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Burn injuries can result from mechanisms such as thermal, chemical, and electrical insults. A prompt and accurate assessment of burns is essential for deciding definitive clinical treatments. Currently, the primary approach for burn assessments, via visual and tactile observations, is approximately 60%-80% accurate. The gold standard is biopsy and a close second would be non-invasive methods like Laser Doppler Imaging (LDI) assessments, which have up to 97% accuracy in predicting burn severity and the required healing time. In this paper, we introduce a machine learning pipeline for assessing burn severities and segmenting the regions of skin that are affected by burn. Segmenting 2D colour images of burns allows for the injured versus non-injured skin to be delineated, clearly marking the extent and boundaries of the localized burn/region-of-interest, even during remote monitoring of a burn patient. We trained a convolutional neural network (CNN) to classify four severities of burns. We built a saliency mapping method, Boundary Attention Mapping (BAM), that utilises this trained CNN for the purpose of accurately localizing and segmenting the burn regions from skin burn images. We demonstrated the effectiveness of our proposed pipeline through extensive experiments and evaluations using two datasets; 1) A larger skin burn image dataset consisting of 1684 skin burn images of four burn severities, 2) An LDI dataset that consists of a total of 184 skin burn images with their associated LDI scans. The CNN trained using the first dataset achieved an average F1-Score of 78% and micro/macro- average ROC of 85% in classifying the four burn severities. Moreover, a comparison between the BAM results and LDI results for measuring injury boundary showed that the segmentations generated by our method achieved 91.60% accuracy, 78.17% sensitivity, and 93.37% specificity.

Additional file 1: Figure S1. Flowchart of patient selection. Table S1. Specification and emulation of a target trial evaluating the effect of NOACs versus warfarin on the risk of incident heart failure using real-world data from Taiwan’s NHIRD. Table S2. Baseline characteristics of elderly patients with atrial fibrillation and diabetes receiving NOAC or warfarin in the original population, without weighting or matching. Table S3. Risk of heart failure in elderly patients with atrial fibrillation and diabetes receiving NOAC versus warfarin in the sensitivity analysis applying propensity score matching and that applying multivariable regression models without propensity score methods. Table S4. Baseline characteristics of elderly patients with atrial fibrillation and diabetes receiving NOAC or warfarin in the population after propensity score matching.

Graphic abstract Droplet and aerosol transmission of COVID-19 are the most important concerns in dental clinics, due to the generation of large amounts of infected aerosol and droplets mixed with patient’s saliva during the procedures. The current approach to prevent airborne disease transmission is an extraoral aerosol suction unit: a stand-alone vacuum module with a segmented arm and cup. Despite the need for disease control in dental offices, these units are rarely seen due to the loud noise produced by vacuum, bulky size, and high cost. This paper describes the aerodynamic design optimization of an affordable, 3D printable, Extraoral Vacuum Aerosol Cup (EVAC) that can be directly connected to existing standard 7/16″ central vacuum high-volume evacuator (HVE) valves used for intraoral saliva absorption in a dental office. These HVEs are typically unsuitable for extraoral suction due to their low vacuum force. However, they can be used for extraoral suction, if the cup attachment is aerodynamically optimized for maximum suction efficiency. Fifteen different designs of EVAC are proposed and their suction processes were simulated with computational fluid dynamics. Droplets of various sizes are released to mimic the droplets produced during dental operation. The suction performances of EVACs with different sizes and shapes were compared to find out the designs with optimal performance. Prototypes of the optimized EVAC are 3D printed and tested at a dental office. Development and manufacturing of such a device will largely reduce the COVID-19 infection risk, thus improving the safety protection for both patients and doctors at dental offices.

Brass (e.g., H80 brass) is widely used in different industrial sectors. To ensure its quality and reliability, the stress damage state of brass has to be monitored and analyzed in practice. This article introduces a new micro-magnetic method for monitoring and analyzing the tensile stress damage state of brass. Specifically, the stress-magnetic flux density model of H80 brass in the micro-magnetic environment was developed based on the stress-magnetic coupling effect. The magnetic flux density value was then used as an indicator of the different stages of tensile damage, and a new model of the spontaneous magnetic signal and structural deformation of H80 brass in the tensile process was established. The tensile test results show that the strain of H80 brass in the elastic deformation stage is 0%-5%, there is a small amount of dislocation, and the magnetic flux density does not change significantly; the strain increases to 5%-48% in the work-hardening stage, the dislocation density increases, a dislocation wall is formed at the internal lattice, and the magnetic flux density decreases with increasing tensile stress; the strain continues to increase to 48%-52% in the necking fracture stage and the internal lattice. When the strain at the necking fracture stage continues to increase to 48%-52%, the dislocation density of the internal lattice gradually stabilizes, accompanied by the appearance of sub-slip bands, and the number and density of slip bands increase; the magnetic flux density drops abruptly when fracturing occurs. These results show that the developed micro-magnetic method is accurate and reliable for monitoring and analyzing the tensile stress damage state of brass.

