Towards Automated Analysis of Grain Spikes in Greenhouse Images Using Neural Network Approaches: A Comparative Investigation of Six Methods.

Automated analysis of small and optically variable plant organs, such as grain spikes, is highly demanded in quantitative plant science and breeding. Previous works primarily focused on the detection of prominently visible spikes emerging on the top of the grain plants growing in field conditions. However, accurate and automated analysis of all fully and partially visible spikes in greenhouse images renders a more challenging task, which was rarely addressed in the past. A particular difficulty for image analysis is represented by leaf-covered, occluded but also matured spikes of bushy crop cultivars that can hardly be differentiated from the remaining plant biomass. To address the challenge of automated analysis of arbitrary spike phenotypes in different grain crops and optical setups, here, we performed a comparative investigation of six neural network methods for pattern detection and segmentation in RGB images, including five deep and one shallow neural network. Our experimental results demonstrate that advanced deep learning methods show superior performance, achieving over 90% accuracy by detection and segmentation of spikes in wheat, barley and rye images. However, spike detection in new crop phenotypes can be performed more accurately than segmentation. Furthermore, the detection and segmentation of matured, partially visible and occluded spikes, for which phenotypes substantially deviate from the training set of regular spikes, still represent a challenge to neural network models trained on a limited set of a few hundreds of manually labeled ground truth images. Limitations and further potential improvements of the presented algorithmic frameworks for spike image analysis are discussed. Besides theoretical and experimental investigations, we provide a GUI-based tool (SpikeApp), which shows the application of pre-trained neural networks to fully automate spike detection, segmentation and phenotyping in images of greenhouse-grown plants.

Using the Negative Soil Adjustment Factor of Soil Adjusted Vegetation Index (SAVI) to Resist Saturation Effects and Estimate Leaf Area Index (LAI) in Dense Vegetation Areas.

Saturation effects limit the application of vegetation indices (VIs) in dense vegetation areas. The possibility to mitigate them by adopting a negative soil adjustment factor X is addressed. Two leaf area index (LAI) data sets are analyzed using the Google Earth Engine (GEE) for validation. The first one is derived from observations of MODerate resolution Imaging Spectroradiometer (MODIS) from 16 April 2013, to 21 October 2020, in the Apiacás area. Its corresponding VIs are calculated from a combination of Sentinel-2 and Landsat-8 surface reflectance products. The second one is a global LAI dataset with VIs calculated from Landsat-5 surface reflectance products. A linear regression model is applied to both datasets to evaluate four VIs that are commonly used to estimate LAI: normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), transformed SAVI (TSAVI), and enhanced vegetation index (EVI). The optimal soil adjustment factor of SAVI for LAI estimation is determined using an exhaustive search. The Dickey-Fuller test indicates that the time series of LAI data are stable with a confidence level of 99%. The linear regression results stress significant saturation effects in all VIs. Finally, the exhaustive searching results show that a negative soil adjustment factor of SAVI can mitigate the SAVIs' saturation in the Apiacás area (i.e., X = -0.148 for mean LAI = 5.35), and more generally in areas with large LAI values (e.g., X = -0.183 for mean LAI = 6.72). Our study further confirms that the lower boundary of the soil adjustment factor can be negative and that using a negative soil adjustment factor improves the computation of time series of LAI.

Modelling leaf spectral properties in a soybean functional-structural plant model by integrating the prospect radiative transfer model.

Background and Aims: Currently, functional-structural plant models (FSPMs) mostly resort to static descriptions of leaf spectral characteristics, which disregard the influence of leaf physiological changes over time. In many crop species, including soybean, these time-dependent physiological changes are of particular importance as leaf chlorophyll content changes with leaf age and vegetative nitrogen is remobilized to the developing fruit during pod filling. Methods: PROSPECT, a model developed to estimate leaf biochemical composition from remote sensing data, is well suited to allow a dynamic approximation of leaf spectral characteristics in terms of leaf composition. In this study, measurements of the chlorophyll content index (CCI) were linked to leaf spectral characteristics within the 400-800 nm range by integrating the PROSPECT model into a soybean FSPM alongside a wavelength-specific light model. Key Results: Straightforward links between the CCI and the parameters of the PROSPECT model allowed us to estimate leaf spectral characteristics with high accuracy using only the CCI as an input. After integration with an FSPM, this allowed digital reconstruction of leaf spectral characteristics on the scale of both individual leaves and the whole canopy. As a result, accurate simulations of light conditions within the canopy were obtained. Conclusions: The proposed approach resulted in a very accurate representation of leaf spectral properties, based on fast and simple measurements of the CCI. Integration of accurate leaf spectral characteristics into a soybean FSPM leads to a better, dynamic understanding of the actual perceived light within the canopy in terms of both light quantity and quality.

