Variation in forest root image annotation by experts, novices, and AI

BACKGROUND

The manual study of root dynamics using images requires huge investments of time and resources and is prone to previously poorly quantified annotator bias. Artificial intelligence (AI) image-processing tools have been successful in overcoming limitations of manual annotation in homogeneous soils, but their efficiency and accuracy is yet to be widely tested on less homogenous, non-agricultural soil profiles, e.g., that of forests, from which data on root dynamics are key to understanding the carbon cycle. Here, we quantify variance in root length measured by human annotators with varying experience levels. We evaluate the application of a convolutional neural network (CNN) model, trained on a software accessible to researchers without a machine learning background, on a heterogeneous minirhizotron image dataset taken in a multispecies, mature, deciduous temperate forest.

RESULTS

Less experienced annotators consistently identified more root length than experienced annotators. Root length annotation also varied between experienced annotators. The CNN root length results were neither precise nor accurate, taking ~ 10% of the time but significantly overestimating root length compared to expert manual annotation (p = 0.01). The CNN net root length change results were closer to manual (p = 0.08) but there remained substantial variation.

CONCLUSIONS

Manual root length annotation is contingent on the individual annotator. The only accessible CNN model cannot yet produce root data of sufficient accuracy and precision for ecological applications when applied to a complex, heterogeneous forest image dataset. A continuing evaluation and development of accessible CNNs for natural ecosystems is required.

Automated seminal root angle measurement with corrective annotation

Measuring seminal root angle is an important aspect of root phenotyping, yet automated methods are lacking. We introduce SeminalRootAngle, a novel open-source automated method that measures seminal root angles from images. To ensure our method is flexible and user-friendly we build on an established corrective annotation training method for image segmentation. We tested SeminalRootAngle on a heterogeneous dataset of 662 spring barley rhizobox images, which presented challenges in terms of image clarity and root obstruction. Validation of our new automated pipeline against manual measurements yielded a Pearson correlation coefficient of 0.71. We also measure inter-annotator agreement, obtaining a Pearson correlation coefficient of 0.68, indicating that our new pipeline provides similar root angle measurement accuracy to manual approaches. We use our new SeminalRootAngle tool to identify single nucleotide polymorphisms (SNPs) significantly associated with angle and length, shedding light on the genetic basis of root architecture.

Shedding Light on the Origin of ^{204}Pb, the Heaviest s-Process-Only Isotope in the Solar System

Asymptotic giant branch stars are responsible for the production of most of the heavy isotopes beyond Sr observed in the solar system. Among them, isotopes shielded from the r-process contribution by their stable isobars are defined as s-only nuclei. For a long time the abundance of ^{204}Pb, the heaviest s-only isotope, has been a topic of debate because state-of-the-art stellar models appeared to systematically underestimate its solar abundance. Besides the impact of uncertainties from stellar models and galactic chemical evolution simulations, this discrepancy was further obscured by rather divergent theoretical estimates for the neutron capture cross section of its radioactive precursor in the neutron-capture flow, ^{204}Tl (t_{1/2}=3.78  yr), and by the lack of experimental data on this reaction. We present the first ever neutron capture measurement on ^{204}Tl, conducted at the CERN neutron time-of-flight facility n_TOF, employing a sample of only 9 mg of ^{204}Tl produced at the Institute Laue Langevin high flux reactor. By complementing our new results with semiempirical calculations we obtained, at the s-process temperatures of kT≈8  keV and kT≈30  keV, Maxwellian-averaged cross sections (MACS) of 580(168) mb and 260(90) mb, respectively. These figures are about 3% lower and 20% higher than the corresponding values widely used in astrophysical calculations, which were based only on theoretical calculations. By using the new ^{204}Tl MACS, the uncertainty arising from the ^{204}Tl(n,γ) cross section on the s-process abundance of ^{204}Pb has been reduced from ∼30% down to +8%/-6%, and the s-process calculations are in agreement with the latest solar system abundance of ^{204}Pb reported by K. Lodders in 2021.

