Reading tea leaves worldwide: Decoupled drivers of initial litter decomposition mass-loss rate and stabilization.

The breakdown of plant material fuels soil functioning and biodiversity. Currently, process understanding of global decomposition patterns and the drivers of such patterns are hampered by the lack of coherent large-scale datasets. We buried 36,000 individual litterbags (tea bags) worldwide and found an overall negative correlation between initial mass-loss rates and stabilization factors of plant-derived carbon, using the Tea Bag Index (TBI). The stabilization factor quantifies the degree to which easy-to-degrade components accumulate during early-stage decomposition (e.g. by environmental limitations). However, agriculture and an interaction between moisture and temperature led to a decoupling between initial mass-loss rates and stabilization, notably in colder locations. Using TBI improved mass-loss estimates of natural litter compared to models that ignored stabilization. Ignoring the transformation of dead plant material to more recalcitrant substances during early-stage decomposition, and the environmental control of this transformation, could overestimate carbon losses during early decomposition in carbon cycle models.

Automatic end-to-end VMAT treatment planning for rectal cancers.

BACKGROUND: The treatment planning process from segmentation to producing a deliverable plan is time-consuming and labor-intensive. Existing solutions automate the segmentation and planning processes individually. The feasibility of combining auto-segmentation and auto-planning for volumetric modulated arc therapy (VMAT) for rectal cancers in an end-to-end process is not clear. PURPOSE: To create and clinically evaluate a complete end-to-end process for auto-segmentation and auto-planning of VMAT for rectal cancer requiring only the gross tumor volume contour and a CT scan as inputs. METHODS: Patient scans and data were retrospectively selected from our institutional records for patients treated for malignant neoplasm of the rectum. We trained, validated, and tested deep learning auto-segmentation models using nnU-Net architecture for clinical target volume (CTV), bowel bag, large bowel, small bowel, total bowel, femurs, bladder, bone marrow, and female and male genitalia. For the CTV, we identified 174 patients with clinically drawn CTVs. We used data for 18 patients for all structures other than the CTV. The structures were contoured under the guidance of and reviewed by a gastrointestinal (GI) radiation oncologist. The predicted results for CTV in 35 patients and organs at risk (OAR) in six patients were scored by the GI radiation oncologist using a five-point Likert scale. For auto-planning, a RapidPlan knowledge-based planning solution was modeled for VMAT delivery with a prescription of 25 Gy in five fractions. The model was trained and tested on 20 and 34 patients, respectively. The resulting plans were scored by two GI radiation oncologists using a five-point Likert scale. Finally, the end-to-end pipeline was evaluated on 16 patients, and the resulting plans were scored by two GI radiation oncologists. RESULTS: In 31 of 35 patients, CTV contours were clinically acceptable without necessary modifications. The CTV achieved a Dice similarity coefficient of 0.85 (±0.05) and 95% Hausdorff distance of 15.25 (±5.59) mm. All OAR contours were clinically acceptable without edits, except for large and small bowel which were challenging to differentiate. However, contours for total, large, and small bowel were clinically acceptable. The two physicians accepted 100% and 91% of the auto-plans. For the end-to-end pipeline, the two physicians accepted 88% and 62% of the auto-plans. CONCLUSIONS: This study demonstrated that the VMAT treatment planning technique for rectal cancer can be automated to generate clinically acceptable and safe plans with minimal human interventions.

Automating the treatment planning process for 3D-conformal pediatric craniospinal irradiation therapy.

