Miracle-dz/UniFAR-FTUs huggingface dataset:
The Influence of External Focus Instruction Characteristics on Children’s Motor Performance

ABSTRACT Purpose: Research has demonstrated that verbal instructions directing attention externally (i.e., toward the effect of the movement) significantly enhance motor skill performance, and this effect is enhanced when the distance of the external focus relative to the body is increased. However, few studies have investigated this distance-of-focus effect in children. The present study aimed to examine the effect of increasing the distance of an external focus on children’s motor performance in 2 experiments. Method: In Experiment 1, children performed standing long jumps under 3 instructional conditions (control, internal attentional focus, and external attentional focus). In Experiment 2, children performed standing long jumps under 4 instructional conditions (control, internal, proximal external attentional focus, and distal external attentional focus). Results: In Experiment 1, results revealed a statistically significant jump distance advantage for the external focus condition. In Experiment 2, a statistically significant jump distance advantage for the distal external focus condition was found. However, instructional and task characteristics beyond distance of focus may have been influential. Conclusions: External focus instructions benefit children’s jump performance, but specifically when they are supported by a concrete movement goal reflecting relevant performance criteria. These findings highlight the importance of examining the content of instructions and relevant task characteristics provided to children beyond attentional focus to consider their motivational characteristics.
Complexity of Brain Activity and Connectivity in Functional Neuroimaging

Understanding the complexity of human brain dynamics and brain connectivity across the repertoire of functional neuroimaging and various conditions, is of paramount importance. Novel measures should be designed tailored to the input focusing on multichannel activity and dynamic functional brain connectivity (DFBC). Here, we defined a novel complexity index (CI) from the field of symbolic dynamics that quantifies patterns of different words up to a length from a symbolic sequence. The CI characterizes the complexity of the brain activity. We analysed dynamic functional brain connectivity by adopting the sliding window approach using imaginary part of phase locking value (iPLV) for EEG/ECoG/MEG and wavelet coherence (WC) for fMRI. Both intra and cross-frequency couplings (CFC) namely phase-to-amplitude were estimated using iPLV/WC at every snapshot of the DFBC. Using proper surrogate analysis, we defined the dominant intrinsic coupling mode (DICM) per pair of regions-of-interest (ROI). The spatio-temporal probability distribution of DICM were reported to reveal the most prominent coupling modes per condition and modality. Finally, a novel flexibility index is defined that quantifies the transition of DICM per pair of ROIs between consecutive time-windows. The whole methodology was demonstrated using four neuroimaging datasets (EEG/ECoG/MEG/fMRI). Finally, we succeeded to totally discriminate healthy controls from schizophrenic using FI and dynamic reconfiguration of DICM. Anesthesia independently of the drug caused a global decreased of complexity in all frequency bands with the exception in δ and alters the dynamic reconfiguration of DICM. CI and DICM of MEG/fMRI resting-state recordings in two spatial scales were high reliable. Significant Statement In the present study, we demonstrated novel indexes for the estimation of complexity in both raw brain activity and dynamic functional connectivity. To ort the universality of both indexes for the majority of functional neuroimaging modalities, we adopted open datasets from electro and magneto-encephalography, from electro-corticography and functional magnetic resonance imaging. Both indexes proved informative and reliable across repeat scan sessions. Moreover, we succeeded to totally discriminate with absolute accuracy healthy controls from schizophrenic patients. Both indexes proved sensitive to common anesthetic drugs effect in monkeys and reliable in MEG and fMRI repeat scan sessions. We first reported the notion of cross-frequency coupling in BOLD activity. Our analysis could be adapted it in any task and modality for any hypothesis driven study.
Quantitative differences in TAD border strength underly the TAD hierarchy in Drosophila chromosomes

