Miracle-dz/UniFAR-FTUs huggingface dataset:
Help-seeking behaviour in men and women with common mental health problems: cross-sectional study

Background Many people with mental health problems do not seek professional help but their use of other sources of help is unclear. Aims To investigate patterns of lay and professional help-seeking in men and women aged 16–64 years in relation to severity of symptoms and sociodemographic variables. Method Postal questionnaire survey, including the 12-item General Health Questionnaire (GHQ–12), sent to a stratified random sample (n=15222) of the population of Somerset. Results The response rate was 76%. Only 28% of people with extremely high GHQ–12 scores (⩾8) had sought help from their general practitioner but most (78%) had sought some form of help. Males, young people and people living in affluent areas were the least likely to seek help. Conclusions Health promotion interventions to encourage appropriate help-seeking behaviour in young people, particularly in men, may lead to improvements in the mental health of this group of the population.
Air quality in the Mexico megacity : an integrated assessment

Prologue. Preface. Chapter One: Air Quality Impacts: A Local and Global Concern. 1. Introduction. 2. Urban Air Pollution: Historical Record. 3. Sources and Transport of Air Pollution. 4. Impacts of Air Pollution. 5. Conclusion. Chapter Two: Cleaning the Air: A Comparative Overview. 1. Introduction. 2. Cleaning the Air: The Los Angeles Experience. 3. Cleaning the Air: The Experience of the Mexico City Metropolitan Area. 4. Discussion and Conclusions. Chapter Three: Forces Driving Pollutant Emissions in the MCMA. 1. Introduction. 2. Population Growth. 3. Urban Growth. 4. Economic Growth. 5. Production and Consumption of Energy. 6. Erosion and Biogenic Emissions Sources. 7. Conclusion. Chapter Four: Health Benefits of Air Pollution Control. 1. Introduction. 2. Evidence of Air Pollution Exposures and Health Effects. 3. 'Back of the Envelope Risk Assessment. 4. Approaches to Valuing Health Effects. 5. Discussion and Conclusions. Chapter Five: Air Pollution Science in the MCMA: Understanding Source-Receptor Relationships Through Emissions Inventories, Measurements and Modeling. 1. Introduction. 2. Basic Scientific Understanding and Important Questions for Policy. 3. Measurements of Meteorology and Air Quality. 4. Emissions Inventories. 5. Air Pollution Modeling Applications. 6. Scientific Understanding, Analysis of Uncertainties, Research Methods, andRecommendations. 7. Summary of Key Recommendations. Chapter Six: The MCMA Transportation System: Mobility and air Pollution. 1. Introduction. 2. Urban Transformation. 3. Transportation Demand. 4. Transportation Supply. 5. Transport-Related Emissions. 6. Regional Architecture Framework. 7. Transportation Strategic Planning by Various Government Agencies. 8. Key Problems and Future Policy Areas. Chapter Seven: Conclusions: Key Findings and Recommendations. 1. Introduction. 2. Integrated Framework for Air Quality Management Plans. 3. Health Benefits of Pollution Control. 4. Air Pollution Science: Understanding Source-Receptor Relationships. 5. Options to Reduce Mobile Source Emissions and Improve Mobility. 6. Options to Reduce Non-Transport Emissions Sources. 7. Institutional Framework for Air Quality Management. 8. Education and Capacity Building. 9. Funding Mechanisms for Air Quality Programs. 10. Summary. Photographs. Appendix A: Mexico Metropolitan Index for Air Quality (Imeca) and the Environmental Contingency Program. Appendix B: 'No Driving Day' (Hoy No circula). Appendix C: List of Acronyms. Appendix D: Glossary. References. About the Authors. Index.
In Search of Politics

Introduction. 1. In Search of Public Space. 2. In Search of Agency. Excursus 1: Ideology in the Postmodern World. Excursus. Excursus 2: Tradition and Autonomy in the Postmodern World. Excursus. Excursus 3: Postmodernity and Moral and Cultural Crisis. 3. In Search of Vision. Notes. Index.
Integrating workplace aggression research: Relational, contextual, and method considerations

