Miracle-dz/UniFAR-FTUs huggingface dataset:
Prevalence of peritonitis and mortality in patients treated with continuous ambulatory peritoneal dialysis (CAPD) in Africa: a protocol for a systematic review and meta-analysis

Introduction Continuous ambulatory peritoneal dialysis (CAPD) is the ideal modality for renal replacement therapy in most African settings given that it is relatively cheaper than haemodialysis (HD) and does not require in-centre care. CAPD is, however, not readily utilised as it is often complicated by peritonitis leading to high rates of technique failure. The objective of this study is to assess the prevalence of CAPD-related peritonitis and all-cause mortality in patients treated with CAPD in Africa. Methods and analysis We will search PubMed, EMBASE, SCOPUS, Africa Journal Online and Google Scholar for studies conducted in Africa from 1 January 1980 to 30 June 2017 with no language restrictions. Eligible studies will include cross-sectional, prospective observational and cohort studies of patients treated with CAPD. Two authors will independently screen, select studies, extract data and conduct risk of bias assessment. Data consistently reported across studies will be pooled using random-effects meta-analysis. Heterogeneity will be evaluated using Cochrane’s Q statistic and quantified using I2 statistics. Graphical and formal statistical tests will be used to assess for publication bias. Ethics and dissemination Ethical approval will not be needed for this study as data used will be extracted from already published studies. Results of this review will be published in a peer-reviewed journal and presented at conferences. The Preferred Reporting Items for Systematic reviews and Meta-Analyses for Protocols 2015 (PRISMA-P 2015) framework guided the development of this protocol. PROSPERO registration number CRD42017072966.
Automatic Focus Annotation: Bringing Formal Pragmatics Alive in Analyzing the Information Structure of Authentic Data

Analyzing language in context, both from a theoretical and from a computational perspective, is receiving increased interest. Complementing the research in linguistics on discourse and information structure, in computational linguistics identifying discourse concepts was also shown to improve the performance of certain applications, for example, Short Answer Assessment systems (Ziai and Meurers, 2014). Building on the research that established detailed annotation guidelines for manual annotation of information structural concepts for written (Dipper et al., 2007; Ziai and Meurers, 2014) and spoken language data (Calhoun et al., 2010), this paper presents the first approach automating the analysis of focus in authentic written data. Our classification approach combines a range of lexical, syntactic, and semantic features to achieve an accuracy of 78.1% for identifying focus.
Automated proof synthesis for propositional logic with deep neural networks

This work explores the application of deep learning, a machine learning technique that uses deep neural networks (DNN) in its core, to an automated theorem proving (ATP) problem. To this end, we construct a statistical model which quantifies the likelihood that a proof is indeed a correct one of a given proposition. Based on this model, we give a proof-synthesis procedure that searches for a proof in the order of the likelihood. This procedure uses an estimator of the likelihood of an inference rule being applied at each step of a proof. As an implementation of the estimator, we propose a proposition-to-proof architecture, which is a DNN tailored to the automated proof synthesis problem. To empirically demonstrate its usefulness, we apply our model to synthesize proofs of propositional logic. We train the proposition-to-proof model using a training dataset of proposition-proof pairs. The evaluation against a benchmark set shows the very high accuracy and an improvement to the recent work of neural proof synthesis.
The Y_dcd_zju Slot Filling System for TAC KBP 2017

This report describes the Y dcd zju Slot Filling System for the TAC KBP 2017 Cold Start Slot Filling task. To accomplish this task, our system uses text-CNN (Convolutional Neural Network) model to detect sentences that contain pre-defined relations, then we use the NER tag to identify slot fillers. To order to increase the recall of our system, a SVM classifier and a manual trigger words matching algorithm are also used to get all possible slot fillers. Distant Supervision is used to generate training data of our CNN model from massive unlabelled text.
Non-parametric estimation of transition probabilities in non-Markov multi-state models: The landmark Aalen–Johansen estimator

