Miracle-dz/UniFAR-FTUs huggingface dataset:
Feature Selection and Classification in Supporting Report-Based Self-Management for People with Chronic Pain

Chronic pain is a common long-term condition that affects a person's physical and emotional functioning. Currently, the integrated biopsychosocial approach is the mainstay treatment for people with chronic pain. Self-reporting (the use of questionnaires) is one of the most common methods to evaluate treatment outcome. The questionnaires can consist of more than 300 questions, which is tedious for people to complete at home. This paper presents a machine learning approach to analyze self-reporting data collected from the integrated biopsychosocial treatment, in order to identify an optimal set of features for supporting self-management. In addition, a classification model is proposed to differentiate the treatment stages. Four different feature selection methods were applied to rank the questions. In addition, four supervised learning classifiers were used to investigate the relationships between the numbers of questions and classification performance. There were no significant differences between the feature ranking methods for each classifier in overall classification accuracy or AUC (p >; 0.05); however, there were significant differences between the classifiers for each ranking method (p <; 0.001). The results showed the multilayer perceptron classifier had the best classification performance on an optimized subset of questions, which consisted of ten questions. Its overall classification accuracy and AUC were 100% and 1, respectively.
Ancient Duplications Have Led to Functional Divergence of Vitellogenin-Like Genes Potentially Involved in Inflammation and Oxidative Stress in Honey Bees

Protection against inflammation and oxidative stress is key in slowing down aging processes. The honey bee (Apis mellifera) shows flexible aging patterns linked to the social role of individual bees. One molecular factor associated with honey bee aging regulation is vitellogenin, a lipoglycophosphoprotein with anti-inflammatory and antioxidant properties. Recently, we identified three genes in Hymenopteran genomes arisen from ancient insect vitellogenin duplications, named vg-like-A, -B, and -C. The function of these vitellogenin homologs is unclear. We hypothesize that some of them might share gene- and protein-level similarities and a longevity-supporting role with vitellogenin. Here, we show how the structure and modifications of the vg-like genes and proteins have diverged from vitellogenin. Furthermore, all three vg-like genes show signs of positive selection, but the spatial location of the selected protein sites differ from those found in vitellogenin. We show that all these genes are expressed in both long-lived winter worker bees and in summer nurse bees with intermediate life expectancy, yet only vg-like-A shows elevated expression in winter bees as found in vitellogenin. Finally, we show that vg-like-A responds more strongly than vitellogenin to inflammatory and oxidative conditions in summer nurse bees, and that also vg-like-B responds to oxidative stress. We associate vg-like-A and, to lesser extent, vg-like-B to the antiaging roles of vitellogenin, but that vg-like-C probably is involved in some other function. Our analysis indicates that an ancient duplication event facilitated the adaptive and functional divergence of vitellogenin and its paralogs in the honey bee.
BioProspector: Discovering Conserved DNA Motifs in Upstream Regulatory Regions of Co-Expressed Genes

The development of genome sequencing and DNA microarray analysis of gene expression gives rise to the demand for data-mining tools. BioProspector, a C program using a Gibbs sampling strategy, examines the upstream region of genes in the same gene expression pattern group and looks for regulatory sequence motifs. BioProspector uses zero to third-order Markov background models whose parameters are either given by the user or estimated from a specified sequence file. The significance of each motif found is judged based on a motif score distribution estimated by a Monte Carlo method. In addition, BioProspector modifies the motif model used in the earlier Gibbs samplers to allow for the modeling of gapped motifs and motifs with palindromic patterns. All these modifications greatly improve the performance of the program. Although testing and development are still in progress, the program has shown preliminary success in finding the binding motifs for Saccharomyces cerevisiae RAP1, Bacillus subtilis RNA polymerase, and Escherichia coli CRP. We are currently working on combining BioProspector with a clustering program to explore gene expression networks and regulatory mechanisms.
Treatment of experimental arthritis with poly(d, l-lactic/glycolic acid) nanoparticles encapsulating betamethasone sodium phosphate

