Reference Dataset Research Articles

A besyian regularisation neural network approach for hepatitis B virus spread prediction and immune system therapy model

The primary aim of the article is to analyze the response of the human immune system when it encounters the hepatitis B virus. This is done using a mathematical system of differential equations. The differential equation system has six components, likely representing various aspects of the immune response or virus dynamics. A Bayesian regularization neural network has been presented in the process of training. These networks are employed to find solutions for different categories or scenarios related to hepatitis B infection. The Adams method is used to generate reference data sets. The back-propagated artificial neural network, based on Bayesian regularization, is trained and validated using the generated data. The data is divided into three sets: 90% for training and 5% each for testing and validation. The correctness and effectiveness of the proposed neural network model have been assessed using various evaluation metrics. The metrics have been used in this study are Mean Square Error (MSE), histogram errors, and regression plots. These measures provide support to the neural network to approximate the immune response to the hepatitis B virus.

Assimilation of Sentinel‐1 Backscatter to Update AquaCrop Estimates of Soil Moisture and Crop Biomass

AbstractThis study assesses the potential of regional microwave backscatter data assimilation (DA) in AquaCrop for the first time, using NASA's Land Information System. The objective is to assess whether the assimilation setup can improve surface soil moisture (SSM) and crop biomass estimates. SSM and crop biomass simulations from AquaCrop were updated using Sentinel‐1 synthetic aperture radar observations, over three regions in Europe in two separate DA experiments. The first experiment concerned updating SSM using VV‐polarized backscatter and the corrections were propagated via the model to the biomass. In the second experiment, the DA setup was extended by also updating the biomass with VH‐polarized backscatter. SSM was evaluated with local in situ data and with downscaled Soil Moisture Active Passive (SMAP) retrievals for all cropland grid cells, whereas crop biomass was compared to SMAP vegetation optical depth and the Copernicus dry matter productivity. The assimilation showed mixed results for root mean square error and Pearson's correlation, with slight overall improvements in the (anomaly) correlations of updated SSM relative to independent in situ and satellite data. By contrast, the biomass estimates obtained with backscatter DA did not agree better with reference data sets. Overall, the SSM evaluation showed that there is potential in using Sentinel‐1 backscatter for assimilation in AquaCrop, but the present setup was not able to improve crop biomass estimates. Our study reveals how the complex interaction between SSM, crop biomass and backscatter affect the impact and performance of DA, offering insight into ways to optimize DA for crop growth estimation.

Evaluating a Natural Language Processing-Driven, AI-Assisted International Classification of Diseases, 10th Revision, Clinical Modification, Coding System for Diagnosis Related Groups in a Real Hospital Environment: Algorithm Development and Validation Study.

International Classification of Diseases codes are widely used to describe diagnosis information, but manual coding relies heavily on human interpretation, which can be expensive, time consuming, and prone to errors. With the transition from the International Classification of Diseases, Ninth Revision, to the International Classification of Diseases, Tenth Revision (ICD-10), the coding process has become more complex, highlighting the need for automated approaches to enhance coding efficiency and accuracy. Inaccurate coding can result in substantial financial losses for hospitals, and a precise assessment of outcomes generated by a natural language processing (NLP)-driven autocoding system thus assumes a critical role in safeguarding the accuracy of the Taiwan diagnosis related groups (Tw-DRGs). This study aims to evaluate the feasibility of applying an International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM), autocoding system that can automatically determine diagnoses and codes based on free-text discharge summaries to facilitate the assessment of Tw-DRGs, specifically principal diagnosis and major diagnostic categories (MDCs). By using the patient discharge summaries from Kaohsiung Medical University Chung-Ho Memorial Hospital (KMUCHH) from April 2019 to December 2020 as a reference data set we developed artificial intelligence (AI)-assisted ICD-10-CM coding systems based on deep learning models. We constructed a web-based user interface for the AI-assisted coding system and deployed the system to the workflow of the certified coding specialists (CCSs) of KMUCHH. The data used for the assessment of Tw-DRGs were manually curated by a CCS with the principal diagnosis and MDC was determined from discharge summaries collected at KMUCHH from February 2023 to April 2023. Both the reference data set and real hospital data were used to assess performance in determining ICD-10-CM coding, principal diagnosis, and MDC for Tw-DRGs. Among all methods, the GPT-2 (OpenAI)-based model achieved the highest F1-score, 0.667 (F1-score 0.851 for the top 50 codes), on the KMUCHH test set and a slightly lower F1-score, 0.621, in real hospital data. Cohen κ evaluation for the agreement of MDC between the models and the CCS revealed that the overall average κ value for GPT-2 (κ=0.714) was approximately 12.2 percentage points higher than that of the hierarchy attention network (κ=0.592). GPT-2 demonstrated superior agreement with the CCS across 6 categories of MDC, with an average κ value of approximately 0.869 (SD 0.033), underscoring the effectiveness of the developed AI-assisted coding system in supporting the work of CCSs. An NLP-driven AI-assisted coding system can assist CCSs in ICD-10-CM coding by offering coding references via a user interface, demonstrating the potential to reduce the manual workload and expedite Tw-DRG assessment. Consistency in performance affirmed the effectiveness of the system in supporting CCSs in ICD-10-CM coding and the judgment of Tw-DRGs.

