Showing papers by "Tohoku University published in 2020"

TRY plant trait database : Enhanced coverage and open access

Abstract: Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.

Wannier90 as a community code: new features and applications.

Abstract: Wannier90 is an open-source computer program for calculating maximally-localised Wannier functions (MLWFs) from a set of Bloch states. It is interfaced to many widely used electronic-structure codes thanks to its independence from the basis sets representing these Bloch states. In the past few years the development of Wannier90 has transitioned to a community-driven model; this has resulted in a number of new developments that have been recently released in Wannier90 v3.0. In this article we describe these new functionalities, that include the implementation of new features for wannierisation and disentanglement (symmetry-adapted Wannier functions, selectively-localised Wannier functions, selected columns of the density matrix) and the ability to calculate new properties (shift currents and Berry-curvature dipole, and a new interface to many-body perturbation theory); performance improvements, including parallelisation of the core code; enhancements in functionality (support for spinor-valued Wannier functions, more accurate methods to interpolate quantities in the Brillouin zone); improved usability (improved plotting routines, integration with high-throughput automation frameworks), as well as the implementation of modern software engineering practices (unit testing, continuous integration, and automatic source-code documentation). These new features, capabilities, and code development model aim to further sustain and expand the community uptake and range of applicability, that nowadays spans complex and accurate dielectric, electronic, magnetic, optical, topological and transport properties of materials.

The Molecular Mechanisms Regulating the KEAP1-NRF2 Pathway.

Abstract: The KEAP1-NRF2 pathway is the principal protective response to oxidative and electrophilic stresses. Under homeostatic conditions, KEAP1 forms part of an E3 ubiquitin ligase, which tightly regulates the activity of the transcription factor NRF2 by targeting it for ubiquitination and proteasome-dependent degradation. In response to stress, an intricate molecular mechanism facilitated by sensor cysteines within KEAP1 allows NRF2 to escape ubiquitination, accumulate within the cell, and translocate to the nucleus, where it can promote its antioxidant transcription program. Recent advances have revealed that KEAP1 contains multiple stress sensors and inactivation modalities, which together allow diverse cellular inputs, from oxidative stress and cellular metabolites to dysregulated autophagy, to regulate NRF2 activity. This integration of the KEAP1-NRF2 system into multiple cellular signaling and metabolic pathways places NRF2 activation as a critical regulatory node in many disease phenotypes and suggests that the pharmaceutical modulation of NRF2's cytoprotective activity will be beneficial for human health in a broad range of noncommunicable diseases.

6G: Opening New Horizons for Integration of Comfort, Security, and Intelligence

Abstract: The innovations provided by sixth generation wireless communication (6G) as compared to fifth generation (5G) are considered in this article based on analysis of related works. With the aim of achieving diverse performance improvements for the various 6G requirements, five 6G core services are identified. Two centricities and eight key performance indices (KPIs) are detailed to describe these services, then enabling technologies to fulfill the KPIs are discussed. A 6G architecture is proposed as an integrated system of the enabling technologies and is then illustrated using four typical urban application scenarios. Potential challenges in the development of 6G technology are then discussed and possible solutions are proposed. Finally, opportunities for exploring 6G are analyzed in order to guide future research.

Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches

Abstract: As a powerful tool, the vehicular network has been built to connect human communication and transportation around the world for many years to come. However, with the rapid growth of vehicles, the vehicular network becomes heterogeneous, dynamic, and large scaled, which makes it difficult to meet the strict requirements, such as ultralow latency, high reliability, high security, and massive connections of the next-generation (6G) network. Recently, machine learning (ML) has emerged as a powerful artificial intelligence (AI) technique to make both the vehicle and wireless communication highly efficient and adaptable. Naturally, employing ML into vehicular communication and network becomes a hot topic and is being widely studied in both academia and industry, paving the way for the future intelligentization in 6G vehicular networks. In this article, we provide a survey on various ML techniques applied to communication, networking, and security parts in vehicular networks and envision the ways of enabling AI toward a future 6G vehicular network, including the evolution of intelligent radio (IR), network intelligentization, and self-learning with proactive exploration.

The 2019 World Health Organization classification of tumours of the breast.

