Miracle-dz/UniFAR-FTUs huggingface dataset:
Clinical and Radiographic Heterogeneity of Interstitial Lung Disease in Systemic Sclerosis Based on Disease-Specific Autoantibodies

Patients with SSc have the presence of several autoantibodies, some of which are SSc-specific antibodies associated with the clinical features of SSc (9-13). The presence of anti-topoisomerase I (topo-I) antibody is associated with the development of skin fibrosis and progressive ILD in SSc. Anti-centromere antibody (ACA) is seen in patients with limited skin disease and is a minor risk factor for the development of ILD. However, it remains unclear whether SSc-specific antibodies are associated with pulmonary function and radiological findings of HRCT. We divided SSc patients into 5 clusters on the basis of SSc-specific autoantibodies present and retrospectively evaluated the clinical and radiological features of ILD among these 5 SSc patient clusters. Abstract Background: Patients with systemic sclerosis have the presence of several autoantibodies, some of which are systemic sclerosis-specific autoantibodies that are related to the clinical features of this disease. We retrospectively evaluated the clinical and radiological features of systemic sclerosis in patients with interstitial lung disease on the basis of these autoantibodies. Method: This study comprised 226 patients with systemic sclerosis. We divided patients with systemic sclerosis into 5 clusters based on the systemic sclerosis-specific autoantibodies present and compared the clinical and radiological features of interstitial lung disease among these 5 systemic sclerosis clusters. Results: Of 226 patients, interstitial lung disease was present in 73 (32.3%) patients. Clustering by Ward cluster analysis subsequently identified 5 major clusters of patients. Clusters of patients with anti-topoisomerase I antibody and without systemic sclerosis-specific autoantibodies had a higher incidence of interstitial lung disease compared to the other clusters (75.0% and 51.9%, respectively). Patients in these two clusters had higher percentages of cough and dyspnea, higher incidence of traction bronchiectasis and honeycomb-like cysts in computed tomography, and a decrease in percentage predicted of vital capacity. In contrast, patients with anti-centromere antibody had few clinical and laboratory findings and the lowest incidence of interstitial lung disease (16.8%). Patients with anti- ribonucleic antibody or nucleolar pattern in anti-nuclear antibody had moderate findings among the 5 clusters. Conclusion: The results showed that there were differences in clinical and radiological findings among patients with systemic sclerosis-specific autoantibodies.
Spatial Modeling Approach for Dynamic Network Formation and Interactions

Abstract This study primarily seeks to answer the following question: How do social networks evolve over time and affect individual economic activity? To provide an adequate empirical tool to answer this question, we propose a new modeling approach for longitudinal data of networks and activity outcomes. The key features of our model are the inclusion of dynamic effects and the use of time-varying latent variables to determine unobserved individual traits in network formation and activity interactions. The proposed model combines two well-known models in the field: latent space model for dynamic network formation and spatial dynamic panel data model for network interactions. This combination reflects real situations, where network links and activity outcomes are interdependent and jointly influenced by unobserved individual traits. Moreover, this combination enables us to (1) manage the endogenous selection issue inherited in network interaction studies, and (2) investigate the effect of homophily and individual heterogeneity in network formation. We develop a Bayesian Markov chain Monte Carlo sampling approach to estimate the model. We also provide a Monte Carlo experiment to analyze the performance of our estimation method and apply the model to a longitudinal student network data in Taiwan to study the friendship network formation and peer effect on academic performance. Supplementary materials for this article are available online.
A mixed-methods approach to demotivating factors among Iranian EFL learners

This study used a mixed-methods approach to investigate Iranian EFL learners’ attitudes towards demotivating factors which may hinder their success in a language learning course. In the quantitative phase, a sample of 337 undergraduate students from universities in Mashhad, Yazd and Gonabad completed a 34-item questionnaire. They also completed open-ended questions which asked them to reflect on any previous EFL (English as a foreign language) learning experiences which had negatively affected their language learning motivation. In the qualitative phase, 15 students participated in semistructured interviews, and 23 in focus group discussions. Quantitative data were subjected to principal component analysis (PCA), which subsequently revealed the presence of six demotivating factors, including lack of success, insufficient technological facilities, teacher’s competence, teaching method and behaviour, lack of interest, class materials, and the class environment. Results of the study revealed that the qualitative findings were compatible with the factors that emerged from the quantitative phase.
Consumers' Motivations and Daily Deal Promotions

