Complete Data Set Research Articles

Abstract PO3-03-09: Clinical Significance of Grade in Triple Negative Breast Cancer

Abstract Triple negative breast cancer (TNBC) is a heterogeneous cancer type that lacks receptors for estrogen (ER), progesterone (PR), and human epidermal growth factor receptor-2 proteins (HER-2). An important prognostic factor for breast cancer patients is the tumour grade, which is the degree of cell proliferation or differentiation of the tumour cells from normal cells. In our initial study involving 305 TNBC patients from 2004-2017 at Windsor Regional Hospital Cancer Centre, we found a statistically significant difference between grade 2 and grade 3 patients, with grade 2 predicting significantly inferior progression-free and overall survival. In our initial study, the overall survival rates for grade 1, 2, and 3 tumours were 90.12%, 64.4%, and 77.2% respectively (p = 0.019) with relapse rates of 70%, 55.6%, and 75.6% respectively (p = 0.04). Our current study is an attempt to validate these initial findings by expanding our data set to include data from the London Health Sciences Centre. A literature review on TNBC and grade was conducted, followed by a retrospective chart review for 305 TNBC patients from the Windsor Regional Hospital and 515 TNBC patients from the London Health Sciences Centre. The parameters for data collection included patient demographics, tumour demographics, therapies, and patient outcomes. We calculated all patient stages using both American Joint Committee on Cancer 7th edition (AJCC7) and the updated AJCC8 staging systems. Initial review of the data supports our earlier findings with a relapse rate of 12.5% in patients with grade 3 tumours and 17% in patients with grade 1 and grade 2 tumours. The completed data set will be analyzed and presented at the conference. Validating our previous findings of significantly inferior patient outcomes for grade 2 compared to grade 3 patients may alter the staging system used for TNBC patients. Citation Format: Mah-noor Malik, Neya Ramanan, Sarang Upneja, Muriel Brackstone, Lisa Porter, Bre-Anne Fifield, Caroline Hamm. Clinical Significance of Grade in Triple Negative Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-03-09.

An industrial IoT-based deformation resistance prediction and thickness control method of cold-rolled strip in steel production systems

Different from traditional analytical and numerical simulation methods, data-driven methods can more effectively describe the effects of nonlinear and coupling factors. Although the fluctuations of deformation resistance and thickness for hot-rolled strips significantly influence the thickness accuracy in strip cold rolling, the traditional deformation resistance prediction model and thickness control mode don't involve that. Thus, based on the Industrial Internet of Things (IIOT) platform and data-driven technology, this work proposed a new method for deformation resistance prediction and a new mode of feed-forward control for strip thickness. IIOT platform can collect production data from the various levels to provide a complete data set. The deformation resistance prediction model is established by adopting the differential evolutionary algorithm to optimize the bi-directional long and short-term memory network. In addition, the feed-forward control (DFFC) strategy for strip thickness is proposed based on deformation resistance prediction. An application results of a 1420 mm production line of CR indicated that the established prediction model could provide a high accuracy prediction for deformation resistance, and compared to traditional thickness gauges-based automatic gauge control methods, the DDFC can capture the effect of the fluctuations of deformation resistance and thickness for the hot-rolled strip to improve thickness accuracy effectively.

Primary results of patients with genitourinary malignancies presented at a Molecular Tumor Board

