Showing papers by "Kyoto University published in 2018"

Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-Years for 29 Cancer Groups, 1990 to 2016: A Systematic Analysis for the Global Burden of Disease Study.

Abstract: Importance The increasing burden due to cancer and other noncommunicable diseases poses a threat to human development, which has resulted in global political commitments reflected in the Sustainable Development Goals as well as the World Health Organization (WHO) Global Action Plan on Non-Communicable Diseases. To determine if these commitments have resulted in improved cancer control, quantitative assessments of the cancer burden are required. Objective To assess the burden for 29 cancer groups over time to provide a framework for policy discussion, resource allocation, and research focus. Evidence Review Cancer incidence, mortality, years lived with disability, years of life lost, and disability-adjusted life-years (DALYs) were evaluated for 195 countries and territories by age and sex using the Global Burden of Disease study estimation methods. Levels and trends were analyzed over time, as well as by the Sociodemographic Index (SDI). Changes in incident cases were categorized by changes due to epidemiological vs demographic transition. Findings In 2016, there were 17.2 million cancer cases worldwide and 8.9 million deaths. Cancer cases increased by 28% between 2006 and 2016. The smallest increase was seen in high SDI countries. Globally, population aging contributed 17%; population growth, 12%; and changes in age-specific rates, −1% to this change. The most common incident cancer globally for men was prostate cancer (1.4 million cases). The leading cause of cancer deaths and DALYs was tracheal, bronchus, and lung cancer (1.2 million deaths and 25.4 million DALYs). For women, the most common incident cancer and the leading cause of cancer deaths and DALYs was breast cancer (1.7 million incident cases, 535 000 deaths, and 14.9 million DALYs). In 2016, cancer caused 213.2 million DALYs globally for both sexes combined. Between 2006 and 2016, the average annual age-standardized incidence rates for all cancers combined increased in 130 of 195 countries or territories, and the average annual age-standardized death rates decreased within that timeframe in 143 of 195 countries or territories. Conclusions and Relevance Large disparities exist between countries in cancer incidence, deaths, and associated disability. Scaling up cancer prevention and ensuring universal access to cancer care are required for health equity and to fulfill the global commitments for noncommunicable disease and cancer control.

Tisagenlecleucel in Children and Young Adults with B-Cell Lymphoblastic Leukemia

Abstract: Background In a single-center phase 1–2a study, the anti-CD19 chimeric antigen receptor (CAR) T-cell therapy tisagenlecleucel produced high rates of complete remission and was associated with serious but mainly reversible toxic effects in children and young adults with relapsed or refractory B-cell acute lymphoblastic leukemia (ALL) Methods We conducted a phase 2, single-cohort, 25-center, global study of tisagenlecleucel in pediatric and young adult patients with CD19+ relapsed or refractory B-cell ALL The primary end point was the overall remission rate (the rate of complete remission or complete remission with incomplete hematologic recovery) within 3 months Results For this planned analysis, 75 patients received an infusion of tisagenlecleucel and could be evaluated for efficacy The overall remission rate within 3 months was 81%, with all patients who had a response to treatment found to be negative for minimal residual disease, as assessed by means of flow cytometry The rates of event-f

Spectral Normalization for Generative Adversarial Networks

Abstract: One of the challenges in the study of generative adversarial networks is the instability of its training. In this paper, we propose a novel weight normalization technique called spectral normalization to stabilize the training of the discriminator. Our new normalization technique is computationally light and easy to incorporate into existing implementations. We tested the efficacy of spectral normalization on CIFAR10, STL-10, and ILSVRC2012 dataset, and we experimentally confirmed that spectrally normalized GANs (SN-GANs) is capable of generating images of better or equal quality relative to the previous training stabilization techniques.

