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Toulouse, France

In this study, we have estimated the different sea level components (observed sea level from satellite altimetry, steric sea level from in situ hydrography-including Argo profiling floats, and ocean mass from Gravity Recovery and Climate Experiment; GRACE), in terms of regional and interannual variability, over 2002-2009. We compute the steric sea level using different temperature (and salinity) data sets processed by different groups (SCRIPPS, CLS, IPRC, and NOAA) and first focus on the regional variability in steric and altimetry-based sea level. In addition to El Nino-La Nina signatures, the observed and steric sea level data show clear impact of three successive Indian Ocean Dipoles in 2006, 2007, and 2008 in the Indian Ocean. We next study the spatial trend patterns in ocean mass signal by comparing GRACE observations over the oceans with observed minus steric sea level. While in some regions, reasonably good agreement is observed, discrepancy is noticed in some others due to still large regional trend errors in Argo and GRACE data, as well as to a possible (unknown) deep ocean contribution. In terms of global mean, interannual variability in altimetry-based minus steric sea level and GRACE-based ocean mass appear significantly correlated. However, large differences are reported when short-term trends are estimated (using both GRACE and Argo data). This prevents us to draw any clear conclusion on the sea level budget over the recent years from the comparison between altimetry-based, steric sea level, and GRACE-based ocean mass trends, nor does it not allow us to constrain the Glacial Isostatic Adjustment correction to apply to GRACE-based ocean mass term using this observational approach. © 2010 Springer-Verlag. Source

Saunders R.A.,Marine Institute of Ireland | Royer F.,CLS | Clarke M.W.,Marine Institute of Ireland
ICES Journal of Marine Science

The porbeagle is one of the top marine predators in the North Atlantic. However, little is known about its biology, abundance, or spatial ecology there. Results are presented on the migration and behaviour of three porbeagles tagged with archival pop-up tags off Ireland between September 2008 and January 2009. One shark migrated >2400 km to the northwest of Morocco, residing around the Bay of Biscay for approximately 30 days. The other two remained more localized in off-shelf regions around the Celtic Sea/Bay of Biscay and off western Ireland. The sharks occupied a broad vertical depth range (0-700 m) and a relatively limited temperature range (∼9-17°C), with notable variations in diving behaviour between individual sharks. There were distinct day-night differences in depth distribution, each shark being positioned higher in the water column by night than by day. Night-time depth distribution also appeared to be driven by the lunar cycle during broad-scale migration through oceanic waters. Our results show that porbeagles occupy and traverse regions of high fishing activity where they are potentially vulnerable to population depletion. Such large-scale movement outside the ICES Area underlines the need for international coordination in their assessment and management. © 2010 International Council for the Exploration of the Sea. Published by Oxford Journals. All rights reserved. Source

News Article
Site: http://www.nature.com/nature/current_issue/

Human ES cell line H9 (WA-09) and derivatives (SOX10::GFP; SYN::ChR2-EYFP; SYN::EYFP;PHOX2B:GFP;EF1::RFP Ednrb−/−) as well as two independent human iPS cell lines (healthy and familial dysautonomia, Sendai-based, OMSK (Cytotune)) were maintained on mouse embryonic fibroblasts (Global Stem) in knockout serum replacement (KSR; Life Technologies, 10828-028) containing human ES cell medium as described previously7. Cells were subjected to mycoplasma testing at monthly intervals and short tandem repeats (STR) profiled to confirm cell identity at the initiation of the study. Human ES cells were plated on matrigel (BD Biosciences, 354234)-coated dishes (105 cells cm−2) in ES cell medium containing 10 nM FGF2 (R&D Systems, 233-FB-001MG/CF). Differentiation was initiated in KSR medium (knockout DMEM plus 15% KSR (Life Technologies, 10828-028), l-glutamine (Life Technologies, 25030-081), NEAA (Life Technologies, 11140-050)) containing LDN193189 (100 nM, Stemgent) and SB431542 (10 μM, Tocris). The KSR medium