bluuebunny/crossref_metadata_2025 huggingface dataset:
A Genome-Wide Association Study of Resistance toPuccinia striiformisf. sp.hordeiandP. graminisf. sp.triticiin Barley and Development of Resistant Germplasm
Authors: Hernandez Javier , del Blanco Alicia , Filichkin Tanya , Fisk Scott , Gallagher Lynn , Helgerson Laura , Meints Brigid , Mundt Chris , Steffenson Brian , Hayes Patrick
Published: February 2020

Stripe rust (incited by Puccinia striiformis f. sp. hordei) and stem rust (incited by P. graminis f. sp. tritici) are two of the most important diseases affecting barley. Building on prior work involving the introgression of the resistance genes rpg4/Rpg5 into diverse genetic backgrounds and the discovery of additional quantitative trait locus (QTLs) for stem rust resistance, we generated an array of germplasm in which we mapped resistance to stripe rust and stem rust. Stem rust races TTKSK and QCCJB were used for resistance mapping at the seedling and adult plant stages, respectively. Resistance to stripe rust, at the adult plant stage, was determined by QTLs on chromosomes 1H, 4H, and 5H that were previously reported in the literature. The rpg4/Rpg5 complex was validated as a source of resistance to stem rust at the seedling stage. Some parental germplasm, selected as potentially resistant to stem rust or susceptible but having other positive attributes, showed resistance at the seedling stage, which appears to be allelic to rpg4/Rpg5. The rpg4/Rpg5 complex, and this new allele, were not sufficient for adult plant resistance to stem rust in one environment. A QTL on 5H, distinct from Rpg5 and a previously reported resistance QTL, was required for resistance at the adult plant stage in all environments. This QTL is coincident with the QTL for stripe rust resistance. Germplasm with mapped genes/QTLs conferring resistance to stripe and stem rust was identified and is available as a resource to the research and breeding communities.
Machine Intelligence in Cardiovascular Medicine
Authors: Miller D. Douglas
Published: February 2020

The computer science technology trend called artificial intelligence (AI) is not new. Both machine learning and deep learning AI applications have recently begun to impact cardiovascular medicine. Scientists working in the AI domain have long recognized the importance of data quality and provenance to AI algorithm efficiency and accuracy. A diverse array of cardiovascular raw data sources of variable quality—electronic medical records, radiological picture archiving and communication systems, laboratory results, omics, etc.—are available to train AI algorithms for predictive modeling of clinical outcomes (in-hospital mortality, acute coronary syndrome risk stratification, etc.), accelerated image interpretation (edge detection, tissue characterization, etc.) and enhanced phenotyping of heterogeneous conditions (heart failure with preserved ejection fraction, hypertension, etc.). A number of software as medical device narrow AI products for cardiac arrhythmia characterization and advanced image deconvolution are now Food and Drug Administration approved, and many others are in the pipeline. Present and future health professionals using AI-infused analytics and wearable devices have 3 critical roles to play in their informed development and ethical application in practice: (1) medical domain experts providing clinical context to computer and data scientists, (2) data stewards assuring the quality, relevance and provenance of data inputs, and (3) real-time and post-hoc interpreters of AI black box solutions and recommendations to patients. The next wave of so-called contextual adaption AI technologies will more closely approximate human decision-making, potentially augmenting cardiologists’ real-time performance in emergency rooms, catheterization laboratories, imaging suites, and clinics. However, before such higher order AI technologies are adopted in the clinical setting and by healthcare systems, regulatory agencies, and industry must jointly develop robust AI standards of practice and transparent technology insertion rule sets.
Natal origin of Pacific bluefin tuna from the California Current Large Marine Ecosystem
Authors: Wells R. J. David , Mohan John A. , Dewar Heidi , Rooker Jay R. , Tanaka Yosuke , Snodgrass Owyn E. , Kohin Suzanne , Miller Nathan R. , Ohshimo Seiji
Published: February 2020

Natal origin of subadult (age-1) Pacific bluefin tuna (PBT,
Thunnus orientalis
) from the California Current Large Marine Ecosystem (CCLME) was determined using natural tracers in ear stones (otoliths). Age-0 PBT collected from the two known spawning areas in the western Pacific Ocean (East China Sea, Sea of Japan) were used to establish baseline signatures from otolith cores over 4 years (2014–2017) based on a suite of trace elements (Li, Mg, Mn, Sr, Zn and Ba). Distinct chemical signatures existed in the otolith cores of age-0 PBT collected from the two spawning areas, with overall classification accuracy ranging 73–93% by year. Subadult PBT collected in the CCLME over the following 4 years (2015–2018) were then age-class matched to baselines using mixed-stock analysis. Natal origin of trans-Pacific migrants in the CCLME ranged 43–78% from the East China Sea and 22–57% from the Sea of Japan, highlighting the importance of both spawning areas for PBT in the CCLME. This study provides the first estimates on the natal origin of subadult PBT in this ecosystem using otolith chemistry and expands upon the application of these natural tracers for population connectivity studies for this species.
Robust subspace methods for outlier detection in genomic data circumvents the curse of dimensionality
Authors: Shetta Omar , Niranjan Mahesan
Published: February 2020

