Miracle-dz/UniFAR-FTUs huggingface dataset:
26 27 2 Nur 77 modulates hepatic lipid metabolism through suppression of SREBP-1 c activity

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Evaluation of Organic Fertilizer Potentials of Different Forms of Siam Weed ( Chromolaena odorata ) Phyto-residues for Improved Performance of Late Season Maize ( Zea mays ) under Guinea Savanna Ecoregion of Nigeria

Application of different organic residues to soils as amendments in diverse forms is a common agronomic practice, especially nowadays that the awareness on the needs for organic agriculture in the tropics is meaningfully increasing. However, there is dearth of information about the best form or nature of applying these phyto-residues which will ensure maximum utilization of their nutrients by crop plants. Field experiment was conducted during the late cropping season of the year 2015, at the Teaching and Research Farms, Ladoke Akintola University of Technology (LAUTECH), Ogbomoso, in Oyo state, Nigeria to evaluate different forms of siam weed (Chromolaena odorata) biomass for improved performance of maize (Zea mays). It was a split plot experiment with three (3) different forms of chromolaena biomass (composted, green and dried), as the main plot, while five (5) different rates (0.0., 1.5, 3.0, 4.0 and 6.0 tons ha-1) were the sub plots. For referencing, NPK fertilizer application at the recommended rate of 400 Kg ha-1 was also included as a treatment. The experiment was laid out in Randomized Complete Block Design (RCBD), replicated thrice. Data collected on growth and yield parameters were subjected to analysis of variance (ANOVA), at p<0.05. Forms of application of chromolaena biomass significantly influenced growth, yield and nutrient uptakes of maize, which increased significantly with increasing rates of application till 4.0 tons ha-1, comparable to the control. Significantly higher grain and total dry biomass yields of 5.5 and 11.10 tons ha-1 were obtained from application of composted chromolaena phyto-residues at 4.0 tons ha-1, comparable to dried chromolaena application and the control. Two distinctive patterns or orders were observed in terms of maize growth / developmental responses to the applied treatments. The growth parameters followed the order; NPK fertilizer > green chromolaena > composted chromolaena > dried chromolaena > control, while the yield parameters followed the order; composted chromolaena ≥ green chromolaena > NPK fertilizer > dried chromolaena > control. Thus, 4.0 tons ha-1 of chromolaena biomass, which could be applied as either green or composted plant residues, is therefore recommended in the study area.
Title Identification of novel common breast cancer risk variants at the 6 q 25 locusamong Latinas Permalink

Background: Breast cancer is a partially heritable trait and genome-wide association studies (GWAS) have identified over 180 common genetic variants associated with breast cancer. We have previously performed breast cancer GWAS in Latinas and identified a strongly protective single nucleotide polymorphism (SNP) at 6q25, with the protective minor allele originating from indigenous American ancestry. Here we report on fine mapping of the 6q25 locus in an expanded sample of Latinas. Methods: We performed GWAS in 2385 cases and 6416 controls who were either US Latinas or Mexican women. We replicated the top SNPs in 2412 cases and 1620 controls of US Latina, Mexican, and Colombian women. In addition, we validated the top novel variants in studies of African, Asian and European ancestry. In each dataset we used logistic regression models to test the association between SNPs and breast cancer risk and corrected for genetic ancestry using either principal components or genetic ancestry inferred from ancestry informative markers using a model-based approach. Results: We identified a novel set of SNPs at the 6q25 locus associated with genome-wide levels of significance (p = 3.3 × 10 8 6.0 × 10 ) not in linkage disequilibrium (LD) with variants previously reported at this locus. These SNPs were in high LD (r > 0.9) with each other, with the top SNP, rs3778609, associated with breast cancer with an odds ratio (OR) and 95% confidence interval (95% CI) of 0.76 (0.70–0.84). In a replication in women of Latin American origin, we also observed a consistent effect (OR 0.88; 95% CI 0.78–0. 99; p = 0.037). We also performed a meta-analysis of these SNPs in East Asians, African ancestry and European ancestry populations and also observed a consistent effect (rs3778609, OR 0.95; 95% CI 0.91–0.97; p = 0.0017). Conclusion: Our study adds to evidence about the importance of the 6q25 locus for breast cancer susceptibility. Our finding also highlights the utility of performing additional searches for genetic variants for breast cancer in non-European populations.
Control for JavaScript

