A plant virus causes symptoms through the deployment of a host-mimicking protein domain to attract the insect vector.

During compatible plant-virus interactions, viruses can interfere with the normal developmental program of their hosts, leading to the appearance of phenotypes that we usually identify as ‘’symptoms of infection’’ (leaf curling and yellowing, stunting, dwarfism, necrosis). Despite their relevance, the molecular mechanisms underlying symptom induction and their biological meaning, if any, remain poorly understood. By using tomato yellow leaf curl virus (TYLCV, Geminivirus) as model, we have isolated C4 as the main protein responsible for the induction of TYLCV-associated symptoms in tomato. C4, by mimicking a host protein domain, the Conserved C-termini in LAZY1 protein family (CCL) domain, physically interacts with the RCC1-like domain-containing plant proteins (RLDs). By interacting with the RLDs through the CCL-like domain, C4 displaces one endogenous interactor, LAZY (LZY), interfering with RLD functions in processes such as auxin signaling and endomembrane trafficking, which correlates with the manifestation of symptoms. Surprisingly, we observed that appearance of C4-mediated symptoms in tomato plants plays no major role in viral replication nor movement, but they serve as attractants for the insect vector, the whitefly Bemisia tabaci, which preferentially feeds on tomato plants exhibiting strong symptoms of viral infection. These results suggest that, during plant-virus co-evolution, symptoms may have appeared as a strategy to promote viral transmission by the insect vector, at least in some specific plant-virus-vector pathosystems.Work in RLD’s lab is partially funded by the Excellence Strategy of the German Federal and State Governments, the ERC-COG GemOmics (101044142), the DeutscheForschungsgemeinschaft (DFG, German Research foundation) (project numbers LO 2314/1-1 and SBF 1101/3, C08), and a Royal Society Newton Advance grant (NA140481 – NAF\R2\180857). EA is the recipient of a Marie Skłodowska-Curie Grant from the European Union’s Horizon 2020 Research and Innovation Program (Grant 896910-GeminiDECODER). Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Sequential chemotherapy and intensity-modulated radiation therapy in the management of locoregionally advanced nasopharyngeal carcinoma: Experience of 370 consecutive cases

<p>Abstract</p> <p>Introduction</p> <p>To investigate the outcome of locoregionally advanced nasopharyngeal carcinoma (NPC) treated with intensity-modulated radiation therapy (IMRT) after induction chemotherapy, with or without concomitant chemotherapy.</p> <p>Methods</p> <p>Between August 2003 and March 2007, 370 patients with locoregionally advanced NPC were treated with IMRT. Presenting stages were stage IIB in 62, stage III in 197, and stage IVA/B in 111 patients. All patients except for 36 patients with cervical lymphadenopathy of 4 cm or less in diameter received 2 cycles of cisplatin-based neoadjuvant chemotherapy. Forty-eight patients received cisplatin-based concurrent chemotherapy as well.</p> <p>Results</p> <p>With a median follow-up time of 31 months (range 5 to 61 months), the 3-year local control, regional control, metastasis-free survival (MFS), disease-free survival (DFS) and overall survival (OS) rates were 95%, 97%, 86%, 81% and 89%, respectively. Multivariate analyses revealed that both age (≤ 60 vs. >60) and N-classification are significant prognosticators for OS (P = 0.001, hazard ratio [HR] 2.395, 95% confidence interval [CI] 1.432-4.003; P = 0.012, hazard ratio [HR] 2.614, 95% confidence interval [CI] 1.235-5.533); And N-classification is the only significant predicative factor for MFS (P = 0.002, [HR] 1.99, 95% CI 1.279-3.098). T-classification and concurrent chemotherapy were not significant prognostic factors for local/regional control, MFS, DFS, or OS. Subgroup analysis revealed that concurrent chemotherapy provided no significant benefit to IMRT in locoregionally advanced NPC, but was responsible for higher rates of grade 3 or 4 acute toxicities (50% vs. 29.8%, P < 0.005). No grade 3 or 4 late toxicity including xerostomia was observed. However, two patients treated with IMRT and neoadjuvant but without concurrent and adjuvant chemotherapy died of treatment related complications.</p> <p>Conclusion</p> <p>IMRT following neoadjuvant chemotherapy produced a superb outcome in terms of local control, regional control, MFS, DFS, and OS rates in patients with stage IIB to IVB NPC. Effective treatment strategy is urgently needed for distant control in patients diagnosed with locoregionally advanced NPC.</p

Quantum Neuronal Sensing of Quantum Many-Body States on a 61-Qubit Programmable Superconducting Processor

Classifying many-body quantum states with distinct properties and phases of matter is one of the most fundamental tasks in quantum many-body physics. However, due to the exponential complexity that emerges from the enormous numbers of interacting particles, classifying large-scale quantum states has been extremely challenging for classical approaches. Here, we propose a new approach called quantum neuronal sensing. Utilizing a 61 qubit superconducting quantum processor, we show that our scheme can efficiently classify two different types of many-body phenomena: namely the ergodic and localized phases of matter. Our quantum neuronal sensing process allows us to extract the necessary information coming from the statistical characteristics of the eigenspectrum to distinguish these phases of matter by measuring only one qubit. Our work demonstrates the feasibility and scalability of quantum neuronal sensing for near-term quantum processors and opens new avenues for exploring quantum many-body phenomena in larger-scale systems.Comment: 7 pages, 3 figures in the main text, and 13 pages, 13 figures, and 1 table in supplementary material

