nils-herrmann/clean_scirepeval huggingface dataset:
Potential of exogenously sourced kinetin in protecting Solanum lycopersicum from NaCl induced oxidative stress through up regulation of the antioxidant system, ascorbate glutathione cycle and glyoxalase system
Venue: PloS one
Year: 2018

The protective role of exogenously applied kinetin (10 mM KN, a cytokinin) against the adverse effects caused by NaCl induced (150 mM) stress in Solanum lycopersicum was investigated. Application of KN significantly enhanced growth and biomass production of normally grown plants (non stressed) and also mitigated the adverse effect of NaCl on stressed plants to a considerable extent. Among the examined parameters, chlorophyll and carotenoid contents, photosynthetic parameters, components of the antioxidant system (both enzymatic and non enzymatic) osmotica accumulation, and mineral uptake exhibited a significant increase following the application of KN. Furthermore, KN application reduced the generation of reactive free radical hydrogen peroxide, coupled with a significant reduction in lipid peroxidation and an increase in membrane stability. The activities of antioxidant enzymes, and glyoxylase system were found to be promoted in plants exposed to NaCl, and the activities were further promoted by KN application, thereby protecting S. lycopersicum plants against NaCl induced oxidative damage. Further strengthening of the antioxidant system in KN supplied plants was ascribed to regulation of ascorbate glutathione cycle, phenols and flavonoids in them. The levels of proline and glycine betaine increased considerably in KN treated plants, thereby maintaining relative water content. Moreover, exogenous KN application reduced the inhibitory effects of NaCl on K+ and Ca2+ uptake, which resulted in a considerable reduction in tissue Na+/K+ ratio.
Agri market Marketing Communication Strategy through agriculture based Online Shopping Application
Year: 2019

This study aims to find out the communication marketing strategy form through one of the agriculture based online shopping applications. This study used a qualitative approach. The descriptive research method with the data collection techniques is literature study and online data search. Then it is analyzed according to the descriptive method. This research is done by discussing the current marketing communication strategies by utilizing technology, paying attention to market share and consumer needs which are the important points in online Agri market business; with an agriculture based online shopping application, it can make marketing easier and get customers. The result of this research is to identify whether the online Agri market has a good promotional strategy and can compete with other online shopping applications that have different bases. Along with technological advances, the online Agri market improves the quality of sales and purchases of fresh agricultural products from producers to consumers.
Replicated Siamese LSTM in Ticketing System for Similarity Learning and Retrieval in Asymmetric Texts
Venue: SemDeep@COLING
Year: 2018

The goal of our industrial ticketing system is to retrieve a relevant solution for an input query, by matching with historical tickets stored in knowledge base. A query is comprised of subject and description, while a historical ticket consists of subject, description and solution. To retrieve a relevant solution, we use textual similarity paradigm to learn similarity in the query and historical tickets. The task is challenging due to significant term mismatch in the query and ticket pairs of asymmetric lengths, where subject is a short text but description and solution are multi sentence texts. We present a novel Replicated Siamese LSTM model to learn similarity in asymmetric text pairs, that gives 22% and 7% gain (Accuracy@10) for retrieval task, respectively over unsupervised and supervised baselines. We also show that the topic and distributed semantic features for short and long texts improved both similarity learning and retrieval.
A Fast and Refined Cancer Regions Segmentation Framework in Whole slide Breast Pathological Images
Venue: Scientific Reports
Year: 2019

Supervised learning methods are commonly applied in medical image analysis. However, the success of these approaches is highly dependent on the availability of large manually detailed annotated dataset. Thus an automatic refined segmentation of whole slide image (WSI) is significant to alleviate the annotation workload of pathologists. But most of the current ways can only output a rough prediction of lesion areas and consume much time in each slide. In this paper, we propose a fast and refined cancer regions segmentation framework v3_DCNN, which first preselects tumor regions using a classification model Inception v3 and then employs a semantic segmentation model DCNN for refined segmentation. Our framework can generate a dense likelihood heatmap with the 1/8 side of original WSI in 11.5 minutes on the Camelyon16 dataset, which saves more than one hour for each WSI compared with the initial DCNN model. Experimental results show that our approach achieves a higher FROC score 83.5% with the champion's method of Camelyon16 challenge 80.7% Based on v3 DCNN model, we further automatically produce heatmap of WSI and extract polygons of lesion regions for doctors, which is very helpful for their pathological diagnosis, detailed annotation and thus contributes to developing a more powerful deep learning model.
Fully Unsupervised Crosslingual Semantic Textual Similarity Metric Based on BERT for Identifying Parallel Data
Venue: CoNLL
Year: 2019

