First-line high-dose therapy and autologous blood stem cell transplantation in patients with primary central nervous system non-Hodgkin lymphomas-a single-centre experience in 61 patients

Primary central nervous system non-Hodgkin lymphomas (PCNS-NHLs) are extranodal B-cell lymphomas with poor prognosis. The role of high-dose therapy (HDT) followed by autologous blood stem cell transplantation (ASCT) as first-line therapy is still not clear. We retrospectively collected long-term follow up data of 61 consecutive patients with PCNS-NHL at the University Hospital Düsseldorf from January 2004 to December 2016. Thirty-six patients were treated with conventional chemoimmunotherapy (cCIT) only (CT-group). Seventeen patients received an induction cCIT followed by HDT and ASCT. In the CT-group, the overall response rate (ORR) was 61% (CR 47%, PR 14%), and there were 8% treatment-related deaths (TRD). Progression-free survival (PFS) was 31.8 months, and overall survival (OS) was 57.3 months. In the HDT-group, the ORR was 88% (59% CR, 29% PR), and there were 6% TRD. Median PFS and OS were not reached at 5 years. The 5-year PFS and OS were 64.7%. After a median follow up of 71 months, 10 patients (59%) were still alive in CR/PR following HDT and ASCT, one patient was treated for progressive disease (PD), and 7 had died (41%, 6 PD, 1 TRD). All patients achieving CR prior to HDT achieved durable CR. In the CT-group, 8 patients (22%) were alive in CR/PR after a median follow-up of 100 months. Twenty-eight patients died (78%, 24 PD, 2 TRD, 2 deaths in remission). In the univariate analysis, the HDT-group patients had significantly better PFS (not reached vs 31.8 months, p = 0.004) and OS (not reached vs 57.3 months, p = 0.021). The multivariate analysis showed HDT was not predictive for survival. Treatment with HDT + ASCT is feasible and offers the chance for long-term survival with low treatment-related mortality in younger patients. In this analysis, ORR, PFS and OS were better with HDT than with conventional cCIT alone. This result was not confirmed in the multivariate analysis, and further studies need to be done to examine the role of HDT in PCNSL

Consistency and diversity of spike dynamics in the neurons of bed nucleus of Stria Terminalis of the rat: a dynamic clamp study

Neurons display a high degree of variability and diversity in the expression and regulation of their voltage-dependent ionic channels. Under low level of synaptic background a number of physiologically distinct cell types can be identified in most brain areas that display different responses to standard forms of intracellular current stimulation. Nevertheless, it is not well understood how biophysically different neurons process synaptic inputs in natural conditions, i.e., when experiencing intense synaptic bombardment in vivo. While distinct cell types might process synaptic inputs into different patterns of action potentials representing specific "motifs'' of network activity, standard methods of electrophysiology are not well suited to resolve such questions. In the current paper we performed dynamic clamp experiments with simulated synaptic inputs that were presented to three types of neurons in the juxtacapsular bed nucleus of stria terminalis (jcBNST) of the rat. Our analysis on the temporal structure of firing showed that the three types of jcBNST neurons did not produce qualitatively different spike responses under identical patterns of input. However, we observed consistent, cell type dependent variations in the fine structure of firing, at the level of single spikes. At the millisecond resolution structure of firing we found high degree of diversity across the entire spectrum of neurons irrespective of their type. Additionally, we identified a new cell type with intrinsic oscillatory properties that produced a rhythmic and regular firing under synaptic stimulation that distinguishes it from the previously described jcBNST cell types. Our findings suggest a sophisticated, cell type dependent regulation of spike dynamics of neurons when experiencing a complex synaptic background. The high degree of their dynamical diversity has implications to their cooperative dynamics and synchronization

Adaptive Algebraic Multigrid Methods

Our ability to simulate physical processes numerically is constrained by our ability to solve the resulting linear systems, prompting substantial research into the development of multiscale iterative methods capable of solving these linear systems with an optimal amount of effort. Overcoming the limitations of geometric multigrid methods to simple geometries and differential equations, algebraic multigrid methods construct the multigrid hierarchy based only on the given matrix. While this allows for efficient black-box solution of the linear systems associated with discretizations of many elliptic differential equations, it also results in a lack of robustness due to assumptions made on the near-null spaces of these matrices. This paper introduces an extension to algebraic multigrid methods that removes the need to make such assumptions by utilizing an adaptive process. The principles which guide the adaptivity are highlighted, as well as their application to algebraic multigrid solution of certain symmetric positive-definite linear systems