The topic non-parametric estimation of transition probabilities in non-Markov multi-state models has seen a remarkable surge of activity recently. Two recent papers have used the idea of subsampling in this context. The first paper, by de Uña Álvarez and Meira-Machado, uses a procedure based on (differences between) Kaplan–Meier estimators derived from a subset of the data consisting of all subjects observed to be in the given state at the given time. The second, by Titman, derived estimators of transition probabilities that are consistent in general non-Markov multi-state models. Here, we show that the same idea of subsampling, used in both these papers, combined with the Aalen–Johansen estimate of the state occupation probabilities derived from that subset, can also be used to obtain a relatively simple and intuitive procedure which we term landmark Aalen–Johansen. We show that the landmark Aalen–Johansen estimator yields a consistent estimator of the transition probabilities in general non-Markov multi-state models under the same conditions as needed for consistency of the Aalen–Johansen estimator of the state occupation probabilities. Simulation studies show that the landmark Aalen–Johansen estimator has good small sample properties and is slightly more efficient than the other estimators.
A feasibility study of colorectal cancer diagnosis via circulating tumor DNA derived CNV detection

Circulating tumor DNA (ctDNA) has shown great promise as a biomarker for early detection of cancer. However, due to the low abundance of ctDNA, especially at early stages, it is hard to detect at high accuracies while keeping sequencing costs low. Here we present a pilot stage study to detect large scale somatic copy numbers variations (CNVs), which contribute more molecules to ctDNA signal compared to point mutations, via cell free DNA sequencing. We show that it is possible to detect somatic CNVs in early stage colorectal cancer (CRC) patients and subsequently discriminate them from normal patients. With 25 normal and 24 CRC samples, we achieve 100% specificity (lower bound confidence interval: 86%) and ~79% sensitivity (95% confidence interval: 63% - 95%,), though the performance should be considered with caution given the limited sample size. We report a lack of concordance between the CNVs detected via cfDNA sequencing and CNVs identified in parent tissue samples. However, recent findings suggest that a lack of concordance is expected for CNVs in CRC because of their sub-clonal nature. Finally, the CNVs we detect very likely contribute to cancer progression as they lie in functionally important regions, and have been shown to be associated with CRC specifically. This study paves the path for a larger scale exploration of the potential of CNV detection for both diagnoses and prognoses of cancer.
Immunomodulatory Effects of Dietary Seaweeds in LPS Challenged Atlantic Salmon Salmo salar as Determined by Deep RNA Sequencing of the Head Kidney Transcriptome

Seaweeds may represent immuno-stimulants that could be used as health-promoting fish feed components. This study was performed to gain insights into the immunomodulatory effects of dietary seaweeds in Atlantic salmon. Specifically tested were 10% inclusion levels of Laminaria digitata (SW1) and a commercial blend of seaweeds (Oceanfeed®) (SW2) against a fishmeal based control diet (FMC). Differences between groups were assessed in growth, feed conversion ratio and blood parameters hematocrit and hemoglobin. After a LPS challenge of fish representing each of the three groups, RNAseq was performed on the head kidney as major immune organ to determine transcriptomic differences in response to the immune activation. Atlantic salmon fed with dietary seaweeds did not show major differences in performance in comparison with fishmeal fed fish. RNAseq resulted in ∼154 million reads which were mapped against a NCBI Salmo salar reference and against a de novo assembled S. salar reference for analyses of expression of immune genes and ontology of immune processes among the 87,600 cDNA contigs. The dietary seaweeds provoked a more efficient immune response which involved more efficient identification of the infection site, and processing and presentation of antigens. More specifically, chemotaxis and the chemokine-mediated signaling were improved and therewith the defense response to Gram-positive bacterium reduced. Specific Laminaria digitata effects included reduction of the interferon-gamma-mediated signaling. Highly upregulated and specific for this diet was the expression of major histocompatibility complex class I-related gene protein. The commercial blend of seaweeds caused more differential expression than Laminaria digitata and improved immune processes such as receptor-mediated endocytosis and cell adhesion, and increased the expression of genes involved in response to lipopolysaccharide and inflammatory response. Particularly, expression of many important immune receptors was up-regulated illustrating increased responsiveness. NF-kappa-B inhibitor alpha is an important gene that marked the difference between both seaweed diets as Laminaria digitata inhibits the expression for this cytokine while the blend of seaweeds stimulates it. It can be concluded that the inclusion of seaweeds such as Laminaria digitata can have important modulatory effects on the immune capacity of Atlantic salmon resulting in a more efficient immune response.
Representative Consensus from Limited‐Size Ensembles

