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At present, there are many aerial-view datasets that contain motion data from vehicles in a variety of traffic scenarios. However, there are few datasets that have been collected under different weather conditions in an urban mixed-traffic scenario. In this study, we propose a framework for extracting vehicle motion data from UAV videos captured under various weather conditions. With this framework, we improve YOLOv5 (you only look once) with image-adaptive enhancement for detecting vehicles in different environments. In addition, a new vehicle-tracking algorithm called SORT++ is proposed to extract high-precision vehicle motion data from the detection results. Moreover, we present a new dataset that includes 7133 traffic images (1311 under sunny conditions, 961 under night, 3366 under rainy, and 1495 under snowy) of 106,995 vehicles. The images were captured by a UAV to evaluate the proposed method for vehicle orientation detection. In order to evaluate the accuracy of the extracted traffic data, we also present a new dataset of four UAV videos, each having 30,000+ frames, of approximately 3K vehicle trajectories collected under sunny, night, rainy, and snowy conditions, respectively. The experimental results show the high accuracy and stability of the proposed methods.

The scientific establishment of the Ecological Security Pattern (ESP) is crucial for fostering the synergistic development of ecological and recreational functions, thereby enhancing urban ecological protection, recreational development, and sustainable growth. This study aimed to propose a novel method of constructing ESP considering both ecological and recreational functions, and to reconstruct ESP by weighing the relationship between ecological protection and recreational development. Utilizing Fuzhou City as a case study, a comprehensive application of methodologies including Morphological Spatial Pattern Analysis (MSPA), landscape connectivity analysis, ArcGIS spatial analysis, social network analysis (SNA), and circuit theory is employed to develop both the ESP and the Recreational Spatial Pattern (RSP). A trade-off matrix is created to facilitate the reconstruction of the ESP, delineate functional zones, and identify strategic points, followed by a thorough optimization and development strategy. The analysis revealed 36 ecological source areas (ESAs) within Fuzhou City, encompassing a total area of 5807.90 km², predominantly situated in the western and northern regions. Additionally, 98 ecological corridors (ECs) were identified, extending over 2500.55 km, alongside 100 ecological pinch points (EPPs) and 146 ecological barrier points (EBPs). The city also contains 57 recreational nodes (RNs),, which display a spatial distribution characterized by a “dense in the east, sparse in the west” pattern. Furthermore, 165 recreational corridors (RCs) were extracted, covering a distance of 3795.21 km. Based on the trade-off matrix, Fuzhou City was categorized into eight functional zones: ecological core zone, ecological important zone, eco-recreation key trade-off zone, eco-recreation secondary trade-off zone, recreational core zone, recreational important zone, recreational development zone, and elastic development zone.The study identified 95 key strategic points and 475 sub-strategic. A multifunctional and complex ESP was constructed, characterized by “one core, five districts, six corridors, and seven wedges”, and a tailored ecological and recreational planning and development strategy for Fuzhou City was proposed. This research contributes a theoretical framework for the construction and optimization of a multifunctional ESP and supports the coordinated high-quality development of ecological protection and recreational activities in urban environments.

Glioblastoma (GBM) cells require large amounts of iron for tumor growth and progression, which makes these cells vulnerable to destruction via ferroptosis induction. Mitochondria are critical for iron metabolism and ferroptosis. Sirtuin-3 (SIRT3) is a deacetylase found in mitochondria that regulates mitochondrial quality and function. This study aimed to characterize SIRT3 expression and activity in GBM and investigate the potential therapeutic effects of targeting SIRT3 while also inducing ferroptosis in these cells. We first found that SIRT3 expression was higher in GBM tissues than in normal brain tissues and that SIRT3 protein expression was upregulated during RAS-selective lethal 3 (RSL3)-induced GBM cell ferroptosis. We then observed that inhibition of SIRT3 expression and activity in GBM cells sensitized GBM cells to RSL3-induced ferroptosis both in vitro and in vivo. Mechanistically, SIRT3 inhibition led to ferrous iron and ROS accumulation in the mitochondria, which triggered mitophagy. RNA-Sequencing analysis revealed that upon SIRT3 knockdown in GBM cells, the mitophagy pathway was upregulated and SLC7A11, a critical antagonist of ferroptosis via cellular import of cystine for glutathione (GSH) synthesis, was downregulated. Forced expression of SLC7A11 in GBM cells with SIRT3 knockdown restored cellular cystine uptake and consequently the cellular GSH level, thereby partially rescuing cell viability upon RSL3 treatment. Furthermore, in GBM cells, SIRT3 regulated SLC7A11 transcription through ATF4. Overall, our study results elucidated novel mechanisms underlying the ability of SIRT3 to protect GBM from ferroptosis and provided insight into a potential combinatorial approach of targeting SIRT3 and inducing ferroptosis for GBM treatment.

