Miracle-dz/UniFAR-FTUs huggingface dataset:
Prognostic and Clinicopathological Value of Rac1 in Cancer Survival: Evidence from a Meta-Analysis

Purpose: The role of Rac1 in cancer survival has been widely studied. However, the prognostic and clinicopathological value of Rac1 remains inconclusive. We performed a meta-analysis to clarify the role of Rac1 in cancer survival as well as its association with clinicopathological features. Methods: Eligible studies were searched from PubMed, Cochrane Library, Embase, and Web of Science databases. The pooled hazard ratios (HRs) and odds ratios (ORs) with corresponding 95% confidence intervals (CIs) were used to detect the prognostic and clinicopathological role of Rac1. Results: A total of 14 studies including 1793 patients were enrolled in the present meta-analysis. Pooled HR for overall survival (OS) (HR=2.02, 95% CI: 1.70-2.39) and disease-free survival (DFS) (HR=2.64, 95% CI: 1.71-4.09) indicated a significant poor prognostic effect for Rac1. Positive Rac1 expression was found to be correlated with tumor stage, blood vessel invasion, and lymph metastasis, but not with histological differentiation. Sensitivity test showed no single study altered OS or DFS significantly. No publication bias was detected by Egger's test and Begg's funnel plot test. Conclusion: This meta-analysis indicated that Rac1 could be used as a potential marker to predict cancer prognosis. Additionally, Rac1 expression was associated with the malignancy-related phenotype.
Visual Domain Adaptation with Manifold Embedded Distribution Alignment

Visual domain adaptation aims to learn robust classifiers for the target domain by leveraging knowledge from a source domain. Existing methods either attempt to align the cross-domain distributions, or perform manifold subspace learning. However, there are two significant challenges: (1) degenerated feature transformation, which means that distribution alignment is often performed in the original feature space, where feature distortions are hard to overcome. On the other hand, subspace learning is not sufficient to reduce the distribution divergence. (2) unevaluated distribution alignment, which means that existing distribution alignment methods only align the marginal and conditional distributions with equal importance, while they fail to evaluate the different importance of these two distributions in real applications. In this paper, we propose a Manifold Embedded Distribution Alignment (MEDA) approach to address these challenges. MEDA learns a domain-invariant classifier in Grassmann manifold with structural risk minimization, while performing dynamic distribution alignment to quantitatively account for the relative importance of marginal and conditional distributions. To the best of our knowledge, MEDA is the first attempt to perform dynamic distribution alignment for manifold domain adaptation. Extensive experiments demonstrate that MEDA shows significant improvements in classification accuracy compared to state-of-the-art traditional and deep methods.
Predicting the Future Need of Walking Device or Assistance by Moderate to Vigorous Physical Activity: A 2-Year Prospective Study of Women Aged 75 Years and Above

Objective To examine the association between daily moderate to vigorous physical activity (MVPA) and the change in mobility function among community-dwelling Japanese women aged 75 years and above. Methods This prospective study included 330 older women aged 75 years and above who could walk without a walking device or assistance. MVPA and light-intensity physical activity (LPA) were assessed using an accelerometer for seven consecutive days. MVPA was defined as an activity with an intensity of >3 metabolic equivalents. The study outcome was a change in mobility function, defined as the need of walking device or assistance, during the two-year period. Results The results of the logistic regression analysis showed that MVPA was inversely associated with a decline in mobility function after controlling for LPA and potential confounders (adjusted odds ratio (OR) = 0.93 per 1 min/d, 95% confidence interval (CI) = 0.88–0.99; P = 0.017), whereas LPA was not when adjusted for MVPA and confounders (adjusted OR = 0.99 per 1 min/d, 95% CI = 0.96–1.01; P = 0.245). The receiver operating characteristics analysis identified a 7.9 min/d of MVPA as the cut-off value. Conclusions The results of this study suggest the importance of promoting daily MVPA for preventing mobility limitation in older women aged 75 years and above.
Modulation of Phytohormone Signaling: A Primary Function of Flavonoids in Plant–Environment Interactions

