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The singular value decomposition order determination method used in nonlinear subspace identification may encounter challenges due to the noise in signals, resulting in the omission of modes or the occurrence of spurious mode. Additionally, the lack of an iterative process in the nonlinear subspace identification, which primarily relies on matrix operations, will lead to suboptimal solutions. To address these challenges, an improved framework for nonlinear subspaces identification is proposed in this paper. False modes are eliminated through data preprocessing and modal stability criteria, followed by the clustering of stable modes using the Density-Based Spatial Clustering of Applications with Noise algorithm for automatic order determination; Simultaneously, an iterative optimization approach based on response prediction error minimization is introduced to enhance the accuracy of the state-space model estimation results. The effectiveness of proposed method is validated through two simulation cases and one experimental verification. The results show that the clustering algorithm effectively distinguishes real modes from false ones and achieves automatic system order determination across various SNR conditions. The iterative optimization process notably enhances state-space model estimation accuracy. Compared to original nonlinear subspace identification, proposed method significantly improves identification accuracy. [ABSTRACT FROM AUTHOR]

Hybrid quantum-classical approaches offer potential solutions to quantum chemistry problems, yet they often manifest as constrained optimization problems. Here, we explore the interconnection between constrained optimization and generalized eigenvalue problems through the Unitary Coupled Cluster (UCC) excitation generators. Inspired by the generator coordinate method, we employ these UCC excitation generators to construct non-orthogonal, overcomplete many-body bases, projecting the system Hamiltonian into an effective Hamiltonian, which bypasses issues such as barren plateaus that heuristic numerical minimizers often encountered in standard variational quantum eigensolver (VQE). Diverging from conventional quantum subspace expansion methods, we introduce an adaptive scheme that robustly constructs the many-body basis sets from a pool of the UCC excitation generators. This scheme supports the development of a hierarchical ADAPT quantum-classical strategy, enabling a balanced interplay between subspace expansion and ansatz optimization to address complex, strongly correlated quantum chemical systems cost-effectively, setting the stage for more advanced quantum simulations in chemistry. [ABSTRACT FROM AUTHOR]

The Gulong Sag in the northern Songliao Basin, China, possesses abundant shale oil resources and represents a highly prospective area for shale oil exploration. However, the Qingshankou formation shale oil reservoir within this region exhibits characteristics such as thin longitudinal thickness, pronounced horizontal heterogeneity, limited frequency range, and significant anisotropy that pose difficulties in accurately predicting the "sweet spot" of shale oil within the target interval. The azimuthal anisotropy characteristics of the target layer in the Qingshankou formation are analyzed in this manuscript, utilizing wide-azimuth and small bin seismic data from the Y3 research area. Considering the limitations of existing methods for fitting elliptical velocities in azimuthal anisotropy correction, the influence of azimuthal anisotropy time difference on the non-in-phase superposition of seismic in-phase axis is eliminated by employing a non-rigid dynamic matching method, thereby enhancing the resolution and imaging accuracy of seismic data. The azimuth anisotropy correction effectively broadens the frequency range of the stack profile by 7 Hz, thereby enhancing the reliability of data for shale oil reservoir prediction in the study area. [ABSTRACT FROM AUTHOR]

Introduction: When switching from premixed insulin to insulin degludec/aspart (IDegAsp), IDegAsp usually starts at the same dose as the premixed insulin according to limited clinical experience or at a dose according to clinician discretion. The dose of insulin degludec used in the real world after switching has been poorly investigated. Methods: A retrospective analysis was conducted on patients with type 2 diabetes who switched from premixed insulin to IDegAsp from October 2016 to December 2023. Repeated measures analysis of variance was used to compare changes in insulin dose, glycated hemoglobin (HbA1c), fasting blood glucose (FBG), and postprandial blood glucose (PBG) before and after switching. Results: Sixty-six patients with prior low-ratio premixed insulin and 22 with prior mid-ratio premixed insulin were included. Among the low-ratio insulin users, the total daily dose of insulin degludec (IDeg) decreased by 21.43% and 19.05% at 3 and 6 months, respectively, after switching, compared with prior basal insulin dose (both p < 0.001). Conversely, among mid-ratio insulin users, the IDeg daily dose increased by 10.71% and 32.14% at 3 and 6 months, respectively, after switching, compared with prior basal insulin dose (both p < 0.001). In all patients, HbA1c levels decreased by 0.70%, FBG decreased by 1.00 mmol/l, and PBG decreased by 1.61 mmol/l after 6 months of switching (all p < 0.05); the total daily insulin dose and injection frequency significantly decreased after switching (both p < 0.05); age and disease duration did not affect IDegAsp effects on HbA1c reduction. Conclusions: In the setting of transition to IDegAsp from premixed insulin, the dose of basal insulin in the premixed formulation can be a valuable reference for adjusting insulin degludec dose. IDegAsp is superior to premixed insulin in blood glucose control with reduced total daily dose and injection frequency. IDegAsp could be the best choice for the management of diabetes in elderly patients. [ABSTRACT FROM AUTHOR]

Oocyte meiotic maturation failure and chromosome abnormality is one of the main causes of infertility, abortion, and diseases. The mono-orientation of sister chromatids during the first meiosis is important for ensuring accurate chromosome segregation in oocytes. MEIKIN is a germ cell-specific protein that can regulate the mono-orientation of sister chromatids and the protection of the centromeric cohesin complex during meiosis I. Here we found that MEIKIN is a maternal protein that was highly expressed in mouse oocytes before the metaphase I (MI) stage, but became degraded by the MII stage and dramatically reduced after fertilization. Strikingly, MEIKIN underwent phosphorylation modification after germinal vesicle breakdown (GVBD), indicating its possible function in subsequent cellular event regulation. We further showed that MEIKIN phosphorylation was mediated by PLK1 at its carboxyl terminal region and its C-terminus was its key functional domain. To clarify the biological significance of meikin degradation during later stages of oocyte maturation, exogenous expression of MEIKIN was employed, which showed that suppression of MEIKIN degradation resulted in chromosome misalignment, cyclin B1 and Securin degradation failure, and MI arrest through a spindle assembly checkpoint (SAC)-independent mechanism. Exogenous expression of MEIKIN also inhibited metaphase II (MII) exit and early embryo development. These results indicate that proper MEIKIN expression level and its C-terminal phosphorylation by PLK1 are critical for regulating the metaphase–anaphase transition in meiotic oocyte. The findings of this study are important for understanding the regulation of chromosome segregation and the prevention meiotic abnormality. [ABSTRACT FROM AUTHOR]

Dental restorative resin composites are widely used to repair tooth decay owing to attractive esthetics, adequate mechanical properties and minimally invasive tooth structure preparations. Nevertheless, dental restorative resin composites still face challenges because of their relatively high failure rate and short lifespan caused by secondary caries and bulk fracture. Thus, attempts have been carried out to explore a new generation of dental restorative resin composites with antibacterial, remineralizing, and self-healing capabilities to inhibit bacteria and lengthen the lifetime of the restorations. Such novel restorative composites can inhibit bacterial activity, reduce acid production, promote mineral regeneration and present a renewable advantage to achieve a higher performance, which are inspiring and provide support for further basic and clinical research. In this review, antibacterial dental restorative resin composites are first introduced, followed by remineralizing, self-healing, and multifunctional dental resin composites with two or more of the functions mentioned above. Meanwhile, we explain the mechanism of the corresponding dental restorative resin composites and describe their characteristics. Finally, we conclude and put forward prospects. This review will attract both researchers and clinicians in this field and help to provide innovative ideas to design new restorative resin composites for biomedical applications. [ABSTRACT FROM AUTHOR]

