Miracle-dz/UniFAR-FTUs huggingface dataset:
The York Gospels: a 1000-year biological palimpsest

Medieval manuscripts, carefully curated and conserved, represent not only an irreplaceable documentary record but also a remarkable reservoir of biological information. Palaeographic and codicological investigation can often locate and date these documents with remarkable precision. The York Gospels (York Minster Ms. Add. 1) is one such codex, one of only a small collection of pre-conquest Gospel books to have survived the Reformation. By extending the non-invasive triboelectric (eraser-based) sampling technique eZooMS, to include the analysis of DNA, we report a cost-effective and simple-to-use biomolecular sampling technique for parchment. We apply this combined methodology to document for the first time a rich palimpsest of biological information contained within the York Gospels, which has accumulated over the 1000-year lifespan of this cherished object that remains an active participant in the life of York Minster. These biological data provide insights into the decisions made in the selection of materials, the construction of the codex and the use history of the object.
Sanitation and Religion in South Asia: What Accounts for Differences across Countries?

Abstract Exposure to open defecation has serious consequences for child mortality, health, and human capital development. South Asia has the highest rates of open defecation worldwide, and although the incidence declines as household income rises, differences across South Asian countries are not explained by differences in per capita income. The rate of open defecation in sub-national regions of Bangladesh, India and Nepal is highly correlated with the fraction of the population that identifies as Hindu, in part because certain rituals of purity and pollution discourage having latrines in close proximity to one’s home. Almost all open defecation occurs in rural areas, and this paper estimates how much the rate could be reduced if rural households in regions that have a higher fraction of Hindus, where open defecation is still common, altered their behaviour to reflect that of non-Hindu households in regions that are predominantly non-Hindu, where the rate of open defecation is much lower. Using nonparametric reweighting methods, this paper projects that rural open defecation in Bangladesh, India, and Nepal could be reduced to rates of between 6 and 8 per cent, compared to the prevailing level of 65 per cent.
When does measurement error in covariates impact causal effect estimates? Analytic derivations of different scenarios and an empirical illustration

The average causal treatment effect (ATE) can be estimated from observational data based on covariate adjustment. Even if all confounding covariates are observed, they might not necessarily be reliably measured and may fail to obtain an unbiased ATE estimate. Instead of fallible covariates, the respective latent covariates can be used for covariate adjustment. But is it always necessary to use latent covariates? How well do analysis of covariance (ANCOVA) or propensity score (PS) methods estimate the ATE when latent covariates are used? We first analytically delineate the conditions under which latent instead of fallible covariates are necessary to obtain the ATE. Then we empirically examine the difference between ATE estimates when adjusting for fallible or latent covariates in an applied example. We discuss the issue of fallible covariates within a stochastic theory of causal effects and analyse data of a within-study comparison with recently developed ANCOVA and PS procedures that allow for latent covariates. We show that fallible covariates do not necessarily bias ATE estimates, but point out different scenarios in which adjusting for latent covariates is required. In our empirical application, we demonstrate how latent covariates can be incorporated for ATE estimation in ANCOVA and in PS analysis.
EdgeSpeechNets: Highly Efficient Deep Neural Networks for Speech Recognition on the Edge

Despite showing state-of-the-art performance, deep learning for speech recognition remains challenging to deploy in on-device edge scenarios such as mobile and other consumer devices. Recently, there have been greater efforts in the design of small, low-footprint deep neural networks (DNNs) that are more appropriate for edge devices, with much of the focus on design principles for hand-crafting efficient network architectures. In this study, we explore a human-machine collaborative design strategy for building low-footprint DNN architectures for speech recognition through a marriage of human-driven principled network design prototyping and machine-driven design exploration. The efficacy of this design strategy is demonstrated through the design of a family of highly-efficient DNNs (nicknamed EdgeSpeechNets) for limited-vocabulary speech recognition. Experimental results using the Google Speech Commands dataset for limited-vocabulary speech recognition showed that EdgeSpeechNets have higher accuracies than state-of-the-art DNNs (with the best EdgeSpeechNet achieving ~97% accuracy), while achieving significantly smaller network sizes (as much as 7.8x smaller) and lower computational cost (as much as 36x fewer multiply-add operations, 10x lower prediction latency, and 16x smaller memory footprint on a Motorola Moto E phone), making them very well-suited for on-device edge voice interface applications.
The CoNLL–SIGMORPHON 2018 Shared Task: Universal Morphological Reinflection