Chromosomes in many organisms, including Drosophila and mammals, are folded into topologically associating domains (TADs). Increasing evidence suggests that TAD folding is hierarchical, wherein subdomains combine to form larger superdomains, instead of a sequence of nonoverlapping domains. Here, we studied the hierarchical structure of TADs in Drosophila. We show that the boundaries of TADs of different hierarchical levels are characterized by the presence of different portions of active chromatin, but do not vary in the binding of architectural proteins, such as CCCTC binding factor or cohesin. The apparent hierarchy of TADs in Drosophila chromosomes is not likely to have functional importance but rather reflects various options of long‐range chromatin folding directed by the distribution of active and inactive chromatin segments and may represent population average.
Identification and Quantification of Novel Major Metabolites of the Steroidal Aromatase Inhibitor, Exemestane

Exemestane (EXE) is an aromatase inhibitor used for the prevention and treatment of estrogen receptor–positive breast cancer. Although the known major metabolic pathway for EXE is reduction to form the active 17β-dihydro-EXE (17β-DHE) and subsequent glucuronidation to 17β-hydroxy-EXE-17-O-β-D-glucuronide (17β-DHE-Gluc), previous studies have suggested that other major metabolites exist for exemestane. In the present study, a liquid chromatography–mass spectrometry (LC-MS) approach was used to acquire accurate mass data in MSE mode, in which precursor ion and fragment ion data were obtained simultaneously to screen novel phase II EXE metabolites in urine specimens from women taking EXE. Two major metabolites predicted to be cysteine conjugates of EXE and 17β-DHE by elemental composition were identified. The structures of the two metabolites were confirmed to be 6-methylcysteinylandrosta-1,4-diene-3,17-dione (6-EXE-cys) and 6-methylcysteinylandrosta-1,4-diene-17β-hydroxy-3-one (6-17β-DHE-cys) after comparison with their chemically synthesized counterparts. Both underwent biosynthesis in vitro in three stepwise enzymatic reactions, with the first involving glutathione conjugation. The cysteine conjugates of EXE and 17β-DHE were subsequently quantified by liquid chromatography–mass spectrometry in the urine and matched plasma samples of 132 subjects taking EXE. The combined 6-EXE-cys plus 6-17β-DHE-cys made up 77% of total EXE metabolites in urine (vs. 1.7%, 0.14%, and 21% for EXE, 17β-DHE, and 17β-DHE-Gluc, respectively) and 35% in plasma (vs. 17%, 12%, and 36% for EXE, 17β-DHE, and 17β-DHE-Gluc, respectively). Therefore, cysteine conjugates of EXE and 17β-DHE appear to be major metabolites of EXE in both urine and plasma.
Propofol for Treatment-Resistant Depression: A Pilot Study

Abstract Background We hypothesized that propofol, a unique general anesthetic that engages N-methyl-D-aspartate and gamma-aminobutyric acid receptors, has antidepressant properties. This open-label trial was designed to collect preliminary data regarding the feasibility, tolerability, and efficacy of deep propofol anesthesia for treatment-resistant depression. Methods Ten participants with moderate-to-severe medication-resistant depression (age 18–45 years and otherwise healthy) each received a series of 10 propofol infusions. Propofol was dosed to strongly suppress electroencephalographic activity for 15 minutes. The primary depression outcome was the 24-item Hamilton Depression Rating Scale. Self-rated depression scores were compared with a group of 20 patients who received electroconvulsive therapy. Results Propofol treatments were well tolerated by all subjects. No serious adverse events occurred. Montreal Cognitive Assessment scores remained stable. Hamilton scores decreased by a mean of 20 points (range 0–45 points), corresponding to a mean 58% improvement from baseline (range 0–100%). Six of the 10 subjects met the criteria for response (>50% improvement). Self-rated depression improved similarly in the propofol group and electroconvulsive therapy group. Five of the 6 propofol responders remained well for at least 3 months. In posthoc analyses, electroencephalographic measures predicted clinical response to propofol. Conclusions These findings demonstrate that high-dose propofol treatment is feasible and well tolerated by individuals with treatment-resistant depression who are otherwise healthy. Propofol may trigger rapid, durable antidepressant effects similar to electroconvulsive therapy but with fewer side effects. Controlled studies are warranted to further evaluate propofol’s antidepressant efficacy and mechanisms of action. ClinicalTrials.gov: NCT02935647.
An early increase of CD56bright natural killer subset as dominant effect and predictor of response to extracorporeal photopheresis for graft‐versus‐host disease