The old observation that plants preferentially synthesize flavonoids with respect to the wide range of phenylpropanoid structures when exposed to high doses of UV-B radiation has supported the view that flavonoids are primarily involved in absorbing the shortest solar wavelengths in photoprotection. However, there is compelling evidence that the biosynthesis of flavonoids is similarly upregulated in response to high photosynthetically active radiation in the presence or in the absence of UV-radiation, as well as in response to excess metal ions and photosynthetic redox unbalance. This supports the hypothesis that flavonoids may play prominent roles as scavengers of reactive oxygen species (ROS) generated by light excess. These ‘antioxidant’ functions of flavonoids appears robust, as maintained between different life kingdoms, e.g., plants and animals. The ability of flavonoids to buffer stress-induced large alterations in ROS homeostasis and, hence, to modulate the ROS-signaling cascade, is at the base of well-known functions of flavonoids as developmental regulators in both plants and animals. There is both long and very recent evidence indeed that, in plants, flavonoids may strongly affect phytohormone signaling, e.g., auxin and abscisic acid signaling. This function is served by flavonoids in a very low (nM) concentration range and involves the ability of flavonoids to inhibit the activity of a wide range of protein kinases, including but not limited to mitogen-activated protein kinases, that operate downstream of ROS in the regulation of cell growth and differentiation. For example, flavonoids inhibit the transport of auxin acting on serine–threonine PINOID (PID) kinases that regulate the localization of auxin efflux facilitators PIN-formed (PIN) proteins. Flavonoids may also determine auxin gradients at cellular and tissue levels, and the consequential developmental processes, by reducing auxin catabolism. Recent observations lead to the hypothesis that regulation/modulation of auxin transport/signaling is likely an ancestral function of flavonoids. The antagonistic functions of flavonoids on ABA-induced stomatal closure also offer novel hypotheses on the functional role of flavonoids in plant–environment interactions, in early as well as in modern terrestrial plants. Here, we surmise that the regulation of phytohormone signaling might have represented a primary function served by flavonols for the conquest of land by plants and it is still of major significance for the successful acclimation of modern terrestrial plants to a severe excess of radiant energy.
Speeding up the Hyperparameter Optimization of Deep Convolutional Neural Networks

Most learning algorithms require the practitioner to manually set the values of many hyperparameters before the learning process can begin. However, with modern algorithms, the evaluation of a given hyperparameter setting can take a considerable amount of time and the search space is often very high-dimensional. We suggest using a lower-dimensional representation of the original data to quickly identify promising areas in the hyperparameter space. This information can then be used to initialize the optimization algorithm for the original, higher-dimensional data. We compare this approach with the standard procedure of optimizing the hyperparameters only on the original input. We perform experiments with various state-of-the-art hyperparameter optimization algorithms such as random search, the tree of parzen estimators (TPEs), sequential model-based algorithm configuration (SMAC), and a genetic algorithm (GA). Our experiments indicate that it is possible to speed up the optimization process by using lower-dimensional data representations at the beginning, while increasing the dimensionality of the input later in the optimization process. This is independent of the underlying optimization procedure, making the approach promising for many existing hyperparameter optimization algorithms.
Partial recovery bounds for clustering with the relaxed K-means

We investigate the clustering performances of the relaxed $K$means in the setting of sub-Gaussian Mixture Model (sGMM) and Stochastic Block Model (SBM). After identifying the appropriate signal-to-noise ratio (SNR), we prove that the misclassification error decay exponentially fast with respect to this SNR. These partial recovery bounds for the relaxed $K$means improve upon results currently known in the sGMM setting. In the SBM setting, applying the relaxed $K$means SDP allows to handle general connection probabilities whereas other SDPs investigated in the literature are restricted to the assortative case (where within group probabilities are larger than between group probabilities). Again, this partial recovery bound complements the state-of-the-art results. All together, these results put forward the versatility of the relaxed $K$means.
Multisite phosphorylation is required for sustained interaction with GRKs and arrestins during rapid μ-opioid receptor desensitization

