Miracle-dz/UniFAR-FTUs huggingface dataset:
Volumetric Analysis of the Hypothalamus in Huntington Disease Using 3T MRI: The IMAGE-HD Study

Huntington disease (HD) is a fatal neurodegenerative disorder caused by an expanded CAG repeat in the huntingtin gene. Non-motor symptoms and signs such as psychiatric disturbances, sleep problems and metabolic dysfunction are part of the disease manifestation. These aspects may relate to changes in the hypothalamus, an area of the brain involved in the regulation of emotion, sleep and metabolism. Neuropathological and imaging studies using both voxel-based morphometry (VBM) of magnetic resonance imaging (MRI) as well as positron emission tomography (PET) have demonstrated pathological changes in the hypothalamic region during early stages in symptomatic HD. In this investigation, we aimed to establish a robust method for measurements of the hypothalamic volume in MRI in order to determine whether the hypothalamic dysfunction in HD is associated with the volume of this region. Using T1-weighted imaging, we describe a reproducible delineation procedure to estimate the hypothalamic volume which was based on the same landmarks used in histologically processed postmortem hypothalamic tissue. Participants included 36 prodromal HD (pre-HD), 33 symptomatic HD (symp-HD) and 33 control participants who underwent MRI scanning at baseline and 18 months follow-up as part of the IMAGE-HD study. We found no evidence of cross-sectional or longitudinal changes between groups in hypothalamic volume. Our results suggest that hypothalamic pathology in HD is not associated with volume changes.
Improved fuzzy clustering for image segmentation based on local and non-local information

Image segmentation is the basis of image analysis, image understanding, video tracking, and etc. However, the complexity of images makes this problem difficult. In this paper, image segmentation algorithms based on fuzzy clustering are investigated and one improved schema is presented. In the proposed schema, local information and non-local information is fused into fuzzy clustering simultaneously, resulting in simple but effective segmentation algorithms. Based on non-local information, the improved algorithms can resist the effect of image artifacts, while image details can be retained with the help of neighbor information. Compared with current segmentation algorithms based on fuzzy clustering, the proposed algorithms can retrieve satisfactory results with acceptable efficiency. Experiments on different images illustrate that the proposed algorithms outperform corresponding fuzzy clustering algorithms.
Feasibility of Cognitive Training in Critically Ill Patients: A Pilot Study

&NA; Background Delirium occurs frequently in the intensive care unit and is associated with detrimental consequences. Cognitive training is a promising, nonpharmacologic, preventive intervention, but it is unknown whether cognitive training is feasible for patients in intensive care units. Objectives To examine the feasibility for both nurses and patients of using cognitive training exercises for intensive care unit patients. Methods A pilot study of a set of cognitive training exercises in a large, academic intensive care unit. Feasibility of the exercises, operationalized as practicability and burden for the patient and the nurse, was tested in multiple rounds and evaluated using Likert scales and open‐ended questions, patients' vital signs, and time investment. Results In total, 75 patients were included. During the first round, 11 exercises were separately tested by nursing researchers in 44 cooperative patients (50% with delirium). Four exercises were evaluated as burdensome and were excluded. Vital signs did not alter during execution. In a second round, the remaining exercises were tested in 31 patients (52% with delirium) by their attending nurse. All exercises were rated as practicable and not burdensome by the patients and the nurses. Total time investment per exercise was a median 4.5 (interquartile range, 3.0‐5.0) minutes. Conclusion Cognitive training exercises used in this study were feasible for intensive care unit patients (including cooperative patients with delirium) and their nurses. More research is needed to determine the clinical effect of the exercises on delirium outcome.
Effects of host traits and land‐use changes on the gut microbiota of the Namibian black‐backed jackal (Canis mesomelas)

