Weight Parameters Research Articles (Page 1)

In our institution, therapeutic drug monitoring of daptomycin is performed routinely and cases of high trough concentrations have been observed in patients without known risk factors. The aim of this study was to identify risk factors of daptomycin overexposure. We performed a case-control study of daptomycin overexposure in patients who received daptomycin between 2013 and 2021. Cases and controls were defined as patients with trough concentration (Cmin) ≥60 mg/L and Cmin <60 mg/L, respectively. Univariate and multivariate analyses were performed with logistic regression models. Retained variables were further analyzed by subgroup analysis and comparison of the pharmacokinetic parameters of daptomycin. We analyzed data from 78 and 26 patients in the control and case groups, respectively. The male-to-female ratio was 1.5. The median (interquartile range) of age, body weight, and creatinine clearance was 66.5 (55-77) years, 77 (65-96) kg, and 98.5 (53-124) mL/min, respectively. Increasing body mass index (BMI) and co-administration of irbesartan were identified as risk factors of daptomycin overexposure with odds ratio (OR) (95% confidence interval [CI]) of 2.9 [1.4-6.2], and 6.1 [1.1-40.8], respectively, whereas increasing creatinine clearance was associated with decreasing risk, with OR of 0.16 [0.05-0.35]. The influence of BMI was attributed to the non-linear relationship between body weight and daptomycin PK parameters and the use of weight-based dosing in patients with high BMI. In addition to renal impairment, high BMI and irbesartan co-administration may be associated with an augmented risk of daptomycin overexposure. Dosing based on actual body weight should be avoided in obese patients.

Abstract We study carbon-priced modal split on capacity-constrained transport networks with piecewise-linear tariff schedules (“tariff blocks”) and quadratic adjustment frictions. We establish turnpike theorems showing that, for any finite horizon, optimal policies spend all but $$O(1)$$ time near the minimizers of a static average-cost program that aggregates logistics costs and carbon charges; the resulting $$O(1)$$ value gap is uniform in the horizon. We then analyze comparative statics of the steady modal mix with respect to the carbon weight and other parameters. Because the steady problem is a parametric convex program with a polyhedral feasible set and piecewise-linear objective, there exists a finite set of policy thresholds at which the optimal steady mix can change discontinuously, while both the steady value and selected minimizers vary Lipschitz-continuously away from those thresholds. We further give envelope identities (e.g., the derivative of the steady value with respect to the carbon weight equals steady emissions) and show that receding-horizon (economic MPC) implementations are near-optimal with bounded, horizon-independent loss. The analysis is purely theoretical; a small synthetic network illustrates threshold locations, jump magnitudes, and boundary-layer dynamics. The results provide transparent diagnostics for pricing vs. capacity instruments and support regime-aware planning on multimodal networks.

Efficient resource management in cloud computing (CC) is critical to maintaining high availability, scalability, and energy efficiency. Traditional methods often struggle to handle dynamic workloads, resulting in resource waste or overload. To address these challenges, this research proposes an Efficient Cloud Resource Management utilizing Complex-Valued Spatio-Temporal Graph Convolutional Neural Networks (CRM-CVSTGCNN). Initially, the input data is collected from the 2019 Google cluster workload traces dataset. It is then pre-processed using Cauchy Robust Correction-Sage Husa Extended Kalman Filter (CRCHEKF) to combine and normalize data vectors. The pre-processed data is then fed into Complex-Valued Spatio-Temporal Graph Convolutional Neural Networks (CVSTGCNN) to forecast future cloud workloads. Since CVSTGCNN alone does not adaptively optimize its weight parameters, hence the Portia Spider Optimization Algorithm (PSOA) is employed in this work to enhance prediction accuracy. The proposed approach is implemented in python and evaluated utilizing metrics like Accuracy, Root Mean Squared Error (RMSE), Mean Squared Error (MSE), Mean Absolute Error (MAE) and Coefficient of Determination (R2). The performance measures of the proposed approach attain 97.5% accuracy, 0.03% RMSE and 0.022% MSE when compared to the existing methods, such as Stable and efficient resource management by deep neural network on cloud computing (SERM-CC-BiLSTM), A proactive auto scaling and energy-efficient VM allocation framework using online multi-resource neural network for cloud data centre (EETS-CCE-GGCN), and AI-based energy-efficient task scheduling for cloud computing surroundings (YPR-RWE-CNN) methods, respectively. These outcomes highlight the effectiveness of the proposed method for dynamic and energy-efficient cloud resource management.