Objective: To examine the therapeutic activity of hydrophilic glucocorticoid encapsulated in PLGA nanoparticles, which have shown slow release and are targeted to inflamed joints after intravenous administration, in experimental arthritis models. Methods: Betamethasone sodium phosphate (BSP) encapsulated in PLGA nanoparticles with a size of 100–200 nm (PLGA-nanosteroid) was prepared using a modified oil in water emulsion solvent diffusion method with Zn ions and coated with lecithin. Rats with adjuvant arthritis (AA rats) and mice with anti-type II collagen antibody induced arthritis (AbIA mice) were treated intravenously with PLGA-nanosteroid after the initial sign of arthritis. Results: In AA rats, a 30% decrease in paw inflammation was obtained in 1 day and maintained for 1 week with a single injection of 100 μg of PLGA-nanosteroid. Soft x ray examination 7 days after this treatment showed decreased soft tissue swelling. Moreover, the PLGA-nanosteroid was also highly effective in AbIA mice. A single injection of 30 μg of the PLGA-nanosteroid resulted in almost complete remission of the inflammatory response after 1 week. In contrast, the same dose of free BSP after three administrations only moderately reduced the severity of inflammation. In addition, a histological examination 7 days after the treatment showed a significant decrease of the inflammatory cells in the joints. Conclusion: The observed strong therapeutic benefit obtained with PLGA-nanosteroid may be due to the targeting of the inflamed joint and its prolonged release in situ. Targeted drug delivery using a sustained release PLGA-nanosteroid is a successful intervention in experimental arthritis.
Glycan heterogeneity on gold nanoparticles increases lectin discrimination capacity in label-free multiplexed bioassays†

The development of new analytical tools as point-of-care biosensors is crucial to combat the spread of infectious diseases, especially in the context of drug-resistant organisms, or to detect biological warfare agents. Glycan/lectin interactions drive a wide range of recognition and signal transduction processes within nature and are often the first site of adhesion/recognition during infection making them appealing targets for biosensors. Glycosylated gold nanoparticles have been developed that change colour from red to blue upon interaction with carbohydrate-binding proteins and may find use as biosensors, but are limited by the inherent promiscuity of some of these interactions. Here we mimic the natural heterogeneity of cell-surface glycans by displaying mixed monolayers of glycans on the surface of gold nanoparticles. These are then used in a multiplexed, label-free bioassay to create ‘barcodes’ which describe the lectin based on its binding profile. The increased information content encoded by using complex mixtures of a few sugars, rather than increased numbers of different sugars makes this approach both scalable and accessible. These nanoparticles show increased lectin identification power at a range of lectin concentrations, relative to single-channel sensors. It was also found that some information about the concentration of the lectins can be extracted, all from just a simple colour change, taking this technology closer to being a realistic biosensor.
Post-processing of Association Rules using Taxonomies

The data mining process enables the end users to analyse, understand and use the extracted knowledge in an intelligent system or to support in the decision-making processes. However, many algorithms used in the process encounter large quantities of patterns, complicating the analysis of the patterns. This fact occurs with association rules, a data mining technique that tries to identify intrinsic patterns in large data sets. A method that can help the analysis of the association rules is the use of taxonomies in the step of post-processing knowledge. In this paper, the GART algorithm is proposed, which uses taxonomies to generalize association rules, and the RulEE-GAR computational module, that enables the analysis of the generalized rules
Photon Journal of Biomedical Engineering Ph ton Biosensor Technology for Chemical and Biological Toxins : Progress and Prospects

McGoron A.J., Research Guide Professor Email: mcgorona@fiu.edu Phone: +1-305-348-1352 Abstract The detection, identification and monitoring of chemical and biological toxins (CBTs) released during warfare and disaster into the environment has critical global importance. The aim of this review article is to give a clear picture of the trend in the development of biosensor technologies for the detection of CBTs. The article introduces the severity of this global problem, followed by the brief description of conventional, standard and advanced state-of-art biosensor technologies for the detection of CBTs. The intrinsic benefits and limitations of these technologies are summarized in context to developing a biosensor device imperative for military and civil defense. Lastly, the scope for further research and innovation in the development of a portable or wearable environmental sensor chip (WESC) for global peace and security is enlightened.
Regulatory T Cells Accumulate in the Lung Allergic Inflammation and Efficiently Suppress T-Cell Proliferation but Not Th2 Cytokine Production