Widely used evaluation metrics for text generation either do not work well with longer texts or fail to evaluate all aspects of text quality. In this paper, we introduce a new metric called SMART to mitigate such limitations. Specifically, We treat sentences as basic units of matching instead of tokens, and use a sentence matching function to soft-match candidate and reference sentences. Candidate sentences are also compared to sentences in the source documents to allow grounding (e.g., factuality) evaluation. Our results show that system-level correlations of our proposed metric with a model-based matching function outperforms all competing metrics on the SummEval summarization meta-evaluation dataset, while the same metric with a string-based matching function is competitive with current model-based metrics. The latter does not use any neural model, which is useful during model development phases where resources can be limited and fast evaluation is required. Finally, we also conducted extensive analyses showing that our proposed metrics work well with longer summaries and are less biased towards specific models., Comment: code coming soon

While large pretrained Transformer models have proven highly capable at tackling natural language tasks, handling long sequence inputs continues to be a significant challenge. One such task is long input summarization, where inputs are longer than the maximum input context of most pretrained models. Through an extensive set of experiments, we investigate what model architectural changes and pretraining paradigms can most efficiently adapt a pretrained Transformer for long input summarization. We find that a staggered, block-local Transformer with global encoder tokens strikes a good balance of performance and efficiency, and that an additional pretraining phase on long sequences meaningfully improves downstream summarization performance. Based on our findings, we introduce PEGASUS-X, an extension of the PEGASUS model with additional long input pretraining to handle inputs of up to 16K tokens. PEGASUS-X achieves strong performance on long input summarization tasks comparable with much larger models while adding few additional parameters and not requiring model parallelism to train.

Supplementary Tables

Adult-onset leukoencephalopathy with axonal spheroids (ALAS) is a group of hereditary, progressive, neurodegenerative disorders involving primarily the central nervous system white matter (WM). ALAS is characterized by patchy, asymmetrical myelin loss and axonal destruction in the WM, predominantly involving the frontoparietal regions. However, the asymmetrical and heterogenous involvement of different brain regions remains poorly characterized. In this study, digital spatial profiling was performed to investigate the region-specific expressions of 60 proteins. Conventional immunohistochemistry methods was used validate intrepretation of probes. Using a high-plex and high-throughput method, we provide evidence of regional heterogeneity in ALAS, particularly involving key markers of microglia composition, glial cell subtype and autophagy.

The private Lookout Observatory (LO) monitored the classic nova V1112 Persei on 37 nights spanning over 80 days, beginning shortly after its discovery by Seiji Ueda on 25 Nov 2020. Images were captured at a high cadence, with exposure lengths of initially less than 2 seconds and with some sessions lasting more than ten hours. The standard error of the photometry was typically better than 5 thousandths of a magnitude (5 mmag). This cadence and precision allowed for not only the observation of the expected dimming of the nova, but also variability having a period of 0.608 +- 0.005 days. This data compliments the publicly available photometry from the American Association of Variable Star Observers (AAVSO) and the resultant data is combined to perform this photometric analysis. This paper does not attempt an in-depth physical analysis of the nova from an astrophysical perspective., Comment: 10 pages, 9 figures Accepted by JAAVSO

An important goal in human-robot-interaction (HRI) is for machines to achieve a close to human level of face perception. One of the important differences between machine learning and human intelligence is the lack of compositionality. This paper introduces a new scheme to enable generative adversarial networks to learn the distribution of face images composed of smaller parts. This results in a more flexible machine face perception and easier generalization to outside training examples. We demonstrate that this model is able to produce realistic high-quality face images by generating and piecing together the parts. Additionally, we demonstrate that this model learns the relations between the facial parts and their distributions. Therefore, the specific facial parts are interchangeable between generated face images.

