nils-herrmann/clean_scirepeval huggingface dataset:
GM1 Ameliorates Lead Induced Cognitive Deficits and Brain Damage Through Activating the SIRT1/CREB/BDNF Pathway in the Developing Male Rat Hippocampus
Venue: Biological Trace Element Research
Year: 2018

Developmental lead (Pb) exposure involves various serious consequences, especially leading to neurotoxicity. In this study, we examined the possible role of monosialoganglioside (GM1) in lead induced nervous impairment in the developing rat. Newborn male Sprague Dawley rat pups were exposed to lead from birth for 30 days and then subjected to GM1 administration (0.4, 2, or 10 mg/kg; i.p. or 0.9% saline. The results showed that developmental lead exposure significantly impaired spatial learning and memory in the Morris water maze test, reduced GM1 content, induced oxidative stress, and weakened the antioxidative systems in the hippocampus. However, co treatment with GM1 reversed these effects. Moreover, GM1 counteracted lead induced apoptosis by decreasing the expression of Bax, cleaved caspase 3, and by increasing the level of Bcl 2 in a dose dependent manner. Furthermore, we found that GM1 upregulated the expression of SIRT1, CREB phosphorylation, and BDNF, which underlie learning and memory in the lead treated developing rat hippocampus. In conclusion, our study demonstrated that GM1 exerts a protective effect on lead induced cognitive deficits via antioxidant activity, preventing apoptosis, and activating SIRT1/CREB/BDNF in the developing rat hippocampus, implying a novel potential assistant therapy for lead poisoning.
A new bound on the block restricted isometry constant in compressed sensing
Venue: Journal of inequalities and applications
Year: 2017

This paper focuses on the sufficient condition of block sparse recovery with the l2/l1$l_{2}/l_{1} minimization. We show that if the measurement matrix satisfies the block restricted isometry property with d2s|I<0.6246$\delta_{2s|\mathcal{I} 0.6246$ then every block s sparse signal can be exactly recovered via the l2/l1$l_{2}/l_{1} minimization approach in the noiseless case and is stably recovered in the noisy measurement case. The result improves the bound on the block restricted isometry constant d2s|I$\delta_{2s|\mathcal {I} of Lin and Li ( 2013)
A Study of the Third Year Students' Attitude towards Learning 01361311 Pali Sanskrit Loanwords in Department of Thai Language, Kasetsart University
Year: 2016

The purpose of this research was to study the third year students' attitude towards learning 01361311 Pali Sanskrit loanwords in department of Thai language, Kasetsart University. The populations in research were the 37 third year students who are studying at department of Thai language, Faculty of Humanities, Bangkhen Campus Kasetsart University. The research tool was a questionnaire on attitude that consists of 2 parts. Part 1 deals with general information of gender. Part 2 deals with 15 questions regarding attitude towards Pali Sanskrit Loanwords. In those 15 questions, the 8 questions were for positive attitude and the 7 questions were for negative attitude. It was found that among the 37 students of third year students, who are studying at department of Thai language, Faculty of Humanities, Bangkhen Campus Kasetsart University, male students were 48.6% and female 51.3% Mean of questionnaire on attitude towards learning 01361311 Pali Sanskrit Loanwords is equal to 3.50. It showed that students have moderate level of attitude towards learning 01361311 Pali Sanskrit Loanwords.
Object based Normalized Cuts for High Resolution Polsar Image Segmentation
Venue: 2019 SAR in Big Data Era (BIGSARDATA)
Year: 2019

