Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of difference of working memory and sensory processing styles in boys and girls with writing-learning disorder

Context: The main problems of children with learning disorder are in memory and their sensory processing and it causes children's academic performance not appropriate. Aims: The aim of this study is to investigate of difference of working memory and sensory processing styles in boys and girls with writing-learning disorder. Settings and Design: This was a descriptive and analytic study in the summer of 2018 at a learning disorders center in Gorgan City, Iran. Materials and Methods: The study population was all girls and boys by the age of 8–10 years suffering from the writing-learning disorder. The available sampling method was used and 44 boys and 36 girls were chosen. The Dunn profile was obtained from the parents of the children. Daneman and Carpenter working memory, questions were asked from the children directly, and the information related to processing and storage in their memory were received. Furthermore, a demographic questionnaire was used. Statistical Analysis Used: Data were analyzed using in descriptive statistics (mean, standard deviation, and frequency), independent t-test, and multivariate analysis of variance. Results: There was a significant difference in the writing learning disorder among processing (P < 0.006) and storage (P < 0.000) of working memory subscales and the indoctrination associated with the physical state of the body motion (P < 0.000), multisensory processing (P < 0.000), and auditory processing (P < 0.002), of the sensory processing styles variable in both the girls and boys. Conclusions: With regard to the results obtained, it can be concluded that the boy's group is in worse conditions in working memory and sensory processing styles, which requires special attention and more focused educational approaches.
Tau Pathology in Chronic Traumatic Encephalopathy and Alzheimer's Disease: Similarities and Differences

Traumatic brain injury (TBI) has been associated with the development of Alzheimer's disease (AD) because these conditions share common pathological hallmarks: amyloid-β and hyperphosphorylated tau accumulation. However, given recent data it is uncertain if a history of TBI leads to the development of AD. Moreover, chronic traumatic encephalopathy (CTE), caused by repetitive mild TBI and characterized by progressive neurodegeneration with hyperphosphorylated tau, has come to be recognized as distinct from AD. Therefore, it is important to elucidate the clinical outcomes and molecular mechanisms underlying tau pathology following TBI. We summarize the histopathological features and clinical course of TBI in CTE, comparing the tau pathology with that in AD. Following brain injury, diffuse axonal injury, and hyperphosphorylated tau aggregates are observed within a shorter period than in AD. Hyperphosphorylated tau deposition usually begins in the perivascular area of the sulci in the cerebral cortex, then spreads unevenly in the cortex in CTE, while AD shows diffuse distribution of hyperphosphorylated tau in the cortical areas. We also highlight the molecular profile of tau and the implications of tau progression throughout the brain in both diseases. Tau contains phosphorylation sites common to both conditions. In particular, phosphorylation at Thr231 triggers a conformational change to the toxic cis form of tau, which is suggested to drive neurodegeneration. Although the mechanism of rapid tau accumulation remains unknown, the structural diversity of tau might result in these different outcomes. Finally, future perspectives on CTE in terms of tau reduction are discussed.
Brain-Dependent Processes Fuel Pain-Induced Hemorrhage After Spinal Cord Injury

Pain (nociceptive) input caudal to a spinal contusion injury can undermine long-term recovery and increase tissue loss (secondary injury). Prior work suggests that nociceptive stimulation has this effect because it fosters the breakdown of the blood-spinal cord barrier (BSCB) at the site of injury, allowing blood to infiltrate the tissue. The present study examined whether these effects impact tissue rostral and caudal to the site of injury. In addition, the study evaluated whether cutting communication with the brain, by means of a rostral transection, affects the development of hemorrhage. Eighteen hours after rats received a lower thoracic (T11–12) contusion injury, half underwent a spinal transection at T2. Noxious electrical stimulation (shock) was applied 6 h later. Cellular assays showed that, in non-transected rats, nociceptive stimulation increased hemoglobin content, activated pro-inflammatory cytokines and engaged signals related to cell death at the site of injury. These effects were not observed in transected animals. In the next experiment, the spinal transection was performed at the time of contusion injury. Nociceptive stimulation was applied 24 h later and tissue was sectioned for microscopy. In non-transected rats, nociceptive stimulation increased the area of hemorrhage and this effect was blocked by spinal transection. These findings imply that the adverse effect of noxious stimulation depends upon spared ascending fibers and the activation of rostral (brain) systems. If true, stimulation should induce less hemorrhage after a severe contusion injury that blocks transmission to the brain. To test this, rats were given a mild, moderate, or severe, injury and electrical stimulation was applied 24 h later. Histological analyses of longitudinal sections showed that nociceptive stimulation triggered less hemorrhage after a severe contusion injury. The results suggest that brain-dependent processes drive pain-induced hemorrhage after spinal cord injury (SCI).
Model-Based Evaluation of Closed-Loop Deep Brain Stimulation Controller to Adapt to Dynamic Changes in Reference Signal