In the last years daily deal (dd) sites have become a substantial part of ecommerce scenario. Every day, for a limited time, such sites offer their subscribers, at very discounted prices, deals for products or services. Despite the worldwide success of daily deal sites, testified by articles in newspapers and magazines, there is an almost complete absence of academic research on this topic. The aim of this paper is to understand the consumers' drivers in online coupon buying. The study is exploratory in nature and authors have collected data by carrying out four synchronous online focus groups involving 21 participants and investigating five areas: level of awareness, perception area, social dimension, consumer behavior, and relationship with ecommerce. Respondents show a high level of awareness of daily deal sites. In the perceptive area, convenience emerges as the driving factor in coupon buying. As to the self-perception area, some ambivalences emerge between smartness and compulsive buying, highlighting also some dissatisfaction. The ‘social dimension’ is almost irrelevant, and as to consumer behavior, daily deal buying seems to be a kind of fashion. Consumers perceive the bargain side of the offer, behaving like “cherry pickers. ” Keywords: Consumer Behavior, E-Commerce, Focus Group, Daily Deal Promotions, Groupon
Analyst Target Prices and Forecast Accuracy around the World

Using a unique analyst-location data covering 11,408 analysts from 41 countries, we find that target price accuracy is negatively associated with the target price level but positively associated with target price revision. Analyst traits that are related to information advantage partially mitigate the negative impact of target price level on forecast accuracy. Analysts in countries with better institutional infrastructure are more accurate; and this accuracy seems to be driven by country-level institutional infrastructure that partially disciplines analysts from inflating target prices. In contrast, institutional infrastructure does not mitigate the impact of target price revision on forecast accuracy. The authors gratefully acknowledge helpful comments from Xi Liu and from conference participants at the EFA 2012 at Boston. We thank the Social Sciences and Humanities Research Council of Canada for financial support. All errors are our own.
Fungal endophytes from two orchid species - pointer towards organ specificity

Fungal endophytes may influence plant communities by altering the host’s fitness either positively or negatively. Little is known, however, about their host/organ specificity, life style and role in plant-fungus symbiosis under varying environmental conditions. We compared the leaf and root endophyte assemblages of two orchids (Bulbophyllum neilgherrense and Pholidota pallida) from natural forests and greenhouse conditions. Xylariaceae species were consistently associated with leaf and root tis-sues, while Guignardia and Pestalotiopsis were found predominantly in the leaf tissues of both or- chids. Correspondence analysis of the endophyte assemblages showed that the endophytes exhibited distinct organ but little host specificity. More endophytes were shared by the two different orchids growing in the same location when compared to endophyte assemblages of a single orchid from differ-ent locations. Considering the influence of endophytes in shaping the host’s community, diverse habitats must be screened vigorously to address questions regarding the role of endophytes in host-endophyte interactions.
Dynamics of Hubbard-band quasiparticles in disordered optical lattices

Quantum degenerate gases trapped in optical lattices are ideal testbeds for fundamental physics because these systems are tunable, well characterized, and isolated from the environment. Controlled disorder can be introduced to explore suppression of quantum diffusion in the absence of conventional dephasing mechanisms such as phonons, which are unavoidable in experiments on electronic solids. Recent experiments use transport of degenerate Fermi gases in optical lattices (Kondov et al. Phys. Rev. Lett. 114, 083002 (2015)) to probe a particularly extreme regime of strong interaction in what can be modeled as an Anderson-Hubbard model. These experiments find evidence for an intriguing insulating phase where quantum diffusion is completely suppressed by strong disorder. Quantitative interpretation of these experiments remains an open problem that requires inclusion of non-zero entropy, strong interaction, and trapping. We argue that the suppression of transport can be thought of as localization of Hubbard-band quasiparticles. We construct a theory of transport of Hubbard-band quasiparticles tailored to trapped optical lattice experiments. We compare the theory directly with center-of-mass transport experiments of Kondov et al. with no fitting parameters. The close agreement between theory and experiments shows that the suppression of transport is only partly due to finite entropy effects. We argue that the complete suppression of transport is consistent with Anderson localization of Hubbard-band quasiparticles. The combination of our theoretical framework and optical lattice experiments offers an important platform for studying localization in isolated many-body quantum systems.
Impact of Fixed Medium-Term National Development Plans and Structural Adjustment Program on Aggregate Economic Activity in Nigeria

