Aim Predicting progression in diabetic kidney disease (DKD) is critical to improving outcomes. We sought to develop/validate a machine-learned, prognostic risk score (KidneyIntelX™) combining electronic health records (EHR) and biomarkers. Methods This is an observational cohort study of patients with prevalent DKD/banked plasma from two EHR-linked biobanks. A random forest model was trained, and performance (AUC, positive and negative predictive values [PPV/NPV], and net reclassification index [NRI]) was compared with that of a clinical model and Kidney Disease: Improving Global Outcomes (KDIGO) categories for predicting a composite outcome of eGFR decline of ≥5 ml/min per year, ≥40% sustained decline, or kidney failure within 5 years. Results In 1146 patients, the median age was 63 years, 51% were female, the baseline eGFR was 54 ml min−1 [1.73 m]−2, the urine albumin to creatinine ratio (uACR) was 6.9 mg/mmol, follow-up was 4.3 years and 21% had the composite endpoint. On cross-validation in derivation (n = 686), KidneyIntelX had an AUC of 0.77 (95% CI 0.74, 0.79). In validation (n = 460), the AUC was 0.77 (95% CI 0.76, 0.79). By comparison, the AUC for the clinical model was 0.62 (95% CI 0.61, 0.63) in derivation and 0.61 (95% CI 0.60, 0.63) in validation. Using derivation cut-offs, KidneyIntelX stratified 46%, 37% and 17% of the validation cohort into low-, intermediate- and high-risk groups for the composite kidney endpoint, respectively. The PPV for progressive decline in kidney function in the high-risk group was 61% for KidneyIntelX vs 40% for the highest risk strata by KDIGO categorisation (p < 0.001). Only 10% of those scored as low risk by KidneyIntelX experienced progression (i.e., NPV of 90%). The NRIevent for the high-risk group was 41% (p < 0.05). Conclusions KidneyIntelX improved prediction of kidney outcomes over KDIGO and clinical models in individuals with early stages of DKD. Graphical abstract

BackgroundIndividuals with type 2 diabetes (T2D) or the apolipoprotein L1 high-risk (APOL1-HR) genotypes are at increased risk of rapid kidney function decline (RKFD) and kidney failure. We hypothesized that a prognostic test using machine learning integrating blood biomarkers and longitudinal electronic health record (EHR) data would improve risk stratification.MethodsWe selected two cohorts from the Mount Sinai BioMe Biobank: T2D (n=871) and African ancestry with APOL1-HR (n=498). We measured plasma tumor necrosis factor receptors (TNFR) 1 and 2 and kidney injury molecule-1 (KIM-1) and used random forest algorithms to integrate biomarker and EHR data to generate a risk score for a composite outcome: RKFD (eGFR decline of ≥5 ml/min per year), or 40% sustained eGFR decline, or kidney failure. We compared performance to a validated clinical model and applied thresholds to assess the utility of the prognostic test (KidneyIntelX) to accurately stratify patients into risk categories.ResultsOverall, 23% of those with T2D and 18% of those with APOL1-HR experienced the composite kidney end point over a median follow-up of 4.6 and 5.9 years, respectively. The area under the receiver operator characteristic curve (AUC) of KidneyIntelX was 0.77 (95% CI, 0.75 to 0.79) in T2D, and 0.80 (95% CI, 0.77 to 0.83) in APOL1-HR, outperforming the clinical models (AUC, 0.66 [95% CI, 0.65 to 0.67] and 0.72 [95% CI, 0.71 to 0.73], respectively; P<0.001). The positive predictive values for KidneyIntelX were 62% and 62% versus 46% and 39% for the clinical models (P<0.01) in high-risk (top 15%) stratum for T2D and APOL1-HR, respectively. The negative predictive values for KidneyIntelX were 92% in T2D and 96% for APOL1-HR versus 85% and 93% for the clinical model, respectively (P=0.76 and 0.93, respectively), in low-risk stratum (bottom 50%).ConclusionsIn patients with T2D or APOL1-HR, a prognostic test (KidneyIntelX) integrating biomarker levels with longitudinal EHR data significantly improved prediction of a composite kidney end point of RKFD, 40% decline in eGFR, or kidney failure over validated clinical models.

