nils-herrmann/clean_scirepeval huggingface dataset:
Overexpression of cytokeratin 17 is associated with the development of papillary thyroid carcinoma and the presence of lymph node metastasis.
Venue: International journal of clinical and experimental pathology
Year: 2015

Cytokeratin 17 (CK17) a basal/myoepithelial cell keratin, appears to play an important role in the progression of several human malignancies. Increased CK17 expression has previously described in cases of papillary thyroid carcinoma (PTC) However, no studies to date have investigated the clinical significance of CK17 expression in patients with PTC. The aim of this study was to compare the expression of CK17 in patients with PTC with that observed in normal thyroid tissue and benign thyroid lesions, and to examine the relationship between CK17 expression and clinicopathologic characteristics of patients with PTC. CK17 protein expression was evaluated by immunohistochemistry on tissue microarrays containing thyroid tissue samples from 108 PTCs, 16 nodular goiters, and 81 healthy controls. Sixty five of the 108 (60.2% PTC tissue samples exhibited positive CK17 expression, whereas all nodular goiters and normal thyroid tissue samples showed a complete absence of CK17 immunoreactivity. The difference in frequency of CK17 positivity between PTC (65/108, 60.2% normal thyroid tissue (0/81, 0.0% and benign thyroid lesions (0/16, 0.0% was statistically significant (P<0.001) Positive CK17 expression in PTC was significantly associated with the presence of lymph node metastasis (P=0.024) and higher pN stage (P=0.028) Expression of CK17 is significantly increased in cases of PTC compared to normal tissue and benign thyroid lesions, and CK17 overexpression is associated with the presence of lymph node metastasis in patients with PTC. These findings suggest that CK17 is involved in the development and metastasis of PTC.
Overview of dengue viruses and their relations.
Year: 2008

Dengue virus (DENV) is one of the most important mosquito borne diseases. It belongs to the genus Flavivirus of the family Flaviviridae and is a small enveloped, positive single stranded RNA virus. Dengue virus comprise four distinct serotypes, DEN 1 through DEN 4, which are transmitted from infected to healthy humans through bites of female Aedes aegypti and A. albopictus. Dengue infection is a major cause of morbidity and mortality in tropical and subtropical countries. Over 2.5 billion people are at risk of dengue infection, and about 100 million cases of dengue fever occur annually. Up to 500,000 of these develop dengue hemorrhagic fever (DHF) or dengue shock syndrome (DSS) the life threatening forms of infection. At present, no proven vaccine is available for all 4 dengue serotypes, nor specific antiviral drugs to treat infection. Studies of the viral structure, clinical presentation and classification, basic to advanced diagnostic tools, and primary treatments, are necessary for effective disease prevention and control. This review summarizes current basic knowledge of dengue virus infection, based on clinical and laboratory studies. This overview of dengue virus infection should be useful for developing disease control programs and further research. Keywords dengue; pathogenesis; diagnosis; treatment; vaccine; review
Stereoselective Nanozyme Based on Ceria Nanoparticles Engineered with Amino Acids.
Venue: Chemistry
Year: 2017

Stereoselectivity towards substrates is one of the most important characteristics of enzymes. Amino acids, as cofactors of many enzymes, play important roles in stereochemistry. Herein, chiral nanozymes were constructed by grafting a series of d or l amino acids onto the surfaces of ceria (cerium oxide) nanoparticles. We selected the most commonly used drug for combating Parkinson's disease, that is, 3,4 dihydroxyphenylalanine (DOPA) enantiomers, as examples for chiral catalysis. Through detailed kinetic studies of cerium oxide nanoparticles (CeNPs) modified with different eight amino acids, we found that phenylalanine modified CeNP was optimal for the DOPA oxidation reaction and showed excellent stereoselectivity towards its enantiomers. l Phenylalanine modified CeNPs showed higher catalytic ability for oxidation of d DOPA, while d phenylalanine modified CeNPs were more effective towards l DOPA. Taken together, the results indicated that stereoselective nanozyme can be constructed by grafting nanoparticles with chiral molecules. This work may inspire better design of chiral nanozymes.
A broadband dual polarized antenna with coupling resonators for base station applications
Venue: 2018 International Workshop on Antenna Technology (iWAT)
Year: 2018

