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Objectives: To evaluate the feasibility of AI-assisted reading of prostate magnetic resonance imaging (MRI) in Organized Prostate cancer Testing (OPT). Methods: Retrospective cohort study including 57 men with elevated prostate-specific antigen (PSA) levels ≥3 µg/L that performed bi-parametric MRI in OPT. The results of a CE-marked deep learning (DL) algorithm for prostate MRI lesion detection wer
Background Prostate-specific membrane antigen (PSMA)-PET usage in patients with prostate cancer is growing rapidly. Thus, novel risk-group definitions based on PSMA-PET are urgently needed for guidelines, clinical use, and trial study design. We report improved risk classification based on PSMA-PET Prostate Cancer Molecular Imaging Standardized Evaluation (PROMISE; PPP) nomograms (PPP3) to prognos
Cutaneous T-cell lymphoma is a chronic disease with a highly heterogeneous and unpre-dictable clinical course, and the impact on psychological resilience is still unknown. The primary aim was to investigate psychological resilience in cutaneous T-cell lymphoma patients over time and to relate it to dermatology-related quality of life (QoL) metrics. Psychological investigations were evaluated in 45
Perceived safety in urban areas depends on factors operating across multiple spatial scales. Nevertheless, most studies address only one scale at a time. The methodological contribution of this work lies on the introduction of a multi-scale approach for the analysis of perceived in urban areas. This approach includes city, neighbourhood, and individual scales. The overall goal is to identify the k
Urban vegetation is a critical nature-based solution for mitigating the urban heat island effect, yet its cooling efficacy on road surfaces varies spatially due to differences in vegetation type, structure, and local urban morphology. This study investigates the spatially varying nonlinear cooling effects of differentiated street greenery, including trees, shrubs, and grass, on land surface temper
The competition, especially for light, is a fundamental determinant of the structure and composition of a forest. Large-scale forest models must balance real-world complexity with computational demand and poorly constrained parameters. The LPJ-GUESS dynamic global vegetation model has a strong track record of simulating forest composition and tree demography with a simple representation of forest
Sulfur compounds in coal release sulfur oxides during thermochemical conversion, causing environmental pollution in the form of acid rain when discharged into the air. There is thus a significant need to develop efficient, environmentally friendly coal desulfurization technologies. This work studied Zhundong coal desulfurization using supercritical carbon dioxide-cosolvent systems experimentally a
Plants take up carbon dioxide (CO2) through photosynthesis. How this will change with rising CO2 concentrations in the atmosphere will strongly determine future climate change. An increase in the seasonal variations of atmospheric CO2 in recent decades indicates a positive trend in photosynthetic carbon uptake. We combined data-driven seasonal cycles of plant productivity with carbon sinks across
Accurately predicting terrestrial ecosystem responses to climate change over long-timescales is crucial for addressing global challenges. This relies on mechanistic modeling of ecosystem processes through land surface models (LSMs). Despite their importance, LSMs face significant uncertainties due to poorly constrained parameters, especially in carbon cycle predictions. This paper reviews the prog
Background: Acute aortic syndrome (AAS) is a life-threatening condition reported as often misdiagnosed. Advancements in CT availability and diagnostic algorithms, including high-sensitivity troponin and D-dimer for acute coronary syndrome (ACS) and pulmonary embolism (PE), may reduce AAS misdiagnosis, but the impact remains unclear. This study evaluated the incidence, characteristics, and clinical
Forests are an important component in the framework of nature-based solutions for mitigating climate change. However, there are still uncertainties about the biogeophysical effects of forest cover changes affecting heat and water fluxes as captured by Earth System Models (ESMs) simulations and observations. In this study, we investigate the differences in the surface temperature response to ideali
Background: Distal pancreatic resection is associated with a high frequency of postoperative complications. The aim of this study was to characterize the effect of diabetes mellitus (DM) on short-term complications after distal pancreatectomy. Methods: Patients who underwent distal pancreatectomy between 1 January 2010 and 31 December 2020 were identified from the Swedish National Pancreatic and P
Non-invasive methods for measuring thermal tolerance and thermoregulation in large numbers of individuals under natural environmental conditions are useful to understand the capacity of species to adapt to future climate scenarios. Infrared thermography (IRT) is one such tool in research on thermal adaptation, but concerns have been raised about its reliability, specifically the correlation betwee
The PRIMARY score was implemented in Prostate Cancer Molecular Imaging Standardized Evaluation (PROMISE) version 2 to improve accuracy for the diagnosis of clinically significant prostate cancer using prostate-specific membrane antigen (PSMA) positron emission tomography (PET). We reviewed overall survival (OS) for patients who underwent PSMA PET for initial staging to evaluate the prognostic valu
The Cox Proportional Hazards (CPH) model has long been the preferred survival model for its explainability. However, to increase its predictive power beyond its linear log-risk, it was extended to utilize deep neural networks, sacrificing its explainability. In this work, we explore the potential of self-explaining neural networks (SENN) for survival analysis. We propose a new locally explainable
We consider the problem of reconstructing the sound field in a room using prior information of the boundary geometry, represented as a point cloud. In general, when no boundary information is available, an accurate sound field reconstruction over a large spatial region and at high frequencies requires numerous microphone measurements. On the other hand, if all geometrical and acoustical aspects of
Deception jamming is a powerful technique used to disrupt various radar systems. In this work, we propose a novel sensor selection scheme for FDA-MIMO radar systems designed to maximize the likelihood of accurate jammer identification. The approach exploits the inherent delay between a jammer measuring incoming waveforms and its response. This delay induces a slight shift in the perceived distance
Many forms of small-size radars suffer from minor clock oscillations due to crystalline impurities in their internal clocks, causing time-varying clock offsets between the transmitter and the receivers. This offset causes a bias and an increased variance in the positioning of close-range targets, limiting the achievable accuracy well beyond the expected estimation limitations. Furthermore, an appr
The expansive carbon reservoirs of the boreal region are becoming some of the most rapidly growing sources of greenhouse gasses under a positive feedback between intensifying fire activity and climate change. However, current regional-scale methods lack the spatial precision needed to improve understanding of the drivers of these fluxes to inform strategies aimed at maximizing landscape carbon sto
