Miracle-dz/UniFAR-FTUs huggingface dataset:
Emerging Theories in Health Promotion Practice and Research (2nd ed.), by DiClemente, R. J., Crosby, R. A., & Kegler, M. (Eds.)

According to the Ottawa Charter (World Health Organization [WHO], 1986), and as referenced in the WHO Health Promotion Glossary (1998), health promotion is “the process of enabling people to increase control over, and to improve their health” (WHO, 1986, p. 1). It is recognized that health promotion occurs at multiple levels, i.e., individual, community, the broader society, and policy. With regard to health promoting strategies, WHO recognizes that “people have to be at the centre of health promotion, action and decision-making processes for them to be effective” (WHO, 1998, p. 12). Social workers are positioned to address health inequities through exercising leadership in health promotion initiatives, particularly in locally focused policies and activities designed to address critical issues such as food security, environmental justice, physical activity, violence prevention, cancer control, women’s health, and so on. The second edition of Emerging Theories in Health Promotion Practice and Research by DiClemente and colleagues provides a useful reference for teaching grant writing, research, and practice. This book acquaints the reader with approaches and theories related to health promotion across ecological levels. Part I presents individual level approaches; part II deals with community-based approaches; and part III presents ecological approaches, a more integrative chapter looking at multilevel strategies, and a concluding chapter on translating theory to practice, including global application. Focusing on individual level strategies, Part I is composed of seven chapters, six of which are dedicated to specific theories and applications that enable individuals to exercise better control over conditions that influence their health. For instance, one chapter describes the Information–Motivation– Behavioral Skills Model as applied to preventing HIV infection. A history of the model is provided, with evidence to date, and helpful figures assist the reader to comprehend how the model can be applied to prevent HIV infection. A detailed table provides an easy-to-understand review of the literature supporting this approach. An evaluation and critique of the model is discussed and the chapter includes a very helpful case example. Extensive references are provided. Other chapters in this section offer a similar format
03 12 28 9 v 2 1 4 Fe b 20 04 Scalar model of the glueball

A scalar model of the glueball is offered. The model is based on the nonperturbative calculation of 2 and 4-points Green’s functions. Approximately they can be expressed via a scalar field. On the basis of the SU(3) Yang-Mills Lagrangian an effective Lagrangian for the scalar field is derived. The corresponding field equations are solved for the spherically symmetric case. The obtained solution is interpreted as a bubble of the SU(3) quantized gauge field.
FPGA BASED ENCRYPTION DESIGN USING VHDL

The job of cryptographers is quite crucial as they are responsible to keep privacy of personal information and indirectly the protection of national security. The efficient encryption method ensures the information security. Here VHDL (Very high speed integrated circuits Hardware Description Language) and FPGA (Field Programmable Gate Arrays technology) are used for highly efficient encryption design to secure the information over open network transmission. The proposed work is a composite encryption technique comprised of transposition and substitution to generate complex encipherment. The design is implemented and tested in Xilinx ISE.9. 2. A final result signifies the efficiency and reliability for FPGA (SPARTAN-3) device.
Bayesian Action&Perception: Representing the World in the Brain

Theories of perception seek to explain how sensory data are processed to identify previously experienced objects, but they usually do not consider the decisions and effort that goes into acquiring the sensory data. Identification of objects according to their tactile properties requires active exploratory movements. The sensory data thereby obtained depend on the details of those movements, which human subjects change rapidly and seemingly capriciously. Bayesian Exploration is an algorithm that uses prior experience to decide which next exploratory movement should provide the most useful data to disambiguate the most likely possibilities. In previous studies, a simple robot equipped with a biomimetic tactile sensor and operated according to Bayesian Exploration performed in a manner similar to and actually better than humans on a texture identification task. Expanding on this, “Bayesian Action&Perception” refers to the construction and querying of an associative memory of previously experienced entities containing both sensory data and the motor programs that elicited them. We hypothesize that this memory can be queried (i) to identify useful next exploratory movements during identification of an unknown entity (“action for perception”) or (ii) to characterize whether an unknown entity is fit for purpose (“perception for action”) or (iii) to recall what actions might be feasible for a known entity (Gibsonian affordance). The biomimetic design of this mechatronic system may provide insights into the neuronal basis of biological action and perception.
Congestion Control based on AMC Scheme for WLAN Mesh Networks