Abstract: The newly published World Health Organization (WHO) Classification of Tumours of the breast features significant changes compared to earlier editions. In this review, we outline the major changes in this important reference source for those diagnosing tumours, or engaged in cancer research, and describe the significant changes. For breast cancer, the overview acknowledges the treatment-relevant subtypes of invasive carcinoma (based on ER and HER2 status) and new data is added to support the differences in pathogenesis, treatment response and prognosis of these clinically relevant groupings. The WHO Classification of Tumours is increasingly evidence-based, with a clear update cycle, improved quality of illustrations, as well as content, led by an editorial board comprising pathologists, but increasingly incorporating input from other disciplines. The advent of the new website allows the use of whole slide images, and hyperlinks to evidence or external bodies that produce guidance on staging or reporting.

Gefitinib Alone Versus Gefitinib Plus Chemotherapy for Non-Small-Cell Lung Cancer With Mutated Epidermal Growth Factor Receptor: NEJ009 Study.

Abstract: PURPOSEEpidermal growth factor receptor (EGFR) tyrosine kinase inhibitor combined with cytotoxic chemotherapy is highly effective for the treatment of advanced non–small-cell lung cancer (NSCLC) wi...

Closed environments facilitate secondary transmission of coronavirus disease 2019 (COVID-19)

Abstract: Objective To identify common features of cases with novel coronavirus disease (COVID-19) so as to better understand what factors promote secondary transmission including superspreading events. Methods A total of 110 cases were examined among eleven clusters and sporadic cases, and investigated who acquired infection from whom. The clusters included four in Tokyo and one each in Aichi, Fukuoka, Hokkaido, Ishikawa, Kanagawa and Wakayama prefectures. The number of secondary cases generated by each primary case was calculated using contact tracing data. Results Of the 110 cases examined, 27 (24.6%) were primary cases who generated secondary cases. The odds that a primary case transmitted COVID-19 in a closed environment was 18.7 times greater compared to an open-air environment (95% confidence interval [CI]: 6.0, 57.9). Conclusions It is plausible that closed environments contribute to secondary transmission of COVID-19 and promote superspreading events. Our findings are also consistent with the declining incidence of COVID-19 cases in China, as gathering in closed environments was prohibited in the wake of the rapid spread of the disease.

Ten Challenges in Advancing Machine Learning Technologies toward 6G

Abstract: As the 5G standard is being completed, academia and industry have begun to consider a more developed cellular communication technique, 6G, which is expected to achieve high data rates up to 1 Tb/s and broad frequency bands of 100 GHz to 3 THz. Besides the significant upgrade of the key communication metrics, Artificial Intelligence (AI) has been envisioned by many researchers as the most important feature of 6G, since the state-of-the-art machine learning technique has been adopted as the top solution in many extremely complex scenarios. Network intelligentization will be the new trend to address the challenges of exponentially increasing number of connected heterogeneous devices. However, compared with the application of machine learning in other fields, such as computer games, current research on intelligent networking still has a long way to go to realize the automatically- configured cellular communication systems. Various problems in terms of communication system, machine learning architectures, and computation efficiency should be addressed for the full use of this technique in 6G. In this paper, we analyze machine learning techniques and introduce 10 most critical challenges in advancing the intelligent 6G system.

Deep Learning for Physical-Layer 5G Wireless Techniques: Opportunities, Challenges and Solutions

Abstract: The new demands for high-reliability and ultra-high capacity wireless communication have led to extensive research into 5G communications. However, current communication systems, which were designed on the basis of conventional communication theories, significantly restrict further performance improvements and lead to severe limitations. Recently, the emerging deep learning techniques have been recognized as a promising tool for handling the complicated communication systems, and their potential for optimizing wireless communications has been demonstrated. In this article, we first review the development of deep learning solutions for 5G communication, and then propose efficient schemes for deep learning-based 5G scenarios. Specifically, the key ideas for several important deep learning-based communication methods are presented along with the research opportunities and challenges. In particular, novel communication frameworks of NOMA, massive multiple-input multiple-output (MIMO), and millimeter wave (mmWave) are investigated, and their superior performances are demonstrated. We envision that the appealing deep learning- based wireless physical layer frameworks will bring a new direction in communication theories and that this work will move us forward along this road.

