Miracle-dz/UniFAR-FTUs huggingface dataset:
Irisin, Two Years Later

In January 2012, Boström and colleagues identified a new muscle tissue secreted peptide, which they named irisin, to highlight its role as a messenger that comes from skeletal muscle to other parts of the body. Irisin is a cleaved and secreted fragment of FNDC5 (also known as FRCP2 and PeP), a member of fibronectin type III repeat containing gene family. Major interest in this protein arose because of its great therapeutic potential in diabetes and perhaps also therapy for obesity. Here we review the most important aspects of irisin's action and discuss its involvement in energy and metabolic homeostasis and whether the beneficial effects of exercise in these disease states could be mediated by this protein. In addition the effects of irisin at the central nervous system (CNS) are highlighted. It is concluded that although current and upcoming research on irisin is very promising it is still necessary to deepen in several aspects in order to clarify its full potential as a meaningful drug target in human disease states.
Dietary surveys indicate vitamin intakes below recommendations are common in representative Western countries

Vitamins play a crucial role in health, but modern lifestyles may lead to suboptimal intakes even in affluent countries. The aim of the present study is to review vitamin intakes in Germany, the UK, The Netherlands and the USA and to compare them with respective national recommendations. Data on adults from the most recently published national dietary intake surveys for the first three countries and data for adults from the US National Health and Nutrition Examination Survey from 2003 to 2008 for the USA were used as a basis for the analysis. The proportions of the populations with intakes below recommendations were categorised as < 5, 5–25, >25–50, >50–75 and >75 % for each vitamin. The data generated are presented in a ‘traffic light display’, using colours from green to red to indicate degrees of sufficiency. The trends found were compared with the results from the European Nutrition and Health Report 2009, even though in that report, only information on mean intakes in the different countries was available. We showed that, although inter-country differences exist, intakes of several vitamins are below recommendations in a significant part of the population in all these countries. The most critical vitamin appears to be vitamin D and the least critical niacin. The variation between the countries is most probably due to differences in recommendations, levels of fortification and local dietary habits. We show that a gap exists between vitamin intakes and requirements for a significant proportion of the population, even though diverse foods are available. Ways to correct this gap need to be investigated.
Multifaceted Predictive Algorithms in Commodity Markets

Commodity price prediction is a notoriously difficult task, and fluctuations in this space have large social, economic, and political consequences, as the recent precipitous drop in oil prices has demonstrated. The ability to predict the movements of commodity prices is highly consequential: a successful prediction algorithm could inform a company on how to hedge against risk or form the basis for a profitable algorithmic trading strategy. In this project, we employ logistic regression, support vector machine, and Naive Bayes classification algorithms. The input to each model is a collection of time series including technical indicators, macro-market features, and other commodities. Each model outputs a vector v ∈ {0, 1} , in which the nth coordinate indicates whether a given commodity index will have achieved net positive return n ∈ {1, . , N} days in the future. We begin our exploration with the findings of Jennings’ commodity ETF prediction project (Stanford MS&E 447) which demonstrate that models constructed on feature spaces consisting of solely price data and corresponding momentum indicators are insufficient for predicting price movements. This agrees with the conclusions of Kinlay, who ran 1,000,000 different machine learning algorithms using only price data and derivative features and found that none of the models’ predictions were distinguishable from noise on out-of-sample data [4]. Kinlay published his 1,000,000 models paper in response to research like that published by Ticlavilca et al [7] and Huang [5] which seems to indicate that machine learning on technical indicators can produce useful and reliable results. Kinlay suspected that the success of many of these researchers is due to sampling bias, and thus performed his exhaustive 1,000,000 model test. The research by Kase [1] and Jegadeesh [3] demonstrating the profitability of momentum-based trading strategies may have inspired recent attempts by machine learning researchers to use feature spaces consisting only of technical indicators. Mindful of this prior work, we conduct an iterative process of feature engineering and exploratory analysis to construct an improved input feature space, and show that our model implementations can successfully generalize to out of sample data. II. DATA AND FEATURES
An information theoretic approach to dynamical systems modeling and identification

The identification and modeling of dynamical systems in a practical situation, where the model set under consideration does not necessarily include the observed system, are treated. A measure of the relevant information in a sequence of observations is shown to possess useful properties, such as the metric property on the parameter set. It is then shown that maximum likelihood and related Bayesian identification procedures converge to a model in the model set, which is closest to the actual system generating the observations in the information distance measure. The convergence analysis is restricted for simplicity to finite sets of models. The analysis naturally suggests methods for approximating a high-order system by a low-order model and for selecting a representative model from a given model set, applicable to infinite and even noncompact model sets.
Designing Grid services for distributed knowledge discovery

