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Pathognomonic PAX3-FOXO1 fusion oncogene expression is associated with poor outcome in rhabdomyosarcoma. Combining genome-wide CRISPR screening with cell-based functional genetic approaches, we here provide evidence that PAX3-FOXO1 induces replication stress, resulting in a synthetic lethal dependency to ATR-mediated DNA damage-response signaling in rhabdomyosarcoma. Expression of PAX3-FOXO1 in muscle progenitor cells was not only sufficient to induce hypersensitivity to ATR inhibition, but PAX3-FOXO1-expressing rhabdomyosarcoma cells also exhibited increased sensitivity to structurally diverse inhibitors of ATR, a dependency that could be validated genetically. Mechanistically, ATR inhibition led to replication stress exacerbation, decreased BRCA1 phosphorylation and reduced homologous recombination-mediated DNA repair pathway activity. Consequently, ATR inhibitor treatment increased sensitivity of rhabdomyosarcoma cells to PARP inhibition in vitro, and combined ATR and PARP inhibition induced regression of primary patient-derived alveolar rhabdomyosarcoma xenografts in vivo. Moreover, a genome-wide CRISPR activation screen (CRISPRa) identified FOS gene family members as inducers of resistance against ATR inhibitors. Mechanistically, FOS gene family members reduced replication stress in rhabdomyosarcoma cells. Lastly, compassionate use of ATR inhibitors in two pediatric patients suffering from relapsed PAX3-FOXO1-expressing alveolar rhabdomyosarcoma showed signs of tolerability, paving the way to clinically exploit this novel synthetic lethal dependency in rhabdomyosarcoma.

Up to one-third of patients with cystic fibrosis (CF) awaiting lung transplantation (LTX) die while waiting. Inclusion of computed tomography (CT) scores may improve survival prediction models such as the lung allocation score (LAS).This study investigated the association between CT and survival in patients with CF screened for LTX.Clinical data and chest CTs of 411 patients with CF screened for LTX between 1990 and 2005 were collected from 17 centers. CTs were scored with the Severe Advanced Lung Disease (SALD) four-category scoring system, including the components infection/inflammation (INF), air trapping/hypoperfusion (AT), normal/hyperperfusion (NOR), and bulla/cysts (BUL). The volume of each component was computed using semiautomated software. Survival analysis included Kaplan-Meier curves and Cox regression models.Three hundred and sixty-six (186 males) of 411 patients entered the waiting list (median age, 23 yr; range, 5-58 yr). Subsequently, 67 of 366 (18%) died while waiting, 263 of 366 (72%) underwent LTX, and 36 of 366 (10%) were awaiting LTX at the census date. INF and LAS were significantly associated with waiting list mortality in univariate analyses. The multivariate Cox model including INF and LAS grouped in tertiles, and comparing tertiles 2 and 3 with tertile 1, showed waiting list mortality hazard ratios of 1.62 (95% confidence interval [95% CI], 0.78-3.36; P = 0.19) and 2.65 (95% CI, 1.35-5.20; P = 0.005) for INF, and 1.42 (95% CI, 0.63-3.24; P = 0.40), and 2.32 (95% CI, 1.17-4.60; P = 0.016) for LAS, respectively. These results indicated that INF and LAS had significant, independent predictive value for survival.CT score INF correlates with survival, and adds to the predictive value of LAS.

Manganese (Mn) ranks twelfth among the most exuberant metal present in the earth's crust and finds its imperative application in the manufacturing steel, chemical, tannery, glass, and battery industries. Solubilisation of Mn can be performed by several bacterial strains which are useful in developing environmental friendly solutions for mining activities. The present investigation aims to isolate and characterize Mn solubilising bacteria from low grade ores from Sanindipur Manganese mine of Sundargh district in Odisha state of India. Four morphologically distinct bacterial strains showing visible growth on Mn supplemented plates were isolated. Mn solubilising ability of the bacterial strains was assessed by visualizing the lightening of the medium appearing around the growing colonies. Three isolates were gram negative and rod shaped while the remaining one was gram positive, coccobacilli. Molecular identification of the isolates was carried out by 16S rRNA sequencing and the bacterial isolates were taxonomically classified as Bacillus anthrasis MSB 2, Acinetobacter sp. MSB 5, Lysinibacillus sp. MSB 11, and Bacillus sp. MMR-1 using BLAST algorithm. The sequences were deposited in NCBI GenBank with the accession number KP635223, KP635224, KP635225 and JQ936966, respectively. Manganese solubilisation efficiency of 40, 96, 97.5 and 48.5% were achieved by MMR-1, MSB 2, MSB 5 and MSB 11 respectively. The efficiency of Mn solubilisation is suggested with the help of a pH variation study. The results are discussed in relation to the possible mechanisms involved in Manganese solubilisation efficiency of bacterial isolates.

This paper develops an algorithmic scheme to determine initial/starting solutions for iterative linear equation solvers, i.e. preconditioned conjugate gradient, multigrid, SOR and Chebeshev semi-iterative. Employing symbolic operators, multi parameter gauges are introduced to enable individual partition level functional fits of domain decomposed problems. Such gauges are metered by a global minimization of the appropriate energy norm. To remove functional discontinuities on subdomain interfaces, successive applications of a partitioned steepest descent algorithm are employed as a smoothing step. The combination of partitioned initialization and iterative smoothing lead to a robust algorithmic pairing which can provide very accurate starting values for very large simulations. If successively iterated in a series of initialization—steepest descent smoothing cycles, the combined scheme can yield converged results in less iterations than the PCG. This is illustrated by a variety of benchmark examples which de...

