Miracle-dz/UniFAR-FTUs huggingface dataset:
T2, T1ρ and TRAFF4 detect early regenerative changes in mouse ischemic skeletal muscle

The identification of areas with regenerative potential in ischemic tissues would allow the targeting of treatments supporting tissue recovery. The regeneration process involves the activation of several cellular and molecular responses which could be detected using magnetic resonance imaging (MRI). However, to date, magnetic resonance (MR) relaxation parameters have received little attention in the diagnosis and follow‐up of limb ischemia. The purpose of this study was to evaluate the feasibility of different MRI relaxation and diffusion tensor imaging parameters in the detection of areas showing early signs of regeneration in ischemic mouse skeletal muscles. T2 and T1ρ relaxation time constants, together with TRAFFn, T1 and diffusion tensor imaging, were evaluated to differentiate areas of regeneration in a mouse hind limb ischemia model before and 0, 1, 4, 7, 14 and 30 days after ischemia. All the measured relaxation times were longer in the areas of early regeneration compared with normal muscle tissue. The relaxation times increased after ischemia in the ischemic muscles, reaching a maximum at 4–7 days after occlusion, coinciding with the appearance of early signs of regeneration. Fractional anisotropy decreased significantly (p < 0.05) on days 1–4, whereas mean diffusivity, λ1 and λ2 decreased later, starting at day 7 after ischemia compared with the pre‐operational time point. The percentages of areas with different tissue morphologies were determined based on histological analysis of the ischemic muscle cross‐sections, and correlations between the percentages obtained and different relaxation times were calculated. The highest correlation between relaxation times and histology was achieved with T2, T1ρ and TRAFF4 (R2 = 0.96, R2 = 0.92 and R2 = 0.84, respectively, p < 0.01). Early regenerative changes were visible using T2, T1ρ and TRAFF4 MR relaxation time constants in skeletal muscle after ischemia. These markers could potentially be used for the identification of targets for therapies supporting muscle regeneration after ischemic injury.
Assessing the Quality of Mobile Exercise Apps Based on the American College of Sports Medicine Guidelines: A Reliable and Valid Scoring Instrument

Background Regular physical activity can not only help with weight management, but also lower cardiovascular risks, cancer rates, and chronic disease burden. Yet, only approximately 20% of Americans currently meet the physical activity guidelines recommended by the US Department of Health and Human Services. With the rapid development of mobile technologies, mobile apps have the potential to improve participation rates in exercise programs, particularly if they are evidence-based and are of sufficient content quality. Objective The goal of this study was to develop and test an instrument, which was designed to score the content quality of exercise program apps with respect to the exercise guidelines set forth by the American College of Sports Medicine (ACSM). Methods We conducted two focus groups (N=14) to elicit input for developing a preliminary 27-item scoring instruments based on the ACSM exercise prescription guidelines. Three reviewers who were no sports medicine experts independently scored 28 exercise program apps using the instrument. Inter- and intra-rater reliability was assessed among the 3 reviewers. An expert reviewer, a Fellow of the ACSM, also scored the 28 apps to create criterion scores. Criterion validity was assessed by comparing nonexpert reviewers’ scores to the criterion scores. Results Overall, inter- and intra-rater reliability was high with most coefficients being greater than . 7. Inter-rater reliability coefficients ranged from . 59 to . 99, and intra-rater reliability coefficients ranged from . 47 to 1.00. All reliability coefficients were statistically significant. Criterion validity was found to be excellent, with the weighted kappa statistics ranging from . 67 to . 99, indicating a substantial agreement between the scores of expert and nonexpert reviewers. Finally, all apps scored poorly against the ACSM exercise prescription guidelines. None of the apps received a score greater than 35, out of a possible maximal score of 70. Conclusions We have developed and presented valid and reliable scoring instruments for exercise program apps. Our instrument may be useful for consumers and health care providers who are looking for apps that provide safe, progressive general exercise programs for health and fitness.
Resource Efficiency: A New Paradigm on Energy Efficiency and Spectral Efficiency Tradeoff

