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Hybridisation and gene flow can have both deleterious and adaptive consequences for natural populations and species. To better understand the extent of hybridisation in nature and the balance between its beneficial and deleterious outcomes in a changing environment, information on naturally hybridising nonmodel organisms is needed. This requires the characterisation of the structure and extent of natural hybrid zones. Here, we study natural populations of five keystone mound-building wood ant species in the Formica rufa group across Finland. No genomic studies across the species group exist, and the extent of hybridisation and genomic differentiation in sympatry is unknown. Combining genome-wide and morphological data, we demonstrate more extensive hybridisation than was previously detected between all five species in Finland. Specifically, we reveal a mosaic hybrid zone between Formica aquilonia, F. rufa and F. polyctena, comprising further generation hybrid populations. Despite this, we find that F. rufa, F. aquilonia, F. lugubris and F. pratensis form distinct gene pools in Finland. We also find that hybrids occupy warmer microhabitats than the nonadmixed populations of cold-adapted F. aquilonia, and suggest that warm winters and springs, in particular, may benefit hybrids over F. aquilonia, the most abundant F. rufa group species in Finland. In summary, our results indicate that extensive hybridisation may create adaptive potential that could promote wood ant persistence in a changing climate. Additionally, they highlight the potentially significant ecological and evolutionary consequences of extensive mosaic hybrid zones, within which independent hybrid populations face an array of ecological and intrinsic selection pressures.

Computational pangenomics is an emerging research field that is changing the way computer scientists are facing challenges in biological sequence analysis. In past decades, contributions from combinatorics, stringology, graph theory and data structures were essential in the development of a plethora of software tools for the analysis of the human genome. These tools allowed computational biologists to approach ambitious projects at population scale, such as the 1000 Genomes Project. A major contribution of the 1000 Genomes Project is the characterization of a broad spectrum of genetic variations in the human genome, including the discovery of novel variations in the South Asian, African and European populations—thus enhancing the catalogue of variability within the reference genome. Currently, the need to take into account the high variability in population genomes as well as the specificity of an individual genome in a personalized approach to medicine is rapidly pushing the abandonment of the traditional paradigm of using a single reference genome. A graph-based representation of multiple genomes, or a graph pangenome, is replacing the linear reference genome. This means completely rethinking well-established procedures to analyze, store, and access information from genome representations. Properly addressing these challenges is crucial to face the computational tasks of ambitious healthcare projects aiming to characterize human diversity by sequencing 1M individuals (Stark et al. 2019). This tutorial aims to introduce readers to the most recent advances in the theory of data structures for the representation of graph pangenomes. We discuss efficient representations of haplotypes and the variability of genotypes in graph pangenomes, and highlight applications in solving computational problems in human and microbial (viral) pangenomes.

Computational pangenomics is an emerging research field that is changing the way computer scientists are facing challenges in biological sequence analysis. In past decades, contributions from combinatorics, stringology, graph theory and data structures were essential in the development of a plethora of software tools for the analysis of the human genome. These tools allowed computational biologists to approach ambitious projects at population scale, such as the 1000 Genomes Project. A major contribution of the 1000 Genomes Project is the characterization of a broad spectrum of genetic variations in the human genome, including the discovery of novel variations in the South Asian, African and European populations—thus enhancing the catalogue of variability within the reference genome. Currently, the need to take into account the high variability in population genomes as well as the specificity of an individual genome in a personalized approach to medicine is rapidly pushing the abandonment of the traditional paradigm of using a single reference genome. A graph-based representation of multiple genomes, or a graph pangenome, is replacing the linear reference genome. This means completely rethinking well-established procedures to analyze, store, and access information from genome representations. Properly addressing these challenges is crucial to face the computational tasks of ambitious healthcare projects aiming to characterize human diversity by sequencing 1M individuals (Stark et al. 2019). This tutorial aims to introduce readers to the most recent advances in the theory of data structures for the representation of graph pangenomes. We discuss efficient representations of haplotypes and the variability of genotypes in graph pangenomes, and highlight applications in solving computational problems in human and microbial (viral) pangenomes.