High-efficacy opioids enhance receptor desensitization due to rapid and sustained GRK2/β-arrestin interactions. Controlling opioid receptor desensitization Chronic opioid use leads to tolerance and the need for increasing doses to achieve pain relief, euphoria, and other effects. At the molecular level, tolerance occurs because of μ-opioid receptor desensitization: Opioid activation of its receptors triggers their phosphorylation and internalization, ultimately reducing the cellular response to opioids. Miess et al. investigated why high-efficacy opioids induce greater receptor internalization than lower-efficacy opioids such as morphine. The kinase GRK2 and the scaffolding protein β-arrestin regulate the desensitization of opioid receptors and other G protein–coupled receptors, and the authors assessed the recruitment of GRK2 and β-arrestin to phosphorylation site receptor mutants. GRK2 was more rapidly recruited to μ-opioid receptors by high-efficacy opioids than by morphine, and GRK2 promoted receptor desensitization to high-efficacy opioids through phosphorylation-dependent and phosphorylation-independent means. These results provide molecular insight into μ-opioid receptor desensitization, which may aid in the development of synthetic opioids that do not induce tolerance and thus have reduced potential for addiction. G protein receptor kinases (GRKs) and β-arrestins are key regulators of μ-opioid receptor (MOR) signaling and trafficking. We have previously shown that high-efficacy opioids such as DAMGO stimulate a GRK2/3-mediated multisite phosphorylation of conserved C-terminal tail serine and threonine residues, which facilitates internalization of the receptor. In contrast, morphine-induced phosphorylation of MOR is limited to Ser375 and is not sufficient to drive substantial receptor internalization. We report how specific multisite phosphorylation controlled the dynamics of GRK and β-arrestin interactions with MOR and show how such phosphorylation mediated receptor desensitization. We showed that GRK2/3 was recruited more quickly than was β-arrestin to a DAMGO-activated MOR. β-Arrestin recruitment required GRK2 activity and MOR phosphorylation, but GRK recruitment also depended on the phosphorylation sites in the C-terminal tail, specifically four serine and threonine residues within the 370TREHPSTANT379 motif. Our results also suggested that other residues outside this motif participated in the initial and transient recruitment of GRK and β-arrestins. We identified two components of high-efficacy agonist desensitization of MOR: a sustained component, which required GRK2-mediated phosphorylation and a potential soluble factor, and a rapid component, which was likely mediated by GRK2 but independent of receptor phosphorylation. Elucidating these complex receptor-effector interactions represents an important step toward a mechanistic understanding of MOR desensitization that leads to the development of tolerance and dependence.
Individual user involvement at Healthy Life Centres: a qualitative study exploring the perspective of health professionals

ABSTRACT The aim of this study was to explore how professionals experience user involvement at an individual level and how they describe involving users at Healthy Life Centres. Four focus group interviews were conducted with a total of 23 professionals. Data were analysed using systematic text condensation. Four themes were identified: (1) Involving users through motivational interviewing; (2) Building a good and trustful relation; (3) Assessing and adjusting to the user’s needs and life situation; and(4) Strengthening the user’s ownership and participation in the lifestyle change process. Motivational interviewing was described by the professionals as a way to induce and ensure user involvement. However, seeing motivational interviewing and user involvement as the same concept might reduce user involvement from being a goal in itself and evolve into a means of achieving lifestyle changes. The professionals might be facing opposing discourses in their practice and a dilemma of promoting autonomy and involvement and at the same time promoting change in a predefined direction. Greater emphasis should thus be put on systematic reflection among professionals about what user involvement implies in the local Healthy Life Centre context and in each user’s situation. Abbreviations: HLC: Healthy Life Centre; MI: Motivational Interviewing; NCD: Non-communicable diseases; STC: Systematic Text Condensation. SDT: Self-determination theory
Adaptive Second-Order Sliding-Mode Observer for PMSM Sensorless Control Considering VSI Nonlinearity

