Miracle-dz/UniFAR-FTUs huggingface dataset:
Prevalence and Predictors of Mild Cognitive Impairment in Xi’an: A Community-Based Study among the Elders

Mild cognitive impairment (MCI) is an intermediate stage between normal cognitive function and dementia among aging individuals. This study was designed to estimate the prevalence of MCI and explore the possible risk factors including gender disparities among community-dwelling older individuals. The study was conducted in Xi’an, China. This is a cross-sectional study. A total of 815 individuals, 60 years and older were selected by stratified random cluster sampling. Cognitive function was measured using the mini-mental status examination (MMSE), the Chinese version of the Dementia Rating Scales (CDRS) was used to apply the diagnostic of non-dementia, and activities of daily living (ADL) and instrumental activities of daily living (IADL) systems were used to functional status. The association between sociodemographic characteristics, lifestyle, history of chronic diseases and MCI were evaluated separately for men and women using the Pearson χ2- test and binary logistic regression. Of the 815 community-dwelling individuals, 145 were found to have MCI. Overall, the prevalence of MCI was 18.5%, with a prevalence of 19.6% in women (105/535), and 15.3% (40/261) in men. The results of the binary logistical regression analysis indicated that age and history of stroke were associated with MCI in men. For women, the risk factors were lower level of educational and lack of religious attendance. Results suggested that the factors capable of influencing MCI differed profoundly between older men and older women. For this reason, different preventative measures should be adopted to delay or reverse cognitive impairment among community-dwelling older men and women.
Synergy between histone deacetylase inhibitors and DNA-damaging agents is mediated by histone deacetylase 2 in colorectal cancer

Previous studies have associated the overexpression of histone deacetylase 2 (HDAC2) and the presence of TP53 mutations with the progression to advanced stage drug resistant colorectal cancer (CRC). However, the mechanistic link between HDAC2 expression and the TP53 mutational status has remained unexplored. Here, we investigated the function of HDAC2 in drug resistance by assessing the synergistic effects of DNA-targeted chemotherapeutic agents and HDAC inhibitors (HDACis) on two TP53-mutated colorectal adenocarcinoma CRC cell lines (SW480 and HT-29) and on the TP53-wild type carcinoma cell line (HCT116 p53+/+) and its TP53 deficient sub-line (HCT116 p53−/−). We showed that in the untreated SW480 and HT-29 cells the steady-state level of HDAC2 was low compared to a TP53-wild type carcinoma cell line (HCT116 p53+/+). Increased expression of HDAC2 correlated with drug resistance, and depletion by shRNA sensitised the multi-drug resistance cell line HT-29 to CRC chemotherapeutic drugs such as 5-fluorouracil (5-FU) and oxaliplatin (Oxa). Combined treatment with the HDACi suberoylanilide hydroxamic acid plus 5-FU or Oxa reduced the level of HDAC2 expression, modified chromatin structure and induced mitotic cell death in HT-29 cells. Non-invasive bioluminescence imaging revealed significant reductions in xenograft tumour growth with HDAC2 expression level reduced to <50% in treated animals. Elevated levels of histone acetylation on residues H3K9, H4K12 and H4K16 were also found to be associated with resistance to VPA/Dox or SAHA/Dox treatment. Our results suggest that HDAC2 expression rather than the p53 mutation status influences the outcome of combined treatment with a HDACi and DNA-damaging agents in CRC.
The yggH Gene of Escherichia coli Encodes a tRNA (m7G46) Methyltransferase

ABSTRACT We cloned, expressed, and purified the Escherichia coli YggH protein and show that it catalyzes the S-adenosyl-l-methionine-dependent formation of N7-methylguanosine at position 46 (m7G46) in tRNA. Additionally, we generated an E. coli strain with a disrupted yggH gene and show that the mutant strain lacks tRNA (m7G46) methyltransferase activity.
Social Trust Prediction via Max-norm Constrained 1-bit Matrix Completion

Social trust prediction addresses the significant problem of exploring interactions among users in social networks. Naturally, this problem can be formulated in the matrix completion framework, with each entry indicating the trustness or distrustness. However, there are two challenges for the social trust problem: 1) the observed data are with sign (1-bit) measurements; 2) they are typically sampled non-uniformly. Most of the previous matrix completion methods do not well handle the two issues. Motivated by the recent progress of max-norm, we propose to solve the problem with a 1-bit max-norm constrained formulation. Since max-norm is not easy to optimize, we utilize a reformulation of max-norm which facilitates an efficient projected gradient decent algorithm. We demonstrate the superiority of our formulation on two benchmark datasets.
Concomitant Variables in Latent Change Models

