Online Interest for Electronic Cigarettes Using Google Trends in the UK: A Correlation Analysis.

BACKGROUND: Google Trends provides an easily accessible and cost-effective method of providing real-time insight into user interest. OBJECTIVE: to address the gap in UK prevalence data for e-cigarettes by analyzing Google Trends to identify correlations with official data from Action on Smoking and Health. The study further evaluates Google Trend's sensitivity to real-time events and the ability for predictive models to forecast future data based on Google Trends. METHODS: UK Google Trends data from 2012 to 2021 was analyzed to assess (a) the most popular electronic nicotine device terminology; (b) statistically significant points in time; (c) correlations between Relative Search Volumes and official reports on electronic cigarette use and (d) whether Google Trends could predict future patterns in data. These were achieved using Locally Weighted Scatterplot Smoothing regression, Pruned Exact Linear Time Method, cross correlation, and Autoregressive Integrated Moving Average algorithms respectively. RESULTS: "Vape" was revealed to be the most popular electronic nicotine device terminology with a correlation coefficient greater than +0.9 when compared to official electronic cigarette consumption data within a one-year timescale (lag 0). Results from ARIMA modeling were varied with the algorithms forecasted trends line occasionally lying outside of a 95% prediction interval. CONCLUSION: Google Trends may correspond to population-based prevalence of electronic cigarette use. The changing trends coincide with changing policy decisions. Google Trends based prediction for online interest in electronic cigarettes requires further validation so should currently be used in conjunction with other traditional methods of data collections.

Measuring environmental awareness: An analysis using google search data.

Environmental awareness is usually measured using surveys. This paper aims to offer an alternative measure: an Environmental Awareness Index (EAI) constructed using Google search data provided by Google Trends. The benefits of using Google search data over surveys are that (i) they are less costly to obtain, (ii) they are available at high frequency, and (iii) they cover countries where no surveys are available. To test the validity of the proposed EAI, this study empirically assesses the impact of the computed index on individuals' pro-environmental behaviors using the Special Eurobarometer: Attitudes of European citizens towards the Environment data. Results show that the EAI is positively related to pro-environmental behaviors with a statistical significance at the one percent level. This finding stays robust in pooled OLS as well as in panel regression analysis when GDP, mean years of schooling, and population are included as control variables and when time-fixed effects are introduced. Further, the results confirm that environmental awareness is not stable over time and underline the importance of having a timely measure of environmental awareness at hand. Finally, the findings offer several practical implications for managers and policymakers, who will be able to use a timely measure of environmental awareness, assess and measure the impact of their policies aiming to raise environmental awareness as well as depict the type of behavior influenced by their policies.

MS Annika 2.0 Identifies Cross-Linked Peptides in MS2-MS3-Based Workflows at High Sensitivity and Specificity.

Cross-linking mass spectrometry has become a powerful tool for the identification of protein-protein interactions and for gaining insight into the structures of proteins. We previously published MS Annika, a cross-linking search engine which can accurately identify cross-linked peptides in MS2 spectra from a variety of different MS-cleavable cross-linkers. In this publication, we present MS Annika 2.0, an updated version implementing a new search algorithm that, in addition to MS2 level, only supports the processing of data from MS2-MS3-based approaches for the identification of peptides from MS3 spectra, and introduces a novel scoring function for peptides identified across multiple MS stages. Detected cross-links are validated by estimating the false discovery rate (FDR) using a target-decoy approach. We evaluated the MS3-search-capabilities of MS Annika 2.0 on five different datasets covering a variety of experimental approaches and compared it to XlinkX and MaXLinker, two other cross-linking search engines. We show that MS Annika detects up to 4 times more true unique cross-links while simultaneously yielding less false positive hits and therefore a more accurate FDR estimation than the other two search engines. All mass spectrometry proteomics data along with result files have been deposited to the ProteomeXchange consortium via the PRIDE partner repository with the dataset identifier PXD041955.

