n the agricultural domain, the deployment of large language models (LLMs) is hindered by the lack of training data and evaluation benchmarks. To mitigate this issue, we propose AgriEval, the first comprehensive Chinese agricultural benchmark with three main characteristics: (1) Comprehensive Capability Evaluation. AgriEval covers six major agriculture categories and 29 subcategories within agriculture, addressing four core cognitive scenarios—memorization, understanding, inference, and generation. (2) High-Quality Data. The dataset is curated from university-level examinations and assignments, providing a natural and robust benchmark for assessing the capacity of LLMs to apply knowledge and make expert-like decisions. (3) Diverse Formats and Extensive Scale. AgriEval comprises 14,697 multiple-choice questions and 2,167 open-ended question-and-answer questions, establishing it as the most extensive agricultural benchmark available to date. We also present comprehensive experimental results over 51 open-source and commercial LLMs. The experimental results reveal that most existing LLMs struggle to achieve 60 percent accuracy, underscoring the developmental potential in agricultural LLMs. Additionally, we conduct extensive experiments to investigate factors influencing model performance and propose strategies for enhancement.

The management of Type 1 Diabetes (T1D) involves comprehensive scenarios, including blood glucose prediction, risk assessment, and insulin dosing control. However, the heterogeneity of information requirements, task objectives, and behavioral logic poses challenges in constructing unified T1D management systems with conventional deep learning models, mainly attributed to insufficient capability of feature alignment and lack of high-quality multi-scenario T1D data. In this paper, we propose T1D-MLLM, the first multimodal large language model designed for unified multi-scenario T1D management, as well as construct LCT1D, a large-scale and cross-scenario T1D dataset. Specifically, T1D-MLLM integrates time-series physiological data with natural language descriptions to capture longterm dependencies across multiple management scenarios while also enhancing fine-grained perception of time-series. Meanwhile, to overcome data scarcity, we proposed a multimodal data generation paradigm based on expert strategies. By constructing task templates and applying a rule-driven alignment mechanism, we generated 150,000 high-quality expert samples with individualized physiological parameters, which provide rich and diverse training samples, significantly improving the T1D-MLLM's capabilities in heterogeneous feature alignment and cross-scenario inference. These experiments demonstrate the effectiveness of the T1D-MLLM in multi-scenarios of various tasks as a unified system, with an excellent performance that surpasses both opensource and proprietary models.

Blood glucose (BG) forecasting aims to help people with Type-1 diabetes (T1D) avoid hyperglycemia or hypoglycemia, which plays a crucial role in medical monitoring. Despite the advancements in deep learning methods for BG forecasting, their ability to predict long-term time series remains limited, and they cannot fully meet the demand for BG forecasting. This limitation stems from the failure to account for both intra- and inter-variable variations simultaneously. To address this challenge, we introduce the$\text{Fi}^{2}$VBlock, which exploits the frequency perspective to fuse intra- and intervariable variations. After transforming to the frequency domain using the Frequency Transform Module, the Frequency Cross Attention between the real and imaginary parts is designed to obtain enhanced frequency representations and capture intravariable variations. In addition, inception blocks are employed to integrate information, thus capturing correlations across different variables. Our backbone network,$\text{Fi}^{2} \mathrm{V}$, employs a residual architecture by concatenating multiple$\text{Fi}^{2}$VBlocks, thereby avoiding degradation problems. Experimental evaluations reveal that$\text{Fi}^{2} \mathrm{V}$outperforms other baselines on the T1DMS and Dnurse datasets and demonstrates zero-shot generalization across patients.

Coronary artery segmentation in X-ray angiography is crucial for cardiovascular diagnosis and treatment planning, yet remains challenging due to the inherently low signal-to-noise ratio (SNR) and low contrast of the images, which degrade obscure vascular structures and fine details. Although the Segment Anything Model (SAM) has demonstrated strong potential in various vision tasks, its capabilities for coronary artery segmentation in X-ray angiography have yet to be fully explored. To overcome these challenges, we propose CoroSAM, a novel promptfree SAM fine-tuning framework tailored for coronary artery segmentation in X-ray angiography. CoroSAM introduces two key innovations: (1) Gated Spatial-Frequency Adapter (GSFA) designed to fine-tune SAM's image encoder by dynamically integrating both spatial- and frequency-domain information to better capture vascular structures under noisy conditions; and (2) Orientation-Guided Adapter (OGA), the first to our knowledge to incorporate orientation maps for refining structural details in SAM predictions, enhancing structural awareness and improving robustness under low-contrast conditions. Extensive experiments on four public datasets demonstrate that CoroSAM consistently outperforms state-of-the-art methods. The code is available at https://github.com/HITZhengGW/CoroSAM

