Sartorius Stedim Biotech GmbH

Russia

Sartorius Stedim Biotech GmbH

Russia
SEARCH FILTERS
Time filter
Source Type

— Cell culture protein surface coating is a procedure in which the cell culture surfaces are coated with proteins or extracellular matrix (ECM) components to enhance the adhesion and proliferation of the cells during in vitro isolation and cultivation. Proteins that are used in coating cells are collagen, fibronectin, vitronectin, laminin, and osteopontin, which are either animal derived, plant derived, synthetic, or human derived. Protein surface coating facilitates the growth of various types of cells such as epithelial, leukocytes, muscle cells, neurons, chinese hamster ovary (CHO) cell lines, fibroblasts, and neurons. Publisher's analysts forecast the global cell culture protein surface coating market to grow at a CAGR of 13.20% during the period 2017-2021. Covered in this report The report covers the present scenario and the growth prospects of the global cell culture protein surface coating market for 2017-2021. To calculate the market size, the report presents a detailed picture of the market by the way of study, synthesis, and summation of data from multiple sources. The market is divided into the following segments based on geography: - Americas - APAC - EMEA Publisher's report, Global Cell Culture Protein Surface Coating Market 2017-2021, has been prepared based on an in-depth market analysis with inputs from industry experts. The report covers the market landscape and its growth prospects over the coming years. The report also includes a discussion of the key vendors operating in this market. Get Sample of the Report at: http://www.reportsweb.com/inquiry&RW0001713445/sample . Other prominent vendors - Abcam - Agilent Technologies - AM-Pharma - BioLamina - BioMedTech Laboratories - Bio-Techne - BioTime - Caladrius Biosciences - Cedarlane Laboratories - Cell Guidance Systems - CellSystems Biotechnologie Vertrieb - Cellular Dynamics International - Cytoskeleton - Full Moon BioSystems - Greiner Bio-One - GlaxoSmithKline - Histocell - Japan Regenerative Medicine - KANGSTEM BIOTECH - Mesoblast - neuVitro - Orla Protein Technologies - Pall - PerkinElmer - PROGEN Biotechnik - PromoCell - RayBiotech - Sartorius Stedim Biotech - SouthernBiotech - Taiwan Bio Therapeutics - Takeda Pharmaceutical Company - Teva Pharmaceutical Industries - Trevigen - TWO CELLS - U.S. Stem Cell - Viogene Market driver - Government and research centers to promote research activities. - For a full, detailed list, view our report Market challenge - Ethical concerns over usage of animal-derived protein coating material. - For a full, detailed list, view our report Market trend - Increasing preference for 3D cell cultures over 2D cell cultures. - For a full, detailed list, view our report PART 01: Executive summary PART 02: Scope of the report PART 03: Market research methodology PART 04: Introduction PART 05: An overview: Cell culture surfaces PART 06: Market landscape PART 07: Market segmentation by product type PART 08: Geographical segmentation PART 09: Decision framework PART 10: Drivers and challenges PART 11: Market trends PART 12: Vendor landscape PART 13: Key vendor analysis PART 14: Appendix For more information, please visit http://www.reportsweb.com/global-cell-culture-protein-surface-coating-market-2017-2021


3 Résultat net courant = résultat net après les participations ne donnant pas le contrôle, corrigé des éléments non récurrents, hors Amortissement et basé sur le résultat financier et le taux d’impôt normalisés. Sartorius Stedim Biotech est l'un des principaux fournisseurs internationaux de produits et services dans l'industrie biopharmaceutique, destinés au développement et à la fabrication de produits pharmaceutiques en toute sécurité et efficacité. En tant que fournisseur de solutions intégrées, le portefeuille de Sartorius Stedim Biotech couvre presque toutes les étapes de la production biopharmaceutique. Avec sa forte concentration sur les technologies à usage unique et les services à valeur ajoutée, Sartorius Stedim Biotech est au cœur de la mutation technologique fulgurante de son secteur d'activité. Basée à Aubagne en France, Sartorius Stedim Biotech est cotée sur Euronext à la Bourse de Paris. Dotée de ses propres sites de production et de R&D en Europe, en Amérique du Nord et en Asie, ainsi que d'un réseau de distribution international, Sartorius Stedim Biotech est présente dans le monde entier. Le groupe Sartorius Stedim Biotech a employé environ 4 700 personnes et a réalisé un chiffre d'affaires de 1 052 millions d'euros sur l'exercice 2016.


This report studies the global High Throughput Process Development market, analyzes and researches the High Throughput Process Development development status and forecast in United States, EU, Japan, China, India and Southeast Asia. This report focuses on the top players in global market, like Market segment by Type, High Throughput Process Development can be split into Instrument Software and Service Market segment by Application, High Throughput Process Development can be split into Monoclonal Antibodies Recombinant Insulin Global High Throughput Process Development Market Size, Status and Forecast 2022 1 Industry Overview of High Throughput Process Development 1.1 High Throughput Process Development Market Overview 1.1.1 High Throughput Process Development Product Scope 1.1.2 Market Status and Outlook 1.2 Global High Throughput Process Development Market Size and Analysis by Regions 1.2.1 United States 1.2.2 EU 1.2.3 Japan 1.2.4 China 1.2.5 India 1.2.6 Southeast Asia 1.3 High Throughput Process Development Market by Type 1.3.1 Instrument 1.3.2 Software and Service 1.4 High Throughput Process Development Market by End Users/Application 1.4.1 Monoclonal Antibodies 1.4.2 Recombinant Insulin 2 Global High Throughput Process Development Competition Analysis by Players 2.1 High Throughput Process Development Market Size (Value) by Players (2016 and 2017) 2.2 Competitive Status and Trend 2.2.1 Market Concentration Rate 2.2.2 Product/Service Differences 2.2.3 New Entrants 2.2.4 The Technology Trends in Future 3 Company (Top Players) Profiles 3.1 Danaher Corporation 3.1.1 Company Profile 3.1.2 Main Business/Business Overview 3.1.3 Products, Services and Solutions 3.1.4 High Throughput Process Development Revenue (Value) (2012-2017) 3.1.5 Recent Developments 3.2 GE Healthcare 3.2.1 Company Profile 3.2.2 Main Business/Business Overview 3.2.3 Products, Services and Solutions 3.2.4 High Throughput Process Development Revenue (Value) (2012-2017) 3.2.5 Recent Developments 3.3 Agilent Technologies 3.3.1 Company Profile 3.3.2 Main Business/Business Overview 3.3.3 Products, Services and Solutions 3.3.4 High Throughput Process Development Revenue (Value) (2012-2017) 3.3.5 Recent Developments 3.4 Merck Millipore 3.4.1 Company Profile 3.4.2 Main Business/Business Overview 3.4.3 Products, Services and Solutions 3.4.4 High Throughput Process Development Revenue (Value) (2012-2017) 3.4.5 Recent Developments 3.5 Thermo Fisher Scientific 3.5.1 Company Profile 3.5.2 Main Business/Business Overview 3.5.3 Products, Services and Solutions 3.5.4 High Throughput Process Development Revenue (Value) (2012-2017) 3.5.5 Recent Developments 3.6 Bio-Rad Laboratories 3.6.1 Company Profile 3.6.2 Main Business/Business Overview 3.6.3 Products, Services and Solutions 3.6.4 High Throughput Process Development Revenue (Value) (2012-2017) 3.6.5 Recent Developments 3.7 Eppendorf AG 3.7.1 Company Profile 3.7.2 Main Business/Business Overview 3.7.3 Products, Services and Solutions 3.7.4 High Throughput Process Development Revenue (Value) (2012-2017) 3.7.5 Recent Developments 3.8 Perkinelmer, Inc 3.8.1 Company Profile 3.8.2 Main Business/Business Overview 3.8.3 Products, Services and Solutions 3.8.4 High Throughput Process Development Revenue (Value) (2012-2017) 3.8.5 Recent Developments 3.9 Sartorius Stedim Biotech 3.9.1 Company Profile 3.9.2 Main Business/Business Overview 3.9.3 Products, Services and Solutions 3.9.4 High Throughput Process Development Revenue (Value) (2012-2017) 3.9.5 Recent Developments 3.10 Tecan Group Ltd. 3.10.1 Company Profile 3.10.2 Main Business/Business Overview 3.10.3 Products, Services and Solutions 3.10.4 High Throughput Process Development Revenue (Value) (2012-2017) 3.10.5 Recent Developments 4 Global High Throughput Process Development Market Size by Type and Application (2012-2017) 4.1 Global High Throughput Process Development Market Size by Type (2012-2017) 4.2 Global High Throughput Process Development Market Size by Application (2012-2017) 4.3 Potential Application of High Throughput Process Development in Future 4.4 Top Consumer/End Users of High Throughput Process Development For more information, please visit https://www.wiseguyreports.com/sample-request/1206536-global-high-throughput-process-development-market-size-status-and-forecast-2022


