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Context Active surveillance for papillary thyroid cancer (PTC) meeting criteria for surgical resection is uncommon. Which patients may prove reasonable candidates for this approach is not well defined. Objective This work aimed to examine the feasibility and safety of active surveillance for patients with known or suspected intrathyroidal PTC up to 4 cm in diameter. Methods A retrospective review was conducted of all consecutive patients who underwent nonoperative active surveillance of suspicious or malignant thyroid nodules over a 20-year period from 2001 to 2021. We included patients with an initial ultrasound–fine-needle aspiration confirming either (a) Bethesda 5 or 6 cytology or (b) a "suspicious" Afirma molecular test. The primary outcomes and measures included the rate of adverse oncologic outcomes (mortality and recurrence), as well as the cumulative incidence of size/volume growth. Results Sixty-nine patients were followed with active surveillance for 1 year or longer (average 55 months), with 26 patients (38%) having nodules 2 cm or larger. No patients were found to develop new-incident occurrence of lymph node or distant metastasis. One patient, however, demonstrated concern for progression to a dedifferentiated cancer on repeat core biopsy 17 years after initial start of nonoperative selection. A total of 21% of patients had an increase in maximum diameter more than 3 mm, while volume increase of 50% or greater was noted in 25% of patients. Thirteen patients ultimately underwent delayed (rescue) surgery, and no disease recurrence was noted after such treatment. Age and initial nodule size were not predictors of nodule growth. Conclusion These data expand consideration of active surveillance of PTC in select patients with intrathyroidal suspected malignancy greater than 1 cm in diameter. Rescue surgery, if required at a later time point, appears effective. [ABSTRACT FROM AUTHOR]

Introduction: We examined the relationship between current tobacco use and functionally important respiratory symptoms.Methods: Longitudinal cohort study of 16 295 US adults without COPD in Waves 2-3 (W2-3, 2014-2016) of the Population Assessment of Tobacco and Health Study. Exposure-Ten mutually exclusive categories of tobacco use including single product, multiple product, former, and never use (reference). Outcome-Seven questions assessing wheezing/cough were summed to create a respiratory symptom index; cutoffs of ≥2 and ≥3 were associated with functional limitations and poorer health. Multivariable regressions examined both cutoffs cross-sectionally and change over approximately 12 months, adjusting for confounders.Results: All tobacco use categories featuring cigarettes (>2/3's of users) were associated with higher risk (vs. never users) for functionally important respiratory symptoms at W2, for example, at symptom severity ≥ 3, risk ratio for exclusive cigarette use was 2.34 [95% CI, 1.92, 2.85] and for worsening symptoms at W3 was 2.80 [2.08, 3.76]. There was largely no increased symptom risk for exclusive use of cigars, smokeless tobacco, hookah, or e-cigarettes (adjustment for pack-years and marijuana attenuated the cross-sectional e-cigarette association from 1.53(95% CI 0.98, 2.40) to 1.05 (0.67, 1.63); RRs for these products were also significantly lower compared to exclusive use of cigarettes. The longitudinal e-cigarette-respiratory symptom association was sensitive to the respiratory index cutoff level; exclusive e-cigarette use was associated with worsening symptoms at an index cutoff ≥ 2 (RR = 1.63 [1.02, 2.59]) and with symptom improvement at an index cutoff of ≥ 3 (RR = 1.64 [1.04, 2.58]).Conclusions: Past and current cigarette smoking drove functionally important respiratory symptoms, while exclusive use of other tobacco products was largely not associated. However, the relationship between e-cigarette use and symptoms was sensitive to adjustment for pack-years and symptom severity.Implications: How noncigarette tobacco products affect respiratory symptoms is not clear; some studies implicate e-cigarettes. We examined functionally important respiratory symptoms (wheezing/nighttime cough) among US adults without COPD. The majority of adult tobacco users smoke cigarettes and have higher risk of respiratory symptoms and worsening of symptoms, regardless of other products used with them. Exclusive use of other tobacco products (e-cigarettes, cigars, smokeless, hookah) was largely not associated with functionally important respiratory symptoms and risks associated with their use was significantly lower than for cigarettes. The association for e-cigarettes was greatly attenuated by adjustment for cigarette pack-years and sensitive to how symptoms were defined. [ABSTRACT FROM AUTHOR]

