Electronic measurement and control of spin transport in Silicon

The electron spin lifetime and diffusion length are transport parameters that define the scale of coherence in spintronic devices and circuits. Since these parameters are many orders of magnitude larger in semiconductors than in metals, semiconductors could be the most suitable for spintronics. Thus far, spin transport has only been measured in direct-bandgap semiconductors or in combination with magnetic semiconductors, excluding a wide range of non-magnetic semiconductors with indirect bandgaps. Most notable in this group is silicon (Si), which (in addition to its market entrenchment in electronics) has long been predicted a superior semiconductor for spintronics with enhanced lifetime and diffusion length due to low spin-orbit scattering and lattice inversion symmetry. Despite its exciting promise, a demonstration of coherent spin transport in Si has remained elusive, because most experiments focused on magnetoresistive devices; these methods fail because of universal impedance mismatch obstacles, and are obscured by Lorentz magnetoresistance and Hall effects. Here we demonstrate conduction band spin transport across 10 microns undoped Si, by using spin-dependent ballistic hot-electron filtering through ferromagnetic thin films for both spin-injection and detection. Not based on magnetoresistance, the hot electron spin-injection and detection avoids impedance mismatch issues and prevents interference from parasitic effects. The clean collector current thus shows independent magnetic and electrical control of spin precession and confirms spin coherent drift in the conduction band of silicon.Comment: Single PDF file with 4 Figure

The phylogenetically-related pattern recognition receptors EFR and XA21 recruit similar immune signaling components in monocots and dicots

During plant immunity, surface-localized pattern recognition receptors (PRRs) recognize pathogen-associated molecular patterns (PAMPs). The transfer of PRRs between plant species is a promising strategy for engineering broad-spectrum disease resistance. Thus, there is a great interest in understanding the mechanisms of PRR-mediated resistance across different plant species. Two well-characterized plant PRRs are the leucine-rich repeat receptor kinases (LRR-RKs) EFR and XA21 from Arabidopsis thaliana (Arabidopsis) and rice, respectively. Interestingly, despite being evolutionary distant, EFR and XA21 are phylogenetically closely related and are both members of the sub-family XII of LRR-RKs that contains numerous potential PRRs. Here, we compared the ability of these related PRRs to engage immune signaling across the monocots-dicots taxonomic divide. Using chimera between Arabidopsis EFR and rice XA21, we show that the kinase domain of the rice XA21 is functional in triggering elf18-induced signaling and quantitative immunity to the bacteria Pseudomonas syringae pv. tomato (Pto) DC3000 and Agrobacterium tumefaciens in Arabidopsis. Furthermore, the EFR:XA21 chimera associates dynamically in a ligand-dependent manner with known components of the EFR complex. Conversely, EFR associates with Arabidopsis orthologues of rice XA21-interacting proteins, which appear to be involved in EFR-mediated signaling and immunity in Arabidopsis. Our work indicates the overall functional conservation of immune components acting downstream of distinct LRR-RK-type PRRs between monocots and dicots

Towards a large-scale quantum simulator on diamond surface at room temperature

Strongly-correlated quantum many-body systems exhibits a variety of exotic phases with long-range quantum correlations, such as spin liquids and supersolids. Despite the rapid increase in computational power of modern computers, the numerical simulation of these complex systems becomes intractable even for a few dozens of particles. Feynman's idea of quantum simulators offers an innovative way to bypass this computational barrier. However, the proposed realizations of such devices either require very low temperatures (ultracold gases in optical lattices, trapped ions, superconducting devices) and considerable technological effort, or are extremely hard to scale in practice (NMR, linear optics). In this work, we propose a new architecture for a scalable quantum simulator that can operate at room temperature. It consists of strongly-interacting nuclear spins attached to the diamond surface by its direct chemical treatment, or by means of a functionalized graphene sheet. The initialization, control and read-out of this quantum simulator can be accomplished with nitrogen-vacancy centers implanted in diamond. The system can be engineered to simulate a wide variety of interesting strongly-correlated models with long-range dipole-dipole interactions. Due to the superior coherence time of nuclear spins and nitrogen-vacancy centers in diamond, our proposal offers new opportunities towards large-scale quantum simulation at room temperatures

