Neural Network Inversion-Based Model for Predicting an Optimal Hardware Configuration

Colonoscopy with biopsy is the "gold" standard for evaluating disease activity in inflammatory bowel diseases (IBD). Current research is geared toward finding non-invasive, cost-efficient methods that estimate disease activity. We aimed to develop a neural network (NN) model for the non-invasive prediction of histologic activity in IBD using routinely available clinical-biological parameters. Standard clinical-biological parameters and histologic activity from 371 ulcerative colitis (UC) and 115 Crohn's disease (CD) patient records were collected. A training set, a test set, and a validation set were used for building/validating 2 models for each disease. All models had binary output predicting the active/inactive histologic disease status. For both diseases, the first model used both clinical and biological inputs, while the second used only biological data. First UC model obtained an accuracy of 95.59% on the test set and 96.67% on the validation set. The second UC model achieved accuracies of 88.24% and 86.67% on the test and validation sets, respectively. The First CD classifier resulted in 90.48% accuracy on the test set and 91.67% on the validation set. Finally, the second CD classifier obtained an accuracy of 85.71% on the test set and 91.67% on the validation set. An accurate and non-invasive artificial intelligence system to predict histologic disease activity in IBD is designed. Our models achieved similar or better results compared to the documented performance of fecal calprotectin (the best non-invasive IBD biomarker to date). Given these favorable results, we anticipate the future utility in the clinical setting of a non-invasive disease activity prediction.

BackgroundNailfold capillary microscopy is used for diagnostic purposes in rheumatology since many years. Abnormal nailfold capillaries are also listed in the 2013 ACR/EULAR classification criteria for systemic sclerosis (SSc) [1]....

10011 Background: We previously presented (ASCO 04 #9506) an exploratory analysis of 13 molecular markers in relation to clinical outcomes in 846 patients treated with fluorouracil (FU), or FU+irinotecan, or FU+oxaliplatin in a large UK-based randomized controlled trial (FOCUS - ASCO ’05 #3518). High TS IHC expression and tumor grade were highly significantly associated with reduced failure-free survival (FFS) in a multivariable model. We now present data from an independent validation set of 449 FOCUS patients and on the combined test and validation sets. Methods: Formalin-fixed, paraffin embedded blocks were retrieved from consenting patients and tissue microarrays made for IHC assessment. Factors assessed for the effect on FFS of first-line therapy were: liver metastases; ALP; grade; mucoid status; age; and IHC for dUTPase and TS. Log-rank univariate analyses and Cox Model multivariable analysis was performed. Results: Univariate analysis of the validation set failed to confirm evidence of the previously observed association between high TS expression and FFS (p=0.2); however high dUTPase IHC (p=0.01), ALP (p<0.0001) and grade (p=0.001) showed significant associations. Table 1 illustrates the results of univariate and multivariable analysis of the combined test and validation sets. In the Cox Model, high TS IHC expression, tumor grade and baseline serum ALP were highly significant predictors of reduced FFS. Conclusions: We failed to confirm a significant association between high TS expression and reduced FFS in an independent validation set. However, in an exploratory analysis of the combined validation and test sets (n>1000), TS IHC expression remained a highly significant predictor of FFS. Overall, our results cannot be taken to demonstrate a powerful and clinically useful influence of TS on treatment efficacy, but are consistent with a moderate effect. [Table: see text] No significant financial relationships to disclose.

Given the compressive strength of concrete, yield strength of steel, span, dead and live loads, singly reinforced concrete simple beams are first optimally designed using genetic algorithms with constraints satisfying the specifications of the ACI code. The objective function is to minimize the total cost of tension steels, stirrups and concrete. A variety of beams are designed for the use of the neural network. To train and test the effectiveness of the neural network, these optimal results are randomly divided into three sets: the training set, validation set and test set. This paper uses a two-layer feed forward neural network: one hidden layer and one output layer. The transfer functions for the hidden layer and output layer are tan-sigmoid and linear functions, respectively. The inputs of the neural network are the compressive strength of concrete, yield strength of steel and span, width and effective depth of the beam, as well as vertical loads the beam is subjected to; the targets of the neural network are the steel ratio and cost of the beam. To evaluate the accuracy, the regression analysis of the target and network output is carried out. Numerical results show good performance of the neural network, which can be used as a model to predict the lowest cost and steel ratio of singly reinforced concrete simple beams. Key words: Reinforced concrete beams, genetic algorithm, neural network, regression analysis.

