Forensic child and Adolescent Psychiatry and mental health in Europe

Background When faced with the discovery of their child’s self-harm, mothers and fathers may re-evaluate their parenting strategies. This can include changes to the amount of support they provide their child and changes to the degree to which they control and monitor their child. Methods We conducted an in-depth qualitative study with 37 parents of young people who had self-harmed in which we explored how and why their parenting changed after the discovery of self-harm. Results Early on, parents often found themselves “walking on eggshells” so as not to upset their child, but later they felt more able to take some control. Parents’ reactions to the self-harm often depended on how they conceptualised it: as part of adolescence, as a mental health issue or as “naughty behaviour”. Parenting of other children in the family could also be affected, with parents worrying about less of their time being available for siblings. Many parents developed specific strategies they felt helped them to be more effective parents, such as learning to avoid blaming themselves or their child for the self-harm and developing new ways to communicate with their child. Parents were generally eager to pass their knowledge on to other people in the same situation. Conclusions Parents reported changes in their parenting behaviours after the discovery of a child’s self-harm. Professionals involved in the care of young people who self-harm might use this information in supporting and advising parents.</p

Practical Tools to Implement Massive Parallel Pyrosequencing of PCR Products in Next Generation Molecular Diagnostics

Despite improvements in terms of sequence quality and price per basepair, Sanger sequencing remains restricted to screening of individual disease genes. The development of massively parallel sequencing (MPS) technologies heralded an era in which molecular diagnostics for multigenic disorders becomes reality. Here, we outline different PCR amplification based strategies for the screening of a multitude of genes in a patient cohort. We performed a thorough evaluation in terms of set-up, coverage and sequencing variants on the data of 10 GS-FLX experiments (over 200 patients). Crucially, we determined the actual coverage that is required for reliable diagnostic results using MPS, and provide a tool to calculate the number of patients that can be screened in a single run. Finally, we provide an overview of factors contributing to false negative or false positive mutation calls and suggest ways to maximize sensitivity and specificity, both important in a routine setting. By describing practical strategies for screening of multigenic disorders in a multitude of samples and providing answers to questions about minimum required coverage, the number of patients that can be screened in a single run and the factors that may affect sensitivity and specificity we hope to facilitate the implementation of MPS technology in molecular diagnostics

Consensus Paper: Cerebellum and Social Cognition.

The traditional view on the cerebellum is that it controls motor behavior. Although recent work has revealed that the cerebellum supports also nonmotor functions such as cognition and affect, only during the last 5 years it has become evident that the cerebellum also plays an important social role. This role is evident in social cognition based on interpreting goal-directed actions through the movements of individuals (social "mirroring") which is very close to its original role in motor learning, as well as in social understanding of other individuals' mental state, such as their intentions, beliefs, past behaviors, future aspirations, and personality traits (social "mentalizing"). Most of this mentalizing role is supported by the posterior cerebellum (e.g., Crus I and II). The most dominant hypothesis is that the cerebellum assists in learning and understanding social action sequences, and so facilitates social cognition by supporting optimal predictions about imminent or future social interaction and cooperation. This consensus paper brings together experts from different fields to discuss recent efforts in understanding the role of the cerebellum in social cognition, and the understanding of social behaviors and mental states by others, its effect on clinical impairments such as cerebellar ataxia and autism spectrum disorder, and how the cerebellum can become a potential target for noninvasive brain stimulation as a therapeutic intervention. We report on the most recent empirical findings and techniques for understanding and manipulating cerebellar circuits in humans. Cerebellar circuitry appears now as a key structure to elucidate social interactions

Avenues for the use of cellular automata in image segmentation

The majority of Cellular Automata (CA) described in the literature are binary or three-state. While several abstractions are possible to generalise to more than three states, only a negligible number of multi–state CA rules exist with concrete practical applications. This paper proposes a generic rule for multi–state CA. The rule allows for any number of states, and allows for the states are semantically related. The rule is illustrated on the concrete example of image segmentation, where the CA agents are pixels in an image, and their states are the pixels’ greyscale values. We investigate in detail the proposed rule and some of its variations, and we also compare its effectiveness against the existing Greenberg–Hastings automaton, as the closest relative of our proposed technique. We apply the proposed methods to both synthetic and real-world images, evaluating the results with a variety of measures. The experimental results demonstrate that our proposed method can segment images accurately and effectively.</p

