Comparison of cognitive-behavioral therapy and acceptance and commitment therapy on cognitive flexibility in mothers with autistic children

Background: Mothers with autistic children encounter with many challenges due to their child's developmental problems. This study aims to compare the effect trainings of cognitive-behavioral therapy (CBT) and acceptance and commitment therapy (ACT) on cognitive flexibility of the mothers with autistic children. Methods: The present research is a quasi-experimental study as pretest and posttest with a control group, which was performed on mothers with autistic children in three autism rehabilitation centers of Mashhad in 2020-2021. 45 people designated by purposive sampling method and randomly settled in three groups of 15 people (2 groups as experimental and 1 group as control). The experimental groups underwent interventions of CBT and ACT in 8 sessions of ninety minutes; whereas, control group didn’t undergo any training. All three groups completed the cognitive flexibility inventory (CFI) in the pretest and posttest. Statistical analyses were performed using SPSS 23 software and method of covariance analysis (ANCOVA). Results: The results indicated that both training methods of CBT and ACT significantly increased the cognitive flexibility of the mothers with autistic children (P <0.05). Based on the results of Tukey's test, CBT training was more effective on cognitive flexibility of the mothers with autistic children compared to ACT training. Conclusion: With regards to the more effectiveness of the CBT, this therapeutic intervention approach can be used to improve emotion regulation strategies and increase the cognitive flexibility the mothers with autistic children

A specialized face-processing model inspired by the organization of monkey face patches explains several face-specific phenomena observed in humans

Converging reports indicate that face images are processed through specialized neural networks in the brain –i.e. face patches in monkeys and the fusiform face area (FFA) in humans. These studies were designed to find out how faces are processed in visual system compared to other objects. Yet, the underlying mechanism of face processing is not completely revealed. Here, we show that a hierarchical computational model, inspired by electrophysiological evidence on face processing in primates, is able to generate representational properties similar to those observed in monkey face patches (posterior, middle and anterior patches). Since the most important goal of sensory neuroscience is linking the neural responses with behavioral outputs, we test whether the proposed model, which is designed to account for neural responses in monkey face patches, is also able to predict well-documented behavioral face phenomena observed in humans. We show that the proposed model satisfies several cognitive face effects such as: composite face effect and the idea of canonical face views. Our model provides insights about the underlying computations that transfer visual information from posterior to anterior face patches

How Can Selection of Biologically Inspired Features Improve the Performance of a Robust Object Recognition Model?

Humans can effectively and swiftly recognize objects in complex natural scenes. This outstanding ability has motivated many computational object recognition models. Most of these models try to emulate the behavior of this remarkable system. The human visual system hierarchically recognizes objects in several processing stages. Along these stages a set of features with increasing complexity is extracted by different parts of visual system. Elementary features like bars and edges are processed in earlier levels of visual pathway and as far as one goes upper in this pathway more complex features will be spotted. It is an important interrogation in the field of visual processing to see which features of an object are selected and represented by the visual cortex. To address this issue, we extended a hierarchical model, which is motivated by biology, for different object recognition tasks. In this model, a set of object parts, named patches, extracted in the intermediate stages. These object parts are used for training procedure in the model and have an important role in object recognition. These patches are selected indiscriminately from different positions of an image and this can lead to the extraction of non-discriminating patches which eventually may reduce the performance. In the proposed model we used an evolutionary algorithm approach to select a set of informative patches. Our reported results indicate that these patches are more informative than usual random patches. We demonstrate the strength of the proposed model on a range of object recognition tasks. The proposed model outperforms the original model in diverse object recognition tasks. It can be seen from the experiments that selected features are generally particular parts of target images. Our results suggest that selected features which are parts of target objects provide an efficient set for robust object recognition

A Stable Biologically Motivated Learning Mechanism for Visual Feature Extraction to Handle Facial Categorization

The brain mechanism of extracting visual features for recognizing various objects has consistently been a controversial issue in computational models of object recognition. To extract visual features, we introduce a new, biologically motivated model for facial categorization, which is an extension of the Hubel and Wiesel simple-to-complex cell hierarchy. To address the synaptic stability versus plasticity dilemma, we apply the Adaptive Resonance Theory (ART) for extracting informative intermediate level visual features during the learning process, which also makes this model stable against the destruction of previously learned information while learning new information. Such a mechanism has been suggested to be embedded within known laminar microcircuits of the cerebral cortex. To reveal the strength of the proposed visual feature learning mechanism, we show that when we use this mechanism in the training process of a well-known biologically motivated object recognition model (the HMAX model), it performs better than the HMAX model in face/non-face classification tasks. Furthermore, we demonstrate that our proposed mechanism is capable of following similar trends in performance as humans in a psychophysical experiment using a face versus non-face rapid categorization task

Adsorption of Ni(II) and Cd(II) Ions from Aqueous Solutions by Modified Surface of Typha latifolia L. Root, as an Economical Adsorbent