We present a fully unsupervised crosslingual semantic textual similarity (STS) metric, based on contextual embeddings extracted from BERT Bidirectional Encoder Representations from Transformers (Devlin et al. 2019) The goal of crosslingual STS is to measure to what degree two segments of text in different languages express the same meaning. Not only is it a key task in crosslingual natural language understanding (XLU) it is also particularly useful for identifying parallel resources for training and evaluating downstream multilingual natural language processing (NLP) applications, such as machine translation. Most previous crosslingual STS methods relied heavily on existing parallel resources, thus leading to a circular dependency problem. With the advent of massively multilingual context representation models such as BERT, which are trained on the concatenation of non parallel data from each language, we show that the deadlock around parallel resources can be broken. We perform intrinsic evaluations on crosslingual STS data sets and extrinsic evaluations on parallel corpus filtering and human translation equivalence assessment tasks. Our results show that the unsupervised crosslingual STS metric using BERT without fine tuning achieves performance on par with supervised or weakly supervised approaches.
A smartphone based chip scale microscope using ambient illumination.
Venue: Lab on a chip
Year: 2014

Portable chip scale microscopy devices can potentially address various imaging needs in mobile healthcare and environmental monitoring. Here, we demonstrate the adaptation of a smartphone's camera to function as a compact lensless microscope. Unlike other chip scale microscopy schemes, this method uses ambient illumination as its light source and does not require the incorporation of a dedicated light source. The method is based on the shadow imaging technique where the sample is placed on the surface of the image sensor, which captures direct shadow images under illumination. To improve the image resolution beyond the pixel size, we perform pixel super resolution reconstruction with multiple images at different angles of illumination, which are captured while the user is manually tilting the device around any ambient light source, such as the sun or a lamp. The lensless imaging scheme allows for sub micron resolution imaging over an ultra wide field of view (FOV) Image acquisition and reconstruction are performed on the device using a custom built Android application, constructing a stand alone imaging device for field applications. We discuss the construction of the device using a commercial smartphone and demonstrate the imaging capabilities of our system.
Kontenjans tablolarinin analizinde log lineer modellerin kullanimi ve sigara bagimliligi uzerine bir uygulama
Year: 2015

Two way cross tables are used chi square analysis of statistical analysis, but is insufficient for the analysis of larger cross tables. Log linear model can be used to determine the structure of three or more multi dimensional cross table relationships. Loglinear model can be queried with the help of more interaction between qualitative variables. Loglinear models examined in this study and application is made to explain the smoking addiction
Similarity based health risk prediction using Domain Fusion and electronic health records data
Venue: J. Biomed. Informatics
Year: 2021

Electronic Health Record (EHR) data represents a valuable resource for individualized prospective prediction of health conditions. Statistical methods have been developed to measure patient similarity using EHR data, mostly using clinical attributes. Only a handful of recent methods have combined clinical analytics with other forms of similarity analytics, and no unified framework exists yet to measure comprehensive patient similarity. Here, we developed a generic framework named Patient similarity based on Domain Fusion (PsDF) PsDF performs patient similarity assessment on each available domain data separately, and then integrate the affinity information over various domains into a comprehensive similarity metric. We used the integrated patient similarity to support outcome prediction by assigning a risk score to each patient. With extensive simulations, we demonstrated that PsDF outperformed existing risk prediction methods including a random forest classifier, a regression based model, and a naive similarity method, especially when heterogeneous signals exist across different domains. Using PsDF and EHR data extracted from the data warehouse of Columbia University Irving Medical Center, we developed two different clinical prediction tools for two different clinical outcomes: incident cases of end stage kidney disease (ESKD) and severe aortic stenosis (AS) requiring valve replacement. We demonstrated that our new prediction method is scalable to large datasets, robust to random missingness, and generalizable to diverse clinical outcomes.
New tetrapeptide ligands designed for antibody purification with biomimetic chromatography: Molecular simulation and experimental validation
Year: 2016

Peptide ligands have been developed as powerful alternatives to Protein A for antibody purification. In this study, a tetrapeptide library with two aromatic residues, one arginine and one aromatic/aliphatic residue ( was constructed considering molecular interactions and applications. The library had 128 peptide ligands and was screened by flexible docking and molecular dynamics simulation. The results indicated that Ac YFRH was the best ligand, and then was coupled onto an aminated argarose matrix. The results of isotherm adsorption showed that the binding capacity of human immunoglobulin G (IgG) was high at pH 78 and low at pH 45. The IgG adsorption also showed obvious salt independence, and the addition of salt could reduce unspecific adsorption caused by electrostatic interaction. After the optimization of pH and salt addition, high IgG purity of 98.4% could be obtained with the recovery of 89.4% from BSA containing feedstock. The purification of monoclonal antibody from CHO cell culture supernatant was tested, and the purity and recovery reached 98.0% and 79.5% respectively. The results demonstrated that the new resin with the tetrapeptide YFRH ligand had high affinity and flexible operation window for IgG purification.
Patient preferences for the integration of mental health counseling and chronic disease care in South Africa
Venue: Patient preference and adherence
Year: 2018

