Showing papers by "Tohoku University published in 2017"

Antiinflammatory Therapy with Canakinumab for Atherosclerotic Disease

Abstract: BackgroundExperimental and clinical data suggest that reducing inflammation without affecting lipid levels may reduce the risk of cardiovascular disease. Yet, the inflammatory hypothesis of atherothrombosis has remained unproved. MethodsWe conducted a randomized, double-blind trial of canakinumab, a therapeutic monoclonal antibody targeting interleukin-1β, involving 10,061 patients with previous myocardial infarction and a high-sensitivity C-reactive protein level of 2 mg or more per liter. The trial compared three doses of canakinumab (50 mg, 150 mg, and 300 mg, administered subcutaneously every 3 months) with placebo. The primary efficacy end point was nonfatal myocardial infarction, nonfatal stroke, or cardiovascular death. ResultsAt 48 months, the median reduction from baseline in the high-sensitivity C-reactive protein level was 26 percentage points greater in the group that received the 50-mg dose of canakinumab, 37 percentage points greater in the 150-mg group, and 41 percentage points greater in t...

Adjuvant Capecitabine for Breast Cancer after Preoperative Chemotherapy.

Abstract: BackgroundPatients who have residual invasive carcinoma after the receipt of neoadjuvant chemotherapy for human epidermal growth factor receptor 2 (HER2)–negative breast cancer have poor prognoses. The benefit of adjuvant chemotherapy in these patients remains unclear. MethodsWe randomly assigned 910 patients with HER2-negative residual invasive breast cancer after neoadjuvant chemotherapy (containing anthracycline, taxane, or both) to receive standard postsurgical treatment either with capecitabine or without (control). The primary end point was disease-free survival. Secondary end points included overall survival. ResultsThe result of the prespecified interim analysis met the primary end point, so this trial was terminated early. The final analysis showed that disease-free survival was longer in the capecitabine group than in the control group (74.1% vs. 67.6% of the patients were alive and free from recurrence or second cancer at 5 years; hazard ratio for recurrence, second cancer, or death, 0.70; 95% ...

Efficient hydrogen production on MoNi 4 electrocatalysts with fast water dissociation kinetics

Abstract: Various platinum-free electrocatalysts have been explored for hydrogen evolution reaction in acidic solutions. However, in economical water-alkali electrolysers, sluggish water dissociation kinetics (Volmer step) on platinum-free electrocatalysts results in poor hydrogen-production activities. Here we report a MoNi4 electrocatalyst supported by MoO2 cuboids on nickel foam (MoNi4/MoO2@Ni), which is constructed by controlling the outward diffusion of nickel atoms on annealing precursor NiMoO4 cuboids on nickel foam. Experimental and theoretical results confirm that a rapid Tafel-step-decided hydrogen evolution proceeds on MoNi4 electrocatalyst. As a result, the MoNi4 electrocatalyst exhibits zero onset overpotential, an overpotential of 15 mV at 10 mA cm-2 and a low Tafel slope of 30 mV per decade in 1 M potassium hydroxide electrolyte, which are comparable to the results for platinum and superior to those for state-of-the-art platinum-free electrocatalysts. Benefiting from its scalable preparation and stability, the MoNi4 electrocatalyst is promising for practical water-alkali electrolysers.

