nils-herrmann/clean_scirepeval huggingface dataset:
Role of ARPC2 in Human Gastric Cancer
Venue: Mediators of inflammation
Year: 2017

Gastric cancer continues to be the second most frequent cause of cancer deaths worldwide. However, the exact molecular mechanisms are still unclear. Further research to find potential targets for therapy is critical and urgent. In this study, we found that ARPC2 promoted cell proliferation and invasion in the human cancer cell line MKN 28 using a cell total number assay, MTT (3 (4,5 dimethyl 2 thiazolyl) 2,5 diphenyl 2 H tetrazolium bromide) assay, cell colony formation assay, migration assay, invasion assay, and wound healing assay. For downstream pathways, CTNND1, EZH2, BCL2L2, CDH2, VIM, and EGFR were upregulated by ARPC2, whereas PTEN, BAK, and CDH1 were downregulated by ARPC2. In a clinical study, we examined the expression of ARPC2 in 110 cases of normal human gastric tissues and 110 cases of human gastric cancer tissues. ARPC2 showed higher expression in gastric cancer tissues than in normal gastric tissues. In the association analysis of 110 gastric cancer tissues, ARPC2 showed significant associations with large tumor size, lymph node invasion, and high tumor stage. In addition, ARPC2 positive patients exhibited lower RFS and OS rates compared with ARPC2 negative patients. We thus identify that ARPC2 plays an aneretic role in human gastric cancer and provided a new target for gastric cancer therapy.
SNUMedinfo at CLEF BioASQ 2015
Venue: CLEF
Year: 2015

This paper describes our participation at the BioASQ Task 3b of CLEF 2015 Question Answering track. We participated at the document retrieval subtask in Phase A and the ideal answer generation subtask in Phase B. As of previous year, in the document retrieval task, we mostly experimented with semantic concept enriched dependence model and sequential dependence model. In the ideal answer generation task, relevant passages are selected and combined to automatically produce answer text.
Public health risks related to food safety issues in the food market: a systematic literature review
Venue: Environmental Health and Preventive Medicine
Year: 2019

BackgroundFood safety in the food market is one of the key areas of focus in public health, because it affects people of every age, race, gender, and income level around the world. The local and international food marketing continues to have significant impacts on food safety and health of the public. Food supply chains now cross multiple national borders which increase the internationalization of health risks. This systematic review of literature was, therefore, conducted to identify common public health risks related to food safety issues in the food market.MethodsAll published and unpublished quantitative, qualitative, and mixed method studies were searched from electronic databases using a three step searching. Analytical framework was developed using the PICo (population, phenomena of interest, and context) method. The methodological quality of the included studies was assessed using mixed methods appraisal tool (MMAT) version 2018. The included full text articles were qualitatively analyzed using emergent thematic analysis approach to identify key concepts and coded them into related non mutually exclusive themes. We then synthesized each theme by comparing the discussion and conclusion of the included articles. Emergent themes were identified based on meticulous and systematic reading. Coding and interpreting the data were refined during analysis.ResultsThe analysis of 81 full text articles resulted in seven common public health risks related with food safety in the food market. Microbial contamination of foods, chemical contamination of foods, food adulteration, misuse of food additives, mislabeling, genetically modified foods (GM foods) and outdated foods or foods past their use by dates were the identified food safety related public health risks in the food market.ConclusionThis systematic literature review identified common food safety related public health risks in the food market. The results imply that the local and international food marketing continues to have significant impacts on health of the public. The food market increases internationalization of health risks as the food supply chains cross multiple national borders. Therefore, effective national risk based food control systems are essential to protect the health and safety of the public. Countries need also assure the safety and quality of their foods entering international trade and ensure that imported foods conform to national requirements.
STUDY AT HOME: ANALISIS KESULITAN BELAJAR MATEMATIKA PADA PROSES PEMBELAJARAN DARING
Year: 2020

The e learning study at home education system is in accordance with the 2013 curriculum. However, when the e learning application is too difficult, it is likely that students are not able to learn mathematics optimally, as well as the amount of data that must be delivered but the internet or network access becomes slow, of course it is will disrupt student activities. This study aims to determine the learning difficulties of students towards mathematics e learning at home. The research method used is descriptive research method. The study involved high school MA students with data collection techniques and information through a student questionnaire response instrument using Google forms and the research subjects numbered 30 students. The student response contained 31 statement items which were the descriptions of the indicators. Indicator to measure students 'responses regarding students' learning difficulties to mathematics through the e learning system at home. Based on the average student achievement regarding learning difficulties in mathematics e learning that is 75% by having the lowest achievement value by students namely the implementation of interactions, assignments and teaching materials in online learning by 73% and the highest indicator achieved by students namely technical signal constraints and the inability to learn online (e learning) that is equal to 77% and for testing the validity shows that all items are valid statements, this is because rcount rtabel and Cronbach Alpha value 0.839> reliability coefficient 0.6. After calculated using SPSS 23.  Difficulty learning mathematics, E learning, studying at home
Impacts of Venture Capital on Online P2P Lending Platforms: Empirical Evidence from China
Venue: Emerging Markets Finance and Trade
Year: 2019