Objective: Insulinomas are rare functional pancreatic neuroendocrine tumours. As previous data on the long-term prognosis of insulinoma patients are scarce, we studied the morbidity and mortality in the Finnish insulinoma cohort. Design: Retrospective cohort study. Methods: Incidence of endocrine, cardiovascular, gastrointestinal and psychiatric disorders, and cancers was compared in all the patients diagnosed with an insulinoma in Finland during 1980–2010 (n = 79, including two patients with multiple endocrine neoplasia type 1 syndrome), vs 316 matched controls, using the Mantel–Haenszel method. Overall survival was analysed with Kaplan–Meier and Cox regression analyses Results: The median length of follow-up was 10.7 years for the patients and 12.2 years for the controls. The long-term incidence of atrial fibrillation (rate ratio (RR): 2.07 (95% CI: 1.02–4.22)), intestinal obstruction (18.65 (2.09–166.86)), and possibly breast (4.46 (1.29–15.39) and kidney cancers (RR not applicable) was increased among insulinoma patients vs controls, P < 0.05 for all comparisons. Endocrine disorders and pancreatic diseases were more frequent in the patients during the first year after insulinoma diagnosis, but not later on. The survival of patients with a non-metastatic insulinoma (n = 70) was similar to that of controls, but for patients with distant metastases (n = 9), the survival was significantly impaired (median 3.4 years). Conclusions: The long-term prognosis of patients with a non-metastatic insulinoma is similar to the general population, except for an increased incidence of atrial fibrillation, intestinal obstruction, and possibly breast and kidney cancers. These results need to be confirmed in future studies. Metastatic insulinomas entail a markedly decreased survival.

The variation graph toolkit (VG) represents genetic variation as a graph. Each path in the graph is a potential haplotype, though most paths are unlikely recombinations of true haplotypes. We augment the VG model with haplotype information to identify which paths are more likely to be correct. For this purpose, we develop a scalable implementation of the graph extension of the positional Burrows-Wheeler transform. We demonstrate the scalability of the new implementation by indexing the 1000 Genomes Project haplotypes. We also develop an algorithm for simplifying variation graphs for k-mer indexing without losing any k-mers in the haplotypes.

Background: Vascular endothelial zinc finger 1 (Vezf1) is a transcription factor previously shown to regulate vasculogenesis and angiogenesis. We aimed to investigate the role of Vezf1 in the postnatal heart. Methods: The role of Vezf1 in regulating cardiac growth and contractile function was studied in zebrafish and in primary cardiomyocytes. Findings: We find that expression of Vezf1 is decreased in diseased human myocardium and mouse hearts. Our experimental data shows that knockdown of zebrafish Vezf1 reduces cardiac growth and results in impaired ventricular contractile response to β-adrenergic stimuli. However, Vezf1 knockdown is not associated with dysregulation of cardiomyocyte Ca2+ transient kinetics. Gene ontology enrichment analysis indicates that Vezf1 regulates cardiac muscle contraction and dilated cardiomyopathy related genes and we identify cardiomyocyte Myh7/β-MHC as key target for Vezf1. We further identify a key role for an MCAT binding site in the Myh7 promoter regulating the response to Vezf1 knockdown and show that TEAD-1 is a binding partner of Vezf1. Interpretation: We demonstrate a role for Vezf1 in regulation of compensatory cardiac growth and cardiomyocyte contractile function, which may be relevant in human cardiac disease.

Objective: Insulinomas are rare pancreatic neoplasms, which can usually be cured by surgery. As the diagnostic delay is often long and the prolonged hyperinsulinemia may have long‐term effects on health and the quality of life, we studied the long‐term health‐related quality of life (HRQoL) in insulinoma patients. Design, patients and measurements: The HRQoL of adults diagnosed with an insulinoma in Finland in 1980‐2010 was studied with the 15D instrument, and the results were compared to those of an age‐ and gender‐matched sample of the general population. The minimum clinically important difference in the total 15D score has been defined as ±0.015. The clinical characteristics, details of insulinoma diagnosis and treatment, and the current health status of the subjects were examined to specify the possible determinants of long‐term HRQoL. Results: Thirty‐eight insulinoma patients participated in the HRQoL survey (response rate 75%). All had undergone surgery with a curative aim, a median of 13 (min 7, max 34) years before the survey. The insulinoma patients had a clinically importantly and statistically significantly better mean 15D score compared with the controls (0.930 ± 0.072 vs 0.903 ± 0.039, P = .046) and were significantly better off regarding mobility, usual activities and eating. Among the insulinoma patients, younger age at the time of survey, higher level of education and smaller number of chronic diseases were associated with better overall HRQoL. Conclusions: In the long term, the overall HRQoL of insulinoma patients is slightly better than that of the general population.

Tiivistelmä. Virulenssin evoluutioon viruksilla vaikuttaa viruksen genomin rakenne, ympäristö sekä isäntäorganismin immuunipuolustus. Yksi eniten vaikuttavista tekijöistä on populaatiotason valintapaine optimoida virulenssin ja leviämiskyvyn suhde. Etenkin isännänvaihdoksen jälkeinen virulenssin evoluutio on tärkeä tutkimuskohde, jonka avulla voidaan ymmärtää uusia infektioita paremmin. Tässä tutkielmassa perehdytään kahden viruksen, myksoomaviruksen ja HIV-1:n virulenssin evoluutioon.

