Miracle-dz/UniFAR-FTUs huggingface dataset:
Use of Autologous Stem Cells for the Regeneration of Periodontal Defects in Animal Studies: a Systematic Review and Meta-Analysis

ABSTRACT Objectives To overview preclinical animal trials and quantify the effect size that stem cell therapy has on the regeneration of periodontal tissue complex. Material and Methods A systematic MEDLINE (PubMed) online library search was conducted for preclinical animal studies in vivo, using autologous periodontal ligament, dental pulp, cementum, alveolar periosteal, gingival margin or adipose stem cell types for periodontal tissue complex regeneration purposes. Studies had to be published between 2007.09. 01 and 2017.09. 01 in the English language. Results Online library search yielded 2099 results. After the title, abstract and full-text screening ten studies fit inclusion criteria and were pooled into meta-analysis. Overall the stem cell regenerative therapy had a statistically significant positive influence on the periodontal tissue regeneration when compared to the control groups. The biggest influence was made to the regeneration of cementum (standardised mean difference [SMD] 2.25 [95% confidence interval (CI) = 1.31 to 3.2]) while the smallest influence was made to the alveolar bone (SMD 1.47 [95% CI = 0.7 to 2.25]) the effect size for periodontal ligament regeneration was (SMD 1.8 [95% CI = 1 to 2.59]). Subgroup analysis showed statistically significant (P < 0.05) differences between different cell types in the alveolar bone and cementum regeneration groups and in alveolar bone group in relation to scaffold materials. Conclusions Stem cell therapy has a positive impact on periodontal tissue complex regeneration. Such therapy has the biggest influence on cementum regeneration meanwhile alveolar bone regeneration is influenced by the least amount. However more and less diverse preclinical studies are needed to have a greater statistical power in future meta-analyses.
Multi-task and Multi-lingual Joint Learning of Neural Lexical Utterance Classification based on Partially-shared Modeling

This paper is an initial study on multi-task and multi-lingual joint learning for lexical utterance classification. A major problem in constructing lexical utterance classification modules for spoken dialogue systems is that individual data resources are often limited or unbalanced among tasks and/or languages. Various studies have examined joint learning using neural-network based shared modeling; however, previous joint learning studies focused on either cross-task or cross-lingual knowledge transfer. In order to simultaneously support both multi-task and multi-lingual joint learning, our idea is to explicitly divide state-of-the-art neural lexical utterance classification into language-specific components that can be shared between different tasks and task-specific components that can be shared between different languages. In addition, in order to effectively transfer knowledge between different task data sets and different language data sets, this paper proposes a partially-shared modeling method that possesses both shared components and components specific to individual data sets. We demonstrate the effectiveness of proposed method using Japanese and English data sets with three different lexical utterance classification tasks.
EMPATHIC: Empathic, Expressive, Advanced Virtual Coach to Improve Independent Healthy-Life-Years of the Elderly

The EMPATHIC project will research, innovate, explore and validate new paradigms and platforms, laying the foundation for future generations of Personalised Virtual Coaches to assist elderly people living independently at and around their home. Innovative multimodal face analytics, adaptive spoken dialogue systems and natural language interfaces are part of what the project will research and innovate, in order to help dependent aging persons and their carers. The project will use remote non-intrusive technologies to extract physiological markers of emotional states in real-time for online adaptive responses of the coach, and advance holistic modelling of behavioural, computational, physical and social aspects of a personalised expressive virtual coach. It will develop causal models of coach-user interactional exchanges that engage elders in emotionally believable interactions keeping off loneliness, sustaining health status, enhancing quality of life and simplifying access to future telecare services
Tailoring Neural Architectures for Translating from Morphologically Rich Languages