A 3-D functional-structural grapevine model that couples the dynamics of water transport with leaf gas exchange.

Background and Aims: Predicting both plant water status and leaf gas exchange under various environmental conditions is essential for anticipating the effects of climate change on plant growth and productivity. This study developed a functional-structural grapevine model which combines a mechanistic understanding of stomatal function and photosynthesis at the leaf level (i.e. extended Farqhuhar-von Caemmerer-Berry model) and the dynamics of water transport from soil to individual leaves (i.e. Tardieu-Davies model). Methods: The model included novel features that account for the effects of xylem embolism (fPLC) on leaf hydraulic conductance and residual stomatal conductance (g0), variable root and leaf hydraulic conductance, and the microclimate of individual organs. The model was calibrated with detailed datasets of leaf photosynthesis, leaf water potential, xylem sap abscisic acid (ABA) concentration and hourly whole-plant transpiration observed within a soil drying period, and validated with independent datasets of whole-plant transpiration under both well-watered and water-stressed conditions. Key Results: The model well captured the effects of radiation, temperature, CO2 and vapour pressure deficit on leaf photosynthesis, transpiration, stomatal conductance and leaf water potential, and correctly reproduced the diurnal pattern and decline of water flux within the soil drying period. In silico analyses revealed that decreases in g0 with increasing fPLC were essential to avoid unrealistic drops in leaf water potential under severe water stress. Additionally, by varying the hydraulic conductance along the pathway (e.g. root and leaves) and changing the sensitivity of stomatal conductance to ABA and leaf water potential, the model can produce different water use behaviours (i.e. iso- and anisohydric). Conclusions: The robust performance of this model allows for modelling climate effects from individual plants to fields, and for modelling plants with complex, non-homogenous canopies. In addition, the model provides a basis for future modelling efforts aimed at describing the physiology and growth of individual organs in relation to water status.

A flexible multivariate random effects proportional odds model with application to adverse effects during radiation therapy.

Radiation therapy in patients with head and neck cancer has a toxic effect on mucosa, the soft tissue in and around the mouth. Hence mucositis is a serious common side effect and is a condition characterized by pain and inflammation of the surface of the mucosa. Although the mucosa recovers during breaks of and following the radiotherapy course, the recovery will depend on the type of tissue involved and on its location. We present a novel flexible multivariate random effects proportional odds model that takes account of the longitudinal course of oral mucositis at different mouth sites and of the radiation dosage (in terms of cumulative dose). The model is an extension of the proportional odds model that is used for ordinal response variables. Our model includes the ordinal multivariate response of the mucositis score by site, random intercepts for individuals, and a nonlinear function of cumulative radiation dose. The model allows to test whether sensitivity differs by mouth sites after having adjusted for site-specific cumulative radiation dose. The model also allows to check whether and how the (nonlinear) effect of site-specific dose differs by site. We fit the model to longitudinal patient data from a prospective observation and find that after adjusting for cumulative dose, upper, lower lips, and mouth floor are associated with the lowest mucositis scores and hard and soft palate are associated with the highest mucositis scores. This implies the possibility that tissues at different mouth sites differ in their sensitivity to the toxic effect of radiation.

Chemoradiotherapy with or without panitumumab in patients with unresected, locally advanced squamous-cell carcinoma of the head and neck (CONCERT-1): a randomised, controlled, open-label phase 2 trial.