CD56bright natural killer (NK) regulatory cells were recently shown to display a differential impact on the risk of developing extensive chronic graft‐versus‐host disease (GVHD). To date no study has definitively established which immune populations are most responsible for the immunomodulatory effects or response to extracorporeal photopheresis (ECP) for GVHD.
Comparison of the ability of Child‐Pugh score, MELD score, and ICG‐R15 to assess preoperative hepatic functional reserve in patients with hepatocellular carcinoma

Assessing hepatic functional reserve before hepatectomy is beneficial to reduce the incidence of posthepatectomy liver failure (PHLF). This study aimed to compare the ability of the Child‐Pugh score, model for end‐stage liver disease (MELD) score, and retention test at 15 minutes (indocyanine green [ICG]‐R15) to assess hepatic functional reserve.
Cannabidiol treatment reduces the motivation to self-administer methamphetamine and methamphetamine-primed relapse in rats

Background: Methamphetamine is an addictive stimulant that can cause many adverse physical, psychological and psychosocial effects. Preliminary evidence shows cannabidiol, a non-intoxicating constituent of the cannabis plant, may have efficacy in treating opioid and nicotine dependence. However, no study has yet examined whether cannabidiol treatment might impact on methamphetamine addiction. Aims: The current study investigated whether cannabidiol administration reduces the motivation to self-administer methamphetamine and relapse to methamphetamine-seeking behavior following abstinence. Methods: Thirty-two male Sprague Dawley rats with implanted jugular vein catheters were initially trained to self-administer methamphetamine via lever press during two-hour sessions on a fixed ratio 1 schedule of reinforcement. Rats in experiment 1 (n=16) then advanced to a progressive ratio reinforcement schedule to examine the effects of cannabidiol (0, 20, 40, and 80 mg/kg intraperitoneal) on motivation to self-administer methamphetamine. Rats in experiment 2 (n=16) were tested for cannabidiol effects on methamphetamine-primed reinstatement following extinction. Results: Cannabidiol (80 mg/kg, but not 40 mg/kg, or 20 mg/kg) reduced the motivation to self-administer methamphetamine and attenuated methamphetamine-primed relapse to methamphetamine-seeking behavior after extinction. Conclusion: This is the first demonstration that cannabidiol can reduce the motivation to seek and consume methamphetamine, and suggests that cannabidiol might be worth trialing as a novel pharmacotherapy for methamphetamine dependence.
Cross-Domain Hallucination Network for Fine-Grained Object Recognition

Existing fine-grained object recognition methods often require high-resolution images to better discriminate the subordinate classes. However, this assumption does not always hold in current surveillance systems, where the distinguished parts may not be clearly presented. Besides, data insufficiency and class imbalance make the problem even more challenging. In this paper, we leverage high-resolution images collected from Internet to improve the vehicle recognition in the surveillance environments. A cross-domain hallucination network is proposed to minimize the domain discrepancy and enhance the quality of low-resolution surveillance images. To better align the cross-domain features and boost the recognition performance, we extend the original framework to part-based hallucination networks, where the parts are automatically extracted based on the maximum responses from the convolution filters. We evaluate our method on a public surveillance vehicle dataset (BoxCars21k). Experimental results demonstrate that our approach outperforms the state-of-the-art methods.
Processing Sampled Big Data

