Miracle-dz/UniFAR-FTUs huggingface dataset:
Bruceine D suppresses viability , metastasis and EMT of human breast cancer MDA-MB-231 cells

Bruceine D (BD), a major quassinoid extracted from the Chinese medicinal herb Brucea javanica, has been demonstrated to exert anticancer effects on various tumor cells. However, the anti-metastatic effect of BD has not been reported. The present study was designed to investigate the anti-metastatic effect of BD on MDA-MB-231 cells. The effects of BD on cell viability, migration and invasion were examined by MTT assay, wound healing assay and transwell assay, respectively. The effect of BD on epithelial-mesenchymal transition (EMT)-associated proteins, including E-cadherin and vimentin was tested by Western blot. At the same time, we detected the expression of β-catenin, which is a key molecular target for promoting cell metastasis. The results showed that BD inhibited the viability of MDA-MB-231 cells in a dose and time-dependent manner, and it also reduced cell migration and invasion in a dose-dependent manner. Western blot showed that BD reversed EMT through suppressing vimentin but enhancing E-cadherin expression. Furthermore, BD obviously decreased the expression of β-catenin in a dosedependent manner. Taken together, all these results suggest that BD suppresses viability, metastasis and EMT of human breast cancer MDA-MB-231 cells.
Glia-Neuron Interactions in Caenorhabditis elegans

Glia are abundant components of animal nervous systems. Recognized 170 years ago, concerted attempts to understand these cells began only recently. From these investigations glia, once considered passive filler material in the brain, have emerged as active players in neuron development and activity. Glia are essential for nervous system function, and their disruption leads to disease. The nematode Caenorhabditis elegans possesses glial types similar to vertebrate glia, based on molecular, morphological, and functional criteria, and has become a powerful model in which to study glia and their neuronal interactions. Facile genetic and transgenic methods in this animal allow the discovery of genes required for glial functions, and effects of glia at single synapses can be monitored by tracking neuron shape, physiology, or animal behavior. Here, we review recent progress in understanding glia-neuron interactions in C. elegans. We highlight similarities with glia in other animals, and suggest conserved emerging principles of glial function. 149 A nn u. R ev . N eu ro sc i. 20 19 . 4 2: 14 916 8. D ow nl oa de d fr om w w w . a nn ua lr ev ie w s. or g A cc es s pr ov id ed b y R oc ke fe lle r U ni ve rs ity o n 07 /1 6/ 19 . F or p er so na l u se o nl y. NE42CH08_Shaham ARjats.cls June 18, 2019 17:32
Finding and Ranking Semantic Web Sites

With the rapid growth of online RDF data,emerging semantic search engines facilitate user's searching of RDF(resource description framework) data.It is an open question to all semantic search engines how to find sites containing semantic Web information resources automatically and collect them efficiently.Firstly,the paper introduces a Linking Model of the Semantic Web Sites.The model characterizes the relations among Semantic Web Sites,Semantic Web Information Resources,RDF Models and Semantic Web Entities.This paper discusses the ownerships of Semantic Web Entities based on this model.It also defines a Site Dependency Graph in virtue of the model,and presents a set of ranking algorithms for Semantic Web Sites.Primary tests of these algorithms have been performed in a real-world semantic search engine.Experimental results show that this approach is effective in finding and ranking Semantic Web Sites.
Review Paper on Sign Language Recognition Techniques

In the fast development of knowledge and communication technology, communication sector has improved continuously. Artificial intelligence could be a field that includes a broad, extremely technical and specialized analysis space that specialize in differing kinds of sub areas and has generated lots of interest with several novel applications within the IT world. Long with recent developments in image process, Artificial intelligence has conjointly been accustomed mechanically acknowledge sign language gestures, in what's called ASLR (Automatic Sign Language Recognition). The most significance of this paper is to review the key finding of the comparison of similar system and also technology used in vision based hand gesture recognition.
STAT 3-mediated Twist 1 upregulation contributes to epithelial-mesenehymal transition in cisplatin resistant ovarian cancer

