Miracle-dz/UniFAR-FTUs huggingface dataset:
All-oral direct antiviral treatment for hepatitis C chronic infection in a real-life cohort: The role of cirrhosis and comorbidities in treatment response

Background Hepatitis C virus (HCV) infection is the major cause of end-stage liver disease (LD) worldwide. The aim of this study was to assess sustained virological response (SVR) rates in a real-world cohort of patients with HCV infection treated with interferon-free direct antiviral agents (DAA). Patients and methods All patients with genotypes 1, 2 or 3 HCV infection who started interferon-free treatment at a university hospital from December 2015 through July 2017 were included. The primary outcome was SVR at post-treatment week 12 by intention-to-treat (ITT) and modified ITT (mITT) analysis. Results Five hundred twenty seven patients were enrolled, 51.6% with cirrhosis. Most patients received sofosbuvir + daclatasvir + ribavirin (60.7%) and sofosbuvir + simeprevir (25.6%). Overall SVR rates were 90.5% for ITT and 96% for mITT. SVR rates were higher in non-cirrhotic (94.2% in ITT and 96.8% in mITT) versus cirrhotic patients (87.1% in ITT and 95.2% in mITT). In ITT and mITT assessments, SVR rates were higher in patients with Child-Pugh A (n = 222, 88.7% and 95.7%, respectively) versus Child-Pugh B or C (n = 40, 80% and 90%, respectively); SVR rates were higher in patients with genotype 1 (n = 405, 92.1% and 98.2%), followed by genotype 2 (n = 13, 84.6% and 92.7%) and genotype 3 (n = 109, 84.4% and 88.4%). Lower comorbidity index (p = 0.0014) and absence of cirrhosis (p = 0.0071) were associated with SVR. Among cirrhotic patients, lower Model for End-Stage Liver Disease (p = 0.0258), higher albumin (p = 0.0015), and higher glomerular filtration rate (p = 0.0366) were related to SVR. Twenty-two cirrhotic patients (8%) had clinical liver decompensation during treatment. Complications of advanced LD were responsible for discontinuation of treatment and death in 12 and 7 patients, respectively. Conclusion Treatment with all-oral DAA achieved high SVR rates, particularly in patients without cirrhosis and few comorbidities. Advanced LD is associated to poor outcome, such as treatment failure and death.
How transferable are the datasets collected by active learners?

Active learning is a widely-used training strategy for maximizing predictive performance subject to a fixed annotation budget. Between rounds of training, an active learner iteratively selects examples for annotation, typically based on some measure of the model's uncertainty, coupling the acquired dataset with the underlying model. However, owing to the high cost of annotation and the rapid pace of model development, labeled datasets may remain valuable long after a particular model is surpassed by new technology. In this paper, we investigate the transferability of datasets collected with an acquisition model A to a distinct successor model S. We seek to characterize whether the benefits of active learning persist when A and S are different models. To this end, we consider two standard NLP tasks and associated datasets: text classification and sequence tagging. We find that training S on a dataset actively acquired with a (different) model A typically yields worse performance than when S is trained with "native" data (i.e., acquired actively using S), and often performs worse than training on i.i. d. sampled data. These findings have implications for the use of active learning in practice,suggesting that it is better suited to cases where models are updated no more frequently than labeled data.
In Vivo Antimalarial Activity of the 80% Methanolic Root Bark Extract and Solvent Fractions of Gardenia ternifolia Schumach. & Thonn. (Rubiaceae) against Plasmodium berghei

Background Evolution of antimalarial drug resistance makes the development of new drugs a necessity. Important source in search of such drugs is medicinal plants. Gardenia ternifolia plant is used in Ethiopian traditional medicine for the treatment of malaria and is endowed with in vitro antimalarial activity. Herein, the in vivo antimalarial activity of the plant was investigated. Methods Acute toxicity was carried out using a standard procedure. A 4-day suppressive test was employed to evaluate the antimalarial effect of methanolic crude extract and solvent fractions of the plant. The curative and prophylactic effect of crude extract was further tested by Ranes's test and residual infection procedure, respectively, using Plasmodium berghei (ANKA strain) in Swiss albino mice. Results The chemosuppressive effect exerted by the crude extract and fractions ranged between 30-59% and 14-51%, respectively. Curative and prophylactic effects of the crude extract were in the range of 36-63% and 24-37%, respectively. All dose levels of the crude extract prevented loss of weight, reduction in temperature, and anemia on early and established infection. Butanol and chloroform fractions also did reverse reduction in temperature, body weight, and packed cell volume. Conclusions The results indicated that the plant has a promising antiplasmodial activity and it could be considered as a potential source to develop new antimalarial agents.
Prevalence of Hearing Impairment Among High-Risk Newborns in Ibadan, Nigeria

