Accurate and efficient simulation of multiphase flow in a heterogeneous reservoir by using error estimate and control in the multiscale finite-volume framework

The multiscale finite-volume (MSFV) method is designed to reduce the computational cost of elliptic and parabolic problems with highly heterogeneous anisotropic coefficients. The reduction is achieved by splitting the original global problem into a set of local problems (with approximate local boundary conditions) coupled by a coarse global problem. It has been shown recently that the numerical errors in MSFV results can be reduced systematically with an iterative procedure that provides a conservative velocity field after any iteration step. The iterative MSFV (i-MSFV) method can be obtained with an improved (smoothed) multiscale solution to enhance the localization conditions, with a Krylov subspace method [e.g., the generalized-minimal-residual (GMRES) algorithm] preconditioned by the MSFV system, or with a combination of both. In a multiphase-flow system, a balance between accuracy and computational efficiency should be achieved by finding a minimum number of i-MSFV iterations (on pressure), which is necessary to achieve the desired accuracy in the saturation solution. In this work, we extend the i-MSFV method to sequential implicit simulation of time-dependent problems. To control the error of the coupled saturation/pressure system, we analyze the transport error caused by an approximate velocity field. We then propose an error-control strategy on the basis of the residual of the pressure equation. At the beginning of simulation, the pressure solution is iterated until a specified accuracy is achieved. To minimize the number of iterations in a multiphase-flow problem, the solution at the previous timestep is used to improve the localization assumption at the current timestep. Additional iterations are used only when the residual becomes larger than a specified threshold value. Numerical results show that only a few iterations on average are necessary to improve the MSFV results significantly, even for very challenging problems. Therefore, the proposed adaptive strategy yields efficient and accurate simulation of multiphase flow in heterogeneous porous media.

Pathological complete response after neoadjuvant chemotherapy is an independent predictive factor irrespective of simplified breast cancer intrinsic subtypes: a landmark and two-step approach analyses from the EORTC 10994/BIG 1-00 phase III trial.

BACKGROUND: Pathological complete response (pCR) following chemotherapy is strongly associated with both breast cancer subtype and long-term survival. Within a phase III neoadjuvant chemotherapy trial, we sought to determine whether the prognostic implications of pCR, TP53 status and treatment arm (taxane versus non-taxane) differed between intrinsic subtypes. PATIENTS AND METHODS: Patients were randomized to receive either six cycles of anthracycline-based chemotherapy or three cycles of docetaxel then three cycles of eprirubicin/docetaxel (T-ET). pCR was defined as no evidence of residual invasive cancer (or very few scattered tumour cells) in primary tumour and lymph nodes. We used a simplified intrinsic subtypes classification, as suggested by the 2011 St Gallen consensus. Interactions between pCR, TP53 status, treatment arm and intrinsic subtype on event-free survival (EFS), distant metastasis-free survival (DMFS) and overall survival (OS) were studied using a landmark and a two-step approach multivariate analyses. RESULTS: Sufficient data for pCR analyses were available in 1212 (65%) of 1856 patients randomized. pCR occurred in 222 of 1212 (18%) patients: 37 of 496 (7.5%) luminal A, 22 of 147 (15%) luminal B/HER2 negative, 51 of 230 (22%) luminal B/HER2 positive, 43 of 118 (36%) HER2 positive/non-luminal, 69 of 221(31%) triple negative (TN). The prognostic effect of pCR on EFS did not differ between subtypes and was an independent predictor for better EFS [hazard ratio (HR) = 0.40, P < 0.001 in favour of pCR], DMFS (HR = 0.32, P < 0.001) and OS (HR = 0.32, P < 0.001). Chemotherapy arm was an independent predictor only for EFS (HR = 0.73, P = 0.004 in favour of T-ET). The interaction between TP53, intrinsic subtypes and survival outcomes only approached statistical significance for EFS (P = 0.1). CONCLUSIONS: pCR is an independent predictor of favourable clinical outcomes in all molecular subtypes in a two-step multivariate analysis. CLINICALTRIALSGOV: EORTC 10994/BIG 1-00 Trial registration number NCT00017095.

