Soil total nitrogen content and pH value estimation method considering spatial heterogeneity: Based on GNNW-XGBoost model.

Soil nitrogen content and pH value are two pivotal factors that critically determine soil fertility and plant growth. As key indicators of soil health, they each play distinct yet complementary roles in the soil ecosystem. Nitrogen is one of the essential nutrients for plant growth, while soil pH directly influences the activity of soil microorganisms.

A Monitoring Device and Grade Prediction System for Grain Mildew.

Mildew infestation is a significant cause of loss during grain storage. The growth and metabolism of mildew leads to changes in gas composition and temperature within granaries. Recent advances in sensor technology and machine learning enable the prediction of grain mildew during storage.

Overcoming CRISPR-Cas9 off-target prediction hurdles: A novel approach with ESB rebalancing strategy and CRISPR-MCA model.

The off-target activities within the CRISPR-Cas9 system remains a formidable barrier to its broader application and development. Recent advancements have highlighted the potential of deep learning models in predicting these off-target effects, yet they encounter significant hurdles including imbalances within datasets and the intricacies associated with encoding schemes and model architectures. To surmount these challenges, our study innovatively introduces an Efficiency and Specificity-Based (ESB) class rebalancing strategy, specifically devised for datasets featuring mismatches-only off-target instances, marking a pioneering approach in this realm.

Effect of Age at Time of Irradiation, Sex, Genetic Diversity, and Granulopoietic Cytokine Radiomitigation on Lifespan and Lymphoma Development in Murine H-ARS Survivors.

Acute, high-dose radiation exposure results in life-threatening acute radiation syndrome (ARS) and debilitating delayed effects of acute radiation exposure (DEARE). The DEARE are a set of chronic multi-organ illnesses that can result in early death due to malignancy and other diseases. Animal models have proven essential in understanding the natural history of ARS and DEARE and licensure of medical countermeasures (MCM) according to the FDA Animal Rule.

HDConv: Heterogeneous kernel-based dilated convolutions.

Dilated convolution has been widely used in various computer vision tasks due to its ability to expand the receptive field while maintaining the resolution of feature maps. However, the critical challenge is the gridding problem caused by the isomorphic structure of the dilated convolution, where the holes filled in the dilated convolution destroy the integrity of the extracted information and cut off the relevance of neighboring pixels. In this work, a novel heterogeneous dilated convolution, called HDConv, is proposed to address this issue by setting independent dilation rates on grouped channels while keeping the general convolution operation.

Prediction of Potential miRNA-Disease Associations Based on a Masked Graph Autoencoder.

Biomedical evidence has demonstrated the relevance of microRNA (miRNA) dysregulation in complex human diseases, and determining the relationship between miRNAs and diseases can aid in the early detection and prevention of diseases. Traditional biological experimental methods have the disadvantages of high cost and low efficiency, which are well compensated by computational methods. However, many computational methods have the challenge of excessively focusing on the neighbor relationship, ignoring the structural information of the graph, and belittling the redundant information of the graph structure.

A novel near infrared spectroscopy analytical strategy for soil nutrients detection based on the DBO-SVR method.

Soil is the basis of agricultural production and accessing accurate information on soil nutrients is essential. Traditional methods of soil composition detection, which are based on chemical analysis, are characterized by being costly and polluting. Spectroscopic analysis has proven to be a rapid, non-destructive and effective technique for predicting soil properties in general and potassium, phosphorus and organic matter in particular.

Prediction of CRISPR-Cas9 off-target activities with mismatches and indels based on hybrid neural network.

The CRISPR/Cas9 system has significantly advanced the field of gene editing, yet its clinical application is constrained by the considerable challenge of off-target effects. Although numerous deep learning models for off-target prediction have been proposed, most struggle to effectively extract the nuanced features of guide RNA (gRNA) and DNA sequence pairs and to mitigate information loss during data transmission within the model. To address these limitations, we introduce a novel Hybrid Neural Network (HNN) model that employs a parallelized network structure to fully extract pertinent features from different positions and types of bases in the sequence to minimize information loss.

DapBCH: a disease association prediction model Based on Cross-species and Heterogeneous graph embedding.

The study of comorbidity can provide new insights into the pathogenesis of the disease and has important economic significance in the clinical evaluation of treatment difficulty, medical expenses, length of stay, and prognosis of the disease. In this paper, we propose a disease association prediction model DapBCH, which constructs a cross-species biological network and applies heterogeneous graph embedding to predict disease association. First, we combine the human disease-gene network, mouse gene-phenotype network, human-mouse homologous gene network, and human protein-protein interaction network to reconstruct a heterogeneous biological network.

Further Characterization of Multi-Organ DEARE and Protection by 16,16 Dimethyl Prostaglandin E2 in a Mouse Model of the Hematopoietic Acute Radiation Syndrome.