Background: Heart failure with preserved ejection fraction (HFpEF) represents a major unmet need in cardiovascular medicine, yet understanding of its causative mechanisms is limited. Recent metabolomic studies using a two-hit murine HFpEF model (high fat diet: HFD + NOS inhibition via L-NAME) revealed reduced myocardial levels of histidyl dipeptides, such as carnosine (β-alanyl-L-histidine). Carnosine is present at 0.1-1 mM in cardiac tissues and is known to sequester lipid peroxidation products from oxidative stress, such as acrolein. Based on these findings, we investigated whether enhancing myocardial carnosine by β-alanine feeding, the precursor amino-acid for carnosine, could modify HFpEF pathophysiology. Hypothesis: Augmenting myocardial carnosine levels by β-alanine supplementation will improve cardiac function in HFpEF. Methods: Eight-week-old wild type C57BL/6J male mice were assigned to three groups; normal chow (NC), HFD (60% kcal from fat) + drinking water with 0.5 g/L L-NAME (HFD/L), and HFD/L plus 20 g/L β-alanine in drinking water (β-ala). After 10 weeks, assessments included body composition by DEXA scan, diastolic function by echo, and glucose and insulin sensitivity by glucose and insulin tolerance tests (GTT and ITT). Histidyl dipeptide levels were measured using LC-MS/MS. Results: HFD+L-NAME feeding increased body weight (NC:33±0.5 vs HFD/L:45±2 g, p&lt;0.05), fat mass (NC:4±0.2 vs HFD/L:16±1 gm, p&lt;0.05), decreased grip strength (NC:2.3±0.2 vs HFD/L:1.2±0.1 N, p&lt;0.05), and induced diastolic dysfunction as evidenced by increased IVRT (NC:10.4±1.1 vs HFD/L:19±2 ms, p&lt;0.05), E/A (NC:1.4±0.1 vs HFD/L:1.5±0.1, p&lt;0.05) and E/e’ (NC:33±9 vs HFD/L:70±7, p&lt;0.05). β-alanine supplementation improved body weight (β-ala:39±2 g, p&lt;0.05), fat mass (12±2 g, p&lt;0.05), and diastolic parameters: IVRT (14±2 g, p&lt;0.05), E/A (1.5±0.2, p&lt;0.05), and E/e’ (33±12, p&lt;0.05). Cardiac carnosine levels were decreased in HFD/L (NC:466.4±40.3 vs HFD/L: 311.9±45.7 pmol/mg, p&lt;0.05) but were increased ~10 fold with β-alanine feeding (4316.3±568.7 pmol/mg, p&lt;0.05). β-alanine hearts also showed increased removal of acrolein via carnosine conjugates (HFD/L: 5.1±0.5 vs β-ala: 54±8 pmol/g, p&lt;0.05). Conclusion: β-alanine supplementation improves diastolic function in a two-hit mouse model of HFpEF, likely through increased myocardial carnosine and its ability to sequester toxic aldehydes. These findings suggest carnosine may be a safe and effective therapeutic intervention in HFpEF.

License Plate Recognition consists of reading and decoding car license plates based on optical character recognition that is captured from pictures or video. License Plate Recognition is used for monitoring traffic, parking, and security applications. This study presents a novel methodology for real-time license plate recognition using an Optical Residual Lightweight Conventional Greylag Goose (ORLCGG). The process begins with image acquisition, where input images are localized using a Region-Based Linear Layer (RLL) to effectively isolate license plates from their backgrounds, enhancing clarity. To improve image quality, the Distortion Blur Preprocessing (DBP) addresses blurriness caused by optical distortions, particularly for side-mounted plates. It employs fuzzy logic for distortion estimation and calculates the Point Spread Function matrix, which normalizes the image to enhance clarity. A canny edge detection algorithm maximizes the features of the license plates and subsequently improves recognition accuracy. For detection with the system, segmentation and ResNeXt101 are combined for feature extraction, and then the appropriate Lightweight Convolutional Neural Network (LCNN) tailored for its number plate recognition problem. Greylag Goose Optimization (GGO) is used to optimize the LCNN weight parameter. The combination increases efficiency for number recognition, with improved accuracy, and makes the system a viable solution for real-time license plate recognition in a number of environments. The proposed method achieved a Mean Absolute Error (MAE) of 0.11, a Mean Squared Error (MSE) of 0.02 and an R 2 value of 0.93, demonstrating its effectiveness in improving detection accuracy across various datasets. Additionally, it attained a confidence level of 81% in real-time data, highlighting its superior feature extraction capabilities.

