Miracle-dz/UniFAR-FTUs huggingface dataset:
Psychological measures of eating behavior and the accuracy of 3 common dietary assessment methods in healthy postmenopausal women.

BACKGROUND Factors affecting the accuracy of reported energy intake (rEI) need to be identified. OBJECTIVE Our objective was to investigate the association of psychological measures of eating behavior with the accuracy of rEI assessed by 7-d weighed intakes, a 24-h recall, and a food-frequency questionnaire. DESIGN Subjects were 26 restrained eaters aged 60.3 +/- 0.6 y (mean +/- SEM) and weighing 63.8 +/- 1.7 kg and 34 unrestrained eaters aged 59.4 +/- 0.6 y and weighing 64.0 kg. rEI was assessed by using 3 dietary assessment methods and total energy expenditure (TEE) was determined by using doubly labeled water. Calculated EI (cEI) was determined as TEE corrected for the estimated change in body energy. Subjects completed the Eating Inventory. RESULTS rEI values were significantly lower than TEE values for all 3 dietary assessment methods (P < 0.05); there was no significant relation between rEI and TEE by any method. There was no significant difference in 100 x rEI:TEE between restrained and unrestrained eaters by any of the dietary assessment methods. When combined data from the 3 methods were used, 100 x rEI:cEI was not significantly different from 100% in unrestrained eaters (99 +/- 6.8%) but was lower in restrained eaters (89.1 +/- 5.3%; P < 0.05). There was a positive relation between hunger and 100 x rEI:TEE (P < 0.05). CONCLUSIONS Low hunger is associated with undereating relative to normal eating during measurement of dietary intake; high dietary restraint may be associated with a reduction in reporting of consumed foods. Dietary hunger and restraint assessed with use of the Eating Inventory may help to identify subjects likely to underreport dietary intake.
Contemporary human‐altered landscapes and oceanic barriers reduce bumble bee gene flow

Much of the world's terrestrial landscapes are being altered by humans in the form of agriculture, urbanization and pastoral systems, with major implications for biodiversity. Bumble bees are one of the most effective pollinators in both natural and cultivated landscapes, but are often the first to be extirpated in human‐altered habitats. Yet, little is known about the role of natural and human‐altered habitats in promoting or limiting bumble bee gene flow. In this study, I closely examine the genetic structure of the yellow‐faced bumble bee, Bombus vosnesenskii, across the southwestern US coast and find strong evidence that natural oceanic barriers, as well as contemporary human‐altered habitats, limit bee gene flow. Heterozygosity and allelic richness were lower in island populations, while private allelic richness was higher in island populations compared to mainland populations. Genetic differentiation, measured for three indices across the 1000 km study region, was significantly greater than the null expectation (FST = 0.041, F’ST = 0.044 and Dest = 0.155) and correlated with geographic distance. Furthermore, genetic differentiation patterns were most strongly correlated with contemporary (2011) not past (2006, 2001) resistance maps calibrated for high dispersal limitation over oceans, impervious habitat and croplands. Despite the incorporation of dramatic elevation gradients, the analyses reveal that oceans and contemporary human land use, not mountains, are the primary dispersal barriers for B. vosnesenskii gene flow. These findings reinforce the importance of maintaining corridors of suitable habitat across the distribution range of native pollinators to promote their persistence and safeguard their ability to provide essential pollination services.
Primary Newcastle disease vaccination of broilers: comparison of the antibody seroresponse and adverse vaccinal reaction after eye–nose drop or coarse spray application, and implication of the results for a previously developed coarse dry powder vaccine

ABSTRACT To compare antibody seroresponse and adverse vaccinal reaction induced by Newcastle disease (ND) vaccination after eye–nose drop or coarse spray, groups of SPF broiler hens were vaccinated at day 4 (day of hatch is day 0) and intratracheally inoculated with Escherichia coli at day 11. Body weight gain (BWG) was assessed between day 4 and day 18; colibacillosis lesions and serum antibodies were determined at day 18. Meaningful comparison requires similar vaccine uptake. Vaccine virus loss during spray relative to eye–nose drop, which was assessed by comparing the results of endpoint titrations, was 3 log10. Colibacillosis lesions in birds spray vaccinated with 106.4 EID50/chicken were significantly more severe (P < 0.05), compared to those in birds eye–nose drop vaccinated with 103.4 EID50/chicken, while the seroresponse was slightly but significantly (P < 0.05) stronger. Colibacillosis lesion scores inversely paralleled BWG. It is concluded that: (1) There is room to improve the coarse ND vaccine spray used regarding adverse vaccinal reaction, while maintaining a sufficient immune response. This is also applicable to the coarse ND powder vaccine studied in previous research, which induced similar antibody response and adverse vaccinal reaction as the spray vaccine used here. (2) The vaccine virus dose influences the colibacillosis susceptibility at seven days post vaccination, as the dynamics of the vaccine virus infection is likely dose-dependent. (3) Low vaccine virus doses likely result in heterogeneous vaccine-take followed by vaccine virus spread from vaccine shedding birds to their non-vaccine virus infected flock mates (“rolling vaccinal reaction”).
Automatic sea floor characterization based on underwater acoustic image processing

