Miracle-dz/UniFAR-FTUs huggingface dataset:
An eHealth Intervention to Increase Physical Activity and Healthy Eating in Older Adult Cancer Survivors: Summative Evaluation Results

Background A healthy lifestyle is associated with improved quality of life among cancer survivors, yet adherence to health behavior recommendations is low. Objective This pilot trial developed and tested the feasibility of a tailored eHealth program to increase fruit and vegetable consumption and physical activity among older, long-term cancer survivors. Methods American Cancer Society (ACS) guidelines for cancer survivors were translated into an interactive, tailored health behavior program on the basis of Social Cognitive Theory. Patients (N=86) with a history of breast (n=83) or prostate cancer (n=3) and less than 5 years from active treatment were randomized 1:1 to receive either provider advice, brief counseling, and the eHealth program (intervention) or advice and counseling alone (control). Primary outcomes were self-reported fruit and vegetable intake and physical activity. Results About half (52.7%, 86/163) of the eligible patients consented to participate. The most common refusal reasons were lack of perceived time for the study (32/163) and lack of interest in changing health behaviors (29/163). Furthermore, 72% (23/32) of the intervention group reported using the program and most would recommend it to others (56%, 14/25). Qualitative results indicated that the intervention was highly acceptable for survivors. For behavioral outcomes, the intervention group reported increased fruit and vegetable consumption. Self-reported physical activity declined in both groups. Conclusions The brief intervention showed promising results for increasing fruit and vegetable intake. Results and participant feedback suggest that providing the intervention in a mobile format with greater frequency of contact and more indepth information would strengthen treatment effects.
Screening effective differential expression genes for hepatic carcinoma with metastasis in the peripheral blood mononuclear cells by RNA-seq

Tumor metastasis is a multistep process involving a number of genetic alterations so that the genetic diagnosis is got increasingly attentions today. The aim of this study was to use RNA-seq to screen the effective differential expression genes in the peripheral blood mononuclear cells for the hepatic carcinoma with metastasis. The results showed that hepatic carcinoma samples gathered according to different metastasis. CCL3, CCL3L1, JUN, IL8, and IL1B were identified in inflammation mediated by chemokine and cytokine signaling pathway (P00031) in the hepatic carcinoma samples with metastasis, and subsequently confirmed by quantitative real-time polymerase chain reaction. In conclusions, CCL3, CCL3L1, JUN, IL8, and IL1B have the potential to be considered as candidates for future molecular diagnosis of the hepatic carcinoma with metastasis. This work may provide us with new visions into the metastasis process and potential efficient clinical diagnosis in the future.
An Automatic and Novel SAR Image Registration Algorithm: A Case Study of the Chinese GF-3 Satellite

The Chinese GF-3 satellite launched in August 2016 is a Synthetic Aperture Radar (SAR) satellite that has the largest number of imaging modes in the world. It achieves a free switch in the spotlight, stripmap, scanSAR, wave, global observation and other imaging modes. In order to further utilize GF-3 SAR images, an automatic and fast image registration procedure needs to be done. In this paper, we propose a novel image registration technique for GF-3 images of different imaging modes. The proposed algorithm consists of two stages: coarse registration and fine registration. In the first stage, we combine an adaptive sampling method with the SAR-SIFT algorithm to efficiently eliminate obvious translation, rotation and scale differences between the reference and sensed images. In the second stage, uniformly-distributed control points are extracted, then the fast normalized cross-correlation of an improved phase congruency model is utilized as a new similarity metric to match the reference image and the coarse-registered image in a local search region. Moreover, a selection strategy is used to remove outliers. Experimental results on several GF-3 SAR images of different imaging modes show that the proposed algorithm gives a robust, efficient and precise registration performance, compared with other state-of-the-art algorithms for SAR image registration.
A novel image quality index for stereo image

