Miracle-dz/UniFAR-FTUs huggingface dataset:
Wide Bandwidth and Enhanced Gain of a Low-Profile Dipole Antenna Achieved by Integrated Suspended Metasurface

A low-profile metasurface-based dipole antenna is proposed, analyzed, and implemented for the wideband operation with improved gain. The metasurface is suspended by separating it from the ground plane with an air gap and positioning it right beneath a horizontally oriented dipole. The wide operating bandwidth is attributed to the simultaneous excitation of the transverse electric surface wave resonance of the suspended metasurface (SMS) and the adjacent metasurface-based dipole resonance. The small propagation constant of transverse magnetic surface wave in the operating band enlarges the SMS aperture along the dipole direction for higher gain compared to a conventional low-profile antenna. The proposed antenna design with an overall thickness of <inline-formula> <tex-math notation="LaTeX">$0.07\lambda $ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> is the wavelength in free space at the center operating frequency) shows the realized boresight gain of 8.5–11.5 dBi over <inline-formula> <tex-math notation="LaTeX">$\vert \text{S}_{11}\vert \le -10$ </tex-math></inline-formula> dB impedance bandwidth of 33.6% (or 4.15–5.85 GHz).
Epidemiologic associations of HPV‐positive oropharyngeal cancer and (pre)cancerous cervical lesions

Human papillomavirus (HPV)‐induced oropharyngeal squamous cell carcinoma (OPSCC) remains increasing worldwide. We aimed to investigate if the HPV‐prevalence of OPSCC in the Netherlands is rising as well, also in female patients. In addition, we evaluated the association between HPV‐positive OPSCC and suspicious Pap results of the cervix in these female patients. Patients with OPSCC treated in the period 2000–2015 at the VU University Medical Center Amsterdam, were included (n = 926). The presence of an oncogenic HPV infection was determined by p16‐immunostaining, followed by a high‐risk HPV general primer 5+/6+ DNA PCR on the p16‐immunopositive cases. A review of pathology reports in all female patients (n = 305) was undertaken to identify cytological signs of HPV‐related (pre)cancer of the cervix. In total 281 of 926 (30.3%) OPSCCs were HPV‐positive. Moreover, a significant increase in the prevalence of HPV‐positive OPSCCs was observed from 14.0% in 2000 to 48.1% in 2015 (p < 0.001). Among the female patients with an HPV‐positive OPSSC (n = 70), the results of cervical smears were available in 56 of 70 patients (80.0%). Of the female patients with HPV‐positive OPSCC, 9 of 56 (16.1%) patients had a vaginal cuff Papanicolaou (Pap) test ≥3b in their medical history compared to 7 of 168 (4.2%) in the HPV‐negative group (p = 0.003). In conclusion, a continuous increase in the HPV‐attributable fraction of OPSCC was demonstrated in the period 2000–2015 in the Amsterdam region. HPV‐positive OPSCC has a significant association with a history of suspicious Pap results of the cervix in female patients.
Performance Assessment of a Custom, Portable, and Low-Cost Brain–Computer Interface Platform

<italic>Objective:</italic> Conventional brain-computer interfaces (BCIs) are often expensive, complex to operate, and lack portability, which confines their use to laboratory settings. Portable, inexpensive BCIs can mitigate these problems, but it remains unclear whether their low-cost design compromises their performance. Therefore, we developed a portable, low-cost BCI and compared its performance to that of a conventional BCI. <italic>Methods:</italic> The BCI was assembled by integrating a custom electroencephalogram (EEG) amplifier with an open-source microcontroller and a touchscreen. The function of the amplifier was first validated against a commercial bioamplifier, followed by a head-to-head comparison between the custom BCI (using four EEG channels) and a conventional 32-channel BCI. Specifically, five able-bodied subjects were cued to alternate between hand opening/closing and remaining motionless while the BCI decoded their movement state in real time and provided visual feedback through a light emitting diode. Subjects repeated the above task for a total of 10 trials, and were unaware of which system was being used. The performance in each trial was defined as the temporal correlation between the cues and the decoded states. <italic> Results:</italic> The EEG data simultaneously acquired with the custom and commercial amplifiers were visually similar and highly correlated (<inline-formula><tex-math notation="LaTeX">$\rho$</tex-math></inline-formula> = 0.79). The decoding performances of the custom and conventional BCIs averaged across trials and subjects were 0.70 <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 0.12 and 0.68 <inline-formula> <tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 0.10, respectively, and were not significantly different. <italic>Conclusion:</italic> The performance of our portable, low-cost BCI is comparable to that of the conventional BCIs. <italic>Significance:</italic> Platforms, such as the one developed here, are suitable for BCI applications outside of a laboratory.
LR3: link reliable reactive routing protocol for wireless sensor networks

