Miracle-dz/UniFAR-FTUs huggingface dataset:
Visual mining in music collections with Emergent SOM

We describe different ways of organiz- ing large collections of music with databionic mining tech- niques. The Emergent Self-Organizing Map is used to clus- ter and visualize similar artists and songs. The first method is the MusicMiner system that utilizes semantic descrip- tions learned from low level audio features for each song. The second method uses tags that have been assigned to songs and artists by the users of the social music platform Last.fm. For both methods we demonstrate the visualiza- tion capabilities of the U-Map. An intuitive browsing of large music collections is offered based on the paradigm of topographic maps. The semantic concepts behind the fea- tures enhance the interpretability of the maps.
Original Contribution

Mycobacterium tuberculosis has diminished capacity to counteract redox stress induced by elevated levels of endogenous superoxide Keywords: Drug resistance Mycobacterium smegmatis Mycobacterium tuberculosis Reactive oxygen species (ROS) Redox regulation Superoxide a b s t r a c t Mycobacterium tuberculosis (Mtb) has evolved protective and detoxification mechanisms to maintain cytoplasmic redox balance in response to exogenous oxidative stress encountered inside host phago-cytes. In contrast, little is known about the dynamic response of this pathogen to endogenous oxidative stress generated within Mtb. Using a noninvasive and specific biosensor of cytoplasmic redox state of Mtb, we for first time discovered a surprisingly high sensitivity of this pathogen to perturbation in redox homeostasis induced by elevated endogenous reactive oxygen species (ROS). We synthesized a series of hydroquinone-based small molecule ROS generators and found that ATD-3169 permeated mycobacteria to reliably enhance endogenous ROS including superoxide radicals. When Mtb strains including multidrug-resistant (MDR) and extensively drug-resistant (XDR) patient isolates were exposed to this compound, a dose-dependent, long-lasting, and irreversible oxidative shift in intramycobacterial redox potential was detected. Dynamic redox potential measurements revealed that Mtb had diminished capacity to restore cytoplasmic redox balance in comparison with Mycobacterium smegmatis (Msm), a fast growing nonpathogenic mycobacterial species. Accordingly, Mtb strains were extremely susceptible to inhibition by ATD-3169 but not Msm, suggesting a functional linkage between dynamic redox changes and survival. Microarray analysis showed major realignment of pathways involved in redox homeostasis, central metabolism, DNA repair, and cell wall lipid biosynthesis in response to ATD-3169, all consistent with enhanced endogenous ROS contributing to lethality induced by this compound. This work provides empirical evidence that the cytoplasmic redox poise of Mtb is uniquely sensitive to manipulation in steady-state endogenous ROS levels, thus revealing the importance of targeting intramycobacterial redox metabolism for controlling TB infection. Introduction Reactive oxygen species (ROS) and reactive nitrogen species (RNS) produced by the host enzymes are critical for controlling Mycobacterium tuberculosis, the causative agent of tuberculosis [1–3]. Moreover, both clinical observations in patients suffering from chronic granulomatous disease and recent experimental studies in zebra fish indicate a major role of superoxide (O 2 À) generated by NOX2 in neutrophil-mediated containment of myco-bacterial infection in vivo [4,5]. Collectively, these studies suggest a beneficial role of host-generated ROS and RNS in limiting survival of Mtb during infection. Despite the toxic effects of ROS and RNS, Mtb survives and persists within macrophages, indicating that Mtb has defense mechanisms to …
A Cellular Data Placement model for Wireless Sensor Network based on Distributed Clustering Approach