Providing security guarantees for systems built out of untrusted components requires the ability to define and enforce access control policies over untrusted code. In Web 2.0 applications, JavaScript code from different origins is often combined on a single page, leading to well-known vulnerabilities. We present a security infrastructure which allows users and content providers to specify access control policies over subsets of a JavaScript program by leveraging the concept of delimited histories with revocation. We implement our proposal in WebKit and evaluate it with three policies on 50 widely used websites with no changes to their JavaScript code and report performance overheads and violations.
Impact of Training Set Batch Size on the Performance of Convolutional Neural Networks for Diverse Datasets

Abstract A problem of improving the performance of convolutional neural networks is considered. A parameter of the training set is investigated. The parameter is the batch size. The goal is to find an impact of training set batch size on the performance. To get consistent results, diverse datasets are used. They areMNIST and CIFAR-10. Simplicity of the MNIST dataset stands against complexity of the CIFAR-10 dataset, although the simpler dataset has 10 classes as well as the more complicated one. To achieve acceptable testing results, various convolutional neural network architectures are selected for the MNIST and CIFAR-10 datasets, with two and five convolutional layers, respectively. The assumption about the dependence of the recognition accuracy on the batch size value is confirmed: the larger the batch size value, the higher the recognition accuracy. Another assumption about the impact of the type of the batch size value on the CNN performance is not confirmed.
HUMAN FACE AND EXPRESSION RECOGNITION WITH KERNEL FISHER ANALYSIS

Human Face recognition is a exigent task in computer vision and pattern detection Face recognition is thorny because it is a real world problem. The Human Face is multifarious, natural object that tends not to have easily acknowledged features and edges. Because of this, it is decisive to develop a mathematical model of that face that can be used as prior knowledge when analyzing a particular image. Kernel fisher Analysis (KFA) method is used to improve the performance of the face reorganization systems. There are other methods PCA, KPCA, KCA, and LDA which also used to improve the performance of the face recognition system. So in this paper we are going to review the different methods and techniques used to for the face and emotion identification.
Numerical simulation of seismic cycles at a subduction zone with a laboratory-derived friction law

We review recent studies of numerical modeling of seismic cycles at subduction zones in Japan. Laboratory-derived rateand state-dependent friction laws are most useful for modeling the entire seismic cycles including aseismic sliding and healing. Among several versions of the rateand state-dependent law, the composite law better fits experimental observations for a wide range of conditions and is most apropriate for application to modeling of seismic cycles. Models for the Tokai earthquake, which is expected to occur in a seismic gap along the Suruga trough, central Japan, predict that accelerating preseismic sliding occurs. We propose a model for aseismic episodic slip events, which have been detected around the Japanese islands with GPS observations.
Factors Attributing to the Service Quality of Railway Station in Bangladesh

This study conducted stated preference survey among 800 passengers to identify the key attributing factors regarding service quality of Airport Railway Station, Dhaka. Based on weighted average scores and factor ranks from 8 factors for station and 13 factors for train interior facility, overall rating was found to be poor (3.04 and 2.93 inside station and inside train respectively). Observed ratings depended significantly on ticketing time, station security and information availability. Toilet facility (2.68) and journey time (2.03) were marked as the worst inside station and train respectively. The outcomes and recommendations of this paper can be used to reframe railway service quality structure to demonstrate it as user friendly mode of transportation in developing countries.
Trajectory Topic Modelling to Characterize Driving Behaviors with GPS-Based Trajectory Data

