Miracle-dz/UniFAR-FTUs huggingface dataset:
De novo synthesis of milk triglycerides in humans.

UNLABELLED Mammary gland (MG) de novo lipogenesis contributes significantly to milk fat in animals but little is known in humans. OBJECTIVE To test the hypothesis that the incorporation of (13)C carbons from [U-(13)C]glucose into fatty acids (FA) and glycerol in triglycerides (TG) will be greater: 1) in milk than plasma TG, 2) during a high-carbohydrate (H-CHO) diet than high-fat (H-FAT) diet, and 3) during feeding than fasting. Seven healthy, lactating women were studied on two isocaloric, isonitrogenous diets. On one occasion, subjects received diets containing H-FAT or H-CHO diet for 1 wk. Incorporation of (13)C from infused [U-(13)C]glucose into FA and glycerol was measured using GC-MS and gene expression in RNA isolated from milk fat globule using microarrays. Incorporation of (13)C2 into milk FA increased with increased FA chain length from C2:0 to C12:0 but progressively declined in C14:0 and C16:0 and was not detected in FA>C16. During feeding, regardless of diets, enrichment of (13)C2 in milk FA and (13)C3 in milk glycerol were ∼ 3- and ∼ 7-fold higher compared with plasma FA and glycerol, respectively. Following an overnight fast during H-CHO and H-FAT diets, 25 and 6%, respectively, of medium-chain FA (MCFA, C6-C12) in milk were derived from glucose but increased to 75 and 25% with feeding. Expression of genes involved in FA or glycerol synthesis was unchanged regardless of diet or fast/fed conditions. The human MG is capable of de novo lipogenesis of primarily MCFA and glycerol, which is influenced by the macronutrient composition of the maternal diet.
Chinese named entity recognition using lexicalized HMMs

This paper presents a lexicalized HMM-based approach to Chinese named entity recognition (NER). To tackle the problem of unknown words, we unify unknown word identification and NER as a single tagging task on a sequence of known words. To do this, we first employ a known-word bigram-based model to segment a sentence into a sequence of known words, and then apply the uniformly lexicalized HMMs to assign each known word a proper hybrid tag that indicates its pattern in forming an entity and the category of the formed entity. Our system is able to integrate both the internal formation patterns and the surrounding contextual clues for NER under the framework of HMMs. As a result, the performance of the system can be improved without losing its efficiency in training and tagging. We have tested our system using different public corpora. The results show that lexicalized HMMs can substantially improve NER performance over standard HMMs. The results also indicate that character-based tagging (viz. the tagging based on pure single-character words) is comparable to and can even outperform the relevant known-word based tagging when a lexicalization technique is applied.
Iterative Enhanced Directional Based Demosaicking Algorithm

Demosaicing is a process of obtaining a full color image by interpolating the missing colors of an image captured from a digital still and video cameras that use a single-sensor array. This paper provides an effective iterative demosaicing algorithm applying a weighted-edge interpolation to handle green pixels followed by a series of color difference interpolation to update red, blue, and green pixels. Then border of the image is enhanced using some adaptive techniques. Experimental results show that the proposed method performs much better than other latest demosaicing techniques in terms of image quality. In comparison to the other algorithms, this algorithm proposed here results in increased PSNR value.
A Novel Gene Coding for a Fas Apoptosis Inhibitory Molecule (FAIM) Isolated from Inducibly Fas-resistant B Lymphocytes