Recently, Wireless Local Area Network (WLAN) mesh networks are attracting significant attention. Because the Institute of Electrical and Electronics Engineers (IEEE) 802.11 Medium Access Control (MAC) protocol has been designed for single- hop networks, the performance of WLAN mesh networks degrades due to congestion, and buffer overflow in intermediate Mesh Nodes (MNs). In this article, we propose new congestion control scheme which applies Adaptive Modulation and Coding (AMC) scheme and effective buffer usage to enhance the performance. AMC scheme uses multiple fragmentation thresholds, modulation levels and coding rates for various data sizes according to Channel State Information (CSI). Because CSI can be used more effectively to squeeze more bits into the medium, the throughput is enhanced. Simulation results show that the proposed scheme achieves much higher throughput than other congestion control schemes.
Expression and Localization of Pituitary Adenylate Cyclase-Activating Polypeptide-Specific Receptor ( PAC 1 R ) After Traumatic Brain Injury in Mice

Pituitary adenylate cyclase-activating polypeptide (PACAP) is a pleiotropic peptide and a well known strong neuroprotectant in various neuronal conditions. PACAP has some receptors and it acts via these receptors. PACAP type 1 receptor (PAC1R) is known as the main receptor of PACAP in neuronal damages. On the other hand, the expression and localization of PAC1R have not been elicited well in traumatic brain injury (TBI). In this study, the expression and the cellular localization of PAC1R were investigated immunohistochemically after TBI in mouse controlled cortical impact (CCI) model. The PAC1R positive cells were expressed from 3 h in the peri-contusional area and observed to 7days after the TBI. Using double-immunohistochemistry, the PAC1R immunopositive cells were co-localized with the microglia on 1day and with microglia and astrocyte on 7 days after TBI. These results suggest that PACAP and PAC1R might play an important role in traumatic brain injury as well as other conditions.
Scalable data center multicast using multi-class Bloom Filter

Multicast benefits data center group communications in saving network bandwidth and increasing application throughput. However, it is challenging to scale Multicast to support tens of thousands of concurrent group communications due to limited forwarding table memory space in the switches, particularly the low-end ones commonly used in modern data centers. Bloom Filter is an efficient tool to compress the Multicast forwarding table, but significant traffic leakage may occur when group membership testing is false positive. To reduce the Multicast traffic leakage, in this paper we bring forward a novel multi-class Bloom Filter (MBF), which extends the standard Bloom Filter by embracing element uncertainty. Specifically, MBF sets the number of hash functions in a per-element level, based on the probability for each Multicast group to be inserted into the Bloom Filter. We design a simple yet effective algorithm to calculate the number of hash functions for each Multicast group. We have prototyped a software based MBF forwarding engine on the Linux platform. Simulation and prototype evaluation results demonstrate that MBF can significantly reduce Multicast traffic leakage compared to the standard Bloom Filter, while causing little system overhead.
Multiple-View Spectral Clustering for Group-wise Functional Community Detection

Functional connectivity analysis yields powerful insights into our understanding of the human brain. Group-wise functional community detection aims to partition the brain into clusters, or communities, in which functional activity is inter-regionally correlated in a common manner across a group of subjects. In this article, we show how to use multiple-view spectral clustering to perform group-wise functional community detection. In a series of experiments on 291 subjects from the Human Connectome Project, we compare three versions of multiple-view spectral clustering: MVSC (uniform weights), MVSCW (weights based on subject-specific embedding quality), and AASC (weights optimized along with the embedding) with the competing technique of Joint Diagonalization of Laplacians (JDL). Results show that multiple-view spectral clustering not only yields group-wise functional communities that are more consistent than JDL when using randomly selected subsets of individual brains, but it is several orders of magnitude faster than JDL.
X-RAY FLARES OF EV Lac: STATISTICS, SPECTRA, AND DIAGNOSTICS

We study the spectral and temporal behavior of X-ray flares from the active M dwarf EV Lac in 200 ks of exposure with the Chandra/HETGS. We derive flare parameters by fitting an empirical function which characterizes the amplitude, shape, and scale. The flares range from very short (<1 ks) to long (∼104 s) duration events with a range of shapes and amplitudes for all durations. We extract spectra for composite flares to study their mean evolution and to compare flares of different lengths. Evolution of spectral features in the density–temperature plane shows probable sustained heating. The short flares are significantly hotter than the longer flares. We determined an upper limit to the Fe K fluorescent flux, the best-fit value being close to what is expected for compact loops.
There’s no Data like More Data? Revisiting the Impact of Data Size on a Classification Task

In the paper we investigate the impact of data size on a Word Sense Disambiguation task (WSD). We question the assumption that the knowledge acquisition bottleneck, which is known as one of the major challenges for WSD, can be solved by simply obtaining more and more training data. Our case study on 1,000 manually annotated instances of the German verb ""drohen"" (threaten) shows that the best performance is not obtained when training on the full data set, but by carefully selecting new training instances with regard to their informativeness for the learning process (Active Learning). We present a thorough evaluation of the impact of different sampling methods on the data sets and propose an improved method for uncertainty sampling which dynamically adapts the selection of new instances to the learning progress of the classifier, resulting in more robust results during the initial stages of learning. A qualitative error analysis identifies problems for automatic WSD and discusses the reasons for the great gap in performance between human annotators and our automatic WSD system.
An Experimental Study on capacitive touch sensor's response to E. coli bacteria

