Classification of Twitter Vaping Discourse Using BERTweet: Comparative Deep Learning Study.

BACKGROUND: Twitter provides a valuable platform for the surveillance and monitoring of public health topics; however, manually categorizing large quantities of Twitter data is labor intensive and presents barriers to identify major trends and sentiments. Additionally, while machine and deep learning approaches have been proposed with high accuracy, they require large, annotated data sets. Public pretrained deep learning classification models, such as BERTweet, produce higher-quality models while using smaller annotated training sets. OBJECTIVE: This study aims to derive and evaluate a pretrained deep learning model based on BERTweet that can identify tweets relevant to vaping, tweets (related to vaping) of commercial nature, and tweets with provape sentiment. Additionally, the performance of the BERTweet classifier will be compared against a long short-term memory (LSTM) model to show the improvements a pretrained model has over traditional deep learning approaches. METHODS: Twitter data were collected from August to October 2019 using vaping-related search terms. From this set, a random subsample of 2401 English tweets was manually annotated for relevance (vaping related or not), commercial nature (commercial or not), and sentiment (positive, negative, or neutral). Using the annotated data, 3 separate classifiers were built using BERTweet with the default parameters defined by the Simple Transformer application programming interface (API). Each model was trained for 20 iterations and evaluated with a random split of the annotated tweets, reserving 10% (n=165) of tweets for evaluations. RESULTS: The relevance, commercial, and sentiment classifiers achieved an area under the receiver operating characteristic curve (AUROC) of 94.5%, 99.3%, and 81.7%, respectively. Additionally, the weighted F1 scores of each were 97.6%, 99.0%, and 86.1%, respectively. We found that BERTweet outperformed the LSTM model in the classification of all categories. CONCLUSIONS: Large, open-source deep learning classifiers, such as BERTweet, can provide researchers the ability to reliably determine if tweets are relevant to vaping; include commercial content; and include positive, negative, or neutral content about vaping with a higher accuracy than traditional natural language processing deep learning models. Such enhancement to the utilization of Twitter data can allow for faster exploration and dissemination of time-sensitive data than traditional methodologies (eg, surveys, polling research).

Machine-learning reinforcement for optimizing multilayered thin films: applications in designing broadband antireflection coatings.

The design and fabrication of nanoscale multilayered thin films play an essential role in regulating the operation efficiency of sensitive optical sensors and filters. In this paper, we introduce a packaged tool that employs flexible electromagnetic calculation software with machine learning in order to find the optimized double-band antireflection coatings in intervals of wavelength from 3 to 5 µm and 8 to 12 µm. Instead of computing or modeling an extremely enormous set of thin film structures, this tool enhanced with machine learning can swiftly predict the optical properties of a given structure with >99.7% accuracy and a substantial reduction in computation costs. Furthermore, the tool includes two learning methods that can infer a global optimal structure or suitable local optimal ones. Specifically, these well-trained models provide the highest accurate double-band average transmission coefficient combined with the lowest number of layers or the thinnest total thickness starting from a reference multilayered structure. Finally, the more sophisticated enhancement method, called the double deep Q-learning network, exhibited the best performance in finding optimal antireflective multilayered structures with the highest double-band average transmission coefficient of about 98.95%.

A New Simple Analytical Method for a Highly Accurate Determination of the Optical Parameters of a Slab from Transmittance Data.

To date, determining with high accuracy the optical parameters (extinction coefficient k and refractive index n) of a slab from the sole transmittance data requires an inverse method based on numerical iteration procedures. In this paper, we propose a new inverse analytical method of extracting (k, n) without numerical iterative processes. The high accuracy of this new inverse method is assessed, and as an application example, the optical parameters of CaF2 and Si substrates are determined in the IR spectral range of 4-8 µm.

Exploring Factors That Predict Marketing of e-Cigarette Products on Twitter: Infodemiology Approach Using Time Series.