Stereo image quality assessment is provided as computa-tional methods to automatically assess the quality of stereo im-ages in perceptually consistent manner. In this paper, a novel full reference stereo image quality index with highlighting double-channel of binocular combination is introduced to assess stereo image quality. First, considering the visual sensitivity of stimu-lus at different spatial frequencies is different, both reference and distortion stereo pairs are filtered by a CSF (contrast sensitivity function). Second, in order to mimic the binocular fusion me-chanism of HVS (human visual system), we extract the binocular energy response of both reference and distortion stereo pairs based on a magnitude response of Gabor filtering measure. Third, since the HVS is enormously complex, especially the binocular rivalry which caused by the asymmetric distortions of stereo pair, we propose to fix the issue of dominant view by using a block-based contrast measure. Finally, a single estimate of overall per-ceived quality of stereo images is obtained by the qualities pool-ing of double-channel. Experimental results show that the pro-posed metric achieves significantly higher consistency with sub-jective scores.
Using spatial hints to improve policy reuse in a reinforcement learning agent

We study the problem of knowledge reuse by a reinforcement learning agent. We are interested in how an agent can exploit policies that were learned in the past to learn a new task more efficiently in the present. Our approach is to elicit spatial hints from an expert suggesting the world states in which each existing policy should be more relevant to the new task. By using these hints with domain exploration, the agent is able to detect those portions of existing policies that are beneficial to the new task, therefore learning a new policy more efficiently. We call our approach Spatial Hints Policy Reuse (SHPR). Experiments demonstrate the effectiveness and robustness of our method. Our results encourage further study investigating how much more efficacy can be gained from the elicitation of very simple advice from humans.
Low-dimensional spike rate models derived from networks of adaptive integrate-and-fire neurons: Comparison and implementation

The spiking activity of single neurons can be well described by a nonlinear integrate-and-fire model that includes somatic adaptation. When exposed to fluctuating inputs sparsely coupled populations of these model neurons exhibit stochastic collective dynamics that can be effectively characterized using the Fokker-Planck equation. This approach, however, leads to a model with an infinite-dimensional state space and non-standard boundary conditions. Here we derive from that description four simple models for the spike rate dynamics in terms of low-dimensional ordinary differential equations using two different reduction techniques: one uses the spectral decomposition of the Fokker-Planck operator, the other is based on a cascade of two linear filters and a nonlinearity, which are determined from the Fokker-Planck equation and semi-analytically approximated. We evaluate the reduced models for a wide range of biologically plausible input statistics and find that both approximation approaches lead to spike rate models that accurately reproduce the spiking behavior of the underlying adaptive integrate-and-fire population. Particularly the cascade-based models are overall most accurate and robust, especially in the sensitive region of rapidly changing input. For the mean-driven regime, when input fluctuations are not too strong and fast, however, the best performing model is based on the spectral decomposition. The low-dimensional models also well reproduce stable oscillatory spike rate dynamics that are generated either by recurrent synaptic excitation and neuronal adaptation or through delayed inhibitory synaptic feedback. The computational demands of the reduced models are very low but the implementation complexity differs between the different model variants. Therefore we have made available implementations that allow to numerically integrate the low-dimensional spike rate models as well as the Fokker-Planck partial differential equation in efficient ways for arbitrary model parametrizations as open source software. The derived spike rate descriptions retain a direct link to the properties of single neurons, allow for convenient mathematical analyses of network states, and are well suited for application in neural mass/mean-field based brain network models.
Older Adults’ Associative Memory Is Modified by Manner of Presentation at Encoding and Retrieval

Relative to young adults, older adults typically exhibit a reduced ability to accurately remember associations between stimuli. Prior research has assumed that this age-related memory impairment affects different types of associations similarly. However, research in young adults has suggested that item–item and item–context associations are supported by different underlying neural mechanisms that could be unequally affected by aging. This experiment compared memory across association types in younger and older adults by presenting the same types of stimuli as either item–item or item–context pairs. Manner of presentation during retrieval was also manipulated so that pairs were presented in a manner that was either congruent or incongruent with their presentation during encoding. Older adults showed a particular benefit of encoding–retrieval congruency for item–context associations, supporting the idea that the associative deficit may be reduced by unitization at encoding and reinstatement of this prior stimulus configuration at retrieval.
Adaptive Cascade Regression Model For Robust Face Alignment

