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There is a critical need for sensitive remote sensing approaches to monitor the parameters governing photosynthesis, at the temporal scales relevant to their natural dynamics. The photochemical reflectance index (PRI) and chlorophyll fluorescence (F) offer a strong potential for monitoring photosynthesis at local, regional, and global scales, however the relationships between photosynthesis and solar induced F (SIF) on diurnal and seasonal scales are not fully understood. This study examines how the fine spatial and temporal scale SIF observations relate to leaf level chlorophyll fluorescence metrics (i.e., PSII yield, YII and electron transport rate, ETR), canopy gross primary productivity (GPP), and PRI. The results contribute to enhancing the understanding of how SIF can be used to monitor canopy photosynthesis. This effort captured the seasonal and diurnal variation in GPP, reflectance, F, and SIF in the O2A (SIFA) and O2B (SIFB) atmospheric bands for corn (Zea mays L.) at a study site in Greenbelt, MD. Positive linear relationships of SIF to canopy GPP and to leaf ETR were documented, corroborating published reports. Our findings demonstrate that canopy SIF metrics are able to capture the dynamics in photosynthesis at both leaf and canopy levels, and show that the relationship between GPP and SIF metrics differs depending on the light conditions (i.e., above or below saturation level for photosynthesis). The sum of SIFA and SIFB (SIFA+B), as well as the SIFA+B yield, captured the dynamics in GPP and light use efficiency, suggesting the importance of including SIFB in monitoring photosynthetic function. Further efforts are required to determine if these findings will scale successfully to airborne and satellite levels, and to document the effects of data uncertainties on the scaling.

BackgroundTransanal minimally invasive surgery (TAMIS) has gained worldwide popularity as a method for the local excision of rectal neoplasms. However, it is technically demanding due to limited working space. Robotic TAMIS offers potential enhanced dexterity and ability while allowing for a more aggressive resection with a stable platform. The objective of this study was to review a single institution experience between laparoscopic (L-TAMIS) and robotic TAMIS (R-TAMIS) for treatment of rectal neoplasms and determine if there are significant differences on outcomes.MethodsForty consecutive patients with rectal neoplasms underwent L-TAMIS or R-TAMIS by two colorectal surgeons from January 2012 to April 2017. We retrospectively reviewed a prospectively maintained database to analyze demographics, peri-operative data, pathology, post-operative complications, and cost.ResultsThere were no significant differences between L- and R-TAMIS on patient demographics. R-TAMIS showed a statically significant increase in cost of surgery by$880. Median direct cost of L-TAMIS was $ 3562 compared to $4440.92 for R-TAMIS (p = 0.04). Wider range of total duration for L-TAMIS is likely due to the variability of body habitus and location of rectal neoplasm, which can significantly limit L-TAMIS compare to R-TAMIS. There was a trend toward decreased blood loss in the R-TAMIS group. Mortality was 0% in both groups.ConclusionsAfter reviewing our experience, we conclude there is no significant difference between L- and R-TAMIS other than total direct cost. We confirmed that both L- and R-TAMIS are safe and associated with low morbidity. The limitations of this study include its small sample size. In the future, we hope to show promising data on R-TAMIS with increased sample size and experience, which may allow for transanal resection not previously feasible. Studies with long-term follow-up assessing oncological and functional results will be mandatory.

Background The potential for malignant transformation varies among pancreatic cystic neoplasms (PCN) subtypes. Imaging and cyst fluid analysis are used to identify premalignant or malignant cases that should undergo operative resection, but the accuracy of operative decision-making process is unclear. The objective of this study was to characterize misdiagnoses of PCN and determine how often operations are undertaken for benign, non-premalignant disease. Methods A retrospective analysis of patients undergoing pancreatic resection for the preoperative diagnosis of PCN was undertaken. Preoperative and pathological diagnoses were compared to measure diagnostic accuracy. Results Between 1999 and 2011, 74 patients underwent pancreatic resection for the preoperative diagnosis of PCN. Preoperative classification of mucinous vs. non-mucinous PCN was correct in 74 %. The specific preoperative PCN diagnosis was correct in 47 %, but half of incorrect preoperative diagnoses were clinically equivalent to the pathological diagnoses. The likelihood that the pathological diagnosis was of higher malignant potential than the preoperative diagnosis was 7 %. In 20 % of cases, the preoperative diagnosis was premalignant or malignant, but the pathological diagnosis was benign. Diagnostic accuracy and the rate of undercall diagnoses and overcall operations did not change with the use of EUS or during the time period of this analysis. Conclusions Precise, preoperative classification of PCN is frequently incorrect but results in appropriate clinical decision-making in three-quarters of cases. However, one in five pancreatic resections performed for PCN was for benign disease with no malignant potential. An appreciation for the rate of diagnostic inaccuracies should inform our operative management of PCN.

