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A fault diagnosis method for rolling bearings in open-set domain adaptation with adversarial learning
Associations of Naples prognostic score with stroke in adults and all cause mortality among stroke patients
Prevalence of congenital malaria in an urban and a semirural area in Lagos: a two-centre cross-sectional study
‘Candidatus liberibacter solanacearum’ protein CKC_05770 interacts in vivo with tomato APX6 and APX7
Decrease in atmospheric pressure could increase endolymphatic space volume in Meniere’s disease
Sensitive and modular amplicon sequencing of Plasmodium falciparum diversity and resistance for research and public health
Abstract Targeted amplicon sequencing is a powerful and efficient tool for interrogating the Plasmodium falciparum genome, generating actionable data from infections to complement traditional malaria epidemiology. For maximum impact, genomic tools should be multi-purpose, robust, sensitive, and reproducible. We developed, characterized, and implemented MAD 4 HatTeR, an amplicon sequencing panel based on Multiplex Amplicons for Drug, Diagnostic, Diversity, and Differentiation Haplotypes using Targeted Resequencing, along with a bioinformatic pipeline for data analysis. Additionally, we introduce an analytical approach to detect gene duplications and deletions from amplicon sequencing data. Laboratory control and field samples were used to demonstrate the panel’s high sensitivity and robustness. MAD 4 HatTeR targets 165 highly diverse loci, focusing on multiallelic microhaplotypes, key markers for drug and diagnostic resistance (including duplications and deletions), and CSP and potential vaccine targets. The panel can also detect non- falciparum Plasmodium species. MAD 4 HatTeR successfully generated data from low-parasite-density dried blood spot and mosquito midgut samples and detected minor alleles at within-sample allele frequencies as low as 1% with high specificity in high-parasite-density dried blood spot samples. Gene deletions and duplications were reliably detected in mono- and polyclonal controls. Data generated by MAD 4 HatTeR were highly reproducible across multiple laboratories. The successful implementation of MAD 4 HatTeR in five laboratories, including three in malaria-endemic African countries, showcases its feasibility and reproducibility in diverse settings. MAD 4 HatTeR is thus a powerful tool for research and a robust resource for malaria public health surveillance and control.
Empowering agricultural ecological quality development through the digital economy with evidence from net carbon efficiency
Authenticable quantum secret sharing based on special entangled state
Fine-tuned deep learning models for early detection and classification of kidney conditions in CT imaging
Abstract The kidney plays a vital role in maintaining homeostasis, but lifestyle factors and diseases can lead to kidney failures. Early detection of kidney disease is crucial for effective intervention, often challenging due to unnoticeable symptoms in the initial stages. Computed tomography (CT) imaging aids specialists in detecting various kidney conditions. The research focuses on classifying CT images of cysts, normal states, stones, and tumors using a hyperparameter fine-tuned approach with convolutional neural networks (CNNs), VGG16, ResNet50, CNNAlexnet, and InceptionV3 transfer learning models. It introduces an innovative methodology that integrates finely tuned transfer learning, advanced image processing, and hyperparameter optimization to enhance the accuracy of kidney tumor classification. By applying these sophisticated techniques, the study aims to significantly improve diagnostic precision and reliability in identifying various kidney conditions, ultimately contributing to better patient outcomes in medical imaging. The methodology implements image-processing techniques to enhance classification accuracy. Feature maps are derived through data normalization and augmentation (zoom, rotation, shear, brightness adjustment, horizontal/vertical flip). Watershed segmentation and Otsu’s binarization thresholding further refine the feature maps, which are optimized and combined using the relief method. Wide neural network classifiers are employed, achieving the highest accuracy of 99.96% across models. This performance positions the proposed approach as a high-performance solution for automatic and accurate kidney CT image classification, significantly advancing medical imaging and diagnostics. The research addresses the pressing need for early kidney disease detection using an innovative methodology, highlighting the proposed approach’s capability to enhance medical imaging and diagnostic capabilities.
Blind HDR image quality assessment based on aggregating perception and inference features
Non-invasive and continuous intra-abdominal pressure assessment using MC sensors
Robustness evaluation of bus-subway composite network considering accessibility
Novel mechanisms of alkyldimethylbenzalkonium chloride in virucidal activity
Productivity, soil fertility and enzyme activity of mixed forage grasslands improved by alfalfa and nitrogen addition in Horqin Sandy Land, China
Traffic safety evaluation of emerging mixed traffic flow at freeway merging area considering driving behavior
Phytochemical and metabolic changes associated with ripening of Lycopersicon esculentum
Development and validation of the Individual Potentials Questionnaire (IP-Q)
Abstract Individual potential has recently been acknowledged by the holistic health model as being essential to successfully addressing life’s demands, both now and in the future. The study employed a cross-sectional survey among Nigerian university undergraduate students, using a convenience sampling method, to assess their subjective individual potential. The study investigated the psychometric properties of the newly developed Individual Potentials Questionnaire (IP-Q). The study involved a total of 730 participants (EFA = 300 and CFA = 430). The I-CVIs and S-CVIs fall within the range of 0.83 to 1, and the I-FVIs and S-FVIs are 1. Two factors (biologically given potential and personally acquired potential) emerged in the EFA analysis, with all 14 items retained due to satisfactory factor loadings (above 0.50) and KMO = 0.905 ( p -value < 0.001). The final CFA model fit indices were: CFI = 0.984, TLI = 0.980, SRMR = 0.034, RMSEA = 0.041, and RMSEA p -value = 0.880. Furthermore, the ICCs for the test–retest are 0.976 (biologically given potential) and 0.953 (personally acquired potential). The results show that the newly developed IP-Q has adequate construct validity and able to assess subjective individual potential.