Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic dissection of complex traits using hierarchical biological knowledge

Despite the growing constellation of genetic loci linked to common traits, these loci have yet to account for most heritable variation, and most act through poorly understood mechanisms. Recent machine learning (ML) systems have used hierarchical biological knowledge to associate genetic mutations with phenotypic outcomes, yielding substantial predictive power and mechanistic insight. Here, we use an ontology-guided ML system to map single nucleotide variants (SNVs) focusing on 6 classic phenotypic traits in natural yeast populations. The 29 identified loci are largely novel and account for ~17% of the phenotypic variance, versus <3% for standard genetic analysis. Representative results show that sensitivity to hydroxyurea is linked to SNVs in two alternative purine biosynthesis pathways, and that sensitivity to copper arises through failure to detoxify reactive oxygen species in fatty acid metabolism. This work demonstrates a knowledge-based approach to amplifying and interpreting signals in population genetic studies.
Gastroenterology Clinical Trials Transparency: An Analysis of Publication Rates From the ClinicalTrials.gov Database

INTRODUCTION: There is limited information on the transparency of gastroenterology clinical trials. METHODS: The ClinicalTrials.gov database was searched for trials focused on most common gastrointestinal diseases up to August 2018. Adherence to reporting of results to the database or in publication form was recorded for each trial along with trial characteristics. RESULTS: Of the 2,429 trials included in the final analysis, 1824 (75%, 95% confidence interval: 73.4%–76.8%) had results on ClinicalTrials.gov or in the form of a publication. However, only 534 (29%) had results posted on ClinicalTrials.gov. DISCUSSION: Improvement of clinical trial transparency is needed in gastroenterology.
Investigation on the Phase Transition and Morphology of TiO2 Nanoparticles with respect to Calcination Temperature

Pure TiO2 nanoparticles have been prepared by a novel Photon Induced Method (PIM) without precursor modification. The prepared sample has been calcinated at 500°C, 700°C and 1000°C to study the effect of calcinations temperature on the stability, phase and morphology of TiO2. XRD analysis reveals an interesting result of increase in phase stability. This may be accounted to the oxygen richness of the sample which has been confirmed through EDAX. XRD results also revel a phase transformation from anatase to rutile with increasing calcination temperature. The morphologic analysis performed with HRSEM and HRTEM revel a transformation from nanoparticles to nanorods. The formation of TiO2 is confirmed through the Ti-O vibrational peaks in FTIR and the band gap of the samples have been examined with UV-vis Spectrophotometer. The prepared samples with such high phase stability may be applied for photocatalytic
Multi-interest Diversification for End-to-end Sequential Recommendation

Sequential recommenders capture dynamic aspects of users’ interests by modeling sequential behavior. Previous studies on sequential recommendations mostly aim to identify users’ main recent interests to optimize the recommendation accuracy; they often neglect the fact that users display multiple interests over extended periods of time, which could be used to improve the diversity of lists of recommended items. Existing work related to diversified recommendation typically assumes that users’ preferences are static and depend on post-processing the candidate list of recommended items. However, those conditions are not suitable when applied to sequential recommendations. We tackle sequential recommendation as a list generation process and propose a unified approach to take accuracy as well as diversity into consideration, called multi-interest, diversified, sequential recommendation. Particularly, an implicit interest mining module is first used to mine users’ multiple interests, which are reflected in users’ sequential behavior. Then an interest-aware, diversity promoting decoder is designed to produce recommendations that cover those interests. For training, we introduce an interest-aware, diversity promoting loss function that can supervise the model to learn to recommend accurate as well as diversified items. We conduct comprehensive experiments on four public datasets and the results show that our proposal outperforms state-of-the-art methods regarding diversity while producing comparable or better accuracy for sequential recommendation.
Grounding NBA Matchup Summaries

The present paper summarizes an attempt we made to meet a shared task challenge on grounding machine-generated summaries of NBA matchups (https://github.com/ehudreiter/accuracySharedTask.git). In the first half, we discuss methods and in the second, we report results, together with a discussion on what feature may have had an effect on the performance.
The MIT-LL/AFRL IWSLT 2012 MT system

