Miracle-dz/UniFAR-FTUs huggingface dataset:
Stress and Eating Behaviour: Implications for Obesity

Background: This report outlines our strategy to examine the influence of workplace stress on eating behaviour, discussing the current literature which explores the relationship between stress and eating behaviour. This research aims to add to and develop the current understanding of the links between stress and eating behaviour. Specifically the aims are to examine the effect of workplace stress in both day workers and shift workers and their subsequent eating behaviour. Methods: The effect of healthy working environment initiatives on stress and eating behaviour will also be examined by comparing a workplace with such an initiative to one with no such initiative. The role of personality on both eating behaviour and stress susceptibility will be examined. In order to achieve this, 450 individuals from 3 public sector workplaces will be recruited. Anthropometric measurements (waist-hip ratio, BMI, visceral fat percentage) will be assessed, as well as personality, eating behaviour profiles, food intake (7-day weighed intake food diary) and both individual daily stressors as well as workplace stress assessed using the demand/control model of job strain will be assessed. Conclusions: Implications for policy and future research are also discussed.
Thermoacoustic Instabilities in a Gas Turbine Combustor

Stationary gas turbines are widely used today for power generation and mechanical drive applications. The introduction of new regulations on emissions in the last decades have led to extensive development and new technologies used within modern gas turbines. The majority of the gas turbines sold today have a so called DLE (Dry Low Emission) combustion system that mainly operates in the leanpremixed combustion regime. The lean-premixed regime is characterized by low emission capabilities but are more likely to exhibit stability issues compared to traditional non-premixed combustion systems. Thermoacoustic instabilities are a highly unwanted phenomena characterized by an interaction between an acoustic field and a combustion process. This interaction may lead to self-sustained large amplitude oscillations which can cause severe structural damage to the gas turbine if it couples with a structural mode. However, since a coupled phenomena, prediction of thermoacoustic stability is a complex topic still under research. In this work, the mechanisms responsible for thermoacoustic instabilities are described and a 1dimensional stability modelling approach is applied to the Siemens SGT-750 combustion system. The complete combustor is modelled by so called acoustic two-port elements in which a 1-dimensional flame model is incorporated. The simulations is done using a generalized network code developed by Siemens. The SGT-750 shows today excellent stability and combustion performance but a deeper knowledge in the thermoacoustic behaviour is highly valued for future development. In addition, measurement data from an engine test is evaluated, post-processed and compared with the results from the 1-dimensional network model. The results are found to be in good agreement and the thermoacoustic response of the SGT-750 is found to be dominated by both global modes including all cans as well as local modes within the individual cans.
Neutropenia in 6 cases of childhood onset type 1 diabetes and its possible mechanisms

Type 1 diabetes (T1D) is a chronic inflammatory disease caused by a selective destruction of the pancreatic β‐cells. There are few reports on peripheral neutropenia in T1D for different reasons. We reported 6 cases of childhood onset T1D combined with neutropenia and explored its possible mechanisms.
Functional domain analysis of SOX18 transcription factor using a single-chain variable fragment-based approach

ABSTRACT Antibodies are routinely used to study the activity of transcription factors, using various in vitro and in vivo approaches such as electrophoretic mobility shift assay, enzyme-linked immunosorbent assay, genome-wide method analysis coupled with next generation sequencing, or mass spectrometry. More recently, a new application for antibodies has emerged as crystallisation scaffolds for difficult to crystallise proteins, such as transcription factors. Only in a few rare cases, antibodies have been used to modulate the activity of transcription factors, and there is a real gap in our knowledge on how to efficiently design antibodies to interfere with transcription. The molecular function of transcription factors is underpinned by complex networks of protein-protein interaction and in theory, setting aside intra-cellular delivery challenges, developing antibody-based approaches to modulate transcription factor activity appears a viable option. Here, we demonstrate that antibodies or an antibody single-chain variable region fragments are powerful molecular tools to unravel complex protein-DNA and protein-protein binding mechanisms. In this study, we focus on the molecular mode of action of the transcription factor SOX18, a key modulator of endothelial cell fate during development, as well as an attractive target in certain pathophysiological conditions such as solid cancer metastasis. The engineered antibody we designed inhibits SOX18 transcriptional activity, by interfering specifically with an 8-amino-acid motif in the C-terminal region directly adjacent to α-Helix 3 of SOX18 HMG domain, thereby disrupting protein-protein interaction. This new approach establishes a framework to guide the study of transcription factors interactomes using antibodies as molecular handles.
Tracking longitudinal retinal changes in experimental ocular hypertension using the cSLO and spectral domain-OCT.

