Seven new taxa from the butterfly subtribe Euptychiina (Lepidoptera: Nymphalidae: Satyrinae) with revisional notes on \u3ci\u3eHarjesia\u3c/i\u3e Forster, 1964 and \u3ci\u3ePseudeuptychia\u3c/i\u3e Forster, 1964

Seven new euptychiine (Lepidoptera: Nymphalidae: Satyrinae) taxa are described and named herein, namely Harjesia argentata Nakahara, Zacca and Lamas, n. sp., Orotaygetis Nakahara and Zacca, n. gen., O. surui Nakahara, Zacca and Lamas, n. sp., Euptychoides sanmarcos Nakahara and Lamas, n. sp., Pseudeuptychia cuzquenya Nakahara and Lamas, n. sp., P. languida austrina Nakahara and Lamas, n. ssp., and Godartiana astronesthes Lamas and Nakahara, n. sp. A revisional note is provided for Harjesia Forster, 1964 and Pseudeuptychia Forster, 1964, and as a result, Taygetis vrazi Kheil, 1896 is removed from Harjesia and a new taxonomic arrangement, Pseudodebis vrazi n. comb., is proposed based on both morphology and molecular data

Genetic and environmental determinants of diastolic heart function

Diastole is the sequence of physiological events that occur in the heart during ventricular filling and principally depends on myocardial relaxation and chamber stiffness. Abnormal diastolic function is related to many cardiovascular disease processes and is predictive of health outcomes, but its genetic architecture is largely unknown. Here, we use machine learning cardiac motion analysis to measure diastolic functional traits in 39,559 participants of the UK Biobank and perform a genome-wide association study. We identified 9 significant, independent loci near genes that are associated with maintaining sarcomeric function under biomechanical stress and genes implicated in the development of cardiomyopathy. Age, sex and diabetes were independent predictors of diastolic function and we found a causal relationship between genetically-determined ventricular stiffness and incident heart failure. Our results provide insights into the genetic and environmental factors influencing diastolic function that are relevant for identifying causal relationships and potential tractable targets

Rare genetic variants affecting urine metabolite levels link population variation to inborn errors of metabolism

Metabolite levels in urine may provide insights into genetic mechanisms shaping their related pathways. We therefore investigate the cumulative contribution of rare, exonic genetic variants on urine levels of 1487 metabolites and 53,714 metabolite ratios among 4864 GCKD study participants. Here we report the detection of 128 significant associations involving 30 unique genes, 16 of which are known to underlie inborn errors of metabolism. The 30 genes are strongly enriched for shared expression in liver and kidney (odds ratio = 65, p-FDR = 3e-7), with hepatocytes and proximal tubule cells as driving cell types. Use of UK Biobank whole-exome sequencing data links genes to diseases connected to the identified metabolites. In silico constraint-based modeling of gene knockouts in a virtual whole-body, organ-resolved metabolic human correctly predicts the observed direction of metabolite changes, highlighting the potential of linking population genetics to modeling. Our study implicates candidate variants and genes for inborn errors of metabolis

Genetic studies of paired metabolomes reveal enzymatic and transport processes at the interface of plasma and urine.

The kidneys operate at the interface of plasma and urine by clearing molecular waste products while retaining valuable solutes. Genetic studies of paired plasma and urine metabolomes may identify underlying processes. We conducted genome-wide studies of 1,916 plasma and urine metabolites and detected 1,299 significant associations. Associations with 40% of implicated metabolites would have been missed by studying plasma alone. We detected urine-specific findings that provide information about metabolite reabsorption in the kidney, such as aquaporin (AQP)-7-mediated glycerol transport, and different metabolomic footprints of kidney-expressed proteins in plasma and urine that are consistent with their localization and function, including the transporters NaDC3 (SLC13A3) and ASBT (SLC10A2). Shared genetic determinants of 7,073 metabolite-disease combinations represent a resource to better understand metabolic diseases and revealed connections of dipeptidase 1 with circulating digestive enzymes and with hypertension. Extending genetic studies of the metabolome beyond plasma yields unique insights into processes at the interface of body compartments

Genetic and environmental determinants of diastolic heart function

Diastole is the sequence of physiological events that occur in the heart during ventricular filling and principally depends on myocardial relaxation and chamber stiffness. Abnormal diastolic function is related to many cardiovascular disease processes and is predictive of health outcomes, but its genetic architecture is largely unknown. Here, we use machine learning cardiac motion analysis to measure diastolic functional traits in 39,559 participants of the UK Biobank and perform a genome-wide association study. We identified 9 significant, independent loci near genes that are associated with maintaining sarcomeric function under biomechanical stress and genes implicated in the development of cardiomyopathy. Age, sex and diabetes were independent predictors of diastolic function and we found a causal relationship between genetically-determined ventricular stiffness and incident heart failure. Our results provide insights into the genetic and environmental factors influencing diastolic function that are relevant for identifying causal relationships and potential tractable targets

Efficient designs for the estimation of mixed and self carryover effects

Biosimilars are copies of biological medicines that are developed by a competitor after the patent for the originator drug has expired. Extensive clinical trials are required to show therapeutic equivalence between the biosimilar and its reference product before a biosimilar can be sold on the market. However, even after more than 10 years of experience with biosimilars in Europe, there is still some uncertainty if the patients who are already taking the reference product can switch between the biosimilar and its reference product. One convenient way to assess the impact of switches is the analysis of mixed and self carryover effects: if the products are switchable, there should not be any difference in the carryover effects. This paper determines a series of simple designs which are highly efficient for the comparison of the mixed and self carryover effects of two treatments. The proof of efficiency is not straightforward because the information matrix of the efficient designs is not completely symmetric