Basketball players typically use the jump shot to score points while playing. Because of this, the jump shot is considered the most important aspect of basketball talent and calls for extraordinary ability. Coaches can make better decisions and significantly improve their athletes’ competitiveness by utilizing body domain network technology in sports training and posture recognition. This manuscript proposes the decision modeling of basketball game tactics based on video analytics (TM-BPJA-SBTT-DVAN). Initially, images were collected from eight male testers performing various basketball-related actions. The input image is fed into the pre-processing stage using Interaction-Aware Labeled Multi-Bernoulli Filter (IALMBF) to remove noise and improve image quality. Then, the pre-processed images are provided for feature extraction using the Signed Cumulative Distribution Transform (SCDT) to extract geometrical features such as area, perimeter, centroid, solidity, and slope. These extracted features are then passed to the Directional Variance Attention Network (DVAN) to recognize the shooting actions of basketball players based on sports biomechanics. However, DVAN does not adopt adapting optimization strategy to determine optimal variables to identify the shooting actions of basketball players. To address this, the Black-winged Kite Optimization Algorithm (BKOA) is applied to improve the weight parameters of DVAN, enhancing its ability to recognize shooting actions. The proposed TM-BPJA-SBTT-DVAN is then implemented in Python, and performance metrics such as Precision, Recall, Accuracy, F1-Score, Specificity, ROC, and Computational Time are analyzed.

ABSTRACT Human pose estimation is widely used in various fields such as sports and fitness, gesture control, unmanned supermarkets, entertainment, and gaming. However, current research on human pose estimation faces challenges such as low accuracy and poor model generalization. This paper proposes a novel human pose estimation framework based on graph neural networks. First, a dynamic adjacency matrix is combined with a static adjacency matrix to adaptively model dynamic, feature‐based connectivity relationships. By introducing learnable weighting parameters, the model can flexibly balance static geometries with feature‐driven dynamic relationships, capturing complex skeletal features more accurately. To further enhance the expressive power of graph convolution, this paper introduces the Chebyshev polynomial convolution mechanism to capture higher‐order neighborhood information. Inspired by Kolmogorov Arnold Networks (KANs), a learnable linear transform layer (KANLinear) is integrated into the Chebyshev graph convolution to learn graph structure feature representations across different layers. This design allows the model to effectively capture both local and global joint point dependencies, demonstrating superior performance in complex skeleton modeling tasks. Experimental results show that the proposed method achieves strong performance on the benchmark dataset Human3.6M, with a P1 error of 41.5 mm and a P2 error of 32.6 mm over 81 frames.

Metabolic syndrome, characterized by dyslipidemia, central obesity, hypertension, hyperglycemia, and insulin resistance, increases the risk of cardiovascular diseases and poses a global health challenge. Ellagic acid (EA), a polyphenol with antioxidant and lipid-lowering properties, shows potential as a therapeutic candidate for modulating metabolic markers. This study aimed to assess the effects of EA on lipid profiles, fat weight, and anthropometric parameters in animals and humans with abnormal metabolic markers or pre-obesity through a systematic review and meta-analysis. Literature searches were conducted using PubMed, Scopus, Embase, and Cochrane Library databases up to October 22, 2024. Data were extracted from 78 studies. A random-effects model calculated the standardized mean difference (SMD) with the 95% CI. EA significantly reduced triglyceride levels in animals (SMD = -2.93; 95% CI, -3.78 to -2.07; P < .0001) and humans (SMD = -0.58; 95% CI, -0.87 to -0.29; P = .0001) and increased high-density-lipoprotein cholesterol levels in animals (SMD = 1.24; 95% CI, 0.58 to 1.90; P = .0002) and humans (SMD = 0.72; 95% CI, 0.37 to 1.07; P < .0001). Ellagic acid also reduced fat weight in epididymal (SMD = -1.40; 95% CI, -1.81 to -0.99; P < .0001), mesenteric (SMD = -1.21; 95% CI, -1.81 to -0.62; P < .0001), and retroperitoneal (SMD = -1.47; 95% CI, -1.94 to -0.99; P < .0001) regions in animals and waist circumference (SMD = -0.55; 95% CI, -1.06 to -0.44; P = .0336) in humans. Ellagic acid improves lipid profiles, reduces fat weight, and optimizes anthropometric parameters in animals and humans with metabolic marker abnormalities or pre-obesity, highlighting its therapeutic potential for managing metabolic syndrome and associated health conditions. Systematic Review Registration: PROSPERO no. CRD42024584248.