A broadband dual polarized antenna is proposed, which consists of two orthogonal dipoles coupled four L shaped resonators. The proposed antenna have two resonant modes in the working band, the lower mode is come from the 1/2 dipole itself, while the upper one is attributed to coupling between two orthogonal dipoles. Moreover, the upper mode can be further controlled by coupling four L shaped resonators to realize more broadband performance for LTE base station applications. The antenna achieves a impedance bandwidth of 45.5% for SWR<1.5 at both ports, a isolation more than 26dB between ports, and a stable Half Power Bandwidth (HPBW) of 65.5 deg+ 2.9 deg over 1.7 2.7GHz band. A good agreement is obtained between the simulation and the measurement results.
g Al2O3 sheet stabilized isolate Co2+ for catalytic propane dehydrogenation
Year: 2020

Co based catalysts are promising alternatives to replace Pt and Cr based catalysts for propane dehydrogenation (PDH) in view of the catalytic activity for C H bond activation, low cost, and low toxicity. However, the reduction of unstable CoOx species and coke deposition cause serious catalyst deactivation. In this work, a sheet shaped Co Al2O3 catalyst comprising of isolate Co2+ sites shows a high intrinsic activity (specific reaction rate 16 mmol g 1 h 1) and catalytic stability in highly selective PDH reaction >97% C3H6 selectivity) Extensive characterization demonstrates that the tetrahedral Co2+ isolated site is stabilized by the g Al2O3 matrix, facilitating propylene desorption to inhibit formation of coke and other byproducts and to free the active Co sites.
Chinese Poetry Generation with Recurrent Neural Networks
Venue: EMNLP
Year: 2014

We propose a model for Chinese poem generation based on recurrent neural networks which we argue is ideally suited to capturing poetic content and form. Our generator jointly performs content selection "what to say" and surface realization "how to say" by learning representations of individual characters, and their combinations into one or more lines as well as how these mutually reinforce and constrain each other. Poem lines are generated incrementally by taking into account the entire history of what has been generated so far rather than the limited horizon imposed by the previous line or lexical n grams. Experimental results show that our model outperforms competitive Chinese poetry generation systems using both automatic and manual evaluation methods.
A Tale of Two Evaluations: The Dynamic Effects of Cause Related Marketing
Venue: Journal of the Association for Consumer Research
Year: 2020

Firms are increasingly engaging in socially responsible business practices, such as cause related marketing (CRM) where firms partner with nonprofits to improve sales and brand value. Such activities, however, often fail to create positive spillover effects on firm outcomes. Building on dual processing models, we distinguish between different firm outcomes and posit that CRM is more likely to influence brand evaluations relative to product evaluations because the former are made affectively and the latter cognitively. Consistent with the notion that a processing mode activated in one task can carry over to subsequent tasks, we show that when customers first evaluate the brand, the positive effect of CRM extends to subsequent product evaluations. However, when customers first evaluate the product, there is no positive effect of CRM on subsequent brand evaluations. These findings illuminate when and why CRM and other socially responsible business practices can have robust dynamic effects for the firm.
Predictors of Treatment Outcome in Preschoolers with Autism Spectrum Disorder: An Observational Study in the Greater Geneva Area, Switzerland
Venue: Journal of autism and developmental disorders
Year: 2020

This study aims to identify predictors of treatment outcome in young children with ASD within a European context, where service provision of intervention remains sporadic. We investigated whether a child's age at baseline, intensity of the intervention provided, type of intervention, child's level of social orienting and cognitive skills at baseline predicted changes in autistic symptoms and cognitive development after 1 year of intervention, in a sample of 60 children with ASD. Our results strongly support early and intensive intervention. We also observed that lower cognitive skills at baseline were related to greater cognitive gains. Finally, we show that a child's interest in social stimuli may contribute to intervention outcome.
Practical Lattice Based Zero Knowledge Proofs for Integer Relations
Venue: IACR Cryptol. ePrint Arch.
Year: 2020