A morphologically complex word (MCW) is a hierarchical constituent with meaning-preserving subunits, so word-based models which rely on surface forms might not be powerful enough to translate such structures. When translating from morphologically rich languages (MRLs), a source word could be mapped to several words or even a full sentence on the target side, which means an MCW should not be treated as an atomic unit. In order to provide better translations for MRLs, we boost the existing neural machine translation (NMT) architecture with a double- channel encoder and a double-attentive decoder. The main goal targeted in this research is to provide richer information on the encoder side and redesign the decoder accordingly to benefit from such information. Our experimental results demonstrate that we could achieve our goal as the proposed model outperforms existing subword- and character-based architectures and showed significant improvements on translating from German, Russian, and Turkish into English.
Protection from chemotherapy- and antibiotic-mediated dysbiosis of the gut microbiota by a probiotic with digestive enzymes supplement

There are numerous downstream consequences of marketed drugs like antineoplastic agents on the gut microbiome, an effect that is suggested to contribute to adverse event profiles and may also influence drug responses. In cancer, progress is needed toward modulation of the host microbiome to prevent off-target side effects of drugs such as gastrointestinal mucositis that result from gut dysbiosis. The objective of this study was evaluation of the bioactivity of a supplement consisting of capsules with a blend of 9 probiotic organisms of the genera Lactobacillus and Bifidobacterium plus 10 digestive enzymes, in protecting the human gastrointestinal tract from chemotherapy and an antibiotic. We used the Simulator of Human Intestinal Microbial Ecosystem (SHIME) model, an in vitro model of a stable colon microbiota, and introduced 5-fluorouracil (5-FU) and vancomycin as microbiome-disrupting drugs. The probiotic with digestive enzymes supplement, added in capsules at in vivo doses, improved fermentation activity in the colon reactors and accelerated the recovery of microbial populations following 5-FU/vancomycin treatment. The supplement restored the Bacteroidetes to Firmicutes ratios in the colon reactors, increased the diversity of microbiota, and induced the production of microbial metabolites that elicited anti-inflammatory cytokines in an in vitro model of intestinal inflammation. In the proximal colon, preventative administration of the supplement resulted in full recovery of the gut microbial community after cessation of 5-FU and vancomycin treatment. These results identify a probiotic with digestive enzymes formulation that protects against drug-induced gut dysbiosis, highlighting its potential utility as a component of routine cancer care.
Exploring Interaction Mechanisms for Map Interfaces in Virtual Reality Environments

Virtual Reality (VR) technologies offer new ways to visualize and use map interfaces in Geographical Information Systems (GIS). But to explore the potential benefits that this technology might provide in this context, it is necessary to deploy an affordable interaction that is both efficient and satisfactory from the users' point of view. This work aims to provide insights on how to approach the interaction design for this type of novel interfaces. We present a study comparing the usability and performance of two popular interaction strategies: body-based interaction and device-based interaction. In the first case, users controlled the map using their own body, their hands and gaze. In the second case, they managed the application using a gamepad, the Oculus Touch®. The results suggest that controlling the map using the gamepad can improve performance of the selection task, in terms of time and error rate. Also, the users' satisfaction responses for system usability favored this interaction strategy.
Comparative effectiveness of hyperthermic intraperitoneal chemotherapy for gastric cancer

Background: Comparative efficacy and safety of different hyperthermic intraperitoneal chemotherapies (HIPEC) in patients with advanced gastric cancer who underwent gastrectomy is unclear. To investigate this question, we conduct a systematic review and network meta-analysis. Methods: The protocol followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols. PubMed, Embase, and the Cochrane Library will be searched systematically for eligible randomized controlled trials without language restriction. The primary outcome is overall survival. The second outcomes are postoperative complications. The surface under the cumulative ranking curve value will be calculated to establish a hierarchy of the treatments. Results: The results will provide useful information about the effectiveness and safety of HIPEC regimens in patients with resected gastric cancer. Conclusion: The findings of the study will be disseminated through peer-reviewed journal.
Physical Layer Security Enhancement for Satellite Communication Among Similar Channels: Relay Selection and Power Allocation

