Miracle-dz/UniFAR-FTUs huggingface dataset:
Adjuvant Treatment in Potentially Curable Pancreatic Cancer: Need to Include Tumor Location in the Equation?

To the Editor: P ancreatic cancer is currently the fourth, but projected to be the second, leading cause of death from cancer in the United States, with an estimated 53,000 new cases and 43,000 deaths in 2017. About 70% of patients with pancreatic cancer die within the first year after diagnosis, with 5-year survival being only 8%. Pancreatic cancer also remains one of the few malignancies with increasing incidence, despite advances in diagnostic imaging, surgical techniques, and systemic treatments. Pancreatic cancer may present in the head or body/tail region of the organ. Patients with tumors located in the pancreatic head
Identification of Cultivable Bacteria from Tropical Marine Sponges and Their Biotechnological Potentials

Marine sponges are acknowledged as bacterial hotspots in the oceanic biome. Aquatic bacteria are being investigated comprehensively for bioactive complexes and secondary metabolites. Cultivable bacteria associated with different species of sea sponges in South China Sea waters adjacent to Bidong Island, Terengganu were identified. Molecular identification was accomplished using 16S rRNA gene cloning and sequencing. Fourteen bacterial species were identified and their phylogenetic relationships were analysed by constructing a neighbour-joining tree with Molecular Evolutionary Genetics Analysis 6. The identified species encompassed four bacterial classes that were Firmicutes, Actinobacteria, Alphaproteobacteria and Gammaproteobacteria known to have been associated with sponges. The potential biotechnological applications of the identified bacteria were compared and reviewed based on relevant past studies. The biotechnological functions of the 14 cultivable isolates have been previously reported, hence reinforcing that bacteria associated with sponges are an abundant resource of scientifically essential compounds. Resilience of psychrotolerant bacteria, Psychrobacter celer, in warm tropical waters holds notable prospects for future research.
Competition of Candida glabrata against Lactobacillus is Hog1 dependent

Candida glabrata is a common human fungal commensal and opportunistic pathogen. This fungus shows remarkable resilience as it can form recalcitrant biofilms on indwelling catheters, has intrinsic resistance against azole antifungals, and is causing vulvovaginal candidiasis. As a nosocomial pathogen, it can cause life‐threatening bloodstream infections in immune‐compromised patients. Here, we investigate the potential role of the high osmolarity glycerol response (HOG) MAP kinase pathway for C. glabrata virulence. The C. glabrata MAP kinase CgHog1 becomes activated by a variety of environmental stress conditions such as osmotic stress, low pH, and carboxylic acids and subsequently accumulates in the nucleus. We found that CgHog1 allows C. glabrata to persist within murine macrophages, but it is not required for systemic infection in a mouse model. C. glabrata and Lactobacilli co‐colonise mucosal surfaces. Lactic acid at a concentration produced by vaginal Lactobacillus spp. causes CgHog1 phosphorylation and accumulation in the nucleus. In addition, CgHog1 enables C. glabrata to tolerate different Lactobacillus spp. and their metabolites when grown in co‐culture. Using a phenotypic diverse set of clinical C. glabrata isolates, we find that the HOG pathway is likely the main quantitative determinant of lactic acid stress resistance. Taken together, our data indicate that CgHog1 has an important role in the confrontation of C. glabrata with the common vaginal flora.
Predictive Analytics with Structured and Unstructured data - a Deep Learning based Approach

— Predictive analytics over structured time-series data has always been an active area of research. With plentitude of textual information generating across different sources on the web, researchers have started combining relevant structured and unstructured data to improve predictions. In this paper, we have present a generic deep learning framework for predictive analytics utilizing both structured and unstructured data. We also present a case-study to validate the functionality and applicability of the proposed framework where we use LSTM for prediction of structured data movement direction using events extracted from news articles. Experimental results shows that the proposed model outperforms existing baseline.
Reducing hospital admissions for COPD: perspectives following the Christchurch Earthquake

