Original Estimates Research Articles

Corrected ERA5 Precipitation by Machine Learning Significantly Improved Flow Simulations for the Third Pole Basins

Abstract Precipitation is one of the most important atmospheric inputs to hydrological models. However, existing precipitation datasets for the Third Pole (TP) basins show large discrepancies in precipitation magnitudes and spatiotemporal patterns, which poses a great challenge to hydrological simulations in the TP basins. In this study, a gridded (10 km × 10 km) daily precipitation dataset is constructed through a random-forest-based machine learning algorithm (RF algorithm) correction of the ERA5 precipitation estimates based on 940 gauges in 11 upper basins of TP for 1951–2020. The dataset is evaluated by gauge observations at point scale and is inversely evaluated by the Variable Infiltration Capacity (VIC) hydrological model linked with a glacier melt algorithm (VIC-Glacier). The corrected ERA5 (ERA5_cor) agrees well with gauge observations after eliminating the severe overestimation in the original ERA5 precipitation. The corrections greatly reduce the original ERA5 precipitation estimates by 10%–50% in 11 basins of the TP and present more details on precipitation spatial variability. The inverse hydrological model evaluation demonstrates the accuracy and rationality, and we provide an updated estimate of runoff components contribution to total runoff in seven upper basins in the TP based on the VIC-Glacier model simulations with the ERA5_cor precipitation. This study provides good precipitation estimates with high spatiotemporal resolution for 11 upper basins in the TP, which are expected to facilitate the hydrological modeling and prediction studies in this high mountainous region. Significance Statement The Third Pole (TP) is the source of water to the people living in the areas downstream. Precipitation is the key driver of the terrestrial hydrological cycle and the most important atmospheric input to land surface hydrological models. However, none of the current precipitation data are equally good for all the TP basins because of high variabilities in their magnitudes and spatiotemporal patterns, posing a great challenge to the hydrological simulation. Therefore, in this study, a gridded daily precipitation dataset (10 km × 10 km) is reconstructed through a random-forest-based machine learning algorithm correction of ERA5 precipitation estimates based on 940 gauges in 11 TP basins for 1951–2020. The data eliminate the severe overestimation of original ERA5 precipitation estimates and present more reasonable spatial variability, and also exhibit a high potential for hydrological application in the TP basins. This study provides long-term precipitation data for climate and hydrological studies and a reference for deriving precipitation in high mountainous regions with complex terrain and limited observations.

327 Parameter Updates in the Davis Growth Model for Growth, Intake, and Composition of Modern Angus-Cross Cattle

Abstract The Davis Growth Model is a dynamic model used to estimate body composition and growth in beef cattle by simulating total body DNA and protein turnover. Since model development and parametrization in the 1980s, the model has been re-structured to account for previous rate of protein accretion and prior nutrition. Original parameter estimates for alpha (maintenance constant, where net energy for maintenance is alpha x shrunk body weight0.75; 0.08406) and K2 (protein synthesis rate constant; 0.0444) may not fit modern, heavier, more productive cattle. Angus-cross steers (n = 24; initial body weight 402 ± 12 kg) were fed using automated feed weight stations in group pens (8 steers/pen). Steers were fed a high energy ration twice daily, at 10% greater than the intake from the previous day. Steers were measured for hip height, body weight (BW), and ultrasonic measurements for ribeye area and back fat every 28 d until harvest (d 128). These measurements were used to calculate empty BW, empty body fat, and empty percent body fat for alpha and K2 estimates. Parameter estimates (± SD) were 0.12661 (0.01587) and 0.04919 (0.00220) for alpha and K2, respectively. Parameter estimates in the current study reflect a large increase in apparent maintenance. Maintenance requirements were moderately correlated with back fat thickness and feed conversion efficiency. Protein turnover had very strong correlations with ribeye area and feed conversion efficiency. Average feed conversion efficiency in the current study was 0.17. To evaluate parameter estimates, the model was re-run using the new values for alpha and K2. On average, the model underpredicted final empty body weight by 0.08 kg and empty body fat percent by 0.01%-units. Considering the relatively large variation among observed alpha values and the magnitude of increase compared with those previously reported, further research is required to determine an accurate alpha.

