Miracle-dz/UniFAR-FTUs huggingface dataset:
Systematic identification of lincRNA‐based prognostic biomarkers by integrating lincRNA expression and copy number variation in lung adenocarcinoma

Copy number alterations (CNAs) of lincRNAs act as one of important mechanisms in disrupting lincRNA expression which may play critical roles during tumorigenesis in lung adenocarcinoma (LUAD). The copy number alterations of lincRNAs can mark the spectrum of cancer progression and may serve as biomarkers for prognosis in LUAD, however it is rarely studied. We analyzed RNASeq data for 488 LUAD patients from TCGA portal and 58 healthy subjects to identify prognostic lincRNAs predictive of patient survival. Computational analysis entailing integration of expression and copy number alteration data revealed five prognostic lincRNAs: RBPMS‐AS1, TDRKH‐AS1, LINC00578, RP11‐470 M17.2 and LINC00941. The copy number alterations in the LINC00578 and RP11‐470 M17.2 genes were positively associated with the longer overall survival of LUAD patients. The CNA in LINC00941 was negatively associated with the longer overall survival. Copy number amplification significantly correlated with increased expression of TDRKH‐AS1, which regulates telomere organization and EZH2‐mediated epigenetic silencing of CDKN1A, CDKN1B and IL24. Decreased survival of LUAD patients was associated with high LINC00941 expression. The LINC00941 regulates the PI3K‐AKT signaling pathway, focal adhesion by influencing potential targets, such as KRAS proto‐oncogene GTPase and VEGFC. These lincRNA‐based prognostic biomarkers may destroy important cancer‐related biological processes contributing to LUAD prognosis. In summary, we demonstrate the prognostic potential of four differentially expressed lincRNAs with copy number alterations (RBPMS‐AS1, TDRKH‐AS1, LINC00578 and RP11‐470 M17.2) that are positively associated with longer overall survival of LUAD patients. One differentially expressed lincRNA LINC00941 with copy number alterations was negatively associated with longer overall survival of LUAD patients.
A Mediated Moderation Model of Work Overload and Mental Health of Professional Service Staff

Professional service has become an important part of the knowledge economy society, and work overload of professional service staff has seriously affected the mental health. Based on Job Demand-Resource theory, this study investigates the mediating mechanism and boundary conditions of work overload on mental health by investigating 113 employees in one of the big four international accounting firms. The results revealed that work overload was positively correlated with job burnout, and negatively correlated with mental health; supervisor support moderate that work overload influence on mental health; job burnout mediating impacts of work overload and supervisor support on mental health. This study provides a new perspective to reduce the negative effects of professional service employees' work overload on mental health.
Non-Euclidean Vector Product for Neural Networks

We present a non-Euclidean vector product for artificial neural networks. The vector product operator does not require any multiplications while providing correlation information between two vectors. Ordinary neurons require inner product of two vectors. We propose a class of neural networks with the universal approximation property over the space of Lebesgue integrable functions based on the proposed non-Euclidean vector product. In this new network, the “product” of two real numbers is defined as the sum of their absolute values, with the sign determined by the sign of the product of the numbers. This “product” is used to construct a vector product in RN. The vector product induces the l1 norm. The additive neural network successfully solves the XOR problem. Experiments on MNIST and CIFAR datasets show that the classification performance of the proposed additive neural network is comparable to the corresponding multi-layer perceptron and convolutional neural networks.
The association between gestational age and otitis media during childhood: a population-based cohort analysis

