A Novel Risk Prediction Score for Clinically Significant Bleeding in Patients Anticoagulated for Venous Thromboembolism with Active Cancer.

BACKGROUND: Cancer-associated venous thromboembolism (Ca-VTE) treatment with anticoagulation is associated with bleeding complications and there are limited data on risk factors. Current models do not provide accurate bleeding risk prediction. METHODS: UK Clinical Practice Research Datalink data (2008-2020) were used to generate a cohort of patients with anticoagulant initiation for first Ca-VTE. Patients were observed up to 180 days for significant bleeding including major bleeding and clinically relevant nonmajor bleeding requiring hospitalization (CRNMB-H). A scoring scheme was developed from sub-distribution hazard ratios, and its discrimination (expressed by the C-statistic) estimated from cross-validation. RESULTS: A total of 15,749 patients with Ca-VTE and anticoagulant treatment were included. In total, 537 significant bleeding events, 161 major bleeds, and 376 CRNMB-H were identified after adjudicated review in 4,914 person-years of observation. Incidence rates of 3.3 and 7.7 per 100 person-years were noted for major bleeding and CRNMB-H. Independent predictors of significant bleeding included cancer of the bladder, central nervous system, cervix, kidney, melanoma, prostate and upper gastrointestinal tract, metastases, minor surgery, minor trauma, and history of major bleeding or CRNMB (before or after the Ca-VTE diagnosis). Patients recognized as low, medium, and high risk (30.4, 56.8, and 1.7% of the population, respectively) had a 6-month significant bleeding incidence rate of 5.1, 19.0, and 56.5 per 100 person-years, respectively. Overall C-statistic for significant bleeding was 0.70 (95% confidence interval: 0.65-0.75), and 0.76 (0.68-0.84) and 0.67 (0.61-0.73) for major bleeding and for CRNMB-H, respectively. CONCLUSION: This risk score may identify patients at risk of significant bleeding, while also helping to determine treatment duration.

Major variation in hepatocellular carcinoma treatment and outcomes in England: a retrospective cohort study.

Objective: Hepatocellular carcinoma (HCC) is increasingly incident in England, while survival remains poor with regional disparities. We aimed to explore the differences in HCC treatment across different geographical regions and to examine the impact on cancer survival. Methods: Incident HCC cases and treatment were identified from the English Hospital Episode Statistics (2016-2017) and then a subset by National Health Service (NHS) regions. Treatment was grouped into curative, palliative and untreated. Median survival was estimated to date of death in the national statistics. Results: The median observed survival was 8.6 months (95% CI 7.5 to 9.9) across all 2160 HCC cases, 52.1 months (CI 50.5, not reached) in 449 (20.8%) treated with curative intent, 21.0 months (CI 18.5 to 24.5) after other cancer-specific treatment in 449 (20.8%), and 2.3 months (CI 2.1 to 2.6) in 1262 (58.4%) untreated. Across NHS regions, <50% of cases received treatment (30.4%-49.6%), while between 14.2% and 27.7% had curative treatment. The 3-year survival was similar (23.5%-29.7%), except in the London region (40.0%). Conclusion: Majority of HCC cases in England are untreated and survival remains low, with variation in outcomes in regions with similar incident rates. A deeper exploration of regional treatments and screening practice is required to improve early detection and survival.

Short-term side effects of COVID-19 vaccines: A cross-sectional study in Jordan.