As more and more high resolution PolSAR images being available, effective methods need to be studied for the processing of high resolution PolSAR images. Since the complexity and speckle noise in high resolution PolSAR image, traditional pixel based image segmentation methods can result in heavy salt and pepper noise and the segments won't be in accord with the edge in PolSAR images. Object based image analysis (OBIA) methods have been proven to be effective for high resolution images. In this paper, a novel object based image segmentation method for high resolution PolSAR image is proposed, combining the superpixel algorithm and Normalized Cuts segmentation (NCuts) The proposed method first generates original objects using simple local iterative clustering (SLIC) which is a new superpixel algorithm; and then Ncuts segmentation is implemented on the original objects to obtain refined object segments and reduce the computational complexity. In addition, both spatial information and polarimetric information will be introduced in the proposed method. Experiments using EMISAR image show that the proposed method can better preserve the edges and homogeneous regions on the image.
Librispeech: An ASR corpus based on public domain audio books
Venue: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2015

This paper introduces a new corpus of read English speech, suitable for training and evaluating speech recognition systems. The LibriSpeech corpus is derived from audiobooks that are part of the LibriVox project, and contains 1000 hours of speech sampled at 16 kHz. We have made the corpus freely available for download, along with separately prepared language model training data and pre built language models. We show that acoustic models trained on LibriSpeech give lower error rate on the Wall Street Journal (WSJ) test sets than models trained on WSJ itself. We are also releasing Kaldi scripts that make it easy to build these systems.
Internet of Things\/Internet of Everything: Structure and Ingredients
Venue: IEEE Potentials
Year: 2019

We are facing a huge revolution called the Internet of Things/Internet of Everything (IoT/IoE) which will enable pervasive computing scenarios. The IoT/IoE is expanding throughout the world at an incredible speed. Although this rapid development has created new opportunit ies, it has also broadened the range of people's expectations. However, the IoT/IoE is still in its infancy, and, therefore, its future structure and ingredients must be further studied and outlined.
Political psycholinguistics: A comprehensive analysis of the language habits of liberal and conservative social media users.
Venue: Journal of personality and social psychology
Year: 2020

For nearly a century social scientists have sought to understand left right ideological differences in values, motives, and thinking styles. Much progress has been made, but as in other areas of research this work has been criticized for relying on small and statistically unrepresentative samples and the use of reactive, self report measures that lack ecological validity. In an effort to overcome these limitations, we employed automated text analytic methods to investigate the spontaneous, naturally occurring use of language in nearly 25,000 Twitter users. We derived 27 hypotheses from the literature on political psychology and tested them using 32 individual dictionaries. In 23 cases, we observed significant differences in the linguistic styles of liberals and conservatives. For instance, liberals used more language that conveyed benevolence, whereas conservatives used more language pertaining to threat, power, tradition, resistance to change, certainty, security, anger, anxiety, and negative emotion in general. In 17 cases, there were also significant effects of ideological extremity. For instance, moderates used more benevolent language, whereas extremists used more language pertaining to inhibition, tentativeness, affiliation, resistance to change, certainty, security, anger, anxiety, negative affect, swear words, and death related language. These research methods, which are easily adaptable, open up new and unprecedented opportunities for conducting unobtrusive research in psycholinguistics and political psychology with large and diverse samples.
The Role of Current Ratio, Operating Cash Flow and Inflation Rate in Predicting Financial Distress: Indonesia Stock Exchange
Year: 2018

We believe company financial statements can be used as a tool to analyze and also as an indicator to know the financial performance. The financial statements contain information for various financial ratios, which are an important tool for assessing the company's financial performance in the future. The purpose of this research is to know the role of the current ratio, operating cash flow, and the inflation rate in predicting financial distress of consumer goods industry sector listed in the Indonesia Stock Exchange period 2011 2015. Financial distress prediction models need to be developed to assist managers in overseeing company performance and help identify important trends. To analyze the current ratio, operating cash flow, and the inflation rate has a probability of occurring financial distress for the company, used logistic regression. From this study resulted in the finding that the probability of a company exposed by financial distress is caused by operating cash flow, while the current ratio and the inflation rate have a smaller probability of the company of consumer goods to be exposed by financial distress.
Productivity and the Welfare of Nations
Year: 2012

