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The 12-lead electrocardiogram (ECG) is inexpensive and widely available. Whether conditions across the human disease landscape can be detected using the ECG is unclear. We developed a deep learning denoising autoencoder and systematically evaluated associations between ECG encodings and ~1,600 Phecode-based diseases in three datasets separate from model development, and meta-analyzed the results. The latent space ECG model identified associations with 645 prevalent and 606 incident Phecodes. Associations were most enriched in the circulatory (n = 140, 82% of category-specific Phecodes), respiratory (n = 53, 62%) and endocrine/metabolic (n = 73, 45%) categories, with additional associations across the phenome. The strongest ECG association was with hypertension (p < 2.2×10 -308 ). The ECG latent space model demonstrated more associations than models using standard ECG intervals, and offered favorable discrimination of prevalent disease compared to models comprising age, sex, and race. We further demonstrate how latent space models can be used to generate disease-specific ECG waveforms and facilitate individual disease profiling., Competing Interests: Competing interests: Dr. Lubitz is a full-time employee of Novartis Institutes for Biomedical Research as of July 18, 2022. Dr. Lubitz has received sponsored research support from Bristol Myers Squibb, Pfizer, Boehringer Ingelheim, Fitbit, Medtronic, Premier, and IBM, and has consulted for Bristol Myers Squibb, Pfizer, Blackstone Life Sciences, and Invitae. Dr. Anderson receives sponsored research support from Bayer AG and Massachusetts General Hospital and has consulted for ApoPharma. Dr. Weng receives sponsored research support from IBM to the Broad Institute. Dr. Ellinor has received sponsored research support from Bayer AG and IBM Health, and he has consulted for Bayer AG, Novartis and MyoKardia. Dr. Batra, Dr. Reeder and Dr. Friedman have received sponsored research support from Bayer AG and IBM Health. Dr. Ho and Dr. Khurshid have received sponsored research support from Bayer AG. The remaining authors declare no competing interests., (© 2025. The Author(s).)

Frogs of the genus Platypelis are known to have their center of species richness in the mountain massifs of northern Madagascar. We here formally describe a new species of Platypelis from this region. Platypelis saikamavo sp. nov. has been known for over 20 years but its taxonomic identity has only recently been robustly clarified as a result of ongoing taxonomic revisions of other Platypelis species. The new species is known from rainforest on the Tsaratanàna Massif and Bemanevika, at elevations of 1000-1530 m above sea level. It is characterized by an isolated phylogenetic position and is genetically highly divergent, with an uncorrected pairwise distance ≥4.5% in the mitochondrial 16S rRNA gene to all other nominal species of Platypelis. It has a unique combination of morphological and chromatic character states such as a relatively large body size of 28-31 mm in snout-vent length, distinct superciliary tubercles, and bright yellow color around the forelimb insertion and on the posterior abdomen where this color extends in the form of flash marks onto the inguinal region.

The Lygodactylus tolampyae complex includes several deep genetic lineages of small diurnal geckos from the West and North West of Madagascar whose taxonomy is largely unsolved. We sequenced DNA fragments of one mitochondrial and four nuclear-encoded genes for up to 70 samples across the entire known range of these geckos. We find as many as 11 mitochondrial lineages differentiated by >4% pairwise distances in the 16S rRNA gene fragment, with >9% pairwise distance for the majority of lineage comparisons. Many of these lineages were concordantly differentiated in all of the nuclear-encoded genes without any haplotype sharing, despite the syntopic occurrence of some of them. We therefore hypothesize that the complex contains seven candidate species, but a comprehensive taxonomic resolution is complicated by various hindrances. These include incomplete sampling, with two lineages each known only from a single specimen, and one further lineage with no voucher specimens available for examination. Further hurdles are the probably lost holotype of L. tolampyae and its imprecise type locality, as well as the apparent lack of any morphological differentiation between the majority of the genetic lineages. Based on a survey of historical literature and the travel routes of the original collector, A. Grandidier, we conclude that the provenance of the holotype of L. tolampyae is likely in the wider Morondava area in the West and assign the sole candidate species from this area to this name. We then proceed to describe three species that represent separate genetic lineages for all markers studied: Lygodactylus morii sp. nov., a species common in Ankarafantsika National Park and several nearby sites in the North West; L. herilalai sp. nov., a species occurring in close syntopy with L. morii in Ankarafantsika without any signal of genetic admixture; and L. schwitzeri sp. nov. from Sahamalaza Peninsula in the North West. This leaves three more lineages without a name and with the need to gather additional samples, two from Namoroka National Park and one from other sites in the North West. We confirm the L. tolampyae complex to be an apparently rare example of truly cryptic reptile species in Madagascar, where even detailed morphological examination does not reveal morphological differences among lineages that are clearly evolutionarily independent and require recognition as distinct species due to their co-occurrence without admixture.

Fibrotic diseases affect multiple organs and are associated with morbidity and mortality. To examine organ-specific and shared biologic mechanisms that underlie fibrosis in different organs, we developed machine learning models to quantify T1 time, a marker of interstitial fibrosis, in the liver, pancreas, heart and kidney among 43,881 UK Biobank participants who underwent magnetic resonance imaging. In phenome-wide association analyses, we demonstrate the association of increased organ-specific T1 time, reflecting increased interstitial fibrosis, with prevalent diseases across multiple organ systems. In genome-wide association analyses, we identified 27, 18, 11 and 10 independent genetic loci associated with liver, pancreas, myocardial and renal cortex T1 time, respectively. There was a modest genetic correlation between the examined organs. Several loci overlapped across the examined organs implicating genes involved in a myriad of biologic pathways including metal ion transport (SLC39A8, HFE and TMPRSS6), glucose metabolism (PCK2), blood group antigens (ABO and FUT2), immune function (BANK1 and PPP3CA), inflammation (NFKB1) and mitosis (CENPE). Finally, we found that an increasing number of organs with T1 time falling in the top quintile was associated with increased mortality in the population. Individuals with a high burden of fibrosis in ≥3 organs had a 3-fold increase in mortality compared to those with a low burden of fibrosis across all examined organs in multivariable-adjusted analysis (hazard ratio = 3.31, 95% confidence interval 1.77-6.19; P = 1.78 × 10 -4 ). By leveraging machine learning to quantify T1 time across multiple organs at scale, we uncovered new organ-specific and shared biologic pathways underlying fibrosis that may provide therapeutic targets., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Background: Disorders affecting cardiac conduction are associated with substantial morbidity. Understanding the epidemiology and risk factors for conduction disorders may enable earlier diagnosis and preventive efforts., Objectives: The purpose of this study was to quantify contemporary frequency and risk factors for electrocardiogram (ECG)-defined cardiac conduction disorders in a large multi-institutional primary care sample., Methods: We quantified prevalence and incidence of conduction disorders among adults receiving longitudinal primary care between 2001 and 2019, each with at least one 12-lead ECG performed prior to the start of follow-up and at least one ECG during follow-up. We defined conduction disorders using curated terms extracted from ECG diagnostic statements by cardiologists. We grouped conduction disorders by inferred anatomic location of abnormal conduction. We tested associations between clinical factors and incident conduction disease using multivariable proportional hazards regression., Results: We analyzed 189,163 individuals (median age 55 years; 58% female). The overall prevalence of conduction disorders was 27% among men and 15% among women. Among 119,926 individuals (median age 55 years; 51% female), 6,802 developed an incident conduction system abnormality over a median of 10 years (Q1, Q3: 6, 15 years) of follow-up. Incident conduction disorders were more common in men (8.78 events/1,000 person-years) vs women (4.34 events/1,000 person-years, P  < 0.05). In multivariable models, clinical factors including older age (HR: 1.25 per 5-year increase [95% CI: 1.24-1.26]) and myocardial infarction (HR: 1.39 [95% CI: 1.26-1.54]) were associated with incident conduction disorders., Conclusions: Cardiac conduction disorders are common in a primary care population, especially among older individuals with cardiovascular risk factors., Competing Interests: Dr Lubitz is a full-time employee of Novartis as of July 2022. He received sponsored research support from Bristol Myers Squibb, Pfizer, Boehringer Ingelheim, Fitbit, Medtronic, Premier, and IBM, and has consulted for Bristol Myers Squibb, Pfizer, Blackstone Life Sciences, and Invitae. Dr Ellinor receives sponsored research support from Bayer AG and IBM and has consulted for Novartis, MyoKardia, and Bayer AG. Dr Ho has received sponsored research support from Bayer AG. Dr Batra receives sponsored research support from Bayer AG and IBM and has consulted for Novartis and Prometheus Biosciences. Dr Philippakis is a Venture Partners and employee at GV, has consulted for Novartis and Rakuten, and receives sponsored research support from Verily, Microsoft, IBM, Intel, Pfizer, Abbvie, Biogen, Ionnis and Bayer. Investigators were supported by 10.13039/100000002National Institutes of Health grants K23HL169839-01 (Dr Khurshid), R38HL150212 (Dr Haimovich); R01HL139731 (Dr Lubitz), R01HL157635 (Dr Lubitz), R01HL134893 (Dr Ho), R01HL140224 (Dr Ho), K24HL153669 (Dr Ho), 2R01HL092577 (Dr Ellinor), and 1R01AG066010, R01HL092577 (Dr Benjamin); 10.13039/100000968American Heart Association grants 18SFRN34250007 (Dr Lubitz), AF 18SFRN34110082 (Dr Benjamin), 2023CDA1050571 (Dr Khurshid) and 18SFRN34110082 (Dr Ellinor); and by a grant from the European Union, MAESTRIA 965286 (Dr Ellinor). Dr Kornej has received funding from the 10.13039/100018694Marie Sklodowska-Curie Actions under the European Union’s 10.13039/100010661Horizon 2020 research and innovation programme (Agreement No 838259). All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (© 2024 The Authors.)