Technical Standards for Respiratory Oscillometry

Abstract: Oscillometry (also known as the forced oscillation technique) measures the mechanical properties of the respiratory system (upper and intrathoracic airways, lung tissue and chest wall) during quiet tidal breathing, by the application of an oscillating pressure signal (input or forcing signal), most commonly at the mouth. With increased clinical and research use, it is critical that all technical details of the hardware design, signal processing and analyses, and testing protocols are transparent and clearly reported to allow standardisation, comparison and replication of clinical and research studies. Because of this need, an update of the 2003 European Respiratory Society (ERS) technical standards document was produced by an ERS task force of experts who are active in clinical oscillometry research.The aim of the task force was to provide technical recommendations regarding oscillometry measurement including hardware, software, testing protocols and quality control.The main changes in this update, compared with the 2003 ERS task force document are 1) new quality control procedures which reflect use of "within-breath" analysis, and methods of handling artefacts; 2) recommendation to disclose signal processing, quality control, artefact handling and breathing protocols (e.g. number and duration of acquisitions) in reports and publications to allow comparability and replication between devices and laboratories; 3) a summary review of new data to support threshold values for bronchodilator and bronchial challenge tests; and 4) updated list of predicted impedance values in adults and children.

Large-scale genome-wide association study in a Japanese population identifies novel susceptibility loci across different diseases

Abstract: The overwhelming majority of participants in current genetic studies are of European ancestry. To elucidate disease biology in the East Asian population, we conducted a genome-wide association study (GWAS) with 212,453 Japanese individuals across 42 diseases. We detected 320 independent signals in 276 loci for 27 diseases, with 25 novel loci (P < 9.58 × 10-9). East Asian-specific missense variants were identified as candidate causal variants for three novel loci, and we successfully replicated two of them by analyzing independent Japanese cohorts; p.R220W of ATG16L2 (associated with coronary artery disease) and p.V326A of POT1 (associated with lung cancer). We further investigated enrichment of heritability within 2,868 annotations of genome-wide transcription factor occupancy, and identified 378 significant enrichments across nine diseases (false discovery rate < 0.05) (for example, NKX3-1 for prostate cancer). This large-scale GWAS in a Japanese population provides insights into the etiology of complex diseases and highlights the importance of performing GWAS in non-European populations.

Engineering a homogeneous alloy-oxide interface derived from metal-organic frameworks for selective oxidation of 5-hydroxymethylfurfural to 2,5-furandicarboxylic acid

Abstract: The selective oxidation of 5-hydroxymethylfurfural (HMF) to 2,5-furandicarboxylic acid (FDCA) is an important biomass conversion reaction. However, the multiple intermediates of the reaction make the catalyst design challenging. We engineered an active and selective catalyst with an active interface between Au-Pd alloy nanoparticles (NPs) and cobalt oxide supports via calcination of a composite of NPs encapsulated in metal-organic frameworks (MOFs). The catalyst shows an effective HMF-to-FDCA oxidation with total conversion and 95 % yield by 10 % hydrogen peroxide solution at 90 °C in one hour under atmospheric pressure. The mechanistic study shows that the engineered interface promotes the formation of hydroperoxyl radicals and dioxygen molecules, which accelerate the oxidation of reactive intermediates to FDCA. This work demonstrates the advantage of using MOF composites as a precursor to generate complex but active interfaces with a strong interaction between the metal and metal oxides.

Measurement of R (D) and R (D?) with a Semileptonic Tagging Method

Abstract: The experimental results on the ratios of branching fractions $\mathcal{R}(D) = {\cal B}(\bar{B} \to D \tau^- \bar{ u}_{\tau})/{\cal B}(\bar{B} \to D \ell^- \bar{ u}_{\ell})$ and $\mathcal{R}(D^*) = {\cal B}(\bar{B} \to D^* \tau^- \bar{ u}_{\tau})/{\cal B}(\bar{B} \to D^* \ell^- \bar{ u}_{\ell})$, where $\ell$ denotes an electron or a muon, show a long-standing discrepancy with the Standard Model predictions, and might hint to a violation of lepton flavor universality. We report a new simultaneous measurement of $\mathcal{R}(D)$ and $\mathcal{R}(D^*)$, based on a data sample containing $772 \times 10^6$ $B\bar{B}$ events recorded at the $\Upsilon(4S)$ resonance with the Belle detector at the KEKB $e^+ e^-$ collider. In this analysis the tag-side $B$ meson is reconstructed in a semileptonic decay mode and the signal-side $\tau$ is reconstructed in a purely leptonic decay. The measured values are $\mathcal{R}(D)= 0.307 \pm 0.037 \pm 0.016$ and $\mathcal{R}(D^*) = 0.283 \pm 0.018 \pm 0.014$, where the first uncertainties are statistical and the second are systematic. These results are in agreement with the Standard Model predictions within $0.2$, $1.1$ and $0.8$ standard deviations for $\mathcal{R}(D)$, $\mathcal{R}(D^*)$ and their combination, respectively. This work constitutes the most precise measurements of $\mathcal{R}(D)$ and $\mathcal{R}(D^*)$ performed to date as well as the first result for $\mathcal{R}(D)$ based on a semileptonic tagging method.