Biometric access system with automatic registration and loyalty check in the system of recognition and database entry

ABSTRACT Against the backdrop of ongoing terrorist threats and uncontrolled migration flows, there is increased interest in biometric human recognition technology and barcoding technology combined with it. As a possible solution, a biometric access system is proposed, designed to implement three main urgent tasks – automatic registration of people crossing the border; recording registration information in a special biometric database; checking registration information and facial image on the database of wanted people. Effective biometric systems combine verification and identification tasks to increase the reliability of person identification and comparison with reference data sets. In the course of the study, various methods of information processing with its subsequent encoding were considered and analyzed, and the experience of previous systems of this type was investigated. As a result of the study, the effectiveness of using the latest biometric technologies in the migration sphere and in law enforcement agencies for the effective search of offenders has been substantiated. This paper will be useful as a material for further study of facial recognition technology and the integration of QR into the field of biometrics.

161 Development, deployment, and calibration of genomic selection tools to improve performance traits for Canadian beef cattle

Abstract Genomic selection has the potential to accelerate the genetic improvement rate for difficult/expensive to measure traits such as feed efficiency and fertility traits in beef cattle. To develop genomic selection tools for Canadian beef cattle, we collected or sourced phenotypic data of feed efficiency along with other traits including carcass merit, female feed intake and fertility related traits, and their genotypes from both research and commercial herds. The phenotypic data were consolidated and recorded for contemporary groups, leading to the development of data sets including 11,292 beef cattle with residual feed intake, dry matter intake, average daily gain, metabolic body weight, 7,299 to 8,081 cattle with carcass merit traits, and 1,802 to 2,792 cows with feed intake and fertility traits after quality controls. Genotype data of the animals from various single nucleotide polymorphisms (SNP) panels were merged to the same allele format of 50K, and then imputed to 770K SNPs, and eventually to whole genome sequence variants. The refined Canadian beef cattle data sets have not only allowed us to investigate genetic architectures of the beef performance traits but also enabled the development of genomic selection tools including molecular breeding values and multiple trait selection indexes with a moderate to moderately high accuracy for the traits through a cross-validation of within reference data set. The genomic selection tools have been successfully deployed to over 10,415 breeding candidates submitted by Canadian beef producers through multiple demonstration projects. The average accuracy of the molecular breeding values and multiple trait selection index of the commercial beef cattle ranged from 0.38 to 0.49 for feed efficiency and carcass merit traits, and from 0.26 to 0.38 for female feed intake and fertility traits. Current research is focusing on calibrating the genomic selection tools with larger data sets and with more advanced genomic prediction methods.

