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The nine banded armadillo (Dasypus novemcinctus) is the only xenarthran mammal to have naturally expanded its range into the middle latitudes of the USA. It is not known to hibernate, but has been associated with unusually labile core body temperatures. Like some other xenarthrans (but unlike mammals in general), Dasypus also shows relatively frequent departures from species-typical thoracic and lumbar vertebral counts. If and how intrauterine temperature variation in xenarthrans might increase skeletal variation during development, and how xenarthran body temperature varies according to season, housing, and gestational status, are unknown. Here, we report temperatures recorded from 19 female armadillos over the course of three seasons (each for at least one month between November and July), tracking internal body temperatures recorded every 6 to 120 minutes. Average temperatures are similar regardless of housing inside or outside, gravid or nongravid. Nongravid individuals housed indoors show significantly higher daily fluctuations than other treatment groups, despite more stable ambient temperatures indoors than outdoors. Assuming that our estimates for implantation are accurate, gravid individuals show, on average, slightly lower daily fluctuations than nongravid, but the difference is not significant and some of our most extreme fluctuations (6-9C within 24 hours) took place in gravid animals. Compared to animals housed outdoors, lab-housed individuals less frequently exhibit body temperatures above their overall mean during typically dormant hours (e.g., afternoon), and body temperatures below their overall mean during typically active hours (e.g., early morning). Temperature periodicity as measured by discrete Fourier transforms shows strong 24 and 12 hour cycles in all individuals, reflecting circadian rhythms. CT scans of monozygotic quadruplets from dams with known body temperatures show a surprising range of phenotypic variation. While all offspring had seven cervicals, and most had ten thoracics, five lumbars, and three sacrals, three of twelve CT-scanned litters had nine rather than 10 thoracics and some offspring of one dam had two sacrals. Dams for two of these exhibited low average temperatures (<35C) early in their gestation. Two dams had offspring with a polymorphic axial skeleton, despite being genetically identical quadruplets. Our data demonstrate the thermal and phenotypic lability of D. novemcinctus but do not decisively link gestational temperatures with particular vertebral phenotypes.
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The Large Binocular Telescope Interferometer (LBTI) enables nulling interferometric observations across the N band (8 to 13 um) to suppress a star's bright light and probe for faint circumstellar emission. We present and statistically analyze the results from the LBTI/HOSTS (Hunt for Observable Signatures of Terrestrial Systems) survey for exozodiacal dust. By comparing our measurements to model predictions based on the Solar zodiacal dust in the N band, we estimate a 1 sigma median sensitivity of 23 zodis for early type stars and 48 zodis for Sun-like stars, where 1 zodi is the surface density of habitable zone (HZ) dust in the Solar system. Of the 38 stars observed, 10 show significant excess. A clear correlation of our detections with the presence of cold dust in the systems was found, but none with the stellar spectral type or age. The majority of Sun-like stars have relatively low HZ dust levels (best-fit median: 3 zodis, 1 sigma upper limit: 9 zodis, 95% confidence: 27 zodis based on our N band measurements), while ~20% are significantly more dusty. The Solar system's HZ dust content is consistent with being typical. Our median HZ dust level would not be a major limitation to the direct imaging search for Earth-like exoplanets, but more precise constraints are still required, in particular to evaluate the impact of exozodiacal dust for the spectroscopic characterization of imaged exo-Earth candidates.
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Alzheimer's disease (AD) is characterised by a dynamic process of neurocognitive changes from normal cognition to mild cognitive impairment (MCI) and progression to dementia. However, not all individuals with MCI develop dementia. Predicting whether individuals with MCI will decline (i.e. progressive MCI) or remain stable (i.e. stable MCI) is impeded by patient heterogeneity due to comorbidities that may lead to MCI diagnosis without progression to AD. Despite the importance of early diagnosis of AD for prognosis and personalised interventions, we still lack robust tools for predicting individual progression to dementia. Here, we propose a novel trajectory modelling approach based on metric learning (Generalised Metric Learning Vector Quantization) that mines multimodal data from MCI patients in the Alzheimer's disease Neuroimaging Initiative (ADNI) cohort to derive individualised prognostic scores of cognitive decline due to AD. We develop an integrated biomarker generation- using partial least squares regression- and classification methodology that extends beyond binary patient classification into discrete subgroups (i.e. stable vs. progressive MCI), determines individual profiles from baseline (i.e. cognitive or biological) data and predicts individual cognitive trajectories (i.e. change in memory scores from baseline). We demonstrate that a metric learning model trained on baseline cognitive data (memory, executive function, affective measurements) discriminates stable vs. progressive MCI individuals with high accuracy (81.4%), revealing an interaction between cognitive (memory, executive functions) and affective scores that may relate to MCI comorbidity (e.g. affective disturbance). Training the model to perform the same binary classification on biological data (mean cortical β-amyloid burden, grey matter density, APOE 4) results in similar prediction accuracy (81.9%). Extending beyond binary classifications, we develop and implement a trajectory modelling approach that shows significantly better performance in predicting individualised rate of future cognitive decline (i.e. change in memory scores from baseline), when the metric learning model is trained with biological (r = -0.68) compared to cognitive (r = -0.4) data. Our trajectory modelling approach reveals interpretable and interoperable markers of progression to AD and has strong potential to guide effective stratification of individuals based on prognostic disease trajectories, reducing MCI patient misclassification, that is critical for clinical practice and discovery of personalised interventions.