Characterizing the uncertainty and extracting reliable visual information from ensemble data have been persistent challenges in various disciplines, specifically in simulation sciences. Many ensemble analysis and visualization techniques take a probabilistic approach to this problem with the assumption that the ensemble size is large enough to extract reliable statistical or probabilistic summaries. However, many real‐life ensembles are rather limited in size, with only a handful of members, due to various restrictions such as storage, computational power, or sampling limitations. As a result, probabilistic inference is subject to imprecision and can potentially result in untrustworthy information in the presence of a limited sample‐size ensemble. In this case, a more reliable approach is to fuse the information present in an ensemble with a limited number of members with minimal assumptions. In this paper, we propose a technique to construct a representative consensus that is particularly suited for ensembles of a relatively small size. The proposed technique casts the problem as an ordering problem in which at each point in the domain, the ensemble members are ranked based on the local neighborhood. This local approach allows us to provide shape and irregularity sensitivity. The local order statistics will then be fused to construct a global consensus using a Bayesian approach to ensure spatial coherency of the local information. We demonstrate the utility of the proposed technique using a synthetic and two real‐life examples.
Identification of HLA-DQA1 as a Susceptibility Gene for Spinal Tuberculosis by Exome Sequencing

Background Spinal tuberculosis (STB) is the main cause of bone and joint tuberculosis. This study aimed to screen and analyze the susceptibility genes for STB using whole-exome sequencing (WES). Material/Methods All exon regions of peripheral blood DNA from 6 STB patients were captured and sequenced using WES and the sequencing data were analyzed by modern bioinformatics methods to identify disease-causing mutations. Sanger sequencing was then used to validate the mutation sites in normal controls (207) and STB patients (193). The mRNA expression of the mutant gene and the serum levels of IL-6 and TNF-α were detected using qPCR or ELISA assay, respectively. Results A nonsynonymous single-nucleotide polymorphism (SNP) in the gene HLA-DQA1 (rs796778515, c.592delCinsG, CAG to GAG, p.Q198E) was identified and further validated by Sanger sequencing. The percentage of the 3 genotypes C/C, C/G and G/G in STB patients and normal controls were 37.3%, 32.1%, and 30.6% and 47.8%, 33.8%, and 18.4%, respectively. Furthermore, the C>G mutation was significantly associated with the occurrence of STB. In addition, the levels of HLA-DQA1 mRNA were significantly lower in blood cells from STB patients compared with normal controls, while the serum levels of IL-6 and TNF-α were significantly higher. Conclusions The C>G mutation in the HLA-DQA1 gene was associated with the occurrence of STB. This variation may result in the decreased level of HLA-DQA1 mRNA and increased serum levels of IL-6 and TNF-α, which finally led the STB susceptibility.
An Extensible, Regular-Expression-Based Tool for Multi-language Mutant Generation