This paper describes the MIT-LL/AFRL statistical MT system and the improvements that were developed during the IWSLT 2012 evaluation campaign. As part of these efforts, we experimented with a number of extensions to the standard phrase-based model that improve performance on the Arabic to English and English to French TED-talk translation task. We also applied our existing ASR system to the TED-talk lecture ASR task, and combined our ASR and MT systems for the TED-talk SLT task. We discuss the architecture of the MIT-LL/AFRL MT system, improvements over our 2011 system, and experiments we ran during the IWSLT-2012 evaluation. Specifically, we focus on 1) cross-domain translation using MAP adaptation, 2) cross-entropy filtering of MT training data, and 3) improved Arabic morphology for MT preprocessing.
The RWTH Aachen machine translation system for IWSLT 2010

In this paper we describe the statistical machine translation system of the RWTH Aachen University developed for the translation task of the IWSLT 2010. This year, We participated in the translation task for the Arabic to English language pair. We experimented with two state-ofthe-art decoders: phrase-based and hierarchical-based decoders. Extensions to the decoders included phrase training (as opposed to heuristic phrase extraction) for the phrase-based decoder, and soft syntactic features for the hierarchical decoder. Additionally, we experimented with various rule-based and statistical-based segmenters for Arabic. Due to the different decoders and the different methodologies that we apply for segmentation, we expect that there will be complimentary variation in the results achieved by each system. The next step would be to exploit these variations and achieve better results by combining the systems. We try different strategies for system combination and report significant improvements over the best single system.
Automatic Verification of Data Summaries

We present a generic method to compute thefactual accuracy of a generated data summarywith minimal user effort. We look at the prob-lem as a fact-checking task to verify the nu-merical claims in the text. The verification al-gorithm assumes that the data used to generatethe text is available. In this paper, we describehow the proposed solution has been used toidentify incorrect claims about basketball tex-tual summaries in the context of the AccuracyShared Task at INLG 2021.
Multi-Sentence Knowledge Selection in Open-Domain Dialogue

Incorporating external knowledge sources effectively in conversations is a longstanding problem in open-domain dialogue research. The existing literature on open-domain knowledge selection is limited and makes certain brittle assumptions on knowledge sources to simplify the overall task, such as the existence of a single relevant knowledge sentence per context. In this work, we evaluate the existing state of open-domain conversation knowledge selection, showing where the existing methodologies regarding data and evaluation are flawed. We then improve on them by proposing a new framework for collecting relevant knowledge, and create an augmented dataset based on the Wizard of Wikipedia (WOW) corpus, which we call WOW++. WOW++ averages 8 relevant knowledge sentences per dialogue context, embracing the inherent ambiguity of open-domain dialogue knowledge selection. We then benchmark various knowledge ranking algorithms on this augmented dataset with both intrinsic evaluation and extrinsic measures of response quality, showing that neural rerankers that use WOW++ can outperform rankers trained on standard datasets.
The RWTH Aachen machine translation systems for IWSLT 2014

This work describes the statistical machine translation (SMT) systems of RWTH Aachen University developed for the evaluation campaign International Workshop on Spoken Language Translation (IWSLT) 2014. We participated in both the MT and SLT tracks for the English→French and German→English language pairs. We apply the identical training pipeline and models on both language pairs. Our state-of-the-art phrase-based baseline systems are augmented with maximum expected BLEU training for phrasal, lexical and reordering models. Further, we apply rescoring with novel recurrent neural language and translation models. The same systems are used for the SLT track, where we additionally perform punctuation prediction on the automatic transcriptions employing hierarchical phrase-based translation. We are able to improve RWTH’s 2013 evaluation systems by 1.7-1.8% BLEU absolute.
Responses of Aspergillus niger to selected environmental factors