The sensitivity of primary splenic B cells to Fas-mediated apoptosis is modulated in a receptor-specific fashion. Here we used a differential display strategy to detect cDNAs present in B cells rendered Fas resistant but absent in those rendered Fas sensitive. This led to the cloning and characterization of a novel 1.2-kb gene that encodes a Fas apoptosis inhibitory molecule (FAIM). faim-transfected BAL-17 B lymphoma cells were less sensitive by half or more to Fas-mediated apoptosis than were vector-transfected controls, using Fas ligand–bearing T cells or a cytotoxic anti-Fas antibody to trigger Fas, and this was associated with inhibition of Fas- induced poly-ADP ribose polymerase (PARP) cleavage. In primary B cells, the time course of faim mRNA and FAIM protein expression correlated with the induction of Fas resistance by surface (s)Ig engagement. Thus, FAIM is an inducible effector molecule that mediates Fas resistance produced by sIg engagement in B cells. However, faim is broadly expressed in various tissues and the faim sequence is highly conserved evolutionarily, suggesting that its role extends beyond lymphocyte homeostasis. As FAIM has no significant regions of homology to other gene products that modulate Fas killing, it appears to represent a distinct, new class of antiapoptotic protein.
Strategic Protection Against Data Injection Attacks on Power Grids

Data injection attacks to manipulate system state estimators on power grids are considered. A unified formulation for the problem of constructing attacking vectors is developed for linearized measurement models. Based on this formulation, a new low-complexity attacking strategy is shown to significantly outperform naive ℓ1 relaxation. It is demonstrated that it is possible to defend against malicious data injection if a small subset of measurements can be made immune to the attacks. However, selecting such subsets is a high-complexity combinatorial problem given the typically large size of electrical grids. To address the complexity issue, a fast greedy algorithm to select a subset of measurements to be protected is proposed. Another greedy algorithm that facilitates the placement of secure phasor measurement units (PMUs) to defend against data injection attacks is also developed. Simulations on the IEEE test systems demonstrate the benefits of the proposed algorithms.
The postmodern perfectionist, the pragmatic hedonist and the materialist maximalist: understanding high school students’ profile choices towards or away from mathematics, science and technology (MST) fields in the Netherlands

ABSTRACT This study focuses on high school students’ profile choices and the choice for or against the Nature and Technology (NT) profile in the Netherlands. A mixed-methods approach is used to study cultural values that affect this choice. The quantitative part of the study shows that being female is negatively correlated with the choice for the NT-profile, irrespective of the grade average for mathematics, chemistry and physics. It further shows that students’ ethnic background does not have a significant effect on this choice. The qualitative part of the study reveals that students’ choice processes towards or away from NT can be categorised in three ideal types: the postmodern perfectionist, the pragmatic hedonist and the materialist maximalist. Gender differences appear to be more pervasive across these types than differences in ethnic background.
The Emergence and Use of GPS Electronic Monitoring in Germany: Current Trends and Findings

ABSTRACT In Germany, Electronic Monitoring (EM) is currently applied in two different areas. As of 2000, in the German state of Hesse, Radio Frequency (RF) EM has been used for probationers to avoid (re)imprisonment. Approximately 4 years ago, a federal law came into force allowing for Global Positioning System (GPS) EM of high-risk offenders after prison release. In spite of these ongoing areas of application, a pilot project featuring GPS-EM of prisoners was carried out as a means of release preparation in the German state of Baden-Württemberg from October 2010 until March 2012. This article gives an overview of the different areas of application of EM in Germany and analyzes its nationwide use. Furthermore, the rationale and the outcome of the pilot project in Baden-Württemberg are presented in detail. In conclusion, the authors raise serious doubts about the rehabilitative potential of EM with respect to the current evaluative findings.
Effects of eicosapentaenoic acid and vitamin C on glycemic indices, blood pressure, and serum lipids in type 2 diabetic Iranian males