PURPOSE: Pediatric patients with medulloblastoma in low- and middle-income countries (LMICs) are most treated with 3D-conformal photon craniospinal irradiation (CSI), a time-consuming, complex treatment to plan, especially in resource-constrained settings. Therefore, we developed and tested a 3D-conformal CSI autoplanning tool for varying patient lengths. METHODS AND MATERIALS: Autocontours were generated with a deep learning model trained:tested (80:20 ratio) on 143 pediatric medulloblastoma CT scans (patient ages: 2-19 years, median = 7 years). Using the verified autocontours, the autoplanning tool generated two lateral brain fields matched to a single spine field, an extended single spine field, or two matched spine fields. Additional spine subfields were added to optimize the corresponding dose distribution. Feathering was implemented (yielding nine to 12 fields) to give a composite plan. Each planning approach was tested on six patients (ages 3-10 years). A pediatric radiation oncologist assessed clinical acceptability of each autoplan. RESULTS: The autocontoured structures' average Dice similarity coefficient ranged from .65 to .98. The average V95 for the brain/spinal canal for single, extended, and multi-field spine configurations was 99.9% ± 0.06%/99.9% ± 0.10%, 99.9% ± 0.07%/99.4% ± 0.30%, and 99.9% ± 0.06%/99.4% ± 0.40%, respectively. The average maximum dose across all field configurations to the brainstem, eyes (L/R), lenses (L/R), and spinal cord were 23.7 ± 0.08, 24.1 ± 0.28, 13.3 ± 5.27, and 25.5 ± 0.34 Gy, respectively (prescription = 23.4 Gy/13 fractions). Of the 18 plans tested, all were scored as clinically acceptable as-is or clinically acceptable with minor, time-efficient edits preferred or required. No plans were scored as clinically unacceptable. CONCLUSION: The autoplanning tool successfully generated pediatric CSI plans for varying patient lengths in 3.50 ± 0.4 minutes on average, indicating potential for an efficient planning aid in a resource-constrained settings.

Resection cavity auto-contouring for patients with pediatric medulloblastoma using only CT information.

PURPOSE: Target delineation for radiation therapy is a time-consuming and complex task. Autocontouring gross tumor volumes (GTVs) has been shown to increase efficiency. However, there is limited literature on post-operative target delineation, particularly for CT-based studies. To this end, we trained a CT-based autocontouring model to contour the post-operative GTV of pediatric patients with medulloblastoma. METHODS: One hundred four retrospective pediatric CT scans were used to train a GTV auto-contouring model. Eighty patients were then preselected for contour visibility, continuity, and location to train an additional model. Each GTV was manually annotated with a visibility score based on the number of slices with a visible GTV (1 = < 25%, 2 = 25-50%, 3 = > 50-75%, and 4 = > 75-100%). Contrast and the contrast-to-noise ratio (CNR) were calculated for the GTV contour with respect to a cropped background image. Both models were tested on the original and pre-selected testing sets. The resulting surface and overlap metrics were calculated comparing the clinical and autocontoured GTVs and the corresponding clinical target volumes (CTVs). RESULTS: Eighty patients were pre-selected to have a continuous GTV within the posterior fossa. Of these, 7, 41, 21, and 11 were visibly scored as 4, 3, 2, and 1, respectively. The contrast and CNR removed an additional 11 and 20 patients from the dataset, respectively. The Dice similarity coefficients (DSC) were 0.61 ± 0.29 and 0.67 ± 0.22 on the models without pre-selected training data and 0.55 ± 13.01 and 0.83 ± 0.17 on the models with pre-selected data, respectively. The DSC on the CTV expansions were 0.90 ± 0.13. CONCLUSION: We successfully automatically contoured continuous GTVs within the posterior fossa on scans that had contrast > ± 10 HU. CT-Based auto-contouring algorithms have potential to positively impact centers with limited MRI access.

Drone-based assessment of microsite-scale hydrological processes promoted by restoration actions in early post-mining ecological restoration stages.

A successful choice of post-mining restoration activities in dry climates may depend on relevant features related to topographic characteristics, hydrological processes and vegetation development, which will determine functional recovery in these ecosystems. The combination of different restoration techniques to reestablish vegetation, such as sowing and plantation, implies the interspersion of bare-soil areas with vegetated areas in early plant development stages, which may result in an associated mosaic of hydrologic functioning. In this study, we conducted a drone-based assessment to disentangle the role played by microsite-scale hydrological processes (i.e., planting hole slope, sink volume capacity, individual catchment area, Flow Length Index) promoted by restoration actions in soil protection and vegetation development on the hillside scale. Based on two contrasting restoration scenarios (Steep hillside and Smooth hillside), the different applied restoration treatments conditioned the microtopographic processes on the planting hole scale and, therefore, resource redistribution. The main results showed higher planting hole functionality on the smooth hillsides than on steep hillside, which resulted in greater water availability and bigger vegetation patches. By addressing the role of hydrological processes on the microsite scale, our study contributes substantially to prior knowledge on the relevant factors for ecosystem development and post-mining restoration success. It also demonstrates that high-resolution drone images can be a very useful tool for monitoring restoration actions, especially in large, inaccessible and unstable restored areas.

