Browse Articles

Enhancement of biodegradation of tar-rich coal through sequential treatment by N. mangyaensis and Ochrobactrum sp.

Development and validation of a modified SOFA score for mortality prediction in candidemia patients

Abstract Candidemia is a life-threatening bloodstream infection associated with high mortality rates, particularly in critically ill patients. Accurate risk stratification is crucial for timely intervention and could improve patient outcomes. This study aimed to enhance the predictive performance of the sequential organ failure assessment (SOFA) score by developing a modified SOFA (mSOFA) score, which is specifically designed for candidemia patients. Using data from MIMIC-III, MIMIC-IV, and ICU-JN databases, we identified key prognostic variables through LASSO regression and integrated into the mSOFA_3 model. The model incorporated respiratory_SOFA, coagulation_SOFA, and circulatory_SOFA along with clinical biomarkers, including lactate, albumin, and blood urea nitrogen. The mSOFA_3 model demonstrated superior predictive performance across multiple machine learning algorithms, with the logistic regression-based model achieving the highest AUC of 0.826 in the internal validation cohort and 0.813 in the test cohort. Kaplan-Meier survival analysis further validated the model’s utility in stratifying patients into high-risk and low-risk groups with distinct survival outcomes. These findings highlight the mSOFA_3 as a robust and clinically relevant tool for early risk stratification, offering potential for improved decision-making and therapeutic management in critically ill patients with candidemia.

Genome-wide analysis of the ammonium transporters gene family in Phaseolus vulgaris and its roles in response to drought and salinity stress

The impact of co-fed plastic diet on Tenebrio molitor gut bacterial community structure

Abstract This study aimed to analyze the long-term impact of co-fed plastic diet on the bacterial community of self-sustaining laboratory populations of T. molitor fed with wheat bran with added polystyrene (PS), and low density polyethylene (LDPE) over a three year period. The most abundant phyla for all three populations were Firmicutes, Bacteroidota and Proteobacteria. PS group microbiota is similar to C group, pointing to a common bacterial species capable for degrading lignocellulose and PS, while consumption of LDPE caused a significant decrease of Bacteroidota and Actinobacteriota compared to both C and PS group, and Campylobacterota compared to PS group. A predictive metabolomics analysis recognized dTDP-L-rhamnose biosynthesis I in PS group as one of five unique pathways, while other five distinctive pathways, like peptidoglycan maturation, were linked to LDPE group. Further studies are needed to determine the plastic degrading properties of the detected bacteria. The results highlight T. molitor’s versatility in biotechnological applications.

Multiclass semantic segmentation for prime disease detection with severity level identification in Citrus plant leaves

Abstract Agriculture provides the basics for producing food, driving economic growth, and maintaining environmental sustainability. On the other hand, plant diseases have the potential to reduce crop productivity and raise expenses, posing a risk to food security and the incomes of farmers. Citrus plants, recognized for their nutritional benefits and economic significance, are especially vulnerable to diseases such as citrus greening, Black spot, and Citrus canker. Due to technological advancements, image processing and Deep learning algorithms can now detect and classify plant diseases early on, which assists in preserving crop health and productivity. The proposed work enables farmers to identify and visualize multiple diseases affecting citrus plants. This study proposes an efficient model to detect multiple citrus diseases (canker, black spot, and greening) that may co-occur on the same leaf. It is achieved using the RSL (Residual Squeeze & Excitation LeakyRelu) Linked-TransNet multiclass segmentation model. The proposed model stands out in its ability to address major limitations in existing models, including spatial inconsistency, loss of fine disease boundaries, and inadequate feature representation. The significance of this proposed RSL Linked-Transnet model lies in its integration of hierarchical feature extraction, global context modeling via transformers, and precise feature reconstruction, ensuring superior segmentation accuracy and robustness. The results of the proposed RSL Linked-TransNet architecture reveal average values of 0.9755 for accuracy, 0.0660 for loss, 0.9779 for precision, 0.9738 for recall, and 0.9308 for IoU. Additionally, the model achieves a mean F1 score of 0.7173 and a mean IoU of 0.7567 for each disease class in images from the test dataset. The segmentation results are further utilized to identify the prime disease affecting the leaves and evaluate disease severity using the prime disease classification and severity detection algorithm.