To address the need for automated sorting of synthetic diamonds based on quality in manufacturing enterprises, this study developed a dedicated dataset and an enhanced YOLOv8n model for synthetic diamonds detection and quality evaluation, named YOLOv8n-adamas. We redesigned the backbone network to improve feature extraction capabilities and introduced a dynamic detection head based on attention mechanisms to further enhance model performance. Experimental results show that on synthetic diamonds dataset, YOLOv8n-adamas achieved a 4.0% improvement in precision (P), a 2.7% increase in recall (R), and improvements of 1.5% and 1.3% in mean average precisions at 50% and 95% Intersection over Union (IoU) thresholds (mAP50 and mAP95) compared to YOLOv8. Furthermore, YOLOv8n-adamas also outperforms other commonly used, high-performing models in various metrics on this dataset, offering effective technical support for the automated quality-based sorting of synthetic diamonds. [ABSTRACT FROM AUTHOR]

To improve the accuracy of photogrammetric joint roughness coefficient (JRC) estimation, this study proposes two optimization models based on ground sample distance (GSD), point density, and the root mean square error (RMSE) of checkpoints. First, an algorithm that automatically generates spatial positions for equipment based on the convergence strategy was developed, using principles of Structure from Motion and Multi-View Stereo (SfM-MVS) and the shooting parameter selection algorithm (SPSA). Second, a portable positioning plate containing ground control points and checkpoints was designed based on optical principles, and a moving camera capture strategy guided by SPSA was proposed. Combining SPSA, portable positioning plate, and moving camera capture strategy, a photogrammetric experiment for small-scale rock samples in the field was conducted, collecting 48 datasets with different shooting parameters. Subsequently, a dataset incorporating GSD, point density, RMSE, and three JRC estimation metrics was established, revealing their correlations and sensitivities. Using seven machine learning algorithms, optimization models for photogrammetric JRC accuracy were developed, with Linear Multidimensional Regression and Gaussian Process Regression models improving JRC accuracy by an average of 85.73%. Finally, the applicability and limitations of the newly proposed method were further discussed. [ABSTRACT FROM AUTHOR]

With countries proposing the goal of carbon neutrality, the clean transformation of energy structure has become a hot and trendy issue internationally. Renewable energy generation will account for the main proportion, but it also leads to the problem of unstable electricity supply. At present, large-scale energy storage technology is not yet mature. Improving the flexibility of coal-fired power plants to suppress the instability of renewable energy generation is a feasible path. Thermal energy storage is a feasible technology to improve the flexibility of coal-fired power plants. This article provides a review of the research on the flexibility transformation of coal-fired power plants based on heat storage technology, mainly including medium to low-temperature heat storage based on hot water tanks and high-temperature heat storage based on molten salt. The current technical difficulties are summarized, and future development prospects are presented. The combination of the thermal energy storage system and coal-fired power generation system is the foundation, and the control of the inclined temperature layer and the selection and development of molten salt are key issues. The authors hope that the research in this article can provide a reference for the flexibility transformation research of coal-fired power plants, and promote the application of heat storage foundation in specific coal-fired power plant transformation projects. [ABSTRACT FROM AUTHOR]

The canonical G406R mutation that increases Ca2+ influx through the CACNA1C-encoded CaV1.2 Ca2+ channel underlies the multisystem disorder Timothy syndrome (TS), characterized by life-threatening arrhythmias. Severe episodic hypoglycemia is among the poorly characterized non-cardiac TS pathologies. While hypothesized from increased Ca2+ influx in pancreatic beta cells and consequent hyperinsulinism, this hypoglycemia mechanism is undemonstrated because of limited clinical data and lack of animal models. We generated a CaV1.2 G406R knockin mouse model that recapitulates key TS features, including hypoglycemia. Unexpectedly, these mice do not show hyperactive beta cells or hyperinsulinism in the setting of normal intrinsic beta cell function, suggesting dysregulated glucose homeostasis. Patient data confirm the absence of hyperinsulinism. We discover multiple alternative contributors, including perturbed counterregulatory hormone responses with defects in glucagon secretion and abnormal hypothalamic control of glucose homeostasis. These data provide new insights into contributions of CaV1.2 channels and reveal integrated consequences of the mutant channels driving life-threatening events in TS. Gain of function mutant CaV1.2 channels drive life-threatening hypoglycemia in the multisystem disorder Timothy syndrome, but the underlying mechanisms are unknown. Here the authors show the mutant channels have adverse effects on counterregulatory hormones and CNS control of glucose homeostasis. [ABSTRACT FROM AUTHOR]

Monoatomic-layered carbon materials, such as graphene1 and amorphous monolayer carbon2,3, have stimulated intense fundamental and applied research owing to their unprecedented physical properties and a wide range of promising applications4,5. So far, such materials have mainly been produced by chemical vapour deposition, which typically requires stringent reaction conditions compared to solution-phase synthesis. Herein, we demonstrate the solution preparation of free-standing nitrogen-doped amorphous monolayer carbon with mixed five-, six- and seven-membered (5-6-7-membered) rings through the polymerization of pyrrole within the confined interlayer cavity of a removable layered-double-hydroxide template. Structural characterizations and first-principles calculations suggest that the nitrogen-doped amorphous monolayer carbon was formed by radical polymerization of pyrrole at the α, β and N sites subjected to confinement of the reaction space, which enables bond rearrangements through the Stone–Wales transformation. The spatial confinement inhibits the C–C bond rotation and chain entanglement during polymerization, resulting in an atom-thick continuous amorphous layer with an in-plane π-conjugation electronic structure. The spatially confined radical polymerization using solid templates and ion exchange strategy demonstrates potential as a universal synthesis approach for obtaining two-dimensional covalent networks, as exemplified by the successful synthesis of monolayers of polythiophene and polycarbazole.Free-standing nitrogen-doped amorphous monolayer carbon consisting of mixed five-, six- and seven-membered rings was prepared through the polymerization of pyrrole within the confined interlayer cavity of a removable layered-double-hydroxide template. [ABSTRACT FROM AUTHOR]

The distribution of data often evolves over time, necessitating classifiers to adjust in order to maintain optimal classification accuracy. This phenomenon, termed "concept drift", poses a significant challenge. Detectors specifically designed for identifying concept drift are typically integrated to bolster classifier performance. In this study, we introduce two innovative methodologies designed to address the prevalent issues of high miss detection, excessive false alarms, and prolonged detection latencies encountered in many contemporary concept drift detection algorithms. The first approach, termed the Hybrid Weighting-based Concept Drift Detection Method (HW_DDM), incorporates both linear and exponential weighting for long and short windows, respectively, within a composite window model. Subsequently, concept drift is detected by calculating the weighted mean value within the window, leveraging the Hoeffding and McDirmid inequality thresholds. The second strategy, named the Dynamic Weighting-based Hoeffding Drift Detection Method (DW_HDDM), employs a mechanism that dynamically adjusts the Hoeffding threshold and dynamically weights classification prediction outcomes, thereby catering to the drift and augmenting detection efficacy. Comparative evaluations using both synthetic and real-world datasets against leading-edge algorithms are presented. The empirical results underscore that HW_DDM exhibits the lowest false detection rate with negligible miss detections on synthetic datasets. In contrast, DW_HDDM shines with minimal detection delay, reduced miss detection rates, and a diminished false detection rate, demonstrating superior classification accuracy on real-world datasets when pitted against benchmark algorithms. [ABSTRACT FROM AUTHOR]

