OBJECTIVE: Accurate diagnosis and quantification of polyps and symptoms are pivotal for planning the therapeutic strategy of Chronic rhinosinusitis with nasal polyposis (CRSwNP). This pilot study aimed to develop an artificial intelligence (AI)-based image analysis system capable of segmenting nasal polyps from nasal endoscopy videos.
METHODS: Recorded nasal videoendoscopies from 52 patients diagnosed with CRSwNP between 2019 and 2022 were retrospectively analyzed. Images extracted were manually segmented on the web application Roboflow. A dataset of 342 images was generated and divided into training (80%), validation (10%), and testing (10%) sets. The Ultralytics YOLOv8.0.28 model was employed for automated segmentation.
RESULTS: The YOLOv8s-seg model consisted of 195 layers and required 42.4 GFLOPs for operation. When tested against the validation set, the algorithm achieved a precision of 0.91, recall of 0.839, and mean average precision at 50% IoU (mAP50) of 0.949. For the segmentation task, similar metrics were observed, including a mAP ranging from 0.675 to 0.679 for IoUs between 50% and 95%.
CONCLUSIONS: The study shows that a carefully trained AI algorithm can effectively identify and delineate nasal polyps in patients with CRSwNP. Despite certain limitations like the focus on CRSwNP-specific samples, the algorithm presents a promising complementary tool to existing diagnostic methods.

Recently, HexNAcQuest was developed to help distinguish peptides modified by HexNAc isomers, more specifically O-linked β-N-acetylglucosamine (O-GlcNAc) and O-linked α-N-acetylgalactosamine (O-GalNAc, Tn antigen). To facilitate its usage (particularly for datasets from glycoproteomics studies), herein we present a detailed protocol. It describes example cases and procedures for which users might need to use HexNAcQuest to distinguish these two modifications.

Timely and precise detection of emerging infections is imperative for effective outbreak management and disease control. Human mobility significantly influences the spatial transmission dynamics of infectious diseases. Spatial sampling, integrating the spatial structure of the target, holds promise as an approach for testing allocation in detecting infections, and leveraging information on individuals\' movement and contact behavior can enhance targeting precision. This study introduces a spatial sampling framework informed by spatiotemporal analysis of human mobility data, aiming to optimize the allocation of testing resources for detecting emerging infections. Mobility patterns, derived from clustering point-of-interest and travel data, are integrated into four spatial sampling approaches at the community level. We evaluate the proposed mobility-based spatial sampling by analyzing both actual and simulated outbreaks, considering scenarios of transmissibility, intervention timing, and population density in cities. Results indicate that leveraging inter-community movement data and initial case locations, the proposed Case Flow Intensity (CFI) and Case Transmission Intensity (CTI)-informed spatial sampling enhances community-level testing efficiency by reducing the number of individuals screened while maintaining a high accuracy rate in infection identification. Furthermore, the prompt application of CFI and CTI within cities is crucial for effective detection, especially in highly contagious infections within densely populated areas. With the widespread use of human mobility data for infectious disease responses, the proposed theoretical framework extends spatiotemporal data analysis of mobility patterns into spatial sampling, providing a cost-effective solution to optimize testing resource deployment for containing emerging infectious diseases.

UNASSIGNED: Renin-expressing cells are myoendocrine cells crucial for the maintenance of homeostasis. Renin is regulated by cAMP, p300 (histone acetyltransferase p300)/CBP (CREB-binding protein), and Brd4 (bromodomain-containing protein 4) proteins and associated pathways. However, the specific regulatory changes that occur following inhibition of these pathways are not clear.
UNASSIGNED: We treated As4.1 cells (tumoral cells derived from mouse juxtaglomerular cells that constitutively express renin) with 3 inhibitors that target different factors required for renin transcription: H-89-dihydrochloride, PKA (protein kinase A) inhibitor; JQ1, Brd4 bromodomain inhibitor; and A-485, p300/CBP inhibitor. We performed ATAC-seq, single-cell RNA sequencing, CUT&Tag, and chromatin immunoprecipitation sequencing for H3K27ac and p300 binding on biological replicates of treated and control As4.1 cells.
UNASSIGNED: In response to each inhibitor, Ren1 expression was significantly reduced and reversible upon washout. Chromatin accessibility at the Ren1 locus did not markedly change but was globally reduced at distal elements. Inhibition of PKA led to significant reductions in H3K27ac and p300 binding specifically within the Ren1 super-enhancer region. Further, we identified enriched TF (transcription factor) motifs shared across each inhibitory treatment. Finally, we identified a set of 9 genes with putative roles across each of the 3 renin regulatory pathways and observed that each displayed differentially accessible chromatin, gene expression, H3K27ac, and p300 binding at their respective loci.
UNASSIGNED: Inhibition of renin expression in cells that constitutively synthesize and release renin is regulated by an epigenetic switch from an active to poised state associated with decreased cell-cell communication and an epithelial-mesenchymal transition. This work highlights and helps define the factors necessary for renin cells to alternate between myoendocrine and contractile phenotypes.

The meta-learning method proposed in this paper addresses the issue of small-sample regression in the application of engineering data analysis, which is a highly promising direction for research. By integrating traditional regression models with optimization-based data augmentation from meta-learning, the proposed deep neural network demonstrates excellent performance in optimizing glass fiber reinforced plastic (GFRP) for wrapping concrete short columns. When compared with traditional regression models, such as Support Vector Regression (SVR), Gaussian Process Regression (GPR), and Radial Basis Function Neural Networks (RBFNN), the meta-learning method proposed here performs better in modeling small data samples. The success of this approach illustrates the potential of deep learning in dealing with limited amounts of data, offering new opportunities in the field of material data analysis.