Mutation testing is widely used in research (even if not in practice). Mutation testing tools usually target only one programming language and rely on parsing a program to generate mutants, or operate not at the source level but on compiled bytecode. Unfortunately, developing a robust mutation testing tool for a new language in this paradigm is a difficult and time-consuming undertaking. Moreover, bytecode/intermediate language mutants are difficult for programmers to read and understand. This paper presents a simple tool, called universalmutator, based on regular-expression-defined transformations of source code. The primary drawback of such an approach is that our tool can generate invalid mutants that do not compile, and sometimes fails to generate mutants that a parser-based tool would have produced. Additionally, it is incompatible with some approaches to improving the efficiency of mutation testing. However, the regexp-based approach provides multiple compensating advantages. First, our tool is easy to adapt to new languages; e.g., we present here the first mutation tool for Apple's Swift programming language. Second, the method makes handling multi-language programs and systems simple, because the same tool can support every language. Finally, our approach makes it easy for users to add custom, project-specific mutations.
A demo of application lifecycle management for IoT collaborative neighborhood in the Fog: Practical experiments and lessons learned around docker

Regarding latency, privacy, resiliency and network scarcity management, only distributed approaches such as proposed by Fog Computing architecture can efficiently address the fantastic growth of the Internet of Things (IoT). IoT applications could be deployed and run hierarchically at different levels in an infrastructure ranging from centralized datacenters to the connected things themselves. Consequently, software entities composing IoT applications could be executed in many different configurations. The heterogeneity of the equipment and devices of the target infrastructure opens opportunities in the placement of the software entities, taking into account their requirements in terms of hardware, cyber-physical interactions and software dependencies. Once the most appropriate place has been found, software entities have to be deployed and run. Container-based virtualization has been considered to overpass the complexity of packaging, deploying and running software entities in a heterogeneous distributed infrastructure at the vicinity of the connected devices. This paper reports a practical experiment presented as a live demo that showcases a “Smart Bell in a Collaborative Neighborhood” IoT application in the Fog. Application Lifecycle Management (ALM) has been put in place based on Docker technologies to deploy and run micro-services in the context of Smart Homes operated by Orange.
A Neural Layered Model for Nested Named Entity Recognition

Entity mentions embedded in longer entity mentions are referred to as nested entities. Most named entity recognition (NER) systems deal only with the flat entities and ignore the inner nested ones, which fails to capture finer-grained semantic information in underlying texts. To address this issue, we propose a novel neural model to identify nested entities by dynamically stacking flat NER layers. Each flat NER layer is based on the state-of-the-art flat NER model that captures sequential context representation with bidirectional Long Short-Term Memory (LSTM) layer and feeds it to the cascaded CRF layer. Our model merges the output of the LSTM layer in the current flat NER layer to build new representation for detected entities and subsequently feeds them into the next flat NER layer. This allows our model to extract outer entities by taking full advantage of information encoded in their corresponding inner entities, in an inside-to-outside way. Our model dynamically stacks the flat NER layers until no outer entities are extracted. Extensive evaluation shows that our dynamic model outperforms state-of-the-art feature-based systems on nested NER, achieving 74.7% and 72.2% on GENIA and ACE2005 datasets, respectively, in terms of F-score.
Efficient Two-level Homomorphic Encryption in Prime-order Bilinear Groups and A Fast Implementation in WebAssembly

We construct an efficient two-level homomorphic public-key encryption in prime-order bilinear groups. Such a scheme supports polynomially many homomorphic additions and one multiplication over encrypted data, similar to the cryptosystem of Boneh, Goh, and Nissim (BGN, presented at TCC 2005), which was constructed in composite-order bilinear groups. Prior to our work, the state-of-the-art for two-level homomorphic public-key encryption is the Freeman scheme (presented at Eurocrypt 2010), which is indeed the prime-order realization of the BGN scheme. Our proposed scheme significantly improves efficiency for almost all the aspects of the Freeman scheme, while retains the same ciphertext sizes. Our scheme is surprisingly simple as it is indeed (a concatenation of two copies of) the ElGamal encryption "in the exponent'' resided in an asymmetric bilinear groups. We provide a software implementation of our scheme in the x86 architecture. Besides this usual implementation, we also implement our scheme in WebAssembly (wasm), which is a portable low-level bytecode format; this allows our scheme to be run (very fast) on any popular web browser, without any plugins required.
Engagement in a mixed-method course: The case of project management

