Miracle-dz/UniFAR-FTUs huggingface dataset:
Carbohydrate-deficient transferrin in serum: a new marker of potentially harmful alcohol consumption reviewed.

During the last 16 years an increasing number of studies have indicated a new diagnostic marker of alcohol abuse, unrelated to any of the conventional markers of alcoholism. This marker, now called carbohydrate-deficient transferrin, consists mainly of one or two isoforms of transferrin that are deficient in their terminal trisaccharides. Such isoforms have so far been detected by methods based on charge, i.e., isoelectric focusing, chromatofocusing, and anion-exchange chromatography of various designs combined with immunological detection techniques. This transferrin abnormality measures an accumulated effect of alcohol consumption, appearing after regular intake of 50-80 g of ethanol/day for at least one week and normalizing slowly during abstinence (half-life = about 15 days). To summarize all studies to date, approximately 2500 individuals have been examined, with a total clinical sensitivity of 82% and a specificity of 97%. False-positive results have only occasionally been reported: in a few patients with severe liver disease, usually primary biliary cirrhosis and chronic active hepatitis; in patients with genetic D variants of transferrin; and in patients with (and some carriers of) a recently identified inborn error of glycoprotein metabolism. The mechanism behind the transferrin abnormality is unknown but an acetaldehyde-mediated inhibition of glycosyl transfer has been suggested. Carbohydrate-deficient transferrin may thus offer a new possibility of diagnosing alcohol-related disorders. Its measurement is little affected by other conditions and, contrary to conventional markers of alcohol abuse, is apparently largely independent of concomitant liver disease.
On the Significance of the Zerotree Hypothesis for Wavelet-Based Image Coding

The significance of the zerotree hypothesis and its importance for the coding efficiency of zerotree-based wavelet image coding schemes is discussed. We apply specific coefficient permutations within the JPEG2000 and SPIHT coding pipelines which allow to control the amount of inter and intra subband correlations present in the visual data to be compressed. Results document and emphasize the importance of the zerotree hypothesis for image coding and suggest that it has a high likelihood of being correct
Vibration prediction in switched reluctance motors with transfer function identification from shaker and force hammer tests

A vibration prediction model for the switched reluctance motor is constructed in this paper. Shaker and force hammer tests for vibration measurement are used for measuring crucial parameters like modal frequency and damping ratio for the Transfer Function. A detailed lookup table of normal force vs. phase current and rotor angle is constructed based on finite element calculations. The model is then verified by experiments, with acceptable accuracy.
Low contrast double slot structure based optomechanical sensor

Slot waveguide structure gained attention due to high confinement of power inside low index slot region. The high e-field confinement is dependent upon various geometrical parameters. A double slot structure where two low index slots of hard material in high index cladding of compressible material is proposed. Power confinement factor dependency upon distance between low index slots has been numerically computed. Sufficient numerical results obtained lead to the proposal of optomechanical sensor based upon proposed double slot structure.
Effects of dispersed oil on reproduction in the cold water copepod Calanus finmarchicus (Gunnerus)

Following a 120‐h exposure period to 3 concentrations of oil dispersions (0.022 mg L−1, 1.8 mg L−1, and 16.5 mg L−1, plus controls) generated from a North Sea crude oil and a subsequent 21‐d recovery, mortality, and several reproduction endpoints (egg production rates, egg hatching success, and fraction of females participating in reproduction) in Calanus finmarchicus were studied. Concentration‐dependent mortality was found during exposure, averaging to 6%, 3%, 15%, and 42% for the controls and 3 exposure levels, respectively. At the start of the recovery period, mean egg production rates of surviving females from the highest concentrations were very low, but reproduction subsequently improved. In a 4‐d single female reproduction test starting 13 d postexposure, no significant differences in egg production rates or hatching success were found between reproducing control and exposed copepods. However, a significantly lower portion of the surviving females from the highest exposure participated in egg production. The results indicate that although short‐term exposure to oil‐polluted water after an oil spill can induce severe mortality and temporarily suspend reproduction, copepods may recover and produce viable offspring soon after exposure. The results might imply that for C. finmarchicus populations, the impact from short‐term exposure to an oil spill might be predicted from acute mortality and that delayed effects make only a limited contribution to population decrease. Environ Toxicol Chem 2013;32:2045–2055. © 2013 The Authors. Environmental Toxicology and Chemistry Published by Wiley Periodicals, Inc., on behalf of SETAC. This is an open access article under the terms of the Creative Commons Attribution‐Non‐Commercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
The SCMPDS Computer and the Basic Semantics of its Instructions 1

