Miracle-dz/UniFAR-FTUs huggingface dataset:
Significance of epithelial-to-mesenchymal transition inducing transcription factors in predicting distance metastasis and survival in patients with colorectal cancer: A systematic review and meta-analysis

Background: The clinical relevance of epithelial-to-mesenchymal transition (EMT) in colorectal cancer (CRC) progression has been highlighted over the last decade. Several EMT-inducing transcription factors (EMT-TFs) have been implicated in the regulation of EMT, including Twist, Snail1, Slug, ZEB1, and ZEB2. Here, this meta-analysis aimed to predict the risk of distance metastasis and overall survival in CRC patients with high expression of EMT-TFs. Materials and Methods: All eligible studies were searched in PubMed, Scopus, and Web of Science databases. The search was carried out to include literatures published as late as September 1, 2018. In overall, 16 studies that investigated the relationship between EMT-TFs with distance metastasis and survival in CRC patients were included. In meta-analysis, a pooled hazard ratio (HR) and odds ratio (OR) were estimated for associations. Results: The results of this review indicated that expressions of all EMT-TFs are significantly correlated with poor overall survival in CRC. Moreover, there are a significant association between Twist (OR, 1.46; 95% confidence interval [CI], 1.03–2.09), Slug (OR, 3.43; 95% CI, 1.98–5.93), and ZEB2 (OR, 2.42; 95% CI, 1.09–5.40) expression with distance metastatic in CRC patients. Conclusion: These findings suggest that the overexpression of EMT-TFs plays a key role in increasing the risk of distance metastasis as well as decreasing overall survival in CRC patients.
Increase in doses of levothyroxine at the age of 3 years and above is useful for distinguishing transient and permanent congenital hypothyroidism

Abstract. There are no recommended diagnostic criteria for transient congenital hypothyroidism (CH) during early childhood. In this study, we aimed to identify the factors that distinguish permanent (P)- and transient (T)-CH. We retrospectively analyzed the clinical, biochemical, and imaging data of 42 children with a definitive diagnosis of P- or T-CH by re-evaluation tests at our institution from November 1986 to October 2019. Patients who continued levothyroxine (L-T4) treatment after the re-evaluation tests were classified as group P (n = 19), while patients who were diagnosed with T-CH and discontinued L-T4 treatment were classified as group T (n = 23). Initial testing performed during infancy showed that the mean serum TSH and free T4 (FT4) levels did not differ significantly between groups P and T. None of the patients in group T required an increased dosage of L-T4 at the age of 3 yr and above while 85% of the patients in group P required increased dosages of L-T4. Hence, T-CH was suspected in patients who did not require an increase in L-T4 dosage at the age of 3 yr and above.
Folate and Vitamin B‐12 Status Is Associated With Bone Mineral Density and Hip Strength of Postmenopausal Chinese‐Singaporean Women

The role of micronutrients such as folate and vitamin B‐12 in bone quality has been widely studied with conflicting results. Ethnicity seems to play a large role on nutrient intake, as diet varies across cultures. In this study, we examined the relationships of BMD, proximal femur strength, and bone resorption with plasma folate and vitamin B‐12 in a cohort of 93 healthy postmenopausal women of Chinese‐Singaporean descent. The parameters examined were areal (aBMD) and volumetric BMD (vBMD) of the proximal femur and the third lumbar vertebra (L3), total body aBMD, proximal femur bending, compressive and impact strength indices (composite strength indices) and circulating levels of C‐telopeptide of type I collagen. Eighteen participants (19.4%) had aBMD in the osteoporotic range (osteoporosis group), 59 (63.4%) in the osteopenic range (osteopenia group), and the remaining 16 (17.2%) in the normal range (normal BMD group). Circulating folate levels were significantly higher in the normal BMD group compared with the osteoporosis group. Using linear regression analysis, we found that overall, aBMD and vBMD are positively associated with folate concentrations, whereas composite strength indices were positively associated with vitamin B‐12 concentrations. These findings support the existing literature and suggest a link between levels of circulating folate/vitamin B‐12 and BMD/bone strength in the cohort examined. Further investigation is needed to examine if individuals with inadequate circulating levels of these nutrients could decrease their risk for fragility fractures through better nutrition or vitamin supplementation. © 2020 The Authors. JBMR Plus published by Wiley Periodicals, Inc. on behalf of American Society for Bone and Mineral Research © 2020 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.
Metronomic Celecoxib Therapy in Clinically Available Dosage Ablates Hepatocellular Carcinoma via Suppressing Cell Invasion, Growth, and Stemness in Pre-Clinical Models

