Universal conductivity and dimensional crossover in multi-layer graphene

We show, by exact Renormalization Group methods, that in multi-layer graphene the dimensional crossover energy scale is decreased by the intra-layer interaction, and that for temperatures and frequencies greater than such scale the conductivity is close to the one of a stack of independent layers up to small corrections

Spin Hall insulators beyond the Helical Luttinger model

We consider the interacting, spin-conserving, extended Kane-Mele-Hubbard model, andwe rigorously establish the exact quantization of the edge spin conductance and the validity of the helical Luttinger liquid relations for Drude weights and susceptibilities. Our analysis takes fully into account lattice effects, typically neglected in the helical Luttinger model approximation, which play an essential role for universality. The analysis is based on exact renormalization-group methods and on a combination of lattice and emergent Ward identities, which enable the emergent chiral anomaly to be related with the finite renormalizations due to lattice corrections

Protein Crosslinks Influence Food Digestion

Enzymatic crosslinking is increasingly applied to confer specific properties to different proteins and, consequently, to food products of which they are components. Among the most investigated enzymes, transglutaminases (in particular the microbial isoform, mTG) and various oxidative biocatalysts are having special attention by food biotechnology researchers. mTG catalyzes isopeptide bond formation among protein molecules, leading to inter-molecular crosslinks and being able to produce both homo- and hetero-polymers. Its peculiar properties, such as the calcium independency, the broad substrate specificity, the stability over a wide range of temperatures and pH values, make such enzyme an effective tool to modify the characteristics of many protein-based foods

You're the Coach: A Guide for Parents of New Drivers, December 8, 2015

This publication is a guide for parents and guardians of teenagers learning to drive. It should be used with the Iowa Driver’s Manual to aid you in instructing your new driver about how to safely and responsibly operate a motor vehicle. Since the task of driving is affected by changing conditions, this manual does not attempt to cover all situations that may arise

The Current and Evolving Landscape of First-Line Treatments for Advanced Renal Cell Carcinoma

Agents targeting the vascular endothelial growth factor (VEGF) and its receptors (VEGFRs), as well as the mammalian target of rapamycin (mTOR) and immune checkpoint receptor programmed death 1 (PD-1) signaling pathway have improved clinical outcomes for patients with advanced renal cell carcinoma (RCC). The VEGFR tyrosine kinase inhibitors (TKIs) pazopanib and sunitinib are FDA-approved first-line treatment options for advanced RCC; however, other treatment options in this setting are available, including the recently approved combination of nivolumab (anti-PD-1) and ipilimumab (anti-cytotoxic T-lymphocyte-associated protein-4 [CTLA-4]) for patients with intermediate or poor risk. Unfortunately, treatment guideline recommendations provide little guidance to aid first-line treatment choice. In addition, several ongoing randomized phase III trials of investigational first-line regimens may complicate the RCC treatment paradigm if these agents gain approval. This article reviews clinical trial and real-world evidence for currently approved and investigational first-line treatment regimens for advanced RCC and provides clinical evidence to aid first-line treatment selection. Implications for Practice: Vascular endothelial growth factor receptor tyrosine kinase inhibitors are approved by the U.S. Food and Drug Administration as first-line treatment options for advanced renal cell carcinoma; however, the treatment paradigm is rapidly evolving. The combination of nivolumab plus ipilimumab was recently approved for intermediate- and poor-risk patients, and other combination strategies and novel first-line agents will likely be introduced soon

Smart dual thermal network

Conventional district heating (DH) systems enable demand aggregation at district level and can provide high centralized heat generation performance values. However, thermal Renewable Energy Sources (RES) deployment at building level still remains low, and exploitation suboptimal, as it is limited by the instantaneous thermal load and storage capacity availability of each building. Buildings play the role of consumers that request a variable amount of heat over time and the thermal network the role of unidirectional heat supplier, without any smart interaction. The FP7 project A2PBEER has developed an innovative Smart Dual Thermal Network concept based on RES and Combined Heat and Power (CHP) as generation technologies, that enables transforming existing suboptimal DH systems, into integrated thermal networks with optimized performance and building level RES system production exploitation. It is based on an innovative Smart Dual Building Thermal Substation concept, which allows a bidirectional heat exchange of the buildings with the thermal network, and to aggregate district level distributed production and storage capacity (Virtual District Plant). With this approach buildings become prosumers maximizing decentralized RES production exploitation, as any possible local heat production surplus on any building of the district, will be delivered to the network to be used by other buildings. Additionally, this thermal network allows the delivery of the energy necessary to meet the heating and cooling demand of the buildings through a single hot water distribution network. In this way, it is possible to upgrade conventional DH systems to district heating and cooling systems, without the construction of a district cooling plant and a dedicated cooling distribution network. Cooling is produced at building level through sorption technologies using locally deployed solar collectors and the thermal network as energy sources. Finally, the district typologies and climatic conditions that maximize the potential of this thermal network concept have been identified.The research activities leading to the described developments and results, were funded by the FP7 project A2PBEER, under grant agreement No 906090. Special thanks to Olof Hallström and ClimateWell AB for making the TRNSYS model of the innovative sorption system and developing the component level simulation work

Correlating densities of centrality and activities in cities : the cases of Bologna (IT) and Barcelona (ES)