Mesenchymal stem cells secretome-induced axonal outgrowth is mediated by BDNF

Mesenchymal stem cells (MSCs) have been used for cell-based therapies in regenerative medicine, with increasing importance in central and peripheral nervous system repair. However, MSCs grafting present disadvantages, such as, a high number of cells required for transplantation and low survival rate when transplanted into the central nervous system (CNS). In line with this, MSCs secretome which present on its composition a wide range of molecules (neurotrophins, cytokines) and microvesicles, can be a solution to surpass these problems. However, the effect of MSCs secretome in axonal elongation is poorly understood. In this study, we demonstrate that application of MSCs secretome to both rat cortical and hippocampal neurons induces an increase in axonal length. In addition, we show that this growth effect is axonal intrinsic with no contribution from the cell body. To further understand which are the molecules required for secretome-induced axonal outgrowth effect, we depleted brain-derived neurotrophic factor (BDNF) from the secretome. Our results show that in the absence of BDNF, secretome-induced axonal elongation effect is lost and that axons present a reduced axonal growth rate. Altogether, our results demonstrate that MSCs secretome is able to promote axonal outgrowth in CNS neurons and this effect is mediated by BDNF.European Regional Development Fund (ERDF), through the Centro 2020 Regional Operational Programme under project CENTRO-01–0145-FEDER-000008:BrainHealth 2020, and through the COMPETE 2020 - Operational Programme for Competitiveness and Internationalisation and Portuguese national funds via FCT – Fundação para a Ciência e a Tecnologia, I.P., under projects PTDC/SAU-NEU/104100/2008, EXPL/NEU-NMC/0541/2012 and UID/NEU/04539/2013. This work was also funded by Marie Curie Actions - International reintegration grant #249288, 7th Framework programme, EU. Partially funded by Prémios Santa Casa Neurociências - Prize Melo e Castro for Spinal Cord Injury Research; Portuguese Foundation for Science and Technology (IF Development Grant to A.J.S.); NORTE-01-0145-FEDER-000013, supported by the Northern Portugal Regional Operational Programme; by FEDER funds, through the Competitiveness Factors Operational Programme (COMPETE), and by national funds, through the Foundation for Science and Technology (FCT), under the scope of the project POCI-01-0145-FEDER-007038. The authors would also like to acknowledge Prof. J.E. Davies from the Institute of Biomaterials and Biomedical Engineering at the University of Toronto, Canada, for kindly providing some of the HUCPVCs lots used in the present workinfo:eu-repo/semantics/publishedVersio

Evaluation of macrophage migration inhibitory factor as an imaging marker for hepatocellular carcinoma in murine models

Objective. Macrophage migration inhibitory factor (MIF) is considered as an important mediator in the pathogenesis of neoplasia. The aim of the present study was to evaluate whether MIF could be used as a marker for hepatocellular carcinoma (HCC) detection. Material and methods. Biodistribution and whole-body autoradiography studies of 131I-labeled anti-MIF monoclonal antibody (McAb) and 131I-labeled control IgG were performed. The HCC-bearing mice were injected with 3.7 MBq of each agent and killed at 24, 48, and 72 h postinjection (p.i.). The organs, blood, and HCC tissues were removed from model mice, weighed, and counted using a gamma-counter. The expression of MIF mRNA and protein within HCC tissues was confirmed by RT-PCR and immunohistochemistry. Results. HCCs in model mice could be adequately visualized at 24 h p.i. The target-to-non-target (T/NT) ratios were 6.72 ± 1.09 (24 h), 9.85 ± 0.81 (48 h), and 12.31 ± 0.57 (72 h) for 131I-labeled anti-MIF McAb group, whereas in the control group of 131I-IgG, T/NT ratios were 4.65 ± 0.63 (24 h), 6.12 ± 0.60 (48 h), and 8.23 ± 0.35 (72 h) (p < 0.05). MIF mRNA expression was twofold higher in the HCC tissues than in the healthy liver tissues. MIF protein expression was much higher in the HCC tissues than in controls. Conclusions. Our findings suggested that 131I-anti-MIF McAb could be rapidly and specifically localized in tumors. Thus, MIF could be used as a marker for HCC tumor detection

Detection of viral respiratory pathogens in mild and severe acute respiratory infections in Singapore.