Foxp3+CD25+CD4+ regulatory T cells are vital for peripheral tolerance and control of tissue inflammation. In this study, we characterized the phenotype and monitored the migration and activity of regulatory T cells present in the airways of allergic or tolerant mice after allergen challenge. To induce lung allergic inflammation, mice were sensitized twice with ovalbumin/aluminum hydroxide gel and challenged twice with intranasal ovalbumin. Tolerance was induced by oral administration of ovalbumin for 5 consecutive days prior to OVA sensitization and challenge. We detected regulatory T cells (Foxp3+CD25+CD4+ T cells) in the airways of allergic and tolerant mice; however, the number of regulatory T cells was more than 40-fold higher in allergic mice than in tolerant mice. Lung regulatory T cells expressed an effector/memory phenotype (CCR4highCD62LlowCD44highCD54highCD69+) that distinguished them from naive regulatory T cells (CCR4intCD62LhighCD44intCD54intCD69−). These regulatory T cells efficiently suppressed pulmonary T-cell proliferation but not Th2 cytokine production.
Art and Social Theory: Sociological Arguments in Aesthetics

List of Illustrations. Acknowledgements. Introduction. 1. Conceptions and Approaches. Metaphysical conceptions of art. Sociological conceptions of art. Humanistic art history. Marxist social history of art. Cultural studies, cultural materialism and postmodernism. Institutional theories of art in analytical philosophy. Anthropological studies of art. Empirical sociology of contemporary arts institutions. Conclusion. 2. Aesthetic Value and Political Value. Value--relevance and value--neutrality. Liberal--humanistic art scholarship. Socialist criticism. Feminist criticism. Postcolonial criticism. Sociology, politics and aesthetics. Conclusion. 3 Production and Socioeconomic Structure. Art and social class structure: Marxist theories. Art and social evolution: Pitirim Sorokin, Arnold Hauser and Robert Witkin. Patronage: the church, the monarchy and the nobility. Arts markets in early modern Europe. The state and the market in twentieth--century arts funding. Conclusion. 4. Consumption and Aesthetic Autonomy. Kantian aesthetics. Leisure, gentility and aesthetic autonomy. Art and cultural capital: Pierre Bourdieu. Arts consumption in the US. Aesthetic validity versus the sociology of taste. Conclusion. 5. Ideology and Utopia. Origins of the critique of mass culture. Art in German idealist philosophy. Marx, Bloch and Lukacs. Art, myth and religion in nineteenth--century high culture. Richard Wagner and Friedrich Nietzsche. Sublimation and civilization: Sigmund Freud and Herbert Marcuse. Conclusion. 6. Modernity and Modernism. Aesthetic modernity after Charles Baudelaire. Max Weber: rationalization and the aesthetic sphere. Georg Simmel: money, style and sociability. Walter Benjamin: mourning and the messianic. Siegfried Kracauer: the redemption of physical reality. Theodor Adorno and the Frankfurt School. The Frankfurt School reassessed. Conclusion. 7. Postmodernism and After. German aesthetic thought since 1945: from Heidegger to Habermas. French aesthetic thought since 1945: literary thinking after the Marquis de Sade. Postmodernism. Beyond postmodernism: autonomy and reflexivity. Globalization and the arts. Conclusion. Further Reading. References. Index
Unsupervised query categorization using automatically-built concept graphs

Automatic categorization of user queries is an important component of general purpose (Web) search engines, particularly for triggering rich, query-specific content and sponsored links. We propose an unsupervised learning scheme that reduces dramatically the cost of setting up and maintaining such a categorizer, while retaining good categorization power. The model is stored as a graph of concepts where graph edges represent the cross-reference between the concepts. Concepts and relations are extracted from query logs by an offline Web mining process, which uses a search engine as a powerful summarizer for building a concept graph. Empirical evaluation indicates that the system compares favorably on publicly available data sets (such as KDD Cup 2005) as well as on portions of the current query stream of Yahoo! Search, where it is already changing the experience of millions of Web search users.
Secure and Efficient Protocol for Route Optimization in PMIPv6-Based Smart Home IoT Networks