High-frequency deep brain stimulation (DBS) of the subthalamic nucleus (STN) is effective in suppressing the motor symptoms of Parkinson's disease (PD). Current clinically-deployed DBS technology operates in an open-loop fashion, i.e., fixed parameter high-frequency stimulation is delivered continuously, invariant to the needs or status of the patient. This poses two major challenges: (1) depletion of the stimulator battery due to the energy demands of continuous high-frequency stimulation, (2) high-frequency stimulation-induced side-effects. Closed-loop deep brain stimulation (CL DBS) may be effective in suppressing parkinsonian symptoms with stimulation parameters that require less energy and evoke fewer side effects than open loop DBS. However, the design of CL DBS comes with several challenges including the selection of an appropriate biomarker reflecting the symptoms of PD, setting a suitable reference signal, and implementing a controller to adapt to dynamic changes in the reference signal. Dynamic changes in beta oscillatory activity occur during the course of voluntary movement, and thus there may be a performance advantage to tracking such dynamic activity. We addressed these challenges by studying the performance of a closed-loop controller using a biophysically-based network model of the basal ganglia. The model-based evaluation consisted of two parts: (1) we implemented a Proportional-Integral (PI) controller to compute optimal DBS frequencies based on the magnitude of a dynamic reference signal, the oscillatory power in the beta band (13–35 Hz) recorded from model globus pallidus internus (GPi) neurons. (2) We coupled a linear auto-regressive model based mapping function with the Routh-Hurwitz stability analysis method to compute the parameters of the PI controller to track dynamic changes in the reference signal. The simulation results demonstrated successful tracking of both constant and dynamic beta oscillatory activity by the PI controller, and the PI controller followed dynamic changes in the reference signal, something that cannot be accomplished by constant open-loop DBS.
A Metabarcoding Analysis of the Mycobiome of Wheat Ears Across a Topographically Heterogeneous Field

Plant associated microbial communities have recently received a lot of attention because thought to play a fundamental role in plant health and development. Focusing on cultivated crops, optimized farming practices must consider the role of these communities when aiming at reducing the impact of pathogens and increasing yields. Typical inhabitants of plant’s phyllosphere are bacteria and microscopic fungi, some of them pathogenic for the plant and dangerous for the consumers, due to the production of toxins. In order to efficiently manage the microbiome, the natural drivers regulating community assembly must be clearly understood. In our study we investigated the within field variation of the phyllosphere mycobiome of wheat ears by metabarcoding of the fungal internal transcribed sequence 1 (ITS1). We selected a field characterized by a high topographic heterogeneity, which is reflected in differences in plant productivity and fitness across it. Samples were taken from 30 sampling points laid across the field where data-loggers were placed, measuring the productivity driven under canopy microclimate. The microclimatic conditions were tested as a source of potential environmental variance. Further independent spatial structures were tested using spatial eigenvector maps (MEMs). Results show considerable differences in the phyllosphere composition across the field. The local under canopy environmental conditions at each point were strong predictors of the community composition. Independent spatial effects given by the geographical position of the sampling points showed also a weaker but significant effect. Moreover we observed different spatial responses from different fungal phyla, with results resembling those described in studies done at a regional scale. This study is the first one to investigate the spatial variation of the phyllosphere mycobiome of a commercial crop within the same field. It contributes to the study of the epidemiology and community assembly dynamics of wheat phyllosphere fungi, showing how in-field community variations are the results of different environmental and spatial processes acting simultaneously. It also shows how heterogeneous fields are a smart and useful system to investigate the ecological mechanisms regulating plant microbiome composition.
Sex, Drugs, and the Medial Amygdala: A Model of Enhanced Sexual Motivation in the Female Rat

Methamphetamine (METH) is a psychomotor stimulant that is reported to enhance sexual desire and behavior in both men and women, leading to increases in unplanned pregnancies, sexually-transmitted infections, and even comorbid psychiatric conditions. Here, we discuss our rodent model of increased sexually-motivated behaviors in which the co-administration of METH and the ovarian hormones, estradiol and progesterone, intensify the incentive properties of a sexual stimulus and increases measures of sexually-motivated behavior in the presence of an androgen-specific cue. We then present the neurobiological mechanisms by which this heightened motivational salience is mediated by the actions of METH and ovarian hormones, particularly progestins, in the posterodorsal medial nucleus of the amygdala (MePD), a key integration site for sexually-relevant sensory information with generalized arousal. We finally demonstrate the cellular and molecular mechanisms underlying this facilitation of sexual motivation by METH, including the upregulation, increased phosphorylation, and activation of progestin receptors (PRs) in the MePD by METH in the presence of ovarian hormones. Taken together, this work extends our understanding of the neurobiology of female sexual motivation.
Team Cognition in Sport: How Current Insights Into How Teamwork Is Achieved in Naturalistic Settings Can Lead to Simulation Studies