The article defines the SCMPDS computer and its instructions. The SCMPDS computer consists of such instructions as conventional arithmetic, “goto”, “return” and “save instruction-counter” (“saveIC” for short). The address used in the “goto” instruction is an offset value rather than a pointer in the standard sense. Thus, we don’t define halting instruction directly but define it by “goto 0” instruction. The “saveIC” and “return” equal almost call and return statements in the usual high programming language. Theoretically, the SCMPDS computer can implement all algorithms described by the usual high programming language including recursive routine. In addition, we describe the execution semantics and halting properties of each instruction.
Debugging With an Enriched Dependency-based Model or How to Distinguish Between Aliasing and Value Assignment

This paper introduces a new model for debugging of Java programs. This model is based on previous functional dependency models that have been developed for the same purpose. In contrast the model makes not only use of dependency information but also of aliasing information. Therefore, the results are better for a large class of examples. The model is basically a qualitative model where values of variables are ignored. Hence, the approach can be seen as an application of qualitative reasoning for debugging software. In the paper we further discuss the compilation of Java programs to a constrained value-flow graph, and the mapping from the graph to its logical representation.
Systems Biology Elucidates Common Pathogenic Mechanisms between Nonalcoholic and Alcoholic-Fatty Liver Disease

The abnormal accumulation of fat in the liver is often related either to metabolic risk factors associated with metabolic syndrome in the absence of alcohol consumption (nonalcoholic fatty liver disease, NAFLD) or to chronic alcohol consumption (alcoholic fatty liver disease, AFLD). Clinical and histological studies suggest that NAFLD and AFLD share pathogenic mechanisms. Nevertheless, current data are still inconclusive as to whether the underlying biological process and disease pathways of NAFLD and AFLD are alike. Our primary aim was to integrate omics and physiological data to answer the question of whether NAFLD and AFLD share molecular processes that lead to disease development. We also explored the extent to which insulin resistance (IR) is a distinctive feature of NAFLD. To answer these questions, we used systems biology approaches, such as gene enrichment analysis, protein–protein interaction networks, and gene prioritization, based on multi-level data extracted by computational data mining. We observed that the leading disease pathways associated with NAFLD did not significantly differ from those of AFLD. However, systems biology revealed the importance of each molecular process behind each of the two diseases, and dissected distinctive molecular NAFLD and AFLD-signatures. Comparative co-analysis of NAFLD and AFLD clarified the participation of NAFLD, but not AFLD, in cardiovascular disease, and showed that insulin signaling is impaired in fatty liver regardless of the noxa, but the putative regulatory mechanisms associated with NAFLD seem to encompass a complex network of genes and proteins, plausible of epigenetic modifications. Gene prioritization showed a cancer-related functional map that suggests that the fatty transformation of the liver tissue is regardless of the cause, an emerging mechanism of ubiquitous oncogenic activation. In conclusion, similar underlying disease mechanisms lead to NAFLD and AFLD, but specific ones depict a particular disease signature that has a different impact on the systemic context.
Spectrum-aware data replication in intermittently connected cognitive radio networks

The opening of under-utilized spectrum creates an opportunity for unlicensed users to achieve substantial performance improvement through cognitive radio techniques. In cognitive radio ad-hoc networks, with node mobility and low node density, the network topology is highly dynamic and end-to-end connection is hard to maintain. We propose data replication techniques to address these problems and improve data access performance in such intermittently connected cognitive radio network. Although data replication has been extensively studied in traditional disruption tolerant networks, existing techniques cannot be directly applied here since they do not consider the effects of primary user appearance on data replication. In this paper, we formulate spectrum-aware data replication as an optimization problem which tries to maximize the average data retrieval probability, subject to storage and time constraints. Since the problem is hard to solve based on mixed integer programming, we further design a distributed replication scheme based on the metric of replication benefit. Extensive simulations based on synthetic and realistic traces show that our scheme outperforms existing schemes in terms of data retrieval probability in various scenarios.
Can We Predict Burnout among Student Nurses? An Exploration of the ICWR-1 Model of Individual Psychological Resilience

The nature of nursing work is demanding and can be stressful. Previous studies have shown a high rate of burnout among employed nurses. Recently, efforts have been made to understand the role of resilience in determining the psychological adjustment of employed nurses. A theoretical model of resilience was proposed recently that includes several constructs identified in the literature related to resilience and to psychological functioning. As nursing students are the future of the nursing workforce it is important to advance our understanding of the determinants of resilience in this population. Student nurses who had completed their final practicum were invited to participate in an online survey measuring the key constructs of the ICWR-1 model. 422 students from across Australia and Canada completed the survey between July 2014 and July 2015. As well as several key demographics, trait negative affect, mindfulness, self-efficacy, coping, resilience, and burnout were measured. We used structural equation modeling and found support for the major pathways of the model; namely that resilience had a significant influence on the relationship between mindfulness, self-efficacy and coping, and psychological adjustment (burnout scores). Furthermore, as predicted, Neuroticism moderated the relationship between coping and burnout. Results are discussed in terms of potential approaches to supporting nursing students who may be at risk of burnout.
Effects of Empathy, Swarming, and the Dilemma between Reactiveness and Proactiveness Incorporated in Caribou Agents on Evolution of their Escaping Behavior in the Wolf-Caribou Problem