Purpose: Personalized medicine poses great opportunities and challenges. While therapeutic landscape markedly expands, descriptions about status, clinical implementation and real-world benefits of precision oncology and molecular tumor boards (MTB) remain sparse, particularly in the field of genitourinary (GU) cancer. Hence, this study characterized urological MTB cases to better understand the potential role of MTB in uro-oncology. Methods: We analyzed patients with complete data sets being reviewed at an MTB from January 2019 to October 2022, focusing on results of molecular analysis and treatment recommendations. Results: We evaluated 102 patients with GU cancer with a mean patient age of 61.7 years. Prostate cancer (PCa) was the most frequent entity with 52.9% (54/102), followed by bladder cancer (18.6%, 19/102) and renal cell carcinoma (14.7%, 15/102). On average, case presentation at MTB took place 54.9 months after initial diagnosis and after 2.7 previous lines of therapy. During the study period 49.0% (50/102) of patients deceased. Additional MTB-based treatment recommendations were achieved in a majority of 68.6% (70/102) of patients, with a recommendation for targeted therapy in 64.3% (45/70) of these patients. Only 6.7% (3/45) of patients – due to different reasons – received the recommended MTB-based therapy tough, with 33% (1/3) of patients reaching disease control. Throughout the MTB study period, GU cancer case presentations and treatment recommendations increased, while the time interval between initial presentation and final therapy recommendation were decreasing over time. Conclusion: Presentation of uro-oncological patients at the MTB is a highly valuable measure for clinical decision-making. Prospectively, earlier presentation of patients at the MTB and changing legislative issues regarding comprehensive molecular testing and targeted treatment approval might further improve patients’ benefits from comprehensive molecular diagnostics.

Online evolution of a phased array for ultrasonic imaging by a novel adaptive data acquisition method

Ultrasonic imaging, using ultrasonic phased arrays, has an enormous impact in science, medicine and society and is a widely used modality in many application fields. The maximum amount of information which can be captured by an array is provided by the data acquisition method capturing the complete data set of signals from all possible combinations of ultrasonic generation and detection elements of a dense array. However, capturing this complete data set requires long data acquisition time, large number of array elements and transmit channels and produces a large volume of data. All these reasons make such data acquisition unfeasible due to the existing phased array technology or non-applicable to cases requiring fast measurement time. This paper introduces the concept of an adaptive data acquisition process, the Selective Matrix Capture (SMC), which can adapt, dynamically, to specific imaging requirements for efficient ultrasonic imaging. SMC is realised experimentally using Laser Induced Phased Arrays (LIPAs), that use lasers to generate and detect ultrasound. The flexibility and reconfigurability of LIPAs enable the evolution of the array configuration, on-the-fly. The SMC methodology consists of two stages: a stage for detecting and localising regions of interest, by means of iteratively synthesising a sparse array, and a second stage for array optimisation to the region of interest. The delay-and-sum is used as the imaging algorithm and the experimental results are compared to images produced using the complete generation-detection data set. It is shown that SMC, without a priori knowledge of the test sample, is able to achieve comparable results, while preforming ∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim$$\\end{document}10 times faster data acquisition and achieving ∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim$$\\end{document} 10 times reduction in data size.

How Good Is Open Bicycle Network Data? A Countrywide Case Study of Denmark

Cycling is a key ingredient for a sustainability shift of Denmark's transportation system. To increase cycling rates, better bicycle infrastructure networks are required. Planning such networks requires high‐quality infrastructure data, yet the quality of bicycle infrastructure data is understudied. Here, we compare the two largest open data sets on dedicated bicycle infrastructure in Denmark, OpenStreetMap (OSM) and GeoDanmark, in a countrywide data quality assessment, asking whether the data are good enough for network‐based analysis of cycling conditions. We find that neither of the data sets is of sufficient quality, and that data conflation is necessary to obtain a more complete data set. Our analysis of the spatial variation of data quality suggests that rural areas are more prone to incomplete data. We demonstrate that the prevalent method of using infrastructure density as a proxy for data completeness is not suitable for bicycle infrastructure data, and that matching of corresponding features is thus necessary to assess data completeness. Based on our data quality assessment, we recommend strategic mapping efforts toward data completeness, consistent standards to support comparability between different data sources, and increased focus on data topology to ensure high‐quality bicycle network data.