News Article | May 17, 2017
Site: www.prnewswire.com

Browse 71 market data Tables and 29 Figures spread through 127 Pages and in-depth TOC on "Depth Filtration Market" Early buyers will receive 10% customization on this report. The report provides a detailed overview of major drivers, restraints, challenges, opportunities, current market trends and strategies impacting the global market along with estimates and forecast of revenue. The growth in this market will majorly be driven by the increasing production of biologics and large molecules, as depth filtration plays a significant role in providing pure and quality products. Depth filters offer advantages such as low cost and ease of use; these advantages are driving their uptake in the market. However, low capacity of depth filters for high-density harvest is a key challenge in this market. The global depth filtration market is broadly segmented on the basis of media type, product, application, and region. On the basis of media type, the depth filtration market is segmented into diatomaceous earth, activated carbon, cellulose, and perlite. In 2017, the diatomaceous earth filters segment is expected to account for the largest share of the depth filtration market followed by activated carbon. This high share is attributed to its high-quality filtration and wide range of applications in the pharmaceutical and biopharmaceutical industry. Furthermore, based on application, the depth filtration market is categorized into final product processing, cell clarification, raw material filtration, and other applications. In 2017, final product processing segment is expected to account the largest share of the global depth filtration market. The final product processing segmented is further divided into small molecule processing and biologics processing. The large share of the final product processing segment is attributed to the rapid growth in the biopharmaceutical industry, expansion in generics production, and the need for high-quality final products. North America (comprising the U.S. and Canada) is expected to account for the largest share of the global depth filtration market in 2017, followed by Europe. North America is the world's largest pharmaceutical market and a leader in biopharmaceutical research. Rising quality requirements have emphasized the need for advanced bioproduction technology in this region. Asia-Pacific is expected to grow at the highest CAGR in the forecast period. Significant investments by key market players, increasing government support, developing R&D infrastructure, and growing expertise and academic excellence are the major factors fueling the growth of the depth filtration market in the Asia-Pacific region. The depth filtration market is dominated by established players such as Merck KGaA (Germany), Sartorius Stedim Biotech S.A. (France), and Pall Corporation (U.S.). Pharmaceutical Filtration Market by Product (Membrane Filters, Single-Use Systems), Technique (Microfiltration, Ultrafiltration), Application (Final Product Processing, Raw material), Scale of Operation (Manufacturing, Pilot, R&D) - Global Forecast to 2021 Downstream Processing Market by Technique (Separation, Concentration, Purification), Product (Chromatography Column & Resin, Filter, Single use), Application (Mab Production), End User (Contract Manufacturing Organization) - Global Forecast to 2021 MarketsandMarkets™ provides quantified B2B research on 30,000 high growth niche opportunities/threats which will impact 70% to 80% of worldwide companies' revenues. Currently servicing 5000 customers worldwide including 80% of global Fortune 1000 companies as clients. Almost 75,000 top officers across eight industries worldwide approach MarketsandMarkets™ for their painpoints around revenues decisions. Our 850 fulltime analyst and SMEs at MarketsandMarkets™ are tracking global high growth markets following the "Growth Engagement Model - GEM". The GEM aims at proactive collaboration with the clients to identify new opportunities, identify most important customers, write "Attack, avoid and defend" strategies, identify sources of incremental revenues for both the company and its competitors. MarketsandMarkets™ now coming up with 1,500 MicroQuadrants (Positioning top players across leaders, emerging companies, innovators, strategic players) annually in high growth emerging segments. MarketsandMarkets™ is determined to benefit more than 10,000 companies this year for their revenue planning and help them take their innovations/disruptions early to the market by providing them research ahead of the curve. MarketsandMarkets' flagship competitive intelligence and market research platform, "RT" connects over 200,000 markets and entire value chains for deeper understanding of the unmet insights along with market sizing and forecasts of niche markets. Visit our Blog @ http://mnmblog.org/market-research/healthcare/biotechnology Connect with us on LinkedIn @ http://www.linkedin.com/company/marketsandmarkets


