Earlier Approaches Research Articles

Microglia and Dendritic Cells as a Source of IL-6 in a Mouse Model of Multiple Sclerosis.

Multiple sclerosis (MS) is a complex autoimmune disease of central nervous system (CNS) characterized by the myelin sheath destruction and compromised nerve signal transmission. Understanding molecular mechanisms driving MS development is critical due to its early onset, chronic course, and therapeutic approaches based only on symptomatic treatment. Cytokines are known to play a pivotal role in the MS pathogenesis with interleukin-6 (IL-6) being one of the key mediators. This study investigates contribution of IL-6 produced by microglia and dendritic cells to the development of experimental autoimmune encephalomyelitis (EAE), a widely used mouse model of MS. Mice with conditional inactivation of IL-6 in the CX3CR1+ cells, including microglia, or CD11c+ dendritic cells, displayed less severe symptoms as compared to their wild-type counterparts. Mice with microglial IL-6 deletion exhibited an elevated proportion of regulatory T cells and reduced percentage of pathogenic IFNγ-producing CD4+ T cells, accompanied by the decrease in pro-inflammatory monocytes in the CNS at the peak of EAE. At the same time, deletion of IL-6 from microglia resulted in the increase of CCR6+ T cells and GM-CSF-producing T cells. Conversely, mice with IL-6 deficiency in the dendritic cells showed not only the previously described increase in the proportion of regulatory T cells and decrease in the proportion of TH17 cells, but also reduction in the production of GM-CSF and IFNγ in the secondary lymphoid organs. In summary, IL-6 functions during EAE depend on both the source and localization of immune response: the microglial IL-6 exerts both pathogenic and protective functions specifically in the CNS, whereas the dendritic cell-derived IL-6, in addition to being critically involved in the balance of regulatory T cells and TH17 cells, may stimulate production of cytokines associated with pathogenic functions of T cells.

Analisis Perkembangan Sosial Emosional Anak pada Usia 3-4 Tahun

This study aims to gain an in-depth understanding of the social-emotional development of children aged 3-4 years. A qualitative descriptive approach was used to explore social interactions, emotional regulation, and language development of children in their everyday contexts. The research methods involved direct observation, interviews with parents, and participatory observations during children's play activities. The collected data were analyzed using a thematic approach to identify patterns and key themes. The findings of the study revealed that children aged 3-4 years show a growing interest in interacting with peers. They begin to learn simple rules of group play and demonstrate the ability to share toys. Children's language skills also undergo significant development, as they are able to use longer and more complex sentences and express their feelings and thoughts through words. Furthermore, children at this age start developing self-awareness, recognizing their own identities, and understanding their roles within a group. They also begin to learn emotional regulation, although they may still struggle with controlling rapid mood changes. The capacity for empathy also begins to develop, as children show concern for injured or sad peers. This study provides a deeper understanding of the social-emotional development of children aged 3-4 years. The implications of these findings can inform early childhood education and care approaches, emphasizing the importance of positive social interactions, healthy emotional regulation, and the development of empathy skills during this period.

Effect of telehealth education on women’s knowledge regarding risk factors and early detection of breast cancer

Background The most frequent cancer that affects women worldwide is breast cancer (BC). Through comprehension of risk factors and early detection approaches, women can become more adept at managing their journey toward breast health. Aim The aim of the study was to evaluate the effect of telehealth education on women’s knowledge regarding risk factors and early detection of BC. Design A quasi-experimental (pre- and post test) design was used to achieve the current study aim. Setting The current study was carried out at family health centers in Cairo City, Egypt. Sample A purposive sample of 300 women was recruited for the current study. Tools Two tools were used for data collection. Tool (I): Demographic and medical-related data form and Tool (II) BC Knowledge Questionnaire. Results The current findings disclosed that there were statistically significant differences between all knowledge dimensions’ mean scores of BC risk factors related to knowledge pre-, post-, and follow-up telehealth sessions (P value= 0.0001). Also, the study finding indicated that 73% of participants got a satisfactory knowledge level regarding BC risk factors and early detection after sessions, which increased to 86.7% after 3 months. Conclusion The implementation of telehealth education has a significant effect on improving women’s knowledge regarding BC risk factors and early detection. Recommendations Promotion and development of telehealth education programs to raise women’s knowledge about BC risk factors and early detection

