Miracle-dz/UniFAR-FTUs huggingface dataset:
Sample Entropy Analysis of Noisy Atrial Electrograms during Atrial Fibrillation

Most cardiac arrhythmias can be classified as atrial flutter, focal atrial tachycardia, or atrial fibrillation. They have been usually treated using drugs, but catheter ablation has proven more effective. This is an invasive method devised to destroy the heart tissue that disturbs correct heart rhythm. In order to accurately localise the focus of this disturbance, the acquisition and processing of atrial electrograms form the usual mapping technique. They can be single potentials, double potentials, or complex fractionated atrial electrogram (CFAE) potentials, and last ones are the most effective targets for ablation. The electrophysiological substrate is then localised by a suitable signal processing method. Sample Entropy is a statistic scarcely applied to electrograms but can arguably become a powerful tool to analyse these time series, supported by its results in other similar biomedical applications. However, the lack of an analysis of its dependence on the perturbations usually found in electrogram data, such as missing samples or spikes, is even more marked. This paper applied SampEn to the segmentation between non-CFAE and CFAE records and assessed its class segmentation power loss at different levels of these perturbations. The results confirmed that SampEn was able to significantly distinguish between non-CFAE and CFAE records, even under very unfavourable conditions, such as 50% of missing data or 10% of spikes.
Beyond Passive Immunity: Is There Priming of the Fetal Immune System Following Vaccination in Pregnancy and What Are the Potential Clinical Implications?

Infection is responsible for over half a million neonatal deaths worldwide every year, and vaccination in pregnancy is becoming increasingly recognized as an important strategy for the protection of young infants. Increasing evidence suggests that exposure to maternal infection in utero may “prime” the developing immune system, even in the absence of infant infection. It is also possible that in utero priming may occur following maternal vaccination, with antigen-specific cellular immune responses detectable in utero and at birth. However, this remains a topic of some controversy. This review focuses on the evidence for in utero priming and the clinical implications for vaccination in pregnancy, considering whether in utero priming following vaccination could provide protection independent of antibody-mediated passive immunity, the possible effects of vaccination on subsequent infant vaccinations, their potential “non-specific” effects, and how the design and timing of vaccination might affect prenatal priming. Looking forward, we describe other possible options for quantifying antigen-specific cellular responses, including MHC tetramers, novel proliferation and cytokine-based assays, and animal models. Together, these may help us address future research questions and establish more robust evidence of fetal immune system priming.
Deep Enhanced Representation for Implicit Discourse Relation Recognition

Implicit discourse relation recognition is a challenging task as the relation prediction without explicit connectives in discourse parsing needs understanding of text spans and cannot be easily derived from surface features from the input sentence pairs. Thus, properly representing the text is very crucial to this task. In this paper, we propose a model augmented with different grained text representations, including character, subword, word, sentence, and sentence pair levels. The proposed deeper model is evaluated on the benchmark treebank and achieves state-of-the-art accuracy with greater than 48% in 11-way and F1 score greater than 50% in 4-way classifications for the first time according to our best knowledge.
Hybrid CTC-Attention based End-to-End Speech Recognition using Subword Units

In this paper, we present an end-to-end automatic speech recognition system, which successfully employs subword units in a hybrid CTC-Attention based system. The subword units are obtained by the byte-pair encoding (BPE) compression algorithm. Compared to using words as modeling units, using characters or subword units does not suffer from the out-of-vocabulary (OOV) problem. Furthermore, using subword units further offers a capability in modeling longer context than using characters. We evaluate different systems over the LibriSpeech 1000h dataset. The subword-based hybrid CTC-Attention system obtains 6.8% word error rate (WER) on the test clean subset without any dictionary or external language model. This represents a significant improvement (a 12.8% WER relative reduction) over the character-based hybrid CTC-Attention system.
Manual acupuncture relieves bile acid-induced itch in mice: the role of microglia and TNF-α