Influenza seriously endangers human health and even causes a large number of deaths every year. Transformers for Influenza-like illness (ILI) forecasting have recently been proven effective. However, these end-to-end deep models are mathematically almost black-box, hindering us from inferring the specific roles and functionalities of each layer within the models, which constitutes a common key challenge in deep neural networks. At the same time, explainability helps trust and use AI systems effectively. In this paper, we propose an efficient ILI forecasting framework incorporating a patching design and variable-channel pairs, which can accommodate any white-box transformer, thereby endowing ILI forecasting with both interpretability and analytical accuracy. Through extensive experimental validation, leveraging white-box transformers such as CRATE, our White-box Time Series Transformer (WhiteTST) framework achieves the state-of-the-art accuracy on ILI datasets. We visually present the self-attention maps within WhiteTST to indicate further explainability. Our results suggest a pathway for designing white-box foundational models for ILI forecasting that concurrently exhibit high accuracy and interpretability. The code is available online in https://github.com/HITshenrj/WhiteTST.

Many studies attempt to explore the underlying mechanisms of depression and distinguish between depression patients and normal controls (NC) using electroencephalography (EEG) techniques. With the advancement of deep learning methods, an increasing number of studies aim to design Computer-Aided Diagnosis (CAD) systems for mild depression (MD) to achieve early identification. However, few studies construct models based on abnormal brain topological structures specific to MD patients. In this study, we investigate the abnormal brain topological structures of individuals with MD based on EEG data obtained during an emotional face paradigm. Functional connectivity analysis reveals a higher proportion of inter-hemispheric connections in the MD group compared to intra-hemispheric connections. Additionally, intra-hemispheric connections are primarily observed within the frontal and parietal lobes of both groups. Hierarchical clustering analysis results indicate impairments in the frontal and parietal lobes in the MD group compared to the NC group. Based on these findings, we propose a novel feature called "cross-brain feature" and introduce a multi-cross-brain attention fusion mechanism to integrate information between brain regions. We train and test our models using 5-fold cross-validation. The results demonstrate that the classification model based on abnormal brain topological structures achieves the highest performance among the three state-of-the-art algorithms, with an accuracy of 80.1%, an area under the ROC curve (AUC) of 80%, and a sensitivity (SEN) of 86.3%. These findings suggest that combining abnormal brain topological structures derived from functional connectivity matrices with deep learning techniques can provide an effective objective approach to the early detection of depression.

This Electroencephalography (EEG)-based research is to explore the effective biomarkers for depression recognition. Resting-state EEG data were collected from 24 major depressive patients (MDD) and 29 normal controls using 128-electrode geodesic sensor net. To better identify depression, we extracted multi-type of EEG features including linear features (L), nonlinear features (NL), functional connectivity features phase lagging index (PLI) and network measures (NM) to comprehensively characterize the EEG signals in patients with MDD. And machine learning algorithms and statistical analysis were used to evaluate the EEG features. Combined multi-types features (All: L+ NL + PLI + NM) outperformed single-type features for classifying depression. Analyzing the optimal features set we found that compared to other type features, PLI occupied the largest proportion of which functional connections in intra-hemisphere were much more than that of in inter-hemisphere. In addition, when using PLI features and All features, high frequency bands (alpha, beta) could achieve obviously higher classification accuracy than low frequency bands (delta, theta). Parietal-occipital lobe in the high frequency bands had great effect in depression identification. In conclusion, combined multi-types EEG features along with a robust classifier can better distinguish depressive patients from normal controls. And intra-hemispheric functional connections might be an effective biomarker to detect depression. Hence, this paper may provide objective and potential electrophysiological characteristics in depression recognition.