This paper proposes an adaptive super-twisting algorithm based sliding-mode observer (STA-SMO) for surface-mounted permanent magnet synchronous machine (PMSM) sensorless control, in which voltage source inverter (VSI) nonlinearity is taken into consideration. An adaptive algorithm for online tuning the sliding-mode coefficients of STA-SMO is proposed based on the existing stable conditions. Thus, position estimation error can be reduced by the proposed adaptive algorithm in wide-speed range. Furthermore, voltage distortion due to VSI nonlinearity is online compensated to improve the accuracy of PMSM model. Because mismatch between reference and actual voltage due to VSI nonlinearity may bring large rotor position and speed estimation error, especially in low-speed range where distorted voltages may become dominant compared to actual voltages. Thus, a STA-SMO with adaptive sliding-mode coefficients and accurate voltage value could be obtained. Finally, the effectiveness of the proposed adaptive STA-SMO with online VSI nonlinearity compensation is verified on a surface-mounted PMSM by experiments.
Circulating tumor cells mirror bone metastatic phenotype in prostate cancer

Circulating tumor cells (CTCs) are promising biomarkers in prostate cancer (PC) because they derive from primary tumor and metastatic tissues. In this study, we used quantitative real-time PCR (qPCR) to compare the expression profiles of 41 PC-related genes between paired CTC and spinal column metastasis samples from 22 PC patients that underwent surgery for spinal cord compression. We observed good concordance between the gene expression profiles in the CTC and metastasis samples in most of the PC patients. Expression of nine genes (AGR2, AKR1C3, AR, CDH1, FOLH1, HER2, KRT19, MDK, and SPINK1) showed a significant correlation between the CTC and metastasis samples. Hierarchical clustering analysis showed a similar grouping of PC patients based on the expression of these nine genes in both CTC and metastasis samples. Our findings demonstrate that CTCs mirror gene expression patterns in tissue metastasis samples from PC patients. Although low detection frequency of certain genes is a limitation in CTCs, our results indicate the potential for CTC phenotyping as a tool to improve individualized therapy in metastatic prostate cancer.
The Association Between Musculoskeletal Symptoms and Traditional Chinese Medicine Use Among Chinese Older Adults in the Greater Chicago Area

Older adults disproportionately experience musculoskeletal symptoms, which are associated with morbidity and mortality. Traditional Chinese medicine (TCM) is an important part of Chinese culture and for millennia have been used to maintain health. This article aims to examine the association between musculoskeletal symptoms and different subtypes of TCM usage. Data were collected through the Population Study of Chinese Elderly in Chicago (PINE) study, a community-engaged, epidemiological study of a U.S. Chinese population. Review of systems was used to measure musculoskeletal symptoms. TCM use was measured by using a 5-point scale. Logistic regression analyses were conducted to control for potential confounding factors. Of 3,157 Chinese, older adults aged 60 and older, the mean age was 72.8 (SD = 8.3) and 58.9% were female. Older adults with any musculoskeletal symptoms had greater utilization of overall TCM use (odds ratio [OR] = 2.10, 95% confidence interval [CI] = [1.76, 2.52]), especially massage therapy (OR = 3.41, 95% CI = [2.51, 4.63]), herbal (OR = 2.68, 95% CI = [2.28, 3.14]), and acupuncture (OR = 2.49, 95% CI = [1.87, 3.32]). However, there was no statistically significant association between the presence of musculoskeletal symptoms and Tai-Chi (OR = 1.18, 95% CI = [0.93, 1.50]). This study demonstrated that musculoskeletal symptoms among Chinese older adults were strongly associated with the use of TCM. Future research is needed to examine the effectiveness of TCM in treating musculoskeletal symptoms.
Loading BMP-2 on nanostructured hydroxyapatite microspheres for rapid bone regeneration

Introduction Tissue engineering is a promising strategy for bone regeneration in repairing massive bone defects. The surface morphology of implanted materials plays a key role in bone healing; these materials incorporate osteoinductive factors to improve the efficiency of bone regeneration. Materials and methods In the current study, nanostructured hydroxyapatite (nHAp) micro-spheres were prepared via a hydrothermal transformation method using calcium silicate (CS) microspheres as precursors; the CS microspheres were obtained by a spray-drying method. The nHAp microspheres constructed by the nano-whiskers significantly improved the ability of the microspheres to adsorb the bioactive protein (BMP-2) and reduce its initial burst release. To evaluate the in vivo bone regeneration of microspheres, both conventional hydroxyapatite (HAp) and nHAp microspheres were either loaded with recombinant human bone morphogenetic protein-2 (rhBMP-2) or not loaded with the protein; these microspheres were implanted in rat femoral bone defects for 4 and 8 weeks. Results and discussion The results of our three-dimensional (3D) micro-computed tomography (CT) and histomorphometric observations showed that the combination of the nano-structured surface and rhBMP-2 obviously improved osteogenesis compared to conventional HAp microspheres loaded with rhBMP-2. Our results suggest that the nHAp microspheres with a nanostructured surface adsorb rhBMP-2 for rapid bone formation; they therefore show the potential to act as carriers in bone tissue regeneration.
Can patient-specific finite element models better predict fractures in metastatic bone disease than experienced clinicians?

