D-Transposition of the great arteries with restrictive foramen ovale in the fetus: the dilemma of predicting the need for postnatal urgent balloon atrial septostomy

OBJECTIVE

Restrictive foramen ovale (FO) in dextro-transposition of the great arteries (d-TGA) with intact ventricular septum may lead to severe life-threatening hypoxia within the first hours of life, making urgent balloon atrial septostomy (BAS) inevitable. Reliable prenatal prediction of restrictive FO is crucial in these cases. However, current prenatal echocardiographic markers show low predictive value, and prenatal prediction often fails with fatal consequences for a subset of newborns. In this study, we described our experience and aimed to identify reliable predictive markers for BAS.

METHODS

We included 45 fetuses with isolated d-TGA that were diagnosed and delivered between 2010 and 2022 in two large German tertiary referral centers. Inclusion criteria were the availability of former prenatal ultrasound reports, of stored echocardiographic videos and still images, which had to be obtained within the last 14 days prior to delivery and that were of sufficient quality for retrospective re-analysis. Cardiac parameters were retrospectively assessed and their predictive value was evaluated.

RESULTS

Among the 45 included fetuses with d-TGA, 22 neonates had restrictive FO postnatally and required urgent BAS within the first 24 h of life. In contrast, 23 neonates had normal FO anatomy, but 4 of them unexpectedly showed inadequate interatrial mixing despite their normal FO anatomy, rapidly developed hypoxia and also required urgent BAS ('bad mixer'). Overall, 26 (58%) neonates required urgent BAS, whereas 19 (42%) achieved good O2 saturation and did not undergo urgent BAS. In the former prenatal ultrasound reports, restrictive FO with subsequent urgent BAS was correctly predicted in 11 of 22 cases (50% sensitivity), whereas a normal FO anatomy was correctly predicted in 19 of 23 cases (83% specificity). After current re-analysis of the stored videos and images, we identified three highly significant markers for restrictive FO: a FO diameter < 7 mm (p < 0.01), a fixed (p = 0.035) and a hypermobile (p = 0.014) FO flap. The maximum systolic flow velocities in the pulmonary veins were also significantly increased in restrictive FO (p = 0.021), but no cut-off value to reliably predict restrictive FO could be identified. If the above markers are applied, all 22 cases with restrictive FO and all 23 cases with normal FO anatomy could correctly be predicted (100% positive predictive value). Correct prediction of urgent BAS also succeeded in all 22 cases with restrictive FO (100% PPV), but naturally failed in 4 of the 23 cases with correctly predicted normal FO ('bad mixer') (82.6% negative predictive value).

CONCLUSION

Precise assessment of FO size and FO flap motility allows a reliable prenatal prediction of both restrictive and normal FO anatomy postnatally. Prediction of likelihood of urgent BAS also succeeds reliably in all fetuses with restrictive FO, but identification of the small subset of fetuses that also requires urgent BAS despite their normal FO anatomy fails, because the ability of sufficient postnatal interatrial mixing cannot be predicted prenatally. Therefore, all fetuses with prenatally diagnosed d-TGA should always be delivered in a tertiary center with cardiac catheter stand-by, allowing BAS within the first 24 h after birth, regardless of their predicted FO anatomy.

High-throughput field phenotyping reveals that selection in breeding has affected the phenology and temperature response of wheat in the stem elongation phase

Crop growth and phenology are driven by seasonal changes in environmental variables, with temperature as one important factor. However, knowledge about genotype-specific temperature response and its influence on phenology is limited. Such information is fundamental to improve crop models and adapt selection strategies. We measured the increase in height of 352 European winter wheat varieties in 4 years to quantify phenology, and fitted an asymptotic temperature response model. The model used hourly fluctuations in temperature to parameterize the base temperature (Tmin), the temperature optimum (rmax), and the steepness (lrc) of growth responses. Our results show that higher Tmin and lrc relate to an earlier start and end of stem elongation. A higher rmax relates to an increased final height. Both final height and rmax decreased for varieties originating from the continental east of Europe towards the maritime west. A genome-wide association study (GWAS) indicated a quantitative inheritance and a large degree of independence among loci. Nevertheless, genomic prediction accuracies (GBLUPs) for Tmin and lrc were low (r≤0.32) compared with other traits (r≥0.59). As well as known, major genes related to vernalization, photoperiod, or dwarfing, the GWAS indicated additional, as yet unknown loci that dominate the temperature response.

International Perspective on Extremity Vascular Trauma in Children: A Scoping Review

OBJECTIVE

Extremity vascular trauma in children can result in significant morbidity and mortality. Most published studies have focused on supracondylar humeral fracture related injuries, with little focus on other injuries. This scoping review describes the current state of knowledge on paediatric vascular injuries in the upper and lower limbs, excluding injuries related to supracondylar humeral fractures.

METHODS

MEDLINE, PubMed, Web of Science, and Cochrane databases were searched for relevant studies evaluating the epidemiology, diagnosis, management, and outcomes of upper and lower limb vascular trauma in those aged under 18 years. Studies related to supracondylar humeral fractures were excluded. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for Scoping Reviews was used.

RESULTS

A total of 39 studies was included, all of which were retrospective, and 74% of which were based in North America or Europe. Extremity vascular trauma was reported to cause 0.6 - 4.4% of all paediatric trauma admissions, with penetrating mechanisms and upper limb injuries being the most common. Operative intervention was reported in 80 - 100% of children in the included studies. Primary repair was the most commonly reported operative intervention, followed by interposition graft and bypass graft. Synthetic graft use was less commonly reported (incidence range 0.5 - 33%). Lower limb fasciotomies and amputations were not commonly reported (incidence range 0 - 23% and 0 - 13%, respectively). The mortality rate appeared low, with 23 studies reporting no deaths (incidence range 0 - 4%). Complications were reported inconsistently, with no uniform outcome or follow up measures used.

CONCLUSION

The incidence of extremity vascular trauma appears low in children, with penetrating mechanisms and upper extremity injuries appearing to dominate. Most studies are from high income countries, with probable selection bias towards those treated by operative intervention. Prospective studies are required focusing on patterns of injury, rates of operative and endovascular intervention, and long term outcomes.

Linking photosynthesis and yield reveals a strategy to improve light use efficiency in a climbing bean breeding population

Photosynthesis drives plant physiology, biomass accumulation, and yield. Photosynthetic efficiency, specifically the operating efficiency of PSII (Fq'/Fm'), is highly responsive to actual growth conditions, especially to fluctuating photosynthetic photon fluence rate (PPFR). Under field conditions, plants constantly balance energy uptake to optimize growth. The dynamic regulation complicates the quantification of cumulative photochemical energy uptake based on the intercepted solar energy, its transduction into biomass, and the identification of efficient breeding lines. Here, we show significant effects on biomass related to genetic variation in photosynthetic efficiency of 178 climbing bean (Phaseolus vulgaris L.) lines. Under fluctuating conditions, the Fq'/Fm' was monitored throughout the growing period using hand-held and automated chlorophyll fluorescence phenotyping. The seasonal response of Fq'/Fm' to PPFR (ResponseG:PPFR) achieved significant correlations with biomass and yield, ranging from 0.33 to 0.35 and from 0.22 to 0.31 in two glasshouse and three field trials, respectively. Phenomic yield prediction outperformed genomic predictions for new environments in four trials under different growing conditions. Investigating genetic control over photosynthesis, one single nucleotide polymorphism (Chr09_37766289_13052) on chromosome 9 was significantly associated with ResponseG:PPFR in proximity to a candidate gene controlling chloroplast thylakoid formation. In conclusion, photosynthetic screening facilitates and accelerates selection for high yield potential.

