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Plant acclimation is a highly complex process, which cannot be fully understood by analysis at any one specific level (i.e. subcellular, cellular or whole plant scale). Various soft-computing techniques, such as neural networks or fuzzy logic, were designed to analyze complex multivariate data sets and might be used to model large such multiscale data sets in plant biology. In this study we assessed the effectiveness of applying neuro-fuzzy logic to modeling the effects of light intensities and sucrose content/concentration in the in vitro culture of kiwifruit on plant acclimation, by modeling multivariate data from 14 parameters at different biological scales of organization. The model provides insights through application of 14 sets of straightforward rules and indicates that plants with lower stomatal aperture areas and higher photoinhibition and photoprotective status score best for acclimation. The model suggests the best condition for obtaining higher quality acclimatized plantlets is the combination of 2.3% sucrose and photonflux of 122-130 µmol m(-2) s(-1). Our results demonstrate that artificial intelligence models are not only successful in identifying complex non-linear interactions among variables, by integrating large-scale data sets from different levels of biological organization in a holistic plant systems-biology approach, but can also be used successfully for inferring new results without further experimental work.

Falls, especially in elderly persons, are an important health problem worldwide. Reliable fall detection systems can mitigate negative consequences of falls. Among the important challenges and issues reported in literature is the difficulty of fair comparison between fall detection systems and machine learning techniques for detection. In this paper, we present UP-Fall Detection Dataset. The dataset comprises raw and feature sets retrieved from 17 healthy young individuals without any impairment that performed 11 activities and falls, with three attempts each. The dataset also summarizes more than 850 GB of information from wearable sensors, ambient sensors and vision devices. Two experimental use cases were shown. The aim of our dataset is to help human activity recognition and machine learning research communities to fairly compare their fall detection solutions. It also provides many experimental possibilities for the signal recognition, vision, and machine learning community.

Background Plant acclimation is a highly complex process, which cannot be fully understood by analysis at any one specific level (i.e. subcellular, cellular or whole plant scale). Various soft-computing techniques, such as neural networks or fuzzy logic, were designed to analyze complex multivariate data sets and might be used to model large such multiscale data sets in plant biology. Methodology and Principal Findings In this study we assessed the effectiveness of applying neuro-fuzzy logic to modeling the effects of light intensities and sucrose content/concentration in the in vitro culture of kiwifruit on plant acclimation, by modeling multivariate data from 14 parameters at different biological scales of organization. The model provides insights through application of 14 sets of straightforward rules and indicates that plants with lower stomatal aperture areas and higher photoinhibition and photoprotective status score best for acclimation. The model suggests the best condition for obtaining higher quality acclimatized plantlets is the combination of 2.3% sucrose and photonflux of 122-130 mu mol m-2 s-1. Conclusions Our results demonstrate that artificial intelligence models are not only successful in identifying complex non-linear interactions among variables, by integrating large-scale data sets from different levels of biological organization in a holistic plant systems-biology approach, but can also be used successfully for inferring new results without further experimental work.

Epigenetic alterations play a pivotal role in conditions influenced by environmental factors such as overweight and obesity. Many of these changes are tissue-specific, which entails a problem in its study since obtaining human tissue is a complex and invasive practice. While blood is widely used as a surrogate biomarker, it cannot directly extrapolate the evidence found in blood to tissue. Moreover, the intricacies of metabolic diseases add a new layer of complexity, as obesity leads to significant alterations in adipose tissue, potentially causing associated pathologies that can disrupt existing correlations seen in healthy individuals. Here, our objective was to determine which epigenetic markers exhibit correlations between blood and adipose tissue, regardless of the metabolic status. We collected paired blood and adipose tissue samples from 64 patients with morbidity obesity and non-obese and employed the MethylationEPIC 850 K array for analysis. We found that only a small fraction, specifically 4.3% (corresponding to 34,825 CpG sites), of the sites showed statistically significant correlations (R ≥ 0.6) between blood and adipose tissue. Within this subset, 5327 CpG sites exhibited a strong correlation (R ≥ 0.8) between blood and adipose tissue. Our findings suggest that the majority of epigenetic markers in peripheral blood do not reliably reflect changes occurring in visceral adipose tissues. However, it is important to note that there exists a distinct set of epigenetic markers that can indeed mirror changes in adipose tissue within blood samples.Key messagesMore than 8% of methylation sites exhibit similarity between blood and adipose tissues, regardless of BMIThe correlation percentage between blood and adipose tissue is strongly influenced by genderThe principal genes implicated in this correlation are related to metabolism or the immunological system