Search Results (35)

Artificial intelligence foundation models are increasingly deployed for prostate cancer Gleason grading, where GP3/GP4 distinction directly impacts treatment decisions (active surveillance vs. intervention). However, these models may achieve high validation accuracy by learning specimen-specific artifacts rather than generalizable biological features, limiting real-world clinical utility. We introduce PANDA-PLUS-Bench, a curated benchmark dataset derived from expertly annotated prostate biopsies designed specifically to quantify this failure mode. The benchmark comprises nine carefully selected whole slide images from nine unique patients containing diverse Gleason patterns, with non-overlapping tissue patches extracted at both 512 × 512 and 224 × 224-pixel resolutions across eight augmentation conditions. Using this benchmark, we evaluate seven foundation models (Virchow, Virchow2, UNI, UNI2, Phikon, Phikon-v2, and HistoEncoder) on their ability to separate biological signals from slide-level confounders. Our results reveal substantial variation in robustness across models: the Virchow models achieved the lowest slide-level encoding among large-scale models (slide ID accuracy: 80.7–81.0%), yet Virchow2 exhibited the lowest cross-slide accuracy (47.2%). HistoEncoder, trained specifically on prostate tissue, demonstrated the highest cross-slide accuracy (59.7%) and the strongest slide-level encoding (slide ID accuracy: 90.3%), suggesting tissue-specific training may enhance both biological feature capture and slide-specific signatures. All models exhibited measurable within-slide vs. cross-slide accuracy gaps, though the magnitude varied from 19.9 percentage points (HistoEncoder) to 26.9 percentage points (Phikon). We provide an open-source Google Colab notebook enabling researchers to evaluate additional foundation models against our benchmark using standardized metrics. PANDA-PLUS-Bench addresses a critical gap in foundation model evaluation by providing a purpose-built resource for robustness assessment in the clinically important context of Gleason grading. Full article

The automatic classification of ISUP (International Society of Urological Pathology) grade groups in prostate histopathological images remains challenging due to the high similarity between adjacent classes, class imbalance, and label noise. In this work, we propose a deep learning pipeline based on EfficientNet convolutional neural networks combined with a hybrid loss function that integrates ordinal regression and Focal Loss to better capture the ordered nature of ISUP grades. A noise-filtering strategy based on the entropy of predictions from multiple EfficientNet models was first applied to identify and remove high-uncertainty samples from the training set. The problem was then reformulated as an ordinal regression task to explicitly model the hierarchical relationship among grades. Experiments conducted on the PANDA dataset demonstrate that removing noisy samples improved performance from $\kappa =0.826$ to $\kappa =0.833$. Incorporating ordinal loss further increased performance to $\kappa =0.851$. The best configuration, combining ordinal regression and Focal Loss, achieved $\kappa =0.857$ and an accuracy of $0.669$, while reducing severe misclassifications and concentrating errors among adjacent classes. These results indicate that explicitly modeling ordinal structure and mitigating label noise are effective strategies for improving prostate cancer grading systems. Full article

As global demands for nature reserve management intensify, intelligent monitoring has become a pivotal trend. Integrating artificial intelligence with infrared camera traps enables automated analysis of endangered species behavior, providing timely insights for conservation. However, complex habitats often degrade the performance of existing detection technologies. Focusing on the giant panda—a flagship conservation species—we constructed a novel dataset from long-term field monitoring videos and developed an improved PandaSlowFast network. Our model employs channel attention to enhance temporal features, uses small-kernel depth-wise convolutions and dilated convolutions to expand receptive fields for spatial feature extraction, and introduces the Adaptive SwisH activation function to improve adaptability and training stability. The results show that PandaSlowFast achieves 85.38% mean average precision (mAP), outperforming existing methods. An FP16-quantized version maintains comparable accuracy (85.16% mAP) while running at 3.2 frames per second on a Raspberry Pi 4, demonstrating practical deployability for on-site monitoring. This work provides technical support for intelligent panda behavior analysis and offers a transferable methodology for monitoring other rare species, contributing to biodiversity conservation. Full article