Abstract Otitis media (OM) carries a tremendous global health burden and potentially severe long-term consequences. The objective of this study was to determine the impact of birth at different gestational ages on the incidence of childhood OM. A population-based cohort analysis was conducted. All singleton deliveries occurring between 1991 and 2014 at a regional tertiary medical center were included. Gestational age on delivery was divided into six subgroups: early (<34 weeks gestation; 0 out of 7) and late (34 weeks gestation; 0 out of 7 to 36 weeks gestation; 6 out of 7) preterm, and early (37 weeks gestation; 0 out of 7 to 38 weeks gestation; 6 out of 7), full (39 weeks gestation; 0 out of 7 to 40 weeks gestation; 6 out of 7), late (41 weeks gestation; 0 out of 7 to 41 weeks gestation; 6 out of 7) and post (⩾42 weeks 0 out of 7) term deliveries. Rates of OM-related hospitalizations up to 18 years of age were assessed. Weibull parametric hazards model was used to study the association between gestational age at birth and the risk for OM-related hospitalizations while controlling for potential confounders. During the study period, 238,622 deliveries met the inclusion criteria. OM-related hospitalizations of the offspring (n=4724) were significantly more common in the preterm (early 3.6%, late 2.4%) and early-term born children (2.2%) and decreased gradually across the full (1.9%), late (1.7%) and post (1.6%) term groups (χ2-test for trends P<0.001). In the Weibull regression model, early preterm, as well as early-term deliveries exhibited an independent association with pediatric OM (adjusted hazard ratios: 1.67 and 1.09, respectively, P<0.02). Deliveries occurring at preterm and early term are associated with higher rates of pediatric OM-related hospitalizations, which decrease gradually as gestational age advances.
Use and the Users of a Patient Portal: Cross-Sectional Study

Background Patient portals offer patients access to their medical information and tools to communicate with health care providers. It has been shown that patient portals have the potential to positively impact health outcomes and efficiency of health care. It is therefore important that health care organizations identify the patients who use or do not use the patient portal and explore the reasons in either case. The Unified Theory of Acceptance and Use of Technology (UTAUT) is a frequently used theory for explaining the use of information technology. It consists of the following constructs: performance expectancy, effort expectancy, social influence, facilitating conditions, and behavioral intention to use. Objective This study aimed to explore the prevalence of patient portal use and the characteristics of patients who use or do not use a patient portal. The main constructs of UTAUT, together with demographics and disease- and care-related characteristics, have been measured to explore the predictive factors of portal use. Methods A cross-sectional study was conducted in the outpatient departments for adult patients of a university hospital in the Netherlands. Following outcomes were included: self-reported portal use, characteristics of users such as demographics, disease- and care-related data, eHealth literacy (modified score), and scores of UTAUT constructs. Descriptive analyses and univariate and multivariate logistic regression were also conducted. Results In the analysis, 439 adult patients were included. Furthermore, 32.1% (141/439) identified as being a user of the patient portal; 31.2% (137/439) indicated as nonusers, but being aware of the existence of the portal; and 36.6% (161/439) as being nonusers not aware of the existence of the portal. In the entire study population, the factors of being chronically ill (odds ratio, OR 1.62, 95% CI 1.04-2.52) and eHealth literacy (modified score; OR 1.12, 95% CI 1.07-1.18) best predicted portal use. In users and nonusers who were aware of the portal, UTAUT constructs were added to the multivariate logistic regression, with chronically ill and modified eHealth literacy sum score. Effort expectancy (OR 13.02, 95% CI 5.68-29.87) and performance expectancy (OR 2.84, 95% CI 1.65-4.90) are shown to significantly influence portal use in this group. Conclusions Approximately one-third of the patients of a university hospital self-reported using the patient portal; most expressed satisfaction. At first sight, being chronically ill and higher scores on the modified eHealth literacy scale explained portal use. Adding UTAUT constructs to the model revealed that effort expectancy (ease of use and knowledge and skills related to portal use) and performance expectancy (perceived usefulness) influenced portal use. Interventions to improve awareness of the portal and eHealth literacy skills of patients and further integration of the patient portal in usual face-to-face care are needed to increase use and potential subsequent patient benefits.
Music Chord Recognition Based on Midi-Trained Deep Feature and BLSTM-CRF Hybird Decoding