Short term Metabolic Effects of Two Novel Oral Androgens, Dimethandrolone Undecanoate and 11β-MNTDC Supplemental File

Supplemental File

Context. Investigations of testosterone (T)-mediated feedback on LH secretion are few and contradictory. Discrepancies may reflect the choice of androgen, as well as dose, route and/or pattern (continuous vspulsatile) of administration. Moreover, for controlled T feedback studies, endogenous T secretion must be shut down and replaced by iv T infusion. Objective. The goal of this study was to quantify feedback in relation to the infused T pattern (pulsatile vs nonpulsatile) with the hypotheses that: 1) feedback by T pulses is less inhibitory, 2) T feedback operates partly at the pituitary level, 3) T feedback is controlled in part by body composition. Design. This was a placebo-controlled, blinded, and prospectively randomized crossover study comprising 16 men (age range 23-54 yr and BMI between 22.3-34.2 kg/m2). Subjects received ketoconazole (1600 mg over 22 h to block endogenous T secretion: serum T below 85 ng/dL) followed by pulsatile or continuous iv T addback (total T dose 5 mg/22 h). Cortisol inhibition was treated with low-dose dexamethasone. Setting. The study was performed in a Clinical Translational Research Unit. Interventions. Subjects underwent 14 h of blood sampling at 10-min intervals, starting at 0600 h. T was given continuously starting at 0900 h or as 45-min squarewave pulses every 90 min. To test pituitary feedback, a bolus iv injection of 33 ng/kg GnRH was given at 1630 h. Outcome measures. Log-transformed LH and T concentration ratios before and after GnRH administration. In addition, a four-parameter logistic regression between serum T concentration and LH secretion rate (calculated by deconvolution analysis) was used to independently estimate feedback of T on LH. Results. Despite higher T (total, bioavailable and free T) concentrations during pulsatile feedback, LH concentrations and secretion rates, whether driven by endogenous or exogenous GnRH, were similar to those during continuous infusion, indicating diminished pulsatile T feedback. This interpretation was corroborated by the right-shift (i.e. towards higher T concentrations) of the T-LH inhibitory dose-response curve. During continuous and pulsatile T infusion, LH concentrations and LH:T ratios depended positively on BMI to a similar extent before and after GnRH administration. Under both feedback regimens, basal but not pulsatile LH secretion correlated negatively with CT-estimated abdominal visceral fat mass. Age was not a significant covariate over the limited range explored. Conclusion. In summary, pulsatile T is less effective as a negative feedback signal on LH secretion than continuous T administration, as demonstrated by two independent methods. This difference in feedback strength was evident for exogenous GnRH-stimulated LH release as well, and thus occurs probably in part at the pituitary level, while not excluding additional hypothalamic involvement.

Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts all text-based language problems into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled data sets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new ``Colossal Clean Crawled Corpus'', we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our data set, pre-trained models, and code., Comment: Final version as published in JMLR

Recent work pre-training Transformers with self-supervised objectives on large text corpora has shown great success when fine-tuned on downstream NLP tasks including text summarization. However, pre-training objectives tailored for abstractive text summarization have not been explored. Furthermore there is a lack of systematic evaluation across diverse domains. In this work, we propose pre-training large Transformer-based encoder-decoder models on massive text corpora with a new self-supervised objective. In PEGASUS, important sentences are removed/masked from an input document and are generated together as one output sequence from the remaining sentences, similar to an extractive summary. We evaluated our best PEGASUS model on 12 downstream summarization tasks spanning news, science, stories, instructions, emails, patents, and legislative bills. Experiments demonstrate it achieves state-of-the-art performance on all 12 downstream datasets measured by ROUGE scores. Our model also shows surprising performance on low-resource summarization, surpassing previous state-of-the-art results on 6 datasets with only 1000 examples. Finally we validated our results using human evaluation and show that our model summaries achieve human performance on multiple datasets., Comment: Added results from mixed+stochastic model, test-set overlapping analysis; Code link added; Accepted for ICML 2020. arXiv admin note: text overlap with arXiv:1605.06560, arXiv:1205.2395, arXiv:0902.4351, arXiv:1610.09932, arXiv:nucl-ex/0512029 by other authors

11B-MNTDC 28 Day Study Supplemental File

Massively multi-label prediction/classification problems arise in environments like health-care or biology where very precise predictions are useful. One challenge with massively multi-label problems is that there is often a long-tailed frequency distribution for the labels, which results in few positive examples for the rare labels. We propose a solution to this problem by modifying the output layer of a neural network to create a Bayesian network of sigmoids which takes advantage of ontology relationships between the labels to help share information between the rare and the more common labels. We apply this method to the two massively multi-label tasks of disease prediction (ICD-9 codes) and protein function prediction (Gene Ontology terms) and obtain significant improvements in per-label AUROC and average precision for less common labels.

