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Improving diagnostic accuracy in atypical melanocytic tumors using p16 immunohistochemistry and 9p21 fluorescence in situ hybridization: analysis of 206 second opinion cases
Efficient face information encryption and verification scheme based on full homomorphic encryption
Correction to at least neutral alignment during high tibial osteotomy is sufficient in reducing the knee adduction moment
Evaluation of ecological consequences on the global distribution of Staphylococcus aureus Rosenbach 1884 due to climate change, using Maxent modeling
Abstract Staphylococcus aureus is a primary cause of many infections in humans, and its rising prevalence and drug resistance are serious public health concerns. While there is evidence that climate change can influence the distribution and abundance of microbial species, the precise effects on S. aureus are not well characterized. The purpose of this study is to predict the potential influence of climate change on the global distribution of Staphylococcus aureus in 2050 and 2070 using GIS and Maxent modeling. S. aureus occurrence data was acquired from global databases and coupled with bioclimatic variables to simulate current and future habitat suitability under several climate change scenarios (RCP 2.6 and 8.5). The Maxent modeling approach was used to forecast geographical patterns of S. aureus distribution, providing insights into locations that may see increased prevalence of this essential species as a result of climate change. The study’s findings can be used to inform public health measures and focused surveillance activities aimed at reducing the burden of Staphylococcus aureus infection.
Shear wave velocity structure at King Saud University, Saudi Arabia, derived from MASW and microtremor arrays
Impact of steatotic liver disease categories on atrial fibrillation in type 2 diabetes: a nationwide study
New antibiotic that kills drug-resistant bacteria discovered in technician’s garden
Trend and multivariate decomposition analysis of modern contraceptive utilization among women in Ethiopia
Results of a multicenter, randomized trial examining a new transition model for post-kidney transplant adolescents
Abstract Allograft loss after pediatric kidney transplantation (KTx) is highest in adolescents and young adults. Non-adherence and Health Care Transition (HCT) are important factors, but others also contribute. In the TransNephro study patients were randomized 1:1. The intervention group was included in the Berlin Transition Program (BTP) and incorporated a central case manager, a communication app, and joined transition rounds for one year before and one year after transfer. Primary endpoint was the coefficient of variation (CoV) of the trough level of the calcineurin inhibitor as a surrogate marker for medication adherence associated with graft loss. Least square (LS) mean differences and corresponding 95% confidence intervals (CIs) were estimated using an analysis of covariance (ANCOVA) model. We assessed 220 patients for eligibility. 49 patients were randomized to the intervention group and 53 to the control group. We analyzed 84 patients in the modified intention-to-treat analysis (38 intervention, 46 controls) and 60 in the per protocol analysis (25 intervention, 35 controls). We found no difference in CoV. We saw low numbers of graft-related events and observed no differences with respect to quality of life. BTP did not improve adherence and other outcome parameters. Non-adherent patients may have decided not to participate, whilst adherence of participants was already good at study start. It is therefore achallenge to design future multicenter trials on HCT that include multiple interventions. Trial registration: ISRCTN22988897, 24/04/2014, https://doi.org/10.1186/ISRCTN22988897 .
Educational disparities in 20-year trajectories of psychological well-being in a national sample
A targeted one dimensional fully convolutional autoencoder network for intelligent compression of magnetic flux leakage data
Abstract In response to the issue of massive data volume generated by magnetic flux leakage (MFL) non-destructive testing in oil and gas pipelines, an intelligent data compression method based on a targeted one-dimensional fully convolutional autoencoder network is proposed. Firstly, a data preprocessing module is designed to generate high-quality data required for subsequent processing, taking into account the characteristics of MFL data. Secondly, a data block classification algorithm is developed to calculate peak values for segmented differential data, and based on a predefined targeted threshold, distinguish different types of MFL data. Subsequently, based on the distinct data types, targeted one-dimensional fully convolutional autoencoder models are constructed to effectively achieve dimensionality reduction compression and reconstruction of the MFL data. Through practical experimental analysis, the reconstruction error such as MAE is reduced by about 27.7% and the compression ratio is improved by about 14% compared with traditional methods such as PCA. In addition, compared with ID-AE, the proposed 1D-FCAE reduces 206.8 k, 1.58G, and 80 s in parameters, memory usage, and training time, respectively, and reduces compression and decompression time by 60 ms and 69 ms, respectively, validating that it is easy to be applied in industrial environments with limited resources.
Exclusive: NIH to cut grants for COVID research, documents reveal
Endogenous DNA damage at sites of terminated transcripts
Characterization and immune-modulatory roles of branched glucan and acetylated gluco-oligosaccharides produced by glucansucrase 40 from Leuconostoc mesenteroides YTU 40
COVID-19 infection was associated with poor sperm quality: a cross-sectional and longitudinal clinical observation study
Sexual dimorphism and allometric patterns in hawkmoth epiphyses (Lepidoptera: Sphingidae)
Polyvinyl alcohol film comprising biochar modified titanium dioxide nanocomposites as decoloring and disinfectant agents
Abstract In this work, titanium dioxide nanowires were prepared hydrothermally in strong alkaline medium. In parallel, nanostructural biochar was obtained via carbonization of rice husk at relatively high temperature. Then, titanate nanowires were modified with the nanorods of biochar via in-situ and ex-situ approaches in order to determine the best way to produce the nanocomposites with improved properties. Polyvinyl alcohol was used as a commercial matrix to include the superlative nanocomposite obtained and casted as a free-standing nanocomposite film. The synthesized nanowires, nanorods, and their nanocomposites were intensively investigated with transmission electron microscope (TEM), scanning electron microscope (SEM), energy dispersive X-ray (EDX), Fourier transform infrared (FTIR), X-ray diffraction (XRD), and N2 gas sorption. The microscopic images confirmed successful preparation and modification of nanostructures. FTIR showed strong interactions between the surface functional groups of the obtained nanomaterials. XRD exhibited a reduction in the crystallite size upon the treatment step. Also, surface texture analysis of titanate nanowires displayed a significant enhancement, particularly in terms of surface area and total pore volume. These superior properties promote the obtained nanocomposites to be evaluated in the water treatment compared with the pristine. The results confirmed complete removal of methylene blue (20 ppm) from the synthetic wastewater within only 20 min. in dark either by using the nanocomposites as powders or even as films. Kinetics and isotherms indicated that the adsorption process obeyed Langmuir model and follows pseudo-second order. On the other hand, the prepared materials depicted a strong biocidal activity against pathogenic microorganisms. The obtained nanocomposites may open opportunities towards developed adsorbents with superior features and performance for applications in the field of water decontamination.