System optimization of an all-silicon IQ modulator : achieving 100 Gbaud dual polarization 32QAM

We experimentally demonstrate the highest, to the best of our knowledge, reported net rate in a SiP IQ modulator. At 100 Gbaud 32QAM (quadrature amplitude modulation), and assuming 20% FEC (forward error correction) overhead, we achieved a dual polarization net rate of 833 Gb/s. This record was achieved by adapting digital signal processing to the challenging pattern dependent distortion encountered in the nonlinear and bandwidth limited regime. First the Mach Zehnder modulator (MZM) operating point (trading off modulation efficiency and 3 dB bandwidth) and linear compensation (electrical and optical) are jointly optimized. Next, the key application of nonlinear preand post-compensation are explored. We show that nonlinear processing at the transmitter, in our case an iterative learning control (ILC) method, is essential as post-processing alone could not achieve reliable communications at 100 Gbaud. Nonlinear post-compensation algorithms pushed the performance under the FEC threshold with the introduction of structured intersymbol interference in post processing and a simple one-step maximum likelihood sequence detector. We provide detailed descriptions of our methodology and results

Study on Differences in the Pathology, T Cell Subsets and Gene Expression in Susceptible and Non-Susceptible Hosts Infected with Schistosoma japonicum

More than 40 kinds of mammals in China are known to be naturally infected with Schistosoma japonicum (S. japonicum); Microtus fortis (M. fortis), a species of vole, is the only mammal in which the schistosomes cannot mature or cause significant pathogenic changes. In the current study, we compared the differences in pathology by Hematoxylin-eosin staining and in changes in the T cell subsets with flow cytometry as well as gene expression using genome oligonucleotide microarrays in the lung and liver, before challenge and 10 days post-infection with schistosomes in a S. japonicum-susceptible mouse model of infection, a non-susceptible rat model and the non-permissive host, M. fortis. The results demonstrated that S. japonicum promoted a more intensive immune response and more pathological lesions in M. fortis and rats than in mice. Hematoxylin-eosin staining revealed that the immune effector cells involved were mainly eosinophilic granulocytes supplemented with heterophilic granulocytes and macrophages. The analysis of splenic T cell subsets showed that CD4+ T cell subsets and the CD4+/CD8+ ratio were increased, while the CD8+ T cell subsets decreased remarkably in rats; whereas the CD8+ T cell subsets were increased, but the CD4+/CD8+ ratio was decreased significantly in mice. The analysis of the pattern of gene expression suggested that some immune-associated genes and apoptosis-inducing genes up-regulated, while some development-associated genes were down-regulated in the infected M. fortis compared to the uninfected controls; the three different hosts have different response mechanisms to schistosome infection. The results of this study will be helpful for identifying the key molecules in the immune response to S. japonicum in M. fortis and for understanding more about the underlying mechanism of the response, as well as for elucidating the interaction between S. japonicum and its hosts

Effects from supplementary feeding of bamboo powder in perinatal period on farrowing process, serum biochemical indexes, and fecal microbes of sows and offspring piglets

IntroductionThis study was conducted to explore the effects of supplementary feeding of bamboo powder on the physical parameters of sows during the perinatal period of 7 days ± in parturition, including farrow duration, serum biochemical indexes, fecal physicochemical indexes, and microbial flora.MethodsThirty pregnant sows were randomly divided into three groups: the control group was fed a basal diet, TRE1 group and TRE2 group were fed a basal diet supplemented with 30 g d−1 and 60 g d−1 bamboo powder, respectively. Multiple parameters of sows and offspring piglets were determined.ResultsThe contents of serum total cholesterol and triglyceride of sows in TRE2 group were significantly lower than those in the control group. The contents of serum malondialdehyde of sows in TRE2 and TRE1 groups were significantly lower than that in control group. The water content of sow feces in TRE2 group was significantly higher than that in control group, and the pH values of sows in TRE2 and TRE1 groups were significantly higher than that in control group. The richness index (Chao) of sow fecal bacterial community in TRE2 group was significantly lower than that of the control group, and the Ace and Sobs indexes tended to be lower than those of the control group. At the phylum level, the relative abundance of Actinobacteriota in the feces of sows in TRE2 group was significantly lower than that of the control group, while that of Fusobacteriota in the feces of suckling piglets in TRE2 group tended to be lower than that of the control group. At the genus level, among the Top10 dominant bacteria, the relative abundance of Tissierella in the feces of sows in TRE2 group was significantly lower than that of the control group while that of Fusobacterium in the feces of suckling piglets in TRE2 group tended to be lower than that of the control group. The relative abundance of Clostridium_sensu_stricto_1, Terrisporobacter, Turicibacter, and Tissierella in the feces of sows in TRE2 group was significantly lower than that of TRE1 group (p < 0.05), while Lactobacillus tended to be higher than that of TRE1 group (p < 0.10).DiscussionThe results suggested that supplementary feeding 60 g d−1 bamboo powder could increase the water content in the feces of sows, reduce the oxidative damage, and tend to reduce the relative abundance of opportunistic pathogenic Fusobacterium for suckling piglets, while it reduced the fecal microbial diversity of sows