Cascade regression is a popular face alignment approach, and it has achieved good performances on the wild databases. However, it depends heavily on local features in estimating reliable landmark locations and therefore suffers from corrupted images, such as images with occlusion, which often exists in real-world face images. In this paper, we present a new adaptive cascade regression model for robust face alignment. In each iteration, the shape-indexed appearance is introduced to estimate the occlusion level of each landmark, and each landmark is then weighted according to its estimated occlusion level. Also, the occlusion levels of the landmarks act as adaptive weights on the shape-indexed features to decrease the noise on the shape-indexed features. At the same time, an exemplar-based shape prior is designed to suppress the influence of local image corruption. Extensive experiments are conducted on the challenging benchmarks, and the experimental results demonstrate that the proposed method achieves better results than the state-of-the-art methods for facial landmark localization and occlusion detection.
Effects of ninety minutes per week of continuous aerobic exercise on blood pressure in hypertensive obese humans

The main objective of this study was to examine the effect of continuous aerobic training (CAT) in hypertensive, obese people. Seven patients of average age (45.3±3.9 years), height (1.63±0.1 m), body weight (89.09±22.0 kg), and body mass index (33.44±8.6 kg/m2) were subjected to the training. CAT was performed in thrice-weekly nonconsecutive sessions (90 min per week) with intervals of 48 hr between each session. The training sessions entailed 30 min of walking at an intensity of 70%–80% of the maximum heart rate (MHR) on a treadmill over a period of eight weeks, giving a total of 24 sessions. Through correlation analyses, we found significant improvement in the systolic pressure (R=0.5675, P=0.0253) and diastolic pressure (R=0.7083, P=0.0088) when the last session was compared to the first session of training. We found no differences in the diastolic pressure and systolic pressure before, during and after 15 min of the protocol exercise. The program showed a large effect size (ES) for systolic pressure (ES=0.85) and a small ES for diastolic pressure (ES=0.33). We found no differences in the blood pressure (BP) and heart rate (HR) during and after the training of obese hypertensive humans, but we found a positively significant correlation between HR and BP in the last session and a large ES, suggesting that this protocol exercise might have significance effect in the long term.
Obstetrical correlates and perinatal consequences of neonatal hypoglycemia in term infants

Abstract Objective: To determine independent perinatal and intrapartum factors associated with neonatal hypoglycemia. Method: Of singleton pregnancies delivered at term in 2013; 318 (3.8%) neonates diagnosed with hypoglycemia were compared to 7955 (96.2%) neonate controls with regression analysis. Results: Regression analysis showed that independent prenatal factors were multiparity (odds-ratio [OR] = 1.61), gestational age (OR = 0.68), gestational diabetes (OR = 0.22), macrosomia (OR = 4.87), small for gestational age neonate [SGA] (OR = 6.83) and admission cervical dilation (OR = 0.79). For intrapartum factors, only cesarean section (OR = 1.57) and last cervical dilation (OR = 0.92) were independently significantly associated with neonatal hypoglycemia. For biologically plausible risk factors, independent factors were cesarean section (OR = 4.18), gentamycin/clindamycin in labor (OR = 5.35), gestational age (OR = 0.59) and macrosomia (OR = 5.62). Mothers of babies with neonatal hypoglycemia had more blood loss and longer hospital stays, while neonates with hypoglycemia had worse umbilical cord gases, more neonatal hypoxic conditions, neonatal morbidities and NICU admissions. Conclusion: Diabetes was protective of neonatal hypoglycemia, which may be explained by optimum maternal glucose management; nevertheless macrosomia was independently predictive of neonatal hypoglycemia. Cesarean section and decreasing gestational age were the most consistent independent risk factors followed by treatment for chorioamnionitis and SGA. Further studies to evaluate these observations and develop preventive strategies are warranted.
Verteporfin exhibits YAP-independent anti-proliferative and cytotoxic effects in endometrial cancer cells

