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Purpose: Consistent assessment of bone metastases is crucial for patient management and clinical trials in prostate cancer (PCa). We aimed to develop a fully automated convolutional neural network (CNN)-based model for calculating PET/CT skeletal tumor burden in patients with PCa. Methods: A total of 168 patients from three centers were divided into training, validation, and test groups. Manual an
Due to the substantial gross exchange fluxes with the atmosphere, the terrestrial carbon cycle plays a significant role in the global carbon budget. Drought commonly affects terrestrial carbon absorption negatively. Terrestrial biosphere models exhibit significant uncertainties in capturing the carbon flux response to drought, which have an impact on estimates of the global carbon budget. Through
Agricultural soils are the most important anthropogenic source of nitrous oxide (N2O) emissions. This occurs via two main pathways: (1) from microbial-mediated oxidation of ammonium to nitrite and nitrate; and (2) denitrification. Most agro-ecological models explicitly deal with these two pathways albeit with different degrees of process understanding and empiricism. Models that integrate the impa
Mouse hematopoietic stem cells (HSCs) have been extensively defined both molecularly and functionally at steady state, while regenerative stress induces immunophenotypical changes that limit high purity isolation and analysis. It is therefore important to identify markers that specifically label activated HSCs to gain further knowledge about their molecular and functional properties. Here, we asse
Atmospheric inversions provide estimates of carbon dioxide (CO2) fluxes between the surface and atmosphere based on atmospheric CO2 concentration observations. The number of CO2 observations is projected to increase severalfold in the next decades from expanding in situ networks and next-generation CO2-observing satellites, providing both an opportunity and a challenge for inversions. This study i
Machine learning, a sub-field of artificial intelligence, is the study of computer algorithms that improve automatically through experience. Although the term was coined in 1959, machine learning builds on questions/methods that were developed earlier in linear algebra, mathematical analysis, optimization, and mathematical statistics. In this chapter we will give a brief overview of machine learni
One of the biggest challenges in urban densifications is securing adequate daylight access. This study examines the potential of using semantic 3D city models as input to daylight simulations. It is focusing on investigating input data requirements to these simulations from a geodata, 3D city model specification and measuring guideline perspective. To achieve this, geodata simulation input require
Background: Primary graft failure (PGF) remains the most common cause of short-term mortality after heart transplantation. The main objective was to develop and validate a risk model for prediction of short-term mortality due to PGF after heart transplantation using the ISHLT Heart Transplant Registry. Methods: We developed a non-linear artificial neural networks (ANN) model to evaluate the associ
The double quantum coherence (DQC) signal in nonlinear spectroscopy gives information about the many-body correlation effects not easily available by other methods. The signal is short-lived, consequently, a significant part of it is generated during the pulse overlap. Since the signal is at two times the laser frequency, one may intuitively expect that the pulse overlap-related artifacts are filt
BACKGROUND: Most cerebral palsy (CP) cases have an unexplained etiology, but a role for environmental exposures has been suggested. One purported environmental risk factor is exposure to endocrine-disrupting pollutants specifically per- and polyfluoroalkyl substances (PFAS).OBJECTIVES: We investigated the association between prenatal PFAS exposures and CP in Swedish children.METHODS: In this case-
BACKGROUND: Retrospective studies have shown promising results using artificial intelligence (AI) to improve mammography screening accuracy and reduce screen-reading workload; however, to our knowledge, a randomised trial has not yet been conducted. We aimed to assess the clinical safety of an AI-supported screen-reading protocol compared with standard screen reading by radiologists following mamm
Climate projections from global circulation models (GCMs), part of the Coupled Model Intercomparison Project 6 (CMIP6), are often employed to study the impact of future climate on ecosystems. However, especially at regional scales, climate projections display large biases in key forcing variables such as temperature and precipitation. These biases have been identified as a major source of uncertai
Baltic Earth is an independent research network of scientists from all Baltic Sea countries that promotes regional Earth system research. Within the framework of this network, the Baltic Earth Assessment Reports (BEARs) were produced in the period 2019-2022. These are a collection of 10 review articles summarising current knowledge on the environmental and climatic state of the Earth system in the
Markers of downstream events are a key component of clinical trials of disease-modifying therapies for Alzheimer's disease. Morphological metrics like cortical thickness are established measures of atrophy but are not sensitive enough to detect amyloid-beta (Aβ)- related changes that occur before overt atrophy become visible. We aimed to investigate to what extent diffusion MRI can provide sensiti
Effectively identifying deviations in real-world medical time-series data is a critical endeavor, essential for early surveillance of disease outbreaks. This paper demonstrates the integration of time-series anomaly detection techniques to develop surveillance systems for disease outbreaks. Utilizing data from Sweden's telephone counseling service (1177), we first illustrate the trends in physical
Post-marketing reports of suspected adverse drug reactions are important for establishing the safety profile of a medicinal product. However, a high influx of reports poses a challenge for regulatory authorities as a delay in identification of previously unknown adverse drug reactions can potentially be harmful to patients. In this study, we use natural language processing (NLP) to predict whether
The handover (HO) procedure is one of the most critical functions in a cellular network driven by measurements of the user channel of the serving and neighboring cells. The success rate of the entire HO procedure is significantly affected by the preparation stage. As massive Multiple-Input Multiple-Output (MIMO) systems with large antenna arrays allow resolving finer details of channel behavior, w
Today, atomic layer deposition (ALD) has become a firm corner stone of thin film deposition technology. The microelectronics industry, an early adopter of ALD, imposes stringent requirements on ALD to produce films with highly defined physical and chemical properties, which becomes even more important as device and component dimensions decrease. This, in turn, means that our understanding of the c
This article addresses the problem of multi-target localization from acoustic time delay measurements in a 3-D underwater environment, taking into account the depth-dependent sound speed of the underwater wave propagation. Specifically, we formulate the problem as an optimal mass transport (OMT) optimization over a grid of potential candidate positions. By exploiting an iterative refinement approa