News Article | May 17, 2017
Site: www.nature.com

No statistical methods were used to predetermine sample size. The experiments were not randomized and the investigators were not blinded to allocation during experiments and outcome assessment. Adult zebrafish (Danio rerio) for breeding were maintained at 28 °C on a 14 h:10 h light:dark cycle following standard methods31. The Tg(elavl3:GCaMP5G)a4598 transgenic line32 used in this study was of genotype elavl3:GCaMP5G+/+; nacre (mitfa–/–), conveying nearly pan-neuronal expression of the calcium indicator GCaMP5G33 and increased transparency due to the nacre mutation34. The larval zebrafish samples described in this study were raised in filtered fish facility water31 until 5–7 dpf. Mice from which support tissue was collected had been previously killed for other experiments. Only unused, to-be-discarded tissue was collected to serve as support tissue. The Standing Committee on the Use of Animals in Research and Training of Harvard University approved all animal experiments. Larval zebrafish were immobilized by immersion in 1 mg ml–1 α-bungarotoxin (Invitrogen) and mounted dorsum-up in 2% low-melting-temperature agarose in a small dish containing a silicone base (Sylgard 184, Dow Corning). Upon agarose hardening, E3 solution (5 mM NaCl, 0.17 mM KCl, 0.33 mM CaCl and 0.33 mM MgSO ) was added to the dish. In vivo structural imaging of elavl3-driven GCaMP5G signal was conducted with a custom-built two-photon microscope equipped with a Ti:Sapphire laser (Mai Tai, Spectra-Physics) excitation source tuned to 800 nm. Frames with a 764.4 × 509.6 μm2 field of view size (1,200 × 800 pixels2) were acquired at 1-μm intervals (0.637 × 0.637 × 1 μm3 per voxel) at approximately 1 Hz with a scan pattern of four evenly spaced, interlaced passes35. A low-noise anatomical snapshot of brain fluorescence was captured in 300 planes, each the sum of 50 single frames. All light-based imaging was performed without any intentional stimulus presentation. Initial attempts at high-quality larval zebrafish brain preservation were impeded by skin and membranes, which prevented sufficient fixation with whole-fish immersion alone (Extended Data Fig. 1a). To overcome this, skin and membranes covering the brain36 were dissected away. Each larval zebrafish, which had been previously immobilized and embedded for two-photon laser-scanning microscopy, was introduced to a dissection solution (64 mM NaCl, 2.9 mM KCl, 10 mM HEPES, 10 mM glucose, 164 mM sucrose, 1.2 mM MgCl , 2.1 mM CaCl , pH 7.5; ref. 37) containing 0.02% (w/v) tricaine mesylate (MS-222, Sigma-Aldrich). Flow of red blood cells through the vasculature was confirmed before proceeding as an indicator of good health. A portion of agarose was removed to expose the dorsum from the posterior hindbrain to the anterior optic tectum. The dissection was initiated by puncturing the thin epithelial layer over the rhombencephalic ventricle above the hindbrain38 with a sharpened tungsten needle. Small incremental anterior-directed incisions were made along the midline as close to the surface as possible until the brain was exposed from the hindbrain entry site to the anterior optic tectum (Extended Data Fig. 1b). The majority of damage associated with this dissection was restricted to medial tectal proliferation zone progenitor cells39 that are unlikely to have integrated into functional neuronal circuits. Dissections lasted 1–2 min, upon which time the complete dish was immersed in a 2.0% formaldehyde and 2.5% glutaraldehyde fixative solution (Electron Microscopy Sciences) overnight at room temperature (Extended Data Fig. 1d). Following washes, larval zebrafish were cut out from the dish in a block of agarose with a scalpel and moved to a round-bottomed microcentrifuge tube. Specimens were then incubated in post-fixation solution containing 1% osmium tetroxide and 1.5% potassium ferricyanide for 2 h (Extended Data Fig. 1e), washed with water, washed with 0.05 M maleate buffer (pH 5.15), and stained with 1% uranyl acetate in maleate buffer overnight (Extended Data Fig. 1f). During the subsequent wash step with maleate buffer, larval zebrafish were freed from the surrounding agarose block and moved to a new microcentrifuge tube. Next, specimens were washed with water, dehydrated with serial dilutions of acetonitrile in water (25%, 50%, 70%, 70%, 80%, 90%, 95%, 100%, 100%, 100%) for 10 min each, and infiltrated with serial dilutions of a diepoxyoctane-based low viscosity resin40 in acetonitrile (25%, 50%, 75%, 100%) for 1 h each. The samples were then embedded in diepoxyoctane-based resin with surrounding support tissue and hardened for 2–3 days at 60 °C (Extended Data Fig. 1g, h). Aqueous solutions were prepared with water passed through a purification system (typically Arium 611VF, Sartorius Stedim Biotech). This process resulted in high-quality ultrastructure preservation (Extended Data Fig. 1c, i). Additional solution, washing, and timing details were described previously in a step-by-step protocol41. Consistent ultrathin sectioning was difficult to achieve in larval zebrafish samples, which contain heterogeneous tissues, but imperative for reconstructing 3D structure from a series of 2D sections. Tests revealed that errors occurred primarily when the sample composition changed markedly (for example, borders between tissue and empty resin). We overcame this by embedding samples surrounded by a mouse cerebral cortex support tissue (Extended Data Figs 1h, 2f, 3a, 4a). We preferred sectioning perpendicular to most axon and dendrite paths for ease and reliability in reconstructing neuronal morphology. For this reason, our cutting plane was oriented perpendicular to the long (anterior–posterior) axis, which required around 2.5× more sections than alternative orientations. This was made possible by customizing an automated tape-collecting ultramicrotome26, 27 by extending the device’s main mounting plate and enlarging its reels (compare Extended Data Fig. 2a with Fig. 1e from ref. 26) to accommodate one long tape stretch capable of collecting all sections. Sections were continuously cut with a diamond knife (Extended Data Fig. 2b, c) affixed to an ultramicrotome (EM UC6, Leica) and collected onto 8-mm-wide and 50–75-μm-thick tape (Kapton polyimide film, DuPont). Restarts were occasionally required for three reasons: fine-tuning of tape positioning or settings is necessary at the beginning of a run; the ultramicrotome design is constrained by a cutting depth range of about 200 μm; and diamond knives must be shifted after cutting several thousand sections to expose the sample to a fresh edge before dulling impairs sectioning quality. When necessary, restarts were completed as quickly as possible (typically 1–2 min) to minimize possible thermal, electrostatic, or other fluctuations. For the same reason, tape reels were fed continuously without ever being reloaded or exchanged. This combination of fast restarts and continuous tape feeding successfully maintained a steady state across restarts. We sectioned two larval zebrafish specimens. These represent the only two samples we have attempted to cut since adopting the surrounding support tissue approach. The primary focus of this study was a 5.5 dpf larval zebrafish sectioned with a 45° ultra diamond knife (Diatome) and a nominal sectioning thickness that averaged 60 nm with a variable setting ranging from 50–70 nm depending on sectioning consistency. Restarts occurred after sections 276, 3,669, 6,967, 10,346, 12,523, 12,916, and 15,956. Knife shifts occurred after sections 6,967 and 12,916. After sectioning, the tape was cut into segments with a razor blade between collected sections and adhered with double-sided conductive carbon adhesive tape (Ted Pella) to 4-in-diameter silicon wafers (University Wafer), which served as an imaging substrate. A total of 17,963 sections, each approximately 60 nm thick, were spread across 80 wafers (Extended Data Figs 2d, e, 3). One potential limitation of the 5.5 dpf larval zebrafish series is the section thickness. Minimizing section thickness is an important factor in the success of axon and dendrite reconstruction1. Small neuronal processes are difficult to reconstruct in thicker sections, especially when they are running roughly parallel to the plane of the section. To be sure that our approach was not fundamentally limited to thicker sections, we sectioned the second sample—a 7 dpf larval zebrafish—with a nominal sectioning thickness that remained constant at 50 nm throughout the entire cutting session using a 45° histo diamond knife (Diatome). Restarts occurred after sections 296, 312, 4,114, 8,233, and 12,333. Knife shifts occurred after sections 4,114 and 12,333. A total of 15,046 sections, each approximately 50 nm thick, were obtained from 15,052 attempted (Extended Data Fig. 4) and spread across 70 wafers. The thinner sections did not