In this paper, we design a novel deep learning based hybrid system for automatic chord recognition. Currently, there is a bottleneck in the amount of enough annotated data for training robust acoustic models, as hand annotating time-synchronized chord labels requires professional musical skills and considerable labor. As a solution to this problem, we construct a large set of time synchronized MIDI-audio pairs, and use these data to train a Deep Residual Network (DRN) feature extractor, which can then estimate pitch class activations of real-world music audio recordings. Sequence classification and decoding are then performed with a trained Bidirectional LSTM and Conditional Random Fields (CRF) network. Experiments show that the proposed model is compatible for both regular major/minor triad chord classification and larger vocabulary chord recognition, the performance is good and no less than other state-of-the-art systems. The proposed system also achieved good evaluation score in MIREX 2017 Automatic Chord Estimation task.
ISL1 promotes cancer progression and inhibits cisplatin sensitivity in triple-negative breast cancer cells

Triple-negative breast cancer (TNBC) is a type of breast cancer that is characterized by the lack of expression of estrogen and progesterone receptors, and epidermal growth factor receptor 2. Therefore, there is an absence of a specific target for effective therapy in TNBC. Cisplatin is usually employed as a first-line chemotherapy agent for patients with TNBC. However, resistance remains an obstacle for cisplatin-based chemotherapy, due to its elusive underlying mechanism. Previously, abnormal expression of Islet 1 (ISL1) was demonstrated to be closely associated with cancer development and progression. The present study revealed that (ISL1) was significantly upregulated in TNBC tissues in comparison with adjacent normal tissues. Overexpression of ISL1 markedly promoted the proliferation and invasion of the TNBC MDA-MB-231 and MDA-MB-468 cell lines, while knockdown of ISL1 inhibited cell invasion and proliferation in these cell lines. In addition, overexpression of ISL1 reversed cisplatin-induced cell apoptosis, while knockdown of ISL1 enhanced apoptosis following cisplatin treatment in MDA-MB-231 and MDA-MB-468 cells. Furthermore, the levels of the anti-apoptotic proteins, phosphorylated-protein kinase B and B-cell lymphoma-2 (Bcl-2), were significantly decreased, while the levels of the pro-apoptotic protein Bcl-2-associated X protein were remarkably increased in response to cisplatin treatment. The present study revealed that ISL1 overexpression reversed the protein expression profile of p-Akt, Bcl-2 and Bax, while ISL1 knockdown promoted cell apoptosis. Therefore, the data of the present study demonstrated that ISL1 contributes to TNBC progression and reverses cell sensitivity towards cisplatin in TNBC cells, suggesting that ISL1 is a potential therapeutic target for the treatment of TNBC.
Ser46-Phosphorylated MARCKS Is a Marker of Neurite Degeneration at the Pre-aggregation Stage in PD/DLB Pathology

Visual Abstract Phosphorylation of myristoylated alanine-rich C kinase substrate (MARCKS) reflects neurite degeneration at the early stage of Alzheimer’s disease (AD), before extracellular Aβ aggregates are histologically detectable. Here, we demonstrate that similar changes in MARCKS occur in Parkinson’s disease (PD) and dementia with Lewy bodies (DLB) pathologies in both mouse models and human patients. The increase in the level of pSer46-MARCKS began before α-synuclein aggregate formation, at a time when human α-Syn-BAC-Tg/GBA-hetero-KO mice exhibited no symptoms, and was sustained during aging, consistent with the pattern in human postmortem brains. The results strongly imply a common mechanism of pre-aggregation neurite degeneration in AD and PD/DLB pathologies.
Revisit Multinomial Logistic Regression in Deep Learning: Data Dependent Model Initialization for Image Recognition