To investigate the performance of laboratory methods and clinical case definitions in detecting the viral pathogens for acute respiratory infections (ARIs) from a prospective community cohort and hospital inpatients, nasopharyngeal swabs from cohort members reporting ARIs (community-ARI) and inpatients admitted with ARIs (inpatient-ARI) were tested by Singleplex Real Time-Polymerase Chain Reaction (SRT-PCR), multiplex RT-PCR (MRT-PCR) and pathogen-chip system (PathChip) between April 2012 and December 2013. Community-ARI and inpatient-ARI was also combined with mild and severe cases of influenza from a historical prospective study as mild-ARI and severe-ARI respectively to evaluate the performance of clinical case definitions. We analysed 130 community-ARI and 140 inpatient-ARI episodes (5 inpatient-ARI excluded because multiple pathogens were detected), involving 138 and 207 samples respectively. Detection by PCR declined with days post-onset for influenza virus; decrease was faster for community-ARI than for inpatient-ARI. No such patterns were observed for non-influenza respiratory virus infections. PathChip added substantially to viruses detected for community-ARI only. Clinical case definitions discriminated influenza from other mild-ARI but performed poorly for severe-ARI and for older participants. Rational strategies for diagnosis and surveillance of influenza and other respiratory virus must acknowledge the differences between ARIs presenting in community and hospital settings

Evolutionary Computation, Optimization and Learning Algorithms for Data Science

A large number of engineering, science and computational problems have yet to be solved in a computationally efficient way. One of the emerging challenges is how evolving technologies grow towards autonomy and intelligent decision making. This leads to collection of large amounts of data from various sensing and measurement technologies, e.g., cameras, smart phones, health sensors, smart electricity meters, and environment sensors. Hence, it is imperative to develop efficient algorithms for generation, analysis, classification, and illustration of data. Meanwhile, data is structured purposefully through different representations, such as large-scale networks and graphs. We focus on data science as a crucial area, specifically focusing on a curse of dimensionality (CoD) which is due to the large amount of generated/sensed/collected data. This motivates researchers to think about optimization and to apply nature-inspired algorithms, such as evolutionary algorithms (EAs) to solve optimization problems. Although these algorithms look un-deterministic, they are robust enough to reach an optimal solution. Researchers do not adopt evolutionary algorithms unless they face a problem which is suffering from placement in local optimal solution, rather than global optimal solution. In this chapter, we first develop a clear and formal definition of the CoD problem, next we focus on feature extraction techniques and categories, then we provide a general overview of meta-heuristic algorithms, its terminology, and desirable properties of evolutionary algorithms

Finite element analysis and calculation method of residual flexural capacity of post-fire RC beams

Fire tests and subsequent bending tests of fourreinforced concrete (RC) beamswere performed. Based on these tests, the post-fire performance of RCbeams was further studied using finite element simulation through reasonable selection of suitable thermal and thermodynamic parameters of steel and concrete materials. A thermodynamic model of RC beams with three sides under fire was built using finite element analysis(FEA)software ABAQUS. The FEA model was validated with the results of fire tests. Different factors were taken into account for further parametric studies in fire using the proposed FE model.The results show that the main factors affecting the fire resistance of the beamsare the thickness of the concretecover, reinforcement ratio of longitudinal steel,the fire exposure timeandthe fire exposure sides. Based on the strength reduction formula at high temperature of steel and concrete, animproved section method was proposed to develop a calculation formula to calculate the flexural capacity of RC beams after fire. The theoretical calculation method proposed in this paper shows good agreement with FEA results, which can be used to calculate the flexuralcapacity of RC beams after fire

Multiscale modelling of auxin transport in the plant-root elongation zone

In the root elongation zone of a plant, the hormone auxin moves in a polar manner due to active transport facilitated by spatially distributed influx and efflux carriers present on the cell membranes. To understand how the cell-scale active transport and passive diffusion combine to produce the effective tissue-scale flux, we apply asymptotic methods to a cell-based model of auxin transport to derive systematically a continuum description from the spatially discrete one. Using biologically relevant parameter values, we show how the carriers drive the dominant tissue-scale auxin flux and we predict how the overall auxin dynamics are affected by perturbations to these carriers, for example, in knockout mutants. The analysis shows how the dominant behaviour depends on the cells' lengths, and enables us to assess the relative importance of the diffusive auxin flux through the cell wall. Other distinguished limits are also identified and their potential roles discussed. As well as providing insight into auxin transport, the study illustrates the use of multiscale (cell to tissue) methods in deriving simplified models that retain the essential biology and provide understanding of the underlying dynamics