ABSTRACT Host traits and environmental factors drive the natural variation in gut microbiota, and disruption in homeostasis can cause infections and chronic diseases. African wildlife is increasingly facing human‐induced agricultural habitats, which also amplifies the contact probability with livestock with unknown consequences for wildlife gut microbiotas and the risk of transmission of potentially pathogenic bacteria. We applied high‐throughput sequencing of bacterial 16S rRNA genes and microsatellite genotyping to investigate the impact of host traits and habitat use on the gut microbiotas of black‐backed jackals (Canis mesomelas). This abundant carnivore inhabits livestock and game farms in central Namibia and is often persecuted as pathogen reservoir and vector. We further compared the gut microbiotas of black‐backed jackals to other wild and domestic carnivores, herbivores and an omnivore, to disentangle the effects of environment, host species and dietary preference. In black‐backed jackals, intrinsic host traits had a stronger impact in shaping the host‐bacteria relationship than environmental factors. Nevertheless, the abundance of bacterial operational taxonomic units (OTUs) differed in individuals from livestock and game farms for specific bacterial genera such as Lactobacillus and Clostridium. We found, however, no evidence that black‐backed jackals harbour abnormal levels of OTUs related to potential bacterial pathogens or that livestock farming has a negative impact on their health. We present here the first study investigating simultaneously the impact of host traits and environmental factors on gut microbiotas of a wildlife carnivore that occurs in a human‐modified habitat.
Why do boys eat less fruit and vegetables than girls?

Abstract Objective To explore why boys eat less fruit and vegetables (F&V) than girls, using longitudinal data following pupils from the age of 12.5 to 15.5 years, including perceived accessibility, modelling, intention, preferences, self-efficacy and knowledge of recommendations as potential mediators. Design/setting/subjects A longitudinal study, based on data collected among the control schools within the intervention project ‘Fruits and Vegetables Make the Marks’. This sample contains 896 6th and 7th graders from 20 randomly selected elementary schools within two Norwegian counties. Questionnaires were administered in May 2002 and May 2005 (when the participants were in 9th and 10th grade in 18 secondary elementary schools). Results In single mediation analyses all determinants mediated parts of the gender difference, but only preferences decreased the gender difference to a level below statistical significance. Preferences alone explained 81% of the gender difference. In the multiple mediation analyses, the six mediators together explained 91% of the gender difference, but only preferences and perceived accessibility contributed uniquely to the explanation, with 25% and 10% respectively. Conclusions Preference appears as the strongest mediator of the difference in F&V intakes between boys and girls. Further research should explore why girls like F&V more than boys.
Integrated Lung and Tracheal mRNA-Seq and miRNA-Seq Analysis of Dogs with an Avian-Like H5N1 Canine Influenza Virus Infection

Avian-like H5N1 canine influenza virus (CIV) causes severe respiratory infections in dogs. However, the mechanism underlying H5N1 CIV infection in dogs is unknown. The present study aimed to identify differentially expressed miRNAs and mRNAs in the lungs and trachea in H5N1 CIV-infected dogs through a next-generation sequencing-based method. Eighteen 40-day-old beagles were inoculated intranasally with CIV, A/canine/01/Guangdong/2013 (H5N1) at a tissue culture infectious dose 50 (TCID50) of 106, and lung and tracheal tissues were harvested at 3 and 7 d post-inoculation. The tissues were processed for miRNA and mRNA analysis. By means of miRNA-gene expression integrative negative analysis, we found miRNA–mRNA pairs. Lung and trachea tissues showed 138 and 135 negative miRNA–mRNA pairs, respectively. One hundred and twenty negative miRNA–mRNA pairs were found between the different tissues. In particular, pathways including the influenza A pathway, chemokine signaling pathways, and the PI3K-Akt signaling pathway were significantly enriched in all groups in responses to virus infection. Furthermore, dysregulation of miRNA and mRNA expression was observed in the respiratory tract of H5N1 CIV-infected dogs and notably, TLR4 (miR-146), NF-κB (miR-34c) and CCL5 (miR-335), CCL10 (miR-8908-5p), and GNGT2 (miR-122) were found to play important roles in regulating pathways that resist virus infection. To our knowledge, the present study is the first to analyze miRNA and mRNA expression in H5N1 CIV-infected dogs; furthermore, the present findings provide insights into the molecular mechanisms underlying influenza virus infection.
Whole-Body Roll Tilt Influences Goal-Directed Upper Limb Movements through the Perceptual Tilt of Egocentric Reference Frame

