Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of some plant extracts for standardization and anticancer activity

In recent times, the trend in cancer research is shifting towards identifying new medicines from natural resources for management of cancer. Medicinal plants such as Sthauneyaka (Taxus baccata L.) and compound formulations like Triphala ghrita, Khadirarista, Madhusnuhi rasayana, Maha triphaladya ghrita, Panchatikta guggulu ghrita are indicated in the Ayurvedic texts for management of cancer/ tumour. The anti-proliferative activities of hydro-alcoholic extracts of some standardized plant materials were screened against a panel of 14 human cancer cell lines representing different tissues (lung, pancreas, colon, cervix, oral, bladder, prostate, breast, leukaemia, etc.) through Sulforhodamine-B (SRB) assay. The findings revealed that Cedrus deodara (Roxb.) ex Lamb. and Berberis aristata (Roxb.) ex DC. have maximum anticancer activity against 3 cell lines while Withania somnifera Dunal. showed activity against two cell lines. In addition to these, Picrorhiza kurroa Royle ex Benth. and Piper longum L. were found active against only one cell line. These results indicate the potential of Ayurvedic medicinal herbs as anti-neoplastic agents mentioned in the Ayurvedic texts. However, further studies are needed for evaluating their mechanism of action and to isolate the active anticancer compounds responsible for this activity.
Electoral Preferences Prediction of the YouGov Social Network Users Based on Computational Intelligence Algorithms

: The contemporary world has witnessed technological advances, such as Online Social Networks (OSN), whose influence in almost every action of the human being is remarkable. Among the human activities most significantly impacted by OSNs are: entertainment, human relationships, education, and political activities, including those related to electoral campaigns and electoral preferences prediction. The research contribution of the current paper regards the usefulness of OSNs users generated data to predict the political context. More specifically, 25 Computational Intelligence (CI) algorithms are used to predict voting intentions on the United States primary presidential elections for 2016, taking as input the data sets generated by 1200 users of the YouGov OSN, as well as the answers they gave to an online study run by the American National Election Studies (ANES). The application of the 25 supervised classification algorithms is done over the Waikato Environment for Knowledge Analysis (WEKA), using a stratified 5-fold cross validation scheme. Also, the experimental results obtained were validated in order to identify significant differences in performance by mean of a non-parametric statistical test (the Friedman test), and a post-hoc test (the Holm test). The hypothesis testing analysis of the experimental results indicates that predicting voting intentions in favour of a democrat or republican candidate is simpler than predicting the particular candidate, given that the prediction performances for a democrat or republican candidate (best performances of 80% and 78%, respectively) are better than those given when predicting a specific candidate (70% for democrat candidates and 56% for republican candidates).
A New Optimization Technique for the Inspector-Executor Method

This paper presents our HPF compiler using our modified inspector-executor method for implementing accesses to a distributed array. In our modified method, a compiler runs an inspector during compile time to obtain the information of data dependency among node processors, and it uses that information to optimize communication code included in the executor. This paper presents our idea, performance improvement shown by our prototype compiler, and limitations of our method.
Cell-Type Specific Oxytocin Gene Expression from AAV Delivered Promoter Deletion Constructs into the Rat Supraoptic Nucleus in vivo

The magnocellular neurons (MCNs) in the hypothalamus selectively express either oxytocin (OXT) or vasopressin (AVP) neuropeptide genes, a property that defines their phenotypes. Here we examine the molecular basis of this selectivity in the OXT MCNs by stereotaxic microinjections of adeno-associated virus (AAV) vectors that contain various OXT gene promoter deletion constructs using EGFP as the reporter into the rat supraoptic nucleus (SON). Two weeks following injection of the AAVs, immunohistochemical assays of EGFP expression from these constructs were done to determine whether the EGFP reporter co-localizes with either the OXT- or AVP-immunoreactivity in the MCNs. The results show that the key elements in the OT gene promoter that regulate the cell-type specific expression the SON are located −216 to −100 bp upstream of the transcription start site. We hypothesize that within this 116 bp domain a repressor exists that inhibits expression specifically in AVP MCNs, thereby leading to the cell-type specific expression of the OXT gene only in the OXT MCNs.
Protective Effect of Tempol against Cisplatin-Induced Ototoxicity