Structure of the neurotensin receptor 1 in complex with β-arrestin 1.

Abstract: Arrestin proteins bind to active, phosphorylated G-protein-coupled receptors (GPCRs), thereby preventing G-protein coupling, triggering receptor internalization and affecting various downstream signalling pathways1,2. Although there is a wealth of structural information detailing the interactions between GPCRs and G proteins, less is known about how arrestins engage GPCRs. Here we report a cryo-electron microscopy structure of full-length human neurotensin receptor 1 (NTSR1) in complex with truncated human β-arrestin 1 (βarr1(ΔCT)). We find that phosphorylation of NTSR1 is critical for the formation of a stable complex with βarr1(ΔCT), and identify phosphorylated sites in both the third intracellular loop and the C terminus that may promote this interaction. In addition, we observe a phosphatidylinositol-4,5-bisphosphate molecule forming a bridge between the membrane side of NTSR1 transmembrane segments 1 and 4 and the C-lobe of arrestin. Compared with a structure of a rhodopsin-arrestin-1 complex, in our structure arrestin is rotated by approximately 85° relative to the receptor. These findings highlight both conserved aspects and plasticity among arrestin-receptor interactions.

Quantum crystal structure in the 250-kelvin superconducting lanthanum hydride

Abstract: The discovery of superconductivity at 200 kelvin in the hydrogen sulfide system at high pressures1 demonstrated the potential of hydrogen-rich materials as high-temperature superconductors. Recent theoretical predictions of rare-earth hydrides with hydrogen cages2,3 and the subsequent synthesis of LaH10 with a superconducting critical temperature (Tc) of 250 kelvin4,5 have placed these materials on the verge of achieving the long-standing goal of room-temperature superconductivity. Electrical and X-ray diffraction measurements have revealed a weakly pressure-dependent Tc for LaH10 between 137 and 218 gigapascals in a structure that has a face-centred cubic arrangement of lanthanum atoms5. Here we show that quantum atomic fluctuations stabilize a highly symmetrical [Formula: see text] crystal structure over this pressure range. The structure is consistent with experimental findings and has a very large electron-phonon coupling constant of 3.5. Although ab initio classical calculations predict that this [Formula: see text] structure undergoes distortion at pressures below 230 gigapascals2,3, yielding a complex energy landscape, the inclusion of quantum effects suggests that it is the true ground-state structure. The agreement between the calculated and experimental Tc values further indicates that this phase is responsible for the superconductivity observed at 250 kelvin. The relevance of quantum fluctuations calls into question many of the crystal structure predictions that have been made for hydrides within a classical approach and that currently guide the experimental quest for room-temperature superconductivity6-8. Furthermore, we find that quantum effects are crucial for the stabilization of solids with high electron-phonon coupling constants that could otherwise be destabilized by the large electron-phonon interaction9, thus reducing the pressures required for their synthesis.

Understanding human health risks caused by antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARG) in water environments: Current knowledge and questions to be answered

Abstract: Aquatic environments are identified as an ideal setting for acquisition and dissemination of antibiotic resistance, and human exposure to antibiotic resistant bacteria (ARB) and antibiotic resistan...

Inhibitory effects of glycopyrronium, formoterol, and budesonide on coronavirus HCoV-229E replication and cytokine production by primary cultures of human nasal and tracheal epithelial cells.

