Miracle-dz/UniFAR-FTUs huggingface dataset:
Mechanisms of Cardiolipin Oxidation by Cytochrome c: Relevance to Pro- and Antiapoptotic Functions of Etoposide

Execution of apoptotic program in mitochondria is associated with accumulation of cardiolipin peroxidation products required for the release of proapoptotic factors into the cytosol. This suggests that lipid antioxidants capable of inhibiting cardiolipin peroxidation may act as antiapoptotic agents. Etoposide, a widely used antitumor drug and a topoisomerase II inhibitor, is a prototypical inducer of apoptosis and, at the same time, an effective lipid radical scavenger and lipid antioxidant. Here, we demonstrate that cardiolipin oxidation during apoptosis is realized not via a random cardiolipin peroxidation mechanism but rather proceeds as a result of peroxidase reaction in a tight cytochrome c/cardiolipin complex that restrains interactions of etoposide with radical intermediates generated in the course of the reaction. Using low-temperature and ambient-temperature electron paramagnetic resonance spectroscopy of H2O2-induced protein-derived (tyrosyl) radicals and etoposide phenoxyl radicals, respectively, we established that cardiolipin peroxidation and etoposide oxidation by cytochrome c/cardiolipin complex takes place predominantly on protein-derived radicals of cytochrome c. We further show that etoposide can inhibit cytochrome c-catalyzed oxidation of cardiolipin competing with it as a peroxidase substrate. Peroxidase reaction of cytochrome c/cardiolipin complexes causes cross-linking and oligomerization of cytochrome c. With nonoxidizable tetraoleoyl-cardiolipin, the cross-linking occurs via dityrosine formation, whereas bifunctional lipid oxidation products generated from tetralinoleoyl-cardiolipin participate in the production of high molecular weight protein aggregates. Protein aggregation is effectively inhibited by etoposide. The inhibition of cardiolipin peroxidation by etoposide, however, is realized at far higher concentrations than those at which it induces apoptotic cell death. Thus, oxidation of cardiolipin by the cytochrome c/cardiolipin peroxidase complex, which is essential for apoptosis, is not inhibited by proapoptotic concentrations of the drug.
Insight into the Assembly Properties and Functional Organisation of the Magnetotactic Bacterial Actin-like Homolog, MamK

Magnetotactic bacteria (MTB) synthesize magnetosomes, which are intracellular vesicles comprising a magnetic particle. A series of magnetosomes arrange themselves in chains to form a magnetic dipole that enables the cell to orient itself along the Earth’s magnetic field. MamK, an actin-like homolog of MreB has been identified as a central component in this organisation. Gene deletion, fluorescence microscopy and in vitro studies have yielded mechanistic differences in the filament assembly of MamK with other bacterial cytoskeletal proteins within the cell. With little or no information on the structural and behavioural characteristics of MamK outside the cell, the mamK gene from Magnetospirillium gryphiswaldense was cloned and expressed to better understand the differences in the cytoskeletal properties with its bacterial homologues MreB and acitin. Despite the low sequence identity shared between MamK and MreB (22%) and actin (18%), the behaviour of MamK monitored by light scattering broadly mirrored that of its bacterial cousin MreB primarily in terms of its pH, salt, divalent metal-ion and temperature dependency. The broad size variability of MamK filaments revealed by light scattering studies was supported by transmission electron microscopy (TEM) imaging. Filament morphology however, indicated that MamK conformed to linearly orientated filaments that appeared to be distinctly dissimilar compared to MreB suggesting functional differences between these homologues. The presence of a nucleotide binding domain common to actin-like proteins was demonstrated by its ability to function both as an ATPase and GTPase. Circular dichroism and structural homology modelling showed that MamK adopts a protein fold that is consistent with the ‘classical’ actin family architecture but with notable structural differences within the smaller domains, the active site region and the overall surface electrostatic potential.
“Fail-safe” system for diagnosis quality control in cervical cancer screening

IT technologies ensure mechanisms for management, connection, interpretation and/or modeling of medical data and images, which assume very large volumes vehiculated under safety and confidential conditions. These must be stored in structured formats like databases or knowledge-databases. The development of a software dedicated for the diagnosis control of a screening program is justified by: (a) Cancer is one of the most important health problems; (b) Cervical cancer is the second most common cancer worlwide (in Romania also), (c) despite a significant progress in early diagnostic and treatment. The major goal of our research is to find an appropriate screening strategy for Romania and thus reducing cancer incidence and mortality rates, improving the quality of life for the patients. Many studies confirmed the efficiency of cervical cancer screening, and our work supports these studies by providing an organized framework, in line with European directives, for collection and validation of resulted screening data.
Constructing High-Resolution Microvascular Models

