Miracle-dz/UniFAR-FTUs huggingface dataset:
PorcineAI-Enhancer: Prediction of Pig Enhancer Sequences Using Convolutional Neural Networks

Simple Summary This study develops a deep learning framework called PorcineAI-enhancer to predict enhancer sequences in pigs. Enhancers play a key role in regulating gene expression. However, identifying enhancers experimentally remains challenging. This study constructs a reliable pig enhancer dataset by integrating multiple data sources. The PorcineAI-enhancer model employs convolutional neural networks to extract features from DNA sequences and classify them into enhancers or non-enhancers. Evaluation on an independent test set shows the model has excellent performance. It also demonstrates strong predictive capability on tissue-specific enhancers from human and pig. This tool facilitates research on gene regulation mechanisms in pigs. It provides valuable resources to understand complex traits related to agriculture and biomedicine. Abstract Understanding the mechanisms of gene expression regulation is crucial in animal breeding. Cis-regulatory DNA sequences, such as enhancers, play a key role in regulating gene expression. Identifying enhancers is challenging, despite the use of experimental techniques and computational methods. Enhancer prediction in the pig genome is particularly significant due to the costliness of high-throughput experimental techniques. The study constructed a high-quality database of pig enhancers by integrating information from multiple sources. A deep learning prediction framework called PorcineAI-enhancer was developed for the prediction of pig enhancers. This framework employs convolutional neural networks for feature extraction and classification. PorcineAI-enhancer showed excellent performance in predicting pig enhancers, validated on an independent test dataset. The model demonstrated reliable prediction capability for unknown enhancer sequences and performed remarkably well on tissue-specific enhancer sequences.The study developed a deep learning prediction framework, PorcineAI-enhancer, for predicting pig enhancers. The model demonstrated significant predictive performance and potential for tissue-specific enhancers. This research provides valuable resources for future studies on gene expression regulation in pigs.
Abnormal and Changing Information Interaction in Adults with Attention-Deficit/Hyperactivity Disorder Based on Network Motifs

Network motif analysis approaches provide insights into the complexity of the brain’s functional network. In recent years, attention-deficit/hyperactivity disorder (ADHD) has been reported to result in abnormal information interactions in macro- and micro-scale functional networks. However, most existing studies remain limited due to potentially ignoring meso-scale topology information. To address this gap, we aimed to investigate functional motif patterns in ADHD to unravel the underlying information flow and analyze motif-based node roles to characterize the different information interaction methods for identifying the abnormal and changing lesion sites of ADHD. The results showed that the interaction functions of the right hippocampus and the right amygdala were significantly increased, which could lead patients to develop mood disorders. The information interaction of the bilateral thalamus changed, influencing and modifying behavioral results. Notably, the capability of receiving information in the left inferior temporal and the right lingual gyrus decreased, which may cause difficulties for patients in processing visual information in a timely manner, resulting in inattention. This study revealed abnormal and changing information interactions based on network motifs, providing important evidence for understanding information interactions at the meso-scale level in ADHD patients.
Defect Classification for Additive Manufacturing with Machine Learning

Additive manufacturing offers significant design freedom and the ability to selectively influence material properties. However, conventional processes like laser powder bed fusion for metals may result in internal defects, such as pores, which profoundly affect the mechanical characteristics of the components. The extent of this influence varies depending on the specific defect type, its size, and morphology. Furthermore, a single component may exhibit various defect types due to the manufacturing process. To investigate these occurrences with regard to other target variables, this study presents a random forest tree model capable of classifying defects in binary images derived from micrographs. Our approach demonstrates a classification accuracy of approximately 95% when distinguishing between keyhole and lack of fusion defects, as well as process pores. In contrast, unsupervised models yielded prediction accuracies below 60%. The model’s accuracy in differentiating between lack of fusion and keyhole defects varies based on the manufacturing process’s parameters, primarily due to the irregular shapes of keyhole defects. We provide the model alongside this paper, which can be utilized on a standard computer without the need for in situ monitoring systems during the additive manufacturing process.
Buckling-Based Method for Evaluating Young's Modulus of Microfiber and Predicting Cell Growth