Survivors of acute radiation exposure suffer from the delayed effects of acute radiation exposure (DEARE), a chronic condition affecting multiple organs, including lung, kidney, heart, gastrointestinal tract, eyes, and brain, and often causing cancer. While effective medical countermeasures (MCM) for the hematopoietic-acute radiation syndrome (H-ARS) have been identified and approved by the FDA, development of MCM for DEARE has not yet been successful. We previously documented residual bone marrow damage (RBMD) and progressive renal and cardiovascular DEARE in murine survivors of H-ARS, and significant survival efficacy of 16,16-dimethyl prostaglandin E2 (dmPGE2) given as a radioprotectant or radiomitigator for H-ARS.

Matrix reconstruction with reliable neighbors for predicting potential MiRNA-disease associations.

Numerous experimental studies have indicated that alteration and dysregulation in mircroRNAs (miRNAs) are associated with serious diseases. Identifying disease-related miRNAs is therefore an essential and challenging task in bioinformatics research. Computational methods are an efficient and economical alternative to conventional biomedical studies and can reveal underlying miRNA-disease associations for subsequent experimental confirmation with reasonable confidence.

Age and Sex Divergence in Hematopoietic Radiosensitivity in Aged Mouse Models of the Hematopoietic Acute Radiation Syndrome.

The hematopoietic system is highly sensitive to stress from both aging and radiation exposure, and the hematopoietic acute radiation syndrome (H-ARS) should be modeled in the geriatric context separately from young for development of age-appropriate medical countermeasures (MCMs). Here we developed aging murine H-ARS models, defining radiation dose response relationships (DRRs) in 12-month-old middle-aged and 24-month-old geriatric male and female C57BL/6J mice, and characterized diverse factors affecting geriatric MCM testing. Groups of approximately 20 mice were exposed to ∼10 different doses of radiation to establish radiation DRRs for estimation of the LD50/30.

Dynamic and Static Representation Learning Network for Recommendation.

Existing review-based recommendation methods learn a latent representation of user and item from user-generated reviews by a static strategy, which are unable to capture the dynamic evolution of users' interests and the dynamic attraction of items. Here, we propose a dynamic and static representation learning network (DSRLN) to improve the rating prediction accuracy by exploring fine-grained representations of users and items. Specifically, we built DSRLN with a dynamic representation extractor to model the dynamic evolution of users' interests by exploring the inner relations of an interaction sequence, and with a static representation extractor to model the users' intrinsic preferences by learning the semantic coherence and feature strength information from reviews.

Digital Twins in Unmanned Aerial Vehicles for Rapid Medical Resource Delivery in Epidemics.

The purposes are to explore the effect of Digital Twins (DTs) in Unmanned Aerial Vehicles (UAVs) on providing medical resources quickly and accurately during COVID-19 prevention and control. The feasibility of UAV DTs during COVID-19 prevention and control is analyzed. Deep Learning (DL) algorithms are introduced.

The accurate prediction and characterization of cancerlectin by a combined machine learning and GO analysis.

Cancerlectins, lectins linked to tumor progression, have become the focus of cancer therapy research for their carbohydrate-binding specificity. However, the specific characterization for cancerlectins involved in tumor progression is still unclear. By taking advantage of the g-gap tripeptide and tetrapeptide composition feature descriptors, we increased the accuracy of the classification model of cancerlectin and lectin to 98.

Establishing Pediatric Mouse Models of the Hematopoietic Acute Radiation Syndrome and the Delayed Effects of Acute Radiation Exposure.

Medical countermeasures (MCMs) for hematopoietic acute radiation syndrome (H-ARS) should be evaluated in well-characterized animal models, with consideration of at-risk populations such as pediatrics. We have developed pediatric mouse models of H-ARS and delayed effects of acute radiation exposure (DEARE) for efficacy testing of MCMs against radiation. Male and female C57BL/6J mice aged 3, 4, 5, 6, 7 and 8 weeks old (±1 day) were characterized for baseline hematopoietic and gastrointestinal parameters, radiation response, efficacy of a known MCM, and DEARE at six and 12 months after total-body irradiation (TBI).

Optimizing and Profiling Prostaglandin E2 as a Medical Countermeasure for the Hematopoietic Acute Radiation Syndrome.

Identification of medical countermeasures (MCM) to mitigate radiation damage and/or protect first responders is a compelling unmet medical need. The prostaglandin E2 (PGE2) analog, 16,16 dimethyl-PGE2 (dmPGE2), has shown efficacy as a radioprotectant and radiomitigator that can enhance hematopoiesis and ameliorate intestinal mucosal cell damage. In this study, we optimized the time of administration of dmPGE2 for protection and mitigation against mortality from the hematopoietic acute radiation syndrome (H-ARS) in young adult mice, evaluated its activity in pediatric and geriatric populations, and investigated potential mechanisms of action.

Multitask Non-Autoregressive Model for Human Motion Prediction.

