Science Progress最新文献

The widespread research and implementation of visual object detection technology have significantly transformed the autonomous driving industry. Autonomous driving relies heavily on visual sensors to perceive and analyze the environment. However, under extreme weather conditions, such as heavy rain, fog, or low light, these sensors may encounter disruptions, resulting in decreased image quality and reduced detection accuracy, thereby increasing the risk for autonomous driving. To address these challenges, we propose adaptive image enhancement (AIE)-YOLO, a novel object detection method to enhance road object detection accuracy under extreme weather conditions. To tackle the issue of image quality degradation in extreme weather, we designed an improved adaptive image enhancement module. This module dynamically adjusts the pixel features of road images based on different scene conditions, thereby enhancing object visibility and suppressing irrelevant background interference. Additionally, we introduce a spatial feature extraction module to adaptively enhance the model's spatial modeling capability under complex backgrounds. Furthermore, a channel feature extraction module is designed to adaptively enhance the model's representation and generalization abilities. Due to the difficulty in acquiring real-world data for various extreme weather conditions, we constructed a novel benchmark dataset named extreme weather simulation-rare object dataset. This dataset comprises ten types of simulated extreme weather scenarios and is built upon a publicly available rare object detection dataset. Extensive experiments conducted on the extreme weather simulation-rare object dataset demonstrate that AIE-YOLO outperforms existing state-of-the-art methods, achieving excellent detection performance under extreme weather conditions.

Double-row planetary gear set (PGS) is a common form of the PGS, which is relatively more complex than the regular PGSs. It consists of one sun gear, several long planets, several short planets, two ring gears, and one carrier. Due to the significantly wider tooth width of the long planet compared to the sun gear, the axial meshing position between the sun gear and the long planet can be adjusted. The vibrations of PGS should vary with different axial meshing positions. If the axial position of the sun gear is optimized, the vibrations of PGS can be reduced. This work establishes a dynamic model of a double-row PGS. The dynamic model considers the mesh forces of the gear pairs and the supporting forces of the bearing. The effect of the sun gear axial position on the sun gear and ring gear #2 vibrations are investigated. Finally, the recommended axial position for the sun gear is provided.

Purpose: Programmed death-1 antibody plus chemotherapy has gained approval for the treatment for (human epidermal growth factor receptor 2 negative locally advanced or metastatic gastric or gastroesophageal junction cancer. This study aims to analyze the efficacy and safety of anti-programmed death-1 antibody combined with chemo- or anti-angiogenesis therapy in Chinese patients with advanced or metastatic gastric or gastroesophageal junction cancer in a real-world setting.

Methods: In total, 122 patients treated with anti-programmed death-1 antibody-based combination therapy between April 2019 and December 2021 were encompassed. Clinical outcomes and safety profile were measured and analyzed.

Results: In the whole cohort, median overall survival was 17.2 months, median progression-free survival was 10.9 months, and median duration of response was 9.4 months. Notably, in the first-line patients, the median overall survival was not reached, median progression-free survival was 14.8 months, objective response rate was 68.4%. In the second-line group, median overall survival, median progression-free survival, median duration of response, and objective response rate were 10.9 months, 5.9 months, 4.5 months, and 41.5%, respectively. Treatment-related adverse events of any grade were observed in 28.2% of the overall cohort, primarily affecting the hematological and liver function. Grade 3 or 4 adverse events were mainly characterized by increased levels of aspartate aminotransferase, alanine aminotransferase, along with decreased lymphocyte and white blood cells, as well as anemia.

Conclusions: Patients in our cohort experienced a clinical benefit from anti-programmed death-1 antibody-combined treatment in first-line treatment settings, with acceptable treatment-related adverse events. The benefit of anti-programmed death-1 antibody combined with chemo- or anti-angiogenesis treatment to the second-line patients should be further confirmed by large multi-center randomized, controlled clinical trials.

