bluuebunny/crossref_metadata_2025_split huggingface dataset:
The impact of the environment of white dwarf mergers on fast radio bursts
Authors: Kundu Esha , Ferrario Lilia 
Published: December 2019

ABSTRACT
Fast radio bursts (FRBs) are transient intense radio pulses with duration of milliseconds. Although, the first FRB was detected more than a decade ago, the progenitors of these energetic events are not yet known. The currently preferred formation channel involves the formation of a neutron star (NS)/magnetar. While these objects are often the end product of the core-collapse (CC) explosion of massive stars, they could also be the outcome of the merging of two massive white dwarfs. In the merger scenario the ejected material interacts with a constant-density circumbinary medium and creates supersonic shocks. We found that when a radio pulse passes through these shocks the dispersion measure (DM) increases with time during the free expansion phase. The rotation measure (RM) displays a similar trend if the power-law index, n, of the outer part of the ejecta is &gt;6. For n = 6, the RM remains constant during this phase. Later, when the ejecta move into the Sedov–Taylor phase while the DM still increases, however, with a different rate, the RM reduces. This behaviour is somewhat similar to that of FRB 121102 for which a marginal increase of DM and a 10 per cent decrease of RM have been observed over time. These features are in contrast to the CC scenario, where the DM and RM contributions to the radio signal always diminish with time.
The emergence of sequence-dependent structural motifs in stretched, torsionally constrained DNA
Authors: Shepherd Jack W , Greenall Robert J , Probert Matt I J , Noy Agnes , Leake Mark C 
Published: December 2019

Abstract
The double-helical structure of DNA results from canonical base pairing and stacking interactions. However, variations from steady-state conformations resulting from mechanical perturbations in cells have physiological relevance but their dependence on sequence remains unclear. Here, we use molecular dynamics simulations showing sequence differences result in markedly different structural motifs upon physiological twisting and stretching. We simulate overextension on different sequences of DNA ((AA)12, (AT)12, (CC)12 and (CG)12) with supercoiling densities at 200 and 50 mM salt concentrations. We find that DNA denatures in the majority of stretching simulations, surprisingly including those with over-twisted DNA. GC-rich sequences are observed to be more stable than AT-rich ones, with the specific response dependent on the base pair order. Furthermore, we find that (AT)12 forms stable periodic structures with non-canonical hydrogen bonds in some regions and non-canonical stacking in others, whereas (CG)12 forms a stacking motif of four base pairs independent of supercoiling density. Our results demonstrate that 20–30% DNA extension is sufficient for breaking B-DNA around and significantly above cellular supercoiling, and that the DNA sequence is crucial for understanding structural changes under mechanical stress. Our findings have important implications for the activities of protein machinery interacting with DNA in all cells.
Human adipose-derived mesenchymal stem cells accelerate decellularized neobladder regeneration
Authors: Moreno-Manzano Victoria , Mellado-López Maravillas , Morera-Esteve Maria Jose , Alastrue-Agudo Ana , Bisbal-Velasco Viviana , Forteza-Vila Jerónimo , Serrano-Aroca Ángel , Vera-Donoso César David 
Published: December 2019

Abstract
Decellularized natural bladder matrices (neobladders) represent an exciting means to regenerate the bladder following bladder cancer-associated cystectomy. In this study, we compare the evolution of decellularized matrices with recellularized matrices by seeding it with human adipose-derived mesenchymal stem cells (ADSC) after implantation following partial cystectomy in rats. We discovered significant anatomical differences since 10 days after neobladder implantation with the ADSC-containing matrices promoting a significant recovery of mature p63- and cytokeratin 7-positive urothelium. We also discovered significantly induced expression of the vimentin mesoderm marker in the submucosal layer in ADSC-seeded matrices. Interestingly, we found a higher expression of smooth muscle actin in transversal and longitudinal smooth muscle layers with ADSC-seeded matrices. Furthermore, ADSC also showed increased vascularization and nerve innervation of the neobladder as determined by the distribution of CD31 and S100β reactivity, respectively. We believe that ADSC and their paracrine-acting pro-regenerative secretome within decellularized matrices represent an efficient bladder substitution strategy; however, we require a fuller understanding of the mechanisms involved before clinical studies can begin.
The Authorship of Donne’s Verse Letters to the Countess of Huntingdon
Authors: May Steven W 
Published: December 2019