Neural networks with low-resolution parameters.

Geographically weighted random forest fusing multi-source environmental covariates for spatial prediction of soil heavy metals.

ObjectivesThe aim of the study was to evaluate the efficacy and safety of capromorelin to manage weight loss in cats with unintended weight loss, as occurs in chronic kidney disease (CKD), in a randomized, masked, placebo-controlled, multicenter clinical field study.MethodsA total of 176 client-owned cats with existing CKD and unintended weight loss of 5% or more were enrolled. Cats were randomized 2:1 to receive capromorelin or a vehicle placebo orally once daily for 55 days. Changes in body weight and safety parameters were monitored throughout the study.ResultsBody weight increased progressively with time in the capromorelin group and decreased in the placebo group. For the effectiveness population data sample (n = 112), mean change in body weight from day 0 to day 55 was +5.18% (95% confidence interval [CI] 3.45-6.91) with capromorelin and -1.65% (95% CI -3.82 to 0.55) with placebo. The treatment effect (capromorelin minus placebo) from day 0 to day 55 was +6.81% (95% CI 4.21-9.42) with P <0.0001, representing +0.25 kg (95% CI 0.15-0.35) body weight. Hypersalivation was observed only in the capromorelin group (P <0.0001). For all other adverse events (AEs), there was no significant difference between the treatment groups: in the capromorelin group 96/118 (81.4%) cats and in the placebo group 41/58 (70.7%) cats had at least one reported AE (P = 0.3650).Conclusions and relevanceCapromorelin was safe and effective, and provides a valuable new option to maintain or increase body weight in cats with CKD.

Data-based decentralized control of nonlinear-constrained interconnected systems using reinforcement learning.

Abstract Developing weak annotations is one of the effective methods to reduce the workload of labeling the training data and extract the valuable priori information. In this paper, we propose a novel weakly supervised variational segmentation model that requires only a single randomly selected point within the target region. First, We construct an anisotropic Riemannian metric that incorporates not only the texture patterns, the local directional information at the boundaries, but also the intensity heterogeneity through a conformal factor. This step is the cornerstone of our method, as it enables the effective integration of multi-scale image features. Second, we calculate two types of distances: geodesic distances based on the new Riemannian metric and Euclidean distances, from all the points in the image to the taken point. These pointwise distances are then integrated into the Chan-Vese model, enabling the simultaneous exclusion of regions with similar intensities both near and far from the target region. The distances serve as adaptive weighting parameters in the variational model, further enhancing segmentation accuracy. We employ the Douglas-Rachford algorithm for efficient numerical implementation. Experimental results show that our method achieves similar or better segmentation performance than other weakly annotated methods and the single-point annotation has better robustness.