BACKGROUND: Eicosapentaenoic acid (EPA) is the principal ω-3 fatty acids in marine oils. Fasting blood sugar (FBS), HbA1c and some of the plasma lipids and lipoproteins has been negatively related to the intake of ω-3 fatty acids and ascorbic acid, in some studies. Therefore, the purpose of this study was to assess the effects of EPA and/or vitamin C on glycemic indices, blood pressure, and plasma lipids in type 2 diabetic Iranian males. METHODS: Sixty five men with type 2 diabetes were enrolled into the study between April 2 and June 27, 2008. Venous blood samples were obtained from all participants after 10 hours of fasting, at the baseline and after the intervention. Subjects received 500 mg EPA and/or 200 mg vitamin C and/or placebo depending on their groups. For eight weeks, 15 participants received EPA supplements with vitamin C (group 1), 16 took EPA supplements and vitamin C placebo (group 2), 17 took EPA placebo and vitamin C (group 3), and 17 received EPA placebo and vitamin C placebo (group 4), daily. RESULTS: There were significant decreases in FBS, HbA1C, LDL-C and TG in groups 1, 2, 3 and 4 (p < 0.01), but significant decreases in TC were shown only in groups 1, 2 and 3 (p < 0.01). There was a significant increase in HDL-C in all groups (p < 0.01). CONCLUSIONS: In summary, it is concluded that, eight weeks of taking EPA + vitamin C supplementation improved the plasma levels of cardiovascular markers but didn’t reduce BP.
A question-and-answer classification technique for constructing and managing spoken dialog system

To recognize user speech accurately and respond to it appropriately, a spoken dialog system usually uses a question-and-answer database (QADB) which contains many question-and-answer pairs. The systems first select a question example which is the most similar to the recognition result for the input voice from the database. An answer sentence which is then paired with the selected question example is output to the user. Many systems have a large database to enable a more appropriate answer to be output. However, when such a database is used, the waiting time increases because the system needs to find the most appropriate question example from a vast number of question examples. We propose a method of classifying the queries in the QADB. By classifying question examples into some clusters using pLSA, an appropriate question example can be found more quickly than when using the conventional method. We evaluated the validity of our proposed method by changing various parameters.
Anatomy of the steroid receptor zinc finger region.

I. Introduction LIKE many transcriptional regulatory proteins, nuclear hormone receptors are single polypeptides that are organized into relatively discrete functional domains (1). This common domain organization groups these receptors into a superfamily of functionally and most likely structurally related, hormonally regulable, transcription factors. The superfamily includes receptors for steroid hormones, such as glucocorticoids, progesterone, estrogen, aldosterone, and androgens, as well as hormonal forms of vitamins A and D, thyroid hormone, and peroxisomal activators (2–6). Several other receptorlike proteins whose ligands have yet to be identified have recently been isolated utilizing molecular cloning techniques. In the same way, several putative nuclear receptors from Drosophila that appear to act as regulators of early development (Refs. 7–10; reviewed in Ref. 11) have been identified, including a receptor for the insect steroid ecdysone (12). Upon association with a particular ligand, nuclear re.
Text and Layout Information Extraction from Document Files of Various Formats Based on the Analysis of Page Description Language

We propose a document analysis method, which extracts text and layout information from document files of various formats. This method analyzes the page description language (PDL) data generated from a printed document. By converting the document to PDL data, this method can handle various document formats. Graphic elements such as text objects, image objects, and path objects in the PDL data are analyzed to extract text and layout information (character size, character position, and table position). By applying OCR to the image objects and the path objects, text images in source documents and vectorized font characters in engineering drawings are converted to text. Moreover, tables in various documents are detected by analyzing path objects. Therefore, it is possible to extract the full content information from document files of various formats as long as the document is printable.
A new metric for dissimilarity data classification based on Support Vector Machines optimization

Dissimilarities are extremely useful in many real-world pattern classification problems, where the data resides in a complicated, complex space, and it can be very difficult, if not impossible, to find useful feature vector representations. In these cases a dissimilarity representation may be easier to come by. The goal of this work is to provide a new technique based on Support Vector Machines (SVM) optimization that can be a good alternative in terms of accuracy compared to known methods using dissimilarities such as k nearest neighbor classifier (kNN), prototype-based dissimilarity classifiers and distance kernel based SVM classifiers.
Using Community Structure for Complex Network Layout

We present a new layout algorithm for complex networks that combines a multi-scale approach for community detection with a standard force-directed design. Since community detection is computationally cheap, we can exploit the multi-scale approach to generate network configurations with close-to-minimal energy very fast. As a further asset, we can use the knowledge of the community structure to facilitate the interpretation of large networks, for example the network defined by protein-protein interactions.
The challenges of sentiment detection in the social programmer ecosystem

