bluuebunny/crossref_metadata_2025_split huggingface dataset:
Applications of miRNAs in cardiac development, disease progression and regeneration
Authors: Pang Jeremy Kah Sheng , Phua Qian Hua , Soh Boon-Seng 
Published: December 2019

AbstractDevelopment of the complex human heart is tightly regulated at multiple levels, maintaining multipotency and proliferative state in the embryonic cardiovascular progenitors and thereafter suppressing progenitor characteristics to allow for terminal differentiation and maturation. Small regulatory microRNAs (miRNAs) are at the level of post-transcriptional gene suppressors, which enhance the degradation or decay of their target protein-coding mRNAs. These miRNAs are known to play roles in a large number of biological events, cardiovascular development being no exception. A number of critical cardiac-specific miRNAs have been identified, of which structural developmental defects have been linked to dysregulation of miRNAs in the proliferating cardiac stem cells. These miRNAs present in the stem cell niche are lost when the cardiac progenitors terminally differentiate, resulting in the postnatal mitotic arrest of the heart. Therapeutic applications of these miRNAs extend to the realm of heart failure, whereby the death of heart cells in the ageing heart cannot be replaced due to the arrest of cell division. By utilizing miRNA therapy to control cell cycling, the regenerative potential of matured myocardium can be restored. This review will address the various cardiac progenitor-related miRNAs that control the development and proliferative potential of the heart.
Current understanding of the administration of mesenchymal stem cells in acute kidney injury to chronic kidney disease transition: a review with a focus on preclinical models
Authors: Zhao Lingfei , Han Fei , Wang Junni , Chen Jianghua 
Published: December 2019

AbstractIncomplete recovery from acute kidney injury (AKI) can result in long-term functional deficits and has been recognized as a major contributor to chronic kidney disease (CKD), which is termed the AKI-CKD transition. Currently, an effective intervention for this disorder is still lacking. Principally, therapeutic strategies targeting the AKI-CKD transition can be divided into those reducing the severity of AKI or promoting the regenerative process towards beneficially adaptive repair pathways. Considering the fact that mesenchymal stem cells (MSCs) have the potential to address both aspects, therapeutic regimens based on MSCs have a promising future. In light of this information, we focus on the currently available evidence associated with MSC therapy involved in the treatment of the AKI-CKD transition and the underlying mechanisms. All of these discussions will contribute to the establishment of a reliable therapeutic strategy for patients with this problem, who can be easily ignored by physicians, and will lead to a better clinical outcome for them.
Community detection in networks: a game-theoretic framework
Authors: Chen Yan , Cao Xuanyu , Liu K. J. Ray 
Published: December 2019

AbstractReal-world networks are often cluttered and hard to organize. Recent studies show that most networks have the community structure, i.e., nodes with similar attributes form a certain community, which enables people to better understand the constitution of the networks and thus gain more insights into the complicated networks. Strategic nodes belonging to different communities interact with each other to decide mutual links in the networks. Hitherto, various community detection methods have been proposed in the literature, yet none of them takes the strategic interactions among nodes into consideration. Additionally, many real-world observations of networks are noisy and incomplete, i.e., with some missing links or fake links, due to either technology constraints or privacy regulations. In this work, a game-theoretic framework of community detection is established, where nodes interact and produce links with each other in a rational way based on mutual benefits, i.e., maximizing their own utility functions when forming a community. Given the proposed game-theoretic generative models for communities, we present a general community detection algorithm based on expectation maximization (EM). Simulations on synthetic networks and experiments on real-world networks demonstrate that the proposed detection method outperforms the state of the art.
A new joint CTC-attention-based speech recognition model with multi-level multi-head attention
Authors: Qin Chu-Xiong , Zhang Wen-Lin , Qu Dan 
Published: December 2019

