A Lesion-aware Edge-based Graph Neural Network for Predicting Language Ability in Patients with Post-stroke Aphasia.

We propose a lesion-aware graph neural network (LEGNet) to predict language ability from resting-state fMRI (rs-fMRI) connectivity in patients with post-stroke aphasia. Our model integrates three components: an edge-based learning module that encodes functional connectivity between brain regions, a lesion encoding module, and a subgraph learning module that leverages functional similarities for prediction. We use synthetic data derived from the Human Connectome Project (HCP) for hyperparameter tuning and model pretraining.

FLUEnT: Transformer for detecting lung consolidations in videos using fused lung ultrasound encodings.

Pneumonia is the leading cause of death among children around the world. According to WHO, a total of 740,180 lives under the age of five were lost due to pneumonia in 2019. Lung ultrasound (LUS) has been shown to be particularly useful for supporting the diagnosis of pneumonia in children and reducing mortality in resource-limited settings.

Graph Attention-Based Fusion of Pathology Images and Gene Expression for Prediction of Cancer Survival.

Multimodal machine learning models are being developed to analyze pathology images and other modalities, such as gene expression, to gain clinical and biological insights. However, most frameworks for multimodal data fusion do not fully account for the interactions between different modalities. Here, we present an attention-based fusion architecture that integrates a graph representation of pathology images with gene expression data and concomitantly learns from the fused information to predict patient-specific survival.

Curated and Annotated Dataset of Lung US Images in Zambian Children with Clinical Pneumonia.

See also the commentary by Sitek in this issue.

Bereavement Counsellors' Experiences Supporting the Families of Deceased Children Within a German Bereavement Network- A Qualitative Interview Study.

The "Bereavement Network Lower Saxony" (BNLS) provides professional bereavement support to families grieving for a child. The present study aimed at exploring the experiences of BNLS bereavement counsellors in providing bereavement support to affected families. 12 semi-structured qualitative interviews were conducted with bereavement counsellors of the BNLS between June and August 2022.

Consistent long-term practice leads to consistent improvement: Benefits of self-managed therapy for language and cognitive deficits using a digital therapeutic.

Background: Although speech-language therapy (SLT) is proven to be beneficial to recovery of post-stroke aphasia, delivering sufficiently high amounts of dosage remains a problem in real-world clinical practice. Self-managed SLT was introduced to solve the problem. Previous research showed in a 10-week period, increased dosage frequency could lead to better performance, however, it is uncertain if dosage still affects performance over a longer period of practice time and whether gains can be seen following practice over several months.

A Graph-Transformer for Whole Slide Image Classification.

Deep learning is a powerful tool for whole slide image (WSI) analysis. Typically, when performing supervised deep learning, a WSI is divided into small patches, trained and the outcomes are aggregated to estimate disease grade. However, patch-based methods introduce label noise during training by assuming that each patch is independent with the same label as the WSI and neglect overall WSI-level information that is significant in disease grading.

Multimodal Neural and Behavioral Data Predict Response to Rehabilitation in Chronic Poststroke Aphasia.

Background: Poststroke recovery depends on multiple factors and varies greatly across individuals. Using machine learning models, this study investigated the independent and complementary prognostic role of different patient-related factors in predicting response to language rehabilitation after a stroke.

Methods: Fifty-five individuals with chronic poststroke aphasia underwent a battery of standardized assessments and structural and functional magnetic resonance imaging scans, and received 12 weeks of language treatment.

Deep-Learning-Driven Quantification of Interstitial Fibrosis in Digitized Kidney Biopsies.

Interstitial fibrosis and tubular atrophy (IFTA) on a renal biopsy are strong indicators of disease chronicity and prognosis. Techniques that are typically used for IFTA grading remain manual, leading to variability among pathologists. Accurate IFTA estimation using computational techniques can reduce this variability and provide quantitative assessment.

Project SEARCH (Scanning EARs for Child Health): validating an ear biometric tool for patient identification in Zambia.

Patient identification in low- to middle-income countries is one of the most pressing public health challenges of our day. Given the ubiquity of mobile phones, their use for health-care coupled with a biometric identification method, present a unique opportunity to address this challenge. Our research proposes an Android-based solution of an ear biometric tool for reliable identification.

