Machine Learning Based Twitter Sentiment Analysis and User Influence

The use of social media platforms, such as Twitter, has grown exponentially over the years, and it has become a valuable source of information for various fields, including marketing, politics, and finance. Sentiment analysis is particularly relevant  in social media analysis. Sentiment analysis involves the use of natural language processing (NLP) techniques to automatically determine the sentiment expressed in a given text, such as positive, negative, or neutral. In this research paper, we focus on Twitter sentiment analysis and identify the most influential users in a given topic. We propose a methodology based on machine learning techniques to perform sentiment analysis and identify the most influential users on Twitter based on popularity. Specifically, we utilize a combination of NLP techniques, sentiment lexicons, and machine learning algorithms to classify tweets as positive, negative, or neutral. We then employ popularity calculations for each user to identify the top 10 most influential users on a given topic. The proposed methodology was tested on a large dataset of US airlines tweets which is related to a specific topic i.e. airlines, and the results show that the approach can effectively classify tweets according to sentiment and identify the most influential users. We evaluated the performance of several machine learning algorithms, including Multinomial Naive Bayes, Support Vector Machines (SVM), Decision Trees, Gradient Boosting, logistic regression, AdaBoost, KNN and Random Forest, and found that the logistic regression algorithm has achieved the highest accuracy. The proposed methodology has several implications for various fields, such as marketing, where sentiment analysis can help companies understand consumer behavior and tailor their marketing strategies accordingly. Moreover, identifying the most influential users can provide insights into opinion leaders in a given topic and help companies and policymakers target their messages more effectively

NMR elucidation of early folding hierarchy in HIV-1 protease

Folding studies on proteases by the conventional hydrogen exchange experiments are severely hampered because of interference from the autolytic reaction in the interpretation of the exchange data. We report here NMR identification of the hierarchy of early conformational transitions (folding propensities) in HIV-1 protease by systematic monitoring of the changes in the state of the protein as it is subjected to different degrees of denaturation by guanidine hydrochloride. Secondary chemical shifts, HN-Hα coupling constants, 1H-15N nuclear Overhauser effects, and 15N transverse relaxation parameters have been used to report on the residual structural propensities, motional restrictions, conformational transitions, etc., and the data suggest that even under the strongest denaturing conditions (6 m guanidine) hydrophobic clusters as well as different native and non-native secondary structural elements are transiently formed. These constitute the folding nuclei, which include residues spanning the active site, the hinge region, and the dimerization domain. Interestingly, the proline residues influence the structural propensities, and the small amino acids, Gly and Ala, enhance the flexibility of the protein. On reducing the denaturing conditions, partially folded forms appear. The residues showing high folding propensities are contiguous along the sequence at many locations or are in close proximity on the native protein structure, suggesting a certain degree of local cooperativity in the conformational transitions. The dimerization domain, the flaps, and their hinges seem to exhibit the highest folding propensities. The data suggest that even the early folding events may involve many states near the surface of the folding funnel

Experiences of sharing results of community based serosurvey with participants in a district of Maharashtra, India.

A growing number of organisations, including medical associations, recommend that research subjects should be given the option of being informed about the general outcome and results of the study. We recently completed a study involving nine serosurveys from 2018 to 2020 in five districts of India among three age groups (children 9 months to < 5 years; 5 to < 15 years of age, and women 15 to < 50 years of age before and after the measles and rubella (MR) vaccination campaigns). In Palghar district of Maharashtra all individuals in 30 selected clusters were enumerated, and 13 individuals per age group were randomly sampled. We established the procedures to return the results to the respondents for each stage of the survey. Of the 1,166 individuals selected for the measles and rubella serosurvey, 971 (83%) agreed to participate and were enrolled. Participants were informed that they will only be contacted if they test seronegative for measles and/or rubella antibodies. Overall, 140 individuals enrolled in the survey tested seronegative for IgG antibodies to measles and/or rubella viruses; were provided the reports and informed to seek medical advice. Upon follow up by phone, 10% (14) of the 140 participants reported to have been vaccinated. In this paper we discuss the procedures, experiences and considerations in returning results to participants in a community-based measles and rubella serosurvey. Although the lessons learned are specific to post measles-rubella vaccine campaign serosurvey in India, they might be helpful to those contemplating sharing results to participants of large scale survey settings

Quantifying Air-Void Distribution and Associated Uncertainty in Fresh and Hardened Concrete