Summary The present article takes an integrative perspective on the field of workplace aggression to highlight areas of ambiguity and opportunities for future research. First, by simultaneously examining the perpetrator- and target-focused literatures, we identify a great deal of overlap between predictors and outcomes in the two literatures, giving rise to the question of whether key constructs are predictors, outcomes, or both. Second, we determine that the question of “who is the perpetrator? ” and “who is the target? ” is considerably more ambiguous than implied within each of these independent literatures. Third, our examination suggests that a greater focus on the relational aspect of workplace aggression is particularly important to enable a more comprehensive understanding of this phenomenon. We examine and critique current methods and measurement and propose different approaches to explore workplace aggression in a more dynamic and contextualized way. Copyright © 2013 John Wiley & Sons, Ltd.
Improved bounds for sampling contingency tables

We study the problem of sampling contingency tables (nonnegative integer matrices with specified row and column sums) uniformly at random. We give an algorithm which runs in polynomial time provided that the row sums ri and the column sums cj satisfy ri = Ω(n3/2m log m), and cj = Ω(m3/2n log n). This algorithm is based on a reduction to continuous sampling from a convex set. The same approach was taken by Dyer, Kannan, and Mount in previous work. However, the algorithm we present is simpler and has weaker requirements on the row and column sums. © 2002 Wiley Periodicals, Inc. Random Struct. Alg. , 21: 135–146, 2002
Counting all equilateral triangles in {0,1,2,...,n}^3

We describe a procedure of counting all equilateral triangles in the three dimensional space whose coordinates are allowed only in the set $\{0,1,. ,n\}$. This sequence is denoted here by ET(n) and it has the entry A102698 in "The On-Line Encyclopedia of Integer Sequences". The procedure is implemented in Maple and its main idea is based on the results in \cite{eji}. Using this we calculated the values ET(n) for n=1.55 which are included here. Some facts and conjectures about this sequence are stated. The main of them is that $\ds \lim_{n\to \infty} \frac{\ln ET(n)}{\ln n+1}$ exists.
Does medial prefrontal cortex activity during self-knowledge reference reflect the uniqueness of self-knowledge? 1

TAKASHI NAKAO* Japan Society for the Promotion of Science; Nagoya University MAKOTO MIYATANI and MIZUKI NAKAO Hiroshima University TOMOHIRO TAKEZAWA National Rehabilitation Center for Persons with Disabilities MASAHARU MARUISHI, HIROYUKI MURANAKA, and HIROFUMI DOJO Hiroshima Prefectural Rehabilitation Center Abstract: For this study, functional magnetic resonance imaging was used to examinewhether medial prefrontal cortex (MPFC) activity during self-knowledge reference reﬂects theuniqueness of self-knowledge. Experiment 1 investigated neural activity during self-knowledgereference (“Does the word describe you? ”) and self-monitoring (“Does the word make youfeel pleasant? ”). The results showed that self-knowledge reference and self-monitoringactivate common neural substrates within the MPFC. Experiment 2 compared neural activityproduced by self-knowledge reference, other-knowledge (acquaintance-knowledge) reference(“Does this word describe the person? ”), and evaluation (“Is this word socially desirable? ”). Results showed no increase in MPFC activity during self-knowledge reference relative toother-knowledge reference. Furthermore, self-knowledge reference and other-knowledgereference share common neural substrates within the MPFC. The results described indicatethat it is unlikely that MPFC activity during self-knowledge reference reﬂects the uniquenessof self-knowledge. The feature, as reﬂected in MPFC activity, is discussed. Key words: self-knowledge, self-monitoring, other-knowledge, emotion, medial prefrontal cortex. Humans have both knowledge of the externalenvironment and knowledge of themselves.The results of many psychological studies havesuggested that self-knowledge, which is know-ledge of one’s own personal traits and abilities,is a unique cognitive structure (Markus, 1977;Rogers, Kuiper, & Kirker, 1977). Kuiper andRogers (1979) performed early inﬂuential workon self-knowledge. They demonstrated thatmemory performance is enhanced when infor-mation is processed as a self-knowledge task(“Does the word describe you? ”) compared withwhen it is processed as an other-knowledgetask (e.g., “Does the word describe an
The perils of policy by p-value: Predicting civil conflicts

