Biological Deacidification Strategies for White Wines

Traditionally, the use of malolactic fermentation gives rise to microbiologically stable wines. However, malolactic fermentation is not free from possible collateral effects that can take place under specific scenarios. The present work tests the influence of different biological deacidification strategies on the volatile and non-volatile components of white must from Germany. The study compared mixed cultures of Lachancea thermotolerans and Schizosaccharomyces pombe and a pure culture of Sc. pombe to the classical biological deacidification process performed by lactic acid bacteria. Strains of Oenococcus oeni and Lactiplantibacillus plantarum were co- or sequentially inoculated with S. cerevisiae to carry out malolactic fermentation. Different fermentation treatments took place at a laboratory scale of 0.6 L in vessels of 0.75 L. The instrumental techniques Fourier-transform mid-infrared spectroscopy (FT-MIR), high performance liquid chromatography (HPLC) and gas chromatography–mass spectrometry (GC-MS) were used to evaluate different chemical parameters in the final wines. The results showed the ability of Sc. pombe to consume malic acid in combination with L. thermotolerans without using S. cerevisiae or lactic acid bacteria. Fermentations involving Sc. pombe consumed all the malic acid, although they reduced the concentrations of higher alcohols, fatty acids and acetic acid. Simultaneous alcoholic and malolactic fermentations reduced malic acid by about 80%, while classical malolactic fermentation reduced it by 100%. Fermentations involving L. thermotolerans produced the highest lactic acid, ester and glycerol concentrations

Computing the inverse of the neurophysiological spike-response transform

Consider the transform from a discrete neuronal spike train to a continuous neurophysiological response such as postsynaptic membrane voltage or muscle contraction. Often, we can record only the response. Here we therefore ask about the inverse of this transform: given the response, how can we estimate from it the spike train that produced it? In previous work, we have developed a kernel-based "decoding" method for system identification of the forward transform. Using several variant approaches based on this method, here we demonstrate how to identify the spike train, in the minimal case from just the response, with synthetic as well as real synaptic and neuromuscular data

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

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

Hands-On Parameter Search for Neural Simulations by a MIDI-Controller

Computational neuroscientists frequently encounter the challenge of parameter fitting – exploring a usually high dimensional variable space to find a parameter set that reproduces an experimental data set. One common approach is using automated search algorithms such as gradient descent or genetic algorithms. However, these approaches suffer several shortcomings related to their lack of understanding the underlying question, such as defining a suitable error function or getting stuck in local minima. Another widespread approach is manual parameter fitting using a keyboard or a mouse, evaluating different parameter sets following the users intuition. However, this process is often cumbersome and time-intensive. Here, we present a new method for manual parameter fitting. A MIDI controller provides input to the simulation software, where model parameters are then tuned according to the knob and slider positions on the device. The model is immediately updated on every parameter change, continuously plotting the latest results. Given reasonably short simulation times of less than one second, we find this method to be highly efficient in quickly determining good parameter sets. Our approach bears a close resemblance to tuning the sound of an analog synthesizer, giving the user a very good intuition of the problem at hand, such as immediate feedback if and how results are affected by specific parameter changes. In addition to be used in research, our approach should be an ideal teaching tool, allowing students to interactively explore complex models such as Hodgkin-Huxley or dynamical 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

Large rivers and orogens: the evolution of the Yarlung Tsangpo–Irrawaddy system and the eastern Himalayan syntaxis

The eastern Himalayan syntaxis has experienced some of the highest rates of deformation and erosion in the orogen during the Late Cenozoic, and the Yarlung Tsangpo, Brahmaputra, Irrawaddy, Salween, and Mekong rivers are the key erosional systems in that region. The Yarlung Tsangpo drains southern Tibet and the deep Siang River gorge through the eastern Himalayan syntaxis before joining the Brahmaputra in northeastern India. It has been proposed that the Yarlung Tsangpo drained into other large rivers of southern Asia, such as the Irrawaddy, Salween and Red River. We have used uranium/lead dating and hafnium measurements of detrital zircons from Cenozoic sedimentary deposits in Central Myanmar to demonstrate that the Yarlung Tsangpo formerly drained into the Irrawaddy River in Myanmar through the eastern syntaxis, and that this ancient river system was established by (at least) the Middle–Late Eocene. The Yarlung Tsangpo–Irrawaddy river disconnected in the Early Miocene driven by increased deformation in the eastern syntaxis and headward erosion by tributaries of the Brahmaputra. Our results highlight the significance of the sedimentary record of large orogen-parallel rivers and provide key chronological constraints on landscape evolution during the Early Miocene phase of the Himalayan orogeny

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