Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of plaque stability by means of high-resolution MRI and finite element analyses of local stresses and strains

To date no adequate diagnostic strategy exists for the assessment of plaque stability. Current approaches focus on identification of high-risk morphological features in cross-sectional images, such as thin caps and large lipid pools. Plaque stability, however, is a 3D mechanical problem, whose solution requires information on both the geometry and the mechanical properties of the involved tissue components. This preliminary in vitro study is aimed to create multicomponent morphological and material models of human diseased arteries from cadavers. Morphological models are based on highly resolved MR images. Material models are fitted to mechanical testing data of single tissue components. Subsequent finite element analysis allows computation of local stresses and strains, whereas consideration of fracture properties provides a basis for the plaque stability assessment. Results demonstrate the complex mechanical behavior of diseased arteries, suggesting that reliable plaque stability assessment requires a combination of appropriate MR protocols, segmentation algorithms and mechanical analysis.
The Use of Biomonitoring Data in Exposure and Human Health Risk Assessments

Biomonitoring uses analytic methods that permit the accurate measurement of low levels of environmental chemicals in human tissues. However, depending on the intended use, biomonitoring, like all exposure tools, may not be a stand-alone exposure assessment tool for some of its environmental public health uses. Although biomonitoring data demonstrate that many environmental chemicals are absorbed in human tissues, uncertainty exists regarding if and at what concentrations many of these chemicals cause adverse health outcomes. Moreover, without exposure pathway information, it is difficult to relate biomonitoring results to sources and routes of exposure and develop effective health risk management strategies. In September 2004, the Health and Environmental Sciences Institute, U.S. Environmental Protection Agency, Centers for Disease Control and Prevention, Agency for Toxic Substances and Disease Registry, and International Council of Chemical Associations co-sponsored the International Biomonitoring Workshop, which explored the processes and information needed for placing biomonitoring data into perspective for risk assessment purposes, with special emphasis on integrating biomarker measurements of exposure, internal dose, and potential health outcome. Scientists from international governments, academia, and industry recommended criteria for applying biomonitoring data for various uses. Six case studies, which are part of this mini-monograph, were examined: inorganic arsenic, methyl eugenol, organophosphorus pesticides, perfluorooctanesulfonate, phthalates, and polybrominated diphenyl ethers. Based on the workshop and follow-up discussions, this overview article summarizes lessons learned, identifies data gaps, outlines research needs, and offers guidance for designing and conducting biomonitoring studies, as well as interpreting biomonitoring data in the context of risk assessment and risk management.
Traditions and innovations

This article gives an overview of the visual culture shared by a number of scientists studying human variation in the first half of the 20th century. This was a time when most scientists shared the conceptual and terminological framework of ‘racial classifications’ to capture the structure of human variation. Clearly, drawings – and later photographs – of people from all over the world constituted a crucial part of the well-established visual culture concerned with human variation. The article, however, focuses on the representational tools for visualizing aspects of human variation that were not so obvious to the eye of the observer. It contextualizes researchers’ strategies of visualizing human diversity within the pictorial traditions of relevant fields, particularly physical anthropology and human genetics. Starting from 1900, the article demonstrates how scientists built up a rich visual repertoire for understanding human diversity, one that integrated maps, tables, photos, drawings, diagrams, family and phylogenetic trees. The representations included abstract and non-abstract elements, but increasingly rested on diagrammatic visualizations. Notably, although these diagrammatic visualizations grew in sophistication and methodological rigor, they were rarely used in academic textbooks. More often, mixed strategies of visualizations – diagrammatic elements integrated into non-diagrammatic images – were chosen as representations for human diversity. In the postwar period, most of those visualizations disappeared from scientific publications, but not from popular media. For the scientific publications, one can argue that a number of scientists paid specific attention to visualizations and their deterministic potential. Some argued that particularly maps and trees were altogether problematic in their visualization of human evolution and human diversity. Others sought out new and better ways of constructing trees and maps to visualize truth-claims about human diversity. Here, diagrammatic elements played a key role. Notably, researchers who employed typological approaches for physical anthropology, and who admitted that there was a moment of intuition in their practices, aspired to meet the demands of mechanical objectivity by establishing abstract inscriptions of their typological assessments.
A personalized mobile travel recommender system using hybrid algorithm

The modern era witnesses the evolution of handheld devices from a simple communication medium to a sophisticated information access platform. Coupling it with intelligent recommendation techniques can transform it to an essential companion for users during their travel, purchases and other routine transactions. A recommender system extracts personalized information relevant to users, thereby limiting the information overload. Our application, Buddy@Move is a hybrid travel recommender which brings together the features of content-based, collaborative and demographic filtering techniques. While travel imprints of the user cater to content-based filtering, resulting in personalized recommendations, the societal transactions provide input for the collaborative portion. The system will make use of the individual demographic information, to suggest what suits the user thereby making it more intelligent and user acceptable.
An Effective Approach for Mixed Noise Removal Using WESNR and Modified Discrete Curvelet Transform