Abstract LB086: Measuring oral DNA damage to evaluate electronic cigarette use as a tobacco harmreduction strategy

Abstract Background: Smokers increase their risk of numerous types of diseases, including lung and oral cancer. Despite this knowledge, approximately 18.7% of U.S. adults are smokers, and millions of smokers across the U.S. struggle to quit tobacco every day. Although difficult due to nicotine addiction, you can increase your chances of quitting with the tools available, and help is an important part of any quitting plan. The prevalence of electronic cigarette (e-cigarette) use has increased as users perceive them to be safer than traditional tobacco smoke. Studies have shown that e-cigarettes contain fewer chemicals than tobacco smoke. Therefore, addressing the effectiveness of e-cigarette use in reducing tobacco harm is essential and is currently a priority for the FDA. Aim: To determine whether combustible tobacco users who are provided with free e-cigarettes and free e-liquids reduce the levels of DNA damage in their oral epithelial cells. Methods: Adult smokers who smoked at least 5 cigarettes per day over the past year and did not plan to quit smoking in the next 3 months were enrolled in a randomized 3-arm clinical trial (NCT03113136): “Usual Brand Cigarettes” (UBC), Low Wattage e-cigarettes (LWe), and High Wattage e-cigarettes (HWe). Free combustible cigarettes or e-cigarettes plus e-liquid were provided during the first 12 weeks according to assigned group. Tobacco product use details, CO level, and oral mucosa samples were collected at 0, 4, and 12 weeks. DNA was extracted from oral cells and damage was quantified by q-PADDA, at three time points (Week 0, 4, 12). Data analysis was performed using ANOVA model. Results: Participants who provided samples at all time points were included in this DNA damage study. 201 complete data sets (UBC=77, LWe=61 and HWe=66) were available for DNA damage quantification at the time of this report. At enrolment (week 0), there were no significant differences in age, sex or tobacco product use across groups. At 12 weeks, combustible tobacco use was significantly reduced in smokers also using LWe or HWe (dual users). At 4 and 12 weeks, the CO levels were reduced in dual users of combustible tobacco and e-cigarettes (LWe or HWe), compared to exclusive combustible tobacco users. Compared to combustible tobacco users, dual users of combustible tobacco and LWe or HWe showed a significant reduction in the levels of oral mucosa DNA damage at 4 and 12 weeks (3 to 5 fold). Conclusion: Our study shows that when provided with free e-cigarette devices and e-liquids, smokers who were dual users or completely switched to LWe or HWe significantly reduced the levels of DNA damage in their oral mucosa compared with exclusive combustible tobacco users. These data suggest that LW and HW e-cigarette devices are an effective harm reduction tool and might help to reduce tobacco-associated health effects. Funding: NIH/NCI (R01CA204891, Wagener; R01CA242168, Queimado). Citation Format: Vengatesh Ganapathy, Mayilvanan Chinnaiyan, Daniel Brobst, Balaji Sadhasivam, Jean D. Nshimiyimana, Yan Zhao, Theodore Wagener, Lurdes Queimado. Measuring oral DNA damage to evaluate electronic cigarette use as a tobacco harmreduction strategy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB086.

Atrioventricular optimization improves cardiac resynchronization response in patients with long interventricular electrical delays: A pooled analysis of the SMART-AV and SMART-CRT trials

BackgroundThe utility of atrioventricular (AV) optimization (AVO) algorithms remains in question. A substudy of the SMART-AV trial found that patients with prolonged interventricular delays ≥70 ms were more likely to benefit from cardiac resynchronization therapy (CRT) with AVO. The SMART-CRT trial evaluated AVO on the basis of these results, but the study was underpowered. ObjectiveTo increase statistical power, data from SMART-AV patients meeting the inclusion criterion of interventricular delay ≥70 ms were pooled with data from SMART-CRT to reassess AVO. MethodsSMART-CRT and SMART-AV were prospective, randomized, multicenter clinical trials. Patients in both studies were randomized to be programmed with an AVO algorithm (SmartDelay) or fixed AV delay (120 ms). Paired echocardiograms obtained at baseline and 6 months were compared, with CRT response defined as ≥15% reduction in left ventricular end-systolic volume. ResultsA total of 451 complete patient data sets were pooled and analyzed. The baseline demographics between studies did not differ statistically in terms of age, sex, left ventricular ejection fraction, or left ventricular end-systolic volume. The AVO group had a greater proportion of CRT responders (SmartDelay, 73.9%; fixed, 63.1%; P = .014) and greater changes in measures of reverse remodeling. SmartDelay patients with a recommended sensed AV delay outside the nominal range (100–120 ms) had 2.3 greater odds of CRT response than fixed AV delay patients. ConclusionGreater CRT response and measures of reverse remodeling were observed in patients with SmartDelay enabled vs a fixed AV delay. This study supports the use of SmartDelay in patients with a CRT indication and interventricular delay ≥70 ms. ClinicalTrials.gov registrationNCT00677014 and NCT03089281