Ultrastrong steel via minimal lattice misfit and high-density nanoprecipitation

Abstract: Next-generation high-performance structural materials are required for lightweight design strategies and advanced energy applications. Maraging steels, combining a martensite matrix with nanoprecipitates, are a class of high-strength materials with the potential for matching these demands. Their outstanding strength originates from semi-coherent precipitates, which unavoidably exhibit a heterogeneous distribution that creates large coherency strains, which in turn may promote crack initiation under load. Here we report a counterintuitive strategy for the design of ultrastrong steel alloys by high-density nanoprecipitation with minimal lattice misfit. We found that these highly dispersed, fully coherent precipitates (that is, the crystal lattice of the precipitates is almost the same as that of the surrounding matrix), showing very low lattice misfit with the matrix and high anti-phase boundary energy, strengthen alloys without sacrificing ductility. Such low lattice misfit (0.03 ± 0.04 per cent) decreases the nucleation barrier for precipitation, thus enabling and stabilizing nanoprecipitates with an extremely high number density (more than 1024 per cubic metre) and small size (about 2.7 ± 0.2 nanometres). The minimized elastic misfit strain around the particles does not contribute much to the dislocation interaction, which is typically needed for strength increase. Instead, our strengthening mechanism exploits the chemical ordering effect that creates backstresses (the forces opposing deformation) when precipitates are cut by dislocations. We create a class of steels, strengthened by Ni(Al,Fe) precipitates, with a strength of up to 2.2 gigapascals and good ductility (about 8.2 per cent). The chemical composition of the precipitates enables a substantial reduction in cost compared to conventional maraging steels owing to the replacement of the essential but high-cost alloying elements cobalt and titanium with inexpensive and lightweight aluminium. Strengthening of this class of steel alloy is based on minimal lattice misfit to achieve maximal precipitate dispersion and high cutting stress (the stress required for dislocations to cut through coherent precipitates and thus produce plastic deformation), and we envisage that this lattice misfit design concept may be applied to many other metallic alloys.

Skyrmion Hall effect revealed by direct time-resolved X-ray microscopy

Abstract: Magnetic skyrmions are promising candidates for future spintronic applications such as skyrmion racetrack memories and logic devices. They exhibit exotic and complex dynamics governed by topology and are less influenced by defects, such as edge roughness, than conventionally used domain walls. In particular, their non-zero topological charge leads to a predicted ‘skyrmion Hall effect’, in which current-driven skyrmions acquire a transverse velocity component analogous to charged particles in the conventional Hall effect. Here, we use nanoscale pump–probe imaging to reveal the real-time dynamics of skyrmions driven by current-induced spin–orbit torques. We find that skyrmions move at a well-defined angle ΘSkH that can exceed 30° with respect to the current flow, but in contrast to conventional theoretical expectations, ΘSkH increases linearly with velocity up to at least 100 ms−1. We qualitatively explain our observation based on internal mode excitations in combination with a field-like spin–orbit torque, showing that one must go beyond the usual rigid skyrmion description to understand the dynamics. Experiments show that when driven by electric currents, magnetic skyrmions experience transverse motion due to their topological charge — similar to the conventional Hall effect experienced by charged particles in a perpendicular magnetic field.

The 2017 Plasma Roadmap: Low temperature plasma science and technology

Abstract: Journal of Physics D: Applied Physics published the first Plasma Roadmap in 2012 consisting of the individual perspectives of 16 leading experts in the various sub-fields of low temperature plasma science and technology. The 2017 Plasma Roadmap is the first update of a planned series of periodic updates of the Plasma Roadmap. The continuously growing interdisciplinary nature of the low temperature plasma field and its equally broad range of applications are making it increasingly difficult to identify major challenges that encompass all of the many sub-fields and applications. This intellectual diversity is ultimately a strength of the field. The current state of the art for the 19 sub-fields addressed in this roadmap demonstrates the enviable track record of the low temperature plasma field in the development of plasmas as an enabling technology for a vast range of technologies that underpin our modern society. At the same time, the many important scientific and technological challenges shared in this roadmap show that the path forward is not only scientifically rich but has the potential to make wide and far reaching contributions to many societal challenges.

