Miracle-dz/UniFAR-FTUs huggingface dataset:
Deleterious SNP prediction: be mindful of your training data!

MOTIVATION To predict which of the vast number of human single nucleotide polymorphisms (SNPs) are deleterious to gene function or likely to be disease associated is an important problem, and many methods have been reported in the literature. All methods require data sets of mutations classified as 'deleterious' or 'neutral' for training and/or validation. While different workers have used different data sets there has been no study of which is best. Here, the three most commonly used data sets are analysed. We examine their contents and relate this to classifiers, with the aims of revealing the strengths and pitfalls of each data set, and recommending a best approach for future studies. RESULTS The data sets examined are shown to be substantially different in content, particularly with regard to amino acid substitutions, reflecting the different ways in which they are derived. This leads to differences in classifiers and reveals some serious pitfalls of some data sets, making them less than ideal for non-synonymous SNP prediction. AVAILABILITY Software is available on request from the authors.
Vehicle trajectories classification using Support Vectors Machines for failure trajectory prediction

The vehicles real trajectories analysis on dangerous zones is an important task to improve the road safety. The objective of this study is to provide tools for driving behaviour identification with the associated risk as regards of handling loss. This study aims to take into account the infrastructure, driver and the vehicle interactions, which are useful to evaluate this risk accurately.We propose in this paper a vehicles trajectories analysis in bend within a suitable Support Vector Machine (SVM) algorithm framework. At first, we will be interested on vehicle trajectory definition and experimental data acquisition. Then, we will make an experimental trajectories classification in order to determine several classes of trajectories. Afterwards, we will make a vehicle trajectories stability analysis in order to identify safe and unsafe fields of the observed trajectories. Lastly, one will use machine learning methods to predict failure trajectories.
A Bacterial Acetyltransferase Destroys Plant Microtubule Networks and Blocks Secretion

The eukaryotic cytoskeleton is essential for structural support and intracellular transport, and is therefore a common target of animal pathogens. However, no phytopathogenic effector has yet been demonstrated to specifically target the plant cytoskeleton. Here we show that the Pseudomonas syringae type III secreted effector HopZ1a interacts with tubulin and polymerized microtubules. We demonstrate that HopZ1a is an acetyltransferase activated by the eukaryotic co-factor phytic acid. Activated HopZ1a acetylates itself and tubulin. The conserved autoacetylation site of the YopJ / HopZ superfamily, K289, plays a critical role in both the avirulence and virulence function of HopZ1a. Furthermore, HopZ1a requires its acetyltransferase activity to cause a dramatic decrease in Arabidopsis thaliana microtubule networks, disrupt the plant secretory pathway and suppress cell wall-mediated defense. Together, this study supports the hypothesis that HopZ1a promotes virulence through cytoskeletal and secretory disruption.
Exploring Metric Sensitivity of Planners for Generation of Pareto Frontiers

This paper explores how current planners behave when exposed to multiple metrics, examining which of the planners are metric sensitive and which are not. For the metric insensitive planners we propose a new method of simulating metric sensitivity for the purpose of generation of diverse plans close to a pareto frontier. It is shown that metric sensitive planners are good candidates for generating sets of pareto optimal plans.
Tetrahedral mesh improvement using swapping and smoothing

Automatic mesh generation and adaptive refinement methods for complex three-dimensional domains have proven to be very successful tools for the efficient solution of complex applications problems. These methods can, however, produce poorly shaped elements that cause the numerical solution to be less accurate and more difficult to compute. Fortunately, the shape of the elements can be improved through several mechanisms, including face- and edge-swapping techniques, which change local connectivity, and optimization-based mesh smoothing methods, which adjust mesh point location. We consider several criteria for each of these two methods and compare the quality of several meshes obtained by using different combinations of swapping and smoothing. Computational experiments show that swapping is critical to the improvement of general mesh quality and that optimization-based smoothing is highly effective in eliminating very small and very large angles. High-quality meshes are obtained in a computationally efficient manner by using optimization-based smoothing to improve only the worst elements and a smart variant of Laplacian smoothing on the remaining elements. Based on our experiments, we offer several recommendations for the improvement of tetrahedral meshes. © 1997 John Wiley & Sons, Ltd.
SUq(2) lattice gauge theory.