The CoNLL--SIGMORPHON 2018 shared task on supervised learning of morphological generation featured data sets from 103 typologically diverse languages. Apart from extending the number of languages involved in earlier supervised tasks of generating inflected forms, this year the shared task also featured a new second task which asked participants to inflect words in sentential context, similar to a cloze task. This second task featured seven languages. Task 1 received 27 submissions and task 2 received 6 submissions. Both tasks featured a low, medium, and high data condition. Nearly all submissions featured a neural component and built on highly-ranked systems from the earlier 2017 shared task. In the inflection task (task 1), 41 of the 52 languages present in last year's inflection task showed improvement by the best systems in the low-resource setting. The cloze task (task 2) proved to be difficult, and few submissions managed to consistently improve upon both a simple neural baseline system and a lemma-repeating baseline.
Heparin-bridging therapy is associated with post-colorectal polypectomy bleeding in patients whose oral anticoagulation therapy is interrupted

Abstract Objectives: The American and Japanese Societies for Gastrointestinal Endoscopy Guidelines recommend heparin-bridging therapy for patients whose oral anticoagulants are interrupted for endoscopic procedures. However, little is known about the potential association between heparin-bridging therapy and post-polypectomy bleeding (PPB). The aim was to investigate the incidence of PPB associated with heparin-bridging therapy administered to patients whose anticoagulants were interrupted. Materials and methods: This was a retrospective observational study using inverse propensity analysis. Between 2013 and 2015, 1004 patients with 2863 lesions were included. The primary outcomes were the rates of PPB and thromboembolism associated with heparin-bridging therapy. The risk factors associated with PPB were identified using multivariate logistic regression analysis involving probability of treatment weighting (IPTW). Results: The patients were categorized into a heparin-bridging therapy group (78 patients with 255 lesions) or a control group (926 patients with 2608 lesions). The PPB rate in the heparin-bridging therapy group (10.2%, 8/78) was significantly higher than in the control group (1.1%, 11/926) (p <.01). Thromboembolism occurred in one patient in the control group. The multivariate analysis revealed that heparin-bridging therapy was an independent risk factor associated with PPB (odds ratio [OR], 8.21; 95% confidence interval [95% CI], 2.32–29.10; p <.01). IPTW showed heparin-bridging therapy increased PPB (OR, 7.68; 95% CI, 1.83–32.28; p <.01). Conclusions: Heparin-bridging therapy administered to patients whose oral anticoagulants were interrupted was associated with an increased PPB risk.
[18F]-florbetaben PET/CT Imaging in the Alzheimer's Disease Mouse Model APPswe/PS1dE9.

BACKGROUND Positron-emission-tomography (PET) using 18F labeled florbetaben allows noninvasive in vivo-assessment of amyloid-beta (Aβ), a pathological hallmark of Alzheimer's disease (AD). In preclinical research, [18F]-florbetaben-PET has already been used to test the amyloid-lowering potential of new drugs, both in humans and in transgenic models of cerebral amyloidosis. The aim of this study was to characterize the spatial pattern of cerebral uptake of [18F]-florbetaben in the APPswe/ PS1dE9 mouse model of AD in comparison to histologically determined number and size of cerebral Aβ plaques. METHODS Both, APPswe/PS1dE9 and wild type mice at an age of 12 months were investigated by smallanimal PET/CT after intravenous injection of [18F]-florbetaben. High-resolution magnetic resonance imaging data were used for quantification of the PET data by volume of interest analysis. The standardized uptake values (SUVs) of [18F]-florbetaben in vivo as well as post mortem cerebral Aβ plaque load in cortex, hippocampus and cerebellum were analyzed. RESULTS Visual inspection and SUVs revealed an increased cerebral uptake of [18F]-florbetaben in APPswe/ PS1dE9 mice compared with wild type mice especially in the cortex, the hippocampus and the cerebellum. However, SUV ratios (SUVRs) relative to cerebellum revealed only significant differences in the hippocampus between the APPswe/PS1dE9 and wild type mice but not in cortex; this differential effect may reflect the lower plaque area in the cortex than in the hippocampus as found in the histological analysis. CONCLUSION The findings suggest that histopathological characteristics of Aβ plaque size and spatial distribution can be depicted in vivo using [18F]-florbetaben in the APPswe/PS1dE9 mouse model.
DNMT3B Functions: Novel Insights From Human Disease

