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It is not uncommon to see fracture or loss of tooth structure due to caries of the anterior teeth, be it primary or permanent dentition. Successful functional, structural and esthetic rehabilitation of anterior teeth has always been a challenge in restorative dentistry. Though there are various accepted treatment modalities, there is always continuous hope of developing newer techniques, in order to overcome the drawback of existing treatments. This paper discusses the use of a simple, yet successful, cost-effective treatment option of restoring anterior teeth with the use of biological posts through two case reports.

Osseointegrated implants have transformed dentists options for restoration of edentulous spaces. A clinicians goal has always been to treat patients with greatest likelihood for success. Screw loosening and fracture of the implant or its components are two of the most typical implant failures and this is observed to occur at the implant abutment junction. It is possible that loss of preload is the root cause for screw loosening. Several factors affect the connection between torque and preload. This review article attempted to explore the numerous factors influencing preload loss and how they affect success of the dental implants.

Background Exposure to PM2.5 (fine particulate matter Objectives We examined whether PM2.5 exposures during pregnancy were associated with birthweight in an integrated rural-urban, mother-child cohort in the state of Tamil Nadu, India. Methods We recruited 1285 pregnant women in the first trimester of pregnancy from primary health care centers and urban health posts and followed them until birth to collect antenatal care data and birthweight. We estimated pregnancy period PM 2.5 exposures through direct serial measurements of 24-h household PM2.5 concentrations, performed across each trimester. Mothers also completed detailed questionnaires to provide data on covariates related to household, socio-economic, demographic and maternal health characteristics. The association between PM2.5 exposures and birth weight was assessed using linear and logistic regression models that controlled for potential confounders. Results A 10-μg/m3 increase in pregnancy period PM2.5 exposures was associated with a 4 g (95% CI: 1.08 g, 6.76 g) decrease in birthweight and 2% increase in prevalence of low birthweight [odds ratio(OR) = 1.02; 95%CI:1.005,1.041] after adjusting for gestational age, infant sex, maternal BMI, maternal age, history of a previous low birth weight child, birth order and season of conception. Conclusions The study provides some of the first quantitative effects estimates for linking rural-urban PM2.5 exposures and birthweight in India, adding important evidence for this association from high exposure settings in LMICs, that also experience dual health burdens from ambient and household air pollution. Study results also point to the need for considering maternal PM2.5 exposures alongside other risk factors for low birthweight in India

Understanding the user intention from text is a problem of growing interest. The social media like Twitter, Facebook etc. extract user intention to analyze the behaviour of a user which in turn is employed for bot recognition, satire detection, fake news detection etc.. The process of identifying stance of a user from the text is called stance detection. This article compares the headline and body pair of a news article and classifies the pair as related or unrelated. The related pair is further classified into agree, disagree, discuss. We call related as detailed classification and unrelated as broad classification. We employ deep neural nets for feature extraction and stance classification. RNN models and its extensions showed significant variations in the classification of detailed class. Bidirectional LSTM model achieved the best accuracy for broad as well as detailed classification.

Social Networks has become the biggest medium of expression of one’s thoughts and emotions. With more than one-third of the global population expressing their thoughts, opinions and events, these networks become rich with direct indicators about the subject. Multimedia based social networking services are gaining popularity than text-based ones. In this work, we analyze the data from Instagram, which is one of the largest image and video-based social networking service with more than one billion users. This paper analyzes the social media posts on Instagram with the goal of finding when the subject feels low that could potentially lead to a suicidal tendency in the initial stages. In this study, the Instagram posts were analysed using different features that are more inclined to exhibit suicidal behaviour. The best accuracy is achieved by J48 binary tree classifier with a classification accuracy of 87.68%.

Robotics has become a rapidly emerging branch of science, addressing the needs of humankind by way of advanced technique, like artificial intelligence (AI). This chapter gives detailed explanation about the background knowledge required in implementing the software robots. This chapter has an in-depth explanation about different types of software robots with respect to different applications. This chapter would also highlight some of the important contributions made in this field. Path planning algorithms are required for performing robot navigation efficiently. This chapter discusses several robot path planning algorithms which help in utilizing the domain knowledge, avoiding the possible obstacles, and successfully accomplishing the tasks in lesser computational time. This chapter would also provide a case study on robot navigation data and explain the significant of machine learning algorithms in decision making. This chapter would also discuss some of the potential simulators used in implementing software robots.

Automated accounts which are otherwise known as bots are rampant in most of the popular online social networks. Similar to email spam, these social media bots are used for spreading information with the goal of propaganda or advertisements for profit. Due to the impact they pose on influencing the user communities, understanding the bot behaviour is important. In this paper, we employ deep neural network analysis on temporal data of bot accounts and have identified the role of temporal activity in bot detection. The bidirectional LSTM network is used for studying the temporal patterns of Twitter bots and its behavioural pattern. The ability of the model to distinguish the tweeting rate and frequency of bot accounts from the genuine accounts has led to a good classification rate.

Highlights • Cross-validation of analytical techniques for 1-hydroxypyrene and 2-naphthol. • Good agreement between low cost HPLC fluorescence (HPLC-FLD) and LC-MSMS methods. • HPLC-FLD method was highly sensitive to 2NAP compared to LC-MSMS. • HPLC-FLD showed large bias for 2NAP with LC-MSMS at levels greater than 20 ng/mL. • HPLC-FLD is a cost-effective and reliable analytical method for low-and middle-income countries., The Household Air Pollution Intervention Network (HAPIN) trial is evaluating health benefits of a liquefied petroleum gas (LPG) stove intervention in biomass cook-fuel using homes (n = 3200) in four low-and middle-income countries (LMICs) that include Peru, Guatemala, Rwanda and India. Longitudinal urine samples (n = 6000) collected from enrolled pregnant women, infants and older women will be analyzed for biomarkers associated with exposure and health outcomes. We report results from cross-validation of a lower cost high-performance liquid chromatography with fluorescence detection (HPLC-FLD) method with a higher resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for the measurement of 1-hydroxypyrene (1PYR) and 2-naphthol (2NAP). Twenty-five split urine samples were analyzed by HPLC-FLD method at the India trial site in Chennai, India and by LC-MSMS method at the trial wide Biomarker Coordinating Center, Emory University, USA. The limits of detection (LOD) for the HPLC-FLD method were 0.02 ng/mL and 0.07 ng/mL for 2NAP and 1PYR, respectively. Bland-Altman analysis estimated a bias of 2.98 ng/ml for 2NAP (95% CI: −5.22, −0.75) and 0.09 ng/mL for 1PYR (95% CI: −0.02, 0.21) with HPLC-FLD levels being lower than LC-MSMS levels at higher concentrations. Analyses of additional urine samples (n = 119) collected during the formative phase of the HAPIN trial in India, showed 2NAP and 1PYR levels to be consistently above the limit of quantification (LOQ) and demonstrated the applicability of the method. The HPLC-FLD method can serve as a cost-effective and reliable analytical method to measure 2NAP and 1PYR in human urine in LMICs, within and beyond the HAPIN trial.

Background: The Household Air Pollution and Intervention Network (HAPIN) trial aims to demonstrate the effects of a liquefied petroleum gas (LPG) intervention on health in biomass using homes acros...

Exposure to PMWe examined whether PMWe recruited 1285 pregnant women in the first trimester of pregnancy from primary health care centers and urban health posts and followed them until birth to collect antenatal care data and birthweight. We estimated pregnancy period PMA 10-μg/mThe study provides some of the first quantitative effects estimates for linking rural-urban PM