Surveillance of Schistosoma japonicum Infection in Domestic Ruminants in the Dongting Lake Region, Hunan Province, China

Background: Schistosomiasis japonica is prevalent in Asian countries and it remains a major public health problem in China. The major endemic foci are the marsh and lake regions of southern China, particularly the Dongting Lake region bordering Hunan and Hubei provinces, and the Poyang Lake region in Jiangxi province. Domestic ruminants, especially bovines, have long been considered to play a major role in the transmission of Schistosoma japonicum to humans. Methods and Findings: A miracidial hatching technique was used to investigate the prevalence of S. japonicum infections in domestic ruminants and field feces collected from two towns located to the south and east of Dongting Lake, Hunan province, between 2005 and 2010. The overall prevalence of infection was not significantly reduced from 4.93 % in 2005 to 3.64 % in 2008, after which it was maintained at this level. Bovines comprised 23.5–58.2 % of the total infected ruminants, while goats comprised 41.8–76.5%. Infection rates in cattle and goats were significantly higher than those found in buffalo in most study years. The prevalence in buffalo younger than three years was significantly higher than that in those aged over three years. All the positive field samples of feces were derived from bovines in Nandashan. In Matang Town, 61.22 % of the positive field feces were from bovines, while the rest were from goats. The positive rates for field feces were approximately the same in April and November/October. Conclusions: The present study found that bovines and goats are major sources of S. japonicum infection in the Dongtin

Apoptosis Governs the Elimination of Schistosoma japonicum from the Non-Permissive Host Microtus fortis

The reed vole, Microtus fortis, is the only known mammalian host in which schistosomes of Schistosoma japonicum are unable to mature and cause significant pathogenesis. However, little is known about how Schistosoma japonicum maturation (and, therefore, the development of schistosomiasis) is prevented in M. fortis. In the present study, the ultrastructure of 10 days post infection schistosomula from BALB/c mice and M. fortis were first compared using scanning electron microscopy and transmission electron microscopy. Electron microscopic investigations showed growth retardation and ultrastructural differences in the tegument and sub-tegumental tissues as well as in the parenchymal cells of schistosomula from M. fortis compared with those in BALB/c mice. Then, microarray analysis revealed significant differential expression between the schistosomula from the two rodents, with 3,293 down-regulated (by ≥2-fold) and 71 up-regulated (≥2 fold) genes in schistosomula from the former. The up-regulated genes included a proliferation-related gene encoding granulin (Grn) and tropomyosin. Genes that were down-regulated in schistosomula from M. fortis included apoptosis-inhibited genes encoding a baculoviral IAP repeat-containing protein (SjIAP) and cytokine-induced apoptosis inhibitor (SjCIAP), genes encoding molecules involved in insulin metabolism, long-chain fatty acid metabolism, signal transduction, the transforming growth factor (TGF) pathway, the Wnt pathway and in development. TUNEL (terminal deoxynucleotidyl transferase dUTP nick end labeling) and PI/Annexin V-FITC assays, caspase 3/7 activity analysis, and flow cytometry revealed that the percentages of early apoptotic and late apoptotic and/or necrotic cells, as well as the level of caspase activity, in schistosomula from M. fortis were all significantly higher than in those from BALB/c mice

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

Construction of grid color mixture model of seven primary-color and modified Stearns-Noechel color matching algorithm for color prediction of full-color-gamut rotor melange yarn

In this paper, the full-color-gamut grid color mixture model containing 601 grid points is constructed by ternary double coupling blending of seven primary-color fibers, and the spinning method of full-color-gamut melange yarn is given by combining with three-channel NC rotor spinning technology. A modified S-N color prediction model was constructed by selecting 55 uniformly distributed grid points for yarn and fabric production from the full-color-gamut grid color mixture model as samples for solving the reflectance conversion coefficients. On this basis, the method of predicting the color value of a melange yarn based on its primary-color fiber composition and blending ratio and predicting the primary-color fiber composition and blending ratio based on the color value of a melange yarn using the parameters of the nearest sample grid point is proposed, and six samples with different blending ratios in six color mixing regions of the full-color-gamut grid color mixture model are designed for validation. The results showed that the average color difference between the predicted color and the actual color of the melange yarn is 1.15, the predicted primary-color fiber composition of the melange yarn is consistent with the actual composition, and the average error between the predicted blending ratio and the actual blending ratio is 3.95%. The method proposed in this paper can effectively predict the color value and blending ratio of melange yarn