Endometrial Carcinoma (EMCA) is the most common gynecologic malignancy and the fourth most common malignancy in women in the United States. Yes-associated protein (YAP) is a potent transcription coactivator acting via binding to the TEAD transcription factor, and plays a critical role in organ size regulation. Verteporfin (VP), a benzoporphyrin derivative, was identified as an inhibitor of YAP-TEAD interaction. We investigated the therapeutic efficacy and mechanism of VP in EMCA. The efficacy of VP on cell viability, cytotoxicity and invasion was assayed in EMCA cell lines. An organoid model system was also developed to test the effect of VP on apoptotic markers in an in vitro model system. Treatment with VP resulted in a decrease in cell viability, invasion and an increase in cytotoxicity of EMCA cells. These effects occurred as early as 15 minutes following treatment. Similarly, VP treatment versus vehicle control increased apoptosis in human organoid model systems. Quantitative RT-PCR, cDNA based RTPCR array analysis and western blotting were performed to investigate the mechanism of VP action. The cytotoxic and anti-proliferative effects appeared to be independent of its effect on YAP. Our results suggest that VP is a promising chemotherapeutic agent for the treatment of endometrial cancer.
Inertial Human Motion Estimation for Physical Human-Robot Interaction Using an Interaction Velocity Update to Reduce Drift

Robots used for physical human-robot interaction (pHRI) are currently advancing from being simple stand-alone manipulators passing tools or parts to human collaborators to becoming autonomous co-workers that continuously share operational control with their human partners. One of the major challenges in this transition is to extend robot capabilities in sensing human motions and behaviour, thereby allowing for more seamless cooperation and ensuring the safety of human partners. Currently, there is a gap between the desire for humans and robots to work closely together and share control of operations, and how robustly we can measure and predict human motions and intentions in pHRI operations. In this paper, we propose to use a set of wireless inertial motion sensors fixed to the body of the human partner to track and estimate human motions, and to use the interaction contact between the robot and the human, as detected by a force/torque (FT) sensor, as an interaction velocity update (IVU) to estimate and reduce drift in the position/orientation estimates. Our hypothesis is that human motion estimates from inertial sensors with an IVU will give sufficiently accurate and robust motion information for safe cooperative pHRI operations.
Single Image Super-Resolution Based on Multi-Scale Competitive Convolutional Neural Network

Deep convolutional neural networks (CNNs) are successful in single-image super-resolution. Traditional CNNs are limited to exploit multi-scale contextual information for image reconstruction due to the fixed convolutional kernel in their building modules. To restore various scales of image details, we enhance the multi-scale inference capability of CNNs by introducing competition among multi-scale convolutional filters, and build up a shallow network under limited computational resources. The proposed network has the following two advantages: (1) the multi-scale convolutional kernel provides the multi-context for image super-resolution, and (2) the maximum competitive strategy adaptively chooses the optimal scale of information for image reconstruction. Our experimental results on image super-resolution show that the performance of the proposed network outperforms the state-of-the-art methods.
Phytoplankton responses and associated carbon cycling during shipboard carbonate chemistry manipulation experiments conducted around Northwest European shelf seas

The ongoing oceanic uptake of anthropogenic carbon dioxide (CO2) is significantly altering the carbon- ate chemistry of seawater, a phenomenon referred to as ocean acidification. Experimental manipulations have been increasingly used to gauge how continued ocean acidifica- tion will potentially impact marine ecosystems and their as- sociated biogeochemical cycles in the future; however, re- sults amongst studies, particularly when performed on nat- ural communities, are highly variable, which may reflect community/environment-specific responses or inconsisten- cies in experimental approach. To investigate the potential for identification of more generic responses and greater ex- perimentally reproducibility, we devised and implemented a series (n = 8) of short-term (2-4 days) multi-level ( 4 con- ditions) carbonate chemistry/nutrient manipulation experi- ments on a range of natural microbial communities sam- pled in Northwest European shelf seas. Carbonate chem- istry manipulations and resulting biological responses were found to be highly reproducible within individual experi- ments and to a lesser extent between geographically sep- arated experiments. Statistically robust reproducible phys- iological responses of phytoplankton to increasing pCO2, characterised by a suppression of net growth for small-sized cells (< 10 µm), were observed in the majority of the ex- periments, irrespective of natural or manipulated nutrient status. Remaining between-experiment variability was po- tentially linked to initial community structure and/or other site-specific environmental factors. Analysis of carbon cy- cling within the experiments revealed the expected increased sensitivity of carbonate chemistry to biological processes at higher pCO2 and hence lower buffer capacity. The results thus emphasise how biogeochemical feedbacks may be al- tered in the future ocean.
On Encryption and Authentication of the DC DCT Coefficient

