Miracle-dz/UniFAR-FTUs huggingface dataset:
Clinical Significance of Quantitative HBs Antigen in the Prediction of Liver Fibrosis in Patients with Chronic Hepatitis B

Abstract The assessment of liver fibrosis in patients with chronic hepatitis B (CHB) is of great importance in evaluating the phases of chronic hepatitis B viral infection, prompt administration of antiviral therapy, prevention of disease progression and late complications of CHB infection. Aim: to investigate the clinical significance of quantitative HBs antigen as a predictor for liver fibrosis in patients with HBe antigen negative chronic hepatitis B and inactive carriers. Material and Methods: the study included 44 treatment naïve patients with chronic hepatitis B, divided into two groups, HBeAg negative chronic HBV infection or inactive carriers (IC) and HBeAg negative chronic hepatitis B patients. All patients underwent laboratory, serologic testing, ultrasound and transient elastography (TE). In both patient groups, quantitative HBs antigen (HBsQ), alanine aminotransferase (ALT), hepatitis B virus deoxyribonucleic acid (HBV DNA) and liver fibrosis were analyzed. Results: The value of HBsQ is significantly higher in patients with HBeAg negative CHB 2477.02±4535.44 IU/ml than in the IC group 8791±11891 IU/ml; Z=3.32, p<0.001 (p=0.0009). In IC patients, 1 (4.76%) had fibrosis and 20 (95.24%)) did not have fibrosis. Out of 23 patients with HBeAg negative chronic hepatitis B, 8 (34.78%) had fibrosis and 15 (65.22%) did not have fibrosis. Patients with HBeAg negative hepatitis B had significantly higher liver fibrosis than IC; Fisher Exact Test p<0.05 (p=0.02). The increase of HBsQ for one single unit (IU/ml) does not have predictive value for fibrosis (Ext (B) =1.00), 95% C.I. for EXP (B): 1.00-1.00 / p>0.05. Conclusion: Quantitative hepatitis B surface antigen has intermediate weak statistically insignificant prediction for liver fibrosis R=0.25 (p<0.10).
Statistical Piano Reduction Controlling Performance Difficulty

We present a statistical-modelling method for piano reduction, i.e. converting an ensemble score into piano scores, that can control performance difficulty. While previous studies have focused on describing the condition for playable piano scores, it depends on player's skill and can change continuously with the tempo. We thus computationally quantify performance difficulty as well as musical fidelity to the original score, and formulate the problem as optimization of musical fidelity under constraints on difficulty values. First, performance difficulty measures are developed by means of probabilistic generative models for piano scores and the relation to the rate of performance errors is studied. Second, to describe musical fidelity, we construct a probabilistic model integrating a prior piano-score model and a model representing how ensemble scores are likely to be edited. An iterative optimization algorithm for piano reduction is developed based on statistical inference of the model. We confirm the effect of the iterative procedure; we find that subjective difficulty and musical fidelity monotonically increase with controlled difficulty values; and we show that incorporating sequential dependence of pitches and fingering motion in the piano-score model improves the quality of reduction scores in high-difficulty cases.
Personalizing Lexical Simplification

A lexical simplification (LS) system aims to substitute complex words with simple words in a text, while preserving its meaning and grammaticality. Despite individual users’ differences in vocabulary knowledge, current systems do not consider these variations; rather, they are trained to find one optimal substitution or ranked list of substitutions for all users. We evaluate the performance of a state-of-the-art LS system on individual learners of English at different proficiency levels, and measure the benefits of using complex word identification (CWI) models to personalize the system. Experimental results show that even a simple personalized CWI model, based on graded vocabulary lists, can help the system avoid some unnecessary simplifications and produce more readable output.
Human Lymph Nodes Maintain TCF-1hi Memory T Cells with High Functional Potential and Clonal Diversity throughout Life

