IFN-gamma inhibits the production of latent transforming growth factor-beta1 by mouse inflammatory macrophages

Transforming growth factor (TGF)-beta is a multifunctional cytokine, which in mammals exists in three isoforms (TGF-beta1, 2 and 3). It is synthesized by a variety of cells including macrophages, and exerts potent immunoregulatory effects such as the inhibition of Th1 development and the suppression or reversal of IFN-gamma-induced macrophage activation. In this study we analyzed the effect of IFN-gamma on the production of TGF-beta1 by thioglycolate-elicited mouse peritoneal macrophages under serum-free conditions. Untreated macrophages released TGF-beta1 in its latent form, which became detectable in a capture ELISA specific for active TGF-beta1 after acid activation of the culture supernatants. Treatment with IFN-gamma reduced the amount of latent TGF-beta1 in the culture supernatants in a dose-dependent fashion. The effect of IFN-gamma was confirmed by a newly developed Western blot system for the detection of mouse TGF-beta1 protein. IFN-gamma only weakly (16-24 %) reduced the levels TGF-beta1 mRNA at early and late time points of stimulation, and no evidence was obtained that IFN-gamma suppresses the secretion of latent TGF-beta1. Thus, inhibition of TGF-beta1 production by IFN-gamma is most likely due to decreased synthesis and/or stability of the TGF-beta1 protein, and might be important for the generation of fully activated macrophages and a Th1 response.     

Detail-driven digital hologram generation

Digital holography is a technology with a potential to provide realistic 3D images. However, generation of digital holograms is a computationally demanding task. Thus, the performance is a major concern. We propose a new method that reduces spatial resolution in order to accelerate hologram generation. It employs the propagation between parallel planes for efficient optical field values evaluation and a computer graphics approach for approximating visibility. Our results show that the proposed reduction has only a minimal impact on the visual quality, while the formal computational complexity confirms performance improvement.     

Atmospheric pressure laser ionization. An analytical technique for highly selective detection of ultralow concentrations in the gas phase

In this contribution a new analytical technique is presented for the direct mass spectrometric (MS) detection of gas-phase trace species at atmospheric pressure. Employing resonance-enhanced multiphoton ionization (REMPI) close to the inlet nozzle orifice, i.e., at high molecule densities, the sensitivity of the instrument has been increased by up to 3 orders of magnitude as compared to conventional REMPI-MS with ionization in a differentially coupled ion source. Furthermore, adiabatic cooling, resonant ionization, and mass-selective detection establish a highly selective analytical technique. Several atmospherically relevant compounds were investigated. The current detection limit for NO is 0.9 pptv, for acetaldehyde 1.7 pptv, for CO 15 pptv, and for 2,5-dichlorotoluene 12 pptv. We discuss APLI with regard to applications in medical and environmental research.     

Efficient model learning methods for actor-critic control

We propose two new actor-critic algorithms for reinforcement learning. Both algorithms use local linear regression (LLR) to learn approximations of the functions involved. A crucial feature of the algorithms is that they also learn a process model, and this, in combination with LLR, provides an efficient policy update for faster learning. The first algorithm uses a novel model-based update rule for the actor parameters. The second algorithm does not use an explicit actor but learns a reference model which represents a desired behavior, from which desired control actions can be calculated using the inverse of the learned process model. The two novel methods and a standard actor-critic algorithm are applied to the pendulum swing-up problem, in which the novel methods achieve faster learning than the standard algorithm.     

Performance Capture of High‐Speed Motion Using Staggered Multi‐View Recording

We present a markerless performance capture system that can acquire the motion and the texture of human actors performing fast movements using only commodity hardware. To this end we introduce two novel concepts: First, a staggered surround multi‐view recording setup that enables us to perform model‐based motion capture on motion‐blurred images, and second, a model‐based deblurring algorithm which is able to handle disocclusion, self‐occlusion and complex object motions. We show that the model‐based approach is not only a powerful strategy for tracking but also for deblurring highly complex blur patterns.     

A computational feedforward model predicts categorization of masked emotional body language for longer, but not for shorter, latencies

Given the presence of massive feedback loops in brain networks, it is difficult to disentangle the contribution of feedforward and feedback processing to the recognition of visual stimuli, in this case, of emotional body expressions. The aim of the work presented in this letter is to shed light on how well feedforward processing explains rapid categorization of this important class of stimuli. By means of parametric masking, it may be possible to control the contribution of feedback activity in human participants. A close comparison is presented between human recognition performance and the performance of a computational neural model that exclusively modeled feedforward processing and was engineered to fulfill the computational requirements of recognition. Results show that the longer the stimulus onset asynchrony (SOA), the closer the performance of the human participants was to the values predicted by the model, with an optimum at an SOA of 100 ms. At short SOA latencies, human performance deteriorated, but the categorization of the emotional expressions was still above baseline. The data suggest that, although theoretically, feedback arising from inferotemporal cortex is likely to be blocked when the SOA is 100 ms, human participants still seem to rely on more local visual feedback processing to equal the model's performance.     

An evolutionary system for ozone concentration forecasting

Nowadays, with more than 50 % of the world’s population living in urban areas, cities are facing important environmental challenges. Among them, air pollution has emerged as one of the most important concerns, taking into account the social costs related to the effect of polluted air. According to a report of the World Health Organization, approximately seven million people die each year from the effects of air pollution. Despite this fact, the same report suggests that cities could greatly improve their air quality through local measures by exploiting modern and efficient solutions for smart infrastructures. Ideally, this approach requires insights of how pollutant levels change over time in specific locations. To tackle this problem, we present an evolutionary system for the prediction of pollutants levels based on a recently proposed variant of genetic programming. This system is designed to predict the amount of ozone level, based on the concentration of other pollutants collected by sensors disposed in critical areas of a city. An analysis of data related to the region of Yuen Long (one of the most polluted areas of China), shows the suitability of the proposed system for addressing the problem at hand. In particular, the system is able to predict the ozone level with greater accuracy with respect to other techniques that are commonly used to tackle similar forecasting problems.     

The Dragonfly Association Index (Insecta: Odonata)—a Tool for the Type‐Specific Assessment of Lowland Rivers

Species traits of 57 Odonata species occurring in the Austrian bioregion Eastern Ridges and Lowlands (ecoregion Hungarian Lowlands; Illies, 1978 ) were defined by factor loadings of 12 habitat parameters: stream sections crenon, rhithron and potamon; flow velocity; standing water; temporary water; size of water body; open water; open banks; submerged macrophytes; reed; and riparian trees. On the basis of the species‐specific configurations of these habitat parameters, cluster analysis revealed seven dragonfly associations with different habitat needs: association of open waters, association of sparsely vegetated banks, association of reed and riparian trees, association of reed and submerged macrophytes, association of temporary waters, rhithron association and potamon association. Correlations between the associations' habitat requirements and the habitat parameters of the seven (near‐)natural river types, which are present in this bioregion were performed to define river type‐specific association compositions. From these results, a dragonfly association index was created to assess the ecological status of these rivers within the five‐tiered system of the European Union Water Framework Directive, emphasizing hydro‐morphological aspects by comparing the type‐specific reference situation with the actual status quo of dragonfly colonization. The method was applied at different rivers, particularly for the purpose of evaluating restoration measures. Copyright © 2014 John Wiley & Sons, Ltd.