Objective: Insulinomas are rare pancreatic tumours. Population-based data on their incidence, clinical picture, diagnosis, and treatment are almost nonexistent. The aim of this study was to clarify these aspects in a nationwide cohort of insulinoma patients diagnosed during three decades. Design and Methods: Retrospective analysis on all adult patients diagnosed with insulinoma in Finland during 1980–2010. Results: Retrospective analysis on all adult patients diagnosed with insulinoma in Finland during 1980–2010. Results. Seventy-nine patients were diagnosed with insulinoma over the research period. The median follow-up from diagnosis to last control visit was one (min 0, max 31) year. The incidence increased from 0.5/million/year in the 1980s to 0.9/million/year in the 2000s (p = 0 002). The median diagnostic delay was 13 months and did not change over the study period. The mean age at diagnosis was 52 (SD 16) years. The overall imaging sensitivity improved from 39% in the 1980s to 98% in the 2000s (p < 0 001). Seventy-one (90%) of the patients underwent surgery with a curative aim, two (3%) had palliative surgery, and 6 (8%) were inoperable. There were no significant differences in the types of surgical procedures between the 1980s, 1990s, and 2000s; tumour enucleations comprised 43% of the operations, distal pancreatic resections 45%, and pancreaticoduodenectomies 12%, over the whole study period. Of the patients who underwent surgery with a curative aim, 89% had a full recovery. Postoperative complications occurred in half of the patients, but postoperative mortality was rare.. Conclusions: The incidence of insulinomas has increased during the past three decades. Despite the improved diagnostic options, diagnostic delay has remained unchanged. To shorten the delay, clinicians should be informed and alert to consider the possibility of hypoglycemia and insulinoma, when symptomatic attacks are investigated in different sectors of the healthcare system.

Purpose This study compared the utility of different statistical methods in differentiating sexual crimes committed by the same person from sexual crimes committed by different persons. Methods Logistic regression, iterative classification tree (ICT), and Bayesian analysis were applied to a dataset of 3,364 solved, unsolved, serial, and apparent one-off sexual assaults committed in five countries. Receiver Operating Characteristic analysis was used to compare the statistical approaches. Results All approaches achieved statistically significant levels of discrimination accuracy. Two out of three Bayesian methods achieved a statistically higher level of accuracy (Areas Under the Curve [AUC]=0.89 [Bayesian coding method 1]; AUC=0.91 [Bayesian coding method 3]) than ICT analysis (AUC=0.88), logistic regression (AUC=0.87), and Bayesian coding method 2 (AUC=0.86). Conclusions The ability to capture/utilize between-offender differences in behavioral consistency appear to be of benefit when linking sexual offenses. Statistical approaches that utilize individual offender behaviors when generating crime linkage predictions may be preferable to approaches that rely on a single summary score of behavioral similarity. Crime linkage decision-support tools should incorporate a range of statistical methods and future research must compare these methods in terms of accuracy, usability, and suitability for practice.

Purpose This study compared the utility of different statistical methods in differentiating sexual crimes committed by the same person from sexual crimes committed by different persons. Methods Logistic regression, iterative classification tree (ICT), and Bayesian analysis were applied to a dataset of 3,364 solved, unsolved, serial, and apparent one-off sexual assaults committed in five countries. Receiver Operating Characteristic analysis was used to compare the statistical approaches. Results All approaches achieved statistically significant levels of discrimination accuracy. Two out of three Bayesian methods achieved a statistically higher level of accuracy (Areas Under the Curve [AUC]=0.89 [Bayesian coding method 1]; AUC=0.91 [Bayesian coding method 3]) than ICT analysis (AUC=0.88), logistic regression (AUC=0.87), and Bayesian coding method 2 (AUC=0.86). Conclusions The ability to capture/utilize between-offender differences in behavioral consistency appear to be of benefit when linking sexual offenses. Statistical approaches that utilize individual offender behaviors when generating crime linkage predictions may be preferable to approaches that rely on a single summary score of behavioral similarity. Crime linkage decision-support tools should incorporate a range of statistical methods and future research must compare these methods in terms of accuracy, usability, and suitability for practice.

Gaussian distribution has for several decades been ubiquitous in the theory and practice of statistical classification. Despite the early proposals motivating the use of predictive inference to design a classifier, this approach has gained relatively little attention apart from certain specific applications, such as speech recognition where its optimality has been widely acknowledged. Here we examine statistical properties of different inductive classification rules under a generic Gaussian model and demonstrate the optimality of considering simultaneous classification of multiple samples under an attractive loss function. It is shown that the simpler independent classification of samples leads asymptotically to the same optimal rule as the simultaneous classifier when the amount of training data increases, if the dimensionality of the feature space is bounded in an appropriate manner. Numerical investigations suggest that the simultaneous predictive classifier can lead to higher classification accuracy than the independent rule in the low-dimensional case, whereas the simultaneous approach suffers more from noise when the dimensionality increases., QC 20160613