Mistle: bringing spectral library predictions to metaproteomics with an efficient search index.

MOTIVATION: Deep learning has moved to the forefront of tandem mass spectrometry-driven proteomics and authentic prediction for peptide fragmentation is more feasible than ever. Still, at this point spectral prediction is mainly used to validate database search results or for confined search spaces. Fully predicted spectral libraries have not yet been efficiently adapted to large search space problems that often occur in metaproteomics or proteogenomics. RESULTS: In this study, we showcase a workflow that uses Prosit for spectral library predictions on two common metaproteomes and implement an indexing and search algorithm, Mistle, to efficiently identify experimental mass spectra within the library. Hence, the workflow emulates a classic protein sequence database search with protein digestion but builds a searchable index from spectral predictions as an in-between step. We compare Mistle to popular search engines, both on a spectral and database search level, and provide evidence that this approach is more accurate than a database search using MSFragger. Mistle outperforms other spectral library search engines in terms of run time and proves to be extremely memory efficient with a 4- to 22-fold decrease in RAM usage. This makes Mistle universally applicable to large search spaces, e.g. covering comprehensive sequence databases of diverse microbiomes. AVAILABILITY AND IMPLEMENTATION: Mistle is freely available on GitHub at https://github.com/BAMeScience/Mistle.

Users choose to engage with more partisan news than they are exposed to on Google Search.

If popular online platforms systematically expose their users to partisan and unreliable news, they could potentially contribute to societal issues such as rising political polarization1,2. This concern is central to the 'echo chamber'3-5 and 'filter bubble'6,7 debates, which critique the roles that user choice and algorithmic curation play in guiding users to different online information sources8-10. These roles can be measured as exposure, defined as the URLs shown to users by online platforms, and engagement, defined as the URLs selected by users. However, owing to the challenges of obtaining ecologically valid exposure data-what real users were shown during their typical platform use-research in this vein typically relies on engagement data4,8,11-16 or estimates of hypothetical exposure17-23. Studies involving ecological exposure have therefore been rare, and largely limited to social media platforms7,24, leaving open questions about web search engines. To address these gaps, we conducted a two-wave study pairing surveys with ecologically valid measures of both exposure and engagement on Google Search during the 2018 and 2020 US elections. In both waves, we found more identity-congruent and unreliable news sources in participants' engagement choices, both within Google Search and overall, than they were exposed to in their Google Search results. These results indicate that exposure to and engagement with partisan or unreliable news on Google Search are driven not primarily by algorithmic curation but by users' own choices.

Analysis of google trends and search results for ophthalmic symptoms of monkeypox.

PURPOSE: In July 2022, the World Health Organization (WHO) declared monkeypox virus's global spread a "public health emergency of international concern." About a quarter of monkeypox cases feature ophthalmic symptoms. We assessed trends in worldwide search interest in monkeypox ophthalmic involvement and inclusion in online search engine queries. METHODS: The following keywords were searched on Google Trends from April 1, 2022, to August 12, 2022: monkeypox + eye, pink eye, eye infection, eyelid, vision, blurry vision, vision loss, blindness, eye symptoms, eye problems, eye pain, eye redness, conjunctivitis, conjunctiva, cornea, keratitis, corneal ulcer, and blepharitis. We analyzed trends, correlated search interest with case count data, and compared popularity of search terms via nonparametric Mann-Whitney-U analysis. Inclusion of ophthalmic symptoms in Google search results for "monkeypox symptoms" was assessed. RESULTS: "Monkeypox eye" had the highest average search interest worldwide and in the United States. Search interest peaked between mid-May and late July 2022. When compared to interest in "monkeypox rash," the most searched monkeypox symptom, the average interest in "monkeypox eye" was lower (p < 0.01). Of the first 50 results from the Google search of "monkeypox symptoms," 10/50 (20%) mentioned ophthalmic symptoms. 6/50 (12%) mentioned the eye as a route of virus transmission. CONCLUSION: Search interest in monkeypox ophthalmic symptoms corresponds with geographic and temporal trends, i.e., timing and location of the first reported non-endemic cases and WHO announcement. Although ophthalmic symptoms are not as widely searched currently, inclusion in public health messaging is key for diagnosis, appropriate management, and reduction of further transmission.