The rapid manufacturing of vaccines has increased hesitancy toward receiving the COVID-19 vaccines. Clarifying what to expect after vaccination and revealing the possible side effects will lower hesitancy toward receiving the COVID-19 vaccine and increase public awareness. This descriptive cross-sectional survey-based study was conducted in Jordan (August 2021) to collect data on the short-term side effects following the COVID-19 vaccines. An extensive literature review was conducted by the research team to assist in developing the first draft of the survey. The survey was tested for face and content validity and piloted test to improve readability and clarity. The survey was organized into two sections (demographics and perceived COVID-19 vaccines' side effects). Data were analyzed using the Statistical Package for Social Science (SPSS). A total of 1,044 participants were enrolled in the study. The most received vaccine among the participants was Pfizer-BioNTech (51.1%). The most frequently reported side effects were sore arm at the injection site (84.65%), fatigue (84.48%), discomfort (65.43%), muscles/joint pain (61.38%), drowsiness (58.73%), and headache (58.38%). More side effects were significantly associated with being older (p = 0.046), having an allergy (p = 0.024) or rheumatoid arthritis (p = 0.023), and participants who take NSAIDs regularly (p = 0.029). Short-term side effects of COVID-19 vaccines seem to be mostly local or transient in nature. Older age and certain comorbidities may increase susceptibility to side effects.

The influence of low- intensity laser irradiation versus hyperbaric oxygen therapy on transcutaneous oxygen tension in chronic diabetic foot ulcers: a controlled randomized trial.

BACKGROUND AND OBJECTIVE: Evaluation of the stage and severity of the chronic diabetic foot ulcer (CDFU) is vital to increase the healing rate and to select the suitable treatment. We aim to assess the influence of low-intensity laser irradiation (LILI) and hyperbaric oxygenation therapy (HBOT) to accelerate the CDFU healing thru the transcutaneous oxygen tension (TcPO2) measurements. MATERIALS AND METHODS: Seventy-five diabetic patients (type 2) of both genders, their ages ranged from 40-65 years with CDFUs (duration of ulcer < 6 weeks). All patients were randomly assigned into LILI, HBOT, and the control group. Measurement of TcPO2 using transcutaneous oximetry was performed for all patients once in the baseline and consequently in the second, fourth, and sixth- weeks duration. LILI utilized by a 33-diode cluster contact applicator with output power 1440 mW, energy density (fluency) was adjusted for 4 J/Cm2 at 10 kHz, and for 8 min per session, three times per week for a total of consecutive 6 weeks. HBOT was pressurized up to 2.5 ATA and patients delivered 100% oxygen for 60 min per session for 30 sessions. The Control group received conventional wound care only, twice daily, with saline and apply a new bandage after cleaning. RESULTS: MANOVA revealed a statistically insignificant difference in the control group, while statistically significant improvement in both the LILI and HBOT groups. The intergroup comparisons showed an insignificant statistical difference in the pre-test, while highly statistically significant differences for the three post-measures in favor of HBOT and LILI groups. The percentage of improvement of the HBOT group was higher than LILI. Post-hoc test using the least significant difference (LSD) revealed statistically significant differences of HBOT in favor of the LILI group. CONCLUSION: Both LILI and HBOT may be used as adjunctive methods to improve TcPO2 that accelerate healing in CDFUs. HBOT may be favorable in the improvement of TcPO2 than LILI.

Nivolumab in gastric/gastroesophageal junction cancer: real-world data from UK Early Access to Medicines Scheme.

Aim: This study provides real-world insight into patient profile, clinical effectiveness and health-related quality of life among patients with advanced gastric/gastroesophageal junction (GEJ) adenocarcinoma treated with nivolumab. Materials & methods: Data were collected from medical records of patients with advanced GEJ adenocarcinoma treated with nivolumab in a UK Early Access to Medicines Scheme and from the patient-reported EuroQoL five dimensions questionnaire. Results: Evaluable patients (n = 113; median age 62 years) were predominantly male (76.1%), White (87.4%) and with GEJ adenocarcinoma (61.9%). Median follow-up was 2.8 months. The 6-month progression-free survival and overall survival were 31.6 and 56.7%, respectively. Mean EuroQoL five dimensions questionnaire index utility scores at baseline, 8, 16 and 24 weeks were 0.795, 0.831, 0.870 and 0.793, respectively. Conclusion: Progression-free survival was consistent with trial results and health-related quality of life remained stable over time.

