The Value of Handheld COPD-6 Spirometry for Early COPD Detection in High Risk Elderly Population in Community

BackgroundChronic obstructive pulmonary disease (COPD) is a common chronic disease of the respiratory tract, and lung function is necessary for the diagnosis of COPD. However, conventional pulmonary function meters are not suitable for a large number of physical examinations in the community. In recent years, it is recommended to use spirometry for COPD screening and management both at home and abroad. However, there is still a lack of relevant data on its consistency and effectiveness with conventional lung function.ObjectiveTo explore the value of handheld COPD-6 spirometry for early COPD detection in high risk elderly population in community.MethodsFrom January 2018 to December 2019 at Dongshan Community Health Service Center, Jiangning District, Nanjing City, a free physical examination was performed on the elder population over 60 years who had high risk factors of COPD. Handheld COPD-6 spirometry was performed pre- and post-bronchodilator, the forced expiratory volume in one second (FEV1) , the forced expiratory volume in one second as a percentage of the predicted value (FEV1%prep) , the forced expiratory volume in six second (FEV6) , the percentage of forced expiratory volume in six second to the predicted value (FEV6%prep) , and the value of FEV1/FEV6 were evaluated and recorded. With FEV1/FEV6<80% as the initial screening positive pre-bronchodilator, retests were performed both with handheld COPD-6 spirometry and confirmatory spirometry after inhaling bronchodilator. Using FEV1/Forced vital capacity (FVC) <70% as the gold standard by confirmatory spirometry, receiver-operator characteristic (ROC) curve analysis was used to obtain the best diagnostic threshold of FEV1/FEV6. Sensitivity, specificity, positive predictive value, and negative predictive value were used to evaluate the diagnostic value of the handheld COPD-6 spirometer.ResultsOut of the 382 participants, COPD was confirmed in 75 according to FEV1/FVC<70% post-bronchodilator. There was no statistically significant difference between FEV1%pred pre- and post-bronchodilator by handheld COPD-6 spirometry (t=-0.971, P=0.703) ; There was no statistically significant difference among FEV1%pred in two tests (t=-2.352, -1.429; P=0.396, 0.058) . The FEV1%pred detected by handheld COPD-6 spirometry post-bronchodilation was positively correlated with confirmatory spirometry (r=0.969, P<0.05) . Compared with FVC%pred and FEV6%pred post-bronchodilation, the difference was statistically significant (t=-3.170, P=0.005) ; and the FEV6%pred was positively correlated with the FVC%pred (r=0.653, P<0.05) . There was no statistically significant difference between FEV1/FEV6 and FEV1/FVC post-bronchodilation (t=1.735, P=0.084) ; and there was substantial agreement between the two diagnostic (r=0.871, P<0.05) . The FEV1/FEV6 cut-off with the greatest sum of sensitivity and specificity was 71% pre-bronchodilator, the sensitivity, specificity, positive and negative predictive values were 80.0%, 79.2%, 90.6% and 48.5% respectively. The greatest sum of sensitivity and specificity was 75% post-bronchodilator, the sensitivity, specificity, positive and negative predictive values were 80.0%, 98.8%, 98.4% and 58.3% respectively.ConclusionIt is feasible to use FEV1/FEV6 as an indicator to screen COPD patients in elderly high-risk populations. It is recommended to use FEV1/FEV6<71% before bronchodilation and FEV1/FEV6<75% after diastole as the screening criteria

A day in the life of a dolphin: Using bio-logging tags for improved animal health and well-being

Little quantitative information on the behavior, health, and activity level of managed marine mammals is currently collected, though it has the potential to significantly contribute to management and welfare of these animals. To address this, highâ resolution motionâ sensing digital acoustic recording tags (DTAGs) collected data from animals under human care (n = 5) during their daily routine, and classification algorithms were used for gait analysis and event detection. We collected and examined ~57 h of data from five bottlenose dolphins (Tursiops truncatus). Dayâ scale changes in behavior and activity level were observed and diurnal changes were detected with lower activity at night (n = 1). During the day, animals spent about 70% of their time swimming. The deepest part of the lagoon is ~3 m and individual dives were typically shallow (~1 m) with the dolphins tending to utilize a fluke and glide gait pattern. Activity level was quantified using overall dynamic body acceleration. A significant relationship between normalized activity level and glide duration during different portions of the dive was measured; animals fluked more during descent and glided more during ascent. This could indicate that even during very shallow dives the dolphins use their positive buoyancy to improve energy economy.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137721/1/mms12408_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137721/2/mms12408.pd

Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges

Artificial General Intelligence (AGI), possessing the capacity to comprehend, learn, and execute tasks with human cognitive abilities, engenders significant anticipation and intrigue across scientific, commercial, and societal arenas. This fascination extends particularly to the Internet of Things (IoT), a landscape characterized by the interconnection of countless devices, sensors, and systems, collectively gathering and sharing data to enable intelligent decision-making and automation. This research embarks on an exploration of the opportunities and challenges towards achieving AGI in the context of the IoT. Specifically, it starts by outlining the fundamental principles of IoT and the critical role of Artificial Intelligence (AI) in IoT systems. Subsequently, it delves into AGI fundamentals, culminating in the formulation of a conceptual framework for AGI's seamless integration within IoT. The application spectrum for AGI-infused IoT is broad, encompassing domains ranging from smart grids, residential environments, manufacturing, and transportation to environmental monitoring, agriculture, healthcare, and education. However, adapting AGI to resource-constrained IoT settings necessitates dedicated research efforts. Furthermore, the paper addresses constraints imposed by limited computing resources, intricacies associated with large-scale IoT communication, as well as the critical concerns pertaining to security and privacy

Rasgrp1 mutation increases naïve T-cell CD44 expression and drives mTOR-dependent accumulation of Helios+ T cells and autoantibodies

Missense variants are a major source of human genetic variation. Here we analyze a new mouse missense variant, Rasgrp1Anaef, with an ENU-mutated EF hand in the Rasgrp1 Ras guanine nucleotide exchange factor. Rasgrp1Anaef mice exhibit anti-nuclear autoantibodies and gradually accumulate a CD44hi Helios+ PD-1+ CD4+ T cell population that is dependent on B cells. Despite reduced Rasgrp1-Ras-ERK activation in vitro, thymocyte selection in Rasgrp1Anaef is mostly normal in vivo, although CD44 is overexpressed on naïve thymocytes and T cells in a T-cell-autonomous manner. We identify CD44 expression as a sensitive reporter of tonic mTOR-S6 kinase signaling through a novel mouse strain, chino, with a reduction-of-function mutation in Mtor. Elevated tonic mTOR-S6 signaling occurs in Rasgrp1Anaef naïve CD4+ T cells. CD44 expression, CD4+ T cell subset ratios and serum autoantibodies all returned to normal in Rasgrp1AnaefMtorchino double-mutant mice, demonstrating that increased mTOR activity is essential for the Rasgrp1Anaef T cell dysregulation

Rasgrp1 mutation increases naïve T-cell CD44 expression and drives mTOR-dependent accumulation of Helios⁺ T cells and autoantibodies

Missense variants are a major source of human genetic variation. Here we analyze a new mouse missense variant, Rasgrp1ᴬⁿᵃᵉᶠ, with an ENU-mutated EF hand in the Rasgrp1 Ras guanine nucleotide exchange factor. Rasgrp1ᴬⁿᵃᵉᶠ mice exhibit anti-nuclear autoantibodies and gradually accumulate a CD44hi Helios⁺ PD-1⁺ CD4⁺ T cell population that is dependent on B cells. Despite reduced Rasgrp1-Ras-ERK activation in vitro, thymocyte selection in Rasgrp1ᴬⁿᵃᵉᶠ is mostly normal in vivo, although CD44 is overexpressed on naïve thymocytes and T cells in a T-cell-autonomous manner. We identify CD44 expression as a sensitive reporter of tonic mTOR-S6 kinase signaling through a novel mouse strain, chino, with a reduction-of-function mutation in Mtor. Elevated tonic mTOR-S6 signaling occurs in Rasgrp1ᴬⁿᵃᵉᶠ naïve CD4⁺ T cells. CD44 expression, CD4⁺ T cell subset ratios and serum autoantibodies all returned to normal in Rasgrp1ᴬⁿᵃᵉᶠMtorᶜʰⁱⁿᵒ double-mutant mice, demonstrating that increased mTOR activity is essential for the Rasgrp1ᴬⁿᵃᵉᶠ T cell dysregulation