Abstract Four wild type strains of A. niger were collected from soil and stream sediments representing environments with variable level of As, Sb, Al, Fe, Cd, Cu, and Zn contamination. Banská Štiavnica-Šobov (S), Pezinok-Kolársky vrch (P) and Slovinky (Sl) represent contaminated localities. Locality Gabčíkovo (G) was as a control site. The influence of toxic elements in these substrates on fungal growth, colony size, enzymatic activity, production of organic acids and their pelletization in water suspensions with montmorillonite was studied. The aim of our study was to find out how the wild type strains from (contaminated) environment will behave in different model solutions. We also wanted to add some new information in this area of study, because that there is some gap in the available knowledge.
Exploiting Aeolus Level-2B Winds to Better Characterize Atmospheric Motion Vector Bias and Uncertainty

Abstract. The need for highly accurate atmospheric wind observations is a high priority in the science community, and in particular numerical weather prediction (NWP). To address this requirement, this study leverages Aeolus wind LIDAR Level-2B data provided by the European Space Agency (ESA) to better characterize atmospheric motion vector (AMV) bias and uncertainty, with the eventual goal of potentially improving AMV algorithms. AMV products from geostationary (GEO) and low-Earth polar orbiting (LEO) satellites are compared with reprocessed Aeolus horizontal line-of-sight (HLOS) global winds observed in August and September 2019. Winds from two of the four Aeolus observing modes are utilized for comparison with AMVs: Rayleigh-clear (derived from the molecular scattering signal) and Mie-cloudy (derived from particle scattering). For the most direct comparison, quality controlled (QC’d) Aeolus winds are collocated with quality controlled AMVs in space and time, and the AMVs are projected onto the Aeolus HLOS direction. Mean collocation differences (MCD) and standard deviation (SD) of those differences (SDCD) are determined from comparisons based on a number of conditions, and their relation to known AMV bias and uncertainty estimates is discussed. GOES-16 and LEO AMV characterizations based on Aeolus winds are described in more detail. Overall, QC’d AMVs correspond well with QC’d Aeolus HLOS wind velocities (HLOSV) for both Rayleigh-clear and Mie-cloudy observing modes, despite remaining biases in Aeolus winds after reprocessing. Comparisons with Aeolus HLOSV are consistent with known AMV bias and uncertainty in the tropics, NH extratropics, and in the Arctic, and at mid- to upper-levels in both clear and cloudy scenes. SH comparisons generally exhibit larger than expected SDCD, which could be attributed to height assignment errors in regions of high winds and enhanced vertical wind shear. GOES-16 water vapor clear-sky AMVs perform best relative to Rayleigh-clear winds, with small MCD (-0.6 m s-1 to 0.1 m s-1) and SDCD (5.4–5.6 m s-1) in the NH and tropics that fall within the accepted range of AMV error values relative to radiosonde winds. Compared to Mie-cloudy winds, AMVs exhibit similar MCD and smaller SDCD (~4.4–4.8 m s-1) throughout the troposphere. In polar regions, Mie-cloudy comparisons have smaller SDCD (5.2 m s-1 in the Arctic, 6.7 m s-1 in the Antarctic) relative to Rayleigh-clear comparisons, which are larger by 1–2 m s-1. The level of agreement between AMVs and Aeolus winds varies per combination of conditions including the Aeolus observing mode coupled with AMV derivation method, geographic region, and height of the collocated winds. It is advised that these stratifications be considered in future comparison studies and impact assessments involving 3D winds. Additional bias corrections to the Aeolus dataset are anticipated to further refine the results.
Does QFII Uphold Value Investment? Evidence from China

The Basic Law and investment idea of value investment is widely used in developed Western financial market, but it still needs to be verified in the emerging and transition China. In this paper, the QFII investment strategy is feasible in China, that is, the ASV-GPD model is the marginal distribution function, and the correlation between QFII and HS300 index is studied by using four kinds of copula cluster methods. It is concerned with the statistical modeling of the dependence structure of QFII and HS300 index using the theory of Copulas. We select four Copulas and identify the type of dependency to capture nonlinear asymmetric and tail dependence. The GPD model needs to estimate the threshold values in order to exactly fit the margin distribution of Copula functions. Our analysis is based on a semi-parametric extreme value model. EV Copulas, Archimax Copulas and Archimedean Copulas simulate the correlation between QFII index and HS300 index. The empirical analysis indicates that the BB1 Copula has a higher correlation in the lower tail than the upper tail for a variety of parameters used in the Copula function. These findings illustrate that two different return series are more likely to correlate with each other during market downturns than upturns. Moreover, the backtesting results show that Copula-ASV-GPD model is suitable for measurement of tail risk of QFII and HS300 portfolio. In addition, we find the striking evidence of QFII value investment in Chinese A-share stock market for the period 2006-2018. Meanwhile QFII institutional investors gradually increase and the improvement of listed companies’ profit sharing system so that there is an opportunity for the rational return of value investment in Chinese stock markets.
DMSO_A_254537 1621..1631