Automatic sea floor characterization is mainly based on the signal or image processing of the data acquired using an active acoustic system called sediment sonar. Each processing method suits a specific type of sonar, such as the monobeam, the multibeam, or the side-scan sonar. Most types of sonar offer a two dimensional view of the sea floor surface. Therefore, a high resolution image results which can be further analyzed. The inconvenient is that the sonar cannot view inside of the sea floor for a deeper analysis. Therefore, lower frequency acoustic systems are used for in-depth sea floor penetration (boomer, sparker, airguns or sub-bottom profilers). In this case, a mono dimensional signal results. Previous studies on the low-frequency systems are mainly based on the visual inspection by a geological human expert. To automatize this process, we propose the use of feature sets based on the transposed expert fuzzy reasoning. Two features are extracted, the first based on the sea floor contour and the second based on the sub-bottom sediment texture.
A machine learning framework for the categorization of elements in images of musical documents

Musical documents may contain heterogeneous information such as music symbols, text, staff lines, ornaments, annotations, and editorial data. Before any attempt at automatically recognizing the information on scores, it is usually necessary to detect and classify each constituent layer of information into different categories. The greatest obstacle of this classification process is the high heterogeneity among music collections, which makes it difficult to propose methods that can be generalizable to a broad range of sources. In this paper we propose a novel machine learning framework that focuses on extracting the different layers within musical documents by categorizing the image at pixel level. The main advantage of our approach is that it can be used regardless of the type of document provided, as long as training data is available. We illustrate some of the capabilities of the framework by showing examples of common tasks that are frequently performed on images of musical documents, such as binarization, staff-line removal, symbol isolation, and complete layout analysis. All these are tasks for which our approach has shown promising performance. We believe our framework will allow the development of generalizable and scalable automatic music recognition systems, thus facilitating the creation of large-scale browsable and searchable repositories of music documents.
Affimer proteins are versatile and renewable affinity reagents

Molecular recognition reagents are key tools for understanding biological processes and are used universally by scientists to study protein expression, localisation and interactions. Antibodies remain the most widely used of such reagents and many show excellent performance, although some are poorly characterised or have stability or batch variability issues, supporting the use of alternative binding proteins as complementary reagents for many applications. Here we report on the use of Affimer proteins as research reagents. We selected 12 diverse molecular targets for Affimer selection to exemplify their use in common molecular and cellular applications including the (a) selection against various target molecules; (b) modulation of protein function in vitro and in vivo; (c) labelling of tumour antigens in mouse models; and (d) use in affinity fluorescence and super-resolution microscopy. This work shows that Affimer proteins, as is the case for other alternative binding scaffolds, represent complementary affinity reagents to antibodies for various molecular and cell biology applications. DOI: http://dx.doi. org/10.7554/eLife.24903. 001
Low-density silicon allotropes for photovoltaic applications

Silicon materials play a key role in many technologically relevant fields, ranging from the electronic to the photovoltaic industry. A systematic search for silicon allotropes was performed by employing a modified ab initio minima hopping crystal structure prediction method. The algorithm was optimized to specifically investigate the hitherto barely explored low-density regime of the silicon phase diagram by imitating the guest-host concept of clathrate compounds. In total 44 metastable phases are presented, of which 11 exhibit direct or quasi-direct band-gaps in the range of $\approx$1.0-1.8 eV, close to the optimal Shockley-Queisser limit of $\approx$1.4 eV, with a stronger overlap of the absorption spectra with the solar spectrum compared to conventional diamond silicon. Due to the structural resemblance to known clathrate compounds it is expected that the predicted phases can be synthesized.
Sex differences in visceral adipose tissue volume among African Americans.