The anatomical structure of the brain microvascular system plays an important role in understanding the function of chemical transport within the brain. However, there have been no imaging techniques that provide the high-resolution and high-throughput data acquisition required to image complete microvascular networks. In this paper, we use a new highthroughput technique to image the entire mouse brain vascular system at a sample rate high enough to resolve all components of the vascular system. We describe the imaging and segmentation methods used to construct a structural model of complex microvascular networks. This model is then used to extract highresolution anatomical statistics while providing a framework for further study.
Partial Suppression of the Radial Orbit Instability in Stellar Systems

It is well known that the simple criterion originally proposed by Polyachenko and Shukhman in their 1981 paper for the onset of the radial orbit instability, although generally a useful tool, faces significant exceptions for both mildly anisotropic systems (with some that can be proved to be unstable) and strongly anisotropic models (with some that can be shown to be stable). In this paper we address two issues: Are there processes of collisionless collapse that can lead to equilibria of the exceptional type? And what is the intrinsic structural property that is responsible for the sometimes-noted exceptional stability behavior? To clarify these issues, we have performed a series of simulations of collisionless collapse that start from homogeneous, highly symmetrized, cold initial conditions and, because of such special conditions, are characterized by very little mixing. For these runs, the end states can be associated with large values of the global pressure anisotropy parameter up to 2Kr/KT ≈ 2.75. The highly anisotropic equilibrium states thus constructed show no significant traces of radial anisotropy in their central region, with a very sharp transition to a radially anisotropic envelope occurring well inside the half-mass radius (around 0.2 rM). To check whether the existence of such an almost perfectly isotropic "nucleus" might be responsible for the apparent suppression of the radial orbit instability, we could not resort to equilibrium models with the above characteristics and with analytically available distribution function. Instead, we studied and confirmed the stability of configurations with those characteristics by initializing N-body approximate equilibria (with given density and pressure anisotropy profiles) with the help of the Jeans equations.
Multibiometric systems based verification technique

Multibiometric systems represent the fusion of two or more unimodal biometric systems. Such systems are expected to be more reliable due to the presence of multiple independent pieces of evidence. Since security is one of the important concerns of twenty first century, multibiometric systems can play an important role in ensuring security. In this paper, a multibiometric system is proposed for human verification i.e. authenticating the identity of an individual. The geometrical dimensions of a human hand, such as hand geometry, finger stripe geometry or palm geometry, contain information that is capable of verifying an individual. Among them, hand geometry based verification is the oldest implemented biometric type (debuting in the market in the late 1980s) and finger stripe is the most recent biometric type proposed. The proposed multibiometric system uses both hand and finger stripe geometry for verification. Artificial Neural Network (ANN) is applied for feature learning and verification process. At the end of the paper, results based on 200 individuals and comparison between proposed method and published alternative methods are demonstrated. The experimental results exhibit that the proposed method outperforms the existing methods.
Software Effort Estimation Using Artificial Neural Networks: A Survey of the Current Practices

The value of Artificial Neural Networks (ANNs) methods in performing complicated pattern recognition and nonlinear estimation tasks has been demonstrated across an impressive spectrum of applications. ANNs methods are also used in software development process, since it is a complex environment with many interrelated factors affecting development effort and productivity. Accurate forecasting has proved difficult since many of these interrelationships are not fully understood. This paper provides an overview on the use Artificial Neural Networks methods to estimate the development effort for software development projects. In this survey an explanation, on why those methods are used and how accurate they are.
Performance comparison of State-of-the-art Missing Value Imputation Algorithms on Some Bench mark Datasets

Decision making from data involves identifying a set of attributes that contribute to effective decision making through computational intelligence. The presence of missing values greatly influences the selection of right set of attributes and this renders degradation in classification accuracies of the classifiers. As missing values are quite common in data collection phase during field experiments or clinical trails appropriate handling would improve the classifier performance. In this paper we present a review of recently developed missing value imputation algorithms and compare their performance on some bench mark datasets.
Robust vehicle categorization from aerial images by 3D-template matching and multiple classifier system

We present a robust method for vehicle categorization in aerial images. This approach relies on a multiple-classifier system that merges the answers of classifiers applied at various camera angle incidences. The single classifiers are built by matching 3D-templates to the vehicle silhouettes with a local projection model that is compatible with the assumption of the little knowledge that we have of the viewing-condition parameters. We assess the validity of our approach on a challenging dataset of images captured in real-world conditions.
Recognizing and Utilizing User Preferences in Collaborative Consultation Dialogues