Epithelial-mesenchymal transition (EMT) is a key process contributing to the invasion and chemoresistance in many tumors, and the critical roles of signal transducer and activator of transcription 3 (STAT3) and Twist basic helix loop helix transcription factor 1 (Twist1) in the progress of epithelial ovarian cancer have been demonstrated. Herein, the relationship between STAT3, Twist1 and EMT pathways and their effect on chemoresistance in ovarian cancer was studied. A cisplatin-sensitive human epithelial ovarian cancer cell line (A2780) and the resistant variant (A2780/DDP) were applied and quantitative real-time PCR (qRTPCR) and Western blot were performed to detect the mRNA and protein expression levels in A2780/DDP and A2780. Loss-of-function assays were performed to analyze the effects of STAT3 and Twsit1 knockdown on the sensitivity of ovarian cancer cells to DDP and the underlying molecular mechanisms in vitro. We found that the activity of STAT3 could contribute to the expression of Twist1 in mRNA and protein levels and further regulate the occurrence of EMT. Furthermore, knocking down the expression of STAT3 could significantly reverse Cisplatin resistance in A2780/DDP cells. However, knocking down Twist1 expression barely affects the expression of STAT3 and simply regulates the process of EMT and sensitivity to Cisplatin. In conclusion, the results suggest that STAT3-mediated Twist1 upregulation may contribute to Cisplatin resistance in epithelial ovarian cancer via EMT, which provides support for a potential novel gene therapy targeting the STAT3-Twist1 signaling network for the treatment of cisplatin resistant ovarian cancer.
Machine Learning for Reliable MIMO Systems

Simulate the working evironment to test NN for Channel mapping in space, frequency, and/or time. Measure real world channels to get realistic data for verification of NN. Compare real world results to simulated results. Specifically look at effect of noise on the accuracy of the NN. Implement system to demonstrate channel mapping in real time. Enable the designed MIMO system to act as a phased array. REFERENCES
Testing the Reliability of Flexible MOX Gas Sensors under Strain

We present flexible chemo-resistive sensors based on AACVD grown tungsten trioxide (WO3) nanowires. The sensor response to gases, before and after a 50-cycle bending test, is reported. Thus, proving that reliable gas sensors, able to withstand repeated bending, have been achieved. Moreover, their integrity and durability have been tested under harsh bending conditions until break down.
Coupled Deep Learning for Multi-Modal Retrieval Student Name :

In past few years, cross-modal information retrieval has drawn much attention due to significant growth in the multimodal data. It takes one type of data as the query to retrieve relevant data of multiple modalities. For example, a user can use a text to retrieve relevant pictures or videos. Since the query and its retrieved results can be of different modalities, how to measure the content similarity between different modalities of data remains a challenge. The existing solutions try to project data from different modalities into a common latent space and then learn a independent mapping from one modality to another. In this paper, we propose a novel fully-coupled deep learning architecture that can effectively exploit the inter-modal and intra-modal associations from heterogeneous data. The proposed learning objective can capture the correlations between the cross-modal data while preserving the intra-modal relationships. We also propose a training method that uses expectation maximization for learning the mapping function from one modality to other. The proposed training method is memory efficient and large training datasets can be split into mini-batches for parameter updations.
A Removing Algorithm for Incremental Support Vector Machine Learning

The training of support vector machine is a difficult issue in classifying large-scale data set.Incremental learning is one of the solutions to the difficulty.After new samples were added to training set,the possible changes of support vector set,were analyzed and a removing algorithm based on density for incremental support vector machine learning was presented.It discarded useless samples,kept the testing accuracy and reduced the training time.Experiments show the validity of this algorithm.
An Evaluation View of an Ensemble Artefact for Decision Support using Action Design Research