We show that generating English Wikipedia articles can be approached as a multi- document summarization of source documents. We use extractive summarization to coarsely identify salient information and a neural abstractive model to generate the article. For the abstractive model, we introduce a decoder-only architecture that can scalably attend to very long sequences, much longer than typical encoder- decoder architectures used in sequence transduction. We show that this model can generate fluent, coherent multi-sentence paragraphs and even whole Wikipedia articles. When given reference documents, we show it can extract relevant factual information as reflected in perplexity, ROUGE scores and human evaluations., Comment: Published as a conference paper at ICLR 2018

We performed a 28-day daily dosing pharmacokinetic and pharmacodynamic study of a novel oral androgen dimethandrolone undecanoate, DMAU, in healthy men. DMAU showed marked suppression of serum gonadotropins and testosterone in men, while being well-tolerated. There were no safety concerns in this 28-day study. Further studies are ongoing to assess the ability of DMAU to suppress spermatogenesis.

ive summarization has been studied using neural sequence transduction methods with datasets of large, paired document-summary examples. However, such datasets are rare and the models trained from them do not generalize to other domains. Recently, some progress has been made in learning sequence-to-sequence mappings with only unpaired examples. In our work, we consider the setting where there are only documents (product or business reviews) with no summaries provided, and propose an end-to-end, neural model architecture to perform unsupervised abstractive summarization. Our proposed model consists of an auto-encoder where the mean of the representations of the input reviews decodes to a reasonable summary-review while not relying on any review-specific features. We consider variants of the proposed architecture and perform an ablation study to show the importance of specific components. We show through automated metrics and human evaluation that the generated summaries are highly abstractive, fluent, relevant, and representative of the average sentiment of the input reviews. Finally, we collect a reference evaluation dataset and show that our model outperforms a strong extractive baseline., Comment: Accepted to ICML 2019

We performed a 28-day daily dosing pharmacokinetic and pharmacodynamic study of a novel oral androgen dimethandrolone undecanoate, DMAU, in healthy men. DMAU showed marked suppression of serum gonadotropins and testosterone in men, while being well-tolerated. There were no safety concerns in this 28-day study. Further studies are ongoing to assess the ability of DMAU to suppress spermatogenesis.

Safety and Pharmacokinetics of Oral 11βMNTDC - supplemental data

Description: representative search strategy. (DOCX 13Â kb)

Additional file 3: Figure S1. Western blot analysis of light chain secretion.

ObjectiveAcutely restricting sleep worsens insulin sensitivity in healthy individuals whose usual sleep is normal in duration and pattern. The effect of recovery or weekend 'catch-up' sleep on insulin sensitivity and metabolically active hormones in individuals with chronic sleep restriction who regularly 'catch-up' on sleep at weekends is as yet unstudied.Design19 men (mean±SEM age 28·6±2·0years, BMI 26·0±0·8kg/m(2) ) with at least 6months' history (5·1±0·9years) of lifestyle-driven, restricted sleep during the working week (373±6·6min/night) with regular weekend 'catch-up' sleep (weekend sleep extension 37·4±2·3%) completed an in-laboratory, randomized, crossover study comprising two of three conditions, stratified by age. Conditions were 3weekend nights of 10 hours, 6 hours or 10hours time-in-bed with slow wave sleep (SWS) suppression using targeted acoustic stimuli.MeasurementsInsulin sensitivity was measured in the morning following the 3rd intervention night by minimal modelling of 19 samples collected during a 2-h oral glucose tolerance test. Glucose, insulin, c-peptide, leptin, peptide YY (PYY), ghrelin, cortisol, testosterone and luteinizing hormone (LH) were measured from daily fasting blood samples; HOMA-IR, HOMA-β and QUICKI were calculated.ResultsInsulin sensitivity was higher following three nights of sleep extension compared to sustained sleep restriction. Fasting insulin, c-peptide, HOMA-IR, HOMA-β, leptin and PYY decreased with 'catch-up' sleep, QUICKI and testosterone increased, while morning cortisol and LH did not change. Targeted acoustic stimuli reduced SWS by 23%, but did not alter insulin sensitivity.ConclusionsThree nights of 'catch-up' sleep improved insulin sensitivity in men with chronic, repetitive sleep restriction. Methods to improve metabolic health by optimizing sleep are plausible.