The application of machine learning to inference problems in biology is dominated by supervised learning problems of regression and classification, and unsupervised learning problems of clustering and variants of low-dimensional projections for visualization. A class of problems that have not gained much attention is detecting outliers in datasets, arising from reasons such as gross experimental, reporting or labelling errors. These could also be small parts of a dataset that are functionally distinct from the majority of a population. Outlier data are often identified by considering the probability density of normal data and comparing data likelihoods against some threshold. This classical approach suffers from the curse of dimensionality, which is a serious problem with omics data which are often found in very high dimensions. We develop an outlier detection method based on structured low-rank approximation methods. The objective function includes a regularizer based on neighbourhood information captured in the graph Laplacian. Results on publicly available genomic data show that our method robustly detects outliers whereas a density-based method fails even at moderate dimensions. Moreover, we show that our method has better clustering and visualization performance on the recovered low-dimensional projection when compared with popular dimensionality reduction techniques.
Phenotypic plasticity of senescence inDaphniaunder predation impact: no ageing acceleration when the perceived risk decreases with age
Authors: Pietrzak Barbara , Rabus Max , Religa Maciej , Laforsch Christian , Dańko Maciej J.
Published: February 2020

Recognising the nature of the predation risk, and responding to it accurately, is crucial to fitness. Yet, even the most accurate adaptive responses to predation risk usually entail costs, both immediate and lifelong. Rooting in life-history theory, we hypothesize that an animal can perceive the nuances of prey size and age selectivity by the predator and modulate its life history accordingly. We test the prediction that—contrary to the faster or earlier senescence under predation risk that increases with prey size and age—under predation risk that decreases with prey size and age either no senescence acceleration or even its deceleration is to be observed. We use two species of indeterminate growers, small crustaceans of the genusDaphnia,Daphnia PulexandDaphnia magna, as the model prey, and their respective gape-limited invertebrate predators, a dipteran, midge larvaChaoborus flavicans, and a notostracan, tadpole shrimpTriops cancriformis. We analyse age-specific survival, mortality and fertility rates, and find no senescence acceleration, as predicted. With this study, we complete the picture of the expected non-consumptive phenotypic effects of perceived predation pressure of different age-dependence patterns.
Basic principles of the genetic code extension
Authors: Błażej Paweł , Wnetrzak Małgorzata , Mackiewicz Dorota , Mackiewicz Paweł
Published: February 2020

Compounds including non-canonical amino acids (ncAAs) or other artificially designed molecules can find a lot of applications in medicine, industry and biotechnology. They can be produced thanks to the modification or extension of the standard genetic code (SGC). Such peptides or proteins including the ncAAs can be constantly delivered in a stable way by organisms with the customized genetic code. Among several methods of engineering the code, using non-canonical base pairs is especially promising, because it enables generating many new codons, which can be used to encode any new amino acid. Since even one pair of new bases can extend the SGC up to 216 codons generated by a six-letter nucleotide alphabet, the extension of the SGC can be achieved in many ways. Here, we proposed a stepwise procedure of the SGC extension with one pair of non-canonical bases to minimize the consequences of point mutations. We reported relationships between codons in the framework of graph theory. All 216 codons were represented as nodes of the graph, whereas its edges were induced by all possible single nucleotide mutations occurring between codons. Therefore, every set of canonical and newly added codons induces a specific subgraph. We characterized the properties of the induced subgraphs generated by selected sets of codons. Thanks to that, we were able to describe a procedure for incremental addition of the set of meaningful codons up to the full coding system consisting of three pairs of bases. The procedure of gradual extension of the SGC makes the whole system robust to changing genetic information due to mutations and is compatible with the views assuming that codons and amino acids were added successively to the primordial SGC, which evolved minimizing harmful consequences of mutations or mistranslations of encoded proteins.
Dynamic inferential NO

x

emission prediction model with delay estimation for SCR de-NO

x

process in coal-fired power plants
Authors: Yan Laiqing , Dong Ze , Jia Hao , Huang Jianan , Meng Lei
Published: February 2020

The selective catalytic reduction (SCR) decomposition of nitrogen oxide (de-NO

x

) process in coal-fired power plants not only displays nonlinearity, large inertia and time variation but also a lag in NO

x

analysis; hence, it is difficult to obtain an accurate model that can be used to control NH
3
injection during changes in the operating state. In this work, a novel dynamic inferential model with delay estimation was proposed for NO

x

emission prediction. First,
k
-nearest neighbour mutual information was used to estimate the time delay of the descriptor variables, followed by reconstruction of the phase space of the model data. Second, multi-scale wavelet kernel partial least square was used to improve the prediction ability, and this was followed by verification using benchmark dataset experiments. Finally, the delay time difference method and feedback correction strategy were proposed to deal with the time variation of the SCR de-NO

x

process. Through the analysis of the experimental field data in the steady state, the variable state and the NO

x

analyser blowback process, the results proved that this dynamic model has high prediction accuracy during state changes and can realize advance prediction of the NO

x

emission.
Fluid–structure interaction simulations outperform computational fluid dynamics in the description of thoracic aorta haemodynamics and in the differentiation of progressive dilation in Marfan syndrome patients
Authors: Pons R. , Guala A. , Rodríguez-Palomares J. F. , Cajas J. C. , Dux-Santoy L. , Teixidó-Tura G. , Molins J. J. , Vázquez M. , Evangelista A. , Martorell J.
Published: February 2020