Lay abstract This study looked at the characteristics and quality of life (QoL) of patients who were taking the drug, nivolumab, and how well it works for advanced gastric/gastroesophageal junction (GEJ) adenocarcinoma. GEJ adenocarcinoma is a rare type of cancer that starts in the GEJ, the area where the esophagus and stomach join. Information was collected from the medical records of patients who had advanced GEJ adenocarcinoma and were treated with nivolumab as part of a UK program that gives people access to new treatments that are not yet licensed. Patients also filled out a questionnaire called the EuroQoL five dimensions questionnaire that focuses on a patient's quality of life (QoL). In total, 113 patients were a part of the study. The midpoint of all patients' ages was 62 years and they were mostly males (76.1%), Whites (87.4%) and with GEJ adenocarcinoma (61.9%). The midpoint of follow-up time was 2.8 months. The percentages of patients meeting progression-free survival for 6 months, a period when a patient lives with GEJ adenocarcinoma but it does not get worse, and overall survival were 31.6 and 56.7%, respectively. Mean EuroQoL five dimensions questionnaire index scores (comprised between zero and one, the higher the better) at treatment start, 8, 16 and 24 weeks were 0.795, 0.831, 0.870 and 0.793, respectively. Progression-free survival was similar to clinical trial results and QoL was constant over time.

Evapotranspiration partitioning assessment using a machine-learning-based leaf area index and the two-source energy balance model with sUAV information.

Accurate quantification of the partitioning of evapotranspiration (ET) into transpiration and evaporation fluxes is necessary to understanding ecosystem interactions among carbon, water, and energy flux components. ET partitioning can also support the description of atmosphere and land interactions and provide unique insights into vegetation water status. Previous studies have identified leaf area index (LAI) estimation as a key descriptor of biomass conditions needed for the estimation of transpiration and evaporation. LAI estimation in clumped vegetation systems, such as vineyards and orchards, has proven challenging and is strongly related to crop phenological status and canopy management. In this study, a feature extraction model based on previous research was built to generate a total of 202 preliminary variables at a 3.6-by-3.6-meter-grid scale based on submeter-resolution information from a small Unmanned Aerial Vehicle (sUAV) in four commercial vineyards across California. Using these variables, a machine learning model called eXtreme Gradient Boosting (XGBoost) was successfully built for LAI estimation. The XGBoost built-in function requires only six variables relating to vegetation indices and temperature to produce high-accuracy LAI estimation for the vineyard. Using the six-variable XGBoost-based LAI map, two versions of the Two-Source Energy Balance (TSEB) model, TSEB-PT and TSEB-2T were used for energy balance and ET partitioning. Comparing these results with the Eddy-Covariance (EC) tower data, showed that TSEB-PT outperforms TSEB-2T on the estimation of sensible heat flux (within 13% relative error) and surface heat flux (within 34% relative error), while TSEB-2T outperforms TSEB-PT on the estimation of net radiation (within 14% relative error) and latent heat flux (within 2% relative error). For the mature vineyard (north block), TSEB-2T performs better than TSEB-PT in partitioning the canopy latent heat flux with 6.8% relative error and soil latent heat flux with 21.7% relative error; however, for the younger vineyard (south block), TSEB-PT performs better than TSEB-2T in partitioning the canopy latent heat flux with 11.7% relative error and soil latent heat flux with 39.3% relative error.