Abstract
Donne’s verse letter to the Countess of Huntingdon beginning ‘That unripe side of earth’ was first printed in the second edition of Donne’s Poems (1635), and was reprinted as his in subsequent editions until 1912, when H. J. C. Grierson demoted it to a merely ‘attributed’ poem. Donne’s authorship, fully supported by the seventeenth-century printed editions, was contradicted by three manuscript copies of the poem that unanimously attributed it to the courtier and diplomat Sir Walter Aston. Scholars and editors since Grierson have wavered on their acceptance of this verse letter as reliably Donne’s work. The Donne Variorum edition of the verse letters (2018) classifies it as a ‘Poem of Disputed Canonicity’. I argue in this essay that textual, stylistic, and biographical evidence establish beyond reasonable doubt that Donne wrote this verse letter to Lady Huntingdon, and that Aston’s name became attached to it through an understandable error in scribal transmission of the poem.
Interpretable Deep Temporal Structure Learning Model for Early Detection of Alzheimer’s Disease
Authors: Wang Xiaoqian , Shen Dinggang , Huang Heng 
Published: December 2019

AbstractIn Alzheimer’s research, Mild Cognitive Impairment (MCI) is an important intermediate stage between normal aging and Alzheimer’s disease. How to distinguish MCI samples that finally convert to AD from those do not is an essential problem in the prevention and diagnosis of Alzheimer’s. Traditional methods use various classification models to distinguish MCI converters from non-converters, while the performance is usually limited by the small number of available data. Moreover, previous methods only use the data at baseline time for training but ignore the longitudinal information at other time points along the disease progression. To tackle with these problems, we propose a novel deep learning framework that uncovers the temporal correlation structure of the longitudinal neuroimaing data in the disease progression. In the meantime, we formulate our new deep learning model in an interpretable style such that it provides insights on the important features Alzheimer’s research. We conduct extensive experiments on the ADNI cohort and outperform the related methods with significant margin.
Image Processing in DNA
Authors: Pan Chao , Tabatabaei Yazdi S. M. Hossein , Tabatabaei S Kasra , Hernandez Alvaro G. , Schroeder Charles , Milenkovic Olgica 
Published: December 2019

ABSTRACTThe main obstacles for the practical deployment of DNA-based data storage platforms are the prohibitively high cost of synthetic DNA and the large number of errors introduced during synthesis. In particular, synthetic DNA products contain both individual oligo (fragment) symbol errors as well as missing DNA oligo errors, with rates that exceed those of modern storage systems by orders of magnitude. These errors can be corrected either through the use of a large number of redundant oligos or through cycles of writing, reading, and rewriting of information that eliminate the errors. Both approaches add to the overall storage cost and are hence undesirable. Here we propose the first method for storing quantized images in DNA that uses signal processing and machine learning techniques to deal with error and cost issues without resorting to the use of redundant oligos or rewriting. Our methods rely on decoupling the RGB channels of images, performing specialized quantization and compression on the individual color channels, and using new discoloration detection and image inpainting techniques. We demonstrate the performance of our approach experimentally on a collection of movie posters stored in DNA.
A Late Cretaceous Lonchodectid?
Authors: Albuquerque Carlos 
Published: December 2019

AbstractA pterosaur ulnar specimen (NZMS CD 467) from the Mangahouanga Stream of New Zealand’s North Island has been first described by Wiffen et al 1988. Assumed to belong to a“Santanadactylus-like” pterosaur, this taxon has not since been extensively described, with only a few tentative claims that it represents an azhdarchid. Here, I re-examine the specimen and compare it to other pterodactyloid taxa, noting peculiar features such as its plug-like (obdurate) ulnar end. ChristenedParirau ataroa, this taxon is found to be a lonchodectid, which alongside the North AmericanNavajodactylus boereiextends this clade into the world’s youngest pterosaur faunas.
An integrated deep mutational scanning approach provides clinical insights onPTENgenotype-phenotype relationships
Authors: Mighell Taylor L. , Thacker Stetson , Fombonne Eric , Eng Charis , O’Roak Brian J. 
Published: December 2019

AbstractGermline variation inPTENresults in variable clinical presentations, including benign and malignant neoplasia and neurodevelopmental disorders. Despite decades of research, it remains unclear howPTENgenotype is related to clinical outcomes. In this study, we combined two recent deep mutational scanning (DMS) datasets probing the effects of single-amino acid variation on enzyme activity and steady-state cellular abundance with the largest well-curated clinical cohort ofPTEN-variant carriers. We sought to connect variant-specific molecular phenotypes to the clinical outcomes of individuals withPTENvariants. We found that DMS data partially explain quantitative clinical traits, including head circumference and Cleveland Clinic (CC) score, which is a semi-quantitative surrogate of disease burden. We built a logistic regression model using DMS and CADD scores to separate clinicalPTENvariation from gnomAD control-only variation with high accuracy (AUC = 0.892). Using a survival-like analysis, we identified molecular phenotype groups with differential risk of early-onset as well as lifetime risk of cancer. Finally, we identified classes of DMS-defined variants with significantly different risk levels for classical hamartoma-related features (odds ratios range of 4.1-102.9). In stark contrast, the risk for developing autism or developmental delay does not significantly change across variant classes (odds ratios range of 5.4-12.4). Together, these findings highlight the potential impact of combining DMS datasets with rich clinical data, and provide new insights that may guide personalized clinical decisions forPTEN-variant carriers.
Estimation of Quasi-Continuous Blood Pressure based on Harmonic Phase-Shifts in Signals using Non-Invasive Photoplethysmographic Measurements
Authors: Kern Fabian , Bernhard Stefan 
Published: December 2019

