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The suppressor of cytokine signaling (SOCS) proteins play important roles in cytokine and growth factor signaling, where they act principally as negative feedback regulators, particularly of the downstream signal transducer and activator of transcription (STAT) transcription factors. This critical mode of regulation impacts on both development and homeostasis. However, understanding of the function of SOCS4 remains limited. To address this, we investigated one of the zebrafish SOCS4 paralogues, socs4a, analyzing its expression and the consequences of its ablation. The socs4a gene had a dynamic expression profile during zebrafish embryogenesis, with initial ubiquitous expression becoming restricted to sensory ganglion within the developing nervous system. The knockdown of zebrafish socs4a revealed novel roles in notochord development, as well as the formation of a functional sensory system.

Developmental dyslexia (DD) is a specific learning disorder concerning reading acquisition that may has a lifelong negative impact on individuals. A reliable estimate of the prevalence of DD serves as the basis for diagnosis, intervention, and evidence-based health resource allocation and policy-making. Hence, the present meta-analysis aims to generate a reliable prevalence estimate of DD worldwide in primary school children and explore the potential variables related to that prevalence.

Studies from the 1950s to June 2021 were collated using a combination of search terms related to DD and prevalence. Study quality was assessed using the STROBE guidelines according to the study design, with study heterogeneity assessed using the



statistic, and random-effects meta-analyses were conducted. Variations in the prevalence of DD in different subgroups were assessed via subgroup meta-analysis and meta-regression.

The pooled prevalence of DD was 7.10% (95% CI 6.27-7.97%). The prevalence in boys was significantly higher than that in girls (boys 9.22%, 95%CI, 8.07-10.44%; girls 4.66%, 95% CI, 3.84-5.54%;

< 0.001), but no significant difference was found in the prevalence across different writing systems (alphabetic scripts 7.26%, 95%CI, 5.94-8.71%; logographic scripts 6.97%, 95%CI, 5.86-8.16%;

> 0.05) or across different orthographic depths (shallow 7.13%, 95% CI, 5.23-9.30%; deep 7.55%, 95% CI, 4.66-11.04%;

> 0.05). It is worth noting that most articles had small sample sizes with diverse operational definitions, making comparisons challenging.

This study provides an estimation of worldwide DD prevalence in primary school children. The prevalence was higher in boys than in girls but was not significantly different across different writing systems.

This study provides an estimation of worldwide DD prevalence in primary school children. The prevalence was higher in boys than in girls but was not significantly different across different writing systems.Mathematical performance implies a series of numerical and mathematical skills (both innate and derived from formal training) as well as certain general cognitive abilities that, if inadequate, can have a cascading effect on mathematics learning. These latter skills were the focus of the present systematic review.

The reviewing process was conducted according to the PRISMA statement. We included 46 studies comparing school-aged children's performance with and without math difficulties in the following cognitive domains processing speed, phonological awareness, short- and long-term memory, executive functions, and attention.

The results showed that some general cognitive domains were compromised in children with mathematical difficulties (i.e., executive functions, attention, and processing speed).

These cognitive functions should be evaluated during the diagnostic process in order to better understand the child's profile and propose individually tailored interventions. However, further studies should investigate the role of skills that have been poorly investigated to date (e.g., long-term memory and phonological awareness).

