Estrogen-induced pancreatitis : transgender females at risk

Estrogen therapy and the consideration of its potential side effects will continue to grow as the number of transgender females presenting to health care services continues to increase. The risks of estrogen therapy in this population are hard to extrapolate from previously identified data in the general population due to variation in birth sex, superior hormone doses required, and extended exposure duration that is often needed. Estrogen therapy is a rare, yet well-known, cause of acute pancreatitis with as many as 40 known reported cases in women and only one other reported case in a transgender female. The presumed mechanism is estrogen-induced hypertriglyceridemia as triglyceride levels were documented as greater than 1,000 mg/dL in most diagnosed patients. The limited data and evidence-based recommendations regarding estrogen therapy treatment and management in transgender women have led to a general lack of understanding among most practitioners. The long-term supraphysiologic doses of sex hormones required for treatment in transgender women and the dose-dependent estrogen-induced elevation in triglycerides are factors that contribute to an increased risk of pancreatitis. Therefore, the utility of regularly scheduled lipid panels in the prevention of pancreatitis increases in this population.Nathan Goodwin (1), Nathanial Nolan (2), Bhavana Chinnakotla (3); 1. University of Missouri School of Medicine. 2. Department of Medicine, University of Missouri. 3. Division of Hospital Medicine, Department of Medicine, University of MissouriIncludes bibliographical reference

Characterization of Laboratory-And Field-Compacted Asphalt Mixtures Based on Dynamic Modulus Testing and Analysis

This first part of the thesis investigates a fast, non-destructive testing method to characterize asphalt mixtures. While dynamic modulus was recommended by NCHRP Project 9-19 as a test to represent pavement performance, the time consumed by the commonly used cyclic test method hampers its adaptation. The possibility of using the resonance test method for determining the complex modulus in a quicker, simpler, and reliable way was evaluated to address this gap. For comparison purposes, complex modulus testing was performed on two asphalt mixtures using the cyclic loading and the resonance frequency methods. The results plotted in Cole-Cole space show that the measurements from both the tests are consistent. The AASHTO R 84 and Havriliak-Negami models were used to estimate the master curves of dynamic modulus and phase angle. The AASHTO R 84 standard procedure could not be extended to fit the resonance test measurements. The second part assesses a new optimum asphalt mixture design procedure using the proposed micromechanics-based performance indicator. The original Superpave mixture design relies only on the material specifications and volumetrics criteria to ensure satisfactory mixture performance. Also, to better predict the asphalt mixture performance, understanding the influence of individual mixture components is necessary along with the effective bulk properties, which is often overlooked. These two shortcomings in the current asphalt mixture design procedure were addressed in this thesis by introducing a new performance indicator. The prediction equations from a micromechanical framework developed by Onifade and Birgisson (2021) were used to find the mixture constituents’ modulus. The microstructure characteristics like the volume fraction of phases, the shape and texture of aggregates, and the arrangement of constituents are also incorporated within the equations used. Based on the predicted stiffness values of the mixture and the constituents, a performance parameter termed the mixture/mastic stiffness ratio is introduced. This parameter can provide preliminary analysis indicating the rutting and fatigue performance of a mixture design without the need for extensive testing. The stiffness ratios correlated well with flow number and critical strain energy at the test temperature and frequency. Further, the ratio was sensitive to mixture gradation and aging

Findings of Factify 2: Multimodal Fake News Detection

With social media usage growing exponentially in the past few years, fake news has also become extremely prevalent. The detrimental impact of fake news emphasizes the need for research focused on automating the detection of false information and verifying its accuracy. In this work, we present the outcome of the Factify 2 shared task, which provides a multi-modal fact verification and satire news dataset, as part of the DeFactify 2 workshop at AAAI'23. The data calls for a comparison based approach to the task by pairing social media claims with supporting documents, with both text and image, divided into 5 classes based on multi-modal relations. In the second iteration of this task we had over 60 participants and 9 final test-set submissions. The best performances came from the use of DeBERTa for text and Swinv2 and CLIP for image. The highest F1 score averaged for all five classes was 81.82%.Comment: Defactify2 @AAAI 202

Overview of Memotion 3: Sentiment and Emotion Analysis of Codemixed Hinglish Memes

Analyzing memes on the internet has emerged as a crucial endeavor due to the impact this multi-modal form of content wields in shaping online discourse. Memes have become a powerful tool for expressing emotions and sentiments, possibly even spreading hate and misinformation, through humor and sarcasm. In this paper, we present the overview of the Memotion 3 shared task, as part of the DeFactify 2 workshop at AAAI-23. The task released an annotated dataset of Hindi-English code-mixed memes based on their Sentiment (Task A), Emotion (Task B), and Emotion intensity (Task C). Each of these is defined as an individual task and the participants are ranked separately for each task. Over 50 teams registered for the shared task and 5 made final submissions to the test set of the Memotion 3 dataset. CLIP, BERT modifications, ViT etc. were the most popular models among the participants along with approaches such as Student-Teacher model, Fusion, and Ensembling. The best final F1 score for Task A is 34.41, Task B is 79.77 and Task C is 59.82.Comment: Defactify2 @AAAI 202

Factify 2: A Multimodal Fake News and Satire News Dataset

The internet gives the world an open platform to express their views and share their stories. While this is very valuable, it makes fake news one of our society's most pressing problems. Manual fact checking process is time consuming, which makes it challenging to disprove misleading assertions before they cause significant harm. This is he driving interest in automatic fact or claim verification. Some of the existing datasets aim to support development of automating fact-checking techniques, however, most of them are text based. Multi-modal fact verification has received relatively scant attention. In this paper, we provide a multi-modal fact-checking dataset called FACTIFY 2, improving Factify 1 by using new data sources and adding satire articles. Factify 2 has 50,000 new data instances. Similar to FACTIFY 1.0, we have three broad categories - support, no-evidence, and refute, with sub-categories based on the entailment of visual and textual data. We also provide a BERT and Vison Transformer based baseline, which acheives 65% F1 score in the test set. The baseline codes and the dataset will be made available at https://github.com/surya1701/Factify-2.0.Comment: Defactify@AAAI202

Observing Human Mobility Internationally During COVID-19

This article analyzes visual data captured from five countries and three U.S. states to evaluate the effectiveness of lockdown policies for reducing the spread of COVID-19. The main challenge is the scale: nearly six million images are analyzed to observe how people respond to the policy changes