The impact of palliative radiotherapy on health-related quality of life in patients with head and neck cancer - Results of a multicenter prospective cohort study.

Purpose: Palliative radiotherapy for patients with head and neck cancer can be used to alleviate symptoms. Only a few studies have investigated its impact on patient-reported outcomes (PRO). Therefore, we conducted a prospective multicenter observational study. The primary objective was to assess changes in health-related quality of life (HrQoL) per PRO. Methods: Eligibility criteria included i.) head and neck cancer and ii.) palliative radiotherapy indicated (EQD2Gy < 60 Gy). The primary follow-up date was eight weeks after radiotherapy (t8w). PRO measures included the EORTC QLQ-C30 and EORTC QLQ-H&N43 and pain per Numeric Rating Scale (NRS). Per protocol, five PRO domains were to be reported in detail as well as PRO domains corresponding to a primary and secondary symptom as determined by the individual patient. We defined a minimal important difference (MID) of 10 points. Results: From 06/2020 to 06/2022, 61 patients were screened and 21 patients were included. Due to death or decline in health-status, HrQoL data was available for 18 patients at the first fraction and for eight patients at t8w. The MID was not met for the predefined domains in terms of mean values as compared from first fraction to t8w. Individually in those patients with available HrQoL data at t8w, 71% (5/7) improved in their primary and 40% (2/5) in their secondary symptom domain reaching the MID from first fraction to t8w, respectively. There was a significant improvement in pain per NRS in those patients with available data at t8w per Wilcoxon signed rank test (p = 0.041). Acute mucositis of grade ≥3 per CTCAE v5.0 occurred in 44% (8/18) of the patients. The median overall survival was 11 months. Conclusion: Despite low patient numbers and risk of selection bias, our study shows some evidence of a benefit from palliative radiotherapy for head and neck cancer as measured by PRO.German Clinical Trial Registry identifier: DRKS00021197.

VegAnn, Vegetation Annotation of multi-crop RGB images acquired under diverse conditions for segmentation.

Applying deep learning to images of cropping systems provides new knowledge and insights in research and commercial applications. Semantic segmentation or pixel-wise classification, of RGB images acquired at the ground level, into vegetation and background is a critical step in the estimation of several canopy traits. Current state of the art methodologies based on convolutional neural networks (CNNs) are trained on datasets acquired under controlled or indoor environments. These models are unable to generalize to real-world images and hence need to be fine-tuned using new labelled datasets. This motivated the creation of the VegAnn - Vegetation Annotation - dataset, a collection of 3775 multi-crop RGB images acquired for different phenological stages using different systems and platforms in diverse illumination conditions. We anticipate that VegAnn will help improving segmentation algorithm performances, facilitate benchmarking and promote large-scale crop vegetation segmentation research.

SegVeg: Segmenting RGB Images into Green and Senescent Vegetation by Combining Deep and Shallow Methods.

Pixel segmentation of high-resolution RGB images into chlorophyll-active or nonactive vegetation classes is a first step often required before estimating key traits of interest. We have developed the SegVeg approach for semantic segmentation of RGB images into three classes (background, green, and senescent vegetation). This is achieved in two steps: A U-net model is first trained on a very large dataset to separate whole vegetation from background. The green and senescent vegetation pixels are then separated using SVM, a shallow machine learning technique, trained over a selection of pixels extracted from images. The performances of the SegVeg approach is then compared to a 3-class U-net model trained using weak supervision over RGB images segmented with SegVeg as groundtruth masks. Results show that the SegVeg approach allows to segment accurately the three classes. However, some confusion is observed mainly between the background and senescent vegetation, particularly over the dark and bright regions of the images. The U-net model achieves similar performances, with slight degradation over the green vegetation: the SVM pixel-based approach provides more precise delineation of the green and senescent patches as compared to the convolutional nature of U-net. The use of the components of several color spaces allows to better classify the vegetation pixels into green and senescent. Finally, the models are used to predict the fraction of three classes over whole images or regularly spaced grid-pixels. Results show that green fraction is very well estimated (R 2 = 0.94) by the SegVeg model, while the senescent and background fractions show slightly degraded performances (R 2 = 0.70 and 0.73, respectively) with a mean 95% confidence error interval of 2.7% and 2.1% for the senescent vegetation and background, versus 1% for green vegetation. We have made SegVeg publicly available as a ready-to-use script and model, along with the entire annotated grid-pixels dataset. We thus hope to render segmentation accessible to a broad audience by requiring neither manual annotation nor knowledge or, at least, offering a pretrained model for more specific use.