Agricultural monitoring is an important application of earth-observing satellite systems. In particular, image time-series data are often fit to functions called shape models that are used to derive phenological transition dates or predict yield. This paper aimed to investigate the impact of imaging frequency on model fitting and estimation of corn phenological transition timing. Images (PlanetScope 4-band surface reflectance) and in situ measurements (Soil Plant Analysis Development (SPAD) and leaf area index (LAI)) were collected over a corn field in the mid-Atlantic during the 2018 growing season. Correlation was performed between candidate vegetation indices and SPAD and LAI measurements. The Normalized Difference Vegetation Index (NDVI) was chosen for shape model fitting based on the ground truth correlation and initial fitting results. Plot-average NDVI time-series were cleaned and fit to an asymmetric double sigmoid function, from which the day of year (DOY) of six different function parameters were extracted. These points were related to ground-measured phenological stages. New time-series were then created by removing images from the original time-series, so that average temporal spacing between images ranged from 3 to 24 days. Fitting was performed on the resampled time-series, and phenological transition dates were recalculated. Average range of estimated dates increased by 1 day and average absolute deviation between dates estimated from original and resampled time-series data increased by 1/3 of a day for every day of increase in average revisit interval. In the context of this study, higher imaging frequency led to greater precision in estimates of shape model fitting parameters used to estimate corn phenological transition timing.

Andrew Russ argues in this book that a closer look at their philosophical underpinnings finds that Rousseau, Marx, and Foucault are much less \"historical\" in their methodology than is widely believed. Instead, they share a more \"timeless\" view, one indebted to principles ordinarily seen as timeless or transcendent

Understanding spatial variability in crop yields across fields is critical for developing precision agricultural strategies that optimize productivity while reducing negative environmental impacts. This variability often arises from a complex interplay of topographic features, soil characteristics, and hydrological conditions. This study investigates the influence of hydro-topographic factors on corn and soybean yield variability from 2016 to 2023 at the well-managed experimental sites in Beltsville, Maryland. A high-resolution surface digital elevation model (DEM) and subsurface DEM derived from ground-penetrating radar (GPR) were used to quantify topographic factors (elevation, slope, and aspect) and hydrological factors (surface flow accumulation, depth from the surface to the subsurface-restricting layer, and distance from each crop pixel to the nearest subsurface flow pathway). Topographic variables alone explained yield variation, with a relative root mean square error (RRMSE) of 23.7% (r2 = 0.38). Adding hydrological variables reduced the error to 15.3% (r2 = 0.73), and further combining with remote sensing data improved the explanatory power to an RRMSE of 10.0% (r2 = 0.87). Notably, even without subsurface data, incorporating surface-derived flow accumulation reduced the RRMSE to 18.4% (r2 = 0.62), which is especially important for large-scale cropland applications where subsurface data are often unavailable. Annual spatial yield variation maps were generated using hydro-topographic variables, enabling the identification of long-term persistent yield regions (LTRs), which served as stable references to reduce spatial anomalies and enhance model robustness. In addition, by combining remote sensing data with interannual meteorological variables, prediction models were evaluated with and without hydro-topographic inputs. The inclusion of hydro-topographic variables improved spatial characterization and enhanced prediction accuracy, reducing error by an average of 4.5% across multiple model combinations. These findings highlight the critical role of hydro-topography in explaining spatial yield variation for corn and soybean and support the development of precise, site-specific management strategies to enhance productivity and resource efficiency.