In our day-to-day life, we can accurately reach for an object in our gravitational environment without any effort. This can be achieved even when the body is tilted relative to gravity. This is accomplished by the central nervous system (CNS) compensation for gravitational forces and torque acting on the upper limbs, based on the magnitude of body tilt. The present study investigated how performance of upper limb movements was influenced by the alteration of body orientation relative to gravity. We observed the spatial trajectory of the index finger while the upper limb reached for a memorized target with the body tilted in roll plane. Results showed that the terminal location of the fingertip shifted toward the direction of body tilt away from the actual target location. The subsequent experiment examined if the perceived direction of the body longitudinal axis shifted relative to the true direction in roll plane. The results showed that the perceived direction of the body longitudinal axis shifted toward the direction of the body tilt, which correlated with the shift of the terminal location in the first experiment. These results suggest that the dissociation between the egocentric and gravitational coordinates induced by whole-body tilt leads to systematic shifts of the egocentric reference frame for action, which in turn influences the motor performance of goal-directed upper limb movements.
Surface integrity evaluation and the effect of machining-induced surface integrity characteristics on part's performance

Surface integrity (SI) is the integrated surface behavior and condition of a material after being modified by a manufacturing process; it describes the influence of surface properties and characteristics upon material functional performance. As the leading-edge field of manufacturing research, SI finishing/machining and the consequent machining-induced complex combination of surface roughness, residual stress, work-hardening, macro and microstructure transformation, strongly affect the fatigue and stress behavior of machined parts. This kind of influence is particularly sensitive and pronounced in the difficult-to-machine materials, which are typically chosen for the most critical applications in the automobile, aerospace and nuclear industry. Thus, well-designed SI processing requirement and accurate SI evaluation model are essential to control and ensure the surface quality and functional performance for these key parts. In this thesis, an SI descriptive model for quantitative characterization and evaluation of surface integrity is proposed based on five principal SI characteristics. Considering the nature of surface integrity, a conceptual framework of an SI model for machined parts is established, in which the SI model is constructed based on the correlations between SI manufacturing processes, SI characteristics and final functionality. This model offers a theoretical basis and guideline for controlling SI characteristics and improving fatigue properties for machined parts. An empirical model for estimating the SI-characteristics-caused effective stress concentration factor (SCF) is established with fatigue life as the evaluating indicator. For a typical difficult-to-machine material, GH4169 superalloy, usually used in internal combustion engines, its grindability and the influence of processing parameters on the five principal SI characteristics are investigated in detail. The correlations between the processing parameters and the SI characteristics, between the processing parameters and the fatigue properties, and between the SI characteristics and the fatigue properties, are analyzed based on an orthogonally-designed grinding experiment and corresponding rotary bending fatigue testing for GH4169 samples within the selective range of grinding processing parameters. The feasibility and effectiveness of the proposed model for estimating the SI effective SCF are also validated by the experimental results, and this has actually offered an equivalent and convenient means for evaluation of SI and fatigue properties. Finally, the conclusions and contribution of the research are discussed, and potential future work to build on this research is identified.
Single-color image motion deblurring using MTV model

Image blind restoration has been a significant subject in various application fields. In the paper, we mainly studied the color image. In the process of converting color image into gray image will result in the loss of information because color image has different channels. In order to solve blind deconvolution of color image effectively, we present a method that estimates kernel result from three channels of color image directly based on multiscale framework. And then we ues the Multichannel Total Variation (MTV) model to protect image edges. By using normalized hyper Laplacian prior term, our method can converge to the real solution. The final clear image can be gotten. Although the MTV model will increase the complexity of computation, The correctness of algorithm and the feasibility of methods are proved by experiments.
The fission yeast MAPK Spc1 senses perturbations in Cdc25 and Wee1 activities and targets Rad24 to restore this balance