The burden of severe hearing impairment is increasing with two-thirds of these hearing impaired people residing in developing countries. Newborn hearing screening helps to identify early, babies who need intervention in order to prevent future disability. Neither universal nor targeted hearing screening programme is available in Nigeria. Objectives: This study was carried out to assess the prevalence of hearing impairment among high-risk newborns in UCH and the associated risk factors. Materials and Methods: Two hundred one newborns in the neonatal unit of UCH with risk factors for hearing impairment had hearing screening done using automated auditory brainstem response (AABR) at 30, 45, and 70 dB at admission and discharge, and those that failed screening at discharge were rescreened at 6 weeks post-discharge. Results: Eighty-three (41.3%) and 32 (15.9%) high-risk newborns failed at admission and discharge screening respectively, and 19 (9.5%) still failed at follow up screening. The majority of hearing loss at follow up was bilateral (94.7%) and severe (52.6%). The risk factors associated with persistent hearing loss at follow up were acute bilirubin encephalopathy (RR = 11.2, CI: 1.4–90.6), IVH (RR = 8.8, CI: 1.1–71.8), meningitis (RR = 4.8, CI: 1.01–29), recurrent apnoea (RR = 2.7, CI: 1.01–7.3), severe perinatal asphyxia NNE III (RR = 7, CI: 2.4–20.2). Conclusion: Severe and bilateral hearing impairment is a common complication among high risk newborns in UCH persisting till 6 weeks post-neonatal care. Severe perinatal asphyxia with NNE III, ABE, IVH, meningitis and administration of amikacin for more than 5 days were significant risk factors. We recommend that SCBU graduates with these risk factors should have mandatory audiologic evaluation at discharge.
Hyperbaric Oxygen Therapy in Liver Diseases

Hyperbaric oxygen therapy (HBOT) is an efficient therapeutic option to improve progress of lots of diseases especially hypoxia-related injuries, and has been clinically established as a wide-used therapy for patients with carbon monoxide poisoning, decompression sickness, arterial gas embolism, problematic wound, and so on. In the liver, most studies positively evaluated HBOT as a potential therapeutic option for liver transplantation, acute liver injury, nonalcoholic steatohepatitis, fibrosis and cancer, especially for hepatic artery thrombosis. This might mainly attribute to the anti-oxidation and anti-inflammation of HBOT. However, some controversies are existed, possibly due to hyperbaric oxygen toxicity. This review summarizes the current understandings of the role of HBOT in liver diseases and hepatic regeneration. Future understanding of HBOT in clinical trials and its in-depth mechanisms may contribute to the development of this novel adjuvant strategy for clinical therapy of liver diseases.
Topological Characterization of Human and Mouse m5C Epitranscriptome Revealed by Bisulfite Sequencing

Background Compared with the well-studied 5-methylcytosine (m5C) in DNA, the role and topology of epitranscriptome m5C remain insufficiently characterized. Results Through analyzing transcriptome-wide m5C distribution in human and mouse, we show that the m5C modification is significantly enriched at 5′ untranslated regions (5′UTRs) of mRNA in human and mouse. With a comparative analysis of the mRNA and DNA methylome, we demonstrate that, like DNA methylation, transcriptome m5C methylation exhibits a strong clustering effect. Surprisingly, an inverse correlation between mRNA and DNA m5C methylation is observed at CpG sites. Further analysis reveals that RNA m5C methylation level is positively correlated with both RNA expression and RNA half-life. We also observed that the methylation level of mitochondrial RNAs is significantly higher than RNAs transcribed from the nuclear genome. Conclusions This study provides an in-depth topological characterization of transcriptome-wide m5C modification by associating RNA m5C methylation patterns with transcriptional expression, DNA methylations, RNA stabilities, and mitochondrial genome.
Evolution of brain network dynamics in neurodevelopment