When encryption and authentication techniques are applied to image or video data, sometimes it is advantageous to limit the operation to the DC DCT coefficient of each 8× 8 block in a picture. In this work, the performance of such an approach is evaluated. This problem is considered as an image quality problem, and the metric structural similarity is used to show that by authenticating the DC coefficient, about 60% of the information can be guaranteed; by encrypting the DC coefficient, about 80% of the information can be
Using Complex-Network Properties for Efficient Graph Analysis

Complex networks are set of entities in a relationship, modeled by graphs where nodes represent entities and edges between nodes represent relationships. Graph algorithms have inherent characteristics, including data-driven computations and poor locality. These characteristics expose graph algorithms to several challenges; this is because most well studied (parallel) abstractions and implementation are not suitable for them. This work shows how we use some complex-network properties, including community structure and heterogeneity of node degree, to tackle one of the main challenges: improving performance, by improving memory location and by providing proper thread scheduling. In this paper, we firstly formalize complex-network ordering for cache misses reducing as a well known NP-Complete problem, the optimal linear arrangement problem; we then propose cn-order an heuristic that outperforms very recent graph orders. Secondly, we formalize degree-aware scheduling problem as another well known NP-Complete problem, the multiple knapsack problem; then we propose two degree-aware heuristics to solve it. We finally validate our theoretical observations with experiments on a 32 cores NUMA machine with some graph algorithms and some stanford graph datasets. For example, some results with Pagerank algorithm and livejournal dataset show that using cn-order improves performance by reducing cache misses and hence time by 41%; and when cn-order is combined with degree-aware scheduling, time is reduced by 50% due to load balancing among threads.
AMPA‐mediated excitotoxicity in oligodendrocytes: role for Na+–K+–Cl− co‐transport and reversal of Na+/Ca2+ exchanger

We investigated the role of Na+–K+–Cl− co‐transporter isoform 1 (NKCC1) and reversal of Na+/Ca2+ exchanger (NCXrev) in glutamate‐mediated excitotoxicity in oligodendrocytes obtained from rat spinal cords (postnatal day 6–8). An immunocytochemical characterization showed that these cultures express NKCC1 and Na+/Ca2+ exchanger isoforms 1, 2, and 3 (NCX1, NCX2, NCX3). Exposing the cultures to alpha‐amino‐3‐hydroxy‐5‐methyl‐4‐isoxazolepropionic acid (AMPA) plus cyclothiazide (CTZ) led to a transient rise in intracellular (), which was followed by a sustained overload, NKCC1 phosphorylation, and a NKCC1‐mediated Na+ influx. In the presence of a specific AMPA receptor inhibitor 6‐cyano‐7‐nitroquinoxaline‐2, 3‐dione (CNQX), the AMPA/CTZ failed to elicit any changes in . The AMPA/CTZ‐induced sustained rise led to mitochondrial Ca2+ accumulation, release of cytochrome c from mitochondria, and cell death. The AMPA/CTZ‐elicited increase, mitochondrial damage, and cell death were significantly reduced by inhibiting NKCC1 or NCXrev. These data suggest that in cultured oligodendrocytes, activation of AMPA receptors leads to NKCC1 phosphorylation that enhances NKCC1‐mediated Na+ influx. The latter triggers NCXrev and NCXrev‐mediated overload and compromises mitochondrial function and cellular viability.