BACKGROUND Men are believed to have more visceral adipose tissue (VAT) than women have, but studies in African Americans that measured VAT from a single computed tomography (CT) slice found no sex difference. OBJECTIVE We used a serial-slice CT scan to investigate whether there is a sex difference in VAT volume among African Americans. DESIGN Single-slice CT measurements of VAT area at lumbar spine L2-3 and L4-5 levels were taken in 110 African Americans (44 men, 66 women). In 59 subjects (24 men, 35 women), VAT volume was also measured with contiguous CT slices from the diaphragm to the iliac crest. Fat mass was determined by dual-energy X-ray absorptiometry. RESULTS Men and women had similar ages (x +/- SD: 36.1 +/- 7.8 and 35.6 +/- 7.8 y, respectively) and body mass indexes in kg/m(2) (29.5 +/- 6.9 and 32.0 +/- 8.9). The percentage of body fat was lower (P < 0.0001) in men (21.8 +/- 7.3%) than in women (37.4 +/- 7.9%). The VAT volume was greater (P = 0.01) in men (1443 +/- 931 cm(3)) than in women (940 +/- 821 cm(3)). There was no sex difference in unadjusted VAT area at L2-3 (men, 88.6 +/- 63.5 cm(2); women, 57.2 +/- 45.4 cm(2)) or L4-5 (men, 65.6 +/- 53.3 cm(2); women, 55.0 +/- 38.3 cm(2)). After adjustment for percentage of body fat or fat mass, men had larger VAT area at both levels (P < 0.01). After adjustment for body mass index, the sex difference in VAT area was detectable at L2-3 (P < 0.001) but not at L4-5 (P = 0.22). CONCLUSIONS VAT volume is greater in men than in women. Detection of sex differences in VAT area among African Americans on single-slice CT requires adjustment for body fat content. At L2-3, adjustment for body mass index alone is adequate to detect sex differences in VAT.
Implementation of Steel-based Fabric Panels in a Heated Garment Design

The smart/interactive textile structures that integrate electronics and textile materials have realized their great potential in recent years. The garments, which can heat the body, will possibly be one of the most widely used products as electrotextiles for future use in daily life. In this study, steel-based conductive yarns were used to produce heating panels within the study about interactive electronic heated garment design. Portable power supplies were applied to fabric-based panels to obtain a heating function. In addition to an electronic circuit, a functional garment containing all of the systems was designed and produced. The performance of the heating garment prototype was evaluated on a thermal mannequin by testing in cold weather environments.
The effect of a wearable soft-robotic glove on motor function and functional performance of older adults

ABSTRACT Reduced grip strength, resulting in difficulties in performing daily activities, is a common problem in the population of older adults. Newly developed soft-robotic devices have the potential to support older adults with reduced grip in daily activities. The objective of this study was to evaluate the direct, assistive effect of grip support from the wearable, soft-robotic ironHand glove. In total, 65 older adults with self-reported decline of hand function resulting from various disorders participated in this cross-sectional study. They performed various hand function tests with and without the glove during a single session. At the end, usability was scored. Participants were able to produce more pinch strength with the glove compared to without glove (p ≤ 0.001) and usability was rated very positively. However, this was not reflected in improved functional performance with the glove, as measured with timed tasks (p < 0.001). Furthermore, no correlation was found between baseline handgrip strength and changes in performance (between without and with glove) of all assessments (ρ ≤ 0.137, p ≥ 0.288). Further design adaptations are desired and more research is needed to investigate if performance with the glove can improve, when taking quality of task performance into account, or when applying a longer acquaintance period with the glove.
An Extreme Function Theory for Novelty Detection

We introduce an extreme function theory as a novel method by which probabilistic novelty detection may be performed with functions, where the functions are represented by time-series of (potentially multivariate) discrete observations. We set the method within the framework of Gaussian processes (GP), which offers a convenient means of constructing a distribution over functions. Whereas conventional novelty detection methods aim to identify individually extreme data points, with respect to a model of normality constructed using examples of “normal” data points, the proposed method aims to identify extreme functions, with respect to a model of normality constructed using examples of “normal” functions, where those functions are represented by time-series of observations. The method is illustrated using synthetic data, physiological data acquired from a large clinical trial, and a benchmark time-series dataset.
Please Scroll down for Article Visual Cognition a Neural Network Model of Inhibitory Processes in Subliminal Priming Psychology Press a Neural Network Model of Inhibitory Processes in Subliminal Priming

This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, redistribution , reselling , loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. Masked priming experiments have revealed a precise set of facilitatory and inhi-bitory visuomotor control processes. Most notably, inhibitory effects have been identified in which prime-target compatibility induces performance costs and prime-target incompatibility induces performance benefits. We argue that this profile of data is commensurate with an "emergency braking mechanism", whereby responses can be retracted as a result of changing sensory evidence. The main contribution of this paper is to provide a neural network-based explanation of this phenomenon. This is obtained through the use of feedfonvard inhibition to implement backward masking, lateral inhibition to implement response competition , and opponent processing mechanisms to implement response retraction. Although the model remains simple, it does a vely good job of reproducing the available masked priming data. For example, it reproduces a large spectrum of reaction time data across a number of different experimental conditions. Perhaps most notably, however, it also reproduces lateralized readiness potentials that have been recorded while subjects perform different conditions. In addition, it provides a concrete set of testable predictions. reviewers of the paper; all of whom gave valuable suggestions on the work presented here. Adam Aron was also involved in preliminary research on this model.
Query Processing using Negative Tuples in Stream Query Engines