Spectral efficiency (SE) and energy efficiency (EE) are the main metrics for designing wireless networks. Rather than focusing on either SE or EE separately, recent works have focused on the relationship between EE and SE and provided good insight into the joint EE-SE tradeoff. However, such works have assumed that the bandwidth was fully occupied regardless of the transmission requirements and therefore are only valid for this type of scenario. In this paper, we propose a new paradigm for EE-SE tradeoff, namely the resource efficiency (RE) for orthogonal frequency division multiple access (OFDMA) cellular network in which we take into consideration different transmission-bandwidth requirements. We analyse the properties of the proposed RE and prove that it is capable of exploiting the tradeoff between EE and SE by balancing consumption power and occupied bandwidth; hence simultaneously optimizing both EE and SE. We then formulate the generalized RE optimization problem with guaranteed quality of service (QoS) and provide a gradient based optimal power adaptation scheme to solve it. We also provide an upper bound near optimal method to jointly solve the optimization problem. Furthermore, a low-complexity suboptimal algorithm based on a uniform power allocation scheme is proposed to reduce the complexity. Numerical results confirm the analytical findings and demonstrate the effectiveness of the proposed resource allocation schemes for efficient resource usage.
Recent advances on uric acid transporters

Uric acid is the product of purine metabolism and its increased levels result in hyperuricemia. A number of epidemiological reports link hyperuricemia with multiple disorders, such as kidney diseases, cardiovascular diseases and diabetes. Recent studies also showed that expression and functional changes of urate transporters are associated with hyperuricemia. Uric acid transporters are divided into two categories: urate reabsorption transporters, including urate anion transporter 1 (URAT1), organic anion transporter 4 (OAT4) and glucose transporter 9 (GLUT9), and urate excretion transporetrs, including OAT1, OAT3, urate transporter (UAT), multidrug resistance protein 4 (MRP4/ABCC4), ABCG-2 and sodium-dependent phosphate transport protein. In the kidney, uric acid transporters decrease the reabsorption of urate and increase its secretion. These transporters’ dysfunction would lead to hyperuricemia. As the function of urate transporters is important to control the level of serum uric acid, studies on the functional role of uric acid transporter may provide a new strategy to treat hyperuricemia associated diseases, such as gout, chronic kidney disease, hyperlipidemia, hypertension, coronary heart disease, diabetes and other disorders. This review article summarizes the physiology of urate reabsorption and excretion transporters and highlights the recent advances on their roles in hyperuricemia and various diseases.
Continuous reinforcement learning from human demonstrations with integrated experience replay for autonomous driving

As a promising subfield of machine learning, Reinforcement Learning (RL) has drawn increasing attention among the academia as well as the public. However, the practical application of RL is still restricted by a variety of reasons. The two most significant challenges of RL are the large exploration domain and the difficulty to converge. Integrating RL with human expertise is technically an interesting way to accelerate the exploration and increase the stability. In this work, we propose a continuous reinforcement learning method which integrates Deep Deterministic Policy Gradient (DDPG) with human demonstrations. The proposed method uses a combined loss function for updating the actor and critic networks. In addition, the experience replay buffer is also drawn from different transition data samples to make the learning more stable. The proposed method is tested with a popular RL task, i.e. the autonomous driving, by simulations with TORCS environment. Experimental results not only show the effectiveness of our method in improving the learning stability, but also manifest the potential capability of our method in mastering human preferences.
Association of Body Mass Index with Suicide Behaviors, Perceived Stress, and Life Dissatisfaction in the Korean General Population

Objective The purpose of the present study was to investigate the association between Body Mass Index, suicide, perceived stress, and life dissatisfaction in a general population sample of Korean adults. Methods A total of 6,022 nationally representative adults aged 18 to 74 were selected using a multistage cross-sectional cluster sampling method. Questionnaires regarding suicide behaviors, perceived stress, and life satisfaction were completed by the participants. They also reported their heights and weights, which were used to calculate BMI. Psychiatric disorders were diagnosed according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, using the Korean version of the Composite International Diagnostic Interview. Results The results showed that being underweight was associated with higher risk for suicide ideation [odds ratio (OR), 1.6; 95% confidence interval (Cl), 1.18–2.05] and suicide attempt (OR, 2.0, 95% Cl, 1.23–3.31). Likewise, obesity also increased the risk of suicide ideation (OR, 1.3; 95% Cl, 1.11–1.56) although not suicide attempt. Furthermore, underweight individuals were more likely to report severe level of perceived stress (OR, 1.7; 95% Cl, 1.26–2.17) and life dissatisfaction (OR, 1.3; 95% Cl, 1.07–1.68). All of the results remained significant after adjusting for age, gender, education, and psychiatric illnesses. Conclusion This study found that being underweight is a significant risk factor for suicide and poor subjective wellbeing in Korea. It suggests that BMI status may be an important modifiable factor for improving mental health in Korea.
A VLC and IMU integration indoor positioning algorithm with weighted unscented Kalman filter