In the current economic landscape, the growing importance of innovation and entrepreneurship underscores an urgent need for accurate market trend prediction. Addressing this challenge, our study introduces an innovative entrepreneurial market trend prediction model based on deep learning principles. Through detailed case studies and performance evaluations, this paper demonstrates the model's effectiveness and its potential to enhance decision-making capabilities in a competitive business environment. Accurate market trend prediction is crucial in the fields of innovation and entrepreneurship, and our approach meets this demand. Our model leverages the power of deep learning technology, combining historical market data with diverse market indicators, including sentiment analysis derived from social media, to create an advanced predictive model that surpasses traditional methods. By analyzing data from multiple channels, our model exhibits exceptional accuracy in forecasting future market trends. The case study provides strong evidence of our model's performance and precision, showcasing its significant support for innovators and entrepreneurs navigating complex market trends. Furthermore, this study highlights the vast potential of deep learning technology in the economic sector. We emphasize the importance of developing innovative entrepreneurial market trend prediction models and foresee an increase in project success rates for innovators and entrepreneurs by enhancing decision quality through the adoption of deep learning.

Objectives: tert-Butylhydroquinone (TBHQ) is an antioxidant and preservative used in unsaturated vegetable oils and processed foods. However, when consumed in higher doses daily, it may pose a threat to public health by potentially increasing the risk of cancer, as it has an affinity with both the aryl hydrocarbon receptor (AhR) and the estrogen receptor alpha (ERα).

Methods: This study aimed to examine the impact of substituting the 1,4-diol of TBHQ with 1,4-dithiol, referred to as TBDT, on the carcinogenic and antioxidant systems using computational methods. The binding affinity of TBHQ and TBDT to the two carcinogenic receptors, AhR and ERα, as well as to the antioxidant receptor Keap1 alone and in connection with Nrf2 (Nrf2-Keap1) was investigated through docking analysis.

Results: The results indicated a decrease in TBDT's binding strength to ERα and AhR when assessed using Molegro Virtual Docker (P-value: 0.0001 and 0.00001, respectively), AutoDock Vina (P-value: 0.0001 and 0.0001), and the online server Fast DRH (P-value: 0.0001 and 0.0001). However, TBDT's binding affinity to Keap1 was predicted to be significantly stronger than TBHQ's by both MVD and AutoDock Vina (P-value: 0.0001 and 0.04), while its binding to Nrf2-Keap1 assessed to be stronger only by MVD (P-value: 0.0001).

Conclusion: These findings suggest that TBDT not only exhibits higher antioxidant activity as a better ligand for the antioxidant system but also shows lower affinity with the AhR and ERα receptors. Therefore, TBDT can be considered a safer compound than TBHQ.

Phononic crystals, which are artificial crystals formed by the periodic arrangement of materials with different elastic coefficients in space, can display modulated sound waves propagating within them. Similar to the natural crystals used in semiconductor research with electronic bandgaps, phononic crystals exhibit the characteristics of phononic bandgaps. A gap design can be utilized to create various resonant cavities, confining specific resonance modes within the defects of the structure. In studies on phononic crystals, phononic band structure diagrams are often used to investigate the variations in phononic bandgaps and elastic resonance modes. As the phononic band frequencies vary nonlinearly with the structural parameters, numerous calculations are required to analyze the gap or mode frequency shifts in phononic band structure diagrams. However, traditional calculation methods are time-consuming. Therefore, this study proposes the use of neural networks to replace the time-consuming calculation processes of traditional methods. Numerous band structure diagrams are initially obtained through the finite-element method and serve as the raw dataset, and a certain proportion of the data is randomly extracted from the dataset for neural network training. By treating each mode point in the band structure diagram as an independent data point, the training dataset for neural networks can be expanded from a small number to a large number of band structure diagrams. This study also introduces another network that effectively improves mode prediction accuracy by training neural networks to focus on specific modes. The proposed method effectively reduces the cost of repetitive calculations.