Secondary structure propensities of the Ebola delta peptide E40 in solution and model membrane environments

The Ebola delta peptide is an amphipathic, 40-residue peptide encoded by the Ebola virus, referred to as E40. The membrane-permeabilising activity of the E40 delta peptide has been demonstrated in cells and lipid vesicles suggesting the E40 delta peptide likely acts as a viroporin. The lytic activity of the peptide increases in the presence of anionic lipids and a disulphide bond in the C-terminal part of the peptide. Previous in silico work predicts the peptide to show a partially helical structure, but there is no experimental information on the structure of E40. Here, we use circular dichroism spectroscopy to report the secondary structure propensities of the reduced and oxidised forms of the E40 peptide in water, detergent micelles, and lipid vesicles composed of neutral and anionic lipids (POPC and POPG, respectively). Results indicate that the peptide is predominately a random coil in solution, and the disulphide bond has a small but measurable effect on peptide conformation. Secondary structure analysis shows large uncertainties and dependence on the reference data set and, in our system, cannot be used to accurately determine the secondary structure motifs of the peptide in membrane environments. Nevertheless, the spectra can be used to assess the relative changes in secondary structure propensities of the peptide depending on the solvent environment and disulphide bond. In POPC-POPG vesicles, the peptide transitions from a random coil towards a more structured conformation, which is even more pronounced in negatively charged SDS micelles. In vesicles, the effect depends on the peptide-lipid ratio, likely resulting from vesicle surface saturation. Further experiments with zwitterionic POPC vesicles and DPC micelles show that both curvature and negatively charged lipids can induce a change in conformation, with the two effects being cumulative. Electrostatic screening from Na+ ions reduced this effect. The oxidised form of the peptide shows a slightly lower propensity for secondary structure and retains a more random coil conformation even in the presence of PG-PC vesicles.

Extended benchmark set for lattice parameters of inorganic solids.

The development of novel methods in solid-state quantum chemistry necessitates reliable reference data sets for their assessment. The most fundamental solid-state property of interest is the crystal structure, quantified by the lattice parameters. In the last decade, several studies were conducted to assess theoretical approaches based on the agreement of calculated lattice parameters with respect to experiment as a measure. However, most of these studies used a limited number of reference systems with high symmetry. The present work offers a more comprehensive reference benchmark denoted as Sol337LC, which consists of 337 inorganic compounds with 553 symmetry-inequivalent lattice parameters, representing every element of the periodic table for atomic numbers between 1 and 86, except noble gases, the radioactive elements and lanthanoids. The reference values were taken from earlier benchmarks and from measurements at very low temperature or extrapolation to 0 K. The experimental low-temperature lattice parameters were then corrected for zero-point energy effects via the quasi-harmonic approximation for direct comparison with quantum-chemical optimized structures. A selection of standard density functional approximations was assessed for their deviations from the experimental reference data. The calculations were performed with the crystal orbital program CRYSTAL23, applying optimized atom-centered basis sets of triple-zeta plus polarization quality. The SCAN functional family and the global hybrid functional PW1PW, augmented with the D3 dispersion correction, were found to provide closest agreement with the Sol337LC reference data.

Innovative thermal management in the presence of ferromagnetic hybrid nanoparticles

In the present work, a simple intelligence-based computation of artificial neural networks with the Levenberg-Marquardt backpropagation algorithm is developed to analyze the new ferromagnetic hybrid nanofluid flow model in the presence of a magnetic dipole within the context of flow over a stretching sheet. A combination of cobalt and iron (III) oxide (Co-Fe2O3) is strategically selected as ferromagnetic hybrid nanoparticles within the base fluid, water. The initial representation of the developed ferromagnetic hybrid nanofluid flow model, which is a system of highly nonlinear partial differential equations, is transformed into a system of nonlinear ordinary differential equations using appropriate similarity transformations. The reference data set of the possible outcomes is obtained from bvp4c for varying the parameters of the ferromagnetic hybrid nanofluid flow model. The estimated solutions of the proposed model are described during the testing, training, and validation phases of the backpropagated neural network. The performance evaluation and comparative study of the algorithm are carried out by regression analysis, error histograms, function fitting graphs, and mean squared error results. The findings of our study analyze the increasing effect of the ferrohydrodynamic interaction parameter β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta$$\\end{document} to enhance the temperature and velocity profiles, while increasing the thermal relaxation parameter α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha$$\\end{document} decreases the temperature profile. The performance on MSE was shown for the temperature and velocity profiles of the developed model about 9.1703e−10, 7.1313ee−10, 3.1462e−10, and 4.8747e−10. The accuracy of the artificial neural networks with the Levenberg-Marquardt algorithm method is confirmed through various analyses and comparative results with the reference data. The purpose of this study is to enhance understanding of ferromagnetic hybrid nanofluid flow models using artificial neural networks with the Levenberg-Marquardt algorithm, offering precise analysis of key parameter effects on temperature and velocity profiles. Future studies will provide novel soft computing methods that leverage artificial neural networks to effectively solve problems in fluid mechanics and expand to engineering applications, improving their usefulness in tackling real-world problems.

