Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

International Consensus Statement on Rhinology and Allergy: Rhinosinusitis

Background: The 5 years since the publication of the first International Consensus Statement on Allergy and Rhinology: Rhinosinusitis (ICAR‐RS) has witnessed foundational progress in our understanding and treatment of rhinologic disease. These advances are reflected within the more than 40 new topics covered within the ICAR‐RS‐2021 as well as updates to the original 140 topics. This executive summary consolidates the evidence‐based findings of the document. Methods: ICAR‐RS presents over 180 topics in the forms of evidence‐based reviews with recommendations (EBRRs), evidence‐based reviews, and literature reviews. The highest grade structured recommendations of the EBRR sections are summarized in this executive summary. Results: ICAR‐RS‐2021 covers 22 topics regarding the medical management of RS, which are grade A/B and are presented in the executive summary. Additionally, 4 topics regarding the surgical management of RS are grade A/B and are presented in the executive summary. Finally, a comprehensive evidence‐based management algorithm is provided. Conclusion: This ICAR‐RS‐2021 executive summary provides a compilation of the evidence‐based recommendations for medical and surgical treatment of the most common forms of RS

Ionic liquid pretreatment of lignocellulosic biomass with ionic liquid-water mixtures

A tongue pressure transducer system was used to assess tongue strength, endurance, fine pressure control and rate of repetitive movement in a group of 20 individuals, aged 17 to 60 years, with dysarthria following severe traumatic brain injury (TBI). Comparison of the TBI group's results against data obtained from a group of 20 age and sex matched control subjects revealed reductions in tongue endurance and rate of repetitive movement. Tongue strength and fine pressure control, however, were found not to differ significantly from the control group. Pearson's product-moment correlations indicated there to be only weak correlations between the physiological nonspeech tongue parameters and the deviant perceptual articulatory features exhibited by the TBI group. Further analysis of the results on an individual subject basis revealed no clear relationships between the physiological and perceptual parameters suggesting that the TBI subjects may have been compensating in different ways for the physiological impairments

Facile, room-temperature pre-treatment of rice husks with tetrabutylphosphonium hydroxide: Enhanced enzymatic and acid hydrolysis yields

Aqueous solutions of tetrabutylphosphonium hydroxide have been evaluated as pretreatment media for rice husks, prior to sulphuric acid hydrolysis or cellulase enzymatic hydrolysis. Varying the water:tetrabutylphosphonium hydroxide ratio varied the rate of delignification, as well as silica, lignin and cellulose solubility. Pre-treatment with 60. wt% hydroxide dissolved the rice husk and the regenerated material was thus heavily disrupted. Sulphuric acid hydrolysis of 60. wt%-treated samples yielded the highest amount of glucose per gram of rice husk. Solutions with good lignin and silica solubility but only moderate to negligible cellulose solubility (10-40. wt% hydroxide) were equally effective as pre-treatment media for both acid and enzymatic hydrolysis. However, pre-treatment with 60. wt% hydroxide solutions was incompatible with downstream enzymatic hydrolysis. This was due to significant incorporation of phosphonium species in the regenerated biomass, which significantly inhibited the activity of the cellulase enzymes

Anti-vaccination attitude trends during the COVID-19 pandemic: A machine learning-based analysis of tweets

Objective: Vaccine hesitancy has been ranked by the World Health Organization among the top 10 threats to global health. With a surge in misinformation and conspiracy theories against vaccination observed during the COVID-19 pandemic, attitudes toward vaccination may be worsening. This study investigates trends in anti-vaccination attitudes during the COVID-19 pandemic and within the United States, Canada, the United Kingdom, and Australia. Methods: Vaccine-related English tweets published between 1 January 2020 and 27 June 2021 were used. A deep learning model using a dynamic word embedding method, Bidirectional Encoder Representations from Transformers (BERTs), was developed to identify anti-vaccination tweets. The classifier achieved a micro F1 score of 0.92. Time series plots and country maps were used to examine vaccination attitudes globally and within countries. Results: Among 9,352,509 tweets, 232,975 (2.49%) were identified as anti-vaccination tweets. The overall number of vaccine-related tweets increased sharply after the implementation of the first vaccination round since November 2020 (daily average of 6967 before vs. 31,757 tweets after 9/11/2020). The number of anti-vaccination tweets increased after conspiracy theories spread on social media. Percentages of anti-vaccination tweets were 3.45%, 2.74%, 2.46%, and 1.86% for the United States, the United Kingdom, Australia, and Canada, respectively. Conclusions: Strategies and information campaigns targeting vaccination misinformation may need to be specifically designed for regions with the highest anti-vaccination Twitter activity and when new vaccination campaigns are initiated

Applying machine learning to identify anti‐vaccination tweets during the covid‐19 pandemic

Anti‐vaccination attitudes have been an issue since the development of the first vaccines. The increasing use of social media as a source of health information may contribute to vaccine hesitancy due to anti‐vaccination content widely available on social media, including Twitter. Being able to identify anti‐vaccination tweets could provide useful information for formulating strategies to reduce anti‐vaccination sentiments among different groups. This study aims to evaluate the performance of different natural language processing models to identify anti‐vaccination tweets that were published during the COVID‐19 pandemic. We compared the performance of the bidirectional encoder representations from transformers (BERT) and the bidirectional long shortterm memory networks with pre‐trained GLoVe embeddings (Bi‐LSTM) with classic machine learning methods including support vector machine (SVM) and naïve Bayes (NB). The results show that performance on the test set of the BERT model was: accuracy = 91.6%, precision = 93.4%, recall = 97.6%, F1 score = 95.5%, and AUC = 84.7%. Bi‐LSTM model performance showed: accuracy = 89.8%, precision = 44.0%, recall = 47.2%, F1 score = 45.5%, and AUC = 85.8%. SVM with linear kernel performed at: accuracy = 92.3%, Precision = 19.5%, Recall = 78.6%, F1 score = 31.2%, and AUC = 85.6%. Complement NB demonstrated: accuracy = 88.8%, precision = 23.0%, recall = 32.8%, F1 score = 27.1%, and AUC = 62.7%. In conclusion, the BERT models outperformed the Bi‐LSTM, SVM, and NB models in this task. Moreover, the BERT model achieved excellent performance and can be used to identify anti‐vaccination tweets in future studies