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Comprehensive genome-wide characterization of NAC transcription factors in Barley influence insights into stress tolerance and evolutionary dynamics
Research on bolt loosening recognition based on sound signal and GA-SVM–RFE
Structural, surface, and theoretical investigation of hydrophobic-modified nanodiamond powders
Deep learning-based video analysis for automatically detecting penetration and aspiration in videofluoroscopic swallowing study
Damage water inrush mechanism of loading unloading stress path of mining floor in deep coal seam
Outcomes of pneumatic retinopexy for primary rhegmatogenous retinal detachment in a Japanese cohort
Model updating method for detect and localize structural damage using generalized flexibility matrix and improved grey wolf optimizer algorithm (I-GWO)
Abstract Various civil engineering-based infrastructures have been strategically planned to implement the structural health monitoring (SHM) system, considering their significance. A key objective faced by this system is the automatic identification and damage detection at the appropriate moment. Employing optimization algorithms in structural model updating is one approach to achieve this objective. This study’s main goal is to evaluate the location and extent of damage by combining two dynamically evolving parameters: the structure’s frequency and the generalized flexibility matrix. It is determined that the suggested approach produces more accurate and effective outcomes than the previous modal flexibility techniques. This is achieved by applying various noises and extracting the damaged structure’s data using the Improved Grey Wolf Optimizer (I-GWO). The accuracy of this method in locating the 15-story shear frame, the 25-member two-dimensional truss bridge, and the 23-member two-dimensional frame, as well as in identifying all damages, is demonstrated by the fact that the error between the simulated and estimated results in an average of twenty runs and each damage scenario was less than 3 percent. The findings demonstrate that the technique can precisely pinpoint the position and extent of damage in various structures, hence increasing the effectiveness of damage identification. Furthermore, they show that when compared to grey wolf optimizer (GWO) and particle swarm optimizer (PSO), I-GWO can offer a dependable method for precisely detecting damage.
Cancer risk from heavy metal contamination in fish and implications for public health
High resolution multiple scenario simulations of future extreme sea levels in hong kong and socioeconomic risks
Aryl hydrocarbon receptor interacting protein and syndromic gene variants detected in Turkish isolated pituitary adenoma families by whole exome sequencing
Smart adaptive learning and optimized feature clustering for enhanced image retrieval
Predictors of female students’ intentions toward urban forest conservation for climate change mitigation in Iran
Evaluation of retinal pigment epithelium changes in serous pigment epithelial detachment using synthesized multi-contrast polarization-sensitive optical coherence tomography
Abstract Retinal pigment epithelium (RPE) melanin thickness maps, derived from multi-contrast images—including the degree of polarization uniformity (DOPU), optical coherence tomography (OCT) angiography, and the attenuation coefficient—are obtained using multi-contrast polarization-sensitive OCT (PS-OCT). These maps have demonstrated utility for three-dimensional assessment of changes in melanin within the RPE (RPE-melanin). While both OCT angiography and the attenuation coefficient can be derived from conventional OCT, measuring the DOPU requires PS-OCT, which is not available on standard commercial OCT systems. To overcome this limitation, we utilized a convolutional neural network to generate DOPU-like images from standard OCT images and used these to calculate a synthesized RPE-melanin thickness map. We evaluated 22 eyes from 20 patients with serous pigment epithelial detachment (PED) secondary to age-related macular degeneration. Both original and synthesized RPE-melanin thickness maps were calculated from multi-contrast PS-OCT datasets. Active RPE lesions were defined as areas with RPE-melanin thickness of ≥ 70 μm (originalRPE70 and synRPE70 for the original and synthesized maps, respectively). Both synthesized and original RPE-melanin thickness maps closely resembled near-infrared autofluorescence imaging. Furthermore, both the originalRPE70 area and synRPE70 area were significantly positively correlated with the PED volume. Synthesized RPE-melanin thickness maps may be useful for clinical quantification of RPE-melanin.
