity is unable to keep up with the spreading of the worm virus.The propagation mode and characteristics of the worm virus in the industrial control system were analyzed.Based on the related works of network isolation and patching, a defense strategy against the worm virus was proposed.This strategy was originated from the fundamental infectious disease model, and then a mathematics model (SEIPQR) was proposed to simulate the trend of worm virus propagation.The model included six situations: Susceptible, Exposed, Infected, Quarantine and Recovered.The state transition diagrams of the model was created, and the calculus equations were obtained from the state transition diagrams.Under the condition that the number of system equipment is fixed, the equations were transformed.The equations were solved by solving the basic regeneration number R0, and six equation expressions of the model ware analyzed when the number of exposed hosts and infected hosts is zero.According to the principle of the Routh-Hurwitz, the system is asymptotically stable when R0＜1, and unstable if R0＞1.Then the dynamic characteristics of the SEIPQR model under different patching probability, different isolation rate and different infection rate were compared by numerical simulation.Furthermore, the disease-free equilibrium point and endemic equilibrium point of the model were obtained.The simulation results showed that, when the whole system is infected with worm virus, timely patching the susceptible devices and isolating the network can effectively inhibit the spread of industrial control worm virus.

The field of intelligent game has gradually become one of the hotspots of AI research.A series of research breakthroughs have been made in the field of game AI and intelligent wargame in recent years.However, how to develop game AI and apply it to the actual intelligent combat deduction is still facing great difficulties.The overall progress of research in the field of intelligent games in domestic and overseas were explored, the main attribute requirements of intelligent combat deduction was tracked, and it was summarized with the latest advancements in reinforcement learning.The feasibility of developing game AI into intelligent combat deduction were comprehensively analyzed from three dimensions: mainstream research technology in the field of intelligent game, relevant intelligent decision technology and technical difficulties of combat deduction, and finally, some suggestions for the development of future intelligent combat deductiongives were given.This paper can introduce a clear development status and provide valuable research ideas for researchers in the field of intelligent game.

Accumulating evidence has recognized that cancer-associated fibroblasts (CAFs) are major players in the desmoplastic stroma of ovarian cancer, modulating tumor progression and therapeutic response. However, it is unclear regarding the signatures of CAFs could be utilized to predict the clinical outcomes of ovarian cancer patients. To fill in this gap, we explored the intratumoral compartment of ovarian cancer by analyzing the single-cell RNA-sequencing (scRNA-seq) datasets of ovarian carcinoma samples, and identified two distinct CAFs (tumor-promoting CAF_c1 subtype and myofibroblasts-like CAF_c2 subtype). The clinical significance of CAF subtypes was further validated in The Cancer Genomics Atlas (TCGA) database and other independent immunotherapy response datasets, and the results revealed that the patients with a higher expression of CAF_c1 signatures had a worse prognosis and showed a tendency of resistance to immunotherapy. This work uncovered the signatures of the CAF_c1 subtype that could serve as a novel prognostic indicator and predictive marker for immunotherapy.

Freeze–thaw damage of rock mass poses a great threat to the safety of rock engineering, ground buildings, and low-temperature storage of liquefied natural gas (LNG) in cold regions. By collecting acoustic emission (AE) signals of sandstone during uniaxial compression failures, this paper analyzed the critical slowdown phenomenon of different types of sandstone during the freeze–thaw failure. According to the auto-correlation coefficients and the variance of AE signals under different windows and steps, the precursors were determined and a warning model of rock engineering failure precursors based on the critical slowdown principle was proposed. Then the Grey Wolf Optimizer (GWO) algorithm was used to optimize the initial weights and thresholds of the back propagation (BP) neural network, and the influence factors of rock engineering failure under different working conditions were input as training sets to train the network. The results showed that the correlation coefficients between the predicted value and real value of the GWO-BP neural network reached 99.90% and 98.81% respectively, indicating that the accuracy of the BP neural network prediction was improved. This study provides a new method for rock engineering failure early warning, and has great theoretical and guiding significance for enriching and improving the rock mass AE monitoring technology.

