First-principles and model simulation of all-optical spin reversal

All-optical spin switching is a potential trailblazer for information storage and communication at an unprecedented fast rate and free of magnetic fields. However, the current wisdom is largely based on semiempirical models of effective magnetic fields and heat pulses, so it is difficult to provide high-speed design protocols for actual devices. Here, we carry out a massively parallel first-principles and model calculation for thirteen spin systems and magnetic layers, free of any effective field, to establish a simpler and alternative paradigm of laser-induced ultrafast spin reversal and to point out a path to a full-integrated photospintronic device. It is the interplay of the optical selection rule and sublattice spin orderings that underlines seemingly irreconcilable helicity-dependent/independent switchings. Using realistic experimental parameters, we predict that strong ferrimagnets, in particular, Laves phase C15 rare-earth alloys, meet the telecommunication energy requirement of 10 fJ, thus allowing a cost-effective subpicosecond laser to switch spin in the GHz region.Comment: 23 pages, 6 figures and one tabl

Derivation of a biomass proxy for dynamic analysis of whole genome metabolic models

A whole genome metabolic model (GEM) is essentially a reconstruction of a network of enzyme-enabled chemical reactions representing the metabolism of an organism, based on information present in its genome. Such models have been designed so that flux balance analysis (FBA) can be applied in order to analyse metabolism under steady state. For this purpose, a biomassfunctionisaddedtothesemodelsasanoverallindicatorofthemodel’s viability. Our objective is to develop dynamic models based on these FBA models in order to observe new and complex behaviours, including transient behaviour. There is however a major challenge in that the biomass function does not operate under dynamic simulation. An appropriate biomass function would enable the estimation under dynamic simulation of the growth of both wildtype and genetically modified bacteria under different, possibly dynamically changing growth conditions. Using data analytics techniques, we have developed a dynamic biomass function which acts as a faithful proxy for the FBA equivalent for a reduced GEM for E. coli. This involved consolidating data for reaction rates and metabolite concentrations generated under dynamic simulation with gold standard target data for biomass obtained by steady state analysis using FBA. It also led to a number of interesting insights regarding biomass fluxes for pairs of conditions. These findings were reproduced in our dynamic proxy function

Impact of Treadmill Running and Sex on Hippocampal Neurogenesis in the Mouse Model of Amyotrophic Lateral Sclerosis

Hippocampal neurogenesis in the subgranular zone (SGZ) of dentate gyrus (DG) occurs throughout life and is regulated by pathological and physiological processes. The role of oxidative stress in hippocampal neurogenesis and its response to exercise or neurodegenerative diseases remains controversial. The present study was designed to investigate the impact of oxidative stress, treadmill exercise and sex on hippocampal neurogenesis in a murine model of heightened oxidative stress (G93A mice). G93A and wild type (WT) mice were randomized to a treadmill running (EX) or a sedentary (SED) group for 1 or 4 wk. Immunohistochemistry was used to detect bromodeoxyuridine (BrdU) labeled proliferating cells, surviving cells, and their phenotype, as well as for determination of oxidative stress (3-NT; 8-OHdG). BDNF and IGF1 mRNA expression was assessed by in situ hybridization. Results showed that: (1) G93A-SED mice had greater hippocampal neurogenesis, BDNF mRNA, and 3-NT, as compared to WT-SED mice. (2) Treadmill running promoted hippocampal neurogenesis and BDNF mRNA content and lowered DNA oxidative damage (8-OHdG) in WT mice. (3) Male G93A mice showed significantly higher cell proliferation but a lower level of survival vs. female G93A mice. We conclude that G93A mice show higher hippocampal neurogenesis, in association with higher BDNF expression, yet running did not further enhance these phenomena in G93A mice, probably due to a ‘ceiling effect’ of an already heightened basal levels of hippocampal neurogenesis and BDNF expression

Using simple artificial intelligence methods for predicting amyloidogenesis in antibodies

<p>Abstract</p> <p>Background</p> <p>All polypeptide backbones have the potential to form amyloid fibrils, which are associated with a number of degenerative disorders. However, the likelihood that amyloidosis would actually occur under physiological conditions depends largely on the amino acid composition of a protein. We explore using a naive Bayesian classifier and a weighted decision tree for predicting the amyloidogenicity of immunoglobulin sequences.</p> <p>Results</p> <p>The average accuracy based on leave-one-out (LOO) cross validation of a Bayesian classifier generated from 143 amyloidogenic sequences is 60.84%. This is consistent with the average accuracy of 61.15% for a holdout test set comprised of 103 AM and 28 non-amyloidogenic sequences. The LOO cross validation accuracy increases to 81.08% when the training set is augmented by the holdout test set. In comparison, the average classification accuracy for the holdout test set obtained using a decision tree is 78.64%. Non-amyloidogenic sequences are predicted with average LOO cross validation accuracies between 74.05% and 77.24% using the Bayesian classifier, depending on the training set size. The accuracy for the holdout test set was 89%. For the decision tree, the non-amyloidogenic prediction accuracy is 75.00%.</p> <p>Conclusions</p> <p>This exploratory study indicates that both classification methods may be promising in providing straightforward predictions on the amyloidogenicity of a sequence. Nevertheless, the number of available sequences that satisfy the premises of this study are limited, and are consequently smaller than the ideal training set size. Increasing the size of the training set clearly increases the accuracy, and the expansion of the training set to include not only more derivatives, but more alignments, would make the method more sound. The accuracy of the classifiers may also be improved when additional factors, such as structural and physico-chemical data, are considered. The development of this type of classifier has significant applications in evaluating engineered antibodies, and may be adapted for evaluating engineered proteins in general.</p