Miracle-dz/UniFAR-FTUs huggingface dataset:
PFP: Automated prediction of gene ontology functional annotations with confidence scores using protein sequence data

Protein function prediction is a central problem in bioinformatics, increasing in importance recently due to the rapid accumulation of biological data awaiting interpretation. Sequence data represents the bulk of this new stock and is the obvious target for consideration as input, as newly sequenced organisms often lack any other type of biological characterization. We have previously introduced PFP (Protein Function Prediction) as our sequence‐based predictor of Gene Ontology (GO) functional terms. PFP interprets the results of a PSI‐BLAST search by extracting and scoring individual functional attributes, searching a wide range of E‐value sequence matches, and utilizing conventional data mining techniques to fill in missing information. We have shown it to be effective in predicting both specific and low‐resolution functional attributes when sufficient data is unavailable. Here we describe (1) significant improvements to the PFP infrastructure, including the addition of prediction significance and confidence scores, (2) a thorough benchmark of performance and comparisons to other related prediction methods, and (3) applications of PFP predictions to genome‐scale data. We applied PFP predictions to uncharacterized protein sequences from 15 organisms. Among these sequences, 60–90% could be annotated with a GO molecular function term at high confidence (≥80%). We also applied our predictions to the protein–protein interaction network of the Malaria plasmodium (Plasmodium falciparum). High confidence GO biological process predictions (≥90%) from PFP increased the number of fully enriched interactions in this dataset from 23% of interactions to 94%. Our benchmark comparison shows significant performance improvement of PFP relative to GOtcha, InterProScan, and PSI‐BLAST predictions. This is consistent with the performance of PFP as the overall best predictor in both the AFP‐SIG ′05 and CASP7 function (FN) assessments. PFP is available as a web service at http://dragon.bio. purdue.edu/pfp/. Proteins 2009. © 2008 Wiley‐Liss, Inc.
The H I Environment of the Sculptor Dwarf Spheroidal Galaxy

New observations of the neutral hydrogen (H I) in and around the line of sight of the Sculptor dwarf spheroidal galaxy (dSph) are presented. The data obtained with the single-dish Parkes telescope cover a large area of 7° × 7° in the direction of the dwarf and have resolutions of 15. ′5 × 1.12 km s-1. The Australia Telescope Compact Array was used to map a smaller area of 2. °2 × 2. °2 centered on the direction of the dwarf with higher resolutions (350″ × 140″ × 1.65 km s-1). Many H I structures having velocities outside the range of the normal Galactic disk velocities were detected, including the two Sculptor clouds (northeast and southwest) of Carignan et al. (C98). The present study shows the total extent of the C98 clouds. We derived heliocentric radial velocities for the northeast and southwest clouds of 100.2 ± 0.9 and 105.1 ± 0.3 km s-1, respectively. The intensity-weighted mean H I velocity for both clouds is 104.1 ± 0.4 km s-1. Three different hypotheses concerning the association of the C98 Sculptor clouds were considered. The case for the clouds belonging to the Sculptor group of galaxies is found to be inconsistent with the observational data. The probability of the C98 Sculptor clouds to be Milky Way features at anomalous velocities (HVCs) superposed by chance on the Sculptor dSph is estimated to be less than 2%. The third hypothesis assumes that the clouds are physically associated with the Sculptor dSph and is supported by the following evidence: (1) the radial velocities for both clouds are very close to the optical velocity of the Sculptor dSph (ΔV = 4 ± 3 km s-1), (2) 88% of the total H I flux is contained within the optical radius of the galaxy, and (3) the clouds are located symmetrically relative to the center of the Sculptor dSph. Arguments are presented that the C98 Sculptor clouds are still gravitationally bound to the dwarf galaxy and are part of its interstellar medium. The mass of each cloud is (4.1 ± 0.2) × 104 M⊙ (northeast cloud) and (1.93 ± 0.02) × 105 M⊙ (southwest cloud) at the Sculptor dSph distance (79 kpc).
Axially symmetric TM-wave radiation from the sphere-conical cavity

