nils-herrmann/clean_scirepeval huggingface dataset:
Metabolic Impacts of Confinement during the COVID 19 Pandemic Due to Modified Diet and Physical Activity Habits
Venue: Nutrients
Year: 2020

While the detrimental effects of a chronic positive energy balance due to a sedentary lifestyle have been well established, the impacts of a short period of abruptly reduced physical activity and overeating arising from strict confinement due to the COVID 19 pandemic will soon start to emerge. To reasonably anticipate major consequences according to the available evidence, we hereby review the literature for studies that have explored the health impacts of several weeks of a reduction in physical activity and daily step count combined with modified eating habits. These studies identify as main metabolic consequences increases in insulin resistance, total body fat, abdominal fat and inflammatory cytokines. All these factors have been strongly associated with the development of metabolic syndrome, which in turn increases the risk of multiple chronic diseases. A plausible mechanism involved in these impacts could be a positive energy balance promoted by maintaining usual dietary intake while reducing energy expenditure. This means that just as calorie intake restriction could help mitigate the deleterious impacts of a bout of physical inactivity, overeating under conditions of home confinement is very likely to exacerbate these consequences. Moreover, hypertension, diabetes, and cardiovascular disease have been identified as potential risk factors for more severely ill patients with COVID 19. Thus, adequate control of metabolic disorders could be important to reduce the risk of severe COVID 19.
Can You Recognize an Effective Teacher When You Recruit One?
Venue: Education Finance and Policy
Year: 2011

Research on the relationship between teacher characteristics and teacher effectiveness has been underway for over a century, yet little progress has been made in linking teacher quality with factors observable at the time of hire. To extend this literature, we administered an in depth survey to new math teachers in New York City and collected information on a number of nontraditional predictors of effectiveness, including teaching specific content knowledge, cognitive ability, personality traits, feelings of self efficacy, and scores on a commercially available teacher selection instrument. We find that only a few of these predictors have statistically significant relationships with student and teacher outcomes. However, the individual variables load onto two factors, which measure what one might describe as teachers' cognitive and noncognitive skills. We find that both factors have a moderately large and statistically significant relationship with student and teacher outcomes, particularly with student test scores.
Differentiable Reasoning on Large Knowledge Bases and Natural Language
Venue: AAAI 2020
Year: 2020

Reasoning with knowledge expressed in natural language and Knowledge Bases (KBs) is a major challenge for Artificial Intelligence, with applications in machine reading, dialogue, and question answering. General neural architectures that jointly learn representations and transformations of text are very data inefficient, and it is hard to analyse their reasoning process. These issues are addressed by end to end differentiable reasoning systems such as Neural Theorem Provers (NTPs) although they can only be used with small scale symbolic KBs. In this paper we first propose Greedy NTPs (GNTPs) an extension to NTPs addressing their complexity and scalability limitations, thus making them applicable to real world datasets. This result is achieved by dynamically constructing the computation graph of NTPs and including only the most promising proof paths during inference, thus obtaining orders of magnitude more efficient models 1. Then, we propose a novel approach for jointly reasoning over KBs and textual mentions, by embedding logic facts and natural language sentences in a shared embedding space. We show that GNTPs perform on par with NTPs at a fraction of their cost while achieving competitive link prediction results on large datasets, providing explanations for predictions, and inducing interpretable models.
Potential Predictors Of Perceived Stress In Helping Profession
Year: 2017

The aim of this study is to explore perceived stress of an individual working in the helping profession, potential predictors and mediators of this stress. The study examines the effect of past traumatic experience, perceived mental pain, adult attachment behavior and their contribution toward present perceived stress in psychologists and social workers. A potentially preventive role of secure adult attachment and self regulation of self care in resulting stress perception are being explored. The methods included the use of the adult attachment behavior Experience in Close Relationships revised ECR R (Fraley, 2000) instrument, Global Measure of Perceived stress (Cohen, Kamarck, Mermelstein, 1983) and Self regulation of self care (Hricova, Lovas, in press) Other instruments used included Mental pain Single item measure and Past traumatic experience Single item used to measure frequency of strong negative incidents experienced before the individual started working in the helping profession. Hypotheses were tested on psychologists and social workers (n=160) using mediation models. Results suggest that a past traumatic experience predicts current mental pain perception. Professional helpers' anxious dimension of adult attachment style partially mediates relationship between perceived mental pain and perceived stress. Avoidance dimension of adult attachment style is related to perceived stress indirectly, through self regulation of self care. Results of the study suggest indirect relationship between past traumatic experience and currently perceived stress.
A Survey of Network Representation Learning Methods for Link Prediction in Biological Network.
Venue: Current pharmaceutical design
Year: 2020