Frost Damage Index: The Antipode of Growing Degree Days

Abiotic stresses such as heat and frost limit plant growth and productivity. Image-based field phenotyping methods allow quantifying not only plant growth but also plant senescence. Winter crops show senescence caused by cold spells, visible as declines in leaf area. We accurately quantified such declines by monitoring changes in canopy cover based on time-resolved high-resolution imagery in the field. Thirty-six winter wheat genotypes were measured in multiple years. A concept termed "frost damage index" (FDI) was developed that, in analogy to growing degree days, summarizes frost events in a cumulative way. The measured sensitivity of genotypes to the FDI correlated with visual scorings commonly used in breeding to assess winter hardiness. The FDI concept could be adapted to other factors such as drought or heat stress. While commonly not considered in plant growth modeling, integrating such degradation processes may be key to improving the prediction of plant performance for future climate scenarios.

Image-based phenomic prediction can provide valuable decision support in wheat breeding

KEY MESSAGE

Genotype-by-environment interactions of secondary traits based on high-throughput field phenotyping are less complex than those of target traits, allowing for a phenomic selection in unreplicated early generation trials. Traditionally, breeders' selection decisions in early generations are largely based on visual observations in the field. With the advent of affordable genome sequencing and high-throughput phenotyping technologies, enhancing breeders' ratings with such information became attractive. In this research, it is hypothesized that G[Formula: see text]E interactions of secondary traits (i.e., growth dynamics' traits) are less complex than those of related target traits (e.g., yield). Thus, phenomic selection (PS) may allow selecting for genotypes with beneficial response-pattern in a defined population of environments. A set of 45 winter wheat varieties was grown at 5 year-sites and analyzed with linear and factor-analytic (FA) mixed models to estimate G[Formula: see text]E interactions of secondary and target traits. The dynamic development of drone-derived plant height, leaf area and tiller density estimations was used to estimate the timing of key stages, quantities at defined time points and temperature dose-response curve parameters. Most of these secondary traits and grain protein content showed little G[Formula: see text]E interactions. In contrast, the modeling of G[Formula: see text]E for yield required a FA model with two factors. A trained PS model predicted overall yield performance, yield stability and grain protein content with correlations of 0.43, 0.30 and 0.34. While these accuracies are modest and do not outperform well-trained GS models, PS additionally provided insights into the physiological basis of target traits. An ideotype was identified that potentially avoids the negative pleiotropic effects between yield and protein content.

The relationship between clinical frailty score, CT-derived body composition, systemic inflammation, and survival in patients with chronic limb threatening ischaemia

INTRODUCTIO

Frailty is a chronic condition with complex aetiology and impaired functional performance, which has been associated altered body composition and chronic inflammation. Chronic Limb Threatening Ischaemia (CLTI) carries significant morbidity and mortality and is associated with poor quality of life. The present study aims to examine these relationships and their prognostic value in patients with CLTI.

METHODS

Consecutive patients presenting as unscheduled admissions to a single tertiary centre with CLTI were included over a 12-month period. Frailty was diagnosed using the clinical frailty scale (CFS). Body composition was assessed using CT at the L3 vertebral level (CT-BC) to generate visceral and subcutaneous fat indices (VFI, SFI), skeletal muscle index (SMI), and skeletal muscle density (SMD). SMI and SMD were combined to form the CT-sarcopenia score (CT-SS). Systemic inflammation was assessed by the modified Glasgow Prognostic Score (mGPS). The primary outcome was overall mortality.

RESULTS

There were 190 patients included with a median (IQR) follow-up of 22 (6) months (range 15-32 months), and 79 deaths during the follow-up period. 100 patients (53%) had a CFS > 4. CFS > 4 (HR 2.14, 95% CI 1.25 - 3.66, p <0.01), CT-SS (HR 1.47, 95% CI 1.03 - 2.09, p <0.05), and mGPS (HR 1.54, 95% CI 1.11 - 2.13, p <0.01) were independently associated with increased mortality. CT-SS (OR 1.88, 95% CI 1.09 - 3.24, p < 0.01) was independently associated with CFS > 4. Patients with CT-SS 0 & CFS ≤4 had 90% (SE 5%) 1-year survival, compared with 35% (SE 9%) in patients with CT-SS 2 & CFS >4 (p <0.001). Patients with mGPS 0 & CFS ≤ 4 had 94% (SE 4%) 1-year survival compared with 44% (SE 6%) in the mGPS 2 & CFS > 4 subgroup (p <0.001).

CONCLUSIONS

Frailty assessed by CFS was associated with CT-BC. CFS, CT-SS and mGPS were associated with poorer survival in patients presenting as unscheduled admissions with CLTI. CT-SS and mGPS may contribute to part of frailty and prognostic assessment in this patient cohort.

Combining High-Resolution Imaging, Deep Learning, and Dynamic Modeling to Separate Disease and Senescence in Wheat Canopies

Maintenance of sufficiently healthy green leaf area after anthesis is key to ensuring an adequate assimilate supply for grain filling. Tightly regulated age-related physiological senescence and various biotic and abiotic stressors drive overall greenness decay dynamics under field conditions. Besides direct effects on green leaf area in terms of leaf damage, stressors often anticipate or accelerate physiological senescence, which may multiply their negative impact on grain filling. Here, we present an image processing methodology that enables the monitoring of chlorosis and necrosis separately for ears and shoots (stems + leaves) based on deep learning models for semantic segmentation and color properties of vegetation. A vegetation segmentation model was trained using semisynthetic training data generated using image composition and generative adversarial neural networks, which greatly reduced the risk of annotation uncertainties and annotation effort. Application of the models to image time series revealed temporal patterns of greenness decay as well as the relative contributions of chlorosis and necrosis. Image-based estimation of greenness decay dynamics was highly correlated with scoring-based estimations (r ≈ 0.9). Contrasting patterns were observed for plots with different levels of foliar diseases, particularly septoria tritici blotch. Our results suggest that tracking the chlorotic and necrotic fractions separately may enable (a) a separate quantification of the contribution of biotic stress and physiological senescence on overall green leaf area dynamics and (b) investigation of interactions between biotic stress and physiological senescence. The high-throughput nature of our methodology paves the way to conducting genetic studies of disease resistance and tolerance.

Dataset on digital technologies as learning content in farm manager training in Switzerland and willingness to use farm information systems

This article describes the data from an online survey conducted at a farm management course in Switzerland. The survey was conducted in German and French between April and May 2021. It was emailed to teachers and students at agricultural education centres across Switzerland that offer a farm management program. In the first part, the survey investigated whether digital technologies were taught in agricultural training, and, more specifically, in basic training or in the farm management course. Next, it investigated teachers' and students' general perceptions of digital technologies in plant production and animal husbandry. The survey further included questions about information sources individuals use to learn more about digital technologies in agriculture. In a subsequent part, students who already owned or co-owned a farm were asked whether they use a farm management information system and were planning to use more digital technologies in the future. For this, we used three items investigating perceived ease of use, which were derived from a previous study and four items using a trans-theoretical model of adoption. Finally, all participants provided basic sociodemographic data and answered items related to environmental concern, based on an existing scale. The survey can be used and adapted to different contents, aiming to investigate perception and adoption of farm management information systems and study the course content, how individuals acquire knowledge or how they perceive digital technologies.

