The physiologic and irreversible process of ageing is accompanied by a wide range of structural and functional shifts at multiple different levels. It is also suggested that variations in the blood ...concentrations of metabolites, hormones, and micronutrients may play a role in the ageing process. Recently, Singh et al. 1,2 investigated a study on Taurine shortage as a driver and biomarker of ageing and its impact on a healthy lifespan.2 They further proposed that functional abnormalities in numerous organs associated with age-related illnesses have been linked to early-life Taurine insufficiency. Taurine deficiency in the elderly and the possible benefits of Taurine supplements One of the reasons for decreasing Taurine concentration is the loss of endogenous synthesis, which may contribute to the decrease in Taurine levels seen in the elderly. While it was previously believed that the liver was responsible for most Taurine synthesis in humans, new research suggests that other organs or common intermediates may play a larger role. The authors experimented with and analysed a life-span examination of various organisms, for example, mice to assess the impacts of Taurine supplementation. They also analysed after the administration of oral Taurine supplementation in conjunction with other interventions using multi-omics data sets (RNA sequencing, metabolomics etc.) across different species.
A united front from all the stakeholders including public, administration and academia alike is required to counter the growing threat of climate change. The recent rise of social media as the new ...public address system, makes it an ideal source of information to assess public discussions and responses in real time. We mine c.1.7 m posts from 55 climate related subreddits on social media platform Reddit since its inception. Using USE, a state-of-the-art sentence encoder, and K-means clustering algorithm, we develop a machine learning based approach to identify, store, process and classify the posts automatically, and at a scale. In the broad and multifaceted theme of climate change, our approach narrows down the focus to 10 critical underlying themes comprising the public discussions on social media over time. Furthermore, we employ a full order partial correlation analysis to assess the relationship between the different identified themes. We show that in line with Paris Agreement, while the climate science community has been successful in influencing the discussions on both the causes and effects of climate change, the public administration has failed to appropriately communicate the causes of climate change and has been able to influence only the discussions on the effects of it. Hence, our study shows a clear gap in the public communication by the administration, wherein counter-intuitively less emphasis has been given on the drivers of climate change. This information can be particularly beneficial to policymakers and climate activists in decision making as they try to close the gap between public and academia.
Rapid non-destructive measurements to predict cassava root yield over the full growing season through large numbers of germplasm and multiple environments is a huge challenge in Cassava breeding ...programs. As opposed to waiting until the harvest season, multispectral imagery using unmanned aerial vehicles (UAV) are capable of measuring the canopy metrics and vegetation indices (VIs) traits at different time points of the growth cycle. This resourceful time series aerial image processing with appropriate analytical framework is very important for the automatic extraction of phenotypic features from the image data. Many studies have demonstrated the usefulness of advanced remote sensing technologies coupled with machine learning (ML) approaches for accurate prediction of valuable crop traits. Until now, Cassava has received little to no attention in aerial image-based phenotyping and ML model testing.
To accelerate image processing, an automated image-analysis framework called CIAT Pheno-i was developed to extract plot level vegetation indices/canopy metrics. Multiple linear regression models were constructed at different key growth stages of cassava, using ground-truth data and vegetation indices obtained from a multispectral sensor. Henceforth, the spectral indices/features were combined to develop models and predict cassava root yield using different Machine learning techniques. Our results showed that (1) Developed CIAT pheno-i image analysis framework was found to be easier and more rapid than manual methods. (2) The correlation analysis of four phenological stages of cassava revealed that elongation (EL) and late bulking (LBK) were the most useful stages to estimate above-ground biomass (AGB), below-ground biomass (BGB) and canopy height (CH). (3) The multi-temporal analysis revealed that cumulative image feature information of EL + early bulky (EBK) stages showed a higher significant correlation (
= 0.77) for Green Normalized Difference Vegetation indices (GNDVI) with BGB than individual time points. Canopy height measured on the ground correlated well with UAV (CHuav)-based measurements (
= 0.92) at late bulking (LBK) stage. Among different image features, normalized difference red edge index (NDRE) data were found to be consistently highly correlated (
= 0.65 to 0.84) with AGB at LBK stage. (4) Among the four ML algorithms used in this study, k-Nearest Neighbours (kNN), Random Forest (RF) and Support Vector Machine (SVM) showed the best performance for root yield prediction with the highest accuracy of R
= 0.67, 0.66 and 0.64, respectively.
