Miracle-dz/UniFAR-FTUs huggingface dataset:
The Association between changes in C reactive protein and pregnancy rate in IVF/ICSI

Objectives: The aim of this study is to evaluate the association between changes in C reactive protein and pregnancy rate in IVF/ICSI. Patients and methods: A prospective cohort study that was conducted in Ain Shams University Maternity Hospital, Infertility Clinic during period time from September 2018 to September 2020. A total of 100 infertile women who were candidate for IVF/ICSI, over a period of 2 years, beginning in September 2018 were recruited to the study after they met the inclusion and exclusion criteria. The participants gave written informed consent and approval for the ethical aspects of the study. Results: This study showed no statistically significant differences according to clinical pregnancy regarding the sociodemographic data as BMI, duration of infertility and type of infertility and regarding Oocyte retrieval and embryo transfer, also no significant differences according to clinical pregnancy regarding CRP at baseline and at Oocyte pickup as well as CRP change at oocyte pickup. But age was significantly lower in cases with clinical pregnancy and CRP at embryo transfer and CRP change at embryo transfer were significantly higher in cases with positive pregnancy. Conclusion: Finally, we concluded that patients whose CRP level decreased on transfer day, had lower chance of pregnancy, whereas patients whose CRP level elevated on embryo transfer day had high chance of pregnancy
[Frontiers in Bioscience 9, 1899-1914, May 1, 2004] 1899 COGNITIVE FUNCTION AND ITS NEURAL MECHANISMS IN NONHUMAN PRIMATE MODELS OF AGING, AlZHEIMER’S DISEASE, AND MENOPAUSE

Nonhuman primates have been used as animal models in which to study cognitive changes associated with aging and age-related disease for decades. There are many advantages to using nonhuman primates for studies of aging including the capability to examine visual nonspatial cognitive processes and the ability to use operationally similar behavioral tasks to what is used with humans. Because some aspects of aging in humans do not develop naturally in nonhuman primates or do not follow the same course of natural development in monkeys, experimental models are necessary for some investigations. Research in our laboratory has identified similarities in the cognitive profiles of nonhuman primate models of aging, Alzheimer’s Disease, and menopause with their human counterparts. In addition, through the use of a variety of different techniques we have used these nonhuman primate models to begin to determine the neural substrates of agerelated cognitive dysfunction noted with advanced age and age-related disease. In this paper, we review our observations made in nonhuman primate models of aging, Alzheimer’s Disease, and menopause and indicate areas for future research.
Understanding How and Why Policy Design Matters: the policy design dynamics of governance reforms in Higher Education. England, Italy and the Netherlands compared

In the past 3 decades, all European HE systems have been exposed to the mantra of reforming governance in higher education. European governments’ HE policies have attempted to converge toward a common template of systemic governance: the so-called “steering at a distance” model. The basic policy tools of this strategy consist of institutional autonomy, new competitive funding mechanisms, and the assessment of the quality of research and teaching. In order to pursue this strategy, most European governments have tried to re-design the systemic dynamics of governance. It has been a complex and demanding process in which policy design has been at the centre of governments’ endeavours. Some governments have proven more effective than others, and have produced genuinely successful governance designs. Other governments have been less effective and have produced weak policy designs or even no policy design at all. This paper compares the dynamics of the policy design pursued in the higher education section in three different countries (Italy, England and the Netherlands), to try and understand how and why policy design develops over the course of time. This comparison of national policy design dynamics aims to show not only that similar policy instruments can produce different content and quality of policy design, but also that good or bad policy design, although contingently driven, depends significantly on the capacity of governments to create a design space for their actions. Paper presented at Workshop on ―Design and Non-Design in Policy-Making: When and How Policy Design Matters‖ ECPR Joint Sessions of Workshops March 31April 3 University of Warsaw Warsaw Poland
Response of wheat (Triticum aestivum L. var. Galaxy-2013) to pre-sowing seed treatment with thiourea under drought stress

A pot experiment was performed to assess the effect of thiourea (TU) as pre-sowing seed treatment to wheat (Triticum aestivum L.) under drought stressed conditions. Seeds of wheat variety named Galaxy-2013 were soaked in various TU levels such as non-soaking (control), water-soaking, 0.1 mM and 1.0 mM thiourea for 12 hours. Drought stress treatments i.e., non stress (100 % field capacity), moderate drought stress (70% FC) and severe drought stress (50% FC) were applied to one week old wheat seedlings. Data of different growth and physicochemical parameters was taken of seven-week-old wheat plants whereas yield was taken at the maturity stage. Drought stress considerably reduced shoot fresh weight, dry weight of shoot, fresh weight of root, shoot length, root length, grain yield plant, 100-grain weight, number of grains plant , total phenolics and total flavonoid contents, while increased hydrogen peroxide (H2O2), proline and glycinebetaine contents. Pre-sowing seed treatment with varying levels of thiourea showed differential effect under drought stress or non stress conditions e.g., under moderate drought stress (70% FC) both 0.1 mM and 1.0 mM TU increased shoot dry weight and decreased total phenolic contents. Furthermore, 0.1 mM TU enhanced root dry weight, while 1.0 mM TU increased proline contents. In conclusion, hydro-priming proved more effective in increasing shoot and root fresh and dry weights, shoot length, total leaf area plant, grain yield and number of grains plant in wheat var. Galaxy-2013 plants under drought stress or non stress conditions.
AMMONIA EMISSIONS FROM LAYER HOUSES IN IOWA