Pruritus, or itch, is a frequent complaint amongst patients with cholestatic hepatobiliary disease and is difficult to manage, with many patients refractory to currently available antipruritic treatments. In this study, we examined whether manual acupuncture (MA) at particular acupoints represses deoxycholic acid (DCA)-induced scratching behavior and microglial activation and compared these effects with those induced by another pruritogen, 5'-guanidinonaltrindole (GNTI, a kappa opioid receptor antagonist). MA at Hegu (LI4) and Quchi (LI11) acupoints significantly attenuated DCA- and GNTI-induced scratching, whereas no such effects were observed at the bilateral Zusanli acupoints (ST36). Interestingly, GNTI-induced scratching was reduced similarly by both MA and electroacupuncture (EA) at the LI4 and LI11 acupoints. MA at non-acupoints did not affect scratching behavior. Intraperitoneal injection of minocycline (a microglial inhibitor) reduced GNTI- and DCA-induced scratching behavior. In Western blot analysis, subcutaneous DCA injection to the back of the neck increased spinal cord expression of ionized calcium-binding adapter molecule 1 (Iba1) and tumor necrosis factor-alpha (TNF-α) as compared with saline injection, while MA at LI4 and LI11 reduced these DCA-induced changes. Immunofluorescence confocal microcopy revealed that DCA-induced Iba1-positive cells with thicker processes emanated from the enlarged cell bodies, while this effect was attenuated by pretreatment with MA. It is concluded that microglia and TNF-α play important roles in the itching sensation and MA reduces DCA-induced scratching behavior by alleviating spinal microglial activation. MA may be an effective treatment for cholestatic pruritus.
Modeling a Production Function to Evaluate the Effect of Medical Staffing on Antimicrobial Stewardship Performance in China, 2009–2016: Static and Dynamic Panel Data Analyses

Background: Antimicrobial resistance (AMR) is an international problem. Emergence and spread of AMR are strongly associated with overuse or inappropriate use of antimicrobials. Antimicrobial stewardship ensures the appropriate use of antimicrobials, and is an effective approach to control AMR. This study aims to understand the relationship between medical staffing and antimicrobial stewardship performance in China. Methods: A provincial-level panel dataset from 2009 to 2016 is used. A macro production function is used to quantify the relationship. The output, antimicrobial stewardship performance, is measured by changes in methicillin resistance rates of Staphylococcus. aureus (S. aureus) and coagulase-negative staphylococci (CoNS). The labor input is measured by the numbers of infectious diseases physicians, pharmacists, clinical microbiologists, and nurses in hospitals per 100,000 populations, whereas the capital input is represented by the number of hospital beds per 100,000 populations. The technology is captured by the time index. Both static and dynamic panel data approaches are employed. Results: The increasing number of clinical microbiologists is a significant predictor of lower resistance of CoNS according to dynamic models (Coef. = −0.191, −0.351; p = 0.070, 0.004, respectively). However, a larger number of nurses is significantly associated with higher resistance of S. aureus (Coef. = 0.648; p = 0.044). In addition, the numbers of the other two groups of medical professionals exhibit no significant associations with stewardship performance. Conclusions: The study demonstrates the crucial role of clinical microbiologists in antimicrobial stewardship. The predicted increased risk of resistance with the higher number of nurses may be attributable to their lack of related knowledge and their unrecognized functions in antimicrobial stewardship.
No-regret algorithms for online k-submodular maximization

We present a polynomial time algorithm for online maximization of $k$-submodular maximization. For online (nonmonotone) $k$-submodular maximization, our algorithm achieves a tight approximate factor in an approximate regret. For online monotone $k$-submodular maximization, our approximate-regret matches to the best-known approximation ratio, which is tight asymptotically as $k$ tends to infinity. Our approach is based on the Blackwell approachability theorem and online linear optimization.
Whole‐exome sequencing in 20,197 persons for rare variants in Alzheimer's disease

The genetic bases of Alzheimer's disease remain uncertain. An international effort to fully articulate genetic risks and protective factors is underway with the hope of identifying potential therapeutic targets and preventive strategies. The goal here was to identify and characterize the frequency and impact of rare and ultra‐rare variants in Alzheimer's disease, using whole‐exome sequencing in 20,197 individuals.
Option pricing in the moderate deviations regime