Personality traits and complex problem solving: Personality disorders and their effects on complex problem-solving ability.

Complex problem solving (CPS) can be interpreted as the number of psychological mechanisms that allow us to reach our targets in difficult situations, that can be classified as complex, dynamic, non-transparent, interconnected, and multilayered, and also polytelic. The previous results demonstrated associations between the personality dimensions neuroticism, conscientiousness, and extraversion and problem-solving performance. However, there are no studies dealing with personality disorders in connection with CPS skills. Therefore, the current study examines a clinical sample consisting of people with personality and/or depressive disorders. As we have data for all the potential personality disorders and also data from each patient regarding to potential depression, we meet the whole range from healthy to impaired for each personality disorder and for depression. We make use of a unique operationalization: CPS was surveyed in a simulation game, making use of the microworld approach. This study was designed to investigate the hypothesis that personality traits are related to CPS performance. Results show that schizotypal, histrionic, dependent, and depressive persons are less likely to successfully solve problems, while persons having the additional behavioral characteristics of resilience, action orientation, and motivation for creation are more likely to successfully solve complex problems.

Radiative transfer model inversion using high-resolution hyperspectral airborne imagery - Retrieving maize LAI to access biomass and grain yield.

Mapping crop within-field yield variability provide an essential piece of information for precision agriculture applications. Leaf Area Index (LAI) is an important parameter that describes maize growth, vegetation structure, light absorption and subsequently maize biomass and grain yield (GY). The main goal for this study was to estimate maize biomass and GY through LAI retrieved from hyperspectral aerial images using a PROSAIL model inversion and compare its performance with biomass and GY estimations through simple vegetation index approaches. This study was conducted in two separate maize fields of 12 and 20 ha located in north-west Mexico. Both fields were cultivated with the same hybrid. One field was irrigated by a linear pivot and the other by a furrow irrigation system. Ground LAI data were collected at different crop growth stages followed by maize biomass and GY at the harvesting time. Through a weekly/biweekly airborne flight campaign, a total of 19 mosaics were acquired between both fields with a micro-hyperspectral Vis-NIR imaging sensor ranging from 400 to 850 nanometres (nm) at different crop growth stages. The PROSAIL model was calibrated and validated for retrieving maize LAI by simulating maize canopy spectral reflectance based on crop-specific parameters. The model was used to retrieve LAI from both fields and to subsequently estimate maize biomass and GY. Additionally, different vegetation indices were calculated from the aerial images to also estimate maize yield and compare the indices with PROSAIL based estimations. The PROSAIL validation to retrieve LAI from hyperspectral imagery showed a R2 value of 0.5 against ground LAI with RMSE of 0.8 m2/m2. Maize biomass and GY estimation based on NDRE showed the highest accuracies, followed by retrieved LAI, GNDVI and NDVI with R2 value of 0.81, 0.73, 0.73 and 0.65 for biomass, and 0.83, 0.69, 0.73 and 0.62 for GY estimation, respectively. Furthermore, the late vegetative growth stage at V16 was found to be the best stage for maize yield prediction for all studied indices.

A Double Swath Configuration for Improving Throughput and Accuracy of Trait Estimate from UAV Images.

Multispectral observations from unmanned aerial vehicles (UAVs) are currently used for precision agriculture and crop phenotyping applications to monitor a series of traits allowing the characterization of the vegetation status. However, the limited autonomy of UAVs makes the completion of flights difficult when sampling large areas. Increasing the throughput of data acquisition while not degrading the ground sample distance (GSD) is, therefore, a critical issue to be solved. We propose here a new image acquisition configuration based on the combination of two focal length (f) optics: an optics with f = 4.2 mm is added to the standard f = 8 mm (SS: single swath) of the multispectral camera (DS: double swath, double of the standard one). Two flights were completed consecutively in 2018 over a maize field using the AIRPHEN multispectral camera at 52 m altitude. The DS flight plan was designed to get 80% overlap with the 4.2 mm optics, while the SS one was designed to get 80% overlap with the 8 mm optics. As a result, the time required to cover the same area is halved for the DS as compared to the SS. The georeferencing accuracy was improved for the DS configuration, particularly for the Z dimension due to the larger view angles available with the small focal length optics. Application to plant height estimates demonstrates that the DS configuration provides similar results as the SS one. However, for both the DS and SS configurations, degrading the quality level used to generate the 3D point cloud significantly decreases the plant height estimates.