The communication in the Smart Home Internet of Things (SH-IoT) comprising various electronic devices and sensors is very sensitive and crucial. In addition, the key requirements of the SH-IoT include channel security, handover support, mobility management, and consistent data rates. Proxy mobile IPv6 (PMIPv6) is considered as one of the core solutions to handle extreme mobility; however, the default PMIPv6 cannot ensure performance enhancement in SH-IoT scenarios, i.e., Route Optimization (RO). The existing security protocols for PMIPv6 cannot support secure RO for smart home IoT services, where mobile nodes (MNs) communicate with home IoT devices not belonging to their domain. Motivated by this, a secure protocol is proposed, which uses trust between PMIPv6 domain and smart home to ensure security as well as performance over the path between MNs and home IoT devices. The proposed protocol includes steps for secure RO and handover management, where mutual authentication, key exchange, perfect forward secrecy, and privacy are supported. The correctness of the proposed protocol is formally analyzed using BAN-logic and Automated Validation of Internet Security Protocols and Applications (AVISPA). Furthermore, network simulations are conducted to evaluate the performance efficiency of the proposed protocol. The results show that the proposed approach is capable of providing secure transmission by resolving the RO problem in PMIPv6 along with the reduction in handover latency, end to end delay and packet loss, and enhancement in throughput and transmission rate even during the handover phase.
For Positivist Organization Theory: Proving the Hard Core

Positivist Organization Theory For Determinism Against Strategic Choice For Determinism Subjective Factors in Strategic Choice For Functionalism Against Politics and 'Parkinson's Law' For Functionalism The Validity of Blau's Theory For Cartesianism Against Organization Types and Quantum Jumps For Generalization in Positivist Organization Theory For Generalization of Organizational Size Conclusions
Methuselah : Intelligent Data Aging

Recent years has seen an ever widening gulf develop between access times for data stored in memory versus data on disk. Concurrently, growth in main memory sizes has led to large gains in the popularity of database systems that keep their working sets primarily in memory. These systems make the assumption that either all data in always in memory, or that access to disk, managed by a standard buffer pool, will suffice. However, with data sizes growing steadily and more quickly than available main memory, it is clear that all in-memory systems will need some way to move data to a cold backing store. This paper proposes a new online, statistics based, batchoriented technique to allow an RDBMS to leverage cold storage to increase data capacity without overly impacting query performance. Our solution couples well with semantic knowledge about an application, making it easy to take advantage of application specific access patterns. We develop a number of techniques for efficient statistics gathering and management, movement of data to cold storage, and querying of data in cold storage. We show this approach fits well into the main memory model, and that it has excellent performance in some industry standard benchmarks, as well as for an Enterprise Resource Planning benchmark we have developed.
Stability analysis of discrete 2-D autonomous systems

In this paper, we analyze the ℓ<sup>2</sup>-stability and the ℓ<sup>∞</sup>-stability of general discrete 2-D autonomous systems. The problem of ℓ<sup>∞</sup>-stability has not been studied before for general 2-D autonomous systems. We give a necessary condition for the ℓ<sup>∞</sup>-stability of discrete 2-D autonomous systems. We also give sufficient conditions for the ℓ<sup>2</sup>-stability and the ℓ<sup>∞</sup>-stability of discrete 2-D autonomous systems, which are easy to check.
ON A STOCHASTIC MODEL FOR THE SPIN-DOWN OF SOLAR-TYPE STARS

