Staffing & Scheduling

Development of an AI Algorithm for Automatic Classification of Gram Stain Images in Microbiology

This paper reflects on the development and performance of an advanced artificial intelligence (AI) algorithm for the automated processing and classification of Gram stain images obtained from actual microbiology samples used in clinical microbiology. The aim of the project was to effectively categorize non-standardized Gram stain images into the six most common categories: Gram-negative rods, Gram...

Environmental Conflict Modulates Pavlovian Bias

Pavlovian avoidance enables rapid defensive responding but can undermine goal-directed behaviour when it overrides instrumental control, a tendency amplified in anxiety. Whether such biases can be flexibly and rapidly modulated by environmental structure remains unknown. Here we show that global conflict in the environment can suppress Pavlovian avoidance and enhance instrumental choice, particula...

Attentional focus and emotion modulate voice recognition deficits in cerebellar stroke patients

The cerebellum, long regarded as a motor structure, is increasingly recognized for its role in higher-order cognitive and socio-emotional functions. I...

Corticofugal gated recurrency captures auditory cortical responses

Memory is essential for the neural processing of natural sounds. It has been proposed that cortical memory is subserved by gated recurrency, a powerfu...

AI-assisted modeling of attention quantifies engagement and predicts cognitive improvement in older adults

Cognitive training aims to prevent or slow cognitive decline in older adults, but outcomes vary widely. Engagement, describing how individuals allocat...

The expression level of CACNA1C-encoded CaV1.2 is a tipping point between promotion and inhibition of dendritic growth in neurons

The CACNA1C gene encodes the CaV1.2 L-type voltage-gated calcium channel, which plays a crucial role in neuronal signaling. CACNA1C is a risk gene for...

Complete NMR assignment for 275 of the most common dipeptides in intrinsically disordered proteins

Accurate NMR chemical shift assignments are essential for atomic-resolution characterization of proteins. Especially for intrinsically disordered prot...

Reinforcement learning enhances training efficiency in high-performance athletes

Skilled athletes need powerful movement strategies to solve tasks effectively. Typically, athletes learn these strategies with instruction-based teach...

Few-Shot Learning for Prostate Cancer Detection on MRI: Comparative Analysis with Radiologists’ Performance

Deep-learning models for prostate cancer detection often require large datasets, which can be challenging to obtain and may lead to domain shift issue...

SLaM Image Bank – a real-world diverse London cohort linking brain MRI to electronic mental health and dementia records for the development of clinical decision support tools using artificial intelligence

Rapid developments are occurring in artificial intelligence (AI) and machine learning (ML) applied to neuroimaging. To date, advances in this space ha...

Wavelet-Based Compression Method for Scale-Preserving SWIR Hyperspectral Data

Hyperspectral imaging (HSI) collects detailed spectral information across hundreds of narrow bands, providing valuable datasets for applications such ...

Projecting climate change impacts on inter-epidemic risk of Rift Valley fever across East Africa

Rift Valley fever (RVF) is a zoonotic disease that causes sporadic, multi-country epidemics. However, RVF virus (RVFV) also circulates during inter-ep...

Dehumanisation and Datafication: Key stressors of Algorithmic Management: A qualitative analysis of Chinese couriers

Technology is an important social determinant of health that has so far been poorly understood. Nevertheless, technologies such as algorithms and arti...

nnDoseNet: Intuitive and Flexible Deep Learning Framework to Train and Evaluate Radiotherapy Dose Prediction Models

Radiotherapy (RT) dose optimization is often labor-intensive, requiring repeated manual adjustments to achieve clinically acceptable plans. In this wo...

A Combined Predictive and Causal Approach for Neighborhood-Level Diabetes Detection

Develop a neighborhood-level framework using machine learning and causal inference to identify socioeconomic and behavioral drivers of Type 2 diabetes...

Screening for anemia using multi-modal machine learning models on smartphones: protocol for a comparative accuracy study in rural India

Anemia, or low blood hemoglobin (Hb), affects one third of the world population, and is particularly prevalent in women and children in lower resource...

A Standard Framework for Converting Coronary Angiography Reports into Machine-Readable Format Using Large Language Models

Coronary angiography (CAG) reports contain many details about coronary anatomy, lesion characteristics, and interventional procedures. However, their ...

Measuring the growth of infectious disease modelling publications and their impact on policymaking: a Large Language Model-assisted bibliometric review

Infectious disease modelling (IDM) is increasingly being used to understand disease transmission and inform public health policy. However, its growth ...

Protocol of the observational study STRATUM-OS: First step in the development and validation of the STRATUM tool based on multimodal data processing to assist surgery in patients affected by intra-axial brain tumours

Integrated digital diagnostics can support complex surgeries in many anatomic sites, and brain tumour surgery represents one of the most complex cases...

Deep-Learning Based Contrast Boosting Improves Lesion Visualization and Image Quality: A Multi-Center Multi-Reader Study on Clinical Performance with Standard Contrast Enhanced MRI of Brain Tumors

Gadolinium-based Contrast Agents (GBCAs) are used in brain MRI exams to improve the visualization of pathology and improve the delineation of lesions....