The axially symmetric TM-wave radiation from the sphere-conical cavity resonator is investigated. The problem is solved rigorously using the analytical regularization technique. Resonator is formed by a perfectly conducting semi-infinite truncated cone joined with the spherical diaphragm by which one of conical sectors is closed. The electric radial dipole is placed in front of the aperture. The problem is reduced to an infinite system of linear algebraic equations (ISLAE) of the second kind. The dependence of the resonance modes radiation through the aperture on the geometrical parameters, as well as the features of the formation of far field patterns are investigated.
AN AUDIO AND MUSIC SIMILARITY AND RETRIEVAL SYSTEM BASED ON SPARSE FEATURE REPRESENTATIONS

In this paper, we present an audio and music similarity and retrieval(AMSR) system, which employed sparse feature representations. Our system first extracts Melscaled spectrum from audio snippet. Next, sparse restricted Boltzmann machine (RBM) is trained for feature representation. In order to enhance computational efficiency in retrieval phase, we employed locality sensitive hashing (LSH) method, which facilitates approximate nearest neighbor (ANN) search.
HIV-1 Vpr-Induced Apoptosis Is Cell Cycle Dependent and Requires Bax but Not ANT

The HIV-1 accessory protein viral protein R (Vpr) causes G2 arrest and apoptosis in infected cells. We previously identified the DNA damage–signaling protein ATR as the cellular factor that mediates Vpr-induced G2 arrest and apoptosis. Here, we examine the mechanism of induction of apoptosis by Vpr and how it relates to induction of G2 arrest. We find that entry into G2 is a requirement for Vpr to induce apoptosis. We investigated the role of the mitochondrial permeability transition pore by knockdown of its essential component, the adenine nucleotide translocator. We found that Vpr-induced apoptosis was unaffected by knockdown of ANT. Instead, apoptosis is triggered through a different mitochondrial pore protein, Bax. In support of the idea that checkpoint activation and apoptosis induction are functionally linked, we show that Bax activation by Vpr was ablated when ATR or GADD45α was knocked down. Certain mutants of Vpr, such as R77Q and I74A, identified in long-term nonprogressors, have been proposed to inefficiently induce apoptosis while activating the G2 checkpoint in a normal manner. We tested the in vitro phenotypes of these mutants and found that their abilities to induce apoptosis and G2 arrest are indistinguishable from those of HIV-1NL4–3 vpr, providing additional support to the idea that G2 arrest and apoptosis induction are mechanistically linked.
Optical Plasmons of Individual Gold Nanosponges

The search for novel plasmonic nanostructures, which can act simultaneously as optical detectors and stimulators, is crucial for many applications in the fields of biosensing, electro- and photocatalysis, electrochemistry, and biofuel generation. In most of these areas, a large surface-to-volume ratio, as well as high density of active surface sites, is desirable. We investigate sponge-like, that is, fully porous, nanoparticles, called nanosponges, where both the gold and the air phase are fully percolated in three dimensions. We correlate, on a single nanoparticle basis, their optical scattering spectra (using dark field microscopy) with their individual morphology (using electron microscopy). We find that the scattering spectra of nanosponges depend only weakly on their size and outer shape, but are greatly influenced by their unique percolation, in qualitative agreement with numerical simulations.
Macrocolonies (Granules) Formation as a Cause of False-Negative Results in the MGIT 960 System: Cause Analysis and Correlation with Mycobacterial Species

