How Necessary is the Vasculature in the Life of Neural Stem and Progenitor Cells? Evidence from Evolution, Development and the Adult Nervous System.

Augmenting evidence suggests that such is the functional dependance of neural stem cells (NSCs) on the vasculature that they normally reside in “perivascular niches”. Two examples are the “neurovascular” and the “oligovascular” niches of the adult brain, which comprise specialized microenvironments where NSCs or oligodendrocyte progenitor cells survive and remain mitotically active in close proximity to blood vessels (BVs). The often observed co-ordination of angiogenesis and neurogenesis led to these processes being described as “coupled”. Here, we adopt an evo-devo approach to argue that some stages in the life of a NSC, such as specification and commitment, are independent of the vasculature, while stages such as proliferation and migration are largely dependent on BVs. We also explore available evidence on the possible involvement of the vasculature in other phenomena such as the diversification of NSCs during evolution and we provide original data on the senescence of NSCs in the subependymal zone stem cell niche. Finally, we will comment on the other side of the story; that is, on how much the vasculature is dependent on NSCs and their progeny

How Necessary is the Vasculature in the Life of Neural Stem and Progenitor Cells? Evidence from Evolution, Development and the Adult Nervous System.

Augmenting evidence suggests that such is the functional dependance of neural stem cells (NSCs) on the vasculature that they normally reside in "perivascular niches". Two examples are the "neurovascular" and the "oligovascular" niches of the adult brain, which comprise specialized microenvironments where NSCs or oligodendrocyte progenitor cells survive and remain mitotically active in close proximity to blood vessels (BVs). The often observed co-ordination of angiogenesis and neurogenesis led to these processes being described as "coupled". Here, we adopt an evo-devo approach to argue that some stages in the life of a NSC, such as specification and commitment, are independent of the vasculature, while stages such as proliferation and migration are largely dependent on BVs. We also explore available evidence on the possible involvement of the vasculature in other phenomena such as the diversification of NSCs during evolution and we provide original data on the senescence of NSCs in the subependymal zone stem cell niche. Finally, we will comment on the other side of the story; that is, on how much the vasculature is dependent on NSCs and their progeny.IK was supported by Action Medical Research, UK (GN2291).This is the final version of the article. It first appeared from Frontiers via http://dx.doi.org/10.3389/fncel.2016.00035

Optical Absorption Effects in Thermal Radiation Barrier Coating Materials

Future gas turbine engines will operate at higher gas temperatures and consequentially hot-section components such as blades, vanes and combustors, will be subject to higher thermal radiation fluxes than today. Current thermal barrier coating materials are translucent over the spectral region of the heat flux so future coatings will also have to provide a barrier to thermal radiation. The effects of optical absorption and scattering properties of coating materials on the temperatures and heat fluxes through coatings are explored using a two-flux heat transfer model, and promising combinations are identified that reduce the coating-alloy interface temperatures. Lower interface temperatures occur for thickness normalized absorptions of $\overline{\kappa} L$ $>$1. The effect of both a narrow and a broad band spectrally selective absorbing Gd${_2}$Zr${_2}$O$_{7}$ based coating materials are then studied. These show that large values of the product of the normalized absorption length and the spectral width of the absorption are required to significantly decrease the radiative heat transport through a coating. The results emphasize the importance of enhancing the optical absorption of the next generation barrier materials as a strategy to increase gas turbine engine efficiency by decreasing compressor bleed air cooling requirements

A survey on the cyber security of Small-to-Medium businesses: Challenges, research focus and recommendations

Small-to-medium sized businesses (SMBs) constitute a large fraction of many countries’ economies but according to the literature SMBs are not adequately implementing cyber security which leaves them susceptible to cyber-attacks. Furthermore, research in cyber security is rarely focused on SMBs, despite them representing a large proportion of businesses. In this paper we review recent research on the cyber security of SMBs, with a focus on the alignment of this research to the popular NIST Cyber Security Framework (CSF). From the literature we also summarise the key challenges SMBs face in implementing good cyber security and conclude with key recommendations on how to implement good cyber security. We find that research in SMB cyber security is mainly qualitative analysis and narrowly focused on the Identify and Protect functions of the NIST CSF with very little work on the other existing functions. SMBs should have the ability to detect, respond and recover from cyber-attacks, and if research lacks in those areas, then SMBs may have little guidance on how to act. Future research in SMB cyber security should be more balanced and researchers should adopt well-established powerful quantitative research approaches to refine and test research whilst governments and academia are urged to invest in incentivising researchers to expand their research focus

Valence, arousal and dominance estimation for English, German, Greek,Portuguese and Spanish lexica using semantic models

We propose and evaluate the use of an affective-semantic model to expand the affective lexica of German, Greek, English, Spanish and Portuguese. Motivated by the assumption that semantic similarity implies affective similarity, we use word level semantic similarity scores as semantic features to estimate their corresponding affective scores. Various context-based semantic similarity metrics are investigated using contextual features that include both words and character n-grams. The model produces continuous affective ratings in three dimensions (valence, arousal and dominance) for all five languages, achieving consistent performance. We achieve classification accuracy (valence polarity task) between 85% and 91% for all five languages. For morphologically rich languages the proposed use of character n-grams is shown to improve performance