The devastating 2011 earthquake in Christchurch destroyed or badly damaged healthcare infrastructure, including Christchurch Hospital. This forced change in management of exacerbations of chronic obstructive pulmonary disease (COPD), which until that point had frequently led to admission to hospital and focused attention on providing safe community options for care. This paper describes the process of understanding factors contributing to high admission frequency with exacerbations of COPD and also describes a process of change, predominantly to healthcare delivery systems and philosophies, and the subsequent outcomes. What became clear in understanding admissions with COPD to Christchurch Hospital was that the behaviour of the patient, in the context of exacerbations, and the subsequent response of the system to the patient, led to admission being the default option, in spite of low severity of the exacerbation itself. By altering systems’ responses to exacerbations, with a linked care process between ambulances, community care and hospitals, we were able to safely reduce admissions for COPD, with a sustained overall reduction in bed-day occupancy for COPD of ~48%. We would encourage these discussions and changes to occur without the stimulus of an earthquake in your healthcare environment!
A Reinforcement Learning Framework for Natural Question Generation using Bi-discriminators

Visual Question Generation (VQG) aims to ask natural questions about an image automatically. Existing research focus on training model to fit the annotated data set that makes it indifferent from other language generation tasks. We argue that natural questions need to have two specific attributes from the perspectives of content and linguistic respectively, namely, natural and human-written. Inspired by the setting of discriminator in adversarial learning, we propose two discriminators, one for each attribute, to enhance the training. We then use the reinforcement learning framework to incorporate scores from the two discriminators as the reward to guide the training of the question generator. Experimental results on a benchmark VQG dataset show the effectiveness and robustness of our model compared to some state-of-the-art models in terms of both automatic and human evaluation metrics.
Predictors of Posttraumatic Growth Among University Students Who Experienced a Recent Earthquake

Abstract A 5.8 magnitude earthquake hit Gyeongju City, South Korea, in 2016, traumatising and psychologically panicking residents. The purpose of this study was to identify predictive factors for posttraumatic growth (PTG) among university students who experienced South Korea’s largest earthquake. A cross-sectional descriptive survey was distributed to a convenience sample of 493 university students in Gyeongju City. Data were collected via 5 self-report questionnaires. PTG was found to be positively correlated with posttraumatic stress disorder (PTSD), resilience and social support. Multiple regression analysis showed that PTSD (β = 0.519, p < .001), resilience (β = 0.213, p < .001), religion (β = 0.125, p = .001) and sex (female; β = 0.090, p = .010) were identified as predictive factors of PTG. These results illustrate a need for health care providers to include individual internal resources such as resilience in intervention strategies to promote PTG and decrease posttraumatic experiences in those who have experienced traumatic events such as earthquakes. A longitudinal study is needed to obtain more definitive evidence of PTSD progression and PTG over time.
Cool English: a Grammatical Error Correction System Based on Large Learner Corpora

This paper presents a grammatical error correction (GEC) system that provides corrective feedback for essays. We apply the sequence-to-sequence model, which is frequently used in machine translation and text summarization, to this GEC task. The model is trained by EF-Cambridge Open Language Database (EFCAMDAT), a large learner corpus annotated with grammatical errors and corrections. Evaluation shows that our system achieves competitive performance on a number of publicly available testsets.
Introducing a new standardized nanomaterial environmental toxicity screening testing procedure, ISO/TS 20787: aquatic toxicity assessment of manufactured nanomaterials in saltwater Lakes using Artemia sp. nauplii

Abstract This paper introduces a new standardized testing procedure for nanomaterial environmental toxicity (International Organization for Standardization/Technical Specification (ISO/TS) 20787): ‘aquatic toxicity assessment of manufactured nanomaterials in saltwater lakes using Artemia sp. Nauplii’ intended to generate more reliable and repeatable aquatic toxicity data testing manufactured nanomaterials, using Artemia sp. , to evaluate their possible ecotoxicity in saltwater lake ecosystems. The principles behind testing with Artemia sp. are reviewed and the paper gives an overview of research published between 2009 and 2018 in which manufactured nanomaterials were tested using Artemia sp.
Should we Dream the Impossible Dream of Reproducibility in Visual Analytics Evaluation?