BACKGROUND: Panitumumab is a fully human monoclonal antibody that targets EGFR. We aimed to compare chemoradiotherapy plus panitumumab with chemoradiotherapy alone in patients with unresected, locally advanced squamous-cell carcinoma of the head and neck. METHODS: In this international, open-label, randomised, controlled, phase 2 trial, we recruited patients with locally advanced squamous-cell carcinoma of the head and neck from 41 sites in nine countries worldwide. Patients aged 18 years and older with stage III, IVa, or IVb, previously untreated, measurable (≥ 10 mm for at least one dimension), locally advanced squamous-cell carcinoma of the head and neck (non-nasopharygeal) and an Eastern Cooperative Oncology Group performance status of 0-1 were randomly assigned (2:3) by an independent vendor to open-label chemoradiotherapy (three cycles of cisplatin 100 mg/m(2)) or panitumumab plus chemoradiotherapy (three cycles of intravenous panitumumab 9.0 mg/kg every 3 weeks plus cisplatin 75 mg/m(2)) using stratified randomisation with a block size of five. All patients received 70 Gy to gross tumour and 50 Gy to areas at risk for subclinical disease with standard fractionation. The primary endpoint was local-regional control at 2 years, analysed in all randomised patients who received at least one dose of their assigned protocol-specific treatment (chemotherapy, radiation, or panitumumab). The trial is closed and this is the final analysis. This trial is registered with ClinicalTrials.gov, number NCT00500760. FINDINGS: Between Oct 26, 2007, and March 26, 2009, 153 patients were enrolled and 150 received treatment (63 in the chemoradiotherapy group and 87 in the panitumumab plus chemoradiotherapy group). Local-regional control at 2 years was 68% (95% CI 54-78) in the chemoradiotherapy group and 61% (50-71) in the panitumumab plus chemoradiotherapy group. The most frequent grade 3-4 adverse events were dysphagia (17 [27%] of 63 patients in the chemoradiotherapy group vs 35 [40%] of 87 in the panitumumab plus chemoradiotherapy group), mucosal inflammation (15 [24%] vs 48 [55%]), and radiation skin injury (eight [13%] vs 27 [31%]). Serious adverse events were reported in 20 (32%) of 63 patients in the chemoradiotherapy group and in 37 (43%) of 87 patients in the panitumumab plus chemoradiotherapy group. INTERPRETATION: In patients with locally advanced squamous-cell carcinoma of the head and neck, the addition of panitumumab to standard fractionation radiotherapy and cisplatin did not confer any benefit, and the role of EGFR inhibition in these patients needs to be reassessed. FUNDING: Amgen.

Tumor cells with low proteasome subunit expression predict overall survival in head and neck cancer patients.

BACKGROUND: Experimental and clinical data suggest that solid cancers contain treatment-resistant cancer stem cells that will impair treatment efficacy. The objective of this study was to investigate if head and neck squamous cell carcinoma (HNSCC) also contain cancer stem cells that can be identified by low 26S proteasome activity and if their presence correlates to clinical outcome. METHODS: Human HNSCC cells, engineered to report lack of proteasome activity based on accumulation of a fluorescent fusion protein, were separated based on high (ZsGreen-cODCneg) or low (ZsGreen-cODCpos) proteasome activity. Self-renewal capacity, tumorigenicity and radioresistance were assessed. Proteasome subunit expression was analyzed in tissue microarrays and correlated to survival and locoregional cancer control of 174 patients with HNSCC. RESULTS: HNSCC cells with low proteasome activity showed a significantly higher self-renewal capacity and increased tumorigenicity. Irradiation enriched for ZsGreen-cODCpos cells. The survival probability of 82 patients treated with definitive radio- or chemo-radiotherapy exhibiting weak, intermediate, or strong proteasome subunit expression were 21.2, 28.8 and 43.8 months (p = 0.05), respectively. Locoregional cancer control was comparably affected. CONCLUSIONS: Subpopulations of HNSCC display stem cell features that affect patients' tumor control and survival. Evaluating cancer tissue for expression of the proteasome subunit PSMD1 may help identify patients at risk for relapse.

An approach to multiscale modelling with graph grammars.