We present a novel lattice based zero knowledge proof system for showing that (arbitrary sized) committed integers satisfy additive and multiplicative relationships. The proof sizes of our schemes are between two to three orders of magnitude smaller than in the lattice proof system of Libert et al. (CRYPTO 2018) for the same relations. Because the proof sizes of our protocols grow linearly in the integer length, our proofs will eventually be longer than those produced by quantum safe succinct proof systems for general circuits (e.g. Ligero, Aurora, etc. But for relations between reasonably sized integers (e.g. $512$ bit) our proofs still result in the smallest zero knowledge proof system based on a quantum safe assumption. Of equal importance, the run time of our proof system is at least an order of magnitude faster than any other quantum safe scheme.
Computational imaging modalities for multi focal whole slide imaging systems.
Venue: Applied optics
Year: 2020

Whole slide imaging systems can generate full color image data of tissue slides efficiently, which are needed for digital pathology applications. This paper focuses on a scanner architecture that is based on a multi line image sensor that is tilted with respect to the optical axis, such that every line of the sensor scans the tissue slide at a different focus level. This scanner platform is designed for imaging with continuous autofocus and inherent color registration at a throughput of the order of 400 MPx/s. Here, single scan multi focal whole slide imaging, enabled by this platform, is explored. In particular, two computational imaging modalities based on multi focal image data are studied. First, 3D imaging of thick absorption stained slides ~60um) is demonstrated in combination with deconvolution to ameliorate the inherently weak contrast in thick tissue imaging. Second, quantitative phase tomography is demonstrated on unstained tissue slides and on fluorescently stained slides, revealing morphological features complementary to features made visible with conventional absorption or fluorescence stains. For both computational approaches simplified algorithms are proposed, targeted for straightforward parallel processing implementation at ~GPx/s throughputs.
Fully Decentralized Federated Learning Based Beamforming Design for UAV Communications.
Year: 2020

To handle the data explosion in the era of internet of things (IoT) it is of interest to investigate the decentralized network, with the aim at relaxing the burden to central server along with keeping data privacy. In this work, we develop a fully decentralized federated learning (FL) framework with an inexact stochastic parallel random walk alternating direction method of multipliers (ISPW ADMM) Performing more communication efficient and enhanced privacy preservation compared with the current state of the art, the proposed ISPW ADMM can be partially immune to the impacts from time varying dynamic network and stochastic data collection, while still in fast convergence. Benefits from the stochastic gradients and biased first order moment estimation, the proposed framework can be applied to any decentralized FL tasks over time varying graphs. Thus to further demonstrate the practicability of such framework in providing fast convergence, high communication efficiency, and system robustness, we study the extreme learning machine(ELM) based FL model for robust beamforming (BF) design in UAV communications, as verified by the numerical simulations.
Detecting Deepfake Video by Learning Two Level Features with Two Stream Convolutional Neural Network
Venue: ICCAI
Year: 2020

Deepfake techniques has made face swapping in video easy to use. Nowadays, the spread of Deepfake videos over networks is concerned worldwide. This work proposes an approach to more accurate and robust detection of them. Since artifacts left by Deepfake tools can be largely categorized into two classes of different levels, i.e. semantic and noise level, we adopt a two stream convolutional neural network (CNN) to capture the 2 level features concurrently. Xception network is trained only as the first stream to detect semantic anomalies such as the editing artifacts around face contour, detail missing, and geometric inconsistence in eyes. Meanwhile, the 2nd stream, which contain the constrained convolution filter and median filter, is designed to capture the tampering traces in local noises. By concatenating the 2 level features learned from the both streams, our method obtains very comprehensive knowledge about the existence of face swapping. The experimental results have shown its advantage over the existing methods on both the accuracy and robustness.
Direct Evidence Revealing Structural Elements Essential for the High Binding Ability of Bisphenol A to Human Estrogen Related Receptor g
Venue: Environmental health perspectives
Year: 2008