Large-n studies of conflict have produced a large number of statistically significant results but little accurate guidance in terms of anticipating the onset of conflict. The authors argue that too much attention has been paid to finding statistically significant relationships, while too little attention has been paid to finding variables that improve our ability to predict civil wars. The result can be a distorted view of what matters most to the onset of conflict. Although these models may not be intended to be predictive models, prescriptions based on these models are generally based on statistical significance, and the predictive attributes of the underlying models are generally ignored. These predictions should not be ignored, but rather need to be heuristically evaluated because they may shed light on the veracity of the models. In this study, the authors conduct a side-by-side comparison of the statistical significance and predictive power of the different variables used in two of the most influential models of civil war. The results provide a clear demonstration of how potentially misleading the traditional focus on statistical significance can be. Until out-of-sample heuristics — especially including predictions — are part of the normal evaluative tools in conflict research, we are unlikely to make sufficient theoretical progress beyond broad statements that point to GDP per capita and population as the major causal factors accounting for civil war onset.
Angelman's syndrome.

I read with the greatest delight the report by Teebi and Al-Saleh' describing (? possibly one of their own families in keeping with the ACHOO2 tradition) the autosomal dominant inheritance of a sneezing disorder provoked by fullness of the stomach. As the authors point out (possibly with tongue in cheek) it may not be all that uncommon a condition, but simply has not been previously reported because it does not lead to major disability. On the other hand it could be socially embarrassing or even stigmatising, albeit highly preferable to a compulsive belching reflex at the end of heavy meals. The basic epidemiological and genetic questions of frequency, prevalence, selective advantage, presence in other species, pathogenic mechanism, linkage, nucleotide sequence, number of introns (perhaps related to the number of sneezes), transposable elements, etc await further investigation. However, I would like to suggest that a catchy acronym may hasten the process of reporting other families (although it hasn't helped ACHOO much). Therefore, I propose the newly described condition be called the SNATIATION reflex-a combination of sneezing and satiation and easily remembered by the acronymous handle of Sneezing Noncontrollably At a Tune of Indulgence of the Appetite-a Trait Inherited and Ordained to be Named. In all seriousness I really was delighted to see the report, both because it tickled my imagination and because I think it is important to report 'normal' traits both structural and behavioural. We tend to teach human genetics by diseases and have few examples of nonpathological traits determined by single genes. Furthermore, the mechanisms involved in producing the sneezes in both the ACHOO and SNATIATION reflexes are totally unknown. Are there other inherited sneezing reflexes? Time to get busy surveying friends, relatives, and clinic personnel! JUDITH G HALL Universit of British Columbia Clinical Genetics Unit, Grace Hospital, Vancouver, Canada.
Prevalence of sickle cell disease and sickle cell trait in national neonatal screening studies

Sickle cell anemia is the best known hereditary blood disorder; there are serious complications associated with the condition. Diagnosis and early intervention reduce morbidity and mortality. These benefits have resulted in the widespread use of newborn screening education programs. In Brazil, the National Neonatal Screening Program established by decree 822/01 included sickle cell disease in the list of diseases tested in the so called "heel prick test". Since then, national studies of the results of this program have been periodically published. To review the literature in order to assess the prevalence of sickle cell trait and sickle cell anemia from data of national neonatal screening studies on hemoglobin S (Hb S). A bibliographic review was carried out using the key words: sickle cell anemia & hemoglobinopathies & neonatal screening & Brazil in the Bireme and SciELO databases. Original Brazilian studies presenting data on prevalence of the sickle cell trait (Hb AS) and sickle cell anemia (Hb SS) based on neonatal screening for Hb S were analysed. Twelve original national studies were identified with prevalences varying from 1.1% to 9.8% for the sickle cell trait and from 0.8 to 60 per 100,000 live births for sickle cell disease in different Brazilian regions. Conclusion: Neonatal screening for Hb S is a very useful method to assess the prevalence of sickle cell trait (Hb AS) and sickle cell anemia (Hb SS) in Brazil. There is a heterogeneous distribution of this disease with the highest prevalence in the northeastern region and the lowest prevalence in the south.
Specificity and robustness of long-distance connections in weighted, interareal connectomes