Existing reliable-oriented routing protocols computes link reliability based on the packet reception ratio and neglects the impact of various parameters such as noise, shadowing, battery-lifespan, uncertainty and geographic locations. In this paper, we propose a Link Reliable Reactive Routing(LR3) protocol for WSNs to accomplish reliability and resilience to out-of-order transmission and path diversity at each hop. The log-normal shadowing model is used to estimate link reliability and a back-off scheme is used to determine delay. A new cost function is estimated to find forwarding nodes on the mentor path that includes link reliability, delay, status of queue, and packet advancement at the forwarding node. LR3 is simulated using NS-2 and the results show that it outperforms other reactive routing protocols in terms of packet delivery ratio, latency, link reliability and data transmission cost[1][2].
A hierachical configuration system for a massively parallel neural hardware platform

Simulation of large networks of neurons is a powerful and increasingly prominent methodology for investigate brain functions and structures. Dedicated parallel hardware is a natural candidate for simulating the dynamic activity of many non-linear units communicating asynchronously. It is only scientifically useful, however, if the simulation tools can be configured and run easily and quickly. We present a method to map network models to computational nodes on the SpiNNaker system, a programmable parallel neurally-inspired hardware architecture, by exploiting the hierarchies built in the model. This PArtitioning and Configuration MANager (PACMAN) system supports arbitrary network topologies and arbitrary membrane potential and synapse dynamics, and (most importantly) decouples the model from the device, allowing a variety of languages (PyNN, Nengo, etc.) to drive the simulation hardware. Model representation operates on a Population/Projection level rather than a single-neuron and connection level, exploiting hierarchical properties to lower the complexity of allocating resources and mapping the model onto the system. PACMAN can be thus be used to generate structures coming from different models and front-ends, either with a host-based process, or by parallelising it on the SpiNNaker machine itself to speed up the generation process greatly. We describe the approach with a first implementation of the framework used to configure the current generation of SpiNNaker machines and present results from a set of key benchmarks. The system allows researchers to exploit dedicated simulation hardware which may otherwise be difficult to program. In effect, PACMAN provides automated hardware acceleration for some commonly used network simulators while also pointing towards the advantages of hierarchical configuration for large, domain-specific hardware systems.
The Prolyl Isomerase Pin1 Is a Novel Target of 6,7,4′-Trihydroxyisoflavone for Suppressing Esophageal Cancer Growth

Intake of soy isoflavones is inversely associated with the risk of esophageal cancer. Numerous experimental results have supported the anticancer activity of soy isoflavones. This study aimed to determine the anti-esophageal cancer activity of 6,7,4′-trihydroxyisoflavone (6,7,4′-THIF), a major metabolite of daidzein, which is readily metabolized in the human body. Notably, 6,7,4′-THIF inhibited proliferation and increased apoptosis of esophageal cancer cells. On the basis of a virtual screening analysis, Pin1 was identified as a target protein of 6,7,4′-THIF. Pull-down assay results using 6,7,4′-THIF Sepharose 4B beads showed a direct interaction between 6,7,4′-THIF and the Pin1 protein. Pin1 is a critical therapeutic and preventive target in esophageal cancer because of its positive regulation of β-catenin and cyclin D1. The 6,7,4′-THIF compound simultaneously reduced Pin1 isomerase activity and the downstream activation targets of Pin1. The specific inhibitory activity of 6,7,4′-THIF was analyzed using Neu/Pin1 wild-type (WT) and Neu/Pin1 knockout (KO) MEFs. 6,7,4′-THIF effected Neu/Pin1 WT MEFs, but not Neu/Pin1 KO MEFs. Furthermore, the results of a xenograft assay using Neu/Pin1 WT and KO MEFs were similar to those obtained from the in vitro assay. Overall, we found that 6,7,4′-THIF specifically reduced Pin1 activity in esophageal cancer models. Importantly, 6,7,4′-THIF directly bound to Pin1 but not FKBP or cyclophilin A, the same family of proteins. Because Pin1 acts like an oncogene by modulating various carcinogenesis-related proteins, this study might at least partially explain the underlying mechanism(s) of the anti-esophageal cancer effects of soy isoflavones. Cancer Prev Res; 10(5); 308–18. ©2017 AACR.
Impact of respiratory motion on variable relative biological effectiveness in 4D-dose distributions of proton therapy