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Immunization is one of the most successful public health initiatives in recent times. It is, therefore, worrying to learn the level of under-vaccination in Pakistan. Diseases that have been successfully eliminated through the aid of vaccination in other countries have not been eliminated in Pakistan. The reasons for this vary and show the uniqueness of the economic, healthcare and environmental landscape of Pakistan, through which public health programmes need to be implemented. The "Expanded Programme of Immunization" (EPI) is the main programme through which routine immunization is provided to the public. Within Pakistan, it has encountered many problems since its inception. This includes logistical obstacles, inefficient health worker attitudes, parental and female awareness, and education, the influence of religious community leaders and the complications that accompany conflict. When compared to globally standardised targets for immunization, Pakistan is trailing behind. Not achieving these targets is worrying from both a global perspective and within the national healthcare landscape of Pakistan. Research is necessary to bring together findings on the failings of routine immunization and polio campaigns; there are many intersecting factors that global health bodies and the Department of Health in Pakistan must address in order to relieve the burden of vaccine-preventable diseases (VPDs).
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Metal-organic frameworks (MOFs) are intriguing host materials in composite electrolytes due to their ability for tailoring host-guest interactions by chemical tuning of the MOF backbone. Here, we introduce particularly high sodium ion conductivity into the zeolitic imidazolate framework ZIF-8 by impregnation with the sodium-salt-containing ionic liquid (IL) (Na0.1EMIM0.9)TFSI. We demonstrate an ionic conductivity exceeding 2 × 10-4 S · cm-1 at room temperature, with an activation energy as low as 0.26 eV, i.e., the highest reported performance for room temperature Na+-related ion conduction in MOF-based composite electrolytes to date. Partial amorphization of the ZIF-backbone by ball-milling results in significant enhancement of the composite stability towards exposure to ambient conditions, up to 20 days. While the introduction of network disorder decelerates IL exudation and interactions with ambient contaminants, the ion conductivity is only marginally affected, decreasing with decreasing crystallinity but still maintaining superionic behavior. This highlights the general importance of 3D networks of interconnected pores for efficient ion conduction in MOF/IL blends, whereas pore symmetry is a less stringent condition.
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Stem cell basic science has sparked a lot of attention because of its use of cells coming from 'destroyed' embryos. An ethnographic study conducted in two developmental biology laboratories located in India and France demonstrates that lab professionals do not see the use of these cells as controversial. What appears to be a major topic of reflection is the killing of mice. A hierarchy of deaths is delineated when biologists evoke the kind of lives at play in their science. A comparison between narrations of cell experimentations and mice sacrifices enriches a biological approach to the living through genetics, which is nonetheless performed in daily scientific practices. Laboratory workers enact other perceptions that point at being alive or having a life. They acknowledge, with personal convictions or expressions of intense affects, lives that are said to be embodied and experienced, while being hierarchised for the sake of science and dying patients. Laboratory workers' narratives of a hierarchy of deaths provide them with arguments to engage with discussions happening outside of their workplace about the handling of living materials in experimental settings.