Computed tomographic features of double aortic arch in six dogs.

Double aortic arch (DAA) is a rare, congenital anomaly in small animals, resulting in a complete vascular ring encircling the esophagus and trachea, and subsequent compression of these organs. Few studies have reported utilizing CT angiography (CTA) for diagnosing DAA in dogs; thus, the imaging features are currently lacking in the literature. The objectives of this retrospective, multicenter, descriptive case series were to report the clinical and CTA characteristics of DAA in surgically treated cases. Medical records and CTA images were reviewed. Six juvenile dogs met the inclusion criteria (median age: 4.2 months; range: 2-5 months). The most common clinical signs included chronic regurgitation (100%), decreased body condition (67%), and coughing (50%). Common CTA features of DAA included a dominant left aortic arch (median diameter: 8.1 mm) and minor right aortic arch (median diameter: 4.3 mm; 83%), an aberrant right subclavian artery arising directly from the right aortic arch (83%), segmental esophageal constriction (100%), and variable degrees of dilation cranial to the heart base, and marked tracheal luminal compression (median percent change: -55%; 100%) and leftward curvature of the trachea at the level of the bifurcation of the aortic arches (100%). All dogs underwent successful surgical correction with only minor postoperative complications. Due to the similarity of clinical and imaging characteristics described to that of other forms of vascular ring anomalies (VRA), CTA is vital for the specific diagnosis of DAA in dogs.

Financed research from Government Delegation grants for the National Plan on Drugs: Research assessment and scientific impact. / Repercusión científica de las ayudas de la Delegación del Gobierno para el Plan Nacional sobre Drogas: Publicaciones derivadas e impacto científico.

Addictive disorders are a serious health problem to which large amounts of research resources are devoted. This study aims to analyze the evolution and scientific impact of the publications derived from the funding of research projects by the Spanish National Plan on Drugs (PNSD). The list of grants awarded was provided by the PNSD. Derived publications were obtained by asking the principal investigators of the grants and searching in the Web of Science and Scopus. Bibliometric indicators and evolutive trends of scientific production per project were calculated. On average, the PNSD conferred 15 annual grants to research projects, with an annual amount close to one million euros (€944,200.64) and an average amount per grant of just over €60,000, being higher in basic research and in alcohol. 71,9% of the grants had derived publications and almost half of them produced between one and three publications, with basic research being the most prolific. The international journal in which most articles were published was Psychopharmacology (50) and among Spanish journals, Adicciones stood out (28). A high level of co-authorship and international collaboration was identified. Most of the PNSD-funded projects produced research articles, many of them in journals belonging to the first and second quartiles of the Journal Citation Reports. The results of this study have revealed the scientific impact of the PNSD research projects funding and may contribute to determining future funding priorities.

Los trastornos adictivos son un grave problema de salud al que se destinan gran cantidad de recursos de investigación. El propósito de este trabajo es analizar la evolución e impacto científico de las publicaciones derivadas de las ayudas a proyectos de investigación financiados por el Plan Nacional Sobre Drogas (PNSD). La relación de ayudas concedidas fue proporcionada por el PNSD. Las publicaciones derivadas se obtuvieron preguntando a los investigadores principales de las ayudas y buscando en Web of Science y Scopus. Se calcularon indicadores bibliométricos y tendencias evolutivas de la producción científica por proyecto. Por término medio, el PNSD concedió 15 ayudas anuales a proyectos de investigación, con un importe anual cercano al millón de euros (944.200,64€) y un importe medio por ayuda de algo más de 60.000€, siendo mayor en la investigación básica y en alcohol. El 71,9% de las ayudas tuvieron publicaciones derivadas y casi la mitad produjeron entre una y tres publicaciones, siendo la investigación básica la más prolífica. La revista extranjera en la que más artículos se publicaron fue Psychopharmacology (50) y entre las españolas destacó Adicciones (28). Se identificó un alto índice de coautoría y de colaboración internacional. La mayoría de los proyectos financiados por el PNSD produjeron artículos de investigación y muchos de ellos en revistas del primer y segundo cuartil del Journal Citation Reports. Los resultados de este estudio han permitido conocer la repercusión científica de las ayudas a proyectos de investigación del PNSD y puede contribuir a determinar futuras prioridades de financiación.