State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems

Abstract: Currently, the network traffic control systems are mainly composed of the Internet core and wired/wireless heterogeneous backbone networks. Recently, these packet-switched systems are experiencing an explosive network traffic growth due to the rapid development of communication technologies. The existing network policies are not sophisticated enough to cope with the continually varying network conditions arising from the tremendous traffic growth. Deep learning, with the recent breakthrough in the machine learning/intelligence area, appears to be a viable approach for the network operators to configure and manage their networks in a more intelligent and autonomous fashion. While deep learning has received a significant research attention in a number of other domains such as computer vision, speech recognition, robotics, and so forth, its applications in network traffic control systems are relatively recent and garnered rather little attention. In this paper, we address this point and indicate the necessity of surveying the scattered works on deep learning applications for various network traffic control aspects. In this vein, we provide an overview of the state-of-the-art deep learning architectures and algorithms relevant to the network traffic control systems. Also, we discuss the deep learning enablers for network systems. In addition, we discuss, in detail, a new use case, i.e., deep learning based intelligent routing. We demonstrate the effectiveness of the deep learning-based routing approach in contrast with the conventional routing strategy. Furthermore, we discuss a number of open research issues, which researchers may find useful in the future.

The Human Phenotype Ontology in 2017

Abstract: Deep phenotyping has been defined as the precise and comprehensive analysis of phenotypic abnormalities in which the individual components of the phenotype are observed and described. The three components of the Human Phenotype Ontology (HPO; www.human-phenotype-ontology.org) project are the phenotype vocabulary, disease-phenotype annotations and the algorithms that operate on these. These components are being used for computational deep phenotyping and precision medicine as well as integration of clinical data into translational research. The HPO is being increasingly adopted as a standard for phenotypic abnormalities by diverse groups such as international rare disease organizations, registries, clinical labs, biomedical resources, and clinical software tools and will thereby contribute toward nascent efforts at global data exchange for identifying disease etiologies. This update article reviews the progress of the HPO project since the debut Nucleic Acids Research database article in 2014, including specific areas of expansion such as common (complex) disease, new algorithms for phenotype driven genomic discovery and diagnostics, integration of cross-species mapping efforts with the Mammalian Phenotype Ontology, an improved quality control pipeline, and the addition of patient-friendly terminology.

Targeting the KEAP1-NRF2 System to Prevent Kidney Disease Progression

Abstract: Background: Nuclear factor erythroid 2-related factor 2 (NRF2) is a critical transcription factor for the antioxidative stress response and it activates a variety

Endothelial dysfunction and vascular disease – a 30th anniversary update

Abstract: The endothelium can evoke relaxations of the underlying vascular smooth muscle, by releasing vasodilator substances. The best-characterized endothelium-derived relaxing factor (EDRF) is nitric oxide (NO) which activates soluble guanylyl cyclase in the vascular smooth muscle cells, with the production of cyclic guanosine monophosphate (cGMP) initiating relaxation. The endothelial cells also evoke hyperpolarization of the cell membrane of vascular smooth muscle (endothelium-dependent hyperpolarizations, EDH-mediated responses). As regards the latter, hydrogen peroxide (H2 O2 ) now appears to play a dominant role. Endothelium-dependent relaxations involve both pertussis toxin-sensitive Gi (e.g. responses to α2 -adrenergic agonists, serotonin, and thrombin) and pertussis toxin-insensitive Gq (e.g. adenosine diphosphate and bradykinin) coupling proteins. New stimulators (e.g. insulin, adiponectin) of the release of EDRFs have emerged. In recent years, evidence has also accumulated, confirming that the release of NO by the endothelial cell can chronically be upregulated (e.g. by oestrogens, exercise and dietary factors) and downregulated (e.g. oxidative stress, smoking, pollution and oxidized low-density lipoproteins) and that it is reduced with ageing and in the course of vascular disease (e.g. diabetes and hypertension). Arteries covered with regenerated endothelium (e.g. following angioplasty) selectively lose the pertussis toxin-sensitive pathway for NO release which favours vasospasm, thrombosis, penetration of macrophages, cellular growth and the inflammatory reaction leading to atherosclerosis. In addition to the release of NO (and EDH, in particular those due to H2 O2 ), endothelial cells also can evoke contraction of the underlying vascular smooth muscle cells by releasing endothelium-derived contracting factors. Recent evidence confirms that most endothelium-dependent acute increases in contractile force are due to the formation of vasoconstrictor prostanoids (endoperoxides and prostacyclin) which activate TP receptors of the vascular smooth muscle cells and that prostacyclin plays a key role in such responses. Endothelium-dependent contractions are exacerbated when the production of nitric oxide is impaired (e.g. by oxidative stress, ageing, spontaneous hypertension and diabetes). They contribute to the blunting of endothelium-dependent vasodilatations in aged subjects and essential hypertensive and diabetic patients. In addition, recent data confirm that the release of endothelin-1 can contribute to endothelial dysfunction and that the peptide appears to be an important contributor to vascular dysfunction. Finally, it has become clear that nitric oxide itself, under certain conditions (e.g. hypoxia), can cause biased activation of soluble guanylyl cyclase leading to the production of cyclic inosine monophosphate (cIMP) rather than cGMP and hence causes contraction rather than relaxation of the underlying vascular smooth muscle.