BACKGROUND: Characterization of microbial growth is of both fundamental and applied interest. Modern platforms can automate collection of high-throughput microbial growth curves, necessitating the development of computational tools to handle and analyze these data to produce insights.
RESULTS: To address this need, here I present a newly-developed R package: gcplyr. gcplyr can flexibly import growth curve data in common tabular formats, and reshapes it under a tidy framework that is flexible and extendable, enabling users to design custom analyses or plot data with popular visualization packages. gcplyr can also incorporate metadata and generate or import experimental designs to merge with data. Finally, gcplyr carries out model-free (non-parametric) analyses. These analyses do not require mathematical assumptions about microbial growth dynamics, and gcplyr is able to extract a broad range of important traits, including growth rate, doubling time, lag time, maximum density and carrying capacity, diauxie, area under the curve, extinction time, and more.
CONCLUSIONS: gcplyr makes scripted analyses of growth curve data in R straightforward, streamlines common data wrangling and analysis steps, and easily integrates with common visualization and statistical analyses.

OBJECTIVE: This study aimed to compare and evaluate the efficiency and accuracy of computerized adaptive testing (CAT) under two stopping rules (SEM 0.3 and 0.25) using both real and simulated data in medical examinations in Korea.
METHODS: This study employed post-hoc simulation and real data analysis to explore the optimal stopping rule for CAT in medical examinations. The real data were obtained from the responses of 3rd-year medical students during examinations in 2020 at Hallym University College of Medicine. Simulated data were generated using estimated parameters from a real item bank in R. Outcome variables included the number of examinees\' passing or failing with SEM values of 0.25 and 0.30, the number of items administered, and the correlation. The consistency of real CAT result was evaluated by examining consistency of pass or fail based on a cut score of 0.0. The efficiency of all CAT designs was assessed by comparing the average number of items administered under both stopping rules.
RESULTS: Both SEM 0.25 and SEM 0.30 provided a good balance between accuracy and efficiency in CAT. The real data showed minimal differences in pass/fail outcomes between the 2 SEM conditions, with a high correlation (r = 0.99) between ability estimates. The simulation results confirmed these findings, indicating similar average item numbers between real and simulated data.
CONCLUSIONS: The findings suggest that both SEM 0.25 and 0.30 are effective termination criteria in the context of the Rasch model, balancing accuracy and efficiency in CAT.

Large language models (LLMs) have emerged as a powerful tool for biomedical researchers, demonstrating remarkable capabilities in understanding and generating human-like text. ChatGPT with its Code Interpreter functionality, an LLM connected with the ability to write and execute code, streamlines data analysis workflows by enabling natural language interactions. Using materials from a previously published tutorial, similar analyses can be performed through conversational interactions with the chatbot, covering data loading and exploration, model development and comparison, permutation feature importance, partial dependence plots, and additional analyses and recommendations. The findings highlight the significant potential of LLMs in assisting researchers with data analysis tasks, allowing them to focus on higher-level aspects of their work. However, there are limitations and potential concerns associated with the use of LLMs, such as the importance of critical thinking, privacy, security, and equitable access to these tools. As LLMs continue to improve and integrate with available tools, data science may experience a transformation similar to the shift from manual to automatic transmission in driving. The advancements in LLMs call for considering the future directions of data science and its education, ensuring that the benefits of these powerful tools are utilized with proper human supervision and responsibility.

To date, the field of transcriptomics has been characterized by rapid methods development and technological advancement, with new technologies continuously rendering older ones obsolete.This chapter traces the evolution of approaches to quantifying gene expression and provides an overall view of the current state of the field of transcriptomics, its applications to the study of the human brain, and its place in the broader emerging multiomics landscape.

BACKGROUND: Previous studies with several limitations have comparatively analyzed the relationship between ambulatory blood pressure (BP) and self-measured BP and biomarkers of organ damage. This study extends this line of research by examining the relationship between ambulatory and self-measured BP and cardiac, renal, and atherosclerotic biomarkers in outpatients at cardiovascular risk.
METHODS: In 1,440 practice outpatients who underwent office, ambulatory, and self-measured BP monitoring, we assessed the relationships of each BP with organ damage biomarkers including b-type natriuretic peptide (BNP), echocardiographic left ventricular mass index (LVMI), urine-albumin-creatinine ratio (UACR), and brachial-ankle pulse wave velocity (baPWV).
RESULTS: In the comparison of correlation, self-measured systolic BP (SBP) was more strongly correlated to log-transformed (Ln) BNP (n=1,435; r=0.123 vs. r = -0.093, P<0.001), LVMI (n=1,278; r=0.223 vs. r=0.094, P<0.001), Ln-UACR (n=1,435; r=0.244 vs. r=0.154, P=0.010), and baPWV (n=1,360; r=0.327 vs. r=0.115, P<0.001) than daytime ambulatory SBP. In the linear regression models including office, ambulatory, and self-measured SBP, only self-measured SBP was significantly related to Ln-BNP (P=0.016) and LVMI (P<0.001). In the logistic regression models for the top quartile of LVMI, adding self-measured SBP improved the model predictability (P=0.027), but adding daytime ambulatory SBP did not. However, adding daytime ambulatory SBP improved the model predictability in the logistic model including office and self-measured SBP.
CONCLUSIONS: Our study findings suggested that self-measured BP was associated with cardiac biomarkers independent of ambulatory BP.