Bridging differences across cohorts in the relationship between prenatal secondhand smoke exposure and birth weight: Transportability of effect estimates in the ECHO consortium

Background and Aim: Transportability of effect estimates in epidemiology can potentially help investigators explain the differences in effect estimates for the same exposure-outcome relationships observed across different cohorts. In this study we aim to explain potential differences in the relationship between prenatal secondhand smoke exposure (SHS) and birth weight across different cohorts of the Environmental influences on Child Health Outcomes (ECHO) consortium that may be due to population differences in sociodemographic characteristics. Methods: We assessed transportability of effect estimates across 5 different cohorts in the ECHO consortium consisting of 6582 mother-offspring dyads. We first estimated individual cohort effects using Targeted Maximum Likelihood Estimation adjusting for several sociodemographic covariates. We then proceeded to estimate transported effects from one cohort to each of the remaining cohorts using a robust non-parametric estimation approach. We compared the transported effect estimates to the original effect estimates for these cohorts with any decrease in differences between cohort-specific estimates potentially attributable to different sociodemographic variable distributions. Results: Individual cohort effects associated with SHS exposure varied across the 5 cohorts with a range of strongly suggestive results for harmful effects of exposure (149.0 gram decrease in birthweight (95% confidence interval (CI): -254.4, -43.5), to results on the protective side of the null albeit with very wide confidence intervals. Transported effect estimates partially explained differences in 3 out of the 4 cohort pairs, explaining 7% to 97% of the differences in the effect point estimates. Conclusion: Differences in sociodemographic characteristics across different populations may partially explain differences observed in the relationship between prenatal SHS exposure and birth weight across cohorts in the ECHO consortium. This framework could have broader implications in in multi-cohort consortiums by aiding in the comparability of cross-cohort results. Keywords: transportability, causal inference, machine learning, secondhand smoke, birth outcomes

Reply.