Significance Interareal communication occurs along physical pathways. The prevailing hypothesis is that long-distance connections reduce the processing length between brain areas, facilitating efficient communication. We show, in five weighted interareal network datasets, that the correlation of connection weight with distance implies that long-distance connections play only a minor role in reducing path length. Instead, long-distance connections add diversity to brain area inputs and outputs, leading to increasingly complex brain dynamics. These findings help to clarify our understanding of how brain structure contributes to interareal communication. Brain areas’ functional repertoires are shaped by their incoming and outgoing structural connections. In empirically measured networks, most connections are short, reflecting spatial and energetic constraints. Nonetheless, a small number of connections span long distances, consistent with the notion that the functionality of these connections must outweigh their cost. While the precise function of long-distance connections is unknown, the leading hypothesis is that they act to reduce the topological distance between brain areas and increase the efficiency of interareal communication. However, this hypothesis implies a nonspecificity of long-distance connections that we contend is unlikely. Instead, we propose that long-distance connections serve to diversify brain areas’ inputs and outputs, thereby promoting complex dynamics. Through analysis of five weighted interareal network datasets, we show that long-distance connections play only minor roles in reducing average interareal topological distance. In contrast, areas’ long-distance and short-range neighbors exhibit marked differences in their connectivity profiles, suggesting that long-distance connections enhance dissimilarity between areal inputs and outputs. Next, we show that—in isolation—areas’ long-distance connectivity profiles exhibit nonrandom levels of similarity, suggesting that the communication pathways formed by long connections exhibit redundancies that may serve to promote robustness. Finally, we use a linearization of Wilson–Cowan dynamics to simulate the covariance structure of neural activity and show that in the absence of long-distance connections a common measure of functional diversity decreases. Collectively, our findings suggest that long-distance connections are necessary for supporting diverse and complex brain dynamics.
Probabilistic Abstraction Lattices : A Computationally Efficient Model for Conditional Probability Estimation

Probabilistic models have shown increasing popularity for machine learning, data mining, and artificial intelligence in general. Probabilistic approaches have been successfully applied to collaborative filtering, robot navigation, text analysis, medical diagnosis, and many other areas. In general, we wish to efficiently represent the joint probability distribution among a set of variables in order to solve these and other problems. Most of the current approaches do so using a graph with a single node for each modeled variable, hidden or observed. While these models have proven effective in many domains, they also have certain limitations. We focus on Bayesian networks since they are the most popular and one of the most general types of probabilistic graphical models. In Bayesian networks, complete inference may take exponential time in the number of variables modeled. Approximation algorithms such as Gibbs sampling can be effective, but this is only a partial solution: iterative sampling is a very slow process as well. While this is fine for problems with limited numbers of variables, special structure, or no time constraints, inference time is an issue for other domains such as collaborative filtering, in which a typical problem may have thousands of variables and require a split-second response. Even ignoring time constraints, a graphical model may not be the most effective representation for some problems. As an alternate representation, association rules remain popular for studying market basket data. An association rule specifies the probability that a user purchasing a certain set of items will also purchase another specific item (Agrawal et al., 1993). For example, an association rule might specify that customers who buy milk are likely to buy bread, with 70% probability, or that customers who buy pens and pencils are likely to buy paper, with 60% percent probability. These association rules may be efficiently mined from data using an algorithm such as APriori (Agrawal and Srikant, 1994). The hope is that some association rules will provide valuable insights into customer behavior and aid in the development of business strategies. Of course, association rules alone do not provide a full joint probability distribution over all variables. Association rules are also somewhat limited, in that they are generally limited to conjunctions of true boolean variables. If these limitations could be overcome, we could construct a new type of probabilistic model where each model component is not a variable but an abstraction that applies to a certain portion of the data as an association rule applies to a certain subset of the observations. In addition to providing rapid inference and perhaps better accuracy in certain domains, an abstraction-based probabilistic model could be easier to understand: while traditional graphical methods depend on an understanding of conditional probability, association rules only require an understanding of cooccurrence.
The Five Vs of Big Data Political Science Introduction to the Virtual Issue on Big Data in Political Science Political Analysis