To implement secure communication among similar channels for satellite communications, a secure communication model based on collaboration with the interference relay of the satellite physical layer is proposed in this paper. Relay selection and power allocation are further studied to enhance the security performance of the satellite communication system based on the model. The relay selection standard under known instantaneous channel state information (CSI) and statistical CSI conditions is theoretically derived, thereby minimizing the secrecy outage probability(SOP). In addition, the power allocation factor is optimized based on the minimization of the SOP. Moreover, a power allocation method based on the statistical CSI is presented. The SOP performance of each relay selection criterion and power allocation scheme are analyzed.
Attending Sentences to detect Satirical Fake News

Satirical news detection is important in order to prevent the spread of misinformation over the Internet. Existing approaches to capture news satire use machine learning models such as SVM and hierarchical neural networks along with hand-engineered features, but do not explore sentence and document difference. This paper proposes a robust, hierarchical deep neural network approach for satire detection, which is capable of capturing satire both at the sentence level and at the document level. The architecture incorporates pluggable generic neural networks like CNN, GRU, and LSTM. Experimental results on real world news satire dataset show substantial performance gains demonstrating the effectiveness of our proposed approach. An inspection of the learned models reveals the existence of key sentences that control the presence of satire in news.
Grape Seed Proanthocyanidin Ameliorates Cardiac Toxicity Induced by Boldenone Undecylenate through Inhibition of NADPH Oxidase and Reduction in the Expression of NOX2 and NOX4

The effect of anabolic androgenic steroids on the cardiovascular system is poorly understood. Increased production of free radicals is coupled to the pathophysiology of many alterations within the circulatory system. The only function of the enzyme family NADPH oxidases (NOXs) is the generation of reactive oxygen species (ROS). Therefore, this study investigated the beneficial role of grape seed proanthocyanidin extract (GSPE) in ameliorating cardiac toxicity induced by the anabolic steroid Boldenone in male rats through NOX inhibition and reduction in the expression of NOX2 and NOX4. This study was conducted on forty male rats which are divided into four groups (normal control, positive control or GSPE, Boldenone, and posttreatment Boldenone with GSPE). A significant increase in relative body weight, relative heart weight, and hemodynamic parameters, as well as serum concentrations of lactate dehydrogenase, creatine kinase, creatine kinase-muscle brain, myoglobin, cholesterol, low-density lipoprotein cholesterol, risk factor 1/2, K+, and Cl−, in treated rats with Boldenone when compared with control. We also noted a significant increase in the levels of cardiac malondialdehyde, H2O2 generation in heart tissues, mRNA expression of NOX2 and NOX4, and immunoreactivity to proliferating cell nuclear antigen (PCNA). Posttreated rats with Boldenone and GSPE ameliorated cardiac toxicity via inhibition of NOX and a reduction in alteration of the expression of NOX2, NOX4, and PCNA induced by Boldenone. These novel insights into the antioxidative activity of GSPE should serve as a basis for the development of improved chemopreventive or therapeutic strategies for cardiac toxicity.
Why do users feel search task difficult?

This paper reports on an examination of why information searchers find search tasks difficult and the specific reasons they feel difficult. Data was collected through a controlled laboratory experiment with 32 participants, each working with 4 search tasks. They were asked in questionnaires, both before and after the tasks, for task difficulty ratings and their reasoning for giving those ratings. We developed a coding scheme based on the difficulty reasons users gave, which covered various aspects of task, user, and user-task interaction. This helps us understand search task difficulty, as well as provides a scheme for task difficulty control in experiment task design.
Infection Analysis on Irregular Networks Through Graph Signal Processing