In this paper, an indoor positioning algorithm based on visible light communication (VLC), combined with inertial measurement unit (IMU) is proposed to achieve the target of high-precision positioning in the indoor environment. A linearization method is proposed to estimate the quaternion errors and velocity errors with adaptive Kalman filtering (AKF). Then, the tilt angle is adopted in VLC received signal strength indicator (RSSI) propagation model to reduce the positioning errors, which is provided by IMU data. Furthermore, an indoor positioning algorithm based on VLC and IMU integration using weighted unscented Kalman filter (WUKF) is developed to obtain the final positioning solutions. Simulation results show that the proposed VLC and IMU integration algorithm can reduce the positioning solution errors to less than 0.23 m.
Image super-resolution using siamese-based neural network

We propose a deep learning method for single image super-resolution using a siamese-based neural network. Our method directly learns an end-to-end mapping between high-resolution image and low-resolution image. Siamese structure is usually used in patch matching and object tracking. This is a try at image super-resolution. We propose that it also perform well on image super-resolution. Our structure, consist of a deep convolutional network(e.g., SRCNN structure) and a three “tower” feature filter with shared parameters. The deep convolutional network is used to get the final model for generating the high-resolution image and the three “tower” filter is used to extract features from the low-resolution patches, generated high-resolution patches and original high-resolution patches. We further explore weights settings to achieve a satisfactory performance. We also use standard images test our model and compare it to the traditional method.
Autism: the aetiology, management and implications for treatment modalities from the dental perspective.

UNLABELLED Autism is defined as a rare and severe psychiatric disorder of childhood. It is marked by severe difficulties in communicating, and forming relationships with other people, in developing language, repetitive and limited patterns of behaviour and obsessive resistance to small changes in familiar surroundings. Hence, affected children offer a special challenge to the practising dentist. To meet this challenge, it is necessary to understand the condition and how to manage its varying presentations in different individuals. The purpose of this article is to review the literature on the topic of autism with an emphasis on the dental perspective. CLINICAL RELEVANCE A better understanding of the effects of autism on the behaviour of an affected individual provides the dental practitioner with the opportunity to deliver oral healthcare in an empathetic and appropriate manner.
Navigating the Dangerous Terrain of Moral Distress: Understanding Response Patterns in the NICU

Moral distress is a well-recognized and ubiquitous aspect of health care professional practice in the neonatal intensive care unit (NICU) context. We used interpretive description methodology to guide a critical exploration of the dynamics of moral distress experience as reflected in the accounts of 28 health care professionals working in this setting. We learned about the kinds of clinical scenarios which triggered distressing experiences, and that the organizational and relational context of clinical work constituted a complex and dynamic working environment that profoundly affected both the individual and the collective experiences with moral distress in these situations. These findings shed light on possibilities for supporting NICU practitioners and developing the collaborative team cultures that may reduce the risk of unresolved effects of moral distress to the benefit of patients as well as the professionals who care for them.
What Can Be Learned from Recent New Drug Applications? A Systematic Review of Drug Interaction Data for Drugs Approved by the US FDA in 2015

As a follow up to previous reviews, the aim of the present analysis was to systematically examine all drug metabolism, transport, pharmacokinetics (PK), and drug-drug interaction (DDI) data available in the 33 new drug applications (NDAs) approved by the Food and Drug Administration (FDA) in 2015, using the University of Washington Drug Interaction Database, and to highlight the significant findings. In vitro, a majority of the new molecular entities (NMEs) were found to be substrates or inhibitors/inducers of at least one drug metabolizing enzyme or transporter. In vivo, 95 clinical DDI studies displayed positive PK interactions, with an area under the curve (AUC) ratio ≥ 1.25 for inhibition or ≤ 0.8 for induction. When NMEs were considered as victim drugs, 21 NMEs had at least one positive clinical DDI, with three NMEs shown to be sensitive substrates of CYP3A (AUC ratio ≥ 5 when coadministered with strong inhibitors): cobimetinib, isavuconazole (the active metabolite of prodrug isavuconazonium sulfate), and ivabradine. As perpetrators, nine NMEs showed positive inhibition and three NMEs showed positive induction, with some of these interactions involving both enzymes and transporters. The most significant changes for inhibition and induction were observed with rolapitant, a moderate inhibitor of CYP2D6 and lumacaftor, a strong inducer of CYP3A. Physiologically based pharmacokinetics simulations and pharmacogenetics studies were used for six and eight NMEs, respectively, to inform dosing recommendations. The effects of hepatic or renal impairment on the drugs’ PK were also evaluated to support drug administration in these specific populations.
Violence, Homelessness, and HIV Risk Among Crack-Using African-American Women

