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Correction: Advancing inclusive research calculator for oncology disease areas: A resource to support the development of enrollment targets in diversity action plans for industry sponsors
A quasi oppositional forensic based investigation algorithm for optimizing distributed generation placement and sizing in power distribution systems
Breast tenderness and swelling experiences related to menstrual cycles and ovulation in healthy premenopausal women: Secondary analysis of the 1-year “Prospective Ovulation Cohort”
Breast tenderness and swelling are associated with premenstrual symptoms but are not well described in healthy women. In this 1-year prospective observational study, we examined daily breast tenderness and swelling to determine whether differences existed between normally ovulatory and ovulatory disturbed (short luteal phase and anovulatory) cycles in a cohort of community dwelling, non-smoking, healthy premenopausal women. Enrolment required two consecutive normal-length and normally ovulatory cycles by Quantitative Basal Temperature© analysis. Women (n = 53) ages 20–41 recorded their daily breast experiences in the Menstrual Cycle Diary© across an average of 13.6 cycles. In all 720 cycles, the median breast tenderness was 1.4 (on a 0–4 scale, range 0.0–3.0), in cycles with a mean length of 28.1 days (95% CI 27.5–28.8). Comparison of breast tenderness and breast size (changes from usual) parameters between all normally ovulatory cycles and all ovulatory disturbed cycles in the whole cohort showed significantly higher levels in normally ovulatory (luteal length ≥10 days) in both Breast Tenderness Score [intensity X duration in days; 6.0 (range 1.0–14.0) vs. 3.0 (0.0–11.0) (P=.005)] and breast size [4.0 (2.0–4.0) vs. 4.0 (0.0–4.0) (P=.034]). However, within-woman in the forty-seven women with both normally ovulatory and ovulatory disturbed cycles, breast tenderness (intensity, duration, and Breast Tenderness Score), did not differ between normally ovulatory cycles and cycles with ovulatory disturbances. This study also demonstrated that in all ovulatory cycles, the timing of breast tenderness increased in parallel with breast swelling; the maximum for both was in the late luteal phase.
Porous hydroxyapatite – β-tricalcium phosphate ceramics produced from a rapid sol-gel process
Threshold-based exploitation of noisy label in black-box unsupervised domain adaptation
How can we perform unsupervised domain adaptation when transferring a black-box source model to a target domain? Black-box Unsupervised Domain Adaptation focuses on transferring the labels derived from a pre-trained black-box source model to an unlabeled target domain. The problem setting is motivated by privacy concerns associated with accessing and utilizing source data or source model parameters. Recent studies typically train the target model by mimicking the labels derived from the black-box source model, which often contain noise due to domain gaps between the source and the target. Directly exploiting such noisy labels or disregarding them may lead to a decrease in the model’s performance. We propose Threshold-Based Exploitation of Noisy Predictions (TEN), a method to accurately learn the target model with noisy labels in Black-box Unsupervised Domain Adaptation. To ensure the preservation of information from the black-box source model, we employ a threshold-based approach to distinguish between clean labels and noisy labels, thereby allowing the transfer of high-confidence knowledge from both labels. We utilize a flexible thresholding approach to adjust the threshold for each class, thereby obtaining an adequate amount of clean data for hard-to-learn classes. We also exploit knowledge distillation for clean data and negative learning for noisy labels to extract high-confidence information. Extensive experiments show that TEN outperforms baselines with an accuracy improvement of up to 9.49%.
Risk identification of coal and gas outburst based on improved CUOWGA weighting TOPSIS model
Abstract In order to accurately assess the risk level of coal and gas outbursts, this study proposes an evaluation method based on an improved CUOWGA-weighted TOPSIS model. The primary challenge faced in evaluating the risk of coal and gas outbursts is the subjectivity of the evaluation indicators, which may lead to unreliable outcomes. To address this issue, a coal and gas outburst evaluation indicator system comprising three key factors—geological conditions, coal seam gas content, and the physical properties of coal and rock—was constructed based on an extensive review of the literature. By introducing an innovative fuzzy semantic quantification operator and a normalized decision matrix, the computation process of the CUOWGA operator is optimized to minimize subjective bias and appropriately allocate weights to the evaluation indicators. By combining the optimized CUOWGA method with TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution), the risk level of coal and gas outbursts was assessed. A case study conducted at Duanshi Coal Mine demonstrated that the risk level of coal and gas outbursts at this mine is classified as Level II, which is consistent with the actual conditions observed in the mining area. These results validate that the evaluation method based on the ICUOWGA-weighted TOPSIS model can effectively assess the risk level of coal and gas outbursts, thereby proving the feasibility of the approach.