Development of High Performance Computing Tools for Estimation of High-Resolution Surface Energy Balance Products Using sUAS Information.

sUAS (small-Unmanned Aircraft System) and advanced surface energy balance models allow detailed assessment and monitoring (at plant scale) of different (agricultural, urban, and natural) environments. Significant progress has been made in the understanding and modeling of atmosphere-plant-soil interactions and numerical quantification of the internal processes at plant scale. Similarly, progress has been made in ground truth information comparison and validation models. An example of this progress is the application of sUAS information using the Two-Source Surface Energy Balance (TSEB) model in commercial vineyards by the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment - GRAPEX Project in California. With advances in frequent sUAS data collection for larger areas, sUAS information processing becomes computationally expensive on local computers. Additionally, fragmentation of different models and tools necessary to process the data and validate the results is a limiting factor. For example, in the referred GRAPEX project, commercial software (ArcGIS and MS Excel) and Python and Matlab code are needed to complete the analysis. There is a need to assess and integrate research conducted with sUAS and surface energy balance models in a sharing platform to be easily migrated to high performance computing (HPC) resources. This research, sponsored by the National Science Foundation FAIR Cyber Training Fellowships, is integrating disparate software and code under a unified language (Python). The Python code for estimating the surface energy fluxes using TSEB2T model as well as the EC footprint analysis code for ground truth information comparison were hosted in myGeoHub site https://mygeohub.org/ to be reproducible and replicable.

Assessing Daily Evapotranspiration Methodologies from One-Time-of-Day sUAS and EC Information in the GRAPEX Project.

Daily evapotranspiration (ET d ) plays a key role in irrigation water management and is particularly important in drought-stricken areas, such as California and high-value crops. Remote sensing allows for the cost-effective estimation of spatial evapotranspiration (ET), and the advent of small unmanned aerial systems (sUAS) technology has made it possible to estimate instantaneous high-resolution ET at the plant, row, and subfield scales. sUAS estimates ET using "instantaneous" remote sensing measurements with half-hourly/hourly forcing micrometeorological data, yielding hourly fluxes in W/m2 that are then translated to a daily scale (mm/day) under two assumptions: (a) relative rates, such as the ratios of ET-to-net radiation (R n ) or ET-to-solar radiation (R s ), are assumed to be constant rather than absolute, and (b) nighttime evaporation (E) and transpiration (T) contributions are negligible. While assumption (a) may be reasonable for unstressed, full cover crops (no exposed soil), the E and T rates may significantly vary over the course of the day for partially vegetated cover conditions due to diurnal variations of soil and crop temperatures and interactions between soil and vegetation elements in agricultural environments, such as vineyards and orchards. In this study, five existing extrapolation approaches that compute the daily ET from the "instantaneous" remotely sensed sUAS ET estimates and the eddy covariance (EC) flux tower measurements were evaluated under different weather, grapevine variety, and trellis designs. Per assumption (b), the nighttime ET contribution was ignored. Each extrapolation technique (evaporative fraction (EF), solar radiation (R s ), net radiation-to-solar radiation (R n /R s ) ratio, Gaussian (GA), and Sine) makes use of clear skies and quasi-sinusoidal diurnal variations of hourly ET and other meteorological parameters. The sUAS ET estimates and EC ET measurements were collected over multiple years and times from different vineyard sites in California as part of the USDA Agricultural Research Service Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). Optical and thermal sUAS imagery data at 10 cm and 60 cm, respectively, were collected by the Utah State University AggieAir sUAS Program and used in the Two-Source Energy Balance (TSEB) model to estimate the instantaneous or hourly sUAS ET at overpass time. The hourly ET from the EC measurements was also used to validate the extrapolation techniques. Overall, the analysis using EC measurements indicates that the R s , EF, and GA approaches presented the best goodness-of-fit statistics for a window of time between 1030 and 1330 PST (Pacific Standard Time), with the R s approach yielding better agreement with the EC measurements. Similar results were found using TSEB and sUAS data. The 1030-1330 time window also provided the greatest agreement between the actual daily EC ET and the extrapolated TSEB daily ET, with the R s approach again yielding better agreement with the ground measurements. The expected accuracy of the upscaled TSEB daily ET estimates across all vineyard sites in California is below 0.5 mm/day, (EC extrapolation accuracy was found to be 0.34 mm/day), making the daily scale results from TSEB reliable and suitable for day-to-day water management applications.

Influence of Model Grid Size on the Estimation of Surface Fluxes Using the Two Source Energy Balance Model and sUAS Imagery in Vineyards.

