Helmholtz-Zentrum Dresden-Rossendorf

See also the editorial by Kuhl and Truhn in this issue.

autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

Hyperspectral image (HSI) classification has become a hot topic in the field of remote sensing. In general, the complex characteristics of hyperspectral data make the accurate classification of such data challenging for traditional machine learning methods. In addition, hyperspectral imaging often deals with an inherently nonlinear relation between the captured spectral information and the corresponding materials. In recent years, deep learning has been recognized as a powerful feature-extraction tool to effectively address nonlinear problems and widely used in a number of image processing tasks. Motivated by those successful applications, deep learning has also been introduced to classify HSIs and demonstrated good performance. This survey paper presents a systematic review of deep learning-based HSI classification literatures and compares several strategies for this topic. Specifically, we first summarize the main challenges of HSI classification which cannot be effectively overcome by traditional machine learning methods, and also introduce the advantages of deep learning to handle these problems. Then, we build a framework that divides the corresponding works into spectral-feature networks, spatial-feature networks, and spectral-spatial-feature networks to systematically review the recent achievements in deep learning-based HSI classification. In addition, considering the fact that available training samples in the remote sensing field are usually very limited and training deep networks require a large number of samples, we include some strategies to improve classification performance, which can provide some guidelines for future studies on this topic. Finally, several representative deep learning-based classification methods are conducted on real HSIs in our experiments.

Science and technologies based on terahertz frequency electromagnetic radiation (100 GHz–30 THz) have developed rapidly over the last 30 years. For most of the 20th Century, terahertz radiation, then referred to as sub-millimeter wave or far-infrared radiation, was mainly utilized by astronomers and some spectroscopists. Following the development of laser based terahertz time-domain spectroscopy in the 1980s and 1990s the field of THz science and technology expanded rapidly, to the extent that it now touches many areas from fundamental science to 'real world' applications. For example THz radiation is being used to optimize materials for new solar cells, and may also be a key technology for the next generation of airport security scanners. While the field was emerging it was possible to keep track of all new developments, however now the field has grown so much that it is increasingly difficult to follow the diverse range of new discoveries and applications that are appearing. At this point in time, when the field of THz science and technology is moving from an emerging to a more established and interdisciplinary field, it is apt to present a roadmap to help identify the breadth and future directions of the field. The aim of this roadmap is to present a snapshot of the present state of THz science and technology in 2017, and provide an opinion on the challenges and opportunities that the future holds. To be able to achieve this aim, we have invited a group of international experts to write 18 sections that cover most of the key areas of THz science and technology. We hope that The 2017 Roadmap on THz science and technology will prove to be a useful resource by providing a wide ranging introduction to the capabilities of THz radiation for those outside or just entering the field as well as providing perspective and breadth for those who are well established. We also feel that this review should serve as a useful guide for government and funding agencies.

Abstract Cancer cells have upregulated glycolysis compared with normal cells, which has led many to the assumption that oxidative phosphorylation (OXPHOS) is downregulated in all cancers. However, recent studies have shown that OXPHOS can be also upregulated in certain cancers, including leukemias, lymphomas, pancreatic ductal adenocarcinoma, high OXPHOS subtype melanoma, and endometrial carcinoma, and that this can occur even in the face of active glycolysis. OXPHOS inhibitors could therefore be used to target cancer subtypes in which OXPHOS is upregulated and to alleviate therapeutically adverse tumor hypoxia. Several drugs including metformin, atovaquone, and arsenic trioxide are used clinically for non-oncologic indications, but emerging data demonstrate their potential use as OXPHOS inhibitors. We highlight novel applications of OXPHOS inhibitors with a suitable therapeutic index to target cancer cell metabolism. Clin Cancer Res; 24(11); 2482–90. ©2018 AACR.

Several machine-learning algorithms have been proposed for remote sensing image classification during the past two decades. Among these machine learning algorithms, Random Forest (RF) and Support Vector Machines (SVM) have drawn attention to image classification in several remote sensing applications. This article reviews RF and SVM concepts relevant to remote sensing image classification and applies a meta-analysis of 251 peer-reviewed journal papers. A database with more than 40 quantitative and qualitative fields was constructed from these reviewed papers. The meta-analysis mainly focuses on 1) the analysis regarding the general characteristics of the studies, such as geographical distribution, frequency of the papers considering time, journals, application domains, and remote sensing software packages used in the case studies, and 2) a comparative analysis regarding the performances of RF and SVM classification against various parameters, such as data type, RS applications, spatial resolution, and the number of extracted features in the feature engineering step. The challenges, recommendations, and potential directions for future research are also discussed in detail. Moreover, a summary of the results is provided to aid researchers to customize their efforts in order to achieve the most accurate results based on their thematic applications.