Networks are powerful resources for describing complex systems. Link prediction is an important issue in network analysis and has important practical application value. Network representation learning has proven to be useful for network analysis, especially for link prediction tasks.  To review the application of network representation learning on link prediction in biological network, we summarize recent methods for link prediction in biological network and discusses the application and significance of network representation learning in link prediction task.  We first introduce the widely used link prediction algorithms, then briefly introduce the development of network representation learning methods, focusing on a few widely used methods, and their application in biological network link prediction. Existing studies demonstrate that using network representation learning to predict link in biological network can achieve better performance. In the end, we discussed some possible future directions.
DDG51 Class Land Based Engineering Site (LBES) The Vision and the Value
Year: 2012

The Arleigh Burke guided missile destroyer program (DDG 51) represents the largest ship class in the US Navy. The DDG 51 Land Based Engineering Site (LBES) is a test complex that was built to provide demonstrate a stable level of operational effectiveness and suitability for mobility and support systems during ship construction and fleet introduction of the class. Integrated systems testing on the LBES proved to be paramount in the success of the overall shipbuilding program as technical risks discovered in Philadelphia were able to be solved with a mix of hardware and computer program changes prior to shipbuilder trials or sailaway. This success in risk/cost avoidance also led to program office investment in LBES upgrades such as Flight IIA DDGs, multi year option class changes, and DDG midlife/modernization changes that are still proving to carry very high cost avoidance and return on investment. LBES has also afforded several indirect benefits such as navy crew training, the genesis of DDG 51 class distant support, and equipment or system vendor ECP testing. Most recently LBES has been used for the navy's Great Green Fleet R&D proof of concept testing (e.g. biofuel in 2010 and hybrid electric drive in 2011 12) This article will describe the DDG 51 LBES contribution to one of the most successful ship acquisition programs in U.S. naval history.(c) 2012 ASME
Game Accessibility Guidelines for People with Sequelae from Macular Chorioretinitis
Venue: 2017 16th Brazilian Symposium on Computer Games and Digital Entertainment (SBGames)
Year: 2017

Macular chorioretinitis is an ocular inflammation that may cause the permanent loss of the central vision. Researches indicate that the formation of macular scars, the potential sequelae of that inflammation, is the leading cause of low vision in children. As we found no game accessibility guidelines in the literature that were specific to users with central vision loss in a preliminary search, this study thus aimed at surveying those targeted at players with low vision. The guidelines were extracted through a systematic literature review conducted on digital libraries and also with the manual selection of other relevant sources. In total, we identified 25 more general game accessibility guidelines, for people with low vision, and 19 of them were considered applicable to people with macular scars. Afterward, we assessed the viability of implementing each guideline according to their impact, reach and cost. The survey also confirmed that no guideline existed that was specific to players with central vision loss. We expect the results of this research to serve as a starting point for the definition of new guidelines specific to players with sequelae from macular chorioretinitis.
The Effect of Copper,Cadmium to Growth and Catalase and Amylase of Wheat
Year: 2008

Through the study the effect of cadmium and copper on metabolize and activities of catalase and amylase of wheat,the result shows that 10 days later with the increasing of Cd and Cu,the concentration of heavy metals in wheat are increasing,and the plant height and leaf decreases.With the increasing of Cd and Cu,the activities of catalase increase,but the activities of amylase increase at first,and then decrease.
Modeling Past and Future for Neural Machine Translation
Venue: Transactions of the Association for Computational Linguistics
Year: 2018

Existing neural machine translation systems do not explicitly model what has been translated and what has not during the decoding phase. To address this problem, we propose a novel mechanism that separates the source information into two parts: translated Past contents and untranslated Future contents, which are modeled by two additional recurrent layers. The Past and Future contents are fed to both the attention model and the decoder states, which provides Neural Machine Translation (NMT) systems with the knowledge of translated and untranslated contents. Experimental results show that the proposed approach significantly improves the performance in Chinese English, German English, and English German translation tasks. Specifically, the proposed model outperforms the conventional coverage model in terms of both the translation quality and the alignment error rate.
Role of big data analytics in developing sustainable capabilities
Venue: Journal of Cleaner Production
Year: 2019

The purpose of this study is to examine the extent of sustainable capabilities driven by corporate commitment resulting from the integration of big data technologies, green supply chain management, and green human resource management practices, and the extent to which these capabilities can enhance the broader firm performance. The study was also designed to investigate the degree to which green human resource management practices influence the integration of big data technologies with processes and enhance the relationships between green supply chain management practices, both internal and external, as well as their influence on sustainable performance. We used dynamic capabilities theory and proposed a conceptual research model which was tested empirically. The findings of our study establish the influence of big data driven strategies on business growth in terms of sustainable performance by considering internal processes that constitute sustainable capabilities. The study recommends the integration of green supply chain management, green human resource management practices, and big data management to enhance firms' sustainable capabilities that lead to better sustainable performance.
International Intellectual Property Rights Protection and Economic Growth with Costly Transfer
Year: 2017