Background. The viable mycobacterial bacilli can sometimes form granules in the Mycobacterium Growth Indicator Tube (MGIT) to produce instrument-negative outcomes when BACTEC MGIT 960 culture is performed. The cause of this phenomenon has never been analyzed. Methods. Thirty-one instrument-negative, granule presenting MGIT vials were collected for conducting acid-fast staining and also liquid and solid subculture. Species identification and drug susceptibility test were performed with the recovered strains. Cultivation test was done by inoculating small amount of bacilli into the MGIT vials. Results. Twenty-four and twenty-nine of the tested MGIT vials were smear and culture positive, respectively. In total, 18, 4, and 7 of the cultivated strains were identified as Mycobacterium tuberculosis complex, M. intracellulare, and M. xenopi, respectively. When a limited amount of bacilli was inoculated, the granule formation was observed for M. xenopi strains in the MGIT system. Conclusions. The granules observed in the instrument-negative MGIT vials consisted of viable bacilli, which emphasized the need of visual inspection to increase recovery rate. Limited bacterial load and specific species might be the cause of granule forming.
Probabilistic Projective Association and Semantic Guided Relocalization for Dense Reconstruction

We present a real-time dense mapping system which uses the predicted 2D semantic labels for optimizing the geometric quality of reconstruction. With a combination of Convolutional Neural Networks (CNNs) for 2D labeling and a Simultaneous Localization and Mapping (SLAM) system for camera trajectory estimation, recent approaches have succeeded in incrementally fusing and labeling 3D scenes. However, the geometric quality of the reconstruction can be further improved by incorporating such semantic prediction results, which is not sufficiently exploited by existing methods. In this paper, we propose to use semantic information to improve two crucial modules in the reconstruction pipeline, namely tracking and loop detection, for obtaining mutual benefits in geometric reconstruction and semantic recognition. Specifically for tracking, we use a novel probabilistic projective association approach to efficiently pick out candidate correspondences, where the confidence of these correspondences is quantified concerning similarities on all available short-term invariant features. For the loop detection, we incorporate these semantic labels into the original encoding through Randomized Ferns to generate a more comprehensive representation for retrieving candidate loop frames. Evaluations on a publicly available synthetic dataset have shown the effectiveness of our approach that considers such semantic hints as a reliable feature for achieving higher geometric quality.
Cluster: an Unsupervised Algorithm for Modeling Gaussian Mixtures

Permission to use, copy, modify, and distribute this software and its documentation for any purpose, without fee, and without written agreement is hereby granted, provided that the above copyright notice and the following two paragraphs appear in all copies of this software. In no event shall Purdue University be liable to any party for direct, indirect, special, incidental, or consequential damages arising out of the use of this software and its documentation, even if Purdue University has been advised of the possibility of such damage. Purdue University specifically disclaims any warranties, including, but not limited to, the implied warranties of merchantability and fitness for a particular purpose. The software provided hereunder is on an " as is " basis, and Purdue Univeristy has no obligation to provide maintenance, support, updates, enhancements, or modifications.
Relay node placement for Wireless Sensor Networks deployed in tunnels

Node placement plays a significant role in the effective and successful deployment of Wireless Sensor Networks (WSNs), i.e., meeting design goals such as cost effectiveness, coverage, connectivity, lifetime and data latency. In this paper, we propose a new strategy to assist in the placement of Relay Nodes (RNs) for a WSN monitoring underground tunnel infrastructure. By applying for the first time an accurate empirical mean path loss propagation model along with a well fitted fading distribution model specifically defined for the tunnel environment, we address the RN placement problem with guaranteed levels of radio link performance. The simulation results show that the choice of appropriate path loss model and fading distribution model for a typical environment is vital in the determination of the number and the positions of RNs. Furthermore, we adapt a two-tier clustering multi-hop framework in which the first tier of the RN placement is modelled as the minimum set cover problem, and the second tier placement is solved using the search-and-find algorithm. The implementation of the proposed scheme is evaluated by simulation, and it lays the foundations for further work in WSN planning for underground tunnel applications.
Accelerating OpenFlow switching with network processors