Fourier transform (FT) is a fundamental step for the data reduction and interpretation of extended x-ray absorption fine structure (EXAFS) spectra. The FT separates backscattering atoms by their radial distance from the absorbing atom (so-called shells). We suggest to routinely complement the FT by a wavelet transform (WT), which provides not only radial distance resolution, but resolves in $k$ space. This information eases the discrimination of atoms by their elemental nature, especially if these atoms are at the same distance. We present an in-depth analysis of the Morlet wavelet, which has specific advantages for EXAFS analysis, including the possibility to estimate Morlet parameter values optimized either for elemental or for spatial resolution. Using an experimental spectrum of $\mathrm{Zn}\text{\ensuremath{-}}\mathrm{Al}$ layered double hydroxide, we demonstrate the discrimination of $\mathrm{Al}$ and $\mathrm{Zn}$ at a similar crystallographic position, in spite of destructive interference substantially reducing signal information. Finally, the extension to multiple scattering paths leads to a deeper understanding of the resolution properties of the WT.

The visualization of an exceptional point in a PT-symmetric directional coupler (DC) is demonstrated. In such a system the exceptional point can be probed by varying only a single parameter. Using the Rayleigh-Schrödinger perturbation theory we prove that the spectrum of a PT-symmetric Hamiltonian is real as long as the radius of convergence has not been reached. We also show how one can use a PT-symmetric directional coupler to measure the radius of convergence for non-PT-symmetric structures. For such systems the physical meaning of the rather mathematical term radius of convergence is exemplified.

The application of nanomaterials (NMs) in biomedicine is increasing rapidly and offers excellent prospects for the development of new non-invasive strategies for the diagnosis and treatment of cancer. In this review, we provide a brief description of cancer pathology and the characteristics that are important for tumor-targeted NM design, followed by an overview of the different types of NMs explored to date, covering synthetic aspects and approaches explored for their application in unimodal and multimodal imaging, diagnosis and therapy. Significant synthetic advances now allow for the preparation of NMs with highly controlled geometry, surface charge, physicochemical properties, and the decoration of their surfaces with polymers and bioactive molecules in order to improve biocompatibility and to achieve active targeting. This is stimulating the development of a diverse range of nanometer-sized objects that can recognize cancer tissue, enabling visualization of tumors, delivery of anti-cancer drugs and/or the destruction of tumors by different therapeutic techniques.

Transition metal dichalcogenides have attracted research interest over the last few decades due to their interesting structural chemistry, unusual electronic properties, rich intercalation chemistry and wide spectrum of potential applications. Despite the fact that the majority of related research focuses on semiconducting transition-metal dichalcogenides (for example, MoS2), recently discovered unexpected properties of WTe2 are provoking strong interest in semimetallic transition metal dichalcogenides featuring large magnetoresistance, pressure-driven superconductivity and Weyl semimetal states. We investigate the sister compound of WTe2, MoTe2, predicted to be a Weyl semimetal and a quantum spin Hall insulator in bulk and monolayer form, respectively. We find that bulk MoTe2 exhibits superconductivity with a transition temperature of 0.10 K. Application of external pressure dramatically enhances the transition temperature up to maximum value of 8.2 K at 11.7 GPa. The observed dome-shaped superconductivity phase diagram provides insights into the interplay between superconductivity and topological physics.

The available data on nuclear fusion cross sections important to energy generation in the Sun and other hydrogen-burning stars and to solar neutrino production are summarized and critically evaluated. Recommended values and uncertainties are provided for key cross sections, and a recommended spectrum is given for $^{8}\mathrm{B}$ solar neutrinos. Opportunities for further increasing the precision of key rates are also discussed, including new facilities, new experimental techniques, and improvements in theory. This review, which summarizes the conclusions of a workshop held at the Institute for Nuclear Theory, Seattle, in January 2009, is intended as a 10-year update and supplement to 1998, Rev. Mod. Phys. 70, 1265.