Overview of pretreatment strategies for enhancing sewage sludge disintegration and subsequent anaerobic digestion: Current advances, full-scale application and future perspectives

Abstract: Sewage sludge management is now becoming a serious issue all over the world. Anaerobic digestion is a simple and well-studied process capable of biologically converting the chemical energy of sewage sludge into methane-rich biogas, as a carbon-neutral alternative to fossil fuels whilst destroying pathogens and removing odors. Hydrolysis is the rate-limiting step because of the sewage sludge complex floc structure (such as extracellular polymeric substances) and hard cell wall. To accelerate the rate-limiting hydrolysis and improve the efficiency of anaerobic digestion, various pretreatment technologies have been developed. This paper presents an up-to-date review of recent research achievements in the pretreatment technologies used for improving biogas production including mechanical (ultrasonic, microwave, electrokinetic and high-pressure homogenization), thermal, chemical (acidic, alkali, ozonation, Fenton and Fe(II)-activated persulfate oxidation), and biological options (temperature-phased anaerobic digestion and microbial electrolysis cell). The effectiveness and relative worth of each of the studied technologies are summarized and compared in terms of the resulting sludge properties, the digester performance, the environmental benefits and the current state of real-world application. The challenge and technical issues encountered during sludge cotreatment are discussed, and the future research needs in promoting full-scale implementations of those approaches are proposed.

mTORC1 and muscle regeneration are regulated by the LINC00961-encoded SPAR polypeptide

Abstract: Although long non-coding RNAs (lncRNAs) are non-protein-coding transcripts by definition, recent studies have shown that a fraction of putative small open reading frames within lncRNAs are translated. However, the biological significance of these hidden polypeptides is still unclear. Here we identify and functionally characterize a novel polypeptide encoded by the lncRNA LINC00961. This polypeptide is conserved between human and mouse, is localized to the late endosome/lysosome and interacts with the lysosomal v-ATPase to negatively regulate mTORC1 activation. This regulation of mTORC1 is specific to activation of mTORC1 by amino acid stimulation, rather than by growth factors. Hence, we termed this polypeptide 'small regulatory polypeptide of amino acid response' (SPAR). We show that the SPAR-encoding lncRNA is highly expressed in a subset of tissues and use CRISPR/Cas9 engineering to develop a SPAR-polypeptide-specific knockout mouse while maintaining expression of the host lncRNA. We find that the SPAR-encoding lncRNA is downregulated in skeletal muscle upon acute injury, and using this in vivo model we establish that SPAR downregulation enables efficient activation of mTORC1 and promotes muscle regeneration. Our data provide a mechanism by which mTORC1 activation may be finely regulated in a tissue-specific manner in response to injury, and a paradigm by which lncRNAs encoding small polypeptides can modulate general biological pathways and processes to facilitate tissue-specific requirements, consistent with their restricted and highly regulated expression profile.