An ongoing project of monitoring ammonia (NH3) emissions from U.S. layer houses with two different housing and management schemes is reported in this paper. Ammonia and carbon dioxide concentration levels were collected bi-weekly from each house with portable monitoring units (PMUs). There exist substantial seasonal variations in NH3 emission from the layer houses. Specifically, daily mean NH3 concentrations in the manure belt house and the high-rise house ranged from 1 to 7 ppm and 9 to 108 ppm, respectively. The NH3 emission rates over the 9-month monitoring period (Feb. – Oct., 2002) averaged 7 mg/hr-hen or 58 g/d-500kg for the manure belt house and 43 mg/h-hen or 326 g/d-500kg for the high-rise house. Ammonia emission rates are higher in summer than in winter, although NH3 concentration may be much lower in summer. KEYWORDS - Ammonia, Emission rates, Ventilation rate.
Reopening Italy’s schools in September 2020: a Bayesian estimation of the change in the growth rate of new SARS-CoV-2 cases

Objectives COVID-19’s second wave started a debate on the potential role of schools as a primary factor in the contagion resurgence. Two opposite positions appeared: those convinced that schools played a major role in spreading SARS-CoV-2 infections and those who were not. We studied the growth rate of the total number of SARS-CoV-2 infections in all the Italian regions, before and after the school reopening (September–October 2020), investigating the hypothesis of an association between schools and the resurgence of the virus. Methods Using a Bayesian piecewise linear regression to scrutinise the number of daily SARS-CoV-2 infections in each region, we looked for an estimate of a changepoint in the growth rate of those confirmed cases. We compared the changepoints with the school opening dates, for each Italian region. The regression allows to discuss the change in steepness of the infection curve, before and after the changepoint. Results In 15 out of 21 Italian regions (71%), an estimated change in the rate of growth of the total number of daily SARS-CoV-2 infection cases occurred after an average of 16.66 days (95% CI 14.47 to 18.73) since the school reopening. The number of days required for the SARS-CoV-2 daily cases to double went from an average of 47.50 days (95% CI 37.18 to 57.61) before the changepoint to an average of 7.72 days (95% CI 7.00 to 8.48) after it. Conclusion Studying the rate of growth of daily SARS-CoV-2 cases in all the regions provides some evidence in favour of a link between school reopening and the resurgence of the virus. The number of factors that could have played a role is too many to give a definitive answer. Still, the temporal correspondence warrants further systematic experiments to investigate on potential confounders that could clarify how much reopening schools mattered.
Reduction in the 2020 Life Expectancy in Brazil after COVID-19

Brazil has the second-largest number of COVID-19 deaths worldwide. We use data on reported deaths to measure and compare the death toll across states from a demographic perspective. We estimate a decline in 2020 life expectancy at birth of 1.94 years, resulting in a mortality level not seen since 2013. The reduction in life expectancy at age 65 was 1.58 years, setting Brazil back to 2009 levels. The decline was larger for males, widening by 2.3% and 5.4% the female-male gap in life expectancy at birth and at age 65, respectively. Among states, Amazonas lost 59.6% of the improvements in life expectancy at birth since 2000. With 2021 COVID-19 deaths at about 43% of the total 2020 figures (as of mid-March) the demographic effect is likely to be even higher this year.
Prevalence of SARS-CoV-2 antibodies in Denmark: Nationwide, population-based seroepidemiological surveys