The synthesis of semiconductor nanowires has been studied intensively worldwide for a wide spectrum of materials. Such low-dimensional nanostructures are not only interesting for fundamental research due to their unique structural and physical properties relative to their bulk counterparts, but also offer fascinating potential for future technological applications. Deeper understanding and sufficient control of the growth of nanowires are central to the current research interest. This Review discusses the various growth processes, with a focus on the vapor-liquid-solid process, which offers an opportunity for the control of spatial positioning of nanowires. Strategies for position-controlled and nanopatterned growth of nanowire arrays are reviewed and demonstrated by selected examples as well as discussed in terms of larger-scale realization and future prospects. Issues on building up nanowire-based electronic and photonic devices are addressed at the end of the Review, accompanied by a brief survey of recent progress demonstrated so far on the laboratory level.

UNLABELLED: This multicenter study examined (18)F-FDG PET measures in the differential diagnosis of Alzheimer's disease (AD), frontotemporal dementia (FTD), and dementia with Lewy bodies (DLB) from normal aging and from each other and the relation of disease-specific patterns to mild cognitive impairment (MCI). METHODS: We examined the (18)F-FDG PET scans of 548 subjects, including 110 healthy elderly individuals ("normals" or NLs), 114 MCI, 199 AD, 98 FTD, and 27 DLB patients, collected at 7 participating centers. Individual PET scans were Z scored using automated voxel-based comparison with generation of disease-specific patterns of cortical and hippocampal (18)F-FDG uptake that were then applied to characterize MCI. RESULTS: Standardized disease-specific PET patterns were developed that correctly classified 95% AD, 92% DLB, 94% FTD, and 94% NL. MCI patients showed primarily posterior cingulate cortex and hippocampal hypometabolism (81%), whereas neocortical abnormalities varied according to neuropsychological profiles. An AD PET pattern was observed in 79% MCI with deficits in multiple cognitive domains and 31% amnesic MCI. (18)F-FDG PET heterogeneity in MCI with nonmemory deficits ranged from absent hypometabolism to FTD and DLB PET patterns. CONCLUSION: Standardized automated analysis of (18)F-FDG PET scans may provide an objective and sensitive support to the clinical diagnosis in early dementia.

The foundation model has recently garnered significant attention due to its potential to revolutionize the field of visual representation learning in a self-supervised manner. While most foundation models are tailored to effectively process RGB images for various visual tasks, there is a noticeable gap in research focused on spectral data, which offers valuable information for scene understanding, especially in remote sensing (RS) applications. To fill this gap, we created for the first time a universal RS foundation model, named SpectralGPT, which is purpose-built to handle spectral RS images using a novel 3D generative pretrained transformer (GPT). Compared to existing foundation models, SpectralGPT 1) accommodates input images with varying sizes, resolutions, time series, and regions in a progressive training fashion, enabling full utilization of extensive RS Big Data; 2) leverages 3D token generation for spatial-spectral coupling; 3) captures spectrally sequential patterns via multi-target reconstruction; and 4) trains on one million spectral RS images, yielding models with over 600 million parameters. Our evaluation highlights significant performance improvements with pretrained SpectralGPT models, signifying substantial potential in advancing spectral RS Big Data applications within the field of geoscience across four downstream tasks: single/multi-label scene classification, semantic segmentation, and change detection.

We apply ultrafast spectroscopy to establish a time-domain hierarchy between structural and electronic effects in a strongly correlated electron system. We discuss the case of the model system ${\mathrm{VO}}_{2}$, a prototypical nonmagnetic compound that exhibits cell doubling, charge localization, and a metal-insulator transition below 340 K. We initiate the formation of the metallic phase by prompt hole photo-doping into the valence band of the low-$T$ insulator. The insulator-to-metal transition is, however, delayed with respect to hole injection, exhibiting a bottleneck time scale, associated with the phonon connecting the two crystallographic phases. This structural bottleneck is observed despite faster depletion of the $d$ bands and is indicative of important bandlike character for this controversial insulator.