The increasing use of computers in all the areas of human activities is resulting in huge collections of digital data. Databases are common everywhere and are used as repositories of every kind of data. Knowledge discovery techniques and tools are used today to analyze those very large data sets to identify interesting patterns and trends in them. When data is maintained over geographically distributed sites the computational power of distributed and parallel systems can be exploited for knowledge discovery in databases. In this scenario the Grid can provide an effective computational support for distributed knowledge discovery on large data sets. To this purpose we designed a system called Knowledge Grid. This paper describes the Knowledge Grid architecture and discusses some related systems and models recently proposed for knowledge discovery on Grids. The paper shows also how to design and implement distributed knowledge discovery services, according to the OGSA model, by using the Knowledge Grid environment starting from searching Grid resources, composing software and data elements, and executing the resulting application on a Grid.
An Analysis of Obligation Patterns for the Department of Defense Operations and Maintenance Appropriations

Abstract : Present appropriation accounting systems are one of the top seven problems facing the Department of Defense according to Deputy Secretary of Defense William Perry. As part of the process of evaluating the potential benefits of a proposed single year accounting system, this research study analyzes obligation patterns of the DoD's Operations and Maintenance (O and M) appropriations between fiscal years 1977 and 1992. Although trends for the entire fiscal year are discussed for 11 appropriations, the analysis focuses more attention on the increase in obligations during the fourth quarter and the reasons for this surge. The patterns, and particularly the peak in obligations near the end of the fiscal year, are assessed in terms of the incentives provided to managers by the current laws and policies governing the obligation of appropriated funds. Finally, the advantages and disadvantages of the single year accounting system are presented as a means to possible change the present incentive structure. Operations and Maintenance obligations, End of fiscal year spending, Wasteful spending.
Capturing collection size for distributed non-cooperative retrieval

Modern distributed information retrieval techniques require accurate knowledge of collection size. In non-cooperative environments, where detailed collection statistics are not available, the size of the underlying collections must be estimated. While several approaches for the estimation of collection size have been proposed, their accuracy has not been thoroughly evaluated. An empirical analysis of past estimation approaches across a variety of collections demonstrates that their prediction accuracy is low. Motivated by ecological techniques for the estimation of animal populations, we propose two new approaches for the estimation of collection size. We show that our approaches are significantly more accurate that previous methods, and are more efficient in use of resources required to perform the estimation.
What Measure of Output Should be Targeted in Interest Rate Rules ? ∗

What measure of output, if any, should central banks target in simple interest rate rules? This paper analyzes the welfare consequences of responding to different measures of real economic activity in simple interest rate rules in a conventional New Keynesian model. Responding to the level of output is always welfare-reducing, while reacting to the level of the output gap reduces welfare if cost-push shocks are sufficiently important. In contrast, reacting to movements in the the growth rate of output improves welfare for most parameterizations and specifications of the model. Reacting to growth rates works to better anchor inflation expectations, which improves central bank tradeoffs and brings the performance of a simple interest rate rule close to that of the targeting rule implied by optimal commitment. ∗I am grateful to Yuriy Gorodnichenko, Robert Lester, and seminar participants at Notre Dame for many helpful comments which have substantially improved the paper.
Building up resilience of construction sector SMEs and their supply chains to extreme weather events

Wider scientific community now accept that the threat of climate change as real and thus acknowledge the importance of implementing adaptation measures in a global context. In the UK, the physical effects of climate change are likely to be directly felt in the form of extreme weather events, which are predicted to escalate in number and severity in future under the changing climatic conditions. Construction industry; which consists of supply chains running across various other industries, economies and regions, will also be affected due to these events. Thus, it is important that the construction organisations are well prepared to withstand the effects of extreme weather events not only directly affecting their organisations but also affecting their supply chains which in turn might affect the organisation concerned. Given the fact that more than 99% of construction sector businesses are SMEs, the area can benefit significantly from policy making to improve SME resilience and coping capacity. This paper presents the literature review and synthesis of a doctoral research study undertaken to address the issue of extreme weather resilience of construction sector SMEs and their supply chains. The main contribution of the paper to both academia and practitioners is a synthesis model that conceptualises the factors that enhances resilience of SMEs and their supply chains against extreme weather events. This synthesis model forms the basis of a decision making framework that will enable SMEs to both reduce their vulnerability and enhance their coping capacity against extreme weather. The value of this paper is further extended by the overall research design that is set forth as the way forward.
Retention of Reading Intervention Effects From Fourth to Fifth Grade for Students With Reading Difficulties