The rapid accumulation of large-scale driving data represents an opportunity to improve our understanding of driving behavior patterns and driver traveling intentions. However, limited efforts have been devoted to understanding these patterns and the travel intentions behind them. This study proposes a new trajectory topic model (TTM) to explore latent driving patterns from driving trajectory data and to qualitatively analyze drivers’ main traveling intentions. These trajectory data were collected from more than 150,000 commercial vehicles in Fujian Province, China. After data preprocessing, the TTM was then established to decompose trajectory data into various topics with corresponding probabilities, which were correlated to drivers’ preferences. Several experiments conducted in Fuzhou City were performed to evaluate the feasibility and efficiency of the TTM using a real trajectory dataset. The results show that the TTM could effectively mine users’ driving behavior patterns with topic probability. The model would enable us to understand the context in which drivers travel and learn their individual preferences. It is also beneficial in that it can predict drivers’ behaviors, analyze traffic patterns in an entire city, and even help autonomous vehicles to learn from drivers.
SpatialNet: A Declarative Resource for Spatial Relations

This paper introduces SpatialNet, a novel resource which links linguistic expressions to actual spatial configurations. SpatialNet is based on FrameNet (Ruppenhofer et al., 2016) and VigNet (Coyne et al., 2011), two resources which use frame semantics to encode lexical meaning. SpatialNet uses a deep semantic representation of spatial relations to provide a formal description of how a language expresses spatial information. This formal representation of the lexical semantics of spatial language also provides a consistent way to represent spatial meaning across multiple languages. In this paper, we describe the structure of SpatialNet, with examples from English and German. We also show how SpatialNet can be combined with other existing NLP tools to create a text-to-scene system for a language.
Sentence Unscrambler : Exploring Deep Learning Models for Word Linearization

Linearization is the task of finding a grammatical order for a given bag of words. This task has versatile applications in the fields of NLP and linguistics, especially in the realms of machine and human translation. In this paper we investigate methods for improving upon the syntax-free linearization model designed by Schmaltz, Rush, and Sheiber (2016). We improve the model of Schmaltz et al. by elaborating on the model’s simple LSTM, adding several encoder mechanisms and a self-attention system. While our baseline model yielded a BLEU score of 20.4 on our NLTK Dataset, we were able to achieve a BLEU score of 25.1 using a 3-convolutional-layer CNN encoder.
Effects of Exogenous PACAP in Small Bowel Ischemia – Reperfusion Injury in Rats

Pituitary adenylate cyclase activating polypeptide (PACAP) is an endogenous neuropeptide widely distributed throughout the body, including the gastrointestinal tract. Several effects have been described in human and animal intestines. Among others, PACAP infl uences secretion of intestinal glands, blood fl ow, and smooth muscle contraction. PACAP is a well-known cytoprotective peptide with strong anti-apoptotic, anti-infl ammatory, and antioxidant effects. The present review gives an overview of the intestinal protective actions of this neuropeptide. Exogenous PACAP treatment was protective in a rat model of small bowel autotransplantation. Radioimmunoassay (RIA) analysis of the intestinal tissue G. Horvath • A. Tamas • D. B. Fulop • B. Opper • D. Reglodi (*) Department of Anatomy, MTA-PTE “Lendulet” PACAP Research Group , University of Pecs , Pecs , Hungary e-mail: dora.reglodi@aok.pte. hu A. Illes Department of Anatomy, MTA-PTE “Lendulet” PACAP Research Group , University of Pecs , Pecs , Hungary Department of Internal Medicine , University of Pecs , Pecs , Hungary M. M. Heimesaat Department of Microbiology and Hygiene , Charite-University Medicine , Berlin , Germany A. Bardosi • S. Bardosi Jakob Henle Institute for Pathology and Neuropathology , Limbach Group , Heidelberg , Germany J. Nemeth Department of Pharmacology and Pharmacotherapy , University of Debrecen , Debrecen , Hungary A. Ferencz Department of Surgical Research and Techniques , Semmelweis University , Budapest , Hungary Andrea Ferencz and Dora Reglodi contributed equally
Performance Comparisons of Basic and Advanced Neighbor Discovery Processes in BLE 4 . 2 and 5 . 0 Specifications