Variety of microfibers have been widely utilized as a promising bioink for 3D bioprinting. However, the mechanical properties of the cell-laden microfiber are difficult to be characterized precisely due to the lack of low-damage loading methods for encapsulated cells that require a relatively short loading time. In this paper, we propose a bulking-based method for rapidly evaluating the Young's modulus, Ec of the microfibers within several seconds. A two-probe based micromanipulation system is utilized to compress the microfiber. Based on the computer vision, the compression force is measured and used for calculating the Ec according to the Euler column formula, and it is found that the evaluation results of Ec can be improved in a high compression speed. Moreover, the precision of measure Ec is guaranteed and the value is close to the other loading methods. The results show the proposed method can realize a high precise measurement in about 2s which is faster than other methods. Additionally, the measured Ec is used to predict the cell growth of cell-laden microfibers in the next few days, demonstrating its potential for speeding up the screening of biocompatible materials.
Personality and Survey Satisficing

Abstract In this paper, we consider the role of personality as a component of motivation in promoting or inhibiting the tendency to exhibit the satisficing response styles of midpoint, straightlining, and Don’t Know responding. We assess whether respondents who are low on the Conscientiousness and Agreeableness dimensions of the Big Five Personality Inventory are more likely to exhibit these satisficing response styles. We find large effects of these personality dimensions on the propensity to satisfice in both face-to-face and self-administration modes and in probability and nonprobability samples. People who score high on Conscientiousness and Agreeableness were less likely to be in the top decile of straightlining and midpoint distributions. The findings for Don’t Know responding were weaker and only significant for Conscientiousness in the nonprobability sample. We also find large effects across all satisficing indicators for a direct measure of cognitive ability, where existing studies have mostly relied on proxy measures of ability such as educational attainment. Sensitivity analysis suggests the personality effects are likely to be causal in nature.
Benchmark for Evaluating Initialization of Visual-Inertial Odometry

In this paper, we present a novel benchmark for the evaluation of the initialization of visual-inertial odometry (VIO). First, we collect a specific set of sequences in three challenging scenarios from existing public datasets. Second, we design benchmarking metrics for VIO initialization algorithms, with which the rapidity and accuracy of state estimation are evaluated comprehensively. We also evaluate state-of-the-art VIO initialization approaches on our benchmark. To facilitate related research, we publicly release the full benchmark described in this paper.
Long-Term LiDAR Place Recognition Based on Phase Congruency in Changing Environments

Robust Place recognition is an open challenging problem under various perceptual conditions, e.g., all weather, times-of-day and seasons shifts. Although LiDAR sensors do not suffer from illumination problem of visual appearance, they are dramatically affected by structural changes across seasons. In this paper, a simple yet effective place recognition method is proposed based on phase congruency to achieve long-term robust localization under severe seasonal changes. To improve the efficiency while retaining geometric information of scene layouts in 3D space, a compact cylindrical description is designed to convert 3D point clouds to 2D images, in which the pixel values are computed based on geometric information rather than heights or numbers of points. Then the histogram of orientated phase congruency are encoded as global descriptor for place retrieval. Extensive real experiments on across-season Oxford RobotCar dataset show that the proposed system outperforms baseline methods, verifying its robust performance in challenging scenarios.
Terrain-Adaptive Gait Planning for Lower Limb Walking Assistance Exoskeleton Robots