AIRPORT: A Data Consistency Constrained Deep Temporal Extrapolation Method To Improve Temporal Resolution In Contrast Enhanced CT Imaging.

Typical tomographic image reconstruction methods require that the imaged object is static and stationary during the time window to acquire a minimally complete data set. The violation of this requirement leads to temporal-averaging errors in the reconstructed images. For a fixed gantry rotation speed, to reduce the errors, it is desired to reconstruct images using data acquired over a narrower angular range, i.e., with a higher temporal resolution. However, image reconstruction with a narrower angular range violates the data sufficiency condition, resulting in severe data-insufficiency-induced errors. The purpose of this work is to decouple the trade-off between these two types of errors in contrast-enhanced computed tomography (CT) imaging. We demonstrated that using the developed data consistency constrained deep temporal extrapolation method (AIRPORT), the entire time-varying imaged object can be accurately reconstructed with 40 frames-per-second temporal resolution, the time window needed to acquire a single projection view data using a typical C-arm cone-beam CT system. AIRPORT is applicable to general non-sparse imaging tasks using a single short-scan data acquisition.

Use of Sequential Hot-Deck Imputation for Missing Health Care Systems Data for Population Health Research.

Electronic medical record (EMR) data present many opportunities for population health research. The use of EMR data for population risk models can be impeded by the high proportion of missingness in key patient variables. Common approaches like complete case analysis and multiple imputation may not be appropriate for some population health initiatives that require a single, complete analytic data set. In this study, we demonstrate a sequential hot-deck imputation (HDI) procedure to address missingness in a set of cardiometabolic measures in an EMR data set. We assessed the performance of sequential HDI within the individual variables and a commonly used composite risk score. A data set of cardiometabolic measures based on EMR data from 2 large urban hospitals was used to create a benchmark data set with simulated missingness. Sequential HDI was applied, and the resulting data were used to calculate atherosclerotic cardiovascular disease risk scores. The performance of the imputation approach was assessed using a set of metrics to evaluate the distribution and validity of the imputed data. Of the 567,841 patients, 65% had at least 1 missing cardiometabolic measure. Sequential HDI resulted in the distribution of variables and risk scores that reflected those in the simulated data while retaining correlation. When stratified by age and sex, risk scores were plausible and captured patterns expected in the general population. The use of sequential HDI was shown to be a suitable approach to multivariate missingness in EMR data. Sequential HDI could benefit population health research by providing a straightforward, computationally nonintensive approach to missing EMR data that results in a single analytic data set.

Homogenized finite element analysis of distal tibia sections: Achievements and limitations