was gradually replaced with increasing amounts of N2 medium from day 4 to day 10 as described previously7. For CNC induction, cells were treated with 3 μM CHIR99021 (Tocris Bioscience, 4423) in addition to LDN193189 and SB431542 from day 2 to day 11. ENC differentiation involves additional treatment with retinoic acid (1 μM) from day 6 to day 11. For deriving MNCs, LDN193189 is replaced with BMP4 (10 nM, R&D, 314-BP) and EDN3 (10 nM, American Peptide company, 88-5-10B) from day 6 to day 11 (ref. 3). The differentiated cells are sorted for CD49D at day 11. CNS precursor control cells were generated by treatment with LDN193189 and SB431542 from day 0 to day 11 as previously described7. Throughout the manuscript, day 0 is the day the medium is switched from human ES cell medium to LDN193189 and SB431542 containing medium. Days of differentiation in text and figures refer to the number of days since the pluripotent stage (day 0). For immunofluorescence, the cells were fixed with 4% paraformaldehyde (Affymetrix-USB, 19943) for 20 min, then blocked and permeabilized using 1% bovine serum albumin (BSA) (Thermo Scientific, 23209) and 0.3% Triton X-100 (Sigma, T8787). The cells were then incubated in primary antibody solutions overnight at 4 °C and stained with fluorophore-conjugated secondary antibodies at room temperature for 1 h. The stained cells were then incubated with DAPI (1 ng ml−1, Sigma, D9542-5MG) and washed several times before imaging. For flow cytometry analysis, the cells are dissociated with Accutase (Innovative Cell Technologies, AT104) and fixed and permeabilized using BD Cytofix/Cytoperm (BD Bioscience, 554722) solution, then washed, blocked and permeabilized using BD Perm/Wash buffer (BD Bioscience, 554723) according to manufacturer’s instructions. The cells are then stained with primary (overnight at 4 °C) and secondary (30 min at room temperature) antibodies and analysed using a flow Cytometer (Flowjo software). A list of primary antibodies and working dilutions is provided in Supplementary Table 4. The PHOX2A antibody was provided by J.-F. Brunet (rabbit, 1:800 dilution). Fertilized eggs (from Charles River Farms) were incubated at 37 °C for 50 h before injections. A total of 2 × 105 CD49D-sorted, RFP-labelled NC cells were injected into the intersomitic space of the vagal region of the embryos targeting a region between somite 2 and 6 (HH 14 embryo, 20–25 somite stage). The embryos were collected 36 h later for whole-mount epifluorescence and histological analyses. For RNA sequencing, total RNA was extracted using RNeasy RNA purification kit (Qiagen, 74106). For qRT–PCR assay, total RNA samples were reverse transcribed to cDNA using Superscript II Reverse Transcriptase (Life Technologies, 18064-014). qRT–PCR reactions were set up using QuantiTect SYBR Green PCR mix (Qiagen, 204148). Each data point represents three independent biological replicates. ENC cells from the 11-day induction protocol were aggregated into 3D spheroids (5 million cells per well) in Ultra Low Attachment 6-well culture plates (Fisher Scientific, 3471) and cultured in Neurobasal (NB) medium supplemented with l-glutamine (Gibco, 25030-164), N2 (Stem Cell Technologies, 07156) and B27 (Life Technologies, 17504044) containing CHIR99021 (3 μM, Tocris Bioscience, 4423) and FGF2 (10 nM, R&D Systems, 233-FB-001MG/CF). After 4 days of suspension culture, the spheroids are plated on poly-ornithine/laminin/fibronectin (PO/LM/FN)-coated dishes (prepared as described previously26) in neurobasal (NB) medium supplemented with l-glutamine (Gibco, 25030-164), N2 (Stem Cell Technologies, 07156) and B27 (Life Technologies, 17504044) containing GDNF (25 ng ml−1, Peprotech, 450-10) and ascorbic acid (100 μM, Sigma, A8960-5G). The ENC precursors migrate out of the plated spheroids and differentiate into neurons in 1–2 weeks. The cells were fixed for immunostaining or collected for gene expression analysis at days 25, 40 and 60 of differentiation. Mesoderm specification is carried out in STEMPRO-34 (Gibco, 10639-011) medium. The ES cells are subjected to activin A treatment (100 ng ml−1, R&D, 338-AC-010) for 24 h followed by BMP4 treatment (10 ng ml−1, R&D, 314-BP) for 4 days9. The cells are then differentiated into SMC progenitors by treatment with PDGF-BB (5 ng ml−1, Peprotech, 100-14B), TGFb3 (5 ng ml−1, R&D systems, 243-B3-200) and 10% FBS. The SMC progenitors are expandable in DMEM