Can Developments in Tissue Optical Clearing Aid Super-Resolution Microscopy Imaging?

The rapid development of super-resolution microscopy (SRM) techniques opens new avenues to examine cell and tissue details at a nanometer scale. Due to compatibility with specific labelling approaches, in vivo imaging and the relative ease of sample preparation, SRM appears to be a valuable alternative to laborious electron microscopy techniques. SRM, however, is not free from drawbacks, with the rapid quenching of the fluorescence signal, sensitivity to spherical aberrations and light scattering that typically limits imaging depth up to few micrometers being the most pronounced ones. Recently presented and robustly optimized sets of tissue optical clearing (TOC) techniques turn biological specimens transparent, which greatly increases the tissue thickness that is available for imaging without loss of resolution. Hence, SRM and TOC are naturally synergistic techniques, and a proper combination of these might promptly reveal the three-dimensional structure of entire organs with nanometer resolution. As such, an effort to introduce large-scale volumetric SRM has already started; in this review, we discuss TOC approaches that might be favorable during the preparation of SRM samples. Thus, special emphasis is put on TOC methods that enhance the preservation of fluorescence intensity, offer the homogenous distribution of molecular probes, and vastly decrease spherical aberrations. Finally, we review examples of studies in which both SRM and TOC were successfully applied to study biological systems.     

Quantification of Dendritic Spines Remodeling under Physiological Stimuli and in Pathological Conditions

Numerous brain diseases are associated with abnormalities in morphology and density of dendritic spines, small membranous protrusions whose structural geometry correlates with the strength of synaptic connections. Thus, the quantitative analysis of dendritic spines remodeling in microscopic images is one of the key elements towards understanding mechanisms of structural neuronal plasticity and bases of brain pathology. In the following article, we review experimental approaches designed to assess quantitative features of dendritic spines under physiological stimuli and in pathological conditions. We compare various methodological pipelines of biological models, sample preparation, data analysis, image acquisition, sample size, and statistical analysis. The methodology and results of relevant experiments are systematically summarized in a tabular form. In particular, we focus on quantitative data regarding the number of animals, cells, dendritic spines, types of studied parameters, size of observed changes, and their statistical significance.     

Forgetting Exceptions is Harmful in Language Learning

We show that in language learning, contrary to received wisdom, keeping exceptional training instances in memory can be beneficial for generalization accuracy. We investigate this phenomenon empirically on a selection of benchmark natural language processing tasks: grapheme-to-phoneme conversion, part-of-speech tagging, prepositional-phrase attachment, and base noun phrase chunking. In a first series of experiments we combine memory-based learning with training set editing techniques, in which instances are edited based on their typicality and class prediction strength. Results show that editing exceptional instances (with low typicality or low class prediction strength) tends to harm generalization accuracy. In a second series of experiments we compare memory-based learning and decision-tree learning methods on the same selection of tasks, and find that decision-tree learning often performs worse than memory-based learning. Moreover, the decrease in performance can be linked to the degree of abstraction from exceptions (i.e., pruning or eagerness). We provide explanations for both results in terms of the properties of the natural language processing tasks and the learning algorithms.     

Utilisation of neural networks to identify the status of the cutting tool point

The paper presents the results of a research work using artificial neural networks to classify the signals of machining forces typical for particular worn cutting tools. Prospectively, a proper classification of these signals may allow the identification of cutter tips condition in multi-tools heads status’ monitoring systems, and facilitate the construction of systems for controlling rocks excavating process.     

Neural navigation interfaces for Information Retrieval: Are they more than an appealing idea?

Neural networks have recently been proposed for the construction of navigation interfaces for Information Retrieval systems. In this paper, we give an overview of some current research in this area. Most of the cited approaches use (variants) of the well-known Kohonen network. The Kohonen network implements a topology-preserving dimensionality-reducing mapping, which can be applied for information visualization. We identify a number of problems in the application of Kohonen networks for Information Retrieval, most notably scalability, reliability and retrieval effectiveness. To solve these problems we propose to use the Growing Cell Structures network, a variant of the Kohonen network which shows a more flexible adaptation to the domain structure.This network was tested on two standard test-collections, using a combined recall and precision measure, and compared to traditional IR methods such as the Vector Space Model and various clustering algorithms. The network performs at a competitive level of effectiveness, and is suitable for visualization purposes. However, the incremental training procedures for the networks result in a reliability problem, and the approach is computationally intensive. Also, the utility of the resulting maps for navigation will need further improvement.     

Signalling and obfuscation for congestion control

We aim to reduce the social cost of congestion in many smart city applications. In our model of congestion, agents interact over limited resources after receiving signals from a central agent that observes the state of congestion in real time. Under natural models of agent populations, we develop new signalling schemes and show that by introducing a non-trivial amount of uncertainty in the signals, we reduce the social cost of congestion, i.e., improve social welfare. The signalling schemes are efficient in terms of both communication and computation, and are consistent with past observations of the congestion. Moreover, the resulting population dynamics converge under reasonable assumptions.     

The study on the model reference adaptive control with proportional derivative action

This article presents a study on the novel method of model reference adaptive control (MRAC). As a new contribution, the derivative action is included into MRAC control to improve the controller performance and efficiency. The stability is analysed using a recent technique called immersion and invariance. The simulations of derived laws are presented based on nonlinear models.     

Modification of physicochemical properties of actin filaments suppresses cell fragmentation in the execution phase of staurosporine-induced apoptotic processes

Effects of jasplakinolide (JSP), a stabilizer of F-actin, and latrunculin A (LTA), a destabilizer of F-actin, on a series of events occurring in the execution phase of staurosporine (STS)-induced apoptotic processes were studied using human osteosarcoma 143B cells. Time-dependent apparent increases of the population of cells with collapsed membrane potential of mitochondria (Delta Psi(m)) caused by STS treatment were not due to actual decreases in the Delta Psi(m) per cell, but due to the fragmentation of cells resulting in decreases in the number of active mitochondria per cell. Decreases in the Delta Psi(m) in fragmented cells occurred late in the execution phase. Both JSP and LAT failed to prevent STS-induced release of cytochrome c from mitochondria followed by the activation of caspases 3 and 9, the cleavage of poly (ADP-ribose) polymerase (PARP) and apoptotic nuclear fragmentation. However, both drugs prevented STS-induced apoptotic cell fragmentation and decreases in the Delta Psi(m). These results indicate that physicochemical states of actin filaments play a certain role in the execution phase of STS-induced apoptotic processes.     

Zapotin,a Polymethoxyflavone,with Potential Therapeutic Attributes

The use of plants as traditional medicines is common and has prevailed in many different cultures over time. Polymethoxyflavones (PMFs) are natural polyphenols from the group of flavonoids. Zapotin, a member of the PMFs, is found mainly in citrus plants and is almost exclusively limited to their peels. The chemical structure of zapotin has been questioned from the very beginning, since the structure of flavonoids with a single oxygen atom in the C2′ position is extremely rare in the plant kingdom. To clarify this, the structural determination and bio-inspired synthesis of zapotin are discussed in detail in this review. Due to the broad biological potential of PMFs, the complication in the isolation process and characterization of PMFs, as well as their purification, have been estimated by adapting various chromatographic methods. According to available data from the literature, zapotin may be a promising curative agent with extensive biological activities, especially as a chemopreventive factor. Apart from that, zapotin acts as an antidepressant-like, anticancer, antifungal, and antioxidant agent. Finally, accessible studies about zapotin metabolism (absorption, distribution, metabolism, excretion, and toxicity) underline its potential in use as a therapeutic substance.