A comparison of various imputation algorithms for missing data
Background Many datasets in medicine and other branches of science are incomplete. In this article we compare various imputation algorithms for missing data. Objectives We take the point of view that it has already been decided that the imputation should be carried out using multiple imputation by chained equation and the only decision left is that of a subroutine for the one-dimensional imputations. The subroutines to be compared are predictive mean matching, weighted predictive mean matching, sampling, classification or regression trees and random forests. Methods We compare these subroutines on real data and on simulated data. We consider the estimation of expected values, variances and coefficients of linear regression models, logistic regression models and Cox regression models. As real data we use data of the survival times after the diagnosis of an obstructive coronary artery disease with systolic blood pressure, LDL, diabetes, smoking behavior and family history of premature heart diseases as variables for which values have to be imputed. While we are mainly interested in statistical properties like biases, mean squared errors or coverage probabilities of confidence intervals, we also have an eye on the computation time. Results Weighted predictive mean matching had to be excluded from the statistical comparison due to its enormous computation time. Among the remaining algorithms, in most situations we tested, predictive mean matching performed best. Novelty This is by far the largest comparison study for subroutines of multiple imputation by chained equations that has been performed up to now.
Advances to IoT security using a GRU-CNN deep learning model trained on SUCMO algorithm
Does family confidence bridge child functioning and caregiver burden in early intervention?
Background: Enhancing caregivers’ confidence and competence in early childhood intervention is a priority, focusing on collaboration between professionals and families to improve family well-being and quality of life. Caregiver burden is crucial in early childhood intervention support services aimed at promoting family well-being and functioning. However, contextual factors and child-related variables may affect caregiver burden. We examined the relationships between family confidence, caregiver burden, and child functioning. Method: A total of 169 Spanish families receiving early intervention services participated in a cross-sectional survey study. Data were analyzed applying single and multiple mediation analyses to examine the influence of socio-demographic variables on family confidence and caregiver burden, as well as the mediating role of family confidence between child functioning and caregiver burden. Results: The study found that child functioning does not directly impact caregiver burden but significantly influences it through family confidence. Confidence in helping the child was a relevant mediator of the impact of child functioning on caregiver burden. However, confidence in helping the family was a mediator across all dimensions of caregiver burden. Conclusions: Higher family confidence predicts lower caregiver burden in early childhood intervention. Confidence in helping family functioning is key to mitigating the negative impact of child functioning on caregiver burden. Practical implications of these findings suggest that early childhood intervention services should focus on capacity-building through collaborative and family-centered practices, empowering families to enhance their confidence, and reduce caregiver burden.
Publisher Correction: Study on spatial pattern and coupling of county traffic superiority degree and new-type urbanization level in Guizhou Province, China
An independently tunable dual control system for RNAi complementation in Trypanosoma brucei
Trypanosoma brucei is a tractable protist parasite for which many genetic tools have been developed to study novel biology. A striking feature of T. brucei is the catenated mitochondrial DNA network called the kinetoplast DNA (kDNA) that is essential for parasite survival and life cycle completion. Maintenance of kDNA requires three independently essential paralogs that have homology to bacterial DNA polymerase I (POLIB, POLIC and POLID). We previously demonstrated that POLIB has a divergent domain architecture that displayed enzymatic properties atypical for replicative DNA polymerases. To evaluate the functional domains required for kDNA replication in vivo , we pursued an RNAi complementation approach based on the widely used tetracycline (Tet) single inducer system. Tet induction of RNAi and complementation with wildtype POLIB (POLIBWT) resulted in a 93% knockdown of endogenous POLIB mRNA but insufficient ectopic POLIBWT expression. This incomplete rescue emphasized the need for a more versatile induction system that will allow independent, tunable, and temporal regulation of gene expression. Hence, we adapted a dual control vanillic acid (Van)-Tet system that can independently control gene expression for robust RNAi complementation. Dual induction with Van and Tet (RNAi + Overexpression) resulted in 91% endogenous POLIB knockdown accompanied by robust and sustained ectopic expression of POLIBWT, and a near complete rescue of the POLIB RNAi defects. To more precisely quantify changes in kDNA size during RNAi, we also developed a semi-automated 3D image analysis tool to measure kDNA volume. Here we provide proof of principle for a dual inducer system that allows more flexible control of gene expression to perform RNAi and overexpression independently or concurrently within a single cell line. This system overcomes limitations of the single inducer system and can be valuable for elegant mechanistic studies in the field.