In a networked system, functionality can be seriously endangered when nodes are infected, due to either internal random failures or a contagious virus that develops into an epidemic. Given a snapshot of the network representing the nodes’ states (infected or healthy), infection analysis refers to distinguishing an epidemic from random failures and gathering information for effective countermeasure design. This analysis is challenging due to irregular network structure, heterogeneous epidemic spreading, and noisy observations. This article treats a network snapshot as a graph signal, and develops effective approaches for infection analysis based on graph signal processing. For the macro (network-level) analysis aiming to distinguish an epidemic from random failures, i) multiple detection metrics are defined based on the graph Fourier transform (GFT) and neighborhood characteristics of the graph signal; ii) a new class of graph wavelets, distance-based graph wavelets (DBGWs), are developed; and iii) a machine learning-based framework is designed employing either the GFT spectrum or the graph wavelet coefficients as features for infection analysis. DBGWs also enable the micro (node-level) infection analysis, through which the performance of epidemic countermeasures can be improved. Extensive simulations are conducted to demonstrate the effectiveness of all the proposed algorithms in various network settings.
N-glycan microheterogeneity regulates interactions of plasma proteins

Significance Glycosylation is one of the most common and complex posttranslation modifications that significantly influences protein structure and function. However, linking individual glycan structures to protein interactions remains challenging and typically requires multiple techniques. Here, we establish a mass-spectrometric approach to systematically dissect the microheterogeneity of two important serum proteins, α1-acid glycoprotein and haptoglobin, and relate glycan features to drug and protein-binding interaction kinetics. We found that the degree of N-glycan branching and extent of terminal fucosylation can attenuate or enhance these interactions, providing important insight into drug transport in plasma. Our study demonstrates an approach capable of investigating how protein glycosylation fine-tunes protein–drug interactions at the glycan-specific level and will prove universally useful for studying glycoprotein interactions. Altered glycosylation patterns of plasma proteins are associated with autoimmune disorders and pathogenesis of various cancers. Elucidating glycoprotein microheterogeneity and relating subtle changes in the glycan structural repertoire to changes in protein–protein, or protein–small molecule interactions, remains a significant challenge in glycobiology. Here, we apply mass spectrometry-based approaches to elucidate the global and site-specific microheterogeneity of two plasma proteins: α1-acid glycoprotein (AGP) and haptoglobin (Hp). We then determine the dissociation constants of the anticoagulant warfarin to different AGP glycoforms and reveal how subtle N-glycan differences, namely, increased antennae branching and terminal fucosylation, reduce drug-binding affinity. Conversely, similar analysis of the haptoglobin–hemoglobin (Hp–Hb) complex reveals the contrary effects of fucosylation and N-glycan branching on Hp–Hb interactions. Taken together, our results not only elucidate how glycoprotein microheterogeneity regulates protein–drug/protein interactions but also inform the pharmacokinetics of plasma proteins, many of which are drug targets, and whose glycosylation status changes in various disease states.
Augmented Reality of the Middle Ear Combining Otoendoscopy and Temporal Bone Computed Tomography.

HYPOTHESIS Augmented reality (AR) may enhance otologic procedures by providing sub-millimetric accuracy and allowing the unification of information in a single screen. BACKGROUND Several issues related to otologic procedures can be addressed through an AR system by providing sub-millimetric precision, supplying a global view of the middle ear cleft, and advantageously unifying the information in a single screen. The AR system is obtained by combining otoendoscopy with temporal bone computer tomography (CT). METHODS Four human temporal bone specimens were explored by high-resolution CT-scan and dynamic otoendoscopy with video recordings. The initialization of the system consisted of a semi-automatic registration between the otoendoscopic video and the 3D CT-scan reconstruction of the middle ear. Endoscope movements were estimated by several computer vision techniques (feature detectors/descriptors and optical flow) and used to warp the CT-scan to keep the correspondence with the otoendoscopic video. RESULTS The system maintained synchronization between the CT-scan image and the otoendoscopic video in all experiments during slow and rapid (5-10 mm/s) endoscope movements. Among tested algorithms, two feature-based methods, scale-invariant feature transform (SIFT); and speeded up robust features (SURF), provided sub-millimeter mean tracking errors (0.38 ± 0.53 mm and 0.20 ± 0.16 mm, respectively) and an adequate image refresh rate (11 and 17 frames per second, respectively) after 2 minutes of procedure with continuous endoscope movements. CONCLUSION A precise augmented reality combining video and 3D CT-scan data can be applied to otoendoscopy without the use of conventional neuronavigation tracking thanks to computer vision algorithms.
Deep Neural Networks at the Service of Multilingual Parallel Sentence Extraction

