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Accurate estimates of snow water equivalent (SWE) based on remote sensing have been elusive, particularly in mountain areas. However, there now appears to be some potential for direct satellite-based SWE observations along ground tracks that only cover a portion of a spatial domain (e.g., watershed). Fortunately, spatiotemporally continuous meteorological and surface variables could be leveraged to infer SWE in the gaps between satellite ground tracks. Here, we evaluate statistical and machine learning (ML) approaches to performing track-to-area (TTA) transformations of SWE observations in California's upper Tuolumne River watershed using synthetic data. The synthetic SWE measurements are designed to mimic a potential future P-band Signals of Opportunity (P-SoOP) satellite mission with a (along-track) spatial resolution of about 500 m. We construct relationships between multiple meteorological and surface variables and synthetic SWE observations along observation tracks, and we then extend these relationships to unobserved areas between ground tracks to estimate SWE over the entire watershed. Domain-wide, SWE inferred on 1 April using two synthetic satellite tracks (∼4.5 % basin coverage) led to percent errors of basin-averaged SWE (PEBAS) of 24.5 %, 4.5 % and 6.3 % in an extremely dry water year (WY2015), a normal water year (WY2008) and an extraordinarily wet water year (WY2017), respectively. Assuming a 10 d overpass interval, percent errors of basin-averaged SWE during both snow accumulation and snowmelt seasons were mostly less than 10 %. We employ a feature sensitivity analysis to overcome the black-box nature of ML methods and increase the explainability of the ML results. Our feature sensitivity analysis shows that precipitation is the dominant variable controlling the TTA SWE estimation, followed by net long-wave radiation (NetLong). We find that a modest increase in the accuracy of SWE estimation occurs when more than two ground tracks are leveraged. The accuracy of 1 April SWE estimation is only modestly improved for track repeats more often than about 15 d. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to investigate practice changes among Southern and Northern California's radiation oncology centers during the COVID‐19 pandemic. Methods: On the online survey platform SurveyMonkey, we designed 10 survey questions to measure changes in various aspects of medical physics practice. The questions covered patient load and travel rules; scopes to work from home; new protocols to reduce corona virus disease‐2019 (COVID‐19) infection risk; availability of telemedicine; and changes in fractionation schedules and/or type of treatment plans. We emailed the survey to radiation oncology centers throughout Northern and Southern California, requesting one completed survey per center. All responses were anonymized, and data were analyzed using both qualitative and quantitative research methods. Results: At the end of a 4‐month collection period (July 2, 2021 to October 11, 2021), we received a total of 61 responses throughout Southern and Northern California. On average, 4111 patients were treated per day across the 61 centers. New COVID‐19‐related department and hospital policies, along with hybrid workflow changes, infectious control policies, and changes in patient load have been reported. Results also showed changes in treatment methods during the pandemic, such as increased use of telemedicine, hypofractionation for palliative, breast cancer, and prostate cancer cases; and simultaneous boosts, compared to sequential boosts. Conclusion: Our California radiation oncology center population study shows changes in various aspects of radiation oncology practices during the COVID‐19 pandemic. This study serves as a pilot study to identify possible correlations and new strategies that allow radiation oncology centers to continue providing quality patient care while ensuring the safety of both staff and patients. [ABSTRACT FROM AUTHOR]

The livestock industry is one of the main contributors to greenhouse gas emissions and there is an increasing demand for the industry to reduce its carbon footprint. Several studies have shown that feed additives 3-nitroxypropanol and nitrate to be effective in reducing enteric methane emissions. The objective of this study was to estimate the net mitigating effect of using 3-nitroxypropanol and nitrate on total greenhouse gas emissions in California dairy industry. A life cycle assessment approach was used to conduct a cradle-to-farm gate environmental impact analysis based on dairy production system in California. Emissions associated with crop production, feed additive production, enteric methane, farm management, and manure storage were calculated and expressed as kg CO2 equivalents (CO2e) per kg of energy corrected milk. The total greenhouse gas emissions from baseline, 3-nitroxypropanol and nitrate offered during lactation were 1.12, 0.993, and 1.08 kg CO2e/kg energy corrected milk, respectively. The average net reduction rates for 3-nitroxypropanol and nitrate were 11.7% and 3.95%, respectively. In both cases, using the feed additives on the whole herd slightly improved overall carbon footprint reduction compared to limiting its use during lactation phase. Although both 3-nitroxypropanol and nitrate had effects on decreasing the total greenhouse gas emission, the former was much more effective with no known safety issues in reducing the carbon footprint of dairy production in California. [ABSTRACT FROM AUTHOR]

