Self-injurious behavior (SIB) presents unique challenges as researchers have identified that some SIB may be resistant to treatment. The unit of analysis in this research is often the frequency of behavior with relatively little attention devoted to the analysis of inter-response time relations. We assessed whether changes in the rate of SIB were also associated with changes in the temporal distribution of this behavior in the presence and absence of systematically manipulated environmental variables. This study included three participants diagnosed with profound intellectual disabilities who engaged in SIB maintained by both negative and automatic reinforcement. For two of the participants, we used a multiple baseline design across participants to assess the effects of noncontingent access to preferred activities on both the rate and temporal distribution of SIB. For the third participant, we used a reversal design to assess the effects of a change in daily schedule (i.e., attending or not attending work) on the rate and temporal distribution of SIB. For all three participants, antecedent manipulations decreased the rate of SIB; however, operant contingency values (a measure of temporal distribution) did not change in a corresponding fashion. These data suggest that although antecedent manipulations may decrease the overall rate of the behavior, once SIB is emitted, additional instances are likely to occur close together in time.
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http://dx.doi.org/10.1037/bar0000151 | DOI Listing |
Cogn Neurodyn
December 2025
Department of Nuclear Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, 730030 China.
Unlabelled: Juvenile myoclonic epilepsy (JME) exhibits abnormal functional connectivity of brain networks at multiple frequencies. We used the multilayer network model to address the heterogeneous features at different frequencies and assess the mechanisms of functional integration and segregation of brain networks in JME patients. To address the possibility of false edges or missing edges during network construction, we combined multilayer networks with link prediction techniques.
View Article and Find Full Text PDFBMC Gastroenterol
January 2025
Department of Gastroenterology, The First Affiliated Hospital of Shihezi University, No.107 North Second Road, Hongshan Street, Shihezi, 832008, China.
Background: Gallbladder and biliary diseases (GABD) represent prevalent disorders of the digestive system.
Methods: Data on age-standardized incidence rate (ASIR), age-standardized mortality rate (ASMR), and age-standardized disability-adjusted life years (DALYs) rate (ASDR) were extracted from the Global Burden of Disease (GBD) 2021 study. The estimated annual percentage change (EAPC) was utilized to quantify temporal trends in GABD.
BMC Bioinformatics
January 2025
Centro de Salud Retiro, Hospital Universitario Gregorio Marañon, C/Lope de Rueda, 43, 28009, Madrid, Spain.
Background: Natural language processing (NLP) enables the extraction of information embedded within unstructured texts, such as clinical case reports and trial eligibility criteria. By identifying relevant medical concepts, NLP facilitates the generation of structured and actionable data, supporting complex tasks like cohort identification and the analysis of clinical records. To accomplish those tasks, we introduce a deep learning-based and lexicon-based named entity recognition (NER) tool for texts in Spanish.
View Article and Find Full Text PDFComput Biol Med
January 2025
Department of Computer Science, Jamia Hamdard University, Near Batra Hospital, New Delhi, 110062, India. Electronic address:
Schizophrenia detection involves identifying the schizophrenia by analyzing specific patterns in Electroencephalogram (EEG) signals, which reflect brain activity associated with symptoms, like hallucinations and cognitive impairments. Existing models face challenges due to the complex and variable nature of EEG data, which may struggle to accurately capture critical temporal dependencies and relevant features. Traditional approaches often lack adaptability, limiting their ability to differentiate schizophrenia patterns from other brain activities.
View Article and Find Full Text PDFWaste Manag
January 2025
Department of Mathematics, University of Padova, Via Trieste, 63, Padova, 35121, Italy; Augmented Intelligence Center, Fondazione Bruno Kessler (FBK), Via Santa Croce, 77, Trento, 38122, Italy; Department of Information Engineering and Computer Science, University of Trento, Via Sommarive, 9, Povo, 38123, Italy.
We explore the application of machine learning (ML) techniques to forecast door-to-door waste collection, addressing the challenges in municipal solid waste (MSW) management. ML models offer a promising solution to optimize waste collection operations, especially amid growing urban populations and evolving waste generation rates. Leveraging comprehensive data from a northeastern Italian municipality, including various waste types, our study investigates ML algorithms' efficacy in predicting household waste collection requirements.
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