All the nodes in the WSN communicate their data to their respective destinations. But it requires a lot of energy to send that particular data. So due to long distance of the source and destination a lot of energy is required by the nodes to send the data. After some time all the energy of the node will be depleted and it cannot send data. Therefore to increase longevity and support scalability, nodes are often grouped into disjoint and mostly non-overlapping clusters. By using the clustering method, the nodes in the clusters do not have to send data directly to the destination instead they send data locally to the cluster head. The cluster head aggregates the data of all the nodes and performs data transfer on behalf of each and every node in the cluster. Thereby saving considerable amount of energy of the various nodes. In this paper, we propose a distributed clustering approach for our proposed real time data placement model for WSNs. Keywords-WSN, Cluster Heads (CH), Energy Efficiency, Centralized Clustering, ARS, Central Control Clustering Algorithm(CCCA).
Feature selection based on sensitivity using evolutionary neural network

A novel evolutionary neural network (ENN) was presented for feature selection. In order to improve the convergent speed of the evolutionary algorithm and the accuracy of classification, a heuristic mutation operator and an adaptive mutation rate was proposed. The importance of each feature was measured with the output sensitivity, and then it was taken as the heuristic to guide the searching procedure. The evolutionary programming was employed to optimize the feature subset, and the back-propagation (BP) algorithm was used to adapt the connection weights of ENN. To evaluate the performance of the proposed approach, the experiments were conducted using three well-known classification problems. The experimental results show that the proposed method has better convergent speed and achieves fewer input features than the traditional method. Furthermore, the proposed method is superior to the traditional method in terms of training accuracy and test accuracy.
Opposite effects of visual and auditory word-likeness on activity in the visual word form area

The present fMRI study investigated the effects of word-likeness of visual and auditory stimuli on activity along the ventral visual stream. In the context of a one-back task, we presented visual and auditory words, pseudowords, and artificial stimuli (i.e., false-fonts and reversed-speech, respectively). Main findings were regionally specific effects of word-likeness on activation in a left ventral occipitotemporal region corresponding to the classic localization of the Visual Word Form Area (VWFA). Specifically, we found an inverse word-likeness effect for the visual stimuli in the form of decreased activation for words compared to pseudowords which, in turn, elicited decreased activation compared to the artificial stimuli. For the auditory stimuli, we found positive word-likeness effects as both words and pseudowords elicited more activation than the artificial stimuli. This resulted from a marked deactivation in response to the artificial stimuli and no such deactivation for words and pseudowords. We suggest that the opposite effects of visual and auditory word-likeness on VWFA activation can be explained by assuming the involvement of visual orthographic memory representations. For the visual stimuli, these representations reduce the coding effort as a function of word-likeness. This results in highest activation to the artificial stimuli and least activation to words for which corresponding representations exist. The positive auditory word-likeness effects may result from activation of orthographic information associated with the auditory words and pseudowords. The view that the VWFA has a primarily visual function is supported by our findings of high activation to the visual artificial stimuli (which have no phonological or semantic associations) and deactivation to the auditory artificial stimuli. According to the phenomenon of cross-modal sensory suppression such deactivations during demanding auditory processing are expected in visual regions.
Combination of Hybrid Chaotic Encryption and LDPC for Secure Transmission of Images over Wireless Networks

Robust and secure transmission strategy for high quality image through wireless networks is considered a great challenge. However, the majority of encrypted image transmission schemes don't consider well the effect of bit errors occurring during transmission. These errors are due to the factors that affect the information such as noise and multipath propagation. That should be handled by an efficient channel coding scheme. Our proposed scheme is based on combining hybrid chaotic encryption, which is based on two- dimensional chaotic maps which is utilized for data security, with an error correction technique based on the Low Density Parity Check (LDPC) code. The LDPC is employed as channel coding for data communication in order to solve the problem of the channel's limited bandwidth and improve throughput. Simulation results show that the proposed scheme achieves a high degree of robustness against channel impairments and wide varieties of attacks as wells as improved reliability of the wireless channel. In addition, LDPC is utilized for error correction in order to solve the limitations of wireless channels.
Effect of Divalent Cation Removal on the Structure of Gram-Negative Bacterial Outer Membrane Models

