Showing papers by "Tokyo Institute of Technology published in 2020"

The genetic architecture of the human cerebral cortex

Abstract: The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder.

Mechanisms governing autophagosome biogenesis.

Abstract: Autophagosomes are double-membrane vesicles newly formed during autophagy to engulf a wide range of intracellular material and transport this autophagic cargo to lysosomes (or vacuoles in yeasts and plants) for subsequent degradation. Autophagosome biogenesis responds to a plethora of signals and involves unique and dynamic membrane processes. Autophagy is an important cellular mechanism allowing the cell to meet various demands, and its disruption compromises homeostasis and leads to various diseases, including metabolic disorders, neurodegeneration and cancer. Thus, not surprisingly, the elucidation of the molecular mechanisms governing autophagosome biogenesis has attracted considerable interest. Key molecules and organelles involved in autophagosome biogenesis, including autophagy-related (ATG) proteins and the endoplasmic reticulum, have been discovered, and their roles and relationships have been investigated intensely. However, several fundamental questions, such as what supplies membranes/lipids to build the autophagosome and how the membrane nucleates, expands, bends into a spherical shape and finally closes, have proven difficult to address. Nonetheless, owing to recent studies with new approaches and technologies, we have begun to unveil the mechanisms underlying these processes on a molecular level. We now know that autophagosome biogenesis is a highly complex process, in which multiple proteins and lipids from various membrane sources, supported by the formation of membrane contact sites, cooperate with biophysical phenomena, including membrane shaping and liquid–liquid phase separation, to ensure seamless segregation of the autophagic cargo. Together, these studies pave the way to obtaining a holistic view of autophagosome biogenesis. Autophagy involves engulfment of cellular components into double-membrane vesicles called autophagosomes. The biogenesis of autophagosomes requires the cooperation of multiple proteins and lipids from various membrane sources. Our understanding of the molecular mechanisms of the initiation, growth, bending and closure of autophagosomal membranes is expanding at a rapid pace.

The 2020 plasma catalysis roadmap

Abstract: Plasma catalysis is gaining increasing interest for various gas conversion applications, such as CO2 conversion into value-added chemicals and fuels, CH4 activation into hydrogen, higher hydrocarbons or oxygenates, and NH3 synthesis Other applications are already more established, such as for air pollution control, eg volatile organic compound remediation, particulate matter and NOx removal In addition, plasma is also very promising for catalyst synthesis and treatment Plasma catalysis clearly has benefits over 'conventional' catalysis, as outlined in the Introduction However, a better insight into the underlying physical and chemical processes is crucial This can be obtained by experiments applying diagnostics, studying both the chemical processes at the catalyst surface and the physicochemical mechanisms of plasma-catalyst interactions, as well as by computer modeling The key challenge is to design cost-effective, highly active and stable catalysts tailored to the plasma environment Therefore, insight from thermal catalysis as well as electro- and photocatalysis is crucial All these aspects are covered in this Roadmap paper, written by specialists in their field, presenting the state-of-the-art, the current and future challenges, as well as the advances in science and technology needed to meet these challenges

Search for electroweak production of charginos and sleptons decaying into final states with two leptons and missing transverse momentum in √s = 13 TeV pp collisions using the ATLAS detector

Abstract: A search for the electroweak production of charginos and sleptons decaying into final states with two electrons or muons is presented. The analysis is based on 139 fb$^{-1}$ of proton–proton collisions recorded by the ATLAS detector at the Large Hadron Collider at $\sqrt{s}=13$ $\text {TeV}$. Three R-parity-conserving scenarios where the lightest neutralino is the lightest supersymmetric particle are considered: the production of chargino pairs with decays via either W bosons or sleptons, and the direct production of slepton pairs. The analysis is optimised for the first of these scenarios, but the results are also interpreted in the others. No significant deviations from the Standard Model expectations are observed and limits at 95% confidence level are set on the masses of relevant supersymmetric particles in each of the scenarios. For a massless lightest neutralino, masses up to 420 $\text {Ge}\text {V}$ are excluded for the production of the lightest-chargino pairs assuming W-boson-mediated decays and up to 1 $\text {TeV}$ for slepton-mediated decays, whereas for slepton-pair production masses up to 700 $\text {Ge}\text {V}$ are excluded assuming three generations of mass-degenerate sleptons.