Amorphous Metallic NiFeP: A Conductive Bulk Material Achieving High Activity for Oxygen Evolution Reaction in Both Alkaline and Acidic Media

Abstract: The intrinsic catalytic activity at 10 mA cm-2 for oxygen evolution reaction (OER) is currently working out at overpotentials higher than 320 mV. A highly efficient electrocatalyst should possess both active sites and high conductivity; however, the loading of powder catalysts on electrodes may often suffer from the large resistance between catalysts and current collectors. This work reports a class of bulk amorphous NiFeP materials with metallic bonds from the viewpoint of electrode design. The materials reported here perfectly combine high macroscopic conductivity with surface active sites, and can be directly used as the electrodes with active sites toward high OER activity in both alkaline and acidic electrolytes. Specifically, a low overpotential of 219 mV is achieved at the geometric current density 10 mA cm-2 in an alkaline electrolyte, with the Tafel slope of 32 mV dec-1 and intrinsic overpotential of 280 mV. Meanwhile, an overpotential of 540 mV at 10 mA cm-2 is attained in an acidic electrolyte and stable for over 30 h, which is the best OER performance in both alkaline and acidic media. This work provides a different angle for the design of high-performance OER electrocatalysts and facilitates the device applications of electrocatalysts.

Temporal Generative Adversarial Nets with Singular Value Clipping

Abstract: In this paper, we propose a generative model, Temporal Generative Adversarial Nets (TGAN), which can learn a semantic representation of unlabeled videos, and is capable of generating videos. Unlike existing Generative Adversarial Nets (GAN)-based methods that generate videos with a single generator consisting of 3D deconvolutional layers, our model exploits two different types of generators: a temporal generator and an image generator. The temporal generator takes a single latent variable as input and outputs a set of latent variables, each of which corresponds to an image frame in a video. The image generator transforms a set of such latent variables into a video. To deal with instability in training of GAN with such advanced networks, we adopt a recently proposed model, Wasserstein GAN, and propose a novel method to train it stably in an end-to-end manner. The experimental results demonstrate the effectiveness of our methods.

A White Paper on keV sterile neutrino Dark Matter

Abstract: We present a comprehensive review of keV-scale sterile neutrino Dark Matter, collecting views and insights from all disciplines involved—cosmology, astrophysics, nuclear, and particle physics—in each case viewed from both theoretical and experimental/observational perspectives. After reviewing the role of active neutrinos in particle physics, astrophysics, and cosmology, we focus on sterile neutrinos in the context of the Dark Matter puzzle. Here, we first review the physics motivation for sterile neutrino Dark Matter, based on challenges and tensions in purely cold Dark Matter scenarios. We then round out the discussion by critically summarizing all known constraints on sterile neutrino Dark Matter arising from astrophysical observations, laboratory experiments, and theoretical considerations. In this context, we provide a balanced discourse on the possibly positive signal from X-ray observations. Another focus of the paper concerns the construction of particle physics models, aiming to explain how sterile neutrinos of keV-scale masses could arise in concrete settings beyond the Standard Model of elementary particle physics. The paper ends with an extensive review of current and future astrophysical and laboratory searches, highlighting new ideas and their experimental challenges, as well as future perspectives for the discovery of sterile neutrinos.

Highly crystalline 2D superconductors

Abstract: Recent advances in materials fabrication have enabled the manufacturing of ordered 2D electron systems, such as heterogeneous interfaces, atomic layers grown by molecular beam epitaxy, exfoliated thin flakes and field-effect devices. These 2D electron systems are highly crystalline, and some of them, despite their single-layer thickness, exhibit a sheet resistance more than an order of magnitude lower than that of conventional amorphous or granular thin films. In this Review, we explore recent developments in the field of highly crystalline 2D superconductors and highlight the unprecedented physical properties of these systems. In particular, we explore the quantum metallic state (or possible metallic ground state), the quantum Griffiths phase observed in out-of-plane magnetic fields and the superconducting state maintained in anomalously large in-plane magnetic fields. These phenomena are examined in the context of weakened disorder and/or broken spatial inversion symmetry. We conclude with a discussion of how these unconventional properties make highly crystalline 2D systems promising platforms for the exploration of new quantum physics and high-temperature superconductors. Highly crystalline 2D superconductors can exhibit very low sheet resistances and unusual physical properties, such as a quantum metallic state, a quantum Griffiths phase and superconductivity robust against in-plane magnetic fields. The origins of these phenomena and the materials in which they are observed are reviewed.

