nils-herrmann/clean_scirepeval huggingface dataset:
Long non-coding RNA UCA1 promotes breast tumor growth by suppression of p27 (Kip1)

Functional genomics studies have led to the discovery of a large amount of non-coding RNAs from the human genome; among them are long non-coding RNAs (lncRNAs). Emerging evidence indicates that lncRNAs could have a critical role in the regulation of cellular processes such as cell growth and apoptosis as well as cancer progression and metastasis. As master gene regulators, lncRNAs are capable of forming lncRNA–protein (ribonucleoprotein) complexes to regulate a large number of genes. For example, lincRNA-RoR suppresses p53 in response to DNA damage through interaction with heterogeneous nuclear ribonucleoprotein I (hnRNP I). The present study demonstrates that hnRNP I can also form a functional ribonucleoprotein complex with lncRNA urothelial carcinoma-associated 1 (UCA1) and increase the UCA1 stability. Of interest, the phosphorylated form of hnRNP I, predominantly in the cytoplasm, is responsible for the interaction with UCA1. Moreover, although hnRNP I enhances the translation of p27 (Kip1) through interaction with the 5′-untranslated region (5′-UTR) of p27 mRNAs, the interaction of UCA1 with hnRNP I suppresses the p27 protein level by competitive inhibition. In support of this finding, UCA1 has an oncogenic role in breast cancer both in vitro and in vivo. Finally, we show a negative correlation between p27 and UCA in the breast tumor cancer tissue microarray. Together, our results suggest an important role of UCA1 in breast cancer.
GCMDDBSCAN: Multi-density DBSCAN Based on Grid and Contribution

Multi Density DBSCAN (Density Based Spatial Clustering of Application with Noise) is an excellent density-based clustering algorithm, which extends DBSCAN algorithm so as to be able to discover the different densities clusters, and retains the advantage of separating noise and finding arbitrary shape clusters. But, because of great memory demand and low calculation efficiency, Multi Density DBSCAN can't deal with large database. Therefore, GCMDDBSCAN is proposed in this paper, and within it 'migration-coefficient' conception is introduced firstly. In GCMDDBSCAN, with the grid technique, the optimization effect of contribution and migration-coefficient, and the efficient SP-tree query index, the runtime is reduced a lot, and the capability of clustering large database is obviously enhanced, at the same time, the accuracy of clustering result is not degraded.
Successful approaches to teaching introductory computer science courses with python

This session will focus on ways that Python is being used to successfully facilitate introductory computer science courses. After a brief introduction, we will present three different models for CS1 and CS2 using Python. Attendees will then participate in a discussion/question-answer session considering the advantages and challenges of using Python in the introductory courses. The presenters will focus on common issues, both positive and negative, that have arisen from the inclusion of Python in the introductory computer science curriculum as well as the impact that this can have on the entire computer science curriculum.
MUSIC, maximum likelihood, and Cramer-Rao bound

The performance of the MUSIC and ML methods is studied, and their statistical efficiency is analyzed. The Cramer-Rao bound (CRB) for the estimation problems is derived, and some useful properties of the CRB covariance matrix are established. The relationship between the MUSIC and ML estimators is investigated as well. A numerical study is reported of the statistical efficiency of the MUSIC estimator for the problem of finding the directions of two plane waves using a uniform linear array. An exact description of the results is included.<<ETX>>
Microbial Surface Colonization and Biofilm Development in Marine Environments.

Biotic and abiotic surfaces in marine waters are rapidly colonized by microorganisms. Surface colonization and subsequent biofilm formation and development provide numerous advantages to these organisms and support critical ecological and biogeochemical functions in the changing marine environment. Microbial surface association also contributes to deleterious effects such as biofouling, biocorrosion, and the persistence and transmission of harmful or pathogenic microorganisms and their genetic determinants. The processes and mechanisms of colonization as well as key players among the surface-associated microbiota have been studied for several decades. Accumulating evidence indicates that specific cell-surface, cell-cell, and interpopulation interactions shape the composition, structure, spatiotemporal dynamics, and functions of surface-associated microbial communities. Several key microbial processes and mechanisms, including (i) surface, population, and community sensing and signaling, (ii) intraspecies and interspecies communication and interaction, and (iii) the regulatory balance between cooperation and competition, have been identified as critical for the microbial surface association lifestyle. In this review, recent progress in the study of marine microbial surface colonization and biofilm development is synthesized and discussed. Major gaps in our knowledge remain. We pose questions for targeted investigation of surface-specific community-level microbial features, answers to which would advance our understanding of surface-associated microbial community ecology and the biogeochemical functions of these communities at levels from molecular mechanistic details through systems biological integration.
Arabinoxylan-mediated synthesis of gold and silver nanoparticles having exceptional high stability.