Translating studies on T cell function and modulation from mouse models to humans requires extrapolating in vivo results on mouse T cell responses in lymphoid organs (spleen and lymph nodes [LN]) to human peripheral blood T cells. However, our understanding of T cell responses in human lymphoid sites and their relation to peripheral blood remains sparse. In this study, we used a unique human tissue resource to study human T cells in different anatomical compartments within individual donors and identify a subset of memory CD8+ T cells in LN, which maintain a distinct differentiation and functional profile compared with memory CD8+ T cells in blood, spleen, bone marrow, and lungs. Whole-transcriptome and high-dimensional cytometry by time-of-flight profiling reveals that LN memory CD8+ T cells express signatures of quiescence and self-renewal compared with corresponding populations in blood, spleen, bone marrow, and lung. LN memory T cells exhibit a distinct transcriptional signature, including expression of stem cell–associated transcription factors TCF-1 and LEF-1, T follicular helper cell markers CXCR5 and CXCR4, and reduced expression of effector molecules. LN memory T cells display high homology to a subset of mouse CD8+ T cells identified in chronic infection models that respond to checkpoint blockade immunotherapy. Functionally, human LN memory T cells exhibit increased proliferation to TCR-mediated stimulation and maintain higher TCR clonal diversity compared with memory T cells from blood and other sites. These findings establish human LN as reservoirs for memory T cells with high capacities for expansion and diverse recognition and important targets for immunotherapies.
Analysing Collective Behaviour in Temporal Networks Using Event Graphs and Temporal Motifs.

Historically studies of behaviour on networks have focused on the behaviour of individuals (node-based) or on the aggregate behaviour of the entire network. We propose a new method to decompose a temporal network into macroscale components and to analyse the behaviour of these components, or collectives of nodes, across time. This method utilises all available information in the temporal network (i.e. no temporal aggregation), combining both topological and temporal structure using temporal motifs and inter-event times. This allows us create an embedding of a temporal network in order to describe behaviour over time and at different timescales. We illustrate this method using an example of digital communication data collected from an online social network.
Timed context-free temporal logics (extended version)

The paper is focused on temporal logics for the description of the behaviour of real-time pushdown reactive systems. The paper is motivated to bridge tractable logics specialized for expressing separately dense-time real-time properties and context-free properties by ensuring decidability and tractability in the combined setting. To this end we introduce two real-time linear temporal logics for specifying quantitative timing context-free requirements in a pointwise semantics setting: Event-Clock Nested Temporal Logic (ECNTL) and Nested Metric Temporal Logic (NMTL). The logic ECNTL is an extension of both the logic CARET (a context-free extension of standard LTL) and Event-Clock Temporal Logic (a tractable real-time logical framework related to the class of Event-Clock automata). We prove that satisfiability of ECNTL and visibly model-checking of Visibly Pushdown Timed Automata VPTA against ECNTL are decidable and EXPTIME-complete. The other proposed logic NMTL is a context-free extension of standard Metric Temporal Logic (MTL). It is well known that satisfiability of future MTL is undecidable when interpreted over infinite timed words but decidable over finite timed words. On the other hand, we show that by augmenting future MTL with future context-free temporal operators, the satisfiability problem turns out to be undecidable also for finite timed words. On the positive side, we devise a meaningful and decidable fragment of the logic NMTL which is expressively equivalent to ECNTL and for which satisfiability and visibly model-checking of VPTA are EXPTIME-complete.
BRAINX3: A New Scientific Instrument for the Acceleration of Hypotheses on Mind and Brain

—“A mind which at a given instant should know all the forces acting in nature, as also the respective situation of the beings of which it consists—provided its powers were sufficiently vast to analyze all these data— could embrace in one formula the movements of the largest bodies in the universe as well as those of the smallest atom; nothing would be uncertain for such a mind, and the future, like the past, would be present to its eyes. ” Laplace (1814).
Quality of Prenatal and Childhood Diet Predicts Neurodevelopmental Outcomes among Children in Mexico City