Background. Seroprevalence studies have proven an important tool to monitor the progression of the coronavirus disease 2019 (COVID-19) epidemic. We present results of consecutive population-based seroprevalence surveys performed in Denmark in 2020. Methods. Invitation letters including a questionnaire covering symptoms were sent to representatively drawn samples of the population in spring, late summer and autumn/winter of 2020. Blood samples from participants taken at public test-centers were analyzed for total Ig and seroprevalence estimates per population segment calculated and compared to other surveillance parameters. Results. From 34,081 participating individuals (response rate 33%), we obtained seroprevalence estimates increasing from 1.1% (95%CI: 0.7%-1.7) in May to 4.0 % (95%CI: 3.4%-4.7%) in December 2020. By December 2020, 1.5% of the population 12 years and older had tested positive by PCR. Seroprevalence estimates were roughly 3 times higher in those aged 12-29 compared to 65+ and higher in metropolitan municipalities. Among seropositives, loss of taste/smell were the more specific symptoms, 32%-56% did not report any symptoms. In half of seroconverted families, we did not see evidence of transmission between generations. Infected individuals in older age groups were hospitalized several fold more often than in younger. Conclusions. Seroprevalence increased during 2020; younger age groups were primarily infected in the autumn/winter surge. Approximately half were asymptomatically infected. Denmark has a high per capita test rate; roughly two undiagnosed infections of COVID-19 were estimated to occur for each diagnosed case. The epidemic appears to have progressed relatively modestly during 2020 in Denmark.
The Chinese Remainder Theorem for Compact, Task-Precise, Efficient and Secure Word Embeddings

The growing availability of powerful mobile devices and other edge devices, together with increasing regulatory and security concerns about the exchange of personal information across networks of these devices has challenged the Computational Linguistics community to develop methods that are at once fast, space-efficient, accurate and amenable to secure encoding schemes such as homomorphic encryption. Inspired by recent work that restricts floating point precision to speed up neural network training in hardware-based SIMD, we have developed a method for compressing word vector embeddings into integers using the Chinese Reminder Theorem that speeds up addition by up to 48.27% and at the same time compresses GloVe word embedding libraries by up to 25.86%. We explore the practicality of this simple approach by investigating the trade-off between precision and performance in two NLP tasks: compositional semantic relatedness and opinion target sentiment classification. We find that in both tasks, lowering floating point number precision results in negligible changes to performance.
A Large-scale Evaluation of Neural Machine Transliteration for Indic Languages

We take up the task of large-scale evaluation of neural machine transliteration between English and Indic languages, with a focus on multilingual transliteration to utilize orthographic similarity between Indian languages. We create a corpus of 600K word pairs mined from parallel translation corpora and monolingual corpora, which is the largest transliteration corpora for Indian languages mined from public sources. We perform a detailed analysis of multilingual transliteration and propose an improved multilingual training recipe for Indic languages. We analyze various factors affecting transliteration quality like language family, transliteration direction and word origin.
What Sounds “Right” to Me? Experiential Factors in the Perception of Political Ideology

In this paper, we challenge the assumption that political ideology is inherently built into text by presenting an investigation into the impact of experiential factors on annotator perceptions of political ideology. We construct an annotated corpus of U.S. political discussion, where in addition to ideology labels for texts, annotators provide information about their political affiliation, exposure to political news, and familiarity with the source domain of discussion, Reddit. We investigate the variability in ideology judgments across annotators, finding evidence that these experiential factors may influence the consistency of how political ideologies are perceived. Finally, we present evidence that understanding how humans perceive and interpret ideology from texts remains a challenging task for state-of-the-art language models, pointing towards potential issues when modeling user experiences that may require more contextual knowledge.
Communicative-Function-Based Sentence Classification for Construction of an Academic Formulaic Expression Database

Formulaic expressions (FEs), such as ‘in this paper, we propose’ are frequently used in scientific papers. FEs convey a communicative function (CF), i.e. ‘showing the aim of the paper’ in the above-mentioned example. Although CF-labelled FEs are helpful in assisting academic writing, the construction of FE databases requires manual labour for assigning CF labels. In this study, we considered a fully automated construction of a CF-labelled FE database using the top–down approach, in which the CF labels are first assigned to sentences, and then the FEs are extracted. For the CF-label assignment, we created a CF-labelled sentence dataset, on which we trained a SciBERT classifier. We show that the classifier and dataset can be used to construct FE databases of disciplines that are different from the training data. The accuracy of in-disciplinary classification was more than 80%, while cross-disciplinary classification also worked well. We also propose an FE extraction method, which was applied to the CF-labelled sentences. Finally, we constructed and published a new, large CF-labelled FE database. The evaluation of the final CF-labelled FE database showed that approximately 65% of the FEs are correct and useful, which is sufficiently high considering practical use.
One-class Text Classification with Multi-modal Deep Support Vector Data Description

This work presents multi-modal deep SVDD (mSVDD) for one-class text classification. By extending the uni-modal SVDD to a multiple modal one, we build mSVDD with multiple hyperspheres, that enable us to build a much better description for target one-class data. Additionally, the end-to-end architecture of mSVDD can jointly handle neural feature learning and one-class text learning. We also introduce a mechanism for incorporating negative supervision in the absence of real negative data, which can be beneficial to the mSVDD model. We conduct experiments on Reuters and 20 Newsgroup datasets, and the experimental results demonstrate that mSVDD outperforms uni-modal SVDD and mSVDD can get further improvements when negative supervision is incorporated.
Dependency parsing with structure preserving embeddings