Randomized trial of the effect of video training on residents' surgical skills in facial skin reconstructive surgery: A SQUIRE study

OBJECTIVES

The purpose of this study was to evaluate the impact of facial skin reconstruction training videos for head and neck and maxillofacial surgery residents.

MATERIAL AND METHODS

This randomized trial, conducted in France, involved residents in head and neck and maxillofacial surgery. A website was created containing facial skin reconstruction training videos. Selected residents performed facial skin flap dissections in the Paris School of Surgery. They were randomized into two groups, one receiving a standard course before the dissection, and the other a standard course plus a video of the flap ("no-video" and "video" groups). Each resident performed 4 facial flaps and was graded (blindly) during dissection. The main study endpoint was intergroup difference in grading score (out of 15). The article was written up following the SQUIRE-EDU (Standards for QUality Improvement Reporting Excellence in EDUcation) criteria.

RESULTS

Eighteen residents were included. For the main endpoint, scores were significantly higher in the "video" than the "no-video" group (6 [IQR, 4: 9] vs. 10 [9: 12]; P<0.001). In addition, as secondary endpoint, "no-video" group residents requested more assistance (3 [2: 4] vs. 1 [1: 2] P<0.001). Power was lacking for any subgroup analysis according to year of residency or to the 4 flaps.

CONCLUSION

Videos improved surgical residents' performance during dissections. However, these results would be difficult to transpose to real clinical conditions. They need validating in a larger study evaluating performance in real-life procedures.

Outdoor Plant Segmentation With Deep Learning for High-Throughput Field Phenotyping on a Diverse Wheat Dataset

Robust and automated segmentation of leaves and other backgrounds is a core prerequisite of most approaches in high-throughput field phenotyping. So far, the possibilities of deep learning approaches for this purpose have not been explored adequately, partly due to a lack of publicly available, appropriate datasets. This study presents a workflow based on DeepLab v3+ and on a diverse annotated dataset of 190 RGB (350 x 350 pixels) images. Images of winter wheat plants of 76 different genotypes and developmental stages have been acquired throughout multiple years at high resolution in outdoor conditions using nadir view, encompassing a wide range of imaging conditions. Inconsistencies of human annotators in complex images have been quantified, and metadata information of camera settings has been included. The proposed approach achieves an intersection over union (IoU) of 0.77 and 0.90 for plants and soil, respectively. This outperforms the benchmarked machine learning methods which use Support Vector Classifier and/or Random Forrest. The results show that a small but carefully chosen and annotated set of images can provide a good basis for a powerful segmentation pipeline. Compared to earlier methods based on machine learning, the proposed method achieves better performance on the selected dataset in spite of using a deep learning approach with limited data. Increasing the amount of publicly available data with high human agreement on annotations and further development of deep neural network architectures will provide high potential for robust field-based plant segmentation in the near future. This, in turn, will be a cornerstone of data-driven improvement in crop breeding and agricultural practices of global benefit.

Rethinking temperature effects on leaf growth, gene expression and metabolism: Diel variation matters

Plants have evolved to grow under prominently fluctuating environmental conditions. In experiments under controlled conditions, temperature is often set to artificial, binary regimes with constant values at day and at night. This study investigated how such a diel (24 hr) temperature regime affects leaf growth, carbohydrate metabolism and gene expression, compared to a temperature regime with a field-like gradual increase and decline throughout 24 hr. Soybean (Glycine max) was grown under two contrasting diel temperature treatments. Leaf growth was measured in high temporal resolution. Periodical measurements were performed of carbohydrate concentrations, carbon isotopes as well as the transcriptome by RNA sequencing. Leaf growth activity peaked at different times under the two treatments, which cannot be explained intuitively. Under field-like temperature conditions, leaf growth followed temperature and peaked in the afternoon, whereas in the binary temperature regime, growth increased at night and decreased during daytime. Differential gene expression data suggest that a synchronization of cell division activity seems to be evoked in the binary temperature regime. Overall, the results show that the coordination of a wide range of metabolic processes is markedly affected by the diel variation of temperature, which emphasizes the importance of realistic environmental settings in controlled condition experiments.

Crop Nitrogen Retrieval Methods for Simulated Sentinel-2 Data Using In-Field Spectrometer Data

Nitrogen (N) is one of the key nutrients supplied in agricultural production worldwide. Over-fertilization can have negative influences on the field and the regional level (e.g., agro-ecosystems). Remote sensing of the plant N of field crops presents a valuable tool for the monitoring of N flows in agro-ecosystems. Available data for validation of satellite-based remote sensing of N is scarce. Therefore, in this study, field spectrometer measurements were used to simulate data of the Sentinel-2 (S2) satellites developed for vegetation monitoring by the ESA. The prediction performance of normalized ratio indices (NRIs), random forest regression (RFR) and Gaussian processes regression (GPR) for plant-N-related traits was assessed on a diverse real-world dataset including multiple crops, field sites and years. The plant N traits included the mass-based N measure, N concentration in the biomass (Nconc), and an area-based N measure approximating the plant N uptake (NUP). Spectral indices such as normalized ratio indices (NRIs) performed well, but the RFR and GPR methods outperformed the NRIs. Key spectral bands for each trait were identified using the RFR variable importance measure and the Gaussian processes regression band analysis tool (GPR-BAT), highlighting the importance of the short-wave infrared (SWIR) region for estimation of plant Nconc-and to a lesser extent the NUP. The red edge (RE) region was also important. The GPR-BAT showed that five bands were sufficient for plant N trait and leaf area index (LAI) estimation and that a surplus of bands effectively reduced prediction performance. A global sensitivity analysis (GSA) was performed on all traits simultaneously, showing the dominance of the LAI in the mixed remote sensing signal. To delineate the plant-N-related traits from this signal, regional and/or national data collection campaigns producing large crop spectral libraries (CSL) are needed. An improved database will likely enable the mapping of N at the agro-ecosystem level or for use in precision farming by farmers in the future.

Temperature response of wheat affects final height and the timing of stem elongation under field conditions

In wheat, temperature affects the timing and intensity of stem elongation. Genetic variation for this process is therefore important for adaptation. This study investigates the genetic response to temperature fluctuations during stem elongation and its relationship to phenology and height. Canopy height of 315 wheat genotypes (GABI wheat panel) was scanned twice weekly in the field phenotyping platform (FIP) of ETH Zurich using a LIDAR. Temperature response was modelled using linear regressions between stem elongation and mean temperature in each measurement interval. This led to a temperature-responsive (slope) and a temperature-irresponsive (intercept) component. The temperature response was highly heritable (H2=0.81) and positively related to a later start and end of stem elongation as well as final height. Genome-wide association mapping revealed three temperature-responsive and four temperature-irresponsive quantitative trait loci (QTLs). Furthermore, putative candidate genes for temperature-responsive QTLs were frequently related to the flowering pathway in Arabidopsis thaliana, whereas temperature-irresponsive QTLs corresponded to growth and reduced height genes. In combination with Rht and Ppd alleles, these loci, together with the loci for the timing of stem elongation, accounted for 71% of the variability in height. This demonstrates how high-throughput field phenotyping combined with environmental covariates can contribute to a smarter selection of climate-resilient crops.

