Miracle-dz/UniFAR-FTUs huggingface dataset:
Dietary patterns in middle-aged Irish men and women defined by cluster analysis

Abstract Objectives: To identify and characterise dietary patterns in a middle-aged Irish population sample and study associations between these patterns, sociodemographic and anthropometric variables and major risk factors for cardiovascular disease. Design: A cross-sectional study. Subjects and methods: A group of 1473 men and women were sampled from 17 general practice lists in the South of Ireland. A total of 1018 attended for screening, with a response rate of 69%. Participants completed a detailed health and lifestyle questionnaire and provided a fasting blood sample for glucose, lipids and homocysteine. Dietary intake was assessed using a standard food-frequency questionnaire adapted for use in the Irish population. The food-frequency questionnaire was a modification of that used in the UK arm of the European Prospective Investigation into Cancer study, which was based on that used in the US Nurses' Health Study. Dietary patterns were assessed primarily by K-means cluster analysis, following initial principal components analysis to identify the seeds. Results: Three dietary patterns were identified. These clusters corresponded to a traditional Irish diet, a prudent diet and a diet characterised by high consumption of alcoholic drinks and convenience foods. Cluster 1 (Traditional Diet) had the highest intakes of saturated fat (SFA), monounsaturated fat (MUFA) and percentage of total energy from fat, and the lowest polyunsaturated fat (PUFA) intake and ratio of polyunsaturated to saturated fat (P:S). Cluster 2 (Prudent Diet) was characterised by significantly higher intakes of fibre, PUFA, P:S ratio and antioxidant vitamins (vitamins C and E), and lower intakes of total fat, MUFA, SFA and cholesterol. Cluster 3 (Alcohol & Convenience Foods) had the highest intakes of alcohol, protein, cholesterol, vitamin B12, vitamin B6, folate, iron, phosphorus, selenium and zinc, and the lowest intakes of PUFA, vitamin A and antioxidant vitamins (vitamins C and E). There were significant differences between clusters in gender distribution, smoking status, physical activity, body mass index, waist circumference and serum homocysteine concentrations. Conclusion: In this general population sample, cluster analysis methods yielded two major dietary patterns: prudent and traditional. The prudent dietary pattern is associated with other health-seeking behaviours. Study of dietary patterns will help elucidate links between diet and disease and contribute to the development of healthy eating guidelines for health promotion.
Low-Complexity Iterative Detection for Spectral Efficient Cooperative Transmission

This paper proposes a low-complexity iterative detection scheme with cooperative transmission for single-carrier frequency-domain equalization (SC-FDE). To increase the spectral efficiency, a node, which acts as a relay, simultaneously transmits its own data block and the data block from the other node. For user separation, each data block is interleaved by a user-specific interleaver. At the destination, linear combining is performed and then the interference from the other source is effectively removed by an iterative detection method in the frequency domain. Simulation results show that the proposed system can obtain diversity gain without sacrificing spectral efficiency and approaches the performance of iterative maximum a posteriori (MAP) equalization at low computational cost.
Institutional Ownership, Analyst Following, and Share Prices

We study the mutual relationships between institutional ownership, analyst following and share prices. We show that the pressure on firms to set lower share prices to attract analysts is attenuated by institutional monitoring. Our theory refutes the assumed causal relation between share price and institutional ownership, attributed to the share price–liquidity relation, and we show empirically that share prices and institutional ownership are positively related after controlling for liquidity. Our study provides a rationale for why better firms generally maintain higher share price levels, and offers new insights into the puzzling empirical linkages observed between nominal share price levels and firm fundamentals.
Caregiver Singing and Background Music in Dementia Care

Caregiver singing and background music were incorporated into the interaction between caregiver and patient, the aim being to illuminate the meaning of verbal communication between persons with severe dementia and their caregivers. In the absence of music, patients communicated with cognitive and behavioral symptoms associated with dementia. In these situations, caregivers devoted their verbal communication to narrating and explaining their caring activities to the patient. The patient and caregiver, however, had difficulties understanding one another. In the presence of background music, caregivers decreased their verbal instructing and narrating while the patient communicated with an increased understanding of the situation, both verbally and behaviorally. During caregiver singing, a paradoxical effect was observed such that despite an evident reduction in the amount of verbal narration and description by the caregiver, the patient implicitly understood what was happening.
Complete Genome Sequence of the Strong Mutator Salmonella enterica subsp. enterica Serotype Heidelberg Strain B182