Smart adaptive ensemble model for multiclass imbalanced nonstationary data streams

Reference intervals for coagulation parameters in chinese adults stratified by sex and age

Formulation of low temperature mixed mode crack propagation behavior of crumb rubber modified HMA using artificial intelligence

Abstract Determining mixed mode fracture parameters asphalt concrete mixtures remains an engineering challenge due to non-homogeneity and inelasticity of the material. In this research, a study was conducted to determine the low-temperature R-curves of unmodified and crumb rubber modified Hot Mix Asphalt (HMA) under mode I and mixed-mode (I/II) loading conditions. Single edge notched beam (SE(B)) testing was employed to collect data, and three key fracture parameters—cohesive energy, energy rate, and fracture energy—were extracted to represent different stages of fracture and crack propagation. Within the scope of this study, it was observed that for the AC 85/100 paving grade bitumen, a temperature of − 20 °C serves as a critical temperature, shifting fracture from quasi-brittle to brittle. At this temperature, the stable crack growth region in the R-curves significantly shrinks, causing abrupt specimen failure. The incorporation of 20% crumb rubber demonstrated favorable material characteristics, with a progressively rising R-curve even during the unsfi crack propagation phase. The central goal of this research is to establish prediction models for the mixed-mode (I/II) crack propagation parameters Gb, Gf, and Gi. The features selected for modeling are Gb0, Gf0, and Gi0 (mode I), percentage of crumb rubber, type of aggregate, binder content, nominal maximum aggregate size, temperature, and normalized offset ratio. Two dataset configurations were used: dataset 1 contains all entries, while dataset 2 excludes Gb0, Gf0, and Gi0 (mode I). Five machine learning techniques, Regression, Multi-Gene Genetic Programming (MGGP), Support Vector Regression (SVR), Random Forest, and Artificial Neural Networks were employed to predict three key fracture parameters. Although slightly less accurate than SVR and Random Forest, MGGP offers the key advantage of yielding explicit mathematical expressions for crack propagation prediction. The R2 index for the MGGP model in Dataset 1 was 0.93 for Gb, 0.94 for Gf, and 0.92 for Gi. For dataset 2, the indices were 0.89, 0.93, and 0.88, respectively.

Regulation of NRF2 by stably associated phosphoinositides and small heat shock proteins in response to stress

Direct printing of metal oxide nanostructures for wearable electrochemical sensing

Digital automatic measurement method for geological information of rock mass structure

NlugOBP1 in Nilaparvata lugens involved in the perception of repellent agent geraniol

Mining, validating, and quantifying circular RNA transcriptome from total RNA as a biomarker or target

A randomized controlled trial comparing romosozumab and denosumab in elderly women with primary osteoporosis and knee osteoarthritis

Abstract We compared the efficacy of romosozumab and denosumab in elderly women with primary osteoporosis and knee osteoarthritis in a randomized controlled trial. A total of 112 participants aged 75–90 years were randomized equally into the romosozumab and denosumab groups. Among these, 49 and 52 participants, respectively, who received their initial dose were included in the analysis. The primary outcome was change in lumbar spine (LS)-bone mineral density (BMD) at 12 months in the romosozumab group versus the denosumab group. Secondary outcomes were changes in knee osteophyte development, patient-reported outcomes (PROs), and the incidence of serious adverse events. Mean age of participants was 80.9 years. There was no difference in baseline LS-BMD between the two groups, with a T-score of -2.6. The mean percentage change in LS-BMD at 12 months was significantly higher in the romosozumab group (13.7%) than in the denosumab group (8.5%; p  = 0.0035). No significant differences were observed in knee osteophyte development and PROs between the two groups. Serious adverse events included a case of mitral regurgitation in the romosozumab group. These findings emphasize the need for refined treatment strategies in high-risk populations, highlighting romosozumab’s benefits and the need to monitor cardiovascular risks.

