Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiple Injections of Cryopreserved Fetal Liver Cells to Ageing Rats Prevent Age-Related Antioxidant System Changes and Increase Lifespan

In this research the possibility of using the cryopreserved fetal liver cells (сFLCs) as anti-aging agents, as well as assessment of their effect on prooxidant-antioxidant balance, physiological parameters and lifespan of rats during ageing was investigated. Multiple injections of сFLCs (every 3 months) to adult rats (from 13 to 22 months of age) have been established to prevent changes in prooxidant-antioxidant balance of liver and blood, forming in older animals by 25 months. Similar multiple injections of сFLCs to aged rats (from 23 months to the end of life) contribute to their body weight gain, increase state of hair coat and prolong lifespan by 100 days. The results of research allow considering the сFLC transplantation as a promising approach to correct the age-related changes associated with oxidative stress.
White Matter Microstructure and Emotional Functioning in Children Treated for Posterior Fossa Tumours

Children treated for brain tumours that arise in the posterior fossa (PF) experience lifelong cognitive and emotional difficulties, and exhibit white matter (WM) damage. This thesis combined diffusion tensor imaging (DTI), eye-tracking and standardized measures of cognitive and emotional functioning in children treated for PF tumours and typically developing children, to: 1) characterize outcomes related to decreases in treatment intensity, 2) objectively evaluate emotional functioning, and 3) examine the associations between WM microstructure and emotional functioning. It was found that treatment with the lowest intensity craniospinal irradiation protocol spared WM in the temporal lobe of children treated for malignant PF tumours. In addition, patients treated on lower intensity protocols had largely preserved cognitive, social and affective functioning. However, novel eye-tracking tasks designed to evaluate emotional functioning uncovered some remaining deficits; children treated for PF tumours had difficulty recognizing facial emotions despite attending to the faces, and difficulty regulating their initial attention away from emotional faces. Notably, this eye-tracking measure of emotion regulation was associated with emotional control in daily life. The current work also revealed that relations between WM microstructure and emotional functioning can diverge in the injured and uninjured brain; WM predicted facial emotion recognition in typically developing iii children only, whereas WM was associated with emotion regulation in patients only. In a field dominated by findings that characterize negative sequelae, this work highlights the possibility of favourable outcomes for PF tumour patients who are eligible for treatment with lower intensity protocols. Despite these positive outcomes, subtle emotional functioning deficits not captured by standardized questionnaires persist. To better understand how emotional processes are altered in children treated for PF tumours, novel objective measures are required. This thesis detailed one such behavioural marker to evaluate emotion regulation, using eye-tracking technology, and characterized an oculomotor response that may warrant interventional follow-up.
Elman neural network optimized by firefly algorithm for forecasting China's carbon dioxide emissions

ABSTRACT With the development of China's economy, more and more energy consumption has led to serious environmental problems. Faced with the enormous pressure of large amounts of carbon dioxide () emissions, China is now actively implementing the development strategy of low-carbon and emission reduction. Through the analysis of the influencing factors of emissions in China, five key influencing factors are selected: urbanization level, gross domestic product (GDP) of secondary industry, thermal power generation, real GDP per capital and energy consumption per unit of GDP. This paper applies the Elman neural network optimized by the Firefly Algorithm (FA) to forecast the emissions in China. And the results show that the performance of the FA–Elman is better than the Elman neural network and Back Propagation Neural Network (BPNN), verifying the effectiveness of the FA–Elman model for the emissions prediction. Finally, we make some suggestions for low-carbon and emission reduction in China by analysing key influencing factors and forecasting emissions using the FA–Elman model from 2017 to 2020.
Lipper: Synthesizing Thy Speech using Multi-View Lipreading