Imaged noising is an important and a challenging task in many fields such as medical, satellite and remote sensing etc., because removal of noise will increase the perceptual quality of an image. In spite of the great success of many denoising algorithms, they were unable to remove the mixed noise, which will be generated by a mixture of random impulse noise (RIN) and additive white Gaussian noise (AWGN) or salt and pepper plus AWGN. To address this problem, in this paper we propose a mixed noise removal method by using weighted encoding sparse non local regularization (WESNR) algorithm and modified discrete Curvelet transform (MD-CVT). These algorithms are developed to risen the texture structures while removing noise. Experimental results shown that the proposed method has given the superior performance to the existing algorithms in terms of quantitative measures such as peak signal to noise ratio (PSNR), featured similarity index (FSIM), mean square error (MSE) and visual quality. INTRODUCTION In many applications, while transmitting the images and/or acquiringan image from both digital cameras will be affected with few or more amount of the noise from a variety of sources. Further processing of these noisy images can be done only after removal of this random noise, because this type of noise elements will create some serious issues in practical applications such as satellite, bio-medical, computer vision, remote sensing, artistic work or marketing and also in many fields. Denoising an image is a primary problem in the applications of image processing. Estimating an original image from the corrupted or sparse image by preserving its edge, texture and structural details is very important.In order to remove the noise from images, prior knowledge about the noise distribution plays a vital role. Mainly, there are two types of noises like impulse noise (IN), additive white Gaussian noise (AWGN). Due to the electrons thermal motion in camera sensors and circuits [22], AWGN will be introduced. In general, when there is a very small change in original pixel value that is known as Gaussian noise. Histogram is a graphical representation of image, which plots a discrete graph of the distortion amount of the pixel values at which frequency it exists, and shows a normal distribution of noise. IN is often introduced due to improper functioning of camera sensors, hardware impairment memory locations or bit errors in transmission [23]. Median filters [1] have been used dominantly to remove IN. Many improvements have been done in median filters to enhance the performance and to preserve the local structures [2-10], which includes weighted median filter (WMF) [3], multistate median filter (MMF) [4]
Protection of Spiral Ganglion Neurons from Degeneration Using Small-Molecule TrkB Receptor Agonists

Neurotrophins (NTs) play essential roles in the development and survival of neurons in PNS and CNS. In the cochlea, NTs [e.g., NT-3, brain-derived neurotrophic factor (BDNF)] are required for the survival of spiral ganglion neurons (SGNs). Preservation of SGNs in the cochlea of patients suffering sensorineural deafness caused by loss of hair cells is needed for the optimal performance of the cochlear implant. Directly applying exogenous BDNF into the cochlea prevents secondary degeneration of SGNs when hair cells are lost. However, a common translational barrier for in vivo applications of BDNF is the poor pharmacokinetics, which severely limits the efficacy. Here we report that 7,8-dihydroxyflavone and 7,8,3′-trihydroxyflavone, both small-molecule agonists of tyrosine receptor kinase B (TrkB), promoted SGN survival with high potency both in vitro and in vivo. These compounds increased the phosphorylated TrkB and downstream MAPK and protected the SGNs in a TrkB-dependent manner. Their applications in the bulla of conditional connexin26 null mice offered significant protection for SGN survival. The function of survived SGNs was assessed by measuring evoked action potentials (APs) in vitro and electrically evoked auditory brainstem response (eABR) thresholds in vivo. APs were reliably evoked in cultured single SGNs treated with the compounds. In addition, eABR thresholds measured from the treated cochleae were significantly lower than untreated controls. Our findings suggest that these novel small-molecule TrkB agonists are promising in vivo therapeutic agents for preventing degeneration of SGNs.
Contextual information in virtual collaboration systems beyond current standards

Context-aware applications are fast becoming popular as a means of enriching users' experiences in various multimedia content access and delivery scenarios. Nevertheless, the definition, identification, and representation of contextual information are still open issues that need to be addressed. In this paper, we briefly present our work developed within the VISNET II Network of Excellence (NoE) project on context-based content adaptation in Virtual Collaboration Systems (VCSs). Based on the conducted research, we conclude that MPEG-21 Digital Item Adaptation (DIA) is the most complete standardization initiative to represent context for content adaptation. However, tools defined in MPEG-21 DIA Usage Environment Descriptors (UEDs) are not adequate for Virtual Collaboration application scenarios, and thus, we propose potential extensions to the available UEDs.
Channel Training Algorithms for Two-Way MIMO Relay Systems

