Miracle-dz/UniFAR-FTUs huggingface dataset:
DMSO_A_274232 4661..4667

1Department of Laboratory Medicine, Zutangshan Hospital, Nanjing 211153, Jiangsu, People’s Republic of China; 2Department of Neurology, Taizhou Second People’s Hospital, Taizhou 225500, Jiangsu, People’s Republic of China Background: Previous studies have reported that diabetes mellitus (DM) is a risk factor for Alzheimer’s disease (AD). Vascular cell adhesion molecule-1 (VCAM-1) plays an important role in the pathological process of atherosclerosis. The aim was to elucidate the relationship between serum VCAM-1 and early AD in DM patients. Methods: Serum samples for VCAM-1 were tested in 208 DM patients. All included DM patients were followed up for a median of 36 months prospectively. The prognostic value of serum VCAM-1 for predicting AD events was analyzed by using Cox proportional hazard. Results: Serum VCAM-1 was independently associated with AD history after adjusting for related confounding factors in patients with DM at baseline by using the logistic regression analysis (OR=1.861; 95% CI, 1.435–2.539; Ptrend=0.020). The Cox proportional hazard model suggested that VCAM-1 was a prognostic factor for AD events in the DM patients (HR=2.728; 95% CI, 1.785–5.439; Ptrend<0.001). Stratified analysis showed that the significant association between AD event and serum VCAM-1 in DM patients was not affective by CVD history. Conclusion: Our results showed that higher VCAM-1 levels were significantly related to a higher risk of AD events in DM patients. The serum biomarker might be beneficial to predict AD early. Serum VCAM-1 might be a good biochemical parameter for predicting AD in DM.
Linguistic Risk-Taking: A Bridge Between the Classroom and the Outside World

This article describes an initiative launched at a Canadian bilingual university in order to encourage L2 French and L2 English learners to take ‘linguistic risks’: authentic, autonomous communicative acts where learners are pushed out of their linguistic comfort zone. The initiative was operationalized through the development of a Linguistic Risk-Taking Passport, which contains 74 linguistic risks that students can take in their L2 across the university campus and in their everyday life. An analysis of interviews with participating teachers (n=6) and learner self-report data from completed passports (n=410) examines how the initiative was integrated into the classroom and which passport items were perceived by students as particularly high-risk. A cyclical process of risk-taking within a broad Task-Based Language Teaching (TBLT) framework is described in which risks are viewed as learner-selected tasks with a dynamic affective slant; risks can be used to connect classroom learning with real-life L2 use and vice versa. The data illustrate that linguistic risk-taking can help TBLT practitioners generate ideas on how to narrow the gap between the classroom and the real-world. The article concludes with a list of practical implications and suggestions for adapting linguistic risk-taking to other institutional contexts.
Supplementary material to "Added value of the EURO-CORDEX high-resolution downscaling over the Iberian Peninsula revisited. Part II: Max and Min Temperature"

Abstract. In the recent past, the increase of computation resources led to the appearance of regional climate models with increasing domains and resolutions, spamming larger temporal periods. A good example is the World Climate Research Program – Coordinated Regional Climate Downscaling Experiment for the European domain (EURO-CORDEX). This set of regional models encompass the entire European continent, for a 130-year common period until the end of the 21st century, while having a 12 Km horizontal resolution. Such simulations are computationally demanding, while at the same time, not always showing added value. This study considers a recently proposed metric in order to assess the added value of the EURO-CORDEX Hindcast (1989–2008) and Historical (1971–2005) simulations, for the maximum and minimum temperature over the Iberian Peninsula. This approach allows an evaluation of the higher against the driving lower resolutions relative to the performance of the whole or partial probability density functions, by having an observational regular gridded dataset as reference. Overall, the gains for maximum temperature are more relevant in comparison to minimum temperature, partially owed to known problems derived from the snow-albedo-atmosphere feedback. For more local scales, areas near the coast reveal significant added value in comparison with the interior, which displays limited gains and sometimes significant detrimental effects around −30 %. Nevertheless, the added value for temperature extremes reveals a similar range, although with stronger gains in coastal regions and in locations from the interior for maximum temperature, contrasting with the losses for locations in the interior of the domain for the minimum temperature.
Discourses about EU Transport Decarbonisation: Towards a Paradigm Shift?