Key message: Transcription factor PpMYB5 promotes lignin synthesis by directly binding to the Pp4CL1/Pp4CL2 promoter and affecting their expression, which may be related to nectarine russeting formation. Nectarine russeting is usually considered to be a non-invasive physiological disease that usually occurs on late-maturing cultivars and seriously affects their appearance quality and commercial value. The cause of nectarine fruit rust is currently unknown. In this study, we compared two flat nectarine cultivars, ‘zhongyoupanweidi’ (HD; russeting-free cultivar) and ‘zhongyoupanweihou’ (TH; russeting-prone cultivar), with respect to nectarine russeting by means of microscopy, transcriptomics, and hormone analysis. Compared to HD fruits, TH fruits had a broken cuticle, missing wax layer, and heavy lignin deposition. RNA sequencing (RNA-seq) revealed significant alternations in the expression of genes related to lignin synthesis. Moreover, structure genes Pp4CL1 and Pp4CL2, MYB transcription factor (TF) gene PpMYB5 were identified through weighted gene co-expression network analysis (WGCNA). Molecular experiments and transgenic evidence suggested that PpMYB5 regulates Pp4CL1/Pp4CL2 expression to promote lignin synthesis. Overall, in addition to providing new insights into the formation of mechanisms for nectarine russeting, our study also establishes a foundation for nectarine russeting prevention. [ABSTRACT FROM AUTHOR]

Purpose: We sought to develop an effective combined model for predicting the survival of patients with diffuse large B-cell lymphoma (DLBCL) based on the multimodal PET-CT deep features radiomics signature (DFR-signature). Methods: 369 DLBCL patients from two medical centers were included in this study. Their PET and CT images were fused to construct the multimodal PET-CT images using a deep learning fusion network. Then the deep features were extracted from those fused PET-CT images, and the DFR-signature was constructed through an Automated machine learning (AutoML) model. Combined with clinical indexes from the Cox regression analysis, we constructed a combined model to predict the progression-free survival (PFS) and the overall survival (OS) of patients. In addition, the combined model was evaluated in the concordance index (C-index) and the time-dependent area under the ROC curve (tdAUC). Results: A total of 1000 deep features were extracted to build a DFR-signature. Besides the DFR-signature, the combined model integrating metabolic and clinical factors performed best in terms of PFS and OS. For PFS, the C-indices are 0.784 and 0.739 in the training cohort and internal validation cohort, respectively. For OS, the C-indices are 0.831 and 0.782 in the training cohort and internal validation cohort. Conclusions: DFR-signature constructed from multimodal images improved the classification accuracy of prognosis for DLBCL patients. Moreover, the constructed DFR-signature combined with NCCN-IPI exhibited excellent potential for risk stratification of DLBCL patients. [ABSTRACT FROM AUTHOR]

Treatment options for patients with esophageal squamous cell carcinoma (ESCC) often result in poor prognosis and declining health-related quality of life. Screening FDA-approved drugs for cancer chemoprevention is a promising and cost-efficient strategy. Here, we found that dronedarone, an antiarrhythmic drug, could inhibit the proliferation of ESCC cells. Moreover, we conducted phosphorylomics analysis to investigate the mechanism of dronedarone-treated ESCC cells. Through computational docking models and pull-down assays, we demonstrated that dronedarone could directly bind to CDK4 and CDK6 kinases. We also proved that dronedarone effectively inhibited ESCC proliferation by targeting CDK4/CDK6 and blocking the G0/G1 phase through RB1 phosphorylation inhibition by in vitro kinase assays and cell cycle assays. Subsequently, we found that knocking out CDK4 and CDK6 decreased the susceptibility of ESCC cells to dronedarone. Furthermore, dronedarone suppressed the growth of ESCC in patient-derived tumor xenograft models in vivo. Thus, our study demonstrated that dronedarone could be repurposed as a CDK4/6 inhibitor for ESCC chemoprevention. [ABSTRACT FROM AUTHOR]

Cross-modal retrieval aims to retrieve related items in one modality using a query from another modality. As the foundational and key challenge of it, image-text retrieval has garnered significant research interest from scholars. In recent years, hashing techniques have gained widespread interest for large-scale dataset retrieval due to their minimal storage requirements and rapid query processing capabilities. However, existing hashing approaches either learn unified representations for both modalities or specific representations within each modality. The former approach lacks modality-specific information, while the latter does not consider the relationships between image-text pairs across various modalities. Therefore, we propose an innovative supervised hashing method that leverages intra-modality and inter-modality matrix factorization. This method integrates semantic labels into the hash code learning process, aiming to understand both inter-modality and intra-modality relationships within a unified framework for diverse data types. The objective is to preserve inter-modal complementarity and intra-modal consistency in multimodal data. Our approach involves: (1) mapping data from various modalities into a shared latent semantic space through inter-modality matrix factorization to derive unified hash codes, and (2) mapping data from each modality into modality-specific latent semantic spaces via intra-modality matrix factorization to obtain modality-specific hash codes. These are subsequently merged to construct the final hash codes. Experimental results demonstrate that our approach surpasses several state-of-the-art cross-modal image-text retrieval hashing methods. Additionally, ablation studies further validate the effectiveness of each component within our model. [ABSTRACT FROM AUTHOR]

Chaohusaurus is an early ichthyosauriform represented by three species known from the Early Triassic of Chaohu, Anhui Province, China, with a fourth species—Chaohusaurus zhangjiawanensis—known from the Nanzhang-Yuan'an region of Hubei Province. In contrast to the Chaohusaurus species from Chaohu, Chaohusaurus zhangjiawanensis remains poorly known, hindering our understanding of early ichthyosauriform evolution. Here, we report a new specimen of Chaohusaurus zhangjiawanensis, which provides new information on its dentition. The new specimen confirms that Chaohusaurus zhangjiawanensis had heterodont dentition consisting of pointed anterior teeth and robust, rounded posterior teeth, indicating a generalist diet. The posterior teeth of Chaohusaurus zhangjiawanensis are more robust (broader and larger) than the posterior dentition of Chaohusaurus chaoxianensis and Chaohusaurus brevifemoralis from Chaohu. This suggests differences in hard-shelled prey preference between species of Chaohusaurus from Chaohu and Nanzhang-Yuan'an, with Chaohusaurus zhangjiawanensis likely capable of feeding on harder and larger prey than Chaohusaurus brevifemoralis and Chaohusaurus chaoxianensis. In turn, this probably reflects differences in durophagous prey availability between the shallow-marine palaeoecosystem of Nanzhang-Yuan'an and the deeper, slope-basin palaeoecosystem of Chaohu. The posterior dentition and forefin of Chaohusaurus zhangjiawanensis are strikingly similar to those of Chaohusaurus geishanensis, the rarest species of Chaohusaurus from the Chaohu fauna. The scarcity of Chaohusaurus geishanensis in the Chaohu fauna, and its morphological similarity to Chaohusaurus zhangjiawanensis, possibly indicate that Chaohusaurus geishanensis was closely related with Chaohusaurus zhangjiawanensis and that it was also a shallow-marine species that was not a typical component of the Chaohu fauna. It probably occasionally wandered out into the deeper waters of Chaohu from a nearby coastal environment. [ABSTRACT FROM AUTHOR]

