Explore the vast range of titles available.

Protein B-factor, also known as the Debye–Waller temperature factor or atomic displacement parameter, measures the thermal fluctuation of an atom around its average position. It serves as a crucial indicator of protein flexibility and dynamics. However, accurately predicting the B-factor of Cα atoms remains challenging. In this work, we introduce OPUS-BFactor, a tool for predicting the normalized protein B-factor. OPUS-BFactor employs a transformer-based module to integrate sequence-level and pair-level features, encompassing structural attributes derived from the protein’s 3D structure and evolutionary profiles obtained from the protein language model ESM-2. Specifically, OPUS-BFactor treats pair features as a bias term, incorporating them into the attention matrix derived from the sequence-level features of each residue pair, thereby effectively merging pair features with sequence features. OPUS-BFactor operates in two modes, enabling predictions based solely on either the protein sequence or the 3D structure of the target protein. Evaluation on three test sets, including recently released targets from CAMEO and CASP15, demonstrated that OPUS-BFactor significantly outperformed other B-factor prediction methods. Therefore, OPUS-BFactor is a valuable tool for predicting protein properties related to the B-factor, such as flexibility, thermal stability, and regional activity.

Fixed double-row photovoltaic (PV) arrays are susceptible to wind-induced damage, while their wind load characteristics remain inadequately investigated. This study employs computational fluid dynamics (CFD) simulations to systematically analyze wind load behavior under varying operational conditions, aiming to identify critical scenarios and structural vulnerabilities. First, the validity of the CFD methodology was verified through direct comparison between wind tunnel pressure measurements of an isolated PV module and corresponding numerical simulations. Subsequently, scaled PV array models were constructed to replicate practical engineering configurations, enabling a systematic evaluation of wind direction effects on mean net wind pressure coefficients and three-component force coefficients. Finally, surface wind pressure distribution patterns were examined for four representative wind angles (0°, 45°, 135°, 180°). Results demonstrate that edge-positioned modules exhibit maximum mean net wind pressure coefficients and three-component force coefficients under oblique wind angles (45° and 135°), which are identified as the most critical operational conditions. In contrast, minimal wind loads were observed at a 90° wind angle, indicating an optimal orientation for array installation. Additionally, significantly higher surface wind pressure coefficients were recorded for edge modules under oblique winds (45°/135°) compared to both interior modules and other wind angles. It was found through the study that under upwind conditions (0–90°), the lower-row components are capable of withstanding greater wind loads, whereas under downwind conditions (90–180°), an increase in the loads exerted on the upper-row components was observed.

Separating point clouds into ground and nonground points is an essential step in the processing of airborne laser scanning (ALS) data for various applications. Interpolation-based filtering algorithms have been commonly used for filtering ALS point cloud data. However, most conventional interpolation-based algorithms have exhibited a drawback in terms of retaining abrupt terrain characteristics, resulting in poor algorithmic precision in these regions. To overcome this drawback, this paper proposes an improved adaptive surface interpolation filter with a multilevel hierarchy by using a cloth simulation and relief amplitude. This method uses three hierarchy levels of provisional digital elevation model (DEM) raster surfaces with thin plate spline (TPS) interpolation to separate ground points from unclassified points based on adaptive residual thresholds. A cloth simulation algorithm is adopted to generate sufficient effective initial ground seeds for constructing topographic surfaces with high quality. Residual thresholds are adaptively constructed by the relief amplitude of the examined area to capture complex landscape characteristics during the classification process. Fifteen samples from the International Society for Photogrammetry and Remote Sensing (ISPRS) commission are used to assess the performance of the proposed algorithm. The experimental results indicate that the proposed method can produce satisfying results in both flat areas and steep areas. In a comparison with other approaches, this method demonstrates its superior performance in terms of filtering results with the lowest omission error rate; in particular, the proposed approach retains discontinuous terrain features with steep slopes and terraces.

