Hierarchical Expert Recommendation on Community Question Answering Platforms

The community question answering (CQA) platforms, such as Stack Overflow, have become the primary source of answers to most questions in various topics. CQA platforms offer an opportunity for sharing and acquiring knowledge at a low cost, where users, many of whom are experts in a specific topic, can potentially provide high-quality solutions to a given question. Many recommendation methods have been proposed to match questions to potential good answerers. However, most existing methods have focused on modelling the user-question interaction — a user might answer multiple questions and a question might be answered by multiple users — using simple collaborative filtering approaches, overlooking the rich information in the question’s title and body when modelling the users’ expertise. This project fills the research gap by thoroughly examining machine learning and deep learning approaches that can be applied to the expert recommendation problem. It proposes a Hierarchical Expert Recommendation (HER) model, a deep learning recommender system that recommends experts to answer a given question in the CQA platform. Although choosing a deep learning over a machine learning solution for this problem can be justified considering the degree of complexity of the available datasets, we assess performance of each family of methods and evaluate the trade-off between them to pick the perfect fit for our problem. We analyzed various machine learning algorithms to determine their performances in the expert recommendation problem, which narrows down the potential ways for tackling this problem using traditional recommendation methods. Furthermore, we investigate the recommendation models based on matrix factorization to establish the baselines for our proposed model and shed light on the weaknesses and strengths of matrix- based solutions, which shape our final deep learning model. In the last section, we introduce the Hierarchical Expert Recommendation System (HER) that utilizes hierarchical attention-based neural networks to rep- resent the questions better and ultimately model the users’ expertise through user-question interactions. We conducted extensive experiments on a large real-world Stack Overflow dataset and benchmarked HER against the state-of-the-art baselines. The results from our extensive experiments show that HER outperforms the state-of-the-art baselines in recommending experts to answer questions in Stack Overflow

Autogenous Transplantation for Replacing a Hopeless Tooth

Autogenous tooth transplantation (ATT) is a simple and reasonable choice for replacing the missing teeth when a proper donor tooth is available. This report presents a case of successful ATT of a maxillary right third molar for replacement of mandibular right second molar with a concomitant endodontic-periodontal disease. The mandibular second molar was believed to be hopeless due to a severe damage to coronal tooth structure, inappropriate root canal treatment and apical radiolucency. After extraction of mandibular second molar and maxillary third molar (the donor), the tooth was re-implanted into the extracted socket of second molar site. Root canal therapy was then performed. After 3 years, clinical and radiographic examinations revealed satisfying results, with no signs and symptoms. The patient is asymptomatic and the transplanted tooth is still functional with no signs of marginal periodontal pathosis. Radiographies showed bone regeneration in the site of previous extensive periapical lesion, normal periodontal ligament with no signs of root resorption.Keywords: Autogenous; Auto-Transplantation; Endodontic; Surgical Procedure; Third Mola

Nonsurgical treatment of an extensive radiolucent lesion, a case report

    Long-term success of endodontic treatment is precisely and completely dependent on adequate and appropriate cleaning and shaping of the root canal along with proper and correct obturation of the entire prepared space.This paper aims to report an exceptionally and novel non-surgical and orthograde endodontic therapy on maxillary right central incisor with an extensive radiolucent lesion. A 17-year-old male with an unusual extensive radiolucent lesion in the anterior part of upper jaw is reported. After cleaning and shaping of the root canal, Calcium Hydroxide was placed in the canal for 6 months and then Obturation was performed. 6 and 20 months follow-ups showed significant changes, including bone formation and periapical healing at the site of the lesion. The patient was asymptomatic. After 20 months, complete radiographic and clinical healing of the periapical lesion was observed

Cohort profile update: Tehran cardiometabolic genetic study

The Tehran cardiometabolic genetic study (TCGS) is a large population-based cohort study that conducts periodic follow-ups. TCGS has created a comprehensive database comprising 20,367 participants born between 1911 and 2015 selected from four main ongoing studies in a family-based longitudinal framework. The study's primary goal is to identify the potential targets for prevention and intervention for non-communicable diseases that may develop in mid-life and late life. TCGS cohort focuses on cardiovascular, endocrine, metabolic abnormalities, cancers, and some inherited diseases. Since 2017, the TCGS cohort has augmented by encoding all health-related complications, including hospitalization outcomes and self-reports according to ICD11 coding, and verifying consanguineous marriage using genetic markers. This research provides an update on the rationale and design of the study, summarizes its findings, and outlines the objectives for precision medicine