The vertical tube indirect evaporative cooler (VTIEC) could be preferred one to air-conditioning buildings if its water film quality could be improved. This paper proposed a new VTIEC with inner grooved tubes to improve the water film quality and obtained its performance by experimental method. The results indicated that inner grooved tubes have positive effects on the water film and performance of the new VTIEC. A low working air enthalpy at inlet can significantly improve the total cooling capacity from evaporation, and then VTIEC performance. In this paper conditions, the velocity ratio of 1.52 of the produced air to the working air is better one condition compared to 1.01 and 2.28. The produced air velocity is positive effect on the cooling capacity per unit area until the produced air velocity of about 4.05 m/s and is negative effect on COP. The maximum unit cooling capacity, wet-bulb efficiency and COP are 307.74W/m2, 81.7% and 44.97.The results will promote the application of the new VTIEC. [ABSTRACT FROM AUTHOR]

The facile reconfiguration of phases plays a pivotal role in enhancing the electrocatalytic production of H2 through heterostructure formation. While chemical methods have been explored extensively for this purpose, plasma-based techniques offer a promising avenue for achieving heterostructured nano-frameworks. However, the conventional plasma approach introduces complexities, leading to a multi-step fabrication process and challenges in precisely controlling partial surface structure modulation due to the intricate interaction environment. In our pursuit of heterostructures with optimized oxygen evolution reaction (OER) behavior, we have designed a facile auxiliary insulator-confined plasma system to directly attain a Ni3N–NiO heterostructure (hNiNO). By meticulously controlling the surface heating process during plasma processing, such approach allows for the streamlined fabrication of hNiNO nano-frameworks. The resulting nano-framework exhibits outstanding catalytic performance, as evidenced by its overpotential of 320 mV at a current density of 10 mA·cm−2, in an alkaline environment. This stands in stark contrast to the performance of NiO-covered Ni3N fabricated using the conventional plasma method (sNiNO). Operando plasma diagnostics, coupled with numerical simulations, further substantiates the influence of surface heating due to auxiliary insulator confinement of the substrate on typical plasma parameters and the formation of the Ni3N–NiO nanostructure, highlighting the pivotal role of controlled surface temperature in creating a high-performance heterostructured electrocatalyst. [ABSTRACT FROM AUTHOR]

Flawed rocks subjected to hydrostatic confinements in deep underground engineering are vulnerable to dynamic disturbances. Understanding the dynamic mechanical responses and fracture characteristics of flawed rocks is crucial for the design and construction safety of deep underground structures. In this research, using a triaxial split Hopkinson pressure bar device, the dynamic triaxial compression tests are performed on multi-flawed sandstone specimens containing different flaw angles and distances. The experimental results revealed the influence of the flaw angle, flaw distance, and hydrostatic confining pressure on the dynamic strength characteristics, deformation properties, failure modes and fracture mechanisms of rocks. Both the dynamic strength and elastic modulus increase with rising hydrostatic confining pressure, while the dynamic strength depicts a positive correlation with strain rate. The dynamic strength fluctuates zigzag with the flaw angle, and decreases first and then increases slightly with flaw distances. Specimens with different flaw angles all demonstrate conjugated "X" shape failure, while the single shear band failure is shown for certain flaw distances. Seven crack categories are identified based on the basis of different trajectories and the fracture characteristics of new cracks are analyzed in multiscale perspective. The scanning electron microscope observation indicates numerous shear traces on the fracture surface while brittle fracture features are relatively few. Furthermore, the fracture surface roughness is quantified by the 3D scanning technique and fractal dimension. Rocks containing higher flaw distance or under higher hydrostatic confining pressure are featured by lower fractal dimension and smoother fracture surfaces. Highlights: Dynamic impacting tests on hydrostatically confined rocks containing multiple flaws were conducted. The influence of hydrostatic confining pressure and flaw configuration on the dynamic strength, deformation properties, and failure characteristics of rocks were investigated. The fracture mechanism of flawed rocks under hydrostatic confinements by the scanning electron microscope and 3D scanning technique was analyzed. [ABSTRACT FROM AUTHOR]

Four species of shellfish, blue mussel (Mytilus galloprovincialis), Pacific abalone (Haliotis discus hannai), zhikong scallops (Chlamys farreri), and Pacific oyster (Crassostrea gigas), were exposed to decoupled carbonate system variables to investigate the impacts of different seawater carbonate parameters on the CO2 excretion process of mariculture shellfish. Six experimental groups with two levels of seawater pH (pH 8.1 and pH 7.7) and three levels of total alkalinity (TA = 1000, 2300, and 3600 μmol/kg, respectively) were established, while pH 8.1 and TA = 2300 μmol/kg was taken as control. Results showed that the CO2 excretion rates of these tested shellfish were significantly affected by the change in carbonate chemistry (P < 0.05). At the same TA level, animals incubated in the acidified group (pH 7.7) had a lower CO2 excretion rate than those in the control group (pH 8.1). In comparison, at the same pH level, the CO2 excretion rate increased when seawater TA level was elevated. No significant correlation between the CO2 excretion rate and seawater pCO2 levels (P > 0.05) was found; however, a significant correlation (P < 0.05) between CO2 excretion rate and TA-DIC (the difference between total alkalinity and dissolved inorganic carbon) was observed. Blue mussel has a significantly higher CO2 excretion rate than the other three species in the CO2 excretions per unit mass of soft parts, with no significant difference observed among these three species. However, in terms of CO2 excretion rate per unit mass of gills, abalone has the highest CO2 excretion rate, while significant differences were found between each species. Our studies indicate that the CO2 buffering capacity impacts the CO2 excretion rate of four shellfish species largely independent of pCO2. Since CO2 excretion is related to acid–base balancing, the results imply that the effects of other carbonate parameters, particularly the CO2 buffering capacity, should be studied to fully understand the mechanism of how acidification affects shellfish. Besides, the species difference in gill to soft parts proportion may contribute to the species difference in responding to ocean acidification. [ABSTRACT FROM AUTHOR]