Increased left atrial volume and decreased left atrial function have long been associated with atrial fibrillation. The availability of large-scale cardiac magnetic resonance imaging data paired with genetic data provides a unique opportunity to assess the genetic contributions to left atrial structure and function, and understand their relationship with risk for atrial fibrillation. Here, we use deep learning and surface reconstruction models to measure left atrial minimum volume, maximum volume, stroke volume, and emptying fraction in 40,558 UK Biobank participants. In a genome-wide association study of 35,049 participants without pre-existing cardiovascular disease, we identify 20 common genetic loci associated with left atrial structure and function. We find that polygenic contributions to increased left atrial volume are associated with atrial fibrillation and its downstream consequences, including stroke. Through Mendelian randomization, we find evidence supporting a causal role for left atrial enlargement and dysfunction on atrial fibrillation risk., (© 2024. The Author(s).)

Aims: To leverage deep learning on the resting 12-lead electrocardiogram (ECG) to estimate peak oxygen consumption (V˙O2peak) without cardiopulmonary exercise testing (CPET)., Methods and Results: V ˙ O 2 peak estimation models were developed in 1891 individuals undergoing CPET at Massachusetts General Hospital (age 45 ± 19 years, 38% female) and validated in a separate test set (MGH Test, n = 448) and external sample (BWH Test, n = 1076). Three penalized linear models were compared: (i) age, sex, and body mass index ('Basic'), (ii) Basic plus standard ECG measurements ('Basic + ECG Parameters'), and (iii) basic plus 320 deep learning-derived ECG variables instead of ECG measurements ('Deep ECG-V˙O2'). Associations between estimated V˙O2peak and incident disease were assessed using proportional hazards models within 84 718 primary care patients without CPET. Inference ECGs preceded CPET by 7 days (median, interquartile range 27-0 days). Among models, Deep ECG-V˙O2 was most accurate in MGH Test [r = 0.845, 95% confidence interval (CI) 0.817-0.870; mean absolute error (MAE) 5.84, 95% CI 5.39-6.29] and BWH Test (r = 0.552, 95% CI 0.509-0.592, MAE 6.49, 95% CI 6.21-6.67). Deep ECG-V˙O2 also outperformed the Wasserman, Jones, and FRIEND reference equations (P < 0.01 for comparisons of correlation). Performance was higher in BWH Test when individuals with heart failure (HF) were excluded (r = 0.628, 95% CI 0.567-0.682; MAE 5.97, 95% CI 5.57-6.37). Deep ECG-V˙O2 estimated V˙O2peak <14 mL/kg/min was associated with increased risks of incident atrial fibrillation [hazard ratio 1.36 (95% CI 1.21-1.54)], myocardial infarction [1.21 (1.02-1.45)], HF [1.67 (1.49-1.88)], and death [1.84 (1.68-2.03)]., Conclusion: Deep learning-enabled analysis of the resting 12-lead ECG can estimate exercise capacity (V˙O2peak) at scale to enable efficient cardiovascular risk stratification., Competing Interests: Conflict of interest: B.M.W. has consulted for Change Healthcare. P.D.A. and P.B. are supported by grants from Bayer AG and IBM applying machine learning in cardiovascular disease. P.B. serves as a consultant for Novartis and Prometheus Biosciences. P.T.E. receives sponsored research support from Bayer AG and IBM Research; he has also served on advisory boards or consulted for Bayer AG, MyoKardia, and Novartis. S.A.L. receives sponsored research support from Bristol Myers Squibb/Pfizer, Bayer AG, Boehringer Ingelheim, Fitbit, and IBM, and has consulted for Bristol Myers Squibb/Pfizer, Bayer AG, and Blackstone Life Sciences. S.A.L. is now an employee of Novartis Institute for Biomedical Research. The remaining authors have no disclosures., (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Deep learning interpretation of echocardiographic images may facilitate automated assessment of cardiac structure and function., Objectives: We developed a deep learning model to interpret echocardiograms and examined the association of deep learning-derived echocardiographic measures with incident outcomes., Methods: We trained and validated a 3-dimensional convolutional neural network model for echocardiographic view classification and quantification of left atrial dimension, left ventricular wall thickness, chamber diameter, and ejection fraction. The training sample comprised 64,028 echocardiograms (n = 27,135) from a retrospective multi-institutional ambulatory cardiology electronic health record sample. Validation was performed in a separate longitudinal primary care sample and an external health care system data set. Cox models evaluated the association of model-derived left heart measures with incident outcomes., Results: Deep learning discriminated echocardiographic views (area under the receiver operating curve >0.97 for parasternal long axis, apical 4-chamber, and apical 2-chamber views vs human expert annotation) and quantified standard left heart measures (R 2 range = 0.53 to 0.91 vs study report values). Model performance was similar in 2 external validation samples. Model-derived left heart measures predicted incident heart failure, atrial fibrillation, myocardial infarction, and death. A 1-SD lower model-left ventricular ejection fraction was associated with 43% greater risk of heart failure (HR: 1.43; 95% CI: 1.23-1.66) and 17% greater risk of death (HR: 1.17; 95% CI: 1.06-1.30). Similar results were observed for other model-derived left heart measures., Conclusions: Deep learning echocardiographic interpretation accurately quantified standard measures of left heart structure and function, which in turn were associated with future clinical outcomes. Deep learning may enable automated echocardiogram interpretation and disease prediction at scale., Competing Interests: Funding Support and Author Disclosures Dr Lau is supported by the National Institutes of Health (NIH) (K23-HL159243) and the American Heart Association (AHA) (853922). Dr Al-Alusi is supported by the NIH (T32-HL007208). Dr Ellinor is supported by the NIH (1R01HL092577, K24HL105780), AHA (18SFRN34110082), Foundation Leducq (14CVD01), and by MAESTRIA (965286). Dr Lubitz was supported by NIH grants 1R01HL139731 and R01HL157635, and AHA 18SFRN34250007 during this work. Dr Ho is supported by the NIH (R01 HL134893, R01 HL140224, R0 1HL160003, and K24 HL153669). Dr Lau has consulted for Roche Diagnostics. Dr Di Achille is a full-time employee of Google as of June 2022. Ms Kopparapu is a full-time employee of DeepMind as of August 2022. Dr Batra is a full-time employee of Flagship Pioneering as of January 2023. Dr Batra has received sponsored research support from Bayer AG and IBM; and has consulted for Novartis and Prometheus Biosciences. Dr Ellinor has received sponsored research support from Bayer AG and IBM Health; and has served on advisory boards or consulted for Bayer AG, Quest Diagnostics, MyoKardia, and Novartis. Dr Picard has consulted for Vertex. Dr Ho has received past sponsored research support from Bayer AG. Dr Lubitz is a full-time employee of Novartis as of July 2022; has received sponsored research support from Bristol Myers Squibb, Pfizer, Boehringer Ingelheim, Fitbit, Medtronic, Premier, and IBM; and has consulted for Bristol Myers Squibb, Pfizer, Blackstone Life Sciences, and Invitae. All other authors have reported that they had no relationships relevant to the contents of this paper to disclose., (Copyright © 2023 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Many studies on reptiles have been conducted across Madagascar but some areas are poorly known in terms of the diversity of reptiles such as the Bobaomby Complex in the northern tip of Madagascar. In February and March 2018, we conducted a biodiversity survey within five sites. This biological survey is to collect scientific information for helping new protected creations. Three main methods were used including pitfall trap, visual and acoustic searching along the transect and refuge examination. In total, we recorded 42 species including 5 chameleons, 8 skinks, 11 geckos, 16 snakes and 2 blinds snake species. All recorded species are endemic to Madagascar except Hemidactylus frenatus , Ebenavia inunguis and Phelsuma abbotti . Rare species known only from a few specimens have been recorded in the Bobaomby Complex: Heteroliodon fohy , Pseudoxyrhopus ambreensis , and Madascincus arenicola . Thirteen species are classified as threatened on the IUCN Red List, of which three are Critically Endangered: Paracontias minimus , Madascincus arenicola , and Paroedura lohatsara ; three are Endangered: Heteroliodon fohy , Lycodryas inopinae , and Phisalixella variabilis ; and seven are Vulnerable: Brookesia ebenaui , Furcifer petteri , Blaesodactylus boivini , Uroplatus ebenaui , Uroplatus henkeli , Liophidium therezieni and Flexiseps ardouini . Our results reveal the importance of the Bobaomby Complex for conserving reptile diversity and highlight the need to protect it.