Two-way relay systems are known to be capable of providing higher spectral efficiency compared with conventional one-way relay systems. However, the channel estimation problem for two-way relay systems is more complicated than that of one-way relay systems. In this paper, we propose and compare two channel estimation algorithms, namely the superimposed channel training scheme and the two-stage channel estimation algorithm, for two-way multiple-input multiple-output (MIMO) relay communication systems, where the individual channel state information (CSI) for the first-hop and second-hop links is estimated. For both algorithms, we derive the optimal structure of the source and relay training sequences which minimize the mean-squared error (MSE) of channel estimation. In the superimposed channel training scheme, the power allocation between the source and relay training sequences is optimized. For the two-stage channel estimation algorithm, we optimize the power allocation at the relay node between two stages to improve the performance of the algorithm. Numerical examples are shown to demonstrate and compare the performance of the proposed channel training algorithms.
Preformed reggie/flotillin caps: stable priming platforms for macrodomain assembly in T cells

T cell activation after contact with an antigen‐presenting cell depends on the regulated assembly of the T cell receptor signaling complex, which involves the polarized assembly of a stable, raft‐like macrodomain surrounding engaged T cell receptors. Here we show that the preformed reggie/flotillin caps present in resting T cells act as priming platforms for macrodomain assembly. Preformed reggie‐1/flotillin‐2 caps are exceptionally stable, as shown by fluorescence recovery after photobleaching (FRAP). Upon T cell stimulation, signaling molecules are recruited to the stable reggie/flotillin caps. Importantly, a trans‐negative reggie‐1/flotillin‐2 deletion mutant, which interferes with assembly of the preformed reggie/flotillin cap, impairs raft polarization and macrodomain formation after T cell activation. Accordingly, expression of the trans‐negative reggie‐1 mutant leads to the incorrect positioning of the guanine nucleotide exchange factor Vav, resulting in defects in cytoskeletal reorganization. Thus, the preformed reggie/flotillin caps are stable priming platforms for the assembly of multiprotein complexes controlling actin reorganization during T cell activation.
Evolutionary history of the TBP-domain superfamily

The TATA binding protein (TBP) is an essential transcription initiation factor in Archaea and Eucarya. Bacteria lack TBP, and instead use sigma factors for transcription initiation. TBP has a symmetric structure comprising two repeated TBP domains. Using sequence, structural and phylogenetic analyses, we examine the distribution and evolutionary history of the TBP domain, a member of the helix-grip fold family. Our analyses reveal a broader distribution than for TBP, with TBP-domains being present across all three domains of life. In contrast to TBP, all other characterized examples of the TBP domain are present as single copies, primarily within multidomain proteins. The presence of the TBP domain in the ubiquitous DNA glycosylases suggests that this fold traces back to the ancestor of all three domains of life. The TBP domain is also found in RNase HIII, and phylogenetic analyses show that RNase HIII has evolved from bacterial RNase HII via TBP-domain fusion. Finally, our comparative genomic screens confirm and extend earlier reports of proteins consisting of a single TBP domain among some Archaea. These monopartite TBP-domain proteins suggest that this domain is functional in its own right, and that the TBP domain could have first evolved as an independent protein, which was later recruited in different contexts.
Time series Forecasting using Holt-Winters Exponential Smoothing

Many industrial time series exhibit seasonal behavior, such as demand for apparel or toys. Consequently, seasonal forecasting problems are of considerable importance. This report concentrates on the analysis of seasonal time series data using Holt-Winters exponential smoothing methods. Two models discussed here are the Multiplicative Seasonal Model and the Additive Seasonal Model.
Intruder Activity Analysis under Unreliable Sensor Networks

This paper addresses the problem of counting intruder activities within a monitored domain by a sensor network. The deployed sensors are unreliable. We characterize imperfect sensors with misdetection and false-alarm probabilities. We model intruder activities with Markov Chains. A set of Hidden Markov Models (HMM) models the imperfect sensors and intruder activities to be monitored. A novel sequential change detection/isolation algorithm is developed to detect and isolate a change from an HMM representing no intruder activity to another HMM representing some intruder activities. Procedures for estimating the entry time and the trace of intruder activities are developed. A domain monitoring example is given to illustrate the presented concepts and computational procedures.
Sensor Networks Routing via Bayesian Exploration

There is increasing research interest in solving routing problems in sensor networks subject to constraints such as data correlation, link reliability and energy conservation. Since information concerning these constraints are unknown in an environment, a reinforcement learning approach is proposed to solve this problem. To this end, we deploy a Bayesian method to offer good balance between exploitation and exploration. It estimates the benefit of exploration by value of information therefore avoids the error-prone process of parameter tuning which usually requires human intervention. Experimental results have shown that this approach outperforms the widely-used Q-routing method
Limit Properties of Evolutionary Algorithms