Abstract Background: Organ motion during radiation therapy with scanned protons leads to deviations between the planned and the delivered physical dose. Using a constant relative biological effectiveness (RBE) of 1.1 linearly maps these deviations into RBE-weighted dose. However, a constant value cannot account for potential nonlinear variations in RBE suggested by variable RBE models. Here, we study the impact of motion on recalculations of RBE-weighted dose distributions using a phenomenological variable RBE model. Material and methods: 4D-dose calculation including variable RBE was implemented in the open source treatment planning toolkit matRad. Four scenarios were compared for one field and two field proton treatments for a liver cancer patient assuming (α∕β)x = 2 Gy and (α∕β)x = 10 Gy: (A) the optimized static dose distribution with constant RBE, (B) a static recalculation with variable RBE, (C) a 4D-dose recalculation with constant RBE and (D) a 4D-dose recalculation with variable RBE. For (B) and (D), the variable RBE was calculated by the model proposed by McNamara. For (C), the physical dose was accumulated with direct dose mapping; for (D), dose-weighted radio-sensitivity parameters of the linear quadratic model were accumulated to model synergistic irradiation effects on RBE. Results: Dose recalculation with variable RBE led to an elevated biological dose at the end of the proton field, while 4D-dose recalculation exhibited random deviations everywhere in the radiation field depending on the interplay of beam delivery and organ motion. For a single beam treatment assuming (α∕β)x = 2 Gy, D95% was 1.98 Gy (RBE) (A), 2.15 Gy (RBE) (B), 1.81 Gy (RBE) (C) and 1.98 Gy (RBE) (D). The homogeneity index was 1.04 (A), 1.08 (B), 1.23 (C) and 1.25 (D). Conclusion: For the studied liver case, intrafractional motion did not reduce the modulation of the RBE-weighted dose postulated by variable RBE models for proton treatments.
Outcomes of a multifaceted physical activity regimen as part of a diabetes self-management intervention

Background: Physical activity (PA) is important for management of diabetes, yet practical interventions that achieve sustained behavior change are rare.Purpose: The goals of this research were to evaluate the effectiveness of a multifaceted PA intervention for people with type 2 diabetes that emphasized participant choice in activity selection. Baseline activity patterns were examined to determine whether they predicted changes in PA at 2 months.Methods: Three hundred thirty-five participants were recruited from 42 primary care physicians and then randomized to either a computer-assisted, tailored self-management intervention (N = 174) or health risk appraisal with feedback control (N = 161). Primary outcome measures included the Community Healthy Activities Model Program for Seniors Questionnaire, diet, and psychosocial assessments at baseline and 2 months.Results: For 301 participants who completed the 2-month follow-up, the intervention significantly improved all PA (p < .01) and moderate PA (metabolic equivalents > 3.0, p < .01) relative to controls. Baseline cluster analyses grouped participant activity patterns into three categories. At 2 months, cluster assignment differentially predicted change in calories expended in moderate, rote, sport, and lifestyle PA.Conclusions: A computer-assisted, multifaceted approach to PA demonstrated improvement after 2 months. The results suggest that individuals are capable of adjusting their activity patterns to maximize their PA.
Deep belief network optimization in speech recognition