PURPOSE Involvement of the outer retina is controversial in glaucoma. The aim of this study was to test, first, whether the outer retina is affected in experimental ocular hypertension (OHT) and, second, whether whole retinal thickness can be used as a surrogate marker of glaucomatous change. METHODS OHT was surgically induced in 20 Dark Agouti rats. Animals were imaged using a modified Spectralis OCT (Heidelberg Engineering, Heidelberg, Germany) at baseline and at 3 and 8 weeks after OHT induction. Measurements were recorded for whole and individual retinal layer thickness in four regions-temporal, superior, nasal, and inferior-around the optic nerve head. RESULTS Whole retinal thickness in normal eyes was 172.19 ± 5.17 μm, with no significant regional differences. OHT caused a significant reduction in whole retinal thickness and the outer nuclear layer (ONL) at 3 and 8 weeks (P < 0.05), along with the expected thinning of the retinal nerve fiber layer (RNFL). Whole retinal thickness correlated well with RNFL (P = 0.035) and ONL (P ≤ 0.001) changes. Sensitivity of RNFL and ONL to IOP exposure appeared greater at 3 than at 8 weeks. In addition, regional profiles were significantly altered in the ONL and RNFL after OHT induction. CONCLUSIONS Adaptation of the Spectralis OCT enables tracking of structural damage in experimental rat OHT. Here the authors show evidence of glaucomatous damage in the outer retinal layers of this model with significant regional changes and highlight whole retinal thickness in the rat as a useful surrogate marker of inner and outer retinal changes. The authors believe that the OCT data can provide useful information with regard to clinical management.
Applying Information Retrieval Techniques to Detect Duplicates and to Rank References in the Preliminary Phases of Systematic Literature Reviews

Systematic Literature Review (SLR) is a means to synthesize relevant and high quality studies related to a specific topic or research questions. In the Primary Selection stage of an SLR, the selection of studies is usually performed manually by reading title, abstract, and keywords of each study. In the last years, the number of published scientific studies has grown increasing the effort to perform this sort of reviews. In this paper, we proposed strategies to detect non-papers and duplicated references in results exported by search engines, and strategies to rank the references in decreasing order of importance for an SLR, regarding the terms in the search string. These strategies are based on Information Retrieval techniques. We implemented the strategies and carried out an experimental evaluation of their applicability using two real datasets. As results, the strategy to detect non-papers presented 100% of precision and 50% of recall; the strategy to detect duplicates detected more duplicates than the manual inspection; and one of the strategies to rank relevant references presented 50% of precision and 80% of recall. Therefore, the results show that the proposed strategies can minimize the effort in the Primary Selection stage of an SLR.
The Role of Responsive Design in Web Development

Responsive design allows software developers to build a Web page that can dynamically adapt to the size of the devices. This development philosophy enables the rendering of Web pages in a fast and optimized way, ensuring a good user experience on mobile devices, tablet and desktop. In the scope of this study, we intend to explore the main advantages and limitations associated with responsive Web design. We adopted a quantitative approach based on a questionnaire filled by 181 professionals in the industry that allowed us to identify the reasons that lead software developers to the adoption of the responsive design and also address the limitations felt by them. The results obtained indicate that offering a good user experience and increasing accessibility stands out as being the most important advantages. On the other hand, the main limitations include the compatibility with older Web browsers, the higher loading time and the difficulties in optimizing user experience. Finally, it was found that the perception of the advantages and limitations of responsive design is distinct for professionals with more professional experience in the field and for freelancer developers.
Damage monitoring of refractory wall in a generic entrained-bed slagging gasification system

Abstract The main cause of performance degradation in entrained-bed slagging gasification systems is attributed to evolution of structural damage in the refractory walls. Early detection of such damage is necessary to avert unscheduled shutdown of a gasification plant. This paper develops an integrated computer simulation model of a generic entrained-bed slagging gasifier for formulation of a damage prediction algorithm with the objective of real-time degradation monitoring and condition-based maintenance of refractory walls. The integrated simulation model yields: (a) quasi-steady-state spatial temperature profiles at any cross-section of the gasification system, and (b) dynamic response of the refractory wall temperature that is measured by an array of sensors installed at specified locations on the external surface of the gasifier wall. The key idea for early detection of refractory-wall damage is built upon the fact that a local anomaly (i.e. deviation from the nominal condition) is likely to influence the temperature gradient in the refractory wall due to changes in the thermal impedance. The information from dynamic response of refractory temperature is extracted in a compressed form as statistical patterns of evolving anomaly through usage of a recently reported data-driven pattern identification tool called symbolic dynamic filtering (SDF). The results of this model-based investigation show that the proposed anomaly detection and damage prediction method is potentially capable of characterizing the health status of refractory walls in particular and the entire gasification system in general. The SDF algorithms in this paper are implemented on the MATLAB platform and are interfaced with the gasification plant simulation model for emulation of real-time degradation monitoring.
Dimensionality Reduction by Spatial–Spectral Preservation in Selected Bands

