Miracle-dz/UniFAR-FTUs huggingface dataset:
Mass Fingerprinting of the Venom and Transcriptome of Venom Gland of Scorpion Centruroides tecomanus

Centruroides tecomanus is a Mexican scorpion endemic of the State of Colima, that causes human fatalities. This communication describes a proteome analysis obtained from milked venom and a transcriptome analysis from a cDNA library constructed from two pairs of venom glands of this scorpion. High perfomance liquid chromatography separation of soluble venom produced 80 fractions, from which at least 104 individual components were identified by mass spectrometry analysis, showing to contain molecular masses from 259 to 44,392 Da. Most of these components are within the expected molecular masses for Na+- and K+-channel specific toxic peptides, supporting the clinical findings of intoxication, when humans are stung by this scorpion. From the cDNA library 162 clones were randomly chosen, from which 130 sequences of good quality were identified and were clustered in 28 contigs containing, each, two or more expressed sequence tags (EST) and 49 singlets with only one EST. Deduced amino acid sequence analysis from 53% of the total ESTs showed that 81% (24 sequences) are similar to known toxic peptides that affect Na+-channel activity, and 19% (7 unique sequences) are similar to K+-channel especific toxins. Out of the 31 sequences, at least 8 peptides were confirmed by direct Edman degradation, using components isolated directly from the venom. The remaining 19%, 4%, 4%, 15% and 5% of the ESTs correspond respectively to proteins involved in cellular processes, antimicrobial peptides, venom components, proteins without defined function and sequences without similarity in databases. Among the cloned genes are those similar to metalloproteinases.
Finding Arguments as Sequence Labeling in Discourse Parsing

This paper describes our system for the CoNLL-2016 Shared Task on Shallow Discourse Parsing on English. We adopt a cascaded framework consisting of nine components, among which six are casted as sequence labeling tasks and the remaining three are treated as classification problems. All our sequence labeling and classification models are implemented based on linear models with averaged perceptron training. Our feature sets are mostly borrowed from previous works. The main focus of our effort is to recall cases when Arg1 locates at sentences far before the connective phrase, with some yet limited success.
A determination of the thick disk chemical abundance distribution: Implications for galaxy evolution

We present a determination of the thick disk iron abundance distribution obtained from an in situ sample of F/G stars. These stars are faint, 15 less than or approximately = V less than or approximately = 18, selected on the basis of color, being a subset of the larger survey of Gilmore and Wyse designed to determine the properties of the stellar populations several kiloparsecs from the Sun. The fields studied in the present paper probe the iron abundance distribution of the stellar populations of the galaxy at 500-3000 pc above the plane, at the solar Galactocentric distance. The derived chemical abundance distributions are consistent with no metallicity gradients in the thick disk over this range of vertical distance, and with an iron abundance distribution for the thick disk that has a peak at -0.7 dex. The lack of a vertical gradient argues against slow, dissipational settling as a mechanism for the formation of the thick disk. The photometric and metallicity data support a turn-off of the thick disk that is comparable in age to the metal-rich globular clusters, or greater than or approximately = 12 Gyr, and are consistent with a spread to older ages.
The -7351C/T Polymorphism in the TPA Gene and Ischemic Stroke Risk: A Meta-Analysis