Evapotranspiration (ET) is a key variable for hydrology and irrigation water management, with significant importance in drought-stricken regions of the western US. This is particularly true for California, which grows much of the high-value perennial crops in the US. The advent of small Unmanned Aerial System (sUAS) with sensor technology similar to satellite platforms allows for the estimation of high-resolution ET at plant spacing scale for individual fields. However, while multiple efforts have been made to estimate ET from sUAS products, the sensitivity of ET models to different model grid size/resolution in complex canopies, such as vineyards, is still unknown. The variability of row spacing, canopy structure, and distance between fields makes this information necessary because additional complexity processing individual fields. Therefore, processing the entire image at a fixed resolution that is potentially larger than the plant-row separation is more efficient. From a computational perspective, there would be an advantage to running models at much coarser resolutions than the very fine native pixel size from sUAS imagery for operational applications. In this study, the Two-Source Energy Balance with a dual temperature (TSEB2T) model, which uses remotely sensed soil/substrate and canopy temperature from sUAS imagery, was used to estimate ET and identify the impact of spatial domain scale under different vine phenological conditions. The analysis relies upon high-resolution imagery collected during multiple years and times by the Utah State University AggieAir™ sUAS program over a commercial vineyard located near Lodi, California. This project is part of the USDA-Agricultural Research Service Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). Original spectral and thermal imagery data from sUAS were at 10 cm and 60 cm per pixel, respectively, and multiple spatial domain scales (3.6, 7.2, 14.4, and 30 m) were evaluated and compared against eddy covariance (EC) measurements. Results indicated that the TSEB2T model is only slightly affected in the estimation of the net radiation (R n ) and the soil heat flux (G) at different spatial resolutions, while the sensible and latent heat fluxes (H and LE, respectively) are significantly affected by coarse grid sizes. The results indicated overestimation of H and underestimation of LE values, particularly at Landsat scale (30 m). This refers to the non-linear relationship between the land surface temperature (LST) and the normalized difference vegetation index (NDVI) at coarse model resolution. Another predominant reason for LE reduction in TSEB2T was the decrease in the aerodynamic resistance (R a ), which is a function of the friction velocity F*) that varies with mean canopy height and roughness length. While a small increase in grid size can be implemented, this increase should be limited to less than twice the smallest row spacing present in the sUAS imagery. The results also indicated that the mean LE at field scale is reduced by 10% to 20% at coarser resolutions, while the with-in field variability in LE values decreased significantly at the larger grid sizes and ranged between approximately 15% and 45%. This implies that, while the field-scale values of LE are fairly reliable at larger grid sizes, the with-in field variability limits its use for precision agriculture applications.

Application of a machine learning algorithm for detection of atrial fibrillation in secondary care.

Atrial fibrillation (AF) is the most common sustained heart arrhythmia and significantly increases risk of stroke. Opportunistic AF testing in high-risk patients typically requires frequent electrocardiogram tests to capture the arrhythmia. Risk-prediction algorithms may help to more accurately identify people with undiagnosed AF and machine learning (ML) may aid in the diagnosis of AF. Here, we applied an AF-risk prediction algorithm to secondary care data linked to primary care data in the DISCOVER database in order to evaluate changes in model performance, and identify patients not previously detected in primary care. We identified an additional 5,444 patients who had an AF diagnosis only in secondary care during the data extraction period. 2,696 (49.5%) were accepted by the algorithm and the algorithm correctly assigned 2,637 (97.8%) patients to the AF cohort. Using a risk threshold of 7.4% in patients aged ≥ 30 years, algorithm sensitivity and specificity was 38% and 95%, respectively. Approximately 15% of AF patients assigned to the AF cohort by the algorithm had a secondary care diagnosis with no record of AF in primary care. These additional patients did not substantially alter algorithm performance. The additional detection of previously undiagnosed AF patients in secondary care highlights unexpected potential utility of this ML algorithm.