Measurement of the τ Lepton Polarization and R (D * ) in the Decay B ̄ →D * τ - ν ̄ τ

Abstract: We report the first measurement of the tau lepton polarization P-tau(D*) in the decay (B) over bar -> D* tau(-) (v) over bar (tau) as well as a newmeasurement of the ratio of the branching fractions R(D*) = B((B) over bar -> D* tau(-) (v) over bar (tau)) / B((B) over bar -> D* l(-) (v) over bar (l)), where l(-) denotes an electron or a muon, and the tau is reconstructed in the modes tau(-) -> pi(-) v(tau) and tau(-) -> rho(-) v(tau). We use the full data sample of 772 x 10(6) B (B) over bar pairs recorded with the Belle detector at the (KEKB) over bar electron-positron collider. Our results, P-tau(D*) = -0.38 +/- 0.51 (stat)(-0.16)(+0.21) (syst) and R(D*) = 0.270 +/- 0.035 (stat)(- 0.025)(+0.028) (syst), are consistent with the theoretical predictions of the standard model.

The global prevalence of IBS in adults remains elusive due to the heterogeneity of studies: a Rome Foundation working team literature review

Abstract: Objectives The global prevalence of IBS is difficult to ascertain, particularly in light of the heterogeneity of published epidemiological studies. The aim was to conduct a literature review, by experts from around the world, of community-based studies on IBS prevalence. Design Searches were conducted using predetermined search terms and eligibility criteria, including papers in all languages. Pooled prevalence rates were calculated by combining separate population survey prevalence estimates to generate an overall combined metaprevalence estimate. The heterogeneity of studies was assessed. Results 1451 papers were returned and 83, including 288 103 participants in 41 countries, met inclusion criteria. The mean prevalence among individual countries ranged from 1.1% in France and Iran to 35.5% in Mexico. There was significant variance in pooled regional prevalence rates ranging from 17.5% (95% CI 16.9% to 18.2%) in Latin America, 9.6% (9.5% to 9.8%) in Asia, 7.1% (8.0% to 8.3%) in North America/ Europe/Australia/New Zealand, to 5.8% (5.6% to 6.0%) in the Middle East and Africa. There was a significant degree of heterogeneity with the percentage of residual variation due to heterogeneity at 99.9%. Conclusions The main finding is the extent of methodological variance in the studies reviewed and the degree of heterogeneity among them. Based on this, we concluded that publication of a single pooled global prevalence rate, which is easily calculated, would not be appropriate or contributory. Furthermore, we believe that future studies should focus on regional and cross-cultural differences that are more likely to shed light on pathophysiology.

Lattice relaxation and energy band modulation in twisted bilayer graphene

Abstract: We theoretically study the lattice relaxation in the twisted bilayer graphene (TBG) and its effect on the electronic band structure. We develop an effective continuum theory to describe the lattice relaxation in general TBGs and obtain the optimized structure to minimize the total energy. At small rotation angles $l{2}^{\ensuremath{\circ}}$, in particular, we find that the relaxed lattice drastically reduces the area of the AA stacking region and forms a triangular domain structure with alternating AB and BA stacking regions. We then investigate the effect of the domain formation on the electronic band structure. The most notable change from the nonrelaxed model is that an energy gap of up to 20 meV opens at the superlattice subband edges on the electron and hole sides. We also find that the lattice relaxation significantly enhances the Fermi velocity, which was strongly suppressed in the nonrelaxed model.

Physiological and structural tradeoffs underlying the leaf economics spectrum.

