Miracle-dz/UniFAR-FTUs huggingface dataset:
EU Exports to the World: Effects on Income

The European Commission identified trade policy as a core component of the European Union's 2020 Strategy. The fast changing global economy, characterised by the dynamic creation of business opportunities and increasingly complex production chains, means that it is now even more important to fully understand how trade flows affect income generation. Gathering comprehensive, reliable and comparable information on this is crucial to support evidence-based policymaking. Guided by that objective, the European Commission's Joint Research Centre (JRC) and the Commission's Directorate General for Trade have collaborated to produce this publication. It aims to be a valuable tool for trade policymakers. Following up the first edition (Arto et al., 2015), the report features a series of indicators to illustrate in detail the relationship between trade and income (i.e. value added) generation for the EU as a whole and for each EU Member State using the World Input-Output Database (WIOD), 2016 release (Timmer et al., 2015, 2016), as the main data source. This information has been complemented with data on labour compensation by skill from EUKLEMS. All the indicators relate to the EU's exports to the rest of the world so as to reflect the scope of EU trade policymaking. Most indicators are available as off 2000 but, due to data constraints, the indicator on labour compensation split by skill is only available from 2008 to 2014. The geographical breakdown of the data includes the 28 EU Member States, Australia, Brazil, Canada, China, India, Indonesia, Japan, Mexico, Norway, Russia, South Korea, Switzerland, Turkey, Taiwan, the United States of America, and an aggregate "Rest of the World" region. On the basis of the value added embodied in every million EUR worth of exports in 2014 and more recent data on international trade in goods and services, this report also provides projections elaborated by the JRC for 2017 using a different methodology, so they should be taken with caution. The information presented in this pocketbook is complemented with an electronic version allowing downloads of the tables with the complete time series (2000-2014 and 2017).
Investigating the urban heat and cool island effects during extreme heat events in high‐density cities: A case study of Hong Kong from 2000 to 2018

Urban heat island (UHI) and cool island (UCI) effects are well‐known and prevalent in cities worldwide. An increasing trend of extreme heat events has been observed over the last few decades and is expected to continue in the foreseeable future. In this study, warm periods (May to September) of 2000–2018 were examined to acquire a comprehensive understanding of the UHI and UCI characteristics for the case study of Hong Kong, China. Twenty‐two weather stations in Hong Kong were classified into four categories, namely urban, urban oasis, suburban, and rural, with reference to the local climate zone (LCZ) scheme, to analyze UHI and UCI phenomena during extreme heat and non‐extreme heat situations. One representative type of extreme heat events was considered in this study: three consecutive hot nights with two very hot days in between (2D3N). Results show that both the UHI and UCI effects are exacerbated during extreme heat events. Using the concept of the UHI degree hours (UHIdh) and UCI degree hours (UCIdh), their spatial patterns in Hong Kong during extreme heat and non‐extreme heat situations were mapped based on multiple linear regression models. It is found that the predictor variable – windward/leeward index is a significant influential factor of both UHIdh and UCIdh during extreme heat events. The resulting UHIdh and UCIdh maps not only enhance our understanding on the spatial pattern and characteristics of the UHI and UCI during extreme heat events, but could also serve as a useful reference in climate change adaptation, heat‐health risk detection, cooling‐energy estimation and policy making.
Remediation of Frogmore Mine Spoiled Soil with Nano Enhanced Materials