This paper shows that the welfare of a country's representative consumer can be measured using just two variables: current and future total factor productivity and the capital stock per capita. These variables suffice to calculate welfare changes within a country, as well as welfare differences across countries. The result holds regardless of the type of production technology and the degree of market competition. It applies to open economies as well, if total factor productivity is constructed using domestic absorption, instead of gross domestic product, as the measure of output. It also requires that total factor productivity be constructed with prices and quantities as perceived by consumers, not firms. Thus, factor shares need to be calculated using after tax wages and rental rates and they will typically sum to less than one. These results are used to calculate welfare gaps and growth rates in a sample of developed countries with high quality total factor productivity and capital data. Under realistic scenarios, the U.K. and Spain had the highest growth rates of welfare during the sample period 1985 2005, but the U.S. had the highest level of welfare.
Oleocanthal Inhibits Catabolic and Inflammatory Mediators in LPS Activated Human Primary Osteoarthritis (OA) Chondrocytes Through MAPKs/NF kB Pathways
Venue: Cellular Physiology and Biochemistry
Year: 2018

Oleocanthal (OC) a phenolic compound present in extra virgin olive oil (EVOO) has attracted attention since its discovery for its relevant pharmacological properties in different pathogenic processes, including inflammation. Here, we investigated the involvement of OC in LPS activated osteoarthritis (OA) human primary chondrocytes.  Human primary chondrocytes were harvested from articular cartilage samples obtained from OA patients. The effects of OC on the viability of chondrocytes were tested by MTT assay. Protein and mRNA expression of several catabolic and pro inflammatory factors after OC treatment were measured by RT qPCR and western blot respectively. Moreover, we analysed the NO production by Griess reaction. Finally, several pathways mediators were analysed by western blot.  We demonstrated that OC did not have any cytotoxic effect. Oleocanthal inhibited NO production and strongly decreased NOS2 and COX 2 protein and mRNA expression in LPS activated human primary OA chondrocytes. Interestingly, OC also inhibits MMP 13 and ADAMTS 5. In addition, OC downregulates several pro inflammatory factors, such as IL 6, IL 8, CCL3, LCN2 and TNF a induced by LPS in human primary OA chondrocytes. Finally, we demonstrated that OC exerts its effects through the MAPK/P38/NF kB pathways.  These data show that OC is able to block LPS mediated inflammatory response and MMP 13 and ADAMTS 5 induction in human primary OA chondrocytes via MAPKs/NF kB pathways, suggesting that OC may be a promising agent for the treatment of inflammation in cartilage and a potential molecule to prevent disease progression by inhibiting metalloproteases and aggrecanases.
What Are They Really Eating? A Review on New Approaches to Dietary Intake Assessment and Validation
Venue: Current Nutrition Reports
Year: 2016

PurposeAssessing food and nutrient intakes is critical to evolving our understanding of diet disease relationships and the refinement of nutrition guidelines to support healthy populations. The aims of this narrative review are to summarise recent advances in dietary assessment methodologies, with a particular focus on approaches using new technologies, as well as strategies to evaluate tools, and to provide directions for future research.Recent FindingsTechnology as a mode to assess dietary intake has gained momentum in recent years, with the development of image based methods and wearable devices, as well as the emergence of online methods of administering traditional paper based approaches to dietary assessment. At the same time, there have been advances in the development of dietary biomarkers to evaluate measures of self reported dietary intake. Common biomarkers, such as plasma carotenoids and red blood cell fatty acids, are still being utilised with new markers including urinary markers of sugar or wholegrain intake, skin carotenoids as a measure of fruit and vegetable intake. As well, the field of metabolomics shows promise.SummaryChallenges remain in dietary intake assessment, and further efforts are required to refine and evaluate methods so that we can better understand diet disease relationships and inform guidelines and interventions to promote health.
Providing Medical Services Online to Children With Chronic Kidney Disease During the COVID 19 Pandemic
Year: 2021

