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Introduction The use of bisphenol A (BPA) has been severely restricted due to proven adverse health effects, (1,2) leading to the widespread utilization of bisphenol S (BPS) and BPS derivatives [...], BACKGROUND: A broad suite of bisphenol S (BPS) derivatives as alternatives for BPS have been identified in various human biological samples, including 4-hydroxyphenyl 4-isopropoxyphenylsulfone (BPSIP) detected in human umbilical cord plasma and breast milk. However, very little is known about the health outcomes of prenatal BPS derivative exposure to offspring. OBJECTIVES: Our study aimed to investigate the response of hepatic cholesterol metabolism by sex in offspring of dams exposed to BPSIP. METHODS: Pregnant ICR mice were exposed to 5 lg=kg body weight (BW)/day of BPSIP, BPS, or E2 through drinking water from gestational day one until the pups were weaned. The concentration of BPSIP, BPS, or E2 in the plasma and liver of pups was determined by liquid chromatographytandem mass spectrometry. Metabolic phenotypes were recorded, and histopathology was examined for liver impairment. Transcriptome analysis was employed to characterize the distribution and expression patterns of differentially expressed genes across sexes. The metabolic regulation was validated by quantitative real-time PCR, immunohistochemistry, and immunoblotting. The role of estrogen receptors (ERs) in mediating sex-dependent effects was investigated using animal models and liver organoids. RESULTS: Pups of dams exposed to BPSIP showed a higher serum cholesterol level, and liver cholesterol levels were higher in females and lower in males than in the controls. BPSIP concentration in the male liver was 1:22 [+ or -] 0:25 ng=g and 0:69 [+ or -] 0:27 ng=g in the female liver. Histopathology analysis showed steatosis and lipid deposition in both male and female offspring. Transcriptome and gene expression analyses identified sex-specific differences in cholesterol biosynthesis, absorption, disposal, and efflux between pups of dams exposed to BPSIP and those in controls. In vivo, chromatin immunoprecipitation analysis revealed that the binding of ERa protein to key genes such as Hmgcr, Pcsk9, and Abcg5 was attenuated in BPSIP-exposed females compared to controls, while it was enhanced in males. In vitro, the liver organoid experiments demonstrated that restoration of differential expression induced by BPSIP in key genes, such as Hmgcr, Ldlr, and Cyp7a1, to levels comparable to the controls was only achieved when treated with a combination of ERa agonist and ERb agonist. DISCUSSION: Findings from this study suggest that perinatal exposure to BPSIP disrupted cholesterol metabolism in a sex-specific manner in a mouse model, in which ERa played a crucial role both in vivo and in vitro. Therefore, it is crucial to systematically evaluate BPS derivatives to protect maternal health during pregnancy and prevent the transmission of metabolic disorders across generations. https://doi.org/10.1289/EHP14643

