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P2020-012 | IBM SPSS Data Collection Technical Support Mastery v1

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P2020-012 - IBM SPSS Data Collection Technical Support Mastery v1 - braindump

Vendor IBM
Exam Number P2020-012
Exam Name IBM SPSS Data Collection Technical Support Mastery v1
Questions 60 Q & A
Recent Update February 11, 2019
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P2020-012 exam Dumps Source : IBM SPSS Data Collection Technical Support Mastery v1

Test Code : P2020-012
Test Name : IBM SPSS Data Collection Technical Support Mastery v1
Vendor Name : IBM
Q&A : 60 Real Questions

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IBM IBM SPSS Data Collection

My Highlights from IBM think 2018: facts Science, SPSS, Augmented fact and the client adventure | killexams.com Real Questions and Pass4sure dumps

I attended IBM’s inaugural suppose adventure in Las Vegas ultimate week. This adventure, IBM’s biggest (estimated 30,000+ attendees!), focused on making your enterprise smarter and blanketed keynotes and periods on such topics as artificial intelligence, statistics science, blockchain, quantum computing and cryptography. i was invited by using IBM as a guest to share some insights from the point of view of an information scientist. beneath are just a few highlights of the experience.

facts Science the use of IBM SPSS SPSS at 50

50 Years of SPSS Innovation. click photograph to enlarge.

IBM SPSS is IBM’s set of predictive analytics items that handle the whole analytical manner, from planning to facts collection to analysis, reporting and deployment. IBM celebrated the 50th anniversary of IBM SPSS with their new beta release of IBM SPSS statistics 25, the largest beta free up in its background. The updated version includes new developments like ebook-in a position charts, MS workplace integration, Bayesian information and advanced facts. also, they added a brand new person interface which is pretty slick.

i was brought to SPSS data in college and have used it for each one of my research tasks considering then. To be sincere, SPSS statistics has aged more desirable than I have! I actually have already all started the use of the new edition and am relatively excited concerning the new elements and person interface. i'll file about adventure in a later post. check out SPSS with a free 14-day trial.

enhancing the client adventure

recent reviews have estimated that 45% of agents are expected to raise the use of synthetic intelligence for customer adventure in the subsequent three years, and 55% of agents are focused on optimizing the consumer event to enhance client loyalty. moreover, eighty five% of all customer interactions with a company may be managed devoid of human interaction by using 2020.

client journey administration (CXM) is the system of figuring out and managing purchasers’ interactions with and perceptions in regards to the business/company. IBM understands that enhancing the consumer adventure is increasingly becoming facts-intensive pastime, and using the combined energy of statistics and nowadays’s processing capabilities can help companies model the strategies that impact the customer journey. I attended a couple of sessions to study how IBM is leveraging the vigor of IBM Watson to aid their customers with Watson Commerce and Watson consumer adventure Analytics solutions. These options use the energy of synthetic intelligence (e.g., predictive analytics) to increase how corporations can more desirable control customer relationships to enhance consumer loyalty and circulate their company ahead.

information Science Meets stronger Analytics and Augmented fact

These statistics authorities from Aginity, IBM Analytics, H2O.ai and IBM Immersive Insights are improving how you get from information to insights.

I noticed a good demonstration of the intersection of statistics science, greater analytics and augmented fact. Getting from information to insights is the purpose of facts science efforts and, as statistics sources proceed to develop, we will need enhanced easy methods to get to those insights. Aginity is working with H2O.ai to demonstrate how you can improve your predictions via augmenting public facts with more advantageous information (with derived attributes) and stronger analytics to make superior predictions. using baseball statistics, Ari Kaplan of Aginity stated that the advancements in predictive models might translate into thousands and thousands of greenbacks per player. whereas his demo focused on the use of those technologies in baseball facts, the concepts are generalizable to any industry vertical, including finance, healthcare and media.

at the identical demonstration station, Alfredo Ruiz, lead of the Augmented truth program at IBM Analytics, showed me how his crew (IBM Immersive Insights) is incorporating augmented reality into statistics Science journey to support agencies improved bear in mind their ever-expanding information sets. I’m anticipating seeing how his efforts in marrying augmented truth and facts science development.

I had the privilege of interviewing Ari Kaplan of Aginity who talked concerning the work he is doing to enhance how Aginity and H2O.ai is improving the information science procedure. try what he has to assert under.

