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Deep learning model for early acute lymphoblastic leukemia detection using microscopic images
Assembled peptoid crystalline nanomaterials as carbonic anhydrase mimics for promoted hydration and sequestration of CO2
Phylogenetic and functional analysis of MYB genes unraveling its role involved in anthocyanin biosynthesis in H macrophylla
Opposing regulation of the K63-linked polyubiquitination of RIPK3 by SMURF1 and USP5 in necroptosis
Prevalence and correlates of restless leg syndrome in psychiatric outpatients in Lebanon
Worldwide genetic diversity of Plasmodium vivax Pv47 is consistent with natural selection by anopheline mosquitoes
Abstract Pv47 is the Plasmodium vivax ortholog of Pfs47, a protein that allows the Plasmodium falciparum malaria parasite to evade mosquito immunity and adapt to diverse vectors. We analyzed global genetic diversity of Pv47 and compared it with Pfs47, finding that most common Pv47 polymorphisms are non-synonymous and cluster in regions similar to those in Pfs47. Pv47 domain 2 presents an excess of non-synonymous substitutions, suggesting positive selection. The greatest haplotype diversity is found in Pv47 from East/Southeast Asia and Oceania. Like Pfs47, Pv47 also exhibits a marked geographic population structure worldwide. Notably, a Pv47 polymorphism (K27E) is associated to differences in infectivity to Anopheles (Nyssorhynchus) albimanus and Anopheles pseudopunctipennis, two phylogenetically distant vectors in Mexico. The striking similarities in genetic diversity, population structure, and signatures of natural selection between Pv47 and Pfs47 suggest that adaptation to different Anopheline mosquito species drives Pv47 diversity by selecting compatible Pv47 haplotypes.
Biodiversity and five novel species of myxozoan parasites in Barbonymus spp. (Cyprinidae, Cypriniformes) from Malaysia
Abstract Up to this time, only five myxosporean species have been documented from fishes of the Barbonymus genus. Due to a limited number of myxozoan studies conducted in Southeast Asia, particularly in Malaysia, the diversity of this parasite group remains largely undiscovered. In this study, a comprehensive parasitology survey was conducted, revealing nine myxozoan parasites, including five different Myxobolus spp., three Thelohanellus sp. and one Myxidium sp. Using morphological and molecular data, we describe here five new species: Myxobolus gonionoti n. sp., found in the gill filaments; Myxobolus barbonymi n. sp., and Myxobolus faizahae n. sp. found in the muscle cells; Thelohanellus gonionoti n. sp. found in the fins; and Thelohanellus barbonymi n. sp. found in the gill arches. Additionally, we identified spores of the previously described Myxobolus dykovae in the gill lamellae of B. schwanefeldii and Thelohanellus zahrahae in the gill filaments of B. gonionotus. Furthermore, two undescribed species were documented solely based on morphology and morphometrics, a Myxobolus sp. from the muscle cells of B. schwanefeldii and a Myxidium sp. from the gallbladder of B. gonionotus.
Molecularly resolved mapping of heterogeneous ice nucleation and crystallization pathways using in-situ cryo-TEM
Applications of bamboo fiber and bamboo stem ash with styrene butadiene rubber in cement mortar for sustainable structural application
Abstract In several developing countries, rapid population growth has led to a shortage of adequate housing. This issue is further intensified by the overuse of traditional construction materials and the environmental concerns linked to their production, particularly the high levels of carbon dioxide emissions. Consequently, the search for environmentally sustainable alternatives has gained momentum. Bamboo, a traditional building resource, has re-emerged as a promising material due to its potential application in construction. This study explored the replacement of Ordinary Portland Cement (OPC) in mortar with bamboo stem ash and alkali-treated bamboo fibers. Various mix ratios were developed and analyzed using the Taguchi method, and the results were validated through analysis of variance (ANOVA), microstructural observations, and energy efficiency evaluation. The optimized mixes demonstrated notable improvements compared to conventional mortar, with an increase in compressive strength of up to 98.42%, flexural strength gains of up to 24%, a 21.7% decrease in dry density, and water absorption limited to 1.52%. Important characteristics of mortar mixes reinforced with bamboo fibers, including as compressive strength, flexural strength, water absorption, and dry density, were efficiently evaluated using the Taguchi technique and is recommend for sustainable bamboo reinforced wall panels.