AbstractAccording to the guidelines of the European Society of Hypertension International Protocol revision 2010, the requirements for long time blood pressure measurement (BPM) are: simple handling, robustness against movements, accuracy of better than ± 5 mmHg, and, above all, that the patient’s motion should not be restricted during measurement. These requirements are in particular important for a reliable interpretation of the blood pressure (BP) of hypertensive patients, because such a diagnosis will usually be confirmed by long-term measurement. Moreover, to be able to correlate the patient’s BP with his normal daily activity, non-obstructive non-invasive methods are desired to reduce the patient’s load.The main concern of this paper is to present a novel method for estimating non-invasive continuous blood pressure (CBP) from a single photoplethysmography (PPG) signal. In contrast to the pulse transit time (PTT) method, our approach is based on the assumption that the phase-velocities of the fundamental and higher harmonics depend on the (non-linear) elastic properties of the arteries. Consequently, phase velocity varies as a function of a vessel’s instantaneous dilation and can be effectively utilised for CBP estimation.In addition to its numerous advantages for a simplified measurement setup, we could show that the method achieves a high degree of correlation for a reliable BP estimation from PPG data. Comparison with state-of-the-art PTT methods was carried out using a dataset from the PhysioBank Database comprising a reference invasive blood pressure (IBP) signal measured at the radial artery, a PPG signal measured at the fingertip and a standard ECG signal.The correlation values obtained from the long-time estimation of the systolic blood pressure (SBP) were as high as r = 0.8945, while the value for the diastolic blood pressure (DBP) was found to be r = 0.9082 and the correlation of the mean blood pressure (MBP) was r = 0.9322. These results were achieved by analysing the dataset in a beat-to-beat manner and regarding several post-processing procedures like coherent averaging (CA) and zero padding with quasi-continuous frequency domain estimation and artificially refined frequency resolution.
The effects of polygenic risk for psychiatric disorders and smoking behaviour on psychotic experiences in UK Biobank
Authors: García-González Judit , Ramírez Julia , Howard David M. , Brennan Caroline H , Munroe Patricia B. , Keers Robert 
Published: December 2019

ABSTRACTWhile psychotic experiences are core symptoms of mental health disorders like schizophrenia, they are also reported by 5-10% of the population. Both smoking behaviour and genetic risk for psychiatric disorders have been associated with psychotic experiences, but the interplay between these factors remains poorly understood. We tested whether smoking status, maternal smoking around birth, and number of packs smoked/year were associated with lifetime occurrence of three psychotic experiences phenotypes: delusions (n=2 067), hallucinations (n=6 689), and any psychotic experience (delusions or hallucinations; n=7 803) in 157 366 UK Biobank participants. We next calculated polygenic risk scores for schizophrenia (PRSSCZ), bipolar disorder (PRSBP), major depression (PRSDEP) and attention deficit hyperactivity disorder (PRSADHD) in 144 818 UK Biobank participants of European ancestry to assess whether association between smoking and psychotic experiences was attenuated after adjustment of diagnosis of psychiatric disorders and the PRSs. Finally, we investigated whether smoking exacerbates the effects of genetic predisposition on the psychotic phenotypes in gene-environment interaction models. Smoking status, maternal smoking, and number of packs smoked/year were associated with psychotic experiences (p<1.77×10-5). Except for packs smoked/year, effects were attenuated but remained significant after adjustment for diagnosis of psychiatric disorders and PRSs (p<1.99×10-3). Gene-environment interaction models showed the effects of PRSDEP and PRSADHD (but not PRSSCZ or PRSBP) on delusions (but not hallucinations) were significantly greater in current smokers compared to never smokers (p<0.002). There were no significant gene-environment interactions for maternal smoking nor for number of packs smoked/year. Our results suggest that both genetic risk of psychiatric disorders and smoking status may have independent and synergistic effects on specific types of psychotic experiences.
Immunologic and epidemiologic drivers of norovirus transmission in daycare and school outbreaks
Authors: Havumaki Joshua , Eisenberg Joseph NS , Mattison Claire P , Lopman Benjamin A , Ortega-Sanchez Ismael R , Hall Aron J , Hutton David W , Eisenberg Marisa C 
Published: December 2019