New interactive machine learning tool for marine image analysis

Advancing imaging technologies are drastically increasing the rate of marine video and image data collection. Often these datasets are not analysed to their full potential as extracting information for multiple species is incredibly time-consuming. This study demonstrates the capability of the open-source interactive machine learning tool, RootPainter, to analyse large marine image datasets quickly and accurately. The ability of RootPainter to extract the presence and surface area of the cold-water coral reef associate sponge species, Mycale lingua, was tested in two datasets: 18 346 time-lapse images and 1420 remotely operated vehicle video frames. New corrective annotation metrics integrated with RootPainter allow objective assessment of when to stop model training and reduce the need for manual model validation. Three highly accurate M. lingua models were created using RootPainter, with an average dice score of 0.94 ± 0.06. Transfer learning aided the production of two of the models, increasing analysis efficiency from 6 to 16 times faster than manual annotation for time-lapse images. Surface area measurements were extracted from both datasets allowing future investigation of sponge behaviours and distributions. Moving forward, interactive machine learning tools and model sharing could dramatically increase image analysis speeds, collaborative research and our understanding of spatiotemporal patterns in biodiversity.

Measurement of the ^{140}Ce(n,γ) Cross Section at n_TOF and Its Astrophysical Implications for the Chemical Evolution of the Universe

^{140}Ce(n,γ) is a key reaction for slow neutron-capture (s-process) nucleosynthesis due to being a bottleneck in the reaction flow. For this reason, it was measured with high accuracy (uncertainty ≈5%) at the n_TOF facility, with an unprecedented combination of a high purity sample and low neutron-sensitivity detectors. The measured Maxwellian averaged cross section is up to 40% higher than previously accepted values. Stellar model calculations indicate a reduction around 20% of the s-process contribution to the Galactic cerium abundance and smaller sizeable differences for most of the heavier elements. No variations are found in the nucleosynthesis from massive stars.

Semantic segmentation of plant roots from RGB (mini-) rhizotron images-generalisation potential and false positives of established methods and advanced deep-learning models

BACKGROUND

Manual analysis of (mini-)rhizotron (MR) images is tedious. Several methods have been proposed for semantic root segmentation based on homogeneous, single-source MR datasets. Recent advances in deep learning (DL) have enabled automated feature extraction, but comparisons of segmentation accuracy, false positives and transferability are virtually lacking. Here we compare six state-of-the-art methods and propose two improved DL models for semantic root segmentation using a large MR dataset with and without augmented data. We determine the performance of the methods on a homogeneous maize dataset, and a mixed dataset of > 8 species (mixtures), 6 soil types and 4 imaging systems. The generalisation potential of the derived DL models is determined on a distinct, unseen dataset.

RESULTS

The best performance was achieved by the U-Net models; the more complex the encoder the better the accuracy and generalisation of the model. The heterogeneous mixed MR dataset was a particularly challenging for the non-U-Net techniques. Data augmentation enhanced model performance. We demonstrated the improved performance of deep meta-architectures and feature extractors, and a reduction in the number of false positives.

CONCLUSIONS

Although correction factors are still required to match human labelled root lengths, neural network architectures greatly reduce the time required to compute the root length. The more complex architectures illustrate how future improvements in root segmentation within MR images can be achieved, particularly reaching higher segmentation accuracies and model generalisation when analysing real-world datasets with artefacts-limiting the need for model retraining.

Prediction of Radiation-induced Lymphopenia following Exposure of the Thoracic Region and Associated Risk of Infections and Mortality

AIMS

Large blood volumes are irradiated when the heart is exposed to radiation. The mean heart dose (MHD) may be a good surrogate for circulating lymphocytes exposure. We investigated the association between MHD and radiation-induced lymphopenia and explored the impact of the end-of-radiation-therapy (EoRT) lymphocyte count on clinical outcomes.

MATERIALS AND METHODS

In total, 915 patients were analysed: 303 patients with breast cancer and 612 with intrathoracic tumours: oesophageal cancer (291), non-small cell lung cancer (265) and small cell lung cancer (56). Heart contours were generated using an interactive deep learning delineation process and an individual dose volume histogram for each heart was obtained. A dose volume histogram for the body was extracted from the clinical systems. We compared different models analysing the effect of heart dosimetry on the EoRT lymphocyte count using multivariable linear regression and assessed goodness of fit. We published interactive nomograms for the best models. The association of the degree of EoRT lymphopenia with clinical outcomes (overall survival, cancer treatment failure and infection) was investigated.