A fast relay feedback auto-tuning tilt-integral-derivative (TID) controller method with the fractional-order Ziegler–Nichols approach

The relay feedback auto-tuning method, which was an early commercialized approach, has maintained its popularity due to its simplicity and robustness. However, the classical proportional integral derivative (PID) controller auto-tuning method often results in unacceptable overshoot, especially for integrating and higher-order processes. By integrating the TID controller with the relay feedback technique, we significantly enhance dynamic control performance without relying on prior knowledge of the system. However, the direct application of the classical auto-tuning method to the TID controller encounters challenges due to the additional fractional-order transfer function s−1n. Therefore, we have developed a fractional-order Ziegler–Nichols (FOZ-N) approach, specifically designed to adjust the parameters of the TID controller. The proposed FOZ-N method is fast, simple, and capable of achieving the desired performance. In contrast to the previous Ziegler–Nichols tuning method, the TID controller parameters Kt and Ki are determined to shift the critical point (−1/Ku,j0) to the FOZ-N point (−0.5,−j0.7) on the Nyquist curve, ensuring system robustness and dynamic performance. The impact of the fractional order parameter s−1n and ratio r is explored through time-domain analysis, where these parameters are determined to ensure optimal dynamic performance. Additionally, we provide a detailed tuning procedure along with a helpful example. To demonstrate the advantages of the proposed auto-tuning TID controller over the Ziegler–Nichols PID controller, Optimal PID controller, simple internal model control (SIMC) PID controller, and Ziegler–Nichols FOPID controller, we present a simulation illustration involving multiple different systems. To validate the practical outcomes of this paper, we present experimental results on the temperature control of a Peltier cell.

Relation between characteristic temperature and elution temperature in temperature programmed gas chromatography – Part II: Influence of column properties

The method development process in gas chromatography can be accelerated by suitable computer simulation tools using knowledge about the solute-column interactions described by thermodynamic retention parameters. Since retention parameters usually are determined under isothermal conditions, the presented work offers a step to estimate one of the most important retention parameters, the characteristic temperature Tchar by less laborious temperature programmed measurements.In the first part an empirical multivariate model was introduced describing the correlation between the elution temperature Telu of a solute and its characteristic temperature Tchar. Now in the second part a simulation model of GC and available retention data from a retention database was used to investigate the correlation between Telu and Tchar for an expanded range of heating rates and initial temperatures. In addition to part I, the simulation is used to investigate the influences of different properties of the separation column such as different phase ratios and column geometries like length and diameter or various stationary phases including SLB-5 ms, SPB-50, Stabilwax, Rtx-Dioxin2, Rxi-17Sil MS, Rxi-5Sil MS, ZB-PAH-CT, DB-5 ms, Rxi-5 ms, Rtx5 and FS5ms. The fit model is valid for all investigated stationary phases. The influence of the phase ratio to the correlation could be determined. Therefore, the model was expanded to this parameter.The expanded range of heating rates and the normalization for the system independent dimensionless heating rate required a further modification of the previously presented correlation model. The model now fits also under isothermal conditions. The results were used for estimation of the Tchar of an analyte from the elution temperature in the temperature program. The prediction performance was investigated and evaluated for 20 different temperature program conditions and at two phase ratios (β=125 and β=250). Under best conditions the estimated and the measured Tchar values show relative differences <0.5 %. With this novel model estimations for Tchar are possible at 20 °C above the initial temperature, which expands the prediction range even for low and medium retained analytes compared to earlier approaches.

Pemetaaan Partisipatif Potensi Wisata Bantaran Sungai Sebagai Upaya Perencanaan Pembangunan Desa

The current management paradigm of the river area is no longer seen as a protected area without any exploitation activity in it. The involvement of the community around the river in the management is an effective alternative inining the sustainable functioning of the riverside area. One of the early approaches to achieving sustainable management of river transportation is by conducting participatory mapping for administrative boundaries and development of tourism activities. The objectives of this activity: 1) provide education on the functioning of the river transport area, potential threats and strategies for sustainable river management; 2) accompany the preparation of participatory maps of the administrative ducks and the development plan of tourist activities in the village of Peniti Besar. Dedication to the community is carried out by the method of community development. The stages are surveys, potential identification, base map making, participatory mapping, map finalisation, and evaluation. Through participatory mapping, the sustainable management of river basin areas based on tourist activities, has provided extensive space for the apparatus of the village, as well as the community in developing its territory independently and intelligently, as listed in the indicator of achievement of the Sustainable Development Goals (SDGs).

