The developmental plasticity of plants relies on the remarkable ability of the meristems to integrate nutrient and energy availability with environmental signals. Meristems in root and shoot apexes share highly similar molecular players but are spatially separated by soil. Whether and how these two meristematic tissues have differential activation requirements for local nutrient, hormone, and environmental cues (e.g., light) remain enigmatic in photosynthetic plants. Here, we report that the activation of root and shoot apexes relies on distinct glucose and light signals. Glucose energy signaling is sufficient to activate target of rapamycin (TOR) kinase in root apexes. In contrast, both the glucose and light signals are required for TOR activation in shoot apexes. Strikingly, exogenously applied auxin is able to replace light to activate TOR in shoot apexes and promote true leaf development. A relatively low concentration of auxin in the shoot and high concentration of auxin in the root might be responsible for this distinctive light requirement in root and shoot apexes, because light is required to promote auxin biosynthesis in the shoot. Furthermore, we reveal that the small GTPase Rho-related protein 2 (ROP2) transduces light-auxin signal to activate TOR by direct interaction, which, in turn, promotes transcription factors E2Fa,b for activating cell cycle genes in shoot apexes. Consistently, constitutively activatedROP2plants stimulate TOR in the shoot apex and cause true leaf development even without light. Together, our findings establish a pivotal hub role of TOR signaling in integrating different environmental signals to regulate distinct developmental transition and growth in the shoot and root.

An important security challenge is to protect the execution of security-sensitive code on legacy systems from malware that may infect the OS, applications, or system devices. Prior work experienced a tradeoff between the level of security achieved and efficiency. In this work, we leverage the features of modern processors from AMD and Intel to overcome the tradeoff to simultaneously achieve a high level of security and high performance.We present TrustVisor, a special-purpose hypervisor that provides code integrity as well as data integrity and secrecy for selected portions of an application. TrustVisor achieves a high level of security, first because it can protect sensitive code at a very fine granularity, and second because it has a very small code base (only around 6K lines of code) that makes verification feasible. TrustVisor can also attest the existence of isolated execution to an external entity. We have implemented TrustVisor to protect security-sensitive code blocks while imposing less than 7% overhead on the legacy OS and its applications in the common case.

Results of a search for new phenomena in final states with an energetic jet and large missing transverse momentum are reported. The search uses proton-proton collision data corresponding to an integrated luminosity of 3.2 fb −1 at ffiffi ffi s p ¼ 13 TeV collected in 2015 with the ATLAS detector at the Large Hadron Collider. Events are required to have at least one jet with a transverse momentum above 250 GeV and no leptons. Several signal regions are considered with increasing missing-transverse-momentum requirements between E miss T > 250 GeV and E miss T > 700 GeV. Good agreement is observed between the number of events in data and Standard Model predictions. The results are translated into exclusion limits in models with large extra spatial dimensions, pair production of weakly interacting dark-matter candidates, and the production of supersymmetric particles in several compressed scenarios.

The luminosity determination for the ATLAS detector at the LHC during pp collisions at  8 TeV in 2012 is presented. The evaluation of the luminosity scale is performed using several luminometers, and comparisons between these luminosity detectors are made to assess the accuracy, consistency and long-term stability of the results. A luminosity uncertainty of is obtained for the of pp collision data delivered to ATLAS at  8 TeV in 2012.

Visual electrophysiology measurements are important for ophthalmic diagnostic testing. Electrodes with combined optical transparency and softness are highly desirable, and sometimes indispensable for many ocular electrophysiology measurements. Here we report the fabrication of soft graphene contact lens electrodes (GRACEs) with broad-spectrum optical transparency, and their application in conformal, full-cornea recording of electroretinography (ERG) from cynomolgus monkeys. The GRACEs give higher signal amplitude than conventional ERG electrodes in recordings of various full-field ERG responses. High-quality topographic mapping of multifocal ERG under simultaneous fundus monitoring is realized. A conformal and tight interface between the GRACEs and cornea is revealed. Neither corneal irritation nor abnormal behavior of the animals is observed after ERG measurements with GRACEs. Furthermore, spatially resolved ERG recordings on rabbits with graphene multi-electrode array reveal a stronger signal at the central cornea than the periphery. These results demonstrate the unique capabilities of the graphene-based electrodes for in vivo visual electrophysiology studies.