Don’t pass over this interview with Ari Kaplan, a true “Moneyball” and smartly usual around most important League Baseball, as he talks in regards to the latest laptop researching technologies powering nowadays’s baseball choices, and check out the great demo.

Posted by means of IBM records Science on Thursday, March 22, 2018

records Science is a crew sport

Bob, Al and Dez. photograph by Dez Blanchfield

I had the possibility to speak with with many industry specialists who come to information science from a distinct standpoint than I do. whereas I focal point basically on the facts and arithmetic elements of records science, lots of my information peers strategy statistics science from a technological and programming perspective. definitely, for an upcoming podcast, Dez Blanchfield and i had been interviewed by using Al Martin of IBM Analytics to talk about our respective roles in statistics science. This dialog became a energetic one, and i am eager for reliving that night once the podcast is released. The final analysis is that facts science requires such a diverse ability set that you simply actually need to work with other people who can complement your advantage.

I’m with records professionals (and actors) Trisha Mahoney, Ryan Arbow and Shadi Copty.

This thought that information science is a crew sport turned into put on full screen in an wonderful session by which a couples therapist (Trisha Mahoney) helped resolve an argument between a knowledge science chief (Shadi Copty) and IT chief (Ryan Arbow). Asking probing questions, the counselor published that the facts science and IT chief were at odds because of an absence of communication. She brought them to IBM’s information Science adventure, an commercial enterprise information science platform that allows them to effortlessly collaborate, use suitable open source tools and get their models into creation sooner.

Analytics: Your aggressive capabilities

For me, IBM think 2018 became all about making your company smarter through analytics. basically, research indicates that businesses which are improved capable of deliver the energy of analytics to undergo on their company issues could be in a far better position to outperform their analytics-challenged opponents. This conception was illustrated via keynotes, classes and conversations. by using bringing diverse statistics science professionals collectively to leverage the tools and methods of AI and laptop/deep studying will support you stream your company ahead. if you have been unable to attend the event, you could watch replays of most of the keynotes here.

(Disclosure: IBM assisted me with commute expenses to IBM consider 2018.)

a look on the IBM SPSS Modeler and IBM SPSS statistics analytics tools | killexams.com Real Questions and Pass4sure dumps

IBM's SPSS predictive analytics equipment include IBM SPSS Modeler and IBM SPSS records. SPSS Modeler gives records mining and textual content evaluation application, while SPSS information is an built-in family unit of items. each tools permit users to build predictive models and execute different analytics tasks.

The IBM SPSS Modeler objectives clients who have little or no programming advantage. clients are offered with a drag-and-drop person interface, enabling them to construct predictive fashions and function other facts analytics. Modeler can apply distinctive methods and algorithms to assist the person find counsel hidden within the statistics. The tool can additionally support in integrating and consolidating all types of statistics units from dispersed information sources throughout the firm.

The IBM SPSS records suite is an built-in set of products geared towards extra expert facts analysts. SPSS statistics addresses the complete analytical method, from planning to records assortment, analysis, reporting and deployment.

IBM SPSS Modeler aspects

version 18 gives the following features:

  • more than 30 base computing device studying algorithms.
  • Extensions that provide continued improvements to be used with open supply products, corresponding to R and Python.
  • better help for a few multithreaded analytical algorithms, together with Random timber, Tree-AS, Generalized Linear Engine, Linear-AS, Linear help Vector machine and Two-Step-AS clustering.
  • The ability to run numerous Python and Spark computer studying, in addition to other Python analytics libraries natively in Modeler devoid of requiring using the Analytic Server, as became required in the outdated edition.
  • SPSS Modeler bundles are deployed on premises, and SPSS Modeler Gold is obtainable as a cloud offering. The customer front end of SPSS Modeler runs under home windows and macOS, while the server component runs on Unix, Linux and home windows.

    IBM SPSS Modeler provides the following editions:

  • SPSS Modeler personal: A single-user laptop product.
  • SPSS Modeler skilled: A desktop product that works with IBM SPSS Analytic Server, proposing superior scalability and efficiency and enabling functions for use across a firm.
  • SPSS Modeler premium: This version comprises superior algorithms and capabilities, corresponding to textual content analytics, entity analytics and social community evaluation, that enhance mannequin accuracy with unstructured information.
  • SPSS Modeler Gold: This edition offers analytical decision administration, collaboration and deployment capabilities. SPSS Modeler Gold is additionally accessible as a cloud providing.
  • IBM SPSS facts elements

    SPSS statistics version 24 contains here new elements:

