Effects of larval exposure to the insecticide flumethrin on the development of honeybee (Apis mellifera) workers

Flumethrin is a widely used acaricide, but its improper use often leads to residue accumulation in honeybee colonies, thus threatening the health of honeybees, especially at the larval stage. Therefore, this study aimed to describe the direct toxicity of flumethrin on honeybee (Apis mellifera) larvae by conducting bioassays for immune and detoxification-related enzymes and transcriptome sequencing to determine the potential effects on newly emerged adults who were exposed to flumethrin during the larval stage. Results showed that the higher the concentration of flumethrin the honeybee larvae were exposed to, the greater the damage to the physiology of honeybee larvae and the newly emerged worker bees. When honeybee larvae were exposed to flumethrin concentrations higher than 0.01 mg/L, the activities of glutathione sulfur transferase and carboxylesterase were affected, and the metabolism-related genes in the head of newly emerged honeybees exposed to flumethrin during the larval stage were down-regulated. Flumethrin concentration higher than 0.1 mg/L significantly increased mixed-functional oxidase content in honeybee larvae, reduced the larval survival rate, and down-regulated the expression levels of olfactory-related and antioxidant-related genes in newly emerged honeybees. Furthermore, a flumethrin concentration of 1 mg/L significantly down-regulated the expression levels of immune and detoxification-related genes in newly emerged honeybees. These findings provide a comprehensive understanding of the response of honeybee larvae to sublethal flumethrin toxicity and could be used to further investigate the complex molecular mechanisms in honeybees under pesticide stress

From Adversarial Arms Race to Model-centric Evaluation: Motivating a Unified Automatic Robustness Evaluation Framework

Textual adversarial attacks can discover models' weaknesses by adding semantic-preserved but misleading perturbations to the inputs. The long-lasting adversarial attack-and-defense arms race in Natural Language Processing (NLP) is algorithm-centric, providing valuable techniques for automatic robustness evaluation. However, the existing practice of robustness evaluation may exhibit issues of incomprehensive evaluation, impractical evaluation protocol, and invalid adversarial samples. In this paper, we aim to set up a unified automatic robustness evaluation framework, shifting towards model-centric evaluation to further exploit the advantages of adversarial attacks. To address the above challenges, we first determine robustness evaluation dimensions based on model capabilities and specify the reasonable algorithm to generate adversarial samples for each dimension. Then we establish the evaluation protocol, including evaluation settings and metrics, under realistic demands. Finally, we use the perturbation degree of adversarial samples to control the sample validity. We implement a toolkit RobTest that realizes our automatic robustness evaluation framework. In our experiments, we conduct a robustness evaluation of RoBERTa models to demonstrate the effectiveness of our evaluation framework, and further show the rationality of each component in the framework. The code will be made public at \url{https://github.com/thunlp/RobTest}.Comment: Accepted to Findings of ACL 202

ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data

Radiology report generation, as a key step in medical image analysis, is critical to the quantitative analysis of clinically informed decision-making levels. However, complex and diverse radiology reports with cross-source heterogeneity pose a huge generalizability challenge to the current methods under massive data volume, mainly because the style and normativity of radiology reports are obviously distinctive among institutions, body regions inspected and radiologists. Recently, the advent of large language models (LLM) offers great potential for recognizing signs of health conditions. To resolve the above problem, we collaborate with the Second Xiangya Hospital in China and propose ChatRadio-Valuer based on the LLM, a tailored model for automatic radiology report generation that learns generalizable representations and provides a basis pattern for model adaptation in sophisticated analysts' cases. Specifically, ChatRadio-Valuer is trained based on the radiology reports from a single institution by means of supervised fine-tuning, and then adapted to disease diagnosis tasks for human multi-system evaluation (i.e., chest, abdomen, muscle-skeleton, head, and maxillofacial $\&$ neck) from six different institutions in clinical-level events. The clinical dataset utilized in this study encompasses a remarkable total of \textbf{332,673} observations. From the comprehensive results on engineering indicators, clinical efficacy and deployment cost metrics, it can be shown that ChatRadio-Valuer consistently outperforms state-of-the-art models, especially ChatGPT (GPT-3.5-Turbo) and GPT-4 et al., in terms of the diseases diagnosis from radiology reports. ChatRadio-Valuer provides an effective avenue to boost model generalization performance and alleviate the annotation workload of experts to enable the promotion of clinical AI applications in radiology reports

Molecular Characterization of Highly Pathogenic H5N1 Avian Influenza A Viruses Isolated from Raccoon Dogs in China