BACKGROUND AND AIMS: Functional-structural plant models (FSPMs) simulate biological processes at different spatial scales. Methods exist for multiscale data representation and modification, but the advantages of using multiple scales in the dynamic aspects of FSPMs remain unclear. Results from multiscale models in various other areas of science that share fundamental modelling issues with FSPMs suggest that potential advantages do exist, and this study therefore aims to introduce an approach to multiscale modelling in FSPMs. METHODS: A three-part graph data structure and grammar is revisited, and presented with a conceptual framework for multiscale modelling. The framework is used for identifying roles, categorizing and describing scale-to-scale interactions, thus allowing alternative approaches to model development as opposed to correlation-based modelling at a single scale. Reverse information flow (from macro- to micro-scale) is catered for in the framework. The methods are implemented within the programming language XL. KEY RESULTS: Three example models are implemented using the proposed multiscale graph model and framework. The first illustrates the fundamental usage of the graph data structure and grammar, the second uses probabilistic modelling for organs at the fine scale in order to derive crown growth, and the third combines multiscale plant topology with ozone trends and metabolic network simulations in order to model juvenile beech stands under exposure to a toxic trace gas. CONCLUSIONS: The graph data structure supports data representation and grammar operations at multiple scales. The results demonstrate that multiscale modelling is a viable method in FSPM and an alternative to correlation-based modelling. Advantages and disadvantages of multiscale modelling are illustrated by comparisons with single-scale implementations, leading to motivations for further research in sensitivity analysis and run-time efficiency for these models.

What is the most prominent factor limiting photosynthesis in different layers of a greenhouse cucumber canopy?

BACKGROUND AND AIMS: Maximizing photosynthesis at the canopy level is important for enhancing crop yield, and this requires insights into the limiting factors of photosynthesis. Using greenhouse cucumber (Cucumis sativus) as an example, this study provides a novel approach to quantify different components of photosynthetic limitations at the leaf level and to upscale these limitations to different canopy layers and the whole plant. METHODS: A static virtual three-dimensional canopy structure was constructed using digitized plant data in GroIMP. Light interception of the leaves was simulated by a ray-tracer and used to compute leaf photosynthesis. Different components of photosynthetic limitations, namely stomatal (S(L)), mesophyll (M(L)), biochemical (B(L)) and light (L(L)) limitations, were calculated by a quantitative limitation analysis of photosynthesis under different light regimes. KEY RESULTS: In the virtual cucumber canopy, B(L) and L(L) were the most prominent factors limiting whole-plant photosynthesis. Diffusional limitations (S(L) + M(L)) contributed <15% to total limitation. Photosynthesis in the lower canopy was more limited by the biochemical capacity, and the upper canopy was more sensitive to light than other canopy parts. Although leaves in the upper canopy received more light, their photosynthesis was more light restricted than in the leaves of the lower canopy, especially when the light condition above the canopy was poor. An increase in whole-plant photosynthesis under diffuse light did not result from an improvement of light use efficiency but from an increase in light interception. Diffuse light increased the photosynthesis of leaves that were directly shaded by other leaves in the canopy by up to 55%. CONCLUSIONS: Based on the results, maintaining biochemical capacity of the middle-lower canopy and increasing the leaf area of the upper canopy would be promising strategies to improve canopy photosynthesis in a high-wire cucumber cropping system. Further analyses using the approach described in this study can be expected to provide insights into the influences of horticultural practices on canopy photosynthesis and the design of optimal crop canopies.

Towards unravelling biological mechanisms behind radiation-induced oral mucositis via mass spectrometry-based proteomics.

