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C2090-011 - IBM SPSS Statistics Level 1 v2 - Dump Information

Vendor : IBM
Exam Code : C2090-011
Exam Name : IBM SPSS Statistics Level 1 v2
Questions and Answers : 55 Q & A
Updated On : October 16, 2018
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C2090-011 Questions and Answers

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C2090-011 IBM SPSS Statistics Level 1 v2

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C2090-011 exam Dumps Source : IBM SPSS Statistics Level 1 v2

Test Code : C2090-011
Test Name : IBM SPSS Statistics Level 1 v2
Vendor Name : IBM
Q&A : 55 Real Questions

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IBM IBM SPSS Statistics Level

Predictive Analytics in 2018: What’s possible, Who’s Doing It, and the way | killexams.com Real Questions and Pass4sure dumps

In 2009, Netflix offered $1 million to anyone who could enhance the nice of its recommendation engine by 10%. It took two years, however a team eventually received. Netflix paid the bounty—then unnoticed the code.

as it turned out, the more suitable algorithms “did not seem to justify the engineering effort mandatory to carry them into a construction environment.”

not handiest did the successful prediction engine fail to scale economically, it also addressed an outdated issue: The shift from mail to streaming throughout that equal two-year window gave Netflix the entire records it necessary to boost more moderen, better algorithms.

Predictive analytics, in different words, wasn’t a panacea. Nor, within the decade on the grounds that, has it turn into one. however, in 2018, incremental positive aspects no longer charge $1 million both:

  • you have got extra information;
  • Storage is inexpensive; and
  • Cloud computing is very nearly infinitely scalable.
  • This publish details these adjustments and indicates how a number of companies—and never just behemoths—have cultivated the predictive analytics panorama.

    What’s modified in the remaining decade? 1. more statistics, extra storage, extra computing vigour

    big, cloud-primarily based repositories of customer interactions, regularly referred to as information lakes, are the uncooked supply material for predictive analytics purposes.

    Many organizations have taken knowledge of low-priced cloud storage to stow away facts for years—without even on account that its expertise use. (what number of overlooked records points do you've got in Google Analytics, Google adverts, MailChimp, Marchex, Stripe, and similar services?)

    That dual growth in scale—of information accrued and accessibility to it—has solved two basic challenges of predictive analytics implementation.

    historically, uncooked computing vigour has been the other. As Andrew Pearson of Intelligencia notes, “without significant hardware investments, predictive analytics programs either weren’t possible or too gradual to be useful.”

    That, Pearson persisted, has additionally changed: “Cloud-primarily based analytics programs have added massive computing device energy into the mix.” more and more potent techniques cracked open the door for precise-time predictive analytics.

    2. a world of true-time predictions

    For some, the age of “real-time” predictive analytics is right here. Judah Phillips, the co-founder and CTO of Vizadata and founder of SmartCurrent, explained:

    We already reside in a global of “real-time” predictive analytics. an easy predictive analysis is your arrival time in Waze. A extra complicated real-time prediction happens billions of instances global every millisecond in matching certain forms of digital promoting.

    extra, groups like Mintigo and Versium now present precise-time solutions for lead scoring, showing that the transition is technically viable. feasible, besides the fact that children, doesn’t mean excellent. Sam Underwood, a vice chairman at Futurety, stated the complexity of critical integrations:

    particularly within the mid-market world, the tools that collect records to become predictive modeling—CRM programs, social media aggregators, logistics, and purchasing methods—regularly don't have friendly APIs or different effortless mechanisms with which to immediately accumulate and interpret facts.

    That disconnect nevertheless thwarts even the most basic business circumstances for real-time predictive analytics. David Longstreet, the chief facts scientist at FanThreeSixty, offered an example:

    In our world of activities and leisure, for example, most activities teams don't know how many americans are in a stadium for a video game. groups recognize what number of tickets had been distributed; although, they have no idea in “real time” what number of individuals are within the venue or stadium throughout the experience.

    That talents gap hampers efforts to personnel and stock the stadium as it should be. It’s also why activity in predictive analytics is nearly familiar, besides the fact that it vastly outpaces adoption.

    3. gradual adoption however soaring hobby

    So what number of corporations are actively using predictive analytics? in response to analysis from Dresner Advisory features, about 23%, a figure nearly unchanged from the prior yr.

    predictive analytics adoption

    under a quarter of organizations are the usage of predictive analytics—although almost all aspire to accomplish that. (graphic source)

    pastime, youngsters, exceeds implementation. The identical analysis suggests that ninety% of groups “connect, at minimum, some magnitude to superior and predictive analytics.”

    So which questions are these 23% answering with predictive analytics? Let’s take a look.

    Which questions can marketers reply with predictive analytics?

    “They want to predict everything,” in accordance with Underwood. And who wouldn’t want to understand the actual foot (or net) site visitors by way of month, day, and hour to streamline staffing (or allocate server elements)?

    but, Underwood persisted, he tries to center of attention purchasers on “the one factor that, if we may predict it for you, would revolutionize your business.”

    In digital advertising, Phillips outlined myriad use cases for predictive analytics, including the capability to foretell:

  • which advertising might be most beneficial—despite the fact you define valuable.
  • which advertising campaigns, channels, touches, behaviors, and demographics are contributing to a business result, a variety of “desktop studying–based attribution.”
  • which section, check, or personalization a person is surely to reply to.
  • the likelihood of clients to click on an ad, to download a whitepaper, to respond to an email, to reply to a suggestion, and different client response you outline.
  • which leads will convert—however you outline conversion.
  • which customers will buy one or more products for a pass-sell or upsell.
  • the number of purchases or profits so that you can occur sooner or later.
  • which clients will have high/medium/low lifetime cost.
  • client churn.
  • the radical chance of predictive analytics, then, is not what which you can predict however the fact that that you could predict. The ancient statistics you at present analyze can doubtless turn into a prediction.

    just make sure you've got the facts.

    What do you deserve to get all started with predictive analytics?

    statistics, records, and records. “precedence 1A and 1B are statistics sources,” brought up Underwood. That’s genuine even if you propose to license software or appoint an outdoor organization. (each alternatives are specified later.)

    All makes use of require practising records. That training information, in turn, is used to build a predictive mannequin to observe to current facts. “The handiest hindrance we’ve run into,” Phillips mentioned, “is an organization’s obtainable information for practising.”

    How a lot information is adequate? based on Phillips:

    just a few thousand information with a satisfactory quantity of positive and bad effects can be enough for advertising and marketing, sales, and product prediction.

    no longer all records is created (or kept) equally

    “You need to consider—I grew up tearing tickets.”