RESULTS

An increasing low dose bath to the body and MHD were associated with a low EoRT lymphocyte count. The best models for intrathoracic tumours included dosimetric parameters, age, gender, number of fractions, concomitant chemotherapy and pre-treatment lymphocyte count. Models for patients with breast cancer showed no improvement when adding dosimetric variables to the clinical predictors. EoRT lymphopenia grade ≥3 was associated with decreased survival and increased risk of infections among patients with intrathoracic tumours.

CONCLUSION

Among patients with intrathoracic tumours, radiation exposure to the heart contributes to lymphopenia and low levels of peripheral lymphocytes after radiotherapy are associated with worse clinical outcomes.

Direct Determination of Fission-Barrier Heights Using Light-Ion Transfer in Inverse Kinematics

We demonstrate a new technique for obtaining fission data for nuclei away from β stability. These types of data are pertinent to the astrophysical r process, crucial to a complete understanding of the origin of the heavy elements, and for developing a predictive model of fission. These data are also important considerations for terrestrial applications related to power generation and safeguarding. Experimentally, such data are scarce due to the difficulties in producing the actinide targets of interest. The solenoidal-spectrometer technique, commonly used to study nucleon-transfer reactions in inverse kinematics, has been applied to the case of transfer-induced fission as a means to deduce the fission-barrier height, among other variables. The fission-barrier height of ^{239}U has been determined via the ^{238}U(d,pf) reaction in inverse kinematics, the results of which are consistent with existing neutron-induced fission data indicating the validity of the technique.

Interfractional dose accumulation for MR-guided liver SBRT: Variation among algorithms is highly patient- and fraction-dependent

BACKGROUND AND PURPOSE

Daily plan adaptations could take the dose delivered in previous fractions into account. Due to high dose delivered per fraction, low number of fractions, steep dose gradients, and large interfractional organ deformations, this might be particularly important for liver SBRT. This study investigates inter-algorithm variation of interfractional dose accumulation for MR-guided liver SBRT.

MATERIALS AND METHODS

We assessed 27 consecutive MR-guided liver SBRT treatments of 67.5 Gy in three (n = 15) or 50 Gy in five fractions (n = 12), both prescribed to the GTV. We calculated fraction doses on daily patient anatomy, warped these doses to the simulation MRI using seven different algorithms, and accumulated the warped doses. Thus, we obtained differences in planned doses and warped or accumulated doses for each algorithm. This enabled us to calculate the inter-algorithm variations in warped doses per fraction and in accumulated doses per treatment course.

RESULTS

The four intensity-based algorithms were more consistent with planned PTV dose than affine or contour-based algorithms. The mean (range) variation of the dose difference for PTV D_95% due to dose warping by these intensity-based algorithms was 10.4 percentage points (0.3 to 43.7) between fractions and 8.6 (0.3 to 24.9) between accumulated treatment doses. As seen by these ranges, the variation was very dependent on the patient and the fraction being analyzed. Nevertheless, no correlations between patient or plan characteristics on the one hand and inter-algorithm dose warping variation on the other hand was found.

CONCLUSION

Inter-algorithm dose accumulation variation is highly patient- and fraction-dependent for MR-guided liver SBRT. We advise against trusting a single algorithm for dose accumulation in liver SBRT.

Relative importance of genotype, gene expression, and DNA methylation on complex traits in perennial ryegrass

The growing demand for food and feed crops in the world because of growing population and more extreme weather events requires high-yielding and resilient crops. Many agriculturally important traits are polygenic, controlled by multiple regulatory layers, and with a strong interaction with the environment. In this study, 120 F₂ families of perennial ryegrass (Lolium perenne L.) were grown across a water gradient in a semifield facility with subsoil irrigation. Genomic (single-nucleotide polymorphism [SNP]), transcriptomic (gene expression [GE]), and DNA methylomic (MET) data were integrated with feed quality trait data collected from control and drought sections in the semifield facility, providing a treatment effect. Deep root length (DRL) below 110 cm was assessed with convolutional neural network image analysis. Bayesian prediction models were used to partition phenotypic variance into its components and evaluated the proportion of phenotypic variance in all traits captured by different regulatory layers (SNP, GE, and MET). The spatial effects and effects of SNP, GE, MET, the interaction between GE and MET (GE × MET) and GE × treatment (GE_Control and GE_Drought ) interaction were investigated. Gene expression explained a substantial part of the genetic and spatial variance for all the investigated phenotypes, whereas MET explained residual variance not accounted for by SNPs or GE. For DRL, MET also contributed to explaining spatial variance. The study provides a statistically elegant analytical paradigm that integrates genomic, transcriptomic, and MET information to understand the regulatory mechanisms of polygenic effects for complex traits.

