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This study outfitted wild-caught white seabass (Atractoscion nobilis) with electronic data storage tags (DSTs) to evaluate subsequent movements of adult fish captured along Baja California, Mexico (BC). Cefas G5 DSTs were surgically implanted into 89 wild-caught white seabass ranging in size from 66 to 152 cm TL between La Salina (32.11°N/116.90°W) and San Quintin (30.25°N/115.83°W), BC. Twenty-four tagged individuals (27%) were recaptured between April, 2010 and May, 2017, following a mean time at liberty of 608 days (range = 18–1424 days) and a mean displacement of approximately 125 km (± 173 km; range = 3–720 km) between the point of release and recapture. Tagged white seabass were recaptured between Santa Rosalillíta, BC (28.66° N/114.27°W) and Monterey Bay, California (36.65°N/121.86°W), with four individuals reported above the U.S-Mexico border. Collectively, 8547 days of archived data revealed that white seabass spent 95% of the time at depths <60 m, with a maximum depth of 158 m. Ambient water temperature ranged from 10.2 to 24.1 °C, with a mean (± SD) of 15.2 ± 0.4 °C. Seasonal shifts in depth profiles to deeper waters during the winter months were comparable with findings from previous tagging studies performed off California. Relatively high recapture rates reaffirm the economic importance of this resource in both the U.S. and Mexico, while tag recoveries within close proximity to particular deployment sites suggest seasonal site fidelity to specific geographic areas. White seabass movements between BC and California further support the transboundary nature of the stock and suggest that future management would benefit from international fishery policies. [ABSTRACT FROM AUTHOR]

This study reports on the horizontal movements of swordfish (Xiphias gladius L.) tagged during deep‐set fishery trials off the California coastline. Position estimates from several electronic tag types were used to better understand swordfish stock structure and regional affiliation with current boundary hypotheses used to manage swordfish in the eastern north Pacific. Swordfish were outfitted with (a) satellite‐linked mark–recapture tags (n = 66), (b) electronic data storage tags that were recaptured (n = 16), (c) fin‐mounted Argos transmitters (n = 6), and (d) satellite‐linked archival tags (n = 4). Twenty‐six percent of tagged swordfish reported close to (<225 km) their deployment location within the southern California Bight (SCB). Of the 50 swordfish that moved outside the SCB, 76% exhibited affiliation to the Eastern Pacific Ocean (EPO) management unit, 20% moved into the Western and Central North Pacific (WCNP) and 4% spent time within both the EPO and WCNP boundaries. Mean displacement between deployment and reporting locations was 1,250 ± 1,375 km, with daily rates of movement up to 55 km/day. Seasonal migrations ranged from the equator (0.8°N.132.4°W) to the Hawaiian Islands (17.0°N/154.2°W), with multiple individuals returning to the initial tagging locations the subsequent season. Seasonal site fidelity exhibited by several individuals highlights the importance of the SCB foraging grounds. While no evidence of trans‐equatorial or trans‐Pacific crossing was documented, extensive movements validate the highly migratory nature of California swordfish and support the need for future inclusion of spatial distribution data in management. Findings suggest that SCB swordfish may exhibit a higher level of EPO connectivity than previously proposed. [ABSTRACT FROM AUTHOR]

Objectives: Abnormalities of the main pancreatic duct may be an early indicator of pancreatic ductal adenocarcinoma (PDAC). We develop and validate algorithms that predict the risk of PDAC using features identified on cross-sectional imaging and other clinical characteristics collected through electronic medical records., Methods: Adult patients with abdominal computed tomography or magnetic resonance imaging in January 2006 to June 2016 demonstrating dilatation of main pancreatic duct were identified. Pancreas-related morphologic features were extracted from radiology reports using natural language processing. The cumulative incidence of PDAC with death as a competing risk was estimated using multistate models. Model discrimination was assessed using c-index. The models were internally validated using bootstrapping., Results: The cohort consisted of 7819 patients (mean age, 71 years; 65% female). A total of 781 patients (10%) developed PDAC within 3 years after the first eligible imaging study. The final models achieved reasonable discrimination (c-index, 0.825-0.833). The 3-year average risk of PDAC in the top 5% of the total eligible patients was 56.0%, more than 20 times of the average risk among the bottom 50% of patients., Conclusions: Prediction models combining imaging features and clinical measures can be used to further stratify the risk of pancreatic cancer among patients with pancreas ductal dilatation.

Sediment budgets for a small mountainous watershed that received large volumes of miningsediment in the 19th century reveal connectivity and dynamics over a 130-year period.Hydraulic mining was invented in California in the mid-twentieth century and produced1.1×109 m3 of distinctive hydraulic mining sediment (HMS) in the northern Sierra Nevadabetween 1853 and 1884. Mining ceased abruptly in response to a legal injunction, sothe sudden start and end of this massive sediment production set up an unusuallywell-defined outdoor experiment in sediment production, storage, and delivery.Meso- to catchment-scale analysis includes the development of spatially distributedsediment budgets for two historical periods: 1853 to 1884 ending at the time of peakmine-sediment production and 1884 to 2014 ending at the time of airborne LiDAR datacollection. Three common methods can be used to develop sediment budgets bycreating DEMs of Difference (DODs) from digital elevation models (DEMs). (1) Themorphometric method differences two DEMs constructed from sequential fieldsurveys. (2) Historical cartometrics can be used to generate the early DEM fordifferencing. (3) This study modeled an early pre-mining surface by interpolation ofcontours manually generated from existing terrace remnants, hill crests, and valleysidewalls and differencing those DEMs with DEMs from 2014 airborne LiDARdata. The resulting HMS storage volumes for pre-mining, penultimate mining, and post-miningperiods indicate 23.5×106 m3 HMS produced in the 54.6 km2 mountainous study catchment,representing 43.0 cm of denudation over a period of 31 years or a mean denudation rate of1.39 cm yr−1. This shows two orders of magnitude increase in denudation ratesfollowing the sudden introduction of post-industrial human activities to the region. Inca. 1884, at the time of maximum channel aggradation, 7.15×106 m3 of HMS or30.4% of the production was stored, representing a sediment delivery ratio (SDR) of69.6%. By 2014, only 3.75×106 m3 or 16% of sediment production remained,indicating that the SDR had increased to 84.1%. Both SDRs are high compared to mostNorth American rates reported in the scientific literature based on reservoir surveys.The dynamic nature of SDR implicated by increasing SDRs with time is intuitive,but rarely observed due to poor temporal resolution and control in most sedimentproduction and storage data. SDRs provide a metric for sediment connectivity and candocument changes in connectivity through time. During this extreme anthropogenicaggradation event, geomorphic changes to the fluvial system included major disruptions tolongitudinal connectivity that greatly reduced initial flux rates but maintained elevatedpost-mining sediment yields at present as the stored HMS continues to be slowly released. [ABSTRACT FROM AUTHOR]

The article presents the corporate profile of Oracle Health Sciences, a provider of strategic software solutions to health sciences industry based in Redwood Shores, California. The company helps biotechnology, pharmaceutical, healthcare, and medical device organizations become successful through innovative products and services. Among its comprehensive industry solutions are eClinical solutions, clinical data warehousing, and safety and risk management.