A recent research trend has emerged to study the role of affect in in the social programmer ecosystem, by applying sentiment analysis to the content available in sites such as GitHub and Stack Overflow. In this paper, we aim at assessing the suitability of a state-of-the-art sentiment analysis tool, already applied in social computing, for detecting affective expressions in Stack Overflow. We also aim at verifying the construct validity of choosing sentiment polarity and strength as an appropriate way to operationalize affective states in empirical studies on Stack Overflow. Finally, we underline the need to overcome the limitations induced by domain-dependent use of lexicon that may produce unreliable results.
Bravo for the Marshallese: Regaining Control in a Post-nuclear, Post-colonial World [Book Review]

Review(s) of: Barker, Holly M. : Bravo for the Marshallese: Regaining Control in a Post-Nuclear, Post-Colonial World, Belmont: Wadsworth, 2004, xv + 172 pp., app. , bib. , figs, glossary, index, photos, tables. n.p. (paper).
Fatigue in patients with chronic widespread pain participating in multidisciplinary rehabilitation treatment: a prospective cohort study

Abstract Purpose: To explore the associations between (improvement in) fatigue and (improvement in) clinical and cognitive factors in patients with chronic widespread pain (CWP), participating in multidisciplinary rehabilitation treatment. Methods: Data were used from baseline, 6 and 18 months of follow-up during a prospective cohort study of 120 CWP patients who completed multidisciplinary rehabilitation treatment. Cross-sectional and longitudinal relationships were analyzed between fatigue, clinical (i.e. pain, interference of pain and depression) and pain related cognitive factors (i.e. negative emotional cognitions, active cognitive coping, and control and chronicity beliefs). Results: Higher levels of pain, interference of pain, depression, negative emotional cognitions, and negative control and chronicity beliefs were associated with a higher level of fatigue. Improvement in depression was related to improvement in fatigue. Conclusions: In CWP patients, worse clinical status, and dysfunctional pain-related cognitions are associated with a higher level of fatigue. Our results suggest that improvement in depression might be a mechanism of improvement in fatigue. Furthermore, improvement in fatigue seems to be independent of improvement in pain related cognitions. Targeting fatigue in multidisciplinary pain treatment may need specific strategies. Implications for Rehabilitation Improvement in depression may be a mechanism of change to improve the level of fatigue in CWP. Improvement in dysfunctional (pain related) cognitions seems to be independent of improvement in fatigue. Targeting fatigue in multidisciplinary treatment may need specific strategies (e.g. additional interventions focusing on reducing fatigue and specific attention to improvement of sleep).
Evaluating Probabilistic Forecasts with Bayesian Signal Detection Models

We propose the use of signal detection theory (SDT) to evaluate the performance of both probabilistic forecasting systems and individual forecasters. The main advantage of SDT is that it provides a principled way to distinguish the response from system diagnosticity, which is defined as the ability to distinguish events that occur from those that do not. There are two challenges in applying SDT to probabilistic forecasts. First, the SDT model must handle judged probabilities rather than the conventional binary decisions. Second, the model must be able to operate in the presence of sparse data generated within the context of human forecasting systems. Our approach is to specify a model of how individual forecasts are generated from underlying representations and use Bayesian inference to estimate the underlying latent parameters. Given our estimate of the underlying representations, features of the classic SDT model, such as the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC), follow immediately. We show how our approach allows ROC curves and AUCs to be applied to individuals within a group of forecasters, estimated as a function of time, and extended to measure differences in forecastability across different domains. Among the advantages of this method is that it depends only on the ordinal properties of the probabilistic forecasts. We conclude with a brief discussion of how this approach might facilitate decision making.
Dabigatran use in elderly patients with atrial fibrillation

