Analysis of cultured neuronal networks using intraburst firing characteristics

It is an open question whether neuronal networks, cultured on multielectrode arrays, retain any capability to usefully process information (learning and memory). A necessary prerequisite for learning is that stimulation can induce lasting changes in the network. To observe these changes, one needs a method to describe the network in sufficient detail, while stable in normal circumstances. We analyzed the spontaneous bursting activity that is encountered in dissociated cultures of rat neocortical cells. Burst profiles (BPs) were made by estimating the instantaneous array-wide firing frequency. The shape of the BPs was found to be stable on a time scale of hours. Spatiotemporal detail is provided by analyzing the instantaneous firing frequency per electrode. The resulting phase profiles (PPs) were estimated by aligning BPs to their peak spiking rate over a period of 15 min. The PPs reveal a stable spatiotemporal pattern of activity during bursts over a period of several hours, making them useful for plasticity and learning studies. We also show that PPs can be used to estimate conditional firing probabilities. Doing so, yields an approach in which network bursting behavior and functional connectivity can be studied.     

Microbial systems biology: new frontiers open to predictive microbiology

The field of Systems Biology is a rapidly evolving area of research. It follows on from the previous experimental and theoretical 'omics' revolution in biology. Now that we have through the use of these tools many 'indices' of biological systems available the next step is to actually start composing the systems that these indices specify. In this paper we will discuss the developments in the field of Systems Biology as they pertain to predictive food microbiology and give an example of state of the art current approaches. The data discussed in the case study deal with the resistance of the yeast Saccharomyces cerevisiae towards environmental temperature changes through adaptation of its metabolism, protein signalling and gene-expression. The results are integrated and its implications for the definition of new experiments discussed; the iteration between experiment driven model definition and model driven experimentation being characteristic for contemporary Systems Biology approaches. The stress condition discussed represents in no way a practical situation in food microbiology but what it teaches may well be applied in such cases. We will indicate how the latter may be achieved.     

Targeting Inflammation after Myocardial Infarction: A Therapeutic Opportunity for Extracellular Vesicles?

After myocardial infarction (MI), a strong inflammatory response takes place in the heart to remove the dead tissue resulting from ischemic injury. A growing body of evidence suggests that timely resolution of this inflammatory process may aid in the prevention of adverse cardiac remodeling and heart failure post-MI. The present challenge is to find a way to stimulate this process without interfering with the reparative role of the immune system. Extracellular vesicles (EVs) are natural membrane particles that are released by cells and carry different macromolecules, including proteins and non-coding RNAs. In recent years, EVs derived from various stem and progenitor cells have been demonstrated to possess regenerative properties. They can provide cardioprotection via several mechanisms of action, including immunomodulation. In this review, we summarize the role of the innate immune system in post-MI healing. We then discuss the mechanisms by which EVs modulate cardiac inflammation in preclinical models of myocardial injury through regulation of monocyte influx and macrophage function. Finally, we provide suggestions for further optimization of EV-based therapy to improve its potential for the treatment of MI.     

Measurements and modeling of absorptive partitioning of volatile organic compounds to painted surfaces

Partitioning to surfaces is an important sink for volatile organic compounds (VOCs) indoors, but the mechanisms are not well understood or quantified. Here, a mass spectrometer was coupled to a portable surface reactor and a flow tube to measure partitioning of VOCs into paint films coated onto glass or wallboard, and their subsequent diffusion. A model was developed to extract values of the effective absorbing organic mass concentration of the film, C_w, which is a measure of absorption capacity, and VOC diffusion coefficients, D_f, from VOC time profiles measured during film passivation and depassivation. Values of C_w agreed well with the value estimated from the paint film mass and flow tube air volume, and D_f values (also measured using attenuated total reflectance-Fourier transform infrared spectroscopy) correlated well with VOC vapor saturation concentrations, C*, estimated using a group contribution method. The value of these relationships for estimating key parameters that control VOC partitioning into paint and the fate of VOCs indoors was demonstrated using a house model, which indicated that >50% of VOCs with C* ≤10⁸ μg/m³ (C* of octane, hexanone, and propanol) that contacted a paint film of typical thickness fully permeated the film regardless of emission duration.     