Abstract
A method called joint connectionist temporal classification (CTC)-attention-based speech recognition has recently received increasing focus and has achieved impressive performance. A hybrid end-to-end architecture that adds an extra CTC loss to the attention-based model could force extra restrictions on alignments. To explore better the end-to-end models, we propose improvements to the feature extraction and attention mechanism. First, we introduce a joint model trained with nonnegative matrix factorization (NMF)-based high-level features. Then, we put forward a hybrid attention mechanism by incorporating multi-head attentions and calculating attention scores over multi-level outputs. Experiments on TIMIT indicate that the new method achieves state-of-the-art performance with our best model. Experiments on WSJ show that our method exhibits a word error rate (WER) that is only 0.2% worse in absolute value than the best referenced method, which is trained on a much larger dataset, and it beats all present end-to-end methods. Further experiments on LibriSpeech show that our method is also comparable to the state-of-the-art end-to-end system in WER.
Time series behavior modeling with digital twin for Internet of Vehicles
Authors: Zhang Tianle , Liu Xiangtao , Luo Zongwei , Dong Fuqiang , Jiang Yu 
Published: December 2019

AbstractElectric vehicle (EV) is considered eco-friendly with low carbon emission and maintenance costs. Given the current battery and charging technology, driving experience of EVs relies heavily on the availability and reachability of EV charging infrastructure. As the number of charging piles increases, carefully designed arrangement of resources and efficient utilization of the infrastructure is essential to the future development of EV industry. The mobility and distribution of EVs determine the charging demand and the load of power distribution grid. Then, dynamic traffic pattern of numerous interconnected EVs poses great impact on charging plans and charging infrastructure.In this paper, we introduce the digital twin of a real-world EV by modeling the mobility based on a time series behaviors of EVs to evaluate the charging algorithm and pile arrangement policy. The introduced digital twin EV is a virtually simulated equivalence with same traffic behaviors and charging activities as the EV in real world. The behavior and route choice of EVs is dynamically simulated base on the time-varying driving operations, travel intent, and charging plan in a simulated large-scale charging scenario composed of concurrently moving EVs and correspondingly equipped charging piles. Different EV navigation algorithms and charging algorithms of Internet of Vehicle can be exactly evaluated in the dynamic simulation of the digital twins of the moving EVs and charging infrastructure. Then we analyze the collected data such as energy consumption, charging capacity, charging frequency, and waiting time in queue on both the EV side and the charging pile side to evaluate the charging efficiency. The simulation is used to study the relations between the scheduled charging operation of EVs and the deployment of piles. The proposed model helps evaluate and validate the design of the charging recommendation and the deployment plan regarding to the arrangement and distribution of charging piles.
Asymptotic behavior for the semilinear reaction–diffusion equations with memory
Authors: Zhang Jiangwei , Xie Yongqin , Luo Qingqing , Tang Zhipiao 
Published: December 2019

AbstractIn this paper, we consider the dynamics of a reaction–diffusion equation with fading memory and nonlinearity satisfying arbitrary polynomial growth condition. Firstly, we prove a criterion in a general setting as an alternative method (or technique) to the existence of the bi-spaces attractors for the nonlinear evolutionary equations (see Theorem 2.14). Secondly, we prove the asymptotic compactness of the semigroup on $L^{2}(\varOmega )\times L_{\mu }^{2}(\mathbb{R}; H_{0}^{1}( \varOmega ))$L2(Ω)×Lμ2(R;H01(Ω)) by using the contractive function, and the global attractor is confirmed. Finally, the bi-spaces global attractor is obtained by verifying the asymptotic compactness of the semigroup on $L^{p}( \varOmega )\times L_{\mu }^{2}(\mathbb{R}; H_{0}^{1}(\varOmega ))$Lp(Ω)×Lμ2(R;H01(Ω)) with initial data in $L^{2}(\varOmega )\times L_{\mu }^{2}(\mathbb{R}; H_{0} ^{1}(\varOmega ))$L2(Ω)×Lμ2(R;H01(Ω)).
Multi-valued backward stochastic differential equations with regime switching
Authors: Deng Ruijuan , Ren Yong 
Published: December 2019