This opinion paper questions current theoretical positions about how teamwork is achieved in sport teams. Based on studies conducted in naturalistic settings, the following assumptions are criticised: (1) expert teams use implicit coordination, (2) implicit coordination is based on shared knowledge within the team, (3) team synergies emerge form the spatiotemporal relationship and affect coordination, and (4) athletes are attuned to local information. An alternative model to explain the successful coordination in sport teams is introduced, and first experiences with a simulation study are reported.
Vaginal Microbiome Signature Is Associated With Spontaneous Preterm Delivery

Background: Preterm delivery (PTD) represents an important public health and therapeutic challenge. Despite the reported link between the composition of vaginal microbiome and PTD, previous studies were inconsistent in their conclusions and utilized non-uniform designs. We performed an independent case-control study carried out on the Slovenian population, where we re-evaluated the role of the vaginal microbiome in PTD. Methods: Vaginal microbiomes of pregnant women who delivered preterm were compared to those delivered at term to examine differences in the microbial richness, diversity, and differential abundance of specific taxa. We obtained vaginal swab samples from 155 Caucasian women who were classified as either term (≥380/7 weeks, n = 107) or preterm (≤366/7 weeks, n = 48) in exclusion of any other medical or obstetric conditions. The vaginal microbiomes of these women were characterized by 16S ribosomal RNA (rRNA) gene sequencing of the V3-V4 region on the MiSeq platform. Results: Women who experienced PTD had a higher microbial richness (Chao1, P = 0.011) and alpha diversity (Shannon, P = 0.00059) than women with term deliveries. We report that overall vaginal microbial community composition (beta-diversity) was significantly different by delivery gestational age category (PWeightedUnifrac < 0.001). Women who delivered preterm had decreased Lactobacilli spp. abundance as well as increased abundance of Gardnerella and other bacterial vaginosis (BV) and aerobic vaginitis (AV) associated genera including Atopobium, Sneathia, Gemella, Megasphaera, Dorea, Streptococcus, and Escherichia/Shigella. Conclusions: In the present study, we provide further evidence that vaginal microbiome composition is associated with PTD.
Rotational speed measurement based on a state-space observation within a tracking demodu1ation method using xMR angular sensors

Sensing of rotational speeds is essential in many application areas such as automotive, industrial or aerospace systems. For speed detection typically magnetic field sensors are used, which are usually combined with frequency measurement systems. Thus, especially for a low number of revolutions near standstill there is either the need of a dramatically increasing measuring time to attain a sufficient accuracy or a significant higher error if the time is kept constant. An alternative are angular measurement setups for rotational speed detection, which suffer from noise that corrupts the velocity calculation. In contrast to existing solutions, we propose a fast observation of rotational speeds with a state-space model. Moreover, apart from fast speed detection the presented approach is also able to suppress interfering magnetic fields, which leads to additional inaccuracies in existing measurement systems. By using an adaptive Kalman filter we are also able to reduce measurement noise. In the following, simulations as well as measurement results based on state of the art magnetoresistive sensors are presented to validate our suggested methodology.
Low-Cost Eye Gesture Communication System for People with Motor Disabilities

In today’s world, the use of electronic interfaces is a daily occurrence that majority of people has taken for granted. People that suffer from motor disabilities are unable to access these simple interfaces due to their inability to direct their body movement to do certain tasks such as pressing a key on the keyboard or activate a button on the device. In most cases, interaction with the digital interfaces or devices is the only means that such individuals can use to communicate with their caregivers. This has become particularly apparent for the people whose only mobility is confined to their eyes. For these individuals, the use of an Eye Gesture Communication System (EGCS) is great importance as it gives them the opportunities to convey their needs to other people through their eye movement. There are various types of EGCS available on the market. However, they are normally quite expensive. This paper discusses how a simple low-cost and yet reasonable accurate EGCS can be achieved with the use a webcam, Open CV’s Haar Cascade Classifiers, and a simple algorithm using various thresholding techniques. The results show the overall accuracy of 86% in interpreting the user’s needs.
A Deformation Sensor based upon Light Attenuation in a Silicone Waveguide: Construction and Characterisation