53 Associations between body composition, ClearCode34, and clinical outcomes in localized clear cell renal cell carcinoma

Abstract Background Emerging evidence suggests that pre-surgical body composition is a prognostic factor for cancer outcomes. Lower skeletal muscle quantity and radiodensity have been associated with renal cell carcinoma (RCC) outcomes in other studies, while reports of the relationship between visceral adipose tissue and RCC outcomes have been inconsistent. Mechanisms underlying these associations are unclear but may relate to tumor biology. We leveraged a large molecular epidemiology study of localized clear cell renal cell carcinoma (ccRCC) patients to examine how pre-surgical body composition features were associated with survival post-nephrectomy as well as ClearCode34 status, a previously validated prognostic gene expression signature. Methods The RESOLVE study at Memorial Sloan Kettering Cancer Center is a retrospective study of 1,239 patients with stage I-III ccRCC undergoing nephrectomy without systemic therapy. Pre-surgical CT scans at the third lumbar vertebrae were segmented for the cross-sectional areas and radiodensities of skeletal muscle, subcutaneous adipose and visceral adipose tissues using Automatica software. Formalin-fixed, paraffin-embedded tumor tissue samples were available for 929 patients, which were sent to the University of North Carolina at Chapel Hill and assayed for gene expression. RNA was extracted from these samples and analyzed using the Nanostring nCounter system on a custom panel of 356 genes, including the 34 genes used in the ClearCode34 assay. Of the 929 patients with tumor tissue available, gene expression data from 837 patients (90.1%) passed RNA extraction and data QC. Samples were classified into either ccA or ccB ClearCode34 classes using a reference data set and PAM (prediction analysis of microarrays) centroid-based classification. We estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for disease-free survival (DFS) within five years of diagnosis using Cox proportional hazards models for both body composition variables and ClearCode34 subtype. We also estimated odds ratios (ORs) and 95% CIs for associations between ClearCode34 subtype and body composition with logistic regression. All multivariable models were adjusted for age, sex, and all body composition variables. Results Of the 837 patients in our analytic sample, 219 (26%) had ccB tumors. Men, patients with high stage or grade tumors, and patients with chronic kidney disease prior to nephrectomy were more likely to present with ccB tumors. Lower skeletal muscle density (SMD) was significantly associated with lower five-year DFS [HR (95% CI) per 10 HU decrease in SMD: 1.7 (1.2, 2.3)]. While no other body composition features were significantly associated, we also observed non-significantly increased HRs for VATI and VATD [HR (95% CIs): 1.3 (0.8, 2.0) and 1.5 (0.9, 2.7), respectively]. ClearCode34 subtype was significantly associated with five-year DFS, where patients with ccB tumors had poorer DFS [HR (95% CI): 1.7 (1.1, 2.5)] compared to patients with ccA tumors. Notably, we observed no significant associations between any body composition feature and ClearCode34 in univariable analyses. However, in multivariable models that accounted for all body composition variables simultaneously as well as age and sex, lower SMD emerged as significantly associated with ccB subtype [OR (95% CI) per 10 HU decrease in SMD: 1.3 (1.0, 1.7)]. Conclusions Our findings suggest that the associations observed between body composition features and clinical outcomes in ccRCC may be related to tumor biology. Lower pre-surgical SMD, which may represent myosteatosis and metabolic dysregulation in ccRCC patients, was associated with both decreased five-year DFS and an increased likelihood of presenting with a more aggressive ccB tumor. Future studies should investigate body composition features related to ccRCC prognosis and additional markers of tumor biology, such as immune-related gene expression patterns, to extend these findings.

Quantitative sensory testing in notalgia paresthetica reveals small fiber-type-specific differences in non-pruritic sensitivity: a pilot study.