Doctor of PhilosophyDepartment of Civil EngineeringChristopher A JonesFreeze-thaw damage in Portland cement concrete pavements (PCCP) has been a significant concern for regions experiencing (numerous) rapid freeze-thaw cycles along with frequent precipitation. In the early 1950s Powers recommended entraining a well-distributed air-void system (comprised of equally spaced, small air voids) through the cementitious paste to minimize frost damage in PCCP. There are several methods to measure total air content in fresh concrete, such as ASTM C231 (volumetric air content using a pressure meter) and ASTM C173 (volumetric air content using a roll-o-meter). Still, air content alone is insufficient to assess an air void distribution. Hardened air-void analysis (ASTM C457) is used to accurately measure distribution in terms of total air content, paste-to-air ratio, spacing factor, and specific surface area. But this test can only be performed in hardened concrete about 28 days after it has been placed on-site, thus limiting its use for making a real-time assessment. According to ACI 201.2R, a minimum 4% total (volumetric) air content, a paste-to-air ratio between 4 to 10, along with a specific surface area in the range of 25 to 45 mm−1 (600 to 1100 in.−1), and a spacing factor in the range of 0.1 to 0.2 mm (0.004 to 0.008 in.), is required to ensure adequate freeze-thaw resistance of PCCP. Accordingly, limitations have been placed by various transportation agencies to ensure the required air­ void distribution in PCCP. Recently, the KT-86/AASHTO T 395 (Sequential Pressure Method, referred to as the Super Air Meter - SAM) and the pressure meter were proposed as field-ready devices to improve quality control/assurance (QC/QA) practices. However, several potential shortcomings are associated with using these devices to evaluate air-void distribution in fresh concrete in real-time on the field. To enrich the predictive capability of these traditional methods, complementary models have been proposed in this study to predict spacing factors using mixture design and fresh concrete test measurements as predictors. A set of 271 observations were used to develop the statistical models for predicting spacing factor (PSF) measured by a commercial air-void scanning system (RapidAir, based on the ASTM C457-16 method). Significant variable(s) for model development were selected using the LASSO, Binary logistic, and Bayesian probit regression analysis models. The predictive models were evaluated on their ability to distinguish between a well-entrained air-void system (spacing factor 0.008 in. or 0.2 mm). It was observed that the predictive models were better and more efficient in assessing the air-void distribution than total air content or SAM number alone. Given that the models were calibrated and validated for a data set dominated by a spacing factor less than 0.008 in. (0.2 mm), its validation was limited for coarsely entrained air-void systems. The given study focuses on examining current & alternate methods and the error associated with them to evaluate air-void distribution in fresh & hardened concrete. Distribution parameters measured by a RapidAir system are assumed to be an actual/accurate representation of the entrained air-void system. But these parameters are regulated by the RapidAir system settings (namely threshold and gain), selected by the operator during evaluation. Thus, to accurately identify and quantify this influence, a set of known air-void distributions were compared to their similar properties measured by the RapidAir system at different system settings. The known air-void distributions were generated as unique synthetic air-void structures using a Python script and printed as luster photographs using a 3200-dpi printer. A RapidAir system at different system settings scanned these photographs. The 'true' distribution parameters for these photographs were calculated from the exact size & location of air voids, recorded by the Python script. As expected, a significant error was observed between the two measurements. Later, these errors were used to minimize the influence of system settings on the distribution parameters measured for a set of concrete samples. As a result, a 'fair ground' to compare air-void structures of the same/different samples evaluated by the same/different systems and settings was provided. Previously, researchers have recommended using alternate parameters and spacing factor definitions to evaluate air-void distribution in hardened concrete. One such parameter is the 'dispersion parameter' (DP), formerly used to quantify the dispersion of fibers in composites. The current study considers this parameter to quantify the dispersion of air voids through its cementitious paste. DP is a scalar quantity calculated as the ratio of maximum work required to disperse air voids from their current locations to their location in 'best' and 'worst' dispersion scenarios. DP helps quantify the distribution of air voids across the sample and identify the location and presence of clusters, thereby offering a better characteristic distance parameter than the 'spacing factor' to quantify the quality of air-void distribution. Overall, the given study serves as a guide to enrich QC/QA practices by assessing the accuracy & uncertainty of current & proposed methods to evaluate air-void distribution in fresh & hardened concrete

Endodontic implications and innovative preventive strategies during novel COVID-19 pandemic requiring emergency endodontic treatment

Endodontics a specialty branch in dentistry frequently deals with endodontic emergencies and during this COVID-19 pandemic Emergency Root Canal Treatement (ERCT) is often required to manage acute pulpitis with or without space infections.There are various routes of disease transmission in endodontics. When treating patients requiring emergency access opening the treating doctor should be extremely vigilant and cautious regarding its spread and containment of COVID-19 virus by utilizing certain innovative strategies such as HEPA filters, High volume extraoral suction, and other devices manufactured from Polyethlene to safeguard from aerosols generated during emergency endodontic therapy.</p

Adjuvant Pembrolizumab versus IFNα2b or Ipilimumab in Resected High-Risk MelanomaAdjuvant Pembrolizumab in High-Risk Melanoma

We conducted a randomized phase III trial to evaluate whether adjuvant pembrolizumab for one year (647 patients) improved recurrence-free survival (RFS) or overall survival (OS) in comparison with high-dose IFNα-2b for one year or ipilimumab for up to three years (654 patients), the approved standard-of-care adjuvant immunotherapies at the time of enrollment for patients with high-risk resected melanoma. At a median follow-up of 47.5 months, pembrolizumab was associated with significantly longer RFS than prior standard-of-care adjuvant immunotherapies [HR, 0.77; 99.62% confidence interval (CI), 0.59-0.99; P = 0.002]. There was no statistically significant association with OS among all patients (HR, 0.82; 96.3% CI, 0.61-1.09; P = 0.15). Proportions of treatment-related adverse events of grades 3 to 5 were 19.5% with pembrolizumab, 71.2% with IFNα-2b, and 49.2% with ipilimumab. Therefore, adjuvant pembrolizumab significantly improved RFS but not OS compared with the prior standard-of-care immunotherapies for patients with high-risk resected melanoma.SignificanceAdjuvant PD-1 blockade therapy decreases the rates of recurrence, but not survival, in patients with surgically resectable melanoma, substituting the prior standard-of-care immunotherapies for this cancer. See related commentary by Smithy and Shoushtari, p. 599. This article is highlighted in the In This Issue feature, p. 587