  • The skill to access more than 100 extensions, enabling clients to take capabilities of free libraries written in R, Python and SPSS syntax.
  • The IBM SPSS Extension Hub to browse, down load, replace, eradicate and generally manage extensions.
  • a big upgrade to the custom Dialog Builder, enabling users to extra with ease build and deploy their personal extensions. Enhancements consist of new controls and new residences for existing controls and several other improvements to the user interface.
  • advancements that enable clients to more conveniently and right now import and export statistics into SPPS statistics.
  • improvements to the custom Tables module, together with new statistical functionality and consumer-requested elements.
  • IBM SPSS information presents here three variations (each with further modules):

  • SPSS facts general equipment provide advanced statistical processes that guide linear and nonlinear statistical fashions, as well as predictive simulation modeling, which accounts for doubtful inputs, geospatial analytics and customised tables.
  • SPSS records professional equipment help data practise, lacking values and records validity, resolution bushes, and forecasting.
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  • Pricing for the SPSS Modeler and SPSS statistics predictive analytics equipment differ reckoning on the bundle alternate options, the number of users and the license period. SPSS facts is now obtainable as a subscription alternative or a perpetual license. IBM offers free trials of each IBM SPSS Modeler and IBM SPSS records.

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    the usage of big records to battle Fraud | killexams.com Real Questions and Pass4sure dumps

    Posted April 18, 2013 by way of Drew Robb     feedback

    while big records may also be advantageous in fighting fraud, common enterprise intelligence tools are not tons help.

    If it changed into convenient to detect fraud, there wouldn’t be plenty of it round. but at the moment, the sheer extent of advice being generated makes it extra difficult than ever to realize.

    “groups today usually have a fragmented strategy to coping with protection and fraud dangers, which leaves them vulnerable to even more attacks as criminals are quick to locate and make the most any points of weakness,” talked about Ellen Joyner-Roberson, global advertising and marketing primary at SAS safety Intelligence solutions. “consequently, safety risks and financial crimes are increasing dramatically as a result of a weak world economy, increase in prepared crime and the expanding sophistication of fraudulent schemes.”

    no longer Your regular Analytics

    regrettably, many current analytics techniques don't assist powerful analytical modeling, making it complicated to method and analyze information speedy adequate to make a change. distinct departments commonly have disparate statistics sources and methods, so it’s hard to identify suspicious endeavor across the business.

    in addition, there's a continuing combat to stability more difficult anti-fraud efforts with an excellent consumer adventure. If carriers cease greater transactions which are suspicious in an try to cut back fraud losses, they can also anger consumers attempting to comprehensive legit transactions.

    Banking, insurance, govt, fitness care and retail are obtrusive ambitions for con artists. The banking business, for instance, has a large issue with id theft and account takeover. There, it is important that selections are made in precise time at the aspect of sale, which requires a monitoring and analytics method that can directly mannequin for behavior changes and prevent that transaction devoid of getting it wrong too time and again.

    due to such demands, the consensus is that usual company intelligence (BI) isn’t subtle ample. It must be backed up via domain-embedded guidelines and adaptive analytics that will also be tailor-made to several types of fraud or fiscal crime. Context additionally performs a essential part, and integrating contextual counsel from cellular apps and other channels can heighten success. And geospatial and pace records seize at transaction origination should still be backed up with the aid of determination making in true time.

    “To fight fraud earlier than it factors fiscal losses, agencies are the usage of true-time interdiction (definition: to forbid or debar) capabilities anywhere they could, and with uncooked response instances beneath 200 milliseconds,” mentioned Joyner-Roberson.

    Her business developed SAS safety Intelligence to tackle this market. It contains a lot of SAS items to address fraud and improper payments, regulatory compliance and protection. This includes SAS Fraud administration, SAS community Analytics, SAS economic Crimes Suite and SAS Fraud Framework.

    SAS protection Intelligence takes an business approach to records administration and consolidation, combining facts integration, data first-rate and grasp data management into a unified environment that brings collectively move-channel business statistics on a single platform. It has the skill to score and determination all transactions in real-time and intercept suspect deals.

    Haystacks, Needles and massive information

    massive records most effective magnifies the complications inherent in tracking down wrongdoers. simply how do you cope with capturing, processing, and storing huge quantities of actual-time data so that you can learn and adapt to changing behavior patterns?