Vacancy-enabled N 2 activation for ammonia synthesis on an Ni-loaded catalyst

Abstract: Ammonia (NH3) is pivotal to the fertilizer industry and one of the most commonly produced chemicals1. The direct use of atmospheric nitrogen (N2) had been challenging, owing to its large bond energy (945 kilojoules per mole)2,3, until the development of the Haber–Bosch process. Subsequently, many strategies have been explored to reduce the activation barrier of the N≡N bond and make the process more efficient. These include using alkali and alkaline earth metal oxides as promoters to boost the performance of traditional iron- and ruthenium-based catalysts4–6 via electron transfer from the promoters to the antibonding bonds of N2 through transition metals7,8. An electride support further lowers the activation barrier because its low work function and high electron density enhance electron transfer to transition metals9,10. This strategy has facilitated ammonia synthesis from N2 dissociation11 and enabled catalytic operation under mild conditions; however, it requires the use of ruthenium, which is expensive. Alternatively, it has been shown that nitrides containing surface nitrogen vacancies can activate N2 (refs. 12–15). Here we report that nickel-loaded lanthanum nitride (LaN) enables stable and highly efficient ammonia synthesis, owing to a dual-site mechanism that avoids commonly encountered scaling relations. Kinetic and isotope-labelling experiments, as well as density functional theory calculations, confirm that nitrogen vacancies are generated on LaN with low formation energy, and efficiently bind and activate N2. In addition, the nickel metal loaded onto the nitride dissociates H2. The use of distinct sites for activating the two reactants, and the synergy between them, results in the nickel-loaded LaN catalyst exhibiting an activity that far exceeds that of more conventional cobalt- and nickel-based catalysts, and that is comparable to that of ruthenium-based catalysts. Our results illustrate the potential of using vacancy sites in reaction cycles, and introduce a design concept for catalysts for ammonia synthesis, using naturally abundant elements. Ammonia is synthesized using a dual-site approach, whereby nitrogen vacancies on LaN activate N2, which then reacts with hydrogen atoms produced over the Ni metal to give ammonia.

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.

Phase separation organizes the site of autophagosome formation.

Abstract: Many biomolecules undergo liquid–liquid phase separation to form liquid-like condensates that mediate diverse cellular functions1,2. Autophagy is able to degrade such condensates using autophagosomes—double-membrane structures that are synthesized de novo at the pre-autophagosomal structure (PAS) in yeast3–5. Whereas Atg proteins that associate with the PAS have been characterized, the physicochemical and functional properties of the PAS remain unclear owing to its small size and fragility. Here we show that the PAS is in fact a liquid-like condensate of Atg proteins. The autophagy-initiating Atg1 complex undergoes phase separation to form liquid droplets in vitro, and point mutations or phosphorylation that inhibit phase separation impair PAS formation in vivo. In vitro experiments show that Atg1-complex droplets can be tethered to membranes via specific protein–protein interactions, explaining the vacuolar membrane localization of the PAS in vivo. We propose that phase separation has a critical, active role in autophagy, whereby it organizes the autophagy machinery at the PAS. The pre-autophagosomal structure in yeast is a liquid-like condensate of Atg proteins whose phase separation may have a critical, active role in autophagy.

The Fourth Catalog of Active Galactic Nuclei Detected by the Fermi Large Area Telescope

Abstract: The fourth catalog of active galactic nuclei (AGNs) detected by the Fermi Gamma-ray Space Telescope Large Area Telescope (4LAC) between 2008 August 4 and 2016 August 2 contains 2863 objects located at high Galactic latitudes (|b|>10{\deg}). It includes 85% more sources than the previous 3LAC catalog based on 4 years of data. AGNs represent at least 79% of the high-latitude sources in the fourth Fermi-Large Area Telescope Source Catalog (4FGL), which covers the energy range from 50 MeV to 1 TeV. In addition, 344 gamma-ray AGNs are found at low Galactic latitudes. Most of the 4LAC AGNs are blazars (98%), while the remainder are other types of AGNs. The blazar population consists of 24% Flat Spectrum Radio Quasars (FSRQs), 38% BL Lac-type objects (BL Lacs), and 38% blazar candidates of unknown types (BCUs). On average, FSRQs display softer spectra and stronger variability in the gamma-ray band than BL Lacs do, confirming previous findings. All AGNs detected by ground-based atmospheric Cherenkov telescopes are also found in the 4LAC.