: We investigate whether socio-psychological aspects such as empathy, grouping (swarming), and the tradeo ﬀ between reactive and proactive behaviors, implemented in caribou agents improves the e ﬃ ciency of the simulated evolution (via genetic programming) of their escape behavior or the e ﬀ ectiveness of such a behavior in the wolf-caribou predator prey pursuit problem. The latter comprises a team of inferior caribou agents attempting to escape from a single yet superior (in terms of sensory abilities, raw speed, and maximum energy) wolf agent in a simulated two-dimensional inﬁnite toroidal world. We empirically veriﬁed the survival value of empathy in that it improves both the e ﬃ ciency of evolution of escape behavior and the e ﬀ ectiveness of such a behavior. Also, we concluded that swarming facilitates a faster evolution of caribou agents while preserving the e ﬀ ectiveness of their evolved behavior. Finally, we investigated the dilemma between the reactiveness and proactiveness of the behavior of caribou agents. The experimental results suggest that the trade-o ﬀ between the reactiveness and proactiveness facilitates a signiﬁcant improvement of both the e ﬃ ciency of evolution and the e ﬀ ectiveness of the evolved escape behavior of caribou agents.
Family and/or Friends? Gene Mapping at Crossroads

Mapping gene(s) underlying a specific trait offers an opportunity to plant breeders to apply marker assisted selection. All gene mapping approaches except LD mapping use family based segregation populations developed by crossing two or more parents. These family based gene mapping approaches include simple interval mapping, composite interval mapping, multiple interval mapping and Bayesian mapping etc. Each approach has its own advantages and disadvantages based on type of population and underlying statistical model. Unlike family based approaches, LD mapping uses population of unrelated individuals which are like friends belonging to different family backgrounds. Relative pros and cons of family and friends based approaches make them complementary to each other. Family based approaches identify wide chromosomal region underlying the trait of interest with relatively lower markers density, and therefore, have low mapping resolution. Conversely, friends based LD mapping identifies chromosomal region of interest with higher resolution using higher marker density. The integration of family and friends based approaches addresses their respective pros and cons successfully to enhance mapping resolution for more valid application of marker assisted selection.
Water Wave Packets Over Variable Depth

In this paper, we develop higher‐order nonlinear Schrödinger equations with variable coefficients to describe how a water wave packet will deform and eventually be destroyed as it propagates shoreward from deep to shallow water. It is well‐known that in the framework of the usual nonlinear Schrödinger equations, a wave packet can only exist in deep water, more precisely when kh > 1.363, where k is the wavenumber and h is the depth. Using a combination of asymptotic analysis and numerical simulations we find that in the framework of the higher‐order nonlinear Schrödinger equations, the wave packet can penetrate into shallow water kh < 1.363 or not even reach kh > 1.363, depending on the sign of the initial value in deep water of a certain parameter of the wave packet that measures its speed.
DEVELOPMENT OF MULTIPLE-INPUT MULTIPLE-OUTPUT (MIMO) AND MULTIPLE-INPUT SINGLE-OUTPUT (MISO) NEURAL NETWORK MODELS FOR CONTINUOUS DISTILLATION COLUMN

Distillation columns are widely used in chemical processes and account for approximately 95 percent of the separation systems in industries. A distillation column is a complex multivariable system and exhibits nonlinear dynamic behavior due to the nonlinear vapor-liquid equilibrium relationships, the complexity processing configurations and high product purities. In order to gain better product quality and lower the energy consumption of the distillation column, an effective nonlinear model based control system is needed to allow the process to be operated over a larger operating range. The availability of a suitable nonlinear model is crucial in the development of a nonlinear model based control. In this study, a nonlinear model to predict the top and bottom product compositions of a methanol-water pilot plant distillation column was developed using the neural network technique. The input-output data for the neural network model was generated from the validated general first principle model. Based on the input-output analyses, reboiler heat duty, reflux flowrate and tray temperatures were selected as the inputs for the neural network model. Seven different profiles were designated to excite the first principle model to generate the input-output data. These sets of data were then divided into training, validation and testing data. Two neural network model structures were developed i.e. a multiple-input multiple-output (MIMO) model and a pair of multiple-input single-output (MISO)
Young Children's Search Strategies and Construction of Search Queries