Importance of the description of light interception in crop growth models.

Canopy light interception determines the amount of energy captured by a crop, and is thus critical to modeling crop growth and yield, and may substantially contribute to the prediction uncertainty of crop growth models (CGMs). We thus analyzed the canopy light interception models of the 26 wheat (Triticum aestivum) CGMs used by the Agricultural Model Intercomparison and Improvement Project (AgMIP). Twenty-one CGMs assume that the light extinction coefficient (K) is constant, varying from 0.37 to 0.80 depending on the model. The other models take into account the illumination conditions and assume either that all green surfaces in the canopy have the same inclination angle (θ) or that θ distribution follows a spherical distribution. These assumptions have not yet been evaluated due to a lack of experimental data. Therefore, we conducted a field experiment with five cultivars with contrasting leaf stature sown at normal and double row spacing, and analyzed θ distribution in the canopies from three-dimensional canopy reconstructions. In all the canopies, θ distribution was well represented by an ellipsoidal distribution. We thus carried out an intercomparison between the light interception models of the AgMIP-Wheat CGMs ensemble and a physically based K model with ellipsoidal leaf angle distribution and canopy clumping (KellC). Results showed that the KellC model outperformed current approaches under most illumination conditions and that the uncertainty in simulated wheat growth and final grain yield due to light models could be as high as 45%. Therefore, our results call for an overhaul of light interception models in CGMs.

A High-Throughput Model-Assisted Method for Phenotyping Maize Green Leaf Area Index Dynamics Using Unmanned Aerial Vehicle Imagery.

The dynamics of the Green Leaf Area Index (GLAI) is of great interest for numerous applications such as yield prediction and plant breeding. We present a high-throughput model-assisted method for characterizing GLAI dynamics in maize (Zea mays subsp. mays) using multispectral imagery acquired from an Unmanned Aerial Vehicle (UAV). Two trials were conducted with a high diversity panel of 400 lines under well-watered and water-deficient treatments in 2016 and 2017. For each UAV flight, we first derived GLAI estimates from empirical relationships between the multispectral reflectance and ground level measurements of GLAI achieved over a small sample of microplots. We then fitted a simple but physiologically sound GLAI dynamics model over the GLAI values estimated previously. Results show that GLAI dynamics was estimated accurately throughout the cycle (R2 > 0.9). Two parameters of the model, biggest leaf area and leaf longevity, were also estimated successfully. We showed that GLAI dynamics and the parameters of the fitted model are highly heritable (0.65 ≤ H2 ≤ 0.98), responsive to environmental conditions, and linked to yield and drought tolerance. This method, combining growth modeling, UAV imagery and simple non-destructive field measurements, provides new high-throughput tools for understanding the adaptation of GLAI dynamics and its interaction with the environment. GLAI dynamics is also a promising trait for crop breeding, and paves the way for future genetic studies.

Estimation of leaf traits from reflectance measurements: comparison between methods based on vegetation indices and several versions of the PROSPECT model.

BACKGROUND: Leaf biochemical composition corresponds to traits related to the plant state and its functioning. This study puts the emphasis on the main leaf absorbers: chlorophyll a and b ([Formula: see text]), carotenoids ([Formula: see text]), water ([Formula: see text]) and dry mater ([Formula: see text]) contents. Two main approaches were used to estimate [[Formula: see text] [Formula: see text], [Formula: see text], [Formula: see text]] in a non-destructive way using spectral measurements. The first one consists in building empirical relationships from experimental datasets using either the raw reflectances or their combination into vegetation indices (VI). The second one relies on the inversion of physically based models of leaf optical properties. Although the first approach is commonly used, the calibration of the empirical relationships is generally conducted over a limited dataset. Consequently, poor predictions may be observed when applying them on cases that are not represented in the training dataset, i.e. when dealing with different species, genotypes or under contrasted environmental conditions. The retrieval performances of the selected VIs were thus compared to the ones of four PROSPECT model versions based on reflectance data acquired at two phenological stages, over six wheat genotypes grown under three different nitrogen fertilizations and two sowing density modalities. Leaf reflectance was measured in the lab with a spectrophotometer equipped with an integrating sphere, the leaf being placed in front of a white Teflon background to increase the sensitivity to leaf biochemical composition. Destructive measurements of [[Formula: see text] [Formula: see text], [Formula: see text], [Formula: see text]] were performed concurrently. RESULTS: The destructive measurements demonstrated that the carotenoid, [Formula: see text], and chlorophyll, [Formula: see text], contents were strongly correlated (r2 = 0.91). The sum of [Formula: see text] and [Formula: see text], i.e. the total chlorophyllian pigment content, [Formula: see text], was therefore used in this study. When inverting the PROSPECT model, accounting for the brown pigment content, [Formula: see text], was necessary when leaves started to senesce. The values of [Formula: see text] and [Formula: see text] were well estimated (r2 = 0.81 and r2 = 0.88 respectively) while the dry matter content, [Formula: see text], was poorly estimated (r2 = 0.00). Retrieval of [Formula: see text] from PROSPECT versions was only slightly biased, while substantial overestimation of [Formula: see text] was observed. The ranking between estimated values of [Formula: see text] and [Formula: see text] from the several PROSPECT versions and that derived using the VIs were similar to the ranking observed over the destructively measured values of [Formula: see text] and [Formula: see text]. CONCLUSIONS: PROSPECT model inversion and empirical VI approach provide similar retrieval performances and are useful methods to estimate leaf biochemical composition from spectral measurements. However, the PROSPECT model inversion gives potential access to additional traits on surface reflectivity and leaf internal structure. This study suggests that non-destructive estimation of leaf chlorophyll and water contents is a relevant method to provide leaf traits with relatively high throughput.