We consider call option prices close to expiry in diffusion models, in an asymptotic regime (“moderately out of the money”) that interpolates between the well‐studied cases of at‐the‐money and out‐of‐the‐money regimes. First and higher order small‐time moderate deviation estimates of call prices and implied volatilities are obtained. The expansions involve only simple expressions of the model parameters, and we show how to calculate them for generic local and stochastic volatility models. Some numerical computations for the Heston model illustrate the accuracy of our results.
Learning-based Regularization for Cardiac Strain Analysis with Ability for Domain Adaptation

Reliable motion estimation and strain analysis using 3D+time echocardiography (4DE) for localization and characterization of myocardial injury is valuable for early detection and targeted interventions. However, motion estimation is difficult due to the low-SNR that stems from the inherent image properties of 4DE, and intelligent regularization is critical for producing reliable motion estimates. In this work, we incorporated the notion of domain adaptation into a supervised neural network regularization framework. We first propose an unsupervised autoencoder network with biomechanical constraints for learning a latent representation that is shown to have more physiologically plausible displacements. We extended this framework to include a supervised loss term on synthetic data and showed the effects of biomechanical constraints on the network's ability for domain adaptation. We validated both the autoencoder and semi-supervised regularization method on in vivo data with implanted sonomicrometers. Finally, we showed the ability of our semi-supervised learning regularization approach to identify infarcted regions using estimated regional strain maps with good agreement to manually traced infarct regions from postmortem excised hearts.
Potential Pathways Associated With Exaggerated T Follicular Helper Response in Human Autoimmune Diseases

Convincing lines of evidence in both mice and humans show that exaggerated T follicular helper (Tfh) responses is pathogenic in autoimmune diseases. However, the cause of exaggerated Tfh response in humans is still much less clear than in mouse models where genetic factors can be manipulated for in vivo testing. Nonetheless, recent advances in our understanding on the mechanisms of human Tfh differentiation and identification of multiple risk loci in genome-wide association studies have revealed several pathways potentially associated with exaggerated Tfh response in human autoimmune diseases. In this review, we will first briefly summarize the differentiation mechanisms of Tfh cells in humans. We describe the features of “Tfh-like” cells recently identified in inflamed tissues of human autoimmune diseases. Then we will discuss how risk loci identified in GWAS are potentially involved in exaggerated Tfh response in human autoimmune diseases.
A Neural Model of Empathic States in Attachment-Based Psychotherapy

We build on a neuroanatomical model of how empathic states can motivate caregiving behavior, via empathy circuit-driven activation of regions in the hypothalamus and amygdala, which in turn stimulate a mesolimbic–ventral pallidum pathway, by integrating findings related to the perception of pain in self and others. On this basis, we propose a network to capture states of personal distress and (weak and strong forms of) empathic concern, which are particularly relevant for psychotherapists conducting attachment-based interventions. This model is then extended for the case of self-attachment therapy, in which conceptualized components of the self serve as both the source of and target for empathic resonance. In particular, we consider how states of empathic concern involving an other that is perceived as being closely related to the self might enhance the motivation for self-directed bonding (which in turn is proposed to lead the individual toward more compassionate states) in terms of medial prefrontal cortex–mediated activation of these caregiving pathways. We simulate our model computationally and discuss the interplay between the bonding and empathy protocols of the therapy.
Exploring the Use of Pre-exposure Prophylaxis (PrEP) for HIV Prevention Among High-Risk People Who Use Drugs in Treatment