The Gram-negative bacterial outer membrane (GNB-OM) is asymmetric in its lipid composition with a phospholipid-rich inner leaflet and an outer leaflet predominantly composed of lipopolysaccharides (LPS). LPS are polyanionic molecules, with numerous phosphate groups present in the lipid A and core oligosaccharide regions. The repulsive forces due to accumulation of the negative charges are screened and bridged by the divalent cations (Mg2+ and Ca2+) that are known to be crucial for the integrity of the bacterial OM. Indeed, chelation of divalent cations is a well-established method to permeabilize Gram-negative bacteria such as Escherichia coli. Here, we use X-ray and neutron reflectivity (XRR and NR, respectively) techniques to examine the role of calcium ions in the stability of a model GNB-OM. Using XRR we show that Ca2+ binds to the core region of the rough mutant LPS (RaLPS) films, producing more ordered structures in comparison to divalent cation free monolayers. Using recently developed solid-supported models of the GNB-OM, we study the effect of calcium removal on the asymmetry of DPPC:RaLPS bilayers. We show that without the charge screening effect of divalent cations, the LPS is forced to overcome the thermodynamically unfavorable energy barrier and flip across the hydrophobic bilayer to minimize the repulsive electrostatic forces, resulting in about 20% mixing of LPS and DPPC between the inner and outer bilayer leaflets. These results reveal for the first time the molecular details behind the well-known mechanism of outer membrane stabilization by divalent cations. This confirms the relevance of the asymmetric models for future studies of outer membrane stability and antibiotic penetration.
Prognostic DNA Methylation Markers for Prostate Cancer

Prostate cancer (PC) is the most commonly diagnosed neoplasm and the third most common cause of cancer-related death amongst men in the Western world. PC is a clinically highly heterogeneous disease, and distinction between aggressive and indolent disease is a major challenge for the management of PC. Currently, no biomarkers or prognostic tools are able to accurately predict tumor progression at the time of diagnosis. Thus, improved biomarkers for PC prognosis are urgently needed. This review focuses on the prognostic potential of DNA methylation biomarkers for PC. Epigenetic changes are hallmarks of PC and associated with malignant initiation as well as tumor progression. Moreover, DNA methylation is the most frequently studied epigenetic alteration in PC, and the prognostic potential of DNA methylation markers for PC has been demonstrated in multiple studies. The most promising methylation marker candidates identified so far include PITX2, C1orf114 (CCDC181) and the GABRE~miR-452~miR-224 locus, in addition to the three-gene signature AOX1/C1orf114/HAPLN3. Several other biomarker candidates have also been investigated, but with less stringent clinical validation and/or conflicting evidence regarding their possible prognostic value available at this time. Here, we review the current evidence for the prognostic potential of DNA methylation markers in PC.
Improved visualization of brain anatomy and function , for surgery , through real-time non-rigid registration

Recent advances in functional MRI (fMRI) and diffusion tensor imaging (DTI) enable the delineation of eloquent cortical areas and the underlying white matter tracts that connect them. Information from each of these modalities can dramatically impact the decision to operate, but the use of pre-surgical data for on-line, intraoperative decision making is hampered by the on-going changes in patient anatomy during surgery. Intraoperative magnetic resonance imaging has been developed to enable ready visualization of intraprocedural changes in the configuration of the patient, and to enable improved surgical navigation, monitoring and targeting [1]. We propose to increase the intraoperative utility of data acquired preoperatively by compensating, in real-time, for brain deformation, or shift, during surgery.
Model-based view-extrapolation for interactive VR Web-systems

High speed networks which guarantee a minimum quality of service (QoS) enable the development of interactive Web systems. The paper introduces a new paradigm for interaction with complex virtual worlds on the Web, which utilizes both the server and the client simultaneously. The client generates the new views by extrapolating a reference view based on the locally available data, including the model. The server transmits only the data necessary to prevent an accumulation of errors. The model based view extrapolation method avoids the use of textures which are view independent primitives and therefore too expensive to be used in interactive applications over the network.
Unfolding Based Algorithms for the Reachability Problem