Background A number of studies assessed the association of tissue plasminogen activator(TPA) gene polymorphisms with ischemic stroke, but the results were contradictory. We aimed to explore the role of TPA -7351C/T SNP in the susceptibility to ischemic stroke through a meta-analysis. Methods The PubMed, MEDLINE, EMBASE, China Biological Medicine Database and WANFANG DATA databases were searched until August 2012. The strict selection criteria and exclusion criteria were determined, and odds ratios (ORs) with 95% confidence intervals (CIs) were used to assess the strength of associations. Stroke subtype was determined using Trial of Org 10172 in Acute Treatment criteria (TOAST). Statistical analyses were performed using the STATA12.0 software. Results A total of 2,299 ischemic stroke cases and 1,948 controls in seven case-control studies were included in the meta-analysis. Significant association between -7351C/T polymorphism in the TPA gene and ischemic stroke was observed in all comparison models (TT+CT versus CC, TT versus CT+CC and T versus C). In the subgroup analysis by ethnicity, TT homozygote carriers had a 142% increased risk of ischemic stroke compared with the C allele carriers among East-Asians (TT versus CT+CC: OR = 2.42, 95% CI = 1.07–5.48), but not in South-Asians and Caucasians, and significantly increased risks were found for T versus C among both East-Asians (OR = 1.33, 95% CI = 1.05–1.68) and Caucasians(OR = 1.16, 95% CI = 1.02–1.31). Further stratification for stroke subtype in three Caucasian studies showed the association between -7351C/T polymorphism and Large-artery atherosclerosis (LAA), but not Small-vessel occlusion (SVO) and Cardioembolism (CE). Conclusions This meta-analysis suggested that the -7351C/T polymorphism in TPA gene would be a risk factor for ischemic stroke, especially among East-Asians compared with Caucasians, but not in South-Asians, and it may play a role in the pathogenesis of LAA in Caucasians, but not in SVO and CE.
Robust morphing of point‐sampled geometry

We propose a novel morphing algorithm for objects represented by point‐sampled geometry. The fundamental problem of point‐sampled geometry morphing is how to set the correspondence between points of the two objects which are usually of different size. The two objects are first parameterized by projecting the sample points onto a common parametric domain. As both objects are densely sampled, we present a novel accelerated parameterization algorithm employing the technique of LOD. The common parameter domain is then split recursively into clusters. The correspondence between sample points of the two objects is established by performing a local mapping in each cluster. As for complex geometries, the establishment of correspondence is facilitated by decomposing the geometry into patches using geodesic decomposition curves.
Discovery of a Candidate Protoplanetary Disk around the Embedded Source IRc9 in Orion

We report the detection of spatially extended mid-infrared emission around the luminous embedded star IRc9 in OMC-1, as seen in 8.8, 11.7, and 18.3 μm images obtained with the Thermal-Region Camera and Spectrograph on Gemini South. The extended emission is asymmetric, and the morphology is reminiscent of warm dust disks around other young stars. The putative disk has a radius of roughly 1. ″5 (700 AU) and a likely dust mass of almost 10 M⊕. The infrared spectral energy distribution of IRc9 indicates a total luminosity of ~100 L☉, implying that it will become an early A-type star when it reaches the main sequence. Thus, the candidate disk around IRc9 may be a young analog of the planetary debris disks around Vega-like stars and the disks of Herbig Ae stars, and may provide a laboratory in which to study the earliest phases of planet formation. A disk around IRc9 may also add weight to the hypothesis that an enhanced T Tauri-like wind from this star has influenced the molecular outflow from the OMC-1 core.
Unsupervised Learning with Mixed Numeric and Nominal Data

Presents a similarity-based agglomerative clustering (SBAC) algorithm that works well for data with mixed numeric and nominal features. A similarity measure proposed by D.W. Goodall (1966) for biological taxonomy, that gives greater weight to uncommon feature value matches in similarity computations and makes no assumptions about the underlying distributions of the feature values, is adopted to define the similarity measure between pairs of objects. An agglomerative algorithm is employed to construct a dendrogram, and a simple distinctness heuristic is used to extract a partition of the data. The performance of the SBAC algorithm has been studied on real and artificially-generated data sets. The results demonstrate the effectiveness of this algorithm in unsupervised discovery tasks. Comparisons with other clustering schemes illustrate the superior performance of this approach.
Color constancy models inspired by human visual system: Survey paper

