Building Reliable Radiomic Models Using Image Perturbation

Radiomic model reliability is a central premise for its clinical translation. Presently, it is assessed using test-retest or external data, which, unfortunately, is often scarce in reality. Therefore, we aimed to develop a novel image perturbation-based method (IPBM) for the first of its kind toward building a reliable radiomic model. We first developed a radiomic prognostic model for head-and-neck cancer patients on a training (70%) and evaluated on a testing (30%) cohort using C-index. Subsequently, we applied the IPBM to CT images of both cohorts (Perturbed-Train and Perturbed-Test cohort) to generate 60 additional samples for both cohorts. Model reliability was assessed using intra-class correlation coefficient (ICC) to quantify consistency of the C-index among the 60 samples in the Perturbed-Train and Perturbed-Test cohorts. Besides, we re-trained the radiomic model using reliable RFs exclusively (ICC>0.75) to validate the IPBM. Results showed moderate model reliability in Perturbed-Train (ICC:0.565, 95%CI:0.518-0.615) and Perturbed-Test (ICC:0.596, 95%CI:0.527-0.670) cohorts. An enhanced reliability of the re-trained model was observed in Perturbed-Train (ICC:0.782, 95%CI:0.759-0.815) and Perturbed-Test (ICC:0.825, 95%CI:0.782-0.867) cohorts, indicating validity of the IPBM. To conclude, we demonstated capability of the IPBM toward building reliable radiomic models, providing community with a novel model reliability assessment strategy prior to prospective evaluation.

Research on Similar Animal Classification Based on CNN Algorithm

Animal image classification with CNN (convolutional neural network) is commonly investigated in aera of image recogniation and classification, but major studies focus on species pictures classification with obvious distinctions. For example, CNN is usually employed to distinghish images between dogs and cats. This article puts the effort on similar animal images classification by applying simple 2D CNN via python. It focus on the binary classification for snub-nosed monkeys and normal monkeys. This distinguishment is hard to be done manually in a short time. For constructing complete convolutional neural network, some preparations are done in advance, such as the database construction and preprocess. The database is constructed by python crawler (downloading from google images), with 800 and 200 images for each class respectively as train data and test data. The pre-work includes image resizing, decoding and standardization. After that, the model is trained and then tested for verifying the model reliability. The training accuracy is 96.67% without any abnormality. On the basis of successful training, the test accuracy almost coincides with train accuracy in each 50 generations and plots in a graph. It indicates similar trends and results for them in the whole process. Because of this, CNN model in the study can help people identify rare animals in time and then people can effectively protect them. Therefore, CNN will be helpful in field of animal conservation, especially for rare species.

Dynamic Modeling of a Front-Loading Type Washing Machine and Model Reliability Investigation

A linear dynamic model of a front-loading type washing machine was developed in this study. The machine was conceptualized with three moving rigid bodies, revolute joints, springs, and dampers along with prescribed rotational drum motion. Kane’s method was employed for deriving the equations of motion of the idealized washing machine. Since the modal and transient characteristics can be conveniently investigated with a linear dynamic model, the linear model can be effectively used for the design of an FL type washing machine. Despite the convenience, however, the reliability of the linear dynamic model is often restricted to a certain range of system parameters. Parameters relevant to the reliability of the linear dynamic model were identified and the parameters’ ranges that could guarantee the reliability of the proposed linear dynamic model were numerically investigated in this study.

Development and Validation of a Short Questionnaire on Dietary and Physical Activity Habits for Patients Submitted to Bariatric Endoscopic Therapies

Purpose Individuals with obesity frequently regain weigh after endoscopic bariatric therapies (EBT) unless they adhere to healthy habits. The objective was to create and validate a short, self-administered questionnaire (EMOVE) to assess healthy dietary and physical activity (PA) habits’ adherence to be used in clinical practice. Materials and Methods In this prospective, unicentric study, 463 patients completed the short, Spanish EMOVE questionnaire, to be validated following the Medical Outcome Trust Criteria. Conceptual and measurement model, reliability (internal consistency and test–retest [subgroup of 93 patients]), construct validity, responsiveness, interpretability, and burden were evaluated. Patients enrolled from January 2017 through August 2018 and auto-filled the EMOVE at baseline and at 3, 6, and 12 months. Results Patients submitted to intragastric ballon for 6 and 12 months or POSE were 82.7% women with a mean age of 42.7 years, and a mean BMI of 37.1 kg/m2. Four factors were extracted with exploratory factor analysis related to intake frequency, portions and proportions, time and place of eating, and physical activity. EMOVE showed adequate internal consistency (α = 0.73), very good test–retest (r = 0.91, CI: 0.86–0.94; p < 0.001), moderate construct validity of dietary (r = 0.24, CI: 0.11–0.37, p < 0.001), and PA habits (r = 0.44, CI 0.30–0.58; p < 0.001). Stable responsiveness, with correlations from 0.29 to 0.39 (p < 0.001) between the EMOVE scores and the % of total weight loss at 3, 6, and 12 months. Participants categorized as having good or excellent habits (score ≥ 30 points) lost significantly more weight (p < 0.05). Finally, the administration burden was 2.96 min. Conclusion The EMOVE is a useful tool in Spanish language to easily assess the level of adherence to healthy dietary and PA habits to be used routinely in clinical practice. Graphical abstract