Mixed Layer Experiment (Mile) and the July-September 1978 Joint Air-Sea Interaction (Jasin) project, moored current measurements were made in the upper ocean with Savonius rotor and vane vector-averaging current meters (VACM), dual orthogonal propeller vector-measuring current meters (VMCM), and dual orthogonal acoustic travel-time vector-averaging current meters (ACM). Wind speeds and significant wave heights reached 20 m s -I and 5 m. The influence of mooring motion upon ACM, VACM, and VMCM measurements are described. In the mixed layer above about 30 m depth where mean currents are relatively large, the effect of a surface-following buoy upon ACM, VACM, and VMCM velocity fluctuations at frequencies less than 0.3 cph was negligible; at frequencies above 4 cph, the VACM data contained the largest mount of mooring induced contamination. Below the mixed layer at depths greater than about 75 m, a subsurface mooring should be used; however, when a surface-following buoy was used, then VMCM data better approximated the spectrum of the fluctuations than VACM data. A spar-buoy should not be used to measure currents at depths as deep as 80 m. The frequency-dependent differences between VACM and VMCM and between VACM and ACM measurements are described. At frequencies less than 0.3 cph, the differences between the VACM and ACM or the VMCM records were not significant with 95% confidence limits, were always positive, and above 80 m depth were less than 20%. At frequencies above 4 cph, the VACM-VMCM differences were about 5 times larger than the VACM-ACM differences.ing buoys needs to be estimated. In this paper, current mea. surements recorded during the Mixed Layer Experiment (Mile) and the Joint Air-Sea Interaction (Jasin) project, when wind speeds and significant wave heights reached 20 m s -• and 5 m (i.e., when mooring motions were expected to be large), are used to investigate the relative performance of different kinds of current meters. VACM, acoustic current meter (ACM), and vector-measuring current meter (VMCM) (described in the instrumentation section) measurements recorded beneath surface-following buoys are compared to similar measurements recorded at approximately the same depth from spar buoys or subsurface moorings to describe the influence of mooring motion upon each type of measurement. In addition, the in situ differences between a VACM, an ACM, and a VMCM mounted on the same mooring are determined. EXPERIMENTS The 19-day Mile observations, which occurred in water about 4200 m deep at ocean weather station P (50øN, 145øW), began August 19, 1977. The average wind speed (measured from the R.V. Oceanographer) and significant wave height (measured with a waverider buoy) were 10.0 m s -• and 2.2 m, respectively (Figure 1), and at 8 m depth the vector-mean current speed was 6 cm s -•. During the initial 5-day period when all moorings remained on-station, an intense low (•992 mbar) atmospheric pressure system moved slowly over the experimental site. For about 2 days before the arrival of the storm, the average ...

GPS is currently being installed; when it is operational, five satellites will be in view virtually 100 percent of the time. However, the full constellation will not be in place for several years, and even then satellite failures may introduce coverage holes. Additionally, even with the full constellation, a GPS receiver will not always be able to monitor signal integrity unless some external aid is used. Loran‐C can be used effectively to aid GPS. A hybrid receiver can combine Loran lines of position with GPS lines of position to provide a system with great integrity, reliability, and time availability. Indeed, Loran is independent of GPS and can be used to detect GPS system or receiver failures. However, the hybrid receivers must include sophisticated Loran propagation models, which account for both the spatial and temporal anomalies of the propagation paths. Fortunately, GPS can be used to efficiently gather the data for these models. Additionally, GPS observations could be used to calibrate (or tune) these models in real time. This paper reviews the theory of Loran propagation and gives a basic Loran pseudorange model. It also presents some basic approaches for combining Loran and GPS pseudoranges in a position‐fixing receiver. Finally, it estimates the accuracy of the hybrid system.

Introduction: Individuals with type 2 diabetes (T2DM) or the APOL1 high-risk genotype (APOL1) are at increased risk of rapid kidney function decline (RKFD) as compared to the general population. Plasma biomarkers representing inflammatory and kidney injury pathways have been validated as predictive of kidney disease progression in several studies. In addition, routine clinical data in the electronic health record (EHR) may also be utilized for predictive purposes. The application of machine learning to integrate biomarkers with clinical data may lead to improved identification of RKFD. Methods:We selected two subpopulations of high-risk individuals: T2DM (n=871) and APOL1 high risk genotype of African Ancestry (n=498), with a baseline eGFR ≥ 45 ml/min/1.73 m 2 from the Mount Sinai BioMe Biobank. Plasma levels of tumor necrosis factor 1/2 (TNFR1/2), and kidney injury molecule-1 (KIM-1) were measured and a series of supervised machine learning approaches including random forest (RF) were employed to combine the biomarker data with longitudinal clinical variables. The primary objective was to accurately predict RKFD (eGFR decline of ≥ 5 ml/min/1.73 m 2 /year) based on an algorithm-produced score and probability cutoffs, with results compared to standard of care. Results:In 871 participants with T2DM, the mean age was 61 years, baseline estimated glomerular filtration rate (eGFR) was 74 ml/min/1.73 m 2 , and median UACR was 13 mg/g. The median follow-up was 4.7 years from the baseline specimen collection with additional retrospective data available for a median of 2.3 years prior to plasma collection. In the 498 African Ancestry patients with high-risk APOL1 genotype, the median age was 56 years, median baseline eGFR was 83 ml/min/1.73 m 2 ,and median UACR was 11 mg/g. The median follow-up was 4.7 years and there was additional retrospective data available for 3.1 years prior to plasma collection. Overall, 19% with T2DM, and 9% of the APOL1 high-risk genotype experienced RKFD. After evaluation of three supervised algorithms: random forest (RF), support vector machine (SVM), and Cox survival, the RF model was selected. In the training and test sets respectively, the RF model had an AUC of 0.82 (95% CI, 0.81-0.83) and 0.80 (95% CI, 0.78-0.82) in T2DM, and an AUC of 0.85 (95% CI, 0.84-0.87) and 0.80 (95% CI, 0.73-0.86) for the APOL1 high-risk group. The combined RF model outperformed standard clinical variables in both patient populations. Discrimination was comparable in two sensitivity analyses: 1) Using only data from ≤ 1 year prior to baseline biomarker measurement and 2) In individuals with eGFR ≤ 60 and/or albuminuria at baseline. The distribution of RFKD probability varied in the two populations. In patients with T2DM, the RKFD score stratified 18%, 49%, and 33% of patients to high-, intermediate-, and lowprobability strata, respectively, with a PPV of 53% in the high-probability group and an NPV of 97% in the lowprobability group. By comparison, in the APOL1 high-risk genotype, the RKFD score stratified 7%, 23%,...