Background: Electronic nicotine delivery systems (known as electronic cigarettes or e-cigarettes) increase risk for adverse health outcomes among naïve tobacco users, particularly youth and young adults. This vulnerable population is also at risk for exposed brand marketing and advertisement of e-cigarettes on social media. Understanding predictors of how e-cigarette manufacturers conduct social media advertising and marketing could benefit public health approaches to addressing e-cigarette use. Objective: This study documents factors that predict changes in daily frequency of commercial tweets about e-cigarettes using time series modeling techniques. Methods: We analyzed data on the daily frequency of commercial tweets about e-cigarettes collected between January 1, 2017, and December 31, 2020. We fit the data to an autoregressive integrated moving average (ARIMA) model and unobserved components model (UCM). Four measures assessed model prediction accuracy. Predictors in the UCM include days with events related to the US Food and Drug Administration (FDA), non-FDA-related events with significant importance such as academic or news announcements, weekday versus weekend, and the period when JUUL maintained an active Twitter account (ie, actively tweeting from their corporate Twitter account) versus when JUUL stopped tweeting. Results: When the 2 statistical models were fit to the data, the results indicate that the UCM was the best modeling technique for our data. All 4 predictors included in the UCM were significant predictors of the daily frequency of commercial tweets about e-cigarettes. On average, brand advertisement and marketing of e-cigarettes on Twitter was higher by more than 150 advertisements on days with FDA-related events compared to days without FDA events. Similarly, more than 40 commercial tweets about e-cigarettes were, on average, recorded on days with important non-FDA events compared to days without such events. We also found that there were more commercial tweets about e-cigarettes on weekdays than on weekends and more commercial tweets when JUUL maintained an active Twitter account. Conclusions: e-Cigarette companies promote their products on Twitter. Commercial tweets were significantly more likely to be posted on days with important FDA announcements, which may alter the narrative about information shared by the FDA. There remains a need for regulation of digital marketing of e-cigarette products in the United States.

A Collaborative Study: Determination of Mycotoxins in Corn, Peanut Butter, and Wheat Flour Using Stable Isotope Dilution Assay (SIDA) and Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS).

A collaborative study was conducted to evaluate stable isotope dilution assay (SIDA) and LC-MS/MS for the simultaneous determination of aflatoxins B1, B2, G1, and G2; deoxynivalenol; fumonisins B1, B2, and B3; ochratoxin A; HT-2 toxin; T-2 toxin; and zearalenone in foods. Samples were fortified with 12 13C uniformly labeled mycotoxins (13C-IS) corresponding to the native mycotoxins and extracted with acetonitrile/water (50:50 v/v), followed by centrifugation, filtration, and LC-MS/MS analysis. In addition to certified reference materials, the six participating laboratories analyzed corn, peanut butter, and wheat flour fortified with the 12 mycotoxins at concentrations ranging from 1.0 to 1000 ng/g. Using their available LC-MS/MS platform, each laboratory developed in-house instrumental conditions for analysis. The majority of recoveries ranged from 80 to 120% with relative standard derivations (RSDs) <20%. Greater than 90% of the average recoveries of the participating laboratories were in the range of 90-110%, with repeatability RSDr (within laboratory) < 10% and reproducibility RSDR (among laboratory) < 15%. All Z scores of the results of certified reference materials were between -2 and 2. Using 13C-IS eliminated the need for matrix-matched calibration standards for quantitation, simplified sample preparation, and achieved simultaneous identification and quantitation of multiple mycotoxins in a simple LC-MS/MS procedure.

Nuclear respiratory factor-1 and bioenergetics in tamoxifen-resistant breast cancer cells.