Radiomics in Abdominopelvic Solid-Organ Oncologic Imaging: Current Status.

Radiomics is the process of extraction of high-throughput quantitative imaging features from medical images. These features represent noninvasive quantitative biomarkers that go beyond the traditional imaging features visible to the human eye. This article first reviews the steps of the radiomics pipeline, including image acquisition, ROI selection and image segmentation, image preprocessing, feature extraction, feature selection, and model development and application. Current evidence for the application of radiomics in abdominopelvic solid-organ cancers is then reviewed. Applications including diagnosis, subtype determination, treatment response assessment, and outcome prediction are explored within the context of hepatobiliary and pancreatic cancer, renal cell carcinoma, prostate cancer, gynecologic cancer, and adrenal masses. This literature review focuses on the strongest available evidence, including systematic reviews, meta-analyses, and large multicenter studies. Limitations of the available literature are highlighted, including marked heterogeneity in radiomics methodology, frequent use of small sample sizes with high risk of overfitting, and lack of prospective design, external validation, and standardized radiomics workflow. Thus, although studies have laid a foundation that supports continued investigation into radiomics models, stronger evidence is needed before clinical adoption.

Artificial Intelligence in Diagnostic Radiology: Where Do We Stand, Challenges, and Opportunities.

ABSTRACT: Artificial intelligence (AI) is the most revolutionizing development in the health care industry in the current decade, with diagnostic imaging having the greatest share in such development. Machine learning and deep learning (DL) are subclasses of AI that show breakthrough performance in image analysis. They have become the state of the art in the field of image classification and recognition. Machine learning deals with the extraction of the important characteristic features from images, whereas DL uses neural networks to solve such problems with better performance. In this review, we discuss the current applications of machine learning and DL in the field of diagnostic radiology.Deep learning applications can be divided into medical imaging analysis and applications beyond analysis. In the field of medical imaging analysis, deep convolutional neural networks are used for image classification, lesion detection, and segmentation. Also used are recurrent neural networks when extracting information from electronic medical records and to augment the use of convolutional neural networks in the field of image classification. Generative adversarial networks have been explicitly used in generating high-resolution computed tomography and magnetic resonance images and to map computed tomography images from the corresponding magnetic resonance imaging. Beyond image analysis, DL can be used for quality control, workflow organization, and reporting.In this article, we review the most current AI models used in medical imaging research, providing a brief explanation of the various models described in the literature within the past 5 years. Emphasis is placed on the various DL models, as they are the most state-of-art in imaging analysis.

An efficient magnetic resonance image data quality screening dashboard.

PURPOSE: Complex data processing and curation for artificial intelligence applications rely on high-quality data sets for training and analysis. Manually reviewing images and their associated annotations is a very laborious task and existing quality control tools for data review are generally limited to raw images only. The purpose of this work was to develop an imaging informatics dashboard for the easy and fast review of processed magnetic resonance (MR) imaging data sets; we demonstrated its ability in a large-scale data review. METHODS: We developed a custom R Shiny dashboard that displays key static snapshots of each imaging study and its annotations. A graphical interface allows the structured entry of review data and download of tabulated review results. We evaluated the dashboard using two large data sets: 1380 processed MR imaging studies from our institution and 285 studies from the 2018 MICCAI Brain Tumor Segmentation Challenge (BraTS). RESULTS: Studies were reviewed at an average rate of 100/h using the dashboard, 10 times faster than using existing data viewers. For data from our institution, 1181 of the 1380 (86%) studies were of acceptable quality. The most commonly identified failure modes were tumor segmentation (9.6% of cases) and image registration (4.6% of cases). Tumor segmentation without visible errors on the dashboard had much better agreement with reference tumor volume measurements (root-mean-square error 12.2 cm3 ) than did segmentations with minor errors (20.5 cm3 ) or failed segmentations (27.4 cm3 ). In the BraTS data, 242 of 285 (85%) studies were acceptable quality after processing. Among the 43 cases that failed review, 14 had unacceptable raw image quality. CONCLUSION: Our dashboard provides a fast, effective tool for reviewing complex processed MR imaging data sets. It is freely available for download at https://github.com/EGates1/MRDQED.

