Hybrid molecular-continuum simulations of water flow through carbon nanotube membranes of realistic thickness

We present new hybrid molecular-continuum simulations of water flow through filtration membranes. The membranes consist of aligned carbon nanotubes (CNTs) of high aspect ratio, where the tube diameters are ~1–2 nm and the tube lengths (i.e. the membrane thicknesses) are 2–6 orders of magnitude larger than this. The flow in the CNTs is subcontinuum, meaning standard continuum fluid equations cannot adequately model the flow; also, full molecular dynamics (MD) simulations are too computationally expensive for modelling these membrane thicknesses. However, various degrees of scale separation in both time and space in this problem can be exploited by a multiscale method: we use the serial-network internal-flow multiscale method (SeN-IMM). Our results from this hybrid method compare very well with full MD simulations of flow cases up to a membrane thickness of 150 nm, beyond which any full MD simulation is computationally intractable. We proceed to use the SeN-IMM to predict the flow in membranes of thicknesses 150 nm–2 μm, and compare these results with both a modified Hagen–Poiseuille flow equation and experimental results for the same membrane configuration. We also find good agreement between experimental and our numerical results for a 1-mm-thick membrane made of CNTs with diameters around 1.1 nm. In this case, the hybrid simulation is orders of magnitude quicker than a full MD simulation would be

Radioimmunotherapy using 131I-rituximab in patients with advanced stage B-cell non-Hodgkin's lymphoma: initial experience

PURPOSE: The aim of this study was to evaluate the safety, toxicity and therapeutic response of non-myeloablative radioimmunotherapy using 131I-rituximab in previously heavily treated patients with B-cell non-Hodgkin's lymphoma (B-NHL). METHODS: Nine patients with relapsed, refractory or transformed B-NHL received ten radioimmunotherapies. Patients had a median of 5 (range 2-7) prior standard therapies. Four patients had received prior high-dose chemotherapy followed by autologous stem cell transplantation, and eight had received prior rituximab therapy. Histopathology consisted of four mantle cell, one follicular and four diffuse large B-cell lymphomas. Rituximab, a monoclonal chimeric anti-CD20 antibody (IDEC-C2B8), was labelled with 131I using the Iodogen method. The administered activity (2,200+/-600 MBq) was based on a dosimetrically calculated 45 cGy total-body radiation dose. All patients received an infusion of 2.5 mg/kg of rituximab prior to administration of the radiopharmaceutical. RESULTS: No acute adverse effects were observed after the administration of 131I-rituximab. Radioimmunotherapy was safe in our patient group and achieved one complete response ongoing at 14 months and two partial responses progressing at 12 and 13 months after treatment. One partial responder was re-treated with radioimmunotherapy and achieved an additional progression-free interval of 7 months. Four non-responders with bulky disease died 4.8+/-2.0 months after therapy. Three patients had an elevated serum lactate dehydrogenase (LDH) level prior to radioimmunotherapy and none of the patients responded. Of two patients who received radioimmunotherapy as an additional treatment after salvage chemotherapy, one continues to be disease-free at 9 months and one relapsed at 5 months' follow-up. Reversible grade 3 or 4 haematological toxicity occurred in seven of nine patients. Median nadirs were 35 days for platelets, 44 days for leucocytes and 57 days for erythrocytes. CONCLUSION: Radioimmunotherapy with 131I-rituximab in previously heavily treated B-NHL patients was safe and well tolerated, and four out of ten therapies induced responses. Radioimmunotherapy was less efficient in patients with bulky disease and elevated LDH. Severe haematological toxicity in seven patients did not cause significant clinical problems. Radioimmunotherapy seems to be an additional therapeutic option in carefully selected therapy-refractory B-NHL patients

Digital imaging of root traits (DIRT): a high-throughput computing and collaboration platform for field-based root phenomics

Plant root systems are key drivers of plant function and yield. They are also under-explored targets to meet global food and energy demands. Many new technologies have been developed to characterize crop root system architecture (CRSA). These technologies have the potential to accelerate the progress in understanding the genetic control and environmental response of CRSA. Putting this potential into practice requires new methods and algorithms to analyze CRSA in digital images. Most prior approaches have solely focused on the estimation of root traits from images, yet no integrated platform exists that allows easy and intuitive access to trait extraction and analysis methods from images combined with storage solutions linked to metadata. Automated high-throughput phenotyping methods are increasingly used in laboratory-based efforts to link plant genotype with phenotype, whereas similar field-based studies remain predominantly manual low-throughput.DescriptionHere, we present an open-source phenomics platform “DIRT”, as a means to integrate scalable supercomputing architectures into field experiments and analysis pipelines. DIRT is an online platform that enables researchers to store images of plant roots, measure dicot and monocot root traits under field conditions, and share data and results within collaborative teams and the broader community. The DIRT platform seamlessly connects end-users with large-scale compute “commons” enabling the estimation and analysis of root phenotypes from field experiments of unprecedented size.ConclusionDIRT is an automated high-throughput computing and collaboration platform for field based crop root phenomics. The platform is accessible at http://​dirt.​iplantcollaborat​ive.​org/​ and hosted on the iPlant cyber-infrastructure using high-throughput grid computing resources of the Texas Advanced Computing Center (TACC). DIRT is a high volume central depository and high-throughput RSA trait computation platform for plant scientists working on crop roots. It enables scientists to store, manage and share crop root images with metadata and compute RSA traits from thousands of images in parallel. It makes high-throughput RSA trait computation available to the community with just a few button clicks. As such it enables plant scientists to spend more time on science rather than on technology. All stored and computed data is easily accessible to the public and broader scientific community. We hope that easy data accessibility will attract new tool developers and spur creative data usage that may even be applied to other fields of science