Big data processing requires extremely powerful and large computing setup. This puts bottleneck not only on processing infrastructure but also many researchers don’t get the freedom to analyze large datasets. This paper thus analyzes the processing of the large amount of data from machine learnt models that are built on the smaller sets of data samples. This work analyzes more than 40 GB data by testing different strategies of reducing the processed data without losing and compromising on the detection and model learning in machine learning. Many alternatives are analyzed and it is observed that 50% reduction does not drastically harm the machine learning model performance. On average, in SVM only 3.6%, and in Random Forest, only 1.8% performance is reduced, if only 50% data is used. The 50% reduction in instances means that in most cases, the data will fit in the RAM and the processing times will be considerably reduced, benefitting in execution times and or resources. From the incremental training and testing experiments, it is found that in special cases, smaller sub-sampled data can be used for model generation in machine learning problems. This is useful in cases, where there are either limitations on hardware or one has to select among many available machine learning algorithms.
NTIRE 2018 Challenge on Spectral Reconstruction from RGB Images

This paper reviews the first challenge on spectral image reconstruction from RGB images, i.e., the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB image. The challenge was divided into 2 tracks: the "Clean" track sought HS recovery from noiseless RGB images obtained from a known response function (representing spectrally-calibrated camera) while the "Real World" track challenged participants to recover HS cubes from JPEG-compressed RGB images generated by an unknown response function. To facilitate the challenge, the BGU Hyperspectral Image Database [4] was extended to provide participants with 256 natural HS training images, and 5+10 additional images for validation and testing, respectively. The "Clean" and "Real World" tracks had 73 and 63 registered participants respectively, with 12 teams competing in the final testing phase. Proposed methods and their corresponding results are reported in this review.
Salient Object Detection Driven by Fixation Prediction

Research in visual saliency has been focused on two major types of models namely fixation prediction and salient object detection. The relationship between the two, however, has been less explored. In this paper, we propose to employ the former model type to identify and segment salient objects in scenes. We build a novel neural network called Attentive Saliency Network (ASNet)1 that learns to detect salient objects from fixation maps. The fixation map, derived at the upper network layers, captures a high-level understanding of the scene. Salient object detection is then viewed as fine-grained object-level saliency segmentation and is progressively optimized with the guidance of the fixation map in a top-down manner. ASNet is based on a hierarchy of convolutional LSTMs (convLSTMs) that offers an efficient recurrent mechanism for sequential refinement of the segmentation map. Several loss functions are introduced for boosting the performance of the ASNet. Extensive experimental evaluation shows that our proposed ASNet is capable of generating accurate segmentation maps with the help of the computed fixation map. Our work offers a deeper insight into the mechanisms of attention and narrows the gap between salient object detection and fixation prediction.
Feature Quantization for Defending Against Distortion of Images

In this work, we address the problem of improving robustness of convolutional neural networks (CNNs) to image distortion. We argue that higher moment statistics of feature distributions can be shifted due to image distortion, and the shift leads to performance decrease and cannot be reduced by ordinary normalization methods as observed in our experimental analyses. In order to mitigate this effect, we propose an approach base on feature quantization. To be specific, we propose to employ three different types of additional non-linearity in CNNs: i) a floor function with scalable resolution, ii) a power function with learnable exponents, and iii) a power function with data-dependent exponents. In the experiments, we observe that CNNs which employ the proposed methods obtain better performance in both generalization performance and robustness for various distortion types for large scale benchmark datasets. For instance, a ResNet-50 model equipped with the proposed method (+HPOW) obtains 6.95%, 5.26% and 5.61% better accuracy on the ILSVRC-12 classification tasks using images distorted with motion blur, salt and pepper and mixed distortions.
Management consultant - client relationships: their impact on consultancy outcomes in SMEs

This paper reports on the findings of an empirical study that examined aspects of the consultant client relationship (CCR) that affect the adoption of a consultant's recommendations in the context of a small or medium enterprise (SME). In addition, this study found that a SNlE client's judgement of the success of a consultancy project was based on three key factors the financial change in the business, the gaining of new knowledge on operating an SME learnt through the consultancy and any new perspective on the business gained as a result of the consultancy. These findings are drawn from three contrasting case studies each developed from interviews of client/owners in a government-sponsored program to assist SMEs through consultant development of business plans. The3e case studies explored the CCR from the client's perspective presenting an alternative perspective to the CCR from that contained in much of the consulting literature.
NTIRE 2018 Challenge on Image Dehazing: Methods and Results