Modeling the rotation history of solar-type stars is an outstanding problem in modern astrophysics. One of the main challenges is to explain the dispersion in the distribution of stellar rotation rate for young stars. Previous works have advocated diverse mechanisms to explain the presence of fast rotators (FRs) and also of slow rotators (SRs). For instance, dynamo saturation can limit the stellar spin-down and explain the presence of FRs but does not produce enough SRs. Here, we present a new model that can account for the presence of both types of rotators by incorporating fluctuations in the angular momentum loss. This renders the spin-down problem probabilistic in nature, some stars experiencing more braking on average than others. We show that random fluctuations in the loss of angular momentum enhance the population of both FR and SR compared to the deterministic cases (with a linear dynamo prescription or with dynamo saturation). The stochastic angular momentum loss thus provides an alternative physical mechanism to that of a saturated dynamo, with an even better agreement with observations.
The novel recognition method with Optimal Wavelet Packet and LSTM based Recurrent Neural Network

In order to adaptively extract the subject-based time-frequency features of motor imagery EEG (MI-EEG) and make full use of the sequential information hidden in MI-EEG features, a Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) is integrated with Optimal Wavelet Packet Transform (OWPT) to yield a novel recognition method, denoted as OWLR. Firstly, OWPT is applied to each channel of MI-EEG, and the improved distance criterion is used to find the optimal wavelet packet subspaces, whose coefficients are further selected as the time-frequency features of MI-EEG. Finally, a LSTM based RNN is used for classifying MI-EEG features. Experiments are conducted on a publicly available dataset, and the 5-fold cross validation experimental results show that OWLR yields relatively higher classification accuracies compared to the existing approaches. This is helpful for the future research and application of RNN in processing of MI-EEG.
Cultural diversity and education : foundations, curriculum, and teaching

This title is only available as a Loose-Leaf version with Pearson eText. This definitive resource gives readers a strong background in the conceptual, theoretical, and philosophical issues in multicultural education from one of the most well respected authorities in the field-James A. Banks. In the opening chapter author Banks presents his well-known and widely used concept of Dimensions of Multicultural Education to help build an understanding of how the various components of multicultural education are interrelated. He then provides vital information on preparing students to function as effective citizens in a global world; discusses the dimensions, history, and goals of multicultural education; presents the conceptual, philosophical, and research issues related to education and diversity; examines the issues involved in curriculum and teaching; looks at gender equity, disability, giftedness, and language diversity; and focuses on intergroup relations and principles for teaching and learning. This new edition incorporates new concepts, theories, research, and developments in the field of multicultural education that have occurred since the last edition was published, and features: 0133830969 / 9780133830965 Cultural Diversity and Education: Foundations, Curriculum, and Teaching, Loose-Leaf Version with Pearson eText -- Access Card Package 0133521516 / 9780133521511 Cultural Diversity and Education: Foundations, Curriculum, and Teaching, Loose-Leaf Version 0133523195 / 9780133523195 Cultural Diversity and Education: Foundations, Curriculum, and Teaching, Pearson eText -- Access Card
Gaussian Process Functional Regression Modeling for Batch Data

Summary A Gaussian process functional regression model is proposed for the analysis of batch data. Covariance structure and mean structure are considered simultaneously, with the covariance structure modeled by a Gaussian process regression model and the mean structure modeled by a functional regression model. The model allows the inclusion of covariates in both the covariance structure and the mean structure. It models the nonlinear relationship between a functional output variable and a set of functional and nonfunctional covariates. Several applications and simulation studies are reported and show that the method provides very good results for curve fitting and prediction.
The 2013 Multi-objective Physical Travelling Salesman Problem Competition

This paper presents the game, framework, rules and results of the Multi-objective Physical Travelling Salesman Problem (MO-PTSP) Competition, that was held at the 2013 IEEE Conference on Computational Intelligence in Games (CIG). The MO-PTSP is a real-time game that can be seen as a modification of the Travelling Salesman Problem, where the player controls a ship that must visit a series of waypoints in a maze while minimizing three opposing goals: time spent, fuel consumed and damage taken. The rankings of the competition are computed using multi-objective concepts, a novel approach in the field of game artificial intelligence competitions. The winning entry of the contest is also explained in detail. This controller is based on the Monte Carlo Tree Search algorithm, and employed Covariance Matrix Adaptation Evolution Strategy (CMA-ES) for parameter tuning.