DeepISLES: a clinically validated ischemic stroke segmentation model from the ISLES'22 challenge
Abstract Diffusion-weighted MRI is critical for diagnosing and managing ischemic stroke, but variability in images and disease presentation limits the generalizability of AI algorithms. We present DeepISLES, a robust ensemble algorithm developed from top submissions to the 2022 Ischemic Stroke Lesion Segmentation challenge we organized. By combining the strengths of best-performing methods from leading research groups, DeepISLES achieves superior accuracy in detecting and segmenting ischemic lesions, generalizing well across diverse axes. Validation on a large external dataset (N = 1685) confirms its robustness, outperforming previous state-of-the-art models by 7.4% in Dice score and 12.6% in F1 score. It also excels at extracting clinical biomarkers and correlates strongly with clinical stroke scores, closely matching expert performance. Neuroradiologists prefer DeepISLES’ segmentations over manual annotations in a Turing-like test. Our work demonstrates DeepISLES’ clinical relevance and highlights the value of biomedical challenges in developing real-world, generalizable AI tools. DeepISLES is freely available at https://github.com/ezequieldlrosa/DeepIsles.
Manufacturing of high-quality green CSA-supported OPC cement with optimum physical and mechanical properties
Abstract In the current study, we aim to enhance the physical and mechanical properties of the ordinary Portland cement (OPC) by incorporating small amounts of calcium sulfoaluminate (CSA) at a rate of 0, 1, 2 and 3% (mixtures A, B, C, and D) to improve its physical and mechanical properties. These mixtures allowed the CSA clinker to emit less CO2 and clink at a low temperature of 1250 °C, which is significantly lower than that of the OPC clinker (1450 °C). The apparent density, specific gravity, porosity, and unconfined compressive strength (UCS) of the pure OPC (0%, mixture A) and the OPC-CSA pastes (B, C, & D mixtures) were measured at curing periods of 1, 3, 7, 28, and 90 days. The scanning electron microscopy (SEM) coupled with the energy dispersive X-ray (EDS) was employed to investigate the internal structure of these pastes, while the XRF technique was employed to determine their chemical composition. Additionally, the optical characteristics of the formulated and disintegrated phases were delineated using FTIR spectroscopy, while the differential thermal analysis (DTA) and thermogravimetric analysis (TGA) were employed to examine them as the curing age increased. The mixture C (2% CSA + 98% OPC) sintered at 90 days is characterized by the best physical and mechanical properties (compressive strength = 52.5 MPa, porosity = 2.86%, apparent density = 2.015 g/cc, and specific gravity = 2.405 g/cc). This procedure is beneficial on a laboratory scale and applicable on the industrial scale to improve the physical and mechanical characteristics of the OPC and decrease its energy consumption.