AbstractThe paper considers a class of multi-valued backward stochastic differential equations with subdifferential of a lower semi-continuous convex function with regime switching, whose generator is a continuous-time Markov chain with a finite state space. Firstly, we get the existence and uniqueness of the solution by the penalization method. Secondly, we prove that the solution of the original system is weakly convergent. Finally, we give an application to the homogenization of a class of multi-valued PDEs with Markov chain.
Convergence of solutions for functional integro-differential equations with nonlinear boundary conditions
Authors: Wang Peiguang , Wang Yameng , Jiang Cuimei , Li Tongxing 
Published: December 2019

AbstractThis paper is concerned with the convergence of solutions for a class of functional integro-differential equations with nonlinear boundary conditions. New comparison principles are obtained. By using the comparison principles and quasilinearization method, we present two monotone iterative sequences uniformly and monotonically converging to the unique solution with rate of order 2. Meanwhile, an example is given to demonstrate applications of the result reported.
Spectral properties and a Parseval’s equality in the singular case for q-Dirac problem
Authors: Mosazadeh Seyfollah 
Published: December 2019

AbstractThis paper is devoted to studying a q-analog of the singular Dirac problem. First, we investigate some spectral properties of the problem. Then we prove the existence of a spectral function and establish a Parseval’s equality, for the singular q-Dirac system in a Hilbert space. Although there were given some results for this type of problem, we think that Parseval’s equality has not been studied yet.
Predictors of psychosocial outcome of bipolar disorder: data from the Stanley Foundation Bipolar Network
Authors: Bennett Francis , Hodgetts Sophie , Close Andrew , Frye Mark , Grunze Heinz , Keck Paul , Kupka Ralph , McElroy Susan , Nolen Willem , Post Robert , Schärer Lars , Suppes Trisha , Sharma Aditya N. 
Published: December 2019

Abstract
Background
Impairments in psychosocial functioning have been demonstrated in 30–60% of adults with bipolar disorder (BD). However, the majority of studies investigating the effect of comorbid mental health disorders and age at onset outcomes in BD have focused on traditional outcome measures such as mood symptoms, mortality and treatment response. Therefore, this project aimed to investigate the impact of comorbid mental health disorders and age at onset on longitudinal psychosocial outcome in participants with BD.

Method
Mixed effects modelling was conducted using data from the Stanley Foundation Bipolar Network. Baseline factors were entered into a model, with Global Assessment of Functioning (GAF) score as the longitudinal outcome measure. Relative model fits were calculated using Akaike’s Information Criterion.

Results
No individual comorbidities predicted lower GAF scores, however an interaction effect was demonstrated between attention deficit hyperactivity disorder (ADHD) and any anxiety disorder (t = 2.180, p = 0.030). Participants with BD I vs BD II (t = 2.023, p = 0.044) and those in the lowest vs. highest income class (t = 2.266, p = 0.024) predicted lower GAF scores. Age at onset (t = 1.672, p = 0.095) did not significantly predict GAF scores.

Conclusions
This is the first study to demonstrate the negative psychosocial effects of comorbid anxiety disorders and ADHD in BD. This study adds to the growing database suggesting that comorbid mental health disorders are a significant factor hindering psychosocial recovery.
Glycine-alanine dipeptide repeats spread rapidly in a repeat length- and age-dependent manner in the fly brain
Authors: Morón-Oset Javier , Supèr Tessa , Esser Jacqueline , Isaacs Adrian M. , Grönke Sebastian , Partridge Linda 
Published: December 2019

AbstractHexanucleotide repeat expansions of variable size in C9orf72 are the most prevalent genetic cause of amyotrophic lateral sclerosis and frontotemporal dementia. Sense and antisense transcripts of the expansions are translated by repeat-associated non-AUG translation into five dipeptide repeat proteins (DPRs). Of these, the polyGR, polyPR and, to a lesser extent, polyGA DPRs are neurotoxic, with polyGA the most abundantly detected DPR in patient tissue. Trans-cellular transmission of protein aggregates has recently emerged as a major driver of toxicity in various neurodegenerative diseases. In vitro evidence suggests that the C9 DPRs can spread. However, whether this phenomenon occurs under more complex in vivo conditions remains unexplored. Here, we used the adult fly brain to investigate whether the C9 DPRs can spread in vivo upon expression in a subset of neurons. We found that only polyGA can progressively spread throughout the brain, which accumulates in the shape of aggregate-like puncta inside recipient cells. Interestingly, GA transmission occurred as early as 3 days after expression induction. By comparing the spread of 36, 100 and 200 polyGA repeats, we found that polyGA spread is enhanced upon expression of longer GA DPRs. Transmission of polyGA is greater in older flies, indicating that age-associated factors exacerbate the spread. These data highlight a unique propensity of polyGA to spread throughout the brain, which could contribute to the greater abundance of polyGA in patient tissue. In addition, we present a model of early GA transmission that is suitable for genetic screens to identify mechanisms of spread and its consequences in vivo.
Computational storage: an efficient and scalable platform for big data and HPC applications
Authors: Torabzadehkashi Mahdi , Rezaei Siavash , HeydariGorji Ali , Bobarshad Hosein , Alves Vladimir , Bagherzadeh Nader 
Published: December 2019