Especially in the ﬁeld of Visual Analytics, where a lot of design decisions have to be taken, researchers strive for reproducible results. We present two different evaluation approaches aiming for more general design knowledge: the isolation of features and the abstraction of results. Both approaches have potentials, but also problems with respect to generating reproducible results. We discuss whether reproducibility is possible or even the right aim in the evaluation of Visual Analytics methods.
Dose‐intensified bendamustine and melphalan (BenMel) conditioning before second autologous transplantation in myeloma patients

Consolidation in myeloma patients with high‐dose melphalan chemotherapy (Mel HDCT) and autologous transplantation (ASCT) is standard of care since more than 2 decades. However, definite cure remains exceptional despite intensive treatment, and improving effectiveness of HDCT remains an unmet clinical need. Combining intensified bendamustine with melphalan may represent an option. We analyzed safety and efficacy of combining dose‐intensified bendamustine (200 mg/m2 on days −4/−3) with high‐dose melphalan (100 mg/m2 on days −2/−1) before a second (tandem) ASCT in adverse risk myeloma patients after Mel HDCT/ASCT1. Twelve patients received BenMel conditioning before ASCT2 because of high‐risk cytogenetics and/or failure to achieve complete remission (CR) after Mel HDCT/ASCT1. Comparing Mel HDCT/ASCT1 and BenMel HDCT/ASCT2, we observed no differences in hematologic recovery and tolerance. Acute renal injury after BenMel conditioning occurred in 3 (25%) patients, but was reversible in all patients, and there were no treatment related deaths. Complete remission rates were increasing from 42% after Mel/ASCT1 to 75% after BenMel/ASCT2. PFS 1 year after ASCT2 was 67%, and OS was 83%. These data suggest that dose‐intensified bendamustine with melphalan conditioning is safe and warrants a prospective randomized comparison to standard melphalan HDCT in myeloma patients.
Ant Colony System for Multi-Document Summarization

This paper proposes an extractive multi-document summarization approach based on an ant colony system to optimize the information coverage of summary sentences. The implemented system was evaluated on both English and Arabic versions of the corpus of the Text Analysis Conference 2011 MultiLing Pilot by using ROUGE metrics. The evaluation results are promising in comparison to those of the participating systems. Indeed, our system achieved the best scores based on several ROUGE metrics.
Ultrastructural aspects of mechano‐ and chemoreceptors in Branchiobdella pentodonta (annelida, oligochaeta)

The surface receptors in Branchiobdella pentodonta consist of “sense buttons” prevalent on the prostomium, isolated sense cells all along the body of the animal, and free nerve endings. The “sense buttons” are uni‐ and multiciliated neurosensitive elements and supporting cells together with mucus glandular processes and muscle fibers. In the neurosensitive elements the cilia are always surrounded by cytoplasmic extroversion. The cytoplasm of the apical zone has abundant small dense granules, mitochondria, bands of tonofilaments, and microtubules. The cilium of uniciliated elements originates from three short roots. The highly vacuolated support cells surround the neurosensitive elements, separating them from each other. The “sense buttons” appear to be mechanoreceptors and chemoreceptors, and the isolated sense cells tactile mechanoreceptors, as are the free nerve endings. The surface receptors are compared with those of other Oligochaeta and Hirudinea.
Why war is a man's game