Effects of low nitrogen supply on tomato (Solanum lycopersicum) fruit yield and quality with special emphasis on sugars, acids, ascorbate, carotenoids, and phenolic compounds.

The objective of this study was to determine the impact of lowering nitrogen supply from 12 to 6 or 4 mM NO(3)(-) on tomato fruit yield and quality during the growing season. Lowering nitrogen supply had a low impact on fruit commercial yield (-7.5%), but it reduced plant vegetative growth and increased fruit dry matter content, improving consequently fruit quality. Fruit quality was improved due to lower acid (10-16%) and increased soluble sugar content (5-17%). The content of some phenolic compounds (rutin, a caffeic acid glycoside, and a caffeic acid derivate) and total ascorbic acid tended to be higher in fruit with the lowest nitrogen supply, but differences were significant in only a few cases (trusses). With regard to carotenoids, data did not show significant and univocal differences related to different levels of nitrogen supply. Thus, reducing nitrogen fertilization limited environmental pollution, on the one hand, and may improve, on the other hand, both growers' profits, by limiting nitrogen inputs, and fruit quality for consumers, by increasing tomato sugars content. It was concluded that primary and secondary metabolites could be affected as a result of a specific response to low nitrogen, combined with a lower degree of vegetative development, increasing fruit irradiance, and therefore modifying fruit composition.

Weight gain after transjugular intrahepatic portosystemic shunt is associated with improvement in body composition in malnourished patients with cirrhosis and hypermetabolism.

BACKGROUND/AIMS: To search for changes in body composition and energy metabolism associated with the repeatedly observed weight gain of cirrhotic patients after portosystemic shunting. METHODS: Twenty-one patients were studied prospectively before and 6 and 12 months after transjugular intrahepatic portosystemic shunt (TIPS) to assess body cell mass by two independent methods (total body potassium counting: body cell mass determined by TBP, BCMTBP, bioelectric impedance analysis: body cell mass determined by BIA, BCMBIA), muscle mass (anthropometry), resting energy expenditure (REECALO) by indirect calorimetry, and nutritional intake by dietary recall analysis. RESULTS: Prior to TIPS patients were hypermetabolic in terms of measured vs. predicted REE (REECALO median 1423 (range 1164-1838) vs. REEPRED 1279 (1067-1687) kcal; P<0.05) and their body cell mass was lower (19.1 (10.9-33.4) vs. 31.7 (16.8-47.1) kg; P=0.001). After TIPS body cell mass (BCMBIA) increased to 23.5 (12.7-44.3) (P<0.025) and 25.7 (14.2-39.7) kg (P=0.05) at 6 and 12 months after TIPS and this was confirmed by total potassium counting (BCMTBP before TIPS: 18.8 (10.6-26.7) vs. 22.4 (12.9-28.5) kg at 6 months; P<0.01). Hypermetabolism persisted throughout the study period. Energy and protein intake increased significantly by 26 and 33%. CONCLUSIONS: An increase of prognostically relevant variables body cell and muscle mass contributes to the weight gain after TIPS in malnourished patients with cirrhosis and hypermetabolism.