    FanThreeSixty’s Longstreet has heard that equal rationalization from venue managers who've spent countless hours counting stacks of stubs after games. It’s a explanation why essential records sources might also not be conveniently obtainable, or available in any respect.

    In stadiums, Longstreet explained, point-of-sale machines and ticket scanners exist for a single goal—to finished transactions rapidly and keep lines moving. those methods do not store records correctly for extraction, nor can they deal with incessant server requests (until hungry fanatics don’t mind waiting).

    For Underwood, purchasers tend to fall into one of two buckets, with half in each and every:

  • “The most fulfilling customer has an inside database deploy and able to go. We pull within the statistics, construct the model, and are off and working.”
  • The other half have a mix of data sources, which inevitably encompass an offshore SQL database (or ten) managed by means of an external dealer whom no person can music down.
  • Stitching records sources collectively is a huge construction task that may additionally require developing customized connectors, developing third-birthday celebration FTP drops, and other advanced but thankless tasks. That work, however, is crucial: fashions and their predictions are simplest as correct because the information they’re constructed upon.

    Don’t forget exterior facts sources

    not all statistics comes from inside sources, either. exterior statistics sources, like climate studies, are sometimes a vital addition to records lakes, peculiarly for small businesses. As Underwood explained:

    eating places can also use analytics to set off e mail sends; as an example, we are able to install the email platform to sync with country wide weather carrier facts to ship an electronic mail about iced tea when the temperature in a given metro enviornment is above 90 levels.

    Likewise, we can set off an e mail to ship to shoppers in a given city if the equipment detects wind gusts of 40+ MPH. both of these use cases attain consumers in a key second of need, negating downstream ad spend and beating competitors to the punch.

    so that you have a huge, neatly-equipped dataset. What do you do with it?

    How do you flip information into predictions?

    while the quandary of insufficient statistics has faded, a further is still:

    groups require both a dedicated group of information scientists to parse through these sets, or a software suite effective sufficient to achieve this rapidly. for most small and medium-sized organizations, this usually capability settling for subpar utility, or forgoing it completely.

    For companies of all sizes, options department into two alternate options:

  • buy software and create predictions in-residence.
  • Pay an outdoor dealer to advance models and visualizations for you.
  • 1. Predictive analytics utility

    The industry for predictive analytics software has ballooned: G2Crowd facts ninety two results within the category. Pricing varies substantially according to the number of users and, in some situations, quantity of records, but commonly starts round $1,000 per year, notwithstanding it may simply scale into six figures.

    G2Crowd lists both IBM’s SPSS records and SAS’s advanced Analytics as market leaders on the business level. together with RStudio, the pair are additionally tagged as leaders for mid-market organizations; most effective IBM retains a place within the “Leaders” quadrant for small agencies.

    traditionally, although, even business-main predictive analytics application hasn’t been a simple, leap-appropriate-in adventure. Take these two examples from IBM’s SPSS information and RapidMiner:

    ibm spss statistics screenshot

    rapid miner screenshot

    while these structures are powerful, clients should format statistics data, hyperlink nodes, and increase visualizations. gaining knowledge of the way to try this—and having the time to do it—is a really expert, full-time job. (To consider in any other case is to expect a Microsoft note license to jot down your notable American Novel.)

    not exceedingly, the market is shifting. RapidMiner has rolled out a SaaS beta that, with just a little of manual adjustment, translates an Excel sheet filled with, say, employment information to a prediction of employee retention:

    rapid miner saas excel sheet

    A dataset in Excel—a place to begin by which most marketers are already relaxed. rapid miner intake

    RapidMiner parses the Excel file in advance of crunching the numbers. rapidminer predictive visualization

    The ensuing visualization shows the device’s prediction efficiency and correlations between datasets and retention.

    Some corporations, like Vizadata’s Phillips, see the user-pleasant SaaS model as the future:

    we're democratizing information science, in order that individuals with confined or no records science or engineering skills can predict. You with ease add your facts and click on subsequent. We do all the heavy lifting.

    Our intelligence determines your based and unbiased variables and the class of evaluation to run. which you can go along with our choices or override them—from regression, the place we are able to do forecasting and optimization, to each binary and multiclass classification, where we are able to predict the probability of outcomes.

    vizadata screenshot

    user-pleasant SaaS models make predictive analytics extra attainable to marketing groups with out data scientists.

    Like Vizadata, MIT’s Endor pursues this direction. The platform makes use of a question-builder to allow anyone to ask questions like “the place should we open our next store?” or “Who is probably going to are attempting product X?” It then mines targeted datasets to give answers, frequently in a be counted of minutes.

    The inclusion of tangential datasets that fall outdoor consideration—or feasibility—for human observers is a routine abilities of predictive analytics. Endor’s creators present an illustration:

    A advertising branch for a financial institution asks, “Who goes to get a mortgage within the subsequent six months?” desktop-getting to know engines may additionally observe a pool of, say, 5,000 consumers who've a financial institution credit card and a excessive credit ranking, and are married—many of which could be false positives.

    Endor detects greater certain clusters of, say, couples about to get married or going through a divorce, founders who currently bought their startups to facebook, or valued clientele who these days graduated from a native precise-estate route.

    Of course, if you are looking to outsource the procedure wholly, backyard providers can prepare your records, construct models, and visualize predictions for you.

    2. outdoor vendorsorganizations providing bespoke options

    for many shoppers, Futurety begins by determining the important thing business query—now not a selected metric or visualization. purchasers may additionally are available for one-off projects, annual re-runs of their information, or ongoing work.

    suggested for You

    Webcast, October 17th: how to solve Your advertising records Nightmare

    “The end result isn't all the time clear at the beginning,” Underwood defined. “when we’re supplying to a person close to the outcomes, like a advertising and marketing manager, they’re usually satisfied with the mannequin, the finding, and the mathematics in the back of it.”

    The “end result” may well be a few things:

  • Integration with a 3rd-birthday celebration platform, like an e mail customer, to automate predictive messaging.
  • simple-text predictive answers to ebook practitioners.
  • potent visualizations to display the method and price to the C-Suite.
  • on the end of every engagement, Futurety gives you the model returned to the client for management and protection.

    Predictive analytics at work
  • Futurety has a small business customer that helps aspiring performing arts majors profit admission to their dream school. however few high schoolers have extensive competencies of good classes. more regularly than no longer, they understand only one name: Julliard.
  • Futurety informed its mannequin on three years of placement records. Then, using new pupil records entered into a standard portal, anticipated where college students would get accredited and succeed academically.
  • The predictive analytics mannequin, which Futurety updates annually, promises a simple record of advised faculties for college kids in keeping with elements like grades and publicity to diverse musical or artistic styles.
  • The mannequin takes under consideration even if previous placements graduated or received awards.
  • All-in-one area of interest suppliers

    FanThreeSixty serves a slender market: sporting venues. as a result of they work with a comparatively constant dataset—season ticket, concession, and souvenir earnings—they understand the range of business questions, records results, and central visualizations.