Implications of Soil and Canopy Temperature Uncertainty in the Estimation of Surface Energy Fluxes Using TSEB2T and High-resolution Imagery in Commercial Vineyards.

Estimation of surface energy fluxes using thermal remote sensing-based energy balance models (e.g., TSEB2T) involves the use of local micrometeorological input data of air temperature, wind speed, and incoming solar radiation, as well as vegetation cover and accurate land surface temperature (LST). The physically based Two-source Energy Balance with a Dual Temperature (TSEB2T) model separates soil and canopy temperature (Ts and Tc) to estimate surface energy fluxes including Rn, H, LE, and G. The estimation of Ts and Tc components for the TSEB2T model relies on the linear relationship between the composite land surface temperature and a vegetation index, namely NDVI. While canopy and soil temperatures are controlling variables in the TSEB2T model, they are influenced by the NDVI threshold values, where the uncertainties in their estimation can degrade the accuracy of surface energy flux estimation. Therefore, in this research effort, the effect of uncertainty in Ts and Tc estimation on surface energy fluxes will be examined by applying a Monte Carlo simulation on NDVI thresholds used to define canopy and soil temperatures. The spatial information used is available from multispectral imagery acquired by the AggieAir sUAS Program at Utah State University over vineyards near Lodi, California as part of the ARS-USDA Agricultural Research Service's Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) project. The results indicate that LE is slightly sensitive to the uncertainty of NDVIs and NDVIc. The observed relative error of LE corresponding to NDVIs uncertainty was between -1% and 2%, while for NDVIc uncertainty, the relative error was between -2.2% and 1.2%. However, when the combined NDVIs and NDVIc uncertainties were used simultaneously, the domain of the observed relative error corresponding to the absolute values of |ΔLE| was between 0% and 4%.

To What Extend Does the Eddy Covariance Footprint Cutoff Influence the Estimation of Surface Energy Fluxes Using Two Source Energy Balance Model and High-Resolution Imagery in Commercial Vineyards?

Validation of surface energy fluxes from remote sensing sources is performed using instantaneous field measurements obtained from eddy covariance (EC) instrumentation. An eddy covariance measurement is characterized by a footprint function / weighted area function that describes the mathematical relationship between the spatial distribution of surface flux sources and their corresponding magnitude. The orientation and size of each flux footprint / source area depends on the micro-meteorological conditions at the site as measured by the EC towers, including turbulence fluxes, friction velocity (ustar), and wind speed, all of which influence the dimensions and orientation of the footprint. The total statistical weight of the footprint is equal to unity. However, due to the large size of the source area / footprint, a statistical weight cutoff of less than one is considered, ranging between 0.85 and 0.95, to ensure that the footprint model is located inside the study area. This results in a degree of uncertainty when comparing the modeled fluxes from remote sensing energy models (i.e., TSEB2T) against the EC field measurements. In this research effort, the sensitivity of instantaneous and daily surface energy flux estimates to footprint weight cutoffs are evaluated using energy balance fluxes estimated with multispectral imagery acquired by AggieAir sUAS (small Unmanned Aerial Vehicle) over commercial vineyards near Lodi, California, as part of the ARS-USDA Agricultural Research Service's Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) project. The instantaneous fluxes from the eddy covariance tower will be compared against instantaneous fluxes obtained from different TSEB2T aggregated footprint weights (cutoffs). The results indicate that the size, shape, and weight of pixels inside the footprint source area are strongly influenced by the cutoff values. Small cutoff values, such as 0.3 and 0.35, yielded high weights for pixels located within the footprint domain, while large cutoffs, such as 0.9 and 0.95, result in low weights. The results also indicate that the distribution of modelled LE values within the footprint source area are influenced by the cutoff values. A wide variation in LE was observed at high cutoffs, such as 0.90 and 0.95, while a low variation was observed at small cutoff values, such as 0.3. This happens due to the large number of pixel units involved inside the footprint domain when using high cutoff values, whereas a limited number of pixels are obtained at lower cutoff values.

