representational learning

BACKGROUND: As global populations age and become susceptible to neurodegenerative illnesses, new therapies for Alzheimer disease (AD) are urgently needed. Existing data resources for drug discovery and repurposing fail to capture relationships central to the disease's etiology and response to drugs. OBJECTIVE: We designed the Alzheimer's Knowledge Base (AlzKB) to alleviate this need by providing a comprehensive knowledge representation of AD etiology and candidate therapeutics...

According to the parallel architecture, syntactic and semantic information processing are two separate streams that interact selectively during language comprehension. While considerable effort is put into psycho- and neurolinguistics to understand the interchange of processing mechanisms in human comprehension, the nature of this interaction in recent neural Large Language Models remains elusive. In this article, we revisit influential linguistic and behavioral experiments and evaluate the ability of a large language model, GPT-3, to perform these tasks...

Congenital heart disease (CHD) is the most frequent birth defect and a leading cause of infant mortality, emphasizing the crucial need for its early diagnosis. Ultrasound is the primary imaging modality for prenatal CHD screening. As a complement to the four-chamber view, the three-vessel view (3VV) plays a vital role in detecting anomalies in the great vessels. However, the interpretation of fetal cardiac ultrasound images is subjective and relies heavily on operator experience, leading to variability in CHD detection rates, particularly in resource-constrained regions...

Aneurysmal subarachnoid hemorrhage is a serious medical emergency of brain that has high mortality and poor prognosis. Treatment effect estimation is of high clinical significance to support the treatment decision-making for aneurysmal subarachnoid hemorrhage. However, most existing studies on treatment decision support of this disease are unable to simultaneously compare the potential outcomes of different treatments for a patient. Furthermore, these studies fail to harmoniously integrate the imaging data with non-imaging clinical data, both of which are significant in clinical scenarios...

This article explores the contribution of the double error dynamic asymptote computational associative learning model to understanding the role of mediated learning mechanisms in the generation of spurious associations, as those postulated to characterize schizophrenia. Three sets of simulations for mediated conditioning, mediated extinction, and a mediated enhancement of latent inhibition, a unique model prediction, are presented. For each set of simulations, a parameter that modulates the impact of associative memory retrieval and the dissipation of nonperceptual activated representations through the network was manipulated...

Despite the ubiquity of healthcare simulation and the humanities in medical education, the two domains of learning remain unintegrated. The stories suffused within healthcare simulation have thus remained unshaped by the developments of narrative medicine and the health humanities. Healthcare simulation, in turn, has yet to utilize concepts like co-construction and narrative competence to enrich learners' understanding of patient experience alongside their clinical competencies. To create a conceptual bridge between these two fields (including narrative-based inquiry more broadly), we redescribe narrative competence via Ronald Heifetz's distinction of "technical" and "adaptive" challenges outlined in his adaptive leadership model...

OBJECTIVE: To develop text classification models for determining whether the checklist items in the CONSORT reporting guidelines are reported in randomized controlled trial publications. MATERIALS AND METHODS: Using a corpus annotated at the sentence level with 37 fine-grained CONSORT items, we trained several sentence classification models (PubMedBERT fine-tuning, BioGPT fine-tuning, and in-context learning with GPT-4) and compared their performance. To address the problem of small training dataset, we used several data augmentation methods (EDA, UMLS-EDA, text generation and rephrasing with GPT-4) and assessed their impact on the fine-tuned PubMedBERT model...

Fully supervised learning methods necessitate a substantial volume of labelled training instances, a process that is typically both labour-intensive and costly. In the realm of medical image analysis, this issue is further amplified, as annotated medical images are considerably more scarce than their unlabelled counterparts. Consequently, leveraging unlabelled images to extract meaningful underlying knowledge presents a formidable challenge in medical image analysis. This paper introduces a simple triple-view unsupervised representation learning model (SimTrip) combined with a triple-view architecture and loss function, aiming to learn meaningful inherent knowledge efficiently from unlabelled data with small batch size...

Automatic and precise polyp segmentation in colonoscopy images is highly valuable for diagnosis at an early stage and surgery of colorectal cancer. Nevertheless, it still posed a major challenge due to variations in the size and intricate morphological characteristics of polyps coupled with the indistinct demarcation between polyps and mucosas. To alleviate these challenges, we proposed an improved dual-aggregation polyp segmentation network, dubbed Dua-PSNet, for automatic and accurate full-size polyp prediction by combining both the transformer branch and a fully convolutional network (FCN) branch in a parallel style...

PURPOSE: Intracranial aneurysm detection from 3D Time-Of-Flight Magnetic Resonance Angiography images is a problem of increasing clinical importance. Recently, a streak of methods have shown promising performance by using segmentation neural networks. However, these methods may be less relevant in a clinical settings where diagnostic decisions rely on detecting objects rather than their segmentation. METHODS: We introduce a 3D single-stage object detection method tailored for small object detection such as aneurysms...