We study in this paper how to initialize the parameters of multinomial logistic regression (a fully connected layer followed with softmax and cross entropy loss), which is widely used in deep neural network (DNN) models for classification problems. As logistic regression is widely known not having a closed-form solution, it is usually randomly initialized, leading to several deficiencies especially in transfer learning where all the layers except for the last task-specific layer are initialized using a pre-trained model. The deficiencies include slow convergence speed, possibility of stuck in local minimum, and the risk of over-fitting. To address those deficiencies, we first study the properties of logistic regression and propose a closed-form approximate solution named regularized Gaussian classifier (RGC). Then we adopt this approximate solution to initialize the task-specific linear layer and demonstrate superior performance over random initialization in terms of both accuracy and convergence speed on various tasks and datasets. For example, for image classification, our approach can reduce the training time by 10 times and achieve 3.2% gain in accuracy for Flickr-style classification. For object detection, our approach can also be 10 times faster in training for the same accuracy, or 5% better in terms of mAP for VOC 2007 with slightly longer training.
Context-Dependent Diffusion Network for Visual Relationship Detection

Visual relationship detection can bridge the gap between computer vision and natural language for scene understanding of images. Different from pure object recognition tasks, the relation triplets of subject-predicate-object lie on an extreme diversity space, such asperson-behind-person andcar-behind-building, while suffering from the problem of combinatorial explosion. In this paper, we propose a context-dependent diffusion network (CDDN) framework to deal with visual relationship detection. To capture the interactions of different object instances, two types of graphs, word semantic graph and visual scene graph, are constructed to encode global context interdependency. The semantic graph is built through language priors to model semantic correlations across objects, whilst the visual scene graph defines the connections of scene objects so as to utilize the surrounding scene information. For the graph-structured data, we design a diffusion network to adaptively aggregate information from contexts, which can effectively learn latent representations of visual relationships and well cater to visual relationship detection in view of its isomorphic invariance to graphs. Experiments on two widely-used datasets demonstrate that our proposed method is more effective and achieves the state-of-the-art performance.
Semantic Priors for Intrinsic Image Decomposition

Intrinsic Image Decomposition (IID) is a challenging and interesting computer vision problem with various applications in several ﬁelds. We present novel semantic priors and an integrated approach for single image IID that involves analyzing image at three hierarchical context levels. Local context priors capture scene properties at each pixel within a small neighborhood. Mid-level context priors encode object level semantics. Global context priors establish correspondences at the scene level. Our semantic priors are designed on both ﬁxed and ﬂexible regions, using selective search method and Convolutional Neural Network features. Experiments and analysis of our method indicate the utility of our weak semantic priors and structured hierarchical analysis in an IID framework. We compare our method with the current state-of-the-art and show results with lesser artifacts. Finally, we highlight that proper choice and encoding of prior knowledge can produce competitive results compared to end-to-end deep learning IID methods, signifying the importance of such priors. We believe that the insights and techniques presented in this paper would be useful in the future IID research.
Mobile Public Service Promotion Strategies for Youth Based on An Empirical Study on University Students' Adoption of Government WeChat Official Accounts

The government WeChat official account in China has become the most signification tool for mobile public services since Internet Plus was proposed in 2015. The government departments are trying their best to attract the public to use their accounts. The young generation is the most significant group to be attracted, because they will be the frequent users soon after they go into the society and also they can teach the older people to use the accounts effectively. Aiming at proposing the promotion strategies for the youth, we conducted an empirical study with structural equation of modeling (SEM) on the university students' adoption of the government WeChat official accounts. In the paper, firstly, we give the profile of the government WeChat official accounts using and the reason why the youth should be paid attention to. Secondly, we construct our research model by employing Technology Acceptance Model, Theory of Reasoned Action, Innovation Diffusion Theory, Network Externality, and the theories on Trust together and considering the characteristics of both WeChat and the young generation, and especially extract the influencing factors from three perspectives, that is, the technical, the personal and the environmental. Then we illustrate the data collection, the methodology and the SEM analysis with AMOS 22.0. At last, we propose four strategies for the promotion of mobile public services for the youth based on the results of the empirical study.
Perioperative Outcomes and Surgical Case Volume in Pediatric Complex Cranial Vault Reconstruction: A Multicenter Observational Study From the Pediatric Craniofacial Collaborative Group.