RootPainter: deep learning segmentation of biological images with corrective annotation

Convolutional neural networks (CNNs) are a powerful tool for plant image analysis, but challenges remain in making them more accessible to researchers without a machine-learning background. We present RootPainter, an open-source graphical user interface based software tool for the rapid training of deep neural networks for use in biological image analysis. We evaluate RootPainter by training models for root length extraction from chicory (Cichorium intybus L.) roots in soil, biopore counting, and root nodule counting. We also compare dense annotations with corrective ones that are added during the training process based on the weaknesses of the current model. Five out of six times the models trained using RootPainter with corrective annotations created within 2 h produced measurements strongly correlating with manual measurements. Model accuracy had a significant correlation with annotation duration, indicating further improvements could be obtained with extended annotation. Our results show that a deep-learning model can be trained to a high accuracy for the three respective datasets of varying target objects, background, and image quality with < 2 h of annotation time. They indicate that, when using RootPainter, for many datasets it is possible to annotate, train, and complete data processing within 1 d.

Dynamics of Deep Water and N Uptake of Oilseed Rape (Brassica napus L.) Under Varied N and Water Supply

Enhanced nitrogen (N) and water uptake from deep soil layers may increase resource use efficiency while maintaining yield under stressed conditions. Winter oilseed rape (Brassica napus L.) can develop deep roots and access deep-stored resources such as N and water to sustain its growth and productivity. Less is known of the performance of deep roots under varying water and N availability. In this study, we aimed to evaluate the effects of reduced N and water supply on deep N and water uptake for oilseed rape. Oilseed rape plants grown in outdoor rhizotrons were supplied with 240 and 80 kg N ha-1, respectively, in 2019 whereas a well-watered and a water-deficit treatment were established in 2020. To track deep water and N uptake, a mixture of 2H2O and Ca(15NO3)2 was injected into the soil column at 0.5- and 1.7-m depths. δ2H in transpiration water and δ15N in leaves were measured after injection. δ15N values in biomass samples were also measured. Differences in N or water supply had less effect on root growth. The low N treatment reduced water uptake throughout the soil profile and altered water uptake distribution. The low N supply doubled the 15N uptake efficiency at both 0.5 and 1.7 m. Similarly, water deficit in the upper soil layers led to compensatory deep water uptake. Our findings highlight the increasing importance of deep roots for water uptake, which is essential for maintaining an adequate water supply in the late growing stage. Our results further indicate the benefit of reducing N supply for mitigating N leaching and altering water uptake from deep soil layers, yet at a potential cost of biomass reduction.

RootPainter3D: Interactive-machine-learning enables rapid and accurate contouring for radiotherapy

PURPOSE

Organ-at-risk contouring is still a bottleneck in radiotherapy, with many deep learning methods falling short of promised results when evaluated on clinical data. We investigate the accuracy and time-savings resulting from the use of an interactive-machine-learning method for an organ-at-risk contouring task.

METHODS

We implement an open-source interactive-machine-learning software application that facilitates corrective-annotation for deep-learning generated contours on X-ray CT images. A trained-physician contoured 933 hearts using our software by delineating the first image, starting model training, and then correcting the model predictions for all subsequent images. These corrections were added into the training data, which was used for continuously training the assisting model. From the 933 hearts, the same physician also contoured the first 10 and last 10 in Eclipse (Varian) to enable comparison in terms of accuracy and duration.