We examined the impacts of venture capital investment (VCI) on the performance of online Peer to Peer (P2P) lending platforms. The research results show that: (1) Gaining the first round of VCI can increase the transaction scale and improve the compliance with regulatory requirement of the online P2P lending platforms; and (2) the platforms acquiring more rounds of VCI have greater turnover. The above research shows that under the background of information asymmetry, the certification function and monitoring mechanism of VCs can work in online P2P lending markets.
Research on Influence Maximization Method for Complex Network
Year: 2020

The influence maximization (IM) is a key algorithm problem in information dissemination research. it aims to select a set of K users (also called seed sets) from a network and maximize the number of affected users (influence spread) through a specific information dissemination model. However, despite its huge application potential, with the advent of the era of big data, all kinds of networks tend to be complicated, and there is relatively little research on influence maximization of multilayer networks in complex networks, because in these networks, nodes are different types. On the other hand, most of the existing research on influence maximization relies on greedy algorithms and can only obtain a single solution. With that in mind, we focus on the influence maximization problem of multilayer networks in complex networks. specifically, we first define some novel concepts about the process of information dissemination in multilayer networks; then, we construct the influence maximization problem in multilayer networks into a multi objective optimization problem. Finally, we do a lot of experiments on the real datasets, and the results show that the algorithm in this paper has a large competitive advantage in the influence spread and running time compared with the existing influence maximization algorithm.
Vulnerabilities at First Sex and Their Association With Lifetime Gender Based Violence and HIV Prevalence Among Adolescent Girls and Young Women Engaged in Sex Work, Transactional Sex, and Casual Sex in Kenya
Venue: Journal of acquired immune deficiency syndromes
Year: 2018

Adolescent girls and young women (AGYW) experience high rates of HIV early in their sexual life course. We estimated the prevalence of HIV associated vulnerabilities at first sex, and their association with lifetime gender based violence (GBV) and HIV.  We conducted a cross sectional biobehavioral survey among AGYW (14 24 years) in Mombasa, Kenya in 2015. We compared the prevalence of first sex vulnerabilities across AGYW who self identified as engaging in sex work (N 408) transactional sex (N 177) or casual sex (N 714) and used logistic regression to identify age adjusted associations between first sex vulnerabilities and outcomes (GBV after first sex;  The median age at first sex was 16 years (interquartile range 14 18) A total of 43.6% received gifts or money at first sex; 41.2% and 11.2% experienced a coerced and forced first sex, respectively. First sex vulnerabilities were generally more common among AGYW in sex work. GBV (prevalence 23.8% and HIV (prevalence 5.6% were associated with first sex before age 15 [GBV adjusted odds ratio (AOR) 1.4, 95% confidence interval (CI) 1.0 to 1.9; HIV AOR 1.9, 95% CI: 1.1 to 1.3] before or within 1 year of menarche (GBV AOR 1.3, 95% CI: 1.0 to 1.7; HIV AOR 2.1, 95% CI: 1.3 to 3.6) and receipt of money (GBV AOR 1.9, 95% CI: 1.4 to 2.5; HIV AOR 2.0, 95% CI: 1.2 to 3.4)  HIV associated vulnerabilities begin at first sex and potentially mediate an AGYW's trajectory of risk. HIV prevention programs should include structural interventions that reach AGYW early, and screening for a history of first sex vulnerabilities could help identify AGYW at risk of ongoing GBV and HIV.
Peculiarities of IoT Based Business Model Transformations in SMEs
Year: 2018

Internet of Things (IoT) is gaining much attention from practitioners and academia and serves as a business mean to increase efficiency and improve business processes. Since small and medium sized enterprises (SMEs) play an important role in the market's value creation, it is particularly important to understand key aspects of IoT based business model transformations in SMEs. However, the existing literature on IoT based business models mainly focused on transformations in large companies. Therefore, this paper aims to identify the preconditions of adopting IoT in SMEs and possible resulting business model transformations. A systematic literature review was chosen as the main research method for this paper. Results of the analysis were summarized as the preconditions for IoT based business model transformations using Osterwalder Pigneur's Business Model Canvas concept as a basis.
Gas fermentation: cellular engineering possibilities and scale up
Venue: Microbial Cell Factories
Year: 2017