Objective To investigate the anti-carcinogenic effect of metronomic Celecoxib (i.e., frequent administration in clinically available doses) against hepatocellular carcinoma (HCC) in the perspective of metastasis, spontaneous hepatocarcinogenesis, cancer invasion, proliferation, and stemness in vivo and in vitro. Background Celecoxib, a selective cyclooxygenase-2 (COX-2) inhibitor, is known to cause anti-carcinogenic effects for HCC in suprapharmacological doses. However, the effects of metronomic Celecoxib treatment on HCC cells remain unclear. Methods The in vivo chemopreventive effect of metronomic Celecoxib (10mg/kg/d) was investigated by the syngeneic HCC implantation model and spontaneous hepatocarcinogenesis in HBV-transgenic(HBVtg) mice individually. HCC cell lines were treated by either suprapharmacological (100 μM) or metronomic (4 μM) Celecoxib therapy. Anti-carcinogenic effects were evaluated using cell invasion, cancer proliferation, angiogenesis, and phenotype of cancer stem/progenitor cells (CSPC). The molecular mechanism of metronomic Celecoxib on HCC was dissected using Luciferase assay. Results In vivo metronomic Celecoxib exerted its chemopreventive effect by significantly reducing tumor growth of implanted syngeneic HCC and spontaneous hepatocarcinogenesis in HBVtg mice. Unlike suprapharmacological dose, metronomic Celecoxib can only inhibit HCC cell invasion after a 7-day course of treatment via NF-κB/MMP9 dependent, COX2/PGE2 independent pathway. Metronomic Celecoxib also significantly suppressed HCC cell proliferation after a 7-day or 30-day culture. Besides, metronomic Celecoxib reduced CSPC phenotype by diminishing sphere formation, percentage of CD90+ population in sphere cells, and expression of CSPC markers. Conclusions Metronomic Celecoxib should be investigated clinically as a chemopreventive agent for selected high-risk HCC patients (e.g., HCC patients after curative treatments).
Multi-classification of Android Applications Based on Convolutional Neural Networks

Google Play App Store, Huawei App Market, Xiaomi App Store, etc. are the main sources for users to download applications. The application category classification is usually based on the category specified by the developer when uploading or by analyzing the description provided by the developer. However, the process is easily manipulated by malicious developers to evade detection. With the popularity of Android phones, the number of Android applications has exploded. How to quickly and accurately classify applications automatically and improve management efficiency is crucial. This paper proposed a multi-classification framework for Android applications based on convolutional neural networks. By extracting multiple static features, we combined natural language processing and deep learning to achieve a classification accuracy of 99.9064%, and the training time was only 158s.
Classifying a Limited Number of the Bamboo Species by the Transformation of Convolution Groups

For agricultural special species, the labeled procedure of large-scale samples is costly, thus, the bamboo species only has a limited number for supervised learning. The fine-tuning strategy is important for deep neural network by transferring learning methods, which utilize the weight of the deep model of the source domain, and can solve the problem associated with insufficient samples to make the model more stability and robustness. In the manuscript, the novelty of the strategy, for images of bamboo species with low-shot classification, mainly proposed an idea that is the transfer of the convolutional group features of deep convolutional models. The deep models with a novel fine-tuning method and three optimizers that are stochastic gradient descent, Adaptive Moment estimation, and Adadelta respectively, are evaluated by the accuracy and the expected calibration error value for the analysis of deep model generalization. An analysis of the results showed that, based on the proportion of training dataset is only 30%, the innovative strategy for bamboo species classification achieved better performance that has an accuracy of 0.82, and the expected calibration error of 0.16, which were better stability and generalization than those of other fine-tuning strategies. Consequently, the novel fine-tuning strategy proposed in this manuscript transfers the features of deep convolutional groups, improves the accuracy and generalizability of the model, and resolves the problems associated with having insufficient samples of bamboo species for low-shot classification.
Effect of Physical-Sports Leisure Activities on Young People’s Psychological Wellbeing