Crop models are useful for evaluating crop growth and yield at the field and regional scales, but their applications and accuracies are restricted by input data availability and quality. To overcome difficulties inherent to crop modeling, input data can be enhanced by the incorporation of remotely sensed and field observations into crop growth models. This approach has been recognized to be an important way to monitor crop growth conditions and to predict yield at the field and regional scale. In recent years, satellite remote sensing has provided high-temporal and high-spatial-resolution data that allow for generating continuous time series of biophysical parameters such as vegetation indices, leaf area index, and phenology. The objectives of this study were to use remote sensing along with field observations as inputs to the Decision Support System for Agro-Technology (DSSAT) model to estimate soybean and maize growth and yield. The study used phenology and leaf area index (LAI) data derived from Planet Fusion (daily, 3 m) satellite imagery along with field observation data on crop growth stage, LAI and yield collected at the United State Department of Agriculture, Agricultural Research Service, Beltsville Agricultural Research Center (BARC), Beltsville, Maryland. For maize, a total of 17 treatments (site years) were used (ten treatments for model calibration and seven treatments for validation), while for soybean (maturity groups three and four), a total of 18 treatments were used (nine for calibration and nine for validation). The calibrated model was tested against an independent, multi-location and multi-year set of phenology and yield data (2017–2020) from BARC fields. The model accurately simulated maize and soybean days to flowering and maturity and produced reasonable yield estimates for most fields and years. Model run for independent locations and years produced good results for phenology and yields for both maize and soybean, as indicated by index of agreement (d) values ranging from 0.65 to 0.93 and normalized root-mean-squared error values ranging from 1 to 20%, except for soybean maturity group four. Overall, model performances with respect to phenology and grain yield for maize and soybean were good and consistent with other DSSAT evaluation studies. The inclusion of remote sensing along with field observations in crop-growth model inputs can provide an effective approach for assessing crop conditions, even in regions lacking ground data.

Background Enhanced recovery after surgery (ERAS) protocols are widely utilized for elective colorectal surgery to improve outcomes and decrease costs, but few studies have evaluated the impact of ERAS protocols on cost with respect to anatomic site of resection. This study evaluated the impact of ERAS protocol on elective colon resections by site and longitudinal impact over time. Methods A single-center retrospective cohort study of 598 consecutive patients undergoing elective colorectal resection before and after implementation of ERAS protocol from 2013 to 2017 was performed. The primary outcomes were length of stay (LOS) and cost. Comparative and multivariate inferential statistics were used to assess additional outcomes. Results A total of 598 patients (100 pre-ERAS vs 498 post-ERAS) were evaluated with an overall median LOS of 4 days for right and left colectomies and 3 days for transverse colectomies. When comparing type of resection before and after ERAS protocol introduction, an increased LOS for left hemicolectomies from 3.09 to 4.03 days (P = .047) was noted, with all other comparisons failing to reach statistical significance. Over time, an initial decrease in LOS for MIS approach after protocol introduction was observed; however, this effect diminished in the ensuing years and had no significant effect overall. Total cost of care was significantly increased post-ERAS for all cohorts except transverse colectomies. No further statistically significant differences were found. Conclusion After an initial improvement in outcomes, continued utilization of ERAS protocols demonstrated no improvement in LOS compared to pre-ERAS data and increased cost overall for patients regardless of site of resection.

Background MUTYH‐associated polyposis is a rare disorder resulting from mutations involved in DNA mismatch repair. This results in an increased susceptibility to colonic adenomatosis and other cancers. Studies have examined the resulting frequency of extracolonic manifestations; however, these typically occur alone, concurrently, or temporally separate from an already diagnosed colorectal cancer in individuals with a biallelic mutation. Case Reported here is a case of five distinct primary neoplasms presenting simultaneously in a patient monoallelic for an MYH mutation. These neoplasms included squamous cell carcinoma of the vulva, rectal adenocarcinoma, synchronous anal adenocarcinoma, papillary thyroid carcinoma, and ovarian serous psammocarcinoma. Throughout her course, she underwent multiple surgical procedures, neoadjuvant chemoradiation, with further adjuvant therapy, and treatment ongoing. Due to her unique presentation, she underwent genetic testing that demonstrated she was monoallelic for an MYH mutation. Conclusion The patient had a positive response to her treatment and surgical procedures with ongoing adjuvant therapy. She will continue to undergo further genetic testing, and testing for her children is being considered. This case demonstrates a unique presentation associated with a monoallelic MYH mutation that is not described in the current literature and warrants further investigation.