A multivariate approach to analyze the spatial-temporal variation of limnological parameters of the reservoir of the Curuá-Una hydroelectric plant

The construction of reservoirs for hydroelectric plants (HP) began in the 1960s and is currently an integral part of the objectives of economic expansion plans in Brazil. The Curuá-Una HP was the first HP constructed in the central Amazon. Due to the great importance of this reservoir for limnological studies in the Amazon, the objective of this study was to analyze and quantify the percentage of spatial-temporal variation of limnological variables upriver of the reservoir of the Curuá-Una hydroelectric plant. Sampling was conducted between 2016 and 2017. The limnological variables analyzed were water transparency, conductivity, turbidity, dissolved oxygen, pH, biological oxygen demand, nitrate, silica, total phosphorus, and chlorophyll-a. Principal components analysis was used to investigate patterns and the size of the gradient in the reservoir, and to select which spatial and temporal variables make significant contributions a canonical redundancy analysis (RDA) was conducted incorporating a partitioning of variance. PCA showed that the samples were seasonally grouped, and the first two axes explained 51.74% of the variability. The RDA and partition of variance showed that the spatial and temporal explanatory variables together explained 66% of the variability (time = 56%, spatial = 10%, time and spatial = 0%, residual = 34%). The results obtained suggest that the seasonal effect is responsible for 56% of the variability, and such changes in time are sufficient enough to alter the biological processes when environmental conditions are turbulent.

Response heterogeneity: Challenges for personalised medicine and big data approaches in psychiatry and chronic pain

Response rates to available treatments for psychological and chronic pain disorders are poor, and there is a substantial burden of suffering and disability for patients, who often cycle through several rounds of ineffective treatment. As individuals presenting to the clinic with symptoms of these disorders are likely to be heterogeneous, there is considerable interest in the possibility that different constellations of signs could be used to identify subgroups of patients that might preferentially benefit from particular kinds of treatment. To this end, there has been a recent focus on the application of machine learning methods to attempt to identify sets of predictor variables (demographic, genetic, etc.) that could be used to target individuals towards treatments that are more likely to work for them in the first instance. Importantly, the training of such models generally relies on datasets where groups of individual predictor variables are labelled with a binary outcome category − usually ‘responder’ or ‘non-responder’ (to a particular treatment). However, as previously highlighted in other areas of medicine, there is a basic statistical problem in classifying individuals as ‘responding’ to a particular treatment on the basis of data from conventional randomized controlled trials. Specifically, insufficient information on the partition of variance components in individual symptom changes mean that it is inappropriate to consider data from the active treatment arm alone in this way. This may be particularly problematic in the case of psychiatric and chronic pain symptom data, where both within-subject variability and measurement error are likely to be high. Here, we outline some possible solutions to this problem in terms of dataset design and machine learning methodology, and conclude that it is important to carefully consider the kind of inferences that particular training data are able to afford, especially in arenas where the potential clinical benefit is so large.

Response heterogeneity: Challenges for personalised medicine and big data approaches in psychiatry and chronic pain

Response rates to available treatments for psychological and chronic pain disorders are poor, and there is a considerable burden of suffering and disability for patients, who often cycle through several rounds of ineffective treatment. As individuals presenting to the clinic with symptoms of these disorders are likely to be heterogeneous, there is considerable interest in the possibility that different constellations of signs could be used to identify subgroups of patients that might preferentially benefit from particular kinds of treatment. To this end, there has been a recent focus on the application of machine learning methods to attempt to identify sets of predictor variables (demographic, genetic, etc.) that could be used to target individuals towards treatments that are more likely to work for them in the first instance. Importantly, the training of such models generally relies on datasets where groups of individual predictor variables are labelled with a binary outcome category − usually ‘responder’ or ‘non-responder’ (to a particular treatment). However, as previously highlighted in other areas of medicine, there is a basic statistical problem in classifying individuals as ‘responding’ to a particular treatment on the basis of data from conventional randomized controlled trials. Specifically, insufficient information on the partition of variance components in individual symptom changes mean that it is inappropriate to consider data from the active treatment arm alone in this way. This may be particularly problematic in the case of psychiatric and chronic pain symptom data, where both within-subject variability and measurement error are likely to be high. Here, we outline some possible solutions to this problem in terms of dataset design and machine learning methodology, and conclude that it is important to carefully consider the kind of inferences that particular training data are able to afford, especially in arenas where the potential clinical benefit is so large.

Gene-culture co-evolution: teaching, learning, and correlations between relatives

Heritability, the fraction of phenotypic variance attributable to the action of genes, is usually derived from a linear statistical partition of variance. In this paper we study a dichotomous phenotype whose transmission from parents to offspring depends on the parents’ phenotypes and the offspring’s genotype. Each individual is then represented as a phenogenotype. We derive expressions for each component of phenotypic variance and for covariances between relatives of various degrees. The resulting heritability estimates vary with the rates of phenotypic transmission as well as with the genetic contribution to the phenotype. Assortative mating by phenotype in parents is also shown to contribute to the correlations between relatives. In addition, we show that the frequency of alleles at genes affecting the phenotypes strongly affects standard heritability measures. This is important because for most complex traits these allele frequencies cannot be ascertained.

The fleece of the Scottish Blackface sheep III. The relative importance of the fleece components

The relative importance of variation in the components of wool production to variation in wool production itself was estimated by two methods of analysis. About 30% of the attributable variation in total fleece weight was found to be due to variation in surface area and 70% was due to wool production per unit skin area. Between 50 and 60% of the variation amongst sheep in the latter character was found to be due to variation in fibre weight, the rest being attributed to variation in number of fibres per unit area (fibre density). The variation in mean fibre weight was further partitioned into 50 to 70% due to variation in mean cross-sectional area and the remainder to variation in mean fibre length.Variation in wool production per unit skin area was also analysed according to growth period and position on the body. The results are discussed in relation to the partition of variance in wool production of other breeds.