Abstract: Background Coronavirus 229E (HCoV-229E), one of the causes of the common cold, exacerbates chronic obstructive pulmonary disease (COPD) and bronchial asthma. Long-acting muscarinic antagonists and β2-agonists and inhaled corticosteroids inhibit the exacerbation of COPD and bronchial asthma caused by infection with viruses, including HCoV-229E. However, the effects of these drugs on HCoV-229E replication and infection-induced inflammation in the human airway are unknown. Methods Primary human nasal (HNE) and tracheal (HTE) epithelial cell cultures were infected with HCoV-229E. Results Pretreatment of HNE and HTE cells with glycopyrronium or formoterol decreased viral RNA levels and/or titers, the expression of the HCoV-229E receptor CD13, the number and fluorescence intensity of acidic endosomes where HCoV-229E RNA enters the cytoplasm, and the infection-induced production of cytokines, including IL-6, IL-8, and IFN-β. Treatment of the cells with the CD13 inhibitor 2′2′-dipyridyl decreased viral titers. Pretreatment of the cells with a combination of three drugs (glycopyrronium, formoterol, and budesonide) exerted additive inhibitory effects on viral titers and cytokine production. Pretreatment of HNE cells with glycopyrronium or formoterol reduced the susceptibility to infection, and pretreatment with the three drugs inhibited activation of nuclear factor-kappa B p50 and p65 proteins. Pretreatment with formoterol increased cAMP levels and treatment with cAMP decreased viral titers, CD13 expression, and the fluorescence intensity of acidic endosomes. Conclusions These findings suggest that glycopyrronium, formoterol, and a combination of glycopyrronium, formoterol, and budesonide inhibit HCoV-229E replication partly by inhibiting receptor expression and/or endosomal function and that these drugs modulate infection-induced inflammation in the airway.

Machine Learning Meets Computation and Communication Control in Evolving Edge and Cloud: Challenges and Future Perspective

Abstract: Mobile Edge Computing (MEC) is considered an essential future service for the implementation of 5G networks and the Internet of Things, as it is the best method of delivering computation and communication resources to mobile devices. It is based on the connection of the users to servers located on the edge of the network, which is especially relevant for real-time applications that demand minimal latency. In order to guarantee a resource-efficient MEC (which, for example, could mean improved Quality of Service for users or lower costs for service providers), it is important to consider certain aspects of the service model, such as where to offload the tasks generated by the devices, how many resources to allocate to each user (specially in the wired or wireless device-server communication) and how to handle inter-server communication. However, in the MEC scenarios with many and varied users, servers and applications, these problems are characterized by parameters with exceedingly high levels of dimensionality, resulting in too much data to be processed and complicating the task of finding efficient configurations. This will be particularly troublesome when 5G networks and Internet of Things roll out, with their massive amounts of devices. To address this concern, the best solution is to utilize Machine Learning (ML) algorithms, which enable the computer to draw conclusions and make predictions based on existing data without human supervision, leading to quick near-optimal solutions even in problems with high dimensionality. Indeed, in scenarios with too much data and too many parameters, ML algorithms are often the only feasible alternative. In this paper, a comprehensive survey on the use of ML in MEC systems is provided, offering an insight into the current progress of this research area. Furthermore, helpful guidance is supplied by pointing out which MEC challenges can be solved by ML solutions, what are the current trending algorithms in frontier ML research and how they could be used in MEC. These pieces of information should prove fundamental in encouraging future research that combines ML and MEC.

Safety and efficacy of rituximab in neuromyelitis optica spectrum disorders (RIN-1 study): a multicentre, randomised, double-blind, placebo-controlled trial