In the chapter limit properties of genetic algorithms and theproblem of their classification are elaborated. Recently one can observe an increasing interest in properties of genetic algorithms modelled by Markov chains (Vose, Rowe). However, the known results are mainly limited to existence theorems. They say that there exists a limit distribution for a Markov chain describing a simple genetic algorithm. In the chapter we perform the next step on this way and present a formula for this limit distribution for a Markov chain. Moreover, we claim that our convergence theorems can be extended to algorithms which admit the change in the mutation rate and others parameters. The formula for a limit distribution requires some knowledge about the distribution of the fitness function on the whole solution space. However, it suggests the methods to control the algorithm parameters to get better convergence rate. The formula can play an important role in deriving new classification tools for genetic algorithms that use methods of the theory of dynamical systems. That tools will exploit real dynamics of the search and be independent of the taxonomic methods of classification that are used nowadays. On the base of the knowledge of the limit distribution we construct an optimal genetic algorithm in the probabilistic sense. Generally this algorithm is impossible to describe. This is an open problem at the moment, however, its existence and its form suggest an improvement of the original algorithm by changing its parameters. Constructed in this way the optimal genetic algorithm is an answer to one of the questions stayed by famous No Free Lunch Theorem. Moreover, it is a complementary result to this theorem. On the base of this theoretical result we perform a classification of algorithms and show empirical (computational) results in getting which the entropy, fractal dimension, or its approximations: the box-counting dimension or information dimension, are used. One of the most difficult, however, of practical importance, problems is the choice of an algorithm to given optimisation problem. The distinguishing between an optimisation problem and the algorithm and its choice creates to the main difficulty. Consequently, the distinguishing is an artificial operation because it abstains from the idea of genetic algorithm (GA), since the fitness function, arises from the cost function (i.e. the function to be optimised) is the main object of the genetic algorithm and it emerges from the formulation of the optimisation problem and it is difficult O pe n A cc es s D at ab as e w w w . ite ch on lin e. co m
Adaptive Strategies for Solving Constraint Satisfaction Problems

A major challenge in constraint programming is to develop efficient generic approaches to solve instances of the constraint satisfaction problem (CSP). In recent years, adaptive approaches for solving CSPs have attracted the interest of many researchers. General speaking, a strategy that uses the results of its own search experience to modify its subsequent behavior does adaptive search. In this dissertationwe explore adaptive strategies for backtracking search on various levels. First, we investigate adaptive search-guiding heuristics for ordering variables in CSPs. These adaptive heuristics learn and use information from every node explored in the search tree, whereas traditional static and dynamic heuristics only use information about the initial and current nodes. We then perform a wide empirical evaluation of the proposed variable ordering heuristics and compare them with the current state-of-the-art variable ordering strategies. Concerning constraint propagationwhich is used as an inferencemechanism in order to simplify a problem so as to make it easier to solve, we explore adaptive strategies for ordering the different revisions performed when enforcing arc consistency algorithms. Next, we propose adaptive branching heuristics for splitting the search tree. The application of these heuristics results in an adaptive branching scheme. Experiments with instantiations of the proposed generic heuristics confirm that search with adaptive branching outperforms search with a fixed branching scheme on a wide range of problem. Finally, we propose a new a generic approach for branching where the variable’s domains are grouped into sets by using the scores assigned to values by a value ordering heuristic, and a clustering algorithm from machine learning. In general, this dissertation contributes to the design and implementation of adaptive and autonomous constraint solvers that have the ability to advantageously modify modelers decisions that typically in mainstream CP solvers are taken prior to search.
Clustering of Authors’ Texts of English Fiction in the Vector Space of Semantic Fields

Abstract This paper describes the analysis of possible differentiation of the author’s idiolect in the space of semantic fields; it also analyzes the clustering of text documents in the vector space of semantic fields and in the semantic space with orthogonal basis. The analysis showed that using the vector space model on the basis of semantic fields is efficient in cluster analysis algorithms of author’s texts in English fiction. The study of the distribution of authors' texts in the cluster structure showed the presence of the areas of semantic space that represent the idiolects of individual authors. Such areas are described by the clusters where only one author dominates. The clusters, where the texts of several authors dominate, can be considered as areas of semantic similarity of author’s styles. SVD factorization of the semantic fields matrix makes it possible to reduce significantly the dimension of the semantic space in the cluster analysis of author’s texts. Using the clustering of the semantic field vector space can be efficient in a comparative analysis of author's styles and idiolects. The clusters of some authors' idiolects are semantically invariant and do not depend on any changes in the basis of the semantic space and clustering method.