Lattice-matched antiperovskite-perovskite system toward all-solid-state batteries
Prognostic comparison of partial and radical nephrectomy for T1b renal cell carcinoma with SEER database analysis
Cell free RNA detection of pancreatic cancer in pre diagnostic high risk and symptomatic patients
Artificial intelligence with feature fusion empowered enhanced brain stroke detection and classification for disabled persons using biomedical images
Abstract Brain stroke is an illness which affects almost every age group, particularly people over 65. There are two significant kinds of strokes: ischemic and hemorrhagic strokes. Blockage of brain vessels causes an ischemic stroke, while cracks in blood vessels in or around the brain cause a hemorrhagic stroke. In the prompt analysis of brain stroke, patients can live an easier life. Recognizing strokes using medical imaging is crucial for early diagnosis and treatment planning. Conversely, access to innovative imaging methods is restricted, particularly in emerging states, so it is challenging to analyze brain stroke cases of disabled people appropriately. Hence, the development of more accurate, faster, and more reliable diagnostic models for the timely recognition and efficient treatment of ischemic stroke is greatly needed. Artificial intelligence technologies, primarily deep learning (DL), have been widely employed in medical imaging, utilizing automated detection methods. This paper presents an Enhanced Brain Stroke Detection and Classification using Artificial Intelligence with Feature Fusion Technologies (EBSDC-AIFFT) model. This paper aims to develop an enhanced brain stroke detection system for individuals with disabilities, utilizing biomedical images to improve diagnostic accuracy. Initially, the image pre-processing stage involves various steps, including resizing, normalization, data augmentation, and data splitting, to enhance image quality. In addition, the EBSDC-AIFFT model combines the Inception-ResNet-v2 model, the convolutional block attention module-ResNet18 method, and the multi-axis vision transformer technique for feature extraction. Finally, the variational autoencoder (VAE) model is implemented for the classification process. The performance validation of the EBSDC-AIFFT technique is performed under the brain stroke CT image dataset. The comparison study of the EBSDC-AIFFT technique demonstrated a superior accuracy value of 99.09% over existing models.
Single-molecule tweezers decode hidden dimerization patterns of membrane proteins within lipid bilayers
Design, analysis and experiment of a novel repeatable buffer landing mechanism
Mitochondrial damage triggers the concerted degradation of negative regulators of neuronal autophagy
Abstract Mutations that disrupt the clearance of damaged mitochondria via mitophagy are causative for neurological disorders including Parkinson’s. Here, we identify a Mitophagic Stress Response (MitoSR) activated by mitochondrial damage in neurons and operating in parallel to canonical Pink1/Parkin-dependent mitophagy. Increasing levels of mitochondrial stress trigger a graded response that induces the concerted degradation of negative regulators of autophagy including Myotubularin-related phosphatase (MTMR)5, MTMR2 and Rubicon via the ubiquitin-proteasome pathway and selective proteolysis. MTMR5/MTMR2 inhibit autophagosome biogenesis; consistent with this, mitochondrial engulfment by autophagosomes is enhanced upon MTMR2 depletion. Rubicon inhibits lysosomal function, blocking later steps of neuronal autophagy; Rubicon depletion relieves this inhibition. Targeted depletion of both MTMR2 and Rubicon is sufficient to enhance mitophagy, promoting autophagosome biogenesis and facilitating mitophagosome-lysosome fusion. Together, these findings suggest that therapeutic activation of MitoSR to induce the selective degradation of negative regulators of autophagy may enhance mitochondrial quality control in stressed neurons.
Surrogate-assisted optimization of roll-to-roll slot die coating
Abstract Roll-to-roll slot die coating is a key wet processing technique, where achieving a specific thickness with minimal variability is crucial. However, the numerous input parameters make optimization complex. Despite its advanced applications, computer-aided optimization remains underutilized, leaving potential performance improvements untapped. Due to the lack of accurate first-principle models, machine learning offers a promising approach. This study employs Radial Basis Function Neural Networks as surrogate models trained on experimental data to optimize roll-to-roll slot die coating. These models predict coating thickness and uniformity with mean absolute errors below 11.5 %. Key process parameters are identified, with shim thickness and substrate velocity having the greatest impact on coating uniformity, while coating gap played a lesser role. An evolutionary optimization approach identified new operating parameters, leading to improved coating properties. Experimentally, these optimized conditions achieved the five lowest recorded uniformity values and increased the hyper-volume fraction from 0.68 to 0.84. Some prediction inconsistencies were observed, likely due to the high sensitivity of lab-scale equipment, which is expected to improve at an industrial scale. This work paves the way for wider adoption of machine learning and accurate metrology in slot die coating.