In the last decade, the explosive surge in multi-modal data has propelled cross-modal retrieval into the forefront of information retrieval research. Exceptional cross-modal retrieval algorithms are crucial for meeting user requirements effectively and offering invaluable support for subsequent tasks, including cross-modal recommendations, multi-modal content generation, and so forth. Previous methods for cross-modal retrieval typically search for a single common subspace, neglecting the possibility of multiple common subspaces that may mutually reinforce each other in reality, thereby resulting in the poor performance of cross-modal retrieval. To address this issue, we propose a Federated Supervised Cross-Modal Retrieval approach (FedSCMR), which leverages competition to learn the optimal common subspace, and adaptively aggregates the common subspaces of multiple clients for dynamic global aggregation. To reduce the differences between modalities, FedSCMR minimizes the semantic discrimination and consistency in the common subspace, in addition to modeling semantic discrimination in the label space. Additionally, it minimizes modal discrimination and semantic invariance across common subspaces to strengthen cross-subspace constraints and promote learning of the optimal common subspace. In the aggregation stage for federated learning, we design an adaptive model aggregation scheme that can dynamically and collaboratively evaluate the model contribution based on data volume, data category, model loss, and mean average precision, to adaptively aggregate multi-party common subspaces. Experimental results on two publicly available datasets demonstrate that our proposed FedSCMR surpasses state-of-the-art cross-modal retrieval methods. [ABSTRACT FROM AUTHOR]

The effect of morphological traits on the body weight of Pacific abalone (Haliotis discus hannai) was investigated by correlation analysis, path analysis and multiple regression analysis. We evaluated the relationships between shell length (SL), shell width (SW) and body weight (BW, the total wet weight) in two different strains of Pacific abalone. One strain is 'Huangxun No. 1', which is from the fifth generation of a novel strain that has experienced higher shell growth rate selection, and the other strain is a first-generation descendant of wild Pacific abalone. The results of the correlation analysis showed that both SL and SW were significantly (p < 0.05) positively correlated with BW in both strains. Path analysis showed that SL significantly affected the BW of the 'Huangxun No. 1' strain, while SW significantly affected BW at only one of the four ages tested. Both SL and SW significantly affected the BW of the wild strain at the four ages tested. The determination coefficients of SL on BW in the 'Huangxun No. 1' strain at 3, 6, 13 and 19 months of age were 0.835, 0.141, 0.834 and 0.708, respectively, while those in the wild strain were 0.476, 0.237, 0.344 and 0.141, respectively. In addition, the SL/SW in the 'Huangxun No. 1' strain ranged from 1.453 to 1.509, while that of the wild-type strain ranged from 1.389 to 1.497. The allometric growth analyses showed positive allometric growth for 'Huangxun No. 1' from 3 to 19 months of age, while the wild strain did not show such growth. These results suggest that multiple generations of selection for a higher SL growth rate may have led to variation in shell morphological traits and that shell width may be a less important factor in determining body weight in the 'Huangxun No. 1' strain, while shell length can be used as an effective morphological trait for selective breeding to increase body weight. [ABSTRACT FROM AUTHOR]

RNA N6-methyladenosine (m6A), as the most abundant modification of messenger RNA, can modulate insect behaviors, but its specific roles in aggregation behaviors remain unexplored. Here, we conducted a comprehensive molecular and physiological characterization of the individual components of the methyltransferase and demethylase in the migratory locust Locusta migratoria. Our results demonstrated that METTL3, METTL14 and ALKBH5 were dominantly expressed in the brain and exhibited remarkable responses to crowding or isolation. The individual knockdown of methyltransferases (i.e., METTL3 and METTL14) promoted locust movement and conspecific attraction, whereas ALKBH5 knockdown induced a behavioral shift toward the solitary phase. Furthermore, global transcriptome profiles revealed that m6A modification could regulate the orchestration of gene expression to fine tune the behavioral aggregation of locusts. In summary, our in vivo characterization of the m6A functions in migratory locusts clearly demonstrated the crucial roles of the m6A pathway in effectively modulating aggregation behaviors. [ABSTRACT FROM AUTHOR]

We propose a scheme to generate robust bipartite entanglement, genuine tripartite entanglement, and one-way steering in a hybrid cavity electro-optomechanical system with the help of a squeezed vacuum field. The system consists of an optical cavity, a mechanical resonator formed by a thin silicon nitride membrane, and two superconducting microwave circuits. The mechanical resonator is coupled to the optical cavity and two superconducting circuits simultaneously. We find there is steady-state entanglement between different modes and genuine tripartite entanglement among the cavity mode and two microwave modes which are robust against the thermal fluctuations of the mechanical mode. In addition, the robust one-way steering between two microwave modes can be generated by selecting appropriate squeezing parameter. Our scheme may have potential applications in quantum information processing and communication. [ABSTRACT FROM AUTHOR]

In the computation of dynamic response sensitivity for rotor-bearing systems using the multicomplex variable derivation method, the presence of nonlinearity, particularly "fractional power" nonlinearity, in the forces generated at the supports may introduce rounding errors, potentially destabilizing the sensitivity calculation results. To address this issue, this paper proposes an enhanced method for sensitivity calculation using multicomplex variable derivation method. The approach leverages De Moivre's theorem to conduct recursive operations on multicomplex numbers, thereby circumventing the rounding errors associated with the "fractional power" nonlinearity. This method facilitates the simultaneous calculation of sensitivity for each order and hybrid sensitivity. Subsequently, the viability of the proposed method is substantiated through a nonlinear single disk rotor system and a simulated gas generator rotor system. The results demonstrate that the proposed method maintains high accuracy and stability in dynamic response sensitivity computations even in the presence of "fractional power" nonlinearity. [ABSTRACT FROM AUTHOR]

Numerical simulation is an important method for verifying the quantum circuits used to simulate low-energy nuclear states. However, real-world applications of quantum computing for nuclear theory often generate deep quantum circuits that place demanding memory and processing requirements on conventional simulation methods. Here, we present advances in high-performance numerical simulations of deep quantum circuits to efficiently verify the accuracy of low-energy nuclear physics applications. Our approach employs novel methods for accelerating the numerical simulation including management of simulated mid-circuit measurements to verify projection based state preparation circuits. We test these methods across a variety of high-performance computing systems and our results show that circuits up to 21 qubits and more than 115,000,000 gates can be efficiently simulated. [ABSTRACT FROM AUTHOR]

Cross-Level High-Utility Itemsets Mining (CLHUIM) aims to discover interesting relationships between hierarchy levels by introducing the taxonomy of items. To tackle this issue of the current CLHUIM algorithms encountering a challenge in dealing with large search spaces, researchers have proposed the concept of mining Top-K cross-level high-utility itemsets(CLHUIs). However, the results obtained by these methods often contain redundant itemsets with significant differences in hierarchy levels, and a large proportion of itemsets with higher abstraction levels, making it neglect some detailed information and unable to provide information of itemsets within the specified hierarchy range. Additionally, they are unable to handle dynamic transactional data. To address the aforementioned problems, this paper proposes Top-K Constrained Cross-Level High-Utility Itemsets Mining (TKCCLHM) algorithm to efficiently mine Top-K itemsets across different hierarchy levels over data streams. Firstly, a new hierarchical level concept is introduced to control the abstraction level of the introduced items, and Top-K itemsets are mined within a specific hierarchy range based on this concept. Secondly, a sliding window-based data structure called Sliding Window-based Utility Projection List (SUPL) is designed, which combined with transaction projection techniques to mine CLHUIs efficiently. Lastly, a Batch and Utility Hash Table (BUHT) structure capable of storing batch and (generalized) item utility information is proposed, along with a new threshold raising strategy. Extensive experiments on six datasets with taxonomy information demonstrated that the proposed algorithm exhibited significant improvements in runtime and scalability performance compared to the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Hippocampal place cells represent the position of a rodent within an environment. In addition, recent experiments show that the CA1 subfield of a passive observer also represents the position of a conspecific performing a spatial task. However, whether this representation is allocentric, egocentric or mixed is less clear. In this study we investigated the representation of others during free behavior and in a task where female mice learned to follow a conspecific for a reward. We found that most cells represent the position of others relative to self-position (social-vector cells) rather than to the environment, with a prevalence of purely egocentric coding modulated by context and mouse identity. Learning of a pursuit task improved the tuning of social-vector cells, but their number remained invariant. Collectively, our results suggest that the hippocampus flexibly codes the position of others in multiple coordinate systems, albeit favoring the self as a reference point. How the position of conspecifics is represented in the brain is not fully understood. Here authors show that the position of conspecifics is represented relative to self-position in the hippocampus of female mice, which is modulated by context and identity and improved through learning. [ABSTRACT FROM AUTHOR]

