Link Prediction Investigation of Dynamic Information Flow in Epilepsy

This work was supported partly by the National Natural Science Foundation of China (Grant No.81460206 and No.81660298), Scientific Research Foundation for Doctors of Guizhou Medical University (No.Yuan Bo He J [2014] 003) and by the 2011 Collaborative Innovation Program of Guizhou Province (No. 2015–04 to ZZ).Peer reviewedPublisher PD

Adaptive Neural Ranking Framework: Toward Maximized Business Goal for Cascade Ranking Systems

Cascade ranking is widely used for large-scale top-k selection problems in online advertising and recommendation systems, and learning-to-rank is an important way to optimize the models in cascade ranking. Previous works on learning-to-rank usually focus on letting the model learn the complete order or top-k order, and adopt the corresponding rank metrics (e.g. OPA and NDCG@k) as optimization targets. However, these targets can not adapt to various cascade ranking scenarios with varying data complexities and model capabilities; and the existing metric-driven methods such as the Lambda framework can only optimize a rough upper bound of limited metrics, potentially resulting in sub-optimal and performance misalignment. To address these issues, we propose a novel perspective on optimizing cascade ranking systems by highlighting the adaptability of optimization targets to data complexities and model capabilities. Concretely, we employ multi-task learning to adaptively combine the optimization of relaxed and full targets, which refers to metrics Recall@m@k and OPA respectively. We also introduce permutation matrix to represent the rank metrics and employ differentiable sorting techniques to relax hard permutation matrix with controllable approximate error bound. This enables us to optimize both the relaxed and full targets directly and more appropriately. We named this method as Adaptive Neural Ranking Framework (abbreviated as ARF). Furthermore, we give a specific practice under ARF. We use the NeuralSort to obtain the relaxed permutation matrix and draw on the variant of the uncertainty weight method in multi-task learning to optimize the proposed losses jointly. Experiments on a total of 4 public and industrial benchmarks show the effectiveness and generalization of our method, and online experiment shows that our method has significant application value.Comment: 12 pages, Accepted by www202

Ten-year Development of General Practice in China：Opportunities and Challenges

ObjectiveTo review the strengths and limitations of the development of general practice in China during the last decade (2010—2020) and to assess the opportunities and challenges for its future development.MethodsData were collected from statistic reports, journal articles and official policies and guidelines regarding general practice development in China from 2010—2020. Donabedian model was applied to examine and assess the quality of essential general practice services in China. SWOT analysis was used to identify internal and external determinants of general practice development in China.Results(1) Structural quality of general practice: the ten-year policies about general practice development were a continuation of the past relevant policies in essence but with developments, with highlights on continuous construction of general practice workforce and discipline, tiered diagnosis and treatment and regional medical consortium, but relevant fiscal and management policies still need improvements. The number of general medical workers has increased rapidly, while the lion's share of them are still allocated at tertiary hospitals. Full-time equivalent is suggested to be used to predict the staffing and assess the performance of these workers. The number of community health centres showed a steady increase, but its growth rate was still slower than that of hospital facilities. Relevant health economics data need to be further supplemented. (2) Process quality of general practice: in 2020, there were 2.045 billion visits in community health centers (stations) and township health centers, that is, 1.5 visits per person per year on average. There was a significant development when found only 1 visit per person per year for primary care in 2010. However, the frequency of visits for primary care was still lower than that of visiting hospital-based outpatients (an average of 2.7 visits per person per year) . The COVID-19 pandemic had a significant impact on community health services/general medical services, and the number of outpatient visits dropped by about 20%. The number of general practice research articles reached a peak in 2018, mainly focusing on bi-directional referrals, tiered diagnosis and treatment, general practitioners (GPs) /family doctors, general medicine, community health services, chronic disease management (especially hypertension and diabetes) , and analysis of factors associated with aspects involved in general medical services. General practice research is expected to provide more support for developing innovative and critical thoughts, more practice-based evidence for clinical services, and more assistance for service quality and patient outcomes improvement as the discipline advances. (3) Results of implementing general medical services: there is no sufficient evidence on the influence of general medical services on people's health. The experiences and views of people including healthy individuals and patients indicated that those receiving general medical services or contracted family doctor services perceived positive experience and expressed high satisfaction, but perceptions and views of general population in the community toward general medical services need to be explored. GPs' own experience and opinions on general practice were quite different. Gender, age, professional title, urban and rural areas, and geographical location may be associated with their experience and job satisfaction. There may be instability in the general practice workforce, mainly due to personal income, workload and time pressure. (4) The major strengths of developing general practice in China are as follows: strong policy-based promotion and government leadership; rapidly constructing and developing GPs teams owing to the excellent resource allocating ability shown by the centralized system from central to all local governments; significantly enhanced general practice education and training systems; increased core professionals as general practice educators and trainers; special development of general practice characterized by the integration of medical sciences and Chinese traditional humanistic theories. (5) The development of general practice in China has been facing limitations similar to those in other countries. Besides that, its special limitations include late development of the discipline, unsatisfactory quality of workforce, high work pressure and high prevalence of burnout in the workforce, as well as impact of generation gap on education and practice among GPs. In addition, the relation between specialists and GPs is on transition of from undifferentiated attachment to self-recognised uniquity, and further seeking transdisciplinary. The teaching competences of GPs teachers, especially those teaching community and clinical care, are inadequate. GPs team building and management need to advance from the formation to the storming and performing phases. (6) Opportunities for further development of general practice in China include strategies for achieving the goals of Healthy China, and an all-round well-off society, the important role of primary health care in sustainable development and universal health coverage reaffirmed by the Declaration of Astana, as well as significantly improved health literacy of people. (7) Challenges for the development of general practice in China include population ageing, and aging-related changes in burden of disease and socio-economic status, the aging and dynamic changes of GPs human resources, the variation of urban and rural areas and regional differences, and the inverted pyramid structure of allocation of medical and health resources (namely, the largest part is allocated to tertiary care while the smallest to primary care) . Relevant recommendations to address these challenges comprise strengthening the advocacy of the development of general practice services, establishing a wide-ranging community collaborative network, and developing general practice professional organizations.ConclusionThe development of general practice in China is advancing, which is manifested as rapidly increased number of general medical workers, strong government promotion, quickly improved accessibility of essential medical services, and notably increased utilization rate of primary care services. However, the development is facing challenges, such as high discipline and social expectations regarding general practice, instability in the workforce due to high work pressure of the knowledge- and labor-intensive job, GPs' insufficient recognition of their self-identity, and unclear status of financial funding for general practice development. Given that there are unprecedented favorable conditions for general practice development, medical industries and general medical workers are suggested to make efforts to turn challenges into opportunities to develop general medical services, thereby universal health outcomes will be improved