State of Iowa Long-Term Economic Recovery Strategy Prepared for the Economic Development Administration, U.S. Department of Commerce, October 2010

The Rebuild Iowa Office (RIO) continues to coordinate the state‘s recovery effort from the storms, tornadoes and floods of 2008. Much has been accomplished since the Office‘s last quarterly report was issued in July 2010. State funding has been disbursed to help Iowans with unmet needs and housing. Local governments and entities are utilizing millions of federal dollars so thousands of disaster-impacted homeowners can be offered a buyout. More infrastructure projects are under construction and new neighborhoods are being built with mitigation efforts in mind. However, as Iowa continues to celebrate many successes along the road to recovery, it must also address the numerous challenges that are encountered along the path. Recovering from the state‘s largest disaster must be looked at as a marathon, not a sprint. Over the past three months, the RIO has especially remained focused on helping small business owners impacted by the 2008 disasters. Many disaster-affected businesses have reopened their doors, however their debt load continues to be overwhelming and many still struggle with the timeliness of the disbursement of funds. This report describes how programs and recent modifications are working to assist recovering businesses. This report contains updates on housing progress while outlining the complexities behind certain programs and the bottlenecks communities are facing due to strict federal guidelines for implementation. This following pages also describe how Iowa is implementing Smart Planning principles, publicizing flood awareness through outreach efforts and preparing a blueprint for the state to follow when future disasters occur. As always, the RIO recognizes and thanks the countless leaders and front-line workers from local, regional, state and federal government, businesses, non-profit organizations and private citizens that have provided input, support and leadership. Their dedication to Iowa‘s disaster recovery has made the plans and projects on the following pages possible.

Rebuild Iowa Office Transition Strategy, December 2010

The closing of the RIO does not mean that the recovery process is complete for Iowa families and communities. The recovery process will continue for many years to come, and the Rebuild Iowa Transition Strategy has been drafted to provide a comprehensive set of recommended action steps to help the state complete long-term recovery efforts while better preparing the state for future disasters. This report begins with a review of the twelve major Rebuild Iowa Advisory Commission (RIAC) recommendations which have guided RIO’s work, followed by a summary of the major accomplishments toward each recommendation. Complete, detailed information on all the work that has been accomplished toward the RIAC recommendations can be found in the RIO’s Quarterly Reports. The identification of remaining needs and issues serves as the basis for the transition strategy.

Iowa’s Drug Control Strategy, 2008

The 2011 Iowa Drug Control Strategy is submitted in satisfaction of Chapter 80E.1 of the Code of Iowa which directs the Drug Policy Coordinator to monitor and coordinate all drug prevention, enforcement and treatment activities in the state. Further, it requires the Coordinator to submit an annual report to the Governor and Legislature concerning the activities and programs of the Coordinator, the Governor’s Office of Drug Control Policy and all other state departments with drug enforcement, substance abuse treatment, and prevention programs. Chapter 80E.2 establishes the Drug Policy Advisory Council (DPAC), chaired by the Coordinator, and consisting of a prosecuting attorney, substance abuse treatment specialist, law enforcement officer, prevention specialist, judge and representatives from the departments of corrections, education, public health, human services, public safety and human rights. This report and strategy was developed in consultation with the DPAC.

Iowa’s Drug Control Strategy, 2010

The 2011 Iowa Drug Control Strategy is submitted in satisfaction of Chapter 80E.1 of the Code of Iowa which directs the Drug Policy Coordinator to monitor and coordinate all drug prevention, enforcement and treatment activities in the state. Further, it requires the Coordinator to submit an annual report to the Governor and Legislature concerning the activities and programs of the Coordinator, the Governor’s Office of Drug Control Policy and all other state departments with drug enforcement, substance abuse treatment, and prevention programs. Chapter 80E.2 establishes the Drug Policy Advisory Council (DPAC), chaired by the Coordinator, and consisting of a prosecuting attorney, substance abuse treatment specialist, law enforcement officer, prevention specialist, judge and representatives from the departments of corrections, education, public health, human services, public safety and human rights. This report and strategy was developed in consultation with the DPAC.