This paper reviews the first challenge on image dehazing (restoration of rich details in hazy image) with focus on proposed solutions and results. The challenge had 2 tracks. Track 1 employed the indoor images (using I-HAZE dataset), while Track 2 outdoor images (using O-HAZE dataset). The hazy images have been captured in presence of real haze, generated by professional haze machines. I-HAZE dataset contains 35 scenes that correspond to indoor domestic environments, with objects with different colors and specularities. O-HAZE contains 45 different outdoor scenes depicting the same visual content recorded in haze-free and hazy conditions, under the same illumination parameters. The dehazing process was learnable through provided pairs of haze-free and hazy train images. Each track had ~ 120 registered participants and 21 teams competed in the final testing phase. They gauge the state-of-the-art in image dehazing.
Lock-In Thermography as an Analytical Tool for Magnetic Nanoparticles: Measuring Heating Power and Magnetic Fields

Magnetic nanoparticles and their ability to convert electromagnetic energy into heat are of explicit interest for various applications. However, precise quantification of their heating efficiency is not always upfront, and several parameters render comparative studies challenging. This paper describes the theory behind lock-in thermography, a new technique for quantifying the heating properties of magnetic nanoparticles. This technique allows the investigation of some of the potential sources of variability: key factors such as magnetic field inhomogeneity and its effects on the heating power are explored in detail. The presented results, obtained from various nanoparticle batches of different origins, highlight the importance of pursuing a standardized and systematic approach when quantifying the heating efficiency of magnetic nanoparticles.
Development and performance analysis of a saturated core high temperature superconducting fault current limiter

Recent international activity in the field of high voltage Fault Current Limiters (FCLs) has intensified since the North American blackout of 2003. A number of national and international peak body groups in the USA, Europe and Japan have been established to assess the need for FCLs and the issues associated with their design, specification, operation, protection and integration into the electricity grids. This paper details the development of a prototype 3-phase saturated core High Temperature Superconducting (HTS) FCL in Australia. Through experimental analysis, the performance of this device is characterised in terms of DC saturation, steady state performance, and fault current limiting ability.
Temperament and reproductive biology: emotional reactivity and reproduction in sheep

Reproductive capacity is controlled by a large number of factors such as season, social interactions and metabolic status. However, the influence of emotional reactivity on reproductive success has not been intensively investigated in farm animals. In this review, we define emotion reactivity and the expression of its inter individual variability named temperament. We briefly describe our protocol to measure temperament in sheep and discuss the heritability of temperament traits. Using the results obtained from our flock of sheep selected for calm or nervous temperament, we illustrate how this selection affects the reproductive biology from changing the inexperienced ewe's response to the male effect to improving lamb survival and ovulation rate. We conclude that the mechanisms by which selection for temperament affects the different steps of the reproductive cycle are not yet understood but nevertheless this type of selection could have a great impact on reproduction efficiency of sheep and other domestic ruminants.
Numerical Prediction of Vadose Zone Behaviour Influenced by Vegetation

Bioengineering aspects of native vegetation are currently, and rapidly, being evolved to improve soil stiffness, slope stabilisation, and erosion control. Apart from the reinforcement effect, tree roots establish sufficient matric suction to increase the shear strength and stiffness of the soil. This paper looks at the way, vegetation influences soil matric suction, shrinkage, and ground settlement. A mathematical model for the rate of root water uptake that considers ground conditions, type of vegetation and climatic parameters, has been developed. Based on this proposed model, the distribution of moisture and the matric suction profile adjacent to the tree are numerically analysed. The model formulation is based on the general effective stress theory of unsaturated soils. Field measurements taken from literature published previously are compared with the authors' numerical model. The predicted results calculated using the soil, plant, and atmospheric parameters contained in the numerical model, compared favourably with the measured results, justifying the assumptions upon which the model was developed.