A basic tenet in modeling preference behavior is that individuals differ in the ways they perceive and evaluate choice options. Latent-class analysis provides a parsimonious and flexible approach to represent these taste differences. This method decomposes a heterogeneous population of decision-makers into several homogeneous classes or subpopulations. Each decision-maker is assigned to one of the latent classes such that preference differences among members of different classes are maximized (Böckenholt, 1993; Croon & Luijkx, 1993; DeSarbo, Ramaswamy, & Lenk, 1993; Winsberg & DeSoete, 1993). The accuracy of this assignment can be greatly improved by taking into account person-specific collateral information (e.g., demographics). The consideration of collateral information is of particular interest in strategic marketing studies. If the segmentation results obtained in a latent class analysis can be characterized by demographic variables, marketing tasks such as positioning and targeting of consumer groups are much simplified (Dillon, Kumar, & Smith de Borrero, 1993; Gupta & Chintagunta, 1994).
Creating Social Contagion Through Firm Mediated Message Design: Evidence from a Randomized Field Experiment

We study whether and how a firm can enhance social contagion by simply varying the message shared by customers with their friends. We focus on two key components of information contained in the messages – information about the sender’s purchase status prior to referral, and information about the existence of referral rewards – and their impacts on the recipient’s purchase decision and further referral behavior. In collaboration with an online daily deal platform we design and conduct a large scale randomized field experiment to identify the effect of each message component, as well as the interaction effects between them, in creating social contagion. We find that small variations in message content can have a significant impact on both recipient’s purchase and referral behaviors. Specifically, we find that i) adding only information about the sender’s purchase status increases the likelihood of recipients’ purchase, but has no impact on follow-up referrals; ii) adding only information about referral reward increases recipients’ follow-up referrals, but has no impact on purchase likelihood; and iii) adding information about both the sender’s purchase as well as information about the referral rewards increases neither the likelihood of purchase or follow-up referrals. The study also examines the underlying mechanisms driving social contagion by exploiting the rich heterogeneity in product, recipient, sender and social tie characteristics. The findings of the study provide valuable guidelines for optimal design of messages at the aggregate as well as at a more granular level.
Inverse bounds for microstructural parameters of composite media derived from complex permittivity measurements

Abstract Bounds on the volume fraction of the constituents in a two-component mixture are derived from measurements of the effective complex permittivity of the mixture, using the analyticity of the effective property. First-order inverse bounds for general anisotropic materials, as well as second-order bounds for isotropic mixtures, are obtained. By exploiting an analytic representation of the effective complex permittivity, the problem of estimating the structural parameters is reduced to a problem of evaluating the moments and support of a measure containing information about the geometrical structure of the material. Rigorous bounds on the volume fraction are found by inverting first- and second-order (Hashin–Shtrikman) forward bounds on the complex permittivity. The inverse bounds are applied to measurements of the effective complex permittivity of sea ice, which is a three-component mixture of ice, brine and air. The sea ice is treated via the two-component theory applied to a mixture of brine and a.
Computer & Information Science A Learnable Spectral Memory Graph for Recognition and Segmentation by Timothée Cour and

Image segmentation is often treated as an unsupervised task. Segmentation by human, in contrast, relies heavily on memory to produce an object-like clustering, through a mechanism of controlled hallucination. This paper presents a learning algorithm for memory-driven object segmentation and recognition. We propose a general spectral graph learning algorithm based on gradient descent in the space of graph weight matrix using derivatives of eigenvectors. The gradients are efficiently computed using the theory of implicit functions. This algorithm effectively learns a graph network capable of memorizing and retrieving multiple patterns given noisy inputs. We demonstrate the validity of this approach on segmentation and recognition tasks, including geometric shape extraction, and hand-written digit recognition.
Rapid Child Growth Raises Blood Pressure in Adolescent Boys Who Were Thin at Birth