result in more lost material: this series contained 6 losses (0.04%; Extended Data Fig. 4d upper), 25 partial sections (0.17%; Extended Data Fig. 4d middle), no adjacent losses, and 6 adjacent lost–partial or partial–partial events (0.04%; Extended Data Fig. 4d lower). The nominal section thickness of approximately 60 nm made it possible to span the entire 5.5 dpf larval zebrafish brain in about 18,000 sections, as determined by finding the location of the spinal cord–hindbrain boundary42. Although the 7 dpf sample was sectioned at 50 nm, it was not made the focus of subsequent imaging because it contained less of the brain. However, improved reliability for this sample despite a 16% reduction in nominal sectioning thickness suggests that yet higher axial resolution is attainable. A section thickness of ≤30 nm would increase confidence in the ability to reconstruct complete neuronal circuit connectivity, and cutting at such thicknesses is known to be possible for mammalian brain sections of comparable sizes27, 43. Wafers containing tape segments were made hydrophilic by brief glow discharging, post-section stained for 1–2 min inside a chamber containing sodium hydroxide pellets using a stabilized lead citrate solution (UltroStain II, Leica) filtered through a 0.2 μm syringe filter, and then washed thoroughly with water. A thin layer of carbon was then deposited onto each wafer to prevent charging during scanning electron microscopy. WaferMapper software was used with light-based wafer overview images to semi-automatically map the positions of all sections and relate them to fiducial markers. This enabled targeted section overview acquisition (758.8 × 758.8 × 60 nm3 per voxel for 5.5 dpf; 741.5 × 741.5 × 50 nm3 per voxel for 7 dpf). Semi-automated alignment of section overviews in WaferMapper then permitted targeting for imaging at higher resolutions26. Field emission scanning electron microscopy of back-scattered electrons was primarily conducted on a Zeiss Merlin equipped with a large-area imaging scan generator (Fibics) and stock detector. An accelerating voltage of 5.0 kV and beam current of 7–10 nA were used for most acquisition. Imaging of back-scattered electrons at the highest resolution (4.0 × 4.0 × 60 nm3 per voxel) was performed on an FEI Magellan XHR 400L with an accelerating voltage of 5.0 kV and beam current of 1.6–3.2 nA. Field of view sizes acquired from a given section varied depending on the cross-sectional area occupied by tissue. All acquisition was performed with a scan rate at or under 1 megapixel per s. For the 5.5 dpf larval zebrafish, this resulted in overhead-inclusive acquisition times of 5.4 days for section overviews (758.8 × 758.8 × 60 nm3 per voxel), 97 days for isotropic full transverse cross-sections (56.4 × 56.4 × 60 nm3 per voxel), and 100 days for high-resolution brain images (18.8 × 18.8 × 60 nm3 per voxel). Continued development of faster electron microscopy technologies44 will hasten the re-imaging process and permit whole-brain studies to be carried out in a fraction of the time required here. Producing anatomically consistent image registration over about 18,000 sections required control of region of interest drift, over-fitting, magnification changes, and intensities. To quickly assess the quality of the dataset and begin reconstructions, we initially performed affine intra- and inter-section image registrations with Fiji45 TrakEM2 alignment plug-ins46. These results revealed that additional nonlinear registration was required in order to compensate for distortions that were likely caused by section compression during cutting and sample charging during imaging. While the state-of-the-art elastic registration method47 also provided in Fiji45 as a TrakEM2 alignment plug-in achieved excellent local registration, we experienced difficulty—at least without modification to the existing implementation—in achieving an anatomically consistent result that preserved the overall larval zebrafish structure, largely due to struggles with constraining region of interest drift across magnification changes and correcting for shearing caused by sectioning. We also determined that the similar AlignTK9 method, which uses Pearson correlation as the matching criterion coupled with spring mesh relaxation to stabilize the global volume, was likely to suffer from similar problems and would require substantial additional data handling to operate on our multi-resolution dataset. Therefore, in order to preserve the overall larval zebrafish structure and simultaneously achieve high-quality local registration, we turned to a new Signal Whitening Fourier Transform Image Registration (SWiFT-IR) method43, 48. Compared to conventional Pearson or phase correlation-based registration approaches, SWiFT-IR produces more robust image matching by using modulated Fourier transform amplitudes, adjusting its spatial frequency response during matching to maximize a signal-to-noise measure as its indicator of alignment quality. This alignment signal better handles variations in biological content and typical data distortions. Additionally, SWiFT-IR achieves higher precision in block matching as a result of the signal whitening, improves processing speeds with the computational complexity advantages of fast-Fourier transforms, and reduces iterative convergence from thousands to dozens of steps. Together, these capabilities enable a model-driven alignment in place of the usual approach of comparing and aligning a given section to a pre-selected number of adjacent sections. The SWiFT-IR model we used consisted of an estimate of local aligned volume content formed by a windowed average, typically spanning 6 μm along the axis orthogonal to the sectioning plane (z, anterior–posterior). Damaged regions, in particular partial sections, were removed from the model to avoid adversely influencing alignment results. This model then served as a registration template, in which raw images were matched to the current model rather than nearby sections. Alignment proceeded in an iterative fashion starting at 758.8 × 758.8 × 60 nm3 per voxel (section overviews) and progressing incrementally to 56.4 × 56.4 × 60 nm3 per voxel for regions outside the brain and 18.8 × 18.8 × 60 nm3 per voxel for regions inside. At each resolution, source images were iteratively aligned to the current model until no further alignment improvement could be achieved, as indicated by the SWiFT-IR signal-to-noise figure of merit. The model was then transferred to higher resolution data by applying the current warpings to the source data for that scale. Iterative model refinement then continued at this subsequent level. Although most computations were locally affine, residual nonlinear deformations, particularly at the highest resolutions, were represented by a triangulation mesh that deformably mapped raw data onto the model volume. Importantly, access to the lowest resolution section overview data permitted us to build an initial model that constrained subsequent registration steps to the overall larval zebrafish structure. Although their resolution and signal quality were intentionally sacrificed in favour of rapid acquisition, the fact that overviews were quickly captured with the same microscope settings and included support tissue provided key constraints for model refinement that resulted in a more accurate global result. More specifically, the 17,963-section overview image volume was processed using SWiFT-IR to produce an initial model at 564 × 564 × 600 nm3 per voxel. Although the lowest-resolution section overview images were each captured at 758.8 × 758.8 × 60 nm3 per voxel, the relative oversampling orthogonal to the sectioning plane enabled a geometrically accurate model at 564 × 564 × 600 nm3 per voxel. This initial model was then cropped and warped using SWiFT-IR–driven matching across the midline axis to remove cutting compression, rotations, and other systematic variations in the specimen pose. The 16,000-section 56.4 × 56.4 × 60 nm3 per voxel volume was next downsampled to 564 × 564 × 600 nm3 per voxel and aligned to the initial overview model, resulting in an improved model. The matching and remodelling process was iterated at this scale until there was no further improvement in SWiFT-IR match quality. The final model at this scale was then expanded to 282 × 282 × 300 nm3 per voxel and similarly aligned in an iterative fashion. This model volume (about 6 gigavoxels; 1,600 × 1,400 × 2,667 voxels) was convenient for rapid viewing to identify and manually correct defects and refine the pose. Further scales at 169.2 × 169.2 × 180 nm3 per voxel and 56.4 × 56.4 × 60 nm3 per voxel were similarly processed by successively expanding the model and aligning until no meaningful improvement in the figure of merit was reached. The 12,546-section 18.8 × 18.8 × 60 nm3 per voxel image set was then registered using the final 56.4 × 56.4 × 60 nm3 per voxel volume as its model. Image intensity was adjusted across sections to achieve a consistent background level by matching the average over a tissue-free region defined by a 256 × 256 pixels2 area. Many images were acquired at 16-bit depth and were converted in this process to 8-bit depth. The target background level was mapped to intensity 250, which left headroom for bright pixels while keeping most tissue of interest from saturating. Next, a linear intensity fit between the background and a second level, typically the average grey level of a continuous trajectory region on the right side of the brain, was made to adjust the intensity values for each section. Correspondence of individual neurons or functional reference atlas regions across imaging modalities was achieved with landmark-based 3D thin-plate spline warping of each fluorescence dataset to the ssEM dataset using BigWarp49. For matching in vivo two-photon laser-scanning microscopy data from the same specimen, we primarily chose landmarks consisting of distinctive arrangements of low-fluorescence regions where GCaMP5G was excluded and could be easily matched to similar patterns of nuclei in the ssEM dataset. This process was difficult in regions with low fluorescence signal (Extended Data Fig. 7e), where many cells were packed closely together (Extended Data Fig. 7f), and at locations where new neurons were likely to have been added between light microscopy and preparation for ssEM (Extended Data Fig. 7g). In the future, improving the light-level data with specific labelling of all nuclei and faster light-based imaging approaches should improve the ease and accuracy of matching neuron identity. Two functional reference atlases with many separate labels were also registered to the ssEM dataset. For matching the Z-Brain atlas50, we chose landmarks based on identifiable structures in the Z-Brain averaged elavl3:H2B-RFP or anti-tERK fluorescence image stacks that were also observed in the ssEM dataset. These structures primarily consisted of region boundaries, discernable clusters of neurons, midline points, ganglia, and the brain outline. The same Z-Brain landmarks were used for transforming a version51 of the Zebrafish Brain Browser52 that was previously registered to the Z-Brain atlas. Reconstruction across multi-resolution ssEM image volumes profits from being able to simultaneously access and view separate but co-registered datasets. Without this, some of the time benefits of our imaging approach would be offset by the need to register and track each structure across volumes that span both low-resolution, large fields of view and high-resolution, specific regions of interest. With this in mind, we added a feature to the Collaborative Annotation Toolkit for Massive Amounts of Image Data (CATMAID) neuronal circuit mapping software53, 54 to overlay and combine image stacks acquired with varying resolutions in a single viewer (Extended Data Fig. 6). This is made possible by rendering using WebGL. Additionally, this new feature combines stacks via a configurable overlay order, introduces blending operations for each overlaid stack, and enables programmatic shaders for dynamic image processing. When overlaid stacks’ resolutions differ, the nearest available zoom level for each stack is interpolated. Missing data regions can be omitted or rendered with interpolation. To account for the increase in data storage and bandwidth when viewing multiple image stacks, the CATMAID image data hierarchy was extended with a shared graphics card memory cache of image tiles using a least-recently-used replacement policy. All additions and modifications to the CATMAID software are now incorporated into the main open-source release. Manual reconstruction was conducted using our modified CATMAID version by placing nodes near the centre of each neuronal structure on every section in which it could be clearly identified. This led to a wire-frame model (‘skeleton’) for each annotated structure. Starting points for reconstruction (‘seeds’) of myelinated processes were manually identified by searching for profiles surrounded by the characteristic thick, densely stained outline associated with staining of the myelin sheath55 (Fig. 1e–g, i). The search protocol consisted of viewing all tissue on a given section from the upper-left to lower-right corner at the highest available resolution. To obtain seeds for the projectome reconstruction, searching was repeated every 50 sections throughout the 16,000 sections acquired at or higher than 56.4 × 56.4 × 60 nm3 per voxel. Many annotations were produced in an affine-only alignment space before being mapped into the final SWiFT-IR alignment space. The reconstructions reported here represent about 450 days of uninterrupted (24 h per day) human annotation. For visualization and reported length measurements, each skeleton was smoothed using a custom python-based implementation of a Kalman smoothing algorithm on a space defined by manually annotated points within unique segments. The initial state variables for smoothing were derived by an optimization of point-to-line distance to connected reconstruction segments. Other variables were tuned with the Estimation Maximization algorithm of the pykalman library to compensate for a lack of human input where data was unavailable because of lost or partial sections. Because the final image alignment was of good quality, smoothing in this manner should produce a slight underestimate in reported reconstruction path lengths. Neurons with known projection patterns or identities were named in the CATMAID database. For example (and subject to change), the reconstruction of a neuron innervating the right anterior macula (utricle) might be named as ‘Ear_AnteriorMacula_R_01’, while an identified neuron such as the left Mauthner neuron was named ‘Mauthner_L’. Two identifiable left–right reticulospinal neuron pairs belonged to the MeM class, which emanates from the nucleus of the medial longitudinal fasciculus (nucMLF). On each side, these were differentiated into dorsal (MeMd) and ventral (MeMv) subclasses based on consistent soma positioning. Image volumes and reconstructions were primarily visualized using Vivaldi56, a domain-specific language for rendering and processing on distributed systems, because it provides access to the parallel computing power of multi-GPU systems with language syntax similar to python. For volume visualizations, we used a direct volume rendering ray-casting technique in which an orthogonal or perspective iterator was marched along a viewing ray while sampled voxel colours were accumulated using an alpha compositing algorithm. We screened out regions containing only support tissue during rendering with labelled volumes constructed by interpolating between manually produced masks indicating which image voxels belong to each separate tissue region. In cases where separate image volumes of the same region were rendered together (for example, ssEM and fluorescence combined), direct volume rendering was performed by combining front-to-back colour and alpha compositions formed from the different transfer function belonging to each image volume. For volume visualizations including reconstructions, direct volume rendering of image data was combined with streamline rendering of reconstructions using two different techniques. The first combined an OpenGL framebuffer with the Vivaldi volume rendering. In this case, each streamline was rendered using OpenGL as a tube into an off-screen buffer (that is, Framebuffer Object). Vivaldi then compared the resulting render and depth buffers to perform direct volume rendering of only the image data above the streamline depth value. This made it possible to ignore image voxels obscured by streamlines, which were treated as opaque. The second technique involved generating a complete streamline volume by 3D rasterization. This streamline volume was then combined with the image volume for direct volume rendering. The former technique is faster and can cope with dynamic streamline changes, but the latter was found to yield better overall rendering quality for our purposes. Visualizations of reconstructions without the image volume context were rendered either in the CATMAID 3D WebGL viewer or plotted in MATLAB. When reconstructions are shown without specific labelling, colours were assigned randomly from a custom palette. Reference plane (for example, horizontal, sagittal, and section) indicators were rendered with Vivaldi by detecting the zero-crossing of each viewing ray and the plane. Support for viewing opaque data views in some spatial regions alongside the semi-transparent volume visualization views in other regions was introduced as a new Vivaldi function, clipping_plane. Similarly, contour (nonplanar) reslice support was added to illustrate a flattened view along a specific reconstruction path