Ikerbasque

About: Ikerbasque is a other organization based out in Bilbao, Spain. It is known for research contribution in the topics: Graphene & Population. The organization has 713 authors who have published 7967 publications receiving 231990 citations. The organization is also known as: Basque Foundation for Science.

Diagnostic Criteria for Mild Cognitive Impairment in Parkinson’s Disease: Movement Disorder Society Task Force Guidelines

Abstract: Mild cognitive impairment is common in nondemented Parkinson's disease (PD) patients and may be a harbinger of dementia. In view of its importance, the Movement Disorder Society commissioned a task force to delineate diagnostic criteria for mild cognitive impairment in PD. The proposed diagnostic criteria are based on a literature review and expert consensus. This article provides guidelines to characterize the clinical syndrome and methods for its diagnosis. The criteria will require validation, and possibly refinement, as additional research improves our understanding of the epidemiology, presentation, neurobiology, assessment, and long-term course of this clinical syndrome. These diagnostic criteria will support future research efforts to identify at the earliest stage those PD patients at increased risk of progressive cognitive decline and dementia who may benefit from clinical interventions at a predementia stage.

Optical nano-imaging of gate-tunable graphene plasmons

Abstract: The ability to manipulate optical fields and the energy flow of light is central to modern information and communication technologies, as well as quantum information processing schemes However, because photons do not possess charge, a way of controlling them efficiently by electrical means has so far proved elusive A promising way to achieve electric control of light could be through plasmon polaritons—coupled excitations of photons and charge carriers—in graphene In this two-dimensional sheet of carbon atoms, it is expected that plasmon polaritons and their associated optical fields can readily be tuned electrically by varying the graphene carrier density Although evidence of optical graphene plasmon resonances has recently been obtained spectroscopically, no experiments so far have directly resolved propagating plasmons in real space Here we launch and detect propagating optical plasmons in tapered graphene nanostructures using near-field scattering microscopy with infrared excitation light We provide real-space images of plasmon fields, and find that the extracted plasmon wavelength is very short—more than 40 times smaller than the wavelength of illumination We exploit this strong optical field confinement to turn a graphene nanostructure into a tunable resonant plasmonic cavity with extremely small mode volume The cavity resonance is controlled in situ by gating the graphene, and in particular, complete switching on and off of the plasmon modes is demonstrated, thus paving the way towards graphene-based optical transistors This successful alliance between nanoelectronics and nano-optics enables the development of active subwavelength-scale optics and a plethora of nano-optoelectronic devices and functionalities, such as tunable metamaterials, nanoscale optical processing, and strongly enhanced light–matter interactions for quantum devices and biosensing applications

Present and Future of Surface-Enhanced Raman Scattering

Abstract: The discovery of the enhancement of Raman scattering by molecules adsorbed on nanostructured metal surfaces is a landmark in the history of spectroscopic and analytical techniques. Significant experimental and theoretical effort has been directed toward understanding the surface-enhanced Raman scattering (SERS) effect and demonstrating its potential in various types of ultrasensitive sensing applications in a wide variety of fields. In the 45 years since its discovery, SERS has blossomed into a rich area of research and technology, but additional efforts are still needed before it can be routinely used analytically and in commercial products. In this Review, prominent authors from around the world joined together to summarize the state of the art in understanding and using SERS and to predict what can be expected in the near future in terms of research, applications, and technological development. This Review is dedicated to SERS pioneer and our coauthor, the late Prof. Richard Van Duyne, whom we lost during the preparation of this article.

Next Generation of Fluorine-Containing Pharmaceuticals, Compounds Currently in Phase II–III Clinical Trials of Major Pharmaceutical Companies: New Structural Trends and Therapeutic Areas

Abstract: Compounds Currently in Phase II−III Clinical Trials of Major Pharmaceutical Companies: New Structural Trends and Therapeutic Areas Yu Zhou,† Jiang Wang,† Zhanni Gu,† Shuni Wang,† Wei Zhu,† Jose ́ Luis Aceña,*,‡,§ Vadim A. Soloshonok,*,‡,∥ Kunisuke Izawa,* and Hong Liu*,† †Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zu Chong Zhi Road, Shanghai 201203, China ‡Department of Organic Chemistry I, Faculty of Chemistry, University of the Basque Country UPV/EHU, Paseo Manuel Lardizab́al 3, 20018 San Sebastiań, Spain Department of Organic Chemistry, Autońoma University of Madrid, Cantoblanco, 28049 Madrid, Spain IKERBASQUE, Basque Foundation for Science, María Díaz de Haro 3, 48013 Bilbao, Spain Hamari Chemicals Ltd., 1-4-29 Kunijima, Higashi-Yodogawa-ku, Osaka, Japan 533-0024

Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification.

Abstract: Summary State-of-the-art light and electron microscopes are capable of acquiring large image datasets, but quantitatively evaluating the data often involves manually annotating structures of interest. This process is time-consuming and often a major bottleneck in the evaluation pipeline. To overcome this problem, we have introduced the Trainable Weka Segmentation (TWS), a machine learning tool that leverages a limited number of manual annotations in order to train a classifier and segment the remaining data automatically. In addition, TWS can provide unsupervised segmentation learning schemes (clustering) and can be customized to employ user-designed image features or classifiers. Availability and implementation TWS is distributed as open-source software as part of the Fiji image processing distribution of ImageJ at http://imagej.net/Trainable_Weka_Segmentation . Contact ignacio.arganda@ehu.eus. Supplementary information Supplementary data are available at Bioinformatics online.