Automatic recognition of endangered animal behavior is crucial for biodiversity conservation and improving animal welfare, yet traditional manual observation remains inefficient and invasive. This work contributes directly to sustainable wildlife management by enabling non-invasive, scalable, and efficient monitoring, which supports long-term ecological balance and aligns with several United Nations Sustainable Development Goals (SDGs), particularly SDG 15 (Life on Land) and SDG 12 (Responsible Consumption and Production). The current deep learning approaches often struggle with the scarcity of behavioral data and complex environments, leading to poor model generalization. To address these challenges, this study focuses on endangered animal behavior monitoring and proposes a multimodal learning framework termed ABCLIP. This model leverages multimodal contrastive learning between video-and-text pairs, utilizing natural language supervision to enhance representation ability. The framework integrates pre-training, prompt learning, and fine-tuning to optimize performance specifically for small-scale animal behavior datasets, with a focus on the specific social and ecological behaviors of giant pandas. The experimental results demonstrate that ABCLIP achieves remarkable accuracy and robustness in recognizing endangered animal behaviors, attaining Top-1 and Top-5 accuracy of 82.50% and 99.25%, respectively, on the LoTE-Animal dataset, which outperforms strong baseline methods such as SlowFast (78.54%/97.55%). Furthermore, in zero-shot recognition scenarios for unseen behaviors, ABCLIP achieves an accuracy of 58.00%. This study highlights the potential of multimodal contrastive learning in wildlife monitoring and provides efficient technical support for precise protection measures and scientific management of endangered species. Full article

Ex situ conservation in breading centers is one of the key strategies for saving giant pandas (Ailuropoda melanoleuca). Abnormal behaviors (e.g., inappetence) are key symptoms of potential health issues (e.g., Klebsiella pneumoniae) for the captives. Therefore, monitoring their normal activity patterns could set a baseline to detect these abnormalities for implementing timely interventions. However, traditional monitoring methods are labor-intensive, which often rely on manual observations. Here, we proposed a deep learning framework, termed as DeepPanda, for automatically recognizing four essential behaviors (i.e., eating, walking, resting and drinking) of giant pandas based on videos from common surveillance cameras. Experimental results demonstrated that the DeepPanda model achieved high performance on the self-established APanda dataset, with the testing mean average precision at an IoU threshold of 0.5 (mAP@0.5) of 98.8%. This methodology provides a powerful tool for monitoring the captive giant panda’s behaviors. Full article

For a long time, low efficiency in the transfer of rural collective land use rights and the ambiguous attribution of collective land property rights have not only restricted the mobility of rural labor factors but have also hindered the release of vitality in the rural collective economy. This has resulted in lagging growth in the income that rural residents obtain from collective economic factors, contributing to the persistent widening of the urban/rural income gap. As an important institutional innovation to address these issues, the effects of the reform of the rural collective property rights system urgently need to be clarified. The reform of the rural collective property rights system constitutes a major initiative in the transformation of the rural land system. Centered on asset verification and valuation, as well as the demarcation of membership rights and the restructuring towards a shareholding cooperative system, it aims to establish a collective property rights regime characterized by clearly defined ownership and fully functional entitlements. This study takes the national pilot reform of rural collective property rights launched in 2016 as a quasi-natural policy experiment, systematically examining the impact of this pilot policy on the internal income gap within households and its spillover effects on the urban–rural income gap. Based on microdata from the China Household Finance Survey (CHFS) and the China Longitudinal Night Light Data Set (PANDA-China), this study constructs a five-period balanced panel dataset covering 2304 rural households across 25 provinces. A relative exploitation index based on the Kawani index is constructed, and empirical analysis is conducted using a combination of multi-period difference-in-differences (Multi-period DID), discrete binary models, and propensity score matching-difference-in-differences (PSM-DID) models. The results show that: First, the pilot reform significantly reduced the level of income inequality within rural areas in the pilot regions, and its policy benefits further generated positive spillovers via market-driven factor allocation mechanisms, effectively bridging the urban–rural income gap. Second, institutional reforms activated the potential of rural non-agricultural economic factors, establishing new channels for a two-way flow of urban and rural factors, becoming an important path to achieve the goal of common prosperity. Third, the policy effects exhibited significant heterogeneity, specifically manifested in the attributes of major grain-producing regions, initial household income levels, and the human capital characteristics of household heads having significant moderating effects on reform outcomes. This study not only provides theoretical support and empirical evidence for deepening rural property rights reforms under the new rural revitalization strategy, but it also reveals the driving role of institutional innovation in factor mobility, thereby influencing the transmission mechanism of income distribution patterns. This finding offers a China-based solution for developing countries to address the imbalance in urban–rural development and the widening income gap. Full article