Effect of Catch Them Early Approach on Expository Reading Skills of Grade 7 Students

Effect of Catch Them Early Approach on Expository Reading Skills of Grade 7 Students

A New Approach to Computationally- Successful Linear and Polynomial Regression Analytics of Large Data in Medicine

Features like assortative mixing; high clustering, short average path lengths, broad degree distributions, and community structure have been the subject of numerous recent social network studies. All of these features are met by the model that is introduced in this study. Additionally, our model enables interactions between various communities, fostering a rich network environment. The asymptotically scale-free degree distribution is maintained by our model, which achieves a high clustering coefficient. In our model, the community structure is generated by a mix of mechanisms involving implicit preferential attachment and random attachment. We expand our consideration to include neighbor of neighbor of Initial Contact (NNIC) as well, in contrast to earlier approaches that solely focused on neighbor of Initial Contact (NIC) as an implicit preferential contact. If a newly added vertex chooses more than one initial contact, this extension makes it possible for contacts between those initial contacts to occur. Consequently, our model facilitates the development of complex social networks beyond those used as basic references. Finally, we conduct centrality calculations on both the existing model and our developed model, providing a comparative analysis of the results.

A Novel Approach for Analyzing Temporal Influence Dynamics in Social Networks

Features like assortative mixing; high clustering, short average path lengths, broad degree distributions, and community structure have been the subject of numerous recent social network studies. All of these features are met by the model that is introduced in this study. Additionally, our model enables interactions between various communities, fostering a rich network environment. The asymptotically scale-free degree distribution is maintained by our model, which achieves a high clustering coefficient. In our model, the community structure is generated by a mix of mechanisms involving implicit preferential attachment and random attachment. We expand our consideration to include neighbor of neighbor of Initial Contact (NNIC) as well, in contrast to earlier approaches that solely focused on neighbor of Initial Contact (NIC) as an implicit preferential contact. If a newly added vertex chooses more than one initial contact, this extension makes it possible for contacts between those initial contacts to occur. Consequently, our model facilitates the development of complex social networks beyond those used as basic references. Finally, we conduct centrality calculations on both the existing model and our developed model, providing a comparative analysis of the results.

Novel Strategies for Acute Relief and Prevention of Migraine

Migraine is a debilitated disorder that is often under diagnosed in clinical practice. Early detection of migraine leads to outstanding improvements in patients’ quality of life. New researchers had developed the central sensitization and cutaneous allodina in chronic migraine which is helpful in early treatment approach with triptans and other agents in acute migraine episodes. Right now, five agents are accepted by US food and drug administration (FDA) for migraine prevention. They are namely Divalporex sodium, Propranolol, Timolol maleate, Topiramate and Methysergide. For so many years, other medications have been used to prevent migraine with not much more success rate. They are Tricyclic anti depressants, anti epileptic agents, Nonsteroidal anti-inflammatory drugs (NSAIDs), Selective serotonin reuptake inhibitors (SSRIS), Calcium channel blockers and muscle relaxants. Calcitonin gene related peptide antagonist, Ditans, Botulinum toxin type A, Orexin receptor antagonists etc were the recent novel agents in the treatment and management of migraine. These novel agents showed more affect than other medications in the acute and prophylactic management of migraines. They play a pivotal role in the standard care for migraine treatment that will improve the patient quality of life with better outcomes.

Intranasal Glioma with Post-Endoscopic Resection in 1 Year Old Infant: Case Report and Literature Review*

Abstract Introduction Nasal gliomas - or nasal glial heterotopias - are rare congenital malformations, which correspond to 5% of the congenital nasal masses. It is a mass composed of mature glial tissue that can be located outside, inside or near the nasal region, and may or may not be connected to the brain by a fibrous pedicle. This report addresses a case of nasal glioma that suffered recurrence after endoscopic treatment. Case Report A 1-year-old boy has, since birth, a mass inside the left nostril, which obstructs and widens the bridge of the nose. Upon physical examination, it is observed that the mass does not increase in size with crying and presents negative transluminescence and Furstenberg test. Upon being biopsied, the lesion reveals malignancy and the presence of inflammatory cells. MRI ruled out communication with intracranial structures. The endoscopic resection of the heterotopia removed a mass of 3,0 × 2,5 × 1,7 cm, whose histological and immunohistochemical analysis revealed glial pattern cell proliferation in the nasal mucosa. Conclusion Considering that nasal glial heterotopy is frequently present at birth, and that newborns breathe predominantly through this route, early diagnosis of the lesion is of great importance, as it can cause signs and symptoms of respiratory distress. In addition, it is worth noting that the early approach also prevents bone deformities.