The paradigm theory of neural network (NN) is expressing from biological of human brain function system. In this study, we deal with the deep belief network (DBN) model using the speech as natural language processing for training the system. The characteristic of a deep belief network is similar to a neural network. Whereas the performance of a neural network depends on the structure itself and it is suitable to select the model and size of the network for the data to handle. In the occasion, deep belief network has the advantage in speech recognition it generates the feature learning with a subsequent stage of supervised learning, once the network initialized, the first way using unsupervised and then fine-tuned with the labeled data to train some neuron in initial weight vectors. Deep belief network (DBN) have many nonlinear hidden layers to produce posterior of probabilities that take several frames of coefficient as input. This paper shows the good performance improvement of modularity in DBN as combination technique of computation.
Using Propensity Score Methods To Assess Causal Effects of Mothers' Dieting Behavior on Daughters' Early Dieting Behavior.

BACKGROUND A high prevalence of dieting has been reported among preadolescent females. It is important to understand factors influencing the emergence of dieting because dieting is associated with increased likelihood of overeating, greater weight gain over time, and other chronic health problems. Previous studies suggest that mothers' own dieting behavior influences their daughters' dieting (i.e., modeling). Because it is not possible to randomly assign girls to a mother who is dieting versus not dieting, causal inference regarding the effects of mothers' modeling behaviors on daughters' dieting is not straightforward. METHODS In an observational study, data were collected on four occasions of measurement across a 6-year period, with 2-year intervals between assessments on 181 girls and their parents. Propensity score methods were used to estimate the causal effects of mothers' dieting on the emergence of daughters' dieting between ages 7 and 11, examining the moderating effect of weight status. RESULTS Girls whose mothers were currently dieting were significantly more likely to diet before age 11 than those whose mothers were not currently dieting, and this effect did not vary by girls' or mothers' weight status. CONCLUSIONS We conclude by discussing the implications of the effects of mothers' dieting on daughters' early dieting as well as the potential of propensity score methods in the field of obesity compared with traditional methodology such as regression analysis.
What accounts for poor functioning in people with schizophrenia: a re-evaluation of the contributions of neurocognitive v. attitudinal and motivational factors

Abstract Background Neurocognitive deficits are often seen as core features of schizophrenia, and as primary determinants of poor functioning. Yet, our clinical observations suggest that individuals who score within the impaired range on standardized tests can reliably perform better in complex real-world situations, especially when performance is embedded within a positive socio-affective context. Methods We analyzed literature on the influence of non-neurocognitive factors on test performance in order to clarify their contributions. Results We identified seven non-neurocognitive factors that significantly contribute to neurocognitive test performance: avolition, dysfunctional attitudes, effort, stress, negative emotions, asociality, and disorganized symptoms. We then proposed an alternative model based on dysfunctional (e.g. defeatist) attitudes and their consequences for motivation and sustained task engagement. We demonstrated that these factors account for substantial variance in negative symptoms, neurocognitive test performance, and functional outcomes. We then demonstrated that recovery-oriented cognitive therapy – which is derived from this alternative model and primarily targets dysfunctional beliefs – has been successful in the treatment of low functioning individuals with schizophrenia. Conclusion The contributions of neurocognitive impairments to poor real-world functioning in people with schizophrenia may be overstated in the literature, and may even be limited relative to non-neurocognitive factors. We offer suggestions for further research to more precisely quantify the contributions of attitudinal/motivation v. neurocognitive factors in schizophrenia.
Multi-scale mutual feature convolutional neural network for depth image denoise and enhancement

RGB-D images captured by consumer camera can provide pair-wise color and depth information but depth image usually contains strong noise and large holes. Due to different modalities of RGB-D, the intensity-guided depth enhancement easily causes artifacts and blurred edges. To solve this problem, we propose the multi-scale mutual feature convolutional neural network (MSMF-CNN) to learn essential mutual features of RGB-D. This end-to-end framework has two stages, i.e., mutual feature learning and depth regeneration. Firstly, we design two parallel separated subnetworks as intensity and depth mutual feature generator. Specifically, we design a multi-scale mutual feature generator to enforce the depth mutual feature learning and incorporate various structure characteristic. Secondly, depth transformation sub-network combines two mutual features to recover clean depth image. We tested our model on two well-known datasets in terms of PSNR, visual effect and computing speed. Compared with state-of-art algorithms, our method provides better performance of all three criteria.
Socioeconomic Inequalities in Green Space Quality and Accessibility—Evidence from a Southern European City