Learning to discriminate overlapping gustatory stimuli that predict distinct outcomes-a feat known as discrimination learning-can mean the difference between ingesting a poison or a nutritive meal. Despite the obvious importance of this process, very little is known about the neural basis of taste discrimination learning. In other sensory modalities, this form of learning can be mediated by either the sharpening of sensory representations or the enhanced ability of "decision-making" circuits to interpret sensory information...

Identifying links within biological networks is important in various biomedical applications. Recent studies have revealed that each node in a network may play a unique role in different links, but most link prediction methods overlook distinctive node roles, hindering the acquisition of effective link representations. Subgraph-based methods have been introduced as solutions but often ignore shared information among subgraphs. To address these limitations, we propose a Subgraph-aware Graph Kernel Neural Network (SubKNet) for link prediction in biological networks...

Previous research on how problem-difficulty affects solution-types of insight-problems has yielded contradictory findings. Thus, we aimed to examine the impact of problem-difficulty on solution-types in both inter- and intra-problem-difficulty contexts. For this, we employed the original 8-coin, and 9-dot problems and four hinted-versions of those that were manipulated by using hints-to-remove-sources-of-difficulty to alter their difficulty level. Those manipulations were executed based on the assumptions of constraint-relaxation and chunk-decomposition as posited by representational change theory...

Behavioral plasticity is subjected to various sensory stimuli, experiences, and physiological states, representing the temporal and spatial patterns of neural circuit dynamics. Elucidation of how genes and neural circuits in our brain actuate behavioral plasticity requires functional imaging during behavioral assays to manifest temporal and spatial neural regulation in behaviors. The exploration of the nervous systems of Caenorhabditis elegans has catalyzed substantial scientific advancements in elucidating the mechanistic link between circuit dynamics and behavioral plasticity...

PURPOSE: The purpose of the present study was to investigate the relationship between speechreading ability, phonological skills, and word reading ability in typically developing children. METHOD: Sixty-six typically developing children (6-7 years old) completed tasks measuring word reading, speechreading (words, sentences, and short stories), alliteration awareness, rhyme awareness, nonword reading, and rapid automatized naming (RAN). RESULTS: Speechreading ability was significantly correlated with rhyme and alliteration awareness, phonological error rate, nonword reading, and reading ability (medium effect sizes) and RAN (small effect size)...

Blind adults display language-specificity in their packaging and ordering of events in speech. These differences affect the representation of events in co-speech gesture--gesturing with speech--but not in silent gesture--gesturing without speech. Here we examine when in development blind children begin to show adult-like patterns in co-speech and silent gesture. We studied speech and gestures produced by 30 blind and 30 sighted children learning Turkish, equally divided into 3 age groups: 5-6, 7-8, 9-10 years...

Image segmentation is commonly used to estimate the location and shape of plants and their external structures. Segmentation masks are then used to localize landmarks of interest and compute other geometric features that correspond to the plant's phenotype. Despite its prevalence, segmentation-based approaches are laborious (requiring extensive annotation to train) and error-prone (derived geometric features are sensitive to instance mask integrity). Here, we present a segmentation-free approach that leverages deep learning-based landmark detection and grouping, also known as pose estimation...

Accurate calculation of drug-target affinity (DTA) is crucial for various applications in the pharmaceutical industry, including drug screening, design, and repurposing. However, traditional machine learning methods for calculating DTA often lack accuracy, posing a significant challenge in accurately predicting DTA. Fortunately, deep learning has emerged as a promising approach in computational biology, leading to the development of various deep learning-based methods for DTA prediction. To support researchers in developing novel and highly precision methods, we have provided a comprehensive review of recent advances in predicting DTA using deep learning...

In this paper we present a method that allows leveraging 3D electron density information to train a deep neural network pipeline to segment regions of high, medium and low electronegativity and classify substances as health hazardous or non-hazardous. We show that this can be used for use-cases such as cosmetics and food products. For this purpose, we first generate 3D electron density cubes using semiempirical molecular calculations for a custom European Chemicals Agency (ECHA) subset consisting of substances labelled as hazardous and non-hazardous for cosmetic usage...

OBJECTIVES: To build self-supervised foundation models for multicontrast MRI of the whole brain and evaluate their efficacy in assisting diagnosis of brain tumors. METHODS: In this retrospective study, foundation models were developed using 57,621 enhanced head MRI scans through self-supervised learning with a pretext task of cross-contrast context restoration with two different content dropout schemes. Downstream classifiers were constructed based on the pretrained foundation models and fine-tuned for brain tumor detection, discrimination, and molecular status prediction...