A green synthesis of highly stable gold and silver nanoparticles (NPs) using arabinoxylan (AX) from ispaghula (Plantago ovata) seed husk is being reported. The NPs were synthesized by stirring a mixture of AX and HAuCl(4)·H(2)O or AgNO(3), separately, below 100 °C for less than an hour, where AX worked as the reducing and the stabilizing agent. The synthesized NPs were characterized by surface plasmon resonance (SPR) spectroscopy, transmission electron microscopy (TEM), atomic force microscopy (AFM), and X-ray diffraction (XRD). The particle size was (silver: 5-20 nm and gold: 8-30 nm) found to be dependent on pH, temperature, reaction time and concentrations of AX and the metal salts used. The NPs were poly-dispersed with a narrow range. They were stable for more than two years time.
Open and closed innovation - different innovation cultures for different strategies

Although strategies, processes, or the role of business models have been addressed in the open innovation literature, the people side of the equation – i.e., the underlying innovation culture – has been neglected so far. Whereas cultural requirements of open innovation have been mentioned, such as the need to overcome the not-invented-here (NIH) syndrome, there exists, to the best of our knowledge, no study that empirically examines open innovation cultures. We attempt to fill this research gap by focusing on innovation cultures within three business units (two follow a closed and one follows an open innovation approach) of a leading multinational company within the specialty chemicals industry. Employing an overall sample of 109 respondents, we focus on the cultural dimensions of NIH syndrome, risk-taking, and management support of innovative behaviour and provide first evidence of cultural differences between Open and Closed Innovation units.
Magnetic needle guidance for neurosurgery: Initial design and proof of concept

A magnetic-tip steerable needle is presented with application to aiding deep brain stimulation electrode placement. The magnetic needle is 1.3mm in diameter at the tip with a 0.7mm diameter shaft, which is selected to match the size of a deep brain stimulation electrode. The tip orientation is controlled by applying torques to the embedded neodymium-iron-boron permanent magnets with a clinically-sized magnetic-manipulation system. The prototype design is capable of following trajectories under human-in-the-loop control with minimum bend radii of 100mm without inducing tissue damage and down to 30mm if some tissue damage is tolerable. The device can be retracted and redirected to reach multiple targets with a single insertion point.
Generative Adversarial Network based Synthesis for Supervised Medical Image Segmentation *

Modern deep learning methods achieve state-ofthe-art results in many computer vision tasks. While these methods perform well when trained on large datasets, deep learning methods suffer from overfitting and lack of generalization given smaller datasets. Especially in medical image analysis, acquisition of both imaging data and corresponding ground-truth annotations (e.g. pixel-wise segmentation masks) as required for supervised tasks, is time consuming and costly, since experts are needed to manually annotate data. In this work we study this problem by proposing a new variant of Generative Adversarial Networks (GANs), which, in addition to synthesized medical images, also generates segmentation masks for the use in supervised medical image analysis applications. We evaluate our approach on a lung segmentation task involving thorax X-ray images, and show that GANs have the potential to be used for synthesizing training data in this specific application.
Progressive compression for lossless transmission of triangle meshes

Lossless transmission of 3D meshes is a very challenging and timely problem for many applications, ranging from collaborative design to engineering. Additionally, frequent delays in transmissions call for progressive transmission in order for the end user to receive useful successive refinements of the final mesh. In this paper, we present a novel, fully progressive encoding approach for lossless transmission of triangle meshes with a very fine granularity. A new valence-driven decimating conquest, combined with patch tiling and an original strategic retriangulation is used to maintain the regularity of valence. We demonstrate that this technique leads to good mesh quality, near-optimal connectivity encoding, and therefore a good rate-distortion ratio throughout the transmission. We also improve upon previous lossless geometry encoding by decorrelating the normal and tangential components of the surface. For typical meshes, our method compresses connectivity down to less than 3.7 bits per vertex, 40% better in average than the best methods previously reported [5, 18]; we further reduce the usual geometry bit rates by 20% in average by exploiting the smoothness of meshes. Concretely, our technique can reduce an ascii VRML 3D model down to 1.7% of its size for a 10-bit quantization (2.3% for a 12-bit quantization) while providing a very progressive reconstruction.
Acetylcodeine, an impurity of illicitly manufactured heroin, elicits convulsions, antinociception, and locomotor stimulation in mice.