DNA methylation plays important roles in gene expression regulation and chromatin structure. Its proper establishment and maintenance are essential for mammalian development and cellular differentiation. DNMT3B is the major de novo DNA methyltransferase expressed and active during the early stage of embryonic development, including implantation. In addition to its well-known role to methylate centromeric, pericentromeric, and subtelomeric repeats, recent observations suggest that DNMT3B acts as the main enzyme methylating intragenic regions of active genes. Although largely studied, much remains unknown regarding how these specific patterns of de novo CpG methylation are established in mammalian cells, and which are the rules governing DNMT3B recruitment and activity. Latest evidence indicates that DNMT3B recruitment is regulated by numerous mechanisms including chromatin modifications, transcription levels, non-coding RNAs, and the presence of DNA-binding factors. DNA methylation abnormalities are a common mark of human diseases involving chromosomal and genomic instabilities, such as inherited disease and cancer. The autosomal recessive Immunodeficiency, Centromeric instability and Facial anomalies syndrome, type I (ICF-1), is associated to hypomorphic mutations in DNMT3B gene, while its altered expression has been correlated with the development of tumors. In both cases, this implies that abnormal DNA hypomethylation and hypermethylation patterns affect gene expression and genomic architecture contributing to the pathological states. We will provide an overview of the most recent research aimed at deciphering the molecular mechanisms by which DNMT3B abnormalities are associated with the onset and progression of these pathologies.
Gray matter volume covariance networks associated with social networks in older adults

ABSTRACT Extensive social networks are associated with better physical, mental, and cognitive health in aging, but the underlying brain substrates remain largely unexplored. Voxel-based morphometry and multivariate statistics were used to identify gray matter volume covariance networks associated with social networks in 86 older adults without dementia (M Age = 75.20 years, 53% women). Gray matter networks associated with the number of high-contact social roles and the total number of network members were identified after adjusting for age, sex, education, global health, and total intracranial volume – and shared nodes included medial, lateral and orbital prefrontal, hippocampal, precuneus, insular, and cingulate regions. Greater expression of these gray matter networks was associated with better memory scores on the Free and Cued Selective Reminding Test. A more distributed network was associated with high-contact social roles than total number of networks members – also extending into amygdala and entorhinal cortex. Thus, high-contact social roles and total number of network members in older adults are associated with gray matter networks composed of regions previously linked to memory and affected by both healthy aging and Alzheimer disease – and high-contact social roles are more strongly associated with brain structures than the total number of network members.
Bioimaging of Intact Polycaprolactone Nanoparticles Using Aggregation‐Caused Quenching Probes: Size‐Dependent Translocation via Oral Delivery

