Simon W. Ginzinger

SimShiftDB; local conformational restraints derived from chemical shift similarity searches on a large synthetic database

We present SimShiftDB, a new program to extract conformational data from protein chemical shifts using structural alignments. The alignments are obtained in searches of a large database containing 13,000 structures and corresponding back-calculated chemical shifts. SimShiftDB makes use of chemical shift data to provide accurate results even in the case of low sequence similarity, and with even coverage of the conformational search space. We compare SimShiftDB to HHSearch, a state-of-the-art sequence-based search tool, and to TALOS, the current standard tool for the task. We show that for a significant fraction of the predicted similarities, SimShiftDB outperforms the other two methods. Particularly, the high coverage afforded by the larger database often allows predictions to be made for residues not involved in canonical secondary structure, where TALOS predictions are both less frequent and more error prone. Thus SimShiftDB can be seen as a complement to currently available methods.

Training of Carbohydrate Estimation for People with Diabetes Using Mobile Augmented Reality

Background: Imprecise carbohydrate counting as a measure to guide the treatment of diabetes may be a source of errors resulting in problems in glycemic control. Exact measurements can be tedious, leading most patients to estimate their carbohydrate intake. In the presented pilot study a smartphone application (BEAR), that guides the estimation of the amounts of carbohydrates, was used by a group of diabetic patients. Methods: Eight adult patients with diabetes mellitus type 1 were recruited for the study. At the beginning of the study patients were introduced to BEAR in sessions lasting 45 minutes per patient. Patients redraw the real food in 3D on the smartphone screen. Based on a selected food type and the 3D form created using BEAR an estimation of carbohydrate content is calculated. Patients were supplied with the application on their personal smartphone or a loaner device and were instructed to use the application in real-world context during the study period. For evaluation purpose a test measuring carbohydrate estimation quality was designed and performed at the beginning and the end of the study. Results: In 44% of the estimations performed at the end of the study the error reduced by at least 6 grams of carbohydrate. This improvement occurred albeit several problems with the usage of BEAR were reported. Conclusions: Despite user interaction problems in this group of patients the provided intervention resulted in a reduction in the absolute error of carbohydrate estimation. Intervention with smartphone applications to assist carbohydrate counting apparently results in more accurate estimations.

CheckShift improved: fast chemical shift reference correction with high accuracy

The construction of a consistent protein chemical shift database is an important step toward making more extensive use of this data in structural studies. Unfortunately, progress in this direction has been hampered by the quality of the available data, particularly with respect to chemical shift referencing, which is often either inaccurate or inconsistently annotated. Preprocessing of the data is therefore required to detect and correct referencing errors. In an earlier study we developed CheckShift, a program for performing this task automatically. Now we spent substantial effort in improving the running time of the CheckShift algorithm, which resulted in an running time decrease of 90%, thereby achieving equivalent quality to the former version of CheckShift. The reason for the running time decrease is twofold. Firstly we improved the search for the optimal re-referencing offset considerably. Secondly, as CheckShift is based on a secondary structure prediction from the amino acid sequence (formally PsiPred was used), we evaluated a wide range of available secondary structure prediction programs focusing on the special needs of the CheckShift algorithm. The results of this evaluation prove empirically that we can use faster secondary structure prediction programs than PsiPred without sacrificing CheckShift’s accuracy. Very recently Wang and Markley (2009) gave a small list of extreme outliers of the former version of the CheckShift web-server. Those were due to the empirical reduction of the search space implemented in the old version. The new version of CheckShift now gives very similar results to RefDB and LACS for all outliers mentioned in Table 1 of Wang and Markley (2009).

No haste, more taste: An EMA study of the effects of stress, negative and positive emotions on eating behavior.

OBJECTIVES Stress and emotions alter eating behavior in several ways: While experiencing negative or positive emotions typically leads to increased food intake, stress may result in either over- or undereating. Several participant characteristics, like gender, BMI and restrained, emotional, or external eating styles seem to influence these relationships. Thus far, most research relied on experimental laboratory studies, thereby reducing the complexity of real-life eating episodes. The aim of the present study was to delineate the effects of stress, negative and positive emotions on two key facets of eating behavior, namely taste- and hunger-based eating, in daily life using ecological momentary assessment (EMA). Furthermore, the already mentioned individual differences as well as time pressure during eating, an important but unstudied construct in EMA studies, were examined. METHODS Fifty-nine participants completed 10days of signal-contingent sampling and data were analyzed using multilevel modeling. RESULTS Results revealed that higher stress led to decreased taste-eating which is in line with physiological stress-models. Time pressure during eating resulted in less taste- and more hunger-eating. In line with previous research, stronger positive emotions went along with increased taste-eating. Emotional eating style moderated the relationship between negative emotions and taste-eating as well as hunger-eating. BMI moderated the relationship between negative as well as positive emotions and hunger-eating. CONCLUSIONS These findings emphasize the importance of individual differences for understanding eating behavior in daily life. Experienced time pressure may be an important aspect for future EMA eating studies.