Interest in the evolutionary origins and drivers of warfare in ancient and contemporary small-scale human societies has greatly increased in the last decade, and has been particularly spurred by exciting archaeological discoveries that suggest our ancestors led more violent lives than previously documented. However, the striking observation that warfare is an almost-exclusively male activity remains unexplained. Three general hypotheses have been proposed, concerning greater male effectiveness in warfare, lower male costs, and patrilocality. But while each of these factors might explain why warfare is more common in men, they do not convincingly explain why women almost never participate. Here, we develop a mathematical model to formally assess these hypotheses. Surprisingly, we find that exclusively male warfare may evolve even in the absence of any such sex differences, though sex biases in these parameters can make this evolutionary outcome more likely. The qualitative observation that participation in warfare is almost exclusive to one sex is ultimately explained by the fundamentally sex-specific nature of Darwinian competition—in fitness terms, men compete with men and women with women. These results reveal a potentially key role for ancestral conditions in shaping our species' patterns of sexual division of labour and violence-related adaptations and behavioural disorders.
WARP-Text: a Web-Based Tool for Annotating Relationships between Pairs of Texts

We present WARP-Text, an open-source web-based tool for annotating relationships between pairs of texts. WARP-Text supports multi-layer annotation and custom definitions of inter-textual and intra-textual relationships. Annotation can be performed at different granularity levels (such as sentences, phrases, or tokens). WARP-Text has an intuitive user-friendly interface both for project managers and annotators. WARP-Text fills a gap in the currently available NLP toolbox, as open-source alternatives for annotation of pairs of text are not readily available. WARP-Text has already been used in several annotation tasks and can be of interest to the researchers working in the areas of Paraphrasing, Entailment, Simplification, and Summarization, among others.
An Empirical Study on Fine-Grained Named Entity Recognition

Named entity recognition (NER) has attracted a substantial amount of research. Recently, several neural network-based models have been proposed and achieved high performance. However, there is little research on fine-grained NER (FG-NER), in which hundreds of named entity categories must be recognized, especially for non-English languages. It is still an open question whether there is a model that is robust across various settings or the proper model varies depending on the language, the number of named entity categories, and the size of training datasets. This paper first presents an empirical comparison of FG-NER models for English and Japanese and demonstrates that LSTM+CNN+CRF (Ma and Hovy, 2016), one of the state-of-the-art methods for English NER, also works well for English FG-NER but does not work well for Japanese, a language that has a large number of character types. To tackle this problem, we propose a method to improve the neural network-based Japanese FG-NER performance by removing the CNN layer and utilizing dictionary and category embeddings. Experiment results show that the proposed method improves Japanese FG-NER F-score from 66.76% to 75.18%.
Adopting the Word-Pair-Dependency-Triplets with Individual Comparison for Natural Language Inference

This paper proposes to perform natural language inference with Word-Pair-Dependency-Triplets. Most previous DNN-based approaches either ignore syntactic dependency among words, or directly use tree-LSTM to generate sentence representation with irrelevant information. To overcome the problems mentioned above, we adopt Word-Pair-Dependency-Triplets to improve alignment and inference judgment. To be specific, instead of comparing each triplet from one passage with the merged information of another passage, we first propose to perform comparison directly between the triplets of the given passage-pair to make the judgement more interpretable. Experimental results show that the performance of our approach is better than most of the approaches that use tree structures, and is comparable to other state-of-the-art approaches.
A Cross-lingual Messenger with Keyword Searchable Phrases for the Travel Domain

We present Qutr (Query Translator), a smart cross-lingual communication application for the travel domain. Qutr is a real-time messaging app that automatically translates conversations while supporting keyword-to-sentence matching. Qutr relies on querying a database that holds commonly used pre-translated travel-domain phrases and phrase templates in different languages with the use of keywords. The query matching supports paraphrases, incomplete keywords and some input spelling errors. The application addresses common cross-lingual communication issues such as translation accuracy, speed, privacy, and personalization.
Population‐based outcome analysis of diffuse large B‐cell lymphoma in people living with HIV infection and competent individuals