Convolutional neural networks: an overview and application in radiology

Abstract: Convolutional neural network (CNN), a class of artificial neural networks that has become dominant in various computer vision tasks, is attracting interest across a variety of domains, including radiology. CNN is designed to automatically and adaptively learn spatial hierarchies of features through backpropagation by using multiple building blocks, such as convolution layers, pooling layers, and fully connected layers. This review article offers a perspective on the basic concepts of CNN and its application to various radiological tasks, and discusses its challenges and future directions in the field of radiology. Two challenges in applying CNN to radiological tasks, small dataset and overfitting, will also be covered in this article, as well as techniques to minimize them. Being familiar with the concepts and advantages, as well as limitations, of CNN is essential to leverage its potential in diagnostic radiology, with the goal of augmenting the performance of radiologists and improving patient care. • Convolutional neural network is a class of deep learning methods which has become dominant in various computer vision tasks and is attracting interest across a variety of domains, including radiology. • Convolutional neural network is composed of multiple building blocks, such as convolution layers, pooling layers, and fully connected layers, and is designed to automatically and adaptively learn spatial hierarchies of features through a backpropagation algorithm. • Familiarity with the concepts and advantages, as well as limitations, of convolutional neural network is essential to leverage its potential to improve radiologist performance and, eventually, patient care.

Shifting the limits in wheat research and breeding using a fully annotated reference genome

Abstract: An annotated reference sequence representing the hexaploid bread wheat genome in 21 pseudomolecules has been analyzed to identify the distribution and genomic context of coding and noncoding elements across the A, B, and D subgenomes. With an estimated coverage of 94% of the genome and containing 107,891 high-confidence gene models, this assembly enabled the discovery of tissue- and developmental stage-related coexpression networks by providing a transcriptome atlas representing major stages of wheat development. Dynamics of complex gene families involved in environmental adaptation and end-use quality were revealed at subgenome resolution and contextualized to known agronomic single-gene or quantitative trait loci. This community resource establishes the foundation for accelerating wheat research and application through improved understanding of wheat biology and genomics-assisted breeding.

SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing

Abstract: This paper describes SentencePiece, a language-independent subword tokenizer and detokenizer designed for Neural-based text processing, including Neural Machine Translation. It provides open-source C++ and Python implementations for subword units. While existing subword segmentation tools assume that the input is pre-tokenized into word sequences, SentencePiece can train subword models directly from raw sentences, which allows us to make a purely end-to-end and language independent system. We perform a validation experiment of NMT on English-Japanese machine translation, and find that it is possible to achieve comparable accuracy to direct subword training from raw sentences. We also compare the performance of subword training and segmentation with various configurations. SentencePiece is available under the Apache 2 license at https://github.com/google/sentencepiece.

Analysis of shared heritability in common disorders of the brain

Abstract: Disorders of the brain can exhibit considerable epidemiological comorbidity and often share symptoms, provoking debate about their etiologic overlap. We quantified the genetic sharing of 25 brain disorders from genome-wide association studies of 265,218 patients and 784,643 control participants and assessed their relationship to 17 phenotypes from 1,191,588 individuals. Psychiatric disorders share common variant risk, whereas neurological disorders appear more distinct from one another and from the psychiatric disorders. We also identified significant sharing between disorders and a number of brain phenotypes, including cognitive measures. Further, we conducted simulations to explore how statistical power, diagnostic misclassification, and phenotypic heterogeneity affect genetic correlations. These results highlight the importance of common genetic variation as a risk factor for brain disorders and the value of heritability-based methods in understanding their etiology.

Spectral Normalization for Generative Adversarial Networks

Abstract: One of the challenges in the study of generative adversarial networks is the instability of its training. In this paper, we propose a novel weight normalization technique called spectral normalization to stabilize the training of the discriminator. Our new normalization technique is computationally light and easy to incorporate into existing implementations. We tested the efficacy of spectral normalization on CIFAR10, STL-10, and ILSVRC2012 dataset, and we experimentally confirmed that spectrally normalized GANs (SN-GANs) is capable of generating images of better or equal quality relative to the previous training stabilization techniques.

Metal-Organic Frameworks as Platforms for Catalytic Applications.