Automation of radiation treatment planning for rectal cancer.

PURPOSE: To develop an automated workflow for rectal cancer three-dimensional conformal radiotherapy (3DCRT) treatment planning that combines deep learning (DL) aperture predictions and forward-planning algorithms. METHODS: We designed an algorithm to automate the clinical workflow for 3DCRT planning with field aperture creations and field-in-field (FIF) planning. DL models (DeepLabV3+ architecture) were trained, validated, and tested on 555 patients to automatically generate aperture shapes for primary (posterior-anterior [PA] and opposed laterals) and boost fields. Network inputs were digitally reconstructed radiographs, gross tumor volume (GTV), and nodal GTV. A physician scored each aperture for 20 patients on a 5-point scale (>3 is acceptable). A planning algorithm was then developed to create a homogeneous dose using a combination of wedges and subfields. The algorithm iteratively identifies a hotspot volume, creates a subfield, calculates dose, and optimizes beam weight all without user intervention. The algorithm was tested on 20 patients using clinical apertures with varying wedge angles and definitions of hotspots, and the resulting plans were scored by a physician. The end-to-end workflow was tested and scored by a physician on another 39 patients. RESULTS: The predicted apertures had Dice scores of 0.95, 0.94, and 0.90 for PA, laterals, and boost fields, respectively. Overall, 100%, 95%, and 87.5% of the PA, laterals, and boost apertures were scored as clinically acceptable, respectively. At least one auto-plan was clinically acceptable for all patients. Wedged and non-wedged plans were clinically acceptable for 85% and 50% of patients, respectively. The hotspot dose percentage was reduced from 121% (σ = 14%) to 109% (σ = 5%) of prescription dose for all plans. The integrated end-to-end workflow of automatically generated apertures and optimized FIF planning gave clinically acceptable plans for 38/39 (97%) of patients. CONCLUSION: We have successfully automated the clinical workflow for generating radiotherapy plans for rectal cancer for our institution.

Role of alkylated residues in the tetrapeptide self-assembly-A molecular dynamics study.

Designing peptide sequences that self-assemble into well-defined nanostructures can open a new venue for the development of novel drug carriers and molecular contrast agents. Current approaches are often based on a linear block-design of amphiphilic peptides where a hydrophilic peptide chain is terminated by a hydrophobic tail. Here, a new template for a self-assembling tetrapeptide (YXKX, Y = tyrosine, X = alkylated tyrosine, K = lysine) is proposed with two distinct sides relative to the peptide's backbone: alkylated hydrophobic residues on one side and hydrophilic residues on the other side. Using all-atom molecular dynamics simulations, the self-assembly pathway of the tetrapeptide is analyzed for two different concentrations. At both concentrations, tetrapeptides self-assembled into a nanosphere structure. The alkylated tyrosines initialize the self-assembly process via a strong hydrophobic effect and to reduce exposure to the aqueous solvent, they formed a hydrophobic core. The hydrophilic residues occupied the surface of the self-assembled nanosphere. Ordered arrangement of tetrapeptides within the nanosphere with the backbone hydrogen bonding led to a beta sheet formation. Alkyl chain length constrained the size and shape of the nanosphere. This study provides foundation for further exploration of self-assembling structures that are based on peptides with hydrophobic and hydrophilic moieties located on the opposite sides of a peptide backbone.

Biochemical and physiological impacts of zinc sulphate, potassium phosphite and hydrogen sulphide in mitigating stress conditions in soybean.

Soybean is the most widely grown oilseed in the world. It is an important source of protein and oil which are derived from its seeds. Drought stress is a major constraint to soybean yields. Finding alternative methods to mitigate the water stress for soybean is useful to maintain adequate crop yields. The aim of this study was to evaluate the morpho-physiological, biochemical and metabolic changes in soybean plants in two ontogenetic stages, under exposure to water deficit and treatment with zinc sulphate (ZS), potassium phosphite (PP) or hydrogen sulphide (HS). We carried out two independent experiments in the V4 and R1 development stages consisting of the following treatments: well-watered control (WW, 100% maximum water holding capacity, MWHC), water deficit (WD, 50% MWHC), PP + WW, PP + WD, HS + WW, HS + WD, ZS + WW and ZS + WD. The experimental design consisted of randomized blocks with eight treatments with five replicates. Morphological, physiological and metabolic analyses were performed 8 days after the start of the treatments for both experiments. We identified two tolerance mechanisms acting in response to compound application during water stress: the first involved the upregulation of antioxidant enzyme activity and the second involved the accumulation of soluble sugars, free amino acids and proline to facilitate osmotic adjustment. Both mechanisms are related to the maintenance of the photosynthetic parameters and cell membrane integrity. This report suggests the potential agricultural use of these compounds to mitigate drought effects in soybean plants.