Acquired tamoxifen (TAM) resistance is a significant clinical problem in treating patients with estrogen receptor α (ERα)+ breast cancer. We reported that ERα increases nuclear respiratory factor-1 (NRF-1), which regulates nuclear-encoded mitochondrial gene transcription, in MCF-7 breast cancer cells and NRF-1 knockdown stimulates apoptosis. Whether NRF-1 and target gene expression is altered in endocrine resistant breast cancer cells is unknown. We measured NRF-1and metabolic features in a cell model of progressive TAM-resistance. NRF-1 and its target mitochondrial transcription factor A (TFAM) were higher in TAM-resistant LCC2 and LCC9 cells than TAM-sensitive MCF-7 cells. Using extracellular flux assays we observed that LCC1, LCC2, and LCC9 cells showed similar oxygen consumption rate (OCR), but lower mitochondrial reserve capacity which was correlated with lower Succinate Dehydrogenase Complex, Subunit B in LCC1 and LCC2 cells. Complex III activity was lower in LCC9 than MCF-7 cells. LCC1, LCC2, and LCC9 cells had higher basal extracellular acidification (ECAR), indicating higher aerobic glycolysis, relative to MCF-7 cells. Mitochondrial bioenergetic responses to estradiol and 4-hydroxytamoxifen were reduced in the endocrine-resistant cells compared to MCF-7 cells. These results suggest the acquisition of altered metabolic phenotypes in response to long term antiestrogen treatment may increase vulnerability to metabolic stress.

Bioenergetic differences between MCF-7 and T47D breast cancer cells and their regulation by oestradiol and tamoxifen.

Oestrogen receptor α (ERα+) breast tumours rely on mitochondria (mt) to generate ATP. The goal of the present study was to determine how oestradiol (E2) and 4-hydroxytamoxifen (4-OHT) affect cellular bioenergetic function in MCF-7 and T47D ERα+ breast cancer cells in serum-replete compared with dextran-coated charcoal (DCC)-stripped foetal bovine serum (FBS)-containing medium ('serum-starved'). Serum-starvation reduced oxygen consumption rate (OCR), extracellular acidification rate (ECAR), ATP-linked OCR and maximum mt capacity, reflecting lower ATP demand and mt respiration. Cellular respiratory stateapparent was unchanged by serum deprivation. 4-OHT reduced OCR independent of serum status. Despite having a higher mt DNA/nuclear DNA ratio than MCF-7 cells, T47D cells have a lower OCR and ATP levels and higher proton leak. T47D express higher nuclear respiratory factor-1 (NRF-1) and NRF-1-regulated, nuclear-encoded mitochondrial transcription factor TFAM and cytochrome c, but lower levels of cytochrome c oxidase, subunit IV, isoform 1 (COX4, COX4I1). Mitochondrial reserve capacity, reflecting tolerance to cellular stress, was higher in serum-starved T47D cells and was increased by 4-OHT, but was decreased by 4-OHT in MCF-7 cells. These data demonstrate critical differences in cellular energetics and responses to 4-OHT in these two ERα+ cell lines, likely reflecting cancer cell avoidance of apoptosis.

Dopant-assisted atmospheric pressure photoionization of patulin in apple juice and apple-based food with liquid chromatography-tandem mass spectrometry.

A dopant-assisted atmospheric pressure photoionization (APPI) with liquid chromatography tandem mass spectrometry (LC-MS/MS) method was developed to determine patulin in apple juice and apple-based food. Different dopants, dopant flow rates, and LC separation conditions were evaluated. Using toluene as the dopant, the LC-APPI-MS/MS method achieved a linear calibration from 12.5 to 2000 µg/L (r(2) > 0.99). Matrix-dependent limits of quantitation (LOQs) were from 8 µg/L (solvent) to 12 µg/L (apple juice). [(13)C]-Patulin-fortified apple juice samples were directly analyzed by the LC-APPI-MS/MS method. Other apple-based food was fortified with [(13)C]-patulin, diluted using water (1% formic acid), centrifuged, and filtered, followed by LC-APPI-MS/MS analysis. In clear apple juice, unfiltered apple cider, applesauce, and apple-based baby food, average recoveries were 101 ± 6% (50 µg/kg), 103 ± 5% (250 µg/kg), and 102 ± 5% (1000 µg/kg) (av ± SD, n = 16). Using the suggested method, patulin was detected in 3 of 30 collected market samples with concentrations ranging from