Lipreading has a lot of potential applications such as in the domain of surveillance and video conferencing. Despite this, most of the work in building lipreading systems has been limited to classifying silent videos into classes representing text phrases. However, there are multiple problems associated with making lipreading a text-based classification task like its dependence on a particular language and vocabulary mapping. Thus, in this paper we propose a multi-view lipreading to audio system, namely Lipper, which models it as a regression task. The model takes silent videos as input and produces speech as the output. With multi-view silent videos, we observe an improvement over single-view speech reconstruction results. We show this by presenting an exhaustive set of experiments for speaker-dependent, out-of-vocabulary and speaker-independent settings. Further, we compare the delay values of Lipper with other speechreading systems in order to show the real-time nature of audio produced. We also perform a user study for the audios produced in order to understand the level of comprehensibility of audios produced using Lipper.
Comparing Upper Limb Intermuscular Coherence between Force- and Myoelectric-Control Tasks

Synchronization between muscles caused by shared neural input has been proposed to have a functional significance in muscle coordination. In this study, intermuscular coherence (IMC), a measure of shared neural input to muscles, was used to explore the neural underpinnings of proximal arm muscle coordination in force- and myoelectric-control tasks. In force-control, the goal was to attain a force magnitude and direction, whereas in myoelectric-control, the goal was to have single or pairwise muscle activation. Muscle coordination was assumed to be more selective in myoelectric-control, being that this task imposed stricter requirements in muscle activation. Accordingly, the expected difference in muscle coordination would be reflected in IMC if shared neural input results from a neural strategy for motor control. The hypothesis was that when single muscle activation was required, IMC in myoelectric-control would be lower than in force-control. Opposite to this, when pairwise muscle activation was required, IMC would be higher in myoelectric-control. Therefore, the presence or not of shared neural input was thought to be the mechanism to correspondingly couple or decouple muscle activation. The main finding was that there was no significant difference in IMC between force- and myoelectric-control tasks for both single and pairwise muscle activation conditions. Furthermore, IMC in single muscle activation was higher than that of pairwise muscle activation conditions in both tasks. Finally, the bands in which IMC was increased were the alpha-band and lower part of the beta-band. These findings are interpreted as that neural constraints have a fundamental role in the distribution of shared neural input to proximal muscles, and thus to their coordination.
Genetic Polymorphisms ofSLC8A1Are Associated with Hypertension and Left Ventricular Hypertrophy in the Korean Population

Hypertension (HTN) is one of the major chronic diseases, and HTN is defined as being in a state of continuous high blood pressure. Left ventricular hypertrophy (LVH) is a condition in which the mass of the left ventricle has increased, and HTN is a leading cause of LVH. HTN and LVH are known to be caused by the interaction of environmental factors and genetic factors. It has been reported that the polymorphisms of SLC8A1, among the genetic factors that affect high blood pressure, are related to salt sensitivity hypertension. In this study, the genetic polymorphisms of SLC8A1 were chosen based on the Korean Genome and Epidemiology data. Logistic regression analysis was then performed for HTN and LVH. Linear regression analysis was also performed for systolic blood pressure (SBP) and diastolic blood pressure (DBP). As a result, 5 SNPs showed statistically significant associations (P ＜ 0.05) with HTN, and 10 SNPs showed statistically significant associations with LVH. rs1002671 and rs9789739 showed significant correlation at the same time with HTN and LVH. These results suggest that the polymorphisms of the SLC8A1 gene are linked to the development of HTN and LVH in Koreans. We expect these results to help us understand the pathogenic mechanisms for HTN and LVH. Copyright 2019 The Korean Society for Clinical Laboratory Science. rights reserved.
Software Defined Wireless Sensor Networks (SDWSN): A Review on Efficient Resources, Applications and Technologies