Abstract: Summary Background Pharmacological prevention against relapses in patients with neuromyelitis optica spectrum disorder (NMOSD) is developing rapidly. We aimed to investigate the safety and efficacy of rituximab, an anti-CD20 monoclonal antibody, against relapses in patients with NMOSD. Methods We did a multicentre, randomised, double-blind, placebo-controlled clinical trial at eight hospitals in Japan. Patients aged 16–80 years with NMOSD who were seropositive for aquaporin 4 (AQP4) antibody, were taking 5–30 mg/day oral steroids, and had an Expanded Disability Status Scale (EDSS) score of 7·0 or less were eligible for the study. Individuals taking any other immunosuppressants were excluded. Participants were randomly allocated (1:1) either rituximab or placebo by a computer-aided dynamic random allocation system. The doses of concomitant steroid (converted to equivalent doses of prednisolone) and relapses in previous 2 years were set as stratification factors. Participants and those assessing outcomes were unaware of group assignments. Rituximab (375 mg/m2) was administered intravenously every week for 4 weeks, then 6-month interval dosing was done (1000 mg every 2 weeks, at 24 weeks and 48 weeks after randomisation). A matching placebo was administered intravenously. Concomitant oral prednisolone was gradually reduced to 2–5 mg/day, according to the protocol. The primary outcome was time to first relapse within 72 weeks. Relapses were defined as patient-reported symptoms or any new signs consistent with CNS lesions and attributable objective changes in MRI or visual evoked potential. The primary analysis was done in the full analysis set (all randomly assigned patients) and safety analyses were done in the safety analysis set (all patients who received at least one infusion of assigned treatment). The primary analysis was by intention-to-treat principles. This trial is registered with the UMIN clinical trial registry, UMIN000013453. Findings Between May 10, 2014, and Aug 15, 2017, 38 participants were recruited and randomly allocated either rituximab (n=19) or placebo (n=19). Three (16%) patients assigned rituximab discontinued the study and were analysed as censored cases. Seven (37%) relapses occurred in patients allocated placebo and none were recorded in patients assigned rituximab (group difference 36·8%, 95% CI 12·3–65·5; log-rank p=0·0058). Eight serious adverse events were recorded, four events in three (16%) patients assigned rituximab (lumbar compression fracture and infection around nail of right foot [n=1], diplopia [n=1], and uterine cancer [n=1]) and four events in two (11%) people allocated to placebo (exacerbation of glaucoma and bleeding in the right eye chamber after surgery [n=1], and visual impairment and asymptomatic white matter brain lesion on MRI [n=1]); all patients recovered. No deaths were reported. Interpretation Rituximab prevented relapses for 72 weeks in patients with NMOSD who were AQP4 antibody-positive. This study is limited by its small sample size and inclusion of participants with mild disease activity. However, our results suggest that rituximab could be useful maintenance therapy for individuals with NMOSD who are AQP4 antibody-positive. Funding Japanese Ministry of Health, Labour and Welfare, Japan Agency for Medical Research and Development, and Zenyaku Kogyo.

Identifying Electrocatalytic Sites of the Nanoporous Copper–Ruthenium Alloy for Hydrogen Evolution Reaction in Alkaline Electrolyte

Abstract: Hydrogen production from electrochemical water splitting is a promising route to pursue clean and sustainable energy sources. Here, a three-dimensional nanoporous Cu–Ru alloy is prepared as a high-...

Stress- and ubiquitylation-dependent phase separation of the proteasome

Abstract: The proteasome is a major proteolytic machine that regulates cellular proteostasis through selective degradation of ubiquitylated proteins1,2. A number of ubiquitin-related molecules have recently been found to be involved in the regulation of biomolecular condensates or membraneless organelles, which arise by liquid-liquid phase separation of specific biomolecules, including stress granules, nuclear speckles and autophagosomes3-8, but it remains unclear whether the proteasome also participates in such regulation. Here we reveal that proteasome-containing nuclear foci form under acute hyperosmotic stress. These foci are transient structures that contain ubiquitylated proteins, p97 (also known as valosin-containing protein (VCP)) and multiple proteasome-interacting proteins, which collectively constitute a proteolytic centre. The major substrates for degradation by these foci were ribosomal proteins that failed to properly assemble. Notably, the proteasome foci exhibited properties of liquid droplets. RAD23B, a substrate-shuttling factor for the proteasome, and ubiquitylated proteins were necessary for formation of proteasome foci. In mechanistic terms, a liquid-liquid phase separation was triggered by multivalent interactions of two ubiquitin-associated domains of RAD23B and ubiquitin chains consisting of four or more ubiquitin molecules. Collectively, our results suggest that ubiquitin-chain-dependent phase separation induces the formation of a nuclear proteolytic compartment that promotes proteasomal degradation.

Non-compulsory measures sufficiently reduced human mobility in Tokyo during the COVID-19 epidemic.

Abstract: While large scale mobility data has become a popular tool to monitor the mobility patterns during the COVID-19 pandemic, the impacts of non-compulsory measures in Tokyo, Japan on human mobility patterns has been under-studied. Here, we analyze the temporal changes in human mobility behavior, social contact rates, and their correlations with the transmissibility of COVID-19, using mobility data collected from more than 200K anonymized mobile phone users in Tokyo. The analysis concludes that by April 15th (1 week into state of emergency), human mobility behavior decreased by around 50%, resulting in a 70% reduction of social contacts in Tokyo, showing the strong relationships with non-compulsory measures. Furthermore, the reduction in data-driven human mobility metrics showed correlation with the decrease in estimated effective reproduction number of COVID-19 in Tokyo. Such empirical insights could inform policy makers on deciding sufficient levels of mobility reduction to contain the disease.