The concept of Negative Tuples (or delete tuples) has been adopted widely in data stream management s.vstems to evaluate continuous sliding-window queries incrementally. The main idea is to produce a negative tuple for each expired tuple from the sliding \vindow. Thus. various query operators can update their state based on the expired tuples. Negative tv,ples avoid the non-deterministic output delays that result from the commonly used input-triggered approach. However, negative tuples double the number of tuples through the query pipeline. thus reducing the pipeline bandwidth. In this paper, we put the negative tuples under a magnifying glass where we show its detailed realization in terms of its generation and its processing in various operators. Then, we present several optimization techniques that aim to reduce the overhead of the negative tuples approach. These optimizations can be applied independentl}' or together to enhance the performance of negative tuples. A detailed experimental study. based on a prototype system implementation. shows the performance gains over the input-triggered approach of the negative tuples approach when accompanied with the proposed optimizations.
A solar irradiance prediction method based on signal decomposition and Informer

The historical data of solar irradiance are characterized by volatility and instability. These characteristics make it difficult to obtain satisfactory results when directly using prediction models to predict solar irradiance data. Therefore, this paper proposes a solar irradiance prediction model that combines signal decomposition methods and deep learning methods. First, feature selection is performed on the collected various meteorological characteristic data to determine the meteorological characteristics used for prediction. Secondly, in order to reduce the error in prediction results caused by the complexity of solar irradiance data, this method uses variational mode decomposition (VMD) to decompose the original data into multiple relatively stable subsequences. Thirdly, multiple sequences are predicted separately through the Informer model, and finally the prediction results are superimposed to obtain the final prediction value. Experiments show that the VMD-Informer model has higher accuracy in solar irradiance prediction than common deep learning models.
Neuroimmune Axes of the Blood–Brain Barriers and Blood–Brain Interfaces: Bases for Physiological Regulation, Disease States, and Pharmacological Interventions

Central nervous system (CNS) barriers predominantly mediate the immune-privileged status of the brain, and are also important regulators of neuroimmune communication. It is increasingly appreciated that communication between the brain and immune system contributes to physiologic processes, adaptive responses, and disease states. In this review, we discuss the highly specialized features of brain barriers that regulate neuroimmune communication in health and disease. In section I, we discuss the concept of immune privilege, provide working definitions of brain barriers, and outline the historical work that contributed to the understanding of CNS barrier functions. In section II, we discuss the unique anatomic, cellular, and molecular characteristics of the vascular blood–brain barrier (BBB), blood–cerebrospinal fluid barrier, and tanycytic barriers that confer their functions as neuroimmune interfaces. In section III, we consider BBB-mediated neuroimmune functions and interactions categorized as five neuroimmune axes: disruption, responses to immune stimuli, uptake and transport of immunoactive substances, immune cell trafficking, and secretions of immunoactive substances. In section IV, we discuss neuroimmune functions of CNS barriers in physiologic and disease states, as well as pharmacological interventions for CNS diseases. Throughout this review, we highlight many recent advances that have contributed to the modern understanding of CNS barriers and their interface functions.
Threshold cointegration and spatial price transmission when expectations matter

We examine the performance of the threshold cointegration approach, specifically Band- TVECM, to price transmission analysis in an explicit context where trade decisions are made based on expectation of final prices, because trade takes time. We find that, following a standard inference strategy, a large portion of three-regime cases are not identified as such. Results show that transfer costs are systematically underestimated, particularly in three- regime models. The speed of price transmission is also biased in three-regime models. Furthermore, inferences about occurrence of trade are poor, with estimated models suggesting far lower market integration than is true in the data generating process.
Learning-Based Constraints on Schemata 1

1 In Proceedings of the Twenty-Second Annual Conference of the Cognitive Science Society, (pp. 776-81, 2000). Mahwah, NJ: Erlbaum. Abstract Schemata are frequently used in cognitive science as a descriptive framework for explaining the units of knowledge. However, the specific properties which comprise a schema are not consistent across authors. In this paper we attempt to ground the concept of a schema based on constraints arising from issues of learning. To do this, we consider the different forms of schemata used in computational models of learning. We propose a framework for comparing forms of schemata which is based on the underlying representation used by each model, and the mechanisms used for learning and retrieving information from its memory. Based on these three characteristics, we compare examples from three classes of model, identified by their underlying representations, specifically: neural network, productionrule and symbolic network models.