    This consistency incentivizes niche companies like FanThreeSixty to improve proprietary dashboards to roll out to all purchasers.

    The interface permits Longstreet’s team to keep statistics science in the history: “the key of desktop gaining knowledge of is if you’re being brought about in the back of the scenes.”

    Distilled fully, FanThreeSixty’s intention (and Longstreet’s explanation of his position at dinner parties) is to “support groups sell greater tickets and scorching canines.”

    Predictive analytics at work
  • FanThreeSixty mines historic facts to see which concessions are most frequently purchased with a scorching dog at an important League Soccer venue.
  • If a consumer purchases a scorching dog, concession group of workers are triggered to ask even if a consumer would like to add the most well-known accompaniment. That recommendation—a prediction of fan want—adjustments in line with other variables.
  • Predictions consider more than 20 datasets—everything from the domestic place of season ticket holders to the weather—to tailor messaging earlier than, throughout, and after fits.
  • all the way through bloodless-weather games, as an example, FanThreeSixty can automate push notifications with tailor-made coupons, like purchase-three-get-one-free sizzling chocolate for a household of four.
  • even if solutions are internally or externally managed, they‘ve long been ordinary in enterprise corporations.

    Predictive analytics use cases on the business stage

    marketing departments in colossal corporations have used predictive analytics for years:

  • AutoTrader. AutoTrader makes use of information from its forty million month-to-month friends to greater bear in mind the occasionally prolonged customer journey. They developed propensity fashions based on search habits and created high-cost lookalike audiences.
  • Editialis. The French publisher uses predictive analytics in its e mail campaigns to “assume engagement at someone stage.” in consequence, they’ve seen click-through-prices increase “dramatically.”
  • Predictive analytics can additionally coordinate offline and online interactions, with two clear use cases for entrepreneurs whose organizations have physical products or storefronts:

  • more suitable pricing. Smartphone information registers in-keep browsing habits to increase on-line or offline advertising concentrated on, approximating the advantages enjoyed by using ecommerce companies.
  • stock management. Full warehouses charge funds; empty cabinets can charge money. Folding on-line statistics, akin to search patterns, into earnings data can better manage stock, especially at a regional and local level.
  • moreover external advertising campaigns, predictive analytics also helps interior mission administration. large marketing campaigns have many relocating constituents—a brand new ad campaign needs new inventive, new copywriting, new touchdown pages, and many others.

    Coordinating the involvement of those groups and precisely estimating the time-to-launch is complicated. Many fail to get it right, once in a while at excellent rate.

    whiteboard planning

    entrepreneurs and software companies might also use predictive analytics for inner venture management in addition to exterior campaigns.

    Predictive algorithms, as McKinsey notes, use a much wider lens that captures historical patterns and interesting project points in a single body:

    while every building project is wonderful, the underlying complexity drivers across tasks are equivalent and might be quantified. If companies have in mind the complexity involved in a brand new challenge, they can estimate the trouble and resources required to complete it.

    Predictive analytics models “take note of now not simplest the complexity of the challenge (both the useful and implementation elements) however also the complexity of the crew environment.”

    Predictive analytics at work:

    greater correct internal assignment administration, in an illustration McKinsey offers, can have an immense influence:

  • an organization in the beginning deliberate a product update to take roughly 300 adult-weeks of effort, an estimate according to the limited variety of adjustments between the current product and a brand new design.
  • however, that estimate failed to bear in mind the proven fact that planned updates would have an effect on many different teams. Predictive analytics models did take it into account and estimated that the challenge would take three to 4 instances as lengthy.
  • subsequently, the company restrained the work to the customary product crew, enabling them to convey the replace on time.
  • apart from helping companies solve inside and exterior challenges, predictive analytics is also the basis for some groups.

    constructing a enterprise on predictive analytics

    Ken Lazarus, CEO of the recruiting platform Scout trade, has an advantage—the company has been round for only 5 years.

    That potential that the business’s facts sources are already primed for extraction into its predictive fashions that pair companies with the appropriate recruiter.

    scout exchange processScout trade’s predictions pair corporations with the correct recruiter.

    the only finest predictor of job placement, Lazarus and his group have found, is the tune list of job recruiters. In contrast, pairing the correct job description with the appropriate resume continues to be highly problematic.

    “Job specs are horrible,” he lamented. “The information isn’t on the paper. CVs are pretty horrible, too.” (statistics augmentation, similar to abilities testing and video interview decoding, Lazarus noted, present skills improvements.)

    however, holes continue to be. Candidates will never divulge negatives on their resume, and significant information may forever remain “non-information,” corresponding to whether a candidate is a very good “tradition healthy.”

    Scaling facts gathering

    Scout exchange has honed its predictions through focusing on enterprise shoppers—its algorithms feast on tons of or thousands of openings from Fortune 500 customers.

    due to this fact, the platform takes in roughly 1 million facts features monthly, with every new job posting yielding an additional 50 data points.

    scout exchange platform

    bigger corporation ratings indicate an enterprise is more responsive than his or her peers. Likewise, greater recruiter rankings indicate a recruiter is greater likely to prevail in submitting suitable candidates than his or her peers.

    nonetheless, human assessment by way of a recruiter—and their customer—is essential. Lazarus drew a parallel: “Would you let desktop studying decide upon your wife? No. but would you let it choose the appropriate matchmaker to assist you discover a better half? yes.”

    people that try to solve the most complex human issues aren’t even within the business world.

    Predictive analytics with life or dying penalties

    The most effective challenges for predictive analytics are folks that deal with complex, individualized human behavior, such as the chance that a patient or crisis-line texter will commit suicide.

    as a result of success or failure is measured in human lives, these challenges are also essentially the most pressing. And whereas these tasks function beyond the scope of advertising and marketing and enterprise, they suggest the skills for predictive analytics as it evolves.

    “reach VET isn't about attempting to find the veteran who’s sitting in the vehicle in a parking lot with a gun in his lap,” Aaron Eagan, Veteran Affairs deputy director for innovation instructed a Washington convention.

    “What we discovered,” Eagan continued, “is that veterans at maximum risk of suicide [also have] significantly accelerated costs of all-cause mortality, accident morality, overdoses, violence, [and] opioids.” Proactive signals that set off health professional determine-ins have enhanced simple-care appointment attendance and decreased sanatorium admissions for intellectual health considerations.

    crisis text line

    The project is akin to a collaboration between Periscope records and disaster textual content Line, a text-primarily based suicide hotline.

