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Identification of m6A methyltransferase-related WTAP and ZC3H13 predicts immune infiltrates in glioblastoma
An atlas of cells in the human brain’s control hub
Coherent Strain-Inhibiting Phase Construction of Lithium-Rich Manganese-Based Oxide Toward High Mechanochemical Stability
Pseudo label refining for semi-supervised temporal action localization
The training of temporal action localization models relies heavily on a large amount of manually annotated data. Video annotation is more tedious and time-consuming compared with image annotation. Therefore, the semi-supervised method that combines labeled and unlabeled data for joint training has attracted increasing attention from academics and industry. This study proposes a method called pseudo-label refining (PLR) based on the teacher-student framework, which consists of three key components. First, we propose pseudo-label self-refinement which features in a temporal region interesting pooling to improve the boundary accuracy of TAL pseudo label. Second, we design a module named boundary synthesis to further refined temporal interval in pseudo label with multiple inference. Finally, an adaptive weight learning strategy is tailored for progressively learning pseudo labels with different qualities. The method proposed in this study uses ActionFormer and BMN as the detector and achieves significant improvement on the THUMOS14 and ActivityNet v1.3 datasets. The experimental results show that the proposed method significantly improve the localization accuracy compared to other advanced SSTAL methods at a label rate of 10% to 60%. Further ablation experiments show the effectiveness of each module, proving that the PLR method can improve the accuracy of pseudo-labels obtained by teacher model reasoning.
Magnitude of self-reported non-fatal work-related injuries and associated factors among construction workers in Aleta Wondo, Sidama, Ethiopia
Why a standard method overlooks the real reason some antibiotics fail
Estimation of true dates of various flowering stages at a centennial scale by applying a Bayesian statistical state space model
Evaluation of long-term detailed cherry flowering phenology is required for a deep understanding of the sensitivity of spring phenology to climate change and its effect on cultural ecosystem services. Neodani Usuzumi-zakura (Cerasus itosakura) is a famous cherry tree in Gifu, Japan. On the basis of detailed decadal flowering phenology information published on the World Wide Web, we estimated the probability distributions of the year-to-year variability of the true dates of first flowering (FFL), first full bloom (FFB), last full bloom (LFB), and last flowering (LFL) from 1924 to 2024 by applying a Bayesian statistical state space model explained by air temperature data. We verified the estimated values against flowering phenology records of the tree from the literature and a private collection. The true dates of FFL and FFB could be explained by means of daily minimum air temperature from 1 December to 28/29 February and that of daily mean air temperature from 1 to 31 March, and those of LFB and LFL by means of daily mean air temperature from 1 to 10 April. Results were similar when we used air temperature data recorded at weather stations both 1 km and 29 km from the tree. These results indicated that our proposed Bayesian statistical state space model can estimate cherry flowering phenology that takes into account centennial-scale air temperature data recorded at a nearby weather station with a coarse temporal resolution.
Structural edge damage detection based on wavelet transform and immune genetic algorithm
A personalized cancer vaccine to prevent the return of high-risk kidney cancer
Diagnosis of carbon monoxide exposure in clinical research and practice: A scoping review
Objective To undertake a scoping review to identify methods and diagnostic levels used in determining unintentional, non-fire related carbon monoxide exposure. Design Online databases and grey literature were searched from 1946 to 2023 identifying 80 papers where carbon monoxide levels were reported. Results 80 papers were included; 71 research studies and 9 clinical guidelines. Four methods were described: blood carboxyhaemoglobin (arterial or venous blood analysis), carbon monoxide oximetry (SpO2), expired carbon monoxide, and ambient carbon monoxide sampling. Blood analysis methods predominated (60.0% of the papers). Multiple methods of measurement were used in 26 (32.5%) of the papers. Diagnostic levels for carboxyhaemoglobin were described in 54 (67.5%) papers, ranging between 2% and 15%. 26 (32.5%) papers reported diagnostic levels that were adjusted for the smoking status of the patient. Conclusions Four methods were found for use in different settings. Variability in diagnostic thresholds impairs diagnostic accuracy. Agreement on standardised diagnostic levels is required to enable consistent diagnosis of unintentional, non-fire related carbon monoxide exposure.