The Xinghongpu Formation is very important for understanding the Devonian tectonic evolution of the South Qinling orogenic belt. Geochemical, detrital zircon U-Pb-Hf isotopic studies were carried out on the Late Devonian metasedimentary rocks of the Xinghongpu Formation to constrain the depositional age, the provenance, and the tectonic setting. The detrital zircon U-Pb dating results revealed that the depositional age of the Xinghongpu Formation of the Late Devonian was not earlier than 363.2 Ma. The whole-rock geochemistry suggested that (1) this suite of metasedimentary rocks was mainly derived from quartzose sediments of mature continental provenance, with a small contribution from mafic and intermediate igneous provenance, and (2) the metasedimentary sandstone of the Xinghongpu Formation from the Late Devonian was deposited in an active continental margin to continental arc setting. The detailed detrital zircon U-Pb dating showed that the age spectra of detrital zircon could be divided into four groups: (1) 416–480 Ma, accounting for about 23%; (2) 740–850 Ma, accounting for about 19%; (3) 889–1017 Ma, accounting for about 19%; and (4) 1072–1146 Ma, accounting for about 12%. It also contained a group of Early Proterozoic zircons. The age and Hf isotope of the detrital zircons suggested that the clastic sediment deposited in the Xinghongpu Formation mainly came from the South Qinling Orogenic Belt and the North Qinling Orogenic Belt. The detrital zircon Lu-Hf isotopes indicated that most zircons were the products of the ancient crustal remelting, and the mantle-derived magmatic sources contributed to the provenance. The Xinghongpu Fm. formed in an oceanic basin in a continental margin environment with arc systems.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), launched in September 2018, has been widely used in forestry and surveying. A high-accuracy digital elevation model (DEM)/digital surface model (DSM) for terrain matching can effectively evaluate the ICESat-2 design requirements and provide essential data support for further study. The conventional terrain-matching methods regard the laser ground track as a whole, ignoring the individual differences caused by the interaction of photons during flight. Therefore, a novel terrain-matching method using a two-dimensional affine transformation model was proposed to describe the deformation of laser tracks. The least-square optimizes the model parameters with the high-accuracy terrain data to obtain the best matching result. The results in McMurdo Dry Valley (MDV), Antarctica, and Zhengzhou (ZZ), China, demonstrate that the proposed method can verify geolocation accuracy and indicate that the average horizontal accuracy of ICESat-2 V5 data is about 3.86 m in MDV and 4.67 m in ZZ. It shows that ICESat-2 has good positioning accuracy, even in mountainous areas with complex terrain. Additionally, the random forest (RF) model was calculated to analyze the influence of four factors on geographic location accuracy. The slope and signal-to-noise ratio (SNR) are considered the crucial factors affecting the accuracy of ICESat-2 data.

In order to explore the evolution mechanism of freeze–thaw disasters and the role of water in the freezing–thawing cycles of rocks, the macro mechanical indexes and microstructural characteristics of seven different saturation sandstones after certain freeze–thaw cycles were analyzed. Electron microscope scanning, nuclear magnetic resonance, and uniaxial compression tests were employed to study the migration law of water in the rock, the crack growth law, and the damage mechanism during freeze–thaw cycles. The results showed that when the saturation was 85%, the peak load curve of sandstone with different saturation appeared at the minimum point, and the porosity of sandstone reached the maximum. The damage variable increased sharply when the saturation was 75–85%. This proves that 85% saturation is the critical value of sandstone after five freeze–thaw cycles. The water migration freezing model is established, and the migration direction of capillary film water during freezing is micropore → mesopore → macropore. The migration of water is accompanied by the expansion and generation of cracks. Then we study the mechanism and law of crack expansion, and the crack propagation rate is positively related to the theoretical suction. The theoretical suction and theoretical ice pressure increased linearly with the decrease in temperature, which accelerated the crack propagation. The crack propagation rate in decreasing order is Vmacropore > Vmesopore > Vmicropore. The research results can provide a theoretical basis for evaluating the stability of rocks under the action of freeze–thaw cycles in cold regions.

The next generation Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2)/Advanced Topographic Laser Altimeter System (ATLAS) was launched in 2018 to provide large amounts of spaceborne laser altimetry data. The joint block adjustment with laser altimetry data and satellite remote sensing images can promote limited stereoscopic positioning accuracy without ground control points (GCPs). However, there are two problems in the joint block adjustment, that is, the reliable ATLAS laser altimetry points (LAPs) selection and the discrepancies of image-object points. To solve the above problems, a surface point cloud extraction algorithm based on wavelet reconstruction was proposed. Then, the LAPs selection constraint model was established, so the terrain influence was resolved. Finally, a block joint adjustment method has been proposed with remote sensing images and the reliable LAPs considering error of their plane coordinates, which could achieve the accurate corresponding image points of the reliable LAPs, remarkably decrease the discrepancies of image-object points. Fourteen pairs of ATLAS data, seven ZY3-02 stereo images and eleven Mapping Satellite-1 stereo images in Zhengzhou, China were collected to validate the method performance. Experiment results have shown that the consistency of the image-object points and the positioning accuracy of stereoscopic images without GCPs have been improved. Compared with the free net block adjustment, the height, east and north positioning accuracy of ZY3-02 and Mapping Satellite-1 stereo images have increased by 61%, 56%, 60% and 56%, 38%, 37%, respectively.