    Leaning on herbal language processing and predictive analytics, the application analyzed conversations, forecasted developments, and trained greater than 13,000 volunteers. The outcomes?

  • Wait instances reduced to under 5 minutes, an operational purpose.
  • ability expanded with the aid of 10% all over height durations.
  • Responses had been prioritized in line with laptop-identified urgency.
  • Endor’s technology has taken on in a similar fashion severe challenges. using 15 million records facets from 50 known ISIS supporters, Endor recognized eighty lookalike bills in below half an hour, with best 35 false positives—knowledgeable investigation become still essential yet possible.

    In a collaborative undertaking with the U.S. protection advanced analysis task company, the platform also analyzed cellular statistics to determine patterns to predict future riots.

    Conclusion

    Predictive analytics is not proof against criticism: GDPR rebuffs one of the crucial identical collection methods that swell information lakes. and not all predictions, even the most accurate, are smartly-obtained. (Famously, goal unwittingly informed a father of his teenage daughter’s pregnancy based on seemingly benign looking habits.)

    Predictive analytics consultants factor out that their algorithms search for patterns among values, no longer the values themselves. Regardless, insufficient facts is not going to hold returned the expansion of the industry—the IoT, wearables, and other information collectors already supplement normal internet and app analytics.

    user-pleasant SaaS structures are still an emerging chance. for many groups, developing models and predictions from historic information still requires a committed worker to navigate advanced software solutions or the outsourcing of that work to a vendor.

    For these suspending predictive analytics projects except the SaaS alternatives are more mature, you can be sensible to hold filling your facts lake.


    finding the information that Makes a change | killexams.com Real Questions and Pass4sure dumps

    2018 have an impact on Awards

    finding the data that Makes a difference

    Southern Connecticut State college has spent 10-plus years homing in on elements that really contribute to scholar success — and how top of the line to assist preserve students on course.

  • by way of Dian Schaffhauser
  • 10/eleven/18
  • class: Administration

    establishment: Southern Connecticut State university

    venture: academic Intelligence: How One college makes use of data to enrich scholar results

    task lead: Michael Ben-Avie, former director, office of assessment and Planning

    Tech lineup: Digital Measures, IBM, Snap Surveys, Watermark

    Southern Connecticut State University

    Southern Connecticut State college

    more than a decade has handed considering that Southern Connecticut State school kicked off its first longitudinal cohort look at in 2007. That effort turned into led by the establishment's workplace of evaluation and Planning. Its mandate: to advertise a "records-pushed method of educational alternate."

    while the college's Institutional research workplace works closely with assessment and Planning and each groups make contributions to protecting the institution's accreditation, the latter's assortment and analysis of records is concentrated on deciding on the "modifications that make a difference."

    And that requires records — some 1.9 million information facets for the newest cohort facts sets. The statistics collection starts with the fundamentals: student profiles as they enter, together with gender, ethnicity and fatherland; SAT ratings; even if college students are athletes or registered with the incapacity resource center or honors faculty. From there it starts off pouring in from distinct sources. all the way through new-student orientation, assessment and Planning administers two surveys, one on the influence of orientation and the different to baseline student engagement or their preparedness for college. Then there may be the primary-yr adventure survey given during the fall semester as well as different surveys on financial literacy, scholar satisfaction with services, satisfaction with admissions and enrollment for transfer college students and lots extra. Plus, every semester college students' GPAs are up-to-date, grades are brought, statistics on math proficiency is factored in, and rankings from the Multi-State Collaborative to boost nice scholar discovering are accumulated, along with standing on the dean's checklist and majors declared.

    Michael Ben-Avie

    Michael Ben-Avie

    each scholar is tracked for at least seven years, including the place they've gone if they've withdrawn from the institution earlier than commencement — whether it be to a non-public in-state university, a community school, a college in another a part of the nation or out of greater schooling altogether, in response to Michael Ben-Avie, former head of the office of assessment and Planning (Ben-Avie is now senior director of getting to know assessment and analysis at Quinnipiac school).

    The cohort statistics is uploaded to IBM's Watson Analytics, which suggests starting features for data analysis, similar to "What drives retention." When a starting point is chosen via the person, Watson will produce a visualization with probably the most critical predictors and provides each one a "predictive power." From there, the consumer can ask follow-up questions the use of natural language. Ben-Avie tells students in the faculty's Watson Analytics internship application, "You do not should know facts because Watson will prefer essentially the most applicable statistical analysis to run. You don't need to code because it's herbal language. What it does is get at broad strokes," he stated. "Then i may go to use SPSS and other statistical programs to probe and explore greater intensive.


    IBM reiterates dedication to Brazil despite uncertainty | killexams.com Real Questions and Pass4sure dumps

    IBM has reinforced its commitment to Brazil regardless of the existing financial, social and political disarray the country is presently experiencing - but the enterprise desires to help form guidelines to place the tech sector as a protagonist in the healing.

    The enterprise has had a native presence for 101 years and it is neatly versed within the native challenges - so the existing instability is nothing new, according to the well-known manager at IBM for Latin the us, Ana Paula Assis.

    "we have lived moments during this market that had been means worse: we went via hyper-inflation, a dictatorship and all manner of disaster and we now have never given up on Brazil. We remain dedicated to the country and that's the way it's going to be in future," the executive instructed ZDNet, at the IBM Cloud Discovery adventure in São Paulo the day prior to this (eight).

    native company spend in know-how has suffered as a result of the recession - but in accordance with Assis, the drop has now not been as severe to the aspect that it will indicate a backwards flow in terms of IT consumption. youngsters, she concedes that spending patters have modified:

    "long-time period commitments were put on hold, however projects as an entire have persisted to exist - what we expect is that strategic projects will come back within the coming years, as this offers us extra security in terms of the degree of funding that we could make and helps us with our projections," Assis says.

    Influencing public coverage

    Innovation records for Brazil are additionally very dangerous and this is without delay significant to the enterprise's enterprise in the neighborhood. Between 2011 and 2018 the nation collapsed 17 positions in the global Innovation Index (GII) by means of Cornell university, company school Insead and the area intellectual Property company. The GII report ranks the nation 64th, with the worst indications across the BRIC nations.

    in an effort to exchange that scenario, Assis facets out that IBM is engaged in tasks such because the Digital Brazil circulate, a lobbying initiative that includes different IT giants akin to Microsoft, Oracle, Cisco and others, aimed at influencing the introduction of public policies that position expertise innovation as a driver for economic growth.

    changing the government's perceptions in that regard is a problem notwithstanding - chiefly so in a climate of austerity: evidence of here's the merger of the Ministry of Science, expertise and Innovation with the Ministry of Communications to reduce public spending. The department had already suffered a number of finances cuts in old years. In that scenario, a special strategy is required, says Assis.