RESULTS

We find strong agreement with manual delineations, with a dice score of 0.95. The annotations created using corrective-annotation also take less time to create as more images are annotated, resulting in substantial time savings compared to manual methods. After 923 images had been delineated, hearts took 2 min and 2 s to delineate on average, which includes time to evaluate the initial model prediction and assign the needed corrections, compared to 7 min and 1 s when delineating manually.

CONCLUSIONS

Our experiment demonstrates that interactive-machine-learning with corrective-annotation provides a fast and accessible way for non computer-scientists to train deep-learning models to segment their own structures of interest as part of routine clinical workflows.

Digging roots is easier with AI

The scale of root quantification in research is often limited by the time required for sampling, measurement, and processing samples. Recent developments in convolutional neural networks (CNNs) have made faster and more accurate plant image analysis possible, which may significantly reduce the time required for root measurement, but challenges remain in making these methods accessible to researchers without an in-depth knowledge of machine learning. We analyzed root images acquired from three destructive root samplings using the RootPainter CNN software that features an interface for corrective annotation for easier use. Root scans with and without non-root debris were used to test if training a model (i.e. learning from labeled examples) can effectively exclude the debris by comparing the end results with measurements from clean images. Root images acquired from soil profile walls and the cross-section of soil cores were also used for training, and the derived measurements were compared with manual measurements. After 200 min of training on each dataset, significant relationships between manual measurements and RootPainter-derived data were noted for monolith (R2=0.99), profile wall (R2=0.76), and core-break (R2=0.57). The rooting density derived from images with debris was not significantly different from that derived from clean images after processing with RootPainter. Rooting density was also successfully calculated from both profile wall and soil core images, and in each case the gradient of root density with depth was not significantly different from manual counts. Differences in root-length density (RLD) between crops with contrasting root systems were captured using automatic segmentation at soil profiles with high RLD (1-5 cm cm-3) as well with low RLD (0.1-0.3 cm cm-3). Our results demonstrate that the proposed approach using CNN can lead to substantial reductions in root sample processing workloads, increasing the potential scale of future root investigations.

Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients

Patients with severe COVID-19 have overwhelmed healthcare systems worldwide. We hypothesized that machine learning (ML) models could be used to predict risks at different stages of management and thereby provide insights into drivers and prognostic markers of disease progression and death. From a cohort of approx. 2.6 million citizens in Denmark, SARS-CoV-2 PCR tests were performed on subjects suspected for COVID-19 disease; 3944 cases had at least one positive test and were subjected to further analysis. SARS-CoV-2 positive cases from the United Kingdom Biobank was used for external validation. The ML models predicted the risk of death (Receiver Operation Characteristics—Area Under the Curve, ROC-AUC) of 0.906 at diagnosis, 0.818, at hospital admission and 0.721 at Intensive Care Unit (ICU) admission. Similar metrics were achieved for predicted risks of hospital and ICU admission and use of mechanical ventilation. Common risk factors, included age, body mass index and hypertension, although the top risk features shifted towards markers of shock and organ dysfunction in ICU patients. The external validation indicated fair predictive performance for mortality prediction, but suboptimal performance for predicting ICU admission. ML may be used to identify drivers of progression to more severe disease and for prognostication patients in patients with COVID-19. We provide access to an online risk calculator based on these findings.

Neutron Capture on the s-Process Branching Point ^{171}Tm via Time-of-Flight and Activation

The neutron capture cross sections of several unstable nuclides acting as branching points in the s process are crucial for stellar nucleosynthesis studies. The unstable ^{171}Tm (t_{1/2}=1.92  yr) is part of the branching around mass A∼170 but its neutron capture cross section as a function of the neutron energy is not known to date. In this work, following the production for the first time of more than 5 mg of ^{171}Tm at the high-flux reactor Institut Laue-Langevin in France, a sample was produced at the Paul Scherrer Institute in Switzerland. Two complementary experiments were carried out at the neutron time-of-flight facility (n_TOF) at CERN in Switzerland and at the SARAF liquid lithium target facility at Soreq Nuclear Research Center in Israel by time of flight and activation, respectively. The result of the time-of-flight experiment consists of the first ever set of resonance parameters and the corresponding average resonance parameters, allowing us to make an estimation of the Maxwellian-averaged cross sections (MACS) by extrapolation. The activation measurement provides a direct and more precise measurement of the MACS at 30 keV: 384(40) mb, with which the estimation from the n_TOF data agree at the limit of 1 standard deviation. This value is 2.6 times lower than the JEFF-3.3 and ENDF/B-VIII evaluations, 25% lower than that of the Bao et al. compilation, and 1.6 times larger than the value recommended in the KADoNiS (v1) database, based on the only previous experiment. Our result affects the nucleosynthesis at the A∼170 branching, namely, the ^{171}Yb abundance increases in the material lost by asymptotic giant branch stars, providing a better match to the available pre-solar SiC grain measurements compared to the calculations based on the current JEFF-3.3 model-based evaluation.