Wireless Sensor Networks (WSNs) play a greater role in short range communication networks. Their application adoption to mission critical systems has increased tremendously in the past decades, due to their deployment simplicity and reasonable implementation costs. However, these technologies have a number of limitations subsequent to their computing capacities. Hence a lot of past research approaches have tried to improve their capabilities in terms of processing, communication, resilience, etc. One approach is to implement WSNs that support some level of Quality of Service (QoS) guarantee in their applications, since it is difficult to provide the desired QoS results on them due to their dynamic architectural nature. However, this is difficult since efficient resource management is key to achieve the desired QoS in WSNs. This paper provides a review on QoS provision in WSNs particularly for mission critical applications. The review focuses on QoS metric implementation, QoS challenges and improved communication opportunities in WSNs. The paper, further proposes Software Defined Wireless Sensor Networking (SDWSN) strategies for efficient resource management and guaranteed QoS support to improve network performance. The proposed strategies are aimed at simplifying QoS provisioning for these constrained technologies using programming methods to advance WSNs applications and their implementations.
Multi-scale graph modeling and analysis of locomotion dynamics towards sensor-based dementia assessment

Abstract Dementia is a general neurodegenerative disorder beyond normal aging, which is not only overwhelming for the patients, but also negatively affects their caregivers and families. In the state of the art, paper-based survey methods such as the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) are widely used for the assessment of dementia conditions. However, these methods require lab visits or administration from nurses, physicians and examiners, and are limited in the ability to track temporal degradation (or daily variations) of dementia conditions. With rapid advances in sensing technology, there is growing interest in the development of new, sensor-based methods that provide more flexibility in dementia monitoring and require minimal interventions from practitioners. In this article, we propose a new, sensor-based method that estimates dementia conditions with daily locomotion data. The proposed methodology is evaluated and validated with both simulation and real-world case studies. Experimental results show the compelling predictive accuracy, with both true positive and true negative rates above 85%. This article shows that sensor-based methods have great potential for real-time monitoring of temporal variations of dementia conditions from daily gait locomotion dynamics.
Assessing the ability of the Fitbit Charge 2 to accurately predict VO2max.

Background The aim of this study was to assess the ability of the Fitbit Charge 2 (FBC2) to accurately estimate VO2max in comparison to both the gold standard VO2max test and a non-exercise VO2max prediction equation. Methods Thirty healthy subjects (17 men, 13 women) between the ages of 18 and 35 (age =21.7±3.1 years) were given a FBC2 to wear for seven days and followed instructions on how to obtain a cardio fitness score (CFS). VO2max was measured with an incremental test on the treadmill followed by a verification phase. VO2max was predicted via a non-exercise prediction model (N-Ex) using self-reported physical activity level. Results Measured VO2max was significantly lower than FBC2 predicted CFS (VO2max =49.91±6.83; CFS =52.53±8.43, P=0.03). N-Ex prediction was significantly lower than CFS but not significantly lower than measured VO2max (N-Ex =48.79±6.32; CFS vs. N-Ex: P=0.01; VO2max vs. N-Ex: P=0.54). Relationships between both VO2max vs. CFS and VO2max vs. N-Ex were good (ICC: VO2max vs. CFS=0.87, VO2max vs. N-Ex =0.87); Bland-Altman analysis indicated consistency of CFS measurement and lack of bias. The coefficient of variation (CV) and mean absolute percent error (MAPE) were greater with CFS than N-Ex (CV: CFS =6.5%±4.1%, N-Ex =5.6%±3.6%; MAPE: CFS =10.2%±6.7%, N-Ex =7.8%±5.0%). Heart rate (HR) estimated by the FBC2 was lower than estimated (Est) HR for pace based on HR extrapolation (FBC2 =155±18 bpm, Est =183±15 bpm, P<0.001). The difference in CFS and VO2max was inversely correlated with the difference in FBC2 HR and Estimated HR (r =-0.45, P<0.001). Conclusions The FBC2 shows consistent, unbiased measurement of CFS while overestimating VO2max in healthy men and women. The non-exercise VO2max prediction equation provides a similar, slightly more accurate, VO2max prediction than the CFS without the need for an exercise test or purchase of a Fitbit.
Characterization of a 5 GHz Modular Radio Frontend for WLAN Based on