Background: Artificial intelligence (AI) models applied to 12-lead ECG waveforms can predict atrial fibrillation (AF), a heritable and morbid arrhythmia. However, the factors forming the basis of risk predictions from AI models are usually not well understood. We hypothesized that there might be a genetic basis for an AI algorithm for predicting the 5-year risk of new-onset AF using 12-lead ECGs (ECG-AI)-based risk estimates., Methods: We applied a validated ECG-AI model for predicting incident AF to ECGs from 39 986 UK Biobank participants without AF. We then performed a genome-wide association study (GWAS) of the predicted AF risk and compared it with an AF GWAS and a GWAS of risk estimates from a clinical variable model., Results: In the ECG-AI GWAS, we identified 3 signals ( P <5×10 -8 ) at established AF susceptibility loci marked by the sarcomeric gene TTN and sodium channel genes SCN5A and SCN10A . We also identified 2 novel loci near the genes VGLL2 and EXT1 . In contrast, the clinical variable model prediction GWAS indicated a different genetic profile. In genetic correlation analysis, the prediction from the ECG-AI model was estimated to have a higher correlation with AF than that from the clinical variable model., Conclusions: Predicted AF risk from an ECG-AI model is influenced by genetic variation implicating sarcomeric, ion channel and body height pathways. ECG-AI models may identify individuals at risk for disease via specific biological pathways., Competing Interests: Disclosures Dr Lubitz is a full-time employee of Novartis as of July 18, 2022. Dr Lubitz has received sponsored research support from Bristol Myers Squibb, Pfizer, Boehringer Ingelheim, Fitbit, Medtronic, Premier, and IBM, and has consulted for Bristol Myers Squibb, Pfizer, Blackstone Life Sciences, and Invitae. Dr Anderson receives sponsored research support from Bayer AG and Massachusetts General Hospital and has consulted for ApoPharma. Dr Weng receives sponsored research support from IBM to the Broad Institute. Dr Ho has received sponsored research support from Bayer AG and research supplies from EcoNugenics, Inc. Dr Ellinor has received sponsored research support from Bayer AG and IBM Health, and he has consulted for Bayer AG, Novartis and MyoKardia. Drs Batra, Reeder, and Friedman have received sponsored research support from Bayer AG and IBM Health, and Dr Batra has consulted for Novartis and Prometheus Biosciences. The other authors report no conflicts.

Speciation rates vary substantially across the tree of life. These rates should be linked to the rate at which population structure forms if a continuum between micro and macroevolutionary patterns exists. Previous studies examining the link between speciation rates and the degree of population formation in clades have been shown to be either correlated or uncorrelated depending on the group, but no study has yet examined the relationship between speciation rates and population structure in a young group that is constrained spatially to a single-island system. We examine this correlation in 109 gemsnakes (Pseudoxyrhophiidae) endemic to Madagascar and originating in the early Miocene, which helps control for extinction variation across time and space. We find no relationship between rates of speciation and the formation rates of population structure over space in 33 species of gemsnakes. Rates of speciation show low variation, yet population structure varies widely across species, indicating that speciation rates and population structure are disconnected. We suspect this is largely due to the persistence of some lineages not susceptible to extinction. Importantly, we discuss how delimiting populations versus species may contribute to problems understanding the continuum between shallow and deep evolutionary processes., Competing Interests: We have no competing interests., (© 2023 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.)

Populations of phytotelmic frogs from northern Madagascar assigned to Guibemantis (Pandanusicola) pulcher are known to differ genetically from populations further south in the eastern rainforest belt of the island, but to date, their status has not been analyzed in depth. We combined molecular genetic data with an examination of color pattern to clarify the taxonomy of these frogs. DNA sequences of both mitochondrial and nuclear-encoded genes were consistently differentiated between the northern populations and those occurring further south. Uncorrected pairwise distance in the 16S rRNA gene was 3.7‒4.3% and thus at a level usually characterizing distinct frog species in Madagascar. Furthermore, the northern specimens were characterized by more and smaller purplish-brown spots on their green dorsal surface, and a less distinct brown patch on the flanks. Although fully conclusive evidence for the species status of the northern lineage from bioacoustic differences, sympatric occurrence or narrow hybrid zone is currently lacking, such species-level distinctness is currently the most likely hypothesis. We therefore name the northern populations as Guibemantis (Pandanusicola) pulcherrimus sp. nov. The new species is known from Makira (type locality) and Bemanevika, and specimens morphologically assignable to this taxon have also been recorded from Masoala, Marojejy and Anjanaharibe-Sud.

Myocardial interstitial fibrosis is associated with cardiovascular disease and adverse prognosis. Here, to investigate the biological pathways that underlie fibrosis in the human heart, we developed a machine learning model to measure native myocardial T1 time, a marker of myocardial fibrosis, in 41,505 UK Biobank participants who underwent cardiac magnetic resonance imaging. Greater T1 time was associated with diabetes mellitus, renal disease, aortic stenosis, cardiomyopathy, heart failure, atrial fibrillation, conduction disease and rheumatoid arthritis. Genome-wide association analysis identified 11 independent loci associated with T1 time. The identified loci implicated genes involved in glucose transport (SLC2A12), iron homeostasis (HFE, TMPRSS6), tissue repair (ADAMTSL1, VEGFC), oxidative stress (SOD2), cardiac hypertrophy (MYH7B) and calcium signaling (CAMK2D). Using a transforming growth factor β1-mediated cardiac fibroblast activation assay, we found that 9 of the 11 loci consisted of genes that exhibited temporal changes in expression or open chromatin conformation supporting their biological relevance to myofibroblast cell state acquisition. By harnessing machine learning to perform large-scale quantification of myocardial interstitial fibrosis using cardiac imaging, we validate associations between cardiac fibrosis and disease, and identify new biologically relevant pathways underlying fibrosis., (© 2023. The Author(s), under exclusive licence to Springer Nature America, Inc.)

A fundamental assumption of evolutionary biology is that phylogeny follows a bifurcating process. However, hybrid speciation and introgression are becoming more widely documented in many groups. Hybrid inference studies have been historically limited to small sets of taxa, while exploration of the prevalence and trends of reticulation at deep time scales remains unexplored. We study the evolutionary history of an adaptive radiation of 109 gemsnakes in Madagascar (Pseudoxyrhophiinae) to identify potential instances of introgression. Using several network inference methods, we find 12 reticulation events within the 22-million-year evolutionary history of gemsnakes, producing 28% of the diversity for the group, including one reticulation that resulted in the diversification of an 18 species radiation. These reticulations are found at nodes with high gene tree discordance and occurred among parental lineages distributed along a north-south axis that share similar ecologies. Younger hybrids occupy intermediate contact zones between the parent lineages showing that post-speciation dispersal in this group has not eroded the spatial signatures of introgression. Reticulations accumulated consistently over time, despite drops in overall speciation rates during the Pleistocene. This suggests that while bifurcating speciation rates may decline as the result of species accumulation and environmental change, speciation by hybridization may be more robust to these processes., (© The Author(s) 2023. Published by Oxford University Press on behalf of The Society for the Study of Evolution (SSE). All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

How environmental factors shape patterns of biotic diversity in tropical ecosystems is an active field of research, but studies examining the possibility of ecological speciation in terrestrial tropical ecosystems are scarce. We use the isolated rainforest herpetofauna on the Montagne d'Ambre (Amber Mountain) massif in northern Madagascar as a model to explore elevational divergence at the level of populations and communities. Based on intensive sampling and DNA barcoding of amphibians and reptiles along a transect ranging from ca. 470-1470 m above sea level (a.s.l.), we assessed a main peak in species richness at an elevation of ca. 1000 m a.s.l. with 41 species. The proportion of local endemics was highest (about 1/3) at elevations >1100 m a.s.l. Two species of chameleons ( Brookesia tuberculata, Calumma linotum ) and two species of frogs ( Mantidactylus bellyi, M. ambony ) studied in depth by newly developed microsatellite markers showed genetic divergence up the slope of the mountain, some quite strong, others very weak, but in each case with genetic breaks between 1100 and 1270 m a.s.l. Genetic clusters were found in transect sections significantly differing in bioclimate and herpetological community composition. A decrease in body size was detected in several species with increasing elevation. The studied rainforest amphibians and reptiles show concordant population genetic differentiation across elevation along with morphological and niche differentiation. Whether this parapatric or microallopatric differentiation will suffice for the completion of speciation is, however, unclear, and available phylogeographic evidence rather suggests that a complex interplay between ecological and allopatric divergence processes is involved in generating the extraordinary species diversity of Madagascar's biota. Our study reveals concordant patterns of diversification among main elevational bands, but suggests that these adaptational processes are only part of the complex of processes leading to species formation, among which geographical isolation is probably also important., Competing Interests: The authors declare no conflicts of interest., (© 2023 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.)

Background: Differentiating among cardiac diseases associated with left ventricular hypertrophy (LVH) informs diagnosis and clinical care., Objective: To evaluate if artificial intelligence-enabled analysis of the 12-lead electrocardiogram (ECG) facilitates automated detection and classification of LVH., Methods: We used a pretrained convolutional neural network to derive numerical representations of 12-lead ECG waveforms from patients in a multi-institutional healthcare system who had cardiac diseases associated with LVH (n = 50,709), including cardiac amyloidosis (n = 304), hypertrophic cardiomyopathy (n = 1056), hypertension (n = 20,802), aortic stenosis (n = 446), and other causes (n = 4766). We then regressed LVH etiologies relative to no LVH on age, sex, and the numerical 12-lead representations using logistic regression ("LVH-Net"). To assess deep learning model performance on single-lead data analogous to mobile ECGs, we also developed 2 single-lead deep learning models by training models on lead I ("LVH-Net Lead I") or lead II ("LVH-Net Lead II") from the 12-lead ECG. We compared the performance of the LVH-Net models to alternative models fit on (1) age, sex, and standard ECG measures, and (2) clinical ECG-based rules for diagnosing LVH., Results: The areas under the receiver operator characteristic curve of LVH-Net by specific LVH etiology were cardiac amyloidosis 0.95 [95% CI, 0.93-0.97], hypertrophic cardiomyopathy 0.92 [95% CI, 0.90-0.94], aortic stenosis LVH 0.90 [95% CI, 0.88-0.92], hypertensive LVH 0.76 [95% CI, 0.76-0.77], and other LVH 0.69 [95% CI 0.68-0.71]. The single-lead models also discriminated LVH etiologies well., Conclusion: An artificial intelligence-enabled ECG model is favorable for detection and classification of LVH and outperforms clinical ECG-based rules., (© 2023 Pub-.)