In recent years, Lower Limb Exoskeletons (LLEs) have gained considerable interest walking assistance applications for paraplegic patients. Assisting patients to walk over different terrains such as slopes and stairs is necessary for the widespread use of LLEs. In this paper, a Kernelized Movement Primitives (KMP) based terrain adaptive gait planning approach has been proposed, which can be used to generate adaptive gait trajectories online and dynamically. The main idea is to model the human-exoskeleton system as a reduced order dynamics model, with the given step length, the foot positions can be generated, and the gait trajectory can be encoded and reproduced by the KMP with the geometric parameters of the terrains that measured by the vision sensor. After learning from demonstrated gaits that sampled from healthy subjects, adaptive gait trajectories can be reproduced on-line to adapt to slopes and stairs with different geometric sizes. The efficiency of the proposed approach has been demonstrated on an lower limb walking assistance exoskeleton robot, and experimental results indicate that the proposed approach can provide exoskeletons with the ability to generate appropriate gait trajectories for different terrains.
Humeral chondrosarcoma associated with lung metastases in a young dog - case report

ABSTRACT Chondrosarcoma, an unusual malignant neoplasm, develops in cartilaginous tissue and presents low rate of metastasis, mainly affecting the axial skeleton from the adult to senile dogs. In the face of unusual occurrence of chondrosarcoma in the long bones of young dogs, the present report aimed to describe it in the right humerus of a two-and-a-half-year-old Siberian Husky, attended at the Veterinary Hospital of the University of Franca, with limping of the right thoracic limb, for 20 days. The radiographic examination of the humerus showed bone lysis and periosteal proliferation. In the incisional biopsy, proliferation of atypical chondrocytes with diffuse distribution, interspersed with compact bone matrix, was observed. The amputation of the limb was performed, and the fragment histopathological analysis showed grade I chondrosarcoma. Periodic returns were made for neoplastic staging, and at 240 days after surgery lung metastases were detected, however, the tutor did not authorize chemotherapy and radiotherapy for financial reasons and due to the absence of respiratory symptoms so far (410 days after surgery). Although uncommon, chondrosarcoma can affect the long bones of young dogs, with clinical signs similar to other bone neoplasms, and, even with the radical limb amputation, can demonstrate systemic metastasis.
A LiDAR SLAM Method Fused with Visual Loop Closure Detection

In simultaneous localization and mapping (SLAM) system, the loop closure detection method based on LiDAR pointcloud suffers from low matching speed and inaccuracy. To address this issue, a LiDAR SLAM method fused with visual loop closure detection is proposed in this paper. In the method, LiDAR pointclouds and images are first synchronized, then Bag-of-Visual-Words (BoVW) model based on ORB features are applied to loop closure detection. The depth of the visual features is calculated by the projection of pointcloud to image, and perspective-n-point (PnP) algorithm is used to obtain loop closure constraints. Finally, more accurate global map will be generated by graph optimization. The proposed algorithm is tested on a KITTI dataset. The result shows that the proposed algorithm, compared with LOAM and LeGO-LOAM, improves the accuracy of mapping and has better realtime performance.
The influence of dredging activities on the morphological development of the Columbia River mouth

This is a numerical modelling study on the impact of dredging and disposal activities on the long-term morphological development of the Columbia River mouth (MCR). Dredging activities removed 120 Mm3 of sand from the MCR during the studied interval between 1958 and 1999. Only one-third of this volume was disposed in the active littoral zone. Therefore, dredging activities might be an important factor for recent erosion trend at the coasts directly adjacent to the MCR. The net influence of both dredging and disposal activities is analyzed by making use of a Delft3D model. Morphological acceleration techniques and wave climate reduction are used to bridge the gap between hydrodynamic and morphological time scales. The model performance is assessed with simulations for the 1926-1958 interval. These simulations indicate that the model captures the large-scale morphological changes that occurred in the study area. Simulations for the 1958-1999 interval are then used for the analysis of the impact of dredging and disposal activities. Based on the model results, dredging activities led to significant volume losses in the inlet itself and in the area west of the river entrance. On the Peacock Spit shoal directly north of the inlet, the volume losses due to dredging are almost completely reversed by disposal of dredged material on the shoal itself. Dredging and disposal activities did not have significant effects for the littoral drift at coastal stretches adjacent to the MCR. In the studied period, the Peacock Spit shoal did absorb the negative effects of dredging and the additional supply due to disposal activities, without reducing its sediment supply function to the adjacent coastal cell. Other effects of dredging and disposal activities were the formation of distinct sand mounds at the locations of some disposal sites and maintaining the southern orientation of the MCR channel at the estuary side of the inlet. Main implication of the model results for disposal strategies is that the sand volume of the Peacock Spit shoal should be maintained to avoid further erosion of the shoal and prevent undermining of the North Jetty and to maintain the sediment supply function of the shoal for the updrift coastal cell.
An Operational Split-Window Algorithm for Generating Long-Term Land Surface Temperature Products From Chinese Fengyun-3 Series Satellite Data