A metal-supported biomimetic micromembrane allowing the recording of single- channel activity and of impedance spectra of membrane proteins

Perceived parental expressed emotions have a substantial effect on adolescents' well-being and non-suicidal self-injury (NSSI). The present study examines the mediating effects of self-criticism and depression in the relationship between perceived parental expressed emotions and NSSI. In total, 358 adolescents between the ages of twelve and twenty were examined. The brief NSSI assessment tool was used to assess NSSI. Depressive symptoms and self-criticism were examined with the Children's Depression Inventory (CDI-NL) and the Self Rating Scale. Finally, the self-report questionnaire of the level of expressed emotions was used to assess perceived parental expressed emotions. The lifetime prevalence of NSSI in the current study was 13.41\ua0%. Results of a mediation analysis show the relationship between self-criticism and NSSI is mediated by depressive symptoms. Furthermore, results of a path model analysis, explaining 20\ua0% of the variance in NSSI frequency, show a direct effect of perceived parental environment (perceived lack of emotional support and perceived parental criticism) on NSSI frequency, as well as indirect paths via adolescent risk factors (depressive symptoms and self-criticism). Perceived lack of parental emotional support had a direct effect on frequency of NSSI, as well as an indirect effect via depressive symptoms. Perceived parental criticism on the other hand, had no direct effect on frequency of NSSI, but showed an indirect effect through self-criticism. This study improves our understanding of the underlying mechanisms involved in NSSI by interrelating significant family and adolescent risk factors. Limitations and clinical implications of these findings are discussed

EvoDynamic: A Framework for the Evolution of Generally Represented Dynamical Systems and Its Application to Criticality

Dynamical systems possess a computational capacity that may be exploited in a reservoir computing paradigm. This paper presents a general representation of dynamical systems which is based on matrix multiplication. That is similar to how an artificial neural network (ANN) is represented in a deep learning library and its computation can be faster because of the optimized matrix operations that such type of libraries have. Initially, we implement the simplest dynamical system, a cellular automaton. The mathematical fundamentals behind an ANN are maintained, but the weights of the connections and the activation function are adjusted to work as an update rule in the context of cellular automata. The advantages of such implementation are its usage on specialized and optimized deep learning libraries, the capabilities to generalize it to other types of networks and the possibility to evolve cellular automata and other dynamical systems in terms of connectivity, update and learning rules. Our implementation of cellular automata constitutes an initial step towards a more general framework for dynamical systems. Our objective is to evolve such systems to optimize their usage in reservoir computing and to model physical computing substrates. Furthermore, we present promising preliminary results toward the evolution of complex behavior and criticality using genetic algorithm in stochastic elementary cellular automata

EvoDynamic: A Framework for the Evolution of Generally Represented Dynamical Systems and Its Application to Criticality

Dynamical systems possess a computational capacity that may be exploited in a reservoir computing paradigm. This paper presents a general representation of dynamical systems which is based on matrix multiplication. That is similar to how an artificial neural network (ANN) is represented in a deep learning library and its computation can be faster because of the optimized matrix operations that such type of libraries have. Initially, we implement the simplest dynamical system, a cellular automaton. The mathematical fundamentals behind an ANN are maintained, but the weights of the connections and the activation function are adjusted to work as an update rule in the context of cellular automata. The advantages of such implementation are its usage on specialized and optimized deep learning libraries, the capabilities to generalize it to other types of networks and the possibility to evolve cellular automata and other dynamical systems in terms of connectivity, update and learning rules. Our implementation of cellular automata constitutes an initial step towards a more general framework for dynamical systems. Our objective is to evolve such systems to optimize their usage in reservoir computing and to model physical computing substrates. Furthermore, we present promising preliminary results toward the evolution of complex behavior and criticality using genetic algorithm in stochastic elementary cellular automata