The highly diverse snake superfamily Elapoidea is considered to be a classic example of ancient, rapid radiation. Such radiations are challenging to fully resolve phylogenetically, with the highly diverse Elapoidea a case in point. Previous attempts at inferring a phylogeny of elapoids produced highly incongruent estimates of their evolutionary relationships, often with very low statistical support. We sought to resolve this situation by sequencing over 4,500 ultraconserved element loci from multiple representatives of every elapoid family/subfamily level taxon and inferring their phylogenetic relationships with multiple methods. Concatenation and multispecies coalescent based species trees yielded largely congruent and well-supported topologies. Hypotheses of a hard polytomy were not retained for any deep branches. Our phylogenies recovered Cyclocoridae and Elapidae as diverging early within Elapoidea. The Afro-Malagasy radiation of elapoid snakes, classified as multiple subfamilies of an inclusive Lamprophiidae by some earlier authors, was found to be monophyletic in all analyses. The genus Micrelaps was consistently recovered as sister to Lamprophiidae. We establish a new family, Micrelapidae fam. nov., for Micrelaps and assign Brachyophis to this family based on cranial osteological synapomorphy. We estimate that Elapoidea originated in the early Eocene and rapidly diversified into all the major lineages during this epoch. Ecological opportunities presented by the post-Cretaceous-Paleogene mass extinction event may have promoted the explosive radiation of elapoid snakes., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Absence of a dicrotic notch on finger photoplethysmography is an easily ascertainable and inexpensive trait that has been associated with age and prevalent cardiovascular disease. However, the trait exists along a continuum, and little is known about its genetic underpinnings or prognostic value for incident cardiovascular disease., Methods: In 169 787 participants in the UK Biobank, we identified absent dicrotic notch on photoplethysmography and created a novel continuous trait reflecting notch smoothness using machine learning. Next, we determined the heritability, genetic basis, polygenic risk, and clinical relations for the binary absent notch trait and the newly derived continuous notch smoothness trait., Results: Heritability of the continuous notch smoothness trait was 7.5%, compared with 5.6% for the binary absent notch trait. A genome-wide association study of notch smoothness identified 15 significant loci, implicating genes including NT5C2 ( P =1.2×10 -26 ), IGFBP3 ( P =4.8×10 -18 ), and PHACTR1 ( P =1.4×10 -13 ), compared with 6 loci for the binary absent notch trait. Notch smoothness stratified risk of incident myocardial infarction or coronary artery disease, stroke, heart failure, and aortic stenosis. A polygenic risk score for notch smoothness was associated with incident cardiovascular disease and all-cause death in UK Biobank participants without available photoplethysmography data., Conclusions: We found that a machine learning derived continuous trait reflecting dicrotic notch smoothness on photoplethysmography was heritable and associated with genes involved in vascular stiffness. Greater notch smoothness was associated with greater risk of incident cardiovascular disease. Raw digital phenotyping may identify individuals at risk for disease via specific genetic pathways.

Background: The left ventricular outflow tract (LVOT) and ascending aorta are spatially complex, with distinct pathologies and embryologic origins. Prior work examined the genetics of thoracic aortic diameter in a single plane., Objectives: We sought to elucidate the genetic basis for the diameter of the LVOT, aortic root, and ascending aorta., Methods: Using deep learning, we analyzed 2.3 million cardiac magnetic resonance images from 43,317 UK Biobank participants. We computed the diameters of the LVOT, the aortic root, and at 6 locations of ascending aorta. For each diameter, we conducted a genome-wide association study and generated a polygenic score. Finally, we investigated associations between these scores and disease incidence., Results: A total of 79 loci were significantly associated with at least 1 diameter. Of these, 35 were novel, and most were associated with 1 or 2 diameters. A polygenic score of aortic diameter approximately 13 mm from the sinotubular junction most strongly predicted thoracic aortic aneurysm (n = 427,016; mean HR: 1.42 per SD; 95% CI: 1.34-1.50; P = 6.67 × 10 -21 ). A polygenic score predicting a smaller aortic root was predictive of aortic stenosis (n = 426,502; mean HR: 1.08 per SD; 95% CI: 1.03-1.12; P = 5 × 10 -6 )., Conclusions: We detected distinct genetic loci underpinning the diameters of the LVOT, aortic root, and at several segments of ascending aorta. We spatially defined a region of aorta whose genetics may be most relevant to predicting thoracic aortic aneurysm. We further described a genetic signature that may predispose to aortic stenosis. Understanding genetic contributions to proximal aortic diameter may enable identification of individuals at risk for aortic disease and facilitate prioritization of therapeutic targets., Competing Interests: Funding Support and Author Disclosures This work was supported by the Fondation Leducq (14CVD01), and by grants from the National Institutes of Health (NIH) to Dr Pirruccello (K08HL159346), Dr Ellinor (1RO1HL092577, K24HL105780), and Dr Ho (R01HL134893, R01HL140224, K24HL153669). This work was also supported by a Sarnoff Cardiovascular Research Foundation Scholar Award to Dr Pirruccello. Dr Nauffal is supported by NIH grant 5T32HL007604-35. Dr Lubitz is supported by NIH grant R01HL139731 and American Heart Association 18SFRN34250007. This work was supported by a grant from the American Heart Association Strategically Focused Research Networks to Dr Ellinor. Dr Lindsay is supported by the Fredman Fellowship for Aortic Disease and the Toomey Fund for Aortic Dissection Research. This work was funded by a collaboration between the Broad Institute and IBM Research. Dr Pirruccello has served as a consultant for Maze Therapeutics. Dr Lubitz receives sponsored research support from Bristol Myers Squibb/Pfizer, Bayer AG, Boehringer Ingelheim, and Fitbit; has consulted for Bristol Myers Squibb/Pfizer and Bayer AG; and participates in a research collaboration with IBM. Dr Ng is employed by IBM Research. Dr Batra receives sponsored research support from Bayer AG and IBM; and has consulted for Novartis and Prometheus Biosciences. Dr Ho has received research grant support from Bayer AG focused on machine learning and cardiovascular disease; and has received research supplies from EcoNugenics. Dr Philippakis is supported by a grant from Bayer AG to the Broad Institute focused on machine learning for clinical trial design. Dr Ellinor has received sponsored research support from Bayer AG and from IBM Research; and has served on advisory boards or consulted for Bayer AG, MyoKardia, and Novartis. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2022 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Background and Objective: Postexercise heart rate recovery (HRR) is an important indicator of cardiac autonomic function and abnormal HRR is associated with adverse outcomes. We hypothesized that deep learning on resting electrocardiogram (ECG) tracings may identify individuals with impaired HRR., Methods: We trained a deep learning model (convolutional neural network) to infer HRR based on resting ECG waveforms (HRR pred ) among UK Biobank participants who had undergone exercise testing. We examined the association of HRR pred with incident cardiovascular disease using Cox models, and investigated the genetic architecture of HRR pred in genome-wide association analysis., Results: Among 56,793 individuals (mean age 57 years, 51% women), the HRR pred model was moderately correlated with actual HRR (r = 0.48, 95% confidence interval [CI] 0.47-0.48). Over a median follow-up of 10 years, we observed 2060 incident diabetes mellitus (DM) events, 862 heart failure events, and 2065 deaths. Higher HRR pred was associated with lower risk of DM (hazard ratio [HR] 0.79 per 1 standard deviation change, 95% CI 0.76-0.83), heart failure (HR 0.89, 95% CI 0.83-0.95), and death (HR 0.83, 95% CI 0.79-0.86). After accounting for resting heart rate, the association of HRR pred with incident DM and all-cause mortality were similar. Genetic determinants of HRR pred included known heart rate, cardiac conduction system, cardiomyopathy, and metabolic trait loci., Conclusion: Deep learning-derived estimates of HRR using resting ECG independently associated with future clinical outcomes, including new-onset DM and all-cause mortality. Inferring postexercise heart rate response from a resting ECG may have potential clinical implications and impact on preventive strategies warrants future study., (© 2022 Heart Rhythm Society.)

Congenital heart diseases often involve maldevelopment of the evolutionarily recent right heart chamber. To gain insight into right heart structure and function, we fine-tuned deep learning models to recognize the right atrium, right ventricle and pulmonary artery, measuring right heart structures in 40,000 individuals from the UK Biobank with magnetic resonance imaging. Genome-wide association studies identified 130 distinct loci associated with at least one right heart measurement, of which 72 were not associated with left heart structures. Loci were found near genes previously linked with congenital heart disease, including NKX2-5, TBX5/TBX3, WNT9B and GATA4. A genome-wide polygenic predictor of right ventricular ejection fraction was associated with incident dilated cardiomyopathy (hazard ratio, 1.33 per standard deviation; P = 7.1 × 10 -13 ) and remained significant after accounting for a left ventricular polygenic score. Harnessing deep learning to perform large-scale cardiac phenotyping, our results yield insights into the genetic determinants of right heart structure and function., (© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Electronic health record (EHR) datasets are statistically powerful but are subject to ascertainment bias and missingness. Using the Mass General Brigham multi-institutional EHR, we approximated a community-based cohort by sampling patients receiving longitudinal primary care between 2001-2018 (Community Care Cohort Project [C3PO], n = 520,868). We utilized natural language processing (NLP) to recover vital signs from unstructured notes. We assessed the validity of C3PO by deploying established risk models for myocardial infarction/stroke and atrial fibrillation. We then compared C3PO to Convenience Samples including all individuals from the same EHR with complete data, but without a longitudinal primary care requirement. NLP reduced the missingness of vital signs by 31%. NLP-recovered vital signs were highly correlated with values derived from structured fields (Pearson r range 0.95-0.99). Atrial fibrillation and myocardial infarction/stroke incidence were lower and risk models were better calibrated in C3PO as opposed to the Convenience Samples (calibration error range for myocardial infarction/stroke: 0.012-0.030 in C3PO vs. 0.028-0.046 in Convenience Samples; calibration error for atrial fibrillation 0.028 in C3PO vs. 0.036 in Convenience Samples). Sampling patients receiving regular primary care and using NLP to recover missing data may reduce bias and maximize generalizability of EHR research., (© 2022. The Author(s).)