Department of Human Physiology, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia Background: Type 2 diabetes mellitus (T2DM) is associated with a high risk of early mortality and morbidity from hypertension. Even though Ethiopia is Africa’s first country among the top five in the prevalence of DM, there is a paucity of data on hypertension and its associated factors among patients with type 2 diabetes mellitus. Therefore, this study aimed to determine the prevalence and associated factors of hypertension among type 2 diabetes mellitus patients at Debre Tabor General Hospital, 2019. Methods and Materials: An institution-based cross-sectional study was employed on 378 T2DM patients. Data were collected using an interviewer-administered questionnaire and analyzed by Stata 14. A multivariable logistic regression model was used to identify associated factors of hypertension among T2DM patients. Associated factors were declared at p < 0.05. Results: The prevalence of hypertension among T2DM patients was 59.5% (95% CI: 54.5–64.5). Stage 1 hypertension was the most common (30.95%). The odds of hypertension was higher among age group of 50–60 years (adjusted odds ratio (AOR)=2.5, 95% confidence interval (CI) (1.27–4.90)), patients from urban area (AOR = 2.8, 95% CI (1.08– 7.18)), with longer duration of T2DM (AOR =1.16, 95% CI (1.08–1.25)), with BMI ≥25 kg/ m (AOR = 3.2, 95% CI (1.71–5.96)), with poor glycemic control (AOR = 3.0, 95% CI (1.75–5.19)), and patients who were current cigarette smokers (AOR = 3.8, 95% CI (1.98–14.96)). Conclusion: The prevalence of hypertension is high and the majority have poor blood pressure control. Hence, DM care providers and other health sector stakeholders have to work in collaboration to prevent it through designing appropriate strategies especially for those at higher risk of developing hypertension.
Heart Patient Triage Prediction of Clinical Outcomes Using Machine Learning Models

In the medical field, the analysis of different clinical and pathological data by medical experts is a complicated process. Therefore, it is important to build a framework that can instantly and effectively identify the prevalence of heart disease in thousands of samples. Hence, the paper proposed a new method for diagnosing heart disease. The proposed machine learning method is based on a hybrid optimization algorithm of random search and grid search of a random forest and a parameter optimization algorithm of support vector machine grid search. The model, in predicting the presence or absence of heart disease in patients, has a maximum accuracy of 89%, which not only aided doctors in accurately predicting and diagnosing various diseases, but also helps patients with early diagnosis.
A CRISPR/Cas9-based method and primer design tool for seamless genome editing in fission yeast [version 3; peer review: 2 approved]

In the fission yeast Schizosaccharomyces pombe the prevailing approach for gene manipulations is based on homologous recombination of a PCR product that contains genomic target sequences and a selectable marker. The CRISPR/Cas9 system has recently been implemented in fission yeast, which allows for seamless genome editing without integration of a selection marker or leaving any other genomic ‘scars’. The published method involves manual design of the single guide RNA (sgRNA), and digestion of a large plasmid with a problematic restriction enzyme to clone the sgRNA. To increase the efficiency of this approach, we have established and optimized a PCR-based system to clone the sgRNA without restriction enzymes into a plasmid with a dominant natMX6 (nourseothricin) selection marker. We also provide a web-tool, CRISPR4P, to support the design of the sgRNAs and the primers required for the entire process of seamless DNA deletion. Moreover, we report the preparation of G1-synchronized and cryopreserved S. pombe cells, which greatly increases the efficiency and speed for transformations, and may also facilitate standard gene manipulations. Applying this optimized CRISPR/Cas9-based approach, we have successfully deleted over 80 different non-coding RNA genes, which are generally lowly expressed, and have inserted 7 point mutations in 4 different genomic regions.
Effect of cadmium doping on structural, spectroscopic and optical properties of samarium manganite multiferroic system