Experimental quantum computational chemistry with optimised unitary coupled cluster ansatz

Simulation of quantum chemistry is one of the most promising applications of quantum computing. While recent experimental works have demonstrated the potential of solving electronic structures with variational quantum eigensolver (VQE), the implementations are either restricted to nonscalable (hardware efficient) or classically simulable (Hartree-Fock) ansatz, or limited to a few qubits with large errors for the more accurate unitary coupled cluster (UCC) ansatz. Here, integrating experimental and theoretical advancements of improved operations and dedicated algorithm optimisations, we demonstrate an implementation of VQE with UCC for H_2, LiH, F_2 from 4 to 12 qubits. Combining error mitigation, we produce high-precision results of the ground-state energy with error suppression by around two orders of magnitude. For the first time, we achieve chemical accuracy for H_2 at all bond distances and LiH at small bond distances in the experiment. Our work demonstrates a feasible path towards a scalable solution to electronic structure calculation, validating the key technological features and identifying future challenges for this goal.Comment: 8 pages, 4 figures in the main text, and 29 pages supplementary materials with 16 figure

Fast character modeling with sketch-based PDE surfaces

© 2020, The Author(s). Virtual characters are 3D geometric models of characters. They have a lot of applications in multimedia. In this paper, we propose a new physics-based deformation method and efficient character modelling framework for creation of detailed 3D virtual character models. Our proposed physics-based deformation method uses PDE surfaces. Here PDE is the abbreviation of Partial Differential Equation, and PDE surfaces are defined as sculpting force-driven shape representations of interpolation surfaces. Interpolation surfaces are obtained by interpolating key cross-section profile curves and the sculpting force-driven shape representation uses an analytical solution to a vector-valued partial differential equation involving sculpting forces to quickly obtain deformed shapes. Our proposed character modelling framework consists of global modeling and local modeling. The global modeling is also called model building, which is a process of creating a whole character model quickly with sketch-guided and template-based modeling techniques. The local modeling produces local details efficiently to improve the realism of the created character model with four shape manipulation techniques. The sketch-guided global modeling generates a character model from three different levels of sketched profile curves called primary, secondary and key cross-section curves in three orthographic views. The template-based global modeling obtains a new character model by deforming a template model to match the three different levels of profile curves. Four shape manipulation techniques for local modeling are investigated and integrated into the new modelling framework. They include: partial differential equation-based shape manipulation, generalized elliptic curve-driven shape manipulation, sketch assisted shape manipulation, and template-based shape manipulation. These new local modeling techniques have both global and local shape control functions and are efficient in local shape manipulation. The final character models are represented with a collection of surfaces, which are modeled with two types of geometric entities: generalized elliptic curves (GECs) and partial differential equation-based surfaces. Our experiments indicate that the proposed modeling approach can build detailed and realistic character models easily and quickly

Towards the biogeography of prokaryotic genes

Funding was provided by the European Union’s Horizon 2020 Research and Innovation Programme (grant 686070: DD-DeCaF to P.B.) and Marie Skłodowska-Curie Actions (grant 713673 to A.R.d.R.), the European Research Council (ERC) MicrobioS (ERC-AdG-669830 to P.B.), JTC project jumpAR (01KI1706 to P.B.), a BMBF Grant (grant 031L0181A: LAMarCK to P.B.), the European Molecular Biology Laboratory (P.B.), the ETH and Helmut Horten Foundation (S.S.), the National Key R&D Program of China (grant 2020YFA0712403 to X.-M.Z.), (grant 61932008 to X.-M.Z.; grant 61772368 to X.-M.Z.; grant 31950410544 to L.P.C.), the Shanghai Municipal Science and Technology Major Project (grant 2018SHZDZX01 to X.-M.Z. and L.P.C.) and Zhangjiang Lab (X.-M.Z. and L.P.C.), the International Development Research Centre (grant 109304, EMBARK under the JPI AMR framework; to L.P.C.), la Caixa Foundation (grant 100010434, fellowship code LCF/BQ/DI18/11660009 to A.R.d.R.), the Severo Ochoa Program for Centres of Excellence in R&D from the Agencia Estatal de Investigación of Spain (grant SEV-2016-0672 (2017–2021) to C.P.C.), the Ministerio de Ciencia, Innovación y Universidades (grant PGC2018-098073-A-I00 MCIU/AEI/FEDER to J.H.-C. and J.G.-L.), the Innovation Fund Denmark (grant 4203-00005B, PNM), the Biotechnology and Biological Sciences research Council (BBSrC) Gut MicroInstitute Strategic Programmebes and Health BB/r012490/1 and its constituent project BBS/e/F/000Pr10355 (F.H.). R.A. is a member of the Collaboration for joint PhD degree between EMBL and Heidelberg University, Faculty of Biosciences.Peer reviewe