Liquid chromatography (LC) coupled with data-independent acquisition (DIA) mass spectrometry (MS) has been increasingly used in quantitative proteomics studies. Here, we present a fast and sensitive approach for direct peptide identification from DIA data, MSFragger-DIA, which leverages the unmatched speed of the fragment ion indexing-based search engine MSFragger. Different from most existing methods, MSFragger-DIA conducts a database search of the DIA tandem mass (MS/MS) spectra prior to spectral feature detection and peak tracing across the LC dimension. To streamline the analysis of DIA data and enable easy reproducibility, we integrate MSFragger-DIA into the FragPipe computational platform for seamless support of peptide identification and spectral library building from DIA, data-dependent acquisition (DDA), or both data types combined. We compare MSFragger-DIA with other DIA tools, such as DIA-Umpire based workflow in FragPipe, Spectronaut, DIA-NN library-free, and MaxDIA. We demonstrate the fast, sensitive, and accurate performance of MSFragger-DIA across a variety of sample types and data acquisition schemes, including single-cell proteomics, phosphoproteomics, and large-scale tumor proteome profiling studies. DIA-MS has emerged as a widely used technological platform for quantitative protein profiling. Here, the authors develop MSFragger-DIA, a robust and fast tool to directly identify peptides from DIA spectra. It demonstrates excellent performance across applications from large-scale tumor studies to single-cell proteomics.

Background Thousands of Coronavirus Disease 2019 (COVID-19) patients have been discharged from hospitals Persistent follow-up studies are required to evaluate the prevalence of post-COVID-19 fibrosis. Methods This study involves 462 laboratory-confirmed patients with COVID-19 who were admitted to Shenzhen Third People’s Hospital from January 11, 2020 to April 26, 2020. A total of 457 patients underwent thin-section chest CT scans during the hospitalization or after discharge to identify the pulmonary lesion. A total of 287 patients were followed up from 90 to 150 days after the onset of the disease, and lung function tests were conducted about three months after the onset. The risk factors affecting the persistence of pulmonary fibrosis were identified through regression analysis and the prediction model of the persistence of pulmonary fibrosis was established. Results Parenchymal bands, irregular interfaces, reticulation and traction bronchiectasis were the most common CT features in all COVID-19 patients. During the 0–30, 31–60, 61–90, 91–120 and > 120 days after onset, 86.87%, 74.40%, 79.56%, 68.12% and 62.03% patients developed with pulmonary fibrosis and 4.53%, 19.61%, 18.02%, 38.30% and 48.98% patients reversed pulmonary fibrosis, respectively. It was observed that Age, BMI, Fever, and Highest PCT were predictive factors for sustaining fibrosis even after 90 days from onset. A predictive model of the persistence with pulmonary fibrosis was developed based-on the Logistic Regression method with an accuracy, PPV, NPV, Sensitivity and Specificity of the model of 76%, 71%, 79%, 67%, and 82%, respectively. More than half of the COVID-19 patients revealed abnormal conditions in lung function after 90 days from onset, and the ratio of abnormal lung function did not differ on a statistically significant level between the fibrotic and non-fibrotic groups. Conclusions Persistent pulmonary fibrosis was more likely to develop in patients with older age, higher BMI, severe/critical condition, fever, a longer viral clearance time, pre-existing disease and delayed hospitalization. Fibrosis developed in COVID-19 patients could be reversed in about a third of the patients after 120 days from onset. The pulmonary function of less than half of COVID-19 patients could turn to normal condition after three months from onset. An effective prediction model with an average area under the curve (AUC) of 0.84 was established to predict the persistence of pulmonary fibrosis in COVID-19 patients for early diagnosis.

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive interstitial lung disease with unknown etiology, high mortality and limited treatment options. It is characterized by myofibroblast proliferation and extensive deposition of extracellular matrix (ECM), which will lead to fibrous proliferation and the destruction of lung structure. Transforming growth factor-β1 (TGF-β1) is widely recognized as a central pathway of pulmonary fibrosis, and the suppression of TGF-β1 or the TGF-β1-regulated signaling pathway may thus offer potential antifibrotic therapies. JAK-STAT is a downstream signaling pathway regulated by TGF-β1. JAK1/2 inhibitor baricitinib is a marketed drug for the treatment of rheumatoid arthritis, but its role in pulmonary fibrosis has not been reported. This study explored the potential effect and mechanism of baricitinib on pulmonary fibrosis in vivo and in vitro. The in vivo studies have shown that baricitinib can effectively attenuate bleomycin (BLM)-induced pulmonary fibrosis, and in vitro studies showed that baricitinib attenuates TGF-β1-induced fibroblast activation and epithelial cell injury by inhibiting TGF-β1/non-Smad and TGF-β1/JAK/STAT signaling pathways, respectively. In conclusion, baricitinib, a JAK1/2 inhibitor, impedes myofibroblast activation and epithelial injury via targeting the TGF-β1 signaling pathway and reduces BLM-induced pulmonary fibrosis in mice.