One of the major adverse effects of cisplatin chemotherapy is hearing loss. Cisplatin-induced ototoxicity hampers treatment because it often necessitates dose reduction, which decreases cisplatin efficacy. This study was performed to investigate the effect of Tempol on cisplatin-induced ototoxicity in an auditory cell line, House Ear Institute-Organ of Corti 1 (HEI-OC1). Cultured HEI-OC1 cells were exposed to 30 μM cisplatin for 24 h with or without a 2 h pre-treatment with Tempol. Cell viability was determined using 3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyltetrazolium bromide (MTT) assay and apoptotic cells were identified using terminal deoxynucleotidyl transferase dUTP nick end labeling of nuclei (TUNEL) assay and flow cytometry. The effects of Tempol on cisplatin-induced cleaved poly(ADP-ribose) polymerase, cleaved caspase, and mitochondrial inducible nitric oxide synthase expression were evaluated using western blot analysis. Levels of intracellular reactive oxygen species (ROS) were measured to assess the effects of Tempol on cisplatin-induced ROS accumulation. Mitochondria were evaluated by confocal microscopy, and the mitochondrial membrane potential was measured to investigate whether Tempol protected against cisplatin-induced mitochondrial dysfunction. Cisplatin treatment decreased cell viability, and increased apoptotic features and markers, ROS accumulation, and mitochondrial dysfunction. Tempol pre-treatment before cisplatin exposure significantly inhibited all these cisplatin-induced effects. These results demonstrate that Tempol inhibits cisplatin-induced cytotoxicity in HEI-OC1, and could play a preventive role against cisplatin-induced ototoxicity.
Nanoscale Feature Composite: An Ensemble Surface for Enhancing Cardiovascular Implant Endothelialization

The establishment and maintenance of functional endothelial cells (ECs) on an engineered surface is central to tissue engineering. As the field advances, the role of cellular mechanisms, particularly the adhesive interaction between the surface of implantable devices and biological systems, becomes more relevant in both research and clinical practice. Knowledge of these interactions can address many fundamental biological questions and would provide key design parameters for medical implants. It has been shown that EC functionality and adhesivity, crucial for the re-endothelialization process, can be induced by nanotopographical modification. Therefore, the goal of this dissertation research was to develop an ensemble surface composing of nanoscale features for the enhancement of endothelial cell adhesion. Without adhesion, subsequent vital mechanism involved in cell alignment, elongation or spreading, proliferation, migration, and ECM proteins deposition will not occur. Experiments in support of this goal were broken down into three specific aims. The first aim was to characterize and develop a size-dependent self-assembly (SDSA) nanoarray of Octamer transcription factor 4 as a demonstration to the fabrication of nanoscale feature surface. This nanoparticle array platform was a pilot studied for the second aim, which was the development of an ensemble surface of nanoscale features for endothelial cell adhesion. The third aim was to evaluate and assess EC response to the ensemble surface.
Deformable boundary finding in medical images by integrating gradient and region information

Accurately segmenting and quantifying structures is a key issue in biomedical image analysis. The two conventional methods of image segmentation, region-based segmentation, and boundary finding, often suffer from a variety of limitations. Here the authors propose a method which endeavors to integrate the two approaches in an effort to form a unified approach that is robust to noise and poor initialization. The authors' approach uses Green's theorem to derive the boundary of a homogeneous region-classified area in the image and integrates this with a gray level gradient-based boundary finder. This combines the perceptual notions of edge/shape information with gray level homogeneity. A number of experiments were performed both on synthetic and real medical images of the brain and heart to evaluate the new approach, and it is shown that the integrated method typically performs better when compared to conventional gradient-based deformable boundary finding. Further, this method yields these improvements with little increase in computational overhead, an advantage derived from the application of the Green's theorem.
Nonlinear inversion for estimating reservoir parameters from time-lapse seismic data

Saturation and pore pressure changes within a reservoir can be estimated by a history matching process based on production data. If time-lapse seismic data are available, the same parameters might be estimated directly from the seismic data as well. There are several ways to combine these data sources for estimating these reservoir parameters. In this work, we formulate a nonlinear inversion scheme to estimate pressure and saturation changes from time-lapse seismic data. We believe that such a formulation will enable us to include seismic data in the reservoir simulator in an efficient way, by including a second term in the leastsquares objective function. A nonlinear Gauss–Newton inversion method is tested on a 2D synthetic dataset inspired by a field offshore from Norway. A conventional reservoir simulator has been used to produce saturation and pore pressure changes as a function of production time. A rock physics model converts these data into synthetic time-lapse seismic data. Finally, the synthetic time-lapse data are used to test the derived inversion algorithm. We find that the inversion results are strongly dependent on the input model, and this is expected since we are dealing with an ill-posed inversion problem. Since we estimate pressure and saturation change for each grid cell in the reservoir model, the number of model parameters is high, and therefore the problem is undetermined. From testing, using this particular dataset, we assume neither pressure nor saturation changes for the initial model. Although uncertainties associated with the proposed method are high, we think this might be a useful tool, since there are ways to reduce the number of model parameters and constrain the objective function by including production data and reservoir simulation data into this algorithm.
Hypoxia-Inducible Factors Modulate the Stemness and Malignancy of Colon Cancer Cells by Playing Opposite Roles in Canonical Wnt Signaling