Adequate nutrition is important for neurodevelopment. Although nutrients are ingested in combination, the impact of specific nutrients within the context of a nutrient mixture has not been studied with respect to health, such as neurodevelopment. Therefore, we examined the impact of prenatal and childhood nutrient mixtures on neurodevelopmental outcomes. Participants included mother–child pairs in the Programming Research in Obesity, Growth, Environment, and Social Stress (PROGRESS) prospective birth cohort in Mexico City. We assessed prenatal and child micro- and macronutrient profiles among 65 and 329 children, respectively, via food frequency questionnaires. Neurodevelopmental outcomes of 4–6 year-old children were measured using the McCarthy Scales of Children’s Abilities (MSCA). We conducted weighted quantile sum (WQS) regression analyses to calculate indices reflecting “good” and “poor” prenatal and childhood nutrition. After adjusting for maternal education, socioeconomic status, the Home Observation for Measurement of the Environment (HOME) score, and total caloric intake, the good prenatal and childhood nutrition indices predicted more favorable neurodevelopment, while both poor nutrition indices predicted poorer neurodevelopment. These associations were stronger in prenatal than childhood models. Monounsaturated fats predicted various neurodevelopmental abilities relatively strongly in both models. Prenatal and childhood consumption of combinations of beneficial nutrients may contribute to more favorable neurodevelopment.
Attributed and Predictive Entity Embedding for Fine-Grained Entity Typing in Knowledge Bases

Fine-grained entity typing aims at identifying the semantic type of an entity in KB. Type information is very important in knowledge bases, but are unfortunately incomplete even in some large knowledge bases. Limitations of existing methods are either ignoring the structure and type information in KB or requiring large scale annotated corpus. To address these issues, we propose an attributed and predictive entity embedding method, which can fully utilize various kinds of information comprehensively. Extensive experiments on two real DBpedia datasets show that our proposed method significantly outperforms 8 state-of-the-art methods, with 4.0% and 5.2% improvement in Mi-F1 and Ma-F1, respectively.
Neuronal population mechanisms of lightness perception

The way that humans and animals perceive the lightness of an object depends on its physical luminance as well as its surrounding context. While neuronal responses throughout the visual pathway are modulated by context, the relationship between neuronal responses and lightness perception is poorly understood. We searched for a neuronal mechanism of lightness by recording responses of neuronal populations in monkey primary visual cortex (V1) and area V4 to stimuli that produce a lightness illusion in humans, in which the lightness of a disk depends on the context in which it is embedded. We found that the way individual units encode the luminance (or equivalently for our stimuli, contrast) of the disk and its context is extremely heterogeneous. This motivated us to ask whether the population representation in either V1 or V4 satisfies three criteria: 1) disk luminance is represented with high fidelity, 2) the context surrounding the disk is also represented, and 3) the representations of disk luminance and context interact to create a representation of lightness that depends on these factors in a manner consistent with human psychophysical judgments of disk lightness. We found that populations of units in both V1 and V4 fulfill the first two criteria, but that we cannot conclude that the two types of information in either area interact in a manner that clearly predicts human psychophysical measurements: the interpretation of our population measurements depends on how subsequent areas read out lightness from the population responses. New & Noteworthy A core question in visual neuroscience is how the brain extracts stable representations of object properties from the retinal image. We searched for a neuronal mechanism of lightness perception by determining whether the responses of neuronal populations in primary visual cortex and area V4 could account for a lightness illusion measured using human psychophysics. Our results suggest that comparing psychophysics with population recordings will yield insight into neuronal mechanisms underlying a variety of perceptual phenomena.
A Pseudo Label based Dataless Naive Bayes Algorithm for Text Classification with Seed Words