The number of vehicles has increased significantly in recent years, which causes high density in traffic and further problems like accidents and road congestions. A solution regarding to this problem is vehicle-to-vehicle communication, which vehicles are able to communicate with their neighboring vehicles even in the absence of a central base station to provide a safer and more efficient roads and to increase passenger safety. The goal of this thesis is to investigate some basic physical layer parameters of inter-vehicle communication signals, like emission power, spectral emission, error vector magnitude, guard interval, ramp-up/down time, and third order intercept point. I have also studied on intelligent transportation system’s channel layout in Europe, the interference of other systems working in co-channel and adjacent channels, and some proposals to use the allocated frequency bands. In the other hand, the fundamental of OFDM transmission and definition of OFDM key parameters in IEEE 802.11p is investigated. The emphasis of this work is on the measurement of transmitter frontend parameters of a new testbed designed and fabricated in order to be used at intervehicle communication based on IEEE 802.11p.
Optimal SIC Ordering and Computation Resource Allocation in MEC-Aware NOMA NB-IoT Networks

Nonorthogonal multiple access (NOMA) and mobile edge computing (MEC) have been emerging as promising techniques in narrowband Internet of Things (NB-IoT) systems to provide ubiquitously connected IoT devices with efficient transmission and computation. However, the successive interference cancellation (SIC) ordering of NOMA has become the bottleneck limiting the performance improvement for the uplink transmission, which is the dominant traffic flow of NB-IoT communications. Also, in order to guarantee the fairness of task execution latency across NB-IoT devices, the computation resource of MEC units has to be fairly allocated to tasks from IoT devices according to the task size. For these reasons, we investigate the joint optimization of SIC ordering and computation resource allocation in this paper. Specifically, we formulate a combinatorial optimization problem with the objective to minimize the maximum task execution latency required per task bit across NB-IoT devices under the limitation of computation resource. We prove the NP-hardness of this joint optimization problem. To tackle this challenging problem, we first propose an optimal algorithm to obtain the optimal SIC ordering and computation resource allocation in two stages: the convex computation resource allocation optimization followed by the combinatorial SIC ordering optimization. To reduce the computational complexity, we design an efficient heuristic algorithm for the SIC ordering optimization. As a good feature, the proposed low-complexity algorithm suffers a negligible performance degradation in comparison with the optimal algorithm. Simulation results demonstrate the benefits of NOMA in reducing the task execution latency.
Cardiovascular drift while rowing on an ergometer

Hartwell MH, Volberding JL, Brennan DK. Cardiovascular Drift while Rowing on an Ergometer. JEPonline 2015;18(2):95-102. Cardiovascular drift (CVdrift) refers to the gradual increase in heart rate (HR) during steady-state exercise. It has been shown to occur after ~15 min into running and cycling. Heart rate variability and CVdrift have yet to be analyzed during rowing on an ergometer. Two groups of 10 male collegiate rowers performed a 60-min session on an ergometer wearing HR monitors with HR measured at 1-min intervals. Demographic variables, post-workout dehydration, and ambient room temperature were measured. Descriptive statistics, dependent and independent samples t-tests, Pearson’s correlation coefficients, and linear regression were conducted to analyze variables. Stabilization of HR following the acclimation phase of exercise differed in rowing versus the role of cycling and running on the CVdrift. These findings may alter the current approach to adapting cycling and running HR training regimens to the sport of rowing and may even necessitate that rowing coaches develop their own training regimens when using HR as a monitoring mechanism. The results suggest that target HR training zones may be unique for the sport of rowing.
Folo: Latency and Quality Optimized Task Allocation in Vehicular Fog Computing