Abstract—Catch-up growth in previously growth-restricted children is a suggested risk factor for chronic disease risk. We use data from 2026 Filipino adolescents to identify periods of growth that matter more for risk of high blood pressure (BP). Subjects were drawn from the Cebu Longitudinal Health and Nutrition Survey, which enrolled pregnant women and followed up their offspring through age14 to 16 years. High BP was defined as the top 10% of residuals from gender-specific regressions of systolic and diastolic BP on age and height. After controlling for birth length, current body mass index, age, and height, the odds of high BP in males were significantly decreased with each kilogram increase in birth weight. The highest odds of elevated BP occurred among males who were relatively thin at birth but relatively heavy as adolescents. Larger weight increments from birth to 2 years decreased the odds of high BP in boys, whereas larger increments from 8 to 11 and 11 to 16 years increased the odds of high BP. Thinness at birth significantly interacted with growth rate after age 8, such that a high rate of weight gain increased risk only among boys who were in the lower two thirds of the body mass index distribution at birth. Results in girls indicated small or no effects of early growth. The synergistic effect on adolescent BP of rapid weight gain from late childhood into adolescence with thinness at birth is further evidence of fetal programming of BP in males and suggests long-term health risks associated with rapid growth, even in the absence of obesity.
An approach for knowledge-driven product, process and resource mappings for assembly automation

The rapid development and reconfiguration of assembly automation systems to accommodate product changes in a short period of time is a challenging task due to the limitations of the contemporary automation systems engineering approach. To improve the engineering process, Product Process Resource (PPR)-based modeling, simulation and data integration systems emerged as a promising approach to compress the development time and engineering cost by enabling collaboration and realization of product and manufacturing resources in a virtual environment. However, due to ineffective coupling of PPR data, design and reconfiguration of assembly systems is still a challenging task and is highly dependent on the knowledge and experience of engineers. This paper presents a knowledge-driven engineering approach to manage the mapping of engineering data sets to interconnect product attributes to manufacturing process and resources. A case study based on a Festo test rig is presented for illustrating the implementation of the approach. The successful identification of the tasks required to manufacture a product proves the approach concept but only for the presented use case.
Correlation of cognitive status, MRI‐ and SPECT‐imaging in CADASIL patients

Although there is evidence for correlations between disability and magnetic resonance imaging (MRI) total lesion volume in autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), the significance of structural MRI abnormalities for cognitive dysfunction remains controversial. We performed detailed neuropsychological testing, high resolution MRI, and Tc‐99m‐ethyl cysteinate‐dimer SPECT in three CADASIL patients. MR‐images were rated independently by two investigators for the presence of white matter lesions, lacunar infarcts, microbleeds, and ventricular enlargement. Cortical atrophy was quantified by the use of automatic morphometric assessment of the cortical thickness. In addition, laboratory and patients’ history data were collected in order to assess the individual vascular risk factor profile. The differences in cognitive performance between the three patients are neither explained by structural‐, or functional neuroimaging, nor by the patient‐specific vascular risk factor profiles. The neuroradiologically least affected patient met criteria for dementia, whereas the most severely affected patient was in the best clinical and cognitive state. Conventional structural and functional neuroimaging is important for the diagnosis of CADASIL, but it is no sufficient surrogate marker for the associated cognitive decline. Detailed neuropsychological assessment seems to be more useful, particularly with respect to the implementation of reliable outcome parameters in possible therapeutic trials.
Knowledge in Economics and Economic Reform: An Analysis of French Survey Data

The coexistence of a predominantly poor opinion of free markets and lack of education in economics are two documented features of France. In this paper, we use data collected through an Internet-based survey conducted in December 2009 in order to test whether this situation is more than a mere coincidence. A first regression model allows us to study how personal characteristics, general and economics education, occupation and personal interest in economics affect knowledge in economics. We then apply factor analysis in order to build an aggregate indicator of opinion on promarket reforms. This opinion indicator becomes the dependent variable in a second multiple regression model; it turns out that knowledge in economics contributes by 3.5% to explain the favorable opinion on pro-market reforms.
Drop-In Control Flow Hijacking Prevention through Dynamic Library Interception

A longstanding issue in computer security is preventing an attacker from gaining arbitrary execution rights from the exploitation of common programming mistakes, which result in opening unintentional breaches in the behavior of executable code. In particular, buffer overflows on the stack and the possibility for an attacker to manipulate format strings in formatted I/O functions still represent, according to the classification provided by the SANS institute, the third and 23rd most significant threats to the security of a system, respectively. We provide a drop-in countermeasure intercepting calls to dynamic libraries, to prevent both stack-based buffer overflows and uncontrolled format strings from providing a viable entry point for an attacker, while keeping the average performance overhead below 4% for I/O intensive applications and within 2% for CPU bound ones. We tested our approach on a large benchmark suite on a common Linux distribution, without making any modifications.
Stochastic collapsed variational Bayesian inference for latent Dirichlet allocation