In the field of data mining, data stream classification is an important research direction. However, the presence of issues such as multi-class imbalance, concept drift, and variable class imbalance ratio in data streams can greatly impact the performance of classification models, and the high cost of sample labeling has always been a focus of research. To address these problems, an online active learning method for multi-class imbalanced data stream (OALM-MI) is proposed. Firstly, a comprehensive sample weighting method based on cross-entropy and margin values is proposed to weight each incoming sample in the data stream according to its classification difficulty and importance, which aims to enhance the learning ability of the classifier for important samples. Besides, a comprehensive weighting and updating strategy for ensemble classifiers is introduced, which combines mean square error, improved square error, recall, and the weights of the classifiers in the previous sliding window of samples to weight and update the classifiers. Additionally, adaptive window is utilized to detect and handle concept drift, enabling better adaptation to the changes in the data stream during the learning process. Finally, a margin matrix label request strategy based on class imbalance ratio is proposed to assign labels to samples according to their imbalance ratio and classification difficulty, which can provide more learning opportunities for minority class samples and important samples. Comprehensive experiments were conducted on 12 synthetic data streams and six real data streams with seven state-of-the-art algorithms, and the results showed that the OALM-MI algorithm achieved the highest performance in terms of recall, precision, F1-score, Kappa, and G-mean. [ABSTRACT FROM AUTHOR]

Anthropogenic environmental changes may affect community assembly through mediating both deterministic (e.g., competitive exclusion and environmental filtering) and stochastic processes (e.g., birth/death and dispersal/colonization). It is traditionally thought that environmental changes have a larger mediation effect on stochastic processes in structuring soil microbial community than aboveground plant community; however, this hypothesis remains largely untested. Here we report an unexpected pattern that nitrogen (N) deposition has a larger mediation effect on stochastic processes in structuring plant community than soil microbial community (those <2 mm in diameter, including archaea, bacteria, fungi, and protists) in the Eurasian steppe. We performed a ten-year nitrogen deposition experiment in a semiarid grassland ecosystem in Inner Mongolia, manipulating nine rates (0–50 g N m−2 per year) at two frequencies (nitrogen added twice or 12 times per year) under two grassland management strategies (fencing or mowing). We separated the compositional variation of plant and soil microbial communities caused by each treatment into the deterministic and stochastic components with a recently-developed method. As nitrogen addition rate increased, the relative importance of stochastic component of plant community first increased and then decreased, while that of soil microbial community first decreased and then increased. On the whole, the relative importance of stochastic component was significantly larger in plant community (0.552±0.035; mean±standard error) than in microbial community (0.427±0.035). Consistently, the proportion of compositional variation explained by the deterministic soil and community indices was smaller for plant community (0.172–0.186) than microbial community (0.240–0.767). Meanwhile, as nitrogen addition rate increased, the linkage between plant and microbial community composition first became weaker and then became stronger. The larger stochasticity in plant community relative to microbial community assembly suggested that more stochastic strategies (e.g., seeds addition) should be adopted to maintain above- than below-ground biodiversity under the pressure of nitrogen deposition. [ABSTRACT FROM AUTHOR]

In the military field, camouflage assessment has significant study implications. In this research, a region segmentation algorithm based on superpixels and color clustering is proposed to address the shortcomings of the current methods, which solely focus on the human eye perception mechanism. This algorithm incorporates the attention process of human eyes and cognition. Compared to the rectangular regions used by the bulk of existing algorithms, the segmented regions with uneven borders have more visual meanings. The low-level perception feature set used in this study is also used to develop a novel camouflage assessment metric that takes into account both the irregularity of the attention perception region and the human eye's feature focus. Due to the difficulty in obtaining the camouflage dataset, this research creates the Green Tank dataset and the Green Car dataset using different targets and similar backgrounds. We ran tests on these two datasets and compared it with 16 other well-known algorithms to verify the efficacy of the strategy suggested in this study. The experimental findings demonstrate that the approach suggested in this research has produced the best outcome. [ABSTRACT FROM AUTHOR]

Deep vein thrombosis (DVT) is a common complication in patients with lower extremity fractures. Once it occurs, it will seriously affect the quality of life and postoperative recovery of patients. Therefore, early prediction and prevention of DVT can effectively improve the prognosis of patients. This study constructed different machine learning models to explore their effectiveness in predicting DVT. Five prediction models were applied to the study, including Extreme Gradient Boosting (XGBoost) model, Logistic Regression (LR) model, RandomForest (RF) model, Multilayer Perceptron (MLP) model, and Support Vector Machine (SVM) model. Afterwards, the performance of the obtained prediction models was evaluated by area under the curve (AUC), accuracy, sensitivity, specificity, F1 score, and Kappa. The prediction performances of the models based on machine learning are as follows: XGBoost model (AUC = 0.979, accuracy = 0.931), LR model (AUC = 0.821, accuracy = 0.758), RF model (AUC = 0.970, accuracy = 0.921), MLP model (AUC = 0.830, accuracy = 0.756), SVM model (AUC = 0.713, accuracy = 0.661). On our data set, the XGBoost model has the best performance. However, the model still needs external verification research before clinical application. [ABSTRACT FROM AUTHOR]

The first record of abnormal body coloration in Sebastes koreanus Kim and Lee, 1994, from the Yellow Sea of China, was documented based on morphological characteristics and DNA barcoding. The two rockfish specimens were collected from the coastal waters of Qingdao, China, and the whole body and all fins of them were red. Of the two red-colored rockfish, there were tiny deep red spots on each fin, 2 red radial stripes behind and below the eyes and 1 large deep red blotch on the opercula, while the similar stripe and spot patterns are also present in the S. koreanus specimens with normal body coloration. The countable characteristics of the two specimens are in the range of the morphometry of S. koreanus. To further clarify the species identity and taxonomic status of the two specimens, DNA barcode analysis was carried out. The genetic distance between the red-colored rockfish and S. koreanus was 0, and the minimum net genetic distances between the red-colored rockfish and other Sebastes species except for S. koreanus were 3.0%, which exceeds the threshold of species delimitation. The phylogenetic analysis showed that the DNA barcoding sequences of the two red-colored rockfish clustered with the S. koreanus sequences. The above results of DNA barcode analysis also support that the two red-colored rockfish could be identified as the species of S. koreanus. The mechanism of color variation in S. koreanus is desirable for further research and the species could be an ideal model to study the color-driven speciation of the rockfishes. [ABSTRACT FROM AUTHOR]