BLE (Bluetooth Low Energy), as one of the most promising technology, has been widely adapted in various IoT (Internet of Things) services. NDP (Neighbor Discovery Process) in BLE networks plays an important role for BLE-based IoT services, which affects the scanning performance and energy consumption directly. Since basic NDP defined in BLE 4.0 specification utilizes only three channels, as the density of BLE objects increases, the possibility of signal collisions also increases and becomes very serious to the IoT services. In order to mitigate the collision probability, an advanced NDP is described in BLE 5.0 specification. In this paper, we analyze the differences between the basic and advanced neighbor discovery processes, and compare their performances by using simulation works. The simulation results show that the discovery delay of advanced NDP is shorter than half of basic NDP.
Development of a Low-cost Humanoid Robot with Neuromorphic Control System

A low-cost middle size humanoid biped robot built using 3D printing techniques and equipped with a neuromorphic control system is presented. The mechanical structure is made of polylactide (PLA), Acrylonitrile Butadiene Styrene (ABS) and aluminum. This allows to keep the overall weight under 3 Kg and still reach a sufficient structural rigidity. The joints are actuated by brush-less DC motors with integrated electronics and gearbox capable to generate up to 8Nm. Angular positions are measured by potentiometers and contact-less magnetic encoders. The control architecture consists of different modules capable to learn and autonomously reproduce complex periodic trajectories. Each module is represented by a recurrent neural network with a core of dynamic neurons randomly and sparsely connected with fixed synapses. A set of read-out units with adaptable connection realize a linear combination of the neurons output in order to reproduce the target signals.
An Accurate Magnetic Equivalent Circuit Model for Analysis of Surface Mounted Permanent Magnet Motors

This paper presents an accurate and at the same time fast technique for performance analysis of surface mounted permanent magnet motors based on magnetic equivalent circuit (MEC) model. The main drawback of the MEC is its poor modeling of air-gap. Therefore, in order to provide accurate permeances of the air-gap for inclusion in the MEC model, the conformal mapping (CM) method is used. This combined model can precisely consider the magnetic saturation and the harmonics of air-gap permeances. To consider the impact of demagnetization of permanent magnets (PMs) and core losses, PMs and core are divided into many elements, and the magnetic scalar potential is calculated for all vertices. The accuracy of this technique is proven by comparing the results obtained through this combined model with corresponding results obtained from the conventional MEC, and finite elements method.
The Optimization of NN Classification : Based on Feature Selection with Genetic Algorithm & Hidden Neuron Pruning

Feed-forward neuron networks of several layers trained by backpropagation has been used to solve plenty of problems for research from the end of last century. Researchers requires more hidden neurons for some research. However, these excessive neurons which apply no efficacy in final products are redundant after learning. Similarly, researchers could specially select effective combination of features to input neuron instead of inputting all features. Thus, some researchers suggested that excess neurons could be removed, but older methods still have some disadvantages in efficiency. In this report, author tried to implement both neuron network pruning and feature selection based on Genetic Algorithm to delete redundant neurons automatically. This report would solve dataset classification problem. As the result, superfluous neurons would be pruned with little negative effects to accuracy (about 90%). Since this report took simple method, the result performs a little worse than some papers with more
CLPsych 2019 Shared Task: Predicting the Degree of Suicide Risk in Reddit Posts

The shared task for the 2019 Workshop on Computational Linguistics and Clinical Psychology (CLPsych’19) introduced an assessment of suicide risk based on social media postings, using data from Reddit to identify users at no, low, moderate, or severe risk. Two variations of the task focused on users whose posts to the r/SuicideWatch subreddit indicated they might be at risk; a third task looked at screening users based only on their more everyday (non-SuicideWatch) posts. We received submissions from 15 different teams, and the results provide progress and insight into the value of language signal in helping to predict risk level.
Cost-benefit analysis of decision support methods for patients with breast cancer in a rural community

Methods Sixty-eight women with breast cancer who were being treated at a rural cancer resource center were randomized to CP in person or by telephone. All participants were then provided with an audio-recording of the physician consultation along with a typed summary for the full intervention (CPRS). Surveys completed by the participants and center staff provided data for measuring costs and willingness-to-pay (WTP) benefits. Societal perspective costs and incremental net benefit (INB) across delivery methods was determined.
Auto-encoding Logic Programs