Mestrado em Tecnologia de Diagnóstico e Intervenção Cardiovascular. Área de especialização: Intervenção Cardiovascular. Introdução: A intervenção coronária percutânea (ICP) é o tratamento de eleição para os doentes com enfarte agudo do miocárdio com supradesnivelamento do segmento ST (EAMST). Importa avaliar qual a influência deste tratamento no prognóstico dos doentes, através da identificação de grupos de elevado risco para eventos adversos. Objectivos: Avaliar a influência da ICP no prognóstico dos doentes com EAM-ST. Identificar factores preditores de mortalidade e de morte ou reenfarte ou revascularização ou acidente vascular cerebral (MACCE). Identificar grupos alto-risco para estes endpoints. Metodologia: Incluíram-se 338 doentes com EAM-ST (62±13 anos; 73% sexo masculino), submetidos a ICP de 01-01-2008 a 31-12-2009. Avaliaram-se doados demográficos e clínicos, procedimento, evolução intra-hospitalar e seguimento clínico (25±13 meses). Resultados: Identificaram-se como factores preditores de mortalidade: idade, doença arterial periférica (DAP), enfarte de localização anterior, doença coronária com envolvimento do tronco comum, insucesso da ICP, choque cardiogénico, colocação de balão intra-aórtico (BIA), Fracção de ejecção (F.Ej)

Through the introduction of multiple two parameter pairs of scaling factors, a modified formulation of the conjugate gradient scheme is derived. The individual pairs are separately assigned to the various substructure of a model so as to gauge the optimal local search direction and length. During the iteration process, this yields a successions of multiparameter dependent energy states. Seeking the appropriate minima, a vectorized multiparameter formulation of the conjugate gradient scheme is derived. Due to the manner of formulation, the scheme provides both global as well as substructural level orthogonality among successive iterates. To generalize the scheme, both pre and unpreconditioned versions are developed. Overall the methodology provides a potentially significant improvement in computational robustness. This is benchmarked in a series of numerical experiments.

We obtain conditions for the convergence of SSOR and USSOR methods when the matrix A has a special form (so-called “red-black” ordering), using some vectorial norms. We give characterizations for the H-matrix with respect to the USSOR iteration matrix. Our results extend the work of Alefeld and Krishna.

To improve the numerical stability of nonlinear equation solvers, a partitioned multiply scaled constraint scheme is developed. This scheme enables hierarchical levels of control for nonlinear equation solvers. To complement the procedure, partitioned convergence checks are established along with self-adaptive partitioning schemes. Overall, such procedures greatly enhance the numerical stability of the original solvers. To demonstrate and motivate the development of the scheme, the problem of nonlinear heat conduction is considered. In this context the main emphasis is given to successive substitution-type schemes. To verify the improved numerical characteristics associated with partitioned multiply scaled solvers, results are presented for several benchmark examples.

The SSOR semi-iterative method is applied to the system of linear equations $A{\bf x}={\bf b}$, where A is an $n \times n$ Hermitian positive definite matrix. We find the bounds for the A-norm of the error vector in terms of the spectral radius of the SSOR iteration matrix.

To improve the numerical stability of nonlinear equation solvers, a partitioned multiply scaled constraint scheme is developed. This scheme enables hierarchical levels of control for nonlinear equation solvers. To complement the procedure, partitioned convergence checks are established along with self-adaptive partitioning schemes. Overall, such procedures greatly enhance the numerical stability of the original solvers. To demonstrate and motivate the development of the scheme, the problem of nonlinear heat conduction is considered. In this context the main emphasis is given to successive substitution-type schemes. To verify the improved numerical characteristics associated with partitioned multiply scaled solvers, results are presented for several benchmark examples.

The symmetric successive overrelaxation (SSOR) iterative method is applied to the solution of the system of linear equations A x = b , where A is an n×n nonsingular matrix. We give bounds for the spectral radius of the SSOR iterative matrix when A is an Hermitian positive definite matrix, and when A is a nonsingular M-matrix. Then, we discuss the convergence of the SSOR iterative method associated with a new splitting of the matrix A which extends the results of Varga and Buoni [1].

The Unsymmetric Successive Overrelaxation (USSOR) iterative method is applied to the solution of the system of linear equationsA x=b, whereA is annxn nonsingular matrix. We find the values of the relaxation parameters ? and $$\bar \omega $$ for which the USSOR iterative method converges. Then we characterize those matrices which are equimodular toA and for which the USSOR iterative method converges.

The shortcomings associated with successive substitution nonlinear solvers are circumvented by introducing a so-called block constraint methodology. Specifically, by partitioning the governing field equations into various subsets, constraints can be applied individually to each separate block. To illustrate the formal properties of the scheme, a series of lemmas and a theorem will be developed. These define its convergence properties. Due to the generality of the approach taken, virtually any form of functional constraint may be employed.