Background. Parathyroid tumors are common endocrine neoplasias associated with primary hyperparathyroidism. Although numerous studies have studied the subject, the predictive value of gene biomarkers nevertheless remains low. Methods. In this study, we performed genomic analysis of abnormal DNA methylation in parathyroid tumors. After data preprocessing, differentially methylated genes were extracted from patients with parathyroid tumors by using t-tests. Results. After refinement of the basic differential methylation, 28241 unique CpGs (634 genes) were identified to be methylated. The methylated genes were primarily involved in 7 GO terms, and the top 3 terms were associated with cyst morphogenesis, ion transport, and GTPase signal. Following pathway enrichment analyses, a total of 10 significant pathways were enriched; notably, the top 3 pathways were cholinergic synapses, glutamatergic synapses, and oxytocin signaling pathways. Based on PPIN and ego-net analysis, 67 ego genes were found which could completely separate the diseased group from the normal group. The 10 most prominent genes included POLA1, FAM155 B, AMMECR1, THOC2, CCND1, CLDN11, IDS, TST, RBPJ, and GNA11. SVM analysis confirmed that this grouping approach was precise. Conclusions. This research provides useful data to further explore novel genes and pathways as therapeutic targets for parathyroid tumors.

The lateral pressure generated by liquefied soil on pile is a critical parameter in the analysis of soil-pile interaction in liquefaction-susceptible sites. Previous studies have shown that liquefied sand behaves like a non-Newton fluid, and its effect on piles has rate-dependent properties. In this study, a simplified pseudo-static method for liquefiable soil-pile interaction analysis is proposed by treating the liquefied soil as a thixotropic fluid, which considers the rate-dependent behavior. The viscous shear force generated by the relative movement between the viscous fluid (whose viscosity coefficient varies with excess pore pressure and shear strain rate) and the pile was assumed to be the lateral load on the pile. The results from the simplified analysis show that the distribution of bending moment is in good agreement with experiments data. Besides, the effects of various parameters, including relative density, thickness ratio of nonliquefiable layer to liquefiable layer, and frequency of input ground motion, on the pile-soil rate-dependent interaction were discussed in detail.

Oleanolic acid is a widely distributed natural product, which possesses promising antitumor, antiviral, antihyperlipidemic, and anti-inflammatory activities. A heterodimeric complex formed by integrin αM (CD11b) and integrin β2 (CD18) is highly expressed on monocytes and macrophages. In the current study, we demonstrate that the I domain of αM (αM-I domain) might present a potential cellular target for oleanolic acid. In vitro data show that oleanolic acid induces clustering of αM on macrophages and reduces their non-directional migration. In accordance with experimental data, molecular docking revealed that oleanolic acid binds to the αM-I domain in its extended-open form, the dominant conformation found in αM clusters. Molecular dynamics simulation revealed that oleanolic acid can increase the flexibility of the α7 helix and promote its movement away from the N-terminus, indicating that oleanolic acid may facilitate the conversion of the αM-I domain from the extended-closed to the extended-open conformation. As demonstrated by metadynamics simulation, oleanolic acid can destabilize the local minimum of the αM-I domain in the open conformation partially through disturbance of the interactions between α1 and α7 helices. In summary, we demonstrate that oleanolic acid might function as an allosteric agonist inducing clustering of αM on macrophages by shifting the balance from the closed to the extended-open conformation. The molecular target identified in this study might hold potential for a purposeful use of oleanolic acid to modulate chronic inflammatory responses.

