92 research outputs found
The Ay Problem for p-3He Elastic Scattering
We present evidence that numerically accurate quantum calculations employing
modern internucleon forces do not reproduce the proton analyzing power, A_y,
for p-3He elastic scattering at low energies. These calculations underpredict
new measured analyzing powers by approximately 30% at E_{c.m.} = 1.20 MeV and
by 40% at E_{c.m.} = 1.69 MeV, an effect analogous to a well-known problem in
p-d and n-d scattering. The calculations are performed using the complex Kohn
variational principle and the (correlated) Hyperspherical Harmonics technique
with full treatment of the Coulomb force. The inclusion of the three-nucleon
interaction does not improve the agreement with the experimental data.Comment: Latex file, 4 pages, 2 figures, to be published on Phys. Rev. Let
Identification by culture, PCR, and immunohistochemistry of mycoplasmas and their molecular typing in sheep and lamb lungs with pneumonia in Eastern Turkey
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. KovacË icÂŽ 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 739â8530 Japan; 28ImmunoGen,
830 Winter Street, Waltham, Massachusetts 02451; 29Department of Medical Physiology, Jagiellonian University Medical College,
ul. Michalowskiego 12, 31â126 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, 363â883 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, 363â700, 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, 5â1 Higashiyama, Myodaiji, Okazaki 444â8787 Japan; 46Graduate School of
Pharmaceutical Sciences, Nagoya City University, 3â1 Tanabe-dori, Mizuhoku, Nagoya 467â8603 Japan; 47Medical & Biological Laboratories
Co., Ltd, 2-22-8 Chikusa, Chikusa-ku, Nagoya 464â0858 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 158â8501 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 20â22 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; 60Childrenâs GMP LLC, St. Jude Childrenâs
Research Hospital, 262 Danny Thomas Place Memphis, Tennessee 38105; 61Sumitomo Bakelite Co., Ltd., 1â5 Muromati 1-Chome, Nishiku,
Kobe, 651â2241 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; 70HorvaÂŽ 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 Tuš 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
Authorâs ChoiceâFinal 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: 11â30, 2020. DOI: 10.1074/mcp.RA119.001677.L
31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two
Background
The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd.
Methods
We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background.
Results
First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001).
Conclusions
In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival
Low-N protein engineering with data-efficient deep learning
Protein engineering has enormous academic and industrial potential. However, it is limited by the lack of experimental assays that are consistent with the design goal and sufficiently high throughput to find rare, enhanced variants. Here we introduce a machine learning-guided paradigm that can use as few as 24 functionally assayed mutant sequences to build an accurate virtual fitness landscape and screen ten million sequences via in silico directed evolution. As demonstrated in two dissimilar proteins, GFP from Aequorea victoria (avGFP) and E. coli strain TEM-1 ÎČ-lactamase, top candidates from a single round are diverse and as active as engineered mutants obtained from previous high-throughput efforts. By distilling information from natural protein sequence landscapes, our model learns a latent representation of 'unnaturalness', which helps to guide search away from nonfunctional sequence neighborhoods. Subsequent low-N supervision then identifies improvements to the activity of interest. In sum, our approach enables efficient use of resource-intensive high-fidelity assays without sacrificing throughput, and helps to accelerate engineered proteins into the fermenter, field and clinic
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Social Safety Theory: Conceptual foundation, underlying mechanisms, and future directions
Classic theories of stress and health are largely based on assumptions regarding how different psychosocial stressors influence biological processes that, in turn, affect human health and behavior. Although theoretically rich, this work has yielded little consensus and led to numerous conceptual, measurement, and reproducibility issues. Social Safety Theory aims to address these issues by using the primary goal and regulatory logic of the human brain and immune system as the basis for specifying the social-environmental situations to which these systems should respond most strongly to maximize reproductive success and survival. This analysis gave rise to the integrated, multi-level formulation described herein, which transforms thinking about stress biology and provides a biologically based, evolutionary account for how and why experiences of social safety and social threat are strongly related to health, well-being, aging, and longevity. In doing so, the theory advances a testable framework for investigating the biopsychosocial roots of health disparities as well as how health-relevant biopsychosocial processes crystalize over time and how perceptions of the social environment interact with childhood microbial environment, birth cohort, culture, air pollution, genetics, sleep, diet, personality, and self-harm to affect health. The theory also highlights several interventions for reducing social threat and promoting resilience
Multi-omics in stress and health research: study designs that will drive the field forward
AbstractDespite decades of stress research, there still exist substantial gaps in our understanding of how social, environmental, and biological factors interact and combine with developmental stressor exposures, cognitive appraisals of stressors, and psychosocial coping processes to shape individualsâ stress reactivity, health, and disease risk. Relatively new biological profiling approaches, called multi-omics, are helping address these issues by enabling researchers to quantify thousands of molecules from a single blood or tissue sample, thus providing a panoramic snapshot of the molecular processes occurring in an organism from a systems perspective. In this review, we summarize two types of research designs for which multi-omics approaches are best suited, and describe how these approaches can help advance our understanding of stress processes and the development, prevention, and treatment of stress-related pathologies. We first discuss incorporating multi-omics approaches into theory-rich, intensive longitudinal study designs to characterize, in high-resolution, the transition to stress-related multisystem dysfunction and disease throughout development. Next, we discuss how multi-omics approaches should be incorporated into intervention research to better understand the transition from stress-related dysfunction back to health, which can help inform novel precision medicine approaches to managing stress and fostering biopsychosocial resilience. Throughout, we provide concrete recommendations for types of studies that will help advance stress research, and translate multi-omics data into better health and health care
A machine learning toolkit for genetic engineering attribution to facilitate biosecurity
© 2020, The Author(s). The promise of biotechnology is tempered by its potential for accidental or deliberate misuse. Reliably identifying telltale signatures characteristic to different genetic designers, termed âgenetic engineering attributionâ, would deter misuse, yet is still considered unsolved. Here, we show that recurrent neural networks trained on DNA motifs and basic phenotype data can reach 70% attribution accuracy in distinguishing between over 1,300 labs. To make these models usable in practice, we introduce a framework for weighing predictions against other investigative evidence using calibration, and bring our model to within 1.6% of perfect calibration. Additionally, we demonstrate that simple models can accurately predict both the nation-state-of-origin and ancestor labs, forming the foundation of an integrated attribution toolkit which should promote responsible innovation and international security alike
En undersĂžkelse av ovin pneumoni i fire besetninger fra midt-Norge. I. Forekomst av pneumoni og mikrobiologiske funn
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