We thank Dr. Duan for the interest in our paper that reported the association between postvaccination and overall and coronavirus disease 2019 (COVID‐19)–related mortality among participants with cirrhosis. We adjusted for the etiology of alcohol‐associated liver disease versus others based on studies that showed a worse prognosis of COVID‐19 associated with alcohol.1–4 However, as suggested, we now include the various etiologies of liver disease in the baseline characteristics (Table S1). The most common cause of cirrhosis in both the postvaccination and unvaccinated COVID‐19 cohorts was NAFLD (28.7% and 28.2%, respectively), and the two groups were well matched with respect to liver disease etiology. We agree that exposure to proton pump inhibitors (PPIs) may be a potential confounder. Participants with postvaccination COVID‐19 were more likely to be exposed to PPIs than those with unvaccinated COVID‐19 (80.3 vs. 65.8%; p < 0.0001). Third, Dr. Duan suggests socioeconomic status as a potential confounder. Although we did not have data on individual income levels, we examined the socioeconomic status by identifying participant locations using residential zip codes and the median household income associated with these locations. Median household incomes were similar between the two groups ($47,400 vs. 47,100; p = 0.64). We repeated the analysis by including these three variables in the multivariable model (Table 1). Compared with NAFLD cirrhosis, alcohol and HCV cirrhosis were not associated with an increase in overall or COVID‐19‐related death. We observed no association between the median household income and overall (per $1000 change in household income; adjusted HR [aHR] 0.98, 95% CI 0.93–1.07; p = 0.16) or COVID‐19‐related death (aHR 0.99, 95% CI 0.97–1.03; p = 0.11). However, PPI exposure was associated with an increase in overall mortality (aHR 1.61, 95% CI 1.06–2.15; p = 0.001), but not COVID‐19‐related death (aHR 1.08, 95% CI 0.55–1.54; p = 0.75). After inclusion of these variables, postvaccination COVID‐19 continued to be associated with a decrease in overall (aHR 0.25, 95% CI 0.12–0.49; p < 0.0001) and COVID‐19‐related death (aHR 0.27, 95% CI 0.13–0.60; p = 0.001). TABLE 1 - Multivariable HRs for the risk of overall death or COVID‐19‐related death in patients with postvaccination COVID‐19 versus unvaccinated COVID‐19 Variable Overall death COVID‐19‐related death aHR (95% CI) p‐Value aHR (95% CI) p‐Value Number of patients 762 – 762 – Number of events 87 – 64 – Group Control REF REF Vaccine 0.25 (0.12, 0.49) <0.0001 0.27 (0.13, 0.60) 0.0011 Location, n (%) Northeast REF REF Southeast 1.13 (0.51, 2.53) 0.7606 1.42 (0.55, 3.65) 0.4726 Midwest 1.30 (0.64, 2.64) 0.4759 1.46 (0.61, 3.48) 0.3914 South 0.61 (0.26, 1.42) 0.2528 0.87 (0.32, 2.38) 0.7897 Northwest 2.47 (0.98, 6.23) 0.0557 2.53 (0.82, 7.81) 0.1067 Southwest 1.54 (0.68, 3.49) 0.2992 2.29 (0.91, 5.80) 0.0801 Age 1.05 (1.02, 1.08) 0.0015 1.06 (1.02, 1.10) 0.0012 BMI 1.00 (0.97, 1.02) 0.6657 1.01 (0.98, 1.03) 0.6598 Diabetes No REF REF Yes 0.92 (0.56, 1.53) 0.9894 0.78 (0.45, 1.34) 0.3673 Etiology at cirrhosis NAFLD REF REF Alcohol 1.01 (0.49, 2.08) 0.9894 1.41 (0.64, 3.12) 0.3942 HCV+Alcohol 0.68 (0.32, 1.43) 0.3043 0.51 (0.20, 1.29) 0.1544 HCV 0.85 (0.45, 1.59) 0.6024 0.93 (0.52, 2.17) 0.8666 Others 0.29 (0.07, 1.18) 0.0841 NA NA AUDIT‐C score Low REF REF High 1.17 (0.53, 2.57) 0.6902 1.21 (0.47, 3.06) 0.6959 eCTP A REF REF B 1.06 (0.60, 1.89) 0.8335 0.83 (0.42, 1.62) 0.5747 C 1.28 (0.11, 4.17) 0.6659 N/A N/A Dexamethasone No REF REF Yes 4.25 (2.08, 8.69) <0.0001 3.78 (1.55, 9.22) 0.0035 Remdesivir No REF REF Yes 0.75 (0.33, 1.70) 0.4926 1.41 (0.55, 3.62) 0.4778 MELD‐Na 1.03 (0.99, 1.08) 0.1183 1.04 (0.99, 1.09) 0.1661 PPI exposure No REF REF Yes 1.61 (1.06, 2.15) 0.0007 1.08 (0.55, 1.54) 0.7511 Median household income per $1000 0.98 (0.93, 1.07) 0.1567 0.99 (0.97, 1.03) 0.1078 Abbreviations: aHR, adjusted HR; AUDIT‐C, Alcohol Use Disorders Identification Test–Concise; BMI, body mass index; COVID‐19, coronavirus disease 2019; eCTP, electronic Child Turcotte Pugh; MELD‐Na, Model for End‐Stage Liver Disease–Sodium; NA, not available; PPI, proton pump inhibitor.Bold indicates p < 0.05. These analyses reveal similar associations described in our original estimates, indicating that postvaccination COVID‐19 is associated with consistent reductions in overall and COVID‐19‐related death.

Local reliability sensitivity method by the reconstructed fraction moment constrained maximum entropy

The local sensitivity of the failure probability (LS-FP) with respect to the distribution parameter of the random input is very important in the field of structural reliability analysis and the reliability based design optimization (RBDO). It can be used not only to measure the effect of distribution parameters of random inputs on the failure probability, but also to provide the exact gradient for the RBDO. In this paper, the LS-FP with respect to the mean parameters and standard deviation parameters are originally derived as the weighted sum of two types of failure probability, one is the original failure probability and the other one is the integration of reconfigurable probability density function (PDF) in failure domain. By reconstructing the optimal PDF, the maximum entropy method based on the fraction moment constraints is then proposed to estimate the sensitivity of the failure probability, which transforms the LS-FP as a byproduct of the original failure probability estimation and avoids the additional computational cost.

