Can one big meal really make you gain weight? The post-holiday scale spike is temporary--unless the leftovers get involved. It's hard not to indulge during the holidays, but can the occasional big meal really harm our long-term health? Breakthroughs, discoveries, and DIY tips sent every weekday. For those of us brave enough to step onto the scale the day after Thanksgiving or Christmas, you can sometimes see an increase of up to five to 10 pounds.

Forecasting mob based cattle weight gain (MB CWG) may benefit large livestock farms, allowing farmers to refine their feeding strategies, make educated breeding choices, and reduce risks linked to climate variability and market fluctuations. In this paper, a novel technique termed MB CWG is proposed to forecast the one month advanced weight gain of herd based cattle using historical data collected from the Charles Sturt University Farm. This research employs a Random Forest (RF) model, comparing its performance against Support Vector Regression (SVR) and Long Short Term Memory (LSTM) models for monthly weight gain prediction. Four datasets were used to evaluate the performance of models, using 756 sample data from 108 herd-based cattle, along with weather data (rainfall and temperature) influencing CWG. The RF model performs better than the SVR and LSTM models across all datasets, achieving an R^2 of 0.973, RMSE of 0.040, and MAE of 0.033 when both weather and age factors were included. The results indicate that including both weather and age factors significantly improves the accuracy of weight gain predictions, with the RF model outperforming the SVR and LSTM models in all scenarios. These findings demonstrate the potential of RF as a robust tool for forecasting cattle weight gain in variable conditions, highlighting the influence of age and climatic factors on herd based weight trends. This study has also developed an innovative automated pre processing tool to generate a benchmark dataset for MB CWG predictive models. The tool is publicly available on GitHub and can assist in preparing datasets for current and future analytical research..

From chewing gum to teabags, microplastics have already been discovered in a range of everyday items. These tiny pieces of plastic measure less than five millimeters long and are not biodegradable - meaning they last for hundreds, if not thousands of years. Now, shocking images have revealed the terrifying effects these tiny pieces of plastic could be having on our bodies. Experts from BusinessWaste.co.uk have used AI to produce images predicting how the average man and woman could look after exposure to microplastics. From weight gain and hair thinning to eczema-like rashes and heavy fatigue, the images paint a bleak picture for our future.

Many cattle farmers still depend on manual methods to measure the live weight gain of cattle at set intervals, which is time consuming, labour intensive, and stressful for both the animals and handlers. A remote and autonomous monitoring system using machine learning (ML) or deep learning (DL) can provide a more efficient and less invasive method and also predictive capabilities for future cattle weight gain (CWG). This system allows continuous monitoring and estimation of individual cattle live weight gain, growth rates and weight fluctuations considering various factors like environmental conditions, genetic predispositions, feed availability, movement patterns and behaviour. Several researchers have explored the efficiency of estimating CWG using ML and DL algorithms. However, estimating CWG suffers from a lack of consistency in its application. Moreover, ML or DL can provide weight gain estimations based on several features that vary in existing research. Additionally, previous studies have encountered various data related challenges when estimating CWG. This paper presents a comprehensive investigation in estimating CWG using advanced ML techniques based on research articles (between 2004 and 2024). This study investigates the current tools, methods, and features used in CWG estimation, as well as their strengths and weaknesses. The findings highlight the significance of using advanced ML approaches in CWG estimation and its critical influence on factors. Furthermore, this study identifies potential research gaps and provides research direction on CWG prediction, which serves as a reference for future research in this area.

However, estimating treatment effects using EHR data poses several challenges, including time-varying confounding, repeated and temporally non-aligned measurements of covariates, treatment assignments and outcomes, and loss-to-follow-up due to dropout. Here, we develop the Subgroup Discovery for Longitudinal Data (SDLD) algorithm, a tree-based algorithm for discovering subgroups with heterogeneous treatment effects using longitudinal data by combining the generalized interaction tree algorithm, a general data-driven method for subgroup discovery, with longitudinal targeted maximum likelihood estimation. We apply the algorithm to EHR data to discover subgroups of people living with human immunodeficiency virus (HIV) who are at higher risk of weight gain when receiving dolutegravir-containing antiretroviral therapies (ARTs) versus when receiving non dolutegravir-containing ARTs. Key words: Causal Inference; Dolutegravir; Electronic health record; Heterogeneity of treatment effects; Longitudinal targeted maximum likelihood estimation; Machine learning; Recursive partitioning; Subgroup discovery.