supplemented with 10% FBS. The SMC progenitors were plated on PO/LM/FN-coated culture dishes (prepared as described previously26) 3 days before addition of ENC-derived neurons. The neurons were dissociated (using accutase, Innovative Cell Technologies, AT104) at day 30 of differentiation and plated onto the SMC monolayer cultures. The culture is maintained in neurobasal (NB) medium supplemented with l-glutamine (Gibco, 25030-164), N2 (Stem Cell Technologies, 07156) and B27 (Life Technologies, 17504044) containing GDNF (25 ng ml−1, Peprotech, 450-10) and ascorbic acid (100 μM, Sigma, A8960-5G). Functional connectivity was assessed at 8–16 weeks of co-culture. SMC-only and SMC-ENC-derived neuron co-cultures were subjected to acetylcholine chloride (50 μM, Sigma, A6625), carbamoylcholine chloride (10 μM, Sigma,C4382) and KCl (55 mM, Fisher Scientific, BP366–500) treatment, 3 months after initiating the co-culture. Optogenetic stimulations were performed using a 450-nm pigtailed diode pumped solid state laser (OEM Laser, PSU-III LED, OEM Laser Systems, Inc.) achieving an illumination between 2 and 4 mW mm−2. The pulse width was 4 ms and stimulation frequencies ranged from 2 to 10 Hz. For the quantification of movement, images were assembled into a stack using Metamorph software and regions with high contrast were identified (labelled yellow in Supplementary Fig. 5). The movement of five representative high-contrast regions per field was automatically traced (Metamorph software). Data are presented in kinetograms as movement in pixels in x and y direction (distance) with respect to the previous frame. We used the previously described method for generation of tissue-engineered colon11. In brief, the donor colon tissue was collected and digested into organoid units using dispase (Life Technologies, 17105-041) and collagenase type 1 (Worthington, CLS-1). The organoid units were then mixed immediately (without any in vitro culture) with CD49D-purified human ES-cell-derived ENC precursors (day 15 of differentiation) and seeded onto biodegradable polyglycolic acid scaffolds (2-mm sheet thickness, 60 mg cm−3 bulk density; porosity >95%, Concordia Fibres) shaped into 2 mm long tubes with poly-l lactide (PLLA) (Durect Corporation). The seeded scaffolds were then placed onto and wrapped in the greater omentum of the adult (>2 months old) NSG mice. Just before the implantation, these mice were irradiated with 350 cGy. The seeded scaffolds were differentiated into colon-like structures inside the omentum for 4 additional weeks before they were surgically removed for tissue analysis. All mouse procedures were performed following NIH guidelines, and were approved by the local Institutional Animal Care and Use Committee (IACUC), the Institutional Biosafety Committee (IBC) as well as the Embryonic Stem Cell Research Committee (ESCRO). We used 3–6-week-old male NSG (NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ) mice or 2–3-week-old Ednrbs-l/s-l (SSL/LeJ) mice27 (n = 12, 6 male, 6 female) for these studies. Animal numbers were based on availability of homozygous hosts and on sufficient statistical power to detect large effects between treatment versus control (Ednrbs-l/s-l) as well as for demonstrating robustness of migration behaviour (NSG). Animals were randomly selected for the various treatment models (NSG and Ednrbs-l/s-l) but assuring for equal distribution of male/female ratio in each group (Ednrbs-l/s-l). All in vivo experiments were performed in a blinded manner. Animals were anaesthetized with isoflurane (1%) throughout the procedure, a small abdominal incision was made, abdominal wall musculature lifted and the caecum is exposed and exteriorized. Warm saline is used to keep the caecum moist. Then 20 μl of cell suspension (2–4 million RFP+ CD49D-purified human ES-cell-derived ENC precursors) in 70% Matrigel (BD Biosciences, 354234) in PBS or 20 μl of 70% Matrigel in PBS only (control-grafted animals) were slowly injected into the caecum (targeting the muscle layer) using a 27-gauge needle. Use of 70% matrigel as carrier for cell injection assured that the cells stayed in place after the injection and prevented backflow into the peritoneum. After injection that needle was withdrawn, and a Q-tip was placed over the injection site for 30 s to prevent bleeding. The caecum was returned to the abdominal cavity and the abdominal wall was closed using 4-0 vicryl and a taper