    "If we're to consult with the govt about know-how for expertise's sake the chances we can prevail are slim. however the factor isn't to improvement the know-how sector, but get the expertise sector to support Brazil turn into extra productive," she features out.

    "We want a change in focus in executive approach and here is what South Korea, China and Vietnam have done. They see technology as a key pillar of economic building and a vital part of the typical agenda."

    When asked no matter if she thinks both leading presidential candidates - Brazil will decide on its new president on October 27 - can be capable of appreciate that need for a more robust innovation plan for the country, Assis says or not it's too early to make any opinions.

    "we will should wait and notice what occurs next year [when the elected president takes over]. What we are able to do at this stage is to stimulate the controversy across the creation of guidelines, current potential proposals that are essential to implement and include the society in that system," the IBM government argues.

    leading the way to inclusive tech offices

    The main presidential candidate, Jair Bolsonaro, is prevalent for its sexist, racist and homophobic rhetoric and human rights campaigners had been voicing their concerns over the advantage corrosion of social equality in the nation if he comes into energy.

    girls and racial minorities already represent a extremely small chunk of the local IT business - so will Bolsonaro's viable presidency pose a danger to developing more inclusive workplaces in tech? according to Assis, businesses akin to IBM have to set the illustration:

    "enterprises have at all times been propellers of progress. IBM added equal alternatives in the united states in 1953, a decade earlier than the Civil Rights Act become enforced. Brazil-selected guidelines include scientific benefits for identical intercourse couples, which we introduced ten years earlier than that became law," the government says.

    "The function of corporations is to retain fostering that classification of agenda, as a result of in addition to being socially dependable, here's good for our enterprise. You do not get innovation and transformation if you do not have a piece atmosphere that's different and inclusive," she adds.

    "in case you preclude your skill pool because of gender, race, sexual orientation or faith, you're preventing your company from accessing what's gold standard when it comes to skill. I believe organizations will proceed to [push an inclusion agenda] as here is primary, exceptionally in an innovation context. we have the mission of developing these situations."


    C2090-011 IBM SPSS Statistics Level 1 v2

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    Deciphering microbial landscapes of fish eggs to mitigate emerging diseases | killexams.com real questions and Pass4sure dumps

    Salmon egg sample collection for microbial community analysis

    Atlantic salmon (Salmo salar L.) eyed eggs and the incubation water from each of the tanks were collected separately from a commercial hatchery in Scotland. The eggs were spawned in November 2010 and incubated in a flow-through system at a flow rate of 25–30 l s−1 at 3–5 °C. The water source for the incubation of the salmon eggs is a catchment area that is an upland peat moor with conifer plantations, grazing cattle, sheep and wild animals, ∼3 km from the hatchery and is delivered via a hydroelectric scheme using a hill loch as a source. Before the water enters the egg incubators, it is filtered through a 10-μm filter and a carbon filter and is subsequently treated with ultraviolet light for a few seconds. Eggs received a 1-h 1000 p.p.m. formalin bath treatment weekly, and were collected on 14 February 2011. For more details on each of the 12 egg samples, see Supplementary Table 1. The microbial community was obtained by shaking the eggs and incubator water at 180 r.p.m. at 6 °C for 12 h, and subsequent centrifugation of the water at 24 000 × g at 4 °C for 20 min. Cell pellets were resuspended in sterile demineralized water, concentrating the original sample 30–80 times, and stored at −80 °C before DNA isolation. One aliquot was stored at −80 °C in 20% (v/v) glycerol for bacterial isolation (Supplementary Figure 1).

    Scanning electron microscopy

    A selection of salmon egg samples showing visual signs of Saprolegnia infection were collected and stored in a 1% gluteraldehyde and 4% formalin solution. Subsequently, samples were fixed in 2.5% gluteraldehyde and then treated with 1% osmium tetroxide for 1 h. Samples were dehydrated with alcohol and subsequently dried using hexamethyldisilazane. Dried samples were coated in gold using an EMitech Limited (Ashford, UK) K550 sputter coater. Coated samples were viewed in a Zeiss (Oberkochen, Germany) EVO MA10 scanning electron microscope at 10 kV.

    Metagenomic DNA isolation and storage

    DNA was isolated from the pellet suspensions (described above) in duplicate with PowerSoil DNA isolation kit (MO BIO Laboratories, Inc., Carlsbad, CA, USA) according to the manufacturer’s instructions. Duplicate DNA extracts were pooled and an aliquot was analysed on a 0.8% (w/v) agarose gel with SmartLadder MW-1700-10 (Eurogentec, Seraing, Belgium) as a size marker. The DNA concentration was measured with a NanoDrop 1000 Spectrophotometer (Thermo Fisher Scientific Inc., Waltham, MA, USA). DNA samples were stored at −80 °C.

    PhyloChip analysis

    PhyloChip analysis of the bacterial and archaeal community structure of each sample (∼0.5 μg DNA) was conducted at Second Genome (South San Francisco, CA, USA). Sample and data processing and data reduction were performed as described previously (Hazen et al., 2010). Annotation of operational taxonomic units (OTUs) was performed based on the Greengenes database (McDonald et al., 2012). Cluster analysis with Bray–Curtis similarity measures was performed in Primer-E v6.1.13 (Primer-E Ltd, Ivybridge, UK). Nonmetric multidimensional scaling and analysis of similarity (Ramette, 2007) were performed in Paleontological Statistics freeware package (PAST v2.10, http://folk.uio.no/ohammer/past/) (Hammer et al., 2001).

    Clone library sequencing of oomycete and fungal communities

    The internal transcribed spacer (ITS) region of fungal and oomycete rRNA genes was PCR amplified with primers ITS1F and ITS4 (White et al., 1990; Gardes and Bruns, 1993), and Oom-Up5.8S (5′-TGCGATACGTAATGCGAATT-3′) and Oom-Lo28S (5′-ACTTGTTCGCTATCGGTCTCGCA-3′) (Mazzola et al., 2002; Tambong et al., 2006), respectively. Amplification reactions were conducted in 50 μl volumes with Taq DNA polymerase (Invitrogen, Grand Island, NY, USA) according to the manufacturer’s instructions with 1 μl DNA (5–10 ng), 1 cycle at 94 °C for 3 min, 40 cycles at 94 °C for 1 min, 50 °C for 45 s, 72 °C for 1 min and 1 cycle at 72 °C for 7 min. The dominant representatives of fungal and oomycete populations were determined by sequencing clone libraries. Amplicons were cloned into the TOPO-TA vector and transformed into chemically competent Escherichia coli Top10 cells according to the manufacturer’s protocol (Invitrogen). Inserts were PCR amplified from intact cells using primer pair M13F/M13R and purified with ExoSAP-IT (USB, Cleveland, OH, USA) according to the manufacturer’s instructions. A total of 20 and 5–6 clones per sample were selected for fungal and oomycete amplicon sequencing, respectively, with primer T7 and GenomeLab DTCS Quick Start Kit using the Beckman Coulter CEQ 8000 Genetic Analysis system (Beckman Coulter, Brea, CA, USA). Sequences of at least 400 bp were analysed by BLASTn (Altschul et al., 1990) on the National Center for Biotechnology Information (NCBI) database website (www.ncbi.nlm.nih.gov/). A cutoff of >93% identity and a 400-bp alignment length were used for identification (Pounder et al., 2007; Nilsson et al., 2008).