Abstract: Metal-organic frameworks (MOFs), also called porous coordination polymers, represent a class of crystalline porous materials built from organic linkers and metal ions/clusters. The unique features of MOFs, including structural diversity and tailorability as well as high surface area, etc., enable them to be a highly versatile platform for potential applications in many fields. Herein, an overview of recent developments achieved in MOF catalysis, including heterogeneous catalysis, photocatalysis, and eletrocatalysis over MOFs and MOF-based materials, is provided. The active sites involved in the catalysts are particularly emphasized. The challenges, future trends, and prospects associated with MOFs and their related materials for catalysis are also discussed.

High performance plasma amyloid-β biomarkers for Alzheimer’s disease

Abstract: To facilitate clinical trials of disease-modifying therapies for Alzheimer's disease, which are expected to be most efficacious at the earliest and mildest stages of the disease, supportive biomarker information is necessary. The only validated methods for identifying amyloid-β deposition in the brain-the earliest pathological signature of Alzheimer's disease-are amyloid-β positron-emission tomography (PET) imaging or measurement of amyloid-β in cerebrospinal fluid. Therefore, a minimally invasive, cost-effective blood-based biomarker is desirable. Despite much effort, to our knowledge, no study has validated the clinical utility of blood-based amyloid-β markers. Here we demonstrate the measurement of high-performance plasma amyloid-β biomarkers by immunoprecipitation coupled with mass spectrometry. The ability of amyloid-β precursor protein (APP)669-711/amyloid-β (Aβ)1-42 and Aβ1-40/Aβ1-42 ratios, and their composites, to predict individual brain amyloid-β-positive or -negative status was determined by amyloid-β-PET imaging and tested using two independent data sets: a discovery data set (Japan, n = 121) and a validation data set (Australia, n = 252 including 111 individuals diagnosed using 11C-labelled Pittsburgh compound-B (PIB)-PET and 141 using other ligands). Both data sets included cognitively normal individuals, individuals with mild cognitive impairment and individuals with Alzheimer's disease. All test biomarkers showed high performance when predicting brain amyloid-β burden. In particular, the composite biomarker showed very high areas under the receiver operating characteristic curves (AUCs) in both data sets (discovery, 96.7%, n = 121 and validation, 94.1%, n = 111) with an accuracy approximately equal to 90% when using PIB-PET as a standard of truth. Furthermore, test biomarkers were correlated with amyloid-β-PET burden and levels of Aβ1-42 in cerebrospinal fluid. These results demonstrate the potential clinical utility of plasma biomarkers in predicting brain amyloid-β burden at an individual level. These plasma biomarkers also have cost-benefit and scalability advantages over current techniques, potentially enabling broader clinical access and efficient population screening.

Quantum circuit learning

Abstract: We propose a classical-quantum hybrid algorithm for machine learning on near-term quantum processors, which we call quantum circuit learning. A quantum circuit driven by our framework learns a given task by tuning parameters implemented on it. The iterative optimization of the parameters allows us to circumvent the high-depth circuit. Theoretical investigation shows that a quantum circuit can approximate nonlinear functions, which is further confirmed by numerical simulations. Hybridizing a low-depth quantum circuit and a classical computer for machine learning, the proposed framework paves the way toward applications of near-term quantum devices for quantum machine learning.

Nanomaterials derived from metal–organic frameworks

Abstract: The thermal transformation of metal–organic frameworks (MOFs) generates a variety of nanostructured materials, including carbon-based materials, metal oxides, metal chalcogenides, metal phosphides and metal carbides. These derivatives of MOFs have characteristics such as high surface areas, permanent porosities and controllable functionalities that enable their good performance in sensing, gas storage, catalysis and energy-related applications. Although progress has been made to tune the morphologies of MOF-derived structures at the nanometre scale, it remains crucial to further our knowledge of the relationship between morphology and performance. In this Review, we summarize the synthetic strategies and optimized methods that enable control over the size, morphology, composition and structure of the derived nanomaterials. In addition, we compare the performance of materials prepared by the MOF-templated strategy and other synthetic methods. Our aim is to reveal the relationship between the morphology and the physico-chemical properties of MOF-derived nanostructures to optimize their performance for applications such as sensing, catalysis, and energy storage and conversion. Nanomaterials derived from metal–organic frameworks (MOFs) show good performance in sensing, gas storage, catalysis and energy-related applications. In this Review, the influence of the morphology of MOF-derived nanostructures on their performance is elucidated, and the opportunities in this field are discussed.