Pose determination of passively cooperative spacecraft in close proximity using a monocular camera and AruCo markers

Missions requiring autonomous, close-proximity operations of spacecraft, such as On-Orbit Servicing, On-Orbit Assembly and Active Debris Removal, have become a thriving topic in the aerospace research community over the last decades, not only from an economic, operative, and scientific perspective, but also as a mean of ensuring the sustainability of the space environment. These operations involve a variety of technological challenges, most of which are related to the need of autonomous and safe Guidance, Navigation and Control systems. Since the future of these mission scenarios is strictly tied to spacecraft standardisation and modularity, relative navigation employing monocular cameras on servicing platforms to approach targets equipped with artificial markers for pose estimation purposes has drawn great attention. Following this trend, this paper presents an original vision-based pose estimation architecture for relative navigation with respect to passively cooperative targets equipped with ArUco markers. The proposed architecture foresees two operative modes, namely Acquisition and Tracking. The first features ArUco's detection through hue-saturation-value image representation, their identification by reading their built-in code and the computation of the pose without a-priori knowledge. The second, instead, takes advantage of prior pose estimates to speed up the entire processing pipeline. Performance is assessed through an extensive numerical simulation campaign, considering as test scenario the final approach phase of a rendezvous manoeuvre to reach a satellite belonging to a large constellation in Low Earth Orbit, and using the Planet and Asteroid Natural scene Generation Utility tool for realistic synthetic image generation. The dedicated tests on the Acquisition mode show that successful marker detection and pose initialization is achieved from up to 99.76% of the possible relative position and attitude states of the chaser with respect to the target at beginning of the final approach trajectory. As the chaser gets closer to the target, results highlight significant robustness of both operative modes against illumination conditions and uncertainties in the knowledge of camera intrinsic parameters. Overall, the architecture shows pose estimation accuracies up to millimetric and sub-degree levels.

Impact of modularization and site staffing on construction schedule of small and large water reactors

Recent nuclear construction project schedules in the US and Europe were 2.5x their original estimates due to productivity misses, supply chain challenges, hiring constraints, and other issues. Small modular reactors are proposed as a remedy. Smaller reactors will require one fourth to one third the person-hours of labor and leverage more common supply chains, putting less strain on each step of the construction process. This study presents a methodology for estimating the construction duration of nuclear projects, so the sensitivity of proposed concepts to staffing and labor constraints can be analyzed. The methodology builds on a previous overnight capital cost estimation tool and builds a Gantt chart out of 226 tasks. The genetic algorithm assigns resources to each task to minimize construction time subject to peak staffing and hiring rate constraints. The results were consistently within 15% of seven benchmark durations. Under tighter labor constraints, the construction duration of large reactors increased 42% on average and only 3% for small reactors. Modularization of systems and structures reduced construction durations 60%, but it increased the sensitivity to labor constraints. Small reactors were more sensitive to the modularization assumptions because of the compact structures and layout, and the power specific cost ($/kW) sensitivity was higher, but the total financial impact was smaller because the total costs were lower.

A novel penalized inverse-variance weighted estimator for Mendelian randomization with applications to COVID-19 outcomes.

Mendelian randomization utilizes genetic variants as instrumental variables (IVs) to estimate the causal effect of an exposure variable on an outcome of interest even in the presence of unmeasured confounders. However, the popular inverse‐variance weighted (IVW) estimator could be biased in the presence of weak IVs, a common challenge in MR studies. In this article, we develop a novel penalized inverse‐variance weighted (pIVW) estimator, which adjusts the original IVW estimator to account for the weak IV issue by using a penalization approach to prevent the denominator of the pIVW estimator from being close to zero. Moreover, we adjust the variance estimation of the pIVW estimator to account for the presence of balanced horizontal pleiotropy. We show that the recently proposed debiased IVW (dIVW) estimator is a special case of our proposed pIVW estimator. We further prove that the pIVW estimator has smaller bias and variance than the dIVW estimator under some regularity conditions. We also conduct extensive simulation studies to demonstrate the performance of the proposed pIVW estimator. Furthermore, we apply the pIVW estimator to estimate the causal effects of five obesity‐related exposures on three coronavirus disease 2019 (COVID‐19) outcomes. Notably, we find that hypertensive disease is associated with an increased risk of hospitalized COVID‐19; and peripheral vascular disease and higher body mass index are associated with increased risks of COVID‐19 infection, hospitalized COVID‐19, and critically ill COVID‐19.