Abstract This study examined and quantified the maintenance of reading comprehension effects from a 4th-grade intervention for students with reading difficulties across the summer into the fall of 5th grade. We randomly assigned 4th-grade students with reading comprehension difficulties to a multicomponent reading intervention (Passport) or to typical school services. Students in the treatment received daily intervention in small groups of 4–7 students across the 4th-grade school year. We followed students (n = 269) into their 5th-grade year. Groups were equivalent at baseline, and the treatment group significantly outperformed the comparison group at posttest. Students in the treatment continued to grow in reading comprehension over the summer at a rate of approximately 25% of the original growth, which suggests partially fleeting effects of the intervention. Treatment students maintained the higher levels of reading comprehension from the end of the 4th-grade intervention to the beginning of 5th grade.
Experimental evaluation of software aging effects on the eucalyptus cloud computing infrastructure

The need for reliability and availability has increased in modern applications, which need to handle rapidly growing demands while providing uninterrupted service. This work investigates the memory leak and memory fragmentation aging effects on the Eucalyptus cloud-computing framework, which considers workloads composed of intensive requests addressing different virtual machines. We experimentally show the existence of the investigated aging effects in the cloud environment under study. Also, a software rejuvenation strategy to mitigate the observed aging effects is proposed and its benefits are evaluated.
The analysis of feature selection methods and classification algorithms in permission based Android malware detection

Android mobile devices have reached a widespread use since the past decade, thus leading to an increase in the number and variety of applications on the market. However, from the perspective of information security, the user control of sensitive information has been shadowed by the fast development and rich variety of the applications. In the recent state of the art, users are subject to responding numerous requests for permission about using their private data to be able run an application. The awareness of the user about data protection and its relationship to permission requests is crucial for protecting the user against malicious software. Nevertheless, the slow adaptation of users to novel technologies suggests the need for developing automatic tools for detecting malicious software. In the present study, we analyze two major aspects of permission-based malware detection in Android applications: Feature selection methods and classification algorithms. Within the framework of the assumptions specified for the analysis and the data used for the analysis, our findings reveal a higher performance for the Random Forest and J48 decision tree classification algorithms for most of the selected feature selection methods.
Closed reduction with or without adductor tenotomy for developmental dysplasia of the hip presenting at walking age

Background: Many children with developmental dislocation of the hip especially in underdeveloped countries reach walking age and still remain undiagnosed, which can be detrimental to their growth and development. Because of the lack medical services often encountered in these regions, it would be attractive to find a cheap and effective treatment. Our work evaluated the results of treatment of these children by closed reduction with or without adductor tenotomy in a prospective study. Methods: We included 20 patients in this study with 29 affected hips (15 right and 14 left). Nine patients (45%) had bilateral DDH and 11 (55%) had unilateral DDH. There were 18 girls (90%) and two boys (10%) who were followed up for a mean of 21 mo (18-24 mo). Ages ranged from 9 to 36 mo (mean age 18.3 mo). Patients were divided according to age into two groups: between 9-18 mo and from 19-36 mo. The first group included nine patients (14 hips) while the second had 11 patients (15 hips). Results: In the first group, closed reduction failed in two patients (two hips) during the follow-up period (14.3%) and this necessitated shift to open reduction, while in the second group only one patient (bilateral DDH) had a similar failure (13.3%). We identified four hips with avascular necrosis. Three of them required no further treatment, the remaining hip was openly reduced. Conclusions: Closed reduction in older children offers a valid and reproducible treatment modality in the hands of an experienced pediatric orthopaedic surgeon as long as there is close follow-up and thorough knowledge of possible complications and their management including the ability to shift timely to open reduction.
Risk stratification of patients with chronic heart failure using cardiac iodine‐123 metaiodobenzylguanidine imaging: incremental prognostic value over right ventricular ejection fraction