Cognitive function evolves significantly over development, enabling flexible control of human behavior. Yet, how these functions are instantiated in spatially distributed and dynamically interacting networks, or graphs, that change in structure from childhood to adolescence is far from understood. Here we applied a novel machine-learning method to track continuously overlapping and time-varying subgraphs in the brain at rest within a sample of 200 healthy youth (ages 8–11 and 19–22) drawn from the Philadelphia Neurodevelopmental Cohort. We uncovered a set of subgraphs that capture surprisingly integrated and dynamically changing interactions among known cognitive systems. We observed that subgraphs that were highly expressed were especially transient, flexibly switching between high and low expression over time. This transience was particularly salient in a subgraph predominantly linking frontoparietal regions of the executive system, which increases in both expression and flexibility from childhood to young adulthood. Collectively, these results suggest that healthy development is accompanied by an increasing precedence of executive networks and a greater switching of the regions and interactions subserving these networks. AUTHOR SUMMARY Our ability to thoughtfully engage with the world around us changes appreciably as we transition from childhood to adulthood. Yet, how our brains develop to enable that change remains far from understood. Here we used network science—traditionally applied to the study of social networks like Facebook or Twitter—and machine learning to show that growing cognitive abilities are accompanied by greater flexibility of brain regions within distributed networks. This flexibility is greatest in the executive system, which is critical for higher-order cognitive functions and increases in expression and flexibility from childhood to young adulthood. These results suggest that healthy development is facilitated by an increasing precedence of executive networks and a greater switching of the regions and interactions subserving these networks.
CSF nonphosphorylated Tau as a biomarker for the discrimination of AD from CJD

Creutzfeldt–Jakob disease and Alzheimer's disease are characterized by the presence of elevated total‐Tau cerebrospinal fluid concentrations while the presence of hyperphosphorylated Tau forms in the cerebrospinal fluid is rather a hallmark of Alzheimer's disease. Here we aimed to investigate potential contribution of nonphospho‐Tau epitopes (non‐P‐Tau) in the discrimination between both diseases. Non‐P‐Tau cerebrospinal fluid concentration was highly increased in Creutzfeldt–Jakob disease (n = 57, 3683 ± 3599 pg/mL) compared to Alzheimer's disease (n = 41, 148 ± 219 pg/mL) and neurological controls (n = 56, 62 ± 40 pg/mL), and significantly improved the proportion of correctly classified patients (99%) compared to that achieved by total‐Tau (90%), P‐Tau (62%) and 14‐3‐3 (91%).
Predictive Value of Combined LIPS and ANG-2 Level in Critically Ill Patients with ARDS Risk Factors

To investigate the predictive value of the acute physiology and chronic health evaluation 2 (APACHE2) score and lung injury prediction score (LIPS) for acute respiratory distress syndrome (ARDS) when combined with biomarkers for this condition in patients with ARDS risk factors. In total, 158 Han Chinese patients with ARDS risk factors were recruited from the Respiratory and Emergency Intensive Care Units. The LIPS, APACHE2 score, primary diagnosis at admission, and ARDS risk factors were determined within 6 h of admission, and PaO2/FiO2 was determined on the day of admission. Blood was collected within 24 h of admission for the measurement of angiopoietin-2 (ANG-2), sE-selectin, interleukin-6 (IL-6), and interleukin-8 (IL-8) levels. ARDS was monitored for the next 7 days. Univariate and multivariate analyses and receiver operating characteristic (ROC) analyses were employed to construct a model for ARDS prediction. Forty-eight patients developed ARDS within 7 days of admission. Plasma ANG-2 level, sE-selectin level, LIPS, and APACHE2 score in ARDS patients were significantly higher than those in non-ARDS patients. ANG-2 level, LIPS, and APACHE2 score were correlated with ARDS (P < 0.001, P < 0.006, and P < 0.042, resp.). When the APACHE2 score was used in combination with the LIPS and ANG-2 level to predict ARDS, the area under the ROC curve (AUC) was not significantly increased. Compared to LIPS or ANG-2 alone, LIPS in combination with ANG-2 had significantly increased positive predictive value (PPV) and AUC for the prediction of ARDS. In conclusion, plasma ANG-2 level, LIPS, and APACHE2 score are correlated with ARDS. Combined LIPS and ANG-2 level displays favorable sensitivity, specificity, and AUC for the prediction of ARDS.
Learning a Decision Boundary from Stochastic Examples: Incremental Algorithms with and without Queries