    “The needle in the haystack analogy is specially appropriate for fraud detection because of the small amount of fraud compared to the entire of legitimate internet traffic,” observed Jesse McKenna, head of possibility analysis for Silver Tail programs, RSA. “despite the fact, as fraud patterns normally shift and evolve, so does the needle you are searching for. nowadays it could be a needle, day after today a pushpin or a nail.”  

    The problem is that you just don’t be aware of exactly what you are seeking apart from it may be distinctive than the rest of the pile. while big facts capability you could be in a position to derive extra insights, finding the significant information and constructing detection algorithms that aren't brittle and may not immediately become beside the point takes a deep knowing of fraud.

    That’s the place behavioral analytics and adaptive detection methods become critical. You want the right way to isolate what's different that you may have certainly not considered earlier than.

    Analytics apps also have to be of price to fraud investigators, who're the ones with the journey of the real world. Machines can burn through the information mountain to locate bizarre occurrences, but they're nowhere close pretty much as good as individuals at evaluating and figuring out context. hence, the app has to facilitate the americans or fraud gadgets it serves, no longer burden them with delays or lengthy approaches.

    RSA’s Silver Tail programs product suite (which include Forensics, Mitigator, and Profile Analyzer equipment) provides behavioral analytics, true-time custom guidelines and mitigation capabilities, as well as a forensics interface for performing investigations.

    for instance, with the aid of monitoring the details of every HTTP/S request that is distributed to a domain, it learns what general conduct appears like for the web page and can then establish crook conduct and alert the acceptable groups for investigation. Alternately, it could actually speak with other network contraptions to redirect or terminate the internet session in real-time.

    user-based mostly behavioral analytics (besides the population-based mostly ones) helps identify when a particular person is deviating from their average habits, as is the case with many fraud and account compromise eventualities.  

    “Silver Tail turned into created by ex-eBay and PayPal fraud authorities who lived the battle towards fraud at a time once they had been protecting the two most focused manufacturers on the information superhighway,” noted McKenna. “Their experiences encouraged them to keep up with shifting web fraud attacks in its place of taking part in capture-up.”

    visible Aids

    IBM is additionally concerned in fraud analytics by means of a combination of IBM SPSS Modeler, SPSS facts, SPSS Collaboration and Deployment features, SPSS records assortment, SPSS decision management, IBM Cognos BI, Cognos categorical, Cognos insight, Cognos Disclosure management and Cognos financial plan Reporting.

    IBM believes that visualization is a a must-have part in making feel of analytics assistance, peculiarly when it involves facilitating the work of authorities in fraud detection.

    “As information volumes proceed to head up, BI initiatives can benefit greatly from using suggestions visualization options,” noted Don Campbell, CTO, IBM company Analytics. “decent visible representations can support people intuitively keep in mind complicated information. Visualization can be a magnificent presentation and advice consumption interface. nevertheless it does not stand on its own.”

    Graphical representation is additionally a spotlight of 21CT, a issuer of analytics for investigation and sample detection. It has released improved graph search for its LYNXeon product to help spot cyber attackers and document crook conduct. The motive at the back of the product is that laptop -based mostly assaults will also be caught by way of machines, however human-primarily based assaults require really expert tools to support americans realize them earlier than the harm is done.

    “health care fraud, peculiarly Medicare, is a giant issue, costing billions a yr,” pointed out Kyle Flaherty, vice president of marketing at 21CT. “The challenge is not best the tremendous quantities of statistics, but also within the disparate connections inside the statistics. moreover, fraud has been rampant during this area and the malicious entities have turn into subtle.”

    while general BI tools deliver insight into one slice of the typical counsel, Flaherty pointed out, they are not good enough for fraud detection, which is dependent upon operational perception and the potential for a human to hunt through visualized information, discover links, and realize average patterns of undertaking.

    “Fraud detection is a human versus human combat, and BI equipment can spit out extra statistics and graphs to help illustrate the problem,” spoke of Flaherty. “within the conclusion you need to be in a position to hop around the facts and examine.”

    Drew Robb is a freelance writer focusing on know-how and engineering. at the moment residing in California, he is at the start from Scotland, the place he acquired a level in geology and geography from the school of Strathclyde. he is the author of Server Disk administration in a home windows environment (CRC Press).