Iowa’s Drug Control Strategy, 2012

The 2011 Iowa Drug Control Strategy is submitted in satisfaction of Chapter 80E.1 of the Code of Iowa which directs the Drug Policy Coordinator to monitor and coordinate all drug prevention, enforcement and treatment activities in the state. Further, it requires the Coordinator to submit an annual report to the Governor and Legislature concerning the activities and programs of the Coordinator, the Governor’s Office of Drug Control Policy and all other state departments with drug enforcement, substance abuse treatment, and prevention programs. Chapter 80E.2 establishes the Drug Policy Advisory Council (DPAC), chaired by the Coordinator, and consisting of a prosecuting attorney, substance abuse treatment specialist, law enforcement officer, prevention specialist, judge and representatives from the departments of corrections, education, public health, human services, public safety and human rights. This report and strategy was developed in consultation with the DPAC.

Iowa’s Drug Control Strategy, 2009

The 2011 Iowa Drug Control Strategy is submitted in satisfaction of Chapter 80E.1 of the Code of Iowa which directs the Drug Policy Coordinator to monitor and coordinate all drug prevention, enforcement and treatment activities in the state. Further, it requires the Coordinator to submit an annual report to the Governor and Legislature concerning the activities and programs of the Coordinator, the Governor’s Office of Drug Control Policy and all other state departments with drug enforcement, substance abuse treatment, and prevention programs. Chapter 80E.2 establishes the Drug Policy Advisory Council (DPAC), chaired by the Coordinator, and consisting of a prosecuting attorney, substance abuse treatment specialist, law enforcement officer, prevention specialist, judge and representatives from the departments of corrections, education, public health, human services, public safety and human rights. This report and strategy was developed in consultation with the DPAC.

How to measure cortical folding from MR images: a step-by-step tutorial to compute local gyrification index.

Cortical folding (gyrification) is determined during the first months of life, so that adverse events occurring during this period leave traces that will be identifiable at any age. As recently reviewed by Mangin and colleagues(2), several methods exist to quantify different characteristics of gyrification. For instance, sulcal morphometry can be used to measure shape descriptors such as the depth, length or indices of inter-hemispheric asymmetry(3). These geometrical properties have the advantage of being easy to interpret. However, sulcal morphometry tightly relies on the accurate identification of a given set of sulci and hence provides a fragmented description of gyrification. A more fine-grained quantification of gyrification can be achieved with curvature-based measurements, where smoothed absolute mean curvature is typically computed at thousands of points over the cortical surface(4). The curvature is however not straightforward to comprehend, as it remains unclear if there is any direct relationship between the curvedness and a biologically meaningful correlate such as cortical volume or surface. To address the diverse issues raised by the measurement of cortical folding, we previously developed an algorithm to quantify local gyrification with an exquisite spatial resolution and of simple interpretation. Our method is inspired of the Gyrification Index(5), a method originally used in comparative neuroanatomy to evaluate the cortical folding differences across species. In our implementation, which we name local Gyrification Index (lGI(1)), we measure the amount of cortex buried within the sulcal folds as compared with the amount of visible cortex in circular regions of interest. Given that the cortex grows primarily through radial expansion(6), our method was specifically designed to identify early defects of cortical development. In this article, we detail the computation of local Gyrification Index, which is now freely distributed as a part of the FreeSurfer Software (http://surfer.nmr.mgh.harvard.edu/, Martinos Center for Biomedical Imaging, Massachusetts General Hospital). FreeSurfer provides a set of automated reconstruction tools of the brain's cortical surface from structural MRI data. The cortical surface extracted in the native space of the images with sub-millimeter accuracy is then further used for the creation of an outer surface, which will serve as a basis for the lGI calculation. A circular region of interest is then delineated on the outer surface, and its corresponding region of interest on the cortical surface is identified using a matching algorithm as described in our validation study(1). This process is repeatedly iterated with largely overlapping regions of interest, resulting in cortical maps of gyrification for subsequent statistical comparisons (Fig. 1). Of note, another measurement of local gyrification with a similar inspiration was proposed by Toro and colleagues(7), where the folding index at each point is computed as the ratio of the cortical area contained in a sphere divided by the area of a disc with the same radius. The two implementations differ in that the one by Toro et al. is based on Euclidian distances and thus considers discontinuous patches of cortical area, whereas ours uses a strict geodesic algorithm and include only the continuous patch of cortical area opening at the brain surface in a circular region of interest.