Metal nanowires show promise in a broad range of applications and can be fabricated via a number of methods, such as vapor–liquid–solid process and template-based electrodeposition. However, the synthesis of Al nanowires (NWs) is still challenging from the stable alumina substrate. In this work, the Ni-catalyzed fabrication of Al NWs has been realized using various Al2O3 substrates. The growth dynamics of Al NWs on Ni/Al2O3 was studied using in situ transmission electron microscopy (TEM). The effect of alumina structures, compositions, and growth temperature were investigated. The growth of Al NWs correlates with the Na addition to the alumina support. Since no eutectic mixture of nickel aluminide was formed, a mechanism of Ni-catalyzed reduction of Al2O3 for Al NWs growth has been proposed instead of the vapor–liquid–solid mechanism. The key insights reported here are not restricted to Ni-catalyzed Al NWs growth but can be extended to understanding the dynamic change and catalytic performance of Ni/Al2O3 under working conditions. [ABSTRACT FROM AUTHOR]

Body-centred cubic refractory multi-principal element alloys (MPEAs), with several refractory metal elements as constituents and featuring a yield strength greater than one gigapascal, are promising materials to meet the demands of aggressive structural applications1–6. Their low-to-no tensile ductility at room temperature, however, limits their processability and scaled-up application7–10. Here we present a HfNbTiVAl10 alloy that shows remarkable tensile ductility (roughly 20%) and ultrahigh yield strength (roughly 1,390 megapascals). Notably, these are among the best synergies compared with other related alloys. Such superb synergies derive from the addition of aluminium to the HfNbTiV alloy, resulting in a negative mixing enthalpy solid solution, which promotes strength and favours the formation of hierarchical chemical fluctuations (HCFs). The HCFs span many length scales, ranging from submicrometre to atomic scale, and create a high density of diffusive boundaries that act as effective barriers for dislocation motion. Consequently, versatile dislocation configurations are sequentially stimulated, enabling the alloy to accommodate plastic deformation while fostering substantial interactions that give rise to two unusual strain-hardening rate upturns. Thus, plastic instability is significantly delayed, which expands the plastic regime as ultralarge tensile ductility. This study provides valuable insights into achieving a synergistic combination of ultrahigh strength and large tensile ductility in MPEAs.A HfNbTiVAl10 alloy shows tensile ductility and ultrahigh yield strength from the addition of aluminium to a HfNbTiV alloy, resulting in a negative mixing enthalpy solid solution, which promotes strength and favours formation of hierarchical chemical fluctuations. [ABSTRACT FROM AUTHOR]

Despite the great progress in unsupervised domain adaptation for semantic segmentation, most previous methods solely consider reducing the inter-domain gap caused by the distribution discrepancy between the source and target domain while not considering the sizeable intra-domain gap among the target domain itself due to the discrepancy among the target data. General intra-domain adaptation methods separate the target data into two splits based on how easily a sample can be segmented, which may not effectively capture the distributions within the target domain. In this paper, based on the observation that there exist diverse styles in the target samples, we propose a style clustering-based unsupervised domain adaptation method to separate the target data into subdomains iteratively. Since the target subdomain labels are unknown, we exploit multi-channel soft labels for adversarial training to close the intra-domain gap among these subdomains. In comparison with general intra-domain adaptation methods, our method can capture the latent distributions within the target data more sufficiently to close the intra-domain gap more effectively. The experiments of unsupervised domain adaptive segmentation tasks on benchmark datasets are conducted and the experimental results show the effectiveness of our method. [ABSTRACT FROM AUTHOR]

With the development of the Internet of Things (IoT), multifunctional integrated wearable smart devices are developing rapidly, but the preparation of fabric-based smart wearables integrated with motion detection, electromagnetic interference (EMI) shielding, electrothermal property, photothermal property and hydrophobicity is still a challenge. Here, nylon and spandex blended knitted fabric (NS) was selected as a flexible substrate, which was surface modified with Ti3C2Tx under ultrasound, followed by microwave-assisted reduction of Ag+ for the deposition of AgNPs, and then finally drop-coated with Ti3C2Tx again to construct a “Ti3C2Tx-AgNPs” bilayer conductive network. The composite fabrics obtained have excellent strain sensing property (GFmax = 9 and strain range = 0–180%), pressure sensing property (GFmax = 0.28KPa−1, pressure range = 0–330KPa), EMI shielding property (SET = 62.6dB), electrothermal property (48.7 °C under infrared light with a light intensity of 100 mW/cm2), photothermal property (61.1 °C under low-voltage drive at 2.5 V) and hydrophobicity (CA = 135°). [ABSTRACT FROM AUTHOR]

Background: Continuous exposure to UVB is the main extrinsic cause of skin photodamage, which is associated with oxidative stress, DNA damage, apoptosis and degradation of collagen. Rapamycin, a mechanistic target inhibitor of rapamycin complex 1 (mTORC1), has been shown to play a crucial role anti-tumor and aging retardation, but its mechanism of action in UVB-induced photodamage still remains unknown. In this study, we investigated the role of rapamycin and Hspb2 (also known as Hsp27) in UVB-induced photodamage in mice. Methods and results: We constructed skin acute photodamage models on the ears of WT and Hspb2 KO mice, respectively, and administered rapamycin treatment. Histological results showed that knockout of the hspb2 exacerbated the skin damage, as evidenced by thickening of the epidermis, breakage and disruption of collagen fibers and reduction in their number, which is reversed by rapamycin treatment. In addition, hspb2 knockout promoted UVB-induced apoptosis and reduced autophagy levels, with a significant increase in p53 levels and Bax/Bcl-2 ratio, a reduction in LC3II/I ratio and an increase in p62 levels in the KO mice compared to those in WT mice after the same dose of UVB irradiation. Rapamycin was also found to inhibit collagen degradation induced by hspb2 knockdown through activation of the TGF-β/Smad signaling pathway. Conclusions: Rapamycin can alleviate skin photodamage from Hspb2 knockout to some extent. It may be a potential therapeutic drug for skin photodamage. In this study, we investigated the role of rapamycin and Hspb2 in UVB-induced photodamage in mice. Histological results showed that knockout of the hspb2 exacerbated the skin damage, as evidenced by thickening of the epidermis, breakage and disruption of collagen fibers and reduction in their number, which is reversed by rapamycin treatment. In addition, hspb2 knockout promoted UVB-induced apoptosis and reduced autophagy levels. Rapamycin was also found to inhibit collagen degradation induced by hspb2 knockdown through activation of the TGF-β/Smad signaling pathway. We conclude that rapamycin and Hspb2 exert a synergistic protective effect in skin photodamage. [ABSTRACT FROM AUTHOR]