ABSTRACT A large amount of waste material and tailings, generated from opencast mineral extraction, released different heavy metals and metalloids that polluted the mine surroundings soil. In the present investigation, an attempt was made to revegetate the open-cut mine affected area. Bulk soil samples were collected from derelict Frogmore tungsten mine site, and after physicochemical characterization of the soil samples, pot culture experiments were conducted in a green house. The different experimental soils like control soil, polluted soil, and polluted soil treated with the combined amendment, viz. Nano chailings (8 t/ha) + Nano composite of Zeolites + Nano Clay (Bentonite) (5%) + Nano composite of Zeolite + Zero valent iron (5%) + 5 t/ha FYM (Farm Yard Manure), were used in the investigation and maize (Zea mays L.) was taken as a test crop. This paper focused on amendments based on nano-enhanced materials. Amongst heavy metals like Cd, Ni, Zn, Cu, Pb, etc., Pb recorded the highest amount (125 mg/kg), which caused drastic reduction in different growth parameters of maize plant grown in mine spoiled soil. Microscopic structural changes, such as decrease in intercellular spaces, stomatal opening, shrinkage of palisade and epidermal cells, occurred in maize leaves grown in the mine polluted soil. Experimental results also revealed significant decrease in the fresh weight, dry weight, height, and girth of maize plants grown in mine spoiled soil. The contaminant distribution in the mine site should be considered in the design of management and remediation strategies. The possible practical consequences of these findings might be the application of nano-enhanced materials for the remediation and revegetation of the abandoned mine spoiled soils.
Chinese Influence through Technical Standardization Power

ABSTRACT Geo-economic rivalry is back on the international agenda, particularly in the field of high technology. Very often, technical standards are regarded as being a central arena of this competition. Surprisingly ignored is the question, how precisely technical standard-setting (such as Wi-Fi or 5G) empowers China. Based on the analysis of quantitative data, primary sources, and in-depth interviews, this article substantiates the widespread hypothesis that China’s growing footprint in technical standardization empowers the Chinese party-state. It introduces seven proxies to measure influence on standard-setting. Next, it explains how technical standards can be utilized by states to gain economic, legal, political, and discursive influence. Finally, it shows thatChina’s growing footprint in technical standardization is the result of party-state engagement, which provides leverage to China’s political leadership.
Bayesian multivariate spatial modeling for crash frequencies by injury severity at daytime and nighttime in traffic analysis zones

ABSTRACT This paper develops a multivariate spatial model for the joint analysis of daytime and nighttime crash frequencies by injury severity in traffic analysis zones (TAZs). The model specification allows for spatial correlation across TAZs, heterogeneous effects specific to crash time and severity, and correlations across response types under a Bayesian multivariate conditional autoregressive framework. One hundred and thirty one TAZs in Hong Kong, China, with traffic crash, traffic flow, roadway network, and land use data for a one-year period are selected to calibrate the advocated model. Considerable spatial and heterogeneous effects are found for each type crash frequency. Significant correlations exist in the heterogeneous effects for various severity levels and those for daytime and nighttime. The Bayesian estimates of the regression coefficients reveal that there are significant inconsistencies in the set of factors contributing to zonal daytime and nighttime crashes at various severity levels.
IgG Anti-ghrelin Immune Complexes Are Increased in Rheumatoid Arthritis Patients Under Biologic Therapy and Are Related to Clinical and Metabolic Markers

Rheumatoid arthritis (RA) is a systemic autoimmune disease associated with increased risk of cardiovascular disease and metabolic alterations. The mechanisms underlying these alterations remain unclear. Ghrelin is a gastrointestinal hormone with potent effects on food intake, body weight, metabolism, and immune response. Recent studies reported the presence of anti-ghrelin autoantibodies in healthy subjects and the levels and affinity of these autoantibodies were altered in anorectic and obese individuals. In this cross-sectional study we analyzed anti-ghrelin autoantibodies in RA patients and evaluated its relationship with clinical, body-composition and metabolic parameters. Clinical measurements of RA patients included the disease activity score-28 (DAS-28), inflammatory biomarkers, autoantibodies (RF and anti-CCP), body composition, glucose and lipid profile. Serum ghrelin levels were measured by enzyme-linked immunosorbent assay (ELISA). Free and total anti-ghrelin autoantibodies quantification (IgG and IgA isotypes) was performed by in-house ELISA. RA patients had lower IgG anti-ghrelin autoantibodies levels and higher immune complexes percentage (IgG+ghrelin) compared to the control group, while the IgA anti-ghrelin autoantibodies showed no significant differences. In the bivariate analysis, the percentage of IgG anti-ghrelin immune complexes positively correlated with BMI and ghrelin whereas in the multivariate regression model, the variables associated were DAS-28, body weight, visceral fat, LDL-C and TG (R2 = 0.72). The percentage of IgA anti-ghrelin immune complexes positively correlated with RF and anti-CCP and the multivariate regression model showed an association with RF and body fat percentage (R2 = 0.22). Our study shows an increased percentage of IgG anti-ghrelin immune complexes in RA patients despite ghrelin levels were similar in both groups, suggesting an increase in the affinity of these autoantibodies toward ghrelin. The associations found in the multiple regression analysis for anti-ghrelin immune complexes support the previously reported functions of these natural autoantibodies as carriers and modulators of the stability and physiological effect of the hormone. However, in RA both the disease activity and the RF appear to influence the formation of these anti-ghrelin immune complexes.
Mapping disaster vulnerability in India using analytical hierarchy process