Mathematical modeling of mass and energy transport for thermoembolization.

Background: Thermoembolization presents a unique treatment alternative for patients diagnosed with hepatocellular carcinoma. The approach delivers a reagent that undergoes an exothermic chemical reaction and combines the benefits of embolic as well as thermal- and chemical-ablative therapy modalities. The target tissue and vascular bed are subjected to simultaneous hyperthermia, ischemia, and chemical denaturation in a single procedure. To guide optimal delivery, we developed a mathematical model for understanding the competing diffusive and convective effects observed in thermoembolization delivery protocols.Methods: A mixture theory formulation was used to mathematically model thermoembolization as chemically reacting transport of an electrophile, dichloroacetyl chloride (DCACl), within porous living tissue. Mass and energy transport of each relevant constituent are considered. Specifically, DCACl is injected into the vessels and exothermically reacts with water in the blood or tissue to form dichloroacetic acid and hydrochloric acid. Neutralization reactions are assumed instantaneous in this approach. We validated the mathematical model predictions of temperature using MR thermometry of the thermoembolization procedure performed in ex vivo kidney.Results: Mathematical modeling predictions of tissue death were highly dependent on the vascular geometry, injection pressure, and intrinsic amount of exothermic energy released from the chemical species, and were able to recapitulate the temperature distributions observed in MR thermometry.Conclusion: These efforts present a first step toward formalizing a mathematical model for thermoembolization and are promising for providing insight for delivery protocol optimization. While our approach captured the observed experimental temperature measurements, larger-scale experimental validation is needed to prioritize additional model complexity and fidelity.

The use of laser interstitial thermal therapy in the treatment of brain metastases: a literature review.

PURPOSE: The aim of this paper is to discuss the current evidence for Laser Interstitial Thermal Therapy (LITT) in the treatment of brain metastases, our current recommendations for patient selection and the future perspectives for this therapy. We have also touched upon the possible complications and role of systemic therapy coupled with LITT for the treatment of brain metastases. MATERIAL AND METHODS: Two authors carried out the literature search using two databases independently, including PubMed, and Web of Science. The review included prospective and retrospective studies using LITT to treat brain metastases. RESULTS: Twenty-two original articles were analyzed in this review, particularly clinical outcomes and complications. We have also provided our institutional experience in the use of LITT to treat brain metastases and addressed future perspectives for the use of this technology. CONCLUSIONS: The current literature supports LITT as a safe and effective therapy for patients with brain metastases that have failed SRS. Larger studies are still required to better evaluate the use of systemic therapy in concomitance with LITT. New images modalities may enable optimized treatment and outcomes.

RADIOGRAPHIC ANATOMY OF THE FORELIMB IN THE NORTHERN TAMANDUA (TAMANDUA MEXICANA).

The thoracic limb anatomy of anteaters in the family Myrmecophagidae is specialized for accessing termite and ant nests and for defense purposes. In the case of the northern tamandua (Tamandua mexicana), the forelimbs are also adapted for arboreal and terrestrial locomotion. Unfortunately, this species faces many conservation threats, such as habitat loss and traffic accidents, and injured individuals are frequently taken to wildlife rehabilitation centers. However, lack of knowledge of the radiographic osteoanatomy of this species may prevent appropriate management of injuries and thereby reduce the chances of successful release and survival. In order to fill this knowledge gap, this article describes for the first time the radiographic anatomy of the thoracic limb of the northern tamandua using four standard views and one additional view. The additional orthogonal view helps visualize structures, such as the hamatus process and the sesamoid bone, that are otherwise difficult to visualize due to the natural forearm position of anteaters. Additionally, some fractures and physeal growth plates were identified in one juvenile individual. Further radiographic investigations should be conducted on anteaters to provide more tools for diagnosis, treatment, and rehabilitation of these animals.