Summary In elderly patients (≥75 years), evidence of dabigatran efficacy is lacking and increased vigilance is warranted. We aimed to assess dabigatran effectiveness and safety in elderly patients in real-world practice. We conducted a population-based study using administrative databases, in Quebec (1999–2013). Dabigatran users (110/150 mg) were compared with matched warfarin users with regard to stroke and bleeding events. Age was categorised into < 75 or ≥ 75 years. Propensity score adjusted models were used. The cohort consisted of 15,918 dabigatran users and 47,192 matched warfarin users, with 67.3 % being elderly patients. The elderly predominantly used the lower dose (80.1 %) while younger patients mainly used the higher dose (80.0 %). In multivariable analyses adjusted for propensity score, the risk of stroke in elderly patients using dabigatran, was no different than the risk in warfarin users (HR 1.05, 95 % CI: 0.93, 1.19) regardless of dabigatran dose. However, dabigatran was associated with lower rates of intracranial haemorrhage (HR 0.60, 95 % CI: 0.47–0.76) and higher rates of gastrointestinal bleeding (HR 1.30 95 % CI: 1.14–1.50) when compared to warfarin. Based on real-life experience, dabigatran can offer an alternative to warfarin in elderly patients, with fewer intracranial bleeding events. However, caution is warranted for gastrointestinal bleeding.
The crack of doom: The uncanny echoes of Steven Moffat's Doctor Who

This paper analyses Steven Moffat's contributions to the British sf series Doctor Who between 2005 and 2010, both as a screenwriter and as executive producer. This analysis is framed specifically by the Freudian notion of the uncanny, and suggests that Moffat's work on Doctor Who confronts unconscious perceptions, repressed fears and death itself through storytelling techniques which attempt to connect directly with the audience by deconstructing the distance between material reality and the series's fantasy space.
A Visual Search Inspired Computational Model for Ship Detection in Optical Satellite Images

In this letter, we propose a novel computational model for automatic ship detection in optical satellite images. The model first selects salient candidate regions across entire detection scene by using a bottom-up visual attention mechanism. Then, two complementary types of top-down cues are employed to discriminate the selected ship candidates. Specifically, in addition to the detailed appearance analysis of candidates, a neighborhood similarity-based method is further exploited to characterize their local context interactions. Furthermore, the framework of our model is designed in a multiscale and hierarchical manner which provides a plausible approximation to a visual search process and reasonably distributes the computational resources. Experiments over panchromatic SPOT5 data prove the effectiveness and computational efficiency of the proposed model.
MINT: maximizing information propagation in predictable delay-tolerant network

Information propagation in delay tolerant networks (DTN) is difficult due to the lack of continues connectivity. Most of previous work put their focus on the information propagation in static network. In this work, we examine two closely related problems on information propagation in predicable DTN. In particular, we assume that during a certain time period, the interacting process among nodes is known a priori or can be predicted. The first problem is to select a set of initial source nodes, subject to budget constraint, in order to maximize the total weight of nodes that receive the information at the final stage. This problem is well-known influence maximization problem which has been extensively studied for static networks. The second problem we want to study is minimum cost initial set problem, in this problem, we aim to select a set of source nodes with minimum cost such that all the other nodes can receive the information with high probability. We conduct extensive experiments using $10,000$ users from real contact trace.
A biologically inspired computational model of Moral Decision Making for autonomous agents

In areas such as psychology and neuroscience a common approach to study human behavior has been the development of theoretical models of cognition. In fields such as artificial intelligence, these cognitive models are usually translated into computational implementations and incorporated into the architectures of intelligent autonomous agents (AAs). The main assumption is that this design approach contributes to the development of intelligent systems capable of displaying very believable and human-like behaviors. Decision Making is one of the most investigated and computationally implemented cognitive functions. The literature reports several computational models designed to allow AAs to make decisions that help achieve their personal goals and needs. However, most models disregard crucial aspects of human decision making such as other agents' needs, ethical values, and social norms. In this paper, we propose a biologically inspired computational model of Moral Decision Making (MDM). This model is designed to enable AAs to make decisions based on ethical and moral judgment. The simulation results demonstrate that the model helps to improve the believability of virtual agents when facing moral dilemmas.