There has been an explosion in the amount of digital text information available in recent years, leading to challenges of scale for traditional inference algorithms for topic models. Recent advances in stochastic variational inference algorithms for latent Dirichlet allocation (LDA) have made it feasible to learn topic models on very large-scale corpora, but these methods do not currently take full advantage of the collapsed representation of the model. We propose a stochastic algorithm for collapsed variational Bayesian inference for LDA, which is simpler and more efficient than the state of the art method. In experiments on large-scale text corpora, the algorithm was found to converge faster and often to a better solution than previous methods. Human-subject experiments also demonstrated that the method can learn coherent topics in seconds on small corpora, facilitating the use of topic models in interactive document analysis software.
An Architecture to Support Dynamic Service Composition in Distributed Real-Time Systems

Recently, new trends in application development for distributed platforms, such as the composable services model, attempt to provide more flexibility in system design, deployment and execution. Such trends, and particularly the referred composable services model, can also be beneficial in real-time distributed embedded systems, also providing a means to support more adaptive behaviors, reacting to the execution environment or coping with system reconfiguration. This paper explores a relatively new direction, which is the extension of the service-based model to dynamic, i.e. at run-time, composition in real-time distributed environments, in order to support the level of flexibility and adaptibility referred above. The paper proposes an architecture to support such dynamic service composition that is based on the flexible time triggered communication paradigm (FTT). To achieve the desired goal, we also redefine the concepts of service and service-based application in the concept of the FTT paradigm. Finally, we show experimental results obtained with a prototype implementation of the proposed architecture that confirm its feasibility and good temporal behavior
ADEPT: a visual tool for organising simulations

ADEPT is a visual tool for creating and managing groups of related biophysical systems simulations that are run by the agricultural modelling framework 'APSIM'. It was written in Java on the Eclipse Rich Client Platform. It features a user-friendly and novel GUI that aids agricultural systems modellers by presenting information about their simulations in a useful and meaningful format and allows them to manipulate that information.
Understanding behavioural patterns in JavaScript

JavaScript is one of the most popular programming languages. How- ever, understanding the dynamic behaviour of JavaScript apps is challenging in practice. There are many factors that hinder JavaScript comprehension, such as its dynamic, asynchronous, and event- driven nature, the dynamic interplay between JavaScript and the Document Object Model, and the asynchronous communication between client and server. In this research work, we have already proposed methods for understanding event-based and asynchronous JavaScript behaviour. To enhance the scalability of our methods, we propose a new technique that adopts bio-informatics algorithms to extract sequences of actions from execution traces that form higher-level patterns.
Pickup protons at quasi-perpendicular shocks: full particle electrodynamic simulations

Abstract. We have performed 3 one-dimensional full particle electromagnetic simulations of a quasi-perpendicular shock with the same Alfven Mach number M A ~5, shock normal-magnetic field angle Θ Bn =87° and ion and electron beta (particle to magnetic field pressure) of 0.1. In the first run we used an ion to electron mass ratio close to the physical one ( m i /m e =1024). As expected from previous high mass ratio simulations the Modified Two-Stream instability develops in the foot of the shock, and the shock periodically reforms itself. We have then self-consistently included in the simulation 10% pickup protons distributed on a shell in velocity space as a third component. In a run with an unrealistically low mass ratios of 200 the shock still reforms itself; reformation is due to accumulation of specularly reflected particles at the upstream edge of the foot. In a third run including pickup protons we used a mass ratio of 1024. The shock reforms periodically as in the low mass ratio run with a somewhat smaller time constant. The specular reflection of pickup protons results in an increase of the shock potential some distance ahead of the shock foot and ramp. The minimum scale of the cross shock potential during reformation is about 7 electron inertial length λ e . We do not find any pickup proton acceleration in the ramp or downstream of the shock beyond the energy which specularly reflected ions gain by the motional electric field of the solar wind during their upstream gyration.
Scaffolding Practice: Supporting Emerging Bilinguals’ Academic Language Use in Two Classroom Communities

For teachers learning to support emerging bilingual students in literacy classrooms, attending to academic language can encourage students’ disciplinary meaning-making as they learn this new language. Taking a community-focused, language-as-practice perspective, this study examines how two teachers scaffold first-grade emerging bilingual students’ academic language use as they explore cause and effect relationships in Ezra Jack Keats’ text, The Snowy Day. Findings show that scaffolds included modeling, recasting, and highlighting academic language use during disciplinary meaning-making, as well as the use of multiple modalities and responsive scaffolding when exploring academic language. Findings also suggest that a strict attention to prescribed uses of academic language at times constrained students’ participation in classroom meaning-making. Implications from this study include the need for integrating content and language learning, the need for responsive and flexible scaffolds, and the importance of community scaffolding.