Temperature response of wheat affects final height and the timing of stem elongation under field conditions

In wheat, temperature affects the timing and intensity of stem elongation. Genetic variation for this process is therefore important for adaptation. This study investigates the genetic response to temperature fluctuations during stem elongation and its relationship to phenology and height. Canopy height of 315 wheat genotypes (GABI wheat panel) was scanned twice weekly in the field phenotyping platform (FIP) of ETH Zurich using a LIDAR. Temperature response was modelled using linear regressions between stem elongation and mean temperature in each measurement interval. This led to a temperature-responsive (slope) and a temperature-irresponsive (intercept) component. The temperature response was highly heritable (H2=0.81) and positively related to a later start and end of stem elongation as well as final height. Genome-wide association mapping revealed three temperature-responsive and four temperature-irresponsive quantitative trait loci (QTLs). Furthermore, putative candidate genes for temperature-responsive QTLs were frequently related to the flowering pathway in Arabidopsis thaliana, whereas temperature-irresponsive QTLs corresponded to growth and reduced height genes. In combination with Rht and Ppd alleles, these loci, together with the loci for the timing of stem elongation, accounted for 71% of the variability in height. This demonstrates how high-throughput field phenotyping combined with environmental covariates can contribute to a smarter selection of climate-resilient crops.

Repeated Multiview Imaging for Estimating Seedling Tiller Counts of Wheat Genotypes Using Drones

Early generation breeding nurseries with thousands of genotypes in single-row plots are well suited to capitalize on high throughput phenotyping. Nevertheless, methods to monitor the intrinsically hard-to-phenotype early development of wheat are yet rare. We aimed to develop proxy measures for the rate of plant emergence, the number of tillers, and the beginning of stem elongation using drone-based imagery. We used RGB images (ground sampling distance of 3 mm pixel-1) acquired by repeated flights (≥ 2 flights per week) to quantify temporal changes of visible leaf area. To exploit the information contained in the multitude of viewing angles within the RGB images, we processed them to multiview ground cover images showing plant pixel fractions. Based on these images, we trained a support vector machine for the beginning of stem elongation (GS30). Using the GS30 as key point, we subsequently extracted plant and tiller counts using a watershed algorithm and growth modeling, respectively. Our results show that determination coefficients of predictions are moderate for plant count (R 2 = 0.52), but strong for tiller count (R 2 = 0.86) and GS30 (R 2 = 0.77). Heritabilities are superior to manual measurements for plant count and tiller count, but inferior for GS30 measurements. Increasing the selection intensity due to throughput may overcome this limitation. Multiview image traits can replace hand measurements with high efficiency (85-223%). We therefore conclude that multiview images have a high potential to become a standard tool in plant phenomics.

The NETest liquid biopsy is diagnostic for gastric neuroendocrine tumors: observations on the blood-based identification of microscopic and macroscopic residual diseaseOK

Background

NETest, a novel multi-gene liquid biopsy has utility in neuroendocrine tumor (NET) diagnosis and identification of residual disease. We independently assessed utility of the NETest to diagnose gastric neuroendocrine neoplasms (GNENs) and identify micro- and macroscopic residual disease.

Methods

Cohorts comprised histologically confirmed GNENs at biopsy, n = 46; GNETs Type 1: 42 (32 NET G1, 10 NET G2), a GNET Type 3: 1 well-differentiated NET G3, neuroendocrine carcinomas (NECs) (n = 3), and controls (n = 63). Disease status at sampling was assessed by gastroscopy, histology (resection margin [R] positivity of polypectomy or biopsy), EUS, CT or MRI, and/or ⁶⁸Ga-DOTA-TATE PET/CT. Groups included image- (gastroscopy, EUS, and anatomical and/or functional imaging) positive or image negative disease. NETest assay by PCR (spotted plates, normal cut-off: 20). Data: mean ± SD.

Results

Disease extent: Image-negative (n = 30) (21 R0, 9 R1); Image-positive, n = 16.

Diagnosis: NETest was increased in GNETs (23 ± 11) vs. controls (7 ± 4, p < 0.0001). In histology-positive, the NETest accuracy was 100% (25/25).

Microscopic disease: In image-negative but R1, NETest was elevated in 100% (9/9; 28 ± 9). Levels were elevated vs. controls (7 ± 4, p < 0.0001), or R0 (16 ± 11, p = 0.02). Eight of 21 R0, exhibited positive NETest.

Macroscopic disease: Gastric lesions were multiple: 38%, single: 62%, submucosal: 13%, or ulcerated: 13%. Lesions size was ≤5 mm (50%), > 5–9.9 mm (17%), 10–19.9 mm (17%), ≥20 mm (17%) [≥10 mm: 34%). The NETest accuracy was 100% (16/16). Levels (28 ± 7) were higher than controls (7 ± 4, p < 0.0001) or R0 (16 ± 11, p = 0.002) but not to R1 (28 ± 9, p = 0.5).

Conclusions

NETest is diagnostic for gastric NETs. Elevated levels identify both microscopic and macroscopic residual disease. In histology/image-negative disease, elevated NETest may reflect early evidence of increased neuroendocrine gene expression of hypergastrinemia-induced neoplastic transformation of enterochromaffin-like (ECL) cells to tumor status. A sensitive liquid biopsy has utility in the management and surveillance of gastric NET disease.

The platform GrowScreen-Agar enables identification of phenotypic diversity in root and shoot growth traits of agar grown plants

BACKGROUND

Root system architecture and especially its plasticity in acclimation to variable environments play a crucial role in the ability of plants to explore and acquire efficiently soil resources and ensure plant productivity. Non-destructive measurement methods are indispensable to quantify dynamic growth traits. For closing the phenotyping gap, we have developed an automated phenotyping platform, GrowScreen-Agar, for non-destructive characterization of root and shoot traits of plants grown in transparent agar medium.

RESULTS

The phenotyping system is capable to phenotype root systems and correlate them to whole plant development of up to 280 Arabidopsis plants within 15 min. The potential of the platform has been demonstrated by quantifying phenotypic differences within 78 Arabidopsis accessions from the 1001 genomes project. The chosen concept 'plant-to-sensor' is based on transporting plants to the imaging position, which allows for flexible experimental size and design. As transporting causes mechanical vibrations of plants, we have validated that daily imaging, and consequently, moving plants has negligible influence on plant development. Plants are cultivated in square Petri dishes modified to allow the shoot to grow in the ambient air while the roots grow inside the Petri dish filled with agar. Because it is common practice in the scientific community to grow Arabidopsis plants completely enclosed in Petri dishes, we compared development of plants that had the shoot inside with that of plants that had the shoot outside the plate. Roots of plants grown completely inside the Petri dish grew 58% slower, produced a 1.8 times higher lateral root density and showed an etiolated shoot whereas plants whose shoot grew outside the plate formed a rosette. In addition, the setup with the shoot growing outside the plate offers the unique option to accurately measure both, leaf and root traits, non-destructively, and treat roots and shoots separately.

CONCLUSIONS

Because the GrowScreen-Agar system can be moved from one growth chamber to another, plants can be phenotyped under a wide range of environmental conditions including future climate scenarios. In combination with a measurement throughput enabling phenotyping a large set of mutants or accessions, the platform will contribute to the identification of key genes.