AbstractIn the era of big data applications, the demand for more sophisticated data centers and high-performance data processing mechanisms is increasing drastically. Data are originally stored in storage systems. To process data, application servers need to fetch them from storage devices, which imposes the cost of moving data to the system. This cost has a direct relation with the distance of processing engines from the data. This is the key motivation for the emergence of distributed processing platforms such as Hadoop, which move process closer to data. Computational storage devices (CSDs) push the “move process to data” paradigm to its ultimate boundaries by deploying embedded processing engines inside storage devices to process data. In this paper, we introduce Catalina, an efficient and flexible computational storage platform, that provides a seamless environment to process data in-place. Catalina is the first CSD equipped with a dedicated application processor running a full-fledged operating system that provides filesystem-level data access for the applications. Thus, a vast spectrum of applications can be ported for running on Catalina CSDs. Due to these unique features, to the best of our knowledge, Catalina CSD is the only in-storage processing platform that can be seamlessly deployed in clusters to run distributed applications such as Hadoop MapReduce and HPC applications in-place without any modifications on the underlying distributed processing framework. For the proof of concept, we build a fully functional Catalina prototype and a CSD-equipped platform using 16 Catalina CSDs to run Intel HiBench Hadoop and HPC benchmarks to investigate the benefits of deploying Catalina CSDs in the distributed processing environments. The experimental results show up to 2.2× improvement in performance and 4.3× reduction in energy consumption, respectively, for running Hadoop MapReduce benchmarks. Additionally, thanks to the Neon SIMD engines, the performance and energy efficiency of DFT algorithms are improved up to 5.4× and 8.9×, respectively.
Educational games and the new forms of interactions
Authors: Zaina Luciana , Castro Elisa , Martinelli Suellen , Sakata Tiemi 
Published: December 2019

AbstractGames are seen as environments that promote the engagement of children to the learning of Computational Thinking (CT) concepts. The new forms of interaction using body movements and gestures have increased in particular in video-games. However, few studies have been conducted to investigate how children feel and interact when using hand gestures in the context of learning tools. And this number becomes low when we look at studies about CT learning. In this article, we explored the feedback of 29 children about their interaction by using hand gestures in a game to stimulate the CT, i.e. a CT game. In the analysis, we explored how these forms of interaction impacted on the accomplishing of the game phases. Our findings revealed the children kept more engaged when using hand gestures, and consequently, this affected their concentration on the game purpose. To complement our investigation, we interviewed 8 elementary school teachers to gather their perceptions about the potential of using hand gestures and our game. The findings showed the teachers agreed that hand gestures are suitable to be used in learning tools. They also demonstrated interested in introducing the CT fundamentals in their classes. However, these teachers demonstrated some concerns about how to match the use of that technology to their teaching activities.
RASAL1 induces to downregulate the SCD1, leading to suppression of cell proliferation in colon cancer via LXRα/SREBP1c pathway
Authors: Wang Guangchuan , Li Zhen , Li Xiao , Zhang Chunqing , Peng Lipan 
Published: December 2019

Abstract
Background
Recent studies have confirmed that RASAL1 has an antitumor effect in many cancers, but its functional role and the molecular mechanism underlying in colon cancer has not been investigated.