Escherichia coli or E. coli is the dominant pathogen in Urinary Tract Infections (UTIs) in more than 80 % of the cases. Laboratory urine culture is the standard detection method which is expensive and time consuming. This paper studies the possibility of using a capacitive sensor to diagnose UTIs. Experimental tests were carried out to investigate the touch sensor's response to artificially prepared E.coli sample. The capacitive touch sensor gave a consistent result that could be used to design a portable diagnostic device for early detection of UTIs. An exponential response was observed to increasing volume (up to 100 microliter) and E.coli presence in the sample. When the volume reached 100 microliter, the capacitance in average RAW Count Difference value became constant. Urine sample mixed with liquid E. coli gave the highest capacitance change (in average RAW Count Difference value) and the sensitivity at 100 microliter was found to be 22 counts per picofarad (pF).
The stimulatory effects of interleukin (IL)-12 on hematopoiesis are antagonized by IL-12-induced interferon gamma in vivo

Interleukin (IL)-12 synergizes with other cytokines to stimulate the proliferation and differentiation of early hematopoietic progenitors in vitro. However, in vivo administration of IL-12 decreases peripheral blood counts and bone marrow hematopoiesis. Here, we used interferon (IFN) gamma receptor-deficient (IFN gamma R-/-) mice to investigate whether the in vivo inhibition of hematopoiesis by IL-12 is indirectly mediated by IL-12-induced IFN-gamma. IL-12 administered for 4 d (1 microgram/mouse per day) resulted in lower peripheral blood counts and a 2-fold decrease in bone marrow cellularity in wild-type mice, but not in IFN gamma R-/- mice. Bone marrow hematopoietic progenitors were decreased after IL-12 treatment in wild-type mice, but rather increased in IFN gamma R-/- mice. Splenic cellularity was 2.3-fold higher after IL-12 administration in wild-type mice, largely due to natural killer (NK) cell and macrophage infiltration together with some extramedullary hematopoiesis. In IFN gamma R-/- mice, spleen cellularity was less increased, there were fewer infiltrating NK cells, but a strong extramedullary hematopoiesis. Thus, alterations mediated by IL-12- induced IFN-gamma include reduction in bone marrow cellularity and hematopoietic progenitors, as well as pronounced splenomegaly, largely caused by NK cell infiltration. In the absence of IFN-gamma signaling, IL-12 promotes hematopoiesis, consistent with its in vitro activities.
The homeodomain factor Gbx1 is required for locomotion and cell specification in the dorsal spinal cord

Dorsal horn neurons in the spinal cord integrate and relay sensory information to higher brain centers. These neurons are organized in specific laminae and different transcription factors are involved in their specification. The murine homeodomain Gbx1 protein is expressed in the mantle zone of the spinal cord at E12.5-13.5, correlating with the appearance of a discernable dorsal horn around E14 and eventually defining a narrow layer in the dorsal horn around perinatal stages. At postnatal stages, Gbx1 identifies a specific subpopulation of GABAergic neurons in the dorsal spinal cord. We have generated a loss of function mutation for Gbx1 and analyzed its consequences during spinal cord development. Gbx1−/− mice are viable and can reproduce as homozygous null mutants. However, the adult mutant mice display an altered gait during forward movement that specifically affects the hindlimbs. This abnormal gait was evaluated by a series of behavioral tests, indicating that locomotion is impaired, but not muscle strength or motor coordination. Molecular analysis showed that the development of the dorsal horn is not profoundly affected in Gbx1−/− mutant mice. However, analysis of terminal neuronal differentiation revealed that the proportion of GABAergic inhibitory interneurons in the superficial dorsal horn is diminished. Our study unveiled a role for Gbx1 in specifying a subset of GABAergic neurons in the dorsal horn of the spinal cord involved in the control of posterior limb movement.
Towards Prediction of Metabolic Products of Polyketide Synthases: An In Silico Analysis