Colorectal cancer (CRC) remains a leading cause of cancer-related death worldwide, with resistance to targeted therapies presenting a significant clinical challenge. This study combines computational and experimental methods to identify and validate Antrocin, a natural sesquiterpene lactone, as a potential multi-target inhibitor of the BRAF/MEK/PI3K oncogenic pathway in CRC. Differential gene expression and mutational analyses were performed using public datasets (TCGA, TNMplot, GEPIA2, GSCA, PANDA, and cBioPortal) to assess the prevalence and clinical significance of BRAF, MEK, and PI3K alterations in CRC. In silico molecular docking, using AutoDock Vina, predicted strong binding affinities of Antrocin to BRAF (ΔG = −8.5 kcal/mol), MEK (ΔG = −7.3 kcal/mol), and PI3K (ΔG = −6.9 kcal/mol), comparable to those of FDA-approved inhibitors for BRAF (Dabrafenib), MEK (Trametinib), and PI3K (Alpelisib). Drug-likeness and ADME properties were evaluated via SwissADME and ADMETlab, supporting Antrocin’s potential as a drug candidate. In vitro assays using HCT116 and RKO CRC cell lines validated that Antrocin treatment suppressed cell viability, spheroid formation, and migration, accompanied by reduced expression levels of the oncogenic BRAF/MEK/PI3K signaling pathway. Antrocin-treated tumor-conditioned medium experiments demonstrated Antrocin’s ability to reduce the differentiation of cancer-associated fibroblasts and the polarization of M2 macrophages. Preclinical mouse xenograft experiments demonstrated a delay in tumor growth following treatment with Antrocin. These results suggest that Antrocin, identified through computational screening and validated experimentally, could be a promising multi-target agent to overcome therapy resistance in CRC. Full article

This study evaluates the performance and energy trade-offs of three popular data processing libraries—Pandas, PySpark, and Polars—applied to GreenNav, a CO₂ emission prediction pipeline for urban traffic. GreenNav is an eco-friendly navigation app designed to predict CO₂ emissions and determine low-carbon routes using a hybrid CNN-LSTM model integrated into a complete pipeline for the ingestion and processing of large, heterogeneous geospatial and road data. Our study quantifies the end-to-end execution time, cumulative CPU load, and maximum RAM consumption for each library when applied to the GreenNav pipeline; it then converts these metrics into energy consumption and CO₂ equivalents. Experiments conducted on datasets ranging from 100 MB to 8 GB demonstrate that Polars in lazy mode offers substantial gains, reducing the processing time by a factor of more than twenty, memory consumption by about two-thirds, and energy consumption by about 60%, while maintaining the predictive accuracy of the model (R² ≈ 0.91). These results clearly show that the careful selection of data processing libraries can reconcile high computing performance and environmental sustainability in large-scale machine learning applications. Full article