ABSTRACT Cadmium doped samarium manganite Sm0.9Cdo. 1MnO3 and Sm0.8Cdo. 2MnO3 were synthesized by solid state reaction technique. X-ray diffraction analysis confirms the single phase formation with pervoskites structure having orthorhombic phase with crystallite size in the range of 30 – 50 nm. Scanning electron micrograph shows particle size distribution is almost homogeneous and spherical. Fourier transformation infrared analysis depicts the presence of the Mn-O bond with a slight shift in wave number. Ultraviolet spectroscopy shows low absorbance in the entire visible region, an essential condition for non-linear optical applications. Absorption spectrum give optical band gap which ranges from 1.70 to 2.28 eV. Magnetization study gives an effective magnetic moment from 0.50 to 0.66 e.m. uwhich confirmthe paramagnetic behavior of the material at room temperature.
Using Neural Language Models to Detect Spoilers vorgelegt

Social media users enjoy discussing crucial plot developments in recently released media, often to the dismay of those users who have yet to watch or read the TV series, book, or movie in question. Users may have their enjoyment spoiled by discussions revealing major turns in the plot. An automated approach, filtering out such spoilers, would be ideal as manual labeling is impossible due to the sheer amount of content. Filtering would allow interested parties to partake in the discussion while leaving others the option to stay uninformed. In this thesis, we first approach the task of identifying spoilers as a classification task on the sentence or paragraph level. Second, we conduct the more challenging task of sequence classification, where each token is classified based on whether it constitutes (part of) a spoiler. Our approach makes use of machine learning methods based on neural networks, relying on user-generated content as training data. More specifically, BERT, a variant of the Transformer architecture, is used, since BERT has recently shown promising results on various language processing tasks. The results show that while our approach outperforms many previous ones, it is still not viable for real-world use. In real-world data, we observe an extreme class imbalance. As a result, all of our models yielding sufficient recall also produce a very high number of false positives. In other words, if enough of the actual spoilers are recognized as such, an unacceptable amount of non-spoilers are also incorrectly marked as spoilers.
OTT_A_244778 4283..4294

Department of General Surgery, Liu Zhou People’s Hospital, Liuzhou, Guangxi 545006, People’s Republic of China Background: Colorectal cancer (CRC) is the most common cause of cancer-related mortality in the world. Long non-coding RNAs (lncRNAs) are involved in the development of many cancers. However, studies on the effect of lncRNA small nucleolar RNA host gene 16 (SNHG16) on the proliferation, metastasis and apoptosis of CRC are still few. Methods: Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to determine the expression levels of SNHG16, microRNA-132-3p (miR-132-3p) and ubiquitin specific peptidase 22 (USP22). The proliferation, apoptosis, migration and invasion of CRC cells were evaluated by the 3-(4,5-dimethyl-2 thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide (MTT) assay, flow cytometry and transwell assay, respectively. Dual-luciferase reporter assay was used to verify the interactions among SNHG16, miR-132-3p and USP22. Also,Western blot analysis was used to assess the protein levels of USP22 and metastasis-related markers. Moreover, mice xenograft models were used to determine the effect of SNHG16 on CRC tumor growth in vivo. Results: SNHG16 was highly expressed in CRC tissues and cells. Knockdown of SNHG16 reduced the proliferation, migration, invasion, and promoted the apoptosis of CRC cells. MiR-1323p could interact with SNHG16, and its inhibitor recovered the suppression effect of silenced SNHG16 on CRC cell progression. Besides, USP22 was a target of miR-132-3p, and its overexpression restored the inhibition effect ofmiR-132-3pmimic onCRCcell progression. In addition, interference of SNHG16 reduced CRC tumor growth in vivo. Conclusion: LncRNA SNHG16 might act as an oncogene in CRC. The discovery of the SNHG16/miR-132-3p/USP22 pathway provided new thinking for the treatment of CRC.