The in-vivo dynamics of Plasmodium falciparum HRP2: implications for the use of rapid diagnostic tests in malaria elimination

BackgroundRapid diagnostic tests (RDTs) that rely on the detection of Plasmodium falciparum histidine-rich protein 2 (PfHRP2) have become key tools for diagnosing P. falciparum infection. The utility of RDTs can be limited by PfHRP2 persistence, however it can be a potential benefit in low transmission settings where detection of persistent PfHRP2 using newer ultra-sensitive PfHRP2 based RDTs can serve as a surveillance tool to identify recent exposure. Better understanding of the dynamics of PfHRP2 over the course of a malaria infection can inform optimal use of RDTs.MethodsA previously published mathematical model was refined to mimic the production and decay of PfHRP2 during a malaria infection. Data from 15 individuals from volunteer infection studies were used to update the original model and estimate key model parameters. The refined model was applied to a cohort of patients from Namibia who received treatment for clinical malaria infection for whom longitudinal PfHRP2 concentrations were measured.ResultsThe refinement of the PfHRP2 dynamic model indicated that in malaria naïve hosts, P. falciparum parasites of the 3D7 strain produce 33.6 × 10−15 g (95% CI 25.0–42.1 × 10−15 g) of PfHRP2 in vivo per parasite replication cycle, with an elimination half-life of 1.67 days (95% CI 1.11–3.40 days). The refined model included these updated parameters and incorporated individualized body fluid volume calculations, which improved predictive accuracy when compared to the original model. The performance of the model in predicting clearance of PfHRP2 post treatment in clinical samples from six adults with P. falciparum infection in Namibia improved when using a longer elimination half-life of 4.5 days, with 14% to 67% of observations for each individual within the predicted range.ConclusionsThe updated mathematical model can predict the growth and clearance of PfHRP2 during the production and decay of a mono-infection with P. falciparum, increasing the understanding of PfHRP2 antigen dynamics. This model can guide the optimal use of PfHRP2-based RDTs for reliable diagnosis of P. falciparum infection and re-infection in endemic settings, but also for malaria surveillance and elimination programmes in low transmission areas.

A Re-Analysis of Podsakoff et al. Using the Reliability-Adjusted Percentage Within-Variance.

1) Do psychological constructs vary more between individuals or within them? 2) What study design characteristics predict within-variance? A recent analysis of Podsakoff et al. (2019) provided answers to these questions and examined the percentage within-variance (PWV) for intraindividual studies. Unfortunately, the PWV overestimates the proportion of within-variance under the condition of measurement error (i.e., non-perfect reliability; Wilms et al., 2020). The original results may be biased and the answers for question 1) and 2) may change after correcting for measurement error. Therefore, I re-analyze the original data with an estimator robust to measurement error. The average reliability-adjusted PWV was 0.45 across all construct categories and thus substantially lower than the original estimate of 0.52 (i.e., 15.56% lower). My results show that only time referent appears to play significant role in predicting the PWV. Implications for researchers and reviewers, and limitations are discussed. The reliability-based adjustment of the analysis of Podsakoff et al. (2019) contributes to the previous research by helping reviewers and researchers alike to interpret, assess and compare a study’s results accounting for bias induced by measurement error.

Catastrophic rupture of lunar rocks: Implications for lunar rock size–frequency distributions

Like many airless planetary surfaces, the surface of the Moon is scattered by populations of blocks and smaller boulders. These features decrease in abundance with increasing exposure time due to comminution by impact bombardment and produce regolith. Here we model the evolution of block size–frequency distributions by updating the model of Hörz et al. (1975) with new input functions: the size–frequency distributions of cm–scale meteoroids observed over the last few tens of years and a rock impact shattering function. The impact shattering function is calibrated using measurements of a lunar block size–frequency distribution of known age. We find that cumulative block size–frequency distributions change with time from a power–law for young populations (<~50 Myr) to an exponential distribution for older populations. The new destruction rates are within the uncertainty of the original model, although, for sizes >5 cm, they are two times faster than the original best estimate. The faster rates are broadly consistent with observations reported by other studies. Since the input functions are known for small rock sizes, the rock abundance can be determined theoretically at sizes below the current image spatial resolution (0.5 m). Surface exposure age of block fields can be estimated together with the initial block abundance from the measurement of block size–frequency distributions.