Objectives In this prospective cohort study, we investigated whether patient-specific finite element (FE) models can identify patients at risk of a pathological femoral fracture resulting from metastatic bone disease, and compared these FE predictions with clinical assessments by experienced clinicians. Methods A total of 39 patients with non-fractured femoral metastatic lesions who were irradiated for pain were included from three radiotherapy institutes. During follow-up, nine pathological fractures occurred in seven patients. Quantitative CT-based FE models were generated for all patients. Femoral failure load was calculated and compared between the fractured and non-fractured femurs. Due to inter-scanner differences, patients were analyzed separately for the three institutes. In addition, the FE-based predictions were compared with fracture risk assessments by experienced clinicians. Results In institute 1, median failure load was significantly lower for patients who sustained a fracture than for patients with no fractures. In institutes 2 and 3, the number of patients with a fracture was too low to make a clear distinction. Fracture locations were well predicted by the FE model when compared with post-fracture radiographs. The FE model was more accurate in identifying patients with a high fracture risk compared with experienced clinicians, with a sensitivity of 89% versus 0% to 33% for clinical assessments. Specificity was 79% for the FE models versus 84% to 95% for clinical assessments. Conclusion FE models can be a valuable tool to improve clinical fracture risk predictions in metastatic bone disease. Future work in a larger patient population should confirm the higher predictive power of FE models compared with current clinical guidelines. Cite this article: F. Eggermont, L. C. Derikx, N. Verdonschot, I. C. M. van der Geest, M. A. A. de Jong, A. Snyers, Y. M. van der Linden, E. Tanck. Can patient-specific finite element models better predict fractures in metastatic bone disease than experienced clinicians? Towards computational modelling in daily clinical practice. Bone Joint Res 2018;7:430–439. DOI: 10.1302/2046-3758.76. BJR-2017-0325.R2.
Breast Tumor Analysis Using Shifted-Excitation Raman Difference Spectroscopy (SERDS)

We used a shifted-excitation Raman difference spectroscopy method for the ex vivo classification of resected and formalin-fixed breast tissue samples as normal (healthy) tissue, fibroadenoma, or invasive carcinoma. We analyzed 8 tissue samples containing invasive carcinoma that were surrounded by normal tissue and 3 tissue samples with fibroadenoma only. We made various measurement sites on various tissue samples, in total 240 measurements for each type of tissue. Although the acquired raw spectra contain enough information to clearly differentiate between normal and tumor (fibroadenoma and invasive carcinoma) tissue, the differentiation between fibroadenoma and invasive carcinoma was possible only after the shifted-excitation Raman difference spectroscopy isolation of pure Raman spectra from the heavily fluorescence interfered raw spectra. We used 784 and 785 nm as excitation wavelengths for the shifted-excitation Raman difference spectroscopy method. The differences in the obtained pure Raman spectra are assigned to the different chemical compositions of normal breast tissue, fibroadenoma, and invasive breast carcinoma. Principal component analysis and linear discriminant analysis showed excellent classification results in the Raman shift range between 1000 and 1800 cm−1. Invasive breast carcinoma was identified with 99.15% sensitivity, and the absence of invasive carcinoma was identified with 90.40% specificity. Tumor tissue in tumor-containing tissue was identified with 100% sensitivity, and the absence of tumor in no-tumor containing tissue was identified with 100% specificity. As gold standard for the determination of the sensitivity and the specificity, we considered the conventional histopathological classification. In summary, shifted-excitation Raman difference spectroscopy could be potentially very useful to support histopathological diagnosis in breast pathology.