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Multiobjective optimization of a pressure maintaining ball valve structure based on RSM and NSGA-II
Characteristic in ground motions between the Mw7.9 Pazarcık earthquake and the Mw7.6 Elbistan earthquake in Türkiye
Advanced hybrid machine learning based modeling for prediction of properties of ionic liquids at different temperatures
Enhanced therapeutic efficacy of silibinin loaded silica coated magnetic nanocomposites against Pseudomonas aeruginosa in Combination with Ciprofloxacin and HepG2 cancer cells
Modulation of leg trajectory by transcranial magnetic stimulation during walking
Abstract The primary motor cortex is involved in initiation and adaptive control of locomotion. However, the role of the motor cortex in controlling gait trajectories remains unclear. In animals, cortical neuromodulation allows for precise control of step height. We hypothesized that a similar control framework applies to humans, whereby cortical stimulation would primarily increase foot elevation. Transcranial magnetic stimulation (TMS) was applied over the motor cortex to assess the involvement of the corticospinal tract over the limb trajectory during human walking. Ten healthy adults (aged 20–32 years) participated in treadmill walking at 1.5 km/h. TMS was applied over the left motor cortex at an intensity of 120% of the threshold to elicit a dorsiflexion of the right ankle during the swing phase of gait. Electromyographic (EMG) measurements and three-dimensional (3D) lower limb kinematics were collected. When delivered during the early swing phase, TMS led to a significant increase in the maximum height of the right toe by a mean of 34.9% ± 9.6% (21.4 mm ± 7.9 mm, p = 0.032) and knee height by 52.8% ± 14.1% (28.8 mm ± 7.7 mm, p = 0.0021) across participants. These findings indicate that TMS can influence limb trajectory during walking, highlighting its potential as a tool for studying cortical control of locomotion.
Cenozoic geoclimatic changes drove the evolutionary dynamics of floristic endemism on the Qinghai–Tibet Plateau
The Qinghai–Tibet Plateau (QTP) harbors extraordinarily high levels of biodiversity and endemism. The region is warming at a rate twice the global average, yet the evolutionary dynamics of its unique biota are poorly understood. Here, we used the endemic land plant genera of the QTP to investigate how its floristic endemism was shaped over time by Cenozoic geoclimatic changes. We first clarified that the QTP hosts 82 endemic land plant genera; we found that the origins of these endemic genera were most likely driven by ecological niche and elevation differentiation, caused by the uplift of the QTP and associated climate change. By sampling 37 land plant clades that together encompass 1,740 species, covering all 82 endemic genera, we show that QTP floristic endemism had emerged by the Early Eocene. Furthermore, the unique biodiversity of the QTP comprises a mix of indigenous elements and immigrants. Among the three subregions of the QTP (Plateau Platform, Himalaya, and the Hengduan Mountains), the processes associated with floristic endemism are asynchronous, reflecting different geoclimatic events with the Miocene as a particularly critical period. The relative contributions of in situ speciation and immigration to the unique biodiversity of the three subregions are also markedly different; in situ speciation dominated in the Hengduan Mountains, which hosts the oldest endemic components of the flora and has served as an important “pump” and “sink” of unique biodiversity. These findings provide insights into how past geoclimatic events may have shaped floristic endemism on the QTP and also have important conservation implications.
Dual asparagine-depriving nanoparticles against solid tumors
Abstract Depletion of circulatory asparagine (Asn) by L-asparaginase (ASNase) has been used for clinical treatment of leukemia, whereas solid tumors are unresponsive to this therapy owing to their active Asn biosynthesis. Herein, we develop a type of core-shell structured cascade-responsive nanoparticles (NPs) for sequential modulation of exogenous Asn supply and endogenous Asn production. The reactive oxygen species-sensitive NP shells disintegrate in the tumor microenvironment and liberate ASNase to scavenge extracellular Asn. The acid-labile NP cores subsequently decompose in the tumor cells and release rotenone to block intracellular Asn biosynthesis. Administration of the dual Asn-depriving NPs in murine models of triple-negative breast cancer and colorectal cancer substantially suppress the growth and epithelial-mesenchymal transition of primary and relapsed tumors, fully eradicate spontaneous and post-surgical metastasis, and confer long-term T cell memory for complete resistance to tumor rechallenge. This study represents a generalized strategy to harness amino acid depletion therapy against solid tumors.