The Embedding Problem for Markov Models of Nucleotide Substitution

10.1371/journal.pone.0069187PLoS ONE87-POLN

Rasgrp1 mutation increases naïve T-cell CD44 expression and drives mTOR-dependent accumulation of Helios+ T cells and autoantibodies

Missense variants are a major source of human genetic variation. Here we analyze a new mouse missense variant, Rasgrp1(Anaef), with an ENU-mutated EF hand in the Rasgrp1 Ras guanine nucleotide exchange factor. Rasgrp1(Anaef) mice exhibit anti-nuclear autoantibodies and gradually accumulate a CD44(hi) Helios(+) PD-1(+) CD4(+) T cell population that is dependent on B cells. Despite reduced Rasgrp1-Ras-ERK activation in vitro, thymocyte selection in Rasgrp1(Anaef) is mostly normal in vivo, although CD44 is overexpressed on naïve thymocytes and T cells in a T-cell-autonomous manner. We identify CD44 expression as a sensitive reporter of tonic mTOR-S6 kinase signaling through a novel mouse strain, chino, with a reduction-of-function mutation in Mtor. Elevated tonic mTOR-S6 signaling occurs in Rasgrp1(Anaef) naïve CD4(+) T cells. CD44 expression, CD4(+) T cell subset ratios and serum autoantibodies all returned to normal in Rasgrp1(Anaef)Mtor(chino) double-mutant mice, demonstrating that increased mTOR activity is essential for the Rasgrp1(Anaef) T cell dysregulation. DOI: http://dx.doi.org/10.7554/eLife.01020.00

NIST Interlaboratory Study on Glycosylation Analysis of Monoclonal Antibodies: Comparison of Results from Diverse Analytical Methods