Traditional supervised text classifiers require a large number of manually labeled documents, which are often expensive to obtain. Recently, dataless text classification has attracted more attention, since it only requires very few seed words of categories that are much cheaper. In this paper, we develop a pseudo-label based dataless Naive Bayes (PL-DNB) classifier with seed words. We initialize pseudo-labels for each document using seed word occurrences, and employ the expectation maximization algorithm to train PL-DNB in a semi-supervised manner. The pseudo-labels are iteratively updated using a mixture of seed word occurrences and estimations of label posteriors. To avoid noisy pseudo-labels, we also consider the information of nearest neighboring documents in the pseudo-label update step, i.e., preserving local neighborhood structure of documents. We empirically show that PL-DNB outperforms traditional dataless text classification algorithms with seed words. Especially, PL-DNB performs well on the imbalanced dataset.
Implicitly Imprinting the Past on the Present: Automatic Partner Attitudes and the Transition to Parenthood

A new model is proposed to explain how automatic partner attitudes affect how couples cope with major life transitions. The automatic partner attitudes in transition (APAT) model assumes that people simultaneously possess contextualized automatic attitudes toward their partner that can differ substantively in valence pre- and posttransition. It further assumes that evaluatively inconsistent pre- and posttransition automatic partner attitudes elicit heightened behavioral angst or uncertainty, self-protective behavior in response to risk, and relationship distress. A longitudinal study of the transition to first parenthood supported the model. People with evaluatively inconsistent automatic partner attitudes, whether more negative pretransition and positive posttransition, or more positive pretransition and negative posttransition, exhibited heightened evidence of cardiovascular threat discussing conflicts, increased self-protective behavior in response to parenting-related transgressions in daily interaction, and steeper declines in relationship well-being in the year following the transition to parenthood.
Recent progress in microneme-based vaccines development against Toxoplasma gondii

Toxoplasmosis is a cosmopolitan zoonotic disease, which infect several warm-blooded mammals. More than one-third of the human population are seropositive worldwide. Due to the high seroprevalence of Toxoplasma gondii infection worldwide, the resulting clinical, mental, and economical complications, as well as incapability of current drugs in the elimination of parasites within tissue cysts, the development of a vaccine against T. gondii would be critical. In the past decades, valuable advances have been achieved in order to identification of vaccine candidates against T. gondii infection. Microneme proteins (MICs) secreted by the micronemes play a critical role in the initial stages of host cell invasion by parasites. In this review, we have summarized the recent progress for MIC-based vaccines development, such as DNA vaccines, recombinant protein vaccines, vaccines based on live-attenuated vectors, and prime-boost strategy in different mouse models. In conclusion, the use of live-attenuated vectors as vehicles to deliver and express the target gene and prime-boost regimens showed excellent outcomes in the development of vaccines against toxoplasmosis, which need more attention in the future studies.
On Being Forgotten: Memory and Forgetting Serve as Signals of Interpersonal Importance

Our memories contain a wealth of social information—including details of past interactions, facts about others, and others’ identities. Yet, human memory is imperfect, and we often find ourselves unable to recall such information in social interactions. Conversely, people routinely find themselves on the receiving end of others’ memory failures; that is, people sometimes find themselves forgotten. Despite the apparent pervasiveness of such experiences, modern science possesses no explanatory framework for understanding the psychological impact of being forgotten in part or in whole. Here, we propose that evidence of memory in social interactions is a powerful signal of the subjective importance attached to an object of memory and that interpretation of such signals has important consequences for interpersonal relationships. We further proposed that attributional explanations for forgetting and that the closeness of the relationship between the people involved in forgetting might moderate the impact of being forgotten. We tested this framework in four studies examining the experience of being forgotten in daily life (Study 1), in experimentally controlled firsthand encounters (Study 2), and in third party perceptions of forgetting (Studies 3 and 4). Results converged to support our proposed framework as well as the moderating role of attribution. Surprisingly, we found no evidence supporting the moderating role of initial relationships closeness. These results advance a systematic model of an understudied but important phenomenon and suggest rich and varied avenues of additional exploration.
Advances in Inference Methods for Markov Logic Networks