Detection of unrealistic molecular environments in protein structures based on expected electron densities

Understanding the relationship between protein structure and biological function is a central theme in structural biology. Advances are severely hampered by errors in experimentally determined protein structures. Detection and correction of such errors is therefore of utmost importance. Electron densities in molecular structures obey certain rules which depend on the molecular environment. Here we present and discuss a new approach that relates electron densities computed from a structural model to densities expected from prior observations on identical or closely related molecular environments. Strong deviations of computed from expected densities reveal unrealistic molecular structures. Most importantly, structure analysis and error detection are independent of experimental data and hence may be applied to any structural model. The comparison to state-of-the-art methods reveals that our approach is able to identify errors that formerly remained undetected. The new technique, called RefDens, is accessible as a public web service at http://refdens.services.came.sbg.ac.at.

Can mobile augmented reality systems assist in portion estimation? A user study

Accurate assessment of nutrition information is an important part in the prevention and treatment of a multitude of diseases, but remains a challenging task. We present a novel mobile augmented reality application, which assists users in the nutrition assessment of their meals. Using the realtime camera image as a guide, the user overlays a 3D form of the food. Additionally the user selects the food type. The corresponding nutrition information is automatically computed. Thus accurate volume estimation is required for accurate nutrition information assessment. This work presents an evaluation of our mobile augmented reality approaches for portion estimation and offers a comparison to conventional portion estimation approaches. The comparison is performed on the basis of a user study (n=28). The quality of nutrition assessment is measured based on the error in energy units. In the results of the evaluation one of our mobile augmented reality approaches significantly outperforms all other methods. Additionally we present results on the efficiency and effectiveness of the approaches.

EatAR tango: portion estimation on mobile devices with a depth sensor

The accurate assessment of nutrition information is a challenging task, but crucial for people with certain diseases, such as diabetes. An important part of the assessment of nutrition information is portion estimation, i.e. volume estimation. Given the volume and the food type, the nutrition information can be computed on the basis of the food type specific nutrition density. Recently mobile devices with depth sensors have been made available for the public (Googles project tango platform). In this work, an app for mobile devices with a depth sensor is presented which assists users in portion estimation. Furthermore, we present the design of a user study for the app and preliminary results.

Smartphone Based Stress Prediction

Smartphone usage has tremendously increased and most users keep their smartphones close throughout the day. Smartphones have a broad variety of sensors, that could automatically map and track the user’s life and behaviour. In this work we investigate whether automatically collected smartphone usage and sensor data can be employed to predict the experienced stress levels of a user using a customized brief version of the Perceived Stress Scale (PSS). To that end we have conducted a user study in which smartphone data and stress (as measured by the PSS seven times a day) were recorded for two weeks. We found significant correlations between stress scores and smartphone usage as well as sensor data, pointing to innovative ways for automatic stress measurements via smartphone technology. Stress is a prevalent risk factor for multiple diseases. Thus accurate and efficient prediction of stress levels could provide means for targeted prevention and intervention.

Real space refinement of crystal structures with canonical distributions of electrons.

Summary Recurring groups of atoms in molecules are surrounded by specific canonical distributions of electrons. Deviations from these distributions reveal unrealistic molecular geometries. Here, we show how canonical electron densities can be combined with classical electron densities derived from X-ray diffraction experiments to drive the real space refinement of crystal structures. The refinement process generally yields superior molecular models with reduced excess electron densities and improved stereochemistry without compromising the agreement between molecular models and experimental data.

An interactive 3D health app with multimodal information representation for frozen shoulder

Patients with Frozen Shoulder suffer from a decreased mobility and pain. Exercise-based physiotherapy is a common treatment and patients mostly perform the exercises at home. Correct exercise performance and compliance are the main issues in home-based therapy of Frozen Shoulder patients. To support patients diagnosed with Frozen Shoulder, a multimodal 3D smartphone app was designed, developed and evaluated. Additional to ten potential users, one physician, five physiotherapists, three computer scientists, two 3D artists and one HCI specialist were involved in the co-creation process. The app was evaluated by five patients during a three-week pilot study, which showed the feasibility of our approach. Exercise correctness, usage of multimodal instructions and user satisfaction were analyzed. Exercise correctness was nearly perfect and the interactive 3D animation was used for exercise instructions. Satisfaction using the app was rated very high according to SUS score. The results confirm that the co-creation process led to an effective, highly satisfactory and actually used system.