Disasters are the coincidences between hazardous events, elements at risk, and conditions of vulnerability. Vulnerability integrates social and environmental systems to reduce the intensity and frequency of these risks. By categorizing regions according to their level of vulnerability, one can examine and assess the possible impacts of developmental and environmental degradation processes. This study is an attempt to map the sub-national areas (districts) in India that are vulnerable to natural and climate-induced disasters. The assessment is considered under the framework of Intergovernmental Panel on Climate Change definition of vulnerability. Using analytical hierarchy process as a multi-criteria decision-mapping method, vulnerability is measured in terms of exposure, sensitivity, and adaptive capacity. Based on this mapping assessment, districts in the state of Arunachal Pradesh, Assam, Bihar, Himachal Pradesh, Jharkhand, Manipur, Meghalaya, Mizoram, Uttar Pradesh, Uttarakhand, and West Bengal are the most vulnerable regions; while districts in the state of Punjab, Haryana, Gujarat, Tamil Nadu, Maharashtra, Goa, Andhra Pradesh, Kerala, and Karnataka are among the least vulnerable regions. The results of this study can serve as the basis for targeting prioritization efforts, emergency response measures, and policy interventions at district level for mitigating disaster vulnerability in the country.
Near real-time landslide monitoring with the smart soil particles

As global warming brings forth extreme precipitation in the typhoon-prone region in Asia, e.g. Hong Kong, Taiwan and Japan, the need for a scalable and yet accurate landslide sensing system becomes increasingly urgent. Natural slopes are the focus since their failures are often large-scale and disastrous; however, it is extremely expensive to monitor every single hazardous natural slope in existence with the current monitoring system. The MEMS (Micro-Electro-Mechanical Systems) technology which makes miniature yet accurate sensors available (e.g. accelerometer, gyroscope and magnetometer), will be pivotal towards the realization of a scalable landslide warning system. In this paper, we present the Smart Soil Particle (SSP) sensing system we build to detect the precursory tilts of a large-scale potential landslide site in LuShan, Taiwan. The SSP consists of the sensing module (i.e., low-noise MEMS accelerometer and a motion sensor) and the processing module (Raspberry Pi, a single-board computer). Both cost less than HKD$850; the price makes it possible to deploy a huge amount of SSPs on the slope surface without the fear of losing any. The processing module is capable of handling a large programmable data transfer rate (up to 800 samples/second/channel) through a 3G modem. In the back-end is a decentralized structured data storage system constructed to handle the massive data inflow from multiple SSPs. The whole system is built to send and receive data at a near real-time speed. With a proper noise control and shielding of the wires, we show that the low-noise SSPs are able to capture a small earthquake and also tiny vibrations due to cars passing by.
Predictive value of a multi-biomarker disease activity score for clinical remission and radiographic progression in patients with early rheumatoid arthritis: a post-hoc study of the OPERA trial