Introduction: Air pollution is a risk factor for a variety of cardiopulmonary diseases and is a contributing factor to cancer, diabetes, and cognitive impairment. The impact on mortality is not clearly elucidated. Objectives: The goal of this study is to determine the impact (if any) of air pollution on the 5-year mortality of patients in the American Family Cohort (AFC) dataset. Methods: The AFC dataset is derived from the American Board of Family Medicine PRIME Registry electronic health record data. It includes longitudinal information from 6.6 million unique patients from an estimated 800 primary care practices across 47 states, with 40% coming from rural areas. The Environmental Protection Agency’s Air Quality Index (AQI) measures were downloaded for the study period (2016–2022). Using the Python library pandas, the AFC and EPA datasets were merged with respect to date, time, and location. Cox Regression Models were performed on the merged dataset to determine the impact (if any) of air quality on patients’ five-year survival. In the model, AQI was handled as a time-independent (time-fixed) covariate. Results: The group with AQI > 50 had an adjusted hazard of death that was 4.02 times higher than the hazard of death in the group with AQI ≤ 50 (95% CI: 3.36, 4.82, p < 0.05). The hazard of death was 6.73 times higher in persons older than 80 years of age (95% CI: 5.47, 8.28; p < 0.05) compared to those younger than 80 years of age. Black/African American patients had a 4.27 times higher hazard of death (95%CI: 3.47, 5.26; p < 0.05) compared to other races. We also found that regional effects played a role in survival. Conclusions: Poor air quality was associated with a higher hazard of mortality, and this phenomenon was particularly pronounced in Black/African American patients and patients older than 80 years of age. Air pollution is an important social determinant of health. Public health initiatives that improve air quality are necessary to improve health outcomes. Full article

Automated giant panda (Ailuropoda melanoleuca) behavior recognition (GPBR) systems are highly beneficial for efficiently monitoring giant pandas in wildlife conservation missions. While video-based behavior recognition attracts a lot of attention, few studies have focused on audio-based methods. In this paper, we propose the exploitation of the audio data recorded by collar-mounted devices on giant pandas for the purpose of GPBR. We construct a new benchmark audio dataset of giant pandas named abPanda-5 for GPBR, which consists of 18,930 samples from five giant panda individuals with five main behaviors. To fully explore the bioacoustic features, we propose an audio-based method for automatic GPBR using competitive fusion learning. The method improves behavior recognition accuracy and robustness, without additional computational overhead in the inference stage. Experiments performed on the abPanda-5 dataset demonstrate the feasibility and effectiveness of our proposed method. Full article

Data cleaning remains one of the most time-consuming and critical steps in modern data science, directly influencing the reliability and accuracy of downstream analytics. In this paper, we present a comprehensive evaluation of five widely used data cleaning tools—OpenRefine, Dedupe, Great Expectations, TidyData (PyJanitor), and a baseline Pandas pipeline—applied to large-scale, messy datasets spanning three domains (healthcare, finance, and industrial telemetry). We benchmark each tool on dataset sizes ranging from 1 million to 100 million records, measuring execution time, memory usage, error detection accuracy, and scalability under increasing data volumes. Additionally, we assess qualitative aspects such as usability and ease of integration, reflecting real-world adoption concerns. We incorporate recent findings on parallelized data cleaning and highlight how domain-specific anomalies (e.g., negative amounts in finance, sensor corruption in industrial telemetry) can significantly impact tool choice. Our findings reveal that no single solution excels across all metrics; while Dedupe provides robust duplicate detection and Great Expectations offers in-depth rule-based validation, tools like TidyData and baseline Pandas pipelines demonstrate strong scalability and flexibility under chunk-based ingestion. The choice of tool ultimately depends on domain-specific requirements (e.g., approximate matching in finance and strict auditing in healthcare) and the magnitude of available computational resources. By highlighting each framework’s strengths and limitations, this study offers data practitioners clear, evidence-driven guidance for selecting and combining tools to tackle large-scale data cleaning challenges. Full article