needle in an interrupted suture pattern and the skin was closed using sterile wound clips. After wound closure animals were put on paper on top of their bedding and attended until conscious and preferably eating and drinking. The tissue was collected at different time points (ranging from two weeks to four months) after transplantation for histological analysis. Ednrbs-l/s-l mice were immunosuppressed by daily injections of cyclosporine (10 mg kg−1 i.p, Sigma, 30024). The collected colon samples were fixed in 4% paraformaldehyde at 4 °C overnight before imaging. Imaging is performed using Maestro fluorescence imaging system (Cambridge Research and Instrumentation). The tissue samples were incubated in 30% sucrose (Fisher Scientific, BP220-1) solutions at 4 °C for 2 days, and then embedded in OCT (Fisher Scientific, NC9638938) and cryosectioned. The sections were then blocked with 1% BSA (Thermo Scientific, 23209) and permeabilized with 0.3% Triton X-100 (Sigma, T8787). The sections are then stained with primary antibody solution at 4 °C overnight and fluorophore-conjugated secondary antibody solutions at room temperature for 30 min. The stained sectioned were then incubated with DAPI (1 ng ml−1, Sigma, D9542-5MG) and washed several times before they were mounted with Vectashield Mounting Medium (vector, H1200) and imaged using fluorescent (Olympus IX70) or confocal microscopes (Zeiss SP5). Mice are gavaged with 0.3 ml of dye solution containing 6% carmine (Sigma, C1022-5G), 0.5% methylcellulose (Sigma, 274429-5G) and 0.9 NaCl, using a #24 round-tip feeding needle. The needle was held inside the mouse oesophagus for a few seconds after gavage to prevent regurgitation. After 1 h, the stool colour was monitored for gavaged mice every 10 min. For each mouse, total gastrointestinal transit time is between the time of gavage and the time when red stool is observed. The double nickase CRISPR/Cas9 system28 was used to target the EDNRB locus in EF1–RFP H9 human ES cells. Two guide RNAs were designed (using the CRISPR design tool; http://crispr.mit.edu/) to target the coding sequence with PAM targets ~20 base pairs apart (qRNA #1 target specific sequence: 5′-AAGTCTGTGCGGACGCGCCCTGG-3′, RNA #2 target specific sequence: 5′-CCAGATCCGCGACAGGCCGCAGG-3′). The cells were transfected with guide RNA constructs and GFP-fused Cas9-D10A nickase. The GFP-expressing cells were FACS purified 24 h later and plated in low density (150 cells cm−2) on mouse embryonic fibroblasts. The colonies were picked 7 days later and passaged twice before genomic DNA isolation and screening. The targeted region of EDNRB gene was PCR amplified (forward primer: 5′-ACGCCTTCTGGAGCAGGTAG-3′, reverse primer: 5′-GTCAGGCGGGAAGCCTCTCT-3′) and cloned into Zero Blunt TOPO vector (Invitrogen, 450245). To ensure that both alleles (from each ES cell colony) are represented and sequenced, we picked 10 bacterial clones (for each ES cell clone) for plasmid purification and subsequent sequencing. The clones with bi-allelic nonsense mutations were expanded and differentiated for follow-up assays. The ENC cells are plated on PO/LM/FN coated (prepared as described previously26) 96-well or 48-well culture plates (30,000 cm−2). After 24 h, the culture lawn is scratched manually using a pipette tip. The cells are given an additional 24–48 h to migrate into the scratch area and fixed for imaging and quantification. The quantification is based on the percentage of the nuclei that are located in the scratch area after the migration period. The scratch area is defined using a reference well that was fixed immediately after scratching. Migration of cells was quantified using the open source data analysis software KNIME29 (http://knime.org) with the ‘quantification in ROI’ plug-in as described in detail elsewhere30. To quantify proliferation, FACS-purified ENC cells were assayed using CyQUANT NF cell proliferation Assay Kit (Life Technologies, C35006) according to manufacturer’s instructions. In brief, to generate a standard, cells were plated at various densities and stained using the fluorescent DNA binding dye reagent. Total fluorescence intensity was then measured using a plate reader (excitation at 485 nm and emission detection at 530 nm). After determining the linear range, the CD49D+ wild-type and Ednrb−/− ENC precursors were plated (6,000 cell cm−2) and assayed at 0, 24, 48 and 72 h. The cells were cultured in neurobasal (NB) medium supplemented with l-glutamine (Gibco, 25030-164), N2 (Stem Cell Technologies, 07156) and B27 (Life Technologies, 17504044) containing CHIR99021 (3 μM, Tocris Bioscience, 4423) and FGF2 (10 nM, R&D Systems, 233-FB-001MG/CF) during the assay. To monitor the viability of wild-type and Ednrb−/− ENC precursors, cells were assayed for lactate dehydrogenase (LDH) activity using CytoTox 96 cytotoxicity assay kit (Promega, G1780). In brief, the cells are plated in 96-well plates at 30,000 cm−2. The supernatant and the cell lysate is collected 24 h later and assayed for LDH activity using a plate reader (490 nm absorbance). Viability is calculated by dividing the LDH signal of the lysate by total LDH signal (from lysate plus supernatant). The cells were cultured in neurobasal (NB) medium supplemented with l-glutamine (Gibco, 25030-164), N2 (Stem Cell Technologies, 07156) and B27 (Life Technologies, 17504044) containing CHIR99021 (3 μM, Tocris Bioscience, 4423) and FGF2 (10 nM, R&D Systems, 233-FB-001MG/CF) during the assay. The chemical compound screening was performed using the Prestwick Chemical Library. The ENC cells were plated in 96-well plates (30,000 cm−2) and scratched manually 24 h before addition of the compounds. The cells were treated with two concentrations of the compounds (10 μM and 1 μM). The plates were fixed 24 h later for total plate imaging. The compounds were scored based on their ability to promote filling of the scratch in 24 h. The compounds that showed toxic effects (based on marked reduction in cell numbers assessed by DAPI staining) were scored 0, compounds with no effects were scored 1, compounds with moderate effects were scored 2, and compounds with strong effects (that resulted in complete filling of the scratch area) were scored 3 and identified as hit compounds. The hits were further validated to ensure reproducibility. The cells were treated with various concentrations of the selected hit compound (pepstatin A) for dose response analysis. The optimal dose (10 μM based on optimal response and viability) was used for follow-up experiments. For the pre-treatment experiments, cells were CD49D purified at day 11 and treated with pepstatin A from day 12 to day 15 followed by transplantation into the colon wall of NSG mice. The cells were cultured in neurobasal (NB) medium supplemented with l-glutamine (Gibco, 25030-164), N2 (Stem Cell Technologies, 07156) and B27 (Life Technologies, 17504044) containing CHIR99021 (3 μM, Tocris Bioscience, 4423) and FGF2 (10 nM, R&D Systems, 233-FB-001MG/CF) during the assay. To inhibit BACE2, the ENC precursors were treated with 1 μM β-secretase inhibitor IV (CAS 797035-11-1; Calbiochem). To knockdown BACE2, cells were dissociated using accutase (Innovative Cell Technologies, AT104) and reverse-transfected (using Lipofectamine RNAiMAX-Life Technologies, 13778-150) with an siRNA pool (SMARTpool: ON-TARGETplus BACE2 siRNA, Dharmacon, L-003802-00-0005) or four different individual siRNAs (Dharmacon, LQ-003802-00-0002, 2 nmol). The knockdown was confirmed by qRT–PCR measurement of BACE2 mRNA levels in cells transfected with the BACE2 siRNAs versus the control siRNA pool (ON-TARGETplus Non-targeting Pool, Dharmacon, D-001810-10-05). The transfected cells were scratched 24 h after plating and fixed 48 h later for migration quantification. The cells were cultured in neurobasal (NB) medium supplemented with l-glutamine (Gibco, 25030-164), N2 (Stem Cell Technologies, 07156) and B27 (Life Technologies, 17504044) containing CHIR99021 (3 μM, Tocris Bioscience, 4423) and FGF2 (10 nM, R&D Systems, 233-FB-001MG/CF) during the assay. Data are presented as mean ± s.e.m. and were derived from at least three independent experiments. Data on replicates (n) is given in figure legends. Statistical analysis was performed using the Student’s t-test (comparing two groups) or ANOVA with Dunnett test (comparing multiple groups against control). Distribution of the raw data approximated normal distribution (Kolmogorov–Smirnov normality test) for data with sufficient number of replicates to test for normality. Survival analysis was performed using a log-rank (Mantel–Cox) test. Z-scores for primary hits were calculated as Z = (x − μ)/σ, in which x is the migration score value and is 3 for all hit compounds; μ is the mean migration score value, and σ is the standard deviation for all compounds and DMSO controls (n = 224).