Background: The provision of green spaces is an important health promotion strategy to encourage physical activity and to improve population health. Green space provision has to be based on the principle of equity. This study investigated the presence of socioeconomic inequalities in geographic accessibility and quality of green spaces across Porto neighbourhoods (Portugal). Methods: Accessibility was evaluated using a Geographic Information System and all the green spaces were audited using the Public Open Space Tool. Kendall’s tau-b correlation coefficients and ordinal regression were used to test whether socioeconomic differences in green space quality and accessibility were statistically significant. Results: Although the majority of the neighbourhoods had an accessible green space, mean distance to green space increased with neighbourhood deprivation. Additionally, green spaces in the more deprived neighbourhoods presented significantly more safety concerns, signs of damage, lack of equipment to engage in active leisure activities, and had significantly less amenities such as seating, toilets, cafés, etc. Conclusions: Residents from low socioeconomic positions seem to suffer from a double jeopardy; they lack both individual and community resources. Our results have important planning implications and might contribute to understanding why deprived communities have lower physical activity levels and poorer health.
A Comprehensive, Multi-Scale Dynamical Model of ErbB Receptor Signal Transduction in Human Mammary Epithelial Cells

The non-receptor tyrosine kinase Src and receptor tyrosine kinase epidermal growth factor receptor (EGFR/ErbB1) have been established as collaborators in cellular signaling and their combined dysregulation plays key roles in human cancers, including breast cancer. In part due to the complexity of the biochemical network associated with the regulation of these proteins as well as their cellular functions, the role of Src in EGFR regulation remains unclear. Herein we present a new comprehensive, multi-scale dynamical model of ErbB receptor signal transduction in human mammary epithelial cells. This model, constructed manually from published biochemical literature, consists of 245 nodes representing proteins and their post-translational modifications sites, and over 1,000 biochemical interactions. Using computer simulations of the model, we find it is able to reproduce a number of cellular phenomena. Furthermore, the model predicts that overexpression of Src results in increased endocytosis of EGFR in the absence/low amount of the epidermal growth factor (EGF). Our subsequent laboratory experiments also suggest increased internalization of EGFR upon Src overexpression under EGF-deprived conditions, further supporting this model-generated hypothesis.
Rho-Kinase Activation in Leukocytes Plays a Pivotal Role in Myocardial Ischemia/Reperfusion Injury

The Rho/Rho-kinase pathway plays an important role in many cardiovascular diseases such as hypertension, atherosclerosis, heart failure, and myocardial infarction. Although previous studies have shown that Rho-kinase inhibitors reduce ischemia/reperfusion (I/R) injury and cytokine production, the role of Rho-kinase in leukocytes during I/R injury is not well understood. Mice were subjected to 30-min ischemia and reperfusion. Rho-kinase activity was significantly greater in leukocytes subjected to myocardial I/R compared to the sham-operated mice. Administration of fasudil, a Rho-kinase inhibitor, significantly reduced the I/R-induced expression of the proinflammatory cytokines interleukin (IL)-6, C-C motif chemoattractant ligand 2 (CCL2), and tumor necrosis factor (TNF)-α, in leukocytes, compared with saline as the vehicle. Furthermore, fasudil decreased I/R-induced myocardial infarction/area at risk (IA) and I/R-induced leukocyte infiltration in the myocardium. Interestingly, IA in fasudil-administered mice with leukocyte depletion was similar to that in fasudil-administered mice. I/R also resulted in remarkable increases in the mRNA expression levels of the proinflammatory cytokines TNF-α, IL-6, and CCL2 in the heart. Inhibition of Rho-kinase activation in leukocytes has an important role in fasudil-induced cardioprotective effects. Hence, inhibition of Rho-kinase may be an additional therapeutic intervention for the treatment of acute coronary syndrome.