Nigeria adopted and implemented four fixed medium-term national development plans between 1960 and 1985 with a view to ultimately achieving optimum societal wellbeing. However, due to the economic problems of the 1980s, an extensive structural adjustment program (SAP) was put in place in 1986. Among other things, SAP was meant to restructure and diversify the Nigerian economy. This paper empirically analyzes the impact of the four plans and the structural adjustment program on aggregate economic activity in Nigeria. The study employs a growth equation that contains major macroeconomic variables such as credit to private sector, foreign direct investment and foreign trade as well as dummy variables that capture eras of the four fixed medium-term national development plans and the structural adjustment program. Contemporary econometric techniques of co integration and autoregressive modeling form part of the methodology for this study. The study is based on annual time series data from 1960 to 2009 obtained from the Central Bank of Nigeria (CBN). The estimates of the study, among other things, reveal that the fixed medium-term national development plans in general had no significant impact on aggregate economic activity in Nigeria whereas the structural adjustment program had some significant positive impact on aggregate economic activity in the country. Thus there is need for optimum state planning in the country, especially if the planning is for fixed medium-term. In general, there is need for optimal management of the country’s economy. Keywords: Development planning, Econometrics, Structural adjustment program, Time series, Nigeria.
Glycosyl Hydrolase Genes and Enzymes of Neurospora crassa

An analysis of the genome of Neurospora crassa has identified genes encoding 84 putative glycosyl hydrolases, representing 24 different families in Henrissat's classification. Functionally, they include enzymes for the degradation of all major polysaccharides (including cellulase, hemicellulase, chitinase and pectinase). There is evidence of high levels of gene amplification, despite the presence of RIP, as there are eight representatives of family 3 (beta-glucosidases and xylosidases), five of family 7 (endoand exo-glucanases), six of family 13 (amylases and maltase), nine of family 18 (chitinase), eight of family 47 (ER alpha-mannosidases), eleven of family 61 (endoglucanases) and seven of family 76 (alpha-mannanases). Creative Commons License This work is licensed under a Creative Commons Attribution-Share Alike 4.0 License. This regular paper is available in Fungal Genetics Reports: http://newprairiepress.org/fgr/vol53/iss1/4
Evaluation of Existing Architecture for M-Mining Market Access Information Among Sugarcane Farmers in Migori County , Kenya

The application of mobile technology in agriculture is expanding rapidly, with new devices and applications being available for everyday use. However, the absence of seamless architecture required to integrate mobile devices with server has limited its use. Ideally this kind of architecture would enhance mining and dissemination of expert information to reach farmers on time. Consequently, farmers can hardly set prices and market their sugarcane products on time hence low income characterized by widespread poverty and low living standards. Although mobile data mining has been applied in other sectors of the economy such as transport and stock market, its application in agriculture is still wanting. There is therefore a need to upscale service oriented mobile data mining techniques to improve discovery and dissemination of agricultural market access knowledge. The objectives of this study were to: (i) examine existing architectures for m-mining agricultural resources; and (ii) evaluate their challenges using scenario and experienced based methods. Results showed the existence of predominantly manual system which was limited by various socio-economic and technological challenges including, inconsistency, error prone, poor internet connectivity. The results also showed that the choice of market for sugarcane greatly depended on the price and mode of payment. The study adopted service oriented architecture as the guiding model. The study recommended integration of mobile phones (SMS technology) as suitable architecture for m-mining and dissemination of market access information.
A Bibliometric Analysis of Soil and Water Conservation in the Loess Tableland-Gully Region of China

The tableland-gully region is one of the main topographic-ecological units in the Chinese Loess Plateau (CLP), and the soil in this region suffers from serious water erosion. In recent years, much work has been conducted to control soil erosion in this area. This paper summarized the development of soil and water conservation researches in the CLP from the bibliometric perspective based on the Science Citation Index (SCI) and Chinese National Knowledge Infrastructure (CNKI) databases. The quantity of SCI literatures has increased rapidly since 2007, with an average annual growth rate of 21.4%, and the quantity of CNKI literatures in the last decade accounted for 62% of the past 30 years. The development trends showed that early SCI research was related to loess geology in the context of ecological remediation, while the CNKI literature focused on agricultural production under comprehensive management. Over time, the research themes of the two databases gradually became unified, i.e., the management of sloping farmland and the improvement of agricultural productivity. Subsequently, the themes gradually extended to the disposition of comprehensive control measures for soil erosion and the environmental effect of agro-fruit ecosystems. The highly cited papers mainly focused on soil reservoir reconstruction, soil erosion factors, and environmental effects of vegetation restoration. Two aspects need further study, including (i) the effect of soil erosion control under different ecological remediation patterns; and, (ii) the ecosystem maintenance mechanism and regulation approaches that are based on the sustainable utilization of soil and water resources in the tableland-gully region of the Loess Plateau.
The Anticipation and Adaptation Effects of Intra- and Interpersonal Wage Changes on Job Satisfaction