Atg9 is a lipid scramblase that mediates autophagosomal membrane expansion

Abstract: The molecular function of Atg9, the sole transmembrane protein in the autophagosome-forming machinery, remains unknown. Atg9 colocalizes with Atg2 at the expanding edge of the isolation membrane (IM), where Atg2 receives phospholipids from the endoplasmic reticulum (ER). Here we report that yeast and human Atg9 are lipid scramblases that translocate phospholipids between outer and inner leaflets of liposomes in vitro. Cryo-EM of fission yeast Atg9 reveals a homotrimer, with two connected pores forming a path between the two membrane leaflets: one pore, located at a protomer, opens laterally to the cytoplasmic leaflet; the other, at the trimer center, traverses the membrane vertically. Mutation of residues lining the pores impaired IM expansion and autophagy activity in yeast and abolished Atg9's ability to transport phospholipids between liposome leaflets. These results suggest that phospholipids delivered by Atg2 are translocated from the cytoplasmic to the luminal leaflet by Atg9, thereby driving autophagosomal membrane expansion.

Evaluating the effect of demographic factors, socioeconomic factors, and risk aversion on mobility during the COVID-19 epidemic in France under lockdown: a population-based study.

Abstract: Summary Background On March 17, 2020, French authorities implemented a nationwide lockdown to respond to the COVID-19 epidemic and curb the surge of patients requiring critical care. Assessing the effect of lockdown on individual displacements is essential to quantify achievable mobility reductions and identify the factors driving the changes in social dynamics that affected viral diffusion. We aimed to use mobile phone data to study how mobility in France changed before and during lockdown, breaking down our findings by trip distance, user age and residency, and time of day, and analysing regional data and spatial heterogeneities. Methods For this population-based study, we used temporally resolved travel flows among 1436 administrative areas of mainland France reconstructed from mobile phone trajectories. Data were stratified by age class (younger than 18 years, 18–64 years, and 65 years or older). We distinguished between residents and non-residents and used population data and regional socioeconomic indicators from the French National Statistical Institute. We measured mobility changes before and during lockdown at both local and country scales using a case-crossover framework. We analysed all trips combined and trips longer than 100 km (termed long trips), and separated trips by daytime or night-time, weekdays or weekends, and rush hours. Findings Lockdown caused a 65% reduction in the countrywide number of displacements (from about 57 million to about 20 million trips per day) and was particularly effective in reducing work-related short-range mobility, especially during rush hour, and long trips. Geographical heterogeneities showed anomalous increases in long-range movements even before lockdown announcement that were tightly localised in space. During lockdown, mobility drops were unevenly distributed across regions (eg, Ile-de-France, the region of Paris, went from 585 000 to 117 000 outgoing trips per day). They were strongly associated with active populations, workers employed in sectors highly affected by lockdown, and number of hospitalisations per region, and moderately associated with the socioeconomic level of the regions. Major cities largely shrank their pattern of connectivity, reducing it mainly to short-range commuting (95% of traffic leaving Paris was contained in a 201 km radius before lockdown, which was reduced to 29 km during lockdown). Interpretation Lockdown was effective in reducing population mobility across scales. Caution should be taken in the timing of policy announcements and implementation, because anomalous mobility followed policy announcements, which might act as seeding events. Conversely, risk aversion might be beneficial in further decreasing mobility in highly affected regions. We also identified socioeconomic and demographic constraints to the efficacy of restrictions. The unveiled links between geography, demography, and timing of the response to mobility restrictions might help to design interventions that minimise invasiveness while contributing to the current epidemic response. Funding Agence Nationale de la Recherche, EU, REACTing.