Human motion prediction, which aims at predicting future human skeletons given the past ones, is a typical sequence-to-sequence problem. Therefore, extensive efforts have been devoted to exploring different RNN-based encoder-decoder architectures. However, by generating target poses conditioned on the previously generated ones, these models are prone to bringing issues such as error accumulation problem.

[Inversion of Water Quality Parameters Based on UAV Multispectral Images and the OPT-MPP Algorithm].

Unmanned aerial vehicle (UAV) multispectral remote sensing can be used to monitor multiple water quality parameters, such as suspended solids, turbidity, total phosphorus, and chlorophyll. Establishing a stable and accurate water quality parameter inversion model is a prerequisite for this work. The matching pixel-by-pixel (MPP) algorithm is an inversion algorithm for high resolution features of UAV images; however, it is associated with problems of excessive computation and over-fitting.

Deep Spectral-Spatial Features of Near Infrared Hyperspectral Images for Pixel-Wise Classification of Food Products.

Hyperspectral imaging (HSI) emerges as a non-destructive and rapid analytical tool for assessing food quality, safety, and authenticity. This work aims to investigate the potential of combining the spectral and spatial features of HSI data with the aid of deep learning approach for the pixel-wise classification of food products. We applied two strategies for extracting spatial-spectral features: (1) directly applying three-dimensional convolution neural network (3-D CNN) model; (2) first performing principal component analysis (PCA) and then developing 2-D CNN model from the first few PCs.

Development of a Model of the Acute and Delayed Effects of High Dose Radiation Exposure in Jackson Diversity Outbred Mice; Comparison to Inbred C57BL/6 Mice.

Development of medical countermeasures against radiation relies on robust animal models for efficacy testing. Mouse models have advantages over larger species due to economics, ease of conducting aging studies, existence of historical databases, and research tools allowing for sophisticated mechanistic studies. However, the radiation dose-response relationship of inbred strains is inherently steep and sensitive to experimental variables, and inbred models have been criticized for lacking genetic diversity.

Lifelong Residual bone Marrow Damage in Murine Survivors of the Hematopoietic Acute Radiation Syndrome (H-ARS): A Compilation of Studies Comprising the Indiana University Experience.

Accurate analyses of the delayed effects of acute radiation exposure in survivors of the hematopoietic acute radiation syndrome are hampered by low numbers of mice for examination due to high lethality from the acute syndrome, increased morbidity and mortality in survivors, high cost of husbandry for long-term studies, biological variability, and inconsistencies of models from different laboratories complicating meta-analyses. To address this, a compilation of 38 similar hematopoietic acute radiation syndrome studies conducted over a 7-y period in the authors' laboratory, comprising more than 1,500 irradiated young adult C57BL/6 mice and almost 600 day-30 survivors, was assessed for hematopoietic delayed effects of acute radiation exposure at various times up to 30 mo of age. Significant loss of long-term repopulating potential of phenotypically defined primitive hematopoietic stem cells was documented in hematopoietic acute radiation syndrome survivors, as well as significant decreases in all hematopoietic lineages in peripheral blood, prominent myeloid skew, significantly decreased bone marrow cellularity, and numbers of lineage-negative Sca-1+ cKit+ CD150+ cells (KSL CD150+; the phenotype known to be enriched for hematopoietic stem cells), and increased cycling of KSL CD150+ cells.

Non-destructive determination of Malondialdehyde (MDA) distribution in oilseed rape leaves by laboratory scale NIR hyperspectral imaging.

The feasibility of hyperspectral imaging with 400-1000 nm was investigated to detect malondialdehyde (MDA) content in oilseed rape leaves under herbicide stress. After comparing the performance of different preprocessing methods, linear and nonlinear calibration models, the optimal prediction performance was achieved by extreme learning machine (ELM) model with only 23 wavelengths selected by competitive adaptive reweighted sampling (CARS), and the result was R = 0.929 and RMSEP = 2.

Effects of chitosan combined with nisin treatment on storage quality of large yellow croaker (Pseudosciaena crocea).

Effects of chitosan combined with different concentrations of nisin on quality enhancement of large yellow croaker (Pseudosciaena crocea) stored at 4 °C were evaluated for 8 days. Changes in sensory score and volatile spoilage products, total viable counts (TVC), and physiochemical indexes including weight loss, colour, pH, total volatile basic nitrogen (TVB-N), and K-value were examined. Results demonstrated that nisin-treated samples presented better quality preservation effects than chitosan alone.

Cell suspension concentration monitoring by using a miniaturized serial high frequency SAWR sensor.

In this paper, a miniaturized cell suspension concentration monitoring method was investigated. The sensing unit was a carbon screen-printed electrode (CSPE) in serial with a 433MHz vacuum-packaged surface acoustic wave resonator (SAWR). SAWR provided a stable and high operating frequency, which helps to keep the stability and sensitivity of the monitoring system.