We study four solutions to the reachability problem for 1-safe Petri nets, all of them based on the unfolding technique. We define the problem as follows: given a set of places of the net, determine if some reachable marking puts a token in all of them. Three of the solutions to the problem are taken from the literature [McM92,Mel98,Hel99], while the fourth one is first introduced here. The new solution shows that the problem can be solved in time O(n^k), where $n$ is the size of the prefix of the unfolding containing all reachable states, and $k$ is the number of places which should hold a token. We compare all four solutions on a set of examples, and extract a recommendation on which algorithms should be used and which ones not.
An angular acceleration sensor inspired by the vestibular system with a fully circular fluid-channel and thermal read-out

We report on an angular accelerometer based on the semicircular channels of the vestibular system. The accelerometer consists of a water-filled circular tube, wherein the fluid flow velocity is measured thermally as a representation for the external angular acceleration. Measurements show a linear response for angular acceleration amplitudes up to 2×105° s-2.
A harmony search algorithm for clustering with feature selection

This paper presents a new clustering algorithm, called IHSK, with feature selection in a linear order of complexity. The algorithm is based on the combination of the harmony search and K-means algorithms. Feature selection uses both the concept of variability and a heuristic method that penalizes the presence of dimensions with a low probability of contributing to the current solution. The algorithm was tested with sets of synthetic and real data, obtaining promising results.
PRAVDA-live: interactive knowledge harvesting

Acquiring high-quality (temporal) facts for knowledge bases is a labor-intensive process. Although there has been recent progress in the area of semi-supervised fact extraction, these approaches still have limitations, including a restricted corpus, a fixed set of relations to be extracted or a lack of assessment capabilities. In this paper we introduce PRAVDA-live, a framework that overcomes these limitations and supports the entire pipeline of interactive knowledge harvesting. To this end, our demo exhibits fact extraction from ad-hoc corpus creation, via relation specification, labeling and assessment all the way to ready-to-use RDF exports.
Indicators of Function in the Geriatric Population

A ssessment of function of an elderly person can be conducted at a number of different levels and using methods which span a wide range of sophistication. On a clinical level, assessment of overall functional capability is a central theme of the field of geriatric medicine. However, with rapid advances in technology, assessment of function of individual organs or organ systems, of individual cells, and even of specific molecules will become increasingly feasible, even in the context of a household survey or evaluation. This chapter will review aspects of function at each of these levels in humans, the various types of total organism functions that are usually assessed clinically and can predict outcomes, and issues related to choice of measures of function for this population. Measurement of function at any of these levels may define the presence of a disease state. For example, a diagnosis of congestive heart failure or renal failure is based on a certain level of functional impairment of the heart or kidneys. However, most diseases are characterized either by overt pathology like tissue damage (e.g., myocardial infarction), inflammation (e.g., infection), or invasion (e.g., cancer); the presence of defined clinical characteristics in a patient (e.g., depression or malnutrition); or a laboratory measurement which predicts subsequent pathology (e.g., high blood pressure or high cholesterol level).
Research on Diesel Engine Piston Wear Fault Diagnosis Method Based on the Local Wave Time-frequency Analysis

A large number of non-stationary dynamic signals are generated in the working machinery and equipment. Es- pecially, diesel engines often encounter non-stationary transient and time-varying modulation signals, such as the impulse response signals caused by cylinder piston wear. These kinds of signals generated from diesel engine are analyzed by the method of the Local Wave time-frequency proposed in this paper, and then according to the analysis to diagnose the working state of the diesel engine. It proved that the proposed method is feasible and effective. Moreover, it provides an effective way for the diesel engine condition monitor and fault diagnosis.
The Nonconscious Influence of Religious Symbols in Motivated Performance Situations