Abstract: The leaf economics spectrum (LES) represents a suite of intercorrelated leaf traits concerning construction costs per unit leaf area, nutrient concentrations, and rates of carbon fixation and tissue turnover. Although broad trade-offs among leaf structural and physiological traits have been demonstrated, we still do not have a comprehensive view of the fundamental constraints underlying the LES trade-offs. Here, we investigated physiological and structural mechanisms underpinning the LES by analysing a novel data compilation incorporating rarely considered traits such as the dry mass fraction in cell walls, nitrogen allocation, mesophyll CO2 diffusion and associated anatomical traits for hundreds of species covering major growth forms. The analysis demonstrates that cell wall constituents are major components of leaf dry mass (18-70%), especially in leaves with high leaf mass per unit area (LMA) and long lifespan. A greater fraction of leaf mass in cell walls is typically associated with a lower fraction of leaf nitrogen (N) invested in photosynthetic proteins; and lower within-leaf CO2 diffusion rates, as a result of thicker mesophyll cell walls. The costs associated with greater investments in cell walls underpin the LES: long leaf lifespans are achieved via higher LMA and in turn by higher cell wall mass fraction, but this inevitably reduces the efficiency of photosynthesis.

Genome-wide association study identifies 112 new loci for body mass index in the Japanese population

Abstract: Obesity is a risk factor for a wide variety of health problems. In a genome-wide association study (GWAS) of body mass index (BMI) in Japanese people (n = 173,430), we found 85 loci significantly associated with obesity (P < 5.0 × 10-8), of which 51 were previously unknown. We conducted trans-ancestral meta-analyses by integrating these results with the results from a GWAS of Europeans and identified 61 additional new loci. In total, this study identifies 112 novel loci, doubling the number of previously known BMI-associated loci. By annotating associated variants with cell-type-specific regulatory marks, we found enrichment of variants in CD19+ cells. We also found significant genetic correlations between BMI and lymphocyte count (P = 6.46 × 10-5, rg = 0.18) and between BMI and multiple complex diseases. These findings provide genetic evidence that lymphocytes are relevant to body weight regulation and offer insights into the pathogenesis of obesity.

The prevalence of biofilms in chronic wounds: a systematic review and meta-analysis of published data

Abstract: The presence of biofilms in chronic non-healing wounds, has been identified through in vitro model and in vivo animal data. However, human chronic wound studies are under-represented and generally report low sample sizes. For this reason we sought to ascertain the prevalence of biofilms in human chronic wounds by undertaking a systematic review and meta-analysis. Our initial search identified 554 studies from the literature databases (Cochrane Library, Embase, Medline). After removal of duplicates, and those not meeting the requirements of inclusion, nine studies involving 185 chronic wounds met the inclusion criteria. Prevalence of biofilms in chronic wounds was 78.2 % (confidence interval [CI 61.6-89, p<0.002]). The results of our meta-analysis support our clinical assumptions that biofilms are ubiquitous in human chronic non-healing wounds.

The Deep Learning Vision for Heterogeneous Network Traffic Control: Proposal, Challenges, and Future Perspective

Abstract: Recently, deep learning, an emerging machine learning technique, is garnering a lot of research attention in several computer science areas. However, to the best of our knowledge, its application to improve heterogeneous network traffic control (which is an important and challenging area by its own merit) has yet to appear because of the difficult challenge in characterizing the appropriate input and output patterns for a deep learning system to correctly reflect the highly dynamic nature of large-scale heterogeneous networks. In this vein, in this article, we propose appropriate input and output characterizations of heterogeneous network traffic and propose a supervised deep neural network system. We describe how our proposed system works and how it differs from traditional neural networks. Also, preliminary results are reported that demonstrate the encouraging performance of our proposed deep learning system compared to a benchmark routing strategy (Open Shortest Path First (OSPF)) in terms of significantly better signaling overhead, throughput, and delay.

Lepton-Flavor-Dependent Angular Analysis of B→K^{*}ℓ^{+}ℓ^{-}

Abstract: We present a measurement of angular observables and a test of lepton flavor universality in the B -> K(+)l(+)l(-) decay, where l is either e or mu. The analysis is performed on a data sample corresponding to an integrated luminosity of 711 fb(-1) containing 772 x 10(6) B (B) over bar pairs, collected at the Upsilon(4S) resonance with the Belle detector at the asymmetric-energy e(+)e(-) collider KEKB. The result is consistent with standard model (SM) expectations, where the largest discrepancy from a SM prediction is observed in the muon modes with a local significance of 2.6 sigma.