ABSTRACT The development and introduction of new technologies are central to achieving sustainable transitions. Policymakers are important in enabling a successful transition. However, discourses about EU transport decarbonisation reveal multiple policy approaches to technology innovation, both in terms of decarbonising the car and building new alternative fuels infrastructure for transport. A discursive institutionalist analysis of these two separate but interdependent communicative discourses on road transport decarbonisation shows the complexities of facilitating transformative change. This shift requires coordination at all levels involving different actors and sector coupling to successfully decarbonise road transport.
Analysis of the thermal rectification in silicon structure with triangular holes

ABSTRACT Silicon semiconductors with excellent thermoelectric characteristics have been widely used in integrated electronic systems. Thermal rectification has important potential applications in semiconductor devices such as thermal management improvement of electronics and energy saving. The thermal conductivities and thermal rectifications of silicon devices with sets of triangular holes are investigated by nonequilibrium molecular dynamic (NEMD) simulations. The influences of the dimension and angle of the distributed triangular-shaped holes are investigated. The results indicate that the phonon scattering on the side and base of the right-angled triangular holes are quite different, which causes the thermal rectification. The thermal rectification effect is strengthened with the increase of the triangle dimension. However, the variations of the triangular angle have little effect on the thermal conductivities of the silicon device. Besides, the phonon spectral energy density along the frequency axis of the silicon system is given to explain the mechanism behind the rectification phenomenon. Based on the present simulation results, a possible design strategy is proposed for developing highly efficient thermoelectric devices.
Europe’s Transition to Sustainability: Actors, Approaches and Policies

In 2019, the European Commission launched the European Green Deal (EGD, see EC [2019]) as a strategic framework for policy development to achieve the aims of the Paris Agreement and the United Nations (UN) 2030 Agenda for Sustainable Development (see Dupont et al. 2020). The EGD offers an opportunity to reflect on the complexity of achieving long-term sustainability through enhanced public action in a number of relevant EU policy areas. Amidst a plethora of policy challenges (such as the refugee crisis or Brexit and Covid-19), this Special Issue uses the new context created by the EGD to engage in the debate on key topics related to this transition towards sustainability. The EGD may become the extension of ecological modernisation (Jänicke 2008), where environmental protection became a perceived chance rather than a cost. By delivering the EGD, the EU may put its action and money behind this idea. The EGD aligns with the aim of the UN 2030 Agenda to leave no one behind. Since the EU has historically been accused of a democratic deficit (see for instance Azman 2011), it is important to explore how the EGD addresses issues of inclusivity of stakeholders (actors) in the governance system. The Special Issue analyses the drivers of policy change and possible barriers to progress. It provides insights on how the process started with the EGD can drive EU member states towards more sustainable policies; what actors, approaches and policies are particularly prominent in the EU multi-level governance system (Heritier 2010; 2017; Kohler-Koch and Larat 2009); and to what extent the EU influences third countries in the adoption of environmental policies showing its actorness on the international stage (Bretherton and Vogler 2005; 2008). The success of the EGD may depend on our understanding of these policy aspects. This is where this Special Issue can contribute insights on European integration. As a baseline definition, the Special Issue retains the original interpretation of sustainable development as comprising social, economic and environmental aspects (Brundtland 1987). The EGD seems to adhere to this most coherent and comprehensive interpretation by employing economic means (investment) for environmental ends, while also paying specific attention to a fair and socially just transition (Laurent 2020). The EU thereby signals that it takes climate change seriously while keeping the economic potential of the sustainability transition in sight. As a result, the EGD allows the EU to strengthen its international environmental leadership by working towards the alignment
Unraveling the COVID-19 hospitalization dynamics in Spain using publicly available data

Background One of the main challenges of the ongoing COVID-19 pandemic is to be able to make sense of available, but often heterogeneous and noisy data, to characterize the evolution of the SARS-CoV-2 infection dynamics, with the additional goal of having better preparedness and planning of healthcare services. This contribution presents a data-driven methodology that allows exploring the hospitalization dynamics of COVID-19, exemplified with a study of 17 autonomous regions in Spain. Methods We use data on new daily cases and hospitalizations reported by the Ministry of Health of Spain to implement a Bayesian inference method that allows making short and mid-term predictions of bed occupancy of COVID-19 patients in each of the autonomous regions of the country. Findings We show how to use given and generated temporal series for the number of daily admissions and discharges from hospital to reproduce the hospitalization dynamics of COVID-19 patients. For the case-study of the region of Aragon, we estimate that the probability of being admitted to hospital care upon infection is 0.090 [0.086-0.094], (95% C.I.), with the distribution governing hospital admission yielding a median interval of 3.5 days and an IQR of 7 days. Likewise, the distribution on the length of stay produces estimates of 12 days for the median and 10 days for the IQR. A comparison between model parameters for the regions analyzed allows to detect differences and changes in policies of the health authorities. Interpretation The amount of data that is currently available is limited, and sometimes unreliable, hindering our understanding of many aspects of this pandemic. We have observed important regional differences, signaling that to properly compare very different populations, it is paramount to acknowledge all the diversity in terms of culture, socio-economic status and resource availability. To better understand the impact of this pandemic, much more data, disaggregated and properly annotated, should be made available.
LncRNA XR_001779380 Primes Epithelial Cells for IFN-γ-Mediated Gene Transcription and Facilitates Age-Dependent Intestinal Antimicrobial Defense