Calcareous sand deposits are widespread along the shoreline in tropical and subtropical regions. Microbially induced calcite precipitation (MICP) treatment is a new method for improving the soil’s stiffness and strength. The small-strain shear modulus and damping ratio of MICP-treated calcareous sand, two critical parameters for predicting the dynamic behavior of soil, are little known. This study conducts a series of resonant column tests to investigate the dynamic characteristics of MICP-treated calcareous sand, emphasizing the influence of treatment duration and confining stress on the stiffness and damping characteristics. It analyzes the relationship between the initial dynamic shear modulus and unconfined compressive strength. In addition, empirical relationships between the reference shear strain and treatment duration or confining stress are provided. The results show that the normalized shear modulus G/G0 of MICP-cemented calcareous sand has a higher strain sensitivity than that of untreated sand, and the Hardin–Drnevich model can describe its attenuation pattern. The effective confining stress σc affects the degradation characteristics of the dynamic shear modulus of MICP-treated calcareous sand with a low cementation level; however, its impact decreases as the treatment duration increases. There is a linear relationship between the reference shear strain and confining stress. While the relationship between the reference shear stain and treatment duration is a power law.

The large intra-class difference and inter-class similarity of scene images bring great challenges to the research of remote-sensing scene image classification. In recent years, many remote-sensing scene classification methods based on convolutional neural networks have been proposed. In order to improve the classification performance, many studies increase the width and depth of convolutional neural network to extract richer features, which increases the complexity of the model and reduces the running speed of the model. In order to solve this problem, a lightweight convolutional neural network based on hierarchical-wise convolution fusion (LCNN-HWCF) is proposed for remote-sensing scene image classification. Firstly, in the shallow layer of the neural network (groups 1–3), the proposed lightweight dimension-wise convolution (DWC) is utilized to extract the shallow features of remote-sensing images. Dimension-wise convolution is carried out in the three dimensions of width, depth and channel, and then, the convoluted features of the three dimensions are fused. Compared with traditional convolution, dimension-wise convolution has a lower number of parameters and computations. In the deep layer of the neural network (groups 4–7), the running speed of the network usually decreases due to the increase in the number of filters. Therefore, the hierarchical-wise convolution fusion module is designed to extract the deep features of remote-sensing images. Finally, the global average pooling layer, the fully connected layer and the Softmax function are used for classification. Using global average pooling before the fully connected layer can better preserve the spatial information of features. The proposed method achieves good classification results on UCM, RSSCN7, AID and NWPU datasets. The classification accuracy of the proposed LCNN-HWCF on the AID dataset (training:test = 2:8) and the NWPU dataset (training:test = 1:9), with great classification difficulty, reaches 95.76% and 94.53%, respectively. A series of experimental results show that compared with some state-of-the-art classification methods, the proposed method not only greatly reduces the number of network parameters but also ensures the classification accuracy and achieves a good trade-off between the model classification accuracy and running speed.

Objective To evaluate survival following afatinib (AF) and erlotinib (ER) treatment in advanced del19 lung adenocarcinoma (AD19LA) with asymptomatic brain metastasis (ABM) after pemetrexed–cisplatin chemotherapy (PCC). Methods Data were retrospectively analysed from individuals with AD19LA and ABM after PCC who received AF or ER for 2 years or until intolerable adverse events (AEs), withdrawal, or death. The primary outcome was survival; secondary outcomes were AEs. Results The final analysis included 174 AD19LA individuals (AF: n = 86; ER: n = 88) with a median follow-up of 24.2 months (IQR 2.1–28.3). Significant differences in overall survival (16.2 months [95%CI 15.4–17.1] for AF vs 7.2 months [95%CI 6.3–8.1] for ER) (HR 0.50, 95%CI 0.36–0.71, p

Summary: Astrocytic glycogen is an important energy reserve in the brain and is believed to supply fuel during energy crisis. However, the pattern of glycogen metabolism in ischemic stroke and its potential therapeutic impact on neurological outcomes are still unknown. Here, we found extensive brain glycogen accumulation after reperfusion in ischemic stroke patients and primates. Glycogenolytic dysfunction in astrocytes is responsible for glycogen accumulation, caused by inactivation of the protein kinase A (PKA)-glycogen phosphorylase kinase (PhK)-glycogen phosphorylase (GP) cascade accompanied by the activation of glycogen synthase kinase-3β (GSK3β). Genetic or pharmacological augmentation of astrocytic GP could promote astrocyte and neuron survival and improve neurological behaviors. In addition, we found that insulin exerted a neuroprotective effect, at least in part by rescuing the PKA-PhK-GP cascade to maintain homeostasis of glycogen metabolism during reperfusion. Together, our findings suggest a promising intervention for undesirable outcomes in ischemic stroke.