Performance of electron and photon triggers in ATLAS during LHC Run 2

Abstract: Electron and photon triggers covering transverse energies from 5 GeV to several TeV are essential for the ATLAS experiment to record signals for a wide variety of physics: from Standard Model processes to searches for new phenomena in both proton–proton and heavy-ion collisions. To cope with a fourfold increase of peak LHC luminosity from 2015 to 2018 (Run 2), to 2.1×1034cm-2s-1, and a similar increase in the number of interactions per beam-crossing to about 60, trigger algorithms and selections were optimised to control the rates while retaining a high efficiency for physics analyses. For proton–proton collisions, the single-electron trigger efficiency relative to a single-electron offline selection is at least 75% for an offline electron of 31 GeV, and rises to 96% at 60 GeV; the trigger efficiency of a 25 GeV leg of the primary diphoton trigger relative to a tight offline photon selection is more than 96% for an offline photon of 30 GeV. For heavy-ion collisions, the primary electron and photon trigger efficiencies relative to the corresponding standard offline selections are at least 84% and 95%, respectively, at 5 GeV above the corresponding trigger threshold.

Ultrafast and energy-efficient all-optical switching with graphene-loaded deep-subwavelength plasmonic waveguides

Abstract: All-optical switches have attracted attention because they can potentially overcome the speed limitation of electric switches. However, ultrafast, energy-efficient all-optical switches have been challenging to realize owing to the intrinsically small optical nonlinearity in existing materials. As a solution, we propose the use of graphene-loaded deep-subwavelength plasmonic waveguides (30 × 20 nm2). Thanks to extreme light confinement, we have greatly enhanced optical nonlinear absorption in graphene, and achieved ultrafast all-optical switching with a switching energy of 35 fJ and a switching time of 260 fs. The switching energy is four orders of magnitude smaller than that in previous graphene-based devices and is the smallest value reported for any all-optical switch operating at a few picoseconds or less. This device can be efficiently connected to conventional silicon waveguides and used in silicon photonic integrated circuits. We believe that this graphene-based device will pave the way towards on-chip ultrafast and energy-efficient photonic processing. All-optical switching with a switching energy of 35 fJ and a switching time of 260 fs is reported in a nanoscale integrated optical circuit.

Search for Heavy Higgs Bosons Decaying into Two Tau Leptons with the ATLAS Detector Using pp Collisions at s =13 TeV

Abstract: A search for heavy neutral Higgs bosons is performed using the LHC Run 2 data, corresponding to an integrated luminosity of 139 fb^{-1} of proton-proton collisions at sqrt[s]=13 TeV recorded with the ATLAS detector. The search for heavy resonances is performed over the mass range 0.2-2.5 TeV for the τ^{+}τ^{-} decay with at least one τ-lepton decaying into final states with hadrons. The data are in good agreement with the background prediction of the standard model. In the M_{h}^{125} scenario of the minimal supersymmetric standard model, values of tanβ>8 and tanβ>21 are excluded at the 95% confidence level for neutral Higgs boson masses of 1.0 and 1.5 TeV, respectively, where tanβ is the ratio of the vacuum expectation values of the two Higgs doublets.

Efficient Visible-Light-Driven CO2 Reduction by a Cobalt Molecular Catalyst Covalently Linked to Mesoporous Carbon Nitride

Abstract: Achieving visible-light-driven carbon dioxide reduction with high selectivity control and durability while using only earth abundant elements requires new strategies. Hybrid catalytic material was prepared upon covalent grafting a Co-quaterpyridine molecular complex to semiconductive mesoporous graphitic carbon nitride (mpg-C3N4) through an amide linkage. The molecular material was characterized by various spectroscopic techniques, including XPS, IR, and impedance spectroscopy. It proved to be a selective catalyst for CO production in acetonitrile using a solar simulator with a high 98% selectivity, while being remarkably robust since no degradation was observed after 4 days of irradiation (ca. 500 catalytic cycles). This unique combination of a selective molecular catalyst with a simple and robust semiconductive material opens new pathways for CO2 catalytic light-driven reduction.

Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback

Abstract: Recommender systems widely use implicit feedback such as click data because of its general availability. Although the presence of clicks signals the users' preference to some extent, the lack of such clicks does not necessarily indicate a negative response from the users, as it is possible that the users were not exposed to the items (positive-unlabeled problem). This leads to a difficulty in predicting the users' preferences from implicit feedback. Previous studies addressed the positive-unlabeled problem by uniformly upweighting the loss for the positive feedback data or estimating the confidence of each data having relevance information via the EM-algorithm. However, these methods failed to address the missing-not-at-random problem in which popular or frequently recommended items are more likely to be clicked than other items even if a user does not have a considerable interest in them. To overcome these limitations, we first define an ideal loss function to be optimized to realize recommendations that maximize the relevance and propose an unbiased estimator for the ideal loss. Subsequently, we analyze the variance of the proposed unbiased estimator and further propose a clipped estimator that includes the unbiased estimator as a special case. We demonstrate that the clipped estimator is expected to improve the performance of the recommender system, by considering the bias-variance trade-off. We conduct semi-synthetic and real-world experiments and demonstrate that the proposed method largely outperforms the baselines. In particular, the proposed method works better for less popular items that are less frequently observed in the training data. The findings indicate that the proposed method can better achieve the objective of recommending items with the highest relevance.

Erratum: The Belle II Physics Book (Progress of Theoretical and Experimental Physics (2019) 2019 (123C01) DOI: 10.1093/ptep/ptz106)

Abstract: In the original version of this manuscript, an error was introduced on pp352. '2.7nb:1.6nb' has been corrected to '2.4nb:1.3nb' in the current online and printed version. doi:10.1093/ptep/ptz106.

Principles of Aggregation‐Induced Emission: Design of Deactivation Pathways for Advanced AIEgens and Applications

Abstract: Twenty years ago, the concept of aggregation-induced emission (AIE) was proposed, and this unique luminescent property has attracted scientific interest ever since. However, AIE denominates only the phenomenon, while the details of its underlying guiding principles remain to be elucidated. This minireview discusses the basic principles of AIE based on our previous mechanistic study of the photophysical behavior of 9,10-bis(N,N-dialkylamino)anthracene (BDAA) and the corresponding mechanistic analysis by quantum chemical calculations. BDAA comprises an anthracene core and small electron donors, which allows the quantum chemical aspects of AIE to be discussed. The key factor for AIE is the control over the non-radiative decay (deactivation) pathway, which can be visualized by considering the conical intersection (CI) on a potential energy surface. Controlling the conical intersection (CI) on the potential energy surface enables the separate formation of fluorescent (CI:high) and non-fluorescent (CI:low) molecules [control of conical intersection accessibility (CCIA)]. The novelty and originality of AIE in the field of photochemistry lies in the creation of functionality by design and in the active control over deactivation pathways. Moreover, we provide a new design strategy for AIE luminogens (AIEgens) and discuss selected examples.

Cohesin and condensin extrude DNA loops in a cell cycle-dependent manner.

Abstract: Loop extrusion by structural maintenance of chromosomes (SMC) complexes has been proposed as a mechanism to organize chromatin in interphase and metaphase. However, the requirements for chromatin organization in these cell cycle phases are different, and it is unknown whether loop extrusion dynamics and the complexes that extrude DNA also differ. Here, we used Xenopus egg extracts to reconstitute and image loop extrusion of single DNA molecules during the cell cycle. We show that loops form in both metaphase and interphase, but with distinct dynamic properties. Condensin extrudes DNA loops non-symmetrically in metaphase, whereas cohesin extrudes loops symmetrically in interphase. Our data show that loop extrusion is a general mechanism underlying DNA organization, with dynamic and structural properties that are biochemically regulated during the cell cycle.

FeGenie: A Comprehensive Tool for the Identification of Iron Genes and Iron Gene Neighborhoods in Genome and Metagenome Assemblies.