Epithelial cells along the mucosal surface provide the front line of defense against luminal pathogen infection in the gastrointestinal tract. These epithelial cells represent an integral component of a highly regulated communication network that can transmit essential signals to cells in the underlying intestinal mucosa that, in turn, serve as targets of mucosal immune mediators. ABSTRACT Interferon (IFN) signaling is key to mucosal immunity in the gastrointestinal tract, but cellular regulatory elements that determine interferon gamma (IFN-γ)-mediated antimicrobial defense in intestinal epithelial cells are not fully understood. We report here that a long noncoding RNA (lncRNA), GenBank accession no. XR_001779380, was increased in abundance in murine intestinal epithelial cells following infection by Cryptosporidium, an important opportunistic pathogen in AIDS patients and a common cause of diarrhea in young children. Expression of XR_001779380 in infected intestinal epithelial cells was triggered by TLR4/NF-κB/Cdc42 signaling and epithelial-specific transcription factor Elf3. XR_001779380 primed epithelial cells for IFN-γ-mediated gene transcription through facilitating Stat1/Swi/Snf-associated chromatin remodeling. Interactions between XR_001779380 and Prdm1, which is expressed in neonatal but not adult intestinal epithelium, attenuated Stat1/Swi/Snf-associated chromatin remodeling induced by IFN-γ, contributing to suppression of IFN-γ-mediated epithelial defense in neonatal intestine. Our data demonstrate that XR_001779380 is an important regulator in IFN-γ-mediated gene transcription and age-associated intestinal epithelial antimicrobial defense. IMPORTANCE Epithelial cells along the mucosal surface provide the front line of defense against luminal pathogen infection in the gastrointestinal tract. These epithelial cells represent an integral component of a highly regulated communication network that can transmit essential signals to cells in the underlying intestinal mucosa that, in turn, serve as targets of mucosal immune mediators. LncRNAs are recently identified long noncoding transcripts that can regulate gene transcription through their interactions with other effect molecules. In this study, we demonstrated that lncRNA XR_001779380 was upregulated in murine intestinal epithelial cells following infection by a mucosal protozoan parasite Cryptosporidium. Expression of XR_001779380 in infected cells primed host epithelial cells for IFN-γ-mediated gene transcription, relevant to age-dependent intestinal antimicrobial defense. Our data provide new mechanistic insights into how intestinal epithelial cells orchestrate intestinal mucosal defense against microbial infection.
The Emergence and Zoonotic Transmission of H10Nx Avian Influenza Virus Infections

Avian influenza viruses pose a continuous threat to both poultry and human health, with significant economic impact. The ability of viruses to reassort and jump the species barrier into mammalian hosts generates a constant pandemic threat. ABSTRACT Avian influenza viruses pose a continuous threat to both poultry and human health, with significant economic impact. The ability of viruses to reassort and jump the species barrier into mammalian hosts generates a constant pandemic threat. H10Nx avian viruses have been shown to replicate in mammalian species without prior adaptation and have caused significant human infection and fatalities. They are able to rapidly reassort with circulating poultry strains and go undetected due to their low pathogenicity in chickens. Novel detections of both human reassortant strains and increasing endemicity of H10Nx poultry infections highlight the increasing need for heightened surveillance and greater understanding of the distribution, tropism, and infection capabilities of these viruses. In this minireview, we highlight the gap in the current understanding of this subtype and its prevalence across a vast range of host species and geographical locations.
Predicting the Degradation of COVID-19 mRNA Vaccine with Graph Convolutional Networks

The COVID-19 pandemic has generated a great demand for effective vaccines to control SARS-CoV-2 that causes the disease. In particular, mRNA vaccines against that virus have taken the lead as the fastest vaccine candidates. However, RNA molecules tend to degrade spontaneously, leading to a key challenge to design super stable mRNA molecules for a refrigerator-stable vaccine. In this paper, to accelerate the research of mRNA vaccines, we develop a machine learning method to predict likely degradation rates at each base of RNA molecules in the Eterna dataset, which has been devised for COVID-19 mRNA vaccines. By employing graph convolutional networks with a multi-head attention mechanism, we successfully achieve the goal to predict the degradation of mRNA molecules based on their structure information. After applying K-Fold cross validation and pseudo-label technique, our model reaches a mean columnwise root mean squared error 0.3512 on the Private Test set at Kaggle platform. Such result ranks top 4% on the leaderboard, and can get a silver medal prize in the Kaggle competition.
Deep Neural Network Based Application Capacity Analysis in Finance System