A family of novel efficient non-oxime compounds exhibited promising reactivation efficacy for VX and sarin inhibited human acetylcholinesterase was discovered. It was found that aromatic groups coupled to Mannich phenols and the introduction of imidazole to the ortho position of phenols would dramatically enhance reactivation efficiency. Moreover, the in vivo experiment was conducted, and the results demonstrated that Mannich phenol L10R1 (30 mg/kg, ip) could afford 100% 48 h survival for mice of 2*LD50 sarin exposure, which is promising for the development of non-oxime reactivators with central efficiency.

In recent years, convolution neural networks (CNNs) have been widely used in the field of remote sensing scene image classification. However, CNN models with good classification performance tend to have high complexity, and CNN models with low complexity are difficult to obtain high classification accuracy. These models hardly achieve a good trade-off between classification accuracy and model complexity. To solve this problem, we made the following three improvements and proposed a lightweight modular network model. First, we proposed a lightweight self-compensated convolution (SCC). Although traditional convolution can effectively extract features from the input feature map, when there are a large number of filters (such as 512 or 1024 common filters), this process takes a long time. To speed up the network without increasing the computational load, we proposed a self-compensated convolution. The core idea of this convolution is to perform traditional convolution by reducing the number of filters, and then compensate the convoluted channels by input features. It incorporates shallow features into the deep and complex features, which helps to improve the speed and classification accuracy of the model. In addition, we proposed a self-compensating bottleneck module (SCBM) based on the self-compensating convolution. The wider channel shortcut in this module facilitates more shallow information to be transferred to the deeper layer and improves the feature extraction ability of the model. Finally, we used the proposed self-compensation bottleneck module to construct a lightweight and modular self-compensation convolution neural network (SCCNN) for remote sensing scene image classification. The network is built by reusing bottleneck modules with the same structure. A lot of experiments were carried out on six open and challenging remote sensing image scene datasets. The experimental results show that the classification performance of the proposed method is superior to some of the state-of-the-art classification methods with less parameters.

Metal/ferroelectric-Si:HfO2/SiN/SiO2/Si structure was fabricated to investigate the charge trapping properties. This device enhances the carrier injection into the nitride from the silicon due to the spontaneous polarization in SiO2:HfO2 layer. Compared with conventional metal/SiO2/SiN/SiO2/Si devices, the proposed devices showed a larger memory window, faster program/erase speeds, and higher endurance with comparable retention properties.

With the development of remote sensing scene image classification, convolutional neural networks have become the most commonly used method in this field with their powerful feature extraction ability. In order to improve the classification performance of convolutional neural networks, many studies extract deeper features by increasing the depth and width of convolutional neural networks, which improves classification performance but also increases the complexity of the model. To solve this problem, a lightweight convolutional neural network based on channel multi-group fusion (LCNN-CMGF) is presented. For the proposed LCNN-CMGF method, a three-branch downsampling structure was designed to extract shallow features from remote sensing images. In the deep layer of the network, the channel multi-group fusion structure is used to extract the abstract semantic features of remote sensing scene images. The structure solves the problem of lack of information exchange between groups caused by group convolution through channel fusion of adjacent features. The four most commonly used remote sensing scene datasets, UCM21, RSSCN7, AID and NWPU45, were used to carry out a variety of experiments in this paper. The experimental results under the conditions of four datasets and multiple training ratios show that the proposed LCNN-CMGF method has more significant performance advantages than the compared advanced method.

With the development of computer vision, attention mechanisms have been widely studied. Although the introduction of an attention module into a network model can help to improve classification performance on remote sensing scene images, the direct introduction of an attention module can increase the number of model parameters and amount of calculation, resulting in slower model operations. To solve this problem, we carried out the following work. First, a channel attention module and spatial attention module were constructed. The input features were enhanced through channel attention and spatial attention separately, and the features recalibrated by the attention modules were fused to obtain the features with hybrid attention. Then, to reduce the increase in parameters caused by the attention module, a group-wise hybrid attention module was constructed. The group-wise hybrid attention module divided the input features into four groups along the channel dimension, then used the hybrid attention mechanism to enhance the features in the channel and spatial dimensions for each group, then fused the features of the four groups along the channel dimension. Through the use of the group-wise hybrid attention module, the number of parameters and computational burden of the network were greatly reduced, and the running time of the network was shortened. Finally, a lightweight convolutional neural network was constructed based on the group-wise hybrid attention (LCNN-GWHA) for remote sensing scene image classification. Experiments on four open and challenging remote sensing scene datasets demonstrated that the proposed method has great advantages, in terms of classification accuracy, even with a very low number of parameters.