Comparison of Database Searching Programs for the Analysis of Single-Cell Proteomics Data.

Single-cell proteomics is emerging as an important subfield in the proteomics and mass spectrometry communities, with potential to reshape our understanding of cell development, cell differentiation, disease diagnosis, and the development of new therapies. Compared with significant advancements in the "hardware" that is used in single-cell proteomics, there has been little work comparing the effects of using different "software" packages to analyze single-cell proteomics datasets. To this end, seven popular proteomics programs were compared here, applying them to search three single-cell proteomics datasets generated by three different platforms. The results suggest that MSGF+, MSFragger, and Proteome Discoverer are generally more efficient in maximizing protein identifications, that MaxQuant is better suited for the identification of low-abundance proteins, that MSFragger is superior in elucidating peptide modifications, and that Mascot and X!Tandem are better for analyzing long peptides. Furthermore, an experiment with different loading amounts was carried out to investigate changes in identification results and to explore areas in which single-cell proteomics data analysis may be improved in the future. We propose that this comparative study may provide insight for experts and beginners alike operating in the emerging subfield of single-cell proteomics.

iBench: A ground truth approach for advanced validation of mass spectrometry identification method.

The discovery of many noncanonical peptides detectable with sensitive mass spectrometry inside, outside, and on cells shepherded the development of novel methods for their identification, often not supported by a systematic benchmarking with other methods. We here propose iBench, a bioinformatic tool that can construct ground truth proteomics datasets and cognate databases, thereby generating a training court wherein methods, search engines, and proteomics strategies can be tested, and their performances estimated by the same tool. iBench can be coupled to the main database search engines, allows the selection of customized features of mass spectrometry spectra and peptides, provides standard benchmarking outputs, and is open source. The proof-of-concept application to tryptic proteome digestions, immunopeptidomes, and synthetic peptide libraries dissected the impact that noncanonical peptides could have on the identification of canonical peptides by Mascot search with rescoring via Percolator (Mascot+Percolator).

Implementing the MSFragger Search Engine as a Node in Proteome Discoverer.

Here, we describe the implementation of the fast proteomics search engine MSFragger as a processing node in the widely used Proteome Discoverer (PD) software platform. PeptideProphet (via the Philosopher tool kit) is also implemented as an additional PD node to allow validation of MSFragger open (mass-tolerant) search results. These two nodes, along with the existing Percolator validation module, allow users to employ different search strategies and conveniently inspect search results through PD. Our results have demonstrated the improved numbers of PSMs, peptides, and proteins identified by MSFragger coupled with Percolator and significantly faster search speed compared to the conventional SEQUEST/Percolator PD workflows. The MSFragger-PD node is available at https://github.com/nesvilab/PD-Nodes/releases/.

Construction and validation of a COVID-19 pandemic trend forecast model based on Google Trends data for smell and taste loss.