Anti-PD-1 and Anti-CTLA-4 Therapies in Cancer: Mechanisms of Action, Efficacy, and Limitations.

Abstract: Melanoma, a skin cancer associated with high mortality rates, is highly radio- and chemotherapy resistant but can also be very immunogenic. These circumstances have led to a recent surge in research into therapies aiming to boost anti-tumor immune responses in cancer patients. Among these immunotherapies, neutralizing antibodies targeting the immune checkpoints T-lymphocyte-associated protein 4 (CTLA-4) and programmed cell death protein 1 (PD-1) are being hailed as particularly successful. These antibodies have resulted in dramatic improvements in disease outcome and are now clinically approved in many countries. However, the majority of advanced stage melanoma patients do not respond or will relapse, and the hunt for the "magic bullet" to treat the disease continues. This review examines the mechanisms of action and the limitations of anti-PD-1/PD-L1 and anti-CTLA-4 antibodies which are the two types of checkpoint inhibitors currently available to patients and further explores the future avenues of their use in melanoma and other cancers.

Prospects for Observing and Localizing Gravitational-Wave Transients with Advanced LIGO, Advanced Virgo and KAGRA

Abstract: We present possible observing scenarios for the Advanced LIGO, Advanced Virgo and KAGRA gravitational-wave detectors over the next decade, with the intention of providing information to the astronomy community to facilitate planning for multi-messenger astronomy with gravitational waves. We estimate the sensitivity of the network to transient gravitational-wave signals, and study the capability of the network to determine the sky location of the source. We report our findings for gravitational-wave transients, with particular focus on gravitational-wave signals from the inspiral of binary neutron star systems, which are the most promising targets for multi-messenger astronomy. The ability to localize the sources of the detected signals depends on the geographical distribution of the detectors and their relative sensitivity, and 90% credible regions can be as large as thousands of square degrees when only two sensitive detectors are operational. Determining the sky position of a significant fraction of detected signals to areas of 5– 20 deg2 requires at least three detectors of sensitivity within a factor of ∼2 of each other and with a broad frequency bandwidth. When all detectors, including KAGRA and the third LIGO detector in India, reach design sensitivity, a significant fraction of gravitational-wave signals will be localized to a few square degrees by gravitational-wave observations alone.

Primordial Black Holes - Perspectives in Gravitational Wave Astronomy -

Abstract: This article reviews current understanding of primordial black holes (PBHs), with particular focus on those massive examples ( ) which remain at the present epoch, not having evaporated through Hawking radiation. With the detection of gravitational waves by LIGO, we have gained a completely novel observational tool to search for PBHs, complementary to those using electromagnetic waves. Taking the perspective that gravitational-wave astronomy will make significant progress in the coming decades, the purpose of this article is to give a comprehensive review covering a wide range of topics on PBHs. After discussing PBH formation, as well as several inflation models leading to PBH production, we summarize various existing and future observational constraints. We then present topics on formation of PBH binaries, gravitational waves from PBH binaries, and various observational tests of PBHs using gravitational waves.

Mimicking Natural Photosynthesis: Solar to Renewable H2 Fuel Synthesis by Z-Scheme Water Splitting Systems.

Abstract: Visible light-driven water splitting using cheap and robust photocatalysts is one of the most exciting ways to produce clean and renewable energy for future generations. Cutting edge research within the field focuses on so-called “Z-scheme” systems, which are inspired by the photosystem II–photosystem I (PSII/PSI) coupling from natural photosynthesis. A Z-scheme system comprises two photocatalysts and generates two sets of charge carriers, splitting water into its constituent parts, hydrogen and oxygen, at separate locations. This is not only more efficient than using a single photocatalyst, but practically it could also be safer. Researchers within the field are constantly aiming to bring systems toward industrial level efficiencies by maximizing light absorption of the materials, engineering more stable redox couples, and also searching for new hydrogen and oxygen evolution cocatalysts. This review provides an in-depth survey of relevant Z-schemes from past to present, with particular focus on mechanist...