Using collision cones to assess biological deconfliction methods.

Biological systems consistently outperform autonomous systems governed by engineered algorithms in their ability to reactively avoid collisions. To better understand this discrepancy, a collision avoidance algorithm was applied to frames of digitized video trajectory data from bats, swallows and fish (Myotis velifer, Petrochelidon pyrrhonota and Danio aequipinnatus). Information available from visual cues, specifically relative position and velocity, was provided to the algorithm which used this information to define collision cones that allowed the algorithm to find a safe velocity requiring minimal deviation from the original velocity.

Perceptual Modalities Guiding Bat Flight in a Native Habitat.

Flying animals accomplish high-speed navigation through fields of obstacles using a suite of sensory modalities that blend spatial memory with input from vision, tactile sensing, and, in the case of most bats and some other animals, echolocation. Although a good deal of previous research has been focused on the role of individual modes of sensing in animal locomotion, our understanding of sensory integration and the interplay among modalities is still meager. To understand how bats integrate sensory input from echolocation, vision, and spatial memory, we conducted an experiment in which bats flying in their natural habitat were challenged over the course of several evening emergences with a novel obstacle placed in their flight path.

A protocol and calibration method for accurate multi-camera field videography.

Stereo videography is a powerful technique for quantifying the kinematics and behavior of animals, but it can be challenging to use in an outdoor field setting. We here present a workflow and associated software for performing calibration of cameras placed in a field setting and estimating the accuracy of the resulting stereoscopic reconstructions. We demonstrate the workflow through example stereoscopic reconstructions of bat and bird flight.

Hierarchical partial matching and segmentation of interacting cells.

We propose a method that automatically tracks and segments living cells in phase-contrast image sequences, especially for cells that deform and interact with each other or clutter. We formulate the problem as a many-to-one elastic partial matching problem between closed curves. We introduce Double Cyclic Dynamic Time Warping for the scenario where a collision event yields a single boundary that encloses multiple touching cells and that needs to be cut into separate cell boundaries.

The kernel semi-least squares method for sparse distance approximation.

We extend the semi-least squares problem defined by Rao and Mitra ( 1971 ) to the kernel semi-least squares problem. We introduce subset projection, a technique that produces a solution to this problem. We show how the results of subset projection can be used to approximate a computationally expensive distance metric.

Explaining clustered ventilation defects via a minimal number of airway closure locations.

Computational models of the human lung have been developed to study lung physiology and have been used to identify the airways responsible for mechanical dysfunction in asthmatics. Tgavalekos et al. used models anatomically consistent with the human lung to link ventilation defects to the heterogeneous closure of small airways.

Applications of thermal infrared imaging for research in aeroecology.

The night sky remains a largely unexplored frontier for biologists studying the behavior and physiology of free-ranging, nocturnal organisms. Conventional imaging tools and techniques such as night-vision scopes, infrared-reflectance cameras, flash cameras, and radar provide insufficient detail for the scale and resolution demanded by field researchers. A new tool is needed that is capable of imaging noninvasively in the dark at high-temporal and spatial resolution.

Brazilian free-tailed bats as insect pest regulators in transgenic and conventional cotton crops.

During the past 12000 years agricultural systems have transitioned from natural habitats to conventional agricultural regions and recently to large areas of genetically engineered (GE) croplands. This GE revolution occurred for cotton in a span of slightly more than a decade during which a switch occurred in major cotton production areas from growing 100% conventional cotton to an environment in which 95% transgenics are grown. Ecological interactions between GE targeted insects and other insectivorous insects have been investigated.

Detecting objects of variable shape structure with hidden state shape models.

This paper proposes a method for detecting object classes that exhibit variable shape structure in heavily cluttered images. The term "variable shape structure" is used to characterize object classes in which some shape parts can be repeated an arbitrary number of times, some parts can be optional, and some parts can have several alternative appearances. Hidden State Shape Models (HSSMs), a generalization of Hidden Markov Models (HMMs), are introduced to model object classes of variable shape structure using a probabilistic framework.