This study examined the role played by hypoxia-inducible factors (HIFs) in malignant phenotype maintenance and canonical Wnt signaling. Under normoxia, we determined that both HIF-1α and HIF-2α are expressed in human colon cancer cells but not in their non-malignant counterparts. The stable knockdown of HIF-1α or HIF-2α expression induced negative effects on the malignant phenotype of colon cancer cells, with lactate production, the rate of apoptosis, migration, CXCR4-mediated chemotaxis, and tumorigenic activity all being significantly affected by HIF knockdown and with HIF-1α depletion exerting greater effects. Knockdown of these two HIF transcripts induced different and even opposite effects on β-catenin transcriptional activity in colon cancer cells with different genetic Wnt signaling pathways. In SW480 cells, HIF-2α knockdown did not affect β-catenin levels, increasing the transcriptional activity of β-catenin by inducing its nuclear accumulation, whereas HIF-1α silencing negatively affected the stability and transcriptional activity of β-catenin, inducing its exit from the nuclei and its recruitment to the cell membrane by E-cadherin. In addition, although HIF-1α depletion induced a reversal of the epithelial-to-mesenchymal transition (EMT), HIF-2α silencing altered the expression of the stem cell markers CD44, Oct4, and CD24 and of the differentiation marker CK20 in the opposite direction as HIF-1α silencing. Remarkably, HIF-2α knockdown also enhanced β-catenin transcriptional activity under hypoxia in cells that displayed normal Wnt signaling, suggesting that the gene negatively modulates canonical Wnt signaling in colon cancer cells. Taken together, our results indicate that HIFs play opposing roles in canonical Wnt signaling and are essential for the stemness and malignancy maintenance of colon cancer cells.
Synonym-Based Expansion and Boosting-Based Re-Ranking: A Two-phase Approach for Genomic Information Retrieval

We describe in this paper the design and evaluation of the system built at Simon Fraser University for the TREC 2005 adhoc retrieval task in the Genomics track. The main approach taken in our system was to expand synonyms by exploiting a fusion of a set of biomedical and general ontology sources, and apply machine learning and natural language processing techniques to re-rank retrieved documents. In our system, we integrated EntrezGene, HUGO, Eugenes, ARGH, GO, MeSH, UMLSKS and WordNet into a large reference database and then used a conventional Information Retrieval (IR) toolkit, the Lemur toolkit (Lemur, 2005), to build an IR system. In the postprocessing phase, we applied a boosting algorithm (Kudo and Matsumoto, 2004) that captured natural language substructures embedded in texts to re-rank the retrieved documents. Experimental results show that the boosting algorithm worked well in cases where a conventional IR system performs poorly, but this re-ranking approach was not robust enough when applied to broad coverage task typically associated with IR.
Efficient cache use for stencil operations on structured discretization grids

We derive tight bounds on the cache misses for evaluation of explicit stencil operators on structured grids. Our lower bound is based on the isoperimetrical property of the discrete octahedron. Our upper bound is based on a good surface to volume ratio of a parallelepiped spanned by a reduced basis of the interference lattice of a grid. Measurements show that our algorithm typically reduces the number of cache misses by a factor of three, relative to a compiler optimized code. We show that stencil calculations on grids whose interference lattice have a short vector feature abnormally high numbers of cache misses. We call such grids unfavorable and suggest to avoid these in computations by appropriate padding. By direct measurements on a MIPS R10000 processor we show a good correlation between abnormally high numbers of cache misses and unfavorable three-dimensional grids.
Applying slicing technique to software architectures

Software architecture is receiving increasing attention as a critical level for software systems. As software architecture design resources (in the form of architectural specifications) are going to be accumulated, the development of techniques and tools to support architectural understanding, testing, reengineering, maintenance, and reuse will become an important issue. This paper introduces a new form of slicing, named architectural slicing, to aid architectural understanding and reuse. In contrast to traditional slicing, architectural slicing is designed to operate on the architectural specification of a software system, rather than the source code of a program. Architectural slicing provides knowledge about the high-level structure of a software system, rather than the low-level implementation details of a program. In order to compute an architectural slice, we present the architecture information flow graph which can be used to represent information flows in a software architecture. Based on the graph, we give a two-phase algorithm to compute an architectural slice.
Haptic Designation Tool to Improve Working Strategy in Collaborative Virtual Environment

Coordinating team's work in a 3D Collaborative Virtual Environments is a real challenge. The communication constraints in such environments reduces the understanding between the partner's and thus complicates the team's coordintation. In order to facilitate the coordination, this paper proposes a hap tic tool for the designation of targets. This tool was experimented in a context of collaborative molecular design where one user designates tasks and coordinates the actions of two operators. The experimental results show an improvement of the working efficiency of the group and a better sharing of the activity between partners. Moreover, we observe that the hap tic guidance enables an accurate selection of targets which leads to an improvement of performance during the deformation process.
Consistency of trace norm minimization