Αναγνώριση ανθρώπινης δραστηριότητας από Δεδομένα Αισθητήρων Κίνησης με χρήση Αρχιτεκτονικών Νευρωνικών Δικτύων Προσοχής

Η αναγνώριση ανθρώπινης δραστηριότητας έχει απασχολήσει αισθητά το ερευνητικό εν- διαφέρον την τελευταία δεκαετία. Συγκεκριμένα, η ταξινόμηση χρονοσειρών με δεδομένα από αισθητήρες κίνησης αποτελεί τον πυρήνα για αρκετές έρευνες οι οποίες κυρίως χρη- σιμοποιούν βαθιά νευρωνικά δίκτυα συνέλιξης και ανατροφοδότησης. Η χρήση τέτοιων δικτύων όμως δεν φαίνεται να είναι επαρκής για μεγάλου μήκους ακολουθίες, καθώς τα χαρακτηριστικά που μαθαίνονται στα αρχικά στάδια δεν διατηρούνται, με αποτέλεσμα την απώλεια πληροφορίας. Η εμφάνιση νευρωνικών δικτύων προσοχής όμως, παρουσιάζει την ικανότητα να διαχειρίζεται τέτοιες αδυναμίες και με κύριο αντιπρόσωπο το μοντέλο βαθιάς μάθησης Transformer, όπως ονομάζεται, να πετυχαίνει υψηλές επιδόσεις σε προβλήματα επεξεργασίας φυσικής γλώσσας και computer vision, καθιστώντας αναπόφευκτη την εφαρμογή του και σε άλλους τομείς, όπως η ταξινόμηση χρονοσειρών. Στην έρευνα που ακολουθεί, αναλύεται σχολαστικά ο μηχανισμός προσοχής που αποτελεί θεμέλιο του μοντέλου, καθώς και η εφαρμογή του σε προβλήματα αναγνώρισης ανθρώπινης δραστηριότητας με δεδομένα χρονοσειρών από αισθητήρες κίνησης. Συγκρίνεται με μοντέλα νευρωνικών δικτύων συνέλιξης και ανατροφοδότησης παρουσιάζοντας καλύτερα αποτελέσματα στην ταξινόμηση δραστηριοτήτων και τέλος εξετάζεται κατά πόσο είναι επαρκής για την αποτελεσματική επίλυση τέτοιων προβλημάτων.Human activity recognition has attracted considerable research interest in the last decade. In particular, the classification of time series with motion sensor data is the core of several researches which mainly use deep convolution and recurrent neural networks. However, the use of such networks does not seem to be sufficient for long sequences, as the features learned at the initial stages are not preserved, resulting in information loss. The emergence of attention neural networks, however, shows the ability to handle such weaknesses and, with the deep learning Transformer model, as it is called, as its main representative, to achieve high performance in natural language and computer vision processing problems, making its application in other areas, such as time series classification, inevitable. In the following research, the attention mechanism that is the foundation of the model is thoroughly analyzed, as well as its application to problems of recognizing human activity with time series data from motion sensors. It is compared with convolution and recurrent neural networks models showing better results in activity classification and finally it is examined whether it is sufficient to effectively solve such problems

Modelling email traffic workloads with RNN and LSTM models

Analysis of time series data has been a challenging research subject for decades. Email traffic has recently been modelled as a time series function using a Recurrent Neural Network (RNN) and RNNs were shown to provide higher prediction accuracy than previous probabilistic models from the literature. Given the exponential rise of email workloads which need to be handled by email servers, in this paper we first present and discuss the literature on modelling email traffic. We then explain the advantages and limitations of different approaches as well as their points of agreement and disagreement. Finally, we present a comprehensive comparison between the performance of RNN and Long Short Term Memory (LSTM) models. Our experimental results demonstrate that both approaches can achieve high accuracy over four large datasets acquired from different universities’ servers, outperforming existing work, and show that the use of LSTM and RNN is very promising for modelling email traffic

Subependymal Zone-Derived Oligodendroblasts Respond to Focal Demyelination but Fail to Generate Myelin in Young and Aged Mice

wo populations of oligodendrogenic progenitors co-exist within the corpus callosum (CC) of the adult mouse. Local, parenchymal oligodendrocyte progenitor cells (pOPCs) and progenitors generated in the subependymal zone (SEZ) cytogenic niche. pOPCs are committed perinatally and retain their numbers through self-renewing divisions, while SEZ-derived cells are relatively “young,” being constantly born from neural stem cells. We compared the behavior of these populations, labeling SEZ-derived cells using hGFAP:Cre$^{Ert2}$ mice, within the homeostatic and regenerating CC of the young-adult and aging brain. We found that SEZ-derived oligodendroglial progenitors have limited self-renewing potential and are therefore not bona fide OPCs but rather “oligodendroblasts” more similar to the neuroblasts of the neurogenic output of the SEZ. In the aged CC their mitotic activity is much reduced, although they still act as a “fast-response element” to focal demyelination. In contrast to pOPCs, they fail to generate mature myelinating oligodendrocytes at all ages studied.This work was supported by a grant from the Biotechnology and Biological Sciences Research Council (UK) ( BB/I013210/1 ) to R.F. and I.K