Abstract: Iron is a micronutrient for nearly all life on Earth. It can be used as an electron donor and electron acceptor by iron-oxidizing and iron-reducing microorganisms and is used in a variety of biological processes, including photosynthesis and respiration. While it is the fourth most abundant metal in the Earth's crust, iron is often limiting for growth in oxic environments because it is readily oxidized and precipitated. Much of our understanding of how microorganisms compete for and utilize iron is based on laboratory experiments. However, the advent of next-generation sequencing and surge in publicly available sequence data has made it possible to probe the structure and function of microbial communities in the environment. To bridge the gap between our understanding of iron acquisition, iron redox cycling, iron storage, and magnetosome formation in model microorganisms and the plethora of sequence data available from environmental studies, we have created a comprehensive database of hidden Markov models (HMMs) based on genes related to iron acquisition, storage, and reduction/oxidation in Bacteria and Archaea. Along with this database, we present FeGenie, a bioinformatics tool that accepts genome and metagenome assemblies as input and uses our comprehensive HMM database to annotate provided datasets with respect to iron-related genes and gene neighborhood. An important contribution of this tool is the efficient identification of genes involved in iron oxidation and dissimilatory iron reduction, which have been largely overlooked by standard annotation pipelines. We validated FeGenie against a selected set of 28 isolate genomes and showcase its utility in exploring iron genes present in 27 metagenomes, 4 isolate genomes from human oral biofilms, and 17 genomes from candidate organisms, including members of the candidate phyla radiation. We show that FeGenie accurately identifies iron genes in isolates. Furthermore, analysis of metagenomes using FeGenie demonstrates that the iron gene repertoire and abundance of each environment is correlated with iron richness. While this tool will not replace the reliability of culture-dependent analyses of microbial physiology, it provides reliable predictions derived from the most up-to-date genetic markers. FeGenie's database will be maintained and continually updated as new genes are discovered. FeGenie is freely available: https://github.com/Arkadiy-Garber/FeGenie.

Unsupervised Learning of Image Segmentation Based on Differentiable Feature Clustering

Abstract: The usage of convolutional neural networks (CNNs) for unsupervised image segmentation was investigated in this study. Similar to supervised image segmentation, the proposed CNN assigns labels to pixels that denote the cluster to which the pixel belongs. In unsupervised image segmentation, however, no training images or ground truth labels of pixels are specified beforehand. Therefore, once a target image is input, the pixel labels and feature representations are jointly optimized, and their parameters are updated by the gradient descent. In the proposed approach, label prediction and network parameter learning are alternately iterated to meet the following criteria: (a) pixels of similar features should be assigned the same label, (b) spatially continuous pixels should be assigned the same label, and (c) the number of unique labels should be large. Although these criteria are incompatible, the proposed approach minimizes the combination of similarity loss and spatial continuity loss to find a plausible solution of label assignment that balances the aforementioned criteria well. The contributions of this study are four-fold. First, we propose a novel end-to-end network of unsupervised image segmentation that consists of normalization and an argmax function for differentiable clustering. Second, we introduce a spatial continuity loss function that mitigates the limitations of fixed segment boundaries possessed by previous work. Third, we present an extension of the proposed method for segmentation with scribbles as user input, which showed better accuracy than existing methods while maintaining efficiency. Finally, we introduce another extension of the proposed method: unseen image segmentation by using networks pre-trained with a few reference images without re-training the networks. The effectiveness of the proposed approach was examined on several benchmark datasets of image segmentation.

Deploying Machine and Deep Learning Models for Efficient Data-Augmented Detection of COVID-19 Infections

Abstract: This generation faces existential threats because of the global assault of the novel Corona virus 2019 (i.e., COVID-19). With more than thirteen million infected and nearly 600000 fatalities in 188 countries/regions, COVID-19 is the worst calamity since the World War II. These misfortunes are traced to various reasons, including late detection of latent or asymptomatic carriers, migration, and inadequate isolation of infected people. This makes detection, containment, and mitigation global priorities to contain exposure via quarantine, lockdowns, work/stay at home, and social distancing that are focused on “flattening the curve”. While medical and healthcare givers are at the frontline in the battle against COVID-19, it is a crusade for all of humanity. Meanwhile, machine and deep learning models have been revolutionary across numerous domains and applications whose potency have been exploited to birth numerous state-of-the-art technologies utilised in disease detection, diagnoses, and treatment. Despite these potentials, machine and, particularly, deep learning models are data sensitive, because their effectiveness depends on availability and reliability of data. The unavailability of such data hinders efforts of engineers and computer scientists to fully contribute to the ongoing assault against COVID-19. Faced with a calamity on one side and absence of reliable data on the other, this study presents two data-augmentation models to enhance learnability of the Convolutional Neural Network (CNN) and the Convolutional Long Short-Term Memory (ConvLSTM)-based deep learning models (DADLMs) and, by doing so, boost the accuracy of COVID-19 detection. Experimental results reveal improvement in terms of accuracy of detection, logarithmic loss, and testing time relative to DLMs devoid of such data augmentation. Furthermore, average increases of 4% to 11% in COVID-19 detection accuracy are reported in favour of the proposed data-augmented deep learning models relative to the machine learning techniques. Therefore, the proposed algorithm is effective in performing a rapid and consistent Corona virus diagnosis that is primarily aimed at assisting clinicians in making accurate identification of the virus.