Notalgia paresthetica (NP) is a chronic condition characterized by pruritus and other unpleasant dysesthetic sensations unilaterally on the subscapular back. Its specific underlying mechanisms are largely unknown, though hypothesized to be neuropathic. Determination of possible somatosensory contributors to the condition could pave the way for novel treatments. Given the potential involvement of non-pruritic mechanisms in NP, our objective was to broadly characterize the somatosensory function in NP-affected and unaffected skin using methods that have been standardized in pain-free controls and painful neuropathic disorders. We hypothesized that if NP is caused by neuropathic mechanisms not targeted directly to pruritoceptors in the skin, somatosensory abnormalities would not be itchspecific. Second, given the lack of symptoms on the contralateral side of the back, we hypothesized that this region would be normally sensitive. In this study, quantitative sensory testing (QST) was used to comprehensively assess the somatosensory function in 15 adult patients with NP. Standardized QST metrics were performed in the NP-affected region and compared with the contralateral asymptomatic skin and itch-free individuals using an age, gender, and site-matched reference data set. There were no significant differences in sensitivity between symptomatic and asymptomatic skin, except for increased mechanical-evoked itch on the itchy side. However, reference data set comparisons revealed bilateral hyposensitivity to innocuous cold and noxious pinprick and higher temporal summation of pain in patients with NP. In addition, compared with reference data, patients with NP demonstrated decreased sensitivity to cold and pinprick, presence of paradoxical heat sensations, and increased wind-up of pain. These results suggest a role for Aδ fiber pathways and central sensitization in NP-associated itch. More research is needed to determine whether sensory differences extend beyond the NP-affected dermatomal level and what might cause neuropathy specifically targeting Aδ fibers.

Discovery of DNA methylation signature in the peripheral blood of individuals with history of antenatal exposure to valproic acid

PurposeValproic acid or valproate is an effective antiepileptic drug; however, embryonic exposure to valproate can result in a teratogenic disorder referred to as fetal valproate syndrome (OMIM #609442). Currently there are no diagnostic biomarkers for the condition. This study aims to define an episignature biomarker for teratogenic antenatal exposure to valproate. MethodsDNA extracted from peripheral blood of individuals with teratogenic antenatal exposure to valproate was processed using DNA methylation microarrays. Subsequently, methylation profiling and construction of support vector machine classifiers were performed in R. ResultsGenomic DNA methylation analysis was applied, and a distinct DNA methylation profile was identified in the majority of affected individuals. This profile was used to develop a diagnostic episignature classifier. The valproate exposure episignature exhibited high sensitivity and specificity relative to a large reference data set of unaffected controls and individuals with a wide spectrum of syndromic disorders with episignatures. Gene set enrichment analysis demonstrated an enrichment for terms associated with cell adhesion, including significant overrepresentation of the cadherin superfamily. ConclusionThis study provides evidence of a robust peripheral blood-based diagnostic epigenetic biomarker for a prenatal teratogenic disorder.

PGAP3 regulates human bronchial epithelial cell mRNAs present in asthma and respiratory virus reference data sets.

PGAP3 is a glycosylphosphatidylinositol (GPI) phospholipase gene localized within chromosome 17q12-21, a region highly linked to asthma. Although much is known about the function of other chromosome 17q12-21 genes expressed at increased levels in bronchial epithelium such as ORMDL3 and GSDMB, little is known about the function of increased PGAP3 expression in bronchial epithelium in the context of asthma. The aim of this study was therefore to determine whether increased PGAP3 expression in human bronchial epithelial cells regulated expression of mRNA pathways important to the pathogenesis of asthma by utilizing RNA-sequencing and bioinformatic analysis. We performed RNA-sequencing on normal human bronchial epithelial cells transfected with PGAP3 for 24 and 48 hours. PGAP3 regulated genes were compared to asthma and respiratory virus (influenza A, rhinovirus, respiratory syncytial virus) reference data sets to identify PGAP3 target genes and pathways. Approximately 9% of the upregulated PGAP3-induced genes were found in an asthma reference data set, 41% in a rhinovirus reference data set, 33% in an influenza A reference data set, and 3% in a respiratory syncytial virus reference data set. PGAP3 significantly upregulated the expression of several genes associated with the innate immune response and viral signatures of respiratory viruses associated with asthma exacerbations. Two of the highest expressed genes induced by PGAP3 are RSAD2, OASL, and IFN-λ, which are anti-viral genes associated with asthma. PGAP3 also upregulated the antiviral gene BST2, which like PGAP3 is a GPI-anchored protein. We conclude that PGAP3 expression in human bronchial epithelial cells regulates expression of genes known to be linked to asthma, and also regulates the bronchial epithelial expression of genes pertinent to the pathogenesis of respiratory viral triggered asthma exacerbations.