ObjectiveAccurate identification of insect pests is crucial for the effective prevention and control of crop infestations. However, existing pest identification methods primarily rely on traditional machine learning or deep learning techniques that are trained on seen classes. These methods falter when they encounter unseen pest species not included in the training set, due to the absence of image samples. An innovative method was proposed to address the zero-shot recognition challenge for pests.MethodsThe novel zero-shot learning (ZSL) method proposed in this study was capable of identifying unseen pest species. First, a comprehensive pest image dataset was assembled, sourced from field photography conducted around Beijing over several years, and from web crawling. The final dataset consisted of 2 000 images across 20 classes of adult Lepidoptera insects, with 100 images per class. During data preprocessing, a semantic dataset was manually curated by defining attributes related to color, pattern, size, and shape for six parts: antennae, back, tail, legs, wings, and overall appearance. Each image was annotated to form a 65-dimensional attribute vector for each class, resulting in a 20×65 semantic attribute matrix with rows representing each class and columns representing attribute values. Subsequently, 16 classes were designated as seen classes, and 4 as unseen classes. Next, a novel zero-shot pest recognition method was proposed, focusing on synthesizing high-quality pseudo-visual features aligned with semantic information using a generator. The wasserstein generative adversarial networks (WGAN) architecture was strategically employed as the fundamental network backbone. Conventional generative adversarial networks (GANs) have been known to suffer from training instabilities, mode collapse, and convergence issues, which can severely hinder their performance and applicability. The WGAN architecture addresses these inherent limitations through a principled reformulation of the objective function. In the proposed method, the contrastive module was designed to capture highly discriminative visual features that could effectively distinguish between different insect classes. It operated by creating positive and negative pairs of instances within a batch. Positive pairs consisted of different views of the same class, while negative pairs were formed from instances belonging to different classes. The contrastive loss function encouraged the learned representations of positive pairs to be similar while pushing the representations of negative pairs apart. Tightly integrated with the WGAN structure, this module substantially improved the generation quality of the generator. Furthermore, the visual-semantic alignment module enforced consistency constraints from both visual and semantic perspectives. This module constructed a cross-modal embedding space, mapping visual and semantic features via two projection layers: One for mapping visual features into the cross-modal space, and another for mapping semantic features. The visual projection layer took the synthesized pseudo-visual features from the generator as input, while the semantic projection layer ingested the class-level semantic vectors. Within this cross-modal embedding space, the module enforced two key constraints: Maximizing the similarity between same-class visual-semantic pairs and minimizing the similarity between different-class pairs. This was achieved through a carefully designed loss function that encourages the projected visual and semantic representations to be closely aligned for instances belonging to the same class, while pushing apart the representations of different classes. The visual-semantic alignment module acted as a regularizer, preventing the generator from producing features that deviated from the desired semantic information. This regularization effect complemented the discriminative power gained from the contrastive module, resulting in a generator that produces high-quality, diverse, and semantically aligned pseudo-visual features.Results and DiscussionsThe proposed method was evaluated on several popular ZSL benchmarks, including CUB, AWA, FLO, and SUN. The results demonstrated that the proposed method achieved state-of-the-art performance across these datasets, with a maximum improvement of 2.8% over the previous best method, CE-GZSL. This outcome fully demonstrated the method's broad effectiveness in different benchmarks and its outstanding generalization ability. On the self-constructed 20-class insect dataset, the method also exhibited exceptional recognition accuracy. Under the standard ZSL setting, it achieved a precise recognition rate of 77.4%, outperforming CE-GZSL by 2.1%. Under the generalized ZSL setting, it achieved a harmonic mean accuracy of 78.3%, making a notable 1.2% improvement. This metric provided a balanced assessment of the model's performance across seen and unseen classes, ensuring that high accuracy on unseen classes does not come at the cost of forgetting seen classes. These results on the pest dataset, coupled with the performance on public benchmarks, firmly validated the effectiveness of the proposed method.ConclusionsThe proposed zero-shot pest recognition method represents a step forward in addressing the challenges of pest management. It effectively generalized pest visual features to unseen classes, enabling zero-shot pest recognition. It can facilitate pests identification tasks that lack training samples, thereby assisting in the discovery and prevention of novel crop pests. Future research will focus on expanding the range of pest species to further enhance the model's practical applicability.

Precrash scenario analysis for autonomous vehicles (AVs) is critical for improving the safety of autonomous driving, yet the scenario differences between different driving modes are unexplored. Using the precrash scenario typology of the USDOT, this study classified 484 AV crash reports from the California DMV from 2018 to 2022, revealing the differences in the scenario proportions of the three modes of autonomous driving, driving takeover, and conventional driving in 34 types of scenarios. The results showed that there were significant differences in the proportion of six scenarios such as 'Lead AV stopped' and 'Lead AV decelerating' among different driving modes (p, Author(s): Tao Wang (corresponding author) [1]; Juncong Chen [1]; Wenyong Li [1]; Jun Chen [2]; Xiaofei Ye [3] 1. Introduction Autonomous driving technology is a scientific technology that brings creative [...]

To describe the microtrajectory change behavior of two-wheeled vehicles when encountering nonrigid obstacles, this paper proposes an obstacle avoidance model of two-wheeled vehicles for nonrigid obstacles based on the improved social force method. In the model, the obstacle avoidance characteristics of two-wheeled vehicles for nonrigid obstacles and the safety operation requirements of two-wheeled vehicles are considered. The calculation methods of the minimum boundary distance of avoidance and the boundary avoidance force range edge function are proposed based on the ultimate turning angle and boundary avoidance characteristics, respectively. The speed control and direction control behavior of the rider to the vehicle is abstracted as the form of social force, and the two-wheeled vehicle/rider individual is subjected to the avoidance force and correction force perpendicular to the direction of the longitudinal axis of the vehicle body. The centrifugal trajectory obstacle avoidance motion obeying the Newtonian mechanics is studied based on the characteristics of the vehicle body turning angle change. Different traffic environments are constructed by MATLAB software and simulated according to the model running logic, and sensitivity analysis is performed based on the actual data collected in the survey. The simulation results show that, under the same conditions, compared with μ [sub.i]=50N, when μ [sub.i]=200N, the X-axis displacement of the simulated vehicle in the avoidance process is shortened by 26.31%, and the body deflection angle increases by 50.75% at the end moment of the avoidance process. Compared with electric motorcycles, the displacement of the X-axis in the avoidance process of the electric bicycle is shortened by 16.92%, and the body deflection angle at the end moment of the avoidance process is increased by 13.68%. The simulation results show that the model can well describe the trajectory change behavior characteristics of two-wheeled vehicles/riders encountering nonrigid obstacles in real situations, and the obstacle avoidance model of two-wheeled vehicles proposed in this paper is considered to be traffic justified., Author(s): Dan Zhou [1]; Qingwei Hu (corresponding author) [2]; Xin Sun [1]; Guobin Gu [3]; Wenyong Li [1,2]; Tao Wang [1] 1. Introduction In recent years, affected by the rapid [...]