Various lines of evidence have shown that bisphenol A [BPA; HO C6H4 C(CH3)2 C6H4 OH] acts as an endocrine disruptor when present in very low doses. We have recently demonstrated that BPA binds strongly to human estrogen related receptor g (ERR g in a binding assay using [3H]4 hydroxytamoxifen [3H]4 OHT) We also demonstrated that BPA inhibits the deactivation activity of 4 OHT.  In the present study, we intended to obtain direct evidence that BPA interacts with ERR g as a strong binder, and also to clarify the structural requirements of BPA for its binding to ERR g.  We examined [3H]BPA in the saturation binding assay using the ligand binding domain of ERR g and analyzed the result using Scatchard plot analysis. A number of BPA derivatives were tested in the competitive binding assay using [3H]BPA as a tracer and in the luciferase reporter gene assay.  [3H]BPA showed a KD of 5.50 nM at a Bmax of 14.4 nmol/mg. When we examined BPA derivatives to evaluate the structural essentials required for the binding of BPA to ERR g we found that only one of the two phenol hydroxyl groups was essential for the full binding. The maximal activity was attained when one of the methyl groups was removed. All of the potent BPA derivatives retained a high constitutive basal activity of ERR g in the luciferase reporter gene assay and exhibited a distinct inhibitory activity against 4 OHT.  These results indicate that the phenol derivatives are potent candidates for the endocrine disruptor that binds to ERR g.
Digital Health Equity and COVID 19: The Innovation Curve Cannot Reinforce the Social Gradient of Health
Venue: Journal of medical Internet research
Year: 2020

Digital health innovations have been rapidly implemented and scaled to provide solutions to health delivery challenges posed by the coronavirus disease (COVID 19) pandemic. This has provided people with ongoing access to vital health services while minimizing their potential exposure to infection and allowing them to maintain social distancing. However, these solutions may have unintended consequences for health equity. Poverty, lack of access to digital health, poor engagement with digital health for some communities, and barriers to digital health literacy are some factors that can contribute to poor health outcomes. We present the Digital Health Equity Framework, which can be used to consider health equity factors. Along with person centered care, digital health equity should be incorporated into health provider training and should be championed at the individual, institutional, and social levels. Important future directions will be to develop measurement based approaches to digital health equity and to use these findings to further validate and refine this model.
Performance Evaluation of Multi operands Floating Point Adder
Year: 2019

In this paper, an architecture is presented for a fused floating point three operand adder unit. This adder executes two additions within a single unit. The purpose of this execution is to lessen total delay, die area, and power consumption in contrast with traditional addition method. Various optimization techniques including exponent comparison, alignment of significands, leading zero detection, addition, and rounding are used to diminish total delay, die area, and power consumption. In addition to this, the comparison is described of different blocks in term for die area, total delay, and power consumption. The proposed scheme is designed and implemented on Xilinx ISE Design 14.7 and synthesized on Synopsis.
Processing Music Rhythm Patterns as Body Accompaniment in an Effort to Increase Body Immunity Against Covid 19
Year: 2020

Physical and spiritual health is one of the most important supporting factors in carrying out all daily activities to maintain body immunity during the Covid 19 pandemic. There are various body movements that are adjusted to the most important supporting factor is music. Music is a sound that can provide a sense of pleasure and enthusiasm, with a wide variety of musical variations in a person's movement patterns will not feel saturated with monotonous rhythm patterns. The purpose of this study is to get results and find out how music art study program students are motivated in responding to musical rhythm patterns as an effort to increase immunity to fight Covid 19. This research uses the Research and Development method. The results of this research are products in the form of accompaniment music for light exercise. Product trials were conducted at the Department of Music, Surabaya State University, with purposive sampling. Data analysis of the product validity results is arranged in a Likert Scale questionnaire. The development of this musical composition showed significant results with a percentage of up to 85% of students feeling the effects of movement with varied rhythmic exercises.
Tourism and sustainable development in China: a review
Venue: Environmental Science and Pollution Research
Year: 2020