consisting of vertical line segments extracted from the image volume. For many cases, the size of the volume being rendered was larger than available memory. In order to support out-of-core processing, we developed and integrated into Vivaldi a slice-based streaming computing framework using the Hadoop distributed file system (HDFS) that will be reported elsewhere. Initial observations of apparent myelinated axon symmetry were found during visual inspection (Fig. 3; Supplementary Videos 8, 9). To quantitatively assess the extent of symmetry, we developed a 3D symmetry plane fitting method and two symmetry analyses: one that produces a cost associated with the 3D shape and position similarity between reconstructed structures and another that compares the relative 2D (cross-sectional) positioning of two identified neuron axons on one side with that for the contralateral axons with the same identities. Only the longest reconstructed path from the soma through the myelinated axon projection was considered in plane fitting and symmetry analyses. Dendrites or short axonal branches were ignored. Each resulting reconstruction path (skeleton) was represented as an ordered list of nodes (points) taken directly from manual reconstructions. Sidedness (left or right) was determined by soma position. The new 3D symmetry plane fitting and 3D symmetry comparison analysis approaches have been described elsewhere57. The symmetry plane fitting, in brief, involves choosing an approximate symmetry plane, reflecting the complete set of points belonging to the reconstruction subset of interest with respect to this plane, registering the original and reflected point clouds with an iterative closest point algorithm, and inferring the optimal symmetry plane from the reflection and registration mappings. The subset of reconstructions from which this plane fitting was performed consisted entirely of identified neurons whose axon projections formed part of the approximately 30-μm-diameter MLF, recognized with the help of refs 29, 58. The 3D symmetry comparison for each template reconstruction on one side, in brief, involved reflecting all contralateral skeletons and computing a matching cost via dynamic time warping (DTW) between the template and each reflected skeleton. The reconstruction subset analysed in this fashion was restricted to identified neuron classes with 1–2 members per side whose axons formed part of the MLF. For our purposes, the DTW cost was taken as the sum of the Euclidian distances between all matched points normalized to the number of matched point pairs (Extended Data Fig. 9a–c). The DTW gap cost parameter for matching a point in one sequence with a gap in another was set to zero because our data was sampled at a nearly constant rate and we sought the optimal subsequence match even in cases where one is shorter than or offset with respect to the other. To compensate for unmatched regions (that is, overhangs), the DTW cost was then multiplied by a penalty factor proportional to the sequence lengths remaining unmatched (total length divided by matched length). Comparing each reconstruction on one side to all reconstructions from the opposite side formed a cost matrix (Fig. 4b) from which an optimal pairwise assignment could be determined without any bias introduced from the previously determined identities. The Munkres algorithm59 was then used to compute a globally optimal pairwise assignment. We also sought to compare the relative 2D positioning for each set of two axons on one side with the contralateral set that had the same identities. The reconstruction subset analysed in this fashion was restricted either to the Mauthner cells and nucMLF neurons (Fig. 3c, 4e, g) or the larger set of 44 identified reticulospinal neurons (Fig. 4b; Extended Data Fig. 9i). To start, we compensated for a small angle offset in the sectioning plane relative to the true transverse plane by projecting the point coordinates of reconstructions such that the previously computed symmetry plane became the plane x = 0. Given the transverse planes z , z and a projected skeleton S containing points s = (s , s , s ), we let . That is, was taken as the subset of points from S whose coordinates are contained in the interval . We refer to the subset of ℝ3 bounded by as the slice . For each slice and skeleton S, we defined as the mean of the elements in . This mean was then taken as representative of the skeleton S in slice for analysis and plotting. Note that all analysis and plotting presented in static form was based on a slice thickness corresponding to a single section (approximately 60 nm), where each slice consisted simply of adjacent sections. Larger slice sizes were used for dynamic presentation (Supplementary Video 10) in order to reduce video duration and size. For comparing a set of two axons with its contralateral counterpart, we then took s ,..., s to be the set of representative points in a fixed slice for skeletons S ,..., S and took t ,..., t to be the representative points (for the same slice) of the respective skeletons T ,..., T that were previously matched to S ,..., S by the Munkres algorithm assignment after 3D symmetry analysis. To quantify the degree of similarity, we devised two measures (Extended Data Fig. 9e). The first, termed the angle difference, a , between a set of two axons and their contralateral counterparts, was defined as: where i, j were skeleton indices, was the reflections of with respect to the computed plane of symmetry, was the dot product between x and y, and was the norm of x. The second, termed the distance difference, d , between a set of two axons and their contralateral counterparts, was defined as: where i, j were skeleton indices, was the reflections of with respect to the computed plane of symmetry, was the absolute value of x, was the norm of x, and M was the maximum of  across all axon sets and all slices. Note that a and d were normalized such that they could vary from 0 (no difference, 0° or 0 μm) to 1 (maximum difference, 180° or 8 μm). Further, when the points s and s were perfectly symmetrical with respect to points t and t , then a  = 0 and d  = 0. To visualize this quantification, a difference matrix, D, was generated for each slice such that D(i,j) = a if j > i and D(i,j) = d if j < i (Fig. 4f; Extended Data Fig. 9f; Supplementary Video 10). Calculating the variance for each element in D across all slices showed which axon sets deviated most with respect to the reflection of their contralateral counterparts (Fig. 4k). Heatmaps of the vectorized upper (j > i) and lower (j < i) triangles of D across slices additionally revealed locations with differences between axon sets and their contralateral counterparts (Fig. 4h, i; Extended Data Fig. 9h, j, k; Supplementary Video 10), with black values representing insufficient data at slice positions where at least one of the compared axons was not annotated. Plotting the sum of all a and d values for a given slice further illustrated positions where differences were present (Fig. 4j). Finally, the same analysis was performed after artificially swapping the identities (assignment) of the two axon reconstructions with the lowest 3D symmetry analysis costs (MeLc and Mauthner) to provide a basis for comparison (Fig. 4j). Custom software tools generated for data handling, visualization, and analysis are publicly available (http://zebrafish.link/hildebrand16/code). Our modifications to CATMAID53, 54 software are included in the main open-source release (http://github.com/catmaid/catmaid). More information on SWiFT-IR alignment software is publicly available (http://www.mmbios.org/swift-ir-home). All aligned ssEM data, reconstructions, transformed functional reference atlases, and an introductory guide are publicly available (hosted by NeuroData at http://neurodata.io/data/hildebrand16 and http://zebrafish.link/hildebrand16). Image data are served as a collection of 8-bit 1,024 × 1,024 pixel2 PNG images with an optional tRNS value of 255 specified to enable transparency. The original resolution for each image stack was downsampled multiple times to create a resolution hierarchy that provides a smooth visualization experience. The entire aligned image dataset requires about 2.7 terabytes of disk space as compressed PNG images (607 gigabytes for 56.4 × 56.4 × 60 nm3 per voxel ssEM data, 1,824 gigabytes for 18.8 × 18.8 × 60 nm3 per voxel ssEM data, 355 gigabytes for 4.0 × 4.0 × 60 nm3 per voxel ssEM of dorsal neuromasts, 1 gigabyte for 600 × 600 × 1200 nm3 per voxel Z-Brain data, and 3 gigabytes for 600 × 600 × 1200 nm3 per voxel Zebrafish Brain Browser data). Data and reconstructions are served to end users via Amazon Web Services (AWS), with an instance of our modified CATMAID53, 54 software deployed on the Elastic Compute Cloud (EC2) that points to static images hosted by the Simple Storage Service (S3) built-in web server.