Stool Protein Mass Spectrometry Identifies Biomarkers for Early Detection of Diffuse-type Gastric Cancer.

There is a high unmet need for early detection approaches for diffuse gastric cancer (DGC). We examined whether the stool proteome of mouse models of gastric cancer (GC) and individuals with hereditary diffuse gastric cancer (HDGC) have utility as biomarkers for early detection. Proteomic mass spectrometry of the stool of a genetically engineered mouse model driven by oncogenic KrasG12D and loss of p53 and Cdh1 in gastric parietal cells [known as Triple Conditional (TCON) mice] identified differentially abundant proteins compared with littermate controls. Immunoblot assays validated a panel of proteins, including actinin alpha 4 (ACTN4), N-acylsphingosine amidohydrolase 2 (ASAH2), dipeptidyl peptidase 4 (DPP4), and valosin-containing protein (VCP), as enriched in TCON stool compared with littermate control stool. Immunofluorescence analysis of these proteins in TCON stomach sections revealed increased protein expression compared with littermate controls. Proteomic mass spectrometry of stool obtained from patients with HDGC with CDH1 mutations identified increased expression of ASAH2, DPP4, VCP, lactotransferrin (LTF), and tropomyosin-2 relative to stool from healthy sex- and age-matched donors. Chemical inhibition of ASAH2 using C6 urea ceramide was toxic to GC cell lines and GC patient-derived organoids. This toxicity was reversed by adding downstream products of the S1P synthesis pathway, which suggested a dependency on ASAH2 activity in GC. An exploratory analysis of the HDGC stool microbiome identified features that correlated with patient tumors. Herein, we provide evidence supporting the potential of analyzing stool biomarkers for the early detection of DGC. Prevention Relevance: This study highlights a novel panel of stool protein biomarkers that correlate with the presence of DGC and has potential use as early detection to improve clinical outcomes.

Diagnosis model of early Pneumocystis jirovecii pneumonia based on convolutional neural network: a comparison with traditional PCR diagnostic method

BackgroundPneumocystis jirovecii pneumonia (PJP) is an interstitial pneumonia caused by pneumocystis jirovecii (PJ). The diagnosis of PJP primarily relies on the detection of the pathogen from lower respiratory tract specimens. However, it faces challenges such as difficulty in obtaining specimens and low detection rates. In the clinical diagnosis process, it is necessary to combine clinical symptoms, serological test results, chest Computed tomography (CT) images, molecular biology techniques, and metagenomics next-generation sequencing (mNGS) for comprehensive analysis.PurposeThis study aims to overcome the limitations of traditional PJP diagnosis methods and develop a non-invasive, efficient, and accurate diagnostic approach for PJP. By using this method, patients can receive early diagnosis and treatment, effectively improving their prognosis.MethodsWe constructed an intelligent diagnostic model for PJP based on the different Convolutional Neural Networks. Firstly, we used the Convolutional Neural Network to extract CT image features from patients. Then, we fused the CT image features with clinical information features using a feature fusion function. Finally, the fused features were input into the classification network to obtain the patient's diagnosis result.ResultsIn this study, for the diagnosis of PJP, the accuracy of the traditional PCR diagnostic method is 77.58%, while the mean accuracy of the optimal diagnostic model based on convolutional neural networks is 88.90%.ConclusionThe accuracy of the diagnostic method proposed in this paper is 11.32% higher than that of the traditional PCR diagnostic method. The method proposed in this paper is an efficient, accurate, and non-invasive early diagnosis approach for PJP.