—Markov Logic Networks (MLNs) are expressive models that can be used to specify complex and uncertain background knowledge in the form of weighted ﬁrst-order logic formulas. However, inference in MLNs is highly challenging since the underlying probabilistic model can be very large even for simple MLN structures. Lifted inference has emerged as dominant approach for probabilistic inference in MLNs, where the idea is to exploit symmetries in the MLN for scalable inference. In this paper, we provide an overview of MLNs, and major advances in inference techniques for MLNs over the last several years.
One vs. Many QA Matching with both Word-level and Sentence-level Attention Network

Question-Answer (QA) matching is a fundamental task in the Natural Language Processing community. In this paper, we first build a novel QA matching corpus with informal text which is collected from a product reviewing website. Then, we propose a novel QA matching approach, namely One vs. Many Matching, which aims to address the novel scenario where one question sentence often has an answer with multiple sentences. Furthermore, we improve our matching approach by employing both word-level and sentence-level attentions for solving the noisy problem in the informal text. Empirical studies demonstrate the effectiveness of the proposed approach to question-answer matching.
ASHuR: Evaluation of the Relation Summary-Content Without Human Reference Using ROUGE

In written documents, the summary is a brief description of important aspects of a text. The degree of similarity between the summary and the content of a document provides reliability about the summary. Some efforts have been done in order to automate the evaluation of a summary. ROUGE metrics can automatically evaluate a summary, but it needs a model summary built by humans. The goal of this study is to find a quantitative relation between an article content and its summary using ROUGE tests without a model summary built by humans. This work proposes a method for automatic text summarization to evaluate a summary (ASHuR) based on extraction of sentences. ASHuR extracts the best sentences of an article based on the frequency of concepts, cue-words, title words, and sentence length. Extracted sentences constitute the essence of the article; these sentences construct the model summary. We performed two experiments to assess the relia510 A. Ramı́rez-Noriega, R. Juárez-Ramı́rez, S. Jiménez, S. Inzunza et al. bility of ASHuR. The first experiment compared ASHuR against similar approaches based on sentences extraction; the experiment placed ASHuR in the first place in each applied test. The second experiment compared ASHuR against human-made summaries, which yielded a Pearson correlation value of 0.86. Assessments made to ASHuR show reliability to evaluate summaries written by users in collaborative sites (e.g. Wikipedia) or to review texts generated by students in online learning systems (e.g. Moodle).
Photothermally Triggered Endosomal Escape and Its Influence on Transfection Efficiency of Gold-Functionalized JetPEI/pDNA Nanoparticles

Plasmonic nanoparticles for drug delivery have attracted increasing interest over the last few years. Their localized surface plasmon resonance causes photothermal effects on laser irradiation, which allows for delivering drugs in a spatio-temporally controlled manner. Here, we explore the use of gold nanoparticles (AuNP) as carriers for pDNA in combination with pulsed laser irradiation to induce endosomal escape, which is currently considered to be one of the major bottlenecks in macromolecular drug delivery on the intracellular level. In particular, we evaluate nanocomplexes composed of JetPEI (polyethylenimine)pDNA and 10 nm AuNP, which do not exhibit endosomal escape by themselves. After incubating HeLa cells with these complexes, we evaluated endosomal escape and transfection efficiency using low- and high-energy laser pulses. At low laser energy heat is produced by the nanocomplexes, while, at higher laser energy, explosive vapour nanobubbles (VNB) are formed. We investigated the ability of heat transfer and VNB formation to induce endosomal escape and we examine the integrity of pDNA cargo after inducing both photothermal effects. We conclude that JetPEI/pDNA/AuNP complexes are unable to induce meaningful transfection efficiencies because laser treatment causes either dysfunctionality of the cargo when VNB are formed or forms too small pores in the endosomal membrane to allow pDNA to escape in case of heating. We conclude that laser-induced VNB is the most suitable to induce effective pDNA endosomal escape, but a different nanocomplex structure will be required to keep the pDNA intact.