Anthropological, sociological, and psychological theories suggest that religious symbols should influence motivational processes during performance of goal-relevant tasks. In two experiments, positive and negative religious (Christian) symbols were presented outside of participants’ conscious awareness. These symbols influenced cardiovascular responses consistent with challenge and threat states during a subsequent speech task, particularly when the speech topic concerned participants’ mortality, and only for Christian participants; similar images lacking Christian meaning were not influential. Results suggested that these effects were due to the learned meaning of the symbols and point to the importance of religion as a coping resource.
Efficient robot-sensor network distributed SEIF range-only SLAM

This paper is motivated by schemes of robot-sensor network cooperation where sensor nodes (beacons) are used as landmarks for Range-Only (RO) Simultaneous Localization and Mapping (SLAM). Most existing RO-SLAM techniques consider beacons as passive devices disregarding the sensing, computing and communication capabilities they are actually endowed with. This paper proposes a Range-Only scheme based on Sparse Extended Information Filters (SEIF) that efficiently exploits their capabilities. The robot computes the SLAM prediction stage and distributes the update stage among beacons within its sensing area. The proposed scheme naturally integrates robot-beacon and inter-beacon measurements, significantly improving map and also robot estimations. Our scheme inherits from SEIF its efficiency and scalability and further reduces robot computational burden by exploiting the beacons computing capability. As a result, it has lower error and lower computer requirements than traditional methods. This paper presents the scheme, evaluates and compares its performance in simulations and real experiments.
A Unifying Hypothesis for Familial and Sporadic Alzheimer's Disease

Alzheimer's disease (AD) is characterised by the aggregation of two quite different proteins, namely, amyloid-beta (Aβ), which forms extracellular plaques, and tau, the main component of cytoplasmic neurofibrillary tangles. The amyloid hypothesis proposes that Aβ plaques precede tangle formation but there is still much controversy concerning the order of events and the linkage between Aβ and tau alterations is still unknown. Mathematical modelling has become an essential tool for generating and evaluating hypotheses involving complex systems. We have therefore used this approach to discover the most probable pathway linking Aβ and tau. The model supports a complex pathway linking Aβ and tau via GSK3β, p53, and oxidative stress. Importantly, the pathway contains a cycle with multiple points of entry. It is this property of the pathway which enables the model to be consistent with both the amyloid hypothesis for familial AD and a more complex pathway for sporadic forms.
Fueling and imaging brain activation

Metabolic signals are used for imaging and spectroscopic studies of brain function and disease and to elucidate the cellular basis of neuroenergetics. The major fuel for activated neurons and the models for neuron–astrocyte interactions have been controversial because discordant results are obtained in different experimental systems, some of which do not correspond to adult brain. In rats, the infrastructure to support the high energetic demands of adult brain is acquired during postnatal development and matures after weaning. The brain's capacity to supply and metabolize glucose and oxygen exceeds demand over a wide range of rates, and the hyperaemic response to functional activation is rapid. Oxidative metabolism provides most ATP, but glycolysis is frequently preferentially up-regulated during activation. Underestimation of glucose utilization rates with labelled glucose arises from increased lactate production, lactate diffusion via transporters and astrocytic gap junctions, and lactate release to blood and perivascular drainage. Increased pentose shunt pathway flux also causes label loss from C1 of glucose. Glucose analogues are used to assay cellular activities, but interpretation of results is uncertain due to insufficient characterization of transport and phosphorylation kinetics. Brain activation in subjects with low blood-lactate levels causes a brain-to-blood lactate gradient, with rapid lactate release. In contrast, lactate flooding of brain during physical activity or infusion provides an opportunistic, supplemental fuel. Available evidence indicates that lactate shuttling coupled to its local oxidation during activation is a small fraction of glucose oxidation. Developmental, experimental, and physiological context is critical for interpretation of metabolic studies in terms of theoretical models.