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.

IMRT with ¹⁸FDG-PET\CT based simultaneous integrated boost for treatment of nodal positive cervical cancer

BACKGROUND To evaluate toxicity and outcome of intensity modulated radiotherapy (IMRT) with simultaneous integrated boost (SIB) to the positive lymph nodes in patients with loco-regional advanced cervical cancer (LRACC). METHODS The study population comprised ten patients with 18FDG-PET\CT positive lymph nodes (LNs), who underwent chemoradiation with IMRT and SIB. A dose of 50.4 Gy, in daily fractions of 1.8 Gy, was delivered to primary tumor and draining LNs. Primary tumor received an additional external beam boost to a total dose of 55.8 Gy. A SIB of 62 Gy, in daily fractions of 2 Gy, was delivered to the 18FDG-PET\CT positive LNs. Finally, a high dose rate brachytherapy (HDRB) boost (15 - 18 Gy) was administered to the primary tumor. The primary goal of this study was to evaluate acute and early late toxicity and loco-regional control. RESULTS The median number of irradiated LNs per patient was 3 (range: 1-6) with a median middle nodal SIB-volume of 26.10 cm3 (range, 11.9-82.50 cm3). Median follow-up was 20 months (range, 12 to 30 months). Acute and late grade 3 toxicity was observed in 1 patient. Three of the patients developed a recurrence, one in the form of a local tumor relapse, one had a paraaortic LN metastasis outside the treated volume and the last one developed a distant metastasis. CONCLUSION IMRT with SIB in the region of 18FDG-PET positive lymph nodes appears to be an effective therapy with acceptable toxicity and might be useful in the treatment of patients with locally advanced cervical cancer.

Search for supersymmetry at √8=8 TeV in final states with jets and two same-sign leptons or three leptons with the ATLAS detector

A search for strongly produced supersymmetric particles is conducted using signatures involving multiple energetic jets and either two isolated leptons (e or μ) with the same electric charge, or at least three isolated leptons. The search also utilises jets originating from b-quarks, missing transverse momentum and other observables to extend its sensitivity. The analysis uses a data sample corresponding to a total integrated luminosity of 20.3 fb−1 of ps = 8TeV proton-proton collisions recorded with the ATLAS detector at the Large Hadron Collider in 2012. No deviation from the Standard Model expectation is observed. New or significantly improved exclusion limits are set on a wide variety of supersymmetric models in which the lightest squark can be of the first, second or third generations, and in which R-parity can be conserved or violated.

Advances in understanding energy consumption behavior and the governance of its change - Outline of an integrated framework

Transforming today’s energy systems in industrialized countries requires a substantial reduction of the total energy consumption at the individual level. Selected instruments have been found to be effective in changing people’s behavior in single domains. However, the so far weak success story on reducing overall energy consumption indicates that our understanding of the determining factors of individual energy consumption as well as of its change is far from being conclusive. Among others, the scientific state of the art is dominated by analyzing single domains of consumption and by neglecting embodied energy. It also displays strong disciplinary splits and the literature often fails to distinguish between explaining behavior and explaining change of behavior. Moreover, there are knowledge gaps regarding the legitimacy and effectiveness of the governance of individual consumption behavior and its change. Against this backdrop, the aim of this paper is to establish an integrated interdisciplinary framework that offers a systematic basis for linking the different aspects in research on energy related consumption behavior, thus paving the way for establishing a better evidence base to inform societal actions. The framework connects the three relevant analytical aspects of the topic in question: (1) It systematically and conceptually frames the objects, i.e. the energy consumption behavior and its change (explananda); (2) it structures the factors that potentially explain the energy consumption behavior and its change (explanantia); (3) it provides a differentiated understanding of change inducing interventions in terms of governance. Based on the existing states of the art approaches from different disciplines within the social sciences the proposed framework is supposed to guide interdisciplinary empirical research.

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.