This paper investigates the integration of content, context and process (CCP) into the Action Design Research (ADR) framework to account for the interplay of organisational issues in artefact design and development. The investigation is conducted through a case study in which successive ICT student teams incrementally build, over several semesters, a tailored, low cost business intelligence (BI) system as an ensemble artefact for an organisation in the not-for-profit (NFP) sector. During project development, CCP’s human-centred approach to evaluation complements ADR’s more prescribed technology-driven software testing. The integration of CCP into ADR as an evaluation view offers an holistic approach to assessing an ensemble artefact. The resultant conceptual framework is presented as a model with an explication of unexpected design and research outcomes.
Survey of Multiview Representation Learning Techniques

This study surveys state of the art methods for scalable multi-view representation learning. We compared kernel and deep neural network techniques for canonical correlation analysis (CCA) to learn representations for each view that jointly maximize total correlation, as well as split autoencoders that attempt to learn a shared representation of the views. We compared these various methods’ performance on the Wisconson X-Ray Micro Beam dataset in terms of classification accuracy and also quality of clusters in the new representation. All of our deep learning was implemented in Google’s newly released TensorFlow library, which obviated the need for explicit gradient computation.
Analysis of Tweets on 2016 US Presidential Election Candidates

Sentiment analysis is a process that focuses on the understanding of the opinions or emotions from text messages. It has been applied to many areas, from product reviews for online marketing to people’s opinions for political events. In this paper, we present a sentiment analysis of text messages extracted from the social media Twitter about the candidates of the 2016 US Presidential Election. The objective of this paper is to find how the traditional method for sentiment analysis compares to the polls and if the sentiment measures can be used as an indicator to predict who will win the election. The statistics software system R with its sentiment tool was used in our analysis. We found that Donald Trump has been leading in the sentiment measures up to the present time, immediately after the two parties’ national conventions. It appears contradicting to the recent pools that show Hilary Clinton is ahead. However, the trend in the sentiment showed Clinton is on the rise. keywords: Sentiment analysis, Sentiment measures, tweet, 2016 US presidential election
Secure Typed Programming Languages Communication

Principals are first-class values that can be used to express security policies. In the example we see that the Runtime class is parameterized by the principal pp. From principals we can build labels to enforce confidentiality (limit the principals that the value can flow to) and integrity (limit the principals that can influence the value). In the language that was defined in class we handled illegal flows due to control flow using a program counter label, which determines which variables are allowed to be written to depending on the label of the boolean that determines the direction of control flow. Since Java has exceptions, Jif needs to handle control flow due to exceptions too. Jif supports dynamic checking of labels: if we check that a label L can flow to L2 with a dynamic check, then code is allowed to let values with label L flow to values with label L2 in the body of the if.
Reduced Order vs. Discretized Lumped System Models with Absolute and Relative States for Continuum Manipulators

—A reliable, accurate, and yet simple dynamic model is important to analyze, design and control continuum manipulators. Such models should be fast, as simple as possible and user-friendly to be widely accepted by the ever-growing robotics research community. In this study, we introduce two new mod- eling methods for continuum manipulators: a general reduced-order model (ROM) and a discretized model with absolute states and Euler-Bernoulli beam segments (EBA). Additionally, a new formulation is presented for a recently introduced discretized model based on Euler-Bernoulli beam segments and relative states (EBR). The models are validated in comparison to ex- perimental results for dynamics of a STIFF-FLOP continuum appendage. Our comparison shows higher simulation accuracy (8-14% normalized error) and numerical robustness of the ROM model for a system with small number of states, and computational efﬁciency of the EBA model with near real-time performances that makes it suitable for large systems. The challenges with designing control and observation scenarios are brieﬂy discussed in the end.
Logic Synthesis for Hybrid CMOS-ReRAM Sequential Circuits

Resistive Random Access Memory (ReRAM) is an emerging non-volatile technology with high scalability and zero standby power which allows to perform logic primitives. ReRAM crossbar arrays combined with a CMOS ubstrate provide a wide range of benefits in logic synthesis. In this paper, we propose to exploit ReRAM in sequential circuits as it provides both required features as a computational and memory element. We propose a fully automated synthesis approach based on graph representations (i.e., BDDs and AIGs) for synthesis of sequential circuits on hybrid CMOS-ReRAM architectures. We propose an algorithm to efficiently divide the target function into two independent computational parts. This allows to merge part of the computation within a ReRAM unit and utilize its computational capabilities besides its function as a sequential element in order to minimize the CMOS overhead. Experimental results show that ReRAM allows for a significant reduction in CMOS size of up to 40.9% for BDDs with an average of 8.7% for BDDs and up to 10.1% with an average of 3.2% for AIGs.
MORPHOLOGICAL CHARACTERISATION AND PRINCIPAL COMPONENT ANALYSIS OF BODY DIMENSIONS IN NIGERIAN POPULATION OF ADULT RABBITS

Body weight and linear body measurements were taken on 139 adult rabbits belonging to a composite population in south-western Nigeria. The objectives of this study were to define the body dimensions of adult heterogeneous rabbits and investigate the morphological variables that contribute to body conformation by the use of principal component analysis. Variables measured included body length, heart girth, ear length, ear width, tail length, face length, fore-arm length and shoulder length, thigh length and thigh circumference. Data were subjected to multivariate analysis using PAST. Mean body weight of all animals was 1880 g with a range of 1500 to 2800 g indicating wide variability. In all, four principal components accounted for 86.85% of total variation in morphological traits for this rabbit population. Variables that make up the first principal component (PC1) which accounted for 55 % of the total variance are heart girth, hind-leg length and thigh length. The second PC (PC2) accounted for 13.74% of total variance included hearth girth, fore-arm length and hind-leg length, while PC3 included body length, fore-arm length and the thigh length and accounted for 10.70 %. Finally, this rabbit population showed no sexual dimorphism between the two sexes.
Auto-Generated User Profile Schemas as a Lens for Understanding Product Experience Trajectories on Reddit

Product reviews offer a spectrum of snap-shot perspectives on product experiences. In contrast, product oriented discussion forums, such as found on Reddit, offer the possibility to examine reactions to products in the context of a user’s posting history. Examination of typical product usage trajectories affords extraction of more nuanced understandings of user experiences that connect opinions on products with features of users or situations in which products are used. This paper presents a computational modeling approach to construction of user proﬁles that enable construction of structured representations of user experiences with products over time. Using this approach it is possible to identify types of experience trajectories and how they are differentially associated with characteristics of users.
Modeling for Style Description of Shape Design

In this paper,Style is looked as a breakthrough of reveal the relation of perception requirement of user and design elements.A systematic method is used to analysis the connotation of style,including the functions of style,the structure of style and components of style,which are the theory bases of style description.Based on the idea of kansei design,maps of different spaces in style design are constructed.With application of the kansei engineering,fuzzy set theory and semantic diffe￣rence method,the process of modeling of style description and framework of style description are discussed.The mathematic(express) of style,evaluation algorithm of class style and subclass style,calculation of similarity of different style and that of(identification) of product style are proposed.The style class and operation by the object oriented method are presented,the(application) paradigm of style description model in the process shape design for style is discussed.
Study on the diagnostic value of various volume-adjusted PSA derivatives in prostate cancer in South Chinese populations

Objective: We aim to investigate the differential value of volume-adjusted prostate specific antigen (PSA) derivatives in the diagnosis of prostate cancer. Method: A retrospective study was performed among 374 cases that needed to receive prostate biopsy procedure in our hospital from 2014.1. 1 to 2015.10. 31. All the patients received a 10-core systemic biopsy procedure with the trans-peritoneal access guided by transrectal ultrasound. Clinical parameters were documented, including PSA, prostate volume, pathological results, etc. Patients were divided into several subgroups, according to different PSA level and transrectal ultrasound findings. The diagnosis of prostate cancer was determined by the pathological evaluation of the biopsy specimen. Result: In the 374 cases, 164 cases (43.9%) were prostate cancer. 154 cases (41.2%) had visually suspicious lesion detected by transrectal ultrasound. 136 cases (36.4%) had a serum level of PSA ≤ 10 ng/ml. Transition zone PSA density showed a better diagnostic efficacy than PSA density and peripheral zone PSA density did in all subgroups, especially in PSA grey-zone (conventionally, 4-10 ng/ml). Besides, transition zone PSA density was elevated consistently with the rise of the grade groups of Gleason score. Conclusion: Transition zone PSA density can be used as a complementary prostate specific antigen derivative for the detection of prostate cancer, and therefore it may help to reduce the incidence of the unnecessary biopsy. In addition, it is a potential predictor for the prognosis of prostate cancer, to some extent.
Resistance improvement of rubberwood treated with zinc oxide nanoparticles and phenolic resin against white-rot fungi, Pycnoporus sanguineus