PhenoCams for Field Phenotyping: Using Very High Temporal Resolution Digital Repeated Photography to Investigate Interactions of Growth, Phenology, and Harvest Traits

Understanding the interaction of plant growth with environmental conditions is crucial to increase the resilience of current cropping systems to a changing climate. Here, we investigate PhenoCams as a high-throughput approach for field phenotyping experiments to assess growth dynamics of many different genotypes simultaneously in high temporal (daily) resolution. First, we develop a method that extracts a daily phenological signal that is normalized for the different viewing geometries of the pixels within the images. Second, we investigate the extraction of the in season traits of early vigor, leaf area index (LAI), and senescence dynamic from images of a soybean (Glycine max) field phenotyping experiment and show that it is possible to rate early vigor, senescence dynamics, and track the LAI development between LAI 1 and 4.5. Third, we identify the start of green up, green peak, senescence peak, and end of senescence in the phenological signal. Fourth, we extract the timing of these points and show how this information can be used to assess the impact of phenology on harvest traits (yield, thousand kernel weight, and oil content). The results demonstrate that PhenoCams can track growth dynamics and fill the gap of high temporal monitoring in field phenotyping experiments.

Assessment of Multi-Image Unmanned Aerial Vehicle Based High-Throughput Field Phenotyping of Canopy Temperature

Canopy temperature (CT) has been related to water-use and yield formation in crops. However, constantly (e.g., sun illumination angle, ambient temperature) as well as rapidly (e.g., clouds) changing environmental conditions make it difficult to compare measurements taken even at short time intervals. This poses a great challenge for high-throughput field phenotyping (HTFP). The aim of this study was to i) set up a workflow for unmanned aerial vehicles (UAV) based HTFP of CT, ii) investigate different data processing procedures to combine information from multiple images into orthomosaics, iii) investigate the repeatability of the resulting CT by means of heritability, and iv) investigate the optimal timing for thermography measurements. Additionally, the approach was v) compared with other methods for HTFP of CT. The study was carried out in a winter wheat field trial with 354 genotypes planted in two replications in a temperate climate, where a UAV captured CT in a time series of 24 flights during 6 weeks of the grain-filling phase. Custom-made thermal ground control points enabled accurate georeferencing of the data. The generated thermal orthomosaics had a high spatial accuracy (mean ground sampling distance of 5.03 cm/pixel) and position accuracy [mean root-mean-square deviation (RMSE) = 4.79 cm] over all time points. An analysis on the impact of the measurement geometry revealed a gradient of apparent CT in parallel to the principle plane of the sun and a hotspot around nadir. Averaging information from all available images (and all measurement geometries) for an area of interest provided the best results by means of heritability. Correcting for spatial in-field heterogeneity as well as slight environmental changes during the measurements were performed with the R package SpATS. CT heritability ranged from 0.36 to 0.74. Highest heritability values were found in the early afternoon. Since senescence was found to influence the results, it is recommended to measure CT in wheat after flowering and before the onset of senescence. Overall, low-altitude and high-resolution remote sensing proved suitable to assess the CT of crop genotypes in a large number of small field plots as is required in crop breeding and variety testing experiments.

Spectral Vegetation Indices to Track Senescence Dynamics in Diverse Wheat Germplasm

The ability of a genotype to stay green affects the primary target traits grain yield (GY) and grain protein concentration (GPC) in wheat. High throughput methods to assess senescence dynamics in large field trials will allow for (i) indirect selection in early breeding generations, when yield cannot yet be accurately determined and (ii) mapping of the genomic regions controlling the trait. The aim of this study was to develop a robust method to assess senescence based on hyperspectral canopy reflectance. Measurements were taken in three years throughout the grain filling phase on >300 winter wheat varieties in the spectral range from 350 to 2500 nm using a spectroradiometer. We compared the potential of spectral indices (SI) and full-spectrum models to infer visually observed senescence dynamics from repeated reflectance measurements. Parameters describing the dynamics of senescence were used to predict GY and GPC and a feature selection algorithm was used to identify the most predictive features. The three-band plant senescence reflectance index (PSRI) approximated the visually observed senescence dynamics best, whereas full-spectrum models suffered from a strong year-specificity. Feature selection identified visual scorings as most predictive for GY, but also PSRI ranked among the most predictive features while adding additional spectral features had little effect. Visually scored delayed senescence was positively correlated with GY ranging from r = 0.173 in 2018 to r = 0.365 in 2016. It appears that visual scoring remains the gold standard to quantify leaf senescence in moderately large trials. However, using appropriate phenotyping platforms, the proposed index-based parameterization of the canopy reflectance dynamics offers the critical advantage of upscaling to very large breeding trials.

Role of the Streptomyces spore wall synthesizing complex SSSC in differentiation of Streptomyces coelicolor A3(2)

A crucial stage of the Streptomyces life cycle is the sporulation septation, a process were dozens of cross walls are synchronously formed in the aerial hyphae in a highly coordinated manner. This process includes the remodeling of the spore envelopes to make Streptomyces spores resistant to detrimental environmental conditions. Sporulation septation and the synthesis of the thickened spore envelope in S. coelicolor A3(2) involves the Streptomyces spore wall synthesizing complex SSSC. The SSSC is a multi-protein complex including proteins directing peptidoglycan synthesis (MreBCD, PBP2, Sfr, RodZ) and cell wall glycopolymer synthesis (PdtA). It also includes two eukaryotic like serin/threonine protein kinases (eSTPK), PkaI and PkaH, which were shown to phosphorylate MreC. Since unbalancing phosphorylation activity by either deleting eSTPK genes or by expressing a second copy of an eSTPK gene affected proper sporulation, a model was developed, in which the activity of the SSSC is controlled by protein phosphorylation.

Phenotyping a Dynamic Trait: Leaf Growth of Perennial Ryegrass Under Water Limiting Conditions

Water limitation is one of the major factors reducing crop productivity worldwide. In order to develop efficient breeding strategies to improve drought tolerance, accurate methods to identify when a plant reduces growth as a consequence of water deficit have yet to be established. In perennial ryegrass (Lolium perenne L.), an important forage grass of the Poaceae family, leaf elongation is a key factor determining plant growth and hence forage yield. Although leaf elongation has been shown to be temperature-dependent under non-stress conditions, the impact of water limitation on leaf elongation in perennial ryegrass is poorly understood. We describe a method for quantifying tolerance to water deficit based on leaf elongation in relation to temperature and soil moisture in perennial ryegrass. With decreasing soil moisture, three growth response phases were identified: first, a "normal" phase where growth is mainly determined by temperature, second a "slow" phase where leaf elongation decreases proportionally to soil water potential and third an "arrest" phase where leaf growth terminates. A custom R function was able to quantify the points which demarcate these phases and can be used to describe the response of plants to water deficit. Applied to different perennial ryegrass genotypes, this function revealed significant genotypic variation in the response of leaf growth to temperature and soil moisture. Dynamic phenotyping of leaf elongation can be used as a tool to accurately quantify tolerance to water deficit in perennial ryegrass and to improve this trait by breeding. Moreover, the tools presented here are applicable to study the plant response to other stresses in species with linear, graminoid leaf morphology.

A spatio temporal spectral framework for plant stress phenotyping

BACKGROUND

Recent advances in high throughput phenotyping have made it possible to collect large datasets following plant growth and development over time, and those in machine learning have made inferring phenotypic plant traits from such datasets possible. However, there remains a dirth of datasets following plant growth under stress conditions along with methods for inferring them using only remotely sensed data, especially under a combination of multiple stress factors such as drought, weeds and nutrient deficiency. Such stress factors and their combinations are commonly encountered during crop production and being able to accurately detect and treat such stress conditions in an automated and timely manner can provide a major boost to farm yields with minimal resource input.

