Fecal Short-Chain Fatty Acid Ratios as Related to Gastrointestinal and Depressive Symptoms in Young Adults

Objective: Short-chain fatty acids (SCFAs) are produced by the gut microbiota and may reflect health. Gut symptoms are common in individuals with depressive disorders, and recent data indicate relationships between gut microbiota and psychiatric health. We aimed to investigate potential associations between SCFAs and self-reported depressive and gut symptoms in young adults.Methods: Fecal samples from 164 individuals (125 were patients with psychiatric disorders: mean [standard deviation] age = 21.9 [2.6] years, 14% men; 39 nonpsychiatric controls: age = 28.5 [9.5] years, 38% men) were analyzed for the SCFA acetate, butyrate, and propionate by nuclear magnetic resonance spectroscopy. We then compared SCFA ratios with dimensional measures of self-reported depressive and gut symptoms.Results: Depressive symptoms showed a positive association with acetate levels (rho = 0.235, p =.003) and negative associations with both butyrate (rho = -0.195, p =.014) and propionate levels (rho = -0.201, p =.009) in relation to total SCFA levels. Furthermore, symptoms of diarrhea showed positive associations with acetate (rho = 0.217, p =.010) and negative associations with propionate in relation to total SCFA levels (rho = 0.229, p = 0-007). Cluster analysis revealed a heterogeneous pattern where shifts in SCFA ratios were observed in individuals with elevated levels of depressive symptoms, elevated levels of gut symptoms, or both.Conclusions: Shifts in SCFAs are associated with both depressive symptoms and gut symptoms in young adults and may have of relevance for treatment

An Ensemble Method: Case-Based Reasoning and the Inverse Problems in Investigating Financial Bubbles

This paper presents an ensemble approach and model; IPCBR, that leverages the capabilities of Case based Reasoning (CBR) and Inverse Problem Techniques (IPTs) to describe and model abnormal stock market fluctuations (often associated with asset bubbles) in time series datasets from historical stock market prices. The framework proposes to use a rich set of past observations and geometric pattern description and then applies a CBR to formulate the forward problem; Inverse Problem formulation is then applied to identify a set of parameters that can statistically be associated with the occurrence of the observed patterns. The technique brings a novel perspective to the problem of asset bubbles predictability. Conventional research practice uses traditional forward approaches to predict abnormal fluctuations in financial time series; conversely, this work proposes a formative strategy aimed to determine the causes of behaviour, rather than predict future time series points. This suggests a deviation from the existing research trend

Self-labeling techniques for semi-supervised time series classification: an empirical study

An increasing amount of unlabeled time series data available render the semi-supervised paradigm a suitable approach to tackle classification problems with a reduced quantity of labeled data. Self-labeled techniques stand out from semi-supervised classification methods due to their simplicity and the lack of strong assumptions about the distribution of the labeled and unlabeled data. This paper addresses the relevance of these techniques in the time series classification context by means of an empirical study that compares successful self-labeled methods in conjunction with various learning schemes and dissimilarity measures. Our experiments involve 35 time series datasets with different ratios of labeled data, aiming to measure the transductive and inductive classification capabilities of the self-labeled methods studied. The results show that the nearest-neighbor rule is a robust choice for the base classifier. In addition, the amending and multi-classifier self-labeled-based approaches reveal a promising attempt to perform semi-supervised classification in the time series context

Gene expression clines reveal local adaptation and associated trade-offs at a continental scale

Local adaptation, where fitness in one environment comes at a cost in another, should lead to spatial variation in trade-offs between life history traits and may be critical for population persistence. Recent studies have sought genomic signals of local adaptation, but often have been limited to laboratory populations representing two environmentally different locations of a species' distribution. We measured gene expression, as a proxy for fitness, in males of Drosophila subobscura, occupying a 20° latitudinal and 11 °C thermal range. Uniquely, we sampled six populations and studied both common garden and semi-natural responses to identify signals of local adaptation. We found contrasting patterns of investment: transcripts with expression positively correlated to latitude were enriched for metabolic processes, expressed across all tissues whereas negatively correlated transcripts were enriched for reproductive processes, expressed primarily in testes. When using only the end populations, to compare our results to previous studies, we found that locally adaptive patterns were obscured. While phenotypic trade-offs between metabolic and reproductive functions across widespread species are well-known, our results identify underlying genetic and tissue responses at a continental scale that may be responsible for this. This may contribute to understanding population persistence under environmental change

Evolution of sex-specific pace-of-life syndromes: genetic architecture and physiological mechanisms

Sex differences in life history, physiology, and behavior are nearly ubiquitous across taxa, owing to sex-specific selection that arises from different reproductive strategies of the sexes. The pace-of-life syndrome (POLS) hypothesis predicts that most variation in such traits among individuals, populations, and species falls along a slow-fast pace-of-life continuum. As a result of their different reproductive roles and environment, the sexes also commonly differ in pace-of-life, with important consequences for the evolution of POLS. Here, we outline mechanisms for how males and females can evolve differences in POLS traits and in how such traits can covary differently despite constraints resulting from a shared genome. We review the current knowledge of the genetic basis of POLS traits and suggest candidate genes and pathways for future studies. Pleiotropic effects may govern many of the genetic correlations, but little is still known about the mechanisms involved in trade-offs between current and future reproduction and their integration with behavioral variation. We highlight the importance of metabolic and hormonal pathways in mediating sex differences in POLS traits; however, there is still a shortage of studies that test for sex specificity in molecular effects and their evolutionary causes. Considering whether and how sexual dimorphism evolves in POLS traits provides a more holistic framework to understand how behavioral variation is integrated with life histories and physiology, and we call for studies that focus on examining the sex-specific genetic architecture of this integration