To describe patient perceptions of the acceptability of integrating mental health counseling within primary care facilities in the Western Cape province of South Africa and their preferences for the way in which this care is delivered.  Qualitative interviews with 30 purposively selected patients receiving treatment for HIV or diabetes within primary care facilities who screened positive for depression using the Center for Epidemiological Studies Depression Scale or hazardous alcohol use through the Alcohol Use Disorders Identification Test.  Participants articulated high levels of unmet need for mental health services and strong associations between poor mental health and the challenges of living with a chronic disease. Consequently, they considered it acceptable to offer screening and mental health counseling within the context of chronic disease care. They thought counseling would be highly relevant if it helped patients develop adaptive strategies for coping with stress and negative emotions. Irrespective of chronic disease, patients indicated a preference for lay counselors rather than existing clinicians as potential delivery agents, supporting a task shared approach to mental health counseling delivery in primary care settings. Some expressed concern about the feasibility of using lay counselors already present in facilities to deliver this service, suggesting that additional counselors might be needed.  Findings demonstrate a need for mental health counseling within the context of chronic disease care in South Africa. Task shared approaches, using lay counselors, seem acceptable to patients provided counselors are selected to ensure they possess the qualities associated with effective counselors. Findings have informed the design of a task shared mental health program that is responsive to the preferences of patients with chronic diseases.
Using Intraoral Scanning to Capture Complete Denture Impressions, Tooth Positions, and Centric Relation Records.
Venue: The International journal of prosthodontics
Year: 2018

Intraoral scanning was used to capture the soft tissue surfaces of both maxillary and mandibular edentulous ridges and the denture borders. Additionally, an intraoral scanner was used to digitize existing dentures with their tooth positions and base forms and a centric relation record obtained with a Gothic arch tracing device. These scans provided all the required records for fabrication of computer aided design/computer assisted manufacturing of complete dentures.
Kernel Based Learning: A Pragmatic Approach in the Face of New Challenges
Year: 2018

This chapter is not aimed at replacing literature on introduction to kernel methods or Fisher kernels. There are some excellent text books and tutorials on the topic by Scholkopf and Smola (Learning with kernels: support vector machines, regularization, optimization, and beyond.  (2002)  Shawe Taylor, Cristianini (Kernel methods for pattern analysis.  (2004)  Kung (Kernel methods and machine learning.  (2014)  In contrast to formal theory and proofs, this chapter briefly describes the evolution of kernel methods and the heuristics and methods that have helped kernel methods evolve over the past many years for solving the challenges faced by current machine learning practitioners and applied scientists.
Protective effect of dexpanthenol on cisplatin induced nephrotoxicity in rats.
Venue: Biotechnic histochemistry official publication of the Biological Stain Commission
Year: 2021

Cisplatin (CIS) is an antineoplastic agent used for treating solid organ tumors. Toxic side effects of CIS treatment include nephrotoxicity, neurotoxicity, ototoxicity, myelosuppression and hepatotoxicity. Dexpanthenol (DEX) exhibits antioxidant and anti inflammatory effects and protective effects against free oxygen radicals. We investigated the protective effects of DEX on CIS induced nephrotoxicity. Animals were divided into four groups of 10. The control group was given saline. The DEX group was treated with DEX for 10 days. The CIS group was treated with a single dose of CIS. The DEX CIS group was given a single dose of CIS followed by DEX for 10 days. We found increased levels of malondialdehyde (MDA) blood urea nitrogen (BUN) and creatinine, while superoxide dismutase (SOD) catalase (CAT) glutathione peroxidase (GPx) and myeloperoxidase (MPO) levels were decreased in the CIS group. MDA, BUN and creatinine levels were decreased, while SOD, CAT, GPx and MPO levels were increased in the DEX CIS group. Renal tubule damage, inflammation and histopathology scores were significantly higher in the CIS group than the control. The DEX CIS group exhibited less renal tubule damage and inflammation, and lower histopathological assessment scores than the CIS group. Significant cortical tubule damage and interstitial inflammation were observed in the CIS group. Tubule damage was slightly less, and mild tubule dilation and less cast formation were observed in the DEX CIS group; also, inflammation was less severe than for the CIS group. DEX may have therapeutic potential for treating CIS induced nephrotoxicity due to its antioxidant and anti inflammatory properties.
Seismic Performance of Reinforced Concrete Columns: P D Effect
Year: 2006