The goal of system capacity analysis is to understand the current capacity usage and forecast future capacity impact based on various business scenarios. The successful capacity analysis is the key to identify the system bottleneck and plan for better resource allocation. However, IT systems for finance company are inherently large and complex with numerous interfaces with other systems. Thus, identifying and selecting a good model to describe the system interdependence from capacity perspective is important but challenging problem. In our paper, we first define the problem we want to solve. We discuss 2 approaches as baselines. Then we propose DNN based multiple linear regression, which is more efficient for complex finance systems. We collected 12 months real production volume data as our dataset. The experiment shows our proposed model can give a better performance compared with baseline approaches. Unlike other research papers, our proposal focuses to solve problem in real finance industry.
Pigmented Skin Lesions Image Classification Based on Residual Network

Pigmented Skin lesions image analysis is an essential diagnostic tool for skin cancer. Since convolutional neural network is an important method to solve image classification problem, using convolutional neural network for skin lesions image classification to assist the diagnosis is practically significant. This paper builds and compares two 50-layer ResNet performances based on different building blocks in skin lesions image classification. Both models performed similarly with an accuracy of about 75%. Nevertheless, ResNet based on bottleneck building block improves training speed by about 51% without sacrificing accuracy. Therefore, using bottleneck block is an effective way to improve model's training speed.
Vendace (Coregonus albula) Disperse Their Eggs Widely during Spawning

Depending on their reproductive strategy, different fish species either aggregate or disperse eggs and larvae in their reproductive habitat. Because yolk-sac larvae of vendace (Coregonus albula) disperse widely across the littoral and pelagic zones of boreal lakes, it is unclear where the exact spawning and egg incubation locations are. Vendace egg and larvae densities were studied in Lake Southern Konnevesi to clarify its spawning strategy. In autumn 2019, 1–2 weeks prior to spawning, 500 egg samplers were installed in five depth zones in 20 sampling plots. Fertilized eggs were found in 18 plots. The mean density of eggs was 74 eggs m–2 and the mean fertilization rate 85%. During spawning, vendace dispersed their offspring throughout the lake. The sampling-plot-specific egg density in autumn 2019 did not correlate with larval density in the spring next year. The reproduction strategy of vendace reduces the effects of high spatial and temporal fluctuation in their reproduction and nursery habitats.
Lots of bots or maybe nots: A process for detecting bots in social media research

The use of bot messaging, that being artificially created messages, has increased since 2010. While not all bots are bad, many have been used to share extreme and divisive views on a range of topics, from policy discussion to brand electronic word of mouth. The issue with bot messaging and its prevalence is that it can affect researchers’ understanding of a topic. For example, if 25% of a dataset is fabricated, decision-making may result in a loss of profit or poor policy formation. To counteract the use of bots, this research note offers a framework to alleviate the potentially destructive nature of bot data and ensure the cleaning of data is thorough and beneficial to decision-making based on social media commentary. The framework is a four-step process, which includes thematic, automated, and characteristic identification stages. We provide three case studies to demonstrate the approach and conclude by providing key practical implications.
1 IMP : A Simple Imperative Language

To illustrate the various semantic styles discussed in this chapter, we have chosen a small imperative language, called IMP, whose syntax is inspired from C and Java. In fact, if we wrap an IMP program in a main(){. } function the we get a valid C program. IMP has arithmetic expressions which include the domain of arbitrarily large integer numbers, Boolean expressions, assignment statements, conditional statements, while loop statements, and sequential composition of statements. Statements can be grouped in blocks surrounded with curly brackets, and the branches of the conditional and the loop body are required to be blocks. All variables used in an IMP program need to be declared at the beginning of the program, can only hold integer values (for simplicity, IMP has no Boolean variables), and are initialized with default value 0.
Measuring Sovereign Credit Risk of the EU countries

Abstract European countries have increased significantly their public debt since the Global Financial Crisis. The increasing trend and the high concentration of public debt in portfolios of financial institutions can lead to a financial turmoil we witnessed during the European Sovereign Debt Crisis. Financial stability authorities therefore look for models to measure the sovereign credit risk and develop“what-if”scenarios to assess a potential repercussion of a financial institution rescue or of an economic contraction on sovereign credit risk. The presented article introduces adjustments to the sovereign contingent claims analysis that is based on the Merton´s Credit Risk Model and the Black-Scholes option pricing techniques. The article proposes adjustments by introducing a new view on a stylised liability side of a central government balance sheet, seniority of its items, and a new proxy for risk measure of junior claims. We show reliable results using derived risk sensitivities for 20 EU countries with decent forward looking ability and propose potential stress-testing framework with an application for the Czech Republic.