With the emerging vehicular applications, such as real-time situational awareness and cooperative lane change, there exist huge demands for sufficient computing resources at the edge to conduct time-critical and data-intensive tasks. This paper proposes Folo, a novel solution for latency and quality optimized task allocation in vehicular fog computing (VFC). Folo is designed to support the mobility of vehicles, including vehicles that generate tasks and the others that serve as fog nodes. Considering constraints on service latency, quality loss, and fog capacity, the process of task allocation across stationary and mobile fog nodes is formulated into a joint optimization problem. This task allocation in VFC is known as a nondeterministic polynomial-time hard problem. In this paper, we present the task allocation to fog nodes as a bi-objective minimization problem, where a tradeoff is maintained between the service latency and quality loss. Specifically, we propose an event-triggered dynamic task allocation framework using linear programming-based optimization and binary particle swarm optimization. To assess the effectiveness of Folo, we simulated the mobility of fog nodes at different times of a day based on real-world taxi traces and implemented two representative tasks, including video streaming and real-time object recognition. Simulation results show that the task allocation provided by Folo can be adjusted according to actual requirements of the service latency and quality, and achieves higher performance compared with naive and random fog node selection. To be more specific, Folo shortens the average service latency by up to 27% while reducing the quality loss by up to 56%.
Observational Study of a Wearable Sensor and Smartphone Application Supporting Unsupervised Exercises to Assess Pain and Stiffness

Background: Evaluation of pain and stiffness in patients with arthritis is largely based on participants retrospectively reporting their self-perceived pain/stiffness. This is subjective and may not accurately reflect the true impact of therapeutic interventions. We now have access to sensor-based systems to continuously capture objective information regarding movement and activity. Objectives: We present an observational study aimed to collect sensor data from participants monitored while performing an unsupervised version of a standard motor task, known as the Five Times Sit to Stand (5×STS) test. The first objective was to explore whether the participants would perform the test regularly in their home environment, and do so in a correct and consistent manner. The second objective was to demonstrate that the measurements collected would enable us to derive an objective signal related to morning pain and stiffness. Methods: We recruited a total of 45 participants, of whom 30 participants fulfilled pre-defined criteria for osteoarthritis, rheumatoid arthritis, or psoriatic arthritis and 15 participants were healthy volunteers. All participants wore accelerometers on their wrists, day and night for about 4 weeks. The participants were asked to perform the 5×STS test in their own home environment at the same time in the morning 3 times per week. We investigated the relationship between pain/stiffness and measurements collected during the 5×STS test by comparing the 5×STS test duration with the patient-reported outcome (PRO) questionnaires, filled in via a smartphone. Results: During the study, we successfully captured accelerometer data from each participant for a period of 4 weeks. The participants performed 56% of the prescribed 5×STS tests. We observed that different tests made by the same participants were performed with subject-specific characteristics that remained consistent throughout the study. We showed that 5×STS test duration (the time taken to complete the 5×STS test) was significantly and robustly associated with the pain and stiffness intensity reported via the PROs, particularly the questions asked in the morning. Conclusions: This study demonstrates the feasibility and usefulness of regular, sensor-based, monitored, unsupervised physical tests to objectively assess the impact of disease on function in the home environment. This approach may permit remote disease monitoring in clinical trials and support the development of novel endpoints from passively collected actigraphy data.
Distributed Multienergy Coordination of Multimicrogrids With Biogas-Solar-Wind Renewables

This paper proposes a distributed multienergy management framework for the coordinated operation of interconnected biogas-solar-wind microgrids. In this framework, each microgrid not only schedules its local hybrid biogas-solar-wind renewables for coupled multicarrier energy supplies based on the concept of energy hub but also exchanges energy with interconnected microgrids and via the transactive market. The multimicrogrid scheduling is a challenging optimization problem due to its severe constraints and strong couplings. A multimicrogrid multienergy coupling matrix is thus formulated to model and exploit the inherent biogas-solar-wind energy couplings among electricity, gas, and heat flows. Furthermore, a distributed stochastic optimal scheduling scheme with minimum information exchange overhead is proposed to dynamically optimize energy conversion and storage devices in the multimicrogrid system. The proposed method has been fully tested and benchmarked on the different scaled multimicrogrid system over a 24-h scheduling horizon. Comparative results demonstrated that the proposed approach can reduce the system operating cost and enhance the system energy-efficiency, and also confirm its scalability in solving large-scale multimicrogrid problems.