Objective: This study intends to investigate the causal association between 35 blood and urine biomarkers and postmenopausal osteoporosis (PMOP) through two-way Mendelian randomization analysis. Methods: This study adopted a two-way Mendelian randomization analysis, with data sourced from the UK Biobank and the Finnish Biobank Study. Among them, the R12 dataset of the Finnish Biobank Study was used as the test set, and the R11 dataset as the validation set. The study regarded 35 biomarkers as exposure factors and PMOP (a condition characterized by decreased bone density after menopause) as the outcome variable. It was analyzed through methods such as the inverse variance weighting method, the weighted median method, and MR-Egger regression, and combined with the MR-PRESSO test to exclude the influence of pleiotropy. Results: In the positive direction analysis, alkaline phosphatase, glomerular filtration rate, sex hormone-binding globulin, and total protein showed statistical significance in both the test set and the validation set, and they were all risk factors for PMOP. Direct bilirubin and uric acid demonstrated statistical significance in both the test and validation sets, and they served as protective factors against PMOP. In the negative direction analysis, alkaline phosphatase showed statistical significance in both the test set and the validation set, being a positive result for PMOP; sex hormone-binding globulin and total bilirubin showed statistical significance in both the test set and the validation set, being negative results for PMOP. Conclusion: Employing bidirectional Mendelian randomization methodology, this investigation elucidated the causal relationships between multiple hematological and urinary biomarkers and PMOP. The results provide promising biomarker candidates for future diagnostic and therapeutic strategies targeting PMOP, while simultaneously establishing a robust framework for subsequent exploration of its underlying pathophysiological mechanisms.

Bioreactor temperature profile controller using inverse neural network (INN) for production of ethanol

The goal of this study was to create a non-invasive cancer detection assay that analyzes flow cytometry data in an objective method. By using an artificial neural network (NN), we were able to distinguish between cancer patients (CPs) and healthy donors (HDs) based upon the flow cytometry profiles of MDSCs and other leukocytes. Myeloid-derived suppressor cells (MDSCs) are known to be key contributors in supporting tumor progression and tumor escape through their ability to suppress anti-tumor responses. Several studies have demonstrated their utility as indicators for tumor development, but there is significant overlap with healthy individuals. We used standard multiparametric flow cytometry techniques to immunophenotype MDSCs and other leukocytes found in the peripheral blood of 65 biopsy-confirmed CPs with solid tumors and 84 HDs. A series of NNs utilizing pattern recognition computational algorithms were created using three data sets: 1) the training set – this ‘teaches’ the two output categories of cancer and not cancer, 2) the validation set – this uses backpropagation to improve the accuracy of the trained networks, and 3) the testing set – this is used to rank the trained networks against each other. Finally, a naïve testing set is then used to determine the overall sensitivity and specificity for the top-ranking networks. Here, we incorporated a standard 12 marker flow cytometry assay with NN technology to achieve a sensitivity of 92% and a specificity of 89%. Pairing the advanced analytical capabilities of our NN with surface biomarker based analysis of MDSCs and certain leukocytes measured in peripheral blood has enabled us the ability to objectively identify patterns indicative for the existence of a solid tumor.

To develop a method for identifying high-risk patients among septic populations requiring mechanical ventilation, and to conduct phenotypic analysis based on this method. Data from four sources were utilized: the Medical Information Mart for Intensive Care (MIMIC-IV 2.0, MIMIC-III 1.4), the Philips eICU-Collaborative Research Database 2.0 (eICU-CRD 2.0), and the Anhui Medical University Second Affiliated Hospital dataset. The adult patients in intensive care unit (ICU) who met Sepsis-3 and received invasive mechanical ventilation (IMV) on the first day of first admission were enrolled. The MIMIC-IV dataset with the highest data integrity was divided into a training set and a test set at a 6:1 ratio, while the remaining datasets were served as validation sets. The demographic information, comorbidities, laboratory indicators, commonly used ICU scores, and treatment measures of patients were extracted. Clinical data collected within first day of ICU admission were used to calculate the sequential organ failure assessment (SOFA) score. K-means clustering was applied to cluster SOFA score components, and the sum of squared errors (SSE) and Davies-Bouldin index (DBI) were used to determine the optimal number of disease subtypes. For clustering results, normalized methods were employed to compare baseline characteristics by visualization, and Kaplan-Meier curves were used to analyze clinical outcomes across phenotypes. This study enrolled patients from MIMIC-IV dataset (n = 11 166), MIMIC-III dataset (n = 4 821), eICU-CRD dataset (n = 6 624), and a local dataset (n = 110), with the four datasets showing similar median ages and male proportions exceeding 50%; using 85% of the MIMIC-IV dataset as the training set, 15% as the test set, and the rest dataset as the validation set. K-means clustering based on the six-item SOFA score was performed to determine the optimal number of clusters as 3, and patients were finally classified into three phenotypes. In the training set, compared with the patients with phenotype II and phenotype III, those with phenotype I had the more severe circulatory and respiratory dysfunction, a higher proportion of vasoactive drug usage, more obvious metabolic acidosis and hypoxia, and a higher incidence of congestive heart failure. The patients with phenotype II was dominated by respiratory dysfunction with higher visceral injury. The patients with phenotype III had relatively stable organ function. The above characteristics were consistent in both the test and validation sets. Analysis of infection-related indicators showed that the patients with phenotype I had the highest SOFA score within 7 days after ICU admission, initial decreases and later increases in platelet count (PLT), and higher counts of neutrophils, lymphocytes, and monocytes as compared with those with phenotype II and phenotype III, their blood cultures had a higher positivity rates for Gram-positive bacteria, Gram-negative bacteria and fungi as compared with those with phenotype II and phenotype III. The Kaplan-Meier curve indicated that in the training, test, and validation sets, the 28-day cumulative mortality of patients with phenotype I was significantly higher than that of patients with phenotypes II and phenotype III. Three distinct phenotypes in septic patients receiving IMV based on unsupervised machine learning is derived, among which phenotype I, characterized by cardiorespiratory failure, can be used for the early identification of high-risk patients in this population. Moreover, this population is more prone to bloodstream infections, posing a high risk and having a poor prognosis.