1AbstractBackgroundNorovirus outbreaks are notoriously explosive, with dramatic symptomology and rapid disease spread. Children are particularly vulnerable to infection and drive norovirus transmission due to their high contact rates with each other and the environment. Despite the explosive nature of norovirus outbreaks, attack rates in schools and daycares remain low with the majority of students not reporting symptoms.MethodsWe explore immunologic and epidemiologic mechanisms that may underlie epidemic norovirus transmission dynamics using a disease transmission model. Towards this end, we compared different model scenarios, including innate resistance and acquired immunity (collectively denoted ‘immunity’), stochastic extinction, and an individual exclusion intervention. We calibrated our model to daycare and school outbreaks from national surveillance data.ResultsRecreating the low attack rates observed in daycare and school outbreaks required a model with immunity. However, immunity alone resulted in shorter duration outbreaks than what was observed. The addition of individual exclusion (to the immunity model) extended outbreak durations by reducing the amount of time that symptomatic people contribute to transmission. Including both immunity and individual exclusion mechanisms resulted in simulations where both attack rates and outbreak durations were consistent with surveillance data.ConclusionsThe epidemiology of norovirus outbreaks in daycare and school settings cannot be well described by a simple transmission model in which all individuals start as fully susceptible. Interventions should leverage population immunity and encourage more rigorous individual exclusion to improve venue-level control measures.
Forecasting Cycles of Seizure Likelihood
Authors: Karoly Philippa J. , Cook Mark J. , Maturana Matias , Nurse Ewan S. , Payne Daniel , Brinkmann Ben , Grayden David B. , Dumanis Sonya B. , Richardson Mark P. , Worrell Greg , Schulze-Bonhage Andreas , Kuhlmann Levin , Freestone Dean R. 
Published: December 2019

SUMMARYObjectiveSeizure unpredictability is rated as one of the most challenging aspects of living with epilepsy. Seizure likelihood can be influenced by a range of environmental and physiological factors that are difficult to measure and quantify. However, some generalizable patterns have been demonstrated in seizure onset. A majority of people with epilepsy exhibit circadian rhythms in their seizure times and many also show slower, multiday patterns. Seizure cycles can be measured using a range of recording modalities, including self-reported electronic seizure diaries. This study aimed to develop personalized forecasts from a mobile seizure diary app.MethodsForecasts based on circadian and multiday seizure cycles were tested pseudo-prospectively using data from 33 app users (mean of 103 seizures per subject). Individual’s strongest cycles were estimated from their reported seizure times and used to derive the likelihood of future seizures. The forecasting approach was validated using self-reported events and electrographic seizures from the Neurovista dataset, an existing database of long-term electroencephalography that has been widely used to develop forecasting algorithms.ResultsThe validation dataset showed that forecasts of seizure likelihood based on self-reported cycles were predictive of electrographic seizures. Forecasts using only mobile app diaries allowed users to spend an average of 62.8% of their time in a low-risk state, with 16.6% of their time in a high-risk warning state. On average, 64.5% of seizures occurred during high-risk states and less than 10% of seizures occurred in low-risk states.SignificanceSeizure diary apps can provide personalized forecasts of seizure likelihood that are accurate and clinically relevant for electrographic seizures. These results have immediate potential for translation to a prospective seizure forecasting trial using a mobile diary app. It is our hope that seizure forecasting apps will one day give people with epilepsy greater confidence in managing their daily activities.
Detection of transient neurotransmitter response using personalized neural networks
Authors: Klyuzhin Ivan S. , Bevington Connor W. J. , Cheng Ju-Chieh (Kevin) , Sossi Vesna 
Published: December 2019

AbstractMeasurement of stimulus-induced dopamine release and other types of transient neurotransmitter response (TNR) from dynamic PET images typically suffers from limited detection sensitivity and high false positive rates. Measurement of TNR of a voxel-level can be particularly problematic due to high image noise. In this work, we perform voxel-level TNR detection using artificial neural networks (ANN) and compare their performance to previously used standard statistical tests. Different ANN architectures were trained and tested using simulated and real human PET imaging data, obtained with the tracer [11C]raclopride (a D2 receptor antagonist). A distinguishing feature of our approach is the use of “personalized” ANNs that are designed to operate on the image from a specific subject and scan. Training of personalized ANNs was performed using simulated images that have been matched with the acquired image in terms of the signal and noise.In our tests of TNR detection performance, the F-test of the linear parametric neurotransmitter PET (lp-ntPET) model fit residuals was used as the reference method. For a moderate TNR magnitude, the areas under the receiver operating characteristic curves in simulated tests were 0.64 for the F-test and 0.77–0.79 for the best ANNs. At a fixed false positive rate of 0.01, the true positive rates were 0.6 for the F-test and 0.8–0.9 for the ANNs. When applied to a real image, the ANNs identified a TNR cluster missed by the F-test. The newly found cluster was verified to contain TNR by direct lp-ntPET model fitting. These results demonstrate that personalized ANNs may offer a greater detection sensitivity of dopamine release and other types of TNR compared to previously used methods.