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BACKGROUND:Multi-drug resistant typhoid fever remains an enormous public health threat in low and middle-income countries. However, we still lack a detailed understanding of the epidemiology and genomics of S. Typhi in many regions. Here we have undertaken a detailed genomic analysis of typhoid in urban Dhaka, Bangladesh to unravel the population structure and antimicrobial resistance patterns in S. Typhi isolated between 2004-2016. PRINCIPAL FINDINGS:Whole genome sequencing of 202 S. Typhi isolates obtained from three study locations in urban Dhaka revealed a diverse range of S. Typhi genotypes and AMR profiles. The bacterial population within Dhaka were relatively homogenous with little stratification between different healthcare facilities or age groups. We also observed evidence of exchange of Bangladeshi genotypes with neighboring South Asian countries (India, Pakistan and Nepal) suggesting these are circulating throughout the region. This analysis revealed a decline in H58 (genotype 4.3.1) isolates from 2011 onwards, coinciding with a rise in a diverse range of non-H58 genotypes and a simultaneous rise in isolates with reduced susceptibility to fluoroquinolones, potentially reflecting a change in treatment practices. We identified a novel S. Typhi genotype, subclade 3.3.2 (previously defined only to clade level, 3.3), which formed two localized clusters (3.3.2.Bd1 and 3.3.2.Bd2) associated with different mutations in the Quinolone Resistance Determining Region (QRDR) of gene gyrA. SIGNIFICANCE:Our analysis of S. Typhi isolates from urban Dhaka, Bangladesh isolated over a twelve year period identified a diverse range of AMR profiles and genotypes. The observed increase in non-H58 genotypes associated with reduced fluoroquinolone susceptibility may reflect a change in treatment practice in this region and highlights the importance of continued molecular surveillance to monitor the ongoing evolution of AMR in Dhaka. We have defined new genotypes and lineages of Bangladeshi S. Typhi which will facilitate the identification of these emerging AMR clones in future surveillance efforts.
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The term neuroscience includes in itself a plethora of research areas devoted to undercover the most fascinating complex organ of our body: the brain. A common denominator of neuroscience areas, is the need for the application of methodologies to integrate different features. In this thesis, we focused on the analysis of two types of brain data: brain data coming from Traumatic Brain Injury (TBI) patients and data collected for the study of neurocognitive healthy ageing. In both cases there was the need of applying computational techniques able to integrate different features. To do so we used multilayer networks. For two groups of TBI patients (adults and paediatrics), time series data were collected from the observations of IntraCranial Pressure (ICP) and Heart Rate (HR). We first detected events of simultaneous increase of HR and ICP, which we called brain-heart crosstalks. Subsequently time series were translated into graphs, and network measures, during brain-heart crosstalks, were obtained. These were then included as predictors in a mortality outcome model, with crosstalks. Causality measures were also investigated, using a Granger causality approach, to understand the dynamics of signals during these events. We further applied multilayer networks to study neurocognitive ageing. To do so, we implemented a pipeline for community detection, which we called NetRank, applying it to the Cam-CAN, a large cross-sectional cohort for the study of healthy neurocognitive ageing. Using multilayer networks modelling, we identified subgroups of individuals, with similar lifestyles, and we related them to structural and functional brain features. We believe that multilayer networks and their extensions represent a powerful tool to be used in integrative and cross modal neuroscience datasets. New insights on cognitive neuroscience and time series analysis, can in fact be gained trough multilayer network, possibly improving patients managements and allowing to develop new predictive tools.
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Degradation by the penetration of oxidation into the Cr12 roller steel is evaluated during thermal fatigue tests in the laboratory in the temperature range of 500−700 °C. A qualitative assessment is carried out with regard to the thermal load, the microstructure and the test temperature. The results show that the specific properties of the microstructure with respect to thermal stress and temperature have a significant influence on the oxidation behavior as well as on the crack propagation mode and crack growth. The conditions that lead to an increase in the oxidation rate and thus to premature and sudden local chipping of the roll surface layer are analyzed and explained.
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Internet of Things (IoT) networks are mostly comprised of power-constrained devices, therefore the most important consideration in designing IoT applications, based on sensor networks is energy efficiency. Minor improvement in energy conservation methods can lead to a significant increase in the lifetime of IoT devices and overall network. To achieve efficient utilisation of energy, different solutions are proposed such as duty cycling optimization, design changes at the MAC layer, etc. In this paper, we propose a new approach to overcome this challenge in cloud-based IoT sensing applications, based on integration of an abstraction layer with constrained application mechanism. To achieve energy conservation and efficient data management in IoT sensing applications, we incorporate modules of efficient web framework with cloud services, in order to minimize the number of round trips for data delivery and graph-based data representation. Our study is the first attempt in the literature, to the best of our knowledge, which introduces the potential of this integration for achieving the aforementioned objectives in the target applications. We implemented the proposed interfacing of abstraction layer in constrained applications, to develop a testbed using Z1 IoT motes, Contiki OS and GraphQL web framework with Google cloud services. Experimental comparisons against baseline REST architecture approach show that our proposed approach achieved significant reductions in data delivery delay and energy consumption (minimum 51.53% and 52.88%, respectively) in IoT applications involving sensor network.
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