Abnormal fluid dynamics at the ascending aorta may be at the origin of aortic aneurysms. This study was aimed at comparing the performance of computational fluid dynamics (CFD) and fluid–structure interaction (FSI) simulations against four-dimensional (4D) flow magnetic resonance imaging (MRI) data; and to assess the capacity of advanced fluid dynamics markers to stratify aneurysm progression risk. Eight Marfan syndrome (MFS) patients, four with stable and four with dilating aneurysms of the proximal aorta, and four healthy controls were studied. FSI and CFD simulations were performed with MRI-derived geometry, inlet velocity field and Young's modulus. Flow displacement, jet angle and maximum velocity evaluated from FSI and CFD simulations were compared to 4D flow MRI data. A dimensionless parameter, the shear stress ratio (SSR), was evaluated from FSI and CFD simulations and assessed as potential correlate of aneurysm progression. FSI simulations successfully matched MRI data regarding descending to ascending aorta flow rates (
R
2
= 0.92) and pulse wave velocity (
R
2
= 0.99). Compared to CFD, FSI simulations showed significantly lower percentage errors in ascending and descending aorta in flow displacement (−46% ascending, −41% descending), jet angle (−28% ascending, −50% descending) and maximum velocity (−37% ascending, −34% descending) with respect to 4D flow MRI. FSI- but not CFD-derived SSR differentiated between stable and dilating MFS patients. Fluid dynamic simulations of the thoracic aorta require fluid–solid interaction to properly reproduce complex haemodynamics. FSI- but not CFD-derived SSR could help stratifying MFS patients.
Continent-wide effects of urbanization on bird and mammal genetic diversity
Authors: Schmidt C. , Domaratzki M. , Kinnunen R. P. , Bowman J. , Garroway C. J.
Published: February 2020

Urbanization and associated environmental changes are causing global declines in vertebrate populations. In general, population declines of the magnitudes now detected should lead to reduced effective population sizes for animals living in proximity to humans and disturbed lands. This is a cause for concern because effective population sizes set the rate of genetic diversity loss due to genetic drift, the rate of increase in inbreeding and the efficiency with which selection can act on beneficial alleles. We predicted that the effects of urbanization should decrease effective population size and genetic diversity, and increase population-level genetic differentiation. To test for such patterns, we repurposed and reanalysed publicly archived genetic datasets for North American birds and mammals. After filtering, we had usable raw genotype data from 85 studies and 41 023 individuals, sampled from 1008 locations spanning 41 mammal and 25 bird species. We used census-based urban–rural designations, human population density and the Human Footprint Index as measures of urbanization and habitat disturbance. As predicted, mammals sampled in more disturbed environments had lower effective population sizes and genetic diversity, and were more genetically differentiated from those in more natural environments. There were no consistent relationships detectable for birds. This suggests that, in general, mammal populations living near humans may have less capacity to respond adaptively to further environmental changes, and be more likely to suffer from effects of inbreeding.
Evidence of five digits in embryonic horses and developmental stabilization of tetrapod digit number
Authors: Kavanagh Kathryn D. , Bailey C. Scott , Sears Karen E.
Published: February 2020

Previous work comparing the developmental mechanisms involved in digit reduction in horses with other mammals reported that horses have only a ‘single digit', with two flanking metapodials identified as remnants of digit II and IV. Here we show that early
Equus
embryos go through a stage with five digit condensations, and that the flanking splint metapodials result from fusions of the two anterior digits I and II and the two posterior digits IV and V, in a striking parallel between ontogeny and phylogeny. Given that even this most extreme case of digit reduction exhibits primary pentadactyly, we re-examined the initial stages of digit condensation of all digit-reduced tetrapods where data are available and found that in all cases, five or four digits initiate (four with digit I missing). The persistent pentadactyl initiation in the horse and other digit-reduced modern taxa underscores a durable developmental stability at the initiation of digits. The digit evodevo model may help illuminate the biological circumstances under which organ systems become highly stabilized versus highly plastic.
Integration of shared-pathogen networks and machine learning reveals the key aspects of zoonoses and predicts mammalian reservoirs
Authors: Wardeh Maya , Sharkey Kieran J. , Baylis Matthew
Published: February 2020