High risk of postoperative complications in dialysis patients undergoing total hip arthroplasty: a database study of Japanese nationwide medical claims
Abstract Dialysis patients who develop degenerative hip disease or femoral neck fractures may require total hip arthroplasty, and their comorbidities predispose them to complications. This study aimed to evaluate whether dialysis was associated with early postoperative complications using a large database of Japanese. In this cohort study, using the Japanese National Administrative Diagnosis Procedure Combination database on THA for patients on hemodialysis or not from December 2011 to March 2023, we assessed the surgical-related complications, medical complications, and mortality during hospitalization after propensity score matching by age, sex, BMI, and comorbidities. A total of 2,111 pairs of patients on hemodialysis and non-dialysis were included. In THA for patients on hemodialysis, the significant odds ratios for various complications were as follows: dislocation (2.616, 95% CI: 1.282 to 5.338, p < 0.01), reoperation (2.104, 95% CI: 1.222 to 3.623, p < 0.01), deep vein thrombosis (0.407, 95% CI: 0.286 to 0.579, p < 0.01), cerebrovascular events (4.426, 95% CI: 1.495 to 13.10, p < 0.01). These findings help identify postoperative THA risks for patients on dialysis, suggesting that more attention should be paid to preoperative planning and postoperative care.
Head and neck tumor organoid grown under simplified media conditions model tumor biology and chemoradiation responses
Abstract Head and neck squamous cell carcinoma (HNSCC) is a prevalent and often fatal malignancy associated with significant treatment-related toxicity. There is an urgent need for a preclinical model to assess therapeutic options and guide clinical decision-making. To define conditions for establishing patient-derived organoid (PDO) models that faithfully recapitulate morphological, histopathological, and genomic characteristics of HNSCC patients and can predict radiation and chemotherapy responses in patients, PDOs were generated from a group of HNSCC patients. The morphological, histological, mutational, and biological characteristics and treatment responses were evaluated. We demonstrate that the PDOs closely resemble resected tumors from which they were derived with respect to histopathology, differentiation state markers, p16 status, and mutation profiling. We observe patient-to-patient variation in cell proliferation rates. Additionally, they exhibit differential responses to radiotherapy and chemotherapy, which were examined using a cell viability assay. This methodology offers potential for drug screening in a pre-clinical context with the potential to mirror clinical outcomes. Our WNT-free growth conditions maintained the differentiation status of PDOs and enabled rapid assessment of drug response and the development of new models to identify new treatment options for head and neck cancer patients.
Optimized biosynthesis and performance enhancement of γ-PGA from Bacillus licheniformis: a study on wettability, microstructure, and environmental performance
Autonomous scheduling mechanism based on energy awareness for improving resource allocation in serverless IoT edge
Abstract The energy-aware scheduling mechanism in serverless computing enables systems to be allocated to IoT devices connected to the network’s edge efficiently based on the energy status of active nodes. In this approach, resource allocation is performed in real-time and according to the energy changes of the nodes, the complexity of which arises from the direct dependence of the energy level on the capacity to respond to requests. This choice is complex since allocating the necessary resources to process requests depends on the energy available in the active nodes. Therefore, to optimize resource allocation and increase access time, selecting and executing pre-schedulers is necessary based on the prediction of the energy level of the active nodes. In this article, we introduce a scheduler selection mechanism called an autonomous energy-aware scheduler, whose design is based on the energy position of the active nodes in the network. In addition, a mechanism for improving system uptime is proposed to increase the duration of the energy reduction of active nodes. The efficiency of the proposed approach was evaluated utilizing three separate load distribution patterns (exponential, Poisson, and exponential-Poisson), and the results indicate the prevention of energy waste and an average reduction of 1.66% in energy consumption. Also, the network uptime is improved by 8.6% compared to other methods. In addition, the proposed method has maintained performance continuity while ensuring failure resistance in all situations. The results demonstrate the high efficiency of the proposed approach in optimizing energy consumption and enhancing the resilience and stability of serverless systems.