Engineers nowadays require knowledge about many topics to succeed in the workplace. The Project Management course provides tools to understand and plan projects in large scale, including financial topics. The course is usually taught in a theoretical manner, with the professor explaining concepts and the students developing homework and written exams. Student performance in the course was good, but engagement during the class was perceived to be very low. As a result, the professor decided to change the course and used three different methods: evidence-centered assessment design, project based learning and experiential learning. At the end of the semester, students took a NSSE (National Survey of Student Engagement) survey [1]. Results show student engagement in different activities performed during the class, favoring a mixed-methods class over a traditional one. Additionally, students in the mixed-method course improved their final grades compared to students in the traditional class.
Predictors of Locally Advanced Disease at Presentation and Clinical Outcomes Among Cervical Cancer Patients Admitted at a Tertiary Hospital in Botswana

Objectives The aim of this study was to determine predictors of locally advanced disease at presentation and clinical outcomes among cervical cancer patients in Botswana to inform interventional strategies. Methods Retrospective review of 149 medical records of new cervical cancer patients was conducted between August 2016 and February 2017 at the Princess Marina Hospital. Data collected included sociodemographics, presenting symptoms, stage of disease, comorbidities, interventions, and clinical outcomes. STATA 12 was used for data analysis. Frequencies were used to describe patient demographics and clinical variables. Bivariate and multivariate binary logistic regression analyses were used to determine association between stage of disease at presentation and patient characteristics. P ⩽ 0.05 was considered significant. Results Mean age was 49.5 years. Nine (89.2%) in 10 patients had locally advanced cervical cancer (stage IB1–IVB). Two thirds (65.1%) were human immunodeficiency virus positive. Previous cervical cancer screening was low at 38.3%. Common symptoms were abnormal vaginal bleeding, low abdominal pain, and malodorous vaginal discharge reported among 75.8%, 66.4%, and 39.6% of cases, respectively. Overall, 32 (21.5%) were declared cured, 52 (34.9%) improved, and 11 (7.4%) opted for home-based care. Hospital deaths were 41 (27.5%). Major causes of death were renal failure (48.7%) and severe anemia (39%). Thirteen (8.7%) were lost to follow-up. Being unmarried (odds ratio [OR], 3.9), lack of cervical cancer screening (OR, 6.68), presentation with vaginal bleeding (OR, 7.69), and low abdominal pain (OR, 4.69) were associated with advanced disease at presentation. Conclusions Lack of cervical cancer screening, vaginal bleeding, low abdominal pain, and unmarried status were associated with advanced disease at presentation. We recommend scale-up of cervical cancer screening and its integration into routine human immunodeficiency virus care. Capacity building in gynecologic oncology and palliative care services is currently critical.
“Everybody Puts Their Whole Life on Facebook”: Identity Management and the Online Social Networks of LGBTQ Youth

Lesbian, gay, bisexual, transgender, and queer (LGBTQ) youth and young adults almost inevitably “come out”, or self-disclose their identity to others. Some LGBTQ youth are more uniformly “out”, while others may disclose to some groups but not others. This selective disclosure is complicated on real name social media sites, which tend to encourage a unified presentation of self across social contexts. We explore these complications with a cohort of LBGTQ youth on Facebook (N = 199, Mage = 24.13). Herein we ask: How do LBGTQ youth manage the disclosure of their sexual orientation and/or gender identity to different people in their lives? Further, are there identifiable differences in the online social network structure for LGBTQ youth who manage outness in different ways? Finally, how do LGBTQ young people describe their experiences on Facebook? We answer these questions using a mixed methods approach, combining statistical cluster analysis, network visualization, and qualitative data. Our findings illustrate patterns in network structure by outness cluster type, highlighting both the work involved in managing one’s online identity as well as the costs to (semi-) closeted individuals including a considerably lower overall network connectivity. In particular, outness to family characterized LGBTQ young people’s experiences on Facebook.