Objective: Head and neck cancer (HNC) accounts for almost 890,000 new cases per year. Radiotherapy (RT) is used to treat the majority of these patients. A common side-effect of RT is the onset of oral mucositis, which decreases the quality of life and represents the major dose-limiting factor in RT. To understand the origin of oral mucositis, the biological mechanisms post-ionizing radiation (IR) need to be clarified. Such knowledge is valuable to develop new treatment targets for oral mucositis and markers for the early identification of "at-risk" patients. Methods: Primary keratinocytes from healthy volunteers were biopsied, irradiated in vitro (0 and 6 Gy), and subjected to mass spectrometry-based analyses 96 h after irradiation. Web-based tools were used to predict triggered biological pathways. The results were validated in the OKF6 cell culture model. Immunoblotting and mRNA validation was performed and cytokines present in cell culture media post-IR were quantified. Results: Mass spectrometry-based proteomics identified 5879 proteins in primary keratinocytes and 4597 proteins in OKF6 cells. Amongst them, 212 proteins in primary keratinocytes and 169 proteins in OKF6 cells were differentially abundant 96 h after 6 Gy irradiation compared to sham-irradiated controls. In silico pathway enrichment analysis predicted interferon (IFN) response and DNA strand elongation pathways as mostly affected pathways in both cell systems. Immunoblot validations showed a decrease in minichromosome maintenance (MCM) complex proteins 2-7 and an increase in IFN-associated proteins STAT1 and ISG15. In line with affected IFN signalling, mRNA levels of IFNß and interleukin 6 (IL-6) increased significantly following irradiation and also levels of secreted IL-1ß, IL-6, IP-10, and ISG15 were elevated. Conclusion: This study has investigated biological mechanisms in keratinocytes post-in vitro ionizing radiation. A common radiation signature in keratinocytes was identified. The role of IFN response in keratinocytes along with increased levels of pro-inflammatory cytokines and proteins could hint towards a possible mechanism for oral mucositis.

Analyzing the Impact of Greenhouse Planting Strategy and Plant Architecture on Tomato Plant Physiology and Estimated Dry Matter.

Determine the level of significance of planting strategy and plant architecture and how they affect plant physiology and dry matter accumulation within greenhouses is essential to actual greenhouse plant management and breeding. We thus analyzed four planting strategies (plant spacing, furrow distance, row orientation, planting pattern) and eight different plant architectural traits (internode length, leaf azimuth angle, leaf elevation angle, leaf length, leaflet curve, leaflet elevation, leaflet number/area ratio, leaflet length/width ratio) with the same plant leaf area using a formerly developed functional-structural model for a Chinese Liaoshen-solar greenhouse and tomato plant, which used to simulate the plant physiology of light interception, temperature, stomatal conductance, photosynthesis, and dry matter. Our study led to the conclusion that the planting strategies have a more significant impact overall on plant radiation, temperature, photosynthesis, and dry matter compared to plant architecture changes. According to our findings, increasing the plant spacing will have the most significant impact to increase light interception. E-W orientation has better total light interception but yet weaker light uniformity. Changes in planting patterns have limited influence on the overall canopy physiology. Increasing the plant leaflet area by leaflet N/A ratio from what we could observe for a rose the total dry matter by 6.6%, which is significantly better than all the other plant architecture traits. An ideal tomato plant architecture which combined all the above optimal architectural traits was also designed to provide guidance on phenotypic traits selection of breeding process. The combined analysis approach described herein established the causal relationship between investigated traits, which could directly apply to provide management and breeding insights on other plant species with different solar greenhouse structures.

Deep Learning Based Greenhouse Image Segmentation and Shoot Phenotyping (DeepShoot).

Background: Automated analysis of large image data is highly demanded in high-throughput plant phenotyping. Due to large variability in optical plant appearance and experimental setups, advanced machine and deep learning techniques are required for automated detection and segmentation of plant structures in complex optical scenes. Methods: Here, we present a GUI-based software tool (DeepShoot) for efficient, fully automated segmentation and quantitative analysis of greenhouse-grown shoots which is based on pre-trained U-net deep learning models of arabidopsis, maize, and wheat plant appearance in different rotational side- and top-views. Results: Our experimental results show that the developed algorithmic framework performs automated segmentation of side- and top-view images of different shoots acquired at different developmental stages using different phenotyping facilities with an average accuracy of more than 90% and outperforms shallow as well as conventional and encoder backbone networks in cross-validation tests with respect to both precision and performance time. Conclusion: The DeepShoot tool presented in this study provides an efficient solution for automated segmentation and phenotypic characterization of greenhouse-grown plant shoots suitable also for end-users without advanced IT skills. Primarily trained on images of three selected plants, this tool can be applied to images of other plant species exhibiting similar optical properties.

Investigation of the impact of supplemental reflective films to improve micro-light climate within tomato plant canopy in solar greenhouses.