This paper analyses how individual job satisfaction is affected by wage changes. In order to account for potential dynamic effects of wage changes on job satisfaction, we include lead and lag effects of income changes in our analysis. Furthermore, we examine the role of social comparisons, i.e., how an individual’s job satisfaction is driven not only by changes in his wages, but also by the size of these changes relative to wage changes within his reference group. Results from an individual fixed effects regression indicate that wage increases have a statistically significant positive effect on job satisfaction. This effect exhibits a dynamic pattern. We observe an anticipation effect of a positive wage change, i.e., individuals are more satisfied with their job one year ahead of the wage increase. Also, we find statistically significant positive, but declining effects on job satisfaction four years after the wage increase, i.e., partial adaptation. We find that an additional increase in job satisfaction is obtained when the individual’s wage increase exceeds the average wage increase for his reference group. However, this effect does not appear to persist, as it is only statistically significant in the first period after the wage change.
Evaluation of wind-induced dynamics of high-rise buildings by means of modal approach *

This work applies a numerical and experimental procedure for investigating the dynamic behavior of tall buildings subjected to multidirectional wind loads. The procedure provides a framework for the calculation of the dynamic response based on the combination of wind tunnel test results and modal information. The procedure is applied to two different tall buildings: a slender tower with section aspect ratio B/D=2.6 and height aspect ratio H/D=9, and a residential tower with "ypsilon" section. Wind tunnel tests were performed on 1:100 scale rigid models to provide the surface pressure data essential for the procedure proposed in this work. A numerical modal approach is then used to evaluate the full-scale dynamics of the building. With the proposed methodology it is possible to evaluate the effects of each mode in the peak acceleration (i.e. torsional mode and second bending modes in the two directions). The methodology can also be used to evaluate interference effects, making new wind tunnel tests including in the set-up all the other tall buildings in the vicinity to the tower studied. These effects are worth to be examined in the design stage since they can greatly modify the response.
Exploring New Forms of Random Projections for Prediction and Dimensionality Reduction in Big-Data Regimes

The story of this work is dimensionality reduction. Dimensionality reduction is a method that takes as input a point-set P of n points in R where d is typically large and attempts to find a lower-dimensional representation of that dataset, in order to ease the burden of processing for down-stream algorithms. In today’s landscape of machine learning, researchers and practitioners work with datasets that either have a very large number of samples, and or include high-dimensional samples. Therefore, dimensionality reduction is applied as a pre-processing technique primarily to overcome the curse of dimensionality. Generally, dimensionality reduction improves time and storage space required for processing the point-set, removes multi-collinearity and redundancies in the dataset where different features may depend on one another, and may enable simple visualizations of the dataset in 2-D and 3-D making the relationships in the data easy for humans to comprehend. Dimensionality reduction methods come in many shapes and sizes. Methods such as Principal Component Analysis (PCA), Multi-dimensional Scaling, IsoMaps, and Locally Linear Embeddings are amongst the most commonly used method of this family of algorithms. However, the choice of dimensionality reduction method proves critical in many applications as there is no one-size-fits-all solution, and special care must be considered for different datasets and tasks. Furthermore, the aforementioned popular methods are data-dependent, and commonly rely on computing either the Kernel / Gram matrix or the covariance matrix of the dataset. These matrices scale with increasing number of samples and increasing number of data dimensions, respectively, and are consequently poor choices in today’s landscape of big-data applications. Therefore, it is pertinent to develop new dimensionality reduction methods that can be efficiently applied to large and high-dimensional datasets, by either reducing the dependency on the data, or side-stepping it altogether. Furthermore, such new dimensionality reduction methods should be able to perform on par with, or better than, traditional methods such as PCA. To achieve this goal, we turn to a simple and powerful method called random projections. Random projections are a simple, efficient, and data-independent method for stably embedding a point-set P of n points in R to R where d is typically large and k is on the order of log n. Random projections have a long history of use in dimensionality reduction literature with great success. In this work we are inspired to build on the ideas of random projection theory, and extend the framework and build a powerful new setup of random projections for large high-dimensional datasets, with comparable performance to state-ofthe-art data-dependent and nonlinear methods. Furthermore, we study the use of random projections in domains other than dimensionality reduction, including prediction, and show