Diseases that spread to humans from animals, zoonoses, pose major threats to human health. Identifying animal reservoirs of zoonoses and predicting future outbreaks are increasingly important to human health and well-being and economic stability, particularly where research and resources are limited. Here, we integrate complex networks and machine learning approaches to develop a new approach to identifying reservoirs. An exhaustive dataset of mammal–pathogen interactions was transformed into networks where hosts are linked via their shared pathogens. We present a methodology for identifying important and influential hosts in these networks. Ensemble models linking network characteristics with phylogeny and life-history traits are then employed to predict those key hosts and quantify the roles they undertake in pathogen transmission. Our models reveal drivers explaining host importance and demonstrate how these drivers vary by pathogen taxa. Host importance is further integrated into ensemble models to predict reservoirs of zoonoses of various pathogen taxa and quantify the extent of pathogen sharing between humans and mammals. We establish predictors of reservoirs of zoonoses, showcasing host influence to be a key factor in determining these reservoirs. Finally, we provide new insight into the determinants of zoonosis-sharing, and contrast these determinants across major pathogen taxa.
Cryolevonia gen. nov. and Cryolevonia schafbergensis sp. nov., a cryophilic yeast from ancient permafrost and melted sea ice
Authors: Pontes Ana , Ruethi Joel , Frey Beat , Aires Andreia , Thomas Amanda , Overy David , Halti Brad , Kerr Russell , Sampaio José Paulo
Published: February 2020

A cryophilic basidiomycetous yeast unable to grow at 18 °C or higher temperatures was isolated from a subsurface permafrost layer collected in the Eastern Swiss Alps and from melted sea ice collected in the Artic at Frobisher Bay, Nunavut, Canada. Phylogenetic analyses employing combined sequences of the D1/D2 domain and ITS region indicated that the two new isolates belong to the family Camptobasidiaceae of the class Microbotryomycetes but are distantly related to any of the currently recognized species and genera. Consequently, the novel genus Cryolevonia, and the novel species Cryolevonia schafbergensis (type strain PYCC 8347T=CBS 16055T) are proposed to accommodate this cryophilic yeast. Although sparse hyphae and teliospore-like stuctures were observed upon prolonged incubation, a sexual cycle was not observed and therefore C. schafbergensis is documented solely from its asexual stage.
Towards a comparative framework of demographic resilience
Authors: Capdevila Pol , Stott Iain , Beger Maria , Salguero-Gómez Roberto
Published: February 2020

In times of global biodiversity crisis, developing tools to define, quantify, compare and predict ecological resilience is essential for understanding species’ responses to global change. Disparate interpretations of ecological resilience have, however, hampered the development of a common currency to quantify and compare resilience across natural systems. Most frameworks of study have focused on upper levels of biological organisation, especially ecosystems or communities, which adds layers of complication to measuring resilience with empirical data. To overcome such limitations, we suggest quantifying resilience using demographic data. Surprisingly, a quantifiable definition of resilience does not exist at the demographic level. Here, we present a framework of demographic resilience with a set of metrics that are comparable across species, and facilitate cost-effective management decisions.
Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study
Authors: Randhawa Gurjit S. , Soltysiak Maximillian P.M. , Roz Hadi El , de Souza Camila P.E. , Hill Kathleen A. , Kari Lila
Published: February 2020

As of February 20, 2020, the 2019 novel coronavirus (renamed to COVID-19) spread to 30 countries with 2130 deaths and more than 75500 confirmed cases. COVID-19 is being compared to the infamous SARS coronavirus, which resulted, between November 2002 and July 2003, in 8098 confirmed cases worldwide with a 9.6% death rate and 774 deaths. Though COVID-19 has a death rate of 2.8% as of 20 February, the 75752 confirmed cases in a few weeks (December 8, 2019 to February 20, 2020) are alarming, with cases likely being under-reported given the comparatively longer incubation period. Such outbreaks demand elucidation of taxonomic classification and origin of the virus genomic sequence, for strategic planning, containment, and treatment. This paper identifies an intrinsic COVID-19 genomic signature and uses it together with a machine learning-based alignment-free approach for an ultra-fast, scalable, and highly accurate classification of whole COVID-19 genomes. The proposed method combines supervised machine learning with digital signal processing for genome analyses, augmented by a decision tree approach to the machine learning component, and a Spearman’s rank correlation coefficient analysis for result validation. These tools are used to analyze a large dataset of over 5000 unique viral genomic sequences, totalling 61.8 million bp. Our results support a hypothesis of a bat origin and classify COVID-19 asSarbecovirus, withinBetacoronavirus. Our method achieves high levels of classification accuracy and discovers the most relevant relationships among over 5,000 viral genomes within a few minutes,ab initio, using raw DNA sequence data alone, and without any specialized biological knowledge, training, gene or genome annotations. This suggests that, for novel viral and pathogen genome sequences, this alignment-free whole-genome machine-learning approach can provide a reliable real-time option for taxonomic classification.
High contiguity long read assembly ofBrassica nigraallows localization of active centromeres and provides insights into the ancestralBrassicagenome
Authors: Perumal Sampath , Koh Chu Shin , Jin Lingling , Buchwaldt Miles , Higgins Erin , Zheng Chunfang , Sankoff David , Robinson Stephen J. , Kagale Sateesh , Navabi Zahra-Katy , Tang Lily , Horner Kyla N. , He Zhesi , Bancroft Ian , Chalhoub Boulos , Sharpe Andrew G , Parkin Isobel AP
Published: February 2020