Topological phases of non-Hermitian systems

Abstract: Recent experimental advances in controlling dissipation have brought about unprecedented flexibility in engineering non-Hermitian Hamiltonians in open classical and quantum systems. A particular interest centers on the topological properties of non-Hermitian systems, which exhibit unique phases with no Hermitian counterparts. However, no systematic understanding in analogy with the periodic table of topological insulators and superconductors has been achieved. In this paper, we develop a coherent framework of topological phases of non-Hermitian systems. After elucidating the physical meaning and the mathematical definition of non-Hermitian topological phases, we start with one-dimensional lattices, which exhibit topological phases with no Hermitian counterparts and are found to be characterized by an integer topological winding number even with no symmetry constraint, reminiscent of the quantum Hall insulator in Hermitian systems. A system with a nonzero winding number, which is experimentally measurable from the wave-packet dynamics, is shown to be robust against disorder, a phenomenon observed in the Hatano-Nelson model with asymmetric hopping amplitudes. We also unveil a novel bulk-edge correspondence that features an infinite number of (quasi-)edge modes. We then apply the K-theory to systematically classify all the non-Hermitian topological phases in the Altland-Zirnbauer classes in all dimensions. The obtained periodic table unifies time-reversal and particle-hole symmetries, leading to highly nontrivial predictions such as the absence of non-Hermitian topological phases in two dimensions. We provide concrete examples for all the nontrivial non-Hermitian AZ classes in zero and one dimensions. In particular, we identify a Z2 topological index for arbitrary quantum channels. Our work lays the cornerstone for a unified understanding of the role of topology in non-Hermitian systems.

Towards K-Ion and Na-Ion Batteries as "Beyond Li-Ion".

Abstract: Li-ion battery commercialized by Sony in 1991 has the highest energy-density among practical rechargeable batteries and is widely used in electronic devices, electric vehicles, and stationary energy storage system in the world. Moreover, the battery market is rapidly growing in the world and further fast-growing is expected. With expansion of the demand and applications, price of lithium and cobalt resources is increasing. We are, therefore, motivated to study Na- and K-ion batteries for stationary energy storage system because of much abundant Na and K resources and the wide distribution in the world. In this account, we review developments of Na- and K-ion batteries with mainly introducing our previous and present researches in comparison to that of Li-ion battery.

Genetic analysis of quantitative traits in the Japanese population links cell types to complex human diseases

Abstract: Clinical measurements can be viewed as useful intermediate phenotypes to promote understanding of complex human diseases. To acquire comprehensive insights into the underlying genetics, here we conducted a genome-wide association study (GWAS) of 58 quantitative traits in 162,255 Japanese individuals. Overall, we identified 1,407 trait-associated loci (P < 5.0 × 10−8), 679 of which were novel. By incorporating 32 additional GWAS results for complex diseases and traits in Japanese individuals, we further highlighted pleiotropy, genetic correlations, and cell-type specificity across quantitative traits and diseases, which substantially expands the current understanding of the associated genetics and biology. This study identified both shared polygenic effects and cell-type specificity, represented by the genetic links among clinical measurements, complex diseases, and relevant cell types. Our findings demonstrate that even without prior biological knowledge of cross-phenotype relationships, genetics corresponding to clinical measurements successfully recapture those measurements’ relevance to diseases, and thus can contribute to the elucidation of unknown etiology and pathogenesis. A genome-wide association study (GWAS) of 58 traits using data from the Biobank Japan Project identifies 1,407 loci, 679 of which are novel. Comparison with disease GWASs and analysis of genetic correlations and cell-type enrichment show that these clinical measurements are relevant to human disease.

