Frequency versus relaxation oscillations in a semiconductor laser with coherent filtered optical feedback

We investigate the dynamics of a semiconductor laser subject to coherent delayed filtered optical feedback. A systematic bifurcation analysis reveals that this system supports two fundamentally different types of oscillations, namely relaxation oscillations and external roundtrip oscillations. Both occur stably in large domains under variation of the feedback conditions, where the feedback phase is identified as a key quantity for controlling this dynamical complexity. We identify two separate parameter regions of stable roundtrip oscillations, which occur throughout in the form of pure frequency oscillations

Shifting boundaries between the normal and the pathological:the case of mild intellectual disability

When disorders fade into normality, how can the threshold between normality and disorder be determined? In considering mild intellectual disability, I argue that economic factors partly determine thresholds. We tend to assume that the relationship between disorder, need and services is such that: first, a cut-off point between the disordered and the normal is determined; second, a needy population is identified; and third, resources are found (or at least should be found) to meet this need. However, the changing definitions of intellectual disability can best be understood if we think of this happening in reverse. That is, first, certain resources are thought obtainable, and then a cut-off point for disorder is selected which supplies an appropriately sized ‘needy population’

Accounting for Location Uncertainty in Azimuthal Telemetry Data Improves Ecological Inference

Background: Characterizing animal space use is critical for understanding ecological relationships. Animal telemetry technology has revolutionized the fields of ecology and conservation biology by providing high quality spatial data on animal movement. Radio-telemetry with very high frequency (VHF) radio signals continues to be a useful technology because of its low cost, miniaturization, and low battery requirements. Despite a number of statistical developments synthetically integrating animal location estimation and uncertainty with spatial process models using satellite telemetry data, we are unaware of similar developments for azimuthal telemetry data. As such, there are few statistical options to handle these unique data and no synthetic framework for modeling animal location uncertainty and accounting for it in ecological models. We developed a hierarchical modeling framework to provide robust animal location estimates from one or more intersecting or non-intersecting azimuths. We used our azimuthal telemetry model (ATM) to account for azimuthal uncertainty with covariates and propagate location uncertainty into spatial ecological models. We evaluate the ATM with commonly used estimators (Lenth (1981) maximum likelihood and M-Estimators) using simulation. We also provide illustrative empirical examples, demonstrating the impact of ignoring location uncertainty within home range and resource selection analyses. We further use simulation to better understand the relationship among location uncertainty, spatial covariate autocorrelation, and resource selection inference. Results: We found the ATM to have good performance in estimating locations and the only model that has appropriate measures of coverage. Ignoring animal location uncertainty when estimating resource selection or home ranges can have pernicious effects on ecological inference. Home range estimates can be overly confident and conservative when ignoring location uncertainty and resource selection coefficients can lead to incorrect inference and over confidence in the magnitude of selection. Furthermore, our simulation study clarified that incorporating location uncertainty helps reduce bias in resource selection coefficients across all levels of covariate spatial autocorrelation. Conclusion: The ATM can accommodate one or more azimuths when estimating animal locations, regardless of how they intersect; this ensures that all data collected are used for ecological inference. Our findings and model development have important implications for interpreting historical analyses using this type of data and the future design of radio-telemetry studies

Psychiatric disorder as clinical presentation of primary Sjögren's syndrome: two case reports

Psychiatric disorders in primary Sjögren's syndrome constitute a possible clinical reality that each practitioner must be able to recognise and treat. In this article, two case reports of mental disorder as clinical presentation of primary Sjögren's syndrome are presented, suggesting that psychiatric manifestations in primary Sjögren's syndrome can occur not only during its longitudinal course, but also at the onset of the autoimmune syndrome. A better adapted prescription of corticosteroids and/or immunosuppressive agents (together with specific psychotropic treatments) can induce rapid relief of mental symptoms

Recognition memory and source memory in autism spectrum disorder: A study of the intention superiority and enactments effects

It is well established that neurotypical individuals generally show better memory for actions they have performed than actions they have observed others perform or merely read about, a so-called “enactment effect”. Strikingly, research has also shown that neurotypical individuals demonstrate superior memory for actions they intend to perform in the future (but have not yet performed), an effect commonly known as the “intention superiority effect”. Although the enactment effect has been studied among people with ASD, the current study is the first to investigate the intention superiority effect in this disorder. This is surprising given the potential importance this issue has for general theory development, as well as for clinical practice. As such, this study aimed to assess the intention superiority and enactment effects in twenty-two children with ASD, and 20 IQ/age-matched neurotypical children. The results showed that children with ASD demonstrated not only undiminished enactment effects in recognition and source memory, but also (surprisingly for some theories) typical intention superiority effects. The implications of these results for theory, as well as clinical practice, are discussed

Anticipating the location of a waste collection point : an application based on Portugal

We study the optimal location of a waste facility in a horizontally differentiated duopoly where firms choose their location and price. The policymaker decides the location of a waste facility targeting social welfare maximization. Consistent with the observation of the location decisions of waste facilities in Portugal, we show that the optimal location of a waste facility is never in the city center under partial expost regulation. Ex-ante regulation ensures the highest level of social welfare, but from a theoretical point of view, it requires a waste facility located in the city center. A robustness check is then provided to justify that, in actual regulatory practice, a first-mover regulator maximizes social welfare without necessarily imposing the installation of a waste facility in the city center.info:eu-repo/semantics/publishedVersio

Drawing a line in the sand: affect and testimony in autism assessment teams in the UK.

