Dataset Information

Massive integration of diverse protein quality assessment methods to improve template based modeling in CASP11.

ABSTRACT: Model evaluation and selection is an important step and a big challenge in template-based protein structure prediction. Individual model quality assessment methods designed for recognizing some specific properties of protein structures often fail to consistently select good models from a model pool because of their limitations. Therefore, combining multiple complimentary quality assessment methods is useful for improving model ranking and consequently tertiary structure prediction. Here, we report the performance and analysis of our human tertiary structure predictor (MULTICOM) based on the massive integration of 14 diverse complementary quality assessment methods that was successfully benchmarked in the 11th Critical Assessment of Techniques of Protein Structure prediction (CASP11). The predictions of MULTICOM for 39 template-based domains were rigorously assessed by six scoring metrics covering global topology of C? trace, local all-atom fitness, side chain quality, and physical reasonableness of the model. The results show that the massive integration of complementary, diverse single-model and multi-model quality assessment methods can effectively leverage the strength of single-model methods in distinguishing quality variation among similar good models and the advantage of multi-model quality assessment methods of identifying reasonable average-quality models. The overall excellent performance of the MULTICOM predictor demonstrates that integrating a large number of model quality assessment methods in conjunction with model clustering is a useful approach to improve the accuracy, diversity, and consequently robustness of template-based protein structure prediction. Proteins 2016; 84(Suppl 1):247-259. © 2015 Wiley Periodicals, Inc.

SUBMITTER: Cao R

PROVIDER: S-EPMC4792798 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

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Massive integration of diverse protein quality assessment methods to improve template based modeling in CASP11.

Cao Renzhi R Bhattacharya Debswapna D Adhikari Badri B Li Jilong J Cheng Jianlin J

Proteins 20150929

Model evaluation and selection is an important step and a big challenge in template-based protein structure prediction. Individual model quality assessment methods designed for recognizing some specific properties of protein structures often fail to consistently select good models from a model pool because of their limitations. Therefore, combining multiple complimentary quality assessment methods is useful for improving model ranking and consequently tertiary structure prediction. Here, we repo ...[more]

PMID: 26369671

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Project description:BackgroundSafe, high-quality surgical care in many African countries is a critical need. Challenges include availability of surgical providers, improving quality of care, and building workforce capacity. Despite growing evidence that mentoring is effective in African healthcare settings, less is known about its role in surgery. We examined a multimodal approach to mentorship as part of a safe surgery intervention (Safe Surgery 2020) to improve surgical quality. Our goal was to distill lessons for policy makers, intervention designers, and practitioners on key elements of a successful surgical mentorship program.MethodsWe used a convergent, mixed-methods design to examine the experiences of mentees, mentors, and facility leaders with mentorship at 10 health facilities in Tanzania's Lake Zone. A multidisciplinary team of mentors worked with surgical providers over 17 months using in-person mentorship, telementoring, and WhatsApp. We conducted surveys, in-depth interviews, and focus groups to capture data in four categories: (1) satisfaction with mentorship; (2) perceived impact; (3) elements of a successful mentoring program; and (4) challenges to implementing mentorship. We analyzed quantitative data using frequency analysis and qualitative data using the constant comparison method. Recurrent and unifying concepts were identified through merging the qualitative and quantitative data.ResultsOverall, 96% of mentees experienced the intervention as positive, 88% were satisfied, and 100% supported continuing the intervention in the future. Mentees, mentors, and facility leaders perceived improvements in surgical practice, the surgical ecosystem, and in reducing postsurgical infections. Several themes related to the intervention's success emerged: (1) the intervention's design, including its multimodality, side-by-side mentorship, and standardization of practices; (2) the mentee-mentor relationship, including a friendly, safe, non-hierarchical, team relationship, as well as mentors' understanding of the local context; and (3) mentorship characteristics, including non-judgmental feedback, experience, and accessibility. Challenges included resistance to change, shortage of providers, mentorship dose, and logistics.ConclusionsOur study suggests a multimodal mentorship approach is promising in building the capacity of surgical providers. By distilling the experiences of the mentees, mentors, and facility leaders, our lessons provide a foundation for future efforts to establish effective surgical mentorship programs that build provider capacity and ultimately improve surgical quality.

Project description:One dimensional Doppler Ultrasound (DUS) is a low cost method for fetal auscultation. However, accuracy of any metrics derived from the DUS signals depends on their quality, which relies heavily on operator skills. In low resource settings, where skill levels are sparse, it is important for the device to provide real time signal quality feedback to allow the re-recording of data. Retrospectively, signal quality assessment can help remove low quality recordings when processing large amounts of data. To this end, we proposed a novel template-based method, to assess DUS signal quality. Data used in this study were collected from 17 pregnant women using a low-cost transducer connected to a smart phone. Recordings were split into 1990 segments of 3.75 s duration, and hand labeled for quality by three independent annotators. The proposed template-based method uses Empirical Mode Decomposition (EMD) to allow detection of the fetal heart beats and segmentation into short, time-aligned temporal windows. Templates were derived for each 15 s window of the recordings. The DUS signal quality index (SQI) was calculated by correlating the segments in each window with the corresponding running template using four different pre-processing steps: (i) no additional preprocessing, (ii) linear resampling of each beat, (iii) dynamic time warping (DTW) of each beat and (iv) weighted DTW of each beat. The template-based SQIs were combined with additional features based on sample entropy and power spectral density. To assess the performance of the method, the dataset was split into training and test subsets. The training set was used to obtain the best combination of features for predicting the DUS quality using cross validation, and the test set was used to estimate the classification accuracy using bootstrap resampling. A median out of sample classification accuracy on the test set of 85.8% was found using three features; template-based SQI, sample entropy and the relative power in the 160 to 660 Hz range. The results suggest that the new automated method can reliably assess the DUS quality, thereby helping users to consistently record DUS signals with acceptable quality for fetal monitoring.

Dataset Information

Massive integration of diverse protein quality assessment methods to improve template based modeling in CASP11.

Publications

Massive integration of diverse protein quality assessment methods to improve template based modeling in CASP11.

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