RESULTS

We present a generic framework for remote plant stress phenotyping that consists of a dataset with spatio-temporal-spectral data following sugarbeet crop growth under optimal, drought, low and surplus nitrogen fertilization, and weed stress conditions, along with a machine learning based methodology for systematically inferring these stress conditions from the remotely measured data. The dataset contains biweekly color images, infra-red stereo image pairs and hyperspectral camera images along with applied treatment parameters and environmental factors like temperature and humidity, collected over two months. We present a plant agnostic methodology for deriving plant trait indicators such as canopy cover, height, hyperspectral reflectance and vegetation indices along with a spectral 3D reconstruction of the plants from the raw data to serve as a benchmark. Additionally, we provide fresh and dry weight measurements for both the above (canopy) and below (beet) ground biomass at the end of the growing period to serve as indicators of expected yield. We further describe a data driven, machine learning based method to infer water, Nitrogen and weed stress using the derived plant trait indicators. We use the plant trait indicators to evaluate 8 different classification approaches from which the best classifier achieved a mean cross validation accuracy of ≈ 93, 76 and 83% for drought, nitrogen and weed stress severity classification respectively. We also show that our multi-modal approach significantly improves classifier performance over using any single modality.

CONCLUSION

The presented framework and dataset can serve as a valuable reference for creating and comparing processing pipelines which extract plant trait indicators and infer prevalent stress factors from remote sensing data under a variety of environments and cropping conditions. These techniques can then be deployed on farm machinery or robots enabling automated, precise and timely corrective interventions for maximising yield.

Diel leaf growth of rapeseed at critically low temperature under winter field conditions

Growth and development of winter crops is strongly limited by low temperature during winter. Monitoring the temporal dynamics and thermal limits of leaf growth in that period can give important insights into the growth physiology at low temperature, crop management and future breeding traits for winter crops. In this study, we focussed on winter rapeseed as a model, dicotyledonous winter crop to study leaf growth under natural winter field conditions. Leaf growth was measured using a high-resolution marker based image sequence analysis method and the results were evaluated in the context of environmental conditions. Leaves stopped growing at a base temperature of 0°C. Above ~4°C, leaves grew with a diel (24h) growth rhythm, which is typically known for dicots at thermally non-limiting growth conditions. Relative leaf growth rates at temperatures above this 4°C threshold were higher at night and showed a pronounced depression during the day, which we could describe by a model based on the environmental factors vapour pressure deficit (VPD), temperature and light with VPD exerting the strongest negative effect on leaf growth. We conclude that leaf growth of the selected model species at low temperatures shows a transition between pronounced environmental regulation and a superposition of environmental and internal, possibly circadian-clock-dependent regulation.

Hyperspectral Sensors and Imaging Technologies in Phytopathology: State of the Art

Plant disease detection represents a tremendous challenge for research and practical applications. Visual assessment by human raters is time-consuming, expensive, and error prone. Disease rating and plant protection need new and innovative techniques to address forthcoming challenges and trends in agricultural production that require more precision than ever before. Within this context, hyperspectral sensors and imaging techniques-intrinsically tied to efficient data analysis approaches-have shown an enormous potential to provide new insights into plant-pathogen interactions and for the detection of plant diseases. This article provides an overview of hyperspectral sensors and imaging technologies for assessing compatible and incompatible plant-pathogen interactions. Within the progress of digital technologies, the vision, which is increasingly discussed in the society and industry, includes smart and intuitive solutions for assessing plant features in plant phenotyping or for making decisions on plant protection measures in the context of precision agriculture.

Hyperspectral Canopy Sensing of Wheat Septoria Tritici Blotch Disease

Producing quantitative and reliable measures of crop disease is essential for resistance breeding, but is challenging and time consuming using traditional phenotyping methods. Hyperspectral remote sensing has shown potential for the detection of plant diseases, but its utility for phenotyping large and diverse populations of plants under field conditions requires further evaluation. In this study, we collected canopy hyperspectral data from 335 wheat varieties using a spectroradiometer, and we investigated the use of canopy reflectance for detecting the Septoria tritici blotch (STB) disease and for quantifying the severity of infection. Canopy- and leaf-level infection metrics of STB based on traditional visual assessments and automated analyses of leaf images were used as ground truth data. Results showed (i) that canopy reflectance and the selected spectral indices show promise for quantifying STB infections, and (ii) that the normalized difference water index (NDWI) showed the best performance in detecting STB compared to other spectral indices. Moreover, partial least squares (PLS) regression models allowed for an improvement in the prediction of STB metrics. The PLS discriminant analysis (PLSDA) model calibrated based on the spectral data of four reference varieties was able to discriminate between the diseased and healthy canopies among the 335 varieties with an accuracy of 93% (Kappa = 0.60). Finally, the PLSDA model predictions allowed for the identification of wheat genotypes that are potentially more susceptible to STB, which was confirmed by the STB visual assessment. This study demonstrates the great potential of using canopy hyperspectral remote sensing to improve foliar disease assessment and to facilitate plant breeding for disease resistance.

Feedbacks between soil penetration resistance, root architecture and water uptake limit water accessibility and crop growth - A vicious circle

Water is the most limiting resource for global crop production. The projected increase of dry spells due to climate change will further increase the problem of water limited crop yields. Besides low water abundance and availability, water limitations also occur due to restricted water accessibility. Soil penetration resistance, which is largely influenced by soil moisture, is the major soil property regulating root elongation and water accessibility. Until now the interactions between soil penetration resistance, root system properties, water uptake and crop productivity are rarely investigated. In the current study we quantified how interactive effects between soil penetration resistance, root architecture and water uptake affect water accessibility and crop productivity in the field. Maize was grown on compacted and uncompacted soil that was either tilled or remained untilled after compaction, which resulted in four treatments with different topsoil penetration resistance. Higher topsoil penetration resistance caused root systems to be shallower. This resulted in increased water uptake from the topsoil and hence topsoil drying, which further increased the penetration resistance in the uppermost soil layer. As a consequence of this feedback, root growth into deeper soil layers, where water would have been available, was reduced and plant growth decreased. Our results demonstrate that soil penetration resistance, root architecture and water uptake are closely interrelated and thereby determine the potential of plants to access soil water pools. Hence, these interactions and their feedbacks on water accessibility and crop productivity have to be accounted for when developing strategies to alleviate water limitations in cropping systems.