The non-uniform growth and development of crops within Chinese Solar Greenhouses (CSG) is directly related to the micro-light climate within canopy. In practice, reflective films are used to improve micro-light climate within plant canopy by homogenizing light distribution and so increasing total plant light interception. However, as to our knowledge, the contributions to light distribution within canopy have not been investigated for passive reflector like reflective films. Field experiments dealing with light conditions and growth behavior over time, are complicated to carry out, time-consuming and hard to control, while however, accurate measurements of how reflective films influence the micro-light climate of canopy are an essential step to improve the growth conditions for any crop. Here, we propose a supplementary light strategy using reflective films to improve light distribution within plant canopy. Based on the example of CSG, a 3D greenhouse model including a detailed 3D tomato canopy structure was constructed to simulate the influence of supplementary reflective films to improve micro-light climate. Comparison of measured solar radiation intensity with predicted model data demonstrated that the model could precisely predict light radiation intensity over time with different time points and positions in the greenhouse. A series of reflective film configurations were investigated based on features analysis of light distribution in the tomato canopy on sunny days using the proposed model. The reflective film configuration scheme with the highest impact significantly improved the evenness of horizontal and vertical light distribution in tomato canopy. The strategy provided here can be used to configure reflective films that will enhance light conditions in CSG, which can be applied and extended in different scenarios.

Integration of p16/HPV DNA Status with a 24-miRNA-Defined Molecular Phenotype Improves Clinically Relevant Stratification of Head and Neck Cancer Patients.

Human papillomavirus (HPV)-driven head and neck squamous cell carcinomas (HNSCC) generally have a more favourable prognosis. We hypothesized that HPV-associated HNSCC may be identified by an miRNA-signature according to their specific molecular pathogenesis, and be characterized by a unique transcriptome compared to HPV-negative HNSCC. We performed miRNA expression profiling of two p16/HPV DNA characterized HNSCC cohorts of patients treated by adjuvant radio(chemo)therapy (multicentre DKTK-ROG n = 128, single-centre LMU-KKG n = 101). A linear model predicting HPV status built in DKTK-ROG using lasso-regression was tested in LMU-KKG. LMU-KKG tumours (n = 30) were transcriptome profiled for differential gene expression and miRNA-integration. A 24-miRNA signature predicted HPV-status with 94.53% accuracy (AUC: 0.99) in DKTK-ROG, and 86.14% (AUC: 0.86) in LMU-KKG. The prognostic values of 24-miRNA- and p16/HPV DNA status were comparable. Combining p16/HPV DNA and 24-miRNA status allowed patient sub-stratification and identification of an HPV-associated patient subgroup with impaired overall survival. HPV-positive tumours showed downregulated MAPK, Estrogen, EGFR, TGFbeta, WNT signaling activity. miRNA-mRNA integration revealed HPV-specific signaling pathway regulation, including PD-L1 expression/PD-1 checkpoint pathway in cancer in HPV-associated HNSCC. Integration of clinically established p16/HPV DNA with 24-miRNA signature status improved clinically relevant risk stratification, which might be considered for future clinical decision-making with respect to treatment de-escalation in HPV-associated HNSCC.

A functional-structural model of rice linking quantitative genetic information with morphological development and physiological processes.

BACKGROUND AND AIMS: Although quantitative trait loci (QTL) analysis of yield-related traits for rice has developed rapidly, crop models using genotype information have been proposed only relatively recently. As a first step towards a generic genotype-phenotype model, we present here a three-dimensional functional-structural plant model (FSPM) of rice, in which some model parameters are controlled by functions describing the effect of main-effect and epistatic QTLs. METHODS: The model simulates the growth and development of rice based on selected ecophysiological processes, such as photosynthesis (source process) and organ formation, growth and extension (sink processes). It was devised using GroIMP, an interactive modelling platform based on the Relational Growth Grammar formalism (RGG). RGG rules describe the course of organ initiation and extension resulting in final morphology. The link between the phenotype (as represented by the simulated rice plant) and the QTL genotype was implemented via a data interface between the rice FSPM and the QTLNetwork software, which computes predictions of QTLs from map data and measured trait data. KEY RESULTS: Using plant height and grain yield, it is shown how QTL information for a given trait can be used in an FSPM, computing and visualizing the phenotypes of different lines of a mapping population. Furthermore, we demonstrate how modification of a particular trait feeds back on the entire plant phenotype via the physiological processes considered. CONCLUSIONS: We linked a rice FSPM to a quantitative genetic model, thereby employing QTL information to refine model parameters and visualizing the dynamics of development of the entire phenotype as a result of ecophysiological processes, including the trait(s) for which genetic information is available. Possibilities for further extension of the model, for example for the purposes of ideotype breeding, are discussed.