A natural language collaborative consultation system must take user preferences into account. This paper presents two strategies: one for dynamically recognizing user preferences during the course of a collaborative planning dialogue and another for exploiting the model of user preferences to detect suboptimal solutions and suggest better alternatives. By modeling and utilizing user preferences, our system is better able to fulfill its role as a collaborative agent.
A Push/Pull scheduling algorithm in P2P VoD

Video-on-Demand (VoD), which is an important application in peer-to-peer (P2P) networks, attracts a lot of researchers' interest at present. Data scheduling is significant in P2P VoD applications to gain desirable user experience and better satisfaction. In unstructured mesh-based overlay, peers in the overlay learn about the chunks availability from neighbors using Buffer Map. The peer can only know the information of its neighbors and is not aware of other peers. So some peers (busy peer) could get a lot of requests and other peers(free peers) receive fewer requests. The busy peer usually ignores the surplus requests and the peers that send these ignored requests will request the chunk again after the timeout. At the same time, the free peers can't effectively utilize the upload bandwidth. In this paper, we propose a hybrid Push/Pull algorithm in P2P VoD. Simulation results shows that the average utilization of upload bandwidth and average playback quality may be improved efficiently.
Recommendations to boost content spread in social networks

Content sharing in social networks is a powerful mechanism for discovering content on the Internet. The degree to which content is disseminated within the network depends on the connectivity relationships among network nodes. Existing schemes for recommending connections in social networks are based on the number of common neighbors, similarity of user profiles, etc. However, such similarity-based connections do not consider the amount of content discovered. In this paper, we propose novel algorithms for recommending connections that boost content propagation in a social network without compromising on the relevance of the recommendations. Unlike existing work on influence propagation, in our environment, we are looking for edges instead of nodes, with a bound on the number of incident edges per node. We show that the content spread function is not submodular, and develop approximation algorithms for computing a near-optimal set of edges. Through experiments on real-world social graphs such as Flickr and Twitter, we show that our approximation algorithms achieve content spreads that are as much as 90 times higher compared to existing heuristics for recommending connections.
Discovering Diverse Subset for Unsupervised Hyperspectral Band Selection

Band selection, as a special case of the feature selection problem, tries to remove redundant bands and select a few important bands to represent the whole image cube. This has attracted much attention, since the selected bands provide discriminative information for further applications and reduce the computational burden. Though hyperspectral band selection has gained rapid development in recent years, it is still a challenging task because of the following requirements: 1) an effective model can capture the underlying relations between different high-dimensional spectral bands; 2) a fast and robust measure function can adapt to general hyperspectral tasks; and 3) an efficient search strategy can find the desired selected bands in reasonable computational time. To satisfy these requirements, a multigraph determinantal point process (MDPP) model is proposed to capture the full structure between different bands and efficiently find the optimal band subset in extensive hyperspectral applications. There are three main contributions: 1) graphical model is naturally transferred to address band selection problem by the proposed MDPP; 2) multiple graphs are designed to capture the intrinsic relationships between hyperspectral bands; and 3) mixture DPP is proposed to model the multiple dependencies in the proposed multiple graphs, and offers an efficient search strategy to select the optimal bands. To verify the superiority of the proposed method, experiments have been conducted on three hyperspectral applications, such as hyperspectral classification, anomaly detection, and target detection. The reliability of the proposed method in generic hyperspectral tasks is experimentally proved on four real-world hyperspectral data sets.
THE STRUCTURE AND NUMBER OF OBSTRUCTIONS TO TREEWIDTH

For each pair of nonadjacent vertices in a graph, consider the greater of the degrees of the two vertices. The minimum of these maxima is a lower bound on the treewidth of a graph, unless it is a complete graph. This bound has three consequences. First, the obstructions of order w + 3 for treewidth w have a simple structural characterization. Second, these graphs are exactly the pathwidth obstructions of order w + 3. Finally, although there is only one obstruction of order w + 2 for width w, the number of obstructions of order w + 3 is bounded below by an exponential function of √ w.
Re-ranking by Multi-feature Fusion with Diffusion for Image Retrieval

We present a re-ranking algorithm for image retrieval by fusing multi-feature information. We utilize pair wise similarity scores between images to exploit the underlying relationships among images. The initial ranked list for a query from each feature is represented as an undirected graph, where edge strength comes from feature-specific image similarity. Graphs from multiple features are combined by a mixture Markov model. In addition, we utilize a probabilistic model based on the statistics of similarity scores of similar and dissimilar image pairs to determine the weight for each graph. The weight for a feature is query specific, where the ranked lists of different queries receive different weights. Our approach for calculating weights is data-driven and does not require any learning. A diffusion process is then applied to the fused graph to reduce noise and achieve better retrieval performance. Experiments demonstrate that our approach significantly improves performance over baseline methods and outperforms many state-of-the-art retrieval methods.
Iris localization algorithm based on gray distribution features