Even if it is not possible to reproduce a target input-output relation, a learning machine should be able to minimize the probability of making errors. A practical learning algorithm should also be simple enough to go without memorizing example data, if possible. Incremental algorithms such as error backpropagation satisfy this requirement. We propose incremental algorithms that provide fast convergence of the machine parameter to its optimal choice o with respect to the number of examples t. We will consider the binary choice model whose target relation has a blurred boundary and the machine whose parameter specifies a decision boundary to make the output prediction. The question we wish to address here is how fast can approach o, depending upon whether in the learning stage the machine can specify inputs as queries to the target relation, or the inputs are drawn from a certain distribution. If queries are permitted, the machine can achieve the fastest convergence, ( - o)2 O(t1). If not, O(t1) convergence is generally not attainable. For learning without queries, we showed in a previous paper that the error minimum algorithm exhibits a slow convergence ( - o)2 O(t2/3). We propose here a practical algorithm that provides a rather fast convergence, O(t4/5). It is possible to further accelerate the convergence by using more elaborate algorithms. The fastest convergence turned out to be O[(lnt)2 t1]. This scaling is considered optimal among possible algorithms, and is not due to the incremental nature of our algorithm.
How Data Drive Early Word Learning: A Cross-Linguistic Waiting Time Analysis

The extent to which word learning is delayed by maturation as opposed to accumulating data is a longstanding question in language acquisition. Further, the precise way in which data influence learning on a large scale is unknown—experimental results reveal that children can rapidly learn words from single instances as well as by aggregating ambiguous information across multiple situations. We analyze Wordbank, a large cross-linguistic dataset of word acquisition norms, using a statistical waiting time model to quantify the role of data in early language learning, building off Hidaka (2013). We find that the model both fits and accurately predicts the shape of children’s growth curves. Further analyses of model parameters suggest a primarily data-driven account of early word learning. The parameters of the model directly characterize both the amount of data required and the rate at which informative data occurs. With high statistical certainty, words require on the order of ∼ 10 learning instances, which occur on average once every two months. Our method is extremely simple, statistically principled, and broadly applicable to modeling data-driven learning effects in development.
A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification

Deep learning approaches for sentiment classification do not fully exploit sentiment linguistic knowledge. In this paper, we propose a Multi-sentiment-resource Enhanced Attention Network (MEAN) to alleviate the problem by integrating three kinds of sentiment linguistic knowledge (e.g., sentiment lexicon, negation words, intensity words) into the deep neural network via attention mechanisms. By using various types of sentiment resources, MEAN utilizes sentiment-relevant information from different representation sub-spaces, which makes it more effective to capture the overall semantics of the sentiment, negation and intensity words for sentiment prediction. The experimental results demonstrate that MEAN has robust superiority over strong competitors.
Diversity of Fungal Communities in Heshang Cave of Central China Revealed by Mycobiome-Sequencing