Towards a functional-structural plant model of cut-rose: simulation of light environment, light absorption, photosynthesis and interference with the plant structure.

BACKGROUND AND AIMS: The production system of cut-rose (Rosa × hybrida) involves a complex combination of plant material, management practice and environment. Plant structure is determined by bud break and shoot development while having an effect on local light climate. The aim of the present study is to cover selected aspects of the cut-rose system using functional-structural plant modelling (FSPM), in order to better understand processes contributing to produce quality and quantity. METHODS: The model describes the production system in three dimensions, including a virtual greenhouse environment with the crop, light sources (diffuse and direct sun light and lamps) and photosynthetically active radiation (PAR) sensors. The crop model is designed as a multiscaled FSPM with plant organs (axillary buds, leaves, internodes, flowers) as basic units, and local light interception and photosynthesis within each leaf. A Monte-Carlo light model was used to compute the local light climate for leaf photosynthesis, the latter described using a biochemical rate model. KEY RESULTS: The model was able to reproduce PAR measurements taken at different canopy positions, different times of the day and different light regimes. Simulated incident and absorbed PAR as well as net assimilation rate in upright and bent shoots showed characteristic spatial and diurnal dynamics for different common cultivation scenarios. CONCLUSIONS: The model of cut-rose presented allowed the creation of a range of initial structures thanks to interactive rules for pruning, cutting and bending. These static structures can be regarded as departure points for the dynamic simulation of production of flower canes. Furthermore, the model was able to predict local (per leaf) light absorption and photosynthesis. It can be used to investigate the physiology of ornamental plants, and provide support for the decisions of growers and consultants.

Fully-automated root image analysis (faRIA).

High-throughput root phenotyping in the soil became an indispensable quantitative tool for the assessment of effects of climatic factors and molecular perturbation on plant root morphology, development and function. To efficiently analyse a large amount of structurally complex soil-root images advanced methods for automated image segmentation are required. Due to often unavoidable overlap between the intensity of fore- and background regions simple thresholding methods are, generally, not suitable for the segmentation of root regions. Higher-level cognitive models such as convolutional neural networks (CNN) provide capabilities for segmenting roots from heterogeneous and noisy background structures, however, they require a representative set of manually segmented (ground truth) images. Here, we present a GUI-based tool for fully automated quantitative analysis of root images using a pre-trained CNN model, which relies on an extension of the U-Net architecture. The developed CNN framework was designed to efficiently segment root structures of different size, shape and optical contrast using low budget hardware systems. The CNN model was trained on a set of 6465 masks derived from 182 manually segmented near-infrared (NIR) maize root images. Our experimental results show that the proposed approach achieves a Dice coefficient of 0.87 and outperforms existing tools (e.g., SegRoot) with Dice coefficient of 0.67 by application not only to NIR but also to other imaging modalities and plant species such as barley and arabidopsis soil-root images from LED-rhizotron and UV imaging systems, respectively. In summary, the developed software framework enables users to efficiently analyse soil-root images in an automated manner (i.e. without manual interaction with data and/or parameter tuning) providing quantitative plant scientists with a powerful analytical tool.

Evaluating erythropoietin-associated tumor progression using archival tissues from a phase III clinical trial.