Influenced by the noises, for example, eyelid and eyelash occlusions, iris localization algorithms are difficult to keep a right balance between real-time quality and accuracy. Therefore, a novel iris localization method with effective noise eliminating is proposed in this paper. For the iris' inner edge location, morphological open is used to eliminate noise based on separating pupil region by binaryzation. Then the pupil's center and radius are located accurately by gray projection. For the iris' outer edge location, morphological close is proposed to eliminate the rich texture within the iris area. Based on this method, an edge detection template to search four directional points within a small scope of the probable boundary is designed and thus the iris's centre and radius can be located. Simulation results show that, compared with the pre-algorithms, the new algorithm not only greatly improves the accuracy and the speed, but also shows good robustness.
Musical training as a potential tool for improving speech perception in background noise

Understanding speech in background noise is a complex and challenging task that causes difficulty for many people, including young children and older adults. Musicians, on the other hand, appear to have an enhanced ability to perceive speech in noise. This has prompted suggestions that musical training could help people who struggle to communicate in complex auditory environments. The experiments presented in this thesis were designed to investigate if and how musical training could be used as an intervention for improving speech perception in noise. The aim of Experiment 1 was to identify specific musical skills which could be targeted for training. Musical beat perception was found to be strongly correlated with speech perception in noise. It was hypothesised that musical beat perception might enhance speech perception in noise by facilitating temporal orienting of attention to important parts of the signal. Experiments 2, 3 and 4 investigated this hypothesis using a rhythmic priming paradigm. Musical rhythm sequences were used to prime temporal expectations, with performance for on-beat targets predicted to be better than that for temporally displaced targets. Rhythmic priming benefits were observed for detection of pure-tone targets in noise and for identification of words in noise. For more complex rhythms, the priming effect was correlated with musical beat perception. Experiment 5 used the metric structure within a sentence context to prime temporal expectations for a target word. There was a significant benefit of rhythmic priming for both children and adults, but the effect was smaller for children. In Experiment 6, a musical beat training programme was devised and evaluated for a group of older adults. After four weeks of training, a small improvement in speech reception thresholds was observed. It was concluded that beat perception is a useful skill to target in a musical intervention for speech perception in noise.
Mitochondrial DNA ligase function in Saccharomyces cerevisiae.

The Saccharomyces cerevisiae CDC9 gene encodes a DNA ligase protein that is targeted to both the nucleus and the mitochondria. While nuclear Cdc9p is known to play an essential role in nuclear DNA replication and repair, its role in mitochondrial DNA dynamics has not been defined. It is also unclear whether additional DNA ligase proteins are present in yeast mitochondria. To address these issues, mitochondrial DNA ligase function in S.cerevisiae was analyzed. Biochemical analysis of mitochondrial protein extracts supported the conclusion that Cdc9p was the sole DNA ligase protein present in this organelle. Inactivation of mitochondrial Cdc9p function led to a rapid decline in cellular mitochondrial DNA content in both dividing and stationary yeast cultures. In contrast, there was no apparent defect in mitochondrial DNA dynamics in a yeast strain deficient in Dnl4p (Deltadnl4). The Escherichia coli ECO:RI endonuclease was targeted to yeast mitochondria. Transient expression of this recombinant ECO:RI endonuclease led to the formation of mitochondrial DNA double-strand breaks. While wild-type and Deltadnl4 yeast were able to rapidly recover from this mitochondrial DNA damage, clones deficient in mitochondrial Cdc9p were not. These results support the conclusion that yeast rely upon a single DNA ligase, Cdc9p, to carry out mitochondrial DNA replication and recovery from both spontaneous and induced mitochondrial DNA damage.
Music Genre Classification with the Million Song Dataset 15-826 Final Report

The field of Music Information Retrieval (MIR) draws from musicology, signal processing, and artificial intelligence. A long line of work addresses problems including: music understanding (extract the musically-meaningful information from audio waveforms), automatic music annotation (measuring song and artist similarity), and other problems. However, very little work has scaled to commercially sized data sets. The algorithms and data are both complex. An extraordinary range of information is hidden inside of music waveforms, ranging from perceptual to auditory—which inevitably makes largescale applications challenging. There are a number of commercially successful online music services, such as Pandora, Last.fm, and Spotify, but most of them are merely based on traditional text IR. Our course project focuses on large-scale data mining of music information with the recently released Million Song Dataset (Bertin-Mahieux et al., 2011),1 which consists of 1http://labrosa.ee. columbia.edu/millionsong/