ABSTRACT In bacteria, normal mutation frequencies are mostly around 10−10 per base pair. However, there exists natural isolates, called “mutators,” that exhibit permanent mutation occurrences up to 1,000-fold greater than usual. As mutations play essential roles, particularly in the evolution of antibiotic resistance, bacteria showing elevated mutation rates could have an important responsibility in the emergence of antibiotic resistance, especially in the clinical background. In this announcement, we report the first complete genome sequence of the Salmonella enterica subsp. enterica serotype Heidelberg B182 mutator strain, isolated from bovine feces (France), which consists of a 4,750,465-bp circular chromosome (cB182_4750; GC, 52.2%) and one circular plasmid of 37,581 bp (pB182_37; GC, 42.8%).
The next 700 programming libraries

Modern software requirements are more diverse than before and can only be timely fulfilled via the extensive use of libraries. As a result, modern programmers spend a significant fraction of their time just mixing and matching these libraries. Programming language success becomes, then, more dependent on the quality and broadness of the accompanying libraries than on the language intrinsic characteristics. In spite of its recognized qualities, Common Lisp lags behind other languages as regards the quality and availability of its libraries. We argue that the best solution to overcome this problem is to automatically translate to Common Lisp the best libraries that are available in other languages. In this paper, we specifically address the translation of Java libraries using the Jnil translator tool and we provide a detailed explanation of the problems found and the lessons learned during the translation of a large Java library. Although many problems remain to be solved, the experiment proved the feasibility of the translation process and significantly increased our confidence in the future of Common Lisp.
Timing offset and frequency offsets in muti-band receiver system

In downlink orthogonal frequency division multiple access (OFDMA) systems, available subcarriers are divided into groups and assigned to multiple uses for simultaneous transmissions. The ideal universal receiver sampling the incoming signals just after a RF stage is very seducing but not realistic at this time due to limitations in sampling capabilities for high frequency systems. Software-defined radio (SDR) principles have attracted more and more attention. As one way, using sub-sampling technique can convert multi-bands signals from RF band to IF band without oscillator. But as the filter performance in RF band, signals converted in low band possibly cause interference and degrades performance. In this paper, we propose one method that uses time division multiplexing (TDM) method as a solution to avoid interference. Combining TDM with sub-sampling at the same can get signals without large changes from the existing structures.
Rapidly internationalizing ventures: how definitions can bridge the gap across contexts

Purpose – The purpose of this article is to develop a contextualized definition of the phenomenon of rapidly internationalizing ventures (RIVs) ‐ such as born globals or international new ventures ‐ building upon the commonly noted dimensions of internationalization: speed, degree and scope.Design/methodology/approach – The study builds on a theory informed review of 62 empirical studies on RIVs from the USA and the European Union and an empirical survey among 103 academics in the field of international entrepreneurship.Findings – After specifying the core characteristics of RIVs (speed, degree, and scope of internationalization), it is shown that the discrepancies in definitions result in a dysfunctional fragmentation of empirical results. Thus, research on the phenomenon of RIVs urgently needs contextualized definitions because the three core characteristics are context‐sensitive, and will therefore manifest themselves differently across contexts.Research limitations/implications – The paper contributes.
Vein resection during pancreaticoduodenectomy for pancreatic adenocarcinoma: Patency rates and outcomes associated with thrombosis

Venous patency rates after pancreaticoduodenectomy (PD) with portal vein (PV) resection are not well established, and the oncologic impact of portal vein thrombosis (PVT) is unknown. The primary aim of this study was to determine rates and predictors of PVT after PD with PV resection for pancreatic adenocarcinoma (PDAC).
Intrusion Detection System Based on Decision Tree over Big Data in Fog Environment

Fog computing, as the supplement of cloud computing, can provide low-latency services between mobile users and the cloud. However, fog devices may encounter security challenges as a result of the fog nodes being close to the end users and having limited computing ability. Traditional network attacks may destroy the system of fog nodes. Intrusion detection system IDS is a proactive security protection technology and can be used in the fog environment. Although IDS in tradition network has been well investigated, unfortunately directly using them in the fog environment may be inappropriate. Fog nodes produce massive amounts of data at all times, and, thus, enabling an IDS system over big data in the fog environment is of paramount importance. In this study, we propose an IDS system based on decision tree. Firstly, we propose a preprocessing algorithm to digitize the strings in the given dataset and then normalize the whole data, to ensure the quality of the input data so as to improve the efficiency of detection. Secondly, we use decision tree method for our IDS system, and then we compare this method with Naive Bayesian method as well as KNN method. Both the 10% dataset and the full dataset are tested. Our proposed method not only completely detects four kinds of attacks but also enables the detection of twenty-two kinds of attacks. The experimental results show that our IDS system is effective and precise. Above all, our IDS system can be used in fog computing environment over big data.
Research on the Brain-inspired Cross-modal Neural Cognitive Computing Framework