Introduction: Despite unequivocal evidence supporting the use of pre-exposure prophylaxis (PrEP), its scale-up has been gradual overall, and nearly absent among people who use drugs (PWUD). In the present study, we implemented the use of PrEP, as a part of an integrated HIV prevention approach, and explored the experiences and attitudes related to PrEP use among PWUD. Methods: Between September 2016 and July 2017, we recruited 40 HIV-uninfected, methadone-maintained people, who reported HIV-risk behaviors, and were currently taking PrEP. We conducted both quantitative and in-depth semi-structured qualitative interviews that primarily focused on experiences, attitudes, acceptability, disclosure status, risk compensation-related attitudes, and barriers related to PrEP adherence. Results: Results showed that participants were highly satisfied and perceived PrEP as valuable and acceptable for HIV prevention. Participants reported high adherence to PrEP. The most highly endorsed facilitators to PrEP adherence were use of memory aids, no out-of-pocket cost, perceived benefit, and support from social network. The barriers to adherence included side-effects, stigmatization, requirement of daily dosing, and accessibility of PrEP services. Additionally, participants expressed disagreement with the overall risk compensation-related attitudes (i.e., decreased personal concern about engaging in HIV risk behavior due to their perception that PrEP is now fully protecting them from contracting HIV) and indicated no increased engagement in risk behaviors while on PrEP. Conclusions: The results from the current study provide preliminary evidence supporting the successful integration of PrEP within the substance abuse treatment setting, where high risk PWUD are concentrated.
Parameterized Distributed Algorithms

In this work, we initiate a thorough study of parameterized graph optimization problems in the distributed setting. In a parameterized problem, an algorithm decides whether a solution of size bounded by a \emph{parameter} $k$ exists and if so, it finds one. We study fundamental problems, including Minimum Vertex Cover (MVC), Maximum Independent Set (MaxIS), Maximum Matching (MaxM), and many others, in both the LOCAL and CONGEST distributed computation models. We present lower bounds for the round complexity of solving parameterized problems in both models, together with optimal and near-optimal upper bounds. Our results extend beyond the scope of parameterized problems. We show that any LOCAL $(1+\epsilon)$-approximation algorithm for the above problems must take $\Omega(\epsilon^{-1})$ rounds. Joined with the algorithm of [GKM17] and the $\Omega(\sqrt{\frac{\log n}{\log\log n}})$ lower bound of [KMW16], this settles the complexity of $(1+\epsilon)$-approximating MVC, MaxM and MaxIS at $(\epsilon^{-1}\log n)^{\Theta(1)}$. We also show that our parameterized approach reduces the runtime of exact and approximate CONGEST algorithms for MVC and MaxM if the optimal solution is small, without knowing its size beforehand. Finally, we propose the first deterministic $o(n^2)$ rounds CONGEST algorithms that approximate MVC and MaxM within a factor strictly smaller than $2$.
Concentration and Subclass Distribution of Anti-ADAMTS13 IgG Autoantibodies in Different Stages of Acquired Idiopathic Thrombotic Thrombocytopenic Purpura

Background The acquired form of idiopathic thrombotic thrombocytopenic purpura (TTP) is an autoimmune disease, in which the underlying deficiency of the ADAMTS13 protease is caused by autoantibodies, predominantly of the IgG isotype. Certain HLA-DR-DQ haplotypes were associated with the risk of developing TTP. Objectives To investigate the development of the ADAMTS13-specific antibody response during the course of the disease, we analyzed the concentration, subclass distribution, and inhibitory potential of anti-ADAMTS13 IgG autoantibodies in samples of TTP patients drawn during the first acute phase, in remission, and during relapse. Additionally, we compared the anti-ADAMTS13 IgG levels between patients carrying and not carrying risk and protective HLA-DR-DQ haplotypes. Patients and Methods We determined the anti-ADAMTS13 IgG concentration and subclass distribution in 101 antibody-positive samples of 81 acquired TTP patients by ELISA methods. The presence and semi-quantitative amount of anti-ADAMTS13 inhibitors were determined in 97 of 100 deficient samples, and the specific inhibitory potential of anti-ADAMTS13 autoantibodies was determined in 49 selected samples, by mixing ADAMTS13-activity assays. HLA-DR-DQ typing and haplotype prediction were performed in 70 of the above patients. Results We found that IgG1 and IgG4 were the predominant subclasses, present in almost all samples. While IgG1 was the dominant subclass in almost half of the samples taken during the first acute episode, IgG4 was dominant in all samples taken during or following a relapse. The inhibitory potential of the samples correlated with levels of the IgG4 subclass. Anti-ADAMTS13 antibodies of IgG4-dominant samples had higher specific inhibitory potentials than IgG1-dominant samples, independently of disease stage. Interestingly, we found that patients carrying the protective DR7-DQ2 and DR13-DQ6 haplotypes had higher anti-ADAMTS13 IgG levels. Conclusion Our results indicate that IgG4 becomes the dominant subtype at some point of the disease course, apparently before the first relapse, parallel to the increase in inhibitory potential of the anti-ADAMTS13 autoantibodies. Furthermore, we found an association between the genetic background and the antibody response in TTP.
Isolated singularities for a semilinear equation for the fractional Laplacian arising in conformal geometry