    T-RFLP analysis of fungal community

    Fluorescently labelled PCR products of the ITS region were generated using the D4-labelled ITS1F primer in conjunction with D3-labelled ITS4 primer. PCR was performed in quadruplicate 25 μl reactions containing 1 × Promega PCR Master mix (Promega, Madison, WI, USA), 200 mM primer, 4 μg μl−1 bovine serum albumin and 0.1–0.5 μl DNA template (0.5–5 ng) with above-mentioned cycling conditions. PCR products were digested in duplicate reactions with 10 U of the restriction endonucleases HaeIII and HhaI at 37 °C for 6 h. Restriction enzymes were heat inactivated at 80 °C for 10 min. Restriction fragments were purified by ethanol precipitation and fragments were resuspended in 40 μl of sample loading solution and 0.25 μl 600 size standards. Terminal restriction fragment length polymorphism (T-RFLP) fragments were separated using the CEQ 8000 Genetic Analysis System with denaturation for 120 s at 90 °C, injection for 15 s at 2 kV, separation for 90 min at 4.8 kV and a capillary temperature of 50 °C. T-RFLP profiles were analysed using the Fragment Analysis Module of the CEQ 8000 Genetic Analysis Software, with a slope threshold of 10, 1% relative peak height threshold, 95% confidence level, quartic calibration curve with PA, ver. 1, dye mobility calibration and calculated dye spectra. The baseline value was set to exclude background peaks that commonly occurred in tandem with the DNA standard peaks. Fragments were binned with a 1-bp margin using the AFLP tool in the Fragment Analysis software. Profiles that consisted of mean fragment length and fluorescence intensities were generated for each sample. As different amounts of DNA were used for amplification between samples, no accurate analysis of T-RF abundance could be carried out and we analysed the data only on the presence/absence of T-RF peaks. Cluster analysis was performed as mentioned above.

    Isolation of oomycetes and fungi from salmon eggs

    Per sample, one to two salmon eggs were placed on potato dextrose agar (PDA, Oxoid, Hampshire, UK). The cultures were incubated for 2 weeks at room temperature (20±1 °C) and for 17–18 days at 18 °C. The oomycete and fungal isolates were purified by subculturing hyphal tips three times on fresh PDA supplemented with 100 μg ml−1 streptomycin and 50 μg ml−1 tetracycline.

    ITS sequencing and phylogenetic analysis of oomycete isolates

    For genomic DNA extraction, hyphae were harvested in 90 μl of 0.5 M NaOH and disrupted by Mixer Mill MM400 (Retsch, Haan, Germany) at a frequency of 30/s for 35 s in the presence of one sterile glass bead. The tube was placed at room temperature for 30 min to 24 h until complete cell wall disruption. The hyphal suspension was diluted with sterile 0.1 M Tris-HCl (pH 7.0) and ITS rRNA genes were amplified with ITS1 and ITS4 primers in 25 μl volumes, each consisting of 2 μl of diluted DNA template with GoTaq DNA polymerase (Promega) according to the manufacturer’s protocol. The amplification program consisted of 1 cycle at 94–95 °C for 2–5 min, 30–35 cycles at 94 °C for 1 min, 55 °C, 60 °C or 65 °C for 30–60 s, 72 °C for 30–120 s and 1 cycle at 72 °C for 5–10 min. PCR products were sequenced by Macrogen Inc. (Amsterdam, The Netherlands). Contigs were created from the sequence fragments using Geneious v6.1.4 (Kearse et al., 2012) and aligned using MAFFT v7.0 with G-INS-i algorithm using default settings (Katoh and Standley, 2013), including previously identified reference sequences (Sandoval-Sierra et al., 2013). Two different approaches were applied: Bayesian inferences and Maximum Likelihood inference. Bayesian inference analysis with Markov-chain Monte Carlo, MrBayes v3.2.1 (Ronquist et al., 2012), was run at temperature parameter of 0.2, sample frequency of 1000 and three independent runs with two hot and six cold chains each for 20 million generations. Estimation of the effective samples size in the Markov-chain Monte Carlo and determination of a suitable burn-in time was checked with Tracer v1.5 (Rambaut and Drummond, 2007). A burn-in of 2 million generations was discarded after checking for stability on the log-likelihood curves, and the remaining trees from the independent runs were combined to build a 50% majority-rule consensus tree. The evolutionary model used for the molecular data in MrBayes was HKY+G and was obtained by running the data sets in jModelTest 2 (Darriba et al., 2012). Maximum Likelihood inference analysis was carried out with RaxmlGUI v7.4.2 (Silvestro and Michalak, 2012) with a random starting tree, included the GTRGAMMA option and employed the rapid hill-climbing algorithm (Stamatakis et al., 2007). Clade support was assessed with 1000 bootstrap replicates, with the rapid-hill climbing algorithm (Stamatakis et al., 2008). Phylogram trees were visualized with FigTree (tree.bio.ed.ac.uk/software/figtree/).