We present herein the first synthesis of the water and air stable organometallic aqua complex [^99mTc(OH₂)₃(CO)₃]⁺ directly from [^99mTcO₄]^- in saline under 1 atm of CO. Subsequent substitution of the labile water ligands by a bifunctional ligand attachable to biomolecules enables the introduction of carbonyl complexes in life sciences in general and in nuclear medicine in particular. [...]

Fragment distributions resulting from $\mathrm{Au}+\mathrm{Au}$ collisions at an incident energy of $E/A\phantom{\rule{0ex}{0ex}}=\phantom{\rule{0ex}{0ex}}600\phantom{\rule{0ex}{0ex}}\mathrm{MeV}$ are studied. From the measured fragment and neutron distributions the mass and the excitation energy of the decaying prefragments were determined. A temperature scale was derived from observed yield ratios of He and Li isotopes. The relation between this isotope temperature and the excitation energy of the system exhibits a behavior which is expected for a phase transition. The nuclear vapor regime takes over at an excitation energy of 10 MeV per nucleon, a temperature of 5 MeV, and may be characterized by a density of 0.15--0.3 normal nuclear density.

The concept of spontaneous symmetry breaking is applied to the rotating mean field of nuclei. The description is based on the tilted-axis cranking model, which takes into account that the rotational axis can take any orientation with respect to the deformed density distribution. The appearance of rotational bands in nuclei is analyzed, focusing on weakly deformed nuclei at high angular momentum. The quantization of the angular momentum of the valence nucleons leads to new phenomena. Magnetic rotation represents the quantized rotation of the anisotropic current distribution in a near spherical nucleus. The restricted amount of angular momentum of the valence particles causes band termination. The discrete symmetries of the mean-field Hamiltonian provide a classification scheme of rotational bands. New symmetries result from the combination of the spatial symmetries of the density distribution with the vector of the angular momentum. The author discusses in detail which symmetries appear for a reflection-symmetric density distribution and how they show up in the properties of the rotational bands. In particular, the consequences of rotation about a nonprincipal axis and of breaking the chiral symmetry are analyzed. Also discussed are which symmetries and band structures appear for non-reflection-symmetric mean fields. The consequences of breaking the symmetry with respect to gauge and isospin rotations are sketched. Some analogies outside nuclear physics are mentioned. The application of symmetry-restoring methods to states with large angular momentum is reviewed.

Magnonics is a budding research field in nanomagnetism and nanoscience that addresses the use of spin waves (magnons) to transmit, store, and process information. The rapid advancements of this field during last one decade in terms of upsurge in research papers, review articles, citations, proposals of devices as well as introduction of new sub-topics prompted us to present the first roadmap on magnonics. This is a collection of 22 sections written by leading experts in this field who review and discuss the current status besides presenting their vision of future perspectives. Today, the principal challenges in applied magnonics are the excitation of sub-100 nm wavelength magnons, their manipulation on the nanoscale and the creation of sub-micrometre devices using low-Gilbert damping magnetic materials and its interconnections to standard electronics. To this end, magnonics offers lower energy consumption, easier integrability and compatibility with CMOS structure, reprogrammability, shorter wavelength, smaller device features, anisotropic properties, negative group velocity, non-reciprocity and efficient tunability by various external stimuli to name a few. Hence, despite being a young research field, magnonics has come a long way since its early inception. This roadmap asserts a milestone for future emerging research directions in magnonics, and hopefully, it will inspire a series of exciting new articles on the same topic in the coming years.

By considering the spectral signature as a sequence, recurrent neural networks (RNNs) have been successfully used to learn discriminative features from hyperspectral images (HSIs) recently. However, most of these models only input the whole spectral bands into RNNs directly, which may not fully explore the specific properties of HSIs. In this paper, we propose a cascaded RNN model using gated recurrent units to explore the redundant and complementary information of HSIs. It mainly consists of two RNN layers. The first RNN layer is used to eliminate redundant information between adjacent spectral bands, while the second RNN layer aims to learn the complementary information from nonadjacent spectral bands. To improve the discriminative ability of the learned features, we design two strategies for the proposed model. Besides, considering the rich spatial information contained in HSIs, we further extend the proposed model to its spectral-spatial counterpart by incorporating some convolutional layers. To test the effectiveness of our proposed models, we conduct experiments on two widely used HSIs. The experimental results show that our proposed models can achieve better results than the compared models.