Multimodal biometric technology provides potential solutions for continuous user-to-device authentication in high-security mobile ad hoc networks (MANETs). This paper studies distributed combined authentication and intrusion detection with data fusion in such MANETs. Multimodal biometrics are deployed to work with intrusion detection systems (IDSs) to alleviate the shortcomings of unimodal biometric systems. Since each device in the network has measurement and estimation limitations, more than one device needs to be chosen, and observations can be fused to increase observation accuracy using Dempster-Shafer theory for data fusion. The system decides whether user authentication (or IDS input) is required and which biosensors (or IDSs) should be chosen, depending on the security posture. The decisions are made in a fully distributed manner by each authentication device and IDS. Simulation results are presented to show the effectiveness of the proposed scheme.

The aggregation of intracellular tau protein is a major hallmark of Alzheimer's disease (AD). The extent and the stereotypical spread of tau pathology in the AD brain are correlated with cognitive decline during disease progression. Here we present an in-depth analysis of endogenous tau fragmentation in a well-characterized cohort of AD and age-matched control subjects. Using protein mass spectrometry and Edman degradation to interrogate endogenous tau fragments in the human brain, we identified two novel proteolytic sites, G323 and G326, as major tau cleavage events in both normal and AD cortex. These sites are located within the sequence recently identified as the structural core of tau protofilaments, suggesting an inhibitory mechanism of fibril formation. In contrast, a different set of novel cleavages showed a distinct increase in late stage AD. These disease-associated sites are located outside of the protofi lament core sequence. We demonstrate that calpain 1 specifically cleaves at both the normal and diseased sites in vitro, and the site selection is conformation-dependent. Monomeric tau is predominantly cleaved at G323/G326 (normal sites), whereas oligomerization increases cleavages at the late-AD-associated sites. The fragmentation patterns specific to disease and healthy states suggest novel regulatory mechanisms of tau aggregation in the human brain.

GACD hypertension consensus variables, suggested collection methods and number of teams using variable.(DOCX 36Â kb)

Echocardiographic Protocol. The standardized echocardiographic protocol outlines all images to be obtained in order to meet the study objectives in the Cardiac Imaging Core. (DOCX 13Â kb)

Description: representative search strategy. (DOCX 13Â kb)

GACD hypertension consensus variables, suggested collection methods and number of teams using variable.(DOCX 36Â kb)

Echocardiographic Protocol. The standardized echocardiographic protocol outlines all images to be obtained in order to meet the study objectives in the Cardiac Imaging Core. (DOCX 13Â kb)

Context: Alongside the growing epidemics of obesity and diabetes mellitus, chronic partial sleep restriction is also increasingly common in modern society, and the metabolic implications of this have not been fully illustrated as yet. Whether recovery sleep is sufficient to offset these detriments is an area of ongoing research. Conclusion: Sleep often seems undervalued in modern society, but this may have widespread metabolic consequences as described in this review. Acute sleep loss is often unavoidable, but chronic sleep restriction ideally should not be. Understanding the implications of both sleep restriction and recovery on metabolic outcomes will guide public health policy and allow clinical recommendations to be prescribed. Data Acquisition: The medical literature from 1970 to March 2012 was reviewed for key articles. Data Synthesis: Epidemiological studies suggest associations between shortened sleep and future obesity and diabetes. Experimental data thus far show a probable link between shortened sleep and altered glucose metabolism as well as appetite dysregulation. Objective: This review seeks to summarize the relevant epidemiological and experimental data in the areas of altered metabolic consequences of both shortened sleep and subsequent recovery sleep. Refereed/Peer-reviewed

Purpose: Large quantities of neurophysiological electroencephalogram (EEG) data are routinely collected in the sleep laboratory. These are underutilised due to the burden of managing artefact contamination. The aim of this study was to develop a new tool for automated artefact rejection that facilitates subsequent quantitative analysis of sleep EEG data collected during routine overnight polysomnography (PSG) in subjects with and without sleep-disordered breathing (SDB). Methods: We evaluated the accuracy of an automated algorithm to detect sleep EEG artefacts against artefacts manually scored by three experienced technologists (reference standard) in 40 PSGs. Spectral power was computed using artefact-free EEG data derived from (1) the reference standard, (2) the algorithm and (3) raw EEG without any prior artefact rejection.Results: The algorithm showed a high level of accuracy of 94.3, 94.7 and 95.8 % for detecting artefacts during the entire PSG, NREM sleep and REM sleep, respectively. There was good to moderate sensitivity and excellent specificity of the algorithm detection capabilities during sleep. The EEG spectral power for the reference standard and algorithm was significantly lower than that of the raw, unprocessed EEG signal.Conclusions: These preliminary findings support an automated way to process EEG artefacts during sleep, providing the opportunity to investigate EEG-based markers of neurobehavioural impairment in sleep disorders in future studies Refereed/Peer-reviewed