Dimensionality reduction (DR) has attracted extensive attention since it provides discriminative information of hyperspectral images (HSI) and reduces the computational burden. Though DR has gained rapid development in recent years, it is difficult to achieve higher classification accuracy while preserving the relevant original information of the spectral bands. To relieve this limitation, in this paper, a different DR framework is proposed to perform feature extraction on the selected bands. The proposed method uses determinantal point process to select the representative bands and to preserve the relevant original information of the spectral bands. The performance of classification is further improved by performing multiple Laplacian eigenmaps (LEs) on the selected bands. Different from the traditional LEs, multiple Laplacian matrices in this paper are defined by encoding spatial–spectral proximity on each band. A common low-dimensional representation is generated to capture the joint manifold structure from multiple Laplacian matrices. Experimental results on three real-world HSIs demonstrate that the proposed framework can lead to a significant advancement in HSI classification compared with the state-of-the-art methods.
Lazy Collaborative Filtering for Data Sets With Missing Values

As one of the biggest challenges in research on recommender systems, the data sparsity issue is mainly caused by the fact that users tend to rate a small proportion of items from the huge number of available items. This issue becomes even more problematic for the neighborhood-based collaborative filtering (CF) methods, as there are even lower numbers of ratings available in the neighborhood of the query item. In this paper, we aim to address the data sparsity issue in the context of neighborhood-based CF. For a given query (user, item), a set of key ratings is first identified by taking the historical information of both the user and the item into account. Then, an auto-adaptive imputation (AutAI) method is proposed to impute the missing values in the set of key ratings. We present a theoretical analysis to show that the proposed imputation method effectively improves the performance of the conventional neighborhood-based CF methods. The experimental results show that our new method of CF with AutAI outperforms six existing recommendation methods in terms of accuracy.
Zero-shot Learning with Complementary Attributes

Zero-shot learning (ZSL) aims to recognize unseen objects using disjoint seen objects via attributes to transfer semantic information from training data to testing data. The generalization performance of ZSL is governed by the attributes, which represent the relatedness between the seen classes and the unseen classes. In this paper, we propose a novel ZSL method using complementary attributes as a supplement to the original attributes. We first expand attributes with their complementary form, and then pre-train classifiers for both original attributes and complementary attributes using training data. After ranking classes for each attribute, we use rank aggregation framework to calculate the optimized rank among testing classes of which the highest order is assigned as the label of testing sample. We empirically demonstrate that complementary attributes have an effective improvement for ZSL models. Experimental results show that our approach outperforms state-of-the-art methods on standard ZSL datasets.
Near-Field Chipless Radio-Frequency Identification (RFID) Sensing and Identification System with Switching Reading

A chipless radio-frequency identification (chipless-RFID) and sensing system, where tags are read by proximity (near-field) through a switch, is presented. The tags consist of a set of identical resonant elements (split-ring resonators or SRRs), printed or etched at predefined and equidistant positions, forming a linear chain, each SRR providing a bit of information. The logic state (‘1’ or ‘0’) associated with each resonator depends on whether it is present or not in the predefined position. The reader is an array of power splitters used to feed a set of SRR-loaded transmission lines (in equal number to the number of resonant elements, or bits, of the tag). The feeding (interrogation) signal is a harmonic (single-tone) signal tuned to a frequency in the vicinity of the fundamental resonance of the SRRs. The set of SRR-loaded lines must be designed so that the corresponding SRRs are in perfect alignment with the SRRs of the tag, provided the tag is positioned on top of the reader. Thus, in a reading operation, as long as the tag is very close to the reader, the SRRs of the tag modify (decrease) the transmission coefficient of the corresponding reader line (through electromagnetic coupling between both SRRs), and the amplitude of the output signal is severely reduced. Therefore, the identification (ID) code of the tag is contained in the amplitudes of the output signals of the SRR-loaded lines, which can be inferred sequentially by means of a switching system. Unlike previous chipless-RFID systems based on near-field and sequential bit reading, the tags in the proposed system can be merely positioned on top of the reader, conveniently aligned, without the need to mechanically place them across the reader. Since tag reading is only possible if the tag is very close to the reader, this system can be also used as a proximity sensor with applications such as target identification. The proposed chipless-RFID and sensing approach is validated by reading a designed 4-bit tag. For identification purposes, this system is of special interest in applications where a low number of bits suffice, and tag reading by proximity is acceptable (or even convenient). Applications mostly related to secure paper, particularly involving a limited number of items (e.g., exams, ballots, etc.), in order to provide authenticity and avoid counterfeiting, are envisaged. As a proximity sensor, the system may be of use in detecting and distinguishing different targets in applications such as smart packaging.
Incremental Validity of Montreal Cognitive Assessment Index Scores in Mild Cognitive Impairment and Alzheimer Disease