Photocatalytic uphill conversion of natural gas beyond the limitation of thermal reaction systems

Abstract: Dry reforming of methane is one of the key reactions to exploit natural gas feedstocks by their catalytic conversion to synthesis gas (CH4 + CO2 → 2H2 + 2CO), which is used in the production of transportable liquid fuel. However, this reaction suffers from thermodynamic conversion limits and high thermal energy requirements. Herein we report that a SrTiO3-supported rhodium (Rh/STO) catalyst efficiently promotes methane reforming under ultraviolet light irradiation without heat supply at low temperatures, which cannot be achieved by conventional thermal catalysis. The photoexcited holes and electrons are used for CH4 oxidation over STO and CO2 reduction over rhodium, respectively. Isotope analysis clarified that the lattice oxygens (O2−) act as mediator to drive dry reforming of methane. The materials design of Rh/STO can be extended in principle to diverse uphill reactions that utilize photon energy to obtain valued products from different carbon resources. Despite its potential, catalytic dry reforming of methane has not yet reached practical application due to high thermal energy requirements. Now, a photocatalytic method is introduced based on strontium titanate-supported rhodium nanoparticles that afford syngas production solely under light irradiation.

New Horizon of Nanoparticle and Cluster Catalysis with Dendrimers

Abstract: Among various approaches synthesizing metal nanoparticles and tiny clusters, a template method using dendrimers has significant advantages over other chemical approaches with respect to their synthetic precision and the scalability. A dendrimer of polydentate ligands assembles metal ions or salts into the interior allowing production of metal nanoparticles in the dendrimer. The dendrimer-encapsulated nanoparticles (DENs) exhibit unique and remarkable catalytic properties depending on the size and elemental formula. Recent advances in dendrimer chemistry even enabled the atom precise synthesis of subnanometer metal clusters that have been impossible to prepare by wet chemical methods. In addition, not only for the synthesis of metal nanoparticles and clusters, the dendrimer itself can also provide the modulation of activity and selectivity in the catalysis. In this review, we summarized the most relevant research in which the dendrimer was employed as the template, modulator, or stabilizer for nanoparticle synthesis for catalytic applications.

Environmental factors involved in SARS-CoV-2 transmission: effect and role of indoor environmental quality in the strategy for COVID-19 infection control.

Abstract: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a new zoonotic agent that emerged in December 2019, causes coronavirus disease 2019 (COVID-19). This infection can be spread by asymptomatic, presymptomatic, and symptomatic carriers. SARS-CoV-2 spreads primarily via respiratory droplets during close person-to-person contact in a closed space, especially a building. This article summarizes the environmental factors involved in SARS-CoV-2 transmission, including a strategy to prevent SARS-CoV-2 transmission in a building environment. SARS-CoV-2 can persist on surfaces of fomites for at least 3 days depending on the conditions. If SARS-CoV-2 is aerosolized intentionally, it is stable for at least several hours. SARS-CoV-2 is inactivated rapidly on surfaces with sunlight. Close-contact aerosol transmission through smaller aerosolized particles is likely to be combined with respiratory droplets and contact transmission in a confined, crowded, and poorly ventilated indoor environment, as suggested by some cluster cases. Although evidence of the effect of aerosol transmission is limited and uncertainty remains, adequate preventive measures to control indoor environmental quality are required, based on a precautionary approach, because COVID-19 has caused serious global damages to public health, community, and the social economy. The expert panel for COVID-19 in Japan has focused on the “3 Cs,” namely, “closed spaces with poor ventilation,” “crowded spaces with many people,” and “close contact.” In addition, the Ministry of Health, Labour and Welfare of Japan has been recommending adequate ventilation in all closed spaces in accordance with the existing standards of the Law for Maintenance of Sanitation in Buildings as one of the initial political actions to prevent the spread of COVID-19. However, specific standards for indoor environmental quality control have not been recommended and many scientific uncertainties remain regarding the infection dynamics and mode of SARS-CoV-2 transmission in closed indoor spaces. Further research and evaluation are required regarding the effect and role of indoor environmental quality control, especially ventilation.