This paper describes a quantitative study focused on two questions: (1) Can children understand and use a hierarchical domain structure to find particular instances of animals? (2) Can children construct search queries to conduct complex searches if sufficiently supported, both visually and conceptually? These two questions have been explored in the context of developing a digital library interface (called “QueryKids”) for children ages 5-10 years old that visualizes the querying process and its results. The results of this study showed that children were able to search very efficiently, primarily using a “fewest-steps” strategy, with the QueryKids software prototype. In addition, children were able to construct search queries with a high degree of accuracy. Results are discussed in terms of the scaffolding support that QueryKids provides, and its effectiveness in helping children to search efficiently and construct complex search queries.
Executive function deficits in children with fetal alcohol spectrum disorders (FASD) measured using the Cambridge Neuropsychological Tests Automated Battery (CANTAB).

BACKGROUND Chronic prenatal alcohol exposure causes a spectrum of deleterious effects in offspring, collectively termed fetal alcohol spectrum disorders (FASD), and deficits in executive function are prevalent in FASD. The goal of this research was to test the hypothesis that children with FASD exhibit performance deficits in tasks that assess attention, planning and spatial working memory. METHODS Subjects (8-15 years male and female children) with a diagnosis of fetal alcohol syndrome (FAS), partial FAS (pFAS), or alcohol-related neurodevelopmental disorder (ARND), and age- and sex-matched controls, completed four tasks selected from the Cambridge Neuropsychological Tests Automated Battery (CANTAB). RESULTS Compared with age-matched control children (n = 92), subjects with FASD (n = 89) exhibited longer reaction and decision times (effect size range; Cohen's d = .51 to .73), suggesting deficits in attention. Children with FASD demonstrated deficits in planning and spatial working memory that became more pronounced when task difficulty increased. The largest effect size in this study population (Cohen's d = 1.1) occurred in the spatial working memory task. Only one outcome measure revealed differences across the diagnostic subgroups, although all groups were different from control. CONCLUSION This study demonstrates that deficits in multiple executive function domains, including set shifting, planning and strategy use, attention and spatial working memory, can be assessed in children with FASD using an easy to administer, brief battery of computer-based neuropsychological tasks. The tasks appear to be equally sensitive for brain injury resulting from prenatal exposure to alcohol, regardless of the presence of facial dysmorphology.
“Mixed Predicates” are, in fact, Atom Predicates

Abstract In this paper, I examine the traditional distinction among distributive predicates, mixed predi- cates, and collective predicates, focusing on mixed predicates and collective predicates. Under the traditional three-way distinction of predicates, a mixed predicate can be both a collective predicate and a distributive predicate because a plural noun in a mixed-predicate sentence is ambiguous be- tween a distributive reading and a collective reading. In this paper, adopting Winter’s (2002) analysis of set/atom predicates, I argue that mixed predicates are atomic predicates, whereas col- lective predicates are set predicates in Japanese. Support for my proposal comes from distributive and collective readings in the Japanese Floating Quantifier Construction (henceforth, JFQC). When a verb composes with a classifier to denote a set of sets in the JFQC, there is a sharp contrast between the mixed-predicate JFQC and the collective-predicate JFQC, which is problem- atic for Link 1983 and Landman 1989. When a verb composes with a classifier to denote a set of sets in the JFQC, a mixed predicate, which is an atom predicate, can have only a distributive read- ing, whereas a collective predicate, which is a set predicate, can have both a distributive reading and a collective reading. In my analysis, this difference can be reduced to the properties of an atom predicate and a set predicate, as proposed by Winter (2002).
“Look It, This is how You Know:” Family Forest Walks as a Context for Knowledge-Building About the Natural World

ABSTRACT This case study focuses on a Native American family's experience on a walk in an urban forest preserve. Drawing on interaction analysis traditions, we analyze video data and transcript data to characterize how learning unfolds in place, in this case an urban forest. We build on this analysis, as well as the work of Indigenous scholars, to theoretically develop walking, reading, and storying land as a methodology for making sense of physical and biological worlds.
Regression clustering with lower error VIA EM algorithm

Data mining is the process of analysing the any dataset to obtain understandable information. These information offer important details to improve decision processes about the data. Data mining is a technique that has a great potential to reveal significant information of consumer behaviours to companies. One of the most important tools for data mining is regression model. In this study, regression models of the dataset including the transaction numbers of several types of bank transactions within three year period are obtained. Besides, subclusters of the data set is generated by using expectation maximisation (EM) algorithm and regression models for every subclusters are created. Regression models of subclusters obtained via EM algorithm and regression model without clustering are investigated. By using root mean square error (RMSE) metric values comparison is made between these regression models. The results demonstrate that clustering the dataset by using EM algorithm creates regression models with lower error.