Protein secondary structure prediction remains a vital topic with broad applications. Due to lack of a widely accepted standard in secondary structure predictor evaluation, a fair comparison of predictors is challenging. A detailed examination of factors that contribute to higher accuracy is also lacking. In this paper, we present: (1) new test sets, Test2018, Test2019, and Test2018-2019, consisting of proteins from structures released in 2018 and 2019 with less than 25% identity to any protein published before 2018; (2) a 4-layer convolutional neural network, SecNet, with an input window of ±14 amino acids which was trained on proteins ≤25% identical to proteins in Test2018 and the commonly used CB513 test set; (3) an additional test set that shares no homologous domains with the training set proteins, according to the Evolutionary Classification of Proteins (ECOD) database; (4) a detailed ablation study where we reverse one algorithmic choice at a time in SecNet and evaluate the effect on the prediction accuracy; (5) new 4- and 5-label prediction alphabets that may be more practical for tertiary structure prediction methods. The 3-label accuracy (helix, sheet, coil) of the leading predictors on both Test2018 and CB513 is 81–82%, while SecNet’s accuracy is 84% for both sets. Accuracy on the non-homologous ECOD set is only 0.6 points (83.9%) lower than the results on the Test2018-2019 set (84.5%). The ablation study of features, neural network architecture, and training hyper-parameters suggests the best accuracy results are achieved with good choices for each of them while the neural network architecture is not as critical as long as it is not too simple. Protocols for generating and using unbiased test, validation, and training sets are provided. Our data sets, including input features and assigned labels, and SecNet software including third-party dependencies and databases, are downloadable from dunbrack.fccc.edu/ss and github.com/sh-maxim/ss.

Food, energy, and water are basic needs that are quite important in livelihood sustainability. In most situations, their interactions make trade‐offs between these key areas of need challenging to attain. For the Food‐Energy‐Water Nexus (FEWN), there exists a need for appropriate decision‐making tools that may help to surmount such challenges. Multiobjective Optimization Approaches (MOAs) have emerged as potential solutions within this domain. The major components explored in this review include the objective functions, decision variables, and optimization techniques within MOAs. Despite their potential, few studies comprehensively address the key concepts of these components, the role of stakeholder involvement, the limitations of existing research, and the emerging technological opportunities that could enhance MOAs in the FEWN. The Preferred Reporting Items for Systematic Reviews and Meta‐Analyses protocol, was combined with Meta‐ethnography to select, review, and analyze the literature. The data synthesis was done by meta‐ethnography, finding key terms to categorize objective functions, decision variables, and optimization techniques and developing key concepts related to MOAs in the FEWN. Key terms for categorizing objective functions are maximizing economic, environmental, and social benefits and food, energy, and water security. For the decision variables, the key terms include availability, accessibility, and sufficiency, while the optimization techniques are Mathematical Programming and Metaheuristics. Besides, emerging technologies create substantial opportunities to improve the performance of MOAs. Therefore, there is a need to develop MOAs that integrate economic, environmental, and social aspects to support the sustainability of the FEWN.