The multidomain target of rapamycin (TOR) is an atypical serine/threonine protein kinase resembling phosphatidylinositol lipid kinases, but retains high sequence identity and serves a remarkably conserved role as a master signalling integrator in yeasts, plants, and humans. TOR dynamically orchestrates cell metabolism, biogenesis, organ growth, and development transitions in response to nutrient, energy, hormone, and environmental cues. Here we review recent findings on the versatile and complex roles of TOR in transcriptome reprogramming, seedling, root, and shoot growth, and root hair production activated by sugar and energy signalling. We explore how coordination of TOR-mediated light and hormone signalling is involved in root and shoot apical meristem activation, proliferation of leaf primordia, cotyledon/leaf greening, and hypocotyl elongation. We also discuss the emerging TOR functions in response to sulfur assimilation and metabolism and consider potential molecular links and positive feedback loops between TOR, sugar, energy, and other essential macronutrients.

This paper presents a measurement of the double-differential cross section for the Drell-Yan Z/γ * → + − and photon-induced γγ → + − processes where is an electron or muon. The measurement is performed for invariant masses of the lepton pairs, m , between 116 GeV and 1500 GeV using a sample of 20.3 fb −1 of pp collisions data at centre-of-mass energy of √ s = 8 TeV collected by the ATLAS detector at the LHC in 2012. The data are presented double differentially in invariant mass and absolute dilepton rapidity as well as in invariant mass and absolute pseudorapidity separation of the lepton pair. The single-differential cross section as a function of m is also reported. The electron and muon channel measurements are combined and a total experimental precision of better than 1% is achieved at low m . A comparison to next-to-next-to-leading order perturbative QCD predictions using several recent parton distribution functions and including next-to-leading order electroweak effects indicates the potential of the data to constrain parton distribution functions. In particular, a large impact of the data on the photon PDF is demonstrated. JHEP08(2016)009The ATLAS collaboration 44 IntroductionThe Drell-Yan (DY) process [1] of lepton pair production in hadronic interactions, pp → Z/γ * + X with Z/γ * → + − , is a powerful tool in understanding the nature of partonic interactions and of hadronic structure in detail. The study of this process has been fundamental in developing theoretical perturbative calculations of quantum chromodynamics (QCD) which are now performed at next-to-next-to-leading-order (NNLO) accuracy [2][3][4][5]. Measurements from the Large Hadron Collider (LHC) of neutral-and charged-current Drell-Yan processes mediated by Z/γ * and W exchange respectively at centre-of-mass energies of √ s = 7 TeV and 8 TeV have been recently published by the ATLAS [6-8], CMS [9][10][11][12] and LHCb [13][14][15][16][17] collaborations. These data provide new constraints on the parton distribution functions (PDFs) of the proton, some of which have been used in recent global PDF fits [18][19][20].Although on-shell Z and W boson measurements provide the greatest experimental precision, they are restricted in the kinematic range of partonic momentum fraction x, and four-momentum transfer Q = m , the invariant mass of the dilepton pair. Offshell measurements provide complementary constraints in a wider range of x and Q. In the neutral-current case, the off-shell measurements are dominated by the electromagnetic quark couplings to the virtual photon γ * , whereas the on-shell measurements are dominated by the weak axial and vector couplings of the quarks to the Z boson. Therefore, the measurements have different sensitivity to the up-type and down-type quarks. At large m the measurements offer constraints on the large-x antiquark PDFs which are poorly known. In addition, off-shell measurements may also be sensitive to the largely unconstrained photon PDF [7, 8, 21, 22] through the photon-induced (PI) process γγ → + ...

Automated brain lesion segmentation provides valuable information for the analysis and intervention of patients. In particular, methods that are based on convolutional neural networks (CNNs) have achieved state-of-the-art segmentation performance. However, CNNs usually require a decent amount of annotated data, which may be costly and time-consuming to obtain. Since unannotated data is generally abundant, it is desirable to use unannotated data to improve the segmentation performance for CNNs when limited annotated data is available. In this work, we propose a semi-supervised learning (SSL) approach to brain lesion segmentation, where unannotated data is incorporated into the training of CNNs. We adapt the mean teacher model, which is originally developed for SSL-based image classification, for brain lesion segmentation. Assuming that the network should produce consistent outputs for similar inputs, a loss of segmentation consistency is designed and integrated into a self-ensembling framework. Self-ensembling exploits the information in the intermediate training steps, and the ensemble prediction based on the information can be closer to the correct result than the single latest model. To exploit such information, we build a student model and a teacher model, which share the same CNN architecture for segmentation. The student and teacher models are updated alternately. At each step, the student model learns from the teacher model by minimizing the weighted sum of the segmentation loss computed from annotated data and the segmentation consistency loss between the teacher and student models computed from unannotated data. Then, the teacher model is updated by combining the updated student model with the historical information of teacher models using an exponential moving average strategy. For demonstration, the proposed approach was evaluated on ischemic stroke lesion segmentation. Results indicate that the proposed method improves stroke lesion segmentation with the incorporation of unannotated data and outperforms competing SSL-based methods.