Phenolic resin or phenol formaldehyde (PF) resin containing different percentage of zinc oxide (ZnO) nanoparticles was prepared and used to treat rubberwood. Three types of treatment solutions were prepared, namely (1) low molecular weight phenol formaldehyde resin (LMwPF), (2) 1.5 wt % nano ZnO dissolved in water (ZnO/H2O), and (3) combination of both LMwPF and 1.5 wt % nano ZnO (LMwPF/ZnO). The rubberwood samples were submerged into the treatment solutions for 60, 90, and 120 min, before vacuum impregnation. The untreated rubberwood samples served as the controlled samples. The thermal stability behaviour and resistance against white-rot fungi (Pycnoporus sanguineus) of the treated rubberwood samples were evaluated. The results reveal that the treated rubberwood had slightly better thermal stability compared to the untreated samples. In terms of decay resistance, the rubberwood treated with LMwPF and LMwPF/ZnO possess very high resistance against white-rot fungi. On the other hand, the rubberwood treated with ZnO/H2O did not attain similar effectiveness as the other two treatments, except for the samples that were submerged in ZnO/H2O for 120 min. The results indicate that 1.5 wt % nano ZnO could be sufficient in imparting superior durability to rubberwood provided that longer submersion time is adopted.
Compressive Properties of Aluminum Matrix Syntactic Foams Prepared by Stir Casting Method

Aluminum matrix syntactic foams with the density of 1.68–2.05 g cm−3 are prepared by the stir casting method. Al2O3 hollow spheres with a volume fraction of 38–48% are chosen as reinforcement and their diameters had three ranges. Ca particles increased melt viscosity to ensure uniform dispersion and good interfacial wettability of the hollow spheres in matrixes. Quasi‐static compression tests are performed. The results show that the mechanical properties and energy absorption capacity increased with decreasing size of the hollow spheres and increasing density of syntactic foams. For the same size hollow spheres and the same density gradient, the T6‐ZL111 matrix syntactic foams had the highest properties, while the cpAl matrix syntactic foams had the lowest values. Macroscopically, relative to plastic collapse, brittle fracture of syntactic foams dominated in compression process. The microscopic deformation and failure mechanism related to matrix plasticity, heat treatment, and density of syntactic foams.
The effect of particle aspect ratio on spatially and angularly resolved vis-NIR spectroscopy of suspensions

The particle characteristics in a suspension affect the performance and quality of the end product of many chemical industries. The shape of the suspended particles can be influenced by changes in the manufacturing process conditions. Thus, there is a need for a robust method for continuous monitoring of particle characteristics through the process. This study investigates the feasibility of using spatially and angularly resolved diffuse reflectance measurements as a method of determining particle shape. A forward calculation was developed using the discrete dipole approximation to estimate optical properties of the single particle and the diffuse approximation for the reflectance of the particle suspension. The method was used to study aqueous suspensions of randomly-oriented polystyrene ellipsoids. Our objectives were to determine and elucidate the contribution of aspect ratio on optical measurement in vis-NIR spectra. The results suggest that the method is suitable for determining particle shape for suspensions where the particle and the solvent have significantly different optical properties. For these systems, the study suggests that diffusion reflectance measurements can be developed into an in-line method for particle shape determination.