High-quality nanopore genome assemblies were generated for twoBrassica nigragenotypes (Ni100 and CN115125); a member of the agronomically importantBrassicaspecies. The N50 contig length for the two assemblies were 17.1 Mb (58 contigs) and 0.29 Mb (963 contigs), respectively, reflecting recent improvements in the technology. Comparison with ade novoshort read assembly for Ni100 corroborated genome integrity and quantified sequence related error rates (0.002%). The contiguity and coverage allowed unprecedented access to low complexity regions of the genome. Pericentromeric regions and coincidence of hypo-methylation enabled localization of active centromeres and identified a novel centromere-associated ALE class I element which appears to have proliferated through relatively recent nested transposition events (<1 million years ago). Computational abstraction was used to define a post-triplicationBrassicaspecific ancestral genome and to calculate the extensive rearrangements that define the genomic distance separatingB. nigrafrom its diploid relatives.
First report of the Eocene bivalveSchedocardia(Mollusca, Cardiidae) from Cuba
Authors: Orihuela Johanset , Izquierdo Yasmani Ceballos , Portell Roger W.
Published: February 2020

Herein we provide the first report of the cardiid bivalveSchedocardiafrom Cuba. The single, partial, valve external mold was derived from the Madruga Formation which is characterized by a richly diverse marine fauna including echinoderms, brachiopods, benthic and planktonic foraminiferans, but from which bivalves were not previously reported. The unit is considered late Paleocene in age (Thanetian), but the presence ofSchedocardiasupports a possible age extension of the formation into the early Eocene (Ypresian). Moreover, we provide a reconsideration of the historical factors that affected the accepted type locality of the outcrop, which allows for an alternative interpretation of the fauna found therein.

Se reporta por primera vez el género bivalvoSchedocardiade la Formación Madruga, para el registro fósil de Cuba. De esta formación se ha reportado una rica fauna de invertebrados incluyendo equinodermos, braquiópodos y, especialmente de foraminíferos, pero no moluscos bivalvos. Se ha considerado que la Formación Madruga se depositó durante el Paleoceno tardío (Thanetiano), no obstante, la presencia deSchedocardiaapoya la extensión de edad de la formación hasta el Eoceno temprano (Ypresiano). Además, se consideran factores antropogénicos que han afectado la localidad tipo de la formación.
The key to complexity in interacting systems with multiple strains
Authors: Gjini Erida , Madec Sten
Published: February 2020

Ecological community structure, persistence and stability are shaped by multiple forces, acting on multiple scales. These include patterns of resource use and limitation, spatial heterogeneities, drift and migration. Pathogen strains co-circulating in a host population are a special type of an ecological community. They compete for colonization of susceptible hosts, and sometimes interact via altered susceptibilities to co-colonization. Diversity in such pairwise interaction traits enables the multiple strains to create dynamically their niches for growth and persistence, and ‘engineer’ their common environment. How such a network of interactions with others mediates collective coexistence remains puzzling analytically and computationally difficult to simulate. Furthermore, the gradients modulating stability-complexity regimes in such multi-player systems remain poorly understood. In a recent study, we presented an analytic framework forN-type coexistence in an SIS epidemiological system with co-colonization interactions. The multi-strain complexity was reduced fromO(N2) dimensions of population structure to onlyNequations for strain frequency evolution on a long timescale. Here, we examine the key drivers of coexistence regimes in such a system. We find the ratio of single to co-colonizationμcritically determines the type of equilibrium for multi-strain dynamics. This key quantity in the model encodes a trade-off between overall transmission intensityR0and mean interaction coefficient in strain spacek. Preserving a given coexistence regime, under fixed trait variation, can only be achieved from a balance between higher competition in favourable environments, and higher cooperation in harsher environments, consistent with the stress gradient hypothesis in ecology. Multi-strain coexistence regimes are more stable whenμis small, whereas asμincreases, dynamics tends to increase in complexity. There is an intermediate ratio that maximizes the existence and stability of a unique coexistence equilibrium between strains. This framework provides a foundation for linking invariant principles in collective coexistence across biological systems, and for understanding critical shifts in community dynamics, driven by simple and random pairwise interactions but potentiated by mean-field and environmental gradients.
Differential vulnerability of the cerebellum in healthy ageing and Alzheimer’s disease
Authors: Gellersen Helena M. , Guell Xavier , Sami Saber
Published: February 2020