In the processes that give rise to new species, changes first occur at the population level. But with the continuous nature of the divergence process, change in biological properties delimiting the shift from "individuals of divergent populations" towards "individuals of distinct species", as well as abiotic factors driving the change, remain largely ambivalent. Here we study diversification processes at the population level in a semi-aquatic frog, Mantidactylus (Brygoomantis) bellyi, across the diverse vegetation types of Montagne d'Ambre National Park (MANP), Madagascar. Genetic diversity was assessed with seven newly developed microsatellite markers as well as mitochondrial DNA sequences and concordance with patterns of ecological, morphological, and bioacoustic divergence evaluated. We found M. bellyi lacking mitochondrial differentiation within MANP, while microsatellite datasets partitioned them into three highly differentiated, geographically separated subpopulations (with indications for up to five subpopulations). The molecular grouping-primarily clustering individuals by geographic proximity-was coincident with differences in mean depth and width of waters, suggesting a possible role of fluvial characteristics in genetic exchange in this stream-breeding species. Genetic clustering not consistent with differences in call properties, except for dominant call frequencies under the two-subpopulations model. Morphological divergence was mostly consistent with the genetic clustering; subpopulations strongly differed by their snout-vent length, with individuals from high-elevation subpopulations smaller than those from populations below 1000 m above sea level. These results exemplify how mountains and environmental conditions might primarily shape genetic and morphological divergence in frog populations, without strongly affecting their calls., Competing Interests: The authors have declared that no competing interests exist.

Background: Artificial intelligence (AI)-enabled analysis of 12-lead ECGs may facilitate efficient estimation of incident atrial fibrillation (AF) risk. However, it remains unclear whether AI provides meaningful and generalizable improvement in predictive accuracy beyond clinical risk factors for AF., Methods: We trained a convolutional neural network (ECG-AI) to infer 5-year incident AF risk using 12-lead ECGs in patients receiving longitudinal primary care at Massachusetts General Hospital (MGH). We then fit 3 Cox proportional hazards models, composed of ECG-AI 5-year AF probability, CHARGE-AF clinical risk score (Cohorts for Heart and Aging in Genomic Epidemiology-Atrial Fibrillation), and terms for both ECG-AI and CHARGE-AF (CH-AI), respectively. We assessed model performance by calculating discrimination (area under the receiver operating characteristic curve) and calibration in an internal test set and 2 external test sets (Brigham and Women's Hospital [BWH] and UK Biobank). Models were recalibrated to estimate 2-year AF risk in the UK Biobank given limited available follow-up. We used saliency mapping to identify ECG features most influential on ECG-AI risk predictions and assessed correlation between ECG-AI and CHARGE-AF linear predictors., Results: The training set comprised 45 770 individuals (age 55±17 years, 53% women, 2171 AF events) and the test sets comprised 83 162 individuals (age 59±13 years, 56% women, 2424 AF events). Area under the receiver operating characteristic curve was comparable using CHARGE-AF (MGH, 0.802 [95% CI, 0.767-0.836]; BWH, 0.752 [95% CI, 0.741-0.763]; UK Biobank, 0.732 [95% CI, 0.704-0.759]) and ECG-AI (MGH, 0.823 [95% CI, 0.790-0.856]; BWH, 0.747 [95% CI, 0.736-0.759]; UK Biobank, 0.705 [95% CI, 0.673-0.737]). Area under the receiver operating characteristic curve was highest using CH-AI (MGH, 0.838 [95% CI, 0.807 to 0.869]; BWH, 0.777 [95% CI, 0.766 to 0.788]; UK Biobank, 0.746 [95% CI, 0.716 to 0.776]). Calibration error was low using ECG-AI (MGH, 0.0212; BWH, 0.0129; UK Biobank, 0.0035) and CH-AI (MGH, 0.012; BWH, 0.0108; UK Biobank, 0.0001). In saliency analyses, the ECG P-wave had the greatest influence on AI model predictions. ECG-AI and CHARGE-AF linear predictors were correlated (Pearson r : MGH, 0.61; BWH, 0.66; UK Biobank, 0.41)., Conclusions: AI-based analysis of 12-lead ECGs has similar predictive usefulness to a clinical risk factor model for incident AF and the approaches are complementary. ECG-AI may enable efficient quantification of future AF risk.

Background: Classical methods for detecting left ventricular (LV) hypertrophy (LVH) using 12-lead ECGs are insensitive. Deep learning models using ECG to infer cardiac magnetic resonance (CMR)-derived LV mass may improve LVH detection., Methods: Within 32 239 individuals of the UK Biobank prospective cohort who underwent CMR and 12-lead ECG, we trained a convolutional neural network to predict CMR-derived LV mass using 12-lead ECGs (left ventricular mass-artificial intelligence [LVM-AI]). In independent test sets (UK Biobank [n=4903] and Mass General Brigham [MGB, n=1371]), we assessed correlation between LVM-AI predicted and CMR-derived LV mass and compared LVH discrimination using LVM-AI versus traditional ECG-based rules (ie, Sokolow-Lyon, Cornell, lead aVL rule, or any ECG rule). In the UK Biobank and an ambulatory MGB cohort (MGB outcomes, n=28 612), we assessed associations between LVM-AI predicted LVH and incident cardiovascular outcomes using age- and sex-adjusted Cox regression., Results: LVM-AI predicted LV mass correlated with CMR-derived LV mass in both test sets, although correlation was greater in the UK Biobank (r=0.79) versus MGB (r=0.60, P<0.001 for both). When compared with any ECG rule, LVM-AI demonstrated similar LVH discrimination in the UK Biobank (LVM-AI c-statistic 0.653 [95% CI, 0.608 -0.698] versus any ECG rule c-statistic 0.618 [95% CI, 0.574 -0.663], P=0.11) and superior discrimination in MGB (0.621; 95% CI, 0.592 -0.649 versus 0.588; 95% CI, 0.564 -0.611, P=0.02). LVM-AI-predicted LVH was associated with incident atrial fibrillation, myocardial infarction, heart failure, and ventricular arrhythmias., Conclusions: Deep learning-inferred LV mass estimates from 12-lead ECGs correlate with CMR-derived LV mass, associate with incident cardiovascular disease, and may improve LVH discrimination compared to traditional ECG rules.

In this work, we describe the CRIMSON (CardiovasculaR Integrated Modelling and SimulatiON) software environment. CRIMSON provides a powerful, customizable and user-friendly system for performing three-dimensional and reduced-order computational haemodynamics studies via a pipeline which involves: 1) segmenting vascular structures from medical images; 2) constructing analytic arterial and venous geometric models; 3) performing finite element mesh generation; 4) designing, and 5) applying boundary conditions; 6) running incompressible Navier-Stokes simulations of blood flow with fluid-structure interaction capabilities; and 7) post-processing and visualizing the results, including velocity, pressure and wall shear stress fields. A key aim of CRIMSON is to create a software environment that makes powerful computational haemodynamics tools accessible to a wide audience, including clinicians and students, both within our research laboratories and throughout the community. The overall philosophy is to leverage best-in-class open source standards for medical image processing, parallel flow computation, geometric solid modelling, data assimilation, and mesh generation. It is actively used by researchers in Europe, North and South America, Asia, and Australia. It has been applied to numerous clinical problems; we illustrate applications of CRIMSON to real-world problems using examples ranging from pre-operative surgical planning to medical device design optimization., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: CAF and CA are Members of CRIMSON Technologies LLC, a company established to support the future development of CRIMSON.

Background: Cardiac magnetic resonance (CMR) is the gold standard for left ventricular hypertrophy (LVH) diagnosis. CMR-derived LV mass can be estimated using proprietary algorithms (eg, InlineVF), but their accuracy and availability may be limited., Objective: To develop an open-source deep learning model to estimate CMR-derived LV mass., Methods: Within participants of the UK Biobank prospective cohort undergoing CMR, we trained 2 convolutional neural networks to estimate LV mass. The first (ML4H reg ) performed regression informed by manually labeled LV mass (available in 5065 individuals), while the second (ML4H seg ) performed LV segmentation informed by InlineVF (version D13A) contours. We compared ML4H reg , ML4H seg , and InlineVF against manually labeled LV mass within an independent holdout set using Pearson correlation and mean absolute error (MAE). We assessed associations between CMR-derived LVH and prevalent cardiovascular disease using logistic regression adjusted for age and sex., Results: We generated CMR-derived LV mass estimates within 38,574 individuals. Among 891 individuals in the holdout set, ML4H seg reproduced manually labeled LV mass more accurately (r = 0.864, 95% confidence interval [CI] 0.847-0.880; MAE 10.41 g, 95% CI 9.82-10.99) than ML4H reg (r = 0.843, 95% CI 0.823-0.861; MAE 10.51, 95% CI 9.86-11.15, P = .01) and InlineVF (r = 0.795, 95% CI 0.770-0.818; MAE 14.30, 95% CI 13.46-11.01, P < .01). LVH defined using ML4H seg demonstrated the strongest associations with hypertension (odds ratio 2.76, 95% CI 2.51-3.04), atrial fibrillation (1.75, 95% CI 1.37-2.20), and heart failure (4.67, 95% CI 3.28-6.49)., Conclusions: ML4H seg is an open-source deep learning model providing automated quantification of CMR-derived LV mass. Deep learning models characterizing cardiac structure may facilitate broad cardiovascular discovery., (© 2021 Heart Rhythm Society.)