Modern neural approaches to dependency parsing are trained to predict a tree structure by jointly learning a contextual representation for tokens in a sentence, as well as a head–dependent scoring function. Whereas this strategy results in high performance, it is difficult to interpret these representations in relation to the geometry of the underlying tree structure. Our work seeks instead to learn interpretable representations by training a parser to explicitly preserve structural properties of a tree. We do so by casting dependency parsing as a tree embedding problem where we incorporate geometric properties of dependency trees in the form of training losses within a graph-based parser. We provide a thorough evaluation of these geometric losses, showing that a majority of them yield strong tree distance preservation as well as parsing performance on par with a competitive graph-based parser (Qi et al., 2018). Finally, we show where parsing errors lie in terms of tree relationship in order to guide future work.
Exploiting Definitions for Frame Identification

Frame identification is one of the key challenges for frame-semantic parsing. The goal of this task is to determine which frame best captures the meaning of a target word or phrase in a sentence. We present a new model for frame identification that uses a pre-trained transformer model to generate representations for frames and lexical units (senses) using their formal definitions in FrameNet. Our frame identification model assesses the suitability of a frame for a target word in a sentence based on the semantic coherence of their meanings. We evaluate our model on three data sets and show that it consistently achieves better performance than previous systems.
Discourse Understanding and Factual Consistency in Abstractive Summarization

We introduce a general framework for abstractive summarization with factual consistency and distinct modeling of the narrative flow in an output summary. Our work addresses current limitations of models for abstractive summarization that often hallucinate information or generate summaries with coherence issues. To generate abstractive summaries with factual consistency and narrative flow, we propose Cooperative Generator-Discriminator Networks (Co-opNet), a novel transformer-based framework where the generator works with a discriminator architecture to compose coherent long-form summaries. We explore four different discriminator objectives which each capture a different aspect of coherence, including whether salient spans of generated abstracts are hallucinated or appear in the input context, and the likelihood of sentence adjacency in generated abstracts. We measure the ability of Co-opNet to learn these objectives with arXiv scientific papers, using the abstracts as a proxy for gold long-form scientific article summaries. Empirical results from automatic and human evaluations demonstrate that Co-opNet learns to summarize with considerably improved global coherence compared to competitive baselines.
Analyzing the Forgetting Problem in Pretrain-Finetuning of Open-domain Dialogue Response Models

In this work, we study how the finetuning stage in the pretrain-finetune framework changes the behavior of a pretrained neural language generator. We focus on the transformer encoder-decoder model for the open-domain dialogue response generation task. Our major finding is that after standard finetuning, the model forgets some of the important language generation skills acquired during large-scale pretraining. We demonstrate the forgetting phenomenon through a set of detailed behavior analysis from the perspectives of knowledge transfer, context sensitivity, and function space projection. As a preliminary attempt to alleviate the forgetting problem, we propose an intuitive finetuning strategy named “mix-review”. We find that mix-review effectively regularizes the finetuning process, and the forgetting problem is alleviated to some extent. Finally, we discuss interesting behavior of the resulting dialogue model and its implications.
Globalizing BERT-based Transformer Architectures for Long Document Summarization

Fine-tuning a large language model on downstream tasks has become a commonly adopted process in the Natural Language Processing (NLP) (CITATION). However, such a process, when associated with the current transformer-based (CITATION) architectures, shows several limitations when the target task requires to reason with long documents. In this work, we introduce a novel hierarchical propagation layer that spreads information between multiple transformer windows. We adopt a hierarchical approach where the input is divided in multiple blocks independently processed by the scaled dot-attentions and combined between the successive layers. We validate the effectiveness of our approach on three extractive summarization corpora of long scientific papers and news articles. We compare our approach to standard and pre-trained language-model-based summarizers and report state-of-the-art results for long document summarization and comparable results for smaller document summarization.
Complex Question Answering on knowledge graphs using machine translation and multi-task learning

Question answering (QA) over a knowledge graph (KG) is a task of answering a natural language (NL) query using the information stored in KG. In a real-world industrial setting, this involves addressing multiple challenges including entity linking, multi-hop reasoning over KG, etc. Traditional approaches handle these challenges in a modularized sequential manner where errors in one module lead to the accumulation of errors in downstream modules. Often these challenges are inter-related and the solutions to them can reinforce each other when handled simultaneously in an end-to-end learning setup. To this end, we propose a multi-task BERT based Neural Machine Translation (NMT) model to address these challenges. Through experimental analysis, we demonstrate the efficacy of our proposed approach on one publicly available and one proprietary dataset.