Regularization by the sum of singular values, also referred to as the trace norm, is a popular technique for estimating low rank rectangular matrices. In this paper, we extend some of the consistency results of the Lasso to provide necessary and sufficient conditions for rank consistency of trace norm minimization with the square loss. We also provide an adaptive version that is rank consistent even when the necessary condition for the non adaptive version is not fulfilled.
The Puzzle of Declining Birth Weight

The recent trends of declining birth weight in the United States are alarming policy makers as well as medical professionals. The trends are puzzling since most factors affecting birth weight have changed in favor of increasing it. We find that the changes in the use of labor induction and Cesarean section explain not only the trends of declining birth weight from 1989 to the mid-2000s, but also the recent trends of increasing birth weight. The changes in the method of delivery also account for the trends of other birth outcomes, such as low birth weight, Apgar score, and macrosomia. We investigate possible reasons for the changes in obstetric interventions.
Predator scent induces differing responses in two sympatric macropodids

When prey species encounter the scent of a predator they must make a decision on how to respond. This may be either to ignore, flee, hide or alarm call. While many species are able to derive detailed information from the chemical cues associated with predator scent, for some the decision to respond is often made without being able to identify the actual location and intentions of the predator. Depending on the sociality and ecology of the species, it may pay to flee or to engage in predator inspection where knowledge is impure. We tested for this in two sympatric marsupial macropodids, the parma wallaby (Macropus parma) and the red-necked pademelon (Thylogale thetis), as little is known of how these species detect and respond to olfactory cues of predation risk. We observed that, when presented with a synthetic predator scent mimicking dog urine, the social forager, T. thetis, tended to spend more time close to the predator odour, while the solitary forager, M. parma, exhibited an aversive response. The results suggest that social and ecological constraints on the sensory modalities used in predator detection may influence how macropodids respond to olfactory predator cues.
ToF-SIMS characterization of modified starch on fine and LWC base papers

The surface chemistry of starch-coated fine and LWC papers was studied using Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS). The starch was modified by varying the degree of acetylation and cationization and spread onto paper surfaces by spraying or bar coating. ToF-SIMS imaging showed a substantially more uniform coating layer of starch when bar coating was used. The spatial distributions of acetyl and quaternary nitrogen functional groups were also assessed. A direct relation between the theoretical cationization/acetylation ratio and the peak ratios obtained by ToF-SIMS indicated that the starch characteristics can significantly influence the surface chemistry of paper. Furthermore, some benefits and drawbacks of this novel characterization technique, especially with regard to quantification of the spectral information, are discussed.
Recommender System for Online Dating Service

Users of online dating sites are facing information overload that requires them to manually construct queries and browse huge amount of matching user profiles. This becomes even more problematic for multimedia profiles. Although matchmaking is frequently cited as a typical application for recommender systems, there is a surprising lack of work published in this area. In this paper we describe a recommender system we implemented and perform a quantitative comparison of two collaborative filtering (CF) and two global algorithms. Results show that collaborative filtering recommenders significantly outperform global algorithms that are currently used by dating sites. A blind experiment with real users also confirmed that users prefer CF based recommendations to global popularity recommendations. Recommender systems show a great potential for online dating where they could improve the value of the service to users and improve monetization of the service.
Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks

Plant phenomics has received increasing interest in recent years in an attempt to bridge the genotype-to-phenotype knowledge gap. There is a need for expanded high-throughput phenotyping capabilities to keep up with an increasing amount of data from high-dimensional imaging sensors and the desire to measure more complex phenotypic traits (Knecht et al., 2016). In this paper, we introduce an open-source deep learning tool called Deep Plant Phenomics. This tool provides pre-trained neural networks for several common plant phenotyping tasks, as well as an easy platform that can be used by plant scientists to train models for their own phenotyping applications. We report performance results on three plant phenotyping benchmarks from the literature, including state of the art performance on leaf counting, as well as the first published results for the mutant classification and age regression tasks for Arabidopsis thaliana.
Theoretical Studies of [2,3]-Sigmatropic Rearrangements of Allylic Selenoxides and Selenimides

Density-functional theory is used to model the endo and exo transition states for [2,3]-sigmatropic rearrangement of allylic aryl-selenoxides and -selenimides. The endo transition state is generally preferred for selenoxides if there is no substitution at the 2 position of the allyl group. Based upon the relative energies of the endo and exo transition states, enantioselectivity of rearrangements is expected to be greatest for molecules with substitutions at the 1- or (E)-3- position of the allyl group. Ortho substitution of a nitro group on the ancillary selenoxide phenyl ring reduces the activation barriers, increases the difference between the endo and exo activation barriers and shifts the equilibrium toward products.