1,721,232 research outputs found
Adult Attachment Style and Suicidality.
Full text linkBACKGROUND:
There is evidence in the literature that adverse early attachment experiences and subsequent attachment insecurities during adulthood would lead to pessimism, low self-esteem, hopelessness and, ultimately, to suicide risk.
SUBJECTS AND METHODS:
This paper aims to review finding on the link between attachment style and suicidality. We searched the literature using the database of the U.S. National Center for Biotechnology Information (NCBI)-MedLine/Pubmed system from January 1992 until December 2016. We started with 1992 because, as far as we know, there are no published studies exploring the relationship between suicide and insecure attachment before that year. We considered reports published on the relationship between attachment style and suicidality. We applied several combinations of the following search terms: attachment, adult attachment style and suicidality, suicide, suicidal ideation, suicidal behavior or suicidal thoughts, and suicide attempts. We selected only English language studies.
RESULTS:
Research suggests that insecure attachment style, mostly anxious, and unresolved traumas are associated with an increased suicide risk. Few studies prospectively examined clinical course, comorbid psychiatric disorders, familial suicidality or other psychosocial factors.
CONCLUSIONS:
Further research is needed to highlight the nature of the link between attachment and suicidality. The presence of suicidal ideation and attempts might be a consequence of an underlying interaction between the emergence of psychiatrics symptoms, and the long-lasting presence of inadequate patterns of attachment. Within this context, Separation Anxiety Disorder, categorized in the DSM-5 as a condition not confined to childhood but as an anxiety disorder that may occur through the entire lifespan, might be the a key for the comprehension of this link. From a neurobiological point of view, the role of oxytocin remains unclear
Approach to bipolar spectrum and subthreshold mood disorders
No full textIn the last decades the concept of bipolar disorder was subjected to many revisions. The complexity in diagnosing mood disorders, on the other hand, stems from the problem to delineate the boundary of these morbid conditions. The current nosographic approach is limited by the lack of attention given to the natural course and to the longitudinal and family characteristics of patients suffering of mood disorders. Considering these limits, some authors developed different nosographic models to include other atypical, non-standardized characteristics of mood disorders. However, regardless of the efforts made so far, a gap in classification still remains, putting restrictions in the clinical and neurobiological range of activities
What Did We Learn from Research on Comorbidity In Psychiatry? Advantages and Limitations in the Forthcoming DSM-V Era.
Full text linkDespite the large amount of research conducted in this area over the last two decades, comorbidity of psychiatric disorders remains a topic of major practical and theoretical significance.Official diagnostic and therapeutic guidelines of psychiatric disorders still do not provide clinicians and researchers with any treatment-specific indications for those cases presenting with psychiatric comorbidity. We will discuss the diagnostic improvement brought about, in clinical practice, by the punctual and refined recognition of threshold and subthreshold comorbidity. From such a perspective, diagnostic procedures and forthcoming systems of classification of mental disorders should attempt to combine descriptive, categorical and dimensional approaches, addressing more attention to the cross-sectional and longitudinal analysis of nuclear, subclinical, and atypical symptoms that may represent a pattern of either full-blown or partially expressed psychiatric comorbidity. This should certainly be regarded as a positive development. Parallel, continuous critical challenge seems to be vital in this area, in order to prevent dangerous trivializations and misunderstandings
Detection and management of mental distress and psychiatric disorders in primary care settings
No full textTecniche di Visione Artificiale per l'Interazione Uomo-Veicolo
No full textNegli ultimi anni, la diffusione di dispositivi digitali in ogni aspetto della vita quotidiana ha portato a nuove opportunità nel campo dell’Interazione Uomo-Macchina. Nel campo automobilistico, dove i sistemi di infotainment sono sempre più importanti per gli utenti finali, la disponibilità di telecamere economiche e miniaturizzate ha permesso lo sviluppo di interfacce utente naturali basate sulla visione artificiale, aprendo a nuove opportunità nell’Interazione Uomo-Veicolo. In questa tesi, si propone uno studio di tecniche di visione artificiale, basate sia su luce visibile che sullo spettro non visibile, che possano formare la base per la prossima generazione di sistemi di infotainment. Come tecnologie di acquisizione, il focus è posto su dispositivi basati su luce infrarossa, come camere termiche e di profondità. Queste tipologie di sensori forniscono dati affidabili in numerose condizioni di illuminazione per cui sono particolarmente adatte al dinamico ambiente automobilistico. Usando questi dispositivi, sono acquisiti due dataset: un dataset di volti, per valutare l’impatto di qualità e risoluzione dei sensori in configurazioni di acquisizione variabile, e un dataset di gesti dinamici della mano, acquisito in un simulatore di auto con molteplici sensori sincronizzati fra loro. Come approcci di visione artificiale, si sceglie di utilizzare tecniche di deep learning stato dell’arte, focalizzandosi su reti neurali efficienti che possano essere utilizzate su dispositivi integrati a basso consumo. In questo contesto, sono esaminati diversi problemi di visione artificiale, con l’obiettivo di coprire la maggior parte delle interazioni uomo-macchina. Innanzitutto, si analizza l’utilizzo di camere di profondità per il riconoscimento facciale, focalizzandosi sull’impatto che la rappresentazione dei dati di profondità e il tipo di architettura neurale utilizzata hanno sulle capacità di riconoscimento. Inoltre, si studia il riconoscimento di gesti dinamici della mano in tempo reale, utilizzando sensori infrarosso e di profondità. Si analizza anche il corpo umano nella sua interezza, in termini di riconoscimento della postura 3D e di stima senza contatto di misure antropometriche. Infine, focalizzandosi sull’area circostante il veicolo, si affronta la ricostruzione 3D di oggetti da immagini 2D, come primo passo verso una visualizzazione 3D navigabile dell’ambiente esterno.In recent years, the widespread adoption of digital devices in all aspects of everyday life has led to new research opportunities in the field of Human-Computer Interaction. In the automotive field, where infotainment systems are becoming more and more important to the final user, the availability of inexpensive miniaturized cameras has enabled the development of vision-based Natural User Interfaces, paving the way for novel approaches to the Human-Vehicle Interaction. In this thesis, we investigate computer vision techniques, based on both visible light and non-visible spectrum, that can form the foundation of the next generation of in-vehicle infotainment systems. As sensing technology, we focus on infrared-based devices, such as depth and thermal cameras. They provide reliable data under different illumination conditions, making them a good fit for the mutable automotive environment. Using these acquisition devices, we collect two novel datasets: a facial dataset, to investigate the impact of sensor resolution and quality in changing acquisition settings, and a dataset of dynamic hand gestures, collected with several synchronized sensors within a car simulator. As vision approaches, we adopt state-of-the-art deep learning techniques, focusing on efficient neural networks that can be easily deployed on computing devices on the edge. In this context, we study several computer vision tasks to cover the majority of human-car interactions. First, we investigate the usage of depth cameras for the face recognition task, focusing on how depth-map representations and deep neural models affect the recognition performance. Secondly, we address the problem of in-car dynamic hand gesture recognition in real-time, using depth and infrared sensors. Then, we focus on the analysis of the human body, both in terms of the 3D human pose estimation and the contact-free estimation of anthropometric measurements. Finally, focusing on the area surrounding the vehicle, we explore the 3D reconstruction of objects from 2D images, as a first step towards the 3D visualization of the external environment from controllable viewpoints
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