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Corrigendum: Silver nanoparticles directly formed on natural macroporous matrix and their anti-microbial activities (2007, Nanotechnology, 18, 055605).
Nanotechnology
January 2025
Guangdong Detection Center for Microbiology, 100 Xianlie Zhong Road, Guangzhou, 510070, CHINA.
In the published article "Silver nanoparticles directly formed on natural macroporous matrix and their anti-microbial activities, Nanotechnology 18 (2007) 055605", the figure caption of Figure 8 has an error in immersion time, and the correct caption is given in this Corrigendum.
View Article and Find Full Text PDFHighly multiplexed spatial transcriptomics in bacteria.
Science
January 2025
Program in Cellular and Molecular Medicine, Boston Children's Hospital, Boston, MA, USA.
Single-cell decisions made in complex environments underlie many bacterial phenomena. Image-based transcriptomics approaches offer an avenue to study such behaviors, yet these approaches have been hindered by the massive density of bacterial messenger RNA. To overcome this challenge, we combined 1000-fold volumetric expansion with multiplexed error-robust fluorescence in situ hybridization (MERFISH) to create bacterial-MERFISH.
View Article and Find Full Text PDFEvaluating Machine Learning and Deep Learning models for predicting Wind Turbine power output from environmental factors.
PLoS One
January 2025
Renewable Energy Science and Engineering Department, Faculty of Postgraduate Studies for Advanced Sciences (PSAS), Beni-Suef University, Beni-Suef, Egypt.
This study presents a comprehensive comparative analysis of Machine Learning (ML) and Deep Learning (DL) models for predicting Wind Turbine (WT) power output based on environmental variables such as temperature, humidity, wind speed, and wind direction. Along with Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN), the following ML models were looked at: Linear Regression (LR), Support Vector Regressor (SVR), Random Forest (RF), Extra Trees (ET), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). Using a dataset of 40,000 observations, the models were assessed based on R-squared, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE).
View Article and Find Full Text PDFVis-NIRS as an auxiliary tool in the classification of bovine carcasses.
PLoS One
January 2025
College of Veterinary Medicine and Animal Science, Federal University of Mato Grosso do Sul, Campo Grande, Mato Grosso do Sul, Brazil.
This work aimed to evaluate the use of Visible and Near-infrared Spectroscopy (Vis-NIRS) as a tool in the classification of bovine carcasses. A total of 133 animals (77 females, 29 males surgically castrated and 27 males immunologically castrated) were used. Vis-NIRS spectra were collected in a chilling room 24 h postmortem directly on the hanging carcasses over the longissimus thoracis between the surface of the 5th and 6th ribs.
View Article and Find Full Text PDFIn Table 7.2, "Common interfering substances and/or conditions that affect glucose meters (for inpatient and outpatient use)," of the article cited above, the effects on glucose values measured by blood glucose meters for high and low hematocrit were incorrect. For high hematocrit, the effect would be falsely lower blood glucose values.
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