This study compares the characteristics of out-of-treatment, homeless, crack-using African-American women with those who are not homeless to determine what risks and protective factors differentiate the two groups. From 1999 to 2001, 683 out-of-treatment, African-American crack-using women (of whom 219 were categorized as homeless) were interviewed and serologically tested. Risk factors that were examined include adverse childhood experiences, psychological distress, physical health, violence and victimization, drug use, and risky sex behaviors. Protective factors that were examined include marital status, education, public assistance, and the responsibility of caring for children. Overall, both groups of women started crack use in their mid-twenties and started drug use with alcohol in their teenage years, though differed significantly on each risk factor examined. Logistic regression analysis found that variables associated with increased odds of being homeless are physical abuse before age 18, crack runs greater than 24 hours, income less than $500 in the last 30 days, depression, and current cigarette smoking. Protective factors found are marital status, living with children under 18, having had a physical in the past year, and receiving money from welfare in the last 30 days. Being sexually assaulted in the past 90 days was marginally associated with homelessness in the model. These findings, specific to crack-using African-American women, suggest that not only do these women overall report painful histories and currently stressful lives, but homeless women are more likely than women who are not homeless to have experienced childhood abuse and are more involved with drug use. Interventions designed for these women need to consider gender, cultural, and contextual issues that not only incorporate aspects of risk reduction related to violence, alcohol use, and comorbid conditions, but also linkages that will address housing issues, education, and skills for independence.
Prediction models for progression of non‐muscle‐invasive bladder cancer: A review

An accurate prediction of progression is critically important in the management of non‐muscle‐invasive bladder cancer. At present, three risk models are widely known for prediction of the risk of tumor recurrence and progression of non‐muscle‐invasive bladder cancer: the European Organization for Research and Treatment of Cancer, Club Urológico Español de Tratamiento Oncológico, and new European Organization for Research and Treatment of Cancer models. Bladder neck involvement has been shown to be one of the significant predictors for progression in non‐muscle‐invasive bladder cancer, and a new scoring model (Tokyo Medical and Dental University model) consisting of bladder neck involvement, tumor grade, and stage has been developed and externally validated. However, the predictive abilities of these models are still unsatisfactory, and more precise models are necessary for accurate individual prediction of prognosis. Until now, time‐fixed analysis has been used for most studies predicting the prognosis and outcome of non‐muscle‐invasive bladder cancer patients. In order to predict progression more precisely, time‐dependent models should be developed using multiple‐event analytical techniques, as non‐muscle‐invasive bladder cancer often progresses to muscle‐invasive bladder cancer after multiple recurrences and changes in tumor characteristics over a long natural history. Integration of molecular markers is also a promising approach. A validated model that accurately predicts the risk of progression would help urologists and patients decide whether and when to choose radical cystectomy on an individual basis.
Perturbation Biology: Inferring Signaling Networks in Cellular Systems

We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology.
A briefly argued case that mitochondria and plastids are descendants of endosymbionts, but that the nuclear compartment is not

Recent findings are summarized in support of the view that mitochondria (including hydrogenosomes) and plastids (including complex ones) descend from symbiotic associations of once free–living organisms. The reasoning behind endosymbiotic hypotheses stems from a comparison of biochemistry and physiology in organelles with that in free–living cells; their strength is shown to lie in the specific testable predictions they generate about expected similarity patterns among genes. Although disdained for many decades, endosymbiotic hypotheses have gradually become very popular. In the wake of that popularity, endosymbiotic hypotheses have been formulated to explain the origins of eukaryotic cell compartments and structures that have no biochemical similarity to free–living cells. In particular, it has become fashionable in recent years to entertain the century–old notion that the nucleus might also descend from an endosymbiotic bacterium. A critique of that hypothesis is formulated and a simple alternative to it is outlined, which derives the nuclear compartment in a mitochondrion–bearing cell.
Practical incremental network coding for multimedia content delivery

Random linear network coding is a promising solution to improve network capacity by allowing intermediate node that combines incoming packets into network coded packets. Nevertheless, there are two main drawbacks: rank deficiency and processing delay. The first drawback can be solved by performing systematic network coding, in which sender transmits all uncoded packets in the first phase and coded packets in the second phase. The processing delay still remains in practical communication system and multimedia content delivery. In this paper, we propose a practical incremental network coding for application of multimedia content delivery over multi-hop network. We implement the proposed scheme using Raspberry Pi as transmission node and evaluate performance in terms of throughput and the attainable video quality (i.e., peak signal-to-noise ratio). The results demonstrate that our proposed incremental network coding outperforms conventional network coding schemes.