Glycosylation is a topic of intense current interest in the development of biopharmaceuticals because it is related to drug safety and efficacy. This work describes results of an interlaboratory study on the glycosylation of the Primary Sample (PS) of NISTmAb, a monoclonal antibody reference material. Seventy-six laboratories from industry, university, research, government, and hospital sectors in Europe, North America, Asia, and Australia submit- Avenue, Silver Spring, Maryland 20993; 22Glycoscience Research Laboratory, Genos, Borongajska cesta 83h, 10 000 Zagreb, Croatia; 23Faculty of Pharmacy and Biochemistry, University of Zagreb, A. Kovacˇ ic´ a 1, 10 000 Zagreb, Croatia; 24Department of Chemistry, Georgia State University, 100 Piedmont Avenue, Atlanta, Georgia 30303; 25glyXera GmbH, Brenneckestrasse 20 * ZENIT / 39120 Magdeburg, Germany; 26Health Products and Foods Branch, Health Canada, AL 2201E, 251 Sir Frederick Banting Driveway, Ottawa, Ontario, K1A 0K9 Canada; 27Graduate School of Advanced Sciences of Matter, Hiroshima University, 1-3-1 Kagamiyama Higashi-Hiroshima 739–8530 Japan; 28ImmunoGen, 830 Winter Street, Waltham, Massachusetts 02451; 29Department of Medical Physiology, Jagiellonian University Medical College, ul. Michalowskiego 12, 31–126 Krakow, Poland; 30Department of Pathology, Johns Hopkins University, 400 N. Broadway Street Baltimore, Maryland 21287; 31Mass Spec Core Facility, KBI Biopharma, 1101 Hamlin Road Durham, North Carolina 27704; 32Division of Mass Spectrometry, Korea Basic Science Institute, 162 YeonGuDanji-Ro, Ochang-eup, Cheongwon-gu, Cheongju Chungbuk, 363–883 Korea (South); 33Advanced Therapy Products Research Division, Korea National Institute of Food and Drug Safety, 187 Osongsaengmyeong 2-ro Osong-eup, Heungdeok-gu, Cheongju-si, Chungcheongbuk-do, 363–700, Korea (South); 34Center for Proteomics and Metabolomics, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands; 35Ludger Limited, Culham Science Centre, Abingdon, Oxfordshire, OX14 3EB, United Kingdom; 36Biomolecular Discovery and Design Research Centre and ARC Centre of Excellence for Nanoscale BioPhotonics (CNBP), Macquarie University, North Ryde, Australia; 37Proteomics, Central European Institute for Technology, Masaryk University, Kamenice 5, A26, 625 00 BRNO, Czech Republic; 38Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstrasse 1, 39106 Magdeburg, Germany; 39Department of Biomolecular Sciences, Max Planck Institute of Colloids and Interfaces, 14424 Potsdam, Germany; 40AstraZeneca, Granta Park, Cambridgeshire, CB21 6GH United Kingdom; 41Merck, 2015 Galloping Hill Rd, Kenilworth, New Jersey 07033; 42Analytical R&D, MilliporeSigma, 2909 Laclede Ave. St. Louis, Missouri 63103; 43MS Bioworks, LLC, 3950 Varsity Drive Ann Arbor, Michigan 48108; 44MSD, Molenstraat 110, 5342 CC Oss, The Netherlands; 45Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, 5–1 Higashiyama, Myodaiji, Okazaki 444–8787 Japan; 46Graduate School of Pharmaceutical Sciences, Nagoya City University, 3–1 Tanabe-dori, Mizuhoku, Nagoya 467–8603 Japan; 47Medical & Biological Laboratories Co., Ltd, 2-22-8 Chikusa, Chikusa-ku, Nagoya 464–0858 Japan; 48National Institute for Biological Standards and Control, Blanche Lane, South Mimms, Potters Bar, Hertfordshire EN6 3QG United Kingdom; 49Division of Biological Chemistry & Biologicals, National Institute of Health Sciences, 1-18-1 Kamiyoga, Setagaya-ku, Tokyo 158–8501 Japan; 50New England Biolabs, Inc., 240 County Road, Ipswich, Massachusetts 01938; 51New York University, 100 Washington Square East New York City, New York 10003; 52Target Discovery Institute, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FZ, United Kingdom; 53GlycoScience Group, The National Institute for Bioprocessing Research and Training, Fosters Avenue, Mount Merrion, Blackrock, Co. Dublin, Ireland; 54Department of Chemistry, North Carolina State University, 2620 Yarborough Drive Raleigh, North Carolina 27695; 55Pantheon, 201 College Road East Princeton, New Jersey 08540; 56Pfizer Inc., 1 Burtt Road Andover, Massachusetts 01810; 57Proteodynamics, ZI La Varenne 20–22 rue Henri et Gilberte Goudier 63200 RIOM, France; 58ProZyme, Inc., 3832 Bay Center Place Hayward, California 94545; 59Koichi Tanaka Mass Spectrometry Research Laboratory, Shimadzu Corporation, 1 Nishinokyo Kuwabara-cho Nakagyo-ku, Kyoto, 604 8511 Japan; 60Children’s GMP LLC, St. Jude Children’s Research Hospital, 262 Danny Thomas Place Memphis, Tennessee 38105; 61Sumitomo Bakelite Co., Ltd., 1–5 Muromati 1-Chome, Nishiku, Kobe, 651–2241 Japan; 62Synthon Biopharmaceuticals, Microweg 22 P.O. Box 7071, 6503 GN Nijmegen, The Netherlands; 63Takeda Pharmaceuticals International Co., 40 Landsdowne Street Cambridge, Massachusetts 02139; 64Department of Chemistry and Biochemistry, Texas Tech University, 2500 Broadway, Lubbock, Texas 79409; 65Thermo Fisher Scientific, 1214 Oakmead Parkway Sunnyvale, California 94085; 66United States Pharmacopeia India Pvt. Ltd. IKP Knowledge Park, Genome Valley, Shamirpet, Turkapally Village, Medchal District, Hyderabad 500 101 Telangana, India; 67Alberta Glycomics Centre, University of Alberta, Edmonton, Alberta T6G 2G2 Canada; 68Department of Chemistry, University of Alberta, Edmonton, Alberta T6G 2G2 Canada; 69Department of Chemistry, University of California, One Shields Ave, Davis, California 95616; 70Horva´ th Csaba Memorial Laboratory for Bioseparation Sciences, Research Center for Molecular Medicine, Doctoral School of Molecular Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Egyetem ter 1, Hungary; 71Translational Glycomics Research Group, Research Institute of Biomolecular and Chemical Engineering, University of Pannonia, Veszprem, Egyetem ut 10, Hungary; 72Delaware Biotechnology Institute, University of Delaware, 15 Innovation Way Newark, Delaware 19711; 73Proteomics Core Facility, University of Gothenburg, Medicinaregatan 1G SE 41390 Gothenburg, Sweden; 74Department of Medical Biochemistry and Cell Biology, University of Gothenburg, Institute of Biomedicine, Sahlgrenska Academy, Medicinaregatan 9A, Box 440, 405 30, Gothenburg, Sweden; 75Department of Clinical Chemistry and Transfusion Medicine, Sahlgrenska Academy at the University of Gothenburg, Bruna Straket 16, 41345 Gothenburg, Sweden; 76Department of Chemistry, University of Hamburg, Martin Luther King Pl. 6 20146 Hamburg, Germany; 77Department of Chemistry, University of Manitoba, 144 Dysart Road, Winnipeg, Manitoba, Canada R3T 2N2; 78Laboratory of Mass Spectrometry of Interactions and Systems, University of Strasbourg, UMR Unistra-CNRS 7140, France; 79Natural and Medical Sciences Institute, University of Tu¨ bingen, Markwiesenstrae 55, 72770 Reutlingen, Germany; 80Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands; 81Division of Bioanalytical Chemistry, Amsterdam Institute for Molecules, Medicines and Systems, Vrije Universiteit Amsterdam, de Boelelaan 1085, 1081 HV Amsterdam, The Netherlands; 82Department of Chemistry, Waters Corporation, 34 Maple Street Milford, Massachusetts 01757; 83Zoetis, 333 Portage St. Kalamazoo, Michigan 49007 Author’s Choice—Final version open access under the terms of the Creative Commons CC-BY license. Received July 24, 2019, and in revised form, August 26, 2019 Published, MCP Papers in Press, October 7, 2019, DOI 10.1074/mcp.RA119.001677 ER: NISTmAb Glycosylation Interlaboratory Study 12 Molecular & Cellular Proteomics 19.1 Downloaded from https://www.mcponline.org by guest on January 20, 2020 ted a total of 103 reports on glycan distributions. The principal objective of this study was to report and compare results for the full range of analytical methods presently used in the glycosylation analysis of mAbs. Therefore, participation was unrestricted, with laboratories choosing their own measurement techniques. Protein glycosylation was determined in various ways, including at the level of intact mAb, protein fragments, glycopeptides, or released glycans, using a wide variety of methods for derivatization, separation, identification, and quantification. Consequently, the diversity of results was enormous, with the number of glycan compositions identified by each laboratory ranging from 4 to 48. In total, one hundred sixteen glycan compositions were reported, of which 57 compositions could be assigned consensus abundance values. These consensus medians provide communityderived values for NISTmAb PS. Agreement with the consensus medians did not depend on the specific method or laboratory type. The study provides a view of the current state-of-the-art for biologic glycosylation measurement and suggests a clear need for harmonization of glycosylation analysis methods. Molecular & Cellular Proteomics 19: 11–30, 2020. DOI: 10.1074/mcp.RA119.001677.L