Overview of the PALM model system 6.0

Abstract: . In this paper, we describe the PALM model system 6.0. PALM (formerly an abbreviation for Parallelized Large-eddy Simulation Model and now an independent name) is a Fortran-based code and has been applied for studying a variety of atmospheric and oceanic boundary layers for about 20 years. The model is optimized for use on massively parallel computer architectures. This is a follow-up paper to the PALM 4.0 model description in Maronga et al. ( 2015 ) . During the last years, PALM has been significantly improved and now offers a variety of new components. In particular, much effort was made to enhance the model with components needed for applications in urban environments, like fully interactive land surface and radiation schemes, chemistry, and an indoor model. This paper serves as an overview paper of the PALM 6.0 model system and we describe its current model core. The individual components for urban applications, case studies, validation runs, and issues with suitable input data are presented and discussed in a series of companion papers in this special issue.

Recent Advances in Electrochemical Systems for Selective Fluorination of Organic Compounds

Abstract: Organofluorine compounds are key materials applied in daily life because of their versatile utility as functional materials, pharmaceuticals, and agrochemicals. Development of the selective fluorination of organic molecules under safe conditions is therefore one of the most important subjects in modern synthetic organofluorine chemistry. Thus, various electrophilic fluorination reagents such as XeF2, (PhSO2)2NF (NFSI), Et2NSF3 (DAST), (MeOCH2CH2)2NSF3 (Deoxofluor), 1-chloromethyl-4-fluoro-1,4-diazoniabicyclo-[2.2.2]octane bis(tetrafluoroborate) (Selectfluor), N-fluoropyridinium salts, and 4-tert-butyl-2,6-dimethylphenylsulfur trifluoride (Fluolead) have been developed for chemical fluorination to date and the development of new fluorinating reagents is still ongoing. Electrochemical synthesis has recently attracted much attention from the perspective of green sustainable chemistry because no hazardous reagents are required and scale-up is generally easy. Although electrochemical perfluorination of organic compounds using a nickel anode in anhydrous HF has been well-established to manufacture perfluoro-functional materials, electrochemical partial fluorination (selective electrochemical fluorination) has been underdeveloped due to the low nucleophilicity of fluoride ions and anode passivation, which interferes with electrolysis. Selective electrochemical fluorination can be commonly achieved in aprotic solvents containing fluoride ions to provide mostly mono- and difluorinated products. Electrolysis is conducted at constant potentials slightly higher than the first oxidation potential of a substrate. Constant current electrolysis is also effective for selective fluorination in many cases. Choice of the combination of a supporting fluoride salt and an electrolytic solvent is most important to accomplish efficient selective fluorination. In this Account, we focus on our recent work on the electrochemical mono- and difluorination of various organic compounds and their synthetic application. We first briefly explain our research background of electrochemical fluorination. Main factors such as the effects of fluoride salts as supporting electrolytes, electrolytic solvents, and anode materials on the selectivity and efficiency of fluorination are discussed. Next, effects of PEG oligomer additives enhancing the nucleophilicity of fluoride ions and organic solvent-free systems using poly(HF) salt ionic liquids as well as recyclable mediatory systems for electrochemical fluorination are described. The desulfurizative monofluorination of xanthate and gem-difluorination of benzothioate and dithioacetals are briefly mentioned. Regioselective anodic fluorination of various heterocyclic compounds having a phenylthio group as electroauxiliary and heterocycles containing sulfur and other heteroatoms are also described. In addition, a boryl group is shown to be a good leaving group for anodic fluorination. Moreover, electrochemically α,α-difluorinated phenylsulfides and phenylselenides are illustrated to be useful for photochemical C-H difluoromethylation of aromatic and heteroaromatic compounds. Finally, this Account also highlights highly diastereoselective fluorination of aliphatic heterocyclic and open-chain compounds, as well as new electrolytic fluorination methods using inorganic fluoride salts such as KF and CsF.