The measurement of the displacement of soft robotic devices and configurations of actuators is essential for the implementation of force and position control. Conventional deformation sensors, such as capacitive and resistive, are unable to acheive the large extensions required to accurately measure these configurations. Recent advances in flexible optical deformation sensors over the last four years have provided sensor technologies that can measure the large extensions that are experienced in soft actuators. However, the majority of existing optical sensors suffer from performance issues relating to unknown losses in optical power and the lack of immunity to external light sources. To address these issues, a simple optical sensor, constructed using a silicone elastomer, has been tested. By measuring the ratio of two wavelengths of light with different attenuation coefficients, the effects of wavelength dependent optical losses and external light interference can be addressed. A clear silicone sensor was characterised using a RGB LED over 500 extension cycles up to 100 % extension, giving a repeatable output and low and linear hysteresis over all 500 cycles. The effect of different optical dyes was investigated, showing that different dyes can controllably attenuate specific wavelengths and thus affect the sensitivity of the measurements. This investigation demonstrates that a ratioed wavelength flexible optical deformation sensor is capable of repeatable measuring large deformations, suitable for use in a range of applications.
Water volume fraction measurement with hydrocyclone-capacitor sensor in horizontal gas-liquid swirling flow

The flow pattern of gas-liquid two-phase flow has a great influence on the water holdup measurement by capacitor sensor. In this paper, the idea of flow pattern regularization is proposed. The complex flow pattern of gasliquid two-phase flow is forcibly changed into the swirling flow pattern with regularized phase distribution by using the hydrocyclone. The structure of the hydrocyclone is designed by means of flow pattern observation to ensure better separation effect and less pressure loss, and the stability of the swirling flow is analyzed. Based on the hydrocyclone-capacitor sensor, the water volume fraction information is converted into liquid film thickness and water holdup information. The experimental results indicate that the sensor has a good resolution to the water volume fraction. A new model of the slip ratio in the swirling flow is presented. Based on the slip ratio in the swirling flow, the water volume fraction can be acquired, realizing the measurement of water volume fraction within 10%. The mean absolute error is 0.244%.
Finger-vein image segmentation based on KFCM and active contour model

Due to inferior contrast and intensity inhomogeneity existing in finger-vein images, current segmentation methods cannot distinguish the venous and non-venous areas effectively. To address this issue, we propose a robust segmentation approach which merges the kernel fuzzy C-means (KFCM) algorithm with an active contour model (ACM). Firstly, the KFCM algorithm is adopted to segment the venous area roughly, which is served as the initial evolution outline of ACMs. Secondly, we present a novel region-based ACM where an edge fitting item is brought in to achieve better segmentation performance. Finally, finger-vein image segmentation is achieved by minimizing the proposed region-based ACM which is served as an energy function, and the level set method is introduced to solve the minimization problem efficiently. The experimental results show that the proposed approach has significant performance in segmenting finger-vein images.
Mitigating the Effect of Obstacles in Narrowband Ultrasonic Localization Systems

This paper develops a method for mitigating the negative effects of obstacles in narrowband, time division multiple access (TDMA), ultrasonic localization systems. The method builds upon the robust Bayesian classifier for ultrasonic localization (RoBCUL) algorithm which utilizes an iteratively reweighted least squares (IRLS) scheme. This algorithm has the advantage of low computational cost but loses performance in the presence of obstacles. The improved version of the RoBCUL algorithm presented in this paper uses a statistical test applied after each iteration of the regression, using a weighted residual vector calculated from the weight matrix and residual vector. The technique was tested using experimental data with its performance being quantified by its ability to correctly classify all the signals received during a single TDMA cycle. The extended version performed significantly better in all obstacle scenarios than the original, correctly classifying 100% of TDMA cycles in the scenarios with no obstacles, 97.6% with one obstacle, and 89.0% with two obstacles.
A CMOS Startup Circuit for Thermoelectric Energy Harvesting Systems

This work presents the design of a fully integrated startup circuit for energy harvesting. The startup circuit can provide an output voltage of 520mV and drives 10uW output power for the minimal input capabilities on a thermoelectric generator. The proposed startup generator reaches a peak efficiency of 72% with an input voltage of 140mV @ 53KHz on switching frequency with only 6.87nW of power consumption. The ultra-low power performance is possible due to an asymmetric design of a ring oscillator and a simple two non-overlapping phase circuit implemented on a CMOS 180nm technology.
A comparison of intelligent classifiers of thermal patterns in diabetic foot

The Diabetic foot ulcer is one of the major complications of Diabetes Mellitus. A suitable technique in detection and prevention of this complication is the analysis of temperature distribution in the plantar region by means of infrared thermography, a non-invasive and non-contact technique. However, due to some complications either from human interactions or related to the data, an emergent alternative is the use of computer-aided diagnosis, taking part in the support decision, and analysis systems with high accuracy rates of classification. This work addresses the employment of computational intelligence-based methods in the classification of thermal images, aiming to compare two of the most powerful techniques such as Support Vector Machines and the Multilayer Perceptron. Both techniques are compared with the recent and renewed usage of Deep Learning structures, a massive structure of artificial neural networks. The research goes from the automatic extraction of the region of interest (ROI) by means of evolutionary algorithms until the final comparison of these three relevant classifiers. The high accuracy of the obtained results shows the utility of these methods as auxiliary tools in the medical diagnosis.