Mitogen‐activated protein kinases (MAPKs) play vital roles in multiple cellular processes and represent prominently pursued targets for development of therapeutic regimes. The MAPK Spc1 (p38 homologue) is known to be very important for both mitotic promotion and delay in Schizosaccharomyces pombe. However, the mechanism responsible for mitotic inhibition has remained elusive. Cdc25 (Cdc2 activator) and Wee1 (Cdc2 inhibtor) are important determinants of mitotic timing in all eukaryotes. Our results show that Spc1 can sense the perturbations in the balance of Cdc25 and Wee1 activities in S. pombe and that its function as a mitotic inhibitor is very important for controlling the same. An Spc1–Srk1–Rad24‐dependent pathway for mitotic inhibition has been reported earlier.Here we report the presence of an alternative mechanism wherein Spc1 targets the 14–3–3 protein, Rad24, independently of Srk1, leading to relocalization of Cdc25 and mitotic inhibition. Our observations suggest that this pathway can serve as a backup mechanism for Cdc2 inactivation in the absence of Wee1.
High-Density NAND-Like Spin Transfer Torque Memory With Spin Orbit Torque Erase Operation

We present a NAND-like spintronics memory (NAND-SPIN) device for high-density non-volatile memory applications. Fast erasing and programming of magnetic tunnel junction (MTJ) are implemented with two unidirectional currents generating spin orbit torque (SOT) and spin transfer torque (STT), respectively. The asymmetric switching drawback of STT mechanism can be definitively overcome as only anti-parallel to parallel operation happens for NAND-SPIN programming, which allows lower switching current, smaller access transistor, and reduced maximum write voltage across the MTJ. By sharing the SOT-induced erase operation in a nanowire, the area overhead due to the three-terminal structure can be also eliminated. Simulation results show that NAND-SPIN can achieve <inline-formula> <tex-math notation="LaTeX">$\text {3}\sim \text {5}\times $ </tex-math></inline-formula> reduction in write energy compared to STT-MRAM, and <inline-formula> <tex-math notation="LaTeX">$\text {2}\sim \text {4}\times $ </tex-math></inline-formula> less bit-cell area than SOT-MRAM at 28 nm node.
High Intensity Exercise in Multiple Sclerosis: Effects on Muscle Contractile Characteristics and Exercise Capacity, a Randomised Controlled Trial

Introduction Low-to-moderate intensity exercise improves muscle contractile properties and endurance capacity in multiple sclerosis (MS). The impact of high intensity exercise remains unknown. Methods Thirty-four MS patients were randomized into a sedentary control group (SED, n = 11) and 2 exercise groups that performed 12 weeks of a high intensity interval (HITR, n = 12) or high intensity continuous cardiovascular training (HCTR, n = 11), both in combination with resistance training. M.vastus lateralis fiber cross sectional area (CSA) and proportion, knee-flexor/extensor strength, body composition, maximal endurance capacity and self-reported physical activity levels were assessed before and after 12 weeks. Results Compared to SED, 12 weeks of high intensity exercise increased mean fiber CSA (HITR: +21±7%, HCTR: +23±5%). Furthermore, fiber type I CSA increased in HCTR (+29±6%), whereas type II (+23±7%) and IIa (+23±6%,) CSA increased in HITR. Muscle strength improved in HITR and HCTR (between +13±7% and +45±20%) and body fat percentage tended to decrease (HITR: -3.9±2.0% and HCTR: -2.5±1.2%). Furthermore, endurance capacity (Wmax +21±4%, time to exhaustion +24±5%, VO2max +17±5%) and lean tissue mass (+1.4±0.5%) only increased in HITR. Finally self-reported physical activity levels increased 73±19% and 86±27% in HCTR and HITR, respectively. Conclusion High intensity cardiovascular exercise combined with resistance training was safe, well tolerated and improved muscle contractile characteristics and endurance capacity in MS. Trial Registration ClinicalTrials.gov NCT01845896
A Sensor-Driven Hierarchical Method for Domain Adaptation in Classification of Remote Sensing Images