Introduction Mesoderm-specific transcript ( Mest ), a paternally expressed imprinted gene, is involved in the modulation of adipose tissue expansion. Mest is also highly expressed in the developing and adult brain, suggesting a role in behavioral phenotypes. Previously, we showed that female mice with paternal Mest inactivation ( Mest pKO ) exhibit no discernible behavioral impairments compared to wild-type mice. In this study, we performed metabolic phenotyping of female Mest pKO mice in response to a dietary challenge. Methods Eight-week-old female and male wild-type and Mest pKO mice were fed a control or Western diet (40 kcal% fat) until 24 weeks of age. Body weight, body composition, and metabolic parameters were measured during the course of the feeding regimens, and gene expression and type-2-deiodinase (DIO2) activity were examined in white adipose tissue and brain at the end of the study. Results Mest pKO female mice fed a Western diet were protected against diet-induced obesity. Strikingly, these mice showed increased ambulatory activity and speed, coupled with reduced resting parameters, suggesting a role for MEST in the regulation of spontaneous physical activity, a form of nonexercise activity thermogenesis. When considering body mass (control diet) and lean mass (Western diet), energy expenditure was increased in the female Mest pKO mice. Male Mest pKO mice did not exhibit these changes. Analyses of hypothalamic gene expression revealed upregulation of Dio2 , and RNA-seq highlighted differential expression of numerous thyroid hormone-responsive genes in Mest pKO female mice. Conclusion Mechanistically, our results suggest that MEST directly or indirectly regulates thyroid hormone-responsive genes in the hypothalamus, thereby modulating the neurobiological control of nonexercise activity thermogenesis in Western diet-fed female mice.

Regions with insufficient resilience in their agriculture industry can usually be exposed to threats of unstable supply of food and agricultural products. Therefore, agricultural resilience is important for regional development and welfare. To support the development of agricultural resilience, proper policies and incentives need to be implemented. To achieve this, the first step is to appropriately evaluate the regional agricultural resilience levels. In this study, a novel agricultural resilience evaluation method was developed based on hybrid weighting approaches and dynamic CoCoSo (i.e., Combined Compromise Solution). The method can capture the temporal change in resilience levels, integrate richer information, and provide more robust output. To confirm its effectiveness, the method was applied to the evaluation of regional agricultural resilience in 21 cities of Sichuan province in China across five years. Over a recent five-year period, the annual average levels of agricultural resilience in Sichuan have increased, although this trend became less significant in more recent years. Also, the resilience levels among cities are diverse, and some cities have experienced significant changes of resilience across years. When considering temporal effects integrating five years, Liangshanzhou city ranks the first and Bazhong city ranks the last in terms of their resilience levels, but such results can depend on CoCoSo parameters and time weight parameters, with the latter having more significant influence. This study can contribute to the existing literature by providing new methodological tools for agricultural resilience research and regional management studies. Also, this study can help identify cities with different agricultural resilience levels and dynamics, informing practitioners’ new perspectives for agricultural policy evaluation as well as business strategy planning.

The ability of Visual Question Answering (VQA) models to provide insightful responses to images based on their content holds the potential to completely transform machine interpretation and interaction. VQA systems face challenges due to noisy image data, making accurate question answering difficult. Several deep learning algorithms are used for predicting answers from images, but they don’t yield enough outcomes. To get over these problems, this work is suggested. In this manuscript, a visual question–answer model-based Transformer-Based Multi-Head Cross Attention Network with optimized Gold Rush Octave Convolution Network (T-MHCAN-OGRCNN) is proposed. To enhance VQA accuracy, three datasets are taken: VQA v2.0, OK-VQA, and FVQA, which include images with embedded questions and answers. These images often contain noise, necessitating pre-processing for improved results. Then, Task-Oriented Homogenized Automatic (TOHA) method for noise reduction is used. For word embedding, Multiscale Parallel MobileNetV3 (WE-MP-MobileNetV3) is employed. Following that, visual and textual features are extracted using Transformer-Based Multi-Head Cross Attention Network (T-MHCAN). The core VQA task is handled by a Fully Octave Convolution Network (OCNN), whose weight parameters are optimized using Gold Rush Optimizer (GRO). Therefore, this method incorporates cutting-edge strategies to improve precision and resilience of VQA systems. The suggested T-MHCAN-OGRCNN technique is implemented on Python platform. Three datasets were used to evaluate the effectiveness of the suggested T-MHCAN-OGRCNN, which achieves superior results than current techniques with 99.9% accuracy and 99.8% recall. This demonstrates approach’s exceptional effectiveness and room for growth in industry.