News Article | March 14, 2016
Site: http://cleantechnica.com

The noted director of automotive research at the University of Duisburg-Essen Ferdinand Dudenhöffer recently published an article in a Swiss paper that argued that the major ICE (internal combustion engine) vehicle manufacturers will have to “adopt the Tesla Principle” in order to stay relevant. The forum member “LST” on the Tesla Motors Club forum (who first posted a link to the article in question) noted in the recent discussion that this is the first time that Dudenhöffer has acknowledged Tesla as being a serious competitor. I suppose that it’s getting a bit hard at this point to deny it. Here are some excerpts from the article in question run through an online translation program (thanks to “sandpiper” for this): Switzerland is certainly not the most important car country in the world. But at the Switzerland trends can be identified. The Swiss do not have private carmaker and are therefore “neutral”, in terms of technologies. The neutrality of Switzerland makes the country interesting to observe trends unadulterated. This is shown at 4 different forms. First, there are no favors for diesel fuel, such as in Germany and many other European countries, but the fuel tax per liter for gasoline and diesel fuels in Switzerland (are) equal. Therefore, Switzerland has not the extreme diesel boom as in other European countries observed. Second, there are no subsidies for electric cars. This must be the vehicles in the “hard” competitive with conventional drives prevail. A Norway- or Holland effect, in both countries are electric cars subsidized by the state, so do not exist. Thirdly, Switzerland is a market for high-quality vehicles. For high-value vehicles, new technologies are usually used first. The Switzerland is thus also a kind of test market for the marketability of future technologies. Whether Rolls-Royce, Ferrari, Porsche and Audi, Mercedes, and BMW – have the Swiss by their high national product per head significantly higher levels of premium as and luxury vehicles of rest Europe. Fourthly, Switzerland does not have a car maker. In countries such as Germany, the propensity for new technologies is “overestimated partly”. The domestic carmakers – such as the VW brand – bring up to 30% of new cars as own admission in the market. So certain are vehicles artificially “pushed”. This distortion there is in Switzerland not. …In 2015, the model Tesla S has with 1556 new registrations, the Swiss dominated full-size cars. From Model S in more sold than on Range Rover (1065), 911 (1027), Porsche Cayenne (894), Mercedes S-Class (776), Audi Q7 (759), BMW X6 (668), Mercedes CLS (500), Mercedes ML and GLE (489), the BMW 7 Series (192), Porsche Panamera (168), the Audi A8 (100) etc. Record for Tesla is impressive and for the upper-class manufacturer rather “sobering”. The entire Mercedes S-class – including its plug-in hybrids – sold worse than the one Tesla model. Even the classic Porsche 911 with its many body styles can not get Tesla. The market share of Tesla’s Switzerland to 0.7% in January 2016 and increased. That sounds like a little, but is relativized when the market shares of other car brands to look. As for the classification: In Germany in 2015 have the brands Honda (0.66%), Land Rover (0.57%), Jeep (0.46%), Subaru (0.2%), Jaguar (0.16 %), Alfa Romeo (0.1%) all less market shares achieved as Tesla with a single model in January 2016 and in Switzerland. Here, the Model X has not yet been delivered in Switzerland. What incidentally applies to Switzerland, may also transfer similar to the United States. 22,635 Model S cars Tesla has sold in 2015 in USA. The Mercedes S-class was only sold 21,934 times, the Audi Q7 only 18,995 times and Porsche Cayenne (16,474) and 911 (9898) as well as BMW 7 Series clearly under Tesla. However, there are in the US government support for the purchase of electric cars. Therefore, the image is “distorted”. …But the customer demand in the neutral test market Switzerland is impressive… The newcomer Tesla is the only one who so far the right jump has made ​​in the electromobility. The strategy of traditional carmaker – to bet on all horses, ie diesel, hybrid, plug-in, fuel cell and a few high-reach poor electric cars seems to have failed. Interesting points. Similar to ones that we’ve made a number of times here at EVObsession, but of course hearing such comments coming from someone like Dudenhöffer has its own implications. I wonder what the CEOs at some of the aforementioned companies thought when hearing about this? Those that speak the language, or are curious to see the original regardless, can find it through the TMC link at the top of this article. Reprinted with permission.    Get CleanTechnica’s 1st (completely free) electric car report → “Electric Cars: What Early Adopters & First Followers Want.”   Come attend CleanTechnica’s 1st “Cleantech Revolution Tour” event → in Berlin, Germany, April 9–10.   Keep up to date with all the hottest cleantech news by subscribing to our (free) cleantech newsletter, or keep an eye on sector-specific news by getting our (also free) solar energy newsletter, electric vehicle newsletter, or wind energy newsletter.  