Acetylcodeine is one of the major impurities present in illicitly manufactured heroin (diacetylmorphine). Data on its pharmacology and toxicology are limited and its ability to alter the toxic effects of diacetylmorphine is not known. The first objective of the present study was to compare the acute pharmacological and toxicological effects of acetylcodeine to those of codeine and diacetylmorphine in mice by assessing nociception in the tail-flick test, locomotor stimulation, and convulsive behavior. The second goal of this study was to determine whether acetylcodeine would alter the convulsant effects of diacetylmorphine. The antinociceptive potencies of acetylcodeine and codeine were similar, as reflected by their ED50 (95% confidence limits) values of 35 (29-44) and 51 (40-65) micromol/kg, respectively. Acetylcodeine was somewhat less potent than codeine in stimulating locomotor behavior, with ED50 values of 28 (22-37) and 12 (6-24) micromol/kg, respectively. Diacetylmorphine was considerably more potent than the other two drugs, producing antinociception and locomotor stimulation at ED50 values of 2.4 (1.4-4.1) and 0.65 (0.36-1.2) micromol/kg, respectively. On the other hand, the convulsant effects of acetylcodeine (ED50=138 (121-157) micromol/kg) and diacetylmorphine (ED50=115 (81-163) micromol/kg) were similar in potency and both were more potent than codeine (ED50=231 (188-283) micromol/kg). Finally, a subthreshold dose of acetylcodeine (72 micromol/kg) decreased the convulsant ED50 dose of diacetylmorphine to 40 (32-49). These findings suggest that the convulsant effects of acetylcodeine are more potent than predicted by its effects on locomotor activity and antinociception. The observation that acetylcodeine potentiated the convulsant effects of diacetylmorphine suggests a mechanism for some of the heroin-related deaths reported in human addicts.
Image Processing Based Disease Detection for Sugarcane Leaves

Sugarcane is one of the most important crops in India. Indian sugar industry is the second largest agro based industry, next only to the textiles. But, being a long durational crop, sugarcane is prone to the number of disease caused by pathogens viz. fungi, bacteria, viruses and phytoplasmas like organisms. Image processing techniques have been proved to be changing the scenario of agriculture in India with a number of research and applications like automatic disease detection, drone based pesticides and fertilizer dispensing, estimation of yield, vegetative growth, fruit sorting etc. This research is carried out to study the effectiveness of Image Processing and computer vision techniques for detection of disease in sugarcane plants by observing the leaves. Few major diseases in sugarcane plant like red rot, mosaic and leaf scald have been studied and detection algorithm for the same has been implemented in this research work.
Decentralized coded caching in wireless networks: Trade-off between storage and latency

This paper studies the decentralized coded caching for a Fog Radio Access Network (F-RAN), whereby two edge-nodes (ENs) connected to a cloud server via fronthaul links with limited capacity are serving the requests of K r users. We consider all ENs and users are equipped with caches. A decentralized content placement is proposed to independently store contents at each network node during the off-peak hours. After that, we design a coded delivery scheme in order to deliver the user demands during the peak-hours under the objective of minimizing the normalized delivery time (NDT), which refers to the worst case delivery latency. An information-theoretic lower bound on the minimum NDT is derived for arbitrary number of ENs and users. We evaluate numerically the performance of the decentralized scheme. Additionally, we prove the approximate optimality of the decentralized scheme for a special case when the caches are only available at the ENs.
Detecting predatory conversations in social media by deep Convolutional Neural Networks

Automatic identification of predatory conversations i chat logs helps the law enforcement agencies act proactively through early detection of predatory acts in cyberspace. In this paper, we describe the novel application of a deep learnin g method to the automatic identification of predatory chat conversations in large volumes of ch at logs. We present a classifier based on Convolutional Neural Network (CNN) to address this problem domain. The proposed CNN architecture outperforms other classification techn iques that are common in this domain including Support Vector Machine (SVM) and regular Neural Network (NN) in terms of classification performance, which is measured by F 1-score. In addition, our experiments show that using existing pre-trained word vectors are no t suitable for this specific domain. Furthermore, since the learning algorithm runs in a m ssively parallel environment (i.e., general-purpose GPU), the approach can benefit a la rge number of computation units (neurons) compared to when CPU is used. To the best of our knowledge, this is the first tim e that CNNs are adapted and applied to this application do main.
Sparse Multi-Modal Hashing

Learning hash functions across heterogenous high-dimensional features is very desirable for many applications involving multi-modal data objects. In this paper, we propose an approach to obtain the sparse codesets for the data objects across different modalities via joint multi-modal dictionary learning, which we call sparse multi-modal hashing (abbreviated as SM2H). In SM2H, both intra-modality similarity and inter-modality similarity are first modeled by a hypergraph, then multi-modal dictionaries are jointly learned by Hypergraph Laplacian sparse coding. Based on the learned dictionaries, the sparse codeset of each data object is acquired and conducted for multi-modal approximate nearest neighbor retrieval using a sensitive Jaccard metric. The experimental results show that SM2H outperforms other methods in terms of mAP and Percentage on two real-world data sets.
Harmonic winding factors and MMF analysis for five-phase fractional-slot concentrated winding PMSM