Carnivore activity across landuse gradients in a Mexican biosphere reserve
Top universities warned against unfair research partnerships on their doorstep
Harnessing Large Language Models to Collect and Analyze Metal–Organic Framework Property Data Set
Impact and perceptions of Active Learning Classrooms on reducing sedentary behaviour and improving physical and mental health and academic indicators in children and adolescents: A scoping review
Prolonged sitting in school harms children’s physical and mental health and reduces the ability to focus on classroom tasks. ’Active Learning Classrooms’ (ALCs) aim to decrease sitting time, following current pedagogical trends, though research on the effects of ALCs on these aspects is still an emerging field. The aims of this review were to: (i) synthesise the available literature on the impact of ALCs on reducing sedentary behaviour, increasing physical activity (PA), physical and mental health, and academic indicators in children and adolescents; and (ii) describe the educational community’s perceptions and teaching practices used in ALCs. This scoping review followed Joanna Briggs Methods and PRISMA guidelines for scoping reviews. We searched for peer-reviewed quantitative and qualitative studies published in English that examined the impact of ALCs on movement patterns, physical or mental health, and academic indicators in children and adolescents, as well as those that explored the perceptions of members of the educational community and the teaching practices used in ALCs. Databases research included MEDLINE (PubMed), ERIC, SCOPUS and ProQuest Education. Nineteen studies were included, of which 11 were experimental, 4 were cross-sectional, and 4 were qualitative. The analysis revealed a predominantly positive influence of ALCs on children’s sedentary behaviour, learning engagement and psychological well-being; and mixed results on PA, physical health and academic performance. Our results also suggest that learning spaces are positively perceived and well accepted by the entire educational community, and that teachers teaching in ALCs are more prone to use student-centered and collaborative pedagogies than in traditional classrooms. Although this review shows a positive impact on key health and education variables, the evidence is limited and lacks depth. In addition, the small number of studies and their methodological weaknesses prevent robust conclusions, but the results still help to guide future decisions.
Author Correction: Pre-therapeutic efficacy of the CDK inhibitor dinaciclib in medulloblastoma cells
Two-Photon-Driven Photoprotection Mechanism in Echinenone-Functionalized Orange Carotenoid Protein
ECP-IEM: Enhancing seasonal crop productivity with deep integrated models
Accurate crop yield forecasting is vital for ensuring food security and making informed decisions. With the increasing population and global warming, addressing food security has become a priority, so accurate yield forecasting is very important. Artificial Intelligence (AI) has increased the yield accuracy significantly. The existing Machine Learning (ML) methods are using statistical measures as regression, correlation and chi square test for predicting crop yield, all such model’s leads to low accuracy when the number of factors (variables) such as the weather and soil conditions, the wind, fertilizer quantity, and the seed quality and climate are increased. The proposed methodology consists of different stages, like Data Collection, Preprocessing, Feature Extraction with Support Vector Machine (SVM), correlation with Normalized Google Distance (NGD), feature ranking with rising star. This study combines Bidirectional Gated Recurrent Unit (Bi-GRU) and Time Series CNN to predict crop yield and then recommendation for further improvement. The proposed model showed very good results in all datasets and showed significant improvement compared to baseline models. The ECP-IEM achieved an accuracy 96.34%, precision 94.56% and recall 95.23% on different datasets. Moreover, the proposed model was also evaluated based on MAE, MSE, and RMSE, which produced values of 0.191, 0.0674, and 0.238, respectively. This will help in improving production of crops by giving an early look about the yield of crops which will than help the farmer in improving the crops yield.
PIPENN-EMB ensemble net and protein embeddings generalise protein interface prediction beyond homology
Federated learning based reference evapotranspiration estimation for distributed crop fields
Water resource management and sustainable agriculture rely heavily on accurate Reference Evapotranspiration (ETo). Efforts have been made to simplify the (ETo) estimation using machine learning models. The existing approaches are limited to a single specific area. There is a need for ETo estimations of multiple locations with diverse weather conditions. The study intends to propose ETo estimation of multiple locations with distinct weather conditions using a federated learning approach. Traditional centralized approaches require aggregating all data in one place, which can be problematic due to privacy concerns and data transfer limitations. However, federated learning trains models locally and combines the knowledge, resulting in more generalized ETo estimates across different regions. The three geographical locations of Pakistan, each with diverse weather conditions, are selected to implement the proposed model using the weather data from 2012 to 2022 of the selected three locations. At each selected location, three machine learning models named Random Forest Regressor (RFR), Support Vector Regressor (SVR), and Decision Tree Regressor (DTR), are evaluated for local Evapotranspiration (ET) estimation and the federated global model. The feature importance-based analysis is also performed to assess the impacts of weather parameters on machine learning performance at each selected local location. The evaluation reveals that Random Forest Regressor (RFR) based federated learning outperformed other models with coefficient of determination (R2) = 0.97%, Root Mean Squared Error (RMSE) = 0.44, Mean Absolute Error (MAE) = 0.33 mm day−1, and Mean Absolute Percentage Error (MAPE) = 8.18%. The Random Forest Regressor (RFR) performance yields the local machine learning models against each selected site. The analysis results suggest that maximum temperature and wind speed are the most influential factors in Evapotranspiration (ET) predictions.