We solve the convergence case of the generalized Baker–Schmidt problem for simultaneous approximation on affine subspaces, under natural diophantine type conditions. In one of our theorems, we do not require monotonicity on the approximation function. In order to prove these results, we establish asymptotic formulae for the number of rational points close to an affine subspace. One key ingredient is a sharp upper bound on a certain sum of reciprocals of fractional parts associated with the matrix defining the affine subspace. [ABSTRACT FROM AUTHOR]

Motivation Mapping distal regulatory elements, such as enhancers, is a cornerstone for elucidating how genetic variations may influence diseases. Previous enhancer-prediction methods have used either unsupervised approaches or supervised methods with limited training data. Moreover, past approaches have implemented enhancer discovery as a binary classification problem without accurate boundary detection, producing low-resolution annotations with superfluous regions and reducing the statistical power for downstream analyses (e.g. causal variant mapping and functional validations). Here, we addressed these challenges via a two-step model called De ep-learning framework for Co n de nsing enhancers and refining boundaries with large-scale functional assays (DECODE). First, we employed direct enhancer-activity readouts from novel functional characterization assays, such as STARR-seq, to train a deep neural network for accurate cell-type-specific enhancer prediction. Second, to improve the annotation resolution, we implemented a weakly supervised object detection framework for enhancer localization with precise boundary detection (to a 10 bp resolution) using Gradient-weighted Class Activation Mapping. Results Our DECODE binary classifier outperformed a state-of-the-art enhancer prediction method by 24% in transgenic mouse validation. Furthermore, the object detection framework can condense enhancer annotations to only 13% of their original size, and these compact annotations have significantly higher conservation scores and genome-wide association study variant enrichments than the original predictions. Overall, DECODE is an effective tool for enhancer classification and precise localization. Availability and implementation DECODE source code and pre-processing scripts are available at decode.gersteinlab.org. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation Traditionally, an individual can only query and retrieve information from a genome browser by using accessories such as a mouse and keyboard. However, technology has changed the way that people interact with their screens. We hypothesized that we could leverage technological advances to use voice recognition as an interactive input to query and visualize genomic information. Results We developed an Amazon Alexa skill called Gene Tracer that allows users to use their voice to find disease-associated gene information, deleterious mutations and gene networks, while simultaneously enjoy a genome browser-like visualization experience on their screen. As the voice can be well recognized and understood, Gene Tracer provides users with more flexibility to acquire knowledge and is broadly applicable to other scenarios. Availabilityand implementation Alexa skill store (https://www.amazon.com/LT-Gene-tracer/dp/B08HCL1V68/) and a demonstration video (https://youtu.be/XbDbx7JDKmI). Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Summary scATAC-seq is a powerful approach for characterizing cell-type-specific regulatory landscapes. However, it is difficult to benchmark the performance of various scATAC-seq analysis techniques (such as clustering and deconvolution) without having a priori a known set of gold-standard cell types. To simulate scATAC-seq experiments with known cell-type labels, we introduce an efficient and scalable scATAC-seq simulation method (SCAN-ATAC-Sim) that down-samples bulk ATAC-seq data (e.g. from representative cell lines or tissues). Our protocol uses a consistent but tunable signal-to-noise ratio across cell types in a scATAC-seq simulation for integrating bulk experiments with different levels of background noise, and it independently samples twice without replacement to account for the diploid genome. Because it uses an efficient weighted reservoir sampling algorithm and is highly parallelizable with OpenMP, our implementation in C++ allows millions of cells to be simulated in less than an hour on a laptop computer. Availability and implementation SCAN-ATAC-Sim is available at scan-atac-sim.gersteinlab.org. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]