High-resolution peripheral quantitative computed tomography (HR-pQCT) based micro-finite element (μFE) analysis allows accurate prediction of stiffness and ultimate load of standardised (∼1 cm) distal radius and tibia sections. An alternative homogenized finite element method (hFE) was recently validated to compute the ultimate load of larger (∼2 cm) distal radius sections that include Colles' fracture sites. Since the mechanical integrity of the weight-bearing distal tibia is gaining clinical interest, it has been shown that the same properties can be used to predict the strength of both distal segments of the radius and the tibia. Despite the capacity of hFE to predict structural properties of distal segments of the radius and the tibia, the limitations of such homogenization scheme remain unclear. Therefore, the objective of this study is to build a complete mechanical data set of the compressive behavior of distal segments of the tibia and to compare quantitatively the structural properties with the hFE predictions. As a further aim, it is intended to verify whether hFE is also able to capture the post-yield strain localisation or fracture zones in such a bone section, despite the absence of strain softening in the constitutive model.Twenty-five fresh-frozen distal parts of tibias of human donors were used in this study. Sections were cut corresponding to an in-house triple-stack protocol HR-pQCT scan, lapped, and scanned using micro computed tomography (μCT). The sections were tested in compression until failure, unloaded and scanned again in μCT. Volumetric bone mineral density (vBMD) and bone mineral content (BMC) were correlated to compression test results. hFE analysis was performed in order to compare computational predictions (stiffness, yield load and plastic deformation field pattern) with the compressive experiment. Namely, strain localization was assessed based on digital volume correlation (DVC) results and qualitatively compared to hFE predictions by comparing mid-slices patterns.Bone mineral content (BMC) showed a good correlation with stiffness (R2 = 0.92) and yield (R2 = 0.88). Structural parameters also showed good agreement between the experiment and hFE for both stiffness (R2 = 0.96, slope = 1.05 with 95 % CI [0.97, 1.14]) and yield (R2 = 0.95, slope = 1.04 [0.94, 1.13]). The qualitative comparison between hFE and DVC strain localization patterns allowed the classification of the samples into 3 categories: bad (15 sections), semi (8), and good agreement (2).The good correlations between BMC or hFE and experiment for structural parameters were similar to those obtained previously for the distal part of the radius. The failure zones determined by hFE corresponded to registration only in 8 % of the cases. We attribute these discrepancies to local elastic/plastic buckling effects that are not captured by the continuum-based FE approach exempt from strain softening. A way to improve strain localization hFE prediction would be to use longer distal segments with intact cortical shells, as done for the radius. To conclude, the used hFE scheme captures the elastic and yield response of the tibia sections reliably but not the subsequent failure process.

Robust deep learning from incomplete annotation for accurate lung nodule detection

Deep learning plays a significant role in the detection of pulmonary nodules in low-dose computed tomography (LDCT) scans, contributing to the diagnosis and treatment of lung cancer. Nevertheless, its effectiveness often relies on the availability of extensive, meticulously annotated dataset. In this paper, we explore the utilization of an incompletely annotated dataset for pulmonary nodules detection and introduce the FULFIL (Forecasting Uncompleted Labels For Inexpensive Lung nodule detection) algorithm as an innovative approach. By instructing annotators to label only the nodules they are most confident about, without requiring complete coverage, we can substantially reduce annotation costs. Nevertheless, this approach results in an incompletely annotated dataset, which presents challenges when training deep learning models.Within the FULFIL algorithm, we employ Graph Convolution Network (GCN) to discover the relationships between annotated and unannotated nodules for self-adaptively completing the annotation. Meanwhile, a teacher–student framework is employed for self-adaptive learning using the completed annotation dataset. Furthermore, we have designed a Dual-Views loss to leverage different data perspectives, aiding the model in acquiring robust features and enhancing generalization. We carried out experiments using the LUng Nodule Analysis (LUNA) dataset, achieving a sensitivity of 0.574 at a False positives per scan (FPs/scan) of 0.125 with only 10% instance-level annotations for nodules. This performance outperformed comparative methods by 7.00%. Experimental comparisons were conducted to evaluate the performance of our model and human experts on test dataset. The results demonstrate that our model can achieve a comparable level of performance to that of human experts. The comprehensive experimental results demonstrate that FULFIL can effectively leverage an incomplete pulmonary nodule dataset to develop a robust deep learning model, making it a promising tool for assisting in lung nodule detection.