1. Kawashima A, Oda Y, Yachie A, et al. Heme oxygenase 1 deficiency: The first autopsy case.  Human heme oxygenase 1 deficiency presenting with hemolysis, nephritis, and asplenia.  2011;33:74 8. 3. Jarmi T, Agarwal A. Heme oxygenase and renal disease. Curr Hyperten Rep. 2009;11:56 62. 4. Yadav SP, Thakkar D, Kohli S, Nivargi S, Rastogi N. Human heme oxygenase 1 deficiency treated successfully by matched sibling donor allogeneic stem cell transplant. Biology of Blood and Marrow Transplantation. S443.
A STAMP Based Causal Analysis of the Korean Sewol Ferry Accident
Year: 2016

The increased complexity of socio technical systems has revealed the limited contributions of existing event based accident analysis methods on sustainable safety improvements. Systems Theoretic Accident Model and Processes (STAMP) constructed upon Systems Theory deploys a holistic approach to safety and provides broad insights into accident causality via the integration of the analysis from both direct and indirect factors involved. A dedicated STAMP based analysis is conducted in this paper by taking the recent Sewol ferry tragedy as an example, to illustrate the utility of applying the STAMP Model to the maritime transportation domain and to stimulate a broader view of accident mechanisms that expands casual analysis beyond immediate physical failures to a systemic view. Some recommendations are developed for continuous improvements and corrective actions to prevent such catastrophic accident from future occurrences.
Using improved gradient boosted decision tree algorithm based on Kalman filter (GBDT KF) in time series prediction
Venue: The Journal of Supercomputing
Year: 2020

In this study, we analyse two mobile phone activity datasets to predict the future traffic of mobile base stations in urban areas. The predicted time series can be used to reflect the trend of human activity flow. Although common methods such as recurrent neural network and long short term memory (LSTM) network often achieve a high precision, they have the short back of time consuming. So, we present the improved gradient boosted decision tree algorithm based on Kalman filter (GBDT KF) due to the noise in the original time series, because the decrease in the performance of GBDT is usually caused by overfitting the noise in the signal. According to our experiments, although the RMSE of the predicted values of our GBDT KF and the ground truth is only 12 14% worse than that of the LSTM model, the proposed GBDT KF algorithm makes a trade off between the precision and time complexity and achieves over 100 time training time reduction compared with the LSTM model. By implementing the result of our work, service providers could predict where and when a network congestion would happen; therefore, they could take actions ahead of time. Such applications are useful especially in the era of 5G.
Peroxisome Proliferator Activated Receptor a Activation Induces Hepatic Steatosis, Suggesting an Adverse Effect
Venue: PloS one
Year: 2014

Non alcoholic fatty liver disease (NAFLD) is characterized by hepatic triglyceride accumulation, ranging from steatosis to steatohepatitis and cirrhosis. NAFLD is a risk factor for cardiovascular diseases and is associated with metabolic syndrome. Antihyperlipidemic drugs are recommended as part of the treatment for NAFLD patients. Although fibrates activate peroxisome proliferator activated receptor a (PPARa) leading to the reduction of serum triglyceride levels, the effects of these drugs on NAFLD remain controversial. Clinical studies have reported that PPARa activation does not improve hepatic steatosis. In the present study, we focused on exploring the effect and mechanism of PPARa activation on hepatic triglyceride accumulation and hepatic steatosis. Male C57BL/6J mice, Ppara null mice and HepG2 cells were treated with fenofibrate, one of the most commonly used fibrate drugs. Both low and high doses of fenofibrate were administered. Hepatic steatosis was detected through oil red O staining and electron microscopy. Notably, in fenofibrate treated mice, the serum triglyceride levels were reduced and the hepatic triglyceride content was increased in a dose dependent manner. Oil red O staining of liver sections demonstrated that fenofibrate fed mice accumulated abundant neutral lipids. Fenofibrate also increased the intracellular triglyceride content in HepG2 cells. The expression of sterol regulatory element binding protein 1c (SREBP 1c) and the key genes associated with lipogenesis were increased in fenofibrate treated mouse livers and HepG2 cells in a dose dependent manner. However, the effect was strongly impaired in Ppara null mice treated with fenofibrate. Fenofibrate treatment induced mature SREBP 1c expression via the direct binding of PPARa to the DR1 motif of the SREBP 1c gene. Taken together, these findings indicate the molecular mechanism by which PPARa activation increases liver triglyceride accumulation and suggest an adverse effect of fibrates on the pathogenesis of hepatic steatosis.
PhyGeoNet: Physics Informed Geometry Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain
Year: 2020