We introduce the study of isolated singularities for a semilinear equation involving the fractional Laplacian. In conformal geometry, it is equivalent to the study of singular metrics with constant fractional curvature. Our main ideas are: first, to set the problem into a natural geometric framework, and second, to perform some kind of phase portrait study for this non-local ODE.
REGMAPR-A Recipe for Textual Matching Anonymous EMNLP submission

We propose a simple model for textual matching problems. Starting from a Siamese architecture, we augment word embeddings with two features based on exact and paraphrase match between words in the two sentences being considered. We train the model using four types of regularization on datasets for textual entailment, paraphrase detection and semantic relatedness. Our model performs comparably or better than more complex architectures; achieving state-of-the-art results for paraphrase detection on the SICK dataset and for textual entailment on the SNLI dataset.
Reducing Distant Supervision Noise with Maxpooled Attention and Sentence-Level Supervision Anonymous EMNLP submission

We propose an effective multitask learning setup for reducing distant supervision noise by leveraging sentence-level supervision. We show how sentence-level supervision can be used to improve the encoding of individual sentences, and to learn which input sentences are more likely to express the relationship between a pair of entities. We also introduce a novel neural architecture for collecting signals from multiple input sentences, which combines the benefits of attention and maxpooling. The proposed method increases AUC by 10% (from 0.261 to 0.284), and outperforms recently published results on the FBNYT dataset.
Language Modeling Teaches You More than Translation Does: Lessons Learned Through Auxiliary Task Analysis Anonymous EMNLP

There is mounting evidence that pretraining can be valuable for neural network language understanding models, but we do not yet have a clear understanding of how the choice of pretraining objective affects the type of linguistic information that models learn. With this in mind, we compare four objectives— language modeling, translation, skip-thought, and autoencoding—on their ability to induce syntactic and part-of-speech information, holding constant the genre and quantity of training data. We ﬁnd that representations from language models consistently perform best on our syntactic auxiliary prediction tasks, even when trained on relatively small amounts of data, which suggests that language modeling may be the best data-rich pretraining task for transfer learning applications requiring syntactic information. We also ﬁnd that a randomly-initialized, frozen model can perform strikingly well on our auxiliary tasks, but that this effect disappears when the amount of training data for the auxiliary tasks is reduced.
What can we learn from Semantic Tagging?

We investigate the effects of multi-task learning using the recently introduced task of semantic tagging. We employ semantic tagging as an auxiliary task for three different NLP tasks: part-of-speech tagging, Universal Dependency parsing, and Natural Language Inference. We compare full neural network sharing, partial neural network sharing, and what we term the learning what to share setting where negative transfer between tasks is less likely. Our findings show considerable improvements for all tasks, particularly in the learning what to share setting which shows consistent gains across all tasks.
El hilo de la vida

Nuestra actualidad se ha visto conmocionada por el estallido del conflicto entre Rusia y Ucrania. Las pérdidas sufridas no consiguen detenerlo. Los psicoanalistas fueron interpelados por estos conflictos con la Primera Guerra Mundial, porque los soldados que volvían presentaban síntomas sin causalidad orgánica resistentes a los tratamientos psiquiátricos de la época. Una pregunta lo conduce a Freud a su teorización sobre la guerra: ¿por qué se hace necesario recordar de manera compulsiva una situación desagradable?