Sequence data arising from an increasing number of partial and complete genome projects is revealing the presence of the polyketide synthase (PKS) family of genes not only in microbes and fungi but also in plants and other eukaryotes. PKSs are huge multifunctional megasynthases that use a variety of biosynthetic paradigms to generate enormously diverse arrays of polyketide products that posses several pharmaceutically important properties. The remarkable conservation of these gene clusters across organisms offers abundant scope for obtaining novel insights into PKS biosynthetic code by computational analysis. We have carried out a comprehensive in silico analysis of modular and iterative gene clusters to test whether chemical structures of the secondary metabolites can be predicted from PKS protein sequences. Here, we report the success of our method and demonstrate the feasibility of deciphering the putative metabolic products of uncharacterized PKS clusters found in newly sequenced genomes. Profile Hidden Markov Model analysis has revealed distinct sequence features that can distinguish modular PKS proteins from their iterative counterparts. For iterative PKS proteins, structural models of iterative ketosynthase (KS) domains have revealed novel correlations between the size of the polyketide products and volume of the active site pocket. Furthermore, we have identified key residues in the substrate binding pocket that control the number of chain extensions in iterative PKSs. For modular PKS proteins, we describe for the first time an automated method based on crucial intermolecular contacts that can distinguish the correct biosynthetic order of substrate channeling from a large number of non-cognate combinatorial possibilities. Taken together, our in silico analysis provides valuable clues for formulating rules for predicting polyketide products of iterative as well as modular PKS clusters. These results have promising potential for discovery of novel natural products by genome mining and rational design of novel natural products.
General linear models in a missing outcome environment of clinical trials incorporating splines for time-invariant continuous adjustment

Missing data is a common occurrence in longitudinal studies of health care research. Although many studies have shown the potential usefulness of current missing analyses, e.g., (1) Complete Case (CC) analysis; (2) imputation methods such as Last Observation Carried Forward (LOCF), multiple imputations, Expectation-Maximization algorithm approach; and (3) methods using all available data such as linear mixed model and generalized estimation equations approach, the CC analysis or LOCF imputation method have been popular due to their simplicity of execution regardless of some critical drawbacks. The proposed approach employs the generalized least squares method using all available data without deletion or imputations for missing outcomes, producing the best linear unbiased estimate. A simulation study was conducted to compare the proposed approach to commonly used missing analyses under each missing data mechanism and showed the validity of the proposed approach, especially with the first order autoregressive correlation structure. B-spline is applied to the proposed model to manage non-linear relationships between outcome and continuous covariate. Application to a cell therapy clinical trial is presented.
Improving Personality/Character Traits in Individuals with Alcohol Dependence: The Influence of Mindfulness-Oriented Meditation

The study of personality is critical to enhance current knowledge of the psychological characteristics of alcohol dependence. Recent evidence shows that mindfulness-oriented meditation positively influences healthy individuals’ character. Here, it was assessed whether 8-week mindfulness-oriented meditation promotes similar changes in a group of alcohol-dependent individuals. A control group with alcohol dependence was also tested. Mindfulness-oriented meditation participants showed an increase in the character scores of the temperament and character inventory together with reduced risks of relapse. These longitudinal data highlight the importance of assessing personality in alcohol-dependent individuals and support the utility of therapeutic interventions for alcohol dependence aimed at enhancing individuals’ character.
Detection of Brucella ovis in ovine from Paraíba State, in the Northeast region of Brazil

To determine the presence of Brucella ovis in ovine from Paraíba State, in the Northeast region of Brazil, 80 animals slaughtered in the public slaughterhouse of Patos city were used. Before slaughter, blood samples were collected by jugular venopuncture from each animal, and after slaughter, testicles, epidydimus and uterus were aseptically collected. For the serological diagnosis of B. ovis and B. abortus infections, the agar gel immunodiffusion (AGID) and Rose Bengal (RBT) tests were carried out, respectively. In addition, microbiological culture and polymerase chain reaction (PCR) were performed on testicle, epidydimus and uterus samples. Six animals (7.5%) tested positive for the presence of B. ovis antibodies and all animals tested negative for the presence of B. abortus antibodies. One AGID-positive animal tested positive at uterine swab culture. PCR was able to amplify DNA of Brucella spp. from the pool of testicle, epidydimus and uterus samples from AGID-positive animals. This is the first report of isolation and detection of B. ovis DNA by PCR in ovine from the Northeast region of Brazil.
Imagery, Affect, and Decision Making

Traditionally, the principal focus of research on judgment and decision making has been largely cognitive and rationalistic. More recently, however, decision-making researchers have acknowledged the role of noncognitive factors and have offered limited accounts of how affect and imagery influence processes associated with judgment and choice. The present paper extends this direction by offering evidence from three studies conducted in widely diverse contexts that support the view that the concepts of mental imagery and affect can provide a powerful framework for predicting both intended and actual behavior from relatively simple image-elicitation techniques. The implications of this evidence are discussed in terms of their significance for imagery and affect to act as organizing principles in theories of judgment and decision making.
Performance in Practice of String Hashing Functions

String hashing is a findamental operation, used in countless applications where fast access to distinct stn’ngs is required. In this paper we describe a class of string hashing functions and explore its performance. In particular, using experiments with both small sets of keys and a large key set from a text database, we show that it is possible to achieve performance close to that theoretically predicted for hashing functions. We also consider criteria for choosing a hashing function and use them to compare our class of finctions to other methods for string hashing. These results show that our class of hashing finctions is reliable and eficient, and is therefore an appropriate choice for general-purpose hashing.