Color constancy is considered to be as the building block for computer vision applications, because color perception may vary based on type of illumination present in the scene. It's well known that human visual system has very efficient color constancy, so it was used as a source of inspiration for some color constancy algorithms. In this survey we present the most recent advances in color constancy models which inspired by the primate visual system. The primate visual system use many functionalities such as, retina adaptation, single opponent cells and double opponent cells, all these functionalities are considered as the basic elements used to achieve color constancy.
hiPathDB: a human-integrated pathway database with facile visualization

One of the biggest challenges in the study of biological regulatory networks is the systematic organization and integration of complex interactions taking place within various biological pathways. Currently, the information of the biological pathways is dispersed in multiple databases in various formats. hiPathDB is an integrated pathway database that combines the curated human pathway data of NCI-Nature PID, Reactome, BioCarta and KEGG. In total, it includes 1661 pathways consisting of 8976 distinct physical entities. hiPathDB provides two different types of integration. The pathway-level integration, conceptually a simple collection of individual pathways, was achieved by devising an elaborate model that takes distinct features of four databases into account and subsequently reformatting all pathways in accordance with our model. The entity-level integration creates a single unified pathway that encompasses all pathways by merging common components. Even though the detailed molecular-level information such as complex formation or post-translational modifications tends to be lost, such integration makes it possible to investigate signaling network over the entire pathways and allows identification of pathway cross-talks. Another strong merit of hiPathDB is the built-in pathway visualization module that supports explorative studies of complex networks in an interactive fashion. The layout algorithm is optimized for virtually automatic visualization of the pathways. hiPathDB is available at http://hiPathDB.kobic. re.kr.
Hybrid Collaborative Filtering with Autoencoders

Collaborative Filtering aims at exploiting the feedback of users to provide personalised recommendations. Such algorithms look for latent variables in a large sparse matrix of ratings. They can be enhanced by adding side information to tackle the well-known cold start problem. While Neu-ral Networks have tremendous success in image and speech recognition, they have received less attention in Collaborative Filtering. This is all the more surprising that Neural Networks are able to discover latent variables in large and heterogeneous datasets. In this paper, we introduce a Collaborative Filtering Neural network architecture aka CFN which computes a non-linear Matrix Factorization from sparse rating inputs and side information. We show experimentally on the MovieLens and Douban dataset that CFN outper-forms the state of the art and benefits from side information. We provide an implementation of the algorithm as a reusable plugin for Torch, a popular Neural Network framework.
Texture for volume character animation

We present an approach to texture mapping volume datasets based on multiple semantic constraints. The approach is based on continuous space mappings to ensure good image quality. It requires only one intervention by the user, to determine key points where the texture must match an intermediate image of the original data. This can also be used to avoid the problem of texture being smeared over too large an area. The method uses three passes. In the first pass the texture is warped to an intermediate surface which is itself a projection of the volume object. This is used to ensure that the texture correctly aligns with user-supplied key points of the object. The use of key points to pin the texture in place ensures that the volume object can be animated. When rendering the object two further passes are used, first inversely mapping each point being rendered to a plenoptic surface and then inversely mapping to the intermediate surface. This three-part aspect additionally allows the texture image to be independent of the volume data. A data-dependent triangulation method is used to retain edge quality in texture images. We demonstrate an extension to 2.5D textures, extruded through the volume, using an approach consistent with 2D texture. This supports interactive sculpting of the volume model. Our overall goal is animated characters using textured volume data.
Morphable 3D models from video

Nonrigid 3D structure-from-motion and 2D optical flow can both be formulated as tensor factorization problems. The two problems can be made equivalent through a noisy affine transform, yielding a combined nonrigid structure-from-intensities problem that we solve via structured matrix decompositions. Often the preconditions for this factorization are violated by image noise and deficiencies of the data visa-vis the sample complexity of the problem. Both issues are remediated with careful use of rank constraints, norm constraints, and integration over uncertainty in the intensity values, yielding novel solutions for SVD under uncertainty, factorization under uncertainty, nonrigid factorization, and subspace optical flow. The resulting integrated algorithm can track and reconstruct in 3D nonrigid surfaces having very little texture, for example the smooth parts of the face. Working with low-resolution low-texture "found video," these methods produce good tracking and 3D reconstruction results where prior algorithms fail.
Distinct Lysosomal Network Protein Profiles in Parkinsonian Syndrome Cerebrospinal Fluid