This paper examines the relationship between street centrality and densities of commercial and service activities in cities. The aim is to verify whether a correlation exists and whether some 'secondary' activities, i.e. those scarcely specialized oriented to the general public and ordinary daily life, are more linked to street centrality than others. The metropolitan area of Barcelona (Spain) is investigated, and results are compared with those found in a previous work on the city of Bologna (Italy). Street centrality is calibrated in a multiple centrality assessment (MCA) model composed of multiple measures such as closeness, betweenness and straightness. Kernel density estimation (KDE) is used to transform data sets of centrality and activities to one scale unit for correlation analysis between them. Results indicate that retail and service activities in both Bologna and Barcelona tend to concentrate in areas with better centralities, and that secondary activities exhibit a higher correlation

PredIG: a predictor of T-cell immunogenicity

The identification of immunogenic epitopes (such as fragments of proteins, in particular peptides, that can trigger an immune response) is a fundamental need for immune-based therapies. A computational tool that could predict such immunogenic epitopes would have vast potential applications in biomedicine ranging, from vaccine design against viruses or bacteria to therapeutic vaccination of cancer patients. While there are several methods that predict whether a peptide will be shown to the immune system via the Human Leukocyte Antigen (HLA) proteins of a patient, most of them cannot predict whether such presentation will indeed trigger an immune response. Additionally, T-cell immunogenicity is determined by multiple cellular processes, some of which are often overlooked by the current state-of-the-art immunogenicity predictors. The aim of this project is to build PredIG, an immunogenicity predictor that discriminates immunogenic from non-immunogenic T-cell epitopes given the peptide sequence and the HLA typing. After a careful study of the drivers of antigen processing and presentation on HLA class I molecules and an assessment of the physicochemical factors influencing epitope recognition by T-cell receptors (TCRs), we have used a selection of publicly available tools and in-house developed algorithms to identify the most relevant features that determine epitope immunogenicity. We then used these features to build an immunogenicity predictor (PredIG) modelled by XGBoost against immunogenically validated epitopes by the ImmunoEpitope DataBase (IEDB)(1), the PRIME dataset(2) and the TANTIGEN database(3). Pondering the feature importance in the model, the in-house developed softwares, NOAH for HLA Binding Affinity and NetCleave for Proteasomal Processing were identified as the major contributors to the performance of the model. Once PredIG was developed, we benchmarked the capacity to predict the immunogenicity of validated T-cell epitopes versus a set of state-of-the-art methods (Fig.1). Relevantly, PredIG showed a greater performance than the Immunogenicity predictors from Prime(2) and IEDB(4). Additionally, our results confirm that predicting T-cell immunogenicity based on data from T-cell assays is more accurate than using HLA Binding assays, the method mostly used in the field. An AUC value of 0.67 and an enrichment factor in the TOP10 epitopes of 90% outperforms the predictive performance of the available methods. In the context of the immune response against cancers, Tcell immunogenicity of tumoral mutations has been described as a response biomarker for immunotherapies such as immune checkpoint inhibitors. Similarly, the presence of immune infiltrate in a tumor has been related to a better prognosis for many cancer types. What is missing is the link between T-cell immunogenicity of tumoral mutations and the capacity of a tumor to attract immune cells. For this reason, we correlated the PredIG immunogenicity score obtained in a dataset of the The Cancer Genome Atlas (TCGA) against the tumor infiltrate in such tumors demonstrating that rather the total number of mutations a tumor accumulates, the tumor mutation burden (TMB), it is the number of immunogenic mutations what should be accounted for as biomarker of response

A stochastic gradient method with variance control and variable learning rate for Deep Learning

In this paper we study a stochastic gradient algorithm which rules the increase of the minibatch size in a predefined fashion and automatically adjusts the learning rate by means of a monotone or non -monotone line search procedure. The mini -batch size is incremented at a suitable a priori rate throughout the iterative process in order that the variance of the stochastic gradients is progressively reduced. The a priori rate is not subject to restrictive assumptions, allowing for the possibility of a slow increase in the mini -batch size. On the other hand, the learning rate can vary non -monotonically throughout the iterations, as long as it is appropriately bounded. Convergence results for the proposed method are provided for both convex and non -convex objective functions. Moreover it can be proved that the algorithm enjoys a global linear rate of convergence on strongly convex functions. The low per -iteration cost, the limited memory requirements and the robustness against the hyperparameters setting make the suggested approach well -suited for implementation within the deep learning framework, also for GPGPU-equipped architectures. Numerical results on training deep neural networks for multiclass image classification show a promising behaviour of the proposed scheme with respect to similar state of the art competitors

Interferometría SAR en las islas Shetland del Sur: modelo numérico de elevaciones de la Isla Decepción

Satellite Synthetic Aperture Radar (SAR) interferometry is a technique that allows the generation of altimetric information. The technique is higly useful in remote areas and this paper shows an application of SAR interferometry in the South Shetland Islands (Antarctica). SAR images obtained by the ERS (European Remote Sensing) satellites of the European Space Agency (ESA) have been processed with an interferometric processor developed by the Departament de Geodinàmica i Geofísica of the Universitat de Barcelona in collaboration with the Institut Cartogràfic de Catalunya