A Novel Deep Reinforcement Learning Approach to Traffic Signal Control with Connected Vehicles

The advent of connected vehicle (CV) technology offers new possibilities for a revolution in future transportation systems. With the availability of real-time traffic data from CVs, it is possible to more effectively optimize traffic signals to reduce congestion, increase fuel efficiency, and enhance road safety. The success of CV-based signal control depends on an accurate and computationally efficient model that accounts for the stochastic and nonlinear nature of the traffic flow. Without the necessity of prior knowledge of the traffic system’s model architecture, reinforcement learning (RL) is a promising tool to acquire the control policy through observing the transition of the traffic states. In this paper, we propose a novel data-driven traffic signal control method that leverages the latest in deep learning and reinforcement learning techniques. By incorporating a compressed representation of the traffic states, the proposed method overcomes the limitations of the existing methods in defining the action space to include more practical and flexible signal phases. The simulation results demonstrate the convergence and robust performance of the proposed method against several existing benchmark methods in terms of average vehicle speeds, queue length, wait time, and traffic density

Non-Embeddability- D4 Microbial Protein Coding Gene (1140 Triads).

<p> Steps to identify Non-embeddability 1. , 2. Negative eigenvalues have odd algebraic multiplicity, 3. Complex eigenvalues occur in non-conjugate pairs, 4. The set of eigenvalues, , lie outside the region in the complex plane, 5. – negative off-diagonals – threshold −0.1, NE =  Non-Embeddable, No. rejections of from parametric bootstrap scheme with a p-value , 584 Alignments failed to find stable estimates.</p