Ranking Quantitative Resistance to Septoria tritici Blotch in Elite Wheat Cultivars Using Automated Image Analysis

Quantitative resistance is likely to be more durable than major gene resistance for controlling Septoria tritici blotch (STB) on wheat. Earlier studies hypothesized that resistance affecting the degree of host damage, as measured by the percentage of leaf area covered by STB lesions, is distinct from resistance that affects pathogen reproduction, as measured by the density of pycnidia produced within lesions. We tested this hypothesis using a collection of 335 elite European winter wheat cultivars that was naturally infected by a diverse population of Zymoseptoria tritici in a replicated field experiment. We used automated image analysis of 21,420 scanned wheat leaves to obtain quantitative measures of conditional STB intensity that were precise, objective, and reproducible. These measures allowed us to explicitly separate resistance affecting host damage from resistance affecting pathogen reproduction, enabling us to confirm that these resistance traits are largely independent. The cultivar rankings based on host damage were different from the rankings based on pathogen reproduction, indicating that the two forms of resistance should be considered separately in breeding programs aiming to increase STB resistance. We hypothesize that these different forms of resistance are under separate genetic control, enabling them to be recombined to form new cultivars that are highly resistant to STB. We found a significant correlation between rankings based on automated image analysis and rankings based on traditional visual scoring, suggesting that image analysis can complement conventional measurements of STB resistance, based largely on host damage, while enabling a much more precise measure of pathogen reproduction. We showed that measures of pathogen reproduction early in the growing season were the best predictors of host damage late in the growing season, illustrating the importance of breeding for resistance that reduces pathogen reproduction in order to minimize yield losses caused by STB. These data can already be used by breeding programs to choose wheat cultivars that are broadly resistant to naturally diverse Z. tritici populations according to the different classes of resistance.

Evaluation of the Active Targeting of Melanin Granules after Intravenous Injection of Dendronized Nanoparticles

The biodistribution of dendronized iron oxides, NPs10@D1_DOTAGA and melanin-targeting NPs10@D1_ICF_DOTAGA, was studied in vivo using magnetic resonance imaging (MRI) and planar scintigraphy through [¹⁷⁷Lu]Lu-radiolabeling. MRI experiments showed high contrast power of both dendronized nanoparticles (DPs) and hepatobiliary and urinary excretions. Little tumor uptake could be highlighted after intravenous injection probably as a consequence of the negatively charged DOTAGA-derivatized shell, which reduces the diffusion across the cells' membrane. Planar scintigraphy images demonstrated a moderate specific tumor uptake of melanoma-targeted [¹⁷⁷Lu]Lu-NPs10@D1_ICF_DOTAGA at 2 h post-intravenous injection (pi), and the highest tumor uptake of the control probe [¹⁷⁷Lu]Lu-NPs10@D1_DOTAGA at 30 min pi, probably due to the enhanced permeability and retention effect. In addition, ex vivo confocal microscopy studies showed a high specific targeting of human melanoma samples impregnated with NPs10@D1_ICF_Alexa647_ DOTAGA.

Changes in the allocation of endogenous strigolactone improve plant biomass production on phosphate-poor soils

Strigolactones (SLs) are carotenoid-derived phytohormones shaping plant architecture and inducing the symbiosis with endomycorrhizal fungi. In Petunia hybrida, SL transport within the plant and towards the rhizosphere is driven by the ABCG-class protein PDR1. PDR1 expression is regulated by phytohormones and by the soil phosphate abundance, and thus SL transport integrates plant development with nutrient conditions. We overexpressed PDR1 (PDR1 OE) to investigate whether increased endogenous SL transport is sufficient to improve plant nutrition and productivity. Phosphorus quantification and nondestructive X-ray computed tomography were applied. Morphological and gene expression changes were quantified at cellular and whole tissue levels via time-lapse microscopy and quantitative PCR. PDR1 OE significantly enhanced phosphate uptake and plant biomass production on phosphate-poor soils. PDR1 OE plants showed increased lateral root formation, extended root hair elongation, faster mycorrhization and reduced leaf senescence. PDR1 overexpression allowed considerable SL biosynthesis by releasing SL biosynthetic genes from an SL-dependent negative feedback. The increased endogenous SL transport/biosynthesis in PDR1 OE plants is a powerful tool to improve plant growth on phosphate-poor soils. We propose PDR1 as an as yet unexplored trait to be investigated for crop production. The overexpression of PDR1 is a valuable strategy to investigate SL functions and transport routes.

Non-destructive measurement of soybean leaf thickness via X-ray computed tomography allows the study of diel leaf growth rhythms in the third dimension

Present-day high-resolution leaf growth measurements provide exciting insights into diel (24-h) leaf growth rhythms and their control by the circadian clock, which match photosynthesis with oscillating environmental conditions. However, these methods are based on measurements of leaf area or elongation and neglect diel changes of leaf thickness. In contrast, the influence of various environmental stress factors to which leaves are exposed to during growth on the final leaf thickness has been studied extensively. Yet, these studies cannot elucidate how variation in leaf area and thickness are simultaneously regulated and influenced on smaller time scales. Only few methods are available to measure the thickness of young, growing leaves non-destructively. Therefore, we evaluated X-ray computed tomography to simultaneously and non-invasively record diel changes and growth of leaf thickness and area. Using conventional imaging and X-ray computed tomography leaf area, thickness and volume growth of young soybean leaves were simultaneously and non-destructively monitored at three cardinal time points during night and day for a period of 80 h under non-stressful growth conditions. Reference thickness measurements on paperboards were in good agreement to CT measurements. Comparison of CT with leaf mass data further proved the consistency of our method. Exploratory analysis showed that measurements were accurate enough for recording and analyzing relative diel changes of leaf thickness, which were considerably different to those of leaf area. Relative growth rates of leaf area were consistently positive and highest during 'nights', while diel changes in thickness fluctuated more and were temporarily negative, particularly during 'evenings'. The method is suitable for non-invasive, accurate monitoring of diel variation in leaf volume. Moreover, our results indicate that diel rhythms of leaf area and thickness show some similarity but are not tightly coupled. These differences could be due to both intrinsic control mechanisms and different sensitivities to environmental factors.

Effect of Enzogenol® Supplementation on Cognitive, Executive, and Vestibular/Balance Functioning in Chronic Phase of Concussion

This study examined the feasibility of Enzogenol® as a potential treatment modality for concussed individuals with residual symptoms in the chronic phase. Forty-two student-athletes with history of sport-related concussion were enrolled, comparing Enzogenol® versus placebo. Testing was conducted using virtual reality (VR) and electroencephalography (EEG), with neuropsychological (NP) tasks primarily used to induce cognitive challenges. After six weeks, the Enzogenol® group showed enhanced frontal-midline theta, and decreased parietal theta power, indicating reduced mental fatigue. Subjects enrolled in the Enzogenol® group also self-reported reduced mental fatigue and sleep problems. This suggests that Enzogenol® has the potential to improve brain functioning in the chronic phase of concussion.

Percutaneous surgery for thoraco-lumbar fractures in ankylosing spondylitis: Study of 31 patients

BACKGROUND

The risk of vertebral fracture is increased 4-fold in patients with ankylosing spondylitis (AS). Diagnostic challenges and the vulnerability associated with AS combine to generate high morbidity and mortality rates. The objective of this study was to assess the outcome of percutaneous thoraco-lumbar fracture surgery in patients with AS, in terms of quality of life, fracture healing, and complications.

HYPOTHESIS

Percutaneous surgery used to treat thoraco-lumbar fractures in patients with AS reliably provides fracture healing, preserves self-sufficiency, and minimises post-operative complications.

METHODS

Two centres included 31 patients with AS who were managed by percutaneous surgery for thoraco-lumbar fractures in 2013-2015. The data were reviewed retrospectively, although admission data were collected prospectively. Clinical outcomes were assessed by comparing the values at baseline and last follow-up of three variables: the Parker score, the visual analogue scale (VAS) pain score, and the EuroQol five dimensions (EQ-5D) quality-of-life score. Computed tomography was performed 1 year after surgery to evaluate bone healing, screw position, and implant loosening. Intra- and post-operative complications were recorded.