Wikipedia provides an invaluable source of parallel multilingual data, which are in high demand for various sorts of linguistic inquiry, including both theoretical and practical studies. We introduce a novel end-to-end neural model for large-scale parallel data harvesting from Wikipedia. Our model is language-independent, robust, and highly scalable. We use our system for collecting parallel German-English, French-English and Persian-English sentences. Human evaluations at the end show the strong performance of this model in collecting high-quality parallel data. We also propose a statistical framework which extends the results of our human evaluation to other language pairs. Our model also obtained a state-of-the-art result on the German-English dataset of BUCC 2017 shared task on parallel sentence extraction from comparable corpora.
The role of acquisition and quantification methods in myocardial blood flow estimability for myocardial perfusion imaging CT

In this work, we clarified the role of acquisition parameters and quantification methods in myocardial blood flow (MBF) estimability for myocardial perfusion imaging using CT (MPI-CT). We used a physiologic model with a CT simulator to generate time-attenuation curves across a range of imaging conditions, i.e. tube current-time product, imaging duration, and temporal sampling, and physiologic conditions, i.e. MBF and arterial input function width. We assessed MBF estimability by precision (interquartile range of MBF estimates) and bias (difference between median MBF estimate and reference MBF) for multiple quantification methods. Methods included: six existing model-based deconvolution models, such as the plug-flow tissue uptake model (PTU), Fermi function model, and single-compartment model (SCM); two proposed robust physiologic models (RPM1, RPM2); model-independent singular value decomposition with Tikhonov regularization determined by the L-curve criterion (LSVD); and maximum upslope (MUP). Simulations show that MBF estimability is most affected by changes in imaging duration for model-based methods and by changes in tube current-time product and sampling interval for model-independent methods. Models with three parameters, i.e. RPM1, RPM2, and SCM, gave least biased and most precise MBF estimates. The average relative bias (precision) for RPM1, RPM2, and SCM was ⩽11% (⩽10%) and the models produced high-quality MBF maps in CT simulated phantom data as well as in a porcine model of coronary artery stenosis. In terms of precision, the methods ranked best-to-worst are: RPM1 > RPM2 > Fermi > SCM > LSVD > MUP other methods. In terms of bias, the models ranked best-to-worst are: SCM > RPM2 > RPM1 > PTU > LSVD other methods. Models with four or more parameters, particularly five-parameter models, had very poor precision (as much as 310% uncertainty) and/or significant bias (as much as 493%) and were sensitive to parameter initialization, thus suggesting the presence of multiple local minima. For improved estimates of MBF from MPI-CT, it is recommended to use reduced models that incorporate prior knowledge of physiology and contrast agent uptake, such as the proposed RPM1 and RPM2 models.
On-Device Neural Language Model Based Word Prediction

Recent developments in deep learning with application to language modeling have led to success in tasks of text processing, summarizing and machine translation. However, deploying huge language models for the mobile device such as on-device keyboards poses computation as a bottle-neck due to their puny computation capacities. In this work, we propose an on-device neural language model based word prediction method that optimizes run-time memory and also provides a real-time prediction environment. Our model size is 7.40MB and has average prediction time of 6.47 ms. Our proposed model outperforms the existing methods for word prediction in terms of keystroke savings and word prediction rate and has been successfully commercialized.