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    Modeling the Impact of Antibiotic Exposure on Human Microbiota | killexams.com real questions and Pass4sure dumps

    Characterization of patients and sputum samples

    The patients and sputum samples included in the training and validation data sets were characterized with respect to two types of variables (Table 1 and 2). Fixed, patient-specific variables included gender, CFTR genotype, and disease severity (or aggressiveness) phenotype21,22. Time-dependent, sample-specific variables included patient age, lung function, and disease stage21 at the time of sample collection. Lung function was measured as percent predicted forced expiratory volume in one sec (%FEV1). Disease stage was defined as early when serial %FEV1 measures were >70; intermediate when %FEV1 measures were between 70 and 40; and advanced when %FEV1 measures were <40. The dominant operational taxonomic unit (OTU) detected in each sample by deep-sequencing was also included as a sample-specific variable. This was defined as the most abundant OTU detected in the sample. Bacterial community diversity of each sputum sample was measured by calculating the inverse Simpson index, which takes into account both the number of OTUs (richness) present in the sample and their relative abundance (evenness).

    Table 1: Characteristics of patients in the training and validation data sets Table 2: Characteristics of samples in the training and validation data sets

    The training set samples (n = 116) were from six men previously described by us in detail21. All six patients had a mild or moderate disease severity phenotype. Samples were collected when these patients were between 18 and 30 years of age. The samples were roughly evenly distributed among periods when these patients were in early, intermediate, or late stages of lung disease. The dominant OTU in most (82%) samples represented the genus Pseudomonas. The patients (n = 60) and sputum samples (n = 362) in the validation set were more heterogeneous. Men represented 53% of patients and there was a greater distribution of patients with mild, moderate and severe disease severity phenotypes. The validation set samples were also more diverse with respect to the dominant OTU detected. Of note, none of the patients in either the training set or the validation set were smokers.

    Antibiotic weighting score development

    The antibiotic exposure associated with each sample was measured by assessing the antibiotic administration to the source patient during a 30-day window prior to sample collection. The duration of exposure (no. of days receiving the antibiotic), the timing of administration relative to the day of sampling (e.g., 20 days vs 2 days prior to sampling), and the antibiotic class and route of administration were determined for each sample.

    These variables were used to develop antibiotic weighting components that yielded scores used as covariates in models predicting bacterial community diversity.

    Weight component A

    (wcA; Equation 1 in Materials and Methods, and Fig. S1) accounts for the duration of antibiotic use during this 30 day window by assessing the number of days an antibiotic was (wcA = 1) or was not (wcA = 0) administered (Fig. 1A). wcA for each antibiotic was determined from observed sample level data without subjective assessment and was constructed similarly for all samples in both the training and validation data sets.

    Figure 1: Antibiotic weight components (A) (wcA) and B (wcB). Figure 1

    Panel (A) depicts daily wcA values for patient P2 during the 30 days prior to collection of sample 27. This patient received four antibiotics during this time (tobramycin-IV, meropenem-IV, ciprofloxacin-PO, and doxycycline-PO). A value of 1 indicates antibiotic administration on that day, while 0 indicates no antibiotic administration. Panel (B) depicts wcB profiles during the 30 days prior to sampling. These profiles indicate equal weighting (black) as well as linear (red), concave (blue), and convex (green) increasing weights as days approach the sampling time. The data points for each profile (circles) were drawn based on values calculated by Equation 2 in the text and each value was listed in Table S1.

    Weight component B

    (wcB; Equation 2 in Materials and Methods) accounts for the proximity of antibiotic use relative to the sampling day. Four weighting schemes, described by the formulas in Equation 2, were assessed, including (i) an equal weight for each day irrespective of proximity to the sampling date, (ii) a linear increase in weights with increasing proximity to the sampling date, and either (iii) a concave or (iv) a convex increase in weights with increasing proximity to the sampling date (Fig. 1B and Table S1). A score for each antibiotic administered during the 30 days prior to the date of each sputum sample was calculated as a product of wcA and wcB (Equation 3 in Materials and Methods). The sum of the scores for all antibiotics administered in association with a sputum sample was calculated to provide the total antibiotic exposure for each sample (Equation 4 in Materials and Methods).

    Using the total antibiotic exposure for each sputum sample, the training dataset was analyzed to determine which of the four wcB weighting schemes best predicted the inverse Simpson index, which had been previously calculated for each sample, based on the Akaike Information Criterion (AIC), after adjusting for age and %FEV1 at the sampling time. A comparison of AICs indicated that the convex increasing weighting scheme provided the best prediction for the inverse Simpson index (Table S2). When the larger and independently sampled validation sample set was similarly analyzed, using the same four wcB weighting schemes, the convex increasing weighing scheme again provided the best prediction of the inverse Simpson index. Analyses on 500 bootstrap samples from the validation set demonstrated the stability of this wcB weighting scheme. The wcB convex increasing weights were ranked as the best fit in 75% of the bootstrap samples, outperforming the other choices, and were therefore used in the remainder of the study whenever wcB was considered.