This article examines the impact of physical-sports activities on the psychological wellbeing of Spanish and Colombian young people. Scientific literature highlights that young people devote leisure time to sports for the purpose of enjoyment, and to maintain good health and body image. In addition, it provides the opportunity to socialize, and come into contact and connect with people who have shared interests. It is also an ideal resource for learning and developing social skills to ensure inclusion and the appropriate strategies for emotional management. Similarly, it favors the learning of values that promote the assumption of responsibilities, decision-making capacity, tolerance to frustration, and the development of resilience. This study considers the inherent benefits of physical-sports activities in order to analyze the impact on young people’s assessment of their own psychological wellbeing. To this effect, a quantitative ex post facto study was designed, and Ryff’s Model of Psychological Wellbeing was used with 1,148 young people from Spain and Colombia aged 16–21. The young people were asked whether or not they performed any type of physical-sports activity in their leisure time and the type of activity performed. The results show that young people who perform such activities have higher overall levels of psychological wellbeing. In turn, they emphasize that the perform of physical-sports activities has a positive impact on three of the dimensions of psychological wellbeing: self-acceptance, positive relations with others, and purpose in life. In addition, significant differences in young people’s psychological wellbeing were found depending on whether they perform individual, team or other physical-sports activities. These results provide a basis for the proposal and design of interventions with young people based on sports and leisure activities as socio-educational strategies.
AutoML and Interpretability: Powering the Machine Learning Revolution in Healthcare

An AutoML and interpretability are both fundamental to the successful uptake of machine learning by non-expert end users. The former will lower barriers to entry and unlock potent new capabilities that are out of reach when working with ad-hoc models, while the latter will ensure that outputs are transparent, trustworthy, and meaningful. In healthcare, AutoML and interpretability are already beginning to empower the clinical community by enabling the crafting of actionable analytics that can inform and improve decision-making by clinicians, administrators, researchers, policymakers, and beyond. This keynote presents state-of-the-art AutoML and interpretability methods for healthcare developed in our lab and how they have been applied in various clinical settings (including cancer, cardiovascular disease, cystic fibrosis, and recently Covid-19), and then explains how these approaches form part of a broader vision for the future of machine learning in healthcare.
Classification of Lung Nodules Based on GAN and 3D CNN

An efficient model based on generative adversarial networks (GAN) and the three-dimensional convolutional neural network (3D CNN) is presented and implemented to classify lung nodules, useful for false positives removal. The model is trained on LIDC-IDRI dataset. In the first stage, the transfer learning based on deep convolutional generative adversarial networks (DCGAN) is proposed to preliminarily classify pulmonary nodules. In the second stage, the three-dimensional convolutional neural network (3D CNN) is introduced to finish the task of removing false positives. In the first stage, the transfer learning network converges more rapidly than the direct training network. Besides, the accuracy of the test set in the first stage is 94.50%. In the second stage, the highest accuracy rate of false positives removal is 95.30%. The research and analysis show that the proposed algorithm has better performance in the classification of lung nodules.
Novel Strategies to Combat CMV-Related Cardiovascular Disease

Cytomegalovirus (CMV), a ubiquitous human pathogen that is never cleared from the host, has long been thought to be relatively innocuous in immunocompetent adults, but causes severe complications including blindness, end-organ disease, and death in newborns and in immuno-compromised individuals, such as organ transplant recipients and those suffering from AIDS. Yet even in persons with intact immunity, CMV infection is associated with profound stimulation of immune and inflammatory pathways. Carriers of CMV infection also have an elevated risk of developing cardiovascular complications. In this review, we define the proposed mechanisms of how CMV contributes to cardiovascular disease (CVD), describe current approaches to target CMV, and discuss how these strategies may or may not alleviate cardiovascular complications in those with CMV infection. In addition, we discuss the special situation of CMV coinfection in people with HIV infection receiving antiretroviral therapy, and describe how these 2 viral infections may interact to potentiate CVD in this especially vulnerable population.
Promoting Public Management Accountability in the Cultural Sector in Brazil