The genus Scaphiophryne (Anura: Microhylidae) contains at least nine species that, together with their probable sister genus Paradoxophyla, form the Madagascar-endemic subfamily Scaphiophryninae. Scaphiophryne are robust burrowing frogs with explosive breeding behavior and are characterized by a unique larval morphology, being intermediate between that of filter-feeding and generalized tadpole types. Based on tadpole morphology, the nominal subgenus Scaphiophryne can be distinguished from the subgenus Pseudohemisus, a clade occurring in arid biomes with a largely unsolved taxonomy and nomenclature. Here we combine a comprehensive molecular phylogeny of all scaphiophrynines based on five mitochondrial and two nuclear genes with a re-examination of the name-bearing type specimens of the seven species-level nomina assignable to the subgenus Pseudohemisus. Our molecular phylogeny supports monophyly of the two subgenera and assigns the enigmatic S. matsoko to the subgenus Scaphiophryne. In this subgenus, our analysis confirms all currently accepted species as independent evolutionary lineages based on concordant differentiation in mitochondrial and nuclear genes, and provides evidence for a combination of low mitochondrial divergence, distinct nuclear divergence, and high nuclear heterozygosity. Yet, contrary to previous hypotheses, we do not find evidence of ongoing hybridization. In the subgenus Pseudohemisus we find support for three clearly delimited species-level lineages, one of which contains two deep conspecific lineages that warrant further study. To stabilize the taxonomy in this subgenus, and considering the very poor state of preservation of several type series and the heterogeneity of some of them, we here designate lectotypes for the nomina: Hemisus obscurus Grandidier, 1872; Calophrynus calcaratus Mocquard, 1895; Pseudohemisus verrucosus Angel, 1930; Pseudohemisus longimanus Angel, 1930; and Pseudohemisus longimanus var. melanopleura Angel, 1934. Taxonomically, we accept Scaphiophryne brevis (Boulenger, 1896) as a well-defined species; restrict S. calcarata (Mocquard, 1995) to the southern lineage of the S. calcarata complex; resurrect Hemisus obscurus Grandidier, 1872 (previously considered to be a dubious name or nomen inquirendum) as Scaphiophryne obscura to refer to the western and north-western lineage of the S. calcarata complex; consider Pseudohemisus verrucosus Angel, 1930 as junior synonym of Scaphiophryne brevis; and consider Pseudohemisus longimanus Angel, 1930 and Pseudohemisus longimanus var. melanopleura Angel, 1934 as junior synonyms of Scaphiophryne obscura. Finally, to accelerate taxonomic progress, we (i) outline a set of criteria by which such old and ambiguous nomina can be applied to known lineages, (ii) suggest that the International Code of Zoological Nomenclature should slightly broaden the conditions under which neotypes are to be designated, and (iii) propose to redouble discussions on the introduction of the concept of the epitype to zoology.

The northern part of Madagascar is well known for its high species diversity and endemism. Exceptional species richness is related to the existence of large forest blocks and mountain complexes. These areas shelter a diverse variety of habitats occupied by a wide diversity of species, including leaf-tailed geckos of the genus Uroplatus. Based on morphological and molecular evidence, we here formally name two evolutionary lineages as new species that previously had been considered as candidate species (Uroplatus spp. Ca3 and Ca4), both small-sized species of the Uroplatus ebenaui group. Genetically, both new species are related to U. finiavana with a genetic divergence (uncorrected pairwise distance) of 10.3-12.8% in a fragment of the mitochondrial 16S rRNA gene, and separated from each other by 10.3-10.7%. Uroplatus fangorn sp. nov. is described from the Marojejy massif and is also known from Andrevorevo, Lohanandroranga and Sorata; it is similar to U. ebenaui and U. fetsy but is distinguishable by its rather short tail and only partially black pigmented oral mucosa. Uroplatus fivehy sp. nov. is described from the Sorata Massif and is wider-ranging, occurring in an area from Marotandrano and Makira to Sorata, comprising Marojejy, Anjanaharibe-Sud, Ankitsika and Betaolana; it is morphologically similar to U. finiavana but distinguishable by tail size and shape. Both newly described species are found in rainforest from mid to high elevation and range respectively from 840-1417 m for U. fivehy sp nov. and 1300-1800 m for U. fangorn sp. nov. According to their respective geographical distributions, we propose to classify the two new species as Vulnerable under the IUCN Red List criteria, due to their occurrence in some forests outside of the protected area network plus the continuing decline of forest patches in the north of Madagascar.

Taxonomic progress is often hindered by intrinsic factors, such as morphologically cryptic species that require a broad suite of methods to distinguish, and extrinsic factors, such as uncertainties in the allocation of scientific names to species. These uncertainties can be due to a wide variety of factors, including old and poorly preserved type specimens (which contain only heavily degraded DNA or have lost important diagnostic characters), inappropriately chosen type specimens (e.g. juveniles without diagnostic characters) or poorly documented type specimens (with unprecise, incorrect, or missing locality data). Thanks to modern sequencing technologies it is now possible to overcome many such extrinsic factors by sequencing DNA from name-bearing type specimens of uncertain assignment and assigning these to known genetic lineages. Here, we apply this approach to frogs of the Mantidactylus ambreensis complex, which was recently shown to consist of two genetic lineages supported by concordant differentiation in mitochondrial and nuclear genes. These lineages co-occur on the Montagne d'Ambre Massif in northern Madagascar but appear to have diverged in allopatry. We use a recently published bait set based on three mitochondrial markers from all known Malagasy frog lineages to capture DNA sequences from the 127-year-old holotype of Mantidactylus ambreensis Mocquard, 1895. With the obtained sequences we are able to assign the name M. ambreensis to the lowland lineage, which is rather widespread in the rainforests of northern Madagascar, leaving the microendemic high-elevation lineage on Montagne d'Ambre in north Madagascar in need of description. We describe this species as Mantidactylus ambony sp. nov., differing from M. ambreensis in call parameters and a smaller body size. Thus, using target enrichment to obtain DNA sequence data from this old specimen, we were able to resolve the extrinsic (nomenclatural) hindrances to taxonomic resolution of this complex. We discuss the broad-scale versatility of this 'barcode fishing' approach, which can draw on the enormous success of global DNA barcoding initiatives to quickly and efficiently assign type specimens to lineages.

Chronic infusion of angiotensin-II in atheroprone (ApoE -/- ) mice provides a reproducible model of dissection in the suprarenal abdominal aorta, often with a false lumen and intramural thrombus that thickens the wall. Such lesions exhibit complex morphologies, with different regions characterized by localized changes in wall composition, microstructure, and properties. We sought to quantify the multiaxial mechanical properties of murine dissecting aneurysm samples by combining in vitro extension-distension data with full-field multimodality measurements of wall strain and thickness to inform an inverse material characterization using the virtual fields method. A key advance is the use of a digital volume correlation approach that allows for characterization of properties not only along and around the lesion, but also across its wall. Specifically, deformations are measured at the adventitial surface by tracking motions of a speckle pattern using a custom panoramic digital image correlation technique while deformations throughout the wall and thrombus are inferred from optical coherence tomography. These measurements are registered and combined in 3D to reconstruct the reference geometry and compute the 3D finite strain fields in response to pressurization. Results reveal dramatic regional variations in material stiffness and strain energy, which reflect local changes in constituent area fractions obtained from histology but emphasize the complexity of lesion morphology and damage within the dissected wall. This is the first point-wise biomechanical characterization of such complex, heterogeneous arterial segments. Because matrix remodeling is critical to the formation and growth of these lesions, we submit that quantification of regional material properties will increase the understanding of pathological mechanical mechanisms underlying aortic dissection.