Background/Aims: The aim of this paper was to evaluate the incremental validity of the Montreal Cognitive Assessment (MoCA) index scores and the MoCA total score in differentiating individuals with normal cognition versus mild cognitive impairment (MCI) or Alzheimer disease (AD). Methods: Effect sizes were calculated for Alzheimer’s Disease Neuroimaging Initiative research participants with normal cognition (n = 295), MCI (n = 471), or AD (n = 150). Results: Effect sizes for the total score were large (> 0.80) and exceeded the index scores in differentiating those with MCI versus normal cognition, MCI versus AD, and AD versus normal cognition. A combined score incorporating the Memory, Executive, and Orientation indexes also improved incremental validity for all 3 group comparisons. Conclusion: Administration of the entire MoCA is more informative than the index scores, especially in distinguishing normal cognition versus MCI. A combined score has stronger incremental validity than the individual index scores.
Computing MaxMin Edge Length Triangulations

In 1991, Edelsbrunner and Tan gave an O(n2) algorithm for finding the MinMax Length triangulation of a set of points in the plane, but stated the complexity of finding a MaxMin Edge Length Triangulation (MELT) as a natural open problem. We resolve this long-standing problem by showing that computing a MELT is NP-complete. Moreover, we prove that (unless P=NP), there is no polynomial-time approximation algorithm that can approximate MELT within any polynomial factor. While this may be taken as conclusive evidence from a theoretical point of view that the problem is hopelessly intractable, it still makes sense to consider powerful optimization methods, such as integer programming (IP), in order to obtain provably optimal solutions for intances of non-trivial size. A straightforward IP based on pairwise disjointness of the Θ(n2) segments between the n points has Θ(n4) constraints, making this IP hopelessly intractable from a practical point of view, even for relatively small n. The main algorithm engineering twist of this paper is to demonstrate how the combination of geometric insights with refined methods of combinatorial optimization can still help to put together an exact method capable of computing optimal MELT solutions for planar point sets up to n = 200. Our key idea is to exploit specific geometric properties in combination with more compact IP formulations, such that we are able to drastically reduce the IPs. On the practical side, we combine two of the most powerful software packages for the individual components: CGAL for carrying out the geometric computations, and CPLEX for solving the IPs. In addition, we discuss specific analytic aspects of the speedup for random point sets.
NTUA-SLP at SemEval-2018 Task 1: Predicting Affective Content in Tweets with Deep Attentive RNNs and Transfer Learning

In this paper we present deep-learning models that submitted to the SemEval-2018 Task 1 competition: “Affect in Tweets”. We participated in all subtasks for English tweets. We propose a Bi-LSTM architecture equipped with a multi-layer self attention mechanism. The attention mechanism improves the model performance and allows us to identify salient words in tweets, as well as gain insight into the models making them more interpretable. Our model utilizes a set of word2vec word embeddings trained on a large collection of 550 million Twitter messages, augmented by a set of word affective features. Due to the limited amount of task-specific training data, we opted for a transfer learning approach by pretraining the Bi-LSTMs on the dataset of Semeval 2017, Task 4A. The proposed approach ranked 1st in Subtask E “Multi-Label Emotion Classification”, 2nd in Subtask A “Emotion Intensity Regression” and achieved competitive results in other subtasks.
Cross-tolerance between nitric oxide synthase inhibition and atypical antipsychotics modify nicotinamide-adenine-dinucleotide phosphate-diaphorase activity in mouse lateral striatum

Previous research indicates that the subchronic administration of NG-nitro-L-arginine (L-NOARG) produces tolerance to haloperidol-induced catalepsy in Swiss mice. The present study aimed to further investigate whether intermittent subchronic systemic administration of L-NOARG induces tolerance to the cataleptic effects of haloperidol as well as olanzapine or clozapine (Clz) in C57Bl mice after subchronic administration for 5 consecutive days. Striatal FosB protein expression was measured in an attempt to gain further insights into striatal mechanisms in antipsychotic-induced extrapyramidal symptoms side effects. An nicotinamide-adenine-dinucleotide phosphate-diaphorase histochemical reaction was also used to investigate whether tolerance could induce changes in the number of nitric oxide synthase-active neurons. Subchronic administration of all antipsychotics produced catalepsy, but cross-tolerance was observed only between L-NOARG (15 mg/kg, intraperitoneally) and Clz (20 mg/kg, intraperitoneally). This cross-tolerance effect was accompanied by decreased FosB protein expression in the dorsal striatum and the nucleus accumbens shell region, and reduced icotinamide-adenine-dinucleotide phosphate-diaphorase activity in the dorsal and ventral lateral striatum. Overall, these results suggest that interference with the formation of nitric oxide, mainly in the dorsal and ventral lateral-striatal regions, appears to improve the cataleptic effects induced by antipsychotics acting as antagonists of low-affinity dopamine D2 receptor, such as Clz.