Results
We collected human colon cancer tissues and adjacent non-tumor tissues, human colon cancer cell lines LoVo, CaCo2, SW1116, SW480 and HCT-116, and normal colonic mucosa cell line NCM460. RT-qPCR was used to detect the RASAL1 level in the clinical tissues and cell lines. In LoVo and HCT-116, RASAL1 was artificially overexpressed. Cell viability and proliferation were measured using CCK-8 assays, and cell cycle was detected via PI staining and flow cytometry analysis. RASAL1 significantly inhibited the cell proliferation via inducing cell cycle arrest, suppressed cell cycle associated protein expression, and decreased the lipid content and inhibited the SCD1 expression. Moreover, SCD1 overexpression induced and downregulation repressed cell proliferation by causing cell cycle arrest. Additionally, luciferase reporter assays were performed to confirm the direct binding between SREBP1c, LXRα and SCD1 promoter, we also demonstrated that RASAL1 inhibit SCD1 3′-UTR activity. RASAL1 inhibited tumor growth in xenograft nude mice models and shows inhibitory effect of SCD1 expression in vivo.

Conclusion
Taken together, we concluded that RASAL1 inhibited colon cancer cell proliferation via modulating SCD1 activity through LXRα/SREBP1c pathway.
An analytical investigation of elastic-plastic deformation of FGM hollow rotors under a high centrifugal effect
Authors: Torabnia Shams , Aghajani Sepideh , Hemati Mohammadreza 
Published: December 2019

AbstractFunctionally graded material shafts are the main part of many modern rotary machines such as turbines and electric motors. The purpose of this study is to present an analytical solution of the elastic-plastic deformation of functionally graded material hollow rotor under a high centrifugal effect and finally determine the maximum allowed angular velocity of a hollow functionally graded material rotating shaft. Introducing non-dimensional parameters, the equilibrium equation has been analytically solved. The results for variable material properties are compared with the homogeneous rotor and the case in which Young’s modulus is the only variable while density and yield stress are considered to be constant. It is shown that material variation has a considerable effect on the stress and strain components and radial displacement. Considering variable density and yield stress causes yielding onset from inner, outer, or simultaneously from both inner and outer rotor shaft radius in contrast to earlier researches that modulus of elasticity was the only variable. The effects of the density on the failure of a functionally graded material elastic fully plastic in a hollow rotating shaft are investigated for the first time in this study with regard to Tresca’s yield criterial. Numerical simulations are used to verify the derived formulations which are in satisfying agreement.
Dental implants in immunocompromised patients: a systematic review and meta-analysis
Authors: Duttenhoefer Fabian , Fuessinger Marc Anton , Beckmann Yasmin , Schmelzeisen Rainer , Groetz Knut A. , Boeker Martin 
Published: December 2019

AbstractObjectiveImpaired health conditions and related lack of adequate host healing are among the most important conditions that account for dental implant failure. Today clinicians face an increasing number of immunocompromised patients requesting implant-based rehabilitation. To provide clinical evidence for prospective decision-making, the aim of this systematic review and meta-analysis was to analyse the influence of immunodeficiency on dental implant survival.MethodsThe study was conducted according to the PRISMA Statement and the principles of the Cochrane Collaboration. MEDLINE and Web of Science were searched. Results were calculated by the pooled incidence of implant loss. Reported odds ratios (OR) from fully adjusted models were preferred. Distinct risk estimates were synthesised with 95% confidence intervals.ResultsA total of 62 publications including 1751 endosseous implants placed in immunocompromised patients were included. For the follow-up of 24 months and longer, the mean survival rate of implants in patients with HIV was 93.1%, chemotherapy was 98.8%, autoimmune disease was 88.75%, after organ transplantation was 100%. Crohn’s disease showed a significant effect on early implant failure and resulted in increased, however not significant, implant loss.ConclusionNo significant effect of immunocompromised conditions on implant survival was detectable. Implant-based therapy in immunocompromised patients should not aggravate the general morbidity and must not interfere in life-saving therapies. A careful risk stratification prior implant therapy is fundamental. To further decipher the role of immunosuppression on dental implantology, more data from controlled and randomised studies are needed.