We employed a pyramidal training model (PTM) to teach staff to correctly implement and collect data for trial-based functional analysis (TBFA) in simulated situations. First, we trained four behavioral consultants (BCs) in a group format, who each trained one behavior technician (BT) in an individual format. We utilized a non-concurrent multiple baseline design to evaluate the effect of the training. During generalization probes, participants implemented TBFA with a novel problem behavior. This study will contribute to the literature on teaching staff how to conduct TBFA. This study demonstrates the application of a two-level PTM. This study illustrates how agencies can utilize the Task Analysis Training Protocol within a PTM to train staff on implementation of TBFA.
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http://dx.doi.org/10.1007/s40617-016-0159-3 | DOI Listing |
Front Neurol
January 2025
Department of Radiology, The First People's Hospital of Lianyungang, Lianyungang Clinical College of Nanjing Medical University, Lianyungang, China.
Objective: To develop a deep learning (DL) model for carotid plaque detection based on CTA images and evaluate the clinical application feasibility and value of the model.
Methods: We retrospectively collected data from patients with carotid atherosclerotic plaques who underwent continuous CTA examinations of the head and neck at a tertiary hospital from October 2020 to October 2022. The model combined ResUNet with the Pyramid Scene Parsing Network (PSPNet) to enhance plaque segmentation.
Physiol Behav
January 2025
Department of Biomedical Sciences, Joan C Edwards School of Medicine at Marshall University, 1700 3(rd) Avenue, Huntington, WV 25703, USA. Electronic address:
With the rise in fast-food culture and the continued high numbers of tobacco-related deaths, there has been a great deal of interest in understanding the relationship between high-fat diet (HFD) and nicotine use behaviors. Using adult mice and a patch-clamp electrophysiology assay, we investigated the influence of HFD on the excitability of ventral tegmental area (VTA) dopamine neurons and pyramidal neurons in the medial prefrontal cortex (mPFC) given their role in modulating the reinforcing effects of nicotine and natural rewards. We then examined whether HFD-induced changes in peripheral markers were associated with nicotine use behaviors.
View Article and Find Full Text PDFAnesthesiology
January 2025
Key Laboratory of Brain Science, Key Laboratory of Anesthesia and Organ Protection of Ministry of Education (In Cultivation), Zunyi Medical University, Zunyi, 563100, Guizhou Province, China.
Background: The medial prefrontal cortex plays a crucial role in regulating consciousness. However, the specific functions of its excitatory and inhibitory networks during anesthesia remain uncertain. Here we explored the hypothesis that somatostatin interneurons in the medial prefrontal cortex enhance the effects of sevoflurane anesthesia by increasing GABA transmission to pyramidal neurons.
View Article and Find Full Text PDFiScience
January 2025
Institute of Neuroscience and Medicine 10, Research Centre Jülich, 52425 Jülich, Germany.
The / gene, linked to fine motor control in vertebrates, is a potential candidate gene thought to play a prominent role in human language production. It is expressed specifically in a subset of corticothalamic (CT) pyramidal cells (PCs) in layer 6 (L6) of the neocortex. These L6 FOXP2+ PCs project exclusively to the thalamus, with L6a PCs targeting first-order or both first- and higher-order thalamic nuclei, whereas L6b PCs connect only to higher-order nuclei.
View Article and Find Full Text PDFJ Dent
January 2025
Department of Orthodontics, School of Stomatology, Capital Medical University, Beijing 100050, China. Electronic address:
Objective: This study constructed a new conditional generative adversarial network (CGAN) model to predict changes in lateral appearance following orthodontic treatment.
Methods: Lateral cephalometric radiographs of adult patients were obtained before (T1) and after (T2) orthodontic treatment. The expanded dataset was divided into training, validation, and test sets by random sampling in a ratio of 8:1:1.
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