Effects of excitation angle strategy on quantitative analysis of hyperpolarized pyruvate.

PURPOSE: Various excitation strategies have been proposed for dynamic imaging of hyperpolarized agents such as [1-13 C]-pyruvate, but the impact of these strategies on quantitative evaluation of signal evolution remains unclear. To better understand their relative performance, we compared the accuracy and repeatability of measurements made using variable excitation angle strategies and conventional constant excitation angle strategies. METHODS: Signal evolution for constant and variable excitation angle schedules was simulated using a pharmacokinetic model of hyperpolarized pyruvate with 2 chemical pools and 2 physical compartments. Noisy synthetic data were then fit using the same pharmacokinetic model with the apparent chemical exchange term as an unknown, and fit results were compared with simulation parameters to determine accuracy and reproducibility. RESULTS: Constant excitations and a variable excitation strategy that maximizes the HP lactate signal yielded data that supported quantitative analyses with similar accuracy and repeatability. Variable excitation angle strategies that were designed to produce a constant signal level resulted in lower signal and worse quantitative accuracy and repeatability, particularly for longer acquisition times. CONCLUSIONS: These results suggest that either constant excitation angle or variable excitation angles that attempt to maximize total signal, as opposed to maintaining a constant signal level, are preferred for metabolic quantification using hyperpolarized pyruvate.

Correction to: Volumetric response of progressing post-SRS lesions treated with laser interstitial thermal therapy.

The original article was published with an incorrect protocol number. The correct protocol number is DR07-0585.

Chemical and isotopic markers detect water deficit and its influence on nutrient allocation in Phaseolus vulgaris.

The impact of drought on plant growth and yield has been widely studied and is considered a major limitation to crops reaching yield potential. Less known is the impact of water deficit on the nutritional quality of the resulting yield. This study characterised the impact of water deficit on carbon assimilation, modelled water use efficiency from carbon isotope discrimination and analysed the concentration of mineral nutrients, amino acids and sugars in leaf, phloem and pod pools collected from Phaseolus vulgaris L. (common bean) grown in a controlled environment. Water deficit led to an isohydric response, impacting on carbon isotope abundance in all tissues though not translating to any significant treatment differences in water use efficiency or nutrient content in tissues over the course of plant development. The results obtained in this study demonstrate that nutrient content of P. vulgaris yield was not impacted by the availability of water. The absence of significant changes in the nutrient content of individual seeds highlights the plasticity of developing reproductive tissue to changes in whole plant water availability.

Automated Volumetric Assessment of Hepatocellular Carcinoma Response to Sorafenib: A Pilot Study.

PURPOSE: This pilot study evaluates the feasibility of automated volumetric quantification of hepatocellular carcinoma (HCC) as an imaging biomarker to assess treatment response for sorafenib. METHODS: In this institutional review board-approved, Health Insurance Portability and Accountability Act-compliant retrospective study, a training database of manually labeled background liver, enhancing and nonenhancing tumor tissue was established using pretherapy and first posttherapy multiphasic computed tomography images from a registry of 13 HCC patients. For each patient, Hounsfield density and geometry-based feature images were generated from registered multiphasic computed tomography data sets and used as the input for a random forest-based classifier of enhancing and nonenhancing tumor tissue. Leave-one-out cross-validation of the dice similarity measure was applied to quantify the classifier accuracy. A Cox regression model was used to confirm volume changes as predictors of time to progression (TTP) of target lesions for both manual and automatic methods. RESULTS: When compared with manual labels, an overall classification accuracy of dice similarity coefficient of 0.71 for pretherapy and 0.66 posttherapy enhancing tumor labels and 0.45 for pretherapy and 0.59 for posttherapy nonenhancing tumor labels was observed. Automated methods for quantifying volumetric changes in the enhancing lesion agreed with manual methods and were observed as a significant predictor of TTP. CONCLUSIONS: Automated volumetric analysis was determined to be feasible for monitoring HCC response to treatment. The information extracted using automated volumetrics is likely to reproduce labor-intensive manual data and provide a good predictor for TTP. Further work will extend these studies to additional treatment modalities and larger patient populations.