    Isolation, in vitro activity and identification of Actinobacterial isolates

    Cell pellets obtained from the salmon eggs samples and stored in 20% glycerol at −80 °C were resuspended in sterile demineralized water and dilution plated on humic acid-vitamin (HA) and starch agar (GA) modified from Zhang (1990). Plates were incubated at 30 °C and bacterial colony-forming units were determined after 6 weeks. HA medium, modified from Hayakawa and Nonomura (1987), contained 1.0 g l−1 humic acid (dissolved in 10 ml of 0.2 N NaCl), 0.5 g l−1 Na2HPO4, 1.71 g l−1 KCl, 0.05 g l−1 MgSO4.7H2O, 0.01 g l−1 FeSO4.7H2O, 0.02 g l−1 CaCO3 and 18.0 g l−1 agar. Starch agar (GA) (Zhang, 1990) contained 15 g l−1 potato starch (soluble starch), 0.5 g l−1 KNO3, 0.5 g l−1 K2HPO4, 0.5 g l−1 MgSO4.7H2O, 5.0 g l−1 NaCl, 5.0 g l−1 KCl, 0.01 g l−1 FeSO4.7H2O and 18.0 g l−1 agar. Both HA and GA were pH adjusted to 7.0–7.4 and were supplemented with 1 ml l−1 of B vitamin solution, 20 μg ml−1 nalidixic acid, 20 μg ml−1 Trimethoprim and 100 μg ml−1 Delvocid after autoclaving. B vitamin solution (Nonomura and Ohara, 1969) (suspended in Milli-Q water) included 0.5 mg ml−1 of thiamine hydrochloride, riboflavin, nicotinic acid, pyridoxine hydrochloride, myo-inositol, calcium pantothenate and 4-aminobenzoic acid and 0.25 mg ml−1 D(+)-biotin. From each sample, 40 single colonies were randomly isolated at different time points from both media and purified on GA or tryptone soya broth (Oxoid) supplemented with 10% sucrose (TSBS) with or without 1.8% (w/v) agar. Isolates were stored at −20 °C and −80 °C in 20–40% glycerol. In vitro antagonistic activity of the isolates against Saprolegnia diclina 1152F4 was determined by placing an agar plug of each Actinobacterial isolate at the edge of 1/5th strength PDA plates. After incubation at 30 °C for 7–10 days, an agar plug from a fresh S. diclina 1152F4 culture was placed in the centre of the plate. Plates were incubated for 4–5 days at 25 °C and inhibition of hyphal growth was scored. For DNA isolation, Actinobacterial cells were disrupted by incubation of 50 μl TSBS culture at 95 °C for 10 min and PCR was performed with Actinobacterial-specific primers Com2xf (5′-AAACTCAAAGGAATTGACGG-3′) and Ac1186r (5′-CTTCCTCCGAGTTGACCC-3′) (Schäfer et al., 2010). Amplification reactions were conducted in 25 μl volumes using GoTaq DNA polymerase (Promega) according to the manufacturer’s instructions, with 1 cycle at 95 °C for 3 min, 35 cycles of 94 °C for 30 s, 60 °C for 30 s, 72 °C for 30 s and 1 cycle at 72 °C for 15 min. All isolates positive for the amplified fragments were sent for 16S rRNA sequencing by Macrogen Inc. Sequence identity was obtained by comparison with sequences deposited in the Greengenes database (McDonald et al., 2012) (http://greengenes.lbl.gov/) and Ribosomal Database Project (Cole et al., 2009) (RDP, http://rdp.cme.msu.edu/). Among the 526 Actinobacterial OTUs with significant differential abundance based on the PhyloChip analyses, the 16S rRNA sequences (850 bp) of 413 OTUs were downloaded from the Greengenes database. These sequences were subjected to phylogenetic analysis with ClustalW in MEGA5 (Tamura et al., 2011) to generate a neighbour-joining (Saitou and Nei, 1987) consensus tree with 1000 bootstrap replicates (Felsenstein, 1985). The evolutionary distances were analysed by Tamura–Nei model (Tamura and Nei, 1993) and the evolutionary rates among sites were modelled by a gamma distribution.

    Salmon eggs for in vivo experiments

    Atlantic salmon (Salmo salar L.) eyed eggs of strain AquaGen Atlantic QTL-innOva IPN from AquaGen AS (Trondheim, Norway) were used for the in vivo experiments. Salmon egg ages at the day of shipment were as follows: for the pathogenicity assay of Saprolegnia isolates, the eggs were 385 degree-days; for the in vivo assays with the Actinobacterial isolates, the eggs were 320 degree-days. All egg batches were disinfected during incubation and before transport with buffodine (1:100, 10 min) and treated with formalin according to Aquagen’s in-house protocols. Upon arrival, blank, pin-eyed and white eggs were removed if observed.

    Preparation of Saprolegnia inocula and pathogenicity assays

    Live eggs were placed in 97–98 °C sterile distilled water or well water and incubated for 80–150 s. The resulting dead eggs were drained and placed on PDA agar plates overgrown with Saprolegnia isolates and incubated overnight at 25 °C. Each egg was then transferred into a well of a 24-well cell-culture plate (Greiner Bio-One, Kremsmünster, Austria) and further incubated for 1 day at 15–25 °C until Saprolegnia hyphal colonization was visible. The slow-growing Saprolegnia isolates 746F3 and 736F2 were further incubated for 2 and 4 days, respectively. After overnight acclimatization of the live eggs, two Saprolegnia-infected dead salmon eggs (inocula) were added to each cup containing 30 live eggs in a similar setup as described in Figure 4c, with the exception that here 3 l of dechlorinated Norwegian tap water, and not well water, was used. Isolate 736F2 was added to the salmon eggs 2 days later as this isolate was a slow grower. Each treatment consisted of an incubator containing three cups with 30 salmon eggs each. In the control treatment no Saprolegnia was added. The water temperature was maintained at 10±1 °C by adding ice blocks into each incubation unit every 12 h. After 8 days post inoculation(6 days post inoculation for isolate 736F2), the mortality percentage of the salmon eggs was determined visually.

    In vivo bioassays to test disease suppression by Actinobacteria

    The incubation setup (Supplementary Figure 4) was located in a ventilated room of constant 15 °C ambient temperature. The egg incubation units, containing 2 l of well water (Supplementary Table 6), were randomly distributed in an incubation tank with circulating water of 5–7 °C. Polystyrene panels were used to cover the incubation tank to maintain darkness and insulation without blocking aeration. Each incubation unit was aerated with an air stone connected to an air pump. Dissolved oxygen levels and temperature, measured with a pH/Oxi 340i multi-parameter instrument (WTW GmbH, Weilheim, Germany), ranged from 101.8% to 103.4% and from 5.7 °C to 6.4 °C, respectively. Within each egg incubation unit, 51±1 live eggs were placed in perforated plastic cups in triplicate. The cups were stabilized by placing a Petri dish (145 mm diameter) and a weight on top. The eggs were introduced into each incubation unit immediately after arrival from Norway and allowed to acclimatize and rehydrate overnight.