Outcomes following medial open reduction in infants aged ≤6 months with developmental dysplasia of the hip

AimTo evaluate mid-long term radiological outcomes following early medial approach open reduction (MAOR) performed for developmental dysplasia of the hip (DDH) in infants aged ≤6 months old at time of surgery, specifically incidence of clinically significant avascular necrosis (AVN), Severin outcomes and rates of further surgery. MethodsThis is a single centre retrospective study of patients treated from 1999 to 2017. Only infants aged ≤6 months old at time of MAOR, and aged at least 6 years old at latest follow-up were included (minimum 5.5 years follow-up). Data was collected from electronic healthcare records and serial radiographs reviewed to assess outcomes. AVN was classified according to Kalamchi and MacEwen, with types 2–4 considered clinically significant. Severin classes I (excellent) and II (good) were considered satisfactory outcomes, and classes III + considered unsatisfactory. ResultsMAOR was performed on 48 hips in 44 patients. Mean age at time of surgery was 4 months (SD 1.4, range 2–6), with mean follow-up of 9.8 years (SD 2.7, range 6.2–16.2). Clinically significant AVN developed in 9/48 hips (19 %), all of which were type 2. Only 1/48 hips (2 %) required a subsequent pelvic osteotomy due to residual dysplasia with subluxation at 2 years post MAOR. At final follow-up, 81 % of patients had excellent or good radiological outcomes (Severin I/II). No statistically significant predictors for developing AVN, including age and presence of ossific nucleus, were identified. ConclusionEarly MAOR in infants aged ≤6 months was associated with a very low rate of significant residual dysplasia requiring further surgery, yet was not associated with unacceptable rates or severe forms of AVN. We therefore recommend MAOR is performed early to optimise acetabular remodelling potential and minimise the need for concurrent or subsequent bony procedures.

Characterisation and biocompatibility of crosslinked hyaluronic acid with BDDE and PEGDE for clinical applications

Hyaluronic acid (HA) is a versatile biomaterial frequently utilized in regenerative medicine due to its gel-like properties, making it well-suited for clinical applications. However, its linear form is susceptible to rapid enzymatic degradation, limiting its longevity within the body. To address this challenge, extensive research has focused on crosslinking mechanisms to enhance the durability of HA gels. One early approach involved crosslinking HA with 1,4-butanediol diglycidyl ether (BDDE) to create HA-BDDE, a clinically used product since the 1990s. However, the manufacturing process for HA-BDDE, primarily used in industry, lacks comprehensive documentation in the literature. More recently, poly(propylene glycol) diglycidyl ether (PEGDE) has emerged as an alternative to BDDE for crosslinking, offering improved gel elasticity and reduced cytotoxicity. In this study, we present the manufacturing process for producing both crosslinked gels, HA-BDDE and HA-PEGDE, with negligible residual crosslinkers, as confirmed by FTIR and NMR analysis. We characterize the crosslinking kinetics and the resulting formulations, revealing that HA-PEGDE gel exhibits comparable stiffness (G' = 60 Pa vs. 75 Pa) to HA-BDDE, despite a lower effective crosslinking ratio (CrR = 0.12 vs. 0.24). Intriguingly, our cytotoxicity testing demonstrates significantly greater cell viability for HA-PEGDE compared to HA-BDDE (151% versus 105%). Overall, both gels can be readily manufactured using a similar process and demonstrate excellent in vitro biocompatibility. This study elucidates why HA-BDDE is widely utilized in clinical settings and underscores the potential promise of HA-PEGDE as an emerging variant for clinical applications.

Functional Solutions of Stochastic Differential Equations

We present an integration condition ensuring that a stochastic differential equation dXt=μ(t,Xt)dt+σ(t,Xt)dBt, where μ and σ are sufficiently regular, has a solution of the form Xt=Z(t,Bt). By generalizing the integration condition we obtain a class of stochastic differential equations that again have a functional solution, now of the form Xt=Z(t,Yt), with Yt an Ito process. These integration conditions, which seem to be new, provide an a priori test for the existence of functional solutions. Then path-independence holds for the trajectories of the process. By Green’s Theorem, it holds also when integrating along any piece-wise differentiable path in the plane. To determine Z at any point (t,x), we may start at the initial condition and follow a path that is first horizontal and then vertical. Then the value of Z can be determined by successively solving two ordinary differential equations. Due to a Lipschitz condition, this value is unique. The differential equations relate to an earlier path-dependent approach by H. Doss, which enables the expression of a stochastic integral in terms of a differential process.