Deciphering of the mycobiome in pristine karst caves has been impeded by constraints of remote locations, inaccessibility to specimens and technical limitations, which greatly restricted in-depth understanding of mycobiomes in subterranean ecosystem. Here, mycobiomes of Heshang Cave in south-western karst region of China were investigated by Illumina HiSeq sequencing of fungal rRNA-ITS1 gene across different habitats. In total 793,502 ITS1 reads and 2,179 OTUs from 778 Mb reads after stringent quality control (Q30) and 453 genera, 72 orders and 19 classes within 6 phyla were detected. Ascomycota (42% OTUs) dominated across the five habitats. Shannon-Wiener index varied from 1.25 to 7.62 and community richness was highest in drip waters, followed by weathered rocks, bat guanos, sediments, and air samples. Mycobiomes displayed specificity to five habitats and more distinct OTUs were found in weathered rocks (12%) and drip waters (9%). In contrast, only 6.60% core OTUs were shared by five habitats. Notably, weathered rocks possessed more indicator groups and were revealed for the first time to be dominated by Sordariomycetes (43%). The community richness of air mycobiomes increased from cave entrance to the innermost part and dominated by the indicator groups of Penicillium mallochii (>30%) and P. herquei (>9%). Our work represents the largest attempt to date to a systematical investigation of oligotrophic solution-cave-associated mycobiomes in China. Our discovery of high diversity of mycobiomes in Heshang Cave also suggests that eukaryotic microorganisms may play a crucial role in subsurface environments.
Using Semantics for Granularities of Tokenization

Depending on downstream applications, it is advisable to extend the notion of tokenization from low-level character-based token boundary detection to identification of meaningful and useful language units. This entails both identifying units composed of several single words that form a several single words that form a, as well as splitting single-word compounds into their meaningful parts. In this article, we introduce unsupervised and knowledge-free methods for these two tasks. The main novelty of our research is based on the fact that methods are primarily based on distributional similarity, of which we use two flavors: a sparse count-based and a dense neural-based distributional semantic model. First, we introduce DRUID, which is a method for detecting MWEs. The evaluation on MWE-annotated data sets in two languages and newly extracted evaluation data sets for 32 languages shows that DRUID compares favorably over previous methods not utilizing distributional information. Second, we present SECOS, an algorithm for decompounding close compounds. In an evaluation of four dedicated decompounding data sets across four languages and on data sets extracted from Wiktionary for 14 languages, we demonstrate the superiority of our approach over unsupervised baselines, sometimes even matching the performance of previous language-specific and supervised methods. In a final experiment, we show how both decompounding and MWE information can be used in information retrieval. Here, we obtain the best results when combining word information with MWEs and the compound parts in a bag-of-words retrieval set-up. Overall, our methodology paves the way to automatic detection of lexical units beyond standard tokenization techniques without language-specific preprocessing steps such as POS tagging.
Dynamic connectivity and the effects of maturation in youth with attention deficit hyperactivity disorder

The analysis of time-varying connectivity by using functional MRI has gained momentum given its ability to complement traditional static methods by capturing additional patterns of variation in human brain function. Attention deficit hyperactivity disorder (ADHD) is a complex, common developmental neuropsychiatric disorder associated with heterogeneous connectivity differences that are challenging to disambiguate. However, dynamic connectivity has not been examined in ADHD, and surprisingly few whole-brain analyses of static functional network connectivity (FNC) using independent component analysis (ICA) exist. We present the first analyses of time-varying connectivity and whole-brain FNC using ICA in ADHD, introducing a novel framework for comparing local and global dynamic connectivity in a 44-network model. We demonstrate that dynamic connectivity analysis captures robust motifs associated with group effects consequent on the diagnosis of ADHD, implicating increased global dynamic range, but reduced fluidity and range localized to the default mode network system. These differentiate ADHD from other major neuropsychiatric disorders of development. In contrast, static FNC based on a whole-brain ICA decomposition revealed solely age effects, without evidence of group differences. Our analysis advances current methods in time-varying connectivity analysis, providing a structured example of integrating static and dynamic connectivity analysis to further investigation into functional brain differences during development. Author Summary Neuropsychiatric disorders represent a central field of inquiry in network neuroscience, akin to lesional analysis of complex brain system dynamics. The prevalence of these conditions increases from mid-childhood to peak in adolescence and regress thereafter, challenging our ability to disambiguate maturational and connectivity effects. ADHD is a paradigmatic example, and the most common neuropsychiatric disorder of development. Here we present the first whole-brain analysis of time-varying connectivity in ADHD. Building on leading-edge methods in dynamic connectivity, our novel approach analyses time-varying connectivity on both a global brain basis, and within local network systems. This framework demonstrates that analysis of time-varying connectivity offers additional ways to characterize group and maturational effects in ADHD that are extensible to other developmental neuropsychiatric disorders.