While the use of deep learning in drug discovery is gaining increasing attention, the lack of methods to compute reliable errors in prediction for Neural Networks prevents their application to guide decision making in domains where identifying unreliable predictions is essential, e.g., precision medicine. Here, we present a framework to compute reliable errors in prediction for Neural Networks using Test-Time Dropout and Conformal Prediction. Specifically, the algorithm consists of training a single Neural Network using dropout, and then applying it N times to both the validation and test sets, also employing dropout in this step. Therefore, for each instance in the validation and test sets an ensemble of predictions are generated. The residuals and absolute errors in prediction for the validation set are then used to compute prediction errors for the test set instances using Conformal Prediction. We show using 24 bioactivity data sets from ChEMBL 23 that Dropout Conformal Predictors are valid (i.e., the fraction of instances whose true value lies within the predicted interval strongly correlates with the confidence level) and efficient, as the predicted confidence intervals span a narrower set of values than those computed with Conformal Predictors generated using Random Forest (RF) models. Lastly, we show in retrospective virtual screening experiments that dropout and RF-based Conformal Predictors lead to comparable retrieval rates of active compounds. Overall, we propose a computationally efficient framework (as only N extra forward passes are required in addition to training a single network) to harness Test-Time Dropout and the Conformal Prediction framework, which is generally applicable to generate reliable prediction errors for Deep Neural Networks in drug discovery and beyond.

An optimization procedure is developed for the design of structural components under crashworthiness requirements. The optimization procedure consists of first developing a system of parallel neural networks able to reproduce the structural behavior during the crash phenomena. Training and test sets are generated by finite element analyses. Then, to find the optimal configuration, both sequential quadratic programming and genetic algorithms are implemented to evaluate the objective and constraint functions using the response surfaces generated by the neural networks. The procedure is applied for the optimization of helicopter subfloor components. In this application, the objective function is taken equal to the sum of two terms: the crush force efficiency and the specific absorbed energy. The values of maximum and mean forces are constrained to meet crashworthiness requirements, whereas other constraints are applied to define the feasibility of the structural solution. The optimal configuration, obtained measuring orders of magnitude savings in the CPU time, allows an increase of the crush force efficiency equal to 18% and a decrease of the mass equal to 8%.

Background and AimsThe prognosis of papillary thyroid carcinoma (PTC) is highly variable, even for high-risk cases. The predictive and prognostic role of the lymphocyte-to-monocyte ratio (LMR) has been reported in other cancers. The aim of our present study was to explore the value of LMR prognostic prediction in high-risk PTC patients.Patients and MethodsTwo hundred and twenty-four PTC high-risk cases at West China Hospital were randomized into a training set (112 cases) and testing set (112 cases), while 48 cases in Shang Jin Nan Fu Hospital were included as the external validation set.ResultsA lower preoperative LMR correlated with larger tumor size, advanced N and M stages, and an increased number of multiple PTC cases in the training, testing, and validation sets (all P<0.05 in the three sets). Patients with a high LMR exhibited significantly improved overall and PTC-free survival compared with those of patients with a low LMR in the training, testing, validation, and combined sets (all P<0.05 in the individual and combined sets). Moreover, multivariate analyses identified the LMR as an independent prognostic factor for overall and PTC-free survival. The nomograms for predicting the 5-year mortality and PTC recurrence were developed based on the risk factors in the training set and validated in the independent testing and validation sets.ConclusionThe preoperative LMR was identified as an independent prognostic factor that could be incorporated into the two nomograms with other risk factors to predict overall survival and PTC-free survival for individual patients.

As mentioned previously in the primary sections of chapter one, there are several, and varied applications of neural networks in geophysics; here we give a short list of some of these applications: Modeling of crustal velocity using Artificial Neural Networks (case study: Iran Geodynamic GPS Network (Ghaffari and Mohammadzadeh 2015) Crustal velocity field modeling with neural network and polynomials, SIDERIS, M.G. (Ed.), Moghtased-Azar and Zaletnyik (2009) Artificial neural network pruning approach for modular neural networks (Yilmaz 2013) Modular neural networks for seismic tomography (Barraez et al. 2002) Estimating one-dimensional models from frequency domain measurements Quantifying sand fraction from seismic attributes using modular artificial neural networks Borehole electrical resistivity modeling using neural networks Determination of facies from well logs using modular neural networks (Bhatt and Helle 2002) Inversion of self-potential anomalies caused by 2D-inclined sheets (Hesham 2009). 2D inverse modeling of residual gravity anomalies from Simple geometric shapes using Modular Feed-forward Neural Network (Eshaghzadeh and Hajian 2018)

The Sixth International Symposium on Neural Networks (ISNN 2009)