Objectives: Measurement of serum biomarkers at disease onset may improve prediction of disease course in patients with early rheumatoid arthritis (RA). We evaluated the multi-biomarker disease activity (MBDA) score and early changes in MBDA score for prediction of 28-joint Disease Activity Score based on C-reactive protein (DAS28-CRP) remission and radiographic progression in the double-blinded OPERA trial. Method: Treatment-naïve RA patients (N = 180) with moderate or high DAS28 were randomized to methotrexate (MTX) + adalimumab (n = 89) or MTX + placebo (n = 91) in combination with glucocorticoid injection into swollen joints. X-rays of hands and feet were evaluated at months 0 and 12 (n = 164) by the total Sharp van der Heijde score (TSS). The smallest detectable change (1.8 TSS units) defined radiographic progression (∆TSS ≥ 2). Clinical remission (DAS28-CRP < 2.6) was assessed at baseline and 6 months. MBDA score was determined at 0 and 3 months and tested in a multivariable logistic regression model for predicting DAS28 remission at 6 months and radiographic progression at 1 year. Results: Baseline MBDA score was independently associated with radiographic progression at 1 year [odds ratio (OR) = 1.03/unit, 95% confidence interval (CI) = 1.01–1.06], and changes in MBDA score from baseline to 3 months with clinical remission at 6 months [OR = 0.98/unit, 95% CI 0.96–1.00). In anti-cyclic citrullinated peptide antibody (anti-CCP)-positive patients, 35 of 89 with high MBDA score (> 44) showed radiographic progression (PPV = 39%), compared with 0 of 15 patients (NPV = 100%) with low/moderate MBDA score (≤ 44) (p = 0.003). Conclusion: Early changes in MBDA score were associated with clinical remission based on DAS28-CRP at 6 months. In anti-CCP-positive patients, a non-high baseline MBDA score (≤ 44) had a clinical value by predicting very low risk of radiographic progression at 12 months.
Taiwan Renal Registry-2004 Annual Data Report

The 2004 annual data report is based on data of incident and prevalent patients collected by the Taiwan Renal Registry in year 2004. This data accounted for 98% of dialysis patients in Taiwan. Patients who expired within one month after start of dialysis were excluded. As of December 31st, 2004, 443 hemodialysis (HD) units and 57 peritoneal dialysis (PD) units provided dialysis services in Taiwan. There were 38,709 patients undergoing maintenance dialysis therapy. The prevalence rate was 1706 per million population (pmp). Among them, 92.3% received hemodialysis (HD) and 7.7% received peritoneal dialysis (PD). Among the PD patients, 74% used continuous ambulatory peritoneal dialysis and 15% received automated peritoneal dialysis. Compared to 2003, the growth rate of prevalence was -4.7%. There were 8525 new patients who initiated dialysis therapy in 2004. The incidence rate was 376 pmp. Compared to 2003, the growth rate of incident patients was -6%. The mean ages of prevalent and incident patients were 59.4±14.5 and 62.3±15.0 years respectively. Aging of our dialysis patients was noted as 39.8% of prevalent patients who were older than 65 years, while 48.9% of incidents patients were older than 65 years. Among the 38709 prevalent patients, there were 17989 males and 20720 females (male to female ratio was 0.87). The three leading causes of end stage renal disease (ESRD) in prevalent patients were chronic glomerulonephritis (38.9%), diabetes mellitus (28.2%), and hypertension (7.2%); while diabetes mellitus (39.5%), chronic glomerulonephritis (29.6%) and hypertension (7%) were three leading causes of ESRD for incident patients. After adjusting for age and gender, the three highest incident districts were Tainan City, Kaohsiung City, and Chiayi City, while three highest prevalent districts were Tainan City, Chiayi City and Kaohsiung County. In 2004, from January 1 through December 31, 6196 patients expired. The crude annual mortality rate was 137.98 per thousand dialysis patients- 141.22 per thousand for HD patients and 96.89 per thousand for PD patients. The main causes of death were cardiovascular disease, infection and malignancy. Both the quality indices of HD and PD in prevalent patients were satisfactory. The rehabilitation status of either PD or HD patients were acceptable. In conclusion, Taiwan remained the top country with the highest ESRD incidence in the world. The dialysis quality and rehabilitation status of dialysis patients were satisfactory. In order to prevent ESRD, we need to focus our prevention programs particularly on elderly and diabetic patients. In addition, we have to pay special attention to the high incident districts, such as Tainan, Kaohsiung and Chiayi areas.
The Impact of Emergency Interventions and Patient Characteristics on the Risk of Heart Failure in Patients with Nontraumatic OHCA