These cognitive functions should be evaluated during the diagnostic process in order to better understand the child's profile and propose individually tailored interventions. However, further studies should investigate the role of skills that have been poorly investigated to date (e.g., long-term memory and phonological awareness).In Parkinson's disease (PD) patients, the progressive nature of the disease and the variability of disabling motor and non-motor symptoms contribute to the growing caregiver burden of PD partners and conflicts in their relationships. Deep brain stimulation (DBS) improves PD symptoms and patients' quality of life but necessitates an intensified therapy optimization after DBS surgery. This review illuminates caregiver burden in the context of DBS, framing both pre- and postoperative aspects. We aim to provide an overview of perioperative factors influencing caregiver burden and wish to stimulate further recognition of caregiver burden of PD patients with DBS.Although 20% of the world's suicides occur in India, suicide prevention efforts in India are lagging (Vijayakumar et al., 2021). Identification of risk factors for suicide in India, as well as the development of accessible interventions to treat these risk factors, could help reduce suicide in India. Interoceptive dysfunction-or an inability to recognize internal sensations in the body-has emerged as a robust correlate of suicidality among studies conducted in the United States. Additionally, a mindfulness-informed intervention designed to reduce interoceptive dysfunction, and thereby suicidality, has yielded promising initial effects in pilot testing (Smith et al., 2021). The current studies sought to replicate these findings in an Indian context. Study 1 (n = 276) found that specific aspects of interoceptive dysfunction were related to current, past, and future likelihood of suicidal ideation. Study 2 (n = 40) was a small, uncontrolled pre-post online pilot of the intervention, Reconnecting to Internal Sensations and Experiences (RISE). The intervention was rated as highly acceptable and demonstrated good retention. Additionally, the intervention was associated with improvements in certain aspects of interoceptive dysfunction and reductions in suicidal ideation and eating pathology. These preliminary results suggest further testing of the intervention among Indian samples is warranted.Everyday life's hygiene and professional realities, especially in economically developed countries, indicate the need to modify the standards of pro-health programs as well as modern hygiene and work ergonomics programs. These observations are based on the problem of premature death caused by civilization diseases. The biological mechanisms associated with financial risk susceptibility are well described, but there is little data explaining the biological basis of neuroaccounting. Therefore, the aim of the study was to present relationships between personality traits, cognitive competences and biological factors shaping behavioral conditions in a multidisciplinary aspect. This critical review paper is an attempt to compile biological and psychological factors influencing the development of professional competences, especially decent in the area of accounting and finance. We analyzed existing literature from wide range of scientific disciplines (including economics, psychology, behavioral genetics) to create background to pursuit multidisciplinary research models in the field of neuroaccounting. This would help in pointing the best genetically based behavioral profile of future successful financial and accounting specialists.This paper investigates the effects of the repetitive block-wise training process on the classification accuracy for a code-modulated visual evoked potentials (cVEP)-based brain-computer interface (BCI). The cVEP-based BCIs are popular thanks to their autocorrelation feature. The cVEP-based stimuli are generated by a specific code pattern, usually the m-sequence, which is phase-shifted between the individual targets. Typically, the cVEP classification requires a subject-specific template (individually created from the user's own pre-recorded EEG responses to the same stimulus target), which is compared to the incoming electroencephalography (EEG) data, using the correlation algorithms. The amount of the collected user training data determines the accuracy of the system. In this offline study, previously recorded EEG data collected during an online experiment with 10 participants from multiple sessions were used. A template matching target identification, with similar models as the task-related component analysis (TRCA), was used for target classification. The spatial filter was generated by the canonical correlation analysis (CCA). When comparing the training models from one session with the same session's data (intra-session) and the model from one session with the data from the other session (inter-session), the accuracies were (94.84%, 94.53%) and (76.67%, 77.34%) for intra-sessions and inter-sessions, respectively. In order to investigate the most reliable configuration for accurate classification, the training data blocks from different sessions (days) were compared interchangeably. In the best training set composition, the participants achieved an average accuracy of 82.66% for models based only on two training blocks from two different sessions. Similarly, at least five blocks were necessary for the average accuracy to exceed 90%. The presented method can further improve cVEP-based BCI performance by reusing previously recorded training data.In recent years, deep learning models have demonstrated an inherently better ability to tackle non-linear classification tasks, due to advances in deep learning architectures. However, much remains to be achieved, especially in designing deep convolutional neural network (CNN) configurations. The number of hyper-parameters that need to be optimized to achieve accuracy in classification problems increases with every layer used, and the selection of kernels in each CNN layer has an impact on the overall CNN performance in the training stage, as well as in the classification process. When a popular classifier fails to perform acceptably in practical applications, it may be due to deficiencies in the algorithm and data processing. RMC-4630 Thus, understanding the feature extraction process provides insights to help optimize pre-trained architectures, better generalize the models, and obtain the context of each layer's features. In this work, we aim to improve feature extraction through the use of a texture amortization map (TAM). An algorithm was developed to obtain characteristics from the filters amortizing the filter's effect depending on the texture of the neighboring pixels. From the initial algorithm, a novel geometric classification score (GCS) was developed, in order to obtain a measure that indicates the effect of one class on another in a classification problem, in terms of the complexity of the learnability in every layer of the deep learning architecture. For this, we assume that all the data transformations in the inner layers still belong to a Euclidean space. In this scenario, we can evaluate which layers provide the best transformations in a CNN, allowing us to reduce the weights of the deep learning architecture using the geometric hypothesis.