Low carbon fuels and chemicals can be sourced from renewable materials such as biomass or from industrial and municipal waste streams. Gasification of these materials allows all of the carbon to become available for product generation, a clear advantage over partial biomass conversion into fermentable sugars. Gasification results into a synthesis stream (syngas) containing carbon monoxide (CO) carbon dioxide (CO2) hydrogen (H2) and nitrogen (N2) Autotrophy the ability to fix carbon such as CO2 is present in all domains of life but photosynthesis alone is not keeping up with anthropogenic CO2 output. One strategy is to curtail the gaseous atmospheric release by developing waste and syngas conversion technologies. Historically microorganisms have contributed to major, albeit slow, atmospheric composition changes. The current status and future potential of anaerobic gas fermenting bacteria with special focus on acetogens are the focus of this review.
A Case Study of a Peer Respite's Integration into a Public Mental Health System.
Venue: Journal of health care for the poor and underserved
Year: 2020

Peer respites are short term residential mental health programs staffed by peers with lived experience of mental distress. This case study examines qualitative data of peer staff and non peer program directors' experiences working at the first peer respite in California. Throughout a five year study (2010 2015) researchers conducted 20 semi structured interviews and three focus groups with 25 peer staff and three program directors. The study found that peer staff reported differences in recovery oriented service delivery between the county health system and their training in Intentional Peer Support. Peer staff indicated that such differences contributed, in part, to the program's gradual acceptance of the service delivery model promoted by the county, and the program's integration enabled its continued support via public funds. This paper concludes with policy recommendations to transform public mental health systems in collaboration with peer staff.
Deep Learning with Limited Numerical Precision
Venue: ICML
Year: 2015

Training of large scale deep neural networks is often constrained by the available computational resources. We study the effect of limited precision data representation and computation on neural network training. Within the context of low precision fixed point computations, we observe the rounding scheme to play a crucial role in determining the network's behavior during training. Our results show that deep networks can be trained using only 16 bit wide fixed point number representation when using stochastic rounding, and incur little to no degradation in the classification accuracy. We also demonstrate an energy efficient hardware accelerator that implements low precision fixed point arithmetic with stochastic rounding.
A structural damage detection technique based on agglomerative clustering applied to the Z 24 Bridge
Year: 2016

In the last decades, the structural health monitoring (SHM) has emerged as a promising approach to provide more reliable and quantitative knowledge about structural condition. Posed in the context of a statistical pattern recognition paradigm, the SHM has evolved and assigned, for instance, to the continuous development of machine learning algorithms that model the normal condition of a structure and, automatically, allow to infer early signs of damage. To overcome the unknown effects of operational and environmental variability, new cluster based algorithms have been required due to their inherent capability to automatically discover the main structural components of a system, allowing a proper learning of the normal condition. Therefore, this paper proposes a novel unsupervised cluster based technique, the agglomerative concentric hypersphere (ACH) to learn the structural response as a small number of structural states, even when operational an environmental influences produce nonlinear effects in the data. Through a simple initialization and an agglomerative clustering procedure, this technique assumes no underlying data distribution and does not require input parameters. The ACH is compared to the Mahalanobis squared distance algorithm on standard data sets from a post tensioned bridge: the Z 24 Bridge. The proposed technique demonstrates to be more suitable to model the normal condition of the test structure as main structural components. Furthermore, the technique reveals a better classification performance than the alternative one in terms of false positive and false negative indications of damage, demonstrating its applicability for real SHM scenarios.
Intelligent Recognition of Fatigue and Sleepiness Based on InceptionV3 LSTM via Multi Feature Fusion
Venue: IEEE Access
Year: 2020

Fatigue is a common state of mankind characterized by a reduction in the level of consciousness and alertness. Therefore, the recognition of fatigue and sleepiness has become indispensable in many alertness dependent situations, such as when driving vehicles on public roads, performing demanding tasks in the workplace, or monitoring intensive care unit patients. This study proposes a method based on novel multi feature fusion to detect fatigue and sleepiness by using traditional image processing and heart rate variability (HRV) The proposed method performs initial feature extraction using InceptionV3 (a convolutional neural network (CNN) following which the second decision is made by a long short term memory network (LSTM) using the features collected by InceptionV3 to process the sequence of video data for recognition. The LSTM provides coherent and precise sequence recognition that avoids static distortions. Then, the final decision is made by the blood volume pulse vector (PBV) method after the features are fused. Because fatigue recognition is usually employed to monitor driver fatigue, we verified the feasibility of our method by testing its ability to successfully recognize driver fatigue. Following the experiments, we compared the different steps in the proposed method with those in existing methods. We selected four other methods to perform the comparison tests and used the same videos for training networks. In comparison with state of the art methods, our method in its entirety achieved an average increase of 5% in terms of both accuracy and stability.
Can Coffee Improve Surgical Robot Accuracy
Year: 2016