Sea Surface Wind Speed Estimation From the Combination of Satellite Scatterometer and Radiometer Parameters

AbstractSatellite research on global sea surface winds is crucial for understanding and monitoring the dynamics of earth's oceans, providing valuable insights into weather patterns, climate changes, and oceanographic processes. This study investigates the fusion of active (microwave scatterometer) and passive (microwave radiometer) satellite data using Machine learning (ML) for sea surface wind speed estimation. Employing Random Forest Regression (RF), Convolutional Neural Network Regression (CNN), and Multiple Linear Regression (MLR). Evaluation against reference data sets (Advanced Scatterometer, ERA5, Cross‐Calibrated Multi‐Platform (CCMP), buoy wind speeds) highlights the robustness of the proposed models. The research findings indicate that both RF and CNN exhibit superior accuracy in the active, passive, and joint active‐passive models compared to the simplistic MLR model. The joint models of the three regression methods outperform the individual active or passive models. The root mean square deviation (RMSD) accuracy of RF and CNN joint models, when compared with ASCAT, is in the order of 0.7 m/s, and when compared with buoys, the RMSD accuracy is around 1.1 m/s. The ML models, especially RF and CNN, demonstrate superior performance, providing accurate and reliable estimations crucial for meteorological and oceanographic applications. These findings underscore the potential operational use of ML techniques in enhancing remote sensing applications.

TORONTO: A trial-oriented multidimensional psychometric testing algorithm.

Bayesian adaptive methods for sensory threshold determination were conceived originally to track a single threshold. When applied to the testing of vision, they do not exploit the spatial patterns that underlie thresholds at different locations in the visual field. Exploiting these patterns has been recognized as key to further improving visual field test efficiency. We present a new approach (TORONTO) that outperforms other existing methods in terms of speed and accuracy. TORONTO generalizes the QUEST/ZEST algorithm to estimate simultaneously multiple thresholds. After each trial, without waiting for a fully determined threshold, the trial-oriented approach updates not only the location currently tested but also all other locations based on patterns in a reference data set. Since the availability of reference data can be limited, techniques are developed to overcome this limitation. TORONTO was evaluated using computer-simulated visual field tests: In the reliable condition (false positive [FP] = false negative [FN] = 3%), the median termination and root mean square error (RMSE) of TORONTO was 153 trials and 2.0dB, twice as fast with equal accuracy as ZEST. In the FP = FN = 15% condition, TORONTO terminated in 151 trials and was 2.2 times faster than ZEST with better RMSE (2.6 vs. 3.7dB). In the FP = FN = 30% condition, TORONTO achieved 4.2dB RMSE in 148 trials, while all other techniques had > 6.5dB RMSE and terminated much slower. In conclusion, TORONTO is a fast and accurate algorithm for determining multiple thresholds under a wide range of reliability and subject conditions.

Evaluation and Uncertainty Analysis of the Land Surface Hydrology in LS3MIP Models Over China

AbstractThe Land Surface, Snow and Soil moisture Model Intercomparison Project (LS3MIP) offers valuable land surface hydrology products from the land modules of current Earth system models (ESMs). Historical hydrological variables from six ESMs driven by four meteorological forcing data sets (GSWP, WFDEI, CRU‐NCEP, and Princeton) in Land Model Intercomparison Project (LMIP) have been extensively evaluated with various high‐quality reference data sets over Chinese mainland. Compared with the reference data sets, the multi‐model ensemble means (MMEs) of most hydrological variables are underestimated, while their annual trends show high spatial consistency, with sign consistency over 56%–85% of land area. After computing and ranking four statistical metrics (bias, correlation coefficient, normalized standard deviation, and unbiased root‐mean‐square biases) between simulations and references, it is found that the CLM5 has the best performance, while the GSWP3 exhibits the highest quality. Furthermore, the analysis of variance method (ANOVA) is then used to trace sources (model, atmospheric forcing data sets and their interactions) of the uncertainty of those modeling hydrological variables for 1900–2012 (1948–2012 for runoff) over China. The results indicate that the total uncertainty and its composition vary with time and decrease significantly in recent decades, reflecting the enhanced forcing data quality. Larger forcing uncertainty existed during the early twentieth century because less available observation data sets have been adopted to constrain climate variables. For all modeling hydrological variables, the model uncertainty plays the dominant role, suggesting that the quality of LMIP products largely relies on Land surface models.