To address modeling problems of brain-inspired intelligence, this thesis is focused on researching in the semantic-oriented framework design for multimedia and multimodal information. The Multimedia Neural Cognitive Computing (MNCC) model was designed based on the nervous mechanism and cognitive architecture. Furthermore, the semantic-oriented hierarchical Cross-modal Neural Cognitive Computing (CNCC) framework was proposed based on MNCC model, and formal description and analysis for CNCC framework was given. It would effectively improve the performance of semantic processing for multimedia and cross-modal information, and has far-reaching significance for exploration and realization brain-inspired computing.
Virtual Sensor of Surface Electromyography in a New Extensive Fault-Tolerant Classification System

A few prosthetic control systems in the scientific literature obtain pattern recognition algorithms adapted to changes that occur in the myoelectric signal over time and, frequently, such systems are not natural and intuitive. These are some of the several challenges for myoelectric prostheses for everyday use. The concept of the virtual sensor, which has as its fundamental objective to estimate unavailable measures based on other available measures, is being used in other fields of research. The virtual sensor technique applied to surface electromyography can help to minimize these problems, typically related to the degradation of the myoelectric signal that usually leads to a decrease in the classification accuracy of the movements characterized by computational intelligent systems. This paper presents a virtual sensor in a new extensive fault-tolerant classification system to maintain the classification accuracy after the occurrence of the following contaminants: ECG interference, electrode displacement, movement artifacts, power line interference, and saturation. The Time-Varying Autoregressive Moving Average (TVARMA) and Time-Varying Kalman filter (TVK) models are compared to define the most robust model for the virtual sensor. Results of movement classification were presented comparing the usual classification techniques with the method of the degraded signal replacement and classifier retraining. The experimental results were evaluated for these five noise types in 16 surface electromyography (sEMG) channel degradation case studies. The proposed system without using classifier retraining techniques recovered of mean classification accuracy was of 4% to 38% for electrode displacement, movement artifacts, and saturation noise. The best mean classification considering all signal contaminants and channel combinations evaluated was the classification using the retraining method, replacing the degraded channel by the virtual sensor TVARMA model. This method recovered the classification accuracy after the degradations, reaching an average of 5.7% below the classification of the clean signal, that is the signal without the contaminants or the original signal. Moreover, the proposed intelligent technique minimizes the impact of the motion classification caused by signal contamination related to degrading events over time. There are improvements in the virtual sensor model and in the algorithm optimization that need further development to provide an increase the clinical application of myoelectric prostheses but already presents robust results to enable research with virtual sensors on biological signs with stochastic behavior.
Social Security: how social and secure should it be?

How will we support ourselves when we grow old? The question is timeless, for age brings a loss of strength, agility, health, and acuity and no one, in any time, can calmly rely on his or her continuing ability to earn a living when old. In the United States, and in other industrial nations, we have come to rely on the government to solve much of the old-age income problem. Public programs today promise a basic pension, health care, and taxsheltered programs for transferring income to our retirement years. But these programs face serious difficulties in the century ahead. Experts of different economic and political persuasions concur that government commitments to the elderly will be a significant economic burden in the coming century. But they disagree on the severity of the problem and on how best to proceed. As a result, proposals for change range all over the map. The purpose of this paper is to document the nature of the problem and to explore some of the fundamental issues that must be addressed in devising a viable response. The paper opens with a brief overview of the goals of the social insurance system. We then review how demographics will produce a worsening old-age dependency problem in coming decades and how this will result in a fiscal crisis if current policies continue. Although we focus on Social Security (that is, on public old-age pensions), we also examine the role other programs are expected to play in the worsening fiscal
Recurrently Aggregating Deep Features for Salient Object Detection

Salient object detection is a fundamental yet challenging problem in computer vision, aiming to highlight the most visually distinctive objects or regions in an image. Recent works benefit from the development of fully convolutional neural networks (FCNs) and achieve great success by integrating features from multiple layers of FCNs. However, the integrated features tend to include non-salient regions (due to low level features of the FCN) or lost details of salient objects (due to high level features of the FCN) when producing the saliency maps. In this paper, we develop a novel deep saliency network equipped with recurrently aggregated deep features (RADF) to more accurately detect salient objects from an image by fully exploiting the complementary saliency information captured in different layers. The RADF utilizes the multi-level features integrated from different layers of a FCN to recurrently refine the features at each layer, suppressing the non-salient noise at low-level of the FCN and increasing more salient details into features at high layers. We perform experiments to evaluate the effectiveness of the proposed network on 5 famous saliency detection benchmarks and compare it with 15 state-of-the-art methods. Our method ranks first in 4 of the 5 datasets and second in the left dataset.
Loss of MYC Confers Resistance to Doxorubicin-induced Apoptosis by Preventing the Activation of Multiple Serine Protease- and Caspase-mediated Pathways*