Recent findings challenge the prior notion that the cerebellum remains unaffected by Alzheimer’s disease (AD). Yet, it is unclear whether AD exacerbates age-related cerebellar grey matter decline or engages distinct structural and functional territories. We performed a meta-analysis of cerebellar grey matter loss in normal ageing and AD. We mapped voxels with structural decline onto established brain networks, functional parcellations, and along gradients that govern the functional organisation of the cerebellum. Importantly, these gradients track continuous changes in cerebellar specialisation providing a more nuanced measure of the functional profile of regions vulnerable to ageing and AD. Gradient 1 progresses from motor to cognitive territories; Gradient 2 isolates attentional processing; Gradient 3 captures lateralisation differences in cognitive functions. We identified bilateral and right-lateralised posterior cerebellar atrophy in ageing and AD, respectively. Age- and AD- related structural decline only showed partial spatial overlap in right lobule VI/Crus I. Despite the seemingly distinct patterns of AD- and age-related atrophy, the functional profiles of these regions were similar. Both participate in the same macroscale networks (default mode, frontoparietal, attention), support executive functions and language processing, and did not exhibit a difference in relative positions along Gradients 1 or 2. However, Gradient 3 values were significantly different in ageing vs. AD, suggesting that the roles of left and right atrophied cerebellar regions exhibit subtle functional differences despite their membership in similar macroscale networks. These findings provide an unprecedented characterisation of structural and functional differences and similarities in cerebellar grey matter loss between normal ageing and AD.
A survey to explore new markers of achievement to assess and monitor gender equity in an NIHR Biomedical Research Centre: A two-factor model
Authors: Henderson Lorna R , Shah Syed Ghulam Sarwar , Ovseiko Pavel V , Dam Rinita , Buchan Alastair M , McShane Helen , Kiparoglou Vasiliki
Published: February 2020

Background

The underrepresentation of women in academic medicine at senior level and in leadership positions is well documented. In the United Kingdom, the National Institute for Health Research (NIHR) announced that eligibility for funding for Biomedical Research Centres (BRCs) required at least Silver award status of the Athena SWAN Charter. However, the evidence base for monitoring gender equity (GE) in BRCs is underdeveloped.

Methods

An exploratory online survey distributed to an entire population of NIHR Oxford BRC affiliates (N=683) who ranked the importance of 13 markers of GE on a five point Likert scale. Data were summarised using frequencies and descriptive statistics. Interrelationships between the markers and underlying latent dimensions (factors) were determined by exploratory and confirmatory factor analyses. Thematic analysis was used to analyse open-ended comments.

Results

The response rate was 36% (243 respondents). Respondents were more frequently female (55%, n=133), aged 41-50 years (33%, n=81), investigators (33%, n=81) and had been affiliated with the BRC for 2-7 years (39.5%, n=96). Participants ranked BRC senior leadership roles and organisational policies on gender equity, as very important, 58% (n=141) and 57% (n=139) respectively. The top two markers ranked as very important by female participants were organisational policies (64.7%, n=86/133) and recruitment and retention (60.9%, n=81/133), whereas male participants ranked leadership development (52.1%, n=50/96) and BRC senior leadership roles (50%, n=48/96) as being very important. The factor analyses showed two distinct latent dimensions: organisational markers and individual markers of GE in BRCs. Open ended comments suggested three key areas of actions: monitoring and benchmarking, organisational support for those with childcare responsibilities, and leadership and Institutional support for GE.

Conclusions

The findings suggest a two-factor model of markers of achievement for GE with organisational and individual dimensions. Implementation and sustainability of gender equity requires commitment at senior leadership and organisational policy level.
Tourism-driven ocean science for sustainable use: A case study of sharks in Fiji
Authors: Ward-Paige C A , Brunnschweiler J , Sykes H
Published: February 2020

The oceans are in a state of rapid change – both negatively, due climate destabilization and misuse, and positively, due to strengthening of policies for sustainable use combined with momentum to grow the blue economy. Globally, more than 121 million people enjoy nature-based marine tourism — e.g., recreational fishing, diving, whale watching — making it one of the largest marine sectors. This industry is increasingly threatened by ocean degradation and management has not kept pace to ensure long-term sustainability. In response, individuals within the industry are taking it upon themselves to monitor the oceans and provide the data needed to assist management decisions. Fiji is one such place where the dive tourism industry is motivated to monitor the oceans (e.g., track sharks). In 2012, 39 dive operators in collaboration with eOceans commenced the Great Fiji Shark Count (GFSC) to document sharks (and other species) on 592 dive sites. Here, using 146,304 shark observations from 30,668 dives we document spatial patterns of 11 shark species. High variability demonstrates the value of longitudinal data that include absences for describing mobile megafauna and the capacity of stakeholders to document the oceans. Our results may be used to guide future scientific questions, provide a baseline for future assessments, or to evaluate conservation needs. It also shows the value of scientists collaborating with stakeholders to address questions that are most important to the local community so that they have the information needed to make science-based decisions.
Artemisinin-resistant malaria parasites show enhanced transmission to mosquitoes under drug pressure
Authors: Witmer Kathrin , Dahalan Farah A. , Delves Michael J , Yahiya Sabrina , Watson Oliver J. , Straschil Ursula , Chiwcharoen Darunee , Sornboon Boodtee , Pukrittayakamee Sasithon , Pearson Richard D. , Howick Virginia M. , Lawniczak Mara K. N. , White Nicholas J. , Dondorp Arjen M. , Okell Lucy C. , Ruecker Andrea , Chotivanich Kesinee , Baum Jake
Published: February 2020