Low-velocity impact localization on a full composite wing of UAV using fiber optic sensors under operating environments

In this paper, low-velocity impact localization algorithms were suggested using fiber optic sensing system. In order to obtain high frequency signals due to low-velocity impact, a commercial high speed fiber Bragg grating (FBG) sensing system (a sampling speed of 100 kHz) was adopted. The test article is a full-scale composite wing box structure as the main wing of an unmanned aerial vehicle. The multiplexed FBG sensor array was attached on the lower surface of the upper skin. Low-velocity impact experiments were performed to obtain the impact signals for the reference data set. The reference data set is composed of the impact signals from the impacts on the reference points. The suggested algorithm in this study is based on the comparisons between impact signals and the reference signals in the database set. The main idea is to determine the reference point which has the most similar data set as the impact location. This idea was validated from the experiments of the non-reference points. As the next step, operational environments were considered to enhance the reliability of the suggested algorithm. The considered environments are the static loads and vibrations. These days, most of UAVs use the electric motors and propellers as the main propulsion system. Thus, harsh vibrations inherently occur and affect the performance of impact localization algorithms. From the experiments under these operating environments, it can be checked that the suggested algorithm has sufficient robustness to such environments.

Using deep learning model to predict micronutrients in food after processing

Understanding the nutritional content of food after processing is important for the food processing industry. Choosing the appropriate processing method allows users to retain healthy micronutrients. In fact, collecting nutrient information of food before and after processing poses many challenges due to biological changes and interactions of nutritional components. Currently, the approach is to collect data on each nutritional component before and after processing. Conventional machine learning models will then use this data to produce good prediction results but with limited stability. Therefore, we proposed to use a deep learning model to conduct training on a data set with 27 nutritional components that change through two processing processes, boil and fry, extracted from a standard reference data set of the United States. The results show accurate prediction and improve the stability by 8.6%. The study shows the potential of improving deep learning models in predicting post-processing nutritional composition in food processing.

Effect of 3D printer, implant analog and angulation on the accuracy of analog position in implant casts

Objectives: To evaluate the accumulative effect of 3D printer, implant analog systems, and implant angulation on the accuracy of analog position in implant casts.Methods: A reference cast, presenting a case of a three-unit implant-supported prosthesis, was scanned with a coordinate measurement machine, producing the first reference data set (CMM, n = 1). The second reference data set (n = 10) was prepared using an intraoral scanner (IOS) (Trios4). Test quadrant casts were produced using three DLP type 3D printers, Max (MAX UV385), Pro (PRO 4K65 UV), and Nex (NextDent 5100), and three implant analog systems, El (Elos), Nt (Nt-trading), and St (Straumann) (n = 90). Stone casts were also produced via analog impressions (Stone, n = 10). After digitization, the accuracy of 3D distance, local angulation (angle between implants) and global angulation (angle between the implant center axis and an axis perpendicular to the global plane) was evaluated by comparing the reference (CMM, IOS), test (3D print), and control (Stone) groups using metrology software. Data were statistically analyzed using three-way ANOVA and Tukey`s tests (α = 0.05).Results: IOS was truer in 3D implant distance and more precise in capturing local angulation than Stone (p ≤ 0.05). Other measurements were similar between both groups (p > 0.05). The amount of error introduced in the workflow by IOS and 3D printing was mostly similar (p > 0.05). 3D printed casts had similar or even higher accuracy than Stone group (p > 0.05). In most cases, higher trueness was achieved when using PRO 4K65 UV 3D printer and Elos implant analog system (p ≤ 0.05).Conclusion: 3D printer, implant analog system, and implant angulation have a significant effect on the accuracy of analog position in implant casts. Limited-span implant-supported cases could be reproduced digitally with similar accuracy as conventional methods.Clinical significance: A fully digital workflow with a carefully selected 3D printer and implant analog system can increase the accuracy of digitally produced implant casts with comparable accuracy to conventional workflow.

Adaptive predictor-set linear model: An imputation-free method for linear regression prediction on data sets with missing values.

Linear regression (LR) is vastly used in data analysis for continuous outcomes in biomedicine and epidemiology. Despite its popularity, LR is incompatible with missing data, which frequently occur in health sciences. For parameter estimation, this shortcoming is usually resolved by complete-case analysis or imputation. Both work-arounds, however, are inadequate for prediction, since they either fail to predict on incomplete records or ignore missingness-induced reduction in prediction accuracy and rely on (unrealistic) assumptions about the missing mechanism. Here, we derive adaptive predictor-set linear model (aps-lm), capable of making predictions for incomplete data without the need for imputation. It is derived by using a predictor-selection operation, the Moore-Penrose pseudoinverse, and the reduced QR decomposition. aps-lm is an LR generalization that inherently handles missing values. It is applied on a reference data set, where complete predictors and outcome are available, and yields a set of privacy-preserving parameters. In a second stage, these are shared for making predictions of the outcome on external data sets with missing entries for predictors without imputation. Moreover, aps-lm computes prediction errors that account for the pattern of missing values even under extreme missingness. We benchmark aps-lm in a simulation study. aps-lm showed greater prediction accuracy and reduced bias compared to popular imputation strategies under a wide range of scenarios including variation of sample size, goodness of fit, missing value type, and covariance structure. Finally, as a proof-of-principle, we apply aps-lm in the context of epigenetic aging clocks, linear models that predict a person's biological age from epigenetic data with promising clinicalapplications.

A Fast, Affordable, and Minimally Invasive Diagnostic Test for Cancer of Unknown Primary Using DNA Methylation Profiling

Currently, we cannot provide a conclusive diagnosis for 3% to 5% of people who are confronted with cancer. These patients have cancer of unknown primary (CUP), ie, a metastasized cancer for which the tissue of origin cannot be determined. Studies have shown that the DNA methylation profile is a unique “fingerprint” that can be used to classify tumors. Here we used cell-free reduced representation bisulfite sequencing (cfRRBS), a technique that allows us to identify the methylation profile starting from minimal amounts of highly fragmented DNA, for CUP diagnosis on formalin-fixed paraffin-embedded (FFPE) tissue and liquid biopsies. We collected 80 primary tumor FFPE samples covering 16 tumor entities together with 15 healthy plasma samples to use as a custom cfRRBS reference data set. Entity-specific methylation regions are defined for each entity to build a classifier based on nonnegative least squares deconvolution. This classification framework was tested on 30 FFPE, 19 plasma, and 40 pleural and peritoneal effusion samples of both known metastatic tumors and clinical CUPs for which pathological investigation finally resulted in a cancer diagnosis. Using this framework, 27 of 30 FFPE (all CUPs) and 16 of 19 plasma samples (10/13 CUPs) obtained an accurate diagnosis, with a minimal DNA input of 400 pg. Diagnosis of the 40 pleural and peritoneal effusion samples is possible in 9 of 27 samples with negative/inconclusive cytology (6/13 CUPs), showing that cell-free DNA (cfDNA) methylation profiling could complement routine cytologic analysis. However, a low “cfDNA – high-molecular weight DNA ratio” has a considerable impact on the prediction accuracy. Moreover, the accuracy improves significantly if the predicted tumor percentage is >7%. This proof-of-concept study shows the feasibility of using DNA methylation profiling on FFPE and liquid biopsy samples such as blood, ascites, and pleural effusions in a fast and affordable way. Our novel RRBS-based technique requires minimal DNA input, can be performed in <1 week, and is highly adaptable to specific diagnostic problems as we only use 5 FFPE references per tumor entity. We believe that cfRRBS methylation profiling could be a valuable addition to the pathologist’s toolbox in the diagnosis of CUPs.