Background: The pivot-shift (PS) examination is used to demonstrate knee instability and detect anterior cruciate ligament (ACL) injury. Prior studies using inertial sensors identified the ACL-deficient knee with reasonable accuracy, but none addressed the more difficult problem of using these sensors to determine whether a subject has an ACL deficiency and to correctly assign a PS grade to a patient's knee. Hypothesis: Inertial sensor data recorded during a PS examination can accurately predict ACL deficiency and the PS score assigned by the examining physician. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: A total of 32 patients with unilateral ACL deficiency and 29 with intact ACLs in both knees had inertial sensor modules strapped to the tibia and femur of each limb for preoperative PS testing under anesthesia. Support vector machine (SVM) methodsassessed PS grades on the basis of these data, with the examiner's clinical grading shift used as ground truth. A fusion of regression and SVM classification techniques diagnosed ACL deficiency. Results: The clinically determined PS grades of all 122 knees were as follows: 0 (n = 69), +1 (n = 23), +2 (n = 27), and +3 (n = 3). The SVM classification analysis was 77% accurate in correctly classifying these grades, with 98% of computed PS grades fallingwithin ±1 grade of the clinically determined value. The system fusion algorithm diagnosed ACL deficiency in an individual with anoverall accuracy of 97%. This method yielded 6% false negatives and 0% false positives. Conclusion: This study used inertial sensor technology with SVM algorithms to accurately determine clinically assigned PS grades in ACL-intact and ACL-deficient knees. By extending the assessment to a separate group of patients without ACL injury, the inertial sensor data demonstrated highly accurate diagnosis of ACL deficiency. [ABSTRACT FROM AUTHOR]

Voting referenda provide direct evidence of the demand for public goods. A number of previous studies have used referenda to analyze the support for public environmental goods. These studies have used aggregate data from large jurisdictional units (usually counties) and summary income measures such as the mean or median, and have usually found that higher income areas offer greater support for environmental propositions. We examine environmental referenda voting in California using census block group data, spatial dependence controls, and detailed income distribution data. We find that household income has a negative marginal effect on environmental referenda voting for most of the income range when using census block data. In addition, controls for spatial dependence significantly reduce the magnitude of most coefficients. This suggests that OLS estimates of referenda determinants are biased. We also show that county level data may be subject to severe aggregation bias and might not be appropriate for referenda studies. [Copyright &y& Elsevier]

The transition from stable to unstable fracture propagation occurs when fractures begin to interact and link. Thus, fracture network coalescence controls how rocks and engineered structures fail. To constrain the factors that influence localization in shear zones under brittle conditions, we build discrete element method models with a rough fault embedded in a shear zone. We add varying numbers of diffuse, randomly-placed weaknesses to examine the influence of diffuse damage on fracture network localization. The number of weaknesses controls the localization behavior of the fault network and the final fault geometry. We quantify localization using the Gini coefficient of the fracture volume, which measures the nonuniformity in a population. Each model generally increases in localization toward failure. However, models with more diffuse damage experience delocalization phases that are superimposed on the overall trend of increasing localization. The observed link between delocalization and host rock damage may help explain the varying localization of low magnitude seismicity in southern California. Models with more diffuse damage produce more complex fault geometries comprised of several parallel strands of wing cracks. The propagation of these wing cracks reduces the shear stress acting on the model boundaries, indicating that this fracture development increases the mechanical efficiency of the system. • Discrete element method simulations of shear zone with wing crack development. • We test the influence of preexisting damage on strain localization. • The fracture networks generally increase in localization. • More damage produces phases of delocalization. • Fault development is associated with increases in mechanical efficiency. [ABSTRACT FROM AUTHOR]

• Anatoxin-a and dihydroanatoxin-a- producing benthic non-heterocystous filamentous cyanobacterium was isolated from the Russian River, California, USA. • Both 16S rRNA and protein sequence phylogenetic analyses showed that the strains represent a distinct new member of the cyanobacterial genus Microcoleus (Oscillatoriales). • A novel species, Microcoleus anatoxicus is described and accompanied with light microscope photomicrographs and toxin profiles. • The complete anatoxin-a gene cassette with the first description of the anaK gene in Microcoleus is presented. Reports of anatoxins poisoning of wildlife and domestic animals by toxigenic cyanobacteria in streams and rivers are increasing globally. Little is known about the taxonomy, morphology and genomics of anatoxins producing species, limiting our knowledge about their environmental preferences. We isolated three benthic non-heterocystous filamentous cyanobacterial strains from the Russian River in Northern California (USA), which produce anatoxin-a and dihydroanatoxin-a. Both 16S rRNA and protein sequence phylogenetic analyses showed that the strains represent a distinct new member of the cyanobacterial genus Microcoleus (Oscillatoriales). A novel species, Microcoleus anatoxicus is described and accompanied with light microscope photomicrographs, toxin profiles and the complete anatoxin-a gene cassette with the first description of the anaK gene in Microcoleus. [ABSTRACT FROM AUTHOR]