We reformulate the Hamiltonian approach to lattice gauge theories such that, at the classical level, the gauge group does not act canonically, but instead as a Poisson-Lie group. At the quantum level, it then gets promoted to a quantum group gauge symmetry. The theory depends on two parameters - the deformation parameter $\lambda$ and the lattice spacing $a$. We show that the system of Kogut and Susskind is recovered when $\lambda \rightarrow 0$, while QCD is recovered in the continuum limit (for any $\lambda$). We thus have the possibility of having a two parameter regularization of QCD.
Histological and Biochemical Features of the Digestive System in the Cage-Reared Gilthead Sea Bream (Sparus aurata)

Five adult cage-reared gilthead sea bream Sparus aurata specimens were sampled to study the histological and biochemical features of its digestive system. Staining using Hematoxylin-eosin, Alcian blue and Orcein were used to elucidate the histological and biochemical features of the digestive organs in cage-reared gilthead sea bream. The digestive system is a hollow tube made of esophagus, stomach and intestines. The esophagus consists of four layers: mucosa, sub-mucosa, muscular layer and the outer layer. The esophageal epithelium contains abundant goblet cells with alkaline or neutral mucopolysaccharides (MPS) inside them. The wall of the fundic and pyloric region of the stomach is also four-layered and goblet cells of the stomach mucosa contain acid MPS. Four pyloric caeca were found between stomach and the intestines. According to its morphology the intestines of gilthead sea bream could be distinguished in three parts: anterior, middle and posterior intestines. In spite the difference in their diameter all three parts of the intestines has shown certain similarity regarding histological features. Goblet cells containing acid MPS were found in all three parts of the intestines although they are more numerous in the upper intestines. The liver in gilthead sea bream consists of two lobes. The structure of the hepatocytes is damaged due to large amounts of fat inside the cells. Pancreatic tissue is scattered down the length of the intestines as well as throughout the liver parenchyma. It contains zymogene cells arranged in serous acing. It can be concluded that the histological and biochemical of the digestive system in cage-reared gilthead sea bream is mostly like in other carnivorous fishes and congruent to its feeding habits.
Avidity maturation of memory CD8 T cells is limited by self-antigen expression

Immune tolerance to self-antigens is a complex process that utilizes multiple mechanisms working in concert to maintain homeostasis and prevent autoimmunity. We developed a system that revealed a population of self-specific CD8 T cells within the endogenous T cell repertoire. Immunization of ovalbumin (OVA)-expressing transgenic mice with recombinant viruses expressing OVA-peptide variants induced self-reactive T cells in vivo that matured into memory T cells able to respond to secondary infection. However, whereas the avidity of memory cells in normal mice increased dramatically with repeated immunizations, avidity maturation was limited for self-specific CD8 T cells. Despite decreased avidity, such memory cells afforded protection against infection, but did not induce overt autoimmunity. Further, up-regulation of self-antigen expression in dendritic cells using an inducible system promoted programmed death-1 expression, but not clonal expansion of preexisting memory cells. Thus, the self-reactive T cell repertoire is controlled by overlapping mechanisms influenced by antigen dose.
Intravitreal bevacizumab vs verteporfin photodynamic therapy for neovascular age-related macular degeneration.