News Article | May 17, 2017
Site: www.prnewswire.co.uk

Browse 71 market data Tables and 29 Figures spread through 127 Pages and in-depth TOC on "Depth Filtration Market" Early buyers will receive 10% customization on this report. The report provides a detailed overview of major drivers, restraints, challenges, opportunities, current market trends and strategies impacting the global market along with estimates and forecast of revenue. The growth in this market will majorly be driven by the increasing production of biologics and large molecules, as depth filtration plays a significant role in providing pure and quality products. Depth filters offer advantages such as low cost and ease of use; these advantages are driving their uptake in the market. However, low capacity of depth filters for high-density harvest is a key challenge in this market. The global depth filtration market is broadly segmented on the basis of media type, product, application, and region. On the basis of media type, the depth filtration market is segmented into diatomaceous earth, activated carbon, cellulose, and perlite. In 2017, the diatomaceous earth filters segment is expected to account for the largest share of the depth filtration market followed by activated carbon. This high share is attributed to its high-quality filtration and wide range of applications in the pharmaceutical and biopharmaceutical industry. Furthermore, based on application, the depth filtration market is categorized into final product processing, cell clarification, raw material filtration, and other applications. In 2017, final product processing segment is expected to account the largest share of the global depth filtration market. The final product processing segmented is further divided into small molecule processing and biologics processing. The large share of the final product processing segment is attributed to the rapid growth in the biopharmaceutical industry, expansion in generics production, and the need for high-quality final products. North America (comprising the U.S. and Canada) is expected to account for the largest share of the global depth filtration market in 2017, followed by Europe. North America is the world's largest pharmaceutical market and a leader in biopharmaceutical research. Rising quality requirements have emphasized the need for advanced bioproduction technology in this region. Asia-Pacific is expected to grow at the highest CAGR in the forecast period. Significant investments by key market players, increasing government support, developing R&D infrastructure, and growing expertise and academic excellence are the major factors fueling the growth of the depth filtration market in the Asia-Pacific region. The depth filtration market is dominated by established players such as Merck KGaA (Germany), Sartorius Stedim Biotech S.A. (France), and Pall Corporation (U.S.). Pharmaceutical Filtration Market by Product (Membrane Filters, Single-Use Systems), Technique (Microfiltration, Ultrafiltration), Application (Final Product Processing, Raw material), Scale of Operation (Manufacturing, Pilot, R&D) - Global Forecast to 2021 Downstream Processing Market by Technique (Separation, Concentration, Purification), Product (Chromatography Column & Resin, Filter, Single use), Application (Mab Production), End User (Contract Manufacturing Organization) - Global Forecast to 2021 MarketsandMarkets™ provides quantified B2B research on 30,000 high growth niche opportunities/threats which will impact 70% to 80% of worldwide companies' revenues. Currently servicing 5000 customers worldwide including 80% of global Fortune 1000 companies as clients. Almost 75,000 top officers across eight industries worldwide approach MarketsandMarkets™ for their painpoints around revenues decisions. Our 850 fulltime analyst and SMEs at MarketsandMarkets™ are tracking global high growth markets following the "Growth Engagement Model - GEM". The GEM aims at proactive collaboration with the clients to identify new opportunities, identify most important customers, write "Attack, avoid and defend" strategies, identify sources of incremental revenues for both the company and its competitors. MarketsandMarkets™ now coming up with 1,500 MicroQuadrants (Positioning top players across leaders, emerging companies, innovators, strategic players) annually in high growth emerging segments. MarketsandMarkets™ is determined to benefit more than 10,000 companies this year for their revenue planning and help them take their innovations/disruptions early to the market by providing them research ahead of the curve. MarketsandMarkets' flagship competitive intelligence and market research platform, "RT" connects over 200,000 markets and entire value chains for deeper understanding of the unmet insights along with market sizing and forecasts of niche markets. Visit our Blog @ http://mnmblog.org/market-research/healthcare/biotechnology Connect with us on LinkedIn @ http://www.linkedin.com/company/marketsandmarkets