Aim: To explore the role of smell and taste changes in preventing and controlling the COVID-19 pandemic, we aimed to build a forecast model for trends in COVID-19 prediction based on Google Trends data for smell and taste loss. Methods: Data on confirmed COVID-19 cases from 6 January 2020 to 26 December 2021 were collected from the World Health Organization (WHO) website. The keywords "loss of smell" and "loss of taste" were used to search the Google Trends platform. We constructed a transfer function model for multivariate time-series analysis and to forecast confirmed cases. Results: From 6 January 2020 to 28 November 2021, a total of 99 weeks of data were analyzed. When the delay period was set from 1 to 3 weeks, the input sequence (Google Trends of loss of smell and taste data) and response sequence (number of new confirmed COVID-19 cases per week) were significantly correlated (P < 0.01). The transfer function model showed that worldwide and in India, the absolute error of the model in predicting the number of newly diagnosed COVID-19 cases in the following 3 weeks ranged from 0.08 to 3.10 (maximum value 100; the same below). In the United States, the absolute error of forecasts for the following 3 weeks ranged from 9.19 to 16.99, and the forecast effect was relatively accurate. For global data, the results showed that when the last point of the response sequence was at the midpoint of the uptrend or downtrend (25 July 2021; 21 November 2021; 23 May 2021; and 12 September 2021), the absolute error of the model forecast value for the following 4 weeks ranged from 0.15 to 5.77. When the last point of the response sequence was at the extreme point (2 May 2021; 29 August 2021; 20 June 2021; and 17 October 2021), the model could accurately forecast the trend in the number of confirmed cases after the extreme points. Our developed model could successfully predict the development trends of COVID-19. Conclusion: Google Trends for loss of smell and taste could be used to accurately forecast the development trend of COVID-19 cases 1-3 weeks in advance.

Variabilidad clínica del síndrome de ovario poliquístico / Clinical variability in polycystic ovary syndrome

Introducción: El síndrome de ovario poliquístico es la forma más común de anovulación crónica relacionada con exceso de andrógenos. La prevalencia oscila según el criterio diagnóstico utilizado entre 4-21 pòr ciento. Objetivo: Describir las características clínicas de las pacientes con síndrome de ovario poliquístico. Métodos: Se seleccionaron los consensos hasta ahora realizados y artículos originales de los último 10 años, disponibles en los siguientes buscadores: Pubmed, Medscape, Scielo, Bireme. Se consideraron otras publicaciones que por su importancia clínica no han sido replicados. Conclusiones: La variedad de fenotipos presentes en el SOP hace que las manifestaciones clínicas y factores de riesgo para otras morbilidades sean heterogéneas. La influencia que ejerce además su etiopatogenia, no completamente dilucidada, hace que el diagnóstico y por consiguiente el manejo actual de estas pacientes tenga un enfoque multidisciplinario, individualizado y enfocado a las prioridades e inconformidades que puedan afectar su calidad de vida(AU)

Introduction: Polycystic ovary syndrome (PCOS) is the most common form of chronic anovulation related to androgen excess. The prevalence ranges according to the diagnostic criteria used between 4-21 percent. Objective: To describe the clinical characteristics of patients with polycystic ovary syndrome. Methods: The consensuses and original articles of the last 10 years were selected, which were available in the following search engines: Pubmed, Medscape, Scielo, and Bireme. Other publications that due to their clinical importance have not been replicated were considered. Conclusions: The variety of phenotypes present in the polycystic ovary syndrome makes the clinical manifestations and risk factors for other morbidities heterogeneous. The influence exerted also by its etiopathogenesis, not completely elucidated, causes the diagnosis and therefore the current management of these patients to have a multidisciplinary approach which is individualized and focused on the priorities and nonconformities that may affect the patients' quality of life(AU)

Sensitive and Specific Spectral Library Searching with CompOmics Spectral Library Searching Tool and Percolator.

Maintaining high sensitivity while limiting false positives is a key challenge in peptide identification from mass spectrometry data. Here, we investigate the effects of integrating the machine learning-based postprocessor Percolator into our spectral library searching tool COSS (CompOmics Spectral library Searching tool). To evaluate the effects of this postprocessing, we have used 40 data sets from 2 different projects and have searched these against the NIST and MassIVE spectral libraries. The searching is carried out using 2 spectral library search tools, COSS and MSPepSearch with and without Percolator postprocessing, and using sequence database search engine MS-GF+ as a baseline comparator. The addition of the Percolator rescoring step to COSS is effective and results in a substantial improvement in sensitivity and specificity of the identifications. COSS is freely available as open source under the permissive Apache2 license, and binaries and source code are found at https://github.com/compomics/COSS.