Fire-extinguishing organic electrolytes for safe batteries

Abstract: Severe safety concerns are impeding the large-scale employment of lithium/sodium batteries. Conventional electrolytes are highly flammable and volatile, which may cause catastrophic fires or explosions. Efforts to introduce flame-retardant solvents into the electrolytes have generally resulted in compromised battery performance because those solvents do not suitably passivate carbonaceous anodes. Here we report a salt-concentrated electrolyte design to resolve this dilemma via the spontaneous formation of a robust inorganic passivation film on the anode. We demonstrate that a concentrated electrolyte using a salt and a popular flame-retardant solvent (trimethyl phosphate), without any additives or soft binders, allows stable charge–discharge cycling of both hard-carbon and graphite anodes for more than 1,000 cycles (over one year) with negligible degradation; this performance is comparable or superior to that of conventional flammable carbonate electrolytes. The unusual passivation character of the concentrated electrolyte coupled with its fire-extinguishing property contributes to developing safe and long-lasting batteries, unlocking the limit toward development of much higher energy-density batteries.

Pristine Metal–Organic Frameworks and their Composites for Energy Storage and Conversion

Abstract: Metal-organic frameworks (MOFs), a new class of crystalline porous organic-inorganic hybrid materials, have recently attracted increasing interest in the field of energy storage and conversion. Herein, recent progress of MOFs and MOF composites for energy storage and conversion applications, including photochemical and electrochemical fuel production (hydrogen production and CO2 reduction), water oxidation, supercapacitors, and Li-based batteries (Li-ion, Li-S, and Li-O2 batteries), is summarized. Typical development strategies (e.g., incorporation of active components, design of smart morphologies, and judicious selection of organic linkers and metal nodes) of MOFs and MOF composites for particular energy storage and conversion applications are highlighted. A broad overview of recent progress is provided, which will hopefully promote the future development of MOFs and MOF composites for advanced energy storage and conversion applications.

Topological Phases of Non-Hermitian Systems

Abstract: Recent experimental advances in controlling dissipation have brought about unprecedented flexibility in engineering non-Hermitian Hamiltonians in open classical and quantum systems. A particular interest centers on the topological properties of non-Hermitian systems, which exhibit unique phases with no Hermitian counterparts. However, no systematic understanding in analogy with the periodic table of topological insulators and superconductors has been achieved. In this paper, we develop a coherent framework of topological phases of non-Hermitian systems. After elucidating the physical meaning and the mathematical definition of non-Hermitian topological phases, we start with one-dimensional lattices, which exhibit topological phases with no Hermitian counterparts and are found to be characterized by an integer topological winding number even with no symmetry constraint, reminiscent of the quantum Hall insulator in Hermitian systems. A system with a nonzero winding number, which is experimentally measurable from the wave-packet dynamics, is shown to be robust against disorder, a phenomenon observed in the Hatano-Nelson model with asymmetric hopping amplitudes. We also unveil a novel bulk-edge correspondence that features an infinite number of (quasi-)edge modes. We then apply the K-theory to systematically classify all the non-Hermitian topological phases in the Altland-Zirnbauer classes in all dimensions. The obtained periodic table unifies time-reversal and particle-hole symmetries, leading to highly nontrivial predictions such as the absence of non-Hermitian topological phases in two dimensions. We provide concrete examples for all the nontrivial non-Hermitian AZ classes in zero and one dimensions. In particular, we identify a Z2 topological index for arbitrary quantum channels. Our work lays the cornerstone for a unified understanding of the role of topology in non-Hermitian systems.

Japan Atherosclerosis Society (JAS) Guidelines for Prevention of Atherosclerotic Cardiovascular Diseases 2017.