This paper presents a sensor-driven hierarchical domain adaptation method that aims at transferring the knowledge from a source domain (RS image where reference data are available) to a different but related target domain (RS image where no labeled reference data are available) for solving a classification problem. Due to the different acquisition conditions, a difference in the source and target distributions of the features representing the same class is generally expected. To solve this problem, the proposed method takes advantage from the availability of multisensor data to hierarchically detect features subspaces where for some classes data manifolds are partially (or completely) aligned. These feature subspaces are associated with invariant physical properties of classes measured by the sensors in the scene, i.e., measures having almost the same behavior in both domains. The detection of these invariant feature subspaces allows us to infer labels of the target samples that result more aligned to the source data for the considered subset of classes. Then, the labeled target samples are analyzed in the full feature space to classify the remaining target samples of the same classes. Finally, for those classes for which none of the sensors can measure invariant features, we perform the adaptation via a standard active learning technique. Experimental results obtained on two real multisensor data sets confirm the effectiveness of the proposed method.
Development of Calorie Tracking Algorithm for Children and Comparison with Commercialized Product

In this study, we developed an appropriate calorie tracking algorithm for children and compared it with the current commercial product. The results of the developed algorithm showed 94.2% accuracy in the classification of intermittent activities: sitting down, standing up, turning around and jumping; and 92.9% accuracy in the classification of continuous activities: walking, running, stair climbing, and jumping rope. This comparison shows that the developed algorithm outperforms the commercialized one and shows it is suitable for actual use of children. This study provides new activity monitor algorithm for children’s self-management.
An improved spatio-temporal context tracking algorithm combining LK optical flow

Recently, the issue of visual tracking has been brought into a focus of public attention. In this paper, we present the improved spatio-temporal context (STC) tracking algorithm combining LK optical flow. Original STC algorithm estimates the best object location by maximizing the confidence map that integrates the spatio-temporal context information. Nevertheless, original STC algorithm only considered positional relationship between the pixels in the context region and object, without considering relative motion and direction. To enhance the accuracy and stability of the tracking, improved STC algorithm applies the idea of partition processing to the context region. Furthermore, The LK optical flow based on Harris corner is utilized to obtain the corresponding weight matrix and integrate it into the Bayesian tracking frame. In the experimental phase, we use the Unmanned Aerial Vehicle (UAV) as a tracking object. Numerous comparative experiments demonstrate that the improved STC algorithm can achieve fast, accurate and continuous tracking for UAV scale and angle variation, rapid flight, complex background changes and occlusion. Overall, it is obvious that the robustness and accuracy of the improved STC algorithm are better than before by comparing the tracking overlap rate and success rate.
Improve object detection via a multi-feature and multi-task CNN model

Current state-of-the-art object detection methods have made dramatic performance improvements in recent few years. However, there are still several challenges. In particular, it still struggles for precise localization of small-sized objects, mainly due to coarse resolutions of feature maps and excessive surroundings such as ground and water. To address the issues, we propose an object detection system based on standard Fast R-CNN object detection branch and DeepLap semantic segmentation branch: (1) multi-feature aggregates hierarchical features for more finer feature maps to detect objects at multiple scales. (2) multi-task uses semantic segmentation for more contextual information to assist object detection via a cross structure between the two tasks. (3) a novel overlap loss function is used for bounding box regression that adjusts region proposals to improve localization. The fusion network improves results over the Fast R-CNN baseline detector by 2.8% mAP and by 4.8% mAP for small objects based on PASCAL VOC datasets.