To enhance torque density by harmonic current injection, optimal slot/pole combinations for five-phase permanent magnet synchronous motors (PMSM) with fractional-slot concentrated windings (FSCW) are chosen. The synchronous and the third harmonic winding factors are calculated for a series of slot/pole combinations. Two five-phase PMSM, with general FSCW (GFSCW) and modular stator and FSCW (MFSCW), are analyzed and compared in detail, including the stator structures, star of slots diagrams, and MMF harmonic analysis based on the winding function theory. The analytical results are verified by finite element method, the torque characteristics and phase back-EMF are also taken into considerations. Results show that the MFSCW PMSM can produce higher average torque, while characterized by more MMF harmonic contents and larger ripple torque.
An Efficient MSB Prediction-Based Method for High-Capacity Reversible Data Hiding in Encrypted Images

Reversible data hiding in encrypted images (RDHEI) is an effective technique to embed data in the encrypted domain. An original image is encrypted with a secret key and during or after its transmission, it is possible to embed additional information in the encrypted image, without knowing the encryption key or the original content of the image. During the decoding process, the secret message can be extracted and the original image can be reconstructed. In the last few years, RDHEI has started to draw research interest. Indeed, with the development of cloud computing, data privacy has become a real issue. However, none of the existing methods allow us to hide a large amount of information in a reversible manner. In this paper, we propose a new reversible method based on MSB (most significant bit) prediction with a very high capacity. We present two approaches, these are: high capacity reversible data hiding approach with correction of prediction errors and high capacity reversible data hiding approach with embedded prediction errors. With this method, regardless of the approach used, our results are better than those obtained with current state of the art methods, both in terms of reconstructed image quality and embedding capacity.
A Distributed Agent Based Web Search using a Genetic Algorithm

In this paper, the problems of current web search engines are analyzed, and the need for a new design is justified. Some ideas on how to improve current web search engines are presented, and then an adaptive method for web meta-search engines with a multi-agent specially the mobile agents is presented to make search engines work more efficiently. In the method, the cooperation between stationary and mobile agents is used to make more efficiency. The meta-search engine gives the user needed documents based on the multi-stage mechanism. The merge of the results obtained from the search engines in the network is done in parallel. Using a reduction parallel algorithm, the efficiency of this method is increased. Furthermore, a feedback mechanism gives the meta-search engine the user’s suggestions about the found documents, which leads to a new query using a genetic algorithm. In the new search stage, more relevant documents are given to the user. The practical experiments were performed in Aglets programming environment. The results achieved from these experiments confirm the efficiency and adaptability of the method.
Applying robust design to study the effects of stratigraphic characteristics on brittle failure and bump potential in a coal mine

Bumps and other types of dynamic failure have been a persistent, worldwide problem in the underground coal mining industry, spanning decades. For example, in just five states in the U.S. from 1983 to 2014, there were 388 reportable bumps. Despite significant advances in mine design tools and mining practices, these events continue to occur. Many conditions have been associated with bump potential, such as the presence of stiff units in the local geology. The effect of a stiff sandstone unit on the potential for coal bumps depends on the location of the stiff unit in the stratigraphic column, the relative stiffness and strength of other structural members, and stress concentrations caused by mining. This study describes the results of a robust design to consider the impact of different lithologic risk factors impacting dynamic failure risk. Because the inherent variability of stratigraphic characteristics in sedimentary formations, such as thickness, engineering material properties, and location, is significant and the number of influential parameters in determining a parametric study is large, it is impractical to consider every simulation case by varying each parameter individually. Therefore, to save time and honor the statistical distributions of the parameters, it is necessary to develop a robust design to collect sufficient sample data and develop a statistical analysis method to draw accurate conclusions from the collected data. In this study, orthogonal arrays, which were developed using the robust design, are used to define the combination of the (a) thickness of a stiff sandstone inserted on the top and bottom of a coal seam in a massive shale mine roof and floor, (b) location of the stiff sandstone inserted on the top and bottom of the coal seam, and (c) material properties of the stiff sandstone and contacts as interfaces using the 3-dimensional numerical model, FLAC3D. After completion of the numerical experiments, statistical and multivariate analysis are performed using the calculated results from the orthogonal arrays to analyze the effect of these variables. As a consequence, the impact of each of the parameters on the potential for bumps is quantitatively classified in terms of a normalized intensity of plastic dissipated energy. By multiple regression, the intensity of plastic dissipated energy and migration of the risk from the roof to the floor via the pillars is predicted based on the value of the variables. The results demonstrate and suggest a possible capability to predict the bump potential in a given rock mass adjacent to the underground excavations and pillars. Assessing the risk of bumps is important to preventing fatalities and injuries resulting from bumps.