Telestroke activity across Europe; The results of a European Stroke Organization survey

IntroductionTelestroke care is likely not inferior to face-to-face care in acute stroke management while it also provides rural sites with access to specialist expertise. However, little is known about the distribution and activity of telestroke networks across Europe. Consequently, the European Stroke Organization (ESO) Telestroke Committee aimed to address this through an online questionnaire.MethodsThe questionnaire was developed through an unstructured consensus process, ratified by the ESO Executive Committee, and emailed to ESO members.ResultsOf 2,147 ESO members contacted, complete data sets were submitted on 25 networks from 10 countries. Among the 25 networks, the mean number of hubs per network was 1.6 (SD 1.2), and the mean number of spokes was 9 (SD 6.7), with considerable variability observed (range 2–24 spokes/network). All sites used audiovisual communication. The mean telemedicine consultations per year per site was 197 (SD 164). The primary reason for consultation was “diagnostic and triage purposes” in all but one network. The median number of strokes per site was 175 (interquartile range 192), and the mean intervention rate was 12.3% (SD 10; thrombolysis or thrombectomy).ConclusionAt 25 networks, this survey probably underrepresents telestroke activity across Europe, yet it is still the first study to provide a continent-wide geographical footprint and report on activity within the networks. There was considerable variability in network size and activity. Spoke sites reported an acceptable intervention rate of 12.3%. This percentage compares favorably with national data from European countries and suggests telestroke care supports reasonable intervention rates.

Improved Approach for ab Initio Calculations of Rate Coefficients for Secondary Reactions in Acrylate Free-Radical Polymerization.

Secondary reactions in radical polymerization pose a challenge when creating kinetic models for predicting polymer structures. Despite the high impact of these reactions in the polymer structure, their effects are difficult to isolate and measure to produce kinetic data. To this end, we used solvation-corrected M06-2X/6-311+G(d,p) ab initio calculations to predict a complete and consistent data set of intrinsic rate coefficients of the secondary reactions in acrylate radical polymerization, including backbiting, β-scission, radical migration, macromonomer propagation, mid-chain radical propagation, chain transfer to monomer and chain transfer to polymer. Two new approaches towards computationally predicting rate coefficients for secondary reactions are proposed: (i) explicit accounting for all possible enantiomers for reactions involving optically active centers; (ii) imposing reduced flexibility if the reaction center is in the middle of the polymer chain. The accuracy and reliability of the ab initio predictions were benchmarked against experimental data via kinetic Monte Carlo simulations under three sufficiently different experimental conditions: a high-frequency modulated polymerization process in the transient regime, a low-frequency modulated process in the sliding regime at both low and high temperatures and a degradation process in the absence of free monomers. The complete and consistent ab initio data set compiled in this work predicts a good agreement when benchmarked via kMC simulations against experimental data, which is a technique never used before for computational chemistry. The simulation results show that these two newly proposed approaches are promising for bridging the gap between experimental and computational chemistry methods in polymer reaction engineering.

A deep learning method to identify and localize large-vessel occlusions from cerebral digital subtraction angiography.

An essential step during endovascular thrombectomy is identifying the occluded arterial vessel on a cerebral digital subtraction angiogram (DSA). We developed an algorithm that can detect and localize the position of occlusions in cerebral DSA. We retrospectively collected cerebral DSAs from a single institution between 2018 and 2020 from 188 patients, 86 of whom suffered occlusions of the M1 and proximal M2 segments. We trained an ensemble of deep-learning models on fewer than 60 large-vessel occlusion (LVO)-positive patients. We evaluated the model on an independent test set and evaluated the truth of its predicted localizations using Intersection over Union and expert review. On an independent test set of 166 cerebral DSA frames with an LVO prevalence of 0.19, the model achieved a specificity of 0.95 (95% confidence interval [CI]: 0.90, 0.99), a precision of 0.7450 (95% CI: 0.64,0.88), and a sensitivity of 0.76 (95% CI: 0.66, 0.91). The model correctly localized the LVO in at least one frame in 13 of the 14 LVO-positive patients in the test set. The model achieved a precision of 0.67 (95% CI: 0.52, 0.79), recall of 0.69 (95% CI: 0.46, 0.81), and a mean average precision of 0.75 (95% CI: 0.56, 0.91). This work demonstrates that a deep learning strategy using a limited dataset can generate effective representations used to identify LVOs. Generating an expanded and more complete dataset of LVOs with obstructed LVOs is likely the best way to improve the model's ability to localize LVOs.

