Brazil's response to public health emergencies in 2023: lessons for the international community.

Objective: To describe the Brazilian experience of responding to public health emergencies in 2023, the organizational structure, and epidemiological characteristics.

Methods: Three emergencies (case studies) that occurred during the study year were analyzed according to the actions implemented during the response and the impacts on the population. The public health emergencies were summarized and analyzed through research on official documents and websites of the Ministry of Health and local health authorities.

A networked station system for high-resolution wind nowcasting in air traffic operations: A data-augmented deep learning approach.

This study introduces a high-resolution wind nowcasting model designed for aviation applications at Madeira International Airport, a location known for its complex wind patterns. By using data from a network of six meteorological stations and deep learning techniques, the produced model is capable of predicting wind speed and direction up to 30-minute ahead with 1-minute temporal resolution. The optimized architecture demonstrated robust predictive performance across all forecast horizons.

A new quantitative reverse transcription PCR assay to improve the routine diagnosis of paracoccidioidomycosis.

Paracoccidioides are dimorphic fungal pathogens and the etiological agents of paracoccidioidomycosis (PCM). This severe systemic mycosis is restricted to Latin America, where it has been historically endemic. Currently, PCM presents the fewest diagnostic tools available when compared to other endemic mycoses.

Predicting noncontact injuries of professional football players using machine learning.

Noncontact injuries are prevalent among professional football players. Yet, most research on this topic is retrospective, focusing solely on statistical correlations between Global Positioning System (GPS) metrics and injury occurrence, overlooking the multifactorial nature of injuries. This study introduces an automated injury identification and prediction approach using machine learning, leveraging GPS data and player-specific parameters.

Expression of beam hardening artifacts on horizontally stitched cone-beam computed tomography images.

Purpose: This study was performed to evaluate the expression of beam hardening artifacts generated by high atomic number materials in stitched cone-beam computed tomography (CBCT) images, compared to the traditional acquisition mode.

Materials And Methods: CBCT volumes were acquired using an acrylic resin phantom embedded with pairs of cylinders made from amalgam dental alloy, cobalt-chromium alloy, gutta-percha, titanium, and zirconium. These cylinders were placed within the overlapping zones of the stitching reconstruction area.