The limited information on biological fate impedes the development of more efficient polymeric nanoparticles for oral delivery of bio‐macromolecules. In this study, the in vivo fate as well as the trans‐epithelia transport of polycaprolactone (PCL) nanoparticles is explored by labeling with aggregation‐caused quenching probes, which is capable of identifying intact nanoparticles. Live imaging and confocal laser scan microscopy confirm size‐dependent absorption of PCL nanoparticles. In general, reducing particle size favors a faster and more oral absorption. Nanoparticles larger than 200 nm, such as 600 and 2000 nm, cannot be efficiently transported across the intestinal membrane. The absorbed nanoparticles (50 and 200 nm) mainly accumulate in the liver. Lymph may be the main absorption route for PCL nanoparticles, transporting 2.39 ± 1.81% and 0.98 ± 0.58% of administered 50 and 200 nm nanoparticles, respectively. Cellular uptake and transportation of PCL nanoparticles are also size dependent. Both enterocytes and M cells mediated transcytosis are involved in the transport of 50 nm PCL nanoparticles, while the M cell pathway is dominative for other nanoparticles. In conclusion, the study provides a valuable tool for bioimaging of intact polymeric nanoparticles as well as solid evidence supporting size‐dependent translocation of the nanoparticles via oral delivery.
Moving Object Detection via Robust Low-Rank and Sparse Separating with High-Order Structural Constraint

Low-rank representation has been successfully applied for moving object detection by assuming the background images are linearly correlated while the moving foreground are sparse. Further, extensive works propose to incorporate the spatial pairwise smoothness of pixels to improve the robustness. In this paper, we investigate the long-range spatiotemporal relationships among pixels, and propose a novel approach to pursue the high-order consistency for moving object detection in the low-rank and sparse separation framework. In particular, we integrate the sparse unary penalty, the spatial pairwise smoothness, and the supervoxel-based high-order consistency into a unified structural constraints on the foreground. Moreover, we propose a single optimization algorithm to learn the background model and the foreground mask at a same time. Extensive experiments on the benchmark datasets GTFD and CDnet suggest that our approach achieves superior performance over several state-of-the-art algorithms.
A Cognitive Agent for Mining Bugs Reports, Feature Suggestions and Sentiment in a Mobile Application Store

Over the last years, mobile applications and their corresponding distribution platforms have gained momentum. Applications stores allow users to write reviews and ratings about the apps, giving feedback to developers. User ratings and reviews may help to improve software quality, solve bugs and develop new features. However, this data is hard to be handled by an individual due to the ever growing amount of textual reviews. This paper proposes the use of cognitive computing technologies for addressing this challenge, by developing a smart agent able to mine bugs reports, feature suggestions and sentiment expressed in mobile app reviews. The main contributions of this paper are: the design of a cognitive agent for assisting developers in managing their interaction with their users, the application of machine learning algorithms for bug and feature request detection, and the agent implementation in a real scenario.
Flow Motifs in Interaction Networks

Many real-world phenomena are best represented as interaction networks with dynamic structures (e.g., transaction networks, social networks, traffic networks). Interaction networks capture flow of data which is transferred between their vertices along a timeline. Analyzing such networks is crucial toward comprehend- ing processes in them. A typical analysis task is the finding of motifs, which are small subgraph patterns that repeat themselves in the network. In this paper, we introduce network flow motifs, a novel type of motifs that model significant flow transfer among a set of vertices within a constrained time window. We design an algorithm for identifying flow motif instances in a large graph. Our algorithm can be easily adapted to find the top-k instances of maximal flow. In addition, we design a dynamic programming module that finds the instance with the maximum flow. We evaluate the performance of the algorithm on three real datasets and identify flow motifs which are significant for these graphs. Our results show that our algorithm is scalable and that the real networks indeed include interesting motifs, which appear much more frequently than in randomly generated networks having similar characteristics.
Penetrance and expressivity of the R858H CACNA1C variant in a five‐generation pedigree segregating an arrhythmogenic channelopathy

Isolated cardiac arrhythmia due to a variant in CACNA1C is of recent knowledge. Most reports have been of singleton cases or of quite small families, and estimates of penetrance and expressivity have been difficult to obtain. We here describe a large pedigree, from which such estimates have been calculated.
CLDN18.1 attenuates malignancy and related signaling pathways of lung adenocarcinoma in vivo and in vitro