Fine-grained Indoor Tracking by Fusing Inertial Sensor and Physical Layer Information in WLANs

Indoor positioning has become an emerging research area because of huge commercial demands for location-based services in indoor environments. Channel State Information (CSI) as a fine-grained physical layer information has been recently proposed to achieve high positioning accuracy by using range-based methods, e.g., trilateration. In this work, we propose to fuse the CSI-based ranges and velocity estimated from inertial sensors by an enhanced particle filter to achieve highly accurate tracking. The algorithm relies on some enhanced ranging methods and further mitigates the remaining ranging errors by a weighting technique. Additionally, we provide an efficient method to estimate the velocity based on inertial sensors. The algorithms are designed in a network-based system, which uses rather cheap commercial devices as anchor nodes. We evaluate our system in a complex environment along three different moving paths. Our proposed tracking method can achieve 1:3m for mean accuracy and 2:2m for 90% accuracy, which is more accurate and stable than pedestrian dead reckoning and range-based positioning.

Bilateral deep brain stimulation: the placement of the second electrode is not necessarily less accurate than that of the first one.

BACKGROUND Deep brain stimulation (DBS) is recognized as an effective treatment for movement disorders. We recently changed our technique, limiting the number of brain penetrations to three per side. OBJECTIVES The first aim was to evaluate the electrode precision on both sides of surgery since we implemented this surgical technique. The second aim was to analyse whether or not the electrode placement was improved with microrecording and macrostimulation. METHODS We retrospectively reviewed operation protocols and MRIs of 30 patients who underwent bilateral DBS. For microrecording and macrostimulation, we used three parallel channels of the 'Ben Gun' centred on the MRI-planned target. Pre- and post-operative MRIs were merged. The distance between the planned target and the centre of the implanted electrode artefact was measured. RESULTS There was no significant difference in targeting precision on both sides of surgery. There was more intra-operative adjustment of the second electrode positioning based on microrecording and macrostimulation, which allowed to significantly approach the MRI-planned target on the medial-lateral axis. CONCLUSION There was more electrode adjustment needed on the second side, possibly in relation with brain shift. We thus suggest performing a single central track with electrophysiological and clinical assessment, with multidirectional exploration on demand for suboptimal clinical responses.

Spatial proteomic and phospho-proteomic organization in three prototypical cell migration modes

BACKGROUND Tight spatio-temporal signaling of cytoskeletal and adhesion dynamics is required for localized membrane protrusion that drives directed cell migration. Different ensembles of proteins are therefore likely to get recruited and phosphorylated in membrane protrusions in response to specific cues. RESULTS HERE, WE USE AN ASSAY THAT ALLOWS TO BIOCHEMICALLY PURIFY EXTENDING PROTRUSIONS OF CELLS MIGRATING IN RESPONSE TO THREE PROTOTYPICAL RECEPTORS: integrins, recepor tyrosine kinases and G-coupled protein receptors. Using quantitative proteomics and phospho-proteomics approaches, we provide evidence for the existence of cue-specific, spatially distinct protein networks in the different cell migration modes. CONCLUSIONS The integrated analysis of the large-scale experimental data with protein information from databases allows us to understand some emergent properties of spatial regulation of signaling during cell migration. This provides the cell migration community with a large-scale view of the distribution of proteins and phospho-proteins regulating directed cell migration.

Fine-grained Indoor Tracking by Fusing Inertial Sensor and Physical Layer Information in WLANs

Indoor positioning has become an emerging research area because of huge commercial demands for location-based services in indoor environments. Channel State Information (CSI) as fine-grained physical layer information has been recently proposed to achieve high positioning accuracy by using range based methods, e.g., trilateration. In this work, we propose to fuse the CSI-based ranging and velocity estimated from inertial sensors by an enhanced particle filter to achieve highly accurate tracking. The algorithm relies on some enhanced ranging methods and further mitigates the remaining ranging errors by a weighting technique. Additionally, we provide an efficient method to estimate the velocity based on inertial sensors. The algorithms are designed in a network-based system, which uses rather cheap commercial devices as anchor nodes. We evaluate our system in a complex environment along three different moving paths. Our proposed tracking method can achieve 1.3m for mean accuracy and 2.2m for 90% accuracy, which is more accurate and stable than pedestrian dead reckoning and range-based positioning.