For remote sensing scene image classification, many convolution neural networks improve the classification accuracy at the cost of the time and space complexity of the models. This leads to a slow running speed for the model and cannot realize a trade-off between the model accuracy and the model running speed. As the network deepens, it is difficult to extract the key features with a sample double branched structure, and it also leads to the loss of shallow features, which is unfavorable to the classification of remote sensing scene images. To solve this problem, we propose a dual branch multi-level feature dense fusion-based lightweight convolutional neural network (BMDF-LCNN). The network structure can fully extract the information of the current layer through 3 × 3 depthwise separable convolution and 1 × 1 standard convolution, identity branches, and fuse with the features extracted from the previous layer 1 × 1 standard convolution, thus avoiding the loss of shallow information due to network deepening. In addition, we propose a downsampling structure that is more suitable for extracting the shallow features of the network by using the pooled branch to downsample and the convolution branch to compensate for the pooled features. Experiments were carried out on four open and challenging remote sensing image scene data sets. The experimental results show that the proposed method has higher classification accuracy and lower model complexity than some state-of-the-art classification methods and realizes the trade-off between model accuracy and model running speed.

The Lower Jurassic reservoir in the Niudong area of the northern margin of Qaidam Basin is a typical low permeability sandstone reservoir and an important target for oil and gas exploration in the northern margin of the Qaidam Basin. In this paper, casting thin section analysis, scanning electron microscopy, X-ray diffraction, and stable isotope analysis among other methods were used to identify the diagenetic characteristics and evolution as well as the main factors influencing reservoir quality in the study area. The predominant types of sandstone in the study area are mainly feldspathic lithic sandstone and lithic arkose, followed by feldspathic sandstone and lithic sandstone. Reservoir porosity ranges from 0.01% to 19.5% (average of 9.9%), and permeability ranges from 0.01 to 32.4 mD (average of 3.8 mD). The reservoir exhibits robust heterogeneity and its quality is mainly influenced by diagenesis. The Lower Jurassic reservoir in the study area has undergone complex diagenesis and reached the middle diagenesis stage (A–B). The quantitative analysis of pore evolution showed that the porosity loss rate caused by compaction and cementation was 69.0% and 25.7% on average, and the porosity increase via dissolution was 4.8% on average. Compaction was the main cause of the reduction in the physical property of the reservoir in the study area, while cementation and dissolution were the main causes of reservoir heterogeneity. Cementation can reduce reservoir space by filling primary intergranular pores and secondary dissolved pores via cementation such as a calcite and illite/smectite mixed layer, whereas high cement content increased the compaction resistance of particles to preserve certain primary pores. δ13C and δ18O isotopes showed that the carbonate cement in the study area was the product of hydrocarbon generation by organic matter. The study area has conditions that are conductive to strong dissolution and mainly occur in feldspar dissolution, which produces a large number of secondary pores. It is important to improve the physical properties of the reservoir. Structurally, the Niudong area is a large nose uplift structure with developed fractures, which can be used as an effective oil and gas reservoir space and migration channel. In addition, the existence of fractures provides favorable conditions for the uninterrupted entry of acid fluid into the reservoir, promoting the occurrence of dissolution, and ultimately improves the physical properties of reservoirs, which is mainly manifested in improving the reservoir permeability.

Epidemiological studies have shown that both lipid metabolism disorder and mitochondrial dysfunction are correlated with the pathogenesis of neurodegenerative diseases (NDDs), including Parkinson’s disease (PD). Emerging evidence suggests that deposition of intracellular lipid droplets (LDs) participates in lipotoxicity and precedes neurodegeneration. Perilipin family members were recognized to facilitate LD movement and cellular signaling interactions. However, the direct interaction between Perilipin-regulated LD deposition and mitochondrial dysfunction in dopaminergic (DA) neurons remains obscure. Here, we demonstrate a novel type of lipid dysregulation involved in PD progression as evidenced by upregulated expression of Plin4 (a coating protein and regulator of LDs), and increased intracellular LD deposition that correlated with the loss of TH-ir (Tyrosine hydroxylase-immunoreactive) neurons in the MPTP/p-induced PD model mouse mesencephalon. Further, in vitro experiments showed that inhibition of LD storage by downregulating Plin4 promoted survival of SH-SY5Y cells. Mechanistically, reduced LD storage restored autophagy, leading to alleviation of mitochondrial damage, which in turn promoted cell survival. Moreover, the parkin-poly-Ub-p62 pathway was involved in this Plin4/LD-induced inhibition of mitophagy. These findings were further confirmed in primary cultures of DA-nergic neurons, in which autophagy inhibitor treatment significantly countermanded the ameliorations conferred by Plin4 silencing. Collectively, these experiments demonstrate that a dysfunctional Plin4/LD/mitophagy axis is involved in PD pathology and suggest Plin4-LDs as a potential biomarker as well as therapeutic strategy for PD.

Based on the dense well pattern and the core data of straight wells and horizontal wells of PI332 unit of Putaohua oil layer, Cretaceous Yaojia Formation in Xingbei area of Daqing Oilfield, according to the model of modern sediments and ancient outcrop, the configuration parameters of lateral interbeds configuration are acquired by rock-electricity combination, which are used to analyze the three-dimensional configuration characteristics of the point bar lateral accretion interbeds located in the meandering distributary channel of the delta distributary plain and the residual oil distribution. The results show that the lateral accretion interbeds of point bar are thin, slow, and dense in PI332 unit of Xingbei area. The thickness of interbeds is mainly 2−25 cm. The upper and lower dip angle of interbeds is smaller at 2°−5°. The middle of interbeds is steeper at 7°−10°. The plane distribution density is 0.11/m. The three-dimensional spatial configuration consists of the plane and profile features. The lateral accretion interbeds are in crescent arrangement on the plane, and in imbricate and echelon distribution on the profile. Under the influence of lateral accretion interbeds architecture and the physical difference of lateral accretions, the middle and lower parts of point bars are seriously flooded, and the residual oil is mainly distributed in the middle and upper parts. The horizontal section of a horizontal well should be designed in the middle and upper parts of point bars, and it should drill out the middle and upper parts of lateral accretion bodies to increase the supply unit and enhance oil recovery. Key words: monosandstone body, point bar, thin interbed, lateral accretion body, lateral accretion interbed, configuration, residual oil, dense well pattern

Airborne LiDAR bathymetry (ALB) has shown great potential in shallow water and coastal mapping. However, due to the variability of the waveforms, it is hard to detect the signals from the received waveforms with a single algorithm. This study proposed a depth-adaptive waveform decomposition method to fit the waveforms of different depths with different models. In the proposed method, waveforms are divided into two categories based on the water depth, labeled as “shallow water (SW)” and “deep water (DW)”. An empirical waveform model (EW) based on the calibration waveform is constructed for SW waveform decomposition which is more suitable than classical models, and an exponential function with second-order polynomial model (EFSP) is proposed for DW waveform decomposition which performs better than the quadrilateral model. In solving the model’s parameters, a trust region algorithm is introduced to improve the probability of convergence. The proposed method is tested on two field datasets and two simulated datasets to assess the accuracy of the water surface detected in the shallow water and water bottom detected in the deep water. The experimental results show that, compared with the traditional methods, the proposed method performs best, with a high signal detection rate (99.11% in shallow water and 74.64% in deep water), low RMSE (0.09 m for water surface and 0.11 m for water bottom) and wide bathymetric range (0.22 m to 40.49 m).

Porous hollow Ga2O3 nanoparticles were successfully synthesized by a hydrolysis method followed by calcination. The prepared samples were characterized by field emission scanning electron microscope, transmission electron microscope, thermogravimetry and differential scanning calorimetry, UV-vis diffuse reflectance spectra and Raman spectrum. The porous structure of Ga2O3 nanoparticles can enhance the light harvesting efficiency, and provide lots of channels for the diffusion of Cr(VI) and Cr(III). Photocatalytic reduction of Cr(VI), with different initial pH and degradation of several organic substrates by porous hollow Ga2O3 nanoparticles in single system and binary system, were investigated in detail. The reduction rate of Cr(VI) in the binary pollutant system is markedly faster than that in the single Cr(VI) system, because Cr(VI) mainly acts as photogenerated electron acceptor. In addition, the type and concentration of organic substrates have an important role in the photocatalytic reduction of Cr(VI).