The highly pathogenic avian influenza H5N1 virus can infect a variety of animals and continually poses a threat to animal and human health. While many genotypes of H5N1 virus can be found in chicken, few are associated with the infection of mammals. Characterization of the genotypes of viral strains in animal populations is important to understand the distribution of different viral strains in various hosts. This also facilitates the surveillance and detection of possible emergence of highly pathogenic strains of specific genotypes from unknown hosts or hosts that have not been previously reported to carry these genotypes.Two H5N1 isolates were obtained from lung samples of two raccoon dogs that had died from respiratory disease in China. Pathogenicity experiments showed that the isolates were highly pathogenic to chicken. To characterize the genotypes of these viruses, their genomic sequences were determined and analyzed. The genetic contents of these isolates are virtually identical and they may come from the same progenitor virus. Phylogenetic analysis indicated that the isolates were genetically closely related to genotype V H5N1 virus, which was first isolated in China in 2003, and were distinct from the dominant virus genotypes (e.g. genotype Z) of recent years. The isolates also contain a multibasic amino acid motif at their HA cleavage sites and have an E residue at position 627 of the PB2 protein similar to the previously-identified avian viruses.This is the first report that genotype V H5N1 virus is found to be associated with a mammalian host. Our results strongly suggest that genotype V H5N1 virus has the ability to cross species barriers to infect mammalian animals. These findings further highlight the risk that avian influenza H5N1 virus poses to mammals and humans, which may be infected by specific genotypes that are not known to infect these hosts

Structural Shape Optimization Based on Multi-Patch Weakly Singular IGABEM and Particle Swarm Optimization Algorithm in Two-Dimensional Elastostatics

In this paper, a multi-patch weakly singular isogeometric boundary element method (WSIGABEM) for two-dimensional elastostatics is proposed. Since the method is based on the weakly singular boundary integral equation, quadrature techniques, dedicated to the weakly singular and regular integrals, are applied in the method. A new formula for the generation of collocation points is suggested to take full advantage of the multi-patch technique. The generated collocation points are essentially inside the patches without any correction. If the boundary conditions are assumed to be continuous in every patch, no collocation point lies on the discontinuous boundaries, thus simplifying the implementation. The multi-patch WSIGABEM is verified by simple examples with analytical solutions. The features of the present multi-patch WSIGABEM are investigated by comparison with the traditional IGABEM. Furthermore, the combination of the present multi-patch WSIGABEM and the particle swarm optimization algorithm results in a shape optimization method in two-dimensional elastostatics. By changing some specific control points and their weights, the shape optimizations of the fillet corner, the spanner, and the arch bridge are verified to be effective

A real-time road detection method based on reorganized lidar data.

Road Detection is a basic task in automated driving field, in which 3D lidar data is commonly used recently. In this paper, we propose to rearrange 3D lidar data into a new organized form to construct direct spatial relationship among point cloud, and put forward new features for real-time road detection tasks. Our model works based on two prerequisites: (1) Road regions are always flatter than non-road regions. (2) Light travels in straight lines in a uniform medium. Based on prerequisite 1, we put forward difference-between-lines feature, while ScanID density and obstacle radial map are generated based on prerequisite 2. According to our method, we construct an array of structures to store and reorganize 3D input firstly. Then, two novel features, difference-between-lines and ScanID density, are extracted, based on which we construct a consistency map and an obstacle map in Bird Eye View (BEV). Finally, the road region is extracted by fusing these two maps and refinement is used to polish up our outcome. We have carried out experiments on the public KITTI-Road benchmark, achieving one of the best performances among the lidar-based road detection methods. To further prove the efficiency of our method on unstructured road, the visual outcomes in rural areas are also proposed

Transformation power or development pressure: economic growth targets and urban carbon productivity

Maintaining moderate economic growth targets (EGTs) is the key for local governments to effectively implement the “carbon peak and carbon neutrality” goals under the refreshed development pattern. Utilizing panel data of 276 prefecture-level cities in mainland China from 2010 to 2020, and employing methods such as intermediary and threshold models, this study empirically analyzes the internal mechanism of EGT’s impact on urban carbon productivity (UCP). Our findings demonstrate that: ① The overall EGT during the analyzed period is not conducive to improving UCP. This conclusion remains valid after a series of robustness tests. This effect is more pronounced in the central region and resource-based cities than in the east-west region and non-resource-based cities. ② EGT not only directly suppresses UCP but also exerts indirect negative impacts on UCP from three aspects: delaying the digital economy (DE), constraining financial expansion (FE), and hindering green technology innovation (GTI). This negative indirect effect is similar to or even surpasses the direct effect, suggesting that the internal relationship between EGT and “dual-carbon” goals should be re-evaluated from a new compound perspective. ③ EGT not only has a simple linear impact on UCP but also significantly exhibits a dynamic evolution pattern in inverted “U” shape. That is, as EGT continuously upgrades, a nonlinear impact on UCP emerges in the form of “promoting first, suppressing later”. This indicates that surpassing the “degree” limit for EGT will be detrimental to the improvement of UCP. This study broadens the scope of carbon productivity analysis by introducing a new perspective centered on EGT. The insights gleaned from this research offer valuable guidance for local governments to effectively manage economic growth expectations and promote the synchronized achievement of dual-carbon objectives