Estimation of surface thermal emissivity in a vineyard for UAV microbolometer thermal cameras using NASA HyTES hyperspectral thermal, Landsat and AggieAir optical data.

Microbolometer thermal cameras in UAVs and manned aircraft allow for the acquisition of high-resolution temperature data, which, along with optical reflectance, contributes to monitoring and modeling of agricultural and natural environments. Furthermore, these temperature measurements have facilitated the development of advanced models of crop water stress and evapotranspiration in precision agriculture and heat fluxes exchanges in small river streams and corridors. Microbolometer cameras capture thermal information at blackbody or radiometric settings (narrowband emissivity equates to unity). While it is customary that the modeler uses assumed emissivity values (e.g. 0.99-0.96 for agricultural and environmental settings); some applications (e.g. Vegetation Health Index), and complex models such as energy balance-based models (e.g. evapotranspiration) could benefit from spatial estimates of surface emissivity for true or kinetic temperature mapping. In that regard, this work presents an analysis of the spectral characteristics of a microbolometer camera with regard to emissivity, along with a methodology to infer thermal emissivity spatially based on the spectral characteristics of the microbolometer camera. For this work, the MODIS UCBS Emissivity Library, NASA HyTES hyperspectral emissivity, Landsat, and Utah State University AggieAir UAV surface reflectance products are employed. The methodology is applied to a commercial vineyard agricultural setting located in Lodi, California, where HyTES, Landsat, and AggieAir UAV spatial data were collected in the 2014 growing season. Assessment of the microbolometer spectral response with regards to emissivity and emissivity modeling performance for the area of study are presented and discussed.

Implications of sensor inconsistencies and remote sensing error in the use of small unmanned aerial systems for generation of information products for agricultural management.

Small, unmanned aerial systems (sUAS) for remote sensing represent a relatively new and growing technology to support decisions for agricultural operations. The size and power limitations of these systems present challenges for the weight, size, and capability of the sensors that can be carried, as well as the geographical coverage that is possible. These factors, together with a lack of standards for sensor technology, its deployment, and data analysis, lead to uncertainties in data quality that can be difficult to detect or characterize. These, in turn, limit comparability between data from different sources and, more importantly, imply limits on the analyses that can be accomplished with the data that are acquired with sUAS. This paper offers a simple statistical examination of the implications toward information products of an array of sensor data uncertainty issues. The analysis relies upon high-resolution data collected in 2016 over a commercial vineyard, located near Lodi, California, for the USD A Agricultural Research Service Grape Remote sensing Atmospheric Profile and Evapotranspiration experiment (GRAPEX) Program. A Monte Carlo analysis is offered of how uncertainty in sensor spectral response and/or orthorectification accuracy can affect the estimation of information products of potential interest to growers, as illustrated in the form of common vegetation indices.

Prevalence of minor musculoskeletal anomalies in children with congenital hypothyroidism.

In the last decade a high frequency of extrathyroidal congenital anomalies has been reported in infants with congenital hypothyroidism (CH) detected by neonatal screening. In the present study the occurrence of additional congenital malformations (CM) in a cohort of children with confirmed primary CH due to thyroid dysgenesis was investigated. A high prevalence of extrathyroidal major congenital anomalies (15.9%), more than 5-fold higher than that reported in the Egyptian population (2.7%), was found. The cardiac and musculoskeletal systems were the most commonly involved, comprising 9.09 and 47.72% of all anomalies, respectively. The high prevalence of musculoskeletal anomalies in this study was mostly due to minor anomalies as brachydactyly and digitalization of thumbs. The type of dysgenesis (i.e. aplastic, ectopic or hypoplastic) as well as the severity of hypothyroidism, as assessed by TSH and T(4) levels at diagnosis, had no relation with the occurrence of extrathyroidal abnormalities.