Claudins are a family of transmembrane proteins integral to the structure and function of tight junctions (TJ). Disruption of TJ and alterations in claudin expression are important features of invasive and metastatic cancer cells. Expression of CLDN18.1, the lung‐specific isoform of CLDN18, is markedly decreased in lung adenocarcinoma (LuAd). Furthermore, we recently observed that aged Cldn18 −/− mice have increased propensity to develop LuAd. We now demonstrate that CLDN18.1 expression correlates inversely with promoter methylation and with LuAd patient mortality. In addition, when restored in LuAd cells that have lost expression, CLDN18.1 markedly attenuates malignant properties including xenograft tumor growth in vivo as well as cell proliferation, migration, invasion and anchorage‐independent colony formation in vitro. Based on high throughput analyses of Cldn18 −/− murine lung alveolar epithelial type II cells, as well as CLDN18.1‐repleted human LuAd cells, we hypothesized and subsequently confirmed by Western analysis that CLDN18.1 inhibits insulin‐like growth factor‐1 receptor (IGF‐1R) and AKT phosphorylation. Consistent with recent data in Cldn18 −/− knockout mice, expression of CLDN18.1 in human LuAd cells also decreased expression of transcriptional co‐activator with PDZ‐binding motif (TAZ) and Yes‐associated protein (YAP) and their target genes, contributing to its tumor suppressor activity. Moreover, analysis of LuAd cells in which YAP and/or TAZ are silenced with siRNA suggests that inhibition of TAZ, and possibly YAP, is also involved in CLDN18.1‐mediated AKT inactivation. Taken together, these data indicate a tumor suppressor role for CLDN18.1 in LuAd mediated by a regulatory network that encompasses YAP/TAZ, IGF‐1R and AKT signaling.
Feasibility of a Skin Dose Reduction for Nasopharyngeal Carcinoma Treated With High-Intensity-Modulated Delivery Techniques

Acute skin toxicity observed in radiotherapy treatment of head and neck cancer is a big concern. The purpose of this work is to evaluate the feasibility of a skin dose reduction in the treatment of nasopharyngeal carcinoma without compromising the overall plan quality. This research focused on comparison of the skin dose reduction that can be obtained for the main high conformal radiotherapy delivery techniques. Sixteen cases of early-stage nasopharyngeal carcinoma were included in this study. For each case, a dynamic intensity-modulated radiation therapy, a volumetric modulated arc therapy, and a helical tomotherapy treatment plans were performed with and without the skin as a sensitive structure in the inverse plan optimization. The dosimetric results obtained for the different treatment techniques and plan optimizations were compared. Dose–volume histogram cutoff points of D95%, D98%, and the homogeneity index were used for target comparison, while Dmean and Dmax/D1cc were used for the organs at risk. The skin volume receiving 5 Gy and then 10 to 70 Gy of radiation dosage registered at step of 10 Gy and Dmean were used for the skin dose comparison. One-way analysis of variance was used to assess the dosimetric results obtained for the different types of treatment plans and techniques investigated. A total of 96 treatment plans were analyzed. When the neck skin was considered in the treatment optimization process, the skin volume that received more than 30 Gy was reduced by 3.7% for dynamic intensity modulated, 4.1% for volumetric modulated arc, and 4.3% for dynamic intensity modulated, while the target dose coverage and organs at risk dosages remained unvaried (p > .05).
Image Reconstruction from Patch Compressive Sensing Measurements

The compressive sensing theory has been successfully applied to image compression in the past few years. Recently, deep network-based compressive sensing image reconstruction algorithms have been proposed, which reduce the computational complexity compared with traditional iterative reconstruction algorithms. But most of those are patch-based reconstruction methods, which leads to blocky artifacts for the full image assembled by patch reconstruction. In this paper, we propose a novel image reconstruction network (CSReNet) from patch compressive sensing measurements. Different from other deep network-based algorithms, our network can not only recovery image from patch compressive sensing measurements, also remove the blocky artifacts. There are two modules, reconstruction module and removal module in our network. Experimental results on test data show that our proposed network outperforms several compressive sensing reconstruction algorithms with patch-based CS measurements.