UAV platforms, time series image acquisition, automated image analytical framework (CIAT Pheno-i), and key vegetation indices (VIs) to estimate phenotyping traits and root yield described in this work have great potential for use as a selection tool in the modern cassava breeding programs around the world to accelerate germplasm and varietal selection. The image analysis software (CIAT Pheno-i) developed from this study can be widely applicable to any other crop to extract phenotypic information rapidly.
Depression is a disorder with variable presentation. Selecting treatments and dose-finding is, therefore, challenging and time-consuming. In addition, novel antidepressants such as ketamine have ...sparse optimization evidence. Insights obtained from metabolomics may improve the management of patients. The objective of this study was to determine whether compounds in the cerebrospinal fluid (CSF) metabolome correlate with scores on questionnaires and response to medication. We performed a retrospective pilot study to evaluate phenotypic and metabolomic variability in patients with treatment-resistant depression using multivariate data compression algorithms. Twenty-nine patients with treatment-resistant depression provided fasting CSF samples. Over 300 metabolites were analyzed in these samples with liquid chromatography-mass spectrometry. Chart review provided basic demographic information, clinical status with self-reported questionnaires, and response to medication. Of the 300 metabolites analyzed, 151 were present in all CSF samples and used in the analyses. Hypothesis-free multivariate analysis compressed the resultant data set into two dimensions using Principal Component (PC) analysis, accounting for ~ 32% of the variance. PC1 accounted for 16.9% of the variance and strongly correlated with age in one direction and 5-methyltetrahydrofolate, homocarnosine, and depression and anxiety scores in the opposite direction. PC2 accounted for 15.4% of the variance, with one end strongly correlated with autism scores, male gender, and cognitive fatigue scores, and the other end with bipolar diagnosis, lithium use, and ethylmalonate disturbance. This small pilot study suggests that complex treatment-resistant depression can be mapped onto a 2-dimensional pathophysiological domain. The results may have implications for treatment selection for depression subtypes.
Class imbalance remains a large problem in high-throughput omics analyses, causing bias towards the over-represented class when training machine learning-based classifiers. Oversampling is a common ...method used to balance classes, allowing for better generalization of the training data. More naive approaches can introduce other biases into the data, being especially sensitive to inaccuracies in the training data, a problem considering the characteristically noisy data obtained in healthcare. This is especially a problem with high-dimensional data. A generative adversarial network-based method is proposed for creating synthetic samples from small, high-dimensional data, to improve upon other more naive generative approaches. The method was compared with 'synthetic minority over-sampling technique' (SMOTE) and 'random oversampling' (RO). Generative methods were validated by training classifiers on the balanced data.
Abstract
Multimorbidity, frequently associated with aging, can be operationally defined as the presence of two or more chronic conditions. Predicting the likelihood of a patient with multimorbidity ...to develop a further particular disease in the future is one of the key challenges in multimorbidity research. In this paper we are using a network-based approach to analyze multimorbidity data and develop methods for predicting diseases that a patient is likely to develop. The multimorbidity data is represented using a temporal bipartite network whose nodes represent patients and diseases and a link between these nodes indicates that the patient has been diagnosed with the disease. Disease prediction then is reduced to a problem of predicting those missing links in the network that are likely to appear in the future. We develop a novel link prediction method for static bipartite network and validate the performance of the method on benchmark datasets. By using a probabilistic framework, we then report on the development of a method for predicting future links in the network, where links are labelled with a time-stamp. We apply the proposed method to three different multimorbidity datasets and report its performance measured by different performance metrics including AUC, Precision, Recall, and F-Score.
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