Estudio descriptivo del perfil de investigadoras hondureñas en Google Académico / Descriptive study of the profile of Honduran female researchers in Google Scholar

Introducción: El rol de la mujer en el área científica ha crecido con el paso de los años, pero aún persiste una gran brecha de género en ciencia. Para conocer y manejar esa brecha, es necesario hacer un mapeo de la situación de las mujeres investigadoras en Honduras. Objetivo: Describir la participación y contribuciones de las investigadoras hondureñas en la producción científica nacional según su perfil en Google Académico. Metodología: Se usó Power BI para analizar las variables sexo, institución, índice H, número de citaciones, número de publicaciones, año de la primera publicación, y áreas de investigación registradas en su perfil, utilizando la versión 2 del Ranking de Investigadores Hondureños según su Perfil de Google Académico disponible en https://bit.ly/38s6YuT, el cual es un registro depurado. Resultados: El 35% de todos los perfiles eran de mujeres, 41% vinculadas a las ciencias médicas y de la salud y en su mayoría (92%), afiliadas a universidades. Pese al creciente número de mujeres investigadoras registradas en Google Académico, el crecimiento no es simétrico respecto a los hombres. Solamente el 3.1% de las investigadoras tenía un índice H que supera los dos dígitos. Conclusión: A pesar de los avances, se mantiene la inequidad de género entre investigadores hondureños según la data de Google Académico. Se requiere de políticas públicas e institucionales, para corregir esa brecha...(AU)

Integration of VarSome API in an existing bioinformatic pipeline for automated ACMG interpretation of clinical variants.

OBJECTIVE: While the bioinformatic workflow, from quality control to annotation, is quite standardized, the interpretation of variants is still a challenge. The decreasing cost of massively parallel NGS has produced hundreds of variants per patient to analyze and interpret. The ACMG "Standards and guidelines for the interpretation of sequence variants", widely adopted in clinical settings, assume that the clinician has a comprehensive knowledge of the literature and the disease. MATERIALS AND METHODS: To semi-automatize the application of the guidelines, we decided to develop an algorithm that exploits VarSome, a widely used platform that interprets variants on the basis of information from more than 70 genome databases. RESULTS: Here we explain how we integrated VarSome API into our existing clinical diagnostic pipeline for NGS data to obtain validated reproducible results as indicated by accuracy, sensitivity and specificity. CONCLUSIONS: We validated the automated pipeline to be sure that it was doing what we expected. We obtained 100% sensitivity, specificity and accuracy, confirming that it was suitable for use in a diagnostic setting.

Does Google Fit provide valid energy expenditure measurements of functional tasks compared to those of Fibion accelerometer in healthy individuals? A cross-sectional study.

BACKGROUND AND AIMS: Smartphone applications (e.g., Google Fit) may be a good alternative tool for accelerometers in estimating energy expenditure of physical activities because they are affordable, easy to use, and freely downloadable on smartphones. We aimed to determine the concurrent validity of the Fibion and Google Fit for measuring energy expenditure of functional tasks in healthy individuals. METHODS: In this cross-sectional study, 28 healthy individuals (21.25 ± 1.84 years) performed certain tasks (lying, standing, 6-min walk test, treadmill walking, stair climbing and cycling) for ∼90 min, while wearing a Fibion accelerometer on their thigh and having the Google Fit application in a smartphone placed in their trouser pocket. Concurrent validity between the energy expenditure data of the Google Fit and Fibion was assessed using the Spearman rho correlation coefficient (data were not normally distributed), Bland-Altman plots and linear regression. RESULTS: Neither energy expenditure for the whole duration nor for the tasks, except sitting + treadmill walking (r = 0.419, p = 0.027), showed significant correlations between the Google Fit and Fibion measurements. A proportional bias was evident for almost all comparisons. CONCLUSIONS: The Google Fit did not provide valid energy expenditure measurements compared to the Fibion for most of the investigated tasks in healthy individuals.