Improved model for intrusion detection in the Internet of Things

Abstract The Internet of Things (IoT) includes many devices generating vast amounts of data that need extensive computation. IoT has several definitions, but the most popular refers to multiple devices, objects, and sensors all connecting via a network to exchange data. IoT has become more efficient in processing large amounts of data in less time than before because it does not require human intervention. Recently, IoT technologies have improved intelligent systems, such as smart cities, healthcare, smart homes, and more. Unfortunately, IoT faces several security issues and is vulnerable to attacks. To prevent damage or losses, we must detect such anomalies. Internet of Things (IoT) devices are developed daily, leading to increased security vulnerabilities. This work presents an improved deep learning (DL) model for intrusion detection in Internet of Things (IoT) environments to improve accuracy and generalization. It uses convolutional neural network (CNN) capabilities to achieve that. The proposed model was tested on several benchmark datasets and demonstrated notable advances over alternative DL as Long-Short Term Memory (LSTM) and machine learning techniques like Decision Tree (DT). The proposed CNN-based model integrates data augmentation and regularization to prevent overfitting. Furthermore, the model achieves a high precision rate equal to 1, and the average precision to multi-class reaches 82%, which is essential to reduce false positives in real-world applications. This work sets a new standard for future IDS development research and emphasizes how deep learning can be used to improve IoT security. Our enhanced model offers an efficient and scalable way for detecting over 10 attacks to defend IoT networks against constantly changing cyber threats by addressing IoT environments’ particular difficulties.

Modeling seawater intrusion along the Alabama coastline using physical and machine learning models to evaluate the effects of multiscale natural and anthropogenic stresses

Abstract Seawater intrusion threatens groundwater resources in coastal regions, including southern Baldwin County, Alabama, where the freshwater-saltwater interface dynamics remain poorly understood. To address this gap, this study uses combined physics-based and machine-learning models to quantify seawater intrusion caused by natural (storm surges) and anthropogenic (human activities) perturbations. The long short-term memory network and wavelet analysis were used to assess vertical aquifer vulnerabilities, revealing that the shallow part of the Coastal lowlands aquifer system (CL1) in the southern Baldwin County region is more susceptible to sea level rise and groundwater extraction than deeper aquifers. Based on these findings, a cross-sectional numerical model (physics approach) for the CL1 aquifer was developed to evaluate tidal and storm surge effects, using Tropical Storm Claudette (June 2021) as a case study. Results showed that tidal fluctuations had a minimal impact on the saltwater-freshwater interface location, whereas storm surges caused substantial inland movement, with effects lasting for nine months. The steady-state version of the three-dimensional (3D) physical model predicted seawater intrusion across the entire area, and convolutional neural network-based modeling further validated the model results. The 3D physical model was also applied to a smaller area to assess human impact on the saltwater interface due to two groundwater pumping scenarios (± 50% of the baseline pumping rate). Results revealed that a 50% increase in groundwater withdrawals caused seawater to advance ~ 320 m inland, whereas a 50% reduction led to a ~ 270-meter retreat. This study highlights the vulnerability of Alabama’s shallow coastal aquifers to seawater intrusion due to storm surges and human activities, and demonstrates that combining physics-based models with machine learning approaches can improve groundwater predictions, though its accuracy depends on the availability of site-specific data.

A new proportional hazard model with applications to breastfeeding data

FPGA implementation of deep learning architecture for ankylosing spondylitis detection from MRI

Tripartite binding mode of cohesin-dockerin complexes from Ruminococcus flavefaciens involving naturally truncated dockerins

Identification of novel gene-based risk score for prognosis in prostate cancer

Abstract Tumor carcinogenesis and progression result from multiple genetic alterations in tumor cells. However, reliable biomarkers for prostate cancer classification remain limited, often leading to either overtreatment or inadequate treatment. Additionally, effective biomarkers for selecting patients who may benefit from immunotherapy are still lacking. Using data from TCGA-PRAD, we established gene selection criteria to develop a gene-based risk score. We identified a novel gene risk panel comprising six genes (SSTR1, CA14, HJURP, KRTAP5-1, VGF, and COMP) for prostate cancer risk classification. Patients in the high-risk group were associated with poor prognosis. The gene panel exhibited significantly enhanced predictive accuracy for progression-free survival compared to conventional clinicopathological parameters, including T stage, N stage, primary Gleason score, and secondary Gleason score. High-risk patients exhibited a higher tumor mutation burden. Notably, immune activity of CD8 + T cells, NK cells, and the type II IFN response was significantly lower in the high-risk group, indicating a more immunosuppressive environment. Furthermore, a nomogram combining the gene-based risk score with T stage and histological grade was constructed. The expression of genes in the gene-based risk score was further validated using clinical samples, and VGF was found to play a significant role in prostate cancer progression. The nomogram could serve as a valuable biomarker for distinguishing between high-risk and low-risk of PFS prostate cancer patients and for selecting patients who might benefit from immunotherapy.