High-quality cottonseed is essential for successful cotton production. The integrity of cottonseed hulls plays a pivotal role in fostering the germination and growth of cotton plants. Consequently, it is crucial to eliminate broken cottonseeds before the cotton planting process. Regrettably, there is a lack of rapid and cost-effective methods for detecting broken cottonseed at this critical stage. To address this issue, this study developed a dual-camera system for acquiring front and back images of multiple cottonseeds. Based on this system, we designed the hardware, software, and control systems required for the online detection of cottonseed breakage. Moreover, to enhance the performance of cottonseed breakage detection, we improved the backbone and YOLO head of YOLOV8m by incorporating MobileOne-block and GhostConv, resulting in Light-YOLO. Light-YOLO achieved detection metrics of 93.8% precision, 97.2% recall, 98.9% mAP50, and 96.1% accuracy for detecting cottonseed breakage, with a compact model size of 41.3 MB. In comparison, YOLOV8m reported metrics of 93.7% precision, 95.0% recall, 99.0% mAP50, and 95.2% accuracy, with a larger model size of 49.6 MB. To further validate the performance of the online detection device and Light-YOLO, this study conducted an online validation experiment, which resulted in a detection accuracy of 86.7% for cottonseed breakage information. The results demonstrate that Light-YOLO exhibits superior detection performance and faster speed compared to YOLOV8m, confirming the feasibility of the online detection technology proposed in this study. This technology provides an effective method for sorting broken cottonseeds.

ASC (Apoptosis-associated Speck-like protein containing a CARD) is a key adaptor protein that assembles inflammasomes by linking sensors such as NLRP3 to effectors like Caspase-1 via its PYD and CARD Death Domains. Due to ASC’s propensity to self-aggregate, most high-resolution structural studies focused on isolated PYD or CARD domains, leaving the atomic basis of full-length ASC assembly unknown. Here we determine atomic-resolution cryo-EM structures of PYD and CARD filaments from full-length ASC, revealing characteristic multitrack bundles composed of alternating ASC PYD and ASC CARD filaments that expose multiple interfaces for flexible assembly and efficient signaling. We further show that Caspase-1 filaments nucleate specifically from the B-end of ASC CARD filaments, and that the interdomain linker modulates bundle formation. The ASC isoform ASCb, with a four-residue linker, adopts a distinct architecture, correlating with reduced Caspase-1 activation efficiency. In ASC ⁻/⁻ THP-1 cells, only wild-type ASC, not interface-disrupting mutants, restored ASC speck formation and Caspase-1 activation, underscoring the requirement for intact multitrack bundles. Cryo-electron tomography captures snapshots of higher-order inflammasome structures. These findings collectively define the structural and functional principles by which ASC organizes inflammasomes to amplify immune signaling. Inflammasomes are immune complexes built around the adaptor ASC (Apoptosis-associated Speck-like protein containing a CARD). Here, authors solve atomic structures of full-length ASC filaments, show that Caspase-1 starts at one filament end, and reveal a multitrack assembly model explaining rapid activation.

This study developed a rapid, non-destructive method for the quantitative detection of protein in cottonseed by integrating near-infrared (NIR) fiber spectroscopy with chemometric machine learning. The establishment of this method holds significant importance for the rational and efficient utilization of cottonseed resources, advancing research on the genetic improvement of cottonseed nutritional quality, and promoting the development of equipment for raw cottonseed protein detection. Fuzzy cottonseed samples from three varieties were collected, and their NIR fiber-optic spectra were acquired. Reference protein contents were measured using the Kjeldahl method. Spectra were denoised through preprocessing, after which informative wavelengths were selected by combining Uninformative Variable Elimination (UVE) with Competitive Adaptive Reweighted Sampling (CARS) and the Random Frog (RF) algorithm. Partial least squares regression (PLSR), least-squares support vector machine (LSSVM), and support vector regression (SVR) models were then constructed to predict protein content. Model performance was assessed using the coefficient of determination (R2), root-mean-square error (RMSE), residual predictive deviation (RPD), and range error ratio (RER). The results indicate that the standard normal variate (SNV) is the most effective preprocessing step. The best performance was achieved by the LSSVM model coupled with UVE + CARS, yielding R2 = 0.8571, RMSE = 0.0033, RPD = 2.7078, and RER = 10.72, outperforming the PLSR and SVR counterparts. These findings provide technical support for the rapid detection of fuzzy cottonseed protein and lay the groundwork for the development of related detection equipment.