This study analyzes 324 captive giant panda breeding events (1998–2023) to unravel maternal and gestational drivers of cub survival and health—the largest dataset of its kind to date. Key variables included gestational duration, maternal age, interbirth interval, number of cubs per breeding event, cub birth weight, and neonatal mortality. Maternal age (5–7 years, ≥20 years) and interbirth intervals ≤1 year were linked to increased neonatal mortality, whereas intermediate gestational durations (110–127 days) and longer interbirth intervals (≥4 years) correlated with higher cub survival ratios. Although no direct relationship was found between gestational duration and birth weight, singleton cubs exhibited significantly higher weights than twins. By quantifying these relationships, we propose actionable strategies to enhance reproductive efficiency in managed populations, such as adjusting breeding schedules and maternal health monitoring. Full article

This paper offers an experimental approach that integrates acoustic emission (AE) monitoring with machine learning (ML) to identify damage mechanisms and predict the mechanical properties of 3D-printed biocomposites. Specimens were fabricated using a bio-filament composed of a PLA matrix reinforced with 10% wt. of Lygeum spartum fibers and were subjected to tensile and flexural tests. The processed dataset, comprising six normalized features (cumulative rise, duration, count, frequency, energy, and amplitude) was used to train four ML models: Random Forest Regression (RFR), Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Decision Trees (DT) implemented in Python using libraries such as scikit-learn, pandas, and numpy. The prediction models were developed using an 80/20 train–test split and further validated by 5-fold cross-validation, with performance evaluated by R-squared (R²) and Mean Squared Error (MSE) metrics. Our results demonstrate robust prediction capabilities, with the RFR model achieving the highest accuracy (R² > 0.98 and MSE as low as 0.013 for tensile stress prediction). Additionally, unsupervised clustering using K-means was applied to group AE signals into distinct clusters corresponding to different damage modes. This comprehensive methodology not only enhances our understanding of damage evolution in composite materials but also establishes a data-driven framework for non-destructive evaluation and structural health monitoring. Full article

Bioplastics have been presented as a sustainable alternative to products derived from fossil sources. In response, industries have developed innovative products using biopolymers across various sectors, such as food, packaging, biomedical, and construction. However, consumer acceptance remains crucial for their widespread adoption. This study aims to explore public sentiment toward bioplastics, focusing on emotions expressed on Reddit. A dataset of 5041 Reddit comments was collected using keywords associated with bioplastics and the extraction process was facilitated by Python-based libraries like pandas, NLTK, and NumPy. The sentiment analysis was conducted using the NRCLex, a broadly used lexicon. The overall findings suggest that trust, anticipation, and joy were the most dominant emotions in the time frame 2014–2024, indicating that the public emotional response towards bioplastics has been mostly positive. Negative emotions such as fear, sadness, and anger were less prevalent, although an intense response was noted in 2018. Findings also indicate a temporal co-occurrence between significant events related to bioplastics and changes in sentiment among Reddit users. Although the representativeness of the sample is limited, the results of this study support the need to develop real-time monitoring of the public’s emotional responses. Thus, it will be possible to design communication campaigns more aligned with public needs. Full article

The giant panda, a rare and iconic species endemic to China, has attracted significant attention from both domestic and international researchers due to its crucial ecological role, unique cultural value, and distinct evolutionary history. While substantial progress has been made in the field of individual identification, behavior recognition remains underdeveloped, facing challenges such as the lack of dynamic temporal features and insufficient extraction of behavioral characteristics. To address these challenges, we propose the Swin-Panda model, which leverages transfer learning based on the Video Swin Transformer architecture within the mmaction2 framework. In addition, we introduce two novel modules: the Comprehensive Perception Auxiliary Module and the Spatiotemporal Shift Attention Module. These modules facilitate the extraction of local and spatiotemporal information, allowing the model to more effectively capture the behavioral and movement patterns of giant pandas. Experimental results on the PACV-8 dataset demonstrate that our model achieves an accuracy of 88.02%, outperforming several benchmark models. This approach significantly enhances behavior recognition accuracy, thereby contributing to the advancement of panda welfare and species conservation efforts. Full article