Pumpkin is widely used as a rootstock to enhance salt tolerance and improve productivity of Cucurbit crops. To date, the morphology and ion parameters of pumpkins at a certain time point under salt stress are well-known. However, the dynamic changes in organ morphology and K+/Na+ content of pumpkin under salt stress and the relationship of them remain unclear. Therefore, this study investigated biomass, root morphology, stem structure, and K+/Na+ content in salt-sensitive (JZ-1) and salt-tolerant (JYZ-1) pumpkins under 0 mM and 120 mM NaCl conditions at 2, 5, and 10 days after treatment (DAT). Our results show that at the beginning, NaCl treatment led to a sharp decrease in shoot fresh weight by 30–53% and a slight decrease in root fresh weight, plant dry weight, and total root length and affects the K+ and Na+ content both in JZ-1 and JYZ-1 at 2 DAT. Subsequently, total root volume and number of tips have changed, in which NaCl treatment resulted in a significant increase of 127% in total root volume and a significant decrease of 38.4% in number of tips in JYZ-1 at 5 DAT, but no significant difference in JZ-1 at 5 DAT was found. At the end, root fresh weight and stem structure parameters were significantly decreased by NaCl treatment at 10 DAT both in JZ-1 and JYZ-1, and stem cross-sectional area under NaCl conditions in JZ-1 and JYZ-1 at 10 DAT (2.133 and 2.316 mm3, respectively) was significantly lower than that under control conditions (2.933 and 4.441 mm3, respectively). Additionally, shoot K+ content showed a trend of first upward and then downward in JZ-1 and a slightly decreasing trend in JYZ-1, and shoot Na+ content displayed a trend of first downward and then upward in JZ-1 and a slightly increasing trend in JYZ-1. It is suggested that shoot K+ content, shoot Na+ content, and total root volume be considered as the important parameters for pumpkin salt tolerance assessment. These findings will help us better understand the mechanisms of salt tolerance and improve the efficiency of identification of salt-tolerant pumpkin.

Plastic waste pollution poses significant challenges and harm to both the environment and human health. Plastics accumulate in the environment and recent researches are focused on developing effective eco-friendly biological process to degrade and remove them. It is on this premise that this preliminary assessment of the ability of Spent Mushroom Substrate (SMS) to degrade plastics by weight parameter technique was carried out. Polyethylene terephthalate (PET) was dried at 24°C for 3 days and afterwards cut into bits of 0.06g. These were later immersed in conc. HCl and incubated in microcosm (micro boxes) containing 400g weathered SMS. These boxes were kept in humid dark environment and samples were collected and analyzed after 30 and 60 days. The results revealed that the SMS had significant effect on weight parameters after 30 and 60 days. Microcosm which had an initial weight of 1.20g was biodegraded to 0.16g and individual piece of the plastic in the box with initial weight of 0.06g decreased to 0.04g after 30 days incubation in SMS. Further incubation for 60 days resulted in the decrease of plastic in the box from 1.20g to 0.02g and individual piece from 0.06g to 0.008g. The control maintained the initial weight till the end of the experiment. This is an indication that the mushroom mycelia in the SMS may have had an interface with the plastic polymer. And this seem to be a promising ecofriendly alternative to deal with plastic waste pollution.

Cotton leafroll dwarf virus (CLRDV) is a polerovirus transmitted by Aphis gossypii Glover. Factors contributing to cotton (Gossypium hirsutum L.) yield losses caused by CLRDV-infection remain unclear, and results from previous studies indicate the environmental component of the disease triangle may significantly influence yield loss outcomes. This three-year study was conducted to compare yield and yield components, fiber quality, root weight and other morphological parameters of CLRDV-infected and non-infected plants grown under high heat conditions. Potted plants were grown in an insect-proof screen house covered with plastic, and data was collected on a per-plant basis. Plants did not exhibit obvious symptoms any year of the study. CLRDV infection significantly reduced lint yield, number of seeds, number of bolls in the first fruiting position, seed index, and root dry weight. Fiber quality analysis showed there was a reduction in content of short fibers in CLRDV-infected plants. Possible interference of CLRDV on translocation of photoassimilates in the phloem, along with changes in net photosynthesis and other physiological processes may explain how this virus affected some of the parameters evaluated, especially lint yield, yield components, and root dry weight.

Differential toxicity of polyhexamethylene guanidine phosphate administered through intravenous injection and intratracheal instillation in Sprague-Dawley rats.