ABSTRACT The modified surface of Typha latifolia L. root (MSTL), as an alternative economical adsorbent, was used for the removal of Ni (II) and Cd(II) ions from aqueous solutions. The effect of initial pH, initial concentration of metal ion, and contact time was investigated in a batch system at room temperature. The kinetics data could be fitted well by pseudo-second-order model with correlation coefficient values greater than 0.99. The mechanism of adsorption process was tested by fitting the experimental data by intraparticle diffusion kinetic and Boyd kinetic equations. The adsorption data could be fitted well by Langmuir and Freundlich adsorption isotherms. The maximum adsorption capacity of MSTL was determined to be 37.31 mg g -1 for Ni 2+ and 28.90 mg g -1 for Cd 2+ at room temperature when the initial concentration of both metal ion was 100 mg/L, and the pH of the solution was 5.00 and 4.00 for Ni 2+ and Cd 2+ , respectively. It has been suggested that the MSTL can be successfully applied for the removal of toxic heavy metal ions such as Ni 2+ and Cd 2+ from aqueous solutions

The impact of intra-sacroiliac joint methylprednisolone injection in the recovery of patients with spondyloarthropathy: a randomized controlled trial

Introduction Spondyloarthropathies are a group of chronic inflammatory diseases with specific clinical symptoms in rheumatic diseases. These patients suffer from pain in the joints. Physicians have tried several ways to decrease the pain in these patients. This study aimed to evaluate the effect of intra-sacroiliac joint methylprednisolone injection under the guidance of ultra- sonography in spondyloarthropathy patients. Material and methods In this randomized control trial we studied 60 patients with spondyloarthropathy (30 patients in the intervention group and 30 patients in the control group) from January 2020 to December 2020. The intervention group patients received 40 mg of intra-sacroiliac joint (SIJ) methylprednisolone injection at the beginning in addition to treatment with nonsteroidal anti-inflammatory drugs (inflammatory dose) and sulfasalazine (2 to 3 g/day). Patients’ pain intensity and symptoms were assessed in the 2nd, 4th, 6th, and 8th weeks after glucocorticosteroid injection. Quantitative factors were compared by independent Student’s t-test. Data analysis was performed using SPSS version 22.0 software. A p-value < 0.005 was considered significant. Results There were no statistically significant differences in Visual Analogue Scale (VAS) and Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) criteria and finger-to-floor (FTF) levels in the intervention and control groups. There were significant differences in VAS and BASDAI criteria and FTF levels 2 weeks after the injection, and this difference remained the same until the end of the 8th week. The p-value was significant (p-value < 0.0001). Conclusions The sacroiliac joint methylprednisolone injection approach with ultrasound guidance seems to be effective in pain relief and function, and patient satisfaction scores. Additionally using the guidance of ultrasonography in this approach is without the risk of radiation exposure

Beyond core object recognition: Recurrent processes account for object recognition under occlusion

Core object recognition, the ability to rapidly recognize objects despite variations in their appearance, is largely solved through the feedforward processing of visual information. Deep neural networks are shown to achieve human-level performance in these tasks, and explain the primate brain representation. On the other hand, object recognition under more challenging conditions (i.e. beyond the core recognition problem) is less characterized. One such example is object recognition under occlusion. It is unclear to what extent feedforward and recurrent processes contribute in object recognition under occlusion. Furthermore, we do not know whether the conventional deep neural networks, such as AlexNet, which were shown to be successful in solving core object recognition, can perform similarly well in problems that go beyond the core recognition. Here, we characterize neural dynamics of object recognition under occlusion, using magnetoencephalography (MEG), while participants were presented with images of objects with various levels of occlusion. We provide evidence from multivariate analysis of MEG data, behavioral data, and computational modelling, demonstrating an essential role for recurrent processes in object recognition under occlusion. Furthermore, the computational model with local recurrent connections, used here, suggests a mechanistic explanation of how the human brain might be solving this problem

Association of erythrocyte sedimentation rate and C-reactive protein and clinical findings with HLA-DQ8 allele in Rheumatoid Arthritis patients

Background ― Rheumatoid arthritis (RA) is an inflammatory, autoimmune disease induced by certain auto-antigens. HLA-DRB1*0401 allele has a significant relationship with RA incident. Additionally, DQβ1*0301, *302(DQ8), *303, and *304 can increase RA risk especially in DQA1*0301 and *302 coincident. Recent studies suggest that distribution of this allele is different in various populations Material and Methods ― 70 patients and 70 healthy controls were analyzed for human leukocyte antigen (HLA) allele by specific primer-polymerase chain reaction (SSP-PCR) method. Patients were evaluated in terms of ESR and CRP. Data analysis was performed in SPSS V.17. Results ― HLA-DQ8 allele was significantly more frequent in RA patients compared to control (P<0.0001). However, no significant relationship was observed between increased ESR (P=0.527), CRP (P=0.505), and mean counts of arthritic (P=0.691) and tender joints (P=0.669) among the patients who were carriers of HLA-DQ8. Conclusion ― There is a significant association between RA and HLA-DQ8 allele, this allele can increase susceptibility to RA. These findings might relate to the ethnical variations of RA patients but we couldn’t find a significant association between CRP and ESR with HLA-DQ8. We recommend to add specific inflammatory markers to CRP as well as assess ESR in larger sample sizes to obtain accurate results

Final and initial population of GA.

<p>(A), The initial population of GA. (B), The final population of GA (each white pixel shows the presence of a patch in learning procedure).</p