Background: Clinical diagnosis of parkinsonian syndromes like Parkinson’s disease (PD), corticobasal degeneration (CBD) and progressive supranuclear palsy (PSP) is hampered by overlapping symptomatology and lack of diagnostic biomarkers, and definitive diagnosis is only possible post-mortem. Objective: Since impaired protein degradation plays an important role in many neurodegenerative disorders, we hypothesized that profiles of select lysosomal network proteins in cerebrospinal fluid could be differentially expressed in these parkinsonian syndromes. Methods: Cerebrospinal fluid samples were collected from PD patients (n = 18), clinically diagnosed 4-repeat tauopathy patients; corticobasal syndrome (CBS) (n = 3) and PSP (n = 8); and pathologically diagnosed PSP (n = 8) and CBD patients (n = 7). Each patient set was compared to its appropriate control group consisting of age and gender matched individuals. Select lysosomal network protein levels were detected via Western blotting. Factor analysis was used to test the diagnostic sensitivity, specificity and accuracy of the select lysosomal network protein expression profiles. Results: PD, CBD and PSP were markedly different in their cerebrospinal fluid lysosomal network protein profiles. Lysosomal-associated membrane proteins 1 and 2 were significantly decreased in PD; early endosomal antigen 1 was decreased and lysozyme increased in PSP; and lysosomal-associated membrane proteins 1 and 2, microtubule-associated protein 1 light chain 3 and lysozyme were increased in CBD. A panel of lysosomal-associated membrane protein 2, lysozyme and microtubule-associated protein 1 light chain discriminated between controls, PD and 4-repeat tauopathies. Conclusions: This study offers proof of concept that select lysosomal network proteins are differentially expressed in cerebrospinal fluid of Parkinson’s disease, corticobasal syndrome and progressive supranuclear palsy. Lysosomal network protein analysis could be further developed as a diagnostic fluid biomarker in parkinsonian syndromes.
Functional Specialization within Medial Frontal Cortex of the Anterior Cingulate for Evaluating Effort-Related Decisions

The rat medial frontal cortex (MFC) has been implicated in allowing animals to work harder to receive larger rewards. However, it is unknown what role the individual MFC regions [anterior cingulate cortex (ACC) and prelimbic-infralimbic cortex (PL-IL)] play in such decision making. To investigate this, we trained rats on a T-maze cost-benefit task with two possible courses of action, shown previously to be affected by complete MFC lesions. One response involved climbing a 30 cm barrier to obtain a large quantity of reward (high cost-high reward), whereas the other had a lower energetic demand but also a smaller reward gain (low cost-low reward). Before surgery, all animals preferred to select the high cost-high reward option. However, after excitotoxic ACC lesions, there was a complete reversal of behavior, with the ACC group selecting the low cost-low reward response on nearly every trial. In contrast, both control animals and rats with PL-IL lesions continued to choose to climb the barrier for the larger reward. When the same rats were tested on a delayed match-to-sample paradigm however, it was the PL-IL group that was significantly impaired at learning the response rule, with the performance of ACC rats being comparable with controls. This double dissociation indicates that the ACC is the important region within the MFC when evaluating how much effort to expend for a specific reward.
An evolutionary based discriminative system for keyword spotting

Keyword spotting refers to detection of all occurrences of any given word in a speech utterance. In this paper, we define the keyword spotting problem as a binary classification problem and propose a discriminative approach for solving it. Our approach exploits evolutionary algorithm to determine the separating hyper plane between two classes: class of sentences containing the target keywords and class of sentences which don't include the target keywords. The results on TIMIT indicate that the proposed method has good performance equal to 95.7 FOM value (average true detection rate for different false alarm per keyword per hour) and acceptable speed equal to 3.3 RTF (Real Time Factor) value.
Markov Logic Networks for Natural Language Question Answering