OpenFlow switching enables flexible management of enterprise network switches and experiments on regular network traffic. We present in this paper a complementary design to OpenFlow's existing reference designs. We apply network processor based acceleration cards to perform OpenFlow switching. We describe the design options and report our experiment results that show a 20% reduction on packet delay and the comparable packet forwarding throughput compared to conventional designs.
Molecular Cytogenetics and Cytogenomics of Brain Diseases

Molecular cytogenetics is a promising field of biomedical research that has recently revolutionized our thinking on genome structure and behavior. This is in part due to discoveries of human genomic variations and their contribution to biodiversity and disease. Since these studies were primarily targeted at variation of the genome structure, it appears apposite to cover them by molecular cytogenomics. Human brain diseases, which encompass pathogenic conditions from severe neurodegenerative diseases and major psychiatric disorders to brain tumors, are a heavy burden for the patients and their relatives. It has been suggested that most of them, if not all, are of genetic nature and several recent studies have supported the hypothesis assuming them to be associated with genomic instabilities (i.e. single-gene mutations, gross and subtle chromosome imbalances, aneuploidy). The present review is focused on the intriguing relationship between genomic instability and human brain diseases. Looking through the data, we were able to conclude that both interindividual and intercellular genomic variations could be pathogenic representing, therefore, a possible mechanism for human brain malfunctioning. Nevertheless, there are still numerous gaps in our knowledge concerning the link between genomic variations and brain diseases, which, hopefully, will be filled by forthcoming studies. In this light, the present review considers perspectives of this dynamically developing field of neurogenetics and genomics.
Fiscal Shocks and Fiscal Risk Management

We use the returns on a set of international financial securities to identify exoge-nous shocks to the Canadian federal surplus. We find that a large portion of the variation in the surplus can be replicated by a linear combination of these returns and that the rising debt observed in the 1980s and 1990s was a result of adverse exogenous shocks and a delayed response by the government to these shocks. We develop a formal framework to evaluate the potential gains from a fiscal risk management strategy, using these securities to hedge against exogenous shocks. We show that fiscal risk management can generate significant welfare gains by enhancing the sustainability of fiscal policy and thereby lowering average tax rates.
Particle filter for state and parameter estimation in passive ranging

On-line state and parameter estimation is important and difficult in passive ranging. This paper proposes particle filter based on sequential Monte Carlo method for state estimation. And a kernel smoothing approach is introduced for the estimation of static model parameters. To demonstrate effectiveness of the proposed algorithms, the static parameters are calculated by kernel smoothing and states are estimated by Auxiliary Particle Filter (APF) in simulation experiment. The proposed algorithm achieves combined state and parameter satisfactory results.
Development of head gesture interface for electric wheelchair

For persons with severe disabilities who find it difficult to use the joystick of an electric wheelchair, we have developed an interface that enables an electric wheelchair to be operated by gestures such as head movements. By using a camera for visual sensing, we plan to implement a non-contact non-constraining interface, thereby making the interface much more convenient to use. We are keeping this study user-oriented by conducting clinical tests with the help of actual users to determine where the camera should be situated, how it should be used, and so on.
Annealed MCMC for Bayesian learning of linear Gaussian state space models with changepoints

Adding changepoints to a linear Gaussian state space model, such that it can switch between multiple regimes of action, provides a powerful extension which allows more complex system behaviour to be described. However, it also means that more sophisticated algorithms are needed for parameter estimation. This paper addresses the task of Bayesian learning of changepoint model parameters using MCMC. Standard Gibbs-based algorithms can suffer from poor mixing, because of the correlation between the changepoints and parameters. To avoid this, algorithms must propose changes to both simultaneously, but naive implementations of this variety generally do not perform well. The idea of using an annealed sequence of distributions to bridge between two changepoint sequences allows us to improve the mixing. We show how to implement such a scheme for a general class of linear changepoint transition models and demonstrate with a simple simulation.
When Informatics Meets Neuroscience: Software and Stat istics for Human Brain Imaging

