64 research outputs found
Manufacture of cream cheese involving the use of dry skim milk
Caption title.Digitized 2006 AES MoU
The relation of dry skim milk to several of the physical and chemical properties of cream cheese
Publication authorized June 28, 1934."The data presented in this bulletin were taken from a paper submitted by the junior author in partial fulfillment of the requirements for the degree of Master of Arts in the Graduate School of the University of Missouri, 1933"--P. [3].Includes bibliographical references (page 40)
EXPERIMENTAL STUDIES OF MITIGATION MATERIALS FOR BLAST INDUCED TBI
The objective of this experimental study is to compare the effects of various materials obstructing the flow of a blast wave and the ability of the given material to reduce the damage caused by the blast. Several methods of energy transfer in blast wave flows are known or expected including: material interfaces with impedance mismatches, density changes in a given material, internal shearing, and particle fracture. The theory applied to this research is that the greatest energy transfer within the obstructing material will yield the greatest mitigation effects to the blast. Sample configurations of foam were varied to introduce material interfaces and filler materials with varying densities and impedances (liquids and powders). The samples were loaded according to a small scale blast produced by an explosive driven shock tube housing gram-range charges. The transmitted blast profiles were analyzed for variations in impulse characteristics and frequency components as compared to standard free field profiles. The results showed a rounding effect of the transmitted blast profile for all samples with the effects of the low density fillers surpassing all others tested.United States. Office of Naval Research (N00014-08-1-0261
Revising the WHO verbal autopsy instrument to facilitate routine cause-of-death monitoring.
OBJECTIVE: Verbal autopsy (VA) is a systematic approach for determining causes of death (CoD) in populations without routine medical certification. It has mainly been used in research contexts and involved relatively lengthy interviews. Our objective here is to describe the process used to shorten, simplify, and standardise the VA process to make it feasible for application on a larger scale such as in routine civil registration and vital statistics (CRVS) systems. METHODS: A literature review of existing VA instruments was undertaken. The World Health Organization (WHO) then facilitated an international consultation process to review experiences with existing VA instruments, including those from WHO, the Demographic Evaluation of Populations and their Health in Developing Countries (INDEPTH) Network, InterVA, and the Population Health Metrics Research Consortium (PHMRC). In an expert meeting, consideration was given to formulating a workable VA CoD list [with mapping to the International Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) CoD] and to the viability and utility of existing VA interview questions, with a view to undertaking systematic simplification. FINDINGS: A revised VA CoD list was compiled enabling mapping of all ICD-10 CoD onto 62 VA cause categories, chosen on the grounds of public health significance as well as potential for ascertainment from VA. A set of 221 indicators for inclusion in the revised VA instrument was developed on the basis of accumulated experience, with appropriate skip patterns for various population sub-groups. The duration of a VA interview was reduced by about 40% with this new approach. CONCLUSIONS: The revised VA instrument resulting from this consultation process is presented here as a means of making it available for widespread use and evaluation. It is envisaged that this will be used in conjunction with automated models for assigning CoD from VA data, rather than involving physicians
A catastrophic meltwater flood event and the formation of the Hudson Shelf Valley
This paper is not subject to U.S. copyright. The definitive version was published in Palaeogeography, Palaeoclimatology, Palaeoecology 246 (2007): 120-136, doi:10.1016/j.palaeo.2006.10.030.The Hudson Shelf Valley (HSV) is the largest physiographic feature on the U.S. mid-Atlantic continental shelf. The 150-km long valley is the submerged extension of the ancestral Hudson River Valley that connects to the Hudson Canyon. Unlike other incised valleys on the mid-Atlantic shelf, it has not been infilled with sediment during the Holocene. Analyses of multibeam bathymetry, acoustic backscatter intensity, and high-resolution seismic reflection profiles reveal morphologic and stratigraphic evidence for a catastrophic meltwater flood event that formed the modern HSV. The valley and its distal deposits record a discrete flood event that carved 15-m high banks, formed a 120-km2 field of 3- to 6-m high bedforms, and deposited a subaqueous delta on the outer shelf. The HSV is inferred to have been carved initially by precipitation and meltwater runoff during the advance of the Laurentide Ice Sheet, and later by the drainage of early proglacial lakes through stable spillways. A flood resulting from the failure of the terminal moraine dam at the Narrows between Staten Island and Long Island, New York, allowed glacial lakes in the Hudson and Ontario basins to drain across the continental shelf. Water level changes in the Hudson River basin associated with the catastrophic drainage of glacial lakes Iroquois, Vermont, and Albany around 11,450 14C year BP (~ 13,350 cal BP) may have precipitated dam failure at the Narrows. This 3200 km3 discharge of freshwater entered the North Atlantic proximal to the Gulf Stream and may have affected thermohaline circulation at the onset of the Intra-Allerød Cold Period. Based on bedform characteristics and fluvial morphology in the HSV, the maximum freshwater flux during the flood event is estimated to be ~ 0.46 Sv for a duration of ~ 80 days.Support for N. Driscoll was provided by the Office of Naval Research and the National Science Foundatio
