Asperger’s Syndrome in a Clinical Sample: Reasons for Referral and Comorbidity

Asperger’s Syndrome (AS) is an autism spectrum disorder without mental retardation and language delay. AS often remains unrecognized until these children fail to adapt to school or kindergarten. The comorbid psychiatric disorders, achieving clinical significance, were considered as another pathway to diagnosis. This study is aimed to elucidate the reasons for referral, the frequency and the kinds of comorbidities in a clinical sample of consecutive cases of children and adolescents with AS. To this objective, clinical records of children and adolescents, who have received a DSM-IV diagnosis of AS after multidisciplinary assessment in a given time period were reviewed. After excluding 3 cases due to insufficient information, 24 cases of children and adolescents with Asperger’s Syndrome (23 boys and one girl) were identified. The mean age at the time of assessment and receiving diagnosis was 9.6 yrs. (age range 4 to 17 years). In twenty-one (87%) of the cases the reason for referral was an episode of disorganized behavior following an attempt to enrollthe child at school or kindergarten, and more rare referral occurred within the significant school transition period. In the remaining 3 cases, the reason for referral was a comorbid condition. Comorbid conditions identified at the moment of assessment include: ADHD documented in 4 cases, tics in 3 cases, obsessive-compulsive behaviors in 4 cases, Stereotypic Movement Disorder or Trichotilomania in 4 of the cases. Within the clinical sample, a priori expected to include relatively severe cases, a higher frequency of comorbidity was found as compared to the rates in the general population. Adjustment reactions and comorbidities occasioned the refer

The stability of the swirling flows with applications to hydraulic turbines

The presence of a large variety of vortex flows in nature and technology has raised many theoretical and numerical problems concerning the stability of such structures. In these conditions, in order to minimize the simulation requirements for nonlinear time-dependent problems, stability analyses of vortexmotions are of main importance in flow control problems. A particular case arises in the Francis turbines operate at partial discharge. The swirling flow downstream the runner becomes unstable inside the draft tube cone, with the development of a precessing helical vortex and associated severe pressure fluctuations [1]

Local Inflammation Induces Complement Crosstalk Which Amplifies the Antimicrobial Response

By eliciting inflammatory responses, the human immunosurveillance system notably combats invading pathogens, during which acute phase proteins (CRP and cytokines) are elevated markedly. However, the Pseudomonas aeruginosa is a persistent opportunistic pathogen prevalent at the site of local inflammation, and its acquisition of multiple antibiotic-resistance factors poses grave challenges to patient healthcare management. Using blood samples from infected patients, we demonstrate that P. aeruginosa is effectively killed in the plasma under defined local infection-inflammation condition, where slight acidosis and reduced calcium levels (pH 6.5, 2 mM calcium) typically prevail. We showed that this powerful antimicrobial activity is provoked by crosstalk between two plasma proteins; CRP∶L-ficolin interaction led to communication between the complement classical and lectin pathways from which two amplification events emerged. Assays for C4 deposition, phagocytosis, and protein competition consistently proved the functional significance of the amplification pathways in boosting complement-mediated antimicrobial activity. The infection-inflammation condition induced a 100-fold increase in CRP∶L-ficolin interaction in a pH- and calcium-sensitive manner. We conclude that the infection-induced local inflammatory conditions trigger a strong interaction between CRP∶L-ficolin, eliciting complement-amplification pathways which are autonomous and which co-exist with and reinforce the classical and lectin pathways. Our findings provide new insights into the host immune response to P. aeruginosa infection under pathological conditions and the potential development of new therapeutic strategies against bacterial infection

High-Fidelity Digital Twin Data Models by Randomized Dynamic Mode Decomposition and Deep Learning with Applications in Fluid Dynamics

The purpose of this paper is the identification of high-fidelity digital twin data models from numerical code outputs by non-intrusive techniques (i.e., not requiring Galerkin projection of the governing equations onto the reduced modes basis). In this paper the author defines the concept of the digital twin data model (DTM) as a model of reduced complexity that has the main feature of mirroring the original process behavior. The significant advantage of a DTM is to reproduce the dynamics with high accuracy and reduced costs in CPU time and hardware for settings difficult to explore because of the complexity of the dynamics over time. This paper introduces a new framework for creating efficient digital twin data models by combining two state-of-the-art tools: randomized dynamic mode decomposition and deep learning artificial intelligence. It is shown that the outputs are consistent with the original source data with the advantage of reduced complexity. The DTMs are investigated in the numerical simulation of three shock wave phenomena with increasing complexity. The author performs a thorough assessment of the performance of the new digital twin data models in terms of numerical accuracy and computational efficiency