Human language as one of the most complex systems has fascinated scientists from various fields for decades. Whether we consider language from a point of view of a classical linguistics, psychology, computational linguistics, medicine or neurolinguistics, it keeps bringing up questions such as "How do we actually comprehend language in our brain? " The most interesting achievements often result from a joined effort of multiple scientific fields. In this paper, we will explore how statistics and informatics contributed to human brain neuroimaging and how this answered some of the linguistic questions about human brain. The purpose of this paper is not to survey these achievements in detail, but rather to offer a comprehensive coverage of methods and techniques on the border of neuroimaging and informatics. To achieve this, we will touch on some of the basic and advanced methods for neuroimaging techniques, ranging from fundamental statistical analysis with the General Linear Model, Bayesian analysis methods to multivariate pattern classification, in the light of neurolinguistic research.
Deferred evaluation of path queries on hierarchical data

This paper is dealing with the on-line processing of queries on hierarchical data. We make use of the structural join method, that we modify so as to achieve the minimum number of I/O operations required for successful identification of the result set. Our technique along with the application server hosting model and distributed architecture allows efficient implementation of information systems extensively using hierarchical data.
Molecular Cloning and Characterization of a P38-Like Mitogen-Activated Protein Kinase from Echinococcus granulosus

Cystic echinococcosis (CE) treatment urgently requires a novel drug. The p38 mitogen-activated protein kinases (MAPKs) are a family of Ser/Thr protein kinases, but still have to be characterized in Echinococcus granulosus. We identified a 1,107 bp cDNA encoding a 368 amino acid MAPK protein (Egp38) in E. granulosus. Egp38 exhibits 2 distinguishing features of p38-like kinases: a highly conserved T-X-Y motif and an activation loop segment. Structural homology modeling indicated a conserved structure among Egp38, EmMPK2, and H. sapiens p38α, implying a common binding mechanism for the ligand domain and downstream signal transduction processing similar to that described for p38α. Egp38 and its phosphorylated form are expressed in the E. granulosus larval stages vesicle and protoscolices during intermediate host infection of an intermediate host. Treatment of in vitro cultivated protoscolices with the p38-MAPK inhibitor ML3403 effectively suppressed Egp38 activity and led to significant protoscolices death within 5 days. Treatment of in vitro-cultivated protoscolices with TGF-β1 effectively induced Egp38 phosphorylation. In summary, the MAPK, Egp38, was identified in E. granulosus, as an anti-CE drug target and participates in the interplay between the host and E. granulosus via human TGF-β1.
String-Corrected Black Holes

We investigate the geometry of four dimensional black hole solutions in the presence of stringy higher curvature corrections to the low energy effective action. For certain supersymmetric two charge black holes these corrections drastically alter the causal structure of the solution, converting seemingly pathological null singularities into timelike singularities hidden behind a finite area horizon. We establish, analytically and numerically, that the string-corrected two-charge black hole metric has the same Penrose diagram as the extremal four-charge black hole. The higher derivative terms lead to another dramatic effect -- the gravitational force exerted by a black hole on an inertial observer is no longer purely attractive! The magnitude of this effect is related to the size of the compactification manifold.
The CHANT Project: From the Synthesis of the Singing Voice to Synthesis in General

The CHANT project was originally concerned with the analysis and synthesis of the singing voice. This work led to a complex program of voice synthesisby-rule: CHANT. This program was enriched with a constantly expanding software environment, consisting of both analysis and composition programs. In time, broader aims than the synthesis of the singing voice imposed themselves. These aims centered on the search for models of the processes involved in the production of musical sound. Our present research encompasses the physical description of sound phenomena (the sonic material), the articulation of these phenomena (organization), and compositional issues. This research is intended to transcend simulation. Our goal is to extrapolate new creative models for music on the basis of knowledge models developed using the synthesis-by-rule methodology. In this research, synthesis is the proof of both our understanding of sound phenomena, and of the music itself.