Segmentation of roots in soil with U-Net

BACKGROUND

Plant root research can provide a way to attain stress-tolerant crops that produce greater yield in a diverse array of conditions. Phenotyping roots in soil is often challenging due to the roots being difficult to access and the use of time consuming manual methods. Rhizotrons allow visual inspection of root growth through transparent surfaces. Agronomists currently manually label photographs of roots obtained from rhizotrons using a line-intersect method to obtain root length density and rooting depth measurements which are essential for their experiments. We investigate the effectiveness of an automated image segmentation method based on the U-Net Convolutional Neural Network (CNN) architecture to enable such measurements. We design a data-set of 50 annotated chicory (Cichorium intybus L.) root images which we use to train, validate and test the system and compare against a baseline built using the Frangi vesselness filter. We obtain metrics using manual annotations and line-intersect counts.

RESULTS

Our results on the held out data show our proposed automated segmentation system to be a viable solution for detecting and quantifying roots. We evaluate our system using 867 images for which we have obtained line-intersect counts, attaining a Spearman rank correlation of 0.9748 and an r 2 of 0.9217. We also achieve an F 1 of 0.7 when comparing the automated segmentation to the manual annotations, with our automated segmentation system producing segmentations with higher quality than the manual annotations for large portions of the image.

CONCLUSION

We have demonstrated the feasibility of a U-Net based CNN system for segmenting images of roots in soil and for replacing the manual line-intersect method. The success of our approach is also a demonstration of the feasibility of deep learning in practice for small research groups needing to create their own custom labelled dataset from scratch.

^{7}Be(n,p)^{7}Li Reaction and the Cosmological Lithium Problem: Measurement of the Cross Section in a Wide Energy Range at n_TOF at CERN

We report on the measurement of the ^{7}Be(n,p)^{7}Li cross section from thermal to approximately 325 keV neutron energy, performed in the high-flux experimental area (EAR2) of the n_TOF facility at CERN. This reaction plays a key role in the lithium yield of the big bang nucleosynthesis (BBN) for standard cosmology. The only two previous time-of-flight measurements performed on this reaction did not cover the energy window of interest for BBN, and they showed a large discrepancy between each other. The measurement was performed with a Si telescope and a high-purity sample produced by implantation of a ^{7}Be ion beam at the ISOLDE facility at CERN. While a significantly higher cross section is found at low energy, relative to current evaluations, in the region of BBN interest, the present results are consistent with the values inferred from the time-reversal ^{7}Li(p,n)^{7}Be reaction, thus yielding only a relatively minor improvement on the so-called cosmological lithium problem. The relevance of these results on the near-threshold neutron production in the p+^{7}Li reaction is also discussed.

A growth hormone receptor SNP promotes lung cancer by impairment of SOCS2-mediated degradation