Despite the prevalence of anemia in cancer, recombinant erythropoietin (Epo) has declined in use because of recent Phase III trials showing more rapid cancer progression and reduced survival in subjects randomized to Epo. Since Epo receptor (EpoR), Jak2, and Hsp70 are well-characterized mediators of Epo signaling in erythroid cells, we hypothesized that Epo might be especially harmful in patients whose tumors express high levels of these effectors. Because of the insensitivity of immunohistochemistry for detecting low level EpoR protein, we developed assays to measure levels of EpoR, Jak2 and Hsp70 mRNA in formalin-fixed paraffin-embedded (FFPE) tumors. We tested 23 archival breast tumors as well as 136 archival head and neck cancers from ENHANCE, a Phase III trial of 351 patients randomized to Epo versus placebo concomitant with radiotherapy following complete resection, partial resection, or no resection of tumor. EpoR, Jak2, and Hsp70 mRNA levels varied >30-fold, >12-fold, and >13-fold across the breast cancers, and >30-fold, >40-fold, and >30-fold across the head and neck cancers, respectively. Locoregional progression-free survival (LPFS) did not differ among patients whose head and neck cancers expressed above- versus below-median levels of EpoR, Jak2 or Hsp70, except in the subgroup of patients with unresected tumors (n = 28), where above-median EpoR, above-median Jak2, and below-median Hsp70 mRNA levels were all associated with significantly poorer LPFS. Our results provide a framework for exploring the relationship between Epo, cancer progression, and survival using archival tumors from other Phase III clinical trials.

Reassessments of ESAs for cancer treatment in the US and Europe.

Anemia is a widely prevalent complication among cancer patients. At the time of diagnosis, 30% to 40% of patients with non-Hodgkin lymphoma or Hodgkin lymphoma and up to 70% of patients with multiple myeloma are anemic; rates are higher among persons with myelodysplastic syndromes. Among patients with solid cancers or lymphomas, up to half develop anemia following chemotherapy. For almost 2 decades, erythropoiesis-stimulating agents (ESAs) were the primary treatment for cancer-related anemia. However, reassessments of benefits and risks of ESAs for cancer-associated anemia have occurred internationally. We reviewed guidelines and notifications from regulatory agencies and manufacturers, reimbursement policies, and utilization for ESAs in the cancer and chronic kidney disease settings within the United States, Europe, and Canada. In 2008 the US Food and Drug Administration (FDA) restricted ESAs from cancer patients seeking cure. Reimbursement is limited to hemoglobin levels < 10 g/dL. In the United States, ESA usage increased 340% between 2001 and 2006, and decreased 60% since 2007. The European Medicines Agency (EMEA) recommended that ESA benefits do not outweigh risks. In Europe between 2001 and 2006, ESA use increased 51%; since 2006, use decreased by 10%. In 2009, Canadian manufacturers recommended usage based on patient preferences. In Canada in 2007, approximately 20% of anemic cancer patients received ESAs, a 20% increase since 2004. In contrast to Europe, where ESA use has increased over time, reassessments of ESA-associated safety concerns in the United States have resulted in marked decrements in ESA use among cancer patients.

A two-step registration-classification approach to automated segmentation of multimodal images for high-throughput greenhouse plant phenotyping.

BACKGROUND: Automated segmentation of large amount of image data is one of the major bottlenecks in high-throughput plant phenotyping. Dynamic optical appearance of developing plants, inhomogeneous scene illumination, shadows and reflections in plant and background regions complicate automated segmentation of unimodal plant images. To overcome the problem of ambiguous color information in unimodal data, images of different modalities can be combined to a virtual multispectral cube. However, due to motion artefacts caused by the relocation of plants between photochambers the alignment of multimodal images is often compromised by blurring artifacts. RESULTS: Here, we present an approach to automated segmentation of greenhouse plant images which is based on co-registration of fluorescence (FLU) and of visible light (VIS) camera images followed by subsequent separation of plant and marginal background regions using different species- and camera view-tailored classification models. Our experimental results including a direct comparison with manually segmented ground truth data show that images of different plant types acquired at different developmental stages from different camera views can be automatically segmented with the average accuracy of 93 % ( S D = 5 % ) using our two-step registration-classification approach. CONCLUSION: Automated segmentation of arbitrary greenhouse images exhibiting highly variable optical plant and background appearance represents a challenging task to data classification techniques that rely on detection of invariances. To overcome the limitation of unimodal image analysis, a two-step registration-classification approach to combined analysis of fluorescent and visible light images was developed. Our experimental results show that this algorithmic approach enables accurate segmentation of different FLU/VIS plant images suitable for application in fully automated high-throughput manner.