State of the art of integrated therapy approaches in the treatment of schizophrenia: empirical evidence and clinical relevance

Objective: Integrated behavior therapy approaches are defined by the combination of behavioral and or cognitive interventions targeting neurocognition combined with other goal-oriented treatment targets such as social cognition, social skills, or educational issues. The Integrated Psychological Therapy Program (IPT) represents one of the very first behavior therapy approaches combining interventions of neurocognition, social cognition, and social competence. This comprehensive group-based bottom-up and top-down approach consists of five subprograms, each with incremental steps. IPT has been successfully implemented in several countries in Europe, America, Australia and in Asia. IPT worked as a model for some other approaches designed in the USA. IPT was undergone two further developments: based on the social competence part of IPT, the three specific therapy programs focusing residential, occupational or recreational topics were developed. Recently, the cognitive part of INT was rigorously expanded into the Integrated Neurocognitive Therapy (INT) designed exclusively for outpatient treatment: INT includes interventions targeting all neurocognitive and social cognitive domains defined by the NIMH-MATRICS initiative. These group and partially PC-based exercises are structured into four therapy modules, each starting with exercises on neurocognitive domains followed by social cognitive targets. Efficacy: The evidence of integrated therapy approaches and its advantage compared to of one-track interventions was becoming a discussion tool in therapy research as well as in mental health systems. Results of meta-analyses support superiority of integrated approaches compared to one-track interventions in more distal outcome areas such as social functioning. These results are in line with the large body of 37 independent IPT studies in 12 countries. Moreover, IPT research indicates the maintenance of therapy effects after the end of therapy and some evidence generalization effects. Additionally, the international randomized multi-center study on INT with 169 outpatients strongly supports the successful therapy of integrated therapy in proximal and distal outcome such as significant effects in cognition, functioning and negative symptoms. Clinical implication: therapy research as well as expert���s clinical experience recommends integrated therapy approaches such as IPT to be successful agents within multimodal psychiatric treatment concepts. Finally, integrated group therapy based on cognitive remediation seems to motivate and stimulate schizophrenia inpatients and outpatients to more successful and independent life also demanded by the recovery movement.

State-of-the-art of imaging detecting endoleaks post-EVAR with special focus on low-flow endoleaks.

Endovascular aortic repair (EVAR) necessitates lifelong surveillance for the patient, in order to detect complications timely. Endoleaks (ELs) are among the most common complications of EVAR. Especially type II ELs can have a very unpredictable clinical course and this can range from spontaneous sealing to aortic rupture. Subgroups of this type of EL need to be identified in order to make a proper risk stratification. Aim of this review is to describe the existing imaging techniques, including their advantages and disadvantages in the context of post-EVAR surveillance with a particular emphasis on low-flow ELs. Low flow ELs cause pressurization of the aortic aneurysm sac with a low velocity filling, leading to difficulty of detection by routine imaging protocols for EVAR surveillance, e.g. bi- or triphasic multislice computed tomographic angiography, magnetic resonance imaging and contrast enhanced ultrasound. In this article, we review the imaging possibilities of ELs and discuss the different imaging strategies available for depicting low flow ELs.

Bathymetry of Lake Constance - State-of-the-art in surveying a large lake

In 2014 the by far largest German lake has been newly surveyed. The transnational project is funded by the European Union and delivers a detailed 3D-model of the lake- floor. The German project name is ��Tiefensch��rfe ��� Hochaufl��sende Vermessung Bo- densee��, which in English roughly means: high-resolution survey of Lake Constance. The German term ��Tiefensch��rfe�� (in optics and photography: depth of field) plays with the meanings of ��Tiefe�� (depth) and ��Sch��rfe�� (sharpness). The result of the sur- vey shall be a clear and sharp image of the deep and shallow lake- floor. At present the LiDAR and multibeam data are still processed, but first results are presented in this article.

State of the art: Reproduction and pregnancy in rheumatic diseases

Throughout the last decade, increasing awareness has been raised on issues related to reproduction in rheumatic diseases including basic research to clarify the important role of estrogens in the etiology and pathophysiology of immune/inflammatory diseases. Sub- or infertility is a heterogeneous condition that can be related to immunological mechanisms, to pregnancy loss, to disease burden, to therapy, and to choices in regard to family size. Progress in reproductive medicine has made it possible for more patients with rheumatic disease to have children. Active disease in women with rheumatoid arthritis (RA) affects their children's birth weight and may have long-term effects on their future health status. Pregnancy complications as preeclampsia and intrauterine growth restriction are still increased in patients with systemic lupus erythematosus (SLE) and antiphospholipid syndrome (APS), however, biomarkers can monitor adverse events, and several new therapies may improve outcomes. Pregnancies in women with APS remain a challenge, and better therapies for the obstetric APS are needed. New prospective studies indicate improved outcomes for pregnancies in women with rare diseases like systemic sclerosis and vasculitis. TNF inhibitors hold promise for maintaining remission in rheumatological patients and may be continued at least in the first half of pregnancy. Pre-conceptional counseling and interdisciplinary management of pregnancies are essential for ensuring optimal pregnancy outcomes.

Quantification of nanoparticles at the single-cell level: an overview about state-of-the-art techniques and their limitations.

With the increasing production and use of engineered nanoparticles it is crucial that their interaction with biological systems is understood. Due to the small size of nanoparticles, their identification and localization within single cells is extremely challenging. Therefore, various cutting-edge techniques are required to detect and to quantify metals, metal oxides, magnetic, fluorescent, as well as electron-dense nanoparticles. Several techniques will be discussed in detail, such as inductively coupled plasma atomic emission spectroscopy, flow cytometry, laser scanning microscopy combined with digital image restoration, as well as quantitative analysis by means of stereology on transmission electron microscopy images. An overview will be given regarding the advantages of those visualization/quantification systems, including a thorough discussion about limitations and pitfalls.

How Should Consumers��� Willingness to Pay Be Measured? An Empirical Comparison of State-of-the-Art Approaches

This study compares the performance of four commonly used approaches to measure consumers��� willingness to pay with real purchase data (REAL): the open-ended (OE) question format; choicebased conjoint (CBC) analysis; Becker, DeGroot, and Marschak���s (BDM) incentive-compatible mechanism; and incentive-aligned choice-based conjoint (ICBC) analysis. With this five-in-one approach, the authors test the relative strengths of the four measurement methods, using REAL as the benchmark, on the basis of statistical criteria and decision-relevant metrics. The results indicate that the BDM and ICBC approaches can pass statistical and decision-oriented tests. The authors find that respondents are more price sensitive in incentive-aligned settings than in non-incentive-aligned settings and the REAL setting. Furthermore, they find a large number of ���none��� choices under ICBC than under hypothetical conjoint analysis. This study uncovers an intriguing possibility: Even when the OE format and CBC analysis generate hypothetical bias, they may still lead to the right demand curves and right pricing decisions.

Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data

Smart homes for the aging population have recently started attracting the attention of the research community. The "health state" of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario.