Average Absolute Difference Research Articles

Deep learning-based prediction and interpretability of physical phenomena for metaporous materials

In this paper, we propose a fast, accurate, and interpretable deep learning (DL)-based method for predicting and interpreting the sound absorption characteristics of metaporous materials. A novel deep convolutional neural network (CNN) model that hybridizes the physical knowledge of metaporous materials, i.e., resonances, is presented to accurately predict the sound-absorbing performances of metaporous materials based solely on their geometric information. Our proposed DL model inspired by locally and globally resonant mechanisms is trained to effectively capture the individual geometric information of embedded split-ring resonators and reflect the global structural interaction between the resonators and their surrounding environments, such as a porous material and a hard-backing layer. Both quantitative and qualitative results demonstrate that the proposed method outperforms the comparative methods, achieving far more accurate predictions of sound absorption coefficients with an average frequency-wise absolute difference of 0.009 and R2 score of 0.98. Besides, the average computation time per single case of the proposed method is observed to be 708 times faster than that of the existing method. Further, we examine the possibility that our physics-inspired proposed model can derive physical relevance appearing in metaporous materials, showing that physically aligned interpretability can be virtually obtained by the proposed approach in the form of visual activation maps. Our study delivers the potential to address various acoustic metamaterials challenges that require real-time, accurate, and interpretable analyses.

Water vapour and ozone in the upper troposphere–lower stratosphere: global climatologies from three Canadian limb-viewing instruments

Abstract. This study presents upper troposphere–lower stratosphere (UTLS) water vapour and ozone climatologies generated from 14 years (June 2004 to May 2018) of measurements made by three Canadian limb-viewing satellite instruments: the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS), the Measurement of Aerosol Extinction in the Stratosphere and Troposphere Retrieved by Occultation (MAESTRO), and the Optical Spectrograph and InfraRed Imaging System (OSIRIS; ozone only). This selection of instruments was chosen to explore the capability of these Canadian instruments in representing the UTLS and to enable analysis of the impact of different measurement sampling patterns. The water vapour and ozone climatologies have been constructed using tropopause-relative potential temperature and equivalent-latitude coordinates in an effort to best represent the distribution of these two gases in the UTLS, which is characterized by a high degree of dynamic and geophysical variability. Zonal-mean multiyear-mean climatologies are provided with 5∘ equivalent latitude and 10 K potential temperature spacing and have been constructed on a monthly, seasonal (3-month), and yearly basis. These climatologies are examined in-depth for two 3-month periods, December–January–February and June–July–August, and are compared to reference climatologies constructed from the Canadian Middle Atmosphere Model 39-year specified dynamics (CMAM39-SD) run, subsampled to the times and locations of the satellite measurements, in order to evaluate the consistency of water vapour and ozone between the datasets. Specifically, this method of using a subsampled model addresses the impact of each instrument's measuring pattern and allows for the quantification of the influence of different measurement patterns on multiyear climatologies. This in turn permits a more consistent evaluation of the distributions of these two gas species, as assessed through the differences between the model and measurement climatologies. For water vapour, the average absolute relative difference between CMAM39-SD and ACE-FTS differed between the two versions of ACE-FTS by less than 8 %, while the MAESTRO climatologies were found to differ by 15 %–41 % from ACE-FTS, depending on the version of ACE-FTS and the season. When considering the ozone climatologies, those constructed from the two ACE-FTS versions agreed to within 2 % overall, and the OSIRIS ozone climatologies agreed with these to within 10 %. The MAESTRO ozone climatologies differ from those from ACE-FTS and OSIRIS by 30 %–35 % and 25 %, respectively, albeit with regions of better agreement within the UTLS. These findings indicate that this set of Canadian limb sounders yields generally similar water vapour and ozone distributions in the UTLS, with some exceptions for MAESTRO depending on the season and gas species.

Body-weight variability and risk of cardiovascular outcomes in patients with type 1 diabetes: a retrospective observational analysis of data from the DCCT/EDIC population

BackgroundCardiovascular risk and body-weight management are both emerging challenges of type 1 diabetes care. We evaluated the association between intraindividual variability of body-weight and risk of cardiovascular events in people with type 1 diabetes.MethodsWe analyzed 1,398 participants from the DCCT/EDIC studies. Five indices of intraindividual variability of body-weight were calculated for each participant taking into account body-weight measures obtained during the DCCT follow-up (average 6 ± 2 years). The Average Successive Variability (ASV) index, the main variable of interest, was defined as the average absolute difference between successive body-weight measures. The primary outcome was a composite of major adverse cardiovascular events (MACE: nonfatal myocardial infarction or stroke, or cardiovascular death) occurring during the subsequent EDIC follow-up (20 ± 3 years). All-cause death was a secondary outcome. Risk of outcomes were assessed by Cox proportional hazards regression analyses, adjusted for traditional cardiovascular risks factors, including BMI.ResultsThe cumulative incidence of MACE and all-cause death during follow-up were 5.6% (n = 79) and 6.8% (n = 95), respectively. The adjusted Hazard Ratio (HR) for MACE by every increase of 1 standard deviation (SD) of ASV was 1.34 (95% CI, 1.06–1.66), p = 0.01. For all-cause death, the adjusted HR for 1 SD increase of ASV was 1.25 (1.03–1.50), p = 0.03. Similar results were observed when considering the other indices of intraindividual variability of body-weight.ConclusionsHigh body-weight variability (body-weight cycling) is associated with increased risk of MACE and all-cause death in people with type 1 diabetes, independently of the BMI and traditional cardiovascular risk factors.

Mining Subsidence Monitoring Based on InSAR Method Fusing Multi-threshold Target

In view of the limitations of traditional InSAR technology in selecting stable target point for orbit refining and surface subsidence inversion in complicated mining area, this paper proposes a time-series InSAR mining area subsidence monitoring method based on the fusion of multi threshold targets. On the basis of the traditional technology, the deviation threshold parameters, the regional window threshold parameters and the coherence threshold parameters are set to extract the relatively stable target points on the ground. Applying this method and traditional InSAR method to practical cases, the monitoring results of surface subsidence in the study area are obtained and verified. The results show that: (1) there are three mining subsidence areas in the mining area, the maximum annual average subsidence rate is -156 mm/a, and the maximum subsidence is -376 mm. Compared with the optical image data, the location of the mining subsidence area is consistent with the mining work area of the coal mine; (2) The absolute average difference of subsidence in the mining area using the two methods shall not exceed 12 mm. It shows that the InSAR method of fusing multi threshold targets can not only effectively overcome the limitations of traditional InSAR, but also ensure high accuracy, and has more advantages in the monitoring of surface subsidence in mining areas.

Sampling Sexual and Gender Minority Youth With UnACoRN (Understanding Affirming Communities, Relationships, and Networks): Lessons From a Web-Based Survey.

Periodic surveys of sexual and gender minority (SGM) populations are essential for monitoring and investigating health inequities. Recent legislative efforts to ban so-called conversion therapy make it necessary to adapt youth surveys to reach a wider range of SGM populations, including those <18 years of age and those who may not adopt an explicit two-spirit, lesbian, gay, bisexual, transgender, and queer (2S/LGBTQ) identity. We aimed to share our experiences in recruiting SGM youth through multiple in-person and online channels and to share lessons learned for future researchers. The Understanding Affirming Communities, Relationships, and Networks (UnACoRN) web-based survey collected anonymous data in English and French from 9679 mostly SGM respondents in the United States and Canada. Respondents were recruited from March 2022 to August 2022 using word-of-mouth referrals, leaflet distribution, bus advertisements, and paid and unpaid campaigns on social media and a pornography website. We analyzed the metadata provided by these and other online resources we used for recruitment (eg, Bitly and Qualtrics) and describe the campaign's effectiveness by recruitment venue based on calculating the cost per completed survey and other secondary metrics. Most participants were recruited through Meta (13,741/16,533, 83.1%), mainly through Instagram; 88.96% (visitors: 14,888/18,179) of our sample reached the survey through paid advertisements. Overall, the cost per survey was lower for Meta than Pornhub or the bus advertisements. Similarly, the proportion of visitors who started the survey was higher for Meta (8492/18,179, 46.7%) than Pornhub (58/18,179, 1.02%). Our subsample of 7037 residents of Canada had a similar geographic distribution to the general population, with an average absolute difference in proportion by province or territory of 1.4% compared to the Canadian census. Our US subsample included 2521 participants from all US states and the District of Columbia. A total of CAD $8571.58 (the currency exchange rate was US $1=CAD $1.25) was spent across 4 paid recruitment channels (Facebook, Instagram, PornHub, and bus advertisements). The most cost-effective tool of recruitment was Instagram, with an average cost per completed survey of CAD $1.48. UnACoRN recruited nearly 10,000 SGM youth in the United States and Canada, and the cost per survey was CAD $1.48. Researchers using online recruitment strategies should be aware of the differences in campaign management each website or social media platform offers and be prepared to engage with their framing (content selection and delivery) to correct any imbalances derived from it. Those who focus on SGM populations should consider how 2S/LGBTQ-oriented campaigns might deter participation from cisgender or heterosexual people or SGM people not identifying as 2S/LGBTQ, if relevant to their research design. Finally, those with limited resources may select fewer venues with lower cost per completed survey or that appeal more to their specific audience, if needed.

Semiautomated intraoperative measurement of Cobb angle and coronal C7 plumb line using deep learning and computer vision for scoliosis correction: a feasibility study.

Scoliosis is a degenerative disease with a 3D deformity in the alignment of the spinal column. Surgical spinal correction outcomes are heavily dependent on the surgeon's expertise and use of visual cues because of time requirements, lack of automation, and radiation exposure associated with current intraoperative measurement techniques. In this study, the authors sought to validate a novel, nonradiographic, and semiautomated device that measures spinal alignment intraoperatively using deep learning and computer vision. To obtain spinal alignment metrics intraoperatively, the surgeon placed 3D-printed markers made of acrylonitrile butadiene styrene (ABS) plastic at designated locations in the surgical field. With the high-definition camera of the device, the surgeon can take an image of the markers in the surgical field. Images are processed through a computer vision model that detects the location of the markers and calculates the Cobb angle and coronal plumb line. The marker detection model was trained on 100 images and tested on 130 images of the ABS markers in various conditions. To verify the Cobb angle calculation, 50 models of angle templates from 0° to 180° in 3.6735° increments were created for testing. To verify the plumb line calculation, 21 models of plumb line measurements from -10 to +10 cm in increments of 1 cm were created for testing. A validation study was performed on a scoliotic cadaver model, and the radiographic calculations for Cobb angle and plumb line were compared with the device's calculations. The area under the curve for the marker detection model was 0.979 for Cobb angle white, 0.791 for Cobb angle black, and 1 for the plumb line model. The average absolute difference between expected and measured Cobb angles on the verification models was 1.726° ± 1.259°, within the clinical acceptable error of 5°. The average absolute difference between the expected and measured plumb lines on the verification models was 0.415 ± 0.255 cm. For the cadaver validation study, the differences between the radiographic and device calculations for the Cobb angle and plumb line were 2.78° and 0.29 cm, respectively. The authors developed and validated a nonradiographic, semiautomated device that utilizes deep learning and computer vision to measure spinal metrics intraoperatively.

PO27 Presentation Time: 7:40 AM: Library-Based MRI-Only Treatment Planning (MRTP) for a Venezia Applicator Using MRI for Catheter Detection

<h3>Purpose</h3> Interstitial HDR brachytherapy currently requires CT, in addition to MRI, to complete the treatment planning workflow. CT is necessary to visualize and localize catheters and template, while MRI is needed for delineation of the clinical target volume (CTV) and the organs-at-risk (OARs). An MRI-only treatment plan/planning (MRTP) approach that can eliminate CT from the workflow using an optimized ‘Pointwise Encoding Time Reduction with Radial Acquisition (PETRA)' MRI, is proposed. Typically, applicators lack MR contrast, making MRI-based digitization difficult for catheters within the applicator. The feasibility of utilizing an applicator library to complete MRTP in gynecological patients was investigated for a Venezia applicator. The clinical translation feasibility of MRTP was evaluated by conducting dosimetric comparisons between the MRTP and the clinical treatment plan (CTP). <h3>Materials and Methods</h3> An optimized PETRA MRI (TR\TE 3.3\0.7ms) was acquired on a 3T MAGNETOM Vida (Siemens) in three gynecological cancer patients treated using a Venezia applicator (Elekta) and interstitial needles. A library-based applicator model tool available on Oncentra Brachy TPS was used to aid catheter digitization of applicators. The library of Venezia applicator was used to digitize two lunar ovoids and tandem. Interstitial needles were manually digitized based on negative contrast between needles and surrounding tissue. The MRTP was generated by activating and assigning the same dwell times as in the CTP. The catheter displacements between two approaches were assessed by comparing dwell positions following the CT-to-MR transformation, as well as the displacement in catheter tip depth. The DVH and EQD₂ metrics for the CTV D₉₀ and the OARs D_2cc (rectum, bladder and sigmoid) were compared between CTP and MRTP. <h3>Results</h3> MRTP workflow corresponded to the average tip depth difference of -1.58 ± 2.49 mm, 0.50 ± 1.55 mm, and -0.38 ± 1.17 mm for lunar ovoids, tandem, and interstitial needles, respectively, compared to the conventional CT-based approach. Differences in catheter displacements were 3.34 ± 1.38mm, 2.49 ± 0.23mm and 2.26 ± 0.48 mm for lunar ovoids, tandem and interstitial needles, respectively. These differences corresponded to absolute DVH differences of -2%, -1% and 3% for CTV D₉₀ in these three patients. The average absolute DVH differences were -3.44 ± 0.02%, 1.77 ± 0.02%, and -0.98 ± 0.02% for OARs (rectum, bladder and sigmoid, respectively). The average relative total EQD2 differences were 0.12 ± 0.02%, -2.62 ± 0.01%, 1.41 ± 0.01% and -0.71 ± 0.02% for CTV, rectum, bladder, and sigmoid, respectively. <h3>Conclusions</h3> Preliminary implementation feasibility of a library-based MRTP for a Venezia applicator was demonstrated in three gynecologic cancer patients.

Accuracy of fat fraction estimation using Dixon: experimental phantom study

Objective. Quantitative assessment of Dixon two-point and three-point technologies operation using phantom modeling in the range from 0 to 70%.Materials and methods. To simulate substances with different concentrations of the fat phase we chose direct oil-in-water emulsions. Tubes with ready-made emulsions were placed in a phantom. Emulsions based on vegetable oils were presented in the range from 0–70%. The phantom was scanned on an Optima MR450w MRI tomograph (GE, USA) in two Dixon modes: the accelerated two-point method “Lava-Flex” and the three-point method “IDEAL IQ”. A scan was performed on a GEM Flex LG Full RF coil. We calculated fat fraction (FF) using two formulas.Results. There is a linear relationship of the determined values when calculating the fat concentration in “IDEAL IQ” mode and using the formula based on Water and Fat. The accuracy of body fat percentage measurement in “IDEAL IQ” mode is higher than in “Lava-Flex” mode. According to the MR-sequence “Lava-Flex” draws attention to the overestimation of the measured values of the concentration of fat in relation to the specified values by an average of 57.6% over the entire range, with an average absolute difference of 17.2%.Conclusion. Using the “IDEAL IQ” sequence, the results of the quantitative determination of fractions by formulas were demonstrated, which are more consistent with the specified values in the phantom. In order to correctly quantify the fat fraction, it is preferable to calculate from the Water and Fat images using Equation 2. Calculations from the In-phase and Out-phase images provide ambiguous results. Phantom modeling with direct emulsions allowed us to detect the shift of the measured fat fraction.

Antibiotic susceptibility of urine culture specimens in Ontario: a population-based cohort study.

Surveillance of antimicrobial resistance is essential to mitigate its impact on population health and inform local empiric treatment practices. Our aims were to evaluate urine culture specimen susceptibility from a range of diverse settings and describe antibiotic susceptibility across all organisms and compare susceptibilities to that of Escherichia coli alone. In this descriptive cohort study, we measured the prevalence of organisms in urine culture specimens using linked province-wide administrative databases. Using positive urine cultures collected in Ontario between Jan. 1, 2016, and Dec. 31, 2017, we measured susceptibility to 6 classes of antibiotics using a weighted antibiogram for all organisms compared with E. coli alone. We included 689 497 cultures derived from 569 399 patients and 879 778 test orders for specimens. For all organisms, the rates of susceptibility in the outpatient, inpatient and long-term care settings were 49.3%, 42.8% and 39.2%, respectively, for ampicillin; 83.1%, 72.7% and 69.7%, respectively, for nitrofurantoin; 80.3%, 64.8% and 73.1%, respectively, for trimethoprim-sulfamethoxazole; 87.2%, 74.1% and 66.2%, respectively, for ciprofloxacin; 90.6%, 73.6% and 85.1%, respectively, for aminoglycosides; and 82.6%, 57.5% and 73.5%, respectively, for cefazolin. We found resistance to 3 or more antibiotic classes in 20.6% of episodes for all organisms compared with 14.0% for E. coli alone. The average absolute difference in antibiotic susceptibility between all organisms and E. coli across all drugs was lowest in the outpatient setting (6.2%) and highest in the inpatient setting (14.6%). In this study, urinary organism prevalence and antimicrobial susceptibility varied across health care settings and patient populations, with implications for both antimicrobial resistance surveillance and clinical decision-making. Weighted antibiograms may be most useful for guiding empiric treatment of urinary infections in inpatient settings where the diversity of infectious organisms is higher than in the community.

Combined electronic medical records and gene polymorphism characteristics to establish an anti-tuberculosis drug-induced hepatic injury (ATDH) prediction model and evaluate the prediction value.

Anti-tuberculosis drug-induced hepatic injury (ATDH) lacks specific diagnostic markers. The characteristics of gene polymorphisms have been preliminarily used for the risk classification of ATDH, and the activation of Pregnane X receptor/aminole-vulinic synthase-1/forkhead box O1 (PXR/ALAS1/FOXO1) axis is closely related to ATDH. Therefore, we consider combining general clinical features of the electronic medical record, laboratory indications, and genetic features of key genes in this axis for predictive model construction to help early clinical diagnosis and treatment. The general characteristics derived from the Hospital Information System (HIS) medical record system, the biochemical tests and hematology tests were detected by Roche automatic biochemical immunoassay analyzer cobas8000 and Sysmex automatic hemocytometer XE2100. The single nucleotide polymorphisms (SNPs) genotyping work was conducted with a custom-designed 48-plex SNP scan® TM Kit. A total of 746 cases were included which were divided into training set and validation set according to the ratio of 3:2 randomly. Taking the occurrence of confirmed ATDH as the outcome variable, lasso regression and logistic regression were used to identify the predictors preliminarily. alanine aminotransferase, aspartate aminotransferase, monocyte, uric acid, albumin, fever, the polymorphisms of rs4435111 (FOXO1) and rs3814055 (PXR) were chosen from all variables to combine the predictive model. The goodness of fit, predictive efficacy, discrimination, and consistency, and clinical decision curve analysis was used to assess the clinical applicability of the models. The best model had a discriminant efficacy C-index of 0.8164, a sensitivity of 34.25%, specificity of 97.99%, a positive predictive value of 78.13% and negative predictive value of 87.69%, the two-tailed value of Spiegelhalter Z test of consistency test S:P =0.896, maximum absolute difference Emax =0.147, and average absolute difference Eave =0.017. In the validation set, performance was close. The clinical decision curve showed the clinical applicability of the prediction model when the prediction risk threshold was between 0.1 and 0.8. The ATDH prediction model was constructed using a machine learning approach, combining general characteristics of the study population, laboratory indications, and SNP features of PXR and FOXO1 genes with good fit and certain predictive value, and has potential and value for clinical application.

A fast GPU-accelerated Monte Carlo engine for calculation of MLC-collimated electron fields.

Although intensity-modulated radiation therapy and volumetric arc therapy have revolutionized photon external beam therapies, the technological advances associated with electron beam therapy have fallen behind. Modern linear accelerators contain technologies that would allow for more advanced forms of electron treatments, such as beam collimation, using the conventional photon multi-leaf collimator (MLC); however, no commercial solutions exist that calculate dose from such beam delivery modes. Additionally, for clinical adoption to occur, dose calculation times would need to be on par with that of modern dose calculation algorithms. This work developed a graphics processing unit (GPU)-accelerated Monte Carlo (MC) engine incorporating the Varian TrueBeam linac head geometry for a rapid calculation of electron beams collimated using the conventional photon MLC. A compute unified device architecture framework was created for the following: (1) transport of electrons and photons through the linac head geometry, considering multiple scattering, Bremsstrahlung, Møller, Compton, and pair production interactions; (2) electron and photon propagation through the CT geometry, considering all interactions plus the photoelectric effect; and (3) secondary particle cascades through the linac head and within the CT geometry. The linac head collimating geometry was modeled according to the specifications provided by the vendor, who also provided phase-space files. The MC was benchmarked against EGSnrc/DOSXYZnrc/GEANT by simulating individual interactions with simple geometries, pencil, and square beam dose calculations in various phantoms. MC-calculated dose distributions for MLC and jaw-collimated electron fields were compared to measurements in a water phantom and with radiochromic film. Pencil and square beam dose distributions are in good agreement with DOSXYZnrc. Angular and spatial distributions for multiple scattering and secondary particle production in thin slab geometries are in good agreement with EGSnrc and GEANT. Dose profiles for MLC and jaw-collimated 6-20-MeV electron beams showed an average absolute difference of 1.1 and 1.9mm for the FWHM and 80%-20% penumbra from measured profiles. Percent depth doses showed differences of <5% for as compared to measurement. The computation time on an NVIDIA Tesla V100 card was 2.5min to achieve a dose uncertainty of <1%, which is ∼300 times faster than published results in a similar geometry using a single-CPU core. The GPU-based MC can quickly calculate dose for electron fields collimated using the conventional photon MLC. The fast calculation times will allow for a rapid calculation of electron fields for mixed photon and electron particle therapy.

The validity of routine individuated programmatic data in HIV surveillance assessed over a 10-year period.

ObjectivesIn the Western Cape, South Africa, public-sector individual-level routine data are consolidated from multiple non-EMR sources through the Provincial Health Data Centre (PHDC). This enables the description of temporal changes in population-wide antenatal HIV sero-prevalence. We evaluated the validity of these data compared to aggregated program data and sentinel surveys. ApproachWe conducted a retrospective cohort analysis of all pregnancies consolidated in the PHDC from January 2011 to December 2020. Evidence of antenatal and HIV care from electronic platforms (each with its own multi-source phenotype algorithm) were linked using a unique patient identifier. HIV prevalence estimates were triangulated with available sentinel national and provincial antenatal survey estimates as well as aggregated programmatic data from registers as recorded in the District Health Information System. Provincial, district-level and age-group HIV prevalence estimates were compared between data systems using correlation coefficients, absolute differences and trend analysis. ResultsIn total, 982,914 pregnancies were ascertained with a median maternal age of 26.9 years. Between 2011–2013, PHDC HIV prevalence estimates were widely disparate from aggregate and survey data (due to incomplete electronic data) whereas, from 2014 onwards, estimates were more closely correlated to aggregate data estimates (r=0.83; p=0.02) with an average absolute prevalence difference of 0.97%. In keeping with survey and aggregate data trends, PHDC data show a relatively stable provincial HIV prevalence from 17.0 (95%CI 16.8%–17.2%) in 2015 to 18.9% (95%CI 18.7–19.1%) in 2020. The highest HIV prevalence was in the Cape Metro district (20.6%; 95%CI 20.4%–20.9%). Prevalence estimates by age group were comparable between sentinel surveys and PHDC from 2014 onwards with an average absolute prevalence difference of 1.6%. ConclusionThis study is the first to compare sentinel sero-prevalence surveys with both register-based aggregate data and consolidated individuated administrative data. We show that in this setting linked individuated data may be reliably used for HIV surveillance and provide more granular estimates with greater efficiency.

Diffusion of Squalene in Nonaqueous Solvents.

Capillary flow techniques have been used to determine the translational diffusion constant, D, of squalene in seven alkanes and five cyclohexanes. The alkanes are n-hexane, n-octane, n-decane, n-dodecane, n-tetradecane, 2,2,4,4,6,8,8-heptamethylnonane (isocetane), and 2,6,10,14-tetramethylpentadecane (pristane). The cyclohexanes are cyclohexane, n-butylcyclohexane, n-hexylcyclohexane, n-octylcyclohexane, and n-dodecylcyclohexane. When combined with published data in CD2Cl2, ethyl acetate, n-hexadecane, squalane, n-octane–squalane mixtures, and supercritical CO2, the 35 diffusion constants and viscosities, η, vary by factors of ∼230 and ∼500, respectively. A fit to the modified Stokes–Einstein equation (MSE, D/T = ASE/ηp) gives an average absolute percentage difference (AAPD) of 7.72% between the experimental and calculated D values where p and ASE are constants, T is the absolute temperature, and the AAPD is the average value of (102) (|Dcalcd – Dexptl|/Dexptl). Two other MSE fits using subsets of the 35 diffusion constants may be useful for (a) estimating the viscosity of the hydrophobic core of lipid droplets, where squalene is a naturally occurring component, and (b) providing estimates of the D values needed to design extraction processes by which squalene is obtained from plant oils. The Wilke–Chang equation also was considered and found to give larger AAPDs than the corresponding MSE fits.

Fibroscan® and Shear Wave correlated well in hepatic fibrosis evaluation of patients with chronic liver diseases "in real life situation".

Background:A new noninvasive medical device based on ultrasound elastography such as the Shear Wave Elastography (SWE) was designed in order to measure the liver hardness. The purpose of this work was to evaluate the correlation of the results of the liver elasticity measurements obtained by Fibroscan® (FS) and SWE for patients with chronic liver diseases.Methods:Between January and October 2017, the patients who were followed during this period of time underwent noninvasive assessments of liver fibrosis by SWE in the intercostal spaces during abdominal ultrasound procedures and/or FS. The correlation between FS and SWE was estimated and tested at a 0.05 significance level.Results:Four hundred and seventy-six patients were included in this study. The main etiologies of chronic liver disease were non alcoholic fatty disease (NAFLD), chronic viral hepatitis B (HBV) and chronic viral hepatitis C (HCV). All patients underwent a SWE and 167 among them underwent a FS procedure. The patients who were followed revealed a median FS measurement of 5.80 kpa (Q25 = 4.90 kPa; Q75 = 8 kPa) and a median SWE measurement of 7.00 kPa (Q25 = 6.10 kPa; Q75 = 8.10 kPa). We could observe a significant correlation between the FS and SWE measurements (0.49; P = .001) in the total cohort. The average absolute difference between the measurements of these 2 methods was of 2.54 kPa (sd = 3.39). There was no significant correlation for patients with NAFLD no matter whether they presented with signs of suspected non alcoholic steatohepatitis (NASH) or not (R = 0.20; P = .108). All patients intending to perform the examination were able to undergo the SWE, allowing 33.3% of the patients who failed the FS to have a noninvasive evaluation of their fibrosis.Conclusion:The SWE technique proved to be as efficient as the FS one for the evaluation of the liver fibrosis in real life situation.

Describing and assessing a new method of approximating categorical individual-level income using community-level income from the census (weighting by income probabilities).

To assess a new approach (weighting by "income probabilities [IP]") that uses US Census data from the patients' communities to approximate individual-level income, an important but often missing variable in health services research. Community (census tract level) income data came from the 2017 5-year American Community Survey (ACS). The patient data included those diagnosed with cancer in 2017 in Ohio (n= 65,759). The reference population was the 2017 5-year ACS Public Use Microdata Sample (n= 564,357 generalizing to 11,288,350 Ohioans). We applied the traditional approach of income approximation using median census tract income along with two IP based approaches to estimate the proportions in the patient data with incomes of 0%-149%, 150%-299%, 300%-499%, and 500%+ of the federal poverty level (FPL) ("class-relevant income grouping") or 0%-138%, 139%-249%, 250%-399%, and 400%+ FPL ("policy-relevant income grouping"). These estimated income distributions were then compared with the known income distributions of the reference population. The patient data came from Ohio's cancer registry. The other data were publicly available. Both IP based approaches consistently outperformed the traditional approach overall and in subgroup analyses, as measured by the weighted average absolute percentage point differences between the proportions of each of the income categories of the reference population and the estimated proportions generated by the income approximation approaches ("average percent difference," or APD). The smallest APD for an IP based method, 0.5%, was seen in non-Hispanic White females in the class-relevant income grouping (compared with 16.5% for the conventional method), while the largest APD, 7.1%, was seen in non-Hispanic Black females in the policy-relevant income grouping (compared with 18.0% for the conventional method). Weighting by IP substantially outperformed the conventional approach of estimating the distribution of incomes in patient data.

Monitoring and Analysis of Water Level–Water Storage Capacity Changes in Ngoring Lake Based on Multisource Remote Sensing Data

Mastering the fluctuation of water levels and the water storage capacity of plateau lakes is greatly important for monitoring the water balance of the Tibetan Plateau and predicting regional and global climate change. The water level of plateau lakes is difficult to measure, and the ground measured data of long-time series are difficult to obtain. Ngoring Lake is considered in this study, using spaceborne single-photon lidar ICESat-2/ATL13 inland lake standard data products, the water level values provided by Hydroweb laboratory, and the image data of an optical remote sensing satellite. A new method is proposed in the absence of measured data. The method uses multisource remote sensing data to estimate the long-term changes in the water levels, surface area, and water storage capacity of Ngoring Lake in the past three decades. The results show that the water level values of ICESat-2 and Hydroweb on overlapping observation days are highly correlated, with R2 = 0.9776, MAE = 0.420 m, RMSE = 0.077 m, and the average absolute height difference is 0.049 m. The fusion of multiple altimetry data can obtain more continuous long-time series water-level observation results. From 1992 to 2021, the water body information of Ngoring Lake basin fluctuated greatly and showed different variation characteristics in different time periods. The lowest water level in January 1997 was approximately 4268.49 m, and it rose to its highest in October 2009, approximately 4272.44 m. The change in the water level in the basin was mainly affected by natural factors, such as precipitation, air temperature, and human activities. The analysis shows that ICESat-2 can be combined with other remote sensing data to realize the long-time series dynamic monitoring of plateau lakes, showing great advantages in the comprehensive observation of plateau lakes in no man’s land.

ANALYSIS OF THORIUM PIN CELL BURN UP OF THE PWR USING WIMS CODE

A thorium-fueled benchmark comparison was made in this study between state-of-the-art codes, WIMSD-5B code to MOCUP (MCNP4B + ORIGEN2), and CASMO-4 for burnup calculations as part of efforts to examine the possible benefits of using thorium in PWR fuel. WIMSD-5B calculations employ the same model as a reference, MOCUP, and CASMO, however, there are some variances in methodology and cross-section libraries. On a PWR pin cell model, eigenvalue and isotope concentrations were examined up to high burnup. The eigenvalue comparison as a function of burnup is good, with a maximum difference of less than 5% and an average absolute difference of less than 1%. The isotope concentration comparisons outperform a set of ThO2-UO2 fuel benchmarks and are comparable to a set of uranium fuel benchmarks previously published in the literature. As a function, the eigenvalue comparison The actinide and fission product data sources for a typical thorium fuel are reported in the WIMSD-5B burnup calculations. The reasons for discrepancies in coding are examined and explored.Keywords: Thorium, PWR Fuel, Burn up, Pin Cell, WIMSD-5B

An approach to high throughput measurement of accurate retention data in liquid chromatography

Efforts to model and simulate various aspects of liquid chromatography (LC) separations (e.g., retention, selectivity, peak capacity, injection breakthrough) depend on experimental retention measurements to use as the basis for the models and simulations. Often these modeling and simulation efforts are limited by datasets that are too small because of the cost (time and money) associated with making the measurements. Other groups have demonstrated improvements in throughput of LC separations by focusing on “overhead” associated with the instrument itself – for example, between-analysis software processing time, and autosampler motions. In this paper we explore the possibility of using columns with small volumes (i.e., 5 mm x 2.1 mm i.d.) compared to conventional columns (e.g., 100 mm x 2.1 mm i.d.) that are typically used for retention measurements. We find that isocratic retention factors calculated for columns with these dimensions are different by about 20%; we attribute this difference – which we interpret as an error in measurements based on data from the 5 mm column – to extra-column volume associated with inlet and outlet frits. Since retention factor is a thermodynamic property of the mobile/stationary phase system under study, it should be independent of the dimensions of the column that is used for the measurement. We propose using ratios of retention factors (i.e., selectivities) to translate retention measurements between columns of different dimensions, so that measurements made using small columns can be used to make predictions for separations that involve conventional columns. We find that this approach reduces the difference in retention factors (5 mm compared to 100 mm columns) from an average of 18% to an average absolute difference of 1.7% (all errors less than 8%). This approach will significantly increase the rate at which high quality retention data can be collected to thousands of measurements per instrument per day, which in turn will likely have a profound impact on the quality of models and simulations that can be developed for many aspects of LC separations.

A "smart" Imputation Approach for Effective Quality Control Across Complex Clinical Data Structures.

The overwhelming need to improve the quality of complex data structures in healthcare is more important than ever. Although data quality has been the point of interest in many studies, none of them has focused on the development of quantitative and explainable methods for data imputation. In this work, we propose a "smart" imputation workflow to address missing data across complex data structures in the context of in silico clinical trials. AI algorithms were utilized to produce high-quality virtual patient profiles. A search algorithm was then developed to extract the best virtual patient profiles through the definition of a profile matching score (PMS). A case study was conducted, where the real dataset was randomly contaminated with multiple missing values (e.g., 10 to 50%). In total, 10000 virtual patient profiles with less than 0.02 Kullback-Leibler (KL) divergence were produced to estimate the PMS distribution. The best generator achieved the lowest average squared absolute difference (0.4) and average correlation difference (0.02) with the real dataset highlighting its increased effectiveness for data imputation across complex clinical data structures.

Evaluation of lateral capacity for flexible drilled shafts in cohesionless soils.

This study aimed to evaluate and improve the p-y curve method for use in the lateral analysis of flexible circular drilled shafts in cohesionless soils. Onsite circular drilled shaft lateral load test data from all over the world were collected, and the applicability of O'Neill and Murchison's p-y curve method was evaluated by comparing the measured values with the method's predicted values. Results showed that the current predictions were underestimated after applying the method to a database of flexible circular drilled shafts in cohesionless soils. Then, the difference (ΔQ) between the predicted value (Qp) and measured value (Qm) was normalized using the measured value (ΔQ/Qm) to determine the percent difference, and an average value of - 47% was produced. The difference ratio between the measured and predicted values was proportional to the diameter of the pile. To calibrate the prediction of the current method, this study applied two parameters (α and β) as modifications to reduce ΔQ. The parameters were decided by using the average difference ratio and average absolute difference methods. The reliability and consistency of the prediction of the p-y curve method were enhanced by applying the recommended modifying parameters. Specific design recommendations were also provided based on the proposed modifications on the original p-y curve.