Transnasal skull base and sinus surgery is often assisted via image guidance systems (e.g. the Brainlab Kolibri  Fig. 1) which show the real time locations of surgical instruments with respect to the 3D map provided by preoperative volumetric medical images. Image guidance systems often employ optical tracking to measure in real time the locations of both surgical instruments and the patient's head. The accuracy of these systems relies on the rigid coupling between optical tracking markers and the patient. However, the markers are often attached to the patient using an elastic headband that can shift if bumped during use  In this paper, we propose the use of both layer jamming and granular jamming to improve the accuracy of image guided robotic transnasal sinus and skull base surgery. Granular jamming involves drawing a vacuum on a bag filled with granules (e.g. coffee grounds)  Layer jamming involves drawing a vacuum on a bag filled with thin layers of material, to increase the normal force between them and thus prevent them from sliding over one another  We use these technologies to securely and non invasively attach optical tracking markers to the patient, which enables accurate registration of preoperative medical images to guide surgical robotic systems, or even manually operated instruments. The prototype described in this paper extends our prior work in which granular jamming alone was used with a plastic frame  Here, we use layer jamming in conjunction with granular jamming to create a headband that conforms to the patient's head, becoming rigid when a vacuum is drawn. An example of where this would be useful is in robotassisted drilling. Drilling is often required as a first step in transnasal skull base and sinus surgeries to access the surgical site. It has been previously shown that collaborative human robot systems (in which both the surgeon and the robot hold the drill simultaneously) can assist the surgeon [5] by enforcing virtual fixtures (see  for a review) around sensitive anatomy. Another example where registered images and virtual boundaries can be useful is in pituitary surgery, where they have been proposed as a means to prevent damage to the optic nerves and carotid arteries in manual [7] and robotic [8] surgical approaches.
Sedna and the cloud of comets surrounding the Solar System in Milgromian dynamics
Year: 2016

We reconsider the hypothesis of a vast cometary reservoir surrounding the solar system the Oort cloud of comets within the framework of Milgromian dynamics (MD or MOND) For this purpose we built a numerical model of the cloud, assuming the theory of modified gravity, QUMOND. In modified gravity versions of MD, the internal dynamics of a system is influenced by the external gravitational field in which the system is embedded, even when this external field is constant and uniform, a phenomenon dubbed the external field effect (EFE) Adopting the popular pair n(x) [1 exp( x1/2) 1 for the MD interpolating function and a0 1.2 x 10 10 m s 2 for the MD acceleration scale, we found that the observationally inferred Milgromian cloud of comets is much more radially compact than its Newtonian counterpart. The comets of the Milgromian cloud stay away from the zone where the Galactic tide can torque their orbits significantly. However, this does not need to be an obstacle for the injection of the comets into the inner solar system as the EFE can induce significant change in perihelion distance during one revolution of a comet around the Sun. Adopting constraints on different interpolating function families and a revised value of a0 (provided recently by the Cassini spacecraft) the aforementioned qualitative results no longer hold, and, in conclusion, the Milgromian cloud is very similar to the Newtonian in its overall size, binding energies of comets and hence the operation of the Jupiter Saturn barrier. However, EFE torquing of perihelia still play a significant role in the inner parts of the cloud. Consequently Sedna like orbits and orbits of large semi major axis Centaurs are easily comprehensible in MD. In MD, they both belong to the same population, just in different modes of their evolution.
SemEval 2015 Task 12: Aspect Based Sentiment Analysis
Venue: NAACL 2015
Year: 2015

SemEval 2015 Task 12, a continuation of SemEval 2014 Task 4, aimed to foster research beyond sentenceor text level sentiment classification towards Aspect Based Sentiment Analysis. The goal is to identify opinions expressed about specific entities (e.g. laptops) and their aspects (e.g. price) The task provided manually annotated reviews in three domains (restaurants, laptops and hotels) and a common evaluation procedure. It attracted 93 submissions from 16 teams.
Measurement and CFD Prediction of Heat Transfer in Air Cooled Disc Type Electrical Machines
Venue: IEEE Transactions on Industry Applications
Year: 2011

Accurate thermal analysis of axial flux permanent magnet (AFPM) machines is crucial in predicting maximum power output. Stator convective heat transfer is one of the most important and least investigated heat transfer mechanisms and is the focus of this paper. Experimental measurements were undertaken using a thin film electrical heating method, providing radially resolved steady state heat transfer data from an experimental rotor stator system designed as a geometric mockup of a through flow ventilated AFPM machine. The measurements are compared with computational fluid dynamics (CFD) simulations using both 2 D axisymmetric and 3 D models. These were found to give a conservative estimate of heat transfer, with inaccuracies near the edge and in the transitional flow regime. Predicted stator heat transfer was found to be relatively insensitive to the choice of turbulence model used in the CFD simulations.