Application research of image classification algorithm based on deep learning in household garbage sorting

The classification of garbage types is an important issue in today's world, and its proper implementation can contribute to environmental conservation and improved efficiency of recycling processes. Unfortunately, the classification of garbage types is currently predominantly performed through human supervision, which leads to high errors and environmental risks. It is crucial to automate this procedure utilizing machine vision methods as a result. This research proposes a revolutionary deep learning-based strategy for classifying domestic waste. The suggested method uses deep learning methods to extract information from images. The Capuchin Search Algorithm (CapSA) is used to improve the hyperparameters of the convolutional neural network (CNN) used as the feature extraction model. Furthermore, for categorizing the retrieved features from the CNN model, a hybrid learning model based on Error-Correcting Output Codes (ECOC) and Artificial Neural Networks (ANN) is used. The classification accuracy may be successfully increased by using this hybrid model, and the benefit becomes more pronounced as the number of target categories rises. The TrashNet and HGCD databases were used to assess the suggested method's effectiveness, and its results in waste type detection were contrasted with those of earlier techniques. Based on the study findings, the suggested approach can identify trash types with an accuracy of 98.81 % and 99.01 % on the TrashNet and HGCD databases, respectively. This is at least a 1.46 % improvement over earlier approaches. The study's conclusions validate that the suggested strategy may be used in real-world scenarios and show how successful the approaches used in it are.

Memupuk Tren Kemandirian dan Kesadaran Lingkungan Program Menanam Sayur untuk Siswa

That vegetable consumption in Indonesian society is still low, and to increase it and foster environmental awareness, an early educational approach is needed about vegetable consumption and cultivation. The vegetable planting program for students at Madrasah Ibtidaiyah Banyugurgul is a concrete example of implementing this approach, through education about vegetable cultivation and practical training on planting in the school yard. This activity succeeded in attracting the enthusiasm of all students and supporting the growing trend of independence and environmental awareness among the younger generation.

Over 30 years of STEP: The Pittsburgh experience with first-episode psychosis.

For over 30 years, combined research and treatment settings in the US have been critical to conceptualizing care for first-episode psychosis (FEP). Here we describe an early example of such a context, the Services for the Treatment of Early Psychosis (STEP) clinic, which is affiliated with the University of Pittsburgh. We describe STEP's historical roots and establishment in the early 1990s; STEP's research and treatment contributions, alongside its growth and ongoing leadership. Research-based clinics, like STEP, preceded and helped pave the way for the Recovery After an Initial Schizophrenia Episode project in the US and the ensuing Coordinated Specialty Care (CSC) approach, now widely adopted in the US. Early clinic-based research at STEP helped establish protocols for psychopharmacology, the relevance of effective early treatment, including psychosocial approaches, and highlighted disparities in treatment outcomes across race/ethnicity. Multidisciplinary collaboration and dialogue with consumers contributed to early treatment, combining psychosocial and pharmacological approaches. STEP adopted CSC and is situated within a bi-state Learning Health System. STEP has retained a relatively unique 5-year treatment model and exists within continuum of care ideally suited to studying psychotic illness and treatment outcomes. STEP remains the largest academic FEP clinic in Pennsylvania. Academic FEP clinics like STEP will have a critical role within Learning Health Systems nationally to model participatory approaches, sustain early intervention treatment quality and ongoing treatment developments.

Yes, I am afraid of the sharks and also wild lions!: A multitask framework for enhancing dialogue generation via knowledge and emotion grounding

Current end-to-end neural conversation models inherently lack the capability to generate coherently engaging responses. Efforts to boost informativeness have an adversarial effect on emotional and factual accuracy, as validated by several sequence-based models. While these issues can be alleviated by access to emotion labels and background knowledge, there is no guarantee of relevance and informativeness in the generated responses. In real dialogue corpus, informative words like named entities, and words that carry specific emotions can often be infrequent and hard to model, and one primary challenge of the dialogue system is how to promote the model’s capability of generating high-quality responses with those informative words. Furthermore, earlier approaches depended on straightforward concatenation techniques that lacked robust representation capabilities in order to account for human emotions. To address this problem, we propose a novel multitask hierarchical encoder–decoder model, which can enhance the multi-turn dialogue response generation by incorporating external textual knowledge and relevant emotions. Experimental results on a benchmark dataset indicate that our model is superior over competitive baselines concerning both automatic and human evaluation.