Our goal is to answer elementary-level science questions using knowledge extracted automatically from science textbooks, expressed in a subset of first-order logic. Given the incomplete and noisy nature of these automatically extracted rules, Markov Logic Networks (MLNs) seem a natural model to use, but the exact way of leveraging MLNs is by no means obvious. We investigate three ways of applying MLNs to our task. In the first, we simply use the extracted science rules directly as MLN clauses. Unlike typical MLN applications, our domain has long and complex rules, leading to an unmanageable number of groundings. We exploit the structure present in hard constraints to improve tractability, but the formulation remains ineffective. In the second approach, we instead interpret science rules as describing prototypical entities, thus mapping rules directly to grounded MLN assertions, whose constants are then clustered using existing entity resolution methods. This drastically simplifies the network, but still suffers from brittleness. Finally, our third approach, called Praline, uses MLNs to align the lexical elements as well as define and control how inference should be performed in this task. Our experiments, demonstrating a 15\% accuracy boost and a 10x reduction in runtime, suggest that the flexibility and different inference semantics of Praline are a better fit for the natural language question answering task.
DNA-graphene interactions during translocation through nanogaps

We study how double-stranded DNA translocates through graphene nanogaps. Nanogaps are fabricated with a novel capillary-force induced graphene nanogap formation technique. DNA translocation signatures for nanogaps are qualitatively different from those obtained with circular nanopores, owing to the distinct shape of the gaps discussed here. Translocation time and conductance values vary by ∼ 100%, which we suggest are caused by local gap width variations. We also observe exponentially relaxing current traces. We suggest that slow relaxation of the graphene membrane following DNA translocation may be responsible. We conclude that DNA-graphene interactions are important, and need to be considered for graphene-nanogap based devices. This work further opens up new avenues for direct read of single molecule activitities, and possibly sequencing.
Impact of machine learning algorithms on analysis of stream ciphers

Stream ciphers are widely used for information security. The keystream produced by a cipher must be unpredictable. Attacks on stream ciphers typically exploit some underlying patterns existing in the keystream. The objective of this paper is to develop such an attack with the help of machine learning algorithms. The Linear Feedback Shift Register (LFSR) has been solved for several test cases using machine learning algorithms. We also study some variants of LFSR and Geffe Generator and propose a model for predicting the future bits of a keystream generator. The results for Geffe Generator using this model have been presented. However the approach did not yield encouraging results when confronted with the keystream generators of the eSTREAM project.
Aspect of Prime Numbers in Public Key Cryptosystem

The secrecy of Public Key cryptosystem relies heavily on the intractability of various mathematical problems. As a result numbers and their complexities play a very important role in keeping the system secure. Prime numbers are of the greatest importance here due to their widespread use in encryption algorithms such as the RSA algorithm. For example, if there were efficient algorithms for factoring large numbers, the vast majority of encrypted electronic communications worldwide would be easily crackable. In most of the public key algorithms large prime numbers (100 decimal digits) have been used as a means of securing the system. In this paper we deal with the subject of handling prime numbers efficiently in cryptosystems and give an overview of certain efficient primality tests. We hope it’s a new look to the prime numbers.
A Web Page Segmentation Algorithm for Extracting Product Information

Nowadays, as the rapid development of Internet, Web is becoming the most popular and also the largest resource for people to acquire information. At the same time, search engine plays an important role while retrieving inform.ation. Nevertheless, the smallest processing unit of search engine is the whole web pages, which contains plenty of noisy information. If the information can be extracted and used as the smallest processing unit, then it can place a positive effect on search engine's precision; so was born the page segmentation algorithm. However, traditional algorithms cannot extract blocks in product level. Hence, a novel algorithm, basing on product features and DOM (document object mode), is proposed. Compared with those traditional algorithms, not only information consistence is greatly enhanced, but also complexity is decreased with this novel page segmentation algorithm.