Recently, the advent of deep learning has spurred interest in the development of physics informed neural networks (PINN) for efficiently solving partial differential equations (PDEs) particularly in a parametric setting. Among all different classes of deep neural networks, the convolutional neural network (CNN) has attracted increasing attention in the scientific machine learning community, since the parameter sharing feature in CNN enables efficient learning for problems with large scale spatiotemporal fields. However, one of the biggest challenges is that CNN only can handle regular geometries with image like format (i.e. rectangular domains with uniform grids) In this paper, we propose a novel physics constrained CNN learning architecture, aiming to learn solutions of \emph{parametric PDEs on irregular domains without any labeled data} In order to leverage powerful classic CNN backbones, elliptic coordinate mapping is introduced to enable coordinate transforms between the irregular physical domain and regular reference domain. The proposed method has been assessed by solving a number of PDEs on irregular domains, including heat equations and steady Navier Stokes equations with parameterized boundary conditions and varying geometries. Moreover, the proposed method has also been compared against the state of the art PINN with fully connected neural network (FC NN) formulation. The numerical results demonstrate the effectiveness of the proposed approach and exhibit notable superiority over the FC NN based PINN in terms of efficiency and accuracy.
The Moderating Role Entrepreneurship Education Programs To Stimulating Students' Entrepreneurial Intention In Universiti Teknikal Malaysia Melaka
Year: 2014

The Entrepreneurship Education Program seeks to provide students with the direction to stimulating them to self employment in futures. By applying the theory of planned behavior, which is an attitude, subjective norms and perceived behavior control, this study have two major purposes which are to investigate the effect of Entrepreneurship Education Programs to the students' entrepreneurial intention in Universiti Teknikal Malaysia Melaka and make a comparison among students' entrepreneurial intention. Data onto this study was collected from structured quantitative questionnaires and responses were made on a 4 Likert scale ranging from strongly disagree to strongly agree. The sample composed of 200 students in Universiti Teknikal Malaysia Melaka. The researcher was conducted a data analysis by using the partial correlation, and independent sample T Test method. The results shown Entrepreneurship Education Programs have influenced the strength of a significant relationship between independent variables and a dependent variable. Besides, the comparison means, among group of students who are joining Entrepreneurship Education Programs is higher than a group of students who do not join the Entrepreneurship Education Programs. The researcher would like to recommend further research to study others criteria that will impact on students' entrepreneurial intention and expand the research location.
Jacobian Adversarially Regularized Networks for Robustness
Venue: ICLR
Year: 2020

Adversarial examples are crafted with imperceptible perturbations with the intent to fool neural networks. Against such attacks, adversarial training and its variants stand as the strongest defense to date. Previous studies have pointed out that robust models that have undergone adversarial training tend to produce more salient and interpretable Jacobian matrices than their non robust counterparts. A natural question is whether a model trained with an objective to produce salient Jacobian can result in better robustness. This paper answers this question with affirmative empirical results. We propose Jacobian Adversarially Regularized Networks (JARN) as a method to optimize the saliency of a classifier's Jacobian by adversarially regularizing the model's Jacobian to resemble natural training images. Image classifiers trained with JARN show improved robust accuracy compared to standard models on the MNIST, SVHN and CIFAR 10 datasets, uncovering a new angle to boost robustness without using adversarial training examples.