This paper develops a product cycle model with costly technology transfer, which requires re sources from both the North and the South. In the basic model we show that strengthening IPR protection induces a large technology transfer and narrows the North South wage gap. However, we obtain an ambiguous result regarding the effect on economic growth, which depends crucially on the size of the transfer cost. Although strengthening IPR protection induces a high growth rate when the transfer cost is small, it can induce a low growth rate when the transfer cost is large. In the extended model, in order to examine what factors determine the transfer cost, we consider the situation where the Southern firms may misbehave and the Northern firms incur a cost to monitor them. We show that the degree of investor protection and the degree of morality in developing countries influence the size of the transfer cost, which affects economic growth.
Cross modal working memory binding and learning of visual phonological associations in children with reading difficulties
Venue: Child neuropsychology a journal on normal and abnormal development in childhood and adolescence
Year: 2019

Reading decoding requires the association of a visual input (the printed word) with a verbal output (the spoken word) and the learning of visual phonological associations via repeated exposure occurs both explicitly (by instruction and training) and implicitly (by exposure to written material) However, a general ability to implicitly learn visual phonological associations (or cross modal bindings) in children with reading difficulties (RD) has not been deeply explored. The present paper examined this issue in two studies comparing groups of children with RD with matched groups of control children in a working memory binding task involving sequences of variable combinations of nonsense shapes and nonwords (odd trials) as well as fixed combinations (even trials) Stimuli presentation was followed by a recognition test in which the nonwords were given one at a time and the children were required to mouse click on their respective shapes. In Study 1, fixed bindings were presented in random sequences across even trials, and the recognition test presented the nonwords at random. In Study 2, fixed bindings were presented in the same order across even trials, and the recognition test presented the nonwords following the same sequence order. In both studies, a consistent and strong learning effect of fixed bindings was observed for the control groups, with significantly better performance than the groups with RD. Such results suggest that poor cross modal binding learning is associated with reading difficulties. Implications for the study and treatment of dyslexia are discussed.
Development and Validation of a Web Based Severe COVID 19 Risk Prediction Model
Venue: The American Journal of the Medical Sciences
Year: 2021

Coronavirus disease 2019 (COVID 19) carries high morbidity and mortality globally. Identification of patients at risk for clinical deterioration upon presentation would aid in triaging, prognostication, and allocation of resources and experimental treatments.  Can we develop and validate a web based risk prediction model for identification of patients who may develop severe COVID 19, defined as intensive care unit (ICU) admission, mechanical ventilation, and/or death?  This retrospective cohort study reviewed 415 patients admitted to a large urban academic medical center and community hospitals. Covariates included demographic, clinical, and laboratory data. The independent association of predictors with severe COVID 19 was determined using multivariable logistic regression. A derivation cohort (n=311, 75% was used to develop the prediction models. The models were tested by a validation cohort (n=104, 25%  The median age was 66 years (Interquartile range [IQR] 54 77) and the majority were male (55% and non White (65.8% The 14 day severe COVID 19 rate was 39.3% 31.7% required ICU, 24.6% mechanical ventilation, and 21.2% died. Machine learning algorithms and clinical judgment were used to improve model performance and clinical utility, resulting in the selection of eight predictors: age, sex, dyspnea, diabetes mellitus, troponin, C reactive protein, D dimer, and aspartate aminotransferase. The discriminative ability was excellent for both the severe COVID 19 (training area under the curve [AUC]=0.82, validation AUC=0.82) and mortality (training AUC= 0.85, validation AUC=0.81) models. These models were incorporated into a mobile friendly website.  This web based risk prediction model can be used at the bedside for prediction of severe COVID 19 using data mostly available at the time of presentation.
A comparison of ASR and human errors for transcription of non native spontaneous speech
Venue: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2016

In this paper, we compare ASR and human transcriptions of non native speech to investigate to what extent the accuracy and the patterns of errors of a modern ASR system match those of human listeners in the context of automated assessment of L2 English language proficiency. We obtained multiple naive transcriptions of short fragments of non native spontaneous speech with different proficiency levels using crowdsourcing and matched these against the output of an ASR system. We compare WER and recall at the fragment level and consider human ASR agreement at the word level. We find that we are able to attain a commensurate level of transcription quality using ASR, but the patterns of errors between the two groups differ at the word level.
Molecular epidemiology and antimicrobial resistance of Salmonella isolates from broilers and pigs in Thailand
Venue: Veterinary world
Year: 2019