Self correction of refractive error among young people in rural China: results of cross sectional investigation

Objective To compare outcomes between adjustable spectacles and conventional methods for refraction in young people

The Embedding Problem for Markov Models of Nucleotide Substitution

10.1371/journal.pone.0069187PLoS ONE87-POLN

HPLC Determination and Pharmacokinetic Study of Homoeriodictyol-7-O-â- D- glucopyranoside in Rat Plasma and Tissues

Homoeriodictyol-7-O-β-D-glucopyranoside (HEDT-Glu) was isolated from Viscum coloratum and identified by MS, 1H- and 13C-NMR. A HPLC method was developed for determination of HEDT-Glu in rat plasma and tissues. All biological samples were pretreated by protein precipitation with acetone. Vanillin was selected as internal standard. The mobile phase consisted of methanol–water–glacial acetic acid (45 : 55 : 0.5, v/v/v). Good linearity were observed over the concentration ranges of 0.1—200.0 μg·ml−1 in rat plasma and 0.05—5.0 μg·ml−1 in tissues. Both intra- and inter-day precisions of HEDT-Glu, expressed as the relative standard deviation, were less than 13.1%. Accuracy, expressed as the relative error, ranged from −0.8 to 5.4% in plasma and from −5.6 to 9.4% in tissues. The mean extraction recovery of HEDT-Glu was above 73.17% in biological samples. The described assay method was successfully applied to the pre-clinical pharmacokinetic study of HEDT-Glu. After intravenous administration of HEDT-Glu to rat, AUC and CLtot were 16.04±3.19 μg·h·ml−1 and 0.85±0.17 l·kg−1·h−1, respectively. T1/2,α and t1/2,β were 0.06±0.01 h and 1.27±0.31 h, respectively. HEDT-Glu was cleared from the blood and mainly distributed to the liver and small intestine

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

ER-organelle contacts: A signaling hub for neurological diseases

Neuronal health is closely linked to the homeostasis of intracellular organelles, and organelle dysfunction affects the pathological progression of neurological diseases. In contrast to isolated cellular compartments, a growing number of studies have found that organelles are largely interdependent structures capable of communicating through membrane contact sites (MCSs). MCSs have been identified as key pathways mediating inter-organelle communication crosstalk in neurons, and their alterations have been linked to neurological disease pathology. The endoplasmic reticulum (ER) is a membrane-bound organelle capable of forming an extensive network of pools and tubules with important physiological functions within neurons. There are multiple MCSs between the ER and other organelles and the plasma membrane (PM), which regulate a variety of cellular processes. In this review, we focus on ER-organelle MCSs and their role in a variety of neurological diseases. We compared the biological effects between different tethering proteins and the effects of their respective disease counterparts. We also discuss how altered ER-organelle contacts may affect disease pathogenesis. Therefore, understanding the molecular mechanisms of ER-organelle MCSs in neuronal homeostasis will lay the foundation for the development of new therapies targeting ER-organelle contacts