News Article
Site: http://techcrunch.com

Google v. Oracle. It’s a sensational case. A battle of tech heavyweights — and a software copyright case that went all the way to the U.S. Supreme Court. Millions of dollars are at stake. And the ramifications for software entrepreneurs are significant. The facts are not in dispute. Google copied a portion of Oracle’s Java application programming interface (API) to create the Android operating system — the most installed mobile operating system in the world. Oracle then sued Google for copyright infringement, but lost in federal district court. Then, surprising many, it won a major victory — a reversal at the U.S. Court of Appeals for the Federal Circuit — an appeals court directly below the U.S. Supreme Court. The Federal Circuit, overturning the district court’s decision, decided that Oracle’s Java API is copyrightable. And in June of this year, the U.S. Supreme Court refused to consider the case. Software developers routinely treat APIs as exempt from copyright protection. That was Google’s assumption when it copied the Java API. That assumption must now change. Under the court’s logic, nearly any API larger than a few words or phrases could be protected by copyright. Now, Google and other developers who copy an API without having a license to do so may have only one defense: a doctrine in copyright law called “fair use.” The jury in the district court case already considered whether Google’s copying was fair use, but was unable to decide. So this high-stakes case now falls back into the hands of a hesitant jury charged with deciding whether Google’s having used Oracle’s Java API for its Android operating system was in fact fair use. There is no way to predict the outcome. Because using APIs is so common, startups, as well as established software companies, need to operate with a clear understanding of this legal battle. They need to understand the implications of this decision in order to avoid running afoul of others’ copyrights — and to leverage their own. The difference between copyright and patent protection is often misunderstood. In the context of software development, copyrights and patents protect two entirely different, non-overlapping aspects. Patents protect functional aspects of software, such as processes and methods of operation. We generally think of these as features of the software. Reproducing patented processes and methods, even in independently written code, is patent infringement. In contrast, copyright only protects artistic aspects of software code, not its functional aspects. Consequently, if software is protected only by copyright, then others may appropriate any of the features implemented by the software by simply writing their own code independently. America’s courts have historically struggled with how to separate artistic aspects of software code from functional aspects. Because software is mostly functional, you might think that copyright protection of software is weak. Generally, you’d be right. But copyright protection is very useful if someone copies either the source code or the compiled code. If someone literally copies all or a portion of such code, then a court would probably decide that at least some artistic aspects were copied, thereby infringing the original developer’s copyright. But, you think, if that is the case, why would Google think it could literally copy a portion of the Java API? The answer is based on a well-established principle of copyright law that tries to prevent granting patent-like rights through copyright. Specifically, according to the Copyright Act, copyright protection in a work must not extend to any functional system or method of operation, regardless of the form in which the system or method is described in the work. One reason for this copyright principle is the differing ease at which protection may be obtained. Copyright protection arises out of the creative effort of writing software. No application for copyright protection is required. In contrast, to obtain patent protection, an application must be filed at the U.S. Patent and Trademark Office. A highly trained patent examiner reviews the application against the state of known technologies. An examiner may only grant a patent when convinced that the claimed invention is new and not obvious over such technologies. Google had argued that if the Java API were copyrightable, then copyright protection would effectively extend to the functional system described in the Java API. Thus, Google claimed, for anyone to create a functional system exactly as described in the Java API, they would have to copy the Java API. Then, said Google, because copying the Java API would infringe Oracle’s copyright, its copyright effectively extends to the functional system described in the Java API — improperly granting patent-like rights through copyright. The Federal Circuit disagreed. It reasoned that “the declaring code [in the Java API] could have been written and organized in any number of ways and still have achieved the same functions.” For example, the Court argued that the Windows Phone API from Microsoft provides “similar functionality” with an entirely different structure, naming scheme and selection than the Java API. Whether Google could copy the exact functional system described in the API is apparently a question of compatibility, which the Court said should be addressed with a “fair use defense.” One tip to avoid copyright infringement is developers should not copy a third-party’s API unless the developers have a license to do so. That sounds straightforward, but it is often difficult or impossible. Say a company