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Onset of deglacial warming in West Antarctica driven by local orbital forcing
The cause of warming in the Southern Hemisphere during the most recent deglaciation remains a matter of debate[superscript 1,2]. Hypotheses for a Northern Hemisphere trigger, through oceanic redistributions of heat, are based in part on the abrupt onset of warming seen in East Antarctic ice cores and dated to 18,000 years ago, which is several thousand years after high-latitude Northern Hemisphere summer insolation intensity began increasing from its minimum, approximately 24,000 years ago[superscript 3,4]. An alternative explanation is that local solar insolation changes cause the Southern Hemisphere to warm independently[superscript 2,5]. Here we present results from a new, annually resolved ice-core record from West Antarctica that reconciles these two views. The records show that 18,000 years ago snow accumulation in West Antarctica began increasing, coincident with increasing carbon dioxide concentrations, warming in East Antarctica and cooling in the Northern Hemisphere[superscript 6] associated with an abrupt decrease in Atlantic meridional overturning circulation[superscript 7]. However, significant warming in West Antarctica began at least 2,000 years earlier. Circum-Antarctic sea-ice decline, driven by increasing local insolation, is the likely cause of this warming. The marine-influenced West Antarctic records suggest a more active role for the Southern Ocean in the onset of deglaciation than is inferred from ice cores in the East Antarctic interior, which are largely isolated from sea-ice changes.Keywords: Last glacial period, Carbon Dioxide, High resolution, Chronology, Ice core, Circulation, Abrupt climate change, Atmospheric Co2, Greenland, Polar ic
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
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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
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
Model Ziegler-Type Hydrogenation Catalyst Precursors, [(1,5-COD)M(mu-O2C8H15)](2) (M = Ir and Rh): Synthesis, Characterization, and Demonstration of Catalytic Activity En Route to Identifying the True Industrial Hydrogenation Catalysts
The compounds [(1,5-COD)M(mu-O2C8H15)](2) (COD = cyclooctadiene, M = Ir (1) or Rh (2), O2C8H15 = 2-ethylhexanoate) were synthesized by addition of Bu3NH(2-ethylhexanoate) or Na(2-ethylhexanoate) to acetone suspensions of [(1,5-COD)Ir(mu-Cl)](2) or [(1,5-COD)Rh(mu-Cl)](2), respectively. The synthesis of such well-defined second and third row model precursors is key to determining the true nature of commercial Ziegler-type hydrogenation catalysts (i.e., catalysts made from the combination of a non-zerovalent, group 8-10 transition metal precatalyst and a trialkylaluminurn cocatalyst), an unsolved, similar to 40 year old problem. The characterizations of 1 and 2 were accomplished by elemental analysis, melting point, FAB-MS, FT-IR, UV-vis, NMR spectroscopy, and single crystal X-ray diffraction. The complexes, C32H54Ir2O4 and C32H54O4Rh2, are isostructural: monoclinic, P2(1)/n, Z = 4. The lattice constants for 1 are a = 15.7748(5) angstrom, b = 9.8962(3) angstrom, c = 20.8847(7) angstrom, ss = 108.408(2)degrees. The lattice constants for 2 are a = 15.7608(4) angstrom, b = 9.9032(3) angstrom, c = 20.8:259(5) angstrom, ss = 108.527(1)degrees. Complexes 1 and 2 are dimeric, bridged by the 2-ethylhexanoates, and with one 1,5-COD ligand bound to each metal. The formally 16 electron metal atoms are in square ligand planes with dihedral angles between the planes of 56.5 degrees for 1 and 58.1 degrees for 2. The M-M distances of 3.2776(2) and 3.3390(4) angstrom for 1 and 2, respectively, fall in the range of similar structures thought to have some M-M interaction despite the lack of a formal M-M bond. Demonstration that active Ziegler-type hydrogenation catalysts are made when 1 or 2 combine with AlEt3 is provided, results that open the door to the use of 1 and 2 as well-defined third and second row congeners, respectively, of Ziegler-type hydrogenation catalysts. These compounds have proven important in addressing the previously unsolved problem of the true nature of the catalyst in industrial Ziegler-type hydrogenation catalyst systems; their high yield synthesis and unequivocal characterization reported herein are the necessary first steps of that work
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