Modeling and Structure Determination of Homo-Oligomeric Proteins: An Overview of Challenges and Current Approaches

Protein homo-oligomerization is a very common phenomenon, and approximately half of proteins form homo-oligomeric assemblies composed of identical subunits. The vast majority of such assemblies possess internal symmetry which can be either exploited to help or poses challenges during structure determination. Moreover, aspects of symmetry are critical in the modeling of protein homo-oligomers either by docking or by homology-based approaches. Here, we first provide a brief overview of the nature of protein homo-oligomerization. Next, we describe how the symmetry of homo-oligomers is addressed by crystallographic and non-crystallographic symmetry operations, and how biologically relevant intermolecular interactions can be deciphered from the ordered array of molecules within protein crystals. Additionally, we describe the most important aspects of protein homo-oligomerization in structure determination by NMR. Finally, we give an overview of approaches aimed at modeling homo-oligomers using computational methods that specifically address their internal symmetry and allow the incorporation of other experimental data as spatial restraints to achieve higher model reliability.

Improvement Plan for Problems of Franchise Industry in South Korea

In this paper, the various problems of the franchise industry in South Korea are discussed and analyzed, and solutions to these based on the research results are presented and discussed. In this study, hypotheses were formulated for creating a model. Conflict, management, fairness, and performance were designated as variables, and how these variables affect one another was investigated. A research was conducted to verify the research model, reliability analysis was conducted through the Cronbach’s coefficient, and the study hypotheses were verified using factor and regression analyses. The conflicts between and the achievements of franchisors and franchisees were examined, along with the relationships among conflict, management, fairness, and performance. Management was subdivided into the overall management by the franchisor and the franchisee’s self-management and autonomy while fairness was subdivided into distribution, procedure, and interaction fairness between the franchisor and franchisee.

Modelling Long-Term Urban Temperatures with Less Training Data: A Comparative Study Using Neural Networks in the City of Madrid

In the last decades, urban climate researchers have highlighted the need for a reliable provision of meteorological data in the local urban context. Several efforts have been made in this direction using Artificial Neural Networks (ANN), demonstrating that they are an accurate alternative to numerical approaches when modelling large time series. However, existing approaches are varied, and it is unclear how much data are needed to train them. This study explores whether the need for training data can be reduced without overly compromising model accuracy, and if model reliability can be increased by selecting the UHI intensity as the main model output instead of air temperature. These two approaches were compared using a common ANN configuration and under different data availability scenarios. Results show that reducing the training dataset from 12 to 9 or even 6 months would still produce reliable results, particularly if the UHI intensity is used. The latter proved to be more effective than the temperature approach under most training scenarios, with an average RMSE improvement of 16.4% when using only 3 months of data. These findings have important implications for urban climate research as they can potentially reduce the duration and cost of field measurement campaigns.

Analyzing the Resilience of Convolutional Neural Networks Implemented on GPUs

There have been an extensive use of Convolutional Neural Networks (CNNs) in healthcare applications. Presently, GPUs are the most prominent and dominated DNN accelerators to increase the execution speed of CNN algorithms to improve their performance as well as the Latency. However, GPUs are prone to soft errors. These errors can impact the behaviors of the GPU dramatically. Thus, the generated fault may corrupt data values or logic operations and cause errors, such as Silent Data Corruption. unfortunately, soft errors propagate from the physical level (microarchitecture) to the application level (CNN model). This paper analyzes the reliability of the AlexNet model based on two metrics: (1) critical kernel vulnerability (CKV) used to identify the malfunction and light- malfunction errors in each kernel, and (2) critical layer vulnerability (CLV) used to track the malfunction and light-malfunction errors through layers. To achieve this, we injected the AlexNet which was popularly used in healthcare applications on NVIDIA’s GPU, using the SASSIFI fault injector as the major evaluator tool. The experiments demonstrate through the average error percentage that caused malfunction of the models has been reduced from 3.7% to 0.383% by hardening only the vulnerable part with the overhead only 0.2923%. This is a high improvement in the model reliability for healthcare applications.

Identifying Controlling Factors of Bioaccumulation of Selected Metal(loid)s in Various Soil–Cereal Crop Systems within Cultivated Fluvisols

The paired Fluvisol and cereal samples in both the field screening and controlled experiments are reported to elucidate the soil–crop relationship for As, Cd, and Pb in relation to changing contamination levels. Significant varietal differences in plant uptake were observed for crop type (barley, triticale) and the harvested part of the crop (oat shoots and grain). When parametrizing the stepwise regression models, the inclusion of soil properties often improved the performance of soil–crop models but diverse critical soil parameters were retained in the model for individual metal(loid)s. The pH value was often a statistically significant variable for Cd uptake. For As and Pb, the more successful model fit was achieved using the indicators of quantity or quality of soil organic matter, but always with lower inherent model reliability compared to Cd. Further, a single correlation analysis was used to investigate the relationship between extractable metal concentrations in soil solution and their crop accumulation. For Cd, there were strong intercorrelations among single extractions, the NH4NO3 extraction stood out with perfect correlation with plant uptake in both experiments. For As and Pb, the CaCl2 and Na2EDTA solutions outperformed other single extractions and were the better choice for the assessment of depositional fluvial substrates.