OBJECTIVE To compare verteporfin photodynamic therapy (PDT) with intravitreal bevacizumab for management of choroidal neovascularization (CNV) associated with age-related macular degeneration. METHODS Patients with predominantly classic CNV were prospectively randomized to receive standard PDT or intravitreal bevacizumab injections (2.5 mg). Best-corrected visual acuity (BCVA) measured by Snellen charts, central retinal thickness by optical coherence tomography, and greatest linear dimension of the CNV by fluorescein angiography were compared between the groups at baseline and at 3 and 6 months. Main outcome measures were stability or improvement in BCVA, decrease in central retinal thickness, and stability in greatest linear dimension. RESULTS Mean baseline BCVA, central retinal thickness, and greatest linear dimension were not statistically different between the bevacizumab (n = 32) and PDT (n = 30) groups. Mean central retinal thickness was significantly better at 3 and 6 months in the bevacizumab group vs the PDT group (P = .04 and P = .002, respectively). At 3 months, mean BCVA and greatest linear dimension were not significantly different between the 2 groups. At 6 months, mean BCVA and greatest linear dimension were significantly better in the bevacizumab group (P < .001 and P = .02, respectively). CONCLUSION During 6 months, intravitreal bevacizumab was superior to PDT in controlling predominantly classic CNV in age-related macular degeneration. Additional randomized clinical trials are necessary to determine if this benefit will remain with longer follow-up.
Uma análise de dados das reações à crise política brasileira no twitter

The analysis of personal opinions on social media has become a source of interest to many types of researches, with a growing focus on platforms like Twitter, in which the aggregated chatter can be used, by data mining, as a source for opinion analysis. This paper creates a dataset based on users’ opinions about the Brazilian political crisis and reactions to the military intervention discussion via data mining from Twitter, with the data being gathered by a keyword search matching the dates of January 2014 to December 2017. The completed dataset is then used by a sentiment analysis algorithm, based on the LIWC dictionary for Brazilian Portuguese, to create data about the public opinion on the subject.
Reviews and Neighbors Influence on Performance of Business

The task of rating prediction has been gaining popularity, especially after several companies come up with competitions, such as the Netflix Challenge and the Yelp dataset challenge[12]. In this paper, we aim to modify and incorporate two methods for rating prediction of businesses, one utilizes the semantics of the review text, while the other uses the influence of the surrounding businesses. For the review text semantics, we combine unsupervised learning technique, Latent Dirichlet Allocation, with supervised learning method, such as SGDRegressor. On the other hand, we use K-D Tree implementation to find the nearest neighbors of a business and incorporate them into the feature vector representation of the business for predicting its rating. For experimentation, we use two datasets, Yelp and Google Local, and compare the performance of the different models on the two datasets. We find that semantics representation of the review text performs quite well, achieving an RMSE of 0.5985 for Yelp dataset and 0.5643 for Google dataset. Adding the neighbors influence only improves the performance by 0.3%, with an RMSE of 0.5970 for Yelp dataset and 0.5626 for Google dataset, which shows that the neighbors do not seem to influence the business’ rating as much. We also discuss the challenges faced while working with Google Local dataset because of its sheer size and incompleteness. Keywords—Yelp, Google Local, Rating prediction, LDA, K-D Tree, SGD Regressor, SVR
Beyond the productivity paradox

of output produced per unit of input. While it is easy to define, it is notoriously difficult to measure, especially in the modern economy. In particular, there are two aspects of productivity that have increasingly defied precise measurement: output and input. Properly measured, output should include not just the number of widgets coming out of a factory, or the lines of code produced by a programming team, but rather the value created for consumers. Fifty years ago, tons of steel or bushels of corn were a reasonable proxy for the value of output. In today’s economy, value depends increasingly on product quality, timeliness, customization, convenience, variety, and other intangibles. Why Should We Care About Productivity?
Ieee Workshop on Machine Learning for Signal Processing Complex-valued Ica Using Second