Developing muscarinic receptor M1 classification models utilizing transfer learning and generative AI techniques
Abstract Muscarinic receptor subtype 1 (M1) is a G protein-coupled receptor (GPCR) and a key pharmacological target for peripheral neuropathy, chronic obstructive pulmonary disease, nerve agent exposures, and cognitive disorders. Screening and identifying compounds with potential to interact with M1 will aid in rational drug design for these disorders. In this work, we developed machine learning-based M1 classification models utilizing publicly available bioactivity data. As inactive compounds are rarely reported in the literature, we encountered the problem of imbalanced datasets. We investigated two strategies to overcome this bottleneck: 1) transfer learning and 2) using generative models to oversample the inactive class. Our analysis shows that these approaches reduced misclassification of the inactive class not only for M1 but also for other GPCR targets. Overall, we have developed classification models for M1 receptor that will enable rapid screening of large chemical databases and advance drug discovery.
Study on tunnel ventilation and pollutant diffusion mechanism during construction period
Ventilation technology is an important means to ensure effective control of pollutant concentration and safe production during tunnel drilling and blasting construction. This study combines theoretical derivation, numerical simulation, and mathematical statistics to explore the extraction of flow field distribution characteristics and pollutant transport and diffusion mechanisms in tunnels. The research results indicate that the instability and turbulence effects of fluids work together to form a vortex zone near the tunnel working face. Fluid instability refers to the tendency of fluids to undergo changes under the influence of tunnel sidewalls or airflow in ducts. Turbulence effect is caused by the chaotic and irregular flow of fluids, leading to fluid mixing and rotation. The complex flow field changes inside the tunnel result in the retention of pollutants generated during construction in specific zone. The main reasons for the formation of pollutant stagnant zones are the bypass effect, low-velocity regions, and vortex of fluid. The emission process of pollutants can be divided into two stages: extraction and dilution. The dilution effect of pollutants is inversely proportional to the distance between the air duct and the working face, and the extraction amount is directly proportional to the airflow of the fan. The shorter distance allows fresh air to directly reach high concentration pollutant zone from the air duct, accelerating the mixing and dilution process. A larger airflow can provide stronger power and carry more pollutants out of the tunnel. The improved Technique for Order Preference by Similarity to an Ideal Solution method can optimize the layout of ventilation parameters and improve ventilation conditions. Finally, an empirical calculation formula for air supply volume is derived through in - depth research and data analysis. This formula takes into account multiple factors related to the tunnel structure, pollutant generation, and ventilation requirements. This empirical formula provides a scientific basis for the selection of ventilation fans in the construction preparation stage. Construction planners can accurately calculate the required air supply volume according to the specific situation of the tunnel, and then select the appropriate ventilation fan, which can not only ensure the ventilation effect but also save energy and reduce costs.
White matter volume and microstructural integrity are associated with fatigue in relapsing multiple sclerosis
Mitochondria-related genes as prognostic signature of endometrial cancer and the effect of MACC1 on tumor cells
Mitochondria are essential organelles involved in cell metabolism and are closely linked to various metabolic disorders. In this study, we aimed to develop a prognostic model for endometrial cancer (EC) patients based on mitochondria-related genes (MRGs), and to investigate the role of MACC1 in EC. As shown in the graphic summary, we retrieved gene expression and clinical data from open-access databases. To construct a predictive signature, we applied the Lasso Cox regression algorithm to MRGs. The predictive performance, immune features, and anti-tumor response of the mitochondrial signature were evaluated through multiple algorithms. Additionally, expression levels of key genes were validated using quantitative Real-Time PCR and Western Blot. A total of 2030 MRGs were retrieved, and 267 were found to be prognostically relevant. Eight MRGs—MACC1, CMPK2, NDUFAF6, DUSP18, TOMM40L, MT-TP, SAMM50, and MAIP1—were identified to construct a prognostic signature for EC. The MRG signature demonstrated significant associations with drug sensitivity, immune therapy, and immune cell infiltration. Based on comprehensive bioinformatic analysis, MACC1 was identified as the most promising MRG candidate in EC. Systematic experimental validation, including both in vitro and in vivo approaches, demonstrated that MACC1 down-regulation significantly suppressed EC progression, highlighting its potential as a therapeutic target.