c-Myc plays an essential role in proliferation, differentiation, and apoptosis. Because of its relevance to cancer, most studies have focused on the cellular consequences of c-Myc overexpression. Here, we address the role of physiological levels of c-Myc in drug-induced apoptosis. By using c-MYC null cells we confirm and extend recent reports showing a c-Myc requirement for the induction of apoptosis by a number of anticancer agents. In particular, we show that c-Myc is required for the induction of apoptosis by doxorubicin and etoposide, whereas it is not required for camptothecin-induced cell death. We have investigated the molecular mechanisms involved in executing doxorubicin-induced apoptosis and show caspase-3 activation by both mitochondria-dependent and -independent pathways. Moreover, serine proteases participate in doxorubicin-induced apoptosis partly by contributing to caspase-3 activation. Finally, a complete rescue from doxorubicin-induced apoptosis is obtained only when serine proteases, caspase-3, and mitochondrial activation are inhibited simultaneously. Interestingly, doxorubicin requires c-Myc for the activation of all of these pathways. Our findings therefore support a model in which doxorubicin simultaneously triggers multiple c-Myc-dependent apoptosis pathways.
Barriers for Rank Methods in Arithmetic Complexity

Arithmetic complexity is considered simpler to understand than Boolean complexity, namely computing Boolean functions via logical gates. And indeed, we seem to have significantly more lower bound techniques and results in arithmetic complexity than in Boolean complexity. Despite many successes and rapid progress, however, challenges like proving super-polynomial lower bounds on circuit or formula size for explicit polynomials, or super-linear lower bounds on explicit 3-dimensional tensors, remain elusive. At the same time, we have plenty more "barrier results" for failing to prove basic lower bounds in Boolean complexity than in arithmetic complexity. Finding barriers to arithmetic lower bound techniques seem harder, and despite some attempts we have no excuses of similar quality for these failures in arithmetic complexity. This paper aims to add to this study. We address rank methods, which were long recognized as encompassing and abstracting almost all known arithmetic lower bounds to-date, including the most recent impressive successes. Rank methods (or flattenings) are also in wide use in algebraic geometry for proving tensor rank and symmetric tensor rank lower bounds. Our main results are barriers to these methods. In particular, 1. Rank methods cannot prove better than $\Omega_d (n^{\lfloor d/2 \rfloor})$ lower bound on the tensor rank of any $d$-dimensional tensor of side $n$. (In particular, they cannot prove super-linear, indeed even $>8n$ tensor rank lower bounds for any 3-dimensional tensors.) 2. Rank methods cannot prove $\Omega_d (n^{\lfloor d/2 \rfloor})$ on the Waring rank of any $n$-variate polynomial of degree $d$. (In particular, they cannot prove such lower bounds on stronger models, including depth-3 circuits.)
Stable distributions in the Black–Litterman approach to asset allocation

The integration of quantitative asset allocation models and the judgment of portfolio managers and analysts (i.e. qualitative view) dates back to a series of papers by Black and Litterman in the early 1990s. In this paper we improve the classical Black–Litterman model by applying more realistic models for asset returns (the normal, the t-student, and the stable distributions) and by using alternative risk measures (dispersion-based risk measures, value at risk, conditional value at risk). Results are reported for monthly data and goodness of the models are tested through a rolling window of fixed size along a fixed horizon. Finally, we find that incorporation of the views of investors into the model provides information as to how the different distributional hypotheses can impact the optimal composition of the portfolio.
Oscillation Frequency Regulates Agonist-stimulated NFk B Transcriptional Activity *

In nonexcitable cells, stimulation by high agonist concentrations typically produces a biphasic increase in cytosolic Ca ([Ca]i). This response is characterized by a transient initial increase because of intracellular Ca release followed by a sustained elevation which varies in amplitude depending on the nature of the stimulus. In contrast, low-level stimulation often evokes oscillatory changes in [Ca]i. The specific information provided by repetitive [Ca]i spikes appears to be encoded in the frequency rather than in the amplitude of [Ca]i oscillations. The specific, membrane-permeable inositol 1,4,5-trisphosphate (Ins-1,4,5-P3) receptor blocker Xestospongin C (XeC, 2–20 mM) was used to affect [Ca]i signaling in human aortic endothelial cells (HAEC) during an established response to low-level (1 mM) histamine stimulation. XeC produced a dosedependent decrease in the frequency of [Ca]i oscillations during histamine stimulation without affecting oscillation amplitude. Histamine stimulated a 14-fold increase in NF-kB-chloramphenicol acetyltransferase reporter gene activity that was dose-dependently decreased by XeC. Thus, during low-level agonist stimulation, [Ca]i oscillation frequency regulates nuclear transcription in HAEC.