News Article | February 15, 2017
Site: www.prweb.com

Worcester Polytechnic Institute will hold its second annual Advanced Biomanufacturing Symposium, a two-day, in-depth event that will focus on the technology and processes of continuous biomanufacturing and the challenges of making novel cell and regenerative tissue therapies that are approaching the clinic. The symposium, which was over-subscribed last year, is set for March 27–28, 2017. Organized by WPI life sciences and bioengineering faculty members and the university’s Biomanufacturing Education and Training Center (BETC), the symposium will bring together industry professionals and academic researchers working with new technologies, processes, and business practices that will have a significant impact on biomanufacturing in the near term. “2017 is shaping up to be an important year for biological products, with increasing public awareness of the industry and advances across the biomanufacturing spectrum that will demand our attention,” said Kamal Rashid, PhD, director of the BETC and research professor at WPI. “Evolving platforms and expression systems, progress towards end-to-end continuous biomanufacturing, the challenges of cell and tissue therapies—all of these topics will be explored in detail at our symposium.” This year’s keynote presenters include Manon Cox, PhD, president and chief executive officer of Protein Sciences Corp.; Jerome Ritz, MD, professor at Harvard Medical School and executive director of the Connell and O'Reilly Cell Manipulation and Gene Transfer Laboratory at Dana-Farber Cancer Institute; and Gail Naughton, PhD, chief executive officer of Histogen Inc. Of note, Kelvin Lee, PhD, Gore Professor of Chemical and Biomolecular Engineering at the University of Delaware, who led the team that organized the recently funded National Institute for Innovation in Manufacturing Biopharmaceuticals(NIIMBL), will also speak at the symposium. WPI is a member of NIIMBL. The symposium will feature session talks by subject matter experts from Biogen, Eppendorf, GE Healthcare, MilliporeSigma, Organovo, Pall Life Sciences, Sartorius Stedim Biotech, and Unum Therapeutics, as well as faculty members from Tufts University and WPI. “The talks will be presented in a single-track so participants will have access to all the content, and not have to choose between concurrent sessions,” Rashid said. “This worked very well last year. It helps maximize interaction and information exchange.” (Click here for photos from last year’s symposium. ) The symposium will take place in the Rubin Campus Center on WPI’s campus in Worcester, Mass. Registration is required and space is limited. (Click here for more event information and registration.) Funded in part by a grant from the Massachusetts Life Sciences Center, the BETC is a multi-faceted resource for the biologics industry, providing a range of hands-on customized programs. The BETC works with biomanufacturers to help them train, and retrain, their employees at a state-of-the-art center removed from their own production facilities. The center also provides research collaboration opportunities and consulting services to help companies manage challenges, explore new technologies, or scale up new processes. Founded in 1865 in Worcester, Mass., WPI is one of the nation’s first engineering and technology universities. Its 14 academic departments offer more than 50 undergraduate and graduate degree programs in science, engineering, technology, business, the social sciences, and the humanities and arts, leading to bachelor’s, master’s and doctoral degrees. WPI’s talented faculty work with students on interdisciplinary research that seeks solutions to important and socially relevant problems in fields as diverse as the life sciences and bioengineering, energy, information security, materials processing, and robotics. Students also have the opportunity to make a difference to communities and organizations around the world through the university’s innovative Global Projects Program. There are more than 45 WPI project centers throughout the Americas, Africa, Asia-Pacific, and Europe.


DUBLIN--(BUSINESS WIRE)--Research and Markets has announced the addition of the "Top 10 Bioprocess Technology Market by Cell Culture, Cell Expansion, Cell Counting, Cell Line Development, Flow Cytometry, Single-Use Bioprocessing, Biologics Safety Testing, Tangential Flow Filtration, Virus Filtration & Region - Forecast to 2021" report to their offering. The top 10 bioprocess technology market is expected to USD 71.03 Billion by 2021 from USD 39.30 Billion in 2016, at a CAGR of 12.4% between 2016 and 2021. Growth in the biopharmaceutical industry, increase in R&D spending, rising demand for vaccine production, and technological advancements form important growth drivers for this market during the forecast period. The rising opportunities in emerging market and increasing pharmaceutical outsourcing provide significant growth opportunities for players operating in the top 10 bioprocess technology market. The report provides an overall understanding of the global top 10 bioprocess technology market. It segments the global market on the basis of type and region. On the basis of type, the market is segmented into cell culture, cell expansion, cell counting, cell line development, flow cytometry, single-use bioprocessing, biologics safety testing, virus filtration, tangential flow filtration, and pyrogen testing. In 2016, the cell culture segment is expected to account for the largest share of the global top 10 bioprocess technology market. The largest share of this segment is primarily attributed to the repeated purchase of consumables as compared to equipment and increase in funding for cell-based research. The major players in top 10 bioprocess technology market include GE Healthcare (U.S.), Danaher Corporation (U.S.), Thermo Fisher Scientific, Inc. (U.S.), Becton, Dickinson and Company (U.S.), Lonza Group AG (Switzerland), Merck Millipore (Germany), Sartorius Stedim Biotech S.A (France), Corning, Inc. (U.S.), Bio-Rad Laboratories (U.S.), and Charles River Laboratories (U.S.). For more information about this report visit http://www.researchandmarkets.com/research/jglv3h/top_10_bioprocess


News Article | February 23, 2017
Site: www.chromatographytechniques.com

Sartorius Stedim Biotech’s SARTOFLOW Smart is an easy benchtop crossflow system for optimized ultra- and dia-filtration applications. It can be used in many downstream processes, such as purification of vaccines, monoclonal antibodies and recombinant proteins. The system is suitable for flexible use in laboratory environments for process development and clinical trials, as well as for cGMP environments. It is equipped with a low shear 4-piston membrane pump that enables high product yields. In addition, the pump provides a wide range of flow rates, allowing users to choose between membrane surface areas from 50 cm² to as much as 0.14 m². The crossflow system is supplied with the company’s intuitive and easy to use DCU-4 control unit, which, when combined with the BioPAT SCADA MFCS-4 software, provides data logging and export. Its touchscreen offers instant access to all critical process parameters and displays control and alarm functions. A logbook function stores alarms, set points and user logs. Sartorius Stedim Biotech www.sartorius.com, 800-635-2906


News Article | March 1, 2017
Site: www.chromatographytechniques.com

Sartorius Stedim Biotech has developed an innovative and fully integrated technology platform to meet the requirements of today’s upstream bioprocessing. It combines a high-performance expression system with excellent equipment and process control for the rapid development and scale-up of robust, high-titer commercial manufacturing processes. Biopharmaceutical manufacturers can reach the clinic in 14 months by leveraging this platform. The royalty-free CHO expression platform of Cellca delivers the titers required to meet companies’ cost of goods objectives, and the automated ambr15 micro bioreactor system is capable of controlling 24 or 48 micro bioreactor experiments. This system accelerates clone selection and scales up readily to BIOSTAT STR single-use bioreactors. To reduce early stage development timelines still further, the company has integrated more than 100 off-the-shelf and pre-qualified assays from BioOutsource into its platform, allowing the rapid testing and analysis of biosimilar products. Sartorius Stedim Biotech www.sartorius.com, 800-635-2906

Loading Sartorius Stedim Biotech GmbH collaborators
Loading Sartorius Stedim Biotech GmbH collaborators