Browse Articles
Discover research articles across all indexed journals
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
Mapping the nuclear landscape with multiplexed super-resolution fluorescence microscopy
Large-scale synaptic dynamics drive the reconstruction of binocular circuits in mouse visual cortex
High-resolution detection of copy number alterations in single cells with HiScanner
Abstract Improvements in single-cell whole-genome sequencing (scWGS) assays have enabled detailed characterization of somatic copy number alterations (CNAs) at the single-cell level. Yet, current computational methods are mostly designed for detecting chromosome-scale changes in cancer samples with low sequencing coverage. Here, we introduce HiScanner (High-resolution Single-Cell Allelic copy Number callER), which combines read depth, B-allele frequency, and haplotype phasing to identify CNAs with high resolution. In simulated data, HiScanner consistently outperforms state-of-the-art methods across various CNA types and sizes. When applied to high-coverage scWGS data from 65 cells across 11 neurotypical human brains, HiScanner shows a superior ability to detect smaller CNAs, uncovering distinct CNA patterns between neurons and oligodendrocytes. We also generated low-coverage scWGS data from 179 cells sampled from the same meningioma patient at two time points. For this serial dataset, integration of CNAs with point mutations revealed evolutionary trajectories of tumor cells. These findings show that HiScanner enables accurate characterization of frequency, clonality, and distribution of CNAs at the single-cell level in both non-neoplastic and neoplastic cells.
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.
Analysis of soil properties and wheat yield in relation to climate smart agricultural practices in cultivated landscape of Bona Dibero, central Ethiopia
Modelling, implementation and analysis of double-side slotted axial flux PMGs suitable to small-scale wind energy conversion systems
Evaluation of comprehensive vitality of Shanghai’s commercial centers using multi-dimensional geospatial big data
Evaluation of different spectral indices for wheat lodging assessment using machine learning algorithms
Abstract Wheat lodging is a recurrent phenomenon that significantly affects grain yield and impedes the harvesting efficiency. Therefore, the precise and rapid assessment of wheat lodging is crucial in minimizing its impact on grain yield and quality. Recently few studies related to machine learning based wheat lodging have been reported; however, the literature still lacks comprehensive assessments of machine learning algorithms for wheat lodging over Indian agricultural fields. This study presented a systematic approach for detecting the wheat lodging occurred during the end of March and April 2023 in the Ludhiana district of Punjab (India) from multi-temporal Sentinel-2 data using the machine learning algorithms. The ground control points for healthy and lodged areas were collected during March and April 2023. The temporal characteristics of crop phenology from November 2022 to April 2023 were analyzed for wheat classification. The normalized difference vegetation index (NDVI) was computed during this period followed by implementation of random forest (RF), decision tree (DT), and support vector machine (SVM) algorithms to evaluate their performance for wheat classification. It was found that RF outperformed the other models in terms of prediction accuracy and wheat area extraction. To distinguish between lodged and non-lodged wheat, eight spectral indices were computed from the visible and infrared bands of Sentinel-2. These indices were used as inputs to RF, DT, and SVM models. The optimal set of features were identified using random forest feature importance selection approach. Among the spectral indices, spectral sum index (SSI) derived from blue, green, red, and near-infrared bands followed by generalized difference vegetation index (GDVI) accurately separated lodged wheat from non-lodged wheat. Among the three algorithms, the RF model combined with SSI and GDVI achieved the highest overall accuracy of 89.2%. These results suggested that SSI and GDVI derived from Sentinel-2 data coupled with random forest model is effective for assessing the wheat lodging on spatio-temporal scale which may be helpful for developing the decision support system to assess the loss of crop yield loss.