Command filtered adaptive output feedback design with novel Lyapunov-based analysis for nonlinear systems with unmodeled dynamics

This paper presents a novel Lyapunov function-based backstepping controller design to tackle the tracking problems for nonlinear systems with unmodeled dynamics and unmeasurable states. The coexistence of unmodeled dynamics and unmeasurable states is the main challenge, which calls for novel techniques to take these two factors into account simultaneously. First, the classical Luenberger observer is extended with a novel transformation function to decouple the original system state and state estimation error. In this way, the effect of unmodeled dynamics on system stability can be separately considered. On this basis, a command-filtered controller is designed to simplify the backstepping design procedures. It is worthy to pointed out that, a novel Lyapunov function is developed to simplify the stability analysis with command filter, where the filter errors, the observer error, compensated tracking errors, and parameter estimation errors can be guaranteed to be semi-globally uniformly ultimate bounded. The simulation studies are investigated to validate the effectiveness of the presented design scheme.

Assessing small-mammal trapping design using spatially explicit capture recapture (SECR) modeling on long-term monitoring data.

Few studies have evaluated the optimal sampling design for tracking small mammal population trends, especially for rare or difficult to detect species. Spatially explicit capture-recapture (SECR) models present an advancement over non-spatial models by accounting for individual movement when estimating density. The salt marsh harvest mouse (SMHM; Reithrodontomys raviventris) is a federal and California state listed endangered species endemic to the San Francisco Bay-Delta estuary, California, USA; where a population in a subembayment has been continually monitored over an 18-year period using mark-recapture methods. We analyzed capture data within a SECR modeling framework that allowed us to account for differences in detection and movement between sexes. We compared the full dataset to subsampling scenarios to evaluate how the grid size (area) of the trap design, trap density (spacing), and number of consecutive trapping occasions (duration) influenced density estimates. To validate the subsampling methods, we ran Monte Carlo simulations based on the true parameter estimates for each specific year. We found that reducing the area of the trapping design by more than 36% resulted in the inability of the SECR model to replicate density estimates within the SE of the original density estimates. However, when trapping occasions were reduced from 4 to 3-nights the density estimates were indistinguishable from the full dataset. Furthermore, reducing trap density by 50% also resulted in density estimates comparable to the full dataset and was a substantially better model than reducing the trap area by 50%. Overall, our results indicated that moderate reductions in the number of trapping occasions or trap density could yield similar density estimates when using a SECR approach. This approach allows the optimization of field trapping efforts and designs by reducing field efforts while maintaining the same population estimate compared to the full dataset. Using a SECR approach may help other wildlife programs identify sampling efficiencies without sacrificing data integrity for long term monitoring of population densities.

Doubt on the statistical significance of the Giant GRB Ring

AbstractBalázs et al. discovered a gigaparsec‐scale ring‐like cluster of gamma‐ray bursts (GRBs) at redshift , the so‐called Giant GRB Ring (GGR), and claimed that the existence of such a gigantic structure challenges the standard cosmological paradigm. We show that the kth nearest neighbor analysis of Balázs et al., by which they estimated the probability of finding by chance a GRB cluster comparable to the GGR to be only 0.05%, has several flaws in the statistical reasoning. After correcting these flaws, the above probability becomes 5–20%, considerably higher than the original estimate and large enough to discard the hypothesis that the GGR has a physical origin. Moreover, this probability is even higher, 60–90%, when using the most recent compilation of GRB data whose redshifts have been measured. We conclude that the GGR is merely a pattern that occurred by chance in the random distribution of GRBs.

Exploring the potential of multispectral satellite images for estimating the contents of cadmium and lead in cropland: The effect of the dimidiate pixel model and random forest