In this paper we propose a novel transformation called strong-uncorrelating transform (SUT) t h a t can h e viewed as a n extension of t h e conventional whitening transform for complex random vectors. T h e S U T is a second order technique that can be used as a fast Independent Component Analysis (ICA) method for complex signals. I t is able t o separate almost all mixtures, if t h e sources belong t o a class of complex non-circular random variables. Moreover, it can be used as a preprocessing technique in any complex ICA algorithm using whitening. If t h e sources are circular, t h e SUT reduces t o t h e usual whitening transform. In other cases i t reduces t h e computational load. Some relevant properties of t h e transform as well as some simulations examples a r e presented. Independent component analysis (ICA)[7] is a relatively new signal processing and data analysis t,echnique. It may be used: for example, in blind source separation (BSS) and identifying or equalizing instantaneous Multiple-Input Multiple-Output (I-MIhIO) systems. It has found applicat,ions e.g. in wireless communicatiorrs, biomedical signal processing and data mining (see [13] for references). In insta,ntaneoris complex-wlued ICA problem x = As: the goal is to recover the original source signal vectors s from the observation vectors x blindly without explicit. knowledge of the sources or the linear mixing system A. ICA is based on a crucial assumption t,hat the underlying unknown source signals are statistically independent. Recent textbooks prw vide excellent. tutorial material and extensive review on ICA methods 16, 131.
Polynomial-Time Resource Allocation in Large Multiflow Wireless Networks with Cooperative Links

An integrated multiflow network model synthesizing physical layer rate control and link layer access control is presented, permitting the study of resource allocation in large networks while allowing multi-terminal cooperation at the physical layer. We discuss how to incorporate the cooperative unit into the broader scheduling and routing framework as a “metalink,” a general notion capable of representing a variety of multiterminal physical layer topologies. Simulation results show the benefits of employing cooperative structures where needed.
Socket: a program for identifying and analysing coiled-coil motifs within protein structures.

The coiled coil is arguably the simplest protein-structure motif and probably the most ubiquitous facilitator of protein-protein interactions. Coiled coils comprise two or more alpha-helices that wind around each other to form "supercoils". The hallmark of most coiled coils is a regular sequence pattern known as the heptad repeat. Despite this apparent simplicity and relatedness at the sequence level, coiled coils display a considerable degree of structural diversity: the helices may be arranged parallel or anti-parallel and may form a variety of oligomer states. To aid studies of coiled coils, we developed SOCKET, a computer program to identify these motifs automatically in protein structures. We used SOCKET to gather a set of unambiguous coiled-coil structures from the RCSB Protein Data Bank. Rather than searching for sequence features, the algorithm recognises the characteristic knobs-into-holes side-chain packing of coiled coils; this proved to be straightforward to implement and was able to distinguish coiled coils from the great majority of helix-helix packing arrangements observed in globular domains. SOCKET unambiguously defines coiled-coil helix boundaries, oligomerisation states and helix orientations, and also assigns heptad registers. Structures retrieved from the Protein Data Bank included parallel and anti-parallel variants of two, three and four-stranded coiled coils, one example of a parallel pentamer and a small number of structures that extend the classical description of a coiled coil. We anticipate that our structural database and the associated sequence data that we have gathered will be of use in identifying principles for coiled-coil assembly, prediction and design. To illustrate this we give examples of sequence and structural analyses of the structures that are possible using the new data bases, and we present amino acid profiles for the heptad repeats of different motifs.
Trust-aware Decentralized Recommender Systems: PhD research proposal

This PhD thesis addresses the following problem: exploiting of trust information in order to enhance the accuracy and the user acceptance of current Recommender Systems (RS). RSs suggest to users items they will probably like. Up to now, current RSs mainly generate recommendations based on users’ opinions on items. Nowadays, with the growth of online communities, emarketplaces, weblogs and peer-to-peer networks, a new kind of information is available: rating expressed by an user on another user (trust). We analyze current RS weaknesses and show how use of trust can overcome them. We proposed a solution about exploiting of trust into RSs and underline what experiments we will run in order to test our solution.
Flexible Backup Supply and the Management of Lead‐Time Uncertainty