This study aimed to determine the prevalence and antimicrobial resistance pattern of Salmonella spp. and the genetic relatedness between isolates from broilers and pigs at slaughterhouses in Thailand.  Fecal samples (604 broilers and 562 pigs) were collected from slaughterhouses from April to July 2018. Salmonella spp. were isolated and identified according to the ISO 6579:2002. Salmonella positive isolates were identified using serotyping and challenged with nine antimicrobial agents: Amoxicillin/clavulanate (AMC, 30 ug) ampicillin (AMP, 10 ug) ceftazidime (30 ug) chloramphenicol (30 ug) ciprofloxacin (CIP, 5 ug) nalidixic acid (NAL, 30 ug) norfloxacin (10 ug) trimethoprim/sulfamethoxazole (SXT, 25 ug) and tetracycline (TET, 30 ug) Isolates of the predominant serovar Salmonella Typhimurium were examined for genetic relatedness using pulsed field gel electrophoresis (PFGE)  Salmonella was detected in 18.05% of broiler isolates and 37.54% of pig isolates. The most common serovars were Kentucky, Give, and Typhimurium in broilers and Rissen, Typhimurium, and Weltevreden in pigs. Among broilers, isolates were most commonly resistant to antibiotics, NAL, AMP, TET, AMC, and CIP. Pig isolates most commonly exhibited antimicrobial resistance against AMP, TET, and SXT. Based on PFGE results among 52 S. Typhimurium isolates from broilers and pigs, a high genetic relatedness between broiler and pig isolates (85% similarity) in Cluster A and C from PFGE result was identified.  The results revealed high cross contamination between these two animal species across various provinces in Thailand.
Tropical diseases of the myocardium: a review
Venue: International journal of general medicine
Year: 2017

Cardiovascular diseases are widely distributed throughout the world. Human parasitic infections are ubiquitous. Tropical parasites are increasingly recognized as causes of cardiovascular diseases. In this review, we address the most frequently reported parasites that directly infect the myocardium, including Trypanosoma cruzi, the protozoal causative agent of American trypanosomiasis (Chagas disease) and Taenia solium, the cestode causative agent of taeniasis and cysticercosis. We also discuss tropical endomyocardial fibrosis, trichinellosis and schistosomiasis. Health systems, attitudes, the perceptions of both patients and physicians as well as socioeconomic factors should all be explored and recognized as crucial factors for improving the control of cardiovascular diseases in the tropics. Clinicians throughout the world must remain aware of imported parasites as potential causes of cardiac diseases.
Psychosocial Determinants of Chronic Disease: Implications for Lifestyle Medicine
Venue: American journal of lifestyle medicine
Year: 2019

We have previously identified a number of "determinants" of chronic disease, using the acronym NASTIE ODOURS. These have been given the collective term "anthropogens," in this journal and other publications, to help direct the management of modern chronic ailments to a monocausal focus, akin to that afforded infectious diseases by the "germ theory." We suggested the acronym NASTIE ODOURS as a starting point for a taxonomy of lifestyle medicine determinants. In the current article, we add 3, less quantifiable, but currently increasingly more important psychosocial experiences to these: Lack of Meaning, Alienation, and Loss of culture, changing the previous acronym to NASTIE MAL ODOURS. As with other determinants, all have accumulating evidence of an underlying low grade, systemic, inflammatory physiological base "metaflammation" but with the need for further research to solidify these findings.
Abundancia estacional de los murcielagos herbivoros y disponibilidad de los recursos quiropterofilos en dos tipos de vegetacion de la Reserva de la Biosfera Barranca de Metztitlan, Hidalgo, Mexico
Year: 2011

We give information on the seasonal abundance of herbivores bats and their plant food resources in Reserva de la Biosfera Barranca de Metztitlan, Hidalgo, Mexico. Two types of vegetation were studied: crasicaule shrub of Isolatocereus dumortieri and tropical deciduous forest. We identified eight species of herbivorous bats that inhabit the Barranca de Metztitlan. Three species of nectar feeding bats Leptonycteris yerbabuenae, Choeronycteris mexicana, Glossophaga soricina and one frugivore Sturnira ludovici were common in the crasicaule scrub and tropical deciduous forest. Four frugivores Artibeus jamaicencis, A. lituratus, A. aztecus and Sturnira lilium were associated only with tropical deciduous forest. At landscape level, we have identified 28 species of chiropterophilous plants that provide food (flowers and fruits) for bats throughout the year. Annually the greatest abundance of bats coincided with the highest chiropterophilous resources abundance. And at local level, we found a positive and significant relationship between the abundance of the pollinator's bats and the productivity of flowers from the giant cacti Isolatocereus dumortieri