Resistance to artemisinin combination therapy (ACT) in thePlasmodium falciparumparasite is threatening to reverse recent gains in reducing global deaths from malaria. Whilst resistance manifests as delayed asexual parasite clearance in patients following ACT treatment, the phenotype can only spread geographically via the sexual cycle and subsequent transmission through the mosquito. Artemisinin and its derivatives (such as dihydroartemisinin, DHA) as well as killing the asexual parasite form are known to sterilize male, sexual-stage gametes from activation. Whether resistant parasites overcome this artemisinin-dependent sterilizing effect has not, however, been fully tested. Here, we analysed fiveP. falciparumclinical isolates from the Greater Mekong Subregion, each of which demonstrated delayed clinical clearance and carried known resistance-associated polymorphisms in theKelch13gene (PfK13var). As well as demonstrating reduced sensitivity to artemisinin-derivates inin vitroasexual growth assays, certain PfK13varisolates also demonstrated a marked reduction in sensitivity to these drugs in anin vitromale gamete activation assay compared to a sensitive control. Importantly, the same reduction in sensitivity to DHA was observed when the most resistant isolate was assayed by standard membrane feeding assays usingAnopheles stephensimosquitoes. These results indicate that ACT use can favour resistant over sensitive parasite transmission. A selective advantage for resistant parasite transmission could also favour acquisition of further polymorphisms, such as mosquito host-specificity or antimalarial partner–drug resistance in mixed infections. Favoured transmission of resistance under ACT coverage could have profound implications for the spread of multidrug resistant malaria beyond Southeast Asia.

ONE SENTENCE SUMMARY

Artemisinin-resistant clinical isolates can also demonstrate resistance to the transmission-blocking effects of artemisinin-based drugs, favouring resistance transmission to the mosquito.
EMBER: Multi-label prediction of kinase-substrate phosphorylation events through deep learning
Authors: Kirchoff Kathryn E. , Gomez Shawn M.
Published: February 2020

Kinase-catalyzed phosphorylation of proteins forms the back-bone of signal transduction within the cell, enabling the coordination of numerous processes such as the cell cycle, apoptosis, and differentiation. While on the order of 105phosphorylation events have been described, we know the specific kinase performing these functions for less than 5% of cases. The ability to predict which kinases initiate specific individual phosphorylation events has the potential to greatly enhance the design of downstream experimental studies, while simultaneously creating a preliminary map of the broader phosphorylation network that controls cellular signaling. To this end, we describe EMBER, a deep learning method that integrates kinase-phylogeny information and motif-dissimilarity information into a multi-label classification model for the prediction of kinase-motif phosphorylation events. Unlike previous deep learning methods that perform single-label classification, we restate the task of kinase-motif phosphorylation prediction as a multi-label problem, allowing us to train a single unified model rather than a separate model for each of the 134 kinase families. We utilize a Siamese network to generate novel vector representations, or an embedding, of motif sequences, and we compare our novel embedding to a previously proposed peptide embedding. Our motif vector representations are used, along with one-hot encoded motif sequences, as input to a classification network while also leveraging kinase phylogenetic relationships into our model via a kinase phylogeny-weighted loss function. Results suggest that this approach holds significant promise for improving our map of phosphorylation relations that underlie kinome signaling.

Availability

https://github.com/gomezlab/EMBER
Discriminating changes in protein structure using PTAD conjugation to tyrosine
Authors: Moinpour Mahta , Barker Natalie K. , Guzman Lindsay E. , Jewett John C. , Langlais Paul R. , Schwartz Jacob C.
Published: February 2020

Chemical modification of proteins has been crucial in engineering protein-based therapies, targeted biopharmaceutics, molecular probes, and biomaterials. Here, we explore the use of a conjugation-based approach to sense alternative conformational states in proteins. Tyrosine has both hydrophobic and hydrophilic qualities, thus allowing it to be positioned at protein surfaces, or binding interfaces, or to be buried within a protein. Tyrosine can be conjugated with 4-phenyl-3H-1,2,4-triazole-3,5(4H)-dione (PTAD). We hypothesized that individual protein conformations could be distinguished by labeling tyrosine residues in the protein with PTAD. We conjugated tyrosine residues in a well-folded protein, bovine serum albumin (BSA), and quantified labeled tyrosine with LC-MS/MS. We applied this approach to alternative conformations of BSA produced in the presence of urea. The amount of PTAD labeling was found to relate to the depth of each tyrosine relative to the protein surface. This study demonstrates a new use of tyrosine conjugation using PTAD as an analytic tool able to distinguish the conformational states of a protein.
Microstructural meal pattern analysis reveals that nicotine is a potent anti-anorectic drug despite producing long-term anorexigenic effects
Authors: Shankar Kokila , Ambroggi Frederic , George Olivier
Published: February 2020