In the last three years, the concept of “Big Data” has emerged from technical obscurity to fully fledged, overblown, memetic phenomenon complete with Rorschach-test perceptions and backlash. For political scientists, the phrase seems to evoke a cognitive mapping onto our old continuum of “small-N” vs. “large-N ,” implying perhaps “really large N . ” This in turn tends to evoke the social science instinct to sample: “You don’t have to eat the whole cow to know that it’s tough. ” (Firebaugh 2008) But this misunderstands the defining characteristics of “big,” the qualitative differences from what has come before, and the challenges and opportunities that big data present to political science. Over a decade ago, IT analyst Doug Laney laid out a framework for understanding the challenges that were then emerging in data management for business, now ubiquitously referred to as “The Three Vs of Big Data”: Volume, Velocity, and Variety (Laney 2001). Simply put, data were beginning to be created at a scale, speed, and diversity of forms sufficient to overwhelm relational databases and other conventional modes of enterprise information management. This framework proved both catchy and useful for the “data science” and “analytics” communities, where technological innovations and new data-driven companies, products, and services are often characterized as addressing the challenges posed by, or extracting new value from, one of the Vs. Broadly conceived, this framework captures much of the challenge and opportunity that big data presents to political science, and social science more generally. As is de rigueur I extend the Vs,1 and those that arise at the fuzzy borders among political science, computer science, and data science. Included in this virtual issue are ten articles from the last decade of Political Analysis, which are individually excellent and worthy of your attention, and which are collectively useful for illustrating these five Vs of big data political science: volume, velocity, variety, vinculation, and validity:
Multimodal Medical Image Registration and Fusion in 3D Conformal Radiotherapy Treatment Planning

Medical image is the technique and process used to create images of the human body for medical science or clinical purposes, including medical medical procedures seeking to reveal, diagnose or examine disease. In the last 100 years, medical imaging technology has grown rapidly and drastically changed the medical profession. Now, physicians can use the images obtained by different medical imaging technologies to both diagnose and track the progress of illnesses and injuries. When 3D conformal radiotherapy planning (3D CRTP) is employed for tumor treatment, the relative position between the tumor and its adjacent tissues, should be obtained accurately. Generally, there are two main kinds of medical images which provide different information for diagnosis in 3D conformal radiotherapy planning (3D CRTP). They are “the anatomical images” and “the functional mages”. The anatomical images, such as Computerized Tomography(CT), depict clearly primarily morphology of human body through the abundant texture, yet it is not very sensitive to the cancer. The functional mages, such as Positron Emission Tomography (PET), depict primarily information on the metabolism of the underlying anatomy. Therefore, the relative position between the tumor and its adjacent tissues could be obtained easily through analyzing the medical data sets which are fused the information of functional mages and anatomical images. Many methods exist to perform image fusion. The very basic one is the high pass filtering technique. Later techniques are based on DWT, uniform rational filter bank, and so on. In this chapter, multimodal medical images are fused by applying wavelet transform with fusion rule of combining the local standard deviation and energy, which will be describe in detail in this chapter. Many documents presented a fusion method based on wavelet transform(Park J H et al., 2001), which is useful for image fusion. But the activity measure of the coefficients reflecting the significant information of multimodal medical images had not been considered in them. In clinic application, physicians are interested in the position signs of the tumor. The anatomical images depict clearly primarily morphology of human body through the abundant texture. So the local standard deviation is regarded as the activity measure of coefficients. Furthermore, the local energy reflects the absolute intensity of the signal change, and the large absolute intensity of the signal change reflect the obvious feature of the image. So the image feature is described in uniform by the local standard,