Brazilian public management in the cultural sector has been historically off-balanced, albeit showing some signs of consolidation during President Lula's government, in the 2000s. During this period, the State enacted several laws to promote public information transparency. Civil Society Organizations of Public Interest (OSCIPs) are Third Sector Organizations (TSOs) which have the prerogative of making agreements with the Brazilian State to carry out public policies, including those in the cultural sector. As government partners, these organizations are also demanded to be accountable to society, being responsive and transparent. However, our research shows this is not the case. We propose the design of accountability mechanisms for these organizations, comprising three artifacts: a conceptual model for OSCIPs for public management in the cultural sector, an information extraction method to collect data from the web based on this model, and an information portal. The research follows the Design Science Research methodology and its results point to the perceived importance of the Portal to cultural business ecosystem.
A Novel Multi-class Classification Framework Based on Local OVR Deep Neural Network

Environmental sound classification (ESC) based on single-classifier-type multi-class deep neural network is gaining growing attention in current audio classification research. In order to improve the performance of multi-class deep neural networks, neural architecture search (NAS) can generally be used, but such methods often demand tremendous computational costs. This paper presents a novel multi-class framework based on local one-versus-rest (OVR) deep neural network, which can improve the classification performance of a pre-trained deep neural network at an affordable computational cost. The main idea of the framework is to first identify the weak classification category group (WeakClass group) of the pre-trained network for the actual sample using only the training data. This is achieved by using the training average confidence matrix of the pre-trained deep neural classification network. The next step is to build a sparse array of OVR subnetwork classifiers according to the WeakClass group. Afterwards, the OVR subnetwork classifiers are integrated into the original pre-trained network to form the final multi-class classifier. We apply this framework to ESC problem and the experimental results show that the proposed framework achieves a classification accuracy of 86.8% on the ESC-50 dataset, which is better than other related algorithms.
Efficient production of few-layer black phosphorus by liquid-phase exfoliation

Phosphorene is a new two-dimensional material that has recently attracted much attention owing to its fascinating electrical, optical, thermal and chemical properties. Here, we report on high-quality exfoliation of black phosphorus nanosheets, with controllable size produced in large quantities by liquid-phase exfoliation using N-methyl-2-pyrrolidone (NMP) as a solvent under ambient conditions. The as-synthesized few layers show a great potential for solar energy conversion based on the optical results shown in this work.
Genotype-phenotype association mining in bipolar disorder: market research meets complex genetics

Disentangling the etiology of common, complex diseases is a major challenge in genetic research. For bipolar disorder (BD), several genome-wide association studies (GWAS) have been performed. Similar to other complex disorders, major breakthroughs in explaining the high heritability of BD through GWAS have remained elusive. To overcome this dilemma, genetic research into BD, has embraced a variety of strategies such as the formation of large consortia to increase sample size and sequencing approaches. Here we advocate a complementary approach making use of already existing GWAS data: applying a data mining procedure to identify yet undetected genotype-phenotype relationships. We adapted association rule mining, a data mining technique traditionally used in retail market research, to identify frequent and characteristic genotype patterns showing strong associations to phenotype clusters. We applied this strategy to three independent GWAS datasets from 2,835 phenotypically characterized patients with BD. In a discovery step, 20,882 candidate association rules were extracted. Two of these - one associated with eating disorder and the other with anxiety - remained significant in an independent dataset after robust correction for multiple testing, showing considerable effect sizes (odds ratio ~ 3.4 and 3.0, respectively). Our approach may help detect novel specific genotype-phenotype relationships in BD typically not explored by analyses like GWAS. While we adapted the data mining tool within the context of BD gene discovery, it may facilitate identifying highly specific genotype-phenotype relationships in subsets of genome-wide data sets of other complex phenotype with similar epidemiological properties and challenges to gene discovery efforts.
Efficient Actor-Critic Reinforcement Learning With Embodiment of Muscle Tone for Posture Stabilization of the Human Arm