Rapid and accurate predictions of heavy metal contents in farmland are of great significance to ensure the safety of agricultural products and maintain ecosystem balance. Combining multispectral images and chemometric modeling provides a feasible means of estimating cadmium (Cd) and lead (Pb) contents in farmland. We collected 640 samples from the surface soils of farmland in Xiangzhou District, Hubei Province, China. The Cd, Pb, soil organic matter (SOM), pH, and Fe of the soil samples were measured in a laboratory. In this study, the dimidiate pixel model was used to process remote sensing images. In particular, we used random forest (RF) to screen the best spectral indices for use as input variables. Partial least squares regression (PLSR), backward propagation neural network (BPNN), and RF were used to calibrate the spectral data with Cd and Pb contents, and the optimal model was used for the regional mapping of soil Cd and Pb contents. Additionally, we explored the potential of using spectral estimation mechanisms to estimate Cd and Pb contents. The mechanism for estimating Cd and Pb contents with multispectral images depended mainly on the covariance of Cd and Pb contents with that of SOM. For both Cd and Pb estimations, the double-date image estimation model performed better than the single-date image estimation model, and the unmixed image estimation model was more accurate than the original image estimation model. Overall, the estimation model using the best spectral indices as input variables performed better than the model using full-band data as input variables. The RF model outperformed the PLSR and BPNN models in all cases. Relatively satisfactory estimates of Cd and Pb contents in farmland of the study area (maximum R2val (determination coefficient of the validation set) = 0.60 for Cd and R2val = 0.63 for Pb) were obtained. Our results show that areas of farmland contaminated by Cd and Pb throughout the study area have increased and that contamination levels have worsened since 1990. In addition, the spatial patterns of Cd and Pb contents in farmland throughout the study area were analyzed and validated using field survey results. The results of the study provide a theoretical basis and methodological reference for the rapid prediction of Cd and Pb contents in regional farmland.

Clinical relevance of the cagA and vacA s1m1 status and antibiotic resistance in Helicobacter pylori: a systematic review and meta-analysis

BackgroundThe role of Helicobacter pylori (H. pylori) virulence factors of such as vacA s1m1 and cagA in designating clinical outcomes and eradication rate has been deeply challenged in the last decade. The goal of this analysis was to identify the potential relevance between cagA and vacA genotypes with reported antibiotic resistance observed in clinical H. pylori isolates.MethodsThis literature search was conducted in databases such as Clarivate analytics, PubMed, Scopus, EMBASE, DOAJ, and Google Scholar by April 2022, regardless of language restrictions and publication date. Quality of the included studies was assessed by the Newcastle–Ottawa scale. Statistical analysis of retrieved studies was fulfilled using Comprehensive Meta-Analysis software version 2.2. Following quality appraisal of eligible studies, potential association between the status of cagA and vacA genes with resistance to clarithromycin, metronidazole, amoxicillin, tetracycline, and levofloxacin was measured using odds ratio with 95% confidence interval. We also used sensitivity analyses and meta-regression to eliminate the source of heterogeneity from the overall estimates. Publication bias was assessed using funnel plot, Egger’s test, Begg’s test with the trim and fill procedure to assess the presence and magnitude of publication bias in the included studies.ResultsOur findings suggested that a significant relationship between cagA status ‎and increase resistance ‎to metronidazole (OR: 2.69; 95% CI: 1.24–5.83‎‏‎). In subgroup analysis, we ‎found that in the Western ‎population, infection with cagA-positive strains could be led to increase in ‎the resistance to ‎metronidazole (OR: 1.59; 95% CI: ‎0.78–3.21‎‏‎), ‎amoxicillin (OR: ‎19.68‎; 95% CI: 2.74–‎‎141.18), ‎and ‎levofloxacin (OR: ‎11.33; 95% CI: ‎1.39–‎‎91.85). After implementation of trim and fill method, the adjusted OR was not significantly differed from original estimates which in turn represented our subgroup analysis was statistically robust. On the other hand, vacA ‎genotypes usually ‎reduce the antibiotic resistance of this bacterium, so that vacA s1m1 significantly reduces the ‎resistance to ‎metronidazole (OR: 0.41; 95% CI: 0.20–0.86‎‏‎). Surprisingly, resistance of vacA s2m2 strains to antibiotics was low, the reason may be due ‎to the non-inflammatory properties of strains containing vacA s2m2. The meta-regression and sensitivity analyses successfully reduced the effect of heterogeneity from the overall estimates. In addition, although the pooled OR is reduced after trim and fill adjustment but results do not change the conclusion regarding vacA genotypes and antibiotic resistance.ConclusionsAccording to our findings, it was clearly demonstrated that cagA-positive strains are resistance to metronidazole, especially in Western countries. In Western countries, vacA s1m1 increases resistance to amoxicillin and levofloxacin. Based on the present findings, the vacA s1m1 genotype significantly increases resistance to metronidazole, while the vacA s1m2 decreases resistance to clarithromycin and metronidazole. Resistance to antibiotics in less virulent (vacA s2m2) strains is statistically significant lower than others.