The research reported in this paper is aimed at identifying the relationship between the curvature ductility, the displacement ductility and the drift capacity of reinforced concrete columns. In order to achieve this goal and to study the effects of aspect ratio (L/h) on the seismic performance of columns, an analytical procedure is developed that can be used to evaluate the deformation capacity of reinforced concrete columns. Experimentally verified constitutive models for reinforcing bar slip, inelastic buckling of bars, and confinement of concrete are used. Model verification is performed by predicting the behavior of columns tested by different researchers. The effect of longitudinal reinforcement ratio, volumetric ratio of confining reinforcement, column aspect ratio, and axial load level on the relationship between various ductility parameters is evaluated and discussed in the paper.
Cellular based machine to machine: overload control
Venue: IEEE Network
Year: 2012

One of the most important problems posed by cellular based machine type communications is congestion. Congestion concerns all the parts of the network, both the radio and the core network impacting both the user data plane and the control plane. In this article, we address the problem of congestion in machine type communications. We propose a congestion aware admission control solution that selectively rejects signaling messages from MTC devices at the radio access network following a probability that is set based on a proportional integrative derivative controller reflecting the congestion level of a relevant core network node. We evaluate the performance of our proposed solution using computer simulations. The obtained results are encouraging. In fact, we succeed in reducing the amount of signaling while maintaining a target utilization ratio of resources in the core network.
Application of Artificial Intelligence in Martial Arts Teaching in Colleges and Universities
Year: 2020

With the increasingly rapid development of society, network science information technology and artificial intelligence technology has been widely penetrated into People's Daily life, especially mobile phones, computers, multimedia and other communication tools, they are convenient, flexible personality, has become the most important means of communication. In addition to communication, it also brings more functions such as videos, topics, we media and self help learning. Based on the background of rapid development of artificial intelligence and multimedia, the integration of mobile network, artificial intelligence and traditional teaching has become an important means of sports information teaching communication. The main purpose of this paper is to better combine artificial intelligence with traditional teaching methods so as to make martial arts teaching more scientific. The research comprehensively used the methods of literature survey and questionnaire survey to understand the scientific research achievements of AI in martial arts teaching in colleges and universities, sort out the research ideas and framework of this paper, and then used the method of questionnaire survey to carry out a sample survey of students and teachers in martial arts colleges and departments. Through the investigation, the demand results and communication conditions of artificial intelligence integrated martial arts teaching are obtained. Results showed, that year until June of 2019, the university sports departments Internet users penetration rate over 95% teachers group can be skilled in the use of artificial intelligence teaching equipment accounted for more than 81.25% and surveyed the crowd is generally accepted that can get advanced teaching ideas on network platform and advanced martial arts movements, eventually make China's sports culture to the world.
Unconstrained high dimensional Bayesian optimization of an airfoil shape design via multi fidelity surrogate modeling
Year: 2019

In engineering, science, and technology, predictions and design decisions can be made or informed by a variety of information sources that range from experimental data to computer models. These information sources typically encompass different mathematical formulations, different grid resolutions, different physics, or different modeling assumptions that simplify the problem. This leads to information sources with varying degrees of fidelity, with varying associated accuracy and querying costs. In this paper we propose a novel and flexible way to use multifidelity information sources optimally in the context of airfoil shape optimization using both a Reynolds averaged Navier Stokes (RANS) solver and a low fidelity approximation based on a simplified physical formulation. The new developments based on Bayesian optimization and kriging metamodeling allow the aerodynamic optimization to be sped up and divide for example (on a 15 design variable unconstrained optimization problem) the total cost by at least two compared to a single fidelity optimization.
A Switch On/Off Relaying Scheme in TDMB Cooperative System
Year: 2013

Terrestrial Digital Multimedia Broadcasting (TDMB) is one popular broadcasting standard that enable digital television transmissions to hand held receivers and cooperative system take advantage of the broadcast nature of wireless channels, uses relay stations as virtual antennas. Relay stations are an attractive solution to penetrate the wireless system with lower transmitting power at the Base station. In this paper, we presented a scheme can switch on/off the power weighting ratio between Base station and Relay station in TDMB cooperative system. It will control the active relays in different channel propagations effectively.
Formal methods in dependable systems engineering: a survey of professionals from Europe and North America
Venue: Empir. Softw. Eng.
Year: 2020

Formal methods (FMs) have been around for a while, still being unclear how to leverage their benefits, overcome their challenges, and set new directions for their improvement towards a more successful transfer into practice. We study the use of formal methods in mission critical software domains, examining industrial and academic views. We perform a cross sectional on line survey. Our results indicate an increased intent to apply FMs in industry, suggesting a positively perceived usefulness. But the results also indicate a negatively perceived ease of use. Scalability, skills, and education seem to be among the key challenges to support this intent. We present the largest study of this kind so far (N 216) and our observations provide valuable insights, highlighting directions for future theoretical and empirical research of formal methods. Our findings are strongly coherent with earlier observations by Austin and Graeme (1993)