RESULTS

The 31 patients had a mean age at surgery of 75.1 years, a mean follow-up of 35.6 months, and a minimum follow-up of 12 months. Three patients died during follow-up. Mean hospital stay duration was 6 days. Cemented screw fixation was used in 18 patients. At last follow-up, all patients had recovered their self-sufficiency; the mean Parker score was 7.14, compared to 6.73 at baseline, the mean VAS pain score was 1.8, and the mean EQ-5D score decrease versus baseline was 0.07 (P=0.02). Bone healing was consistently achieved. Loosening of an uncemented pedicle screw was noted in 1 patient. Of the 228 screws implanted, 6 (2.6%) were improperly positioned, including 1 within the spinal canal in a patient free of neurological manifestations. Asymptomatic cement leakage was noted in 2 patients.

DISCUSSION

Percutaneous fixation of thoraco-lumbar fractures in patients with AS is a reliable method that produces a high healing rate and allows prompt patient mobilisation with preservation of self-sufficiency. The post-operative complication rate is low.

LEVEL OF EVIDENCE

IV, retrospective observational study.

Comparison of visible imaging, thermography and spectrometry methods to evaluate the effect of Heterodera schachtii inoculation on sugar beets

BACKGROUND

Phenotyping technologies are expected to provide predictive power for a range of applications in plant and crop sciences. Here, we use the disease pressure of Beet Cyst Nematodes (BCN) on sugar beet as an illustrative example to test the specific capabilities of different methods. Strong links between the above and belowground parts of sugar beet plants have made BCN suitable targets for use of non-destructive phenotyping methods. We compared the ability of visible light imaging, thermography and spectrometry to evaluate the effect of BCN on the growth of sugar beet plants.

RESULTS

Two microplot experiments were sown with the nematode susceptible cultivar Aimanta and the nematode tolerant cultivar BlueFox under semi-field conditions. Visible imaging, thermal imaging and spectrometry were carried out on BCN infested and non-infested plants at different times during the plant development. Effects of a chemical nematicide were also evaluated using the three phenotyping methods. Leaf and beet biomass were measured at harvest. For both susceptible and tolerant cultivar, canopy area extracted from visible images was the earliest nematode stress indicator. Using such canopy area parameter, delay in leaf growth as well as benefit from a chemical nematicide could be detected already 15 days after sowing. Spectrometry was suitable to identify the stress even when the canopy reached full coverage. Thermography could only detect stress on the susceptible cultivar. Spectral Vegetation Indices related to canopy cover (NDVI and MCARI2) and chlorophyll content (CHLG) were correlated with the final yield (R = 0.69 on average for the susceptible cultivar) and the final nematode population in the soil (R = 0.78 on average for the susceptible cultivar).

CONCLUSION

In this paper we compare the use of visible imaging, thermography and spectrometry over two cultivars and 2 years under outdoor conditions. The three different techniques have their specific strengths in identifying BCN symptoms according to the type of cultivars and the growth stages of the sugar beet plants. Early detection of nematicide benefit and high yield predictability using visible imaging and spectrometry suggests promising applications for agricultural research and precision agriculture.

Root Tip Shape Governs Root Elongation Rate under Increased Soil Strength

Increased soil strength due to soil compaction or soil drying is a major limitation to root growth and crop productivity. Roots need to exert higher penetration force, resulting in increased penetration stress when elongating in soils of greater strength. This study aimed to quantify how the genotypic diversity of root tip geometry and root diameter influences root elongation under different levels of soil strength and to determine the extent to which roots adjust to increased soil strength. Fourteen wheat (Triticum aestivum) varieties were grown in soil columns packed to three bulk densities representing low, moderate, and high soil strength. Under moderate and high soil strength, smaller root tip radius-to-length ratio was correlated with higher genotypic root elongation rate, whereas root diameter was not related to genotypic root elongation. Based on cavity expansion theory, it was found that smaller root tip radius-to-length ratio reduced penetration stress, thus enabling higher root elongation rates in soils with greater strength. Furthermore, it was observed that roots could only partially adjust to increased soil strength. Root thickening was bounded by a maximum diameter, and root tips did not become more acute in response to increased soil strength. The obtained results demonstrated that root tip geometry is a pivotal trait governing root penetration stress and root elongation rate in soils of greater strength. Hence, root tip shape needs to be taken into account when selecting for crop varieties that may tolerate high soil strength.

IRF6 and SPRY4 Signaling Interact in Periderm Development

Rare mutations in IRF6 and GRHL3 cause Van der Woude syndrome, an autosomal dominant orofacial clefting disorder. Common variants in IRF6 and GRHL3 also contribute risk for isolated orofacial clefting. Similarly, variants within genes that encode receptor tyrosine kinase (RTK) signaling components, including members of the FGF pathway, EPHA3 and SPRY2, also contribute risk for isolated orofacial clefting. In the mouse, loss of Irf6 or perturbation of Fgf signaling leads to abnormal oral epithelial adhesions and cleft palate. Oral adhesions can result from a disruption of periderm formation. Here, we find that IRF6 and SPRY4 signaling interact in periderm function. We crossed Irf6 heterozygous ( Irf6^+/-) mice with transgenic mice that express Spry4 in the basal epithelial layer ( Tg^KRT14::Spry4). While embryos with either of these mutations can have abnormal oral adhesions, using a new quantitative assay, we observed a nonadditive effect of abnormal oral epithelial adhesions in the most severely affected double mutant embryos ( Irf6^+/-;Tg^KRT14::Spry4). At the molecular level, the sites of abnormal oral adhesions maintained periderm-like cells that express keratin 6, but we observed abnormal expression of GRHL3. Together, these data suggest that Irf6 and RTK signaling interact in regulating periderm differentiation and function, as well as provide a rationale to screen for epistatic interactions between variants in IRF6 and RTK signaling pathway genes in human orofacial clefting populations.

Evaluation of the efficacy and toxicity profile associated with intraperitoneal chemotherapy use in older women

OBJECTIVE

Intraperitoneal (IP) chemotherapy (CT) for treatment of epithelial ovarian cancer (EOC) has been shown to provide a substantial OS advantage. This study aims to compare the toxicity and benefits of IP CT in patients ≥70 with those <70.

METHODS

We performed a single institution retrospective review of patients diagnosed with Stage IIA-IIIC EOC from 2000 to 2013 who received IP CT. Clinicopathologic characteristics were extracted, and survival was calculated.

RESULTS

133 patients were included with 100 pts. <70years old and 33 pts. ≥70years old. Clinical trial enrollment was similar despite age. In trial enrolled patients, older patients received statistically fewer cycles of therapy (6.4 vs 5.8, p=0.002) but had similar dose delays (0.9 vs 0.7, p=0.72), and modifications (0.9 vs 0.36, p=0.11). Median PFS (27 vs 31months) and OS (71 and 62months) were not statistically different. Grade 3/4 neutropenia was significantly worse in the older patients (82% vs 100%, p=0.04). Neuropathy grade ≥2 and other non-hematologic toxicities were not different between age groups.

CONCLUSIONS

Despite completing fewer cycles of IP CT, older EOC patients had comparable survival to younger patients. The population of older patients receiving IP CT in this study were on clinical trial and likely to be heartier than the general older population. IP CT appears well tolerated and effective among select older patients and is likely under-utilized outside of clinical trials.