Personal conceptions of intelligence seem to make a significant contribution to overcoming a reading deficit, as indicated in our earlier research. The present aim was to assess improvements in reading-decoding following training of children with reading-decoding problems and different conceptions of intelligence (incremental or entity). It was expected that treatment of children with an incremental representation would improve more. Participants were 20 children (10 girls, 10 boys) whose average age was 8.6 yr., who attended Grade 3 of elementary school, and who were selected from 675 pupils. Children were given a multimedia test to measure motivational factors such as conceptions of intelligence, achievement goals, perception of controllability, and causal attributions. The participants took part in a multimedia training. Posttest evaluations showed more improvement in reading-decoding by children holding an incremental theory of intelligence. The importance of treatment programmes in which account is taken of both specificity of deficits and motivational factors should be explored further as the present sample was very small.
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http://dx.doi.org/10.2466/pms.107.3.963-973 | DOI Listing |
Oral Maxillofac Surg
January 2025
Department of Oral and Maxillofacial Diseases, Helsinki University and Helsinki University Hospital, Helsinki, Finland.
Purpose: Preoperative virtual planning and osteosynthesis with patient-specific implants (PSIs) have become a quotidian approach to many maxillofacial elective surgery setups. When a process is well-organized, a similar approach can be harnessed to serve the needs of exact primary reconstructions, especially in midfacial trauma cases. PSI osteosynthesis of the mandible is, however, more challenging because a mirror technique of the facial sides is often unreliable due to inherent lack of symmetry, and movement of the mandible increases the risk of loosening of the osteosynthesis.
View Article and Find Full Text PDFAm J Kidney Dis
January 2025
Hereditary Kidney Diseases Laboratory, Inserm UMR 1163, Imagine Institute, Paris Cité University, Paris, France; Department of Genomic Medicine for Rare Diseases, Necker-Enfants Malades Hospital, Assistance publique, Hôpitaux de Paris (AP-HP), Paris, France. Electronic address:
Rationale & Objective: Molecular diagnosis of autosomal dominant tubulointerstitial kidney disease (ADTKD) due to variants in the MUC1 gene has long been challenging since variants lie in a large Variable Number of Tandem Repeat (VNTR) region, making identification impossible using standard short read techniques. Previously, we addressed this diagnostic limitation by developing a computational pipeline, named VNtyper, for easier reliable detection of MUC1 VNTR pathogenic variants from short read sequences. This led to unexpected diagnoses of ADTKD-MUC1 among patients with kidney disease referred for genetic testing, which we report here.
View Article and Find Full Text PDFFront Sports Act Living
January 2025
Department of Development and Educational Psychology, University of Murcia, Murcia, Spain.
Introduction: Attitudes and beliefs guide our decision-making. In the educational context, prior research has noted the existence of prejudices and stereotypes among teachers that make it difficult to identify and care for gifted students. Stereotypes towards gifted students can hinder the identification and development of potential and the development of personality.
View Article and Find Full Text PDFISA Trans
January 2025
School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, BT9 5BN Belfast, United Kingdom. Electronic address:
In recent years, exoskeleton robots have attracted great interest from researchers in the area of robotics due to their ability to assist human functionality improvement. A wearable lower limb exoskeleton is aimed at supporting the limb functionality rehabilitation process and to assist physical therapists. Development of a stable and robust control system for multi-joint rehabilitation robots is a challenging task due to their non-linear dynamic systems.
View Article and Find Full Text PDFEur J Obstet Gynecol Reprod Biol
January 2025
Center for Advanced Reproductive Medicine, Department of Obstetrics & Gynecology, University of Kansas Medical Center, Overland Park, KS 66211, USA. Electronic address:
Background: The majority of machine learning applications in assisted reproduction have been focused on predicting the likelihood of pregnancy. In the present study, we aim to investigate which machine learning models are most effective in predicting the occurrence of a high proportion (>30 %) of 3PN/MPN zygotes in individual IVF cycles.
Methods: Eight machine learning algorithms were trained and compared, including the AdaBoost and Gaussian NB.
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