The Reliability Inference for Multicomponent Stress–Strength Model under the Burr X Distribution

The reliability of the multicomponent stress–strength system was investigated under the two-parameter Burr X distribution model. Based on the structure of the system, the type II censored sample of strength and random sample of stress were obtained for the study. The maximum likelihood estimators were established by utilizing the type II censored Burr X distributed strength and complete random stress data sets collected from the multicomponent system. Two related approximate confidence intervals were achieved by utilizing the delta method under the asymptotic normal distribution theory and parametric bootstrap procedure. Meanwhile, point and confidence interval estimators based on alternative generalized pivotal quantities were derived. Furthermore, a likelihood ratio test to infer the equality of both scalar parameters is provided. Finally, a practical example is provided for illustration.

Assessing the multivariate distributional accuracy of common imputation methods

Imputation methods are popular tools that allow for a wide range of subsequent analyses on complete data sets. However, in order for these analyses to be trustworthy, it is important that the imputation procedure reflects the true distribution of the unobserved data sufficiently well. This raises the question how well different imputation methods can reproduce multivariate correlations, associations or even the entire multivariate distribution. The paper gives first answers to this question by means of an extensive comparative simulation study. In particular, we evaluate the multivariate distributional accuracy for six state-of-the art imputation algorithms with respect to different measures and give practical recommendations.

Assessment of fatty infiltration of the hamstring muscles in chronic proximal hamstring ruptures and effect on clinical outcomes after surgical repair: a novel application of the Goutallier classification.

Increased time to surgery has been previously associated with poorer clinical outcomes after surgical treatment of proximal hamstring ruptures, though the etiology remains unclear. The purpose of this study was to evaluate whether degree of muscle atrophy, as assessed using the Goutallier classification system, is associated with worse outcomes following surgical treatment of chronic proximal hamstring ruptures. This was a retrospective case series of patients who underwent repair of proximal hamstring ruptures from 2012 to 2020 with minimum 2-year follow-up. Patients were included if they underwent primary repair of a proximal hamstring rupture ≥ 6weeks after the date of injury and had accessible preoperative magnetic resonance imaging (MRI). Exclusion criteria were allograft reconstruction, endoscopic repair, or prior ipsilateral hip surgery. Patients were administered validated surveys: the modified Harris Hip Score (mHHS) and Perth Hamstring Assessment Tool (PHAT). Fatty atrophy on preoperative MRI was independently graded by two musculoskeletal radiologists using the Goutallier classification. Multivariate regression analysis was performed to evaluate associations of preoperative characteristics with muscle atrophy, as well as mHHS and PHAT scores. Complete data sets were obtained for 27 patients. A majority of this cohort was male (63.0%), with a mean age of 51.5 ± 11.8years and BMI of 26.3 ± 3.8. The mean follow-up time was 62.6 ± 23.1months, and the mean time from injury-to-surgery was 20.4 ± 15.3weeks. The Goutallier grading inter-reader weighted kappa coefficient was 0.655. Regression analysis demonstrated that atrophy was not significantly associated with PHAT (p = 0.542) or mHHS (p = 0.574) at latest follow-up. Increased age was significantly predictive of muscle atrophy (β = 0.62, p = 0.005) and was also found to be a significant predictor of poorer mHHS (β = - 0.75; p = 0.037). The degree of atrophy was not found to be an independent predictor of clinical outcomes following repair of chronic proximal hamstring ruptures. Increasing age was significantly predictive of increased atrophy and poorer patient-reported outcomes.

[The German Transplant Registry - An Analysis of Legacy Data 2006-2016].