Abstract: Toray Industries, Inc., Tokyo, Japan Department of Diabetes, Metabolism and Endocrinology, Chiba University Graduate School of Medicine, Chiba, Japan National Center for Geriatrics and Gerontology, Aichi, Japan Department of Internal Medicine and Cardiology, Gifu Prefectural General Medical Center, Gifu, Japan Division of Diabetes and Metabolism, Department of Internal Medicine, Iwate Medical University, Iwate, Japan Division of Endocrinology and Metabolism, Department of Medicine, Jichi Medical University, Tochigi, Japan Center for Integrated Medical Research, Hiroshima University Hospital, Hiroshima, Japan Egusa Genshi Clinic, Hiroshima, Japan Department of Cardiovascular Medicine, Juntendo University, Tokyo, Japan Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan Biomedical Informatics, Osaka University, Osaka, Japan Division of Cardiology, Department of Medicine, Showa University School of Medicine, Tokyo, Japan Department of Community Health Systems Nursing, Ehime University Graduate School of Medicine, Ehime, Japan Department of Vascular Medicine, Osaka City University Graduate School of Medicine, Osaka, Japan Department of Internal Medicine, Teikyo University School of Medicine, Tokyo, Japan Department of Vascular Surgery, Saitama Medical Center, Saitama, Japan Chief Health Management Department, Mitsui Chemicals Inc., Tokyo, Japan Department of Pediatrics, Showa University School of Medicine, Tokyo, Japan Department of Neurology, Kita-Harima Medical Center, Hyogo, Japan Department of Internal Medicine, Mizonokuchi Hospital, Teikyo University School of Medicine, Kanagawa, Japan Division of Cardiology, Department of Medicine, Nihon University School of Medicine, Tokyo, Japan Tsukasa Health Care Hospital, Kagoshima, Japan Faculty of Nutrition, Division of Clinical Nutrition, Kobe Gakuin University, Hyogo, Japan Department of Food and Nutrition, Faculty of Human Sciences and Design, Japan Women’s University, Tokyo, Japan 25 Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, Osaka, Japan Department of Medical Statistics, Toho University, Tokyo, Japan Department of Clinical Innovative Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan Department of Laboratory Medicine, Jikei University Kashiwa Hospital, Chiba, Japan Department of Geriatric and General Medicine, Osaka University Graduate School of Medicine, Osaka, Japan Department of Obstetrics and Gynecology, Aichi Medical University, Aichi, Japan 31 Department of Community Medicine, Department of Cardiovascular Medicine, Osaka University Graduate School of Medicine, Osaka, Japan Rinku General Medical Center, Osaka, Japan

Expanding frontiers in materials chemistry and physics with multiple anions

Abstract: During the last century, inorganic oxide compounds laid foundations for materials synthesis, characterization, and technology translation by adding new functions into devices previously dominated by main-group element semiconductor compounds. Today, compounds with multiple anions beyond the single-oxide ion, such as oxyhalides and oxyhydrides, offer a new materials platform from which superior functionality may arise. Here we review the recent progress, status, and future prospects and challenges facing the development and deployment of mixed-anion compounds, focusing mainly on oxide-derived materials. We devote attention to the crucial roles that multiple anions play during synthesis, characterization, and in the physical properties of these materials. We discuss the opportunities enabled by recent advances in synthetic approaches for design of both local and overall structure, state-of-the-art characterization techniques to distinguish unique structural and chemical states, and chemical/physical properties emerging from the synergy of multiple anions for catalysis, energy conversion, and electronic materials.

Core microbiomes for sustainable agroecosystems.

Abstract: In an era of ecosystem degradation and climate change, maximizing microbial functions in agroecosystems has become a prerequisite for the future of global agriculture. However, managing species-rich communities of plant-associated microbiomes remains a major challenge. Here, we propose interdisciplinary research strategies to optimize microbiome functions in agroecosystems. Informatics now allows us to identify members and characteristics of ‘core microbiomes’, which may be deployed to organize otherwise uncontrollable dynamics of resident microbiomes. Integration of microfluidics, robotics and machine learning provides novel ways to capitalize on core microbiomes for increasing resource-efficiency and stress-resistance of agroecosystems.

cGANs with Projection Discriminator

Abstract: We propose a novel, projection based way to incorporate the conditional information into the discriminator of GANs that respects the role of the conditional information in the underlining probabilistic model. This approach is in contrast with most frameworks of conditional GANs used in application today, which use the conditional information by concatenating the (embedded) conditional vector to the feature vectors. With this modification, we were able to significantly improve the quality of the class conditional image generation on ILSVRC2012 (ImageNet) 1000-class image dataset from the current state-of-the-art result, and we achieved this with a single pair of a discriminator and a generator. We were also able to extend the application to super-resolution and succeeded in producing highly discriminative super-resolution images. This new structure also enabled high quality category transformation based on parametric functional transformation of conditional batch normalization layers in the generator.