Land surface temperature (LST) is an important parameter that characterizes the energy balance of the land surface, and it is widely used in various research fields. This article proposes an operational split-window (SW) algorithm for use with the Chinese Fengyun-3 (FY-3) series satellite data, with the purpose of generating long-term global LST products. The algorithm primarily involves three steps. First, the brightness temperatures of the FY-3 Visible and Infrared Radiometer (VIRR) were recalibrated using historical recalibration coefficients to improve the accuracy of the absolute radiometric calibration. Second, daily dynamic emissivity maps were estimated using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) global emissivity dataset (GED) and vegetation/snow cover products based on the vegetation cover method. Finally, the coefficients of the SW algorithm were simulated using MODTRAN 5 combined with the SeeBor V5.0 atmospheric profile library and ASTER spectral library, and then, the coefficients were stratified by the view zenith angle (VZA) and atmospheric water vapor content (WVC) to improve the fitting accuracy. The proposed SW algorithm was integrated into the MUlti-source data SYnergized Quantitative (MUSYQ) remote sensing production system to then generate FY-3 VIRR LST products. Ten land surface sites from the Heihe Watershed Allied Telemetry Experimental Research (HiWATER), Surface Radiation Budget (SURFRAD) networks, and nine water surface sites from the National Data Buoy Center (NDBC) were used to evaluate the accuracy of the FY-3 VIRR LST products. The results demonstrated that the accuracy of the historical recalibration coefficients of the FY-3A/B VIRR is higher than that of the operational calibration coefficients for LST retrieval. The evaluation results revealed that the FY-3A VIRR LST products (2009–2013) had a bias of 0.13 K and an RMSE of 2.77 K, and the FY-3B VIRR LST products (2011–2020) had a bias of −0.07 K and an RMSE of 2.83 K. These results demonstrate that the proposed operational SW algorithm has reasonable accuracy and can be used to produce global LST products from the FY-3 VIRR data.
A Bibliometric Analysis of the Rise of ChatGPT in Medical Research

The rapid emergence of publicly accessible artificial intelligence platforms such as large language models (LLMs) has led to an equally rapid increase in articles exploring their potential benefits and risks. We performed a bibliometric analysis of ChatGPT literature in medicine and science to better understand publication trends and knowledge gaps. Following title, abstract, and keyword searches of PubMed, Embase, Scopus, and Web of Science databases for ChatGPT articles published in the medical field, articles were screened for inclusion and exclusion criteria. Data were extracted from included articles, with citation counts obtained from PubMed and journal metrics obtained from Clarivate Journal Citation Reports. After screening, 267 articles were included in the study, most of which were editorials or correspondence with an average of 7.5 +/− 18.4 citations per publication. Published articles on ChatGPT were authored largely in the United States, India, and China. The topics discussed included use and accuracy of ChatGPT in research, medical education, and patient counseling. Among non-surgical specialties, radiology published the most ChatGPT-related articles, while plastic surgery published the most articles among surgical specialties. The average citation number among the top 20 most-cited articles was 60.1 +/− 35.3. Among journals with the most ChatGPT-related publications, there were on average 10 +/− 3.7 publications. Our results suggest that managing the inevitable ethical and safety issues that arise with the implementation of LLMs will require further research exploring the capabilities and accuracy of ChatGPT, to generate policies guiding the adoption of artificial intelligence in medicine and science.
A Stackelberg Game Approach for Three-Player Autonomous Racing