In 2018, medical transplant data from three institutions were merged to create a German transplant registry. Since June 2021, access to data of the registry has been available. It was planned to analyze the registry data in order to compare special allocation rules with regular allocation for heart, liver, lung, and kidney transplantation. Our approach led to a quality analysis of the registry. Upon request, legacy data (2006-2016) of the registry was provided, divided into 61 elements. From these elements, the user had to compile the required dataset. Data checks were performed for completeness, correct allocation of information, and consistency among different sources. Software used for these tasks included R, SQL, and Excel. The initial elements ("waiting list" elements) of the four types of transplantations contained data from a total of 80,259 originally listed patients. However, these patients were only partially present in other elements resulting in complete datasets reflecting waiting time in only 23%, 30%, 50%, and 96%, and for post-transplantation outcomes in 14%, 11%, 38%, and 13% (heart, liver, lung, and kidney transplantation, respectively). The linking of urgency information with clinical data was successful in only a small proportion, with only 6% for heart transplantation. Incorrect and thus implausible allocations in the case of special allocation rules indicated incorrect entries in the registry. Data from different data providers were inconsistent. The incompleteness and incorrect data allocation raise doubts about the reliability of scientific studies based on the transplant registry. The complex structure also hinders the compilation of a reliable dataset, which is uncommon internationally. New data (acquisition since 2017) has only been available since December 2023. The transplant registry urgently needs restructuring. Competent clinical data management, involving transplant medical expertise, and continuous quality controls are essential in this process.

Single-cell operando SOC and SOH diagnosis in a 24 V lithium iron phosphate battery with a voltage-controlled model

Batteries typically consist of multiple individual cells connected in series. Here we demonstrate single-cell state of charge (SOC) and state of health (SOH) diagnosis in a 24 V class lithium-ion battery. To this goal, we introduce and apply a novel, highly efficient algorithm based on a voltage-controlled model (VCM). The battery, consisting of eight single cells, is cycled over a duration of five months under a simple cycling protocol between 20 % and 100 % SOC. The cell-to-cell standard deviations obtained with the novel algorithm were 1.25 SOC-% and 1.07 SOH-% at beginning of cycling. A cell-averaged capacity loss of 9.9 % after five months cycling was observed. While the accuracy of single-cell SOC estimation was limited (probably owed to the flat voltage characteristics of the lithium iron phosphate, LFP, chemistry investigated here), single-cell SOH estimation showed a high accuracy (2.09 SOH-% mean absolute error compared to laboratory reference tests). Because the algorithm does not require observers, filters, or neural networks, it is computationally very efficient (three seconds analysis time for the complete data set consisting of eight cells with approx. 780.000 measurement points per cell).

TOMMicroNet: Convolutional Neural Networks for Smartphone-Based Microscopic Detection of Tomato Biotic and Abiotic Plant Health Issues.

The image-based detection and classification of plant diseases has become increasingly important to the development of precision agriculture. We consider the case of tomato, a high-value crop supporting the livelihoods of many farmers around the world. Many biotic and abiotic plant health issues impede the efficient production of this crop, and laboratory-based diagnostics are inaccessible in many remote regions. Early detection of these plant health issues is essential for efficient and accurate response, prompting exploration of alternatives for field detection. Considering the availability of low-cost smartphones, artificial intelligence-based classification facilitated by mobile phone imagery can be a practical option. This study introduces a smartphone-attachable 30x microscopic lens, used to produce the novel tomato microimaging dataset of 8500 images representing 34 tomato plant conditions on the upper and lower sides of leaves as well as on the surface of tomato fruits. We introduce TOMMicroNet, a 14-layer convolutional neural network (CNN) trained to classify amongst biotic and abiotic plant health issues, and we compare it against six existing pre-trained CNN models. We compared two separate pipelines of grouping data for training TOMMicroNet, either presenting all data at once or separating into subsets based on the three parts of the plant. Comparing configurations based on cross-validation and F1 scores, we determined that TOMMicroNet attained the highest performance when trained on the complete dataset, with 95% classification accuracy on both training and external datasets. Given TOMMicroNet's capabilities when presented with unfamiliar data, this approach has the potential for the identification of plant health issues.