BACKGROUND Complex cranial vault reconstruction (CCVR) performed to treat craniosynostosis can be associated with significant blood loss, transfusion, and perioperative complications. The aim of this study was to examine the effect of CCVR surgical case volume on perioperative outcomes. We hypothesized that surgical case volume is not associated with differences in perioperative outcomes. The study primary outcome was total perioperative blood donor exposures. Secondary outcomes included the total perioperative transfusion volume, major complications, and intensive care unit and hospital length of stay. METHODS The multicenter Pediatric Surgery Perioperative Registry was queried for infants and children undergoing CCVR between June 2012 and September 2016. Institutions were categorized into low, middle, or high surgical case volume groups based on tertiles of the average number of cases performed per month. Primary and secondary outcomes were analyzed with respect to these groupings. RESULTS The query yielded 1814 CCVR cases from 33 institutions. Demographics were similar among the 3 study groups. An inverse relationship between surgical case volume and total perioperative blood donor exposures was observed (P < .001). The low-volume group had higher perioperative transfusion volumes (P = .02 versus middle; P = .01 versus high). There was no significant relationship between surgical case volume and the incidence of major postoperative complications or hospital length of stay. CONCLUSIONS In this study, low surgical case volumes were associated with increased total blood donor exposures and increased perioperative transfusion volumes. Hospital length of stay was homogeneous in the 3 groups, suggesting a limited overall clinical impact of the observed transfusion outcome differences.
Modulation of Cortical Activity by High-Frequency Whole-Body Vibration Exercise: An fNIRS Study.

CONTEXT Whole-body vibration (WBV) has shown many positive effects on the human body in rehabilitation and clinical settings in which vibration has been used to elicit muscle contractions in spastic and paretic muscles. OBJECTIVE The purpose of this study was to investigate whether WBV exercise (WBVe) differently modulates the cortical activity associated with motor and prefrontal function based on its frequency. METHODS A total of 18 healthy male adults (mean age: 25.3 [2.4] y) participated in this study and performed WBVe (Galileo Advanced plus; Novotec Medical, Pforzheim, Germany) under 3 different vibration frequency conditions (4-mm amplitude with 10-, 20-, and 27-Hz frequencies) and a control condition (0-mm amplitude with 0-Hz frequency). Each condition consisted of 2 alternating tasks (squatting and standing) every 30 seconds for 5 repetitions. All subjects performed the 4 conditions in a randomized order. MAIN OUTCOME MEASURE Cortical activation during WBVe was measured by relative changes in oxygenated hemoglobin concentration over the primary motor cortex, premotor cortex, supplementary motor area, and prefrontal and somatosensory cortices using functional near-infrared spectroscopy. RESULTS Oxygenated hemoglobin concentration was higher during the 27-Hz vibration condition than the control and 10-Hz vibration conditions. Specifically, these changes were pronounced in the bilateral primary motor cortex (P < .05) and right prefrontal cortex (P < .05). In contrast, no significant changes in oxygenated hemoglobin concentration were observed in any of the cortical areas during the 10-Hz vibration condition compared with the control condition. CONCLUSION This study provides evidence that the motor network and prefrontal cortical areas of healthy adult males can be activated by 27-Hz WBVe. However, WBVe at lower frequencies did not induce significant changes in cortical activation.
Effects of Tea Catechins on Alzheimer’s Disease: Recent Updates and Perspectives

Alzheimer’s disease (AD) is one of the most common neurodegenerative disorders worldwide. Its incidence is gradually increasing because of an aging demographic. Therefore, AD prevention and modification is important to improve the health status of older adults. Oxidative stress is a component of the pathological mechanisms underlying AD. It is caused by a disruption of the balance between reactive oxygen species and antioxidant molecules. This imbalance also causes neuroinflammation. Catechins, which are bioactive components of tea, have antioxidative and anti-inflammatory effects. Moreover, other potential properties related to AD prevention and modification have been reported in in vitro and in vivo studies. Several clinical studies have also been conducted to date. The current review summarizes recent updates and perspectives of the effects of catechins on AD based on the molecular mechanisms and related clinical studies.