Building virtual patients using simulation-based inference.

In the context of clinical trials, mechanistic computer models for pathophysiology and pharmacology (here Quantitative Systems Pharmacology models, QSP) can greatly support the decision making for drug candidates and elucidate the (potential) response of patients to existing and novel treatments. These models are built on disease mechanisms and then parametrized using (clinical study) data. Clinical variability among patients is represented by alternative model parameterizations, called virtual patients.

SugarViT-Multi-objective regression of UAV images with Vision Transformers and Deep Label Distribution Learning demonstrated on disease severity prediction in sugar beet.

Remote sensing and artificial intelligence are pivotal technologies of precision agriculture nowadays. The efficient retrieval of large-scale field imagery combined with machine learning techniques shows success in various tasks like phenotyping, weeding, cropping, and disease control. This work will introduce a machine learning framework for automatized large-scale plant-specific trait annotation for the use case of disease severity scoring for CLS in sugar beet.

Extending from Adaptation to Resilience Pathways: Perspectives from the Conceptual Framework to Key Insights.

The extent and timescale of climate change impacts remain uncertain, including global temperature increase, sea level rise, and more frequent and intense extreme events. Uncertainties are compounded by cascading effects. Nevertheless, decision-makers must take action.

Utilizing natural language processing to identify pediatric patients experiencing status epilepticus.

Purpose: Compare the identification of patients with established status epilepticus (ESE) and refractory status epilepticus (RSE) in electronic health records (EHR) using human review versus natural language processing (NLP) assisted review.

Methods: We reviewed EHRs of patients aged 1 month to 21 years from Boston Children's Hospital (BCH). We included all patients with convulsive ESE or RSE during admission.

Sharing for Caring? A Patients' and Clinicians' View on Handling Personal Medical Data in the Context of Digitization: An Exploratory Study.

Background: The healthcare sector is currently undergoing a significant transformation, driven by an increased utilization of data. In this evolving landscape, surveys are of pivotal importance to the comprehension of patient needs and preferences. Moreover, the digital affinity of patients and physicians within the healthcare system is reforming the manner in which healthcare services are accessed and delivered.

Development of image-based decision support systems utilizing information extracted from radiological free-text report databases with text-based transformers.

Objectives: To investigate the potential and limitations of utilizing transformer-based report annotation for on-site development of image-based diagnostic decision support systems (DDSS).

Methods: The study included 88,353 chest X-rays from 19,581 intensive care unit (ICU) patients. To label the presence of six typical findings in 17,041 images, the corresponding free-text reports of the attending radiologists were assessed by medical research assistants ("gold labels").

Short-term predictor for COVID-19 severity from a longitudinal multi-omics study for practical application in intensive care units.

Background: The COVID-19 pandemic challenged the management of technical and human resources in intensive care units (ICU) across the world. Several long-term predictors for COVID-19 disease progression have been discovered. However, predictors to support short-term planning of resources and medication that can be translated to future pandemics are still missing.

Paving the Way for Climate Neutral and Resilient Historic Districts.

Climate change is a major global threat to our society's urban areas, with the majority of Europe's population living in cities and their cultural heritage. Historic districts of significant cultural value and the communities connected to these places have an important role to play in fostering location-based identity and economy, social cohesion, innovation, urban regeneration, and climate change adaptation. Thus, it is important to make historic districts climate resilient, by jointly considering climate change adaptation, disaster risk management, heritage management, and sustainable urban development.

Application of clinical and molecular profiling data to improve patient outcomes in psoriatic arthritis.

Achieving a good outcome for a person with Psoriatic Arthritis (PsA) is made difficult by late diagnosis, heterogenous clinical disease expression and in many cases, failure to adequately suppress inflammatory disease features. Single-centre studies have certainly contributed to our understanding of disease pathogenesis, but to adequately address the major areas of unmet need, multi-partner, collaborative research programmes are now required. HIPPOCRATES is a 5-year, Innovative Medicines Initiative (IMI) programme which includes 17 European academic centres experienced in PsA research, 5 pharmaceutical industry partners, 3 small-/medium-sized industry partners and 2 patient-representative organizations.

An integrative review on the acceptance of artificial intelligence among healthcare professionals in hospitals.

Artificial intelligence (AI) in the domain of healthcare is increasing in prominence. Acceptance is an indispensable prerequisite for the widespread implementation of AI. The aim of this integrative review is to explore barriers and facilitators influencing healthcare professionals' acceptance of AI in the hospital setting.

Transformer-based structuring of free-text radiology report databases.

Objectives: To provide insights for on-site development of transformer-based structuring of free-text report databases by investigating different labeling and pre-training strategies.

Methods: A total of 93,368 German chest X-ray reports from 20,912 intensive care unit (ICU) patients were included. Two labeling strategies were investigated to tag six findings of the attending radiologist.

Agricultural plant cataloging and establishment of a data framework from UAV-based crop images by computer vision.

Background: Unmanned aerial vehicle (UAV)-based image retrieval in modern agriculture enables gathering large amounts of spatially referenced crop image data. In large-scale experiments, however, UAV images suffer from containing a multitudinous amount of crops in a complex canopy architecture. Especially for the observation of temporal effects, this complicates the recognition of individual plants over several images and the extraction of relevant information tremendously.

A hybrid approach for identifying drug repurposing candidates and their mechanisms: An interview with Vanessa Lage-Rupprecht and two co-authors.

Senior researcher Vanessa Lage-Rupprecht and two collaborators talk about what data science means to them and illustrate how they managed to create a data and lab coexistence in their drug-repurposing project, which was recently published in . In this article, they have developed a drug-target-mechanism-oriented data model, Human Brain PHARMACOME, and have presented it as a resource to the community.

A hybrid approach unveils drug repurposing candidates targeting an Alzheimer pathophysiology mechanism.

The high number of failed pre-clinical and clinical studies for compounds targeting Alzheimer disease (AD) has demonstrated that there is a need to reassess existing strategies. Here, we pursue a holistic, mechanism-centric drug repurposing approach combining computational analytics and experimental screening data. Based on this integrative workflow, we identified 77 druggable modifiers of tau phosphorylation (pTau).

Prediction of memory formation based on absolute electroencephalographic phases in rhinal cortex and hippocampus outperforms prediction based on stimulus-related phase shifts.

Absolute (i.e. measured) rhinal and hippocampal phase values are predictive for memory formation.

E2mC: Improving Emergency Management Service Practice through Social Media and Crowdsourcing Analysis in Near Real Time.

In the first hours of a disaster, up-to-date information about the area of interest is crucial for effective disaster management. However, due to the delay induced by collecting and analysing satellite imagery, disaster management systems like the Copernicus Emergency Management Service (EMS) are currently not able to provide information products until up to 48-72 h after a disaster event has occurred. While satellite imagery is still a valuable source for disaster management, information products can be improved through complementing them with user-generated data like social media posts or crowdsourced data.

Prediction of successful memory encoding based on single-trial rhinal and hippocampal phase information.

Mediotemporal EEG characteristics are closely related to long-term memory formation. It has been reported that rhinal and hippocampal EEG measures reflecting the stability of phases across trials are better suited to distinguish subsequently remembered from forgotten trials than event-related potentials or amplitude-based measures. Theoretical models suggest that the phase of EEG oscillations reflects neural excitability and influences cellular plasticity.