plans to develop a new software product that will compete with a competitor’s existing software product. Customers will access the new software product through an API. Competitor customers also access the competitor’s existing software product through an API. To encourage the competitor customers to switch to the company’s new software product, the company may be tempted to copy the competitor’s API. If the new API is the same as the competitor’s API, then the competitor customers would not need to make any changes to their code to use the company’s new software product. In such a scenario, the company should not copy the competitor’s API without a license and rely on the fair use defense without first consulting a copyright attorney. Fair use is highly fact-sensitive — and highly subjective. A jury weighs multiple factors, and it is difficult to know in advance whether any fair use defense will succeed. Surprisingly, if the innovating company develops a new API instead of making an identical copy of the competitor’s, it still risks copyright infringement. A copyright owner may prove that an original work has been copied by first proving that the accused infringer had access to the original work and second showing that the accused work is “substantially similar” to the original. In this “copy test,” direct evidence of copying is not a requirement. So, developing a new API may risk infringement even if the new API is not identical to the competitor’s and was not actually consciously copied from the competitor. If the two APIs are found to be “substantially similar” and the developers of the new API had access to the competitor’s, then that competitor could successfully argue that the new API infringes their copyright. You can surely imagine the scene in court where an attorney shows a jury the two APIs side-by-side, and points out all the similarities! One way to mitigate this risk is to develop any new API using “clean room” development. In this approach, the desired functionality is given to a developer, who writes the new API from scratch without ever accessing the competitor’s API. This technique addresses the issue of whether one’s developers had access to the competitor’s API in the “copy test.” If a company is a startup, though, all its developers may already have had access to the competitor’s API. That may make a “clean room” option economically unviable because the startup would have to hire one or more other developers who demonstrably had no access to the competitor’s API. Instead, the company may deliberately try to make the new API different from the competitor’s. It may first instruct its developers to develop a unique new API based on the desired functionality without referring back to the competitor’s API; then it could compare the resulting new API to the competitor’s and modify any elements that are too similar. However, this “deliberately different” option is much riskier than the “clean room” option. How can a company protect its own software? One way is to publish its APIs just to make it easier to show that a competitor had access to them. Ideally, there also is another legitimate reason to publish its APIs, such as to provide online documentation for users. Then, if the competitor creates a substantially similar API, a company might succeed in a copyright infringement claim. That brings us back to patent protection, which should be sought for new and innovative features. Although recent Supreme Court rulings such as the notable 2014 decision in Alice Corporation v. CLS Bank Intl. have made patenting certain categories of software inventions more difficult, patents on software features for many different technologies are alive and well. As just explained, the decision in Google v. Oracle is inconsistent with many software developers’ past view of APIs. As a result, many APIs now probably infringe another’s copyright. For better or worse, this creates opportunities for entrepreneurs to make money based on litigation or a threat of litigation. Some such scenarios might be viewed as the copyright analog of what have commonly become known as “patent trolls.” Specifically, entities may emerge that purchase copyrights in original APIs, search for instances of others’ copying those APIs without a license and sue for damages. That surely would be an unintended outcome of the Federal Circuit’s decision — and one worth keeping an eye on. This article is intended to provide information of general interest to the public and is not intended to offer legal advice about specific situations or problems. You should consult a lawyer if you have a legal matter requiring attention. About The Author Michael Hussey is an intellectual property attorney at Brinks Gilson & Lione, one of the nation’s largest IP law firms. Before earning his law degree and joining Brinks, he worked for more than a decade in software development. At Oracle, he was a senior software development manager responsible for database development on Windows and Apple operating systems. As a director of product development at Made2Manage, Michael developed ERP software for manufacturers. As a software architect at Saba Corp., he designed and wrote web-based enterprise applications in Java using XML and J2EE technologies. Michael helps clients, ranging from Fortune 500 companies to startups, navigate and leverage the complex world of intellectual property. He prosecutes patent applications globally for software and hardware technologies such as computer networking, data storage, telecommunications, VOIP, lighting control systems and electronic circuitry.

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