    Actinobacterial isolates were inoculated in TSBS and incubated at 30 °C at 200 r.p.m. for 2–4 days. Cultures were centrifuged at 3400 × g for 5–15 min at room temperature. The cell pellets were washed and resuspended in sterile well water and OD600 values determined by spectrophotometry for the Frondihabitans sp. and Arthrobacter sp. For the Streptomyces isolates, the OD600 could not be determined because of aggregation of cells. Approximate equal amounts of cells were determined based on an estimate of the size of the cell pellet after centrifugation. On day 1, the Frondihabitans sp. and Arthrobacter sp. were added to the incubation units at a final concentration of 107 cells per ml or 0.5 ml and 1.5 ml of Streptomyces sp. 1 and 2, respectively. In the consortium of all four isolates, one quarter of the cell suspensions of each bacterium was added. After 3 days of incubation, two S. diclina 1152F4 inocula were placed at opposite sides in each cup. As control treatments, salmon eggs were treated with S. diclina only or with malachite green and S. diclina. Each treatment was conducted in duplicate, that is, two independent experimental incubation units with three technical replicates per incubation unit. For those eggs that received malachite green treatment, the eggs were placed in 1.5 l of 2 p.p.m. malachite green solution for 60–70 min with aeration. Subsequently, eggs were gently rinsed in 500 ml fresh well water and placed back into their incubation units. This was repeated every 4 days, replacing the incubation unit water with fresh well water. Hyphal expansion was photographed and assessed every other day. Based on the photos of each group of eggs, ImageJ v1.44n (Schneider et al., 2012) was used to measure the diameter of hyphal expansion. As a proxy for infection of salmon eggs by Saprolegnia, we determined hyphal attachment by lifting infection inocula with a pair of tweezers and counting the number of eggs attached to the hyphal patch (Supplementary Movie 1). Hyphal attachment was determined at day 14 (10 days post inoculation) when attachment in the control, S. diclina only, was >50% (Supplementary Table 5).

    Nucleotide sequence accession numbers

    The sequences of the 16S rRNA sequences of Frondihabitans sp., Arthrobacter sp. and Streptomyces sp. 1 and 2 have been deposited in GenBank under accession numbers KF741274, KF741275, KF741276 and KF741277, respectively.

    Colonization of Actinobacteria on the egg surface

    Colonization of the applied Actinobacterial isolates on the salmon egg surfaces was determined by harvesting one to two eggs from each cup at 0 and 10 days post inoculation, rinsing with 1 ml of sterile well water three times, draining briefly on sterile filter paper and rolling gently over the surface of HA and GA agar plates (Supplementary Movie 2). All plates were incubated at 30 °C for 5–6 weeks, and 2–8 colonies that were morphologically similar to each of the four isolates used for inoculation were purified and subjected to 16S rRNA sequencing.

    Statistical analyses

    For each experiment, obtained data were tested for normal distribution and homogeneity of variances before conducting parametric or nonparametric statistical analyses. Disease incidence of salmon egg samples was analysed with Mann–Whitney U-test (P<0.05). The pathogenicity of Saprolegnia isolates were compared with one-way analysis of variance and with post hoc Dunnett analysis (P<0.05). Cluster analysis and nonmetric multidimensional scaling and analysis of similarity of T-RFLP-derived data for the fungal community and PhyloChip-derived data for the bacterial and archaeal communities were conducted using Bray–Curtis similarity measures in Primer-E and in PAST. The differences in total number of bacterial and archaeal OTUs associated with diseased versus healthy eggs was analysed by Student’s t-test (P>0.05). Phylogenetic groups containing OTUs that have a significant differential abundance between diseased and healthy eggs were identified with Student’s t-test (P<0.005) in Microsoft Excel. Percentage of hyphal attachment was analysed with Kruskal–Wallis one-way analysis of variance (P<0.05). Area under hyphal expansion curve and area under hatching percentage curve of different treatments in the bioassay were compared with Kruskal–Wallis one-way analysis of variance (P<0.05). All statistical analyses were performed in IBM SPSS Statistics v20 (Armonk, NY, USA), unless mentioned otherwise.


    Family decision maker perspectives on the return of genetic results in biobanking research | killexams.com real questions and Pass4sure dumps

    The authors thank Maureen Wilson, PhD, and Anthony Molisani, MA, for their assistance in the preparation of the manuscript.

    The GTEx Consortium Members: John Lonsdale, Jeffrey Thomas, Mike Salvatore, Rebecca Phillips, Edmund Lo, Saboor Shad, Richard Hasz, Gary Walters, Fernando Garcia, Nancy Young, Barbara Foster, Mike Moser, Ellen Karasik, Bryan Gillard, Kimberley Ramsey, Susan Sullivan, Jason Bridge, Harold Magazine, John Syron, Johnelle Fleming, Scott Jewell, Dan Rohrer, Dan Maxim, Dana Filkins, Philip Harbach, Eddie Cortadillo, Bree Berghuis, Lisa Turner, Eric Hudson, Kristin Feenstra, Leslie Sobin, James Robb, Phillip Branton, Greg Korzeniewski, Charles Shive, David Tabor, Liqun Qi, Kevin Groch, Sreenath Nampally, Steve Buia, Angela Zimmerman, Anna Smith, Robin Burges, Karna Robinson, Kim Valentino, Deborah Bradbury, Mark Cosentino, Norma Diaz-Mayoral, Mary Kennedy, Theresa Engel, Penelope Williams, Kenyon Erickson, Kristin Ardlie, Wendy Winckler, Gad Getz, David DeLuca, Daniel MacArthur, Manolis Kellis, Alexander Thomson, Taylor Young, Ellen Gelfand, Molly Donovan, Yan Meng, George Grant, Deborah Mash, Yvonne Marcus, Margaret Basile, Jun Liu, Jun Zhu, Zhidong Tu, Nancy J Cox, Dan L Nicolae, Eric R Gamazon, Hae Kyung Im, Anuar Konkashbaev, Jonathan Pritchard, Matthew Stevens, Timothèe Flutre, Xiaoquan Wen, Emmanouil T Dermitzakis, Tuuli Lappalainen, Roderic Guigo, Jean Monlong, Michael Sammeth, Daphne Koller, Alexis Battle, Sara Mostafavi, Mark McCarthy, Manual Rivas, Julian Maller, Ivan Rusyn, Andrew Nobel, Fred Wright, Andrey Shabalin, Mike Feolo, Nataliya Sharopova, Anne Sturcke, Justin Paschal, James M Anderson, Elizabeth L Wilder, Leslie K Derr, Eric D Green, Jeffery P Struewing, Gary Temple, Simona Volpi, Joy T Boyer, Elizabeth J Thomson, Mark S Guyer, Cathy Ng, Assya Abdallah, Deborah Colantuoni, Thomas R Insel, Susan E Koester, A Roger Little, Patrick K Bender, Thomas Lehner, Yin Yao, Carolyn C Compton, Jimmie B Vaught, Sherilyn Sawyer, Nicole C Lockhart, Joanne Demchok & Helen F Moore



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    IBM C2090-011 Exam (IBM SPSS Statistics Level 1 v2 ) Detailed Information



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