Globally, the number of obese patients has doubled due to sedentary lifestyles and improper dieting. The tremendous increase altered human genetics, and health. According to the world health organization, Life expectancy dropped from 80 to 75 years, as obese people struggle with different chronic diseases. This report will address the problems of obesity in children and adults using ML datasets to feature, predict, and analyze the causes of obesity. By engaging neural ML networks, we will explore neural control using diffusion tensor imaging to consider body fats, BMI, waist \& hip ratio circumference of obese patients. To predict the present and future causes of obesity with ML, we will discuss ML techniques like decision trees, SVM, RF, GBM, LASSO, BN, and ANN and use datasets implement the stated algorithms. Different theoretical literature from experts ML \& Bioinformatics experiments will be outlined in this report while making recommendations on how to advance ML for predicting obesity and other chronic diseases.

We all have a craving for chocolate now and again, but not usually when we first wake up. However, a new study has claimed that eating the sugary snack for breakfast could actually have'unexpected benefits' by helping your body burn fat. Researchers in Boston, Massachusetts gave 100 grams of milk chocolate to 19 post-menopausal women within one hour after waking up and one hour before bedtime. Starting the day with chocolate could actually help your body burn fat, scientists at Brigham and Women's Hospital in Boston say That is about the equivalent of two standard-sized Mars bars (58g) – although the researchers used standard milk chocolate containing 18.1g of cocoa. Amazingly, the team discovered that neither morning or night time milk chocolate intake led to weight gain, likely because it acted as an appetite suppressant.

Artificial neurons are already far more human-like than traditional computers, and now it turns out they might also need sleep to function at their peak. And it's not just a matter of turning them off every now and then – a new study shows that the neurons benefit from exposure to slow-wave signals like those in a sleeping biological brain. Neural networks are made up of artificial neurons, which all signal to each other like real neurons do in a real brain. Commonly used connections are reinforced over time, effectively allowing neural networks to learn on their own. Unlike the sequential processing of traditional computers, neural networks can process different streams of information in parallel, which makes them powerful tools for things like image and speech recognition.

Parents letting their child play lots of video games are signing the youngster up for weight gain a decade later, a study has revealed. More than 16,000 children were tracked from age five through to age 14 and scientists assessed the relationship between video games and weight. Results revealed children who regularly played video games as a five-year-old had a higher BMI nine years later, compared to those who did not play video games. Drinking sugary drinks and irregular bedtimes also have a significant impact on children, the study found, and could partly be to blame for the weight change. The study, funded by Cancer Research UK, is the first to look at the potential effect of video game use on children's BMI over time.

In the last decade, improvements in genomic, transcrip-tomic, and proteomic technologies have enabled personalized medicine (also called precision medicine) to become an essential part of contemporary medicine. Personalized medicine takes into account individual variability in genes, proteins, environment, and lifestyle to decide on optimal disease treatment and prevention [14]. The use of a patient's genetic and epigenetic information has already proven to be highly effective to tailor drug therapies or preventive care in a number of applications, such as breast [7], prostate [23], ovarian [17], and pancreatic cancers [24], cardiovascular disease [11], cystic fibrosis [36], and psychiatry [10]. The subfield of pharmacogenomics studies specifically how genes affect a person's response to particular drugs to develop more efficient and safer medications [37]. Genomic, epigenomic, and transcriptomic data used in precision medicine, such as gene expression, copy number variants, or methylation levels are typically high-dimensional with a number of variables that rivals or exceeds the number of observations.