Two-step chromatographic purification of human plasma alpha(1)-acid glycoprotein: its application to the purification of rare phenotype samples of the protein and their study by chromatography on immobilized metal chelate affinity adsorbent.

Alpha1-Acid glycoprotein (AAG) or orosomucoid was purified to homogeneity from human plasma by a separate two-step method using chromatography on immobilized Cibacron Blue F3G-A to cross-linked agarose and chromatography on hydroxyapatite. The conditions for the pre-purification of AAG by chromatography on immobilized Cibacron Blue F3G-A were first optimized using different buffer systems with different pH values. The overall yield of the combined techniques was 80% and ca. 12 mg of AAG were purified from an initial total amount of ca. 15 mg in a ca. 40 ml sample of human plasma. This method was applied to the purification of AAG samples corresponding to the three main phenotypes of the protein (FI*S/A, F1/A and S/A), from individual human plasma previously phenotyped for AAG. A study by isoelectric focusing with carrier ampholytes showed that the microheterogeneity of the purified F1*S/A, F1/A and S/A AAG samples was similar to that of AAG in the corresponding plasma, thus suggesting that no apparent desialylation of the glycoprotein occurred during the purification steps. This method was also applied to the purification of AAG samples corresponding to rare phenotypes of the protein (F1/A*AD, S/A*X0 and F1/A*C1) and the interactions of these variants with immobilized copper(II) ions were then studied at pH 7, by chromatography on an iminodiacetate Sepharose-Cu(II) gel. It was found that the different variants encoded by the first of the two genes coding for AAG in humans (i.e. the F1 and S variants) interacted non-specifically with the immobilized ligand, whereas those encoded by the second gene of AAG (i.e. the A, AD, X0 and C1 variants) strongly bound to immobilized Cu(II) ions. These results suggested that chromatography on an immobilized affinity Cu(II) adsorbent could be helpful to distinguish between the respective products of the two highly polymorphic genes which code for human AAG.

A clinical pattern-based etiological diagnostic strategy for sensory neuronopathies: a French collaborative study.

Sensory neuronopathies (SNNs) encompass paraneoplastic, infectious, dysimmune, toxic, inherited, and idiopathic disorders. Recently described diagnostic criteria allow SNN to be differentiated from other forms of sensory neuropathy, but there is no validated strategy based on routine clinical investigations for the etiological diagnosis of SNN. In a multicenter study, the clinical, biological, and electrophysiological characteristics of 148 patients with SNN were analyzed. Multiple correspondence analysis and logistic regression were used to identify patterns differentiating between forms of SNNs with different etiologies. Models were constructed using a study population of 88 patients and checked using a test population of 60 cases. Four patterns were identified. Pattern A, with an acute or subacute onset in the four limbs or arms, early pain, and frequently affecting males over 60 years of age, identified mainly paraneoplastic, toxic, and infectious SNN. Pattern B identified patients with progressive SNN and was divided into patterns C and D, the former corresponding to patients with inherited or slowly progressive idiopathic SNN with severe ataxia and electrophysiological abnormalities and the latter to patients with idiopathic, dysimmune, and sometimes paraneoplastic SNN with a more rapid course than in pattern C. The diagnostic strategy based on these patterns correctly identified 84/88 and 58/60 patients in the study and test populations, respectively.