Nicotine consumption in both human and animal studies has been strongly associated with changes in feeding-related behaviors and metabolism. The current dogma is that chronic nicotine decreases food intake and increases metabolism, leading to lower body weight. However, the effect of acute nicotine intake on feeding is unclear. The present study employed microstructural and macrostructural behavioral analyses to elucidate changes in feeding behavior in animals that intravenously self-administered nicotine. At the microstructural level (seconds to minutes), nicotine increased feeding and drinking behavior during the first 5 minutes after nicotine self-administration. This effect was also observed in animals that passively received nicotine, but the effect was not observed in animals that self-administered saline or passively received saline. At the macrostructural level (hours to days), nicotine decreased body weight gain, decreased feeding, and was associated with increases in feeding and body weight gain during abstinence. These results suggest that nicotine first produces anti-anorectic effects before producing long-term anorexigenic effects. These results challenge the notion that nicotine is an anorexigenic drug and paradoxically suggest that the anorexigenic effects of nicotine may be a long-term consequence of acute anti-anorectic effects of nicotine.
Bacterial Growth Stimulation and Antifungal Effects Of The Essential-oil-less-extracts Of The Food SpiceDysphania ambrosioides
Authors: Nitsch-Velásquez Lucía
Published: February 2020

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D. ambrosioidesleaves (DaL) are utilized as food spice. DaL infusion is utilized as antihelminthic in traditional medicine, the extracted essential oil (EO) has been repurposed as biopesticide,in vitroactivities include nematocidal and phytotoxic activities by hydrophillic compounds from leaves and the roots. As Da might be a good candidate for circular economy, more potential applications of the essential–oil–less–Da extracts were pursued by applying green chemistry based extraction methods.

DaL extracts were prepared by the autoclave method for the sterile–essential–oil–less aqueous extract (SALAEL–Da), butanol fractionating for saponins extraction (SAP) and ethanol boiling method for the saponin–free extract (EtOH–Da). Their effects over clinical isolates of fungi (Candida albicans/ CA), gram–negative bacteria (Erwinia carotovora/ ErC) and –positive (Methicillin resistantStaphylococcus aureusUSA 300 / MRSA–USA–300). were explored.

The raw extracts stimulated the bacterial growth of all strains in the pre-screening phase.SALAEL-DaL (at 25 mg/mL) estimulated ErC growth, reducint its doubling time by 35%, microdilutions of EtOH-DaL (at 180 mg/mL) stimulated the growth of MRSA–USA–300 even in the GEN presence at sub-lethal concentrations (MICGEN=1.75µg/mL). SALAEL-Da (at 137 mg/mL) inhibited the growth of CA in agar dilutions, and its fraction SAP showed a moderated fungistatic effect at 100 mg/mL in disk diffusion pre-screening tests. SAP fraction may partially account for the observed antifungal activity. The essential–oil–less–Da aqueous extracts analyzed contained bacterial growth stimulating and antifungal components. Further investigation may lead to commercial opportunities for probiotics and antifungals.

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Importance

The leaves ofD. ambrosioides(Da) are utilized as food spice and its infusion as antihelminthic in Latin American folk medicine, with a wide variety ofin vitrobioactivities reported. The most studied is Da essential oil (Da–EO) and has been repurposed as insect repellent and pesticide. Which would leave the extracted plant material as a potential raw material for other products. Additionally, Da could be an interesting candidate tobe produced at high–scale in rural communities. The ecofriendly extraction processes yielded an aqueous extract (free of the EO) that enhances the bacterial growth rate of two bacterial strains, which can eventually be useful in the probiotics industry. This extract also inhibited the growth of the opportunistic fungiCandida albicans, becoming a potential source of new antifungals. Further investigation may lead to a circular economy of the agroindustry aroundD. ambrosioides, probiotics and antifungals, among others.
Towards the spatial resolution of metalloprotein charge states by detailed modeling of XFEL crystallographic diffraction
Authors: Sauter Nicholas K. , Kern Jan , Yano Junko , Holton James M.
Published: February 2020

Oxidation states of individual metal atoms within a metalloprotein can be assigned by examining X-ray absorption edges, which shift to higher energy for progressively more positive valence numbers. Indeed, X-ray crystallography is well suited for such a measurement, owing to its ability to spatially resolve the scattering contributions of individual metal atoms that have distinct electronic environments contributing to protein function. However, as the magnitude of the shift is quite small, about +2 eV per valence state for iron, it has only been possible to measure the effect when performed with monochromated X-ray sources at synchrotron facilities with energy resolutions in the range 2–3 × 10−4 (ΔE/E). This paper tests whether X-ray free-electron laser (XFEL) pulses, which have a broader bandpass (ΔE/E = 3 × 10−3) when used without a monochromator, might also be useful for such studies. The program nanoBragg is used to simulate serial femtosecond crystallography (SFX) diffraction images with sufficient granularity to model the XFEL spectrum, the crystal mosaicity and the wavelength-dependent anomalous scattering factors contributed by two differently charged iron centers in the 110-amino-acid protein, ferredoxin. Bayesian methods are then used to deduce, from the simulated data, the most likely X-ray absorption curves for each metal atom in the protein, which agree well with the curves chosen for the simulation. The data analysis relies critically on the ability to measure the incident spectrum for each pulse, and also on the nanoBragg simulator to predict the size, shape and intensity profile of Bragg spots based on an underlying physical model that includes the absorption curves, which are then modified to produce the best agreement with the simulated data. This inference methodology potentially enables the use of SFX diffraction for the study of metalloenzyme mechanisms and, in general, offers a more detailed approach to Bragg spot data reduction.