Background Since out-of-hospital cardiac arrest- (OHCA-) related dysfunction (ischemic/reperfusion injury and inflammatory response) might result in long-term impairment, we suspect that new-onset heart failure might be common in long-term survivors. However, these relationships had not been well addressed, and we aimed to analyze the impact of emergency interventions and patient characteristics on the risk of new-onset heart failure in patients with nontraumatic OHCA. Methods The Taiwanese government healthcare database contains data for 49,101 nontraumatic OHCA adult patients from 2011-2012, which were analyzed in this study. Nontraumatic OHCA patients who survived to the intensive care unit (ICU) were included as the study group (n = 7,321). Matched patients (n = 21,963) were recruited as a comparison group. Patients with any history of heart failure or cardiac arrest were not included in either group. All patients were followed-up for 6 months for the identification of new-onset heart failure. Adjustments were made for demographics, age, emergency interventions, and comorbidities as potential risk factors. Results In all, 3.84% (n = 281) of OHCA patients suffered new-onset heart failure, while only 1.24% (n = 272) of matched patients in the comparison group suffered new-onset heart failure. Strong risk factors for heart failure were age (60–75 years, HR: 11.4; 95% CI: 9–14.4), medical history (myocardial infarction, HR: 2.47; 95% CI: 2.05–2.98 and cardiomyopathy, HR: 2.94; 95% CI: 1.45–5.94), and comorbidities during hospitalization (ischemic heart disease, HR: 4.5; 95% CI: 3.46–5.86). Only extracorporeal membrane oxygenation (ECMO) decreased the risk of heart failure. Most (53.6%) heart failure events occurred within 60 days after OHCA. Conclusion An age from 61 to 75 years, a history of myocardial infarction or cardiomyopathy, and ischemic heart disease or infection as comorbidities occurring during hospitalization were strong risk factors for new-onset heart failure in OHCA patients. However, ECMO could decrease this risk. More importantly, most heart failure events occurred within 60 days after OHCA.
Introduction to Chlamydomonas and its laboratory use

Dr. Harris has played a major role in the development of this organism as a model system. Her previous version of the Chlamydomonas Sourcebook which published in 1989, has been a classic in the field and is considered required reading for anyone working with this organism. This latest edition has been expanded to include three volumes providing molecular techniques, analysis of the recently sequenced genome, and reviews of the current status of the diverse fields in which Chlamydomonas is used as a model organism. Methods for Chlamydomonas research and best practices for applications in research, including methods for culture, preservation of cultures, preparation of media, lists of inhibitors and other additives to culture media, are included. Additions to this volume also include help with common laboratory problems such as contamination, student demonstrations, and properties of particular strains and mutants. This volume is part of a 3-Volume Set (ISBN: 978-0-12-370873-1) and is also sold individually. * Expanded revision of gold standard reference * Includes latest advances in research, including completion of the genome * Provides broad perspective with studies in cell and molecular biology, genetics, plant physiology and related fields * Available as part of a 3-Volume Set or sold individually
Understanding Trolls with Efficient Analytics of Large Graphs in Neo4j

: Analytics of large graph data set has become an important means of understanding and influencing the world. The use of graph database technology in the International Consortium of Investigative Journalists’ (ICIJ) investigation of the Panama Papers and Paradise Papers or in cancer research illustrates how analysing graph-structured data helps to uncover important but hidden relationships. A very current example in that regards shows how graph analytics can help shed light on the operations of social media troll-networks, e.g. on Twitter. In similar fashion, graph analytics can help enterprises to unearth hidden patterns and structures within connected data, to make more accurate predictions and faster decisions. All this requires efficient graph analytics well-integrated with management of graph data. The Neo4j Graph Platform provides such an environment. It provides transactional processing and analytical processing of graph data including data management and analytics tooling. A central element for graph analytics in the Graph Platform are the Neo4j graph algorithms. Neo4j graph algorithms provide efficiently implemented, parallel versions of common graph algorithms, integrated and optimized for the Neo4j transactional database. In this paper, we will describe the design and integration Neo4j Graph Algorithms, demonstrate its utility of our approach with a Twitter Troll analysis, and show case its performance with a few experiments on large graphs.