Dramatic Declines of Evening Grosbeak Numbers at a Spring Migration Stop-Over Site

Evening Grosbeak (Coccothraustes vespertinus) populations have been hypothesized to be in steep decline across North America. Data characterizing long-term changes are needed to quantify the magnitude of the declines. We surveyed grosbeaks at a spring migratory stop-over site in Corvallis, Oregon, USA, where birds gather annually during April and May to feast on elm (Ulmus spp.) seeds before departing to breeding sites. An estimate produced by a statistics professor in the 1970s indicated peak numbers were 150,000 to 250,000 birds. Our surveys in 2013–2015 found annually variable numbers, from a few hundred grosbeaks in the lowest year to less than five thousand birds in the highest year. If the original estimate is approximately true, Evening Grosbeak numbers have experienced dramatic declines, averaging −2.6%/year, over the last four decades. Our local observation of declines during spring aligns with declines documented in winter across North America by bird feeder studies and in summer by the Breeding Bird Survey. We explore potential explanations for the changes in population size, such as influences of spruce budworm outbreaks, disease, and decreased structural diversity of forests owing to harvest practices. We also consider the challenges of interpreting changes in abundance of species with exceptionally variable populations, especially if population fluctuations or cycles may have long periodicities. Finally, we call for additional planned surveys to track the numbers of this enigmatic and charismatic species.

DOPE++: 6D pose estimation algorithm for weakly textured objects based on deep neural networks.

This paper focuses on 6D pose estimation for weakly textured targets from RGB-D images. A 6D pose estimation algorithm (DOPE++) based on a deep neural network for weakly textured objects is proposed to solve the poor real-time pose estimation and low recognition efficiency in the robot grasping process of parts with weak texture. More specifically, we first introduce the depthwise separable convolution operation to lighten the original deep object pose estimation (DOPE) network structure to improve the network operation speed. Second, an attention mechanism is introduced to improve network accuracy. In response to the low recognition efficiency of the original DOPE network for parts with occlusion relationships and the false recognition problem in recognizing parts with scales that are too large or too small, a random mask local processing method and a multiscale fusion pose estimation module are proposed. The results show that our proposed DOPE++ network improves the real-time performance of 6D pose estimation and enhances the recognition of parts at different scales without loss of accuracy. To address the problem of a single background representation of the part pose estimation dataset, a virtual dataset is constructed for data expansion to form a hybrid dataset.

On Reverse Shrinkage Effects and Shrinkage Overshoot

Given a squared Euclidean norm penalty, we examine some less well-known properties of shrinkage estimates. In particular, we highlight that it is possible for some components of the shrinkage estimator to be placed further away from the prior mean than the original estimate. An analysis of this effect is provided within three different modeling settings—encompassing linear, logistic, and ordinal regression models. Additional simulations show that the outlined effect is not a mathematical artefact, but likely to occur in practice. As a byproduct, they also highlight the possibilities of sign reversals (“overshoots”) for shrinkage estimates. We point out practical consequences and challenges, which might arise from the observed effects with special emphasis on psychometrics.

Corporate Sustainability: A Model Uncertainty Analysis of Materiality

ABSTRACTFor decades, scholars searched for a connection between a corporation's current performance with respect to sustainability and the future returns of its stock. In 2016, Khan, Serafeim, and Yoon published an apparent breakthrough in this quest: guidance on materiality from the Sustainability Accounting Standards Board allowed the construction of corporate sustainability scales that reliably predicted stock returns. Their finding had immediate and broad impact, but it remains, in its authors' own words, just “first evidence.” Here, we further explore the relationship between material-sustainability and stock returns by performing a “model uncertainty analysis.” We reproduce the original estimate but conclude that it is a statistical artifact. We then use machine learning to explore the practicality of employing historical associations to determine which aspects of sustainability are material to investors. We conclude that, for one popular source of data on corporate sustainability, accurate guidance on materiality may be difficult to achieve.JEL Classifications: Q51; D22; L25; C11; C18.