Although deep learning (Goodfellow et al., 2016) has achieved a tremendous progress over the last decade, the majority of the tasks addressed focus on signal data such as images, speech and text. Ubiquitous relational data, e.g. biological and social networks, has only recently started to receive attention. Learning and reasoning with such complex data has typically been in the domain of logicbased approaches to Artificial intelligence such as Statistical relational learning (SRL) (Getoor & Taskar, 2007; de Raedt et al., 2016). Exceptions to that are knowledge graph embedding (Nickel et al., 2016) and differentiable interpreter/programmer (Rocktäschel & Riedel, 2017; Reed & de Freitas, 2016) approaches. Both lines of research focus on developing ways to represent relational data (or programs) in Euclidean spaces or distributed amongst the weights of a neural network.
Variable Density PRM Waypoint Generation and Connection Radii for Energy-Efficient Flight through Wind Fields

This paper presents a method for planning lowcost paths for an unmanned aerial vehicle (UAV) in a spatially varying wind field using a biased sampling-based path search technique. The objective of the path planning problem is to minimise the energy consumption of a UAV by finding timeefficient paths through the available flight region. A Gaussian process (GP) model of the wind field is constructed from sparse samples of the wind, and the gradient of this model over the entire sample space is used to guide the waypoint sampling density and connection radius across the flight region. For a UAV planning problem in strong winds the proposed method is demonstrated to produce paths with comparable cost to a uniformly-sampled technique whilst using fewer than one tenth of the number of nodes and edges.
Tooth Retention and Implant Placement: Developing Treatment Algorithms

Resective Therapy: Applicable Today? The Rationale for Pocket Elimination Procedures through the Use of Osseous Resective Techniques Results of Longitudinal Human Studies Clinical Example One Clinical Example Two Financial Algorithms Specific Clinical Scenarios Scenario One: The Single-Rooted Decayed Tooth Clinical Example Three Clinical Example Four Scenario Two: A Single Missing Tooth Clinical Example Five Clinical Example Six Scenario Three: Multiple Missing Adjacent Posterior Teeth Scenario Four: A Missing Maxillary First Molar, When the Second Molar Is Present Eliminating less predictable therapies through implant use Clinical Example Seven The influence of patient health on treatment plan selection: Conclusions
Evaluation of antioxidant , antimicrobial activities and phytochemical content of some Egyptian plants

This study evaluated antioxidant and antimicrobial activities of methanol extracts of Artocarpus heterophyllus, Parkia africana, Derris rubosta and Cichorium intybus stems and also phytochemical analysis of the four plants was detected. Antioxidant activity was evaluated by radical scavenging assay and phosphomolybdenic assay. Total phenolics and flavonoids were estimated by Folin-Ciocalteu reagent and aluminium chloride method. The antimicrobial screenings were carried out via disc diffusion method and minimal inhibition concentration against three species of gram negative bacteria: Escherichia coli CCM 3988, Salmonella enterica subsp. enterica CCM 3807, Yersinia enterocolitica CCM 5671 and three grampositive bacteria: Bacillus thuringiensis CCM 19, Listeria monocytogenes CCM 4699, Stapylococcus aureus subsp. aereus CCM 2461. All the plants tested had some antioxidant activity. Parkia africana had the highest antioxidant activity values of 47.99 μg TEAC/g and 1036.56 μg TEAC/g in the DPPH• and phosphomolybdenum assays respectively; along with the highest amount of total flavonoids (0.165 μg QE/g). The inhibition zones against three gram-negative strains of different tested plants were ranged from 10-24 mm. From our results the Parkia africana extract showed the strongest action against E. coli (24 mm), S. enterica subsp. enterica (21 mm) and Y. enterocolitica (22 mm). The minimum inhibitory affect of plants ranged from 16 to 256 μg/ml. The best antibacterial activity was found at Parkia africana against gramnegative bacteria strains. The results indicated that the plants tested may be potential sources for isolation of natural antioxidant and antimicrobial compounds.