Sealing mechanisms and enrichment patterns of deep coalbed methane: insights from the Jiaxian block, Ordos Basin
Genetic insights for enhancing conservation strategies in captive and wild Asian elephants through improved non-invasive DNA-based individual identification
Asian elephant is a key umbrella species that plays a crucial role in maintaining biodiversity and ecological balance. As an iconic symbol of Thailand, it also significantly contributes to the nation's tourism industry. However, human activities pose serious threats to their long-term survival and population health. To tackle these challenges and develop effective conservation strategies, extensive genetic reference data were collected to enhance both captive and wild elephant conservation, improve non-invasive DNA-based individual identification, and assess genetic diversity using 18 microsatellite markers. High genetic diversity was observed across all populations; however, high levels of inbreeding were evident in NEI, EKS, BCEP, and wild elephant populations, except for the MEP population, which recorded low inbreeding levels. Significant variation in the gene pool estimates was observed across different populations, with three maternal haplogroups (α, β1, and a tentative β3) identified. A reduced panel of six microsatellite markers proved highly efficient for individual identification. Additionally, non-invasive DNA samples were tested using 18 microsatellite loci for individual identification. Using only 7 out of the 18 microsatellite loci tested, individuals were successfully identified, demonstrating enough discriminatory power to distinguish between individuals. Among these, four loci (LaT08, LaT13, FH19, and FH67) were both effective and efficient for reliable individual identification in fecal samples. These findings offer valuable insights for optimizing conservation efforts, including the design of tailored strategies to protect Asian elephants in Thailand and ensure the long-term viability of their populations.
High efficiency wideband printed monopole antenna with enhanced gain using artificial magnetic conductor surface
Abstract Combining the benefits of a low profile, high gain, high efficiency, and wideband operation in a planar antenna presents a significant challenge for antenna designers. Low-profile wideband antennas often suffer from low gain. This study introduces a compact wideband artificial magnetic conducting surface (AMCS) positioned behind a wideband omnidirectional antenna to enhance its gain across the operational frequency range. This integration allows the radiating structure to achieve both high gain and wideband functionality in a single design. In this research, a wideband planar monopole printed antenna is developed to function as an omnidirectional radiator, delivering excellent impedance matching and radiation efficiency across the frequency range of 3.9–7.2 GHz (60% bandwidth) in free space. The free-standing antenna dimensions are 30 mm × 20 mm (0.39 λo × 0.3 λo), where λo corresponds to the lowest operating frequency of the antenna). It exhibits a gain ranging from 2 dBi to 4.5 dBi over this frequency band. To improve gain, a wideband AMCS is designed, consisting of just 3 × 3 unit cells with overall dimensions of 9 × 9 cm (1.1 λo × 1.1 λo). The AMCS is placed parallel to the planar antenna at a distance of 1.75 cm behind it. The gain of the AMCS-backed antenna reaches up to 9 dBi without compromising bandwidth or impedance matching. Furthermore, the radiation efficiency remains above 98% across the operational band of 3.6–7.2 GHz (66% bandwidth). The wideband antenna and AMCS are fabricated to experimentally validate the performance of the AMCS-based antenna. Measurements of impedance matching, gain, and radiation efficiency demonstrate close alignment with simulation results, confirming the effectiveness of the proposed design.
Selection of dual-channel supply chain cooperation mode of older adults care service under the government subsidy strategy
This study develops a dual-channel supply chain coordination model for services aimed at older adults, taking into account differentiated government subsidies. Utilizing Hotelling and Stackelberg game models, we systematically examine optimal strategies across three distinct scenarios: a non-cooperative mode, cooperation between online channels and logistics suppliers, and a tripartite collaboration involving both online and offline channels alongside logistics suppliers. The results demonstrate that the optimal pricing and service levels attained in cooperative scenarios exceed those observed in non-cooperative settings. Furthermore, within the framework of tripartite collaboration, the influence of enhanced service levels on the optimization of both service pricing and quality is particularly significant. It is noteworthy that government subsidies tend to exert a marginally greater incentive effect on offline service channels compared to online ones, thereby increasing the focus on addressing the emotional needs of older adults. Overall, this research represents a pioneering effort to compare these three service cooperation models, leveraging government subsidies as a catalyst. It not only enhances the advantages of differentiated dual-channel services but also promotes the efficient allocation of resources in elder care through the identification of suitable collaborative strategies.