This letter proposes a new idea to improve learning efficiency in reinforcement learning (RL) with the actor-critic method used as a muscle controller for posture stabilization of the human arm. Actor-critic RL (ACRL) is used for simulations to realize posture controls in humans or robots using muscle tension control. However, it requires very high computational costs to acquire a better muscle control policy for desirable postures. For efficient ACRL, we focused on embodiment that is supposed to potentially achieve efficient controls in research fields of artificial intelligence or robotics. According to the neurophysiology of motion control obtained from experimental studies using animals or humans, the pedunculopontine tegmental nucleus (PPTn) induces muscle tone suppression, and the midbrain locomotor region (MLR) induces muscle tone promotion. PPTn and MLR modulate the activation levels of mutually antagonizing muscles such as flexors and extensors in a process through which control signals are translated from the substantia nigra reticulata to the brain stem. Therefore, we hypothesized that the PPTn and MLR could control muscle tone, that is, the maximum values of activation levels of mutually antagonizing muscles using different sigmoidal functions for each muscle; then we introduced antagonism function models (AFMs) of PPTn and MLR for individual muscles, incorporating the hypothesis into the process to determine the activation level of each muscle based on the output of the actor in ACRL. ACRL with AFMs representing the embodiment of muscle tone successfully achieved posture stabilization in five joint motions of the right arm of a human adult male under gravity in predetermined target angles at an earlier period of learning than the learning methods without AFMs. The results obtained from this study suggest that the introduction of embodiment of muscle tone can enhance learning efficiency in posture stabilization disorders of humans or humanoid robots.
Diagnosing COVID-19 pneumonia in a pandemic setting: Lung Ultrasound versus CT (LUVCT) – a multicentre, prospective, observational study

Background In this coronavirus disease 2019 (COVID-19) pandemic, fast and accurate testing is needed to profile patients at the emergency department (ED) and efficiently allocate resources. Chest imaging has been considered in COVID-19 workup, but evidence on lung ultrasound (LUS) is sparse. We therefore aimed to assess and compare the diagnostic accuracy of LUS and computed tomography (CT) in suspected COVID-19 patients. Methods This multicentre, prospective, observational study included adult patients with suspected COVID-19 referred to internal medicine at the ED. We calculated diagnostic accuracy measures for LUS and CT using both PCR and multidisciplinary team (MDT) diagnosis as reference. We also assessed agreement between LUS and CT, and between sonographers. Results One hundred and eighty-seven patients were recruited between March 19 and May 4, 2020. Area under the receiver operating characteristic (AUROC) was 0.81 (95% CI 0.75–0.88) for LUS and 0.89 (95% CI 0.84–0.94) for CT. Sensitivity and specificity for LUS were 91.9% (95% CI 84.0–96.7) and 71.0% (95% CI 61.1–79.6), respectively, versus 88.4% (95% CI 79.7–94.3) and 82.0% (95% CI 73.1–89.0) for CT. Negative likelihood ratio was 0.1 (95% CI 0.06–0.24) for LUS and 0.14 (95% CI 0.08–0.3) for CT. No patient with a false negative LUS required supplemental oxygen or admission. LUS specificity increased to 80% (95% CI 69.9–87.9) compared to MDT diagnosis, with an AUROC of 0.85 (95% CI 0.79–0.91). Agreement between LUS and CT was 0.65. Interobserver agreement for LUS was good: 0.89 (95% CI 0.83–0.93). Conclusion LUS and CT have comparable diagnostic accuracy for COVID-19 pneumonia. LUS can safely exclude clinically relevant COVID-19 pneumonia and may aid COVID-19 diagnosis in high prevalence situations. The #luvct study found that LUS and CT have comparable diagnostic accuracy for #COVID19 pneumonia. LUS safely excludes COVID-19 pneumonia and may aid diagnosis of COVID-19. This simple tool may prove especially useful in resource constrained settings. https://bit.ly/33P71fq