We consider replenishment decisions for a constant rate demand environment from a supplier with uncertain lead times. We study the potential use of a flexible backup supplier as an emergency response to accurate lead‐time information arriving at (or close after) the beginning of the demand interval and well after an original order with the stochastic lead‐time supplier has been placed. The emergency response decisions involve whether to order and how much from the flexible backup supplier, with the objective of minimizing the cost of meeting demand. We derive the optimal emergency‐response policy and clearly outline its implications on the optimized safety lead time of the original order placement and on the cost of meeting demand. We examine the impact on the use of the flexible backup supplier of factors like the arrival time of accurate lead‐time information and the response lead time of the backup supplier. We further study the potential benefits of the use of the flexible backup supplier in a dual role: as one of the two suppliers in a redundant supply system assigned to originally meet the demand and as an emergency response to later‐arriving lead‐time information. Our numerical studies illustrate the benefits from the use of the flexible backup supplier as an emergency response, but for reasonable purchase premiums and short lead times of flexible backup supply options, their use in a dual (regular and emergency response) role often leads to improved performance over safety lead‐time single and uncertain lead‐time supplier‐replenishment strategies. The benefits of the backup supply options are accentuated the higher the lead‐time uncertainty of the stochastic lead‐time supplier is.
Biomimetic segmentation of unconstrained touching numeral strings

This paper presents a new bionic segmentation method which chooses principal curves to extract strokes of characters and form the stroke sequence. The training sample space is constructed by the Affinity Propagation (AP) algorithm and Bionic Pattern Recognition (BPR) theory. The stroke sequence is divided by calculating the distance of each subsequence to the relative subspace. Experimental results indicate that the method is effective in the segmentation of unconstrained handwritten numeral strings.
The Motivation, Architecture and Demonstration of UltralightNetwork Testbed

In this paper we describe progress in the NSF-funded Ultralight project and a recent demonstration of Ultralight technologies at SuperComputing 2005 (SC|05). The goal of the Ultralight project is to help meet the data-intensive computing challenges of the next generation of particle physics experiments with a comprehensive, network-focused approach. Ultralight adopts a new approach to networking: instead of treating it traditionally, as a static, unchanging and unmanaged set of inter-computer links, we are developing and using it as a dynamic, configurable, and closely monitored resource that is managed from end-to-end. Thus we are constructing a next-generation global system that is able to meet the data processing, distribution, access and analysis needs of the particle physics community. In this paper we present the motivation for, and an overview of, the Ultralight project. We then cover early results in the various working areas of the project. The remainder of the paper describes our experiences of the Ultralight network architecture, kernel setup, application tuning and configuration used during the bandwidth challenge event at SC|05. During this Challenge, we achieved a record-breaking aggregate data rate in excess of 150 Gbps while moving physics datasets between many sites interconnected by the Ultralight backbone network. The exercise highlighted the benefits of Ultralight's research and development efforts that are enabling new and advanced methods of distributed scientific data analysis.
The Random Walk Metropolis: Linking Theory and Practice Through a Case Study

The random walk Metropolis (RWM) is one of the most common Markov Chain Monte Carlo algorithms in practical use today. Its theoretical properties have been extensively explored for certain classes of target, and a number of results with important practical implications have been derived. This article draws together a selection of new and existing key results and concepts and describes their implications. The impact of each new idea on algorithm efficiency is demonstrated for the practical example of the Markov modulated Poisson process (MMPP). A reparameterisation of the MMPP which leads to a highly efficient RWM within Gibbs algorithm in certain circumstances is also developed.
Acoustic training system for speaker independent continuous Arabic speech recognition system

The paper presents an acoustic training system for building acoustic models for a medium vocabulary speaker independent continuous speech recognition system. A speech database is constructed to train the acoustic models. The acoustic models are constructed, and trained. A test set database is constructed to test the accuracy of the acoustic models. Also 4 language models of two main types: bigram and context free grammar, were built and used in tests. Our results show 5.26% and 2.72% word error rates for 1340 and 306 words bigram based language models, respectively. Our results show also 0.19% and 0.99% word error rates for 1340 and 306 words context free grammar based language models, respectively.