The KEAP1–NRF2 System in Cancer

Abstract: Cancer cells first adapt to the microenvironment and then propagate. Mutations in tumor suppressor genes or oncogenes are frequently found in cancer cells. Comprehensive genomic analyses have identified somatic mutations and other alterations in the KEAP1 or NRF2 genes and in well-known tumor suppressor genes or oncogenes, such as TP53, CDKN2A, PTEN, and PIK3CA, in various types of cancer. Aberrant NRF2 activation in cancer cells occurs through somatic mutations in the KEAP1 or NRF2 gene as well as through other mechanisms that disrupt the binding of KEAP1 to NRF2. Unregulated NRF2 confers on cancer cells high-level resistance to anti-cancer drugs and reactive oxygen species (ROS) and directs cancer cells toward metabolic reprogramming. Therefore, NRF2 has been studied as a therapeutic target molecule in cancer. Two strategies have been used to target NRF2 via therapeutic drugs: inhibition of NRF2 and induction of NRF2. NRF2 inhibitors may be effective against NRF2-addicted cancer cells in which NRF2 is aberrantly activated. These inhibitors have not yet been established as NRF2-targeted anti-cancer drugs for the treatment of human cancers. Diagnosis of NRF2 activation could facilitate the use of NRF2 inhibitors for the treatment of patients with NRF2-addicted cancers. Conversely, NRF2 inducers have been used or are being developed for non-cancer diseases. In addition, NRF2 inducers may be useful for cancer chemotherapy in combination with conventional anti-cancer agents or even NRF2 inhibitors.

Hybrid Method for Minimizing Service Delay in Edge Cloud Computing Through VM Migration and Transmission Power Control

Abstract: Due to physical limitations, mobile devices are restricted in memory, battery, processing, among other characteristics. This results in many applications that cannot be run in such devices. This problem is fixed by Edge Cloud Computing, where the users offload tasks they cannot run to cloudlet servers in the edge of the network. The main requirement of such a system is having a low Service Delay, which would correspond to a high Quality of Service. This paper presents a method for minimizing Service Delay in a scenario with two cloudlet servers. The method has a dual focus on computation and communication elements, controlling Processing Delay through virtual machine migration and improving Transmission Delay with Transmission Power Control. The foundation of the proposal is a mathematical model of the scenario, whose analysis is used on a comparison between the proposed approach and two other conventional methods; these methods have single focus and only make an effort to improve either Transmission Delay or Processing Delay, but not both. As expected, the proposal presents the lowest Service Delay in all study cases, corroborating our conclusion that a dual focus approach is the best way to tackle the Service Delay problem in Edge Cloud Computing.

A Survey on Network Methodologies for Real-Time Analytics of Massive IoT Data and Open Research Issues

Abstract: With the widespread adoption of the Internet of Things (IoT), the number of connected devices is growing at an exponential rate, which is contributing to ever-increasing, massive data volumes. Real-time analytics on the massive IoT data, referred to as the “real-time IoT analytics” in this paper, is becoming the mainstream with an aim to provide an immediate or non-immediate actionable insights and business intelligence. However, the analytics network of the existing IoT systems does not adequately consider the requirements of the real-time IoT analytics. In fact, most researchers overlooked an appropriate design of the IoT analytics network while focusing much on the sensing and delivery networks of the IoT system. Since much of the IoT analytics network has often been taken as granted, the survey, in this paper, we aim to review the state-of-the-art of the analytics network methodologies, which are suitable for real-time IoT analytics. In this vein, we first describe the basics of the real-time IoT analytics, use cases, and software platforms, and then explain the shortcomings of the network methodologies to support them. To address those shortcomings, we then discuss the relevant network methodologies which may support the real-time IoT analytics. Also, we present a number of prospective research problems and future research directions focusing on the network methodologies for the real-time IoT analytics.