Both humans and mice lacking functional growth hormone (GH) receptors are known to be resistant to cancer. Further, autocrine GH has been reported to act as a cancer promoter. Here we present the first example of a variant of the GH receptor (GHR) associated with cancer promotion, in this case lung cancer. We show that the GHRP495T variant located in the receptor intracellular domain is able to prolong the GH signal in vitro using stably expressing mouse pro-B-cell and human lung cell lines. This is relevant because GH secretion is pulsatile, and extending the signal duration makes it resemble autocrine GH action. Signal duration for the activated GHR is primarily controlled by suppressor of cytokine signalling 2 (SOCS2), the substrate recognition component of the E3 protein ligase responsible for ubiquitinylation and degradation of the GHR. SOCS2 is induced by a GH pulse and we show that SOCS2 binding to the GHR is impaired by a threonine substitution at Pro 495. This results in decreased internalisation and degradation of the receptor evident in TIRF microscopy and by measurement of mature (surface) receptor expression. Mutational analysis showed that the residue at position 495 impairs SOCS2 binding only when a threonine is present, consistent with interference with the adjacent Thr494. The latter is key for SOCS2 binding, together with nearby Tyr487, which must be phosphorylated for SOCS2 binding. We also undertook nuclear magnetic resonance spectroscopy approach for structural comparison of the SOCS2 binding scaffold Ile455-Ser588, and concluded that this single substitution has altered the structure of the SOCS2 binding site. Importantly, we find that lung BEAS-2B cells expressing GHRP495T display increased expression of transcripts associated with tumour proliferation, epithelial-mesenchymal transition and metastases (TWIST1, SNAI2, EGFR, MYC and CCND1) at 2 h after a GH pulse. This is consistent with prolonged GH signalling acting to promote cancer progression in lung cancer.

Erythropoiesis-stimulating agents significantly delay the onset of a regular transfusion need in nontransfused patients with lower-risk myelodysplastic syndrome

BACKGROUND

The EUMDS registry is an unique prospective, longitudinal observational registry enrolling newly diagnosed patients with lower-risk myelodysplastic syndrome (MDS) from 17 European countries from both university hospitals and smaller regional hospitals.

OBJECTIVE

The aim of this study was to describe the usage and clinical impact of erythropoiesis-stimulating agents (ESAs) in 1696 patients enrolled between 2008 and 2014.

METHODS

The effects of ESAs on outcomes were assessed using proportional hazards models weighting observations by propensity to receive ESA treatment within a subset of anaemic patients with or without a regular transfusion need.

RESULTS

ESA treatment (median duration of 27.5 months, range 0-77 months) was administered to 773 patients (45.6%). Outcomes were assessed in 897 patients (484 ESA treated and 413 untreated). ESA treatment was associated with a nonsignificant survival benefit (HR 0.82, 95% CI: 0.65-1.04, P = 0.09); this benefit was larger amongst patients without prior transfusions (P = 0.07). Amongst 539 patients for whom response to ESA treatment could be defined, median time to first post-ESA treatment transfusion was 6.1 months (IQR: 4.3-15.9 months) in those transfused before ESA treatment compared to 23.3 months (IQR: 7.0-47.8 months) in patients without prior transfusions (HR 2.4, 95% CI: 1.7-3.3, P < 0.0001). Responding patients had a better prognosis in terms of a lower risk of death (HR 0.65, 95% CI: 0.45-0.893, P = 0.018), whereas there was no significant effect on the risk of progression to acute myeloid leukaemia (HR 0.71, 95% CI: 0.39-1.29, P = 0.27).

CONCLUSION

Appropriate use of ESAs can significantly delay the onset of a regular transfusion need in patients with lower-risk MDS.

^{7}Be(n,α)^{4}He Reaction and the Cosmological Lithium Problem: Measurement of the Cross Section in a Wide Energy Range at n_TOF at CERN

The energy-dependent cross section of the ^{7}Be(n,α)^{4}He reaction, of interest for the so-called cosmological lithium problem in big bang nucleosynthesis, has been measured for the first time from 10 meV to 10 keV neutron energy. The challenges posed by the short half-life of ^{7}Be and by the low reaction cross section have been overcome at n_TOF thanks to an unprecedented combination of the extremely high luminosity and good resolution of the neutron beam in the new experimental area (EAR2) of the n_TOF facility at CERN, the availability of a sufficient amount of chemically pure ^{7}Be, and a specifically designed experimental setup. Coincidences between the two alpha particles have been recorded in two Si-^{7}Be-Si arrays placed directly in the neutron beam. The present results are consistent, at thermal neutron energy, with the only previous measurement performed in the 1960s at a nuclear reactor. The energy dependence reported here clearly indicates the inadequacy of the cross section estimates currently used in BBN calculations. Although new measurements at higher neutron energy may still be needed, the n_TOF results hint at a minor role of this reaction in BBN, leaving the long-standing cosmological lithium problem unsolved.