The study aimed to fabricate and evaluate a bone tissue engineering scaffold made from a composite of polylactic-co-glycolic acid (PLGA), nano-hydroxyapatite (nHA), and graphene oxide (GO) using low-temperature 3D printing and freeze-drying techniques. The scaffolds were produced with varying compositions: PLGA alone and in combination with nHA and GO. The macro and microstructure, pore size, porosity, mechanical properties, and in vitro biocompatibility were assessed. Bone marrow mesenchymal stem cells (BMSCs) were co-cultured with the scaffolds to evaluate cell adhesion, proliferation, and cytotoxicity. The PLGA/nHA/GO composite scaffolds exhibited optimal pore size and microtopography, enhanced mechanical properties, excellent water absorption, and appropriate degradability. The co-culture with BMSCs demonstrated improved cell adhesion and proliferation, indicating good biocompatibility. The PLGA/nHA/GO composite scaffolds show potential as a bone tissue engineering material due to their favorable properties and biocompatibility, suggesting their suitability for bone defect repair applications.

As one of the ship energy efficiency optimization measures with the most energy saving and emission reduction potential, ship speed optimization has been recommended by the International Maritime Organization. In ship speed optimization, considering the influence of weather conditions, route segmentation and weather data loading methods significantly affect the reliability of speed optimization results. Therefore, taking the ocean-going container ship as the research object, on the basis of constructing the main engine fuel consumption prediction model and shaft speed prediction model based on machine learning methods, a route segmentation and weather loading-speed optimization iterative algorithm is proposed in this study. Single-objective speed optimization research is then conducted based on the algorithm. The research results show that the proposed algorithm effectively reduces the difference between optimized fuel consumption and actual fuel consumption, and can achieve a fuel-saving rate between 2.1% and 5.2%. This study achieves an accurate and reliable prediction of ship fuel consumption and shaft speed, and solves the strong coupling problem between route segmentation, weather loading, and speed optimization by iterative optimization of ship speed. The proposed algorithm provides strong technical support for ships to achieve the goal of energy saving and emission reduction.

Elucidating the dynamics of the neutralizing antibody (nAb) response in coronavirus disease 2019 (COVID-19) convalescents is crucial in controlling the pandemic and informing vaccination strategies. Here we measured nAb titres across 411 sequential plasma samples collected during 1–480 d after illness onset or laboratory confirmation (d.a.o.) from 214 COVID-19 convalescents, covering the clinical spectrum of disease and without additional exposure history after recovery or vaccination against SARS-CoV-2, using authentic SARS-CoV-2 microneutralization (MN) assays. Forty-eight samples were also tested for neutralizing activities against the circulating variants using pseudotyped neutralization assay. Results showed that anti-RBD IgG and MN titres peaked at ~120 d.a.o. and subsequently declined, with significantly reduced nAb responses found in 91.67% of COVID-19 convalescents (≥50% decrease in current MN titres compared with the paired peak MN titres). Despite this decline, majority of the COVID-19 convalescents maintained detectable anti-RBD IgG and MN titres at 400–480 d.a.o., with undetectable neutralizing activity found in 14.41% (16/111) of the mild and 50% (5/10) of the asymptomatic infections at 330–480 d.a.o. Persistent antibody-dependent immunity could provide protection against circulating variants after one year, despite significantly decreased neutralizing activities against Beta, Delta and Mu variants. In conclusion, these data show that despite a marked decline in neutralizing activity over time, nAb responses persist for up to 480 d in most convalescents of symptomatic COVID-19, whereas a high rate of undetectable nAb responses was found in convalescents from asymptomatic infections. A longitudinal analysis of the neutralizing antibody response dynamics in 214 COVID-19 convalescents up to 16 months after infection shows that despite substantial declines in antibody levels over time, they could still provide protection against circulating variants even after one year of infection in most individuals, although neutralizing activities were reduced, particularly against Beta, Delta and Mu SARS-CoV-2 variants.