Beyond element-wise interactions: identifying complex interactions in biological processes

Background: Biological processes typically involve the interactions of a number of elements (genes, cells) acting on each others. Such processes are often modelled as networks whose nodes are the elements in question and edges pairwise relations between them (transcription, inhibition). But more often than not, elements actually work cooperatively or competitively to achieve a task. Or an element can act on the interaction between two others, as in the case of an enzyme controlling a reaction rate. We call “complex” these types of interaction and propose ways to identify them from time-series observations. Methodology: We use Granger Causality, a measure of the interaction between two signals, to characterize the influence of an enzyme on a reaction rate. We extend its traditional formulation to the case of multi-dimensional signals in order to capture group interactions, and not only element interactions. Our method is extensively tested on simulated data and applied to three biological datasets: microarray data of the Saccharomyces cerevisiae yeast, local field potential recordings of two brain areas and a metabolic reaction. Conclusions: Our results demonstrate that complex Granger causality can reveal new types of relation between signals and is particularly suited to biological data. Our approach raises some fundamental issues of the systems biology approach since finding all complex causalities (interactions) is an NP hard problem

Extending the applicability of multigrid methods

Multigrid methods are ideal for solving the increasingly large-scale problems that arise in numerical simulations of physical phenomena because of their potential for computational costs and memory requirements that scale linearly with the degrees of freedom. Unfortunately, they have been historically limited by their applicability to elliptic-type problems and the need for special handling in their implementation. In this paper, we present an overview of several recent theoretical and algorithmic advances made by the TOPS multigrid partners and their collaborators in extending applicability of multigrid methods. Specific examples that are presented include quantum chromodynamics, radiation transport, and electromagnetics

Competition-based model of pheromone component ratio detection in the moth

For some moth species, especially those closely interrelated and sympatric, recognizing a specific pheromone component concentration ratio is essential for males to successfully locate conspecific females. We propose and determine the properties of a minimalist competition-based feed-forward neuronal model capable of detecting a certain ratio of pheromone components independently of overall concentration. This model represents an elementary recognition unit for the ratio of binary mixtures which we propose is entirely contained in the macroglomerular complex (MGC) of the male moth. A set of such units, along with projection neurons (PNs), can provide the input to higher brain centres. We found that (1) accuracy is mainly achieved by maintaining a certain ratio of connection strengths between olfactory receptor neurons (ORN) and local neurons (LN), much less by properties of the interconnections between the competing LNs proper. An exception to this rule is that it is beneficial if connections between generalist LNs (i.e. excited by either pheromone component) and specialist LNs (i.e. excited by one component only) have the same strength as the reciprocal specialist to generalist connections. (2) successful ratio recognition is achieved using latency-to-first-spike in the LN populations which, in contrast to expectations with a population rate code, leads to a broadening of responses for higher overall concentrations consistent with experimental observations. (3) when longer durations of the competition between LNs were observed it did not lead to higher recognition accuracy

Modality and uncertainty in data visualizations : A corpus approach to the use of connecting lines

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Enabling Model Testing of Cyber-Physical Systems

Applying traditional testing techniques to Cyber-Physical Systems (CPS) is challenging due to the deep intertwining of software and hardware, and the complex, continuous interactions between the system and its environment. To alleviate these challenges we propose to conduct testing at early stages and over executable models of the system and its environment. Model testing of CPSs is however not without difficulties. The complexity and heterogeneity of CPSs renders necessary the combination of different modeling formalisms to build faithful models of their different components. The execution of CPS models thus requires an execution framework supporting the co-simulation of different types of models, including models of the software (e.g., SysML), hardware (e.g., SysML or Simulink), and physical environment (e.g., Simulink). Furthermore, to enable testing in realistic conditions, the co-simulation process must be (1) fast, so that thousands of simulations can be conducted in practical time, (2) controllable, to precisely emulate the expected runtime behavior of the system and, (3) observable, by producing simulation data enabling the detection of failures. To tackle these challenges, we propose a SysML-based modeling methodology for model testing of CPSs, and an efficient SysML-Simulink co-simulation framework. Our approach was validated on a case study from the satellite domain

Infrastructure for Detector Research and Development towards the International Linear Collider

The EUDET-project was launched to create an infrastructure for developing and testing new and advanced detector technologies to be used at a future linear collider. The aim was to make possible experimentation and analysis of data for institutes, which otherwise could not be realized due to lack of resources. The infrastructure comprised an analysis and software network, and instrumentation infrastructures for tracking detectors as well as for calorimetry.Comment: 54 pages, 48 picture