Eco-driving has garnered considerable research attention owing to its potential socio-economic impact, including enhanced public health and mitigated climate change effects through the reduction of greenhouse gas emissions. With an expectation of more autonomous vehicles (AVs) on the road, an eco-driving strategy in hybrid traffic networks encompassing AV and human-driven vehicles (HDVs) with the coordination of traffic lights is a challenging task. The challenge is partially due to the insufficient infrastructure for collecting, transmitting, and sharing real-time traffic data among vehicles, facilities, and traffic control centers, and the following decision-making of agents involved in traffic control. Additionally, the intricate nature of the existing traffic network, with its diverse array of vehicles and facilities, contributes to the challenge by hindering the development of a mathematical model for accurately characterizing the traffic network. In this study, we utilized the Simulation of Urban Mobility (SUMO) simulator to tackle the first challenge through computational analysis. To address the second challenge, we employed a model-free reinforcement learning (RL) algorithm, proximal policy optimization, to decide the actions of AV and traffic light signals in a traffic network. A novel eco-driving strategy was proposed by introducing different percentages of AV into the traffic flow and collaborating with traffic light signals using RL to control the overall speed of the vehicles, resulting in improved fuel consumption efficiency. Average rewards with different penetration rates of AV (5%, 10%, and 20% of total vehicles) were compared to the situation without any AV in the traffic flow (0% penetration rate). The 10% penetration rate of AV showed a minimum time of convergence to achieve average reward, leading to a significant reduction in fuel consumption and total delay of all vehicles.

Background: Ulinastatin has been applied in a series of diseases associated with inflammation but its clinical effects remain somewhat elusive.

Objective: We aimed to investigate the potential effects of ulinastatin on organ failure patients admitted to the intensive care unit (ICU).

Methods: This is a single-center retrospective study on organ failure patients from 2013 to 2019. Patients were divided into two groups according to using ulinastatin or not during hospitalization. Propensity score matching was applied to reduce bias. The outcomes of interest were 28-day all-cause mortality, length of ICU stay, and mechanical ventilation duration.

Results: Of the 841 patients who fulfilled the entry criteria, 247 received ulinastatin. A propensity-matched cohort of 608 patients was created. No significant differences in 28-day mortality between the two groups. Sequential organ failure assessment (SOFA) was identified as the independent risk factor associated with mortality. In the subgroup with SOFA ≤ 10, patients received ulinastatin experienced significantly shorter time in ICU (10.0 d [interquartile range, IQR: 7.0∼20.0] vs 15.0 d [IQR: 7.0∼25.0]; p = .004) and on mechanical ventilation (222 h [IQR:114∼349] vs 251 h [IQR: 123∼499]; P = .01), but the 28-day mortality revealed no obvious difference (10.5% vs 9.4%; p = .74).

Conclusion: Ulinastatin was beneficial in treating patients in ICU with organ failure, mainly by reducing the length of ICU stay and duration of mechanical ventilation.

The primrose-willow (Ludwigia L.), a well-defined genus of the Onagraceae family, comprises 87 species widely distributed worldwide. In this study, we sequenced and characterized the complete chloroplast (cp) genomes of three species in the genus, including Ludwigia adscendens, Ludwigia hyssopifolia, and Ludwigia prostrata. Three Ludwigia cp genomes ranged from 158,354 to 159,592 bp in size, and each contained 113 genes, including 79 unique protein-coding genes (PCGs), four rRNA genes, and 30 tRNA genes. A comparison of the Ludwigia cp genomes revealed that they were highly conserved in gene composition, gene orientation, and GC content. Moreover, we compared the structure of cp genomes and reconstructed phylogenetic relationships with related species in the Onagraceae family. Regarding contraction/expansion of inverted repeat (IR) region, two kinds of expansion IR region structures were found in Oenothera, Chamaenerion, and Epilobium genera, with primitive IR structures in Ludwigia and Circeae genera. The regions clpP, ycf2, and ycf1 genes possessed highly divergent nucleotides among all available cp genomes of the Onagraceae family. The phylogenetic reconstruction using 79 PCGs from 39 Onagraceae cp genomes inferred that Ludwigia (including L. adscendens, L. hyssopifolia, L. prostrata, and Ludwigia octovalvis) clade was monophyletic and well-supported by the bootstrap and posterior probability values. This study provides the reference cp genomes of three Ludwigia species, which can be used for species identification and phylogenetic reconstruction of Ludwigia and Onagraceae taxa.