Models for estimating urban rental house prices in the real estate market continue to pose a challenging problem due to the insufficiency of algorithms and comprehensive perspectives. Existing rental house price models based on either the geographically weighted regression (GWR) or deep-learning methods can hardly predict very satisfactory prices, since the rental house prices involve both complicated nonlinear characteristics and spatial heterogeneity. The linear-based GWR model cannot characterize the nonlinear complexity of rental house prices, while existing deep-learning methods cannot explicitly model the spatial heterogeneity. This paper proposes a fully connected neural network–geographically weighted regression (FCNN–GWR) model that combines deep learning with GWR and can handle both of the problems above. In addition, when calculating the geographical location of a house, we propose a set of locational and neighborhood variables based on the quantities of nearby points of interests (POIs). Compared with traditional locational and neighborhood variables, the proposed “quantity-based” locational and neighborhood variables can cover more geographic objects and reflect the locational characteristics of a house from a comprehensive geographical perspective. Taking four major Chinese cities (Wuhan, Nanjing, Beijing, and Xi’an) as study areas, we compare the proposed method with other commonly used methods, and this paper presents a more precise estimation model for rental house prices. The method proposed in this paper may serve as a useful reference for individuals and enterprises in their transactions relevant to rental houses, and for the government in terms of the policies and positions of public rental housing.

Traffic congestion, especially during peak hours, has become a challenge for transportation systems in many metropolitan areas, and such congestion causes delays and negative effects for passengers. Many studies have examined the prediction of congestion; however, these studies focus mainly on road traffic, and subway transit, which is the main form of transportation in densely populated cities, such as Tokyo, Paris, and Beijing and Shenzhen in China, has seldom been examined. This study takes Shenzhen as a case study for predicting congestion in a subway system during peak hours and proposes a hybrid method that combines a static traffic assignment model with an agent-based dynamic traffic simulation model to estimate recurrent congestion in this subway system. The homes and work places of the residents in this city are collected and taken to represent the traffic demand for the subway system of Shenzhen. An origin-destination (OD) matrix derived from the data is used as an input in this method of predicting traffic, and the traffic congestion is presented in simulations. To evaluate the predictions, data on the congestion condition of subway segments that are released daily by the Shenzhen metro operation microblog are used as a reference, and a comparative analysis indicates the appropriateness of the proposed method. This study could be taken as an example for similar studies that model subway traffic in other cities.

Exploring the spatial patterns of COVID-19 transmission and its key determinants could provide a deeper understanding of the evolution of the COVID-19 pandemic. The goal of this study is to investigate the spatial patterns of COVID-19 transmission in different periods in Singapore, as well as their relationship with demographic and built-environment factors. Based on reported cases from 23 January to 30 September 2020, we divided the research time into six phases and used spatial autocorrelation analysis, the ordinary least squares (OLS) model, the multiscale geographically weighted regression (MGWR) model, and dominance analysis to explore the spatial patterns and influencing factors in each phase. The results showed that the spatial patterns of COVID-19 cases differed across time, and imported cases presented a random pattern, whereas local cases presented a clustered pattern. Among the selected variables, the supermarket density, elderly population density, hotel density, business land proportion, and park density may be particular fitting indicators explaining the different phases of pandemic development in Singapore. Furthermore, the associations between determinants and COVID-19 transmission changed dynamically over time. This study provides policymakers with valuable information for developing targeted interventions for certain areas and periods.

Currently, coronavirus disease 2019 (COVID-19) remains a global pandemic, but the prevention and control of the disease in various countries have also entered the normalization stage. To achieve economic recovery and avoid a waste of resources, different regions have developed prevention and control strategies according to their social, economic, and medical conditions and culture. COVID-19 disparities under the interaction of various factors, including interventions, need to be analyzed in advance for effective and precise prevention and control. Considering the United States as the study case, we investigated statistical and spatial disparities based on the impact of the county-level social vulnerability index (SVI) on the COVID-19 infection rate. The county-level COVID-19 infection rate showed very significant heterogeneity between states, where 67% of county-level disparities in COVID-19 infection rates come from differences between states. A hierarchical linear model (HLM) was adopted to examine the moderating effects of state-level social distancing policies on the influence of the county-level SVI on COVID-19 infection rates, considering the variation in data at a unified level and the interaction of various data at different levels. Although previous studies have shown that various social distancing policies inhibit COVID-19 transmission to varying degrees, this study explored the reasons for the disparities in COVID-19 transmission under various policies. For example, we revealed that the state-level restrictions on the internal movement policy significantly attenuate the positive effect of county-level economic vulnerability indicators on COVID-19 infection rates, indirectly inhibiting COVID-19 transmission. We also found that not all regions are suitable for the strictest social distancing policies. We considered the moderating effect of multilevel covariates on the results, allowing us to identify the causes of significant group differences across regions and to tailor measures of varying intensity more easily. This study is also necessary to accomplish targeted preventative measures and to allocate resources.