The semantics of the combination of atomized statements and parallel choice

In this paper we define a uniform language that is an extension of the language underlying the process algebraPA. One of the main extensions of this language overPA is given by so-called atomizing brackets. If we place these brackets around a statement then we treat this statement as an atomic action. Put differently, these brackets remove all interleaving points. We present a transition system for the language and derive its operational semantics. We show that there are several options for defining a transition system such that the resulting operational semantics is a conservative extension of the semantics forPA. We define a semantic domain and a denotational model for the language. Next we define a closure operator on the semantic domain and show how to use this closure operator to derive a fully abstract denotational semantics. Then the algebraic theory of the language is considered. We define a collection of axioms and a term rewrite system based on these axioms. Using this term rewrite system we are able to identify normal forms for the language. It is shown that these axioms capture the denotational equality. It follows that if two terms are provably equal then they have the same operational semantics. Finally, we show how to extend the axiomatization in order to axiomatize its operational equivalence.     

Generating flat patterns for folded structures: Search space reduction

A significant number of packaging products can be represented as folded structures. This contribution demonstrates an approach for generating a set of feasible flat patterns based upon a graph representation of the folded structures. It presents algorithms to detect several typical design features. In order to present alternative flat patterns with different properties to the designer, it is preferred to first generate all feasible spanning trees. Two options to decrease the number of generated spanning trees are then proposed: user constraints and overlapping features. Each of these reduction possibilities is examined, and several practical examples with outcomes are shown. The presented framework shows a decrease of up to 98.5% in the number of possible flat patterns for the investigated parts.     

Scale coding bag of deep features for human attribute and action recognition

Most approaches to human attribute and action recognition in still images are based on image representation in which multi-scale local features are pooled across scale into a single, scale-invariant encoding. Both in bag-of-words and the recently popular representations based on convolutional neural networks, local features are computed at multiple scales. However, these multi-scale convolutional features are pooled into a single scale-invariant representation. We argue that entirely scale-invariant image representations are sub-optimal and investigate approaches to scale coding within a bag of deep features framework. Our approach encodes multi-scale information explicitly during the image encoding stage. We propose two strategies to encode multi-scale information explicitly in the final image representation. We validate our two scale coding techniques on five datasets: Willow, PASCAL VOC 2010, PASCAL VOC 2012, Stanford-40 and Human Attributes (HAT-27). On all datasets, the proposed scale coding approaches outperform both the scale-invariant method and the standard deep features of the same network. Further, combining our scale coding approaches with standard deep features leads to consistent improvement over the state of the art.     

Road to Metastasis: The TWEAK Pathway as a Discriminant between Metastasizing and Non-Metastasizing Thick Melanomas

Cutaneous melanoma (CM) is the most aggressive form of skin cancer, and its worldwide incidence is rapidly increasing. Early stages can be successfully treated by surgery, but once metastasis has occurred, the prognosis is poor. However, some 5–10% of thick (≥2 mm) melanomas do not follow this scenario and run an unpredictable course. Little is known about the factors that contribute to metastasis in some patient with thick melanomas and the lack thereof in thick melanoma patients who never develop metastatic disease. We were therefore interested to study differential gene expression and pathway analysis and compare non-metastatic and metastatic thick melanomas. We found that the TNF-like weak inducer of apoptosis (TWEAK) pathway was upregulated in thick non-metastasizing melanomas. MAP3K14 (NIK1), BIRC2 (cIAP1), RIPK1, CASP7, CASP8, and TNF play an important role in inhibiting proliferation and invasion of tumor cells via the activation of the non-canonical NF-κB signaling pathway. In particular, this pathway sensitizes melanoma cells to TNF-alpha and activates the apoptosis module of the TWEAK pathway in thick non-metastasizing melanomas. Hence, our study suggests a potential role of the TWEAK pathway in inhibiting thick melanoma from metastasis. Exploitation of these genes and the pathway they control may open future therapeutic avenues.