Diagnosis of autism in the UK is generally made within a multidisciplinary team setting and is primarily based on observation and clinical interview. We examined how clinicians diagnose autism in practice by observing post-assessment meetings in specialist autism teams. Eighteen meetings across four teams based in the south of England and covering 88 cases were audio-recorded, transcribed and analysed using thematic analysis. We drew out two themes, related to the way in which clinicians expressed their specialist disciplinary knowledge to come to diagnostic consensus: Feeling Autism in the Encounter; and Evaluating Testimonies of Non-present Actors. We show how clinicians produce objective accounts through their situated practices and perform diagnosis as an act of interpretation, affect and evaluation to meet the institutional demands of the diagnostic setting. Our study contributes to our understanding of how diagnosis is accomplished in practice.Wellcome Trust Investigator Awar

A comparison of the development of audiovisual integration in children with autism spectrum disorders and typically developing children

This study aimed to investigate the development of audiovisual integration in children with Autism Spectrum Disorder (ASD). Audiovisual integration was measured using the McGurk effect in children with ASD aged 7–16 years and typically developing children (control group) matched approximately for age, sex, nonverbal ability and verbal ability. Results showed that the children with ASD were delayed in visual accuracy and audiovisual integration compared to the control group. However, in the audiovisual integration measure, children with ASD appeared to ‘catch-up’ with their typically developing peers at the older age ranges. The suggestion that children with ASD show a deficit in audiovisual integration which diminishes with age has clinical implications for those assessing and treating these children

Language abilities in children with autism and language impairment: using narrative as a additional source of clinical information

Autistic Spectrum Disorder (ASD) and Specific Language Impairment (SLI) are disorders of communication that are sometimes thought to show similar structural language difficulties. Recent research has even suggested that they might be aetiologically related. However, it may be that standardized language tasks are not sensitive enough to detect similarities and differences accurately. This study involved 26 Greek children with either ASD or SLI and compared them on standardized measures of structural and pragmatic language as well as using a structured narrative task. Children with ASD were more impaired on receptive but not expressive scores from standardized language tests. In contrast, narrative measures showed significantly poorer ASD performance in expressive skills involving wider story-telling skill and in some sentence-level skills, in particular referencing, compared to peers with SLI. ASD and SLI groups also showed different relationships between structural language and other measures. The data suggests that narrative is a useful tool for revealing qualitative differences in language between overlapping communication disorders both at the clinical and theoretical level, since it provides information that is lost in more formalized testing. This may be particularly true where norms are not available or testing is difficult

Machine learning for developing a prediction model of hospital admission of emergency department patients:Hype or hope?

Objective: Early identification of emergency department (ED) patients who need hospitalization is essential for quality of care and patient safety. We aimed to compare machine learning (ML) models predicting the hospitalization of ED patients and conventional regression techniques at three points in time after ED registration. Methods: We analyzed consecutive ED patients of three hospitals using the Netherlands Emergency Department Evaluation Database (NEED). We developed prediction models for hospitalization using an increasing number of data available at triage, similar to 30 min (including vital signs) and similar to 2 h (including laboratory tests) after ED registration, using ML (random forest, gradient boosted decision trees, deep neural networks) and multivariable logistic regression analysis (including spline transformations for continuous predictors). Demographics, urgency, presenting complaints, disease severity and proxies for comorbidity, and complexity were used as covariates. We compared the performance using the area under the ROC curve in independent validation sets from each hospital. Results: We included 172,104 ED patients of whom 66,782 (39 %) were hospitalized. The AUC of the multi-variable logistic regression model was 0.82 (0.78-0.86) at triage, 0.84 (0.81-0.86) at similar to 30 min and 0.83 (0.75-0.92) after similar to 2 h. The best performing ML model over time was the gradient boosted decision trees model with an AUC of 0.84 (0.77-0.88) at triage, 0.86 (0.82-0.89) at similar to 30 min and 0.86 (0.74-0.93) after similar to 2 h. Conclusions: Our study showed that machine learning models had an excellent but similar predictive performance as the logistic regression model for predicting hospital admission. In comparison to the 30-min model, the 2-h model did not show a performance improvement. After further validation, these prediction models could support management decisions by real-time feedback to medical personal