This paper considers the autonomous racing of three cars, including a team of two cars and an opponent car. The competition is modeled as a two-leader one-follower Stackelberg game which can be called the sequential game. Leaders select the trajectories which make the max payoffs of the team and the follower does its best response to the actions of the team. Given that the Stackelberg equilibrium is not unique in both games, rather than selecting one strategy stochastically, the cost functions are designed for the players to cope with multiple strategies that have the same progress. The competitions are performed in a receding horizon fashion, and the aim of the research is to study the effects of cooperation and make the team win the competition under collision and road constraints.
Sepsis-induced coagulopathy is associated with new episodes of atrial fibrillation in patients admitted to critical care in sinus rhythm

Background Sepsis is a life-threatening disease commonly complicated by activation of coagulation and immune pathways. Sepsis-induced coagulopathy (SIC) is associated with micro- and macrothrombosis, but its relation to other cardiovascular complications remains less clear. In this study we explored associations between SIC and the occurrence of atrial fibrillation (AF) in patients admitted to the Intensive Care Unit (ICU) in sinus rhythm. We also aimed to identify predictive factors for the development of AF in patients with and without SIC. Methods Data were extracted from the publicly available AmsterdamUMCdb database. Patients with sepsis and documented sinus rhythm on admission to ICU were included. Patients were stratified into those who fulfilled the criteria for SIC and those who did not. Following univariate analysis, logistic regression models were developed to describe the association between routinely documented demographics and blood results and the development of at least one episode of AF. Machine learning methods (gradient boosting machines and random forest) were applied to define the predictive importance of factors contributing to the development of AF. Results Age was the strongest predictor for the development of AF in patients with and without SIC. Routine coagulation tests activated Partial Thromboplastin Time (aPTT) and International Normalized Ratio (INR) and C-reactive protein (CRP) as a marker of inflammation were also associated with AF occurrence in SIC-positive and SIC-negative patients. Cardiorespiratory parameters (oxygen requirements and heart rate) showed predictive potential. Conclusion Higher INR, elevated CRP, increased heart rate and more severe respiratory failure are risk factors for occurrence of AF in critical illness, suggesting an association between cardiac, respiratory and immune and coagulation pathways. However, age was the most dominant factor to predict the first episodes of AF in patients admitted in sinus rhythm with and without SIC.
Plant and Disease Recognition Based on PMF Pipeline Domain Adaptation Method: Using Bark Images as Meta-Dataset

Efficient image recognition is important in crop and forest management. However, it faces many challenges, such as the large number of plant species and diseases, the variability of plant appearance, and the scarcity of labeled data for training. To address this issue, we modified a SOTA Cross-Domain Few-shot Learning (CDFSL) method based on prototypical networks and attention mechanisms. We employed attention mechanisms to perform feature extraction and prototype generation by focusing on the most relevant parts of the images, then used prototypical networks to learn the prototype of each category and classify new instances. Finally, we demonstrated the effectiveness of the modified CDFSL method on several plant and disease recognition datasets. The results showed that the modified pipeline was able to recognize several cross-domain datasets using generic representations, and achieved up to 96.95% and 94.07% classification accuracy on datasets with the same and different domains, respectively. In addition, we visualized the experimental results, demonstrating the model’s stable transfer capability between datasets and the model’s high visual correlation with plant and disease biological characteristics. Moreover, by extending the classes of different semantics within the training dataset, our model can be generalized to other domains, which implies broad applicability.