Madagascar's biota is characterized by a high degree of microendemism at different taxonomic levels, but how colonization and in-situ speciation contribute to the assembly of local species communities has rarely been studied on this island. Here we analyze the phylogenetic relationships of riparian frogs of the Mantidactylus ambreensis species complex, which is distributed in the north of Madagascar and was originally described from Montagne d'Ambre, an isolated mountain of volcanic origin, currently protected within Montagne d'Ambre National Park (MANP). Data from mitochondrial DNA, and phylogenomic data from FrogCap, a sequence capture method, independently confirm that this species complex is monophyletic within the subgenus Ochthomantis, and identify two main clades within it. These two clades are separated by 5.6-6.8% pairwise distance in the mitochondrial 16S rRNA gene and co-occur in MANP, with one distributed at high elevations (940-1375 m a.s.l.) and the other at lower elevations (535-1010 m a.s.l.), but show almost no haplotype sharing in the nuclear RAG1 gene. This occurrence in syntopy without admixture confirms them as independent evolutionary lineages that merit recognition as separate species, and we here refer to them as high-elevation (HE) and low-elevation (LE) lineage; they will warrant taxonomic assessment to confidently assign the name ambreensis to one or the other. Populations of the M. ambreensis complex from elsewhere in northern Madagascar all belong to the LE lineage, although they do occur over a larger elevational range than in Montagne d'Ambre (285-1040 m a.s.l.). Within LE there are several phylogroups (LE1-LE4) of moderately deep divergence (1.5-2.8% in 16S), but phylogroup LE4 that occurs in MANP has a deeply nested phylogenetic position, as recovered separately by mitochondrial and sequence capture datasets. This suggests that HE and LE did not diverge by a local fission of lower and upper populations, but instead arose through a more complex biogeographic scenario. The branching pattern of phylogroups LE1-LE4 shows a clear south-to-north phylogeographic pattern. We derive from these results a testable hypothesis of vicariant speciation that restricted the HE lineage to MANP and the LE candidate species to a climatic refugium further south, with subsequent northwards range expansion and secondary colonization of MANP by LE. These results provide an example for complex assembly of local microendemic amphibian faunas on Madagascar., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Computational models of the cardiovascular system and specifically heart function are currently being investigated as analytic tools to assist medical practice and clinical trials. To achieve clinical utility, models should be able to assimilate the diagnostic multi-modality data available for each patient and generate consistent representations of the underlying cardiovascular physiology. While finite element models of the heart can naturally account for patient-specific anatomies reconstructed from medical images, optimizing the many other parameters driving simulated cardiac functions is challenging due to computational complexity. With the goal of streamlining parameter adaptation, in this paper we present a novel, multifidelity strategy for model order reduction of 3-D finite element models of ventricular mechanics. Our approach is centered around well established findings on the similarity between contraction of an isolated muscle and the whole ventricle. Specifically, we demonstrate that simple linear transformations between sarcomere strain (tension) and ventricular volume (pressure) are sufficient to reproduce global pressure-volume outputs of 3-D finite element models even by a reduced model with just a single myocyte unit. We further develop a procedure for congruency training of a surrogate low-order model from multi-scale finite elements, and we construct an example of parameter optimization based on medical images. We discuss how the presented approach might be employed to process large datasets of medical images as well as databases of echocardiographic reports, paving the way towards application of heart mechanics models in the clinical practice., Competing Interests: PA, JP, JK and VG are employees of IBM. This does not alter our adherence to PLOS ONE policies on sharing data and materials. The cardiac mechanics models were implemented in the IBM Cardiac Modeling Platform. The IBM Cardiac Modeling Platform software is experimental. Readers are therefore encouraged to contact the authors if interested in using the software.

Processes leading to spectacular diversity of both form and species on islands have been well-documented under island biogeography theory, where distance from source and island size are key factors determining immigration and extinction resistance. But far less understood are the processes governing in situ diversification on the world's mega islands, where large and isolated land masses produced morphologically distinct radiations from related taxa on continental regions. Madagascar has long been recognized as a natural laboratory due to its isolation, lack of influence from adjacent continents, and diversification of spectacular vertebrate radiations. However, only a handful of studies have examined rate shifts of in situ diversification for this island. Here, we examine rates of diversification in the Malagasy snakes of the family Pseudoxyrhophiinae (gemsnakes) to understand if rates of speciation were initially high, enhanced by diversification into distinct biomes, and associated with key dentition traits. Using a genomic sequence-capture data set for 366 samples, we determine that all previously described and newly discovered species are delimitable and therefore useful candidates for understanding diversification trajectories through time. Our analysis detected no shifts in diversification rate between clades or changes in biome or dentition type. Remarkably, we demonstrate that rates of diversification of the gemsnake radiation, which originated in Madagascar during the early Miocene, remained steady throughout the Neogene. However, we do detect a significant slowdown in diversification during the Pleistocene. We also comment on the apparent paradox where most living species originated in the Pleistocene, despite diversification rates being substantially higher during the earlier 15 myr., (© The Author(s) 2019. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Previous studies on leaf-tailed geckos of the genus Uroplatus identified a lineage from the Ankarana karst massif in northern Madagascar as genetically highly divergent, but only fragmentary information was available on these geckos. Here, we provide an integrative analysis based on molecular and morphological data, including a newly collected specimen from this locality. Phylogenetic analysis placed the Ankarana lineage sister to U. ebenaui, but with a surprisingly high genetic divergence of over 19% uncorrected pairwise distance in the mitochondrial 16S rRNA gene. We formally describe the Ankarana lineage as Uroplatus fetsy sp. nov. and provide new information on the distribution and genetic differentiation of its sister species, Uroplatus ebenaui. Based on DNA sequences of newly examined samples this latter species is confirmed as widespread, ranging from low-elevation dry forests from Beanka in western Madagascar to Forêt d'Ambre and Analalava/Fanambana in northern Madagascar, and thus far has not been found in the Ankarana Massif, where U. fetsy occurs.

Multiscale computational models of the heart are being extensively investigated for improved assessment of drug-induced torsades de pointes (TdP) risk, a fatal side effect of many drugs. Model-derived metrics such as action potential duration and net charge carried by ionic currents ( qNet ) have been proposed as potential candidates for TdP risk stratification after being tested on small datasets. Unlike purely statistical approaches, model-derived metrics are thought to provide mechanism-based classification. In particular, qNet has been recently proposed as a surrogate metric for early afterdepolarizations (EADs), which are known to be cellular triggers of TdP. Analysis of critical model components and of the ion channels that have major impact on model-derived metrics can lead to improvements in the confidence of the prediction. In this paper, we analyze large populations of virtual drugs to systematically examine the influence of different ion channels on model-derived metrics that have been proposed for proarrhythmic risk assessment. We demonstrate via global sensitivity analysis (GSA) that model-derived metrics are most sensitive to different sets of input parameters. Similarly, important differences in sensitivity to specific channel blocks are highlighted when classifying drugs into different risk categories by either qNet or a metric directly based on simulated EADs. In particular, the higher sensitivity of qNet to the block of the late sodium channel might explain why its classification accuracy is better than that of the EAD-based metric, as shown for a small set of known drugs. Our results highlight the need for a better mechanistic interpretation of promising metrics like qNet based on a formal analysis of models. GSA should, therefore, constitute an essential component of the in silico workflow for proarrhythmic risk assessment, as an improved understanding of the structure of model-derived metrics could increase confidence in model-predicted risk., (Copyright © 2019 Parikh, Di Achille, Kozloski and Gurev.)

The Marojejy Massif in northern Madagascar is a constant source of herpetological surprises. Herein we describe a new species of leaf-mimicking leaf-tailed gecko, Uroplatus finaritra sp. nov., based on morphological and molecular phylogenetic evidence. This new species inhabits the rainforests of Marojejy National Park at low elevations and is morphologically similar to Uroplatus phantasticus, but differs by having a larger body size, relatively shorter tail, and dark-red pigmentation of the oral mucosa. Molecular data reveals a distance of 14.0-14.7% in the 16S rRNA gene to U. phantasticus, and haplotype sharing in the c-mos nuclear gene only with species of much shorter tails (i.e., with U. kelirambo and two undescribed candidate species). Similar to other members of the leaf-mimicking Uroplatus species in northern Madagascar, Uroplatus finaritra sp. nov. likely has a small geographic distribution, and is currently known only from the lowland rainforests of Marojejy. We discuss the value of the buccal mucosa colouration and tail dimensions of Uroplatus as taxonomic characters, and the potential drivers of their evolution. In order to reduce the risk of international trade under incorrect species names we suggest that exported CITES species should be obligatorily accompanied by information about their precide geographic origin within Madagascar.

Considerable insight into effectors of cardiovascular function can be gleaned from controlled studies on mice, especially given the diverse models that are available. Toward this end, however, there is a need for consistent and complementary methods of in vivo and in vitro data analysis, synthesis, and interpretation. The overall objective of this study is twofold. First, we present new semi-automated methods to quantify in vivo measurements of vascular function in anesthetized mice as well as new approaches to synthesize these data with those from in vitro biaxial mechanical characterizations. Second, we contrast regional differences in biomechanical behaviors along the central vasculature by combining biaxial strains measured in vivo with data on the unloaded geometry and biaxial material properties measured in vitro. Results support the hypothesis that the healthy ascending aorta stores significant elastic energy during systole, which is available to work on the heart and blood during diastole, particularly during periods of physical exertion, and further suggest that perivascular tethering allows arteries to work at lower values of wall stress and material stiffness than often assumed. The numerous measurements of vascular function and properties provided herein can also serve as reference values for normal wild-type male and female mice, to which values for myriad genetic, surgical, and pharmacological models can be compared in future studies., Competing Interests: The authors have declared that no competing interests exist.

Over 75% of abdominal aortic aneurysms harbor an intraluminal thrombus, and increasing evidence suggests that biologically active thrombus contributes to the natural history of these potentially lethal lesions. Thrombus formation depends on the local hemodynamics, which in turn depends on morphological features of the aneurysm and near vasculature. We previously presented a hemodynamically motivated "thrombus formation potential" that predicts where and when thrombus might form. Herein, we combine detailed studies of the three-dimensional hemodynamics with methods of sparse grid collocation and interpolation via kriging to examine roles of five key morphological features of aneurysms on thrombus formation: lesion diameter, axial position, length, curvature, and renal artery position. Computational simulations suggest that maximum diameter is a key determinant of thrombogenicity, but other morphological features modulate this dependence. More distally located lesions tend to have a higher thrombus formation potential and shorter lesions tend to have a higher potential than longer lesions, given the same aneurysmal dilatation. Finally, movement of vortical structures through the infrarenal aorta and lesion can significantly affect thrombogenicity. Formation of intraluminal thrombus within an evolving abdominal aortic aneurysm thus depends on coupled morphological features, not all intuitive, and computational simulations can be useful for predicting thrombogenesis.