Understanding the near-surface structure of the Earth requires accurate prediction of physical properties of the subsurface, such as velocity estimated from tomographic inversion of seismic refraction data. The predicted velocity values are often uncertain due to epistemic uncertainty in the inversion process (i.e., imperfectly known underlying physics) and aleatoric variability in the data (i.e., inherent noise in observations). Although seismic refraction is widely used in near-surface applications, the associated uncertainty is rarely quantified and presented alongside the inverted velocity tomograms. In this study, the effect of epistemic uncertainty due to local variability in the initial model and aleatoric variability due to first-arrival picking error on the velocity prediction uncertainty are investigated. A stochastic framework is implemented based on a statistical approach where multiple realizations of stochastically perturbed initial models and travel time picks are generated and the uncertainty in the predicted velocity models is quantified. The two sources of uncertainty are first studied independently and then the combined effect is investigated. The results show that both sources affect the posterior uncertainty, but the uncertainty in the initial model has a greater effect than picking error on the uncertainty of the posterior velocity model. In addition, joint analysis of both sources of uncertainty shows that the uncertainty in the inverted model depends on predicted velocity values, depths, velocity gradients and ray coverages. [ABSTRACT FROM AUTHOR]

In this paper, a cost-effective and miniaturized instrument is proposed, which is based on a tunable modulated grating Y-branch (MG-Y) laser for rapid temperature measurement using a Fabry-Perot interferometer (FPI) sensor. The FPI sensor with a 1 463-µm cavity length is a short segment of a capillary tube sandwiched by two sections of single-mode fibers (SMFs). This system has a broad tunable range (1 527 nm–1 567 nm) with a wavelength interval of 8 pm and a tuning rate of 100 Hz. Temperature sensing experiments are carried out to investigate the performance of the system by demodulating the absolute cavity length of the FPI sensor using a cross-correlation algorithm. Experimental results show that the sensor can reach the response time as short as 94 ms with the sensitivity of 802 pm/°C. Benefiting from the homemade and integrated essential electrical circuits, the entire system has the small size, low cost, and practical application potential to be used in the harsh environment for rapid temperature measurement. [ABSTRACT FROM AUTHOR]

Published literature considering the association between ambient air pollution and blood pressure is highly inconsistent, which may be explained by the different proportions of susceptible subpopulations. We hypothesized that hypertensive patients are more sensitive to air pollution due to the disruption of neurohumoral system. The study aimed to reveal the association between PM2.5 and its carbon components and blood pressure, and whether this association is modified by hypertension status. We conducted a panel study in Beijing, China. Four repeated measurements were performed from 2016 to 2018. Linear mixed-effects models and generalized additive mixed models were performed to investigate the associations between PM2.5 and its carbon components and blood pressure. Subgroup analyses were performed by hypertension status to reveal potential effect modification. Among hypertensive patients, for every 1 μg/m3 increment of PM2.5, TC, OC, and EC in 1-day to 2-day MA, SBP increased from 0.16 mmHg (95% CI, 0.03 to 0.29) to 6.75 mmHg (95% CI, 2.82 to 10.68), and PP increased from 0.14 mmHg (95% CI, 0.02 to 0.26) to 6.03% (95% CI, 2.46 to 9.59%), but no significant association was observed among non-hypertensive subjects. The p values for the interaction between pollutants and hypertension status in 1-day to 2-day MA were less than 0.05. These findings suggest that hypertensive patients may be more susceptible to the adverse effects of air pollution than non-hypertensive subjects, which might provide guidance to hypertensive patients living in areas with high levels of particle pollution. [ABSTRACT FROM AUTHOR]

Virtual reality (VR) applications could be beneficial for education, training, and treatment. However, VR may induce symptoms of simulator sickness (SS) such as difficulty focusing, difficulty concentrating, or dizziness that could impair autonomic nervous system function, affect mental workload, and worsen interventional outcomes. In the original randomized controlled trial, which explored the effectiveness of using a 360° VR video versus a two-dimensional VR video to learn history taking and physical examination skills, only the former group participants had SS. Therefore, 28 undergraduate medical students who participated in a 360° VR learning module were included in this post hoc study using a repeated measures design. Data of the Simulator Sickness Questionnaire (SSQ), heart rate variability (HRV) analysis, Task Load Index, and Mini-Clinical Evaluation Exercise were retrospectively reviewed and statistically analyzed. Ten (36%) participants had mild SS (total score > 0 and ≤ 20), and 18 (64%) had no SS symptom. Total SSQ score was positively related to the very low frequency (VLF) band power, physical demand subscale, and frustration subscale, and inversely related to physical examination score. Using multilevel modeling, the VLF power mediated the relationship between total SSQ score and physical examination score. Furthermore, frustration subscale moderated the mediating effects of the VLF power. Our results highlight the importance of documenting SS to evaluate a 360° VR training program. Furthermore, the combination of HRV analysis with mental workload measurement and outcome assessments provided the important clinical value in evaluating the effects of SS in VR applications in medical education. [ABSTRACT FROM AUTHOR]

Neuroendocrine prostate cancer is a rapidly progressive and lethal disease characterized by early visceral metastasis, poor prognosis, and limited treatment options. Uncovering the oncogenic mechanisms could lead to the discovery of potential therapeutic avenues. Here, we demonstrate that the RNA-binding protein ELAVL3 is specifically upregulated in neuroendocrine prostate cancer and that overexpression of ELAVL3 alone is sufficient to induce the neuroendocrine phenotype in prostate adenocarcinoma. Mechanistically, ELAVL3 is transcriptionally regulated by MYCN and subsequently binds to and stabilizes MYCN and RICTOR mRNA. Moreover, ELAVL3 is shown to be released in extracellular vesicles and induce neuroendocrine differentiation of adenocarcinoma cells via an intercellular mechanism. Pharmacological inhibition of ELAVL3 with pyrvinium pamoate, an FDA-approved drug, effectively suppresses tumor growth, reduces metastatic risk, and improves survival in neuroendocrine prostate cancer mouse models. Our results identify ELAVL3 as a critical regulator of neuroendocrine differentiation in prostate cancer and propose a drug repurposing strategy for targeted therapies. Neuroendocrine prostate cancer (NEPC) is an aggressive subtype with limited effective therapeutic options. Here, the authors identify the RNA-binding protein ELAVL3 as a driver of differentiation into NEPC via a positive feedback loop with MYCN and demonstrate the use of the repurposed drug pyrvinium pamoate to target this axis using preclinical models of NEPC. [ABSTRACT FROM AUTHOR]

In the background environment of the serious problem of high temperature heat damage in deep mining, some mines have complex and interlocking forms of roadway arrangement, with the innovative concept of cooling on demand as the principle, this paper develops a mobile ice cooling equipment, and introduces and explains the equipment from the perspective of principle, composition and dimensions. and uses Comsol simulation software to simulate and analyze the main heat exchange process of the mobile ice cooling equipment under the conditions of two cooling sources, obtains quantitative results on the finned tube arrangement parameters and the heat exchange cooling effect of the equipment under ideal conditions, which provides data for the optimization and upgrading of this mobile ice cooling equipment. The results show that the mobile cooling equipment is capable of feeding the desired temperature of the cooling air into deep mine, and with flexible, convenient, efficient, and cost effective. This research and development is a new exploration of deep ventilation and cooling technology and equipment means, puts forward a new concept, accumulates valuable experience, and lays the foundation for the subsequent related research and optimization. [ABSTRACT FROM AUTHOR]