The prognostic factors and outcome of 58 acquired immunodeficiency syndrome‐related diffuse large B‐cell lymphoma (AR‐DLBCL) patients from the Swiss HIV Cohort Study, diagnosed from 2004 to 2011, were compared with those of 326 immunocompetent (IC)‐DLBCL from the Hematology Division of the Amedeo Avogadro University (Italy) and the Oncology Institute of Southern Switzerland. Median follow‐up was 6 years; 5‐year overall survival (OS) was 68% (95% CI: 63%‐73%) in IC‐DLBCL and 63% (95% CI: 49%‐75%) in AR‐DLBCL (P = .220). The acquired immunodeficiency syndrome‐related lymphoma international prognostic index predicted OS in AR‐DLBCL. Among 148 patients younger than 61 years (40 AR‐DLBCL and 108 IC‐DLBCL) treated with RCHOP/RCHOP‐like regimens, 20 IC‐DLBCL and 9 AR‐DLBCL patients died and OS was not significantly different. A higher proportion of early deaths occurred in the AR‐DLBCL: indeed, 1‐year OS was 94% (95% CI: 87%‐97%) in IC‐DLBCL and 82% (95% CI: 66%‐91%) in AR‐DLBCL patients. After rituximab and active antiretroviral therapy introduction, AR‐DLBCL and IC‐DLBCL patients treated with curative intent have similar long‐term survival.
Effective Attention Modeling for Aspect-Level Sentiment Classification

Aspect-level sentiment classification aims to determine the sentiment polarity of a review sentence towards an opinion target. A sentence could contain multiple sentiment-target pairs; thus the main challenge of this task is to separate different opinion contexts for different targets. To this end, attention mechanism has played an important role in previous state-of-the-art neural models. The mechanism is able to capture the importance of each context word towards a target by modeling their semantic associations. We build upon this line of research and propose two novel approaches for improving the effectiveness of attention. First, we propose a method for target representation that better captures the semantic meaning of the opinion target. Second, we introduce an attention model that incorporates syntactic information into the attention mechanism. We experiment on attention-based LSTM (Long Short-Term Memory) models using the datasets from SemEval 2014, 2015, and 2016. The experimental results show that the conventional attention-based LSTM can be substantially improved by incorporating the two approaches.
Automatic Assessment of Conceptual Text Complexity Using Knowledge Graphs

Complexity of texts is usually assessed only at the lexical and syntactic levels. Although it is known that conceptual complexity plays a significant role in text understanding, no attempts have been made at assessing it automatically. We propose to automatically estimate the conceptual complexity of texts by exploiting a number of graph-based measures on a large knowledge base. By using a high-quality language learners corpus for English, we show that graph-based measures of individual text concepts, as well as the way they relate to each other in the knowledge graph, have a high discriminative power when distinguishing between two versions of the same text. Furthermore, when used as features in a binary classification task aiming to choose the simpler of two versions of the same text, our measures achieve high performance even in a default setup.
Probabilistic classification of tumour habitats in soft tissue sarcoma

The purpose of this work is to propose a method to characterize tumour heterogeneity on MRI, using probabilistic classification based on a reference tissue. The method uses maps of the apparent diffusion coefficient (ADC), T2 relaxation, and a calculated map representing high‐b‐value diffusion‐weighted MRI (denoted simDWI) to identify up to five habitats (i.e. sub‐regions) of tumours.
Encoding Sentiment Information into Word Vectors for Sentiment Analysis

General-purpose pre-trained word embeddings have become a mainstay of natural language processing, and more recently, methods have been proposed to encode external knowledge into word embeddings to benefit specific downstream tasks. The goal of this paper is to encode sentiment knowledge into pre-trained word vectors to improve the performance of sentiment analysis. Our proposed method is based on a convolutional neural network (CNN) and an external sentiment lexicon. Experiments on four popular sentiment analysis datasets show that this method improves the accuracy of sentiment analysis compared to a number of benchmark methods.