Right ventricular (RV) systolic dysfunction has been shown to be an independent predictor of clinical outcome in patients with chronic heart failure (CHF), and cardiac metaiodobenzylguanidine (MIBG) imaging also provides prognostic information. We aimed to evaluate the long‐term predictive value of combining RV systolic dysfunction and abnormal findings of cardiac MIBG imaging on outcome in CHF patients.
Spatial trends of genetic variation of domestic ruminants in Europe

The introduction of livestock species in Europe has been followed by various genetic events, which created a complex spatial pattern of genetic differentiation. Spatial principal component (sPCA) analysis and spatial metric multidimensional scaling (sMDS) incorporate geography in multivariate analysis. This method was applied to three microsatellite data sets for 45 goat breeds, 46 sheep breeds, and 101 cattle breeds from Europe, Southwest Asia, and India. The first two sPCA coordinates for goat and cattle, and the first sPCA coordinate of sheep, correspond to the coordinates of ordinary PCA analysis. However, higher sPCA coordinates suggest, for all three species, additional spatial structuring. The goat is the most geographically structured species, followed by cattle. For all three species, the main genetic cline is from southeast to northwest, but other geographic patterns depend on the species. We propose sPCA and sMDS to be useful tools for describing the correlation of genetic variation with geography.
A SPITZER HIGH-RESOLUTION MID-INFRARED SPECTRAL ATLAS OF STARBURST GALAXIES

We present an atlas of Spitzer/IRS high-resolution (R ∼ 600) 10–37 μm spectra for 24 well known starburst galaxies. The spectra are dominated by fine-structure lines, molecular hydrogen lines, and emission bands of polycyclic aromatic hydrocarbons (PAHs). Six out of the eight objects with a known active galactic nucleus (AGN) component show emission of the high excitation [Ne v] line. This line is also seen in one other object (NGC 4194) with, a priori, no known AGN component. In addition to strong PAH emission features in this wavelength range (11.3, 12.7, 16.4 μm), the spectra reveal other weak hydrocarbon features at 10.6, 13.5, 14.2 μm, and a previously unreported emission feature at 10.75 μm. An unidentified absorption feature at 13.7 μm is detected in many of the starbursts. We use the fine-structure lines to derive the abundance of neon and sulfur for 14 objects where the H i 7-6 line is detected. We further use the molecular hydrogen lines to sample the properties of the warm molecular gas. Several basic diagrams characterizing the properties of the sample are also shown. We have combined the spectra of all the pure starburst objects to create a high signal-to-noise ratio template, which is available to the community.
Learning Multiple Tasks using Manifold Regularization

We present a novel method for multitask learning (MTL) based on manifold regularization: assume that all task parameters lie on a manifold. This is the generalization of a common assumption made in the existing literature: task parameters share a common linear subspace. One proposed method uses the projection distance from the manifold to regularize the task parameters. The manifold structure and the task parameters are learned using an alternating optimization framework. When the manifold structure is fixed, our method decomposes across tasks which can be learnt independently. An approximation of the manifold regularization scheme is presented that preserves the convexity of the single task learning problem, and makes the proposed MTL framework efficient and easy to implement. We show the efficacy of our method on several datasets.
Paravertebral Block Combined with Sedation for a Myasthenic Patient Undergoing Breast Augmentation

Paravertebral block is a unilateral analgesic technique that can provide adequate surgical anesthesia and great advantages in many types of surgery with a low side-effect profile. In this case we present combination of bilateral thoracic paravertebral block under ultrasound guidance with sedation which provides complete anesthesia and postoperative analgesia in a myasthenic patient undergoing cosmetic breast surgery. In myasthenic patients paravertebral blocks may be a better option for breast surgery with avoiding the need for muscle relaxants and opioids and risk of respiratory failure in postoperative period.
A Comparison of the Beliefs and Priorities of Early Intervention Providers and the Families they Serve

This study was conducted to compare the perceptions and beliefs held by parents of children, aged three to five years, receiving early intervention to those of early interventionists. The participants included parents whose children were receiving early intervention provided by a regional education services agency in southeastern Pennsylvania and the early interventionists who worked for the same agency. Professional participants reported differences between parents and early interventionists regarding the role of the parent and the role of the early interventionist. Early interventionists recommended improvements in parent involvement and responsiveness. Parents offered recommendations for improving early intervention programs, expressing a need for improved communication, collaboration, responsiveness and coordination of services. The study had implications for early interventionists, parents, program design and early intervention practices. Recommendations for training for parents and early interventionists were offered. Well-designed family outcomes as an aspect of the individual education planning process were also recommended.