Patient specific models of ventricular mechanics require the optimization of their many parameters under the uncertainties associated with imaging of cardiac function. We present a strategy to reduce the complexity of parametric searches for 3-D FE models of left ventricular contraction. The study employs automatic image segmentation and analysis of an image database to gain geometric features for several classes of patients. Statistical distributions of geometric parameters are then used to design parametric studies investigating the effects of: (1) passive material properties during ventricular filling, and (2) infarct geometry on ventricular contraction in patients after a heart attack. Gaussian Process regression is used in both cases to build statistical models trained on the results of biophysical FEM simulations. The first statistical model estimates unloaded configurations based on either the intraventricular pressure or the end-diastolic fiber strain. The technique provides an alternative to the standard fixed-point iteration algorithm, which is more computationally expensive when used to unload more than 10 ventricles. The second statistical model captures the effects of varying infarct geometries on cardiac output. For training, we designed high resolution models of non-transmural infarcts including refinements of the border zone around the lesion. This study is a first effort in developing a platform combining HPC models and machine learning to investigate cardiac function in heart failure patients with the goal of assisting clinical diagnostics.

Background: Few details are available on the consumption of ectoparasites, specifically bat flies (Diptera: Nycteribiidae and Streblidae), by their chiropteran hosts while grooming. Such details are important to document consumption rates of ectoparasites by their bat host provide details on the dynamics of host-parasite interactions. We present data on ectoparasite consumption rates for an endemic Malagasy fruit bat (Pteropodidae: Rousettus madagascariensis) occupying a cave day roost colony in northern Madagascar. Using quantified behavioral analyses, grooming and associated ingestion rates were measured from infrared videos taken in close proximity to day-roosting bats. The recorded individual bats could be visually identified to age (adult, juvenile) and sex (male, female), allowing analyses of the proportion of time these different classes allocated to consuming ectoparasites via auto-grooming (self) or allo-grooming (intraspecific) per 10 min video recording session. These figures could then be extrapolated to estimates of individual daily consumption rates., Results: Based on video recordings, adults spent significantly more time auto-grooming and allo-grooming than juveniles. The latter group was not observed consuming ectoparasites. Grooming rates and the average number of ectoparasites consumed per day did not differ between adult males and females. The mean extrapolated number consumed on a daily basis for individual adults was 37 ectoparasites. When these figures are overlaid on the estimated number of adult Rousettus occurring at the roost site during the dry season, the projected daily consumption rate was 57,905 ectoparasites., Conclusions: The details presented here represent the first quantified data on bat consumption rates of their ectoparasites, specifically dipterans. These results provide new insights in host-parasite predation dynamics. More research is needed to explore the mechanism zoonotic diseases isolated from bat flies might be transmitted to their bat hosts, specifically those pathogens that can be communicated via an oral route.

Aortic dissection is a pathology that manifests due to microstructural defects in the aortic wall. Blood enters the damaged wall through an intimal tear, thereby creating a so-called false lumen and exposing the blood to thrombogenic intramural constituents such as collagen. The natural history of this acute vascular injury thus depends, in part, on thrombus formation, maturation, and possible healing within the false lumen. A key question is: Why do some false lumens thrombose completely while others thrombose partially or little at all? An ability to predict the location and extent of thrombus in subjects with dissection could contribute significantly to clinical decision-making, including interventional design. We develop, for the first time, a data-driven particle-continuum model for thrombus formation in a murine model of aortic dissection. In the proposed model, we simulate a final-value problem in lieu of the original initial-value problem with significantly fewer particles that may grow in size upon activation, thus representing the local concentration of blood-borne species. Numerical results confirm that geometry and local hemodynamics play significant roles in the acute progression of thrombus. Despite geometrical differences between murine and human dissections, mouse models can provide considerable insight and have gained popularity owing to their reproducibility. Our results for three classes of geometrically different false lumens show that thrombus forms and extends to a greater extent in regions with lower bulk shear rates. Dense thrombi are less likely to form in high-shear zones and in the presence of strong vortices. The present data-driven study suggests that the proposed model is robust and can be employed to assess thrombus formation in human aortic dissections.

A multi-modality imaging-based modeling approach was used to study complex unsteady hemodynamics and lesion growth in a dissecting abdominal aortic aneurysm model. We combined in vivo ultrasound (geometry and flow) and in vitro optical coherence tomography(OCT) (geometry) to obtain the high resolution needed to construct detailed hemodynamic simulations over large portions of the murine vasculature, which include fine geometric complexities. We illustrate this approach for a spectrum of dissecting abdominal aortic aneurysms induced in male apolipoprotein E-null mice by high-dose angiotensin II infusion. In vivo morphological and hemodynamic data provide information on volumetric lesion growth and changes in blood flow dynamics, respectively, occurring from the day of initial aortic expansion. We validated the associated computational models by comparing results on time-varying outlet flows and vortical structures within the lesions. Three out of four lesions exhibited abrupt formation of thrombus, though different in size. We determined that a lesion without thrombus formed with a thickened vessel wall, which was resolvable by OCT and histology. We attribute differences in final sizes and compositions of these lesions to the different computed flow and vortical structures we obtained in our mouse-specific fluid dynamic models. Differences in morphology and hemodynamics play crucial roles in determining the evolution of dissecting abdominal aortic aneurysms. Coupled high resolution in vivo and in vitro imaging approaches provide much-improved geometric models for hemodynamic simulations. Our imaging-based computational findings suggest a link between perturbations in hemodynamic metrics and aneurysmal disease heterogeneity.

Accumulating evidence suggests that intraluminal thrombus plays many roles in the natural history of abdominal aortic aneurysms. There is, therefore, a pressing need for computational models that can describe and predict the initiation and progression of thrombus in aneurysms. In this paper, we introduce a phenomenological metric for thrombus deposition potential and use hemodynamic simulations based on medical images from 6 patients to identify best-fit values of the 2 key model parameters. We then introduce a shape optimization method to predict the associated radial growth of the thrombus into the lumen based on the expectation that thrombus initiation will create a thrombogenic surface, which in turn will promote growth until increasing hemodynamically induced frictional forces prevent any further cell or protein deposition. Comparisons between predicted and actual intraluminal thrombus in the 6 patient-specific aneurysms suggest that this phenomenological description provides a good first estimate of thrombus deposition. We submit further that, because the biologically active region of the thrombus appears to be confined to a thin luminal layer, predictions of morphology alone may be sufficient to inform fluid-solid-growth models of aneurysmal growth and remodeling., (Copyright © 2016 John Wiley & Sons, Ltd.)

Background: Granulocyte colony-stimulating factors (G-CSFs) are biological products for which the main indication of use is chemotherapy-induced neutropenia. Biosimilars of G-CSFs have been available in Europe since 2007., Objective: The objective of this study was to investigate the prescribing pattern of G-CSFs in five Italian centres using different healthcare policy interventions to promote the use of biosimilars in routine care., Methods: This retrospective, population-based drug utilization study was conducted during the years 2009-2014 using the administrative databases of the Caserta, Treviso and Palermo Local Health Units (LHUs) and the Tuscany and Umbria regions. G-CSF users were characterized and the prevalence of use, proportion of biosimilar users and switching pattern of different G-CSFs were evaluated over time and across centres., Results: Overall, 30,247 patients were treated with G-CSFs in the years 2009-2014, of which 29,083 (96.2 %) were naïve users. The overall prevalence of G-CSF use increased from 0.8 per 1000 inhabitants in 2009 to 1.1 per 1000 in 2014. An increase in the proportion of the use of the biosimilar filgrastim by the total G-CSF users was observed in all centres: from 0.2 % (2009) to 66.2 % (2014). However, heterogeneity across different centres was reported, with the largest increase in Treviso LHU (from 0 to 89.1 % from 2009 to 2014). During the first year of treatment, switching between different G-CSFs was frequent (20.3 %)., Conclusions: Heterogeneity in the use of G-CSF and, in particular, biosimilar filgrastim across different Italian centres was observed, probably due to different regional healthcare policy interventions. During the first year of treatment, switching between different G-CSFs was frequent. Considering the impact of biological drugs on pharmaceutical expenses, it is necessary to harmonize healthcare policies promoting the use of biological drugs with the lowest cost.

Many vascular disorders, including aortic aneurysms and dissections, are characterized by localized changes in wall composition and structure. Notwithstanding the importance of histopathologic changes that occur at the microstructural level, macroscopic manifestations ultimately dictate the mechanical functionality and structural integrity of the aortic wall. Understanding structure-function relationships locally is thus critical for gaining increased insight into conditions that render a vessel susceptible to disease or failure. Given the scarcity of human data, mouse models are increasingly useful in this regard. In this paper, we present a novel inverse characterization of regional, nonlinear, anisotropic properties of the murine aorta. Full-field biaxial data are collected using a panoramic-digital image correlation (p-DIC) system. An inverse method, based on the principle of virtual power (PVP), is used to estimate values of material parameters regionally for a microstructurally motivated constitutive relation. We validate our experimental-computational approach by comparing results to those from standard biaxial testing. The results for the nondiseased suprarenal abdominal aorta from apolipoprotein-E null mice reveal material heterogeneities, with significant differences between dorsal and ventral as well as between proximal and distal locations, which may arise in part due to differential perivascular support and localized branches. Overall results were validated for both a membrane and a thick-wall model that delineated medial and adventitial properties. Whereas full-field characterization can be useful in the study of normal arteries, we submit that it will be particularly useful for studying complex lesions such as aneurysms, which can now be pursued with confidence given the present validation.