State-of-the-art neonatal cerebral ultrasound: technique and reporting

In the past three decades, cerebral ultrasound (CUS) has become a trusted technique to study the neonatal brain. It is a relatively cheap, non-invasive, bedside neuroimaging method available in nearly every hospital. Traditionally, CUS was used to detect major abnormalities, such as intraventricular hemorrhage (IVH), periventricular hemorrhagic infarction, post-hemorrhagic ventricular dilatation, and (cystic) periventricular leukomalacia (cPVL). The use of different acoustic windows, such as the mastoid and posterior fontanel, and ongoing technological developments, allows for recognizing other lesion patterns (e.g., cerebellar hemorrhage, perforator stroke, developmental venous anomaly). The CUS technique is still being improved with the use of higher transducer frequencies (7.5-18 MHz), 3D applications, advances in vascular imaging (e.g. ultrafast plane wave imaging), and improved B-mode image processing. Nevertheless, the helpfulness of CUS still highly depends on observer skills, knowledge, and experience. In this special article, we discuss how to perform a dedicated state-of-the-art neonatal CUS, and we provide suggestions for structured reporting and quality assessment.Developmen

Auditory and Visual Selective Attention and Reading Ability

158 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1980.While selective attention has been the subject of a considerable number of research studies, comparatively few of those studies have examined that variable in relation to reading ability. Similarly there is a dearth of studies looking at selective attention in both auditory and visual modalities.In this study 96 subjects were involved, 48 from Grade Three and 48 from Grade Six. Subjects were selected for participation according to their reading scores in the Metropolitan Achievement Test. At each grade level 16 subjects were selected from each of three percentile ranges on that test - the 20-39 range, the 50-69 range, and the 80-99 range. Subjects were required to (a) read, silently, a grade level passage while ignoring intrusion words typed in red, and (b) listen to female voice reading a grade level passage while ignoring intrusion words spoken by a male voice. After a series of multiple choice comprehension questions, checks were made to establish whether subjects had ignored the intrusion material.The four principal findings of this study were - (1) good readers displayed better selective attention abilities than did poor readers; (2) in the visual area embedded intrusion material was more distracting than was peripheral intrusion material; (3) auditory intrusion material was more difficult to ignore then visual intrusion material; (4) poor readers performed at least as well on auditory material as they did on visual material.The results of the research are discussed both in the terms of their implications for the teacher, and in terms of selective attention theory.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

Simulating Emotions With an Integrated Computational Model of Appraisal and Reinforcement Learning

Predicting users’ emotional states during interaction is a long-standing goal of affective computing. However, traditional methods based on sensory data alone fall short due to the interplay between users’ latent cognitive states and emotional responses. To address this, we introduce a computational cognitive model that simulates emotion as a continuous process, rather than a static state, during interactive episodes. This model integrates cognitive-emotional appraisal mechanisms with computational rationality, utilizing value predictions from reinforcement learning. Experiments with human participants demonstrate the model’s ability to predict and explain the emergence of emotions such as happiness, boredom, and irritation during interactions. Our approach opens the possibility of designing interactive systems that adapt to users’ emotional states, thereby improving user experience and engagement. This work also deepens our understanding of the potential of modeling the relationship between reward processing, reinforcement learning, goal-directed behavior, and appraisal.peerReviewe

Source data for 'The sex of organ geometry' Blackie, Gaspar et al, 2024, Nature

Source data for 'The sex of organ geometry' Blackie, Gaspar et al, 2024, Nature.Contains microCT scans as .nii.gz files, gut centrelines as .swc and .trace files and organ segmentations as .obj mesh files for genotypes and figures in the paper. See readme file for which genotypes are present in which figures.</p