Unraveling the mystery of recent shortened response time of ENSO to Atlantic forcing
Fully non-fused electron acceptor solar cells with 18% efficiency via a synergistic peripheral substituent strategy
Impacts of autonomous vehicles on freeway with conditional isolated and dedicated lanes
The impact of microburst-induced load variations on aircraft performance through numerical simulation
Spatial and temporal variation in vibroscape composition in two grassland habitats
Alternative audio-graphic method for presenting structural information in mathematical graphs designed for low-vision users
Mapping mangrove multi-trait functional diversity from satellite observations across dense and fragmented stands using spectral-biophysical derivatives
Metabolomic analysis of rumen fluid in Tan sheep reveals sex-specific key metabolites and pathways associated with residual feed intake
Evaluation of the diagnostic concordance of FDA-approved PD-L1 assays in clear cell renal cell carcinoma
Artificial intelligence derived grading of mustard gas induced corneal injury and opacity
Abstract Artificial intelligence (AI) has emerged as a transformative tool in ophthalmology for disease diagnosis and prognosis. However, use of AI for assessing corneal damage due to chemical injury in live rabbits remains lacking. This study aimed to develop an AI-derived clinical classification model for an objective grading of corneal injury and opacity levels in live rabbits following ocular exposure of sulfur mustard (SM). An automated method to grade corneal injury minimizes diagnostic errors and enhances translational application of preclinical research in better human eyecare. SM induced corneal injury and opacity from 401 in-house rabbit corneal images captured with a clinical stereomicroscope were used. Three independent subject matter specialists classified corneal images into four health grades: healthy, mild, moderate, and severe. Mask-RCNN was employed for precise corneal segmentation and extraction, followed by classification using baseline convolutional neural network and transfer learning algorithms, including VGG16, ResNet101, DenseNet121, InceptionV3, and ResNet50. The ResNet50-based model demonstrated the best performance, achieving 87% training accuracy, and 85% and 83% prediction accuracies on two independent test sets. This deep learning framework, combining Mask-RCNN with ResNet50 allows reliable and uniform grading of SM-induced corneal injury and opacity levels in affected eyes.
Global overlooked multidimensional water scarcity
Freshwater resources are fundamental to supporting humanity, and measures of water scarcity have been critical for identifying where water requirements and water availability are imbalanced. Existing water scarcity metrics typically account for blue water withdrawals (i.e., from surface-/groundwater), while the contribution of green water (i.e., soil moisture) and water quality—dimensions with important implications for multiple societal sectors—to water scarcity remains unclear. Here, we introduce the concept of multidimensional water scarcity that explicitly assesses all three of these dimensions of water scarcity and evaluates their individual and combined effects. We find that 22 to 26% of the global land area and 58 to 64% of the global population are exposed to some form of water scarcity annually, with multidimensional (i.e., blue, green, and quality) water scarcity particularly high in India, China, and Pakistan. Examining seasonal water scarcity, we estimate that 5.9 billion people (or 80% of the world’s population in 2015) were exposed to at least one dimension of water scarcity for at least 1 mo per year and that 1-in-10 people (10%) were exposed to multidimensional water scarcity at least 1 mo per year. Our findings demonstrate that the challenges of water scarcity are far more widespread than previously understood. As such, our assessment provides a more holistic view of global water scarcity issues and points to overlooked scarcity where action needs to bring human pressure on freshwater resources into balance with water quantity and quality.