The adaption of the open up reform policies in China some three decades ago has resulted in a rapid economic transformation of which the tourism sector has equally witnessed fast development. Therefore, the essence of this article is to review the evolution and expansion of the tourism industry in China and its obligation to observing international sustainable development policies and practices. Indications of the current policy regime, establishment, and institutions, sustainable development strategies to ensure continuity and availability of resources for future use, environmental sustainability laws and regulations, and promotional events for the development of the tourism industry are made available in this article. In effect, this article reviews how the activities of the tourism sector impacts on the environment. The findings show that China in its quest to be a world leader of tourists' destination has impacted negatively on the environment which by extension affect the economy and society at large. On the other hand, as a leading nation in the United Nations, China has in contemporary times adapted sustainable development strategies to help safeguard the environment. However, more needs to be done in the area of advanced technology and renewable energy.
Going up to the Spirit in the Sky
Year: 2006

'The relationship between religion and science has for a long time been a central area in the history of science and ideas. Cosmology, although distinctly a modern science and undeniably of great relevance to theology, has received curiously little attention' (Preface) With these words Helge Kragh begins his latest venture into the history of cosmology. It is hard to imagine a timelier topic. Science and religion are interacting more and more intensely everywhere these days, especially in the United States; and it is increasingly evident that the interactions could proceed more beneficially to both traditions were the long, rich history of their dynamic better known and better kept in view. Here, for sure, is a locus where history can teach us something. Kragh s book is an excellent place for the lesson to begin: there is no domain more replete with science religion interactions than cosmology, and Kragh, with his usual care and skill, has exploited the domain fully. In the introductory chapter 'Cosmo theology before Einstein' Kragh sets out six cosmic questions he finds to have remained essentially the same since before the time of Kepler, and then contextualises these questions in 'an impressionistic look at a few earlier episodes' (p. 3) The questions are familiar ones:
Impact of graph based features on Bitcoin prices
Year: 2019

Predicting the trends in Bitcoin market prices is a very challenging task due to the many uncertainties and variables influencing the market value. The market is susceptible to quick changes, causing seemingly random fluctuations in the Bitcoin price. Due to the chaotic and highly volatile nature of Bitcoin behavior, investments come with high risk. To minimize the risk involved, knowledge of the Bitcoin price movement in the future is desirable. Different studies have shown that Machine Learning algorithms can predict, to varying degrees, the price fluctuations of Bitcoin. However, most researches do not explore the relationship between the price and other features outside the transaction network, such as market capitalization, Bitcoin mining speed, or entity behavior. Also, most of the features are extracted from the network level, which means obtaining the number of transactions, users, Bitcoins mined, etc. In this research, we focus on additional features, such as features outside the transaction network and node based features inside the transaction network, which could improve the price prediction of Bitcoin. The investigated features are the "fairness and goodness" measure and the "1 ARW betweenness cen trality" measure. Fairness and goodness are entity behavior measures. The goodness of a Bitcoin address captures how much this address is liked/trusted by other addresses, while the fairness of a Bitcoin address captures how fair the address is in rating other addresses' likeability or trust level. The 1 ARW betweenness centrality is a feature based on absorbing random walks. The feature captures the extent to which a Bitcoin address has control over the money flow between different addresses. A benchmark, based on the machine learning algorithm Random Forest with commonly used features, is used to test the impact of the additional features. The Random Forest tries to predict the sign (up down movement) of the price per day, using data from the two previous days. Comparing this benchmark with a similar model, but then including the additional features, will gain more information about how these additional features influence the Bitcoin price.