    Weight component C

    (wcC; Equation 5 in Materials and Methods) accounts for the effects of antibiotic type and route of administration on predicting the inverse Simpson index. Since only 16 of the 37 antibiotic types observed in the validation set were used in the training set, we based our initial evaluation of this weight component on analyses of the combined training and validation sets. First, the ability of each antibiotic associated with a sputum sample (i.e., administered within 30 days of sample collection) to predict the inverse Simpson index was assessed, based on the wcA and wcB weighting components and adjusting for age and %FEV1 at sampling. No significant interactions between multiple antibiotics were detected. Next, the AICs for the 37 antibiotic types were modeled and ranked from lowest (best prediction model) to highest (worst prediction model). Antibiotic type coefficients from the 37 AIC models were also ranked based on largest to smallest impact on inverse Simpson index (Table S3).

    The AIC and coefficient ranks were summed and sorted from lowest to highest and grouped into terciles. wcC values of 0.5, 0.33 or 0.17 were assigned to each combination of antibiotic type and route of administration for the best, intermediate, and worst predictors of the inverse Simpson index, respectively (Equation 5 in Materials and Methods and Table S3). Antibiotics administered by the IV route were more likely to be in the top tercile (i.e., wcC value of 0.5) than were antibiotics administered orally or by inhalation (50% of IV administered antibiotics had wcC of 0.5 compared to 16% of oral/inhaled antibiotics; Fisher's exact test p = 0.04).

    The combined antibiotic weighting score (Equation 6 in Materials and Methods) for each sputum sample was calculated by multiplying wcA, wcB, and wcC for each of the 30 days prior to sputum collection and then summing these scores across the 30 days. Inclusion of wcC improved the prediction of community diversity (inverse Simpson index) in each of the training, validation and combined sets as opposed to using only wcA and wcB alone. A permutation test indicated that inclusion of wcC in the combined antibiotic weighting score gave a significantly lower AIC value (better model fit; p < 0.001) than would have occurred under 5000 random permutations of the wcC values across the 37 antibiotic types. The distribution of the combined antibiotic load score across validation samples is shown in Figure S2.

    Predictors of community diversity in CF

    To illustrate the utility of the antibiotic weighting scoring schemes, we included the combined antibiotic weighting score as a covariate in a multivariate model predicting community diversities in our collection of CF sputum samples. This allowed us to explore associations between bacterial community diversity (inverse Simpson index) and the patient- and sample-specific variables associated with these samples. Since disease stage was defined based on lung function (%FEV1), these correlated variables could not both be included in the model. We therefore included disease stage as a covariate in the model since it has a stronger predictive ability (AIC 1572.14) than lung function (AIC 1579.56).

    Table 3 shows results from the multivariate linear mixed model predicting the inverse Simpson index in the validation set. An increase of one unit in the antibiotic weighting score calculated over the month prior to sampling is associated with a 1.25 point decrease in the inverse Simpson index on average (95% CI, 0.44, 2.05 point decrease, p = 0.002) after adjusting for dominant OTU, disease severity, gender, CFTR genotype, disease stage, and patient age. We observed significant differences in community diversity with respect to dominant OTU (composite P < 0.001). Samples dominated by Pseudomonas or Burkholderia showed the least diversity, while samples dominated by Streptococcus showed the greatest diversity (approximately 3 inverse Simpson index points higher than Pseudomonas and Burkholderia, on average) after adjusting for age, gender, disease severity, disease stage, CFTR genotype, and antibiotic weighting in the previous month. On average, men had an approximately 1 point higher inverse Simpson index than women after adjusting for other factors (p = 0.004). We observed decreasing diversity with advancing disease stage after adjusting for other factors; on average, early disease stage had a 1.15 point higher inverse Simpson index than late disease stage (p = 0.02). We did not observe significant associations between community diversity and disease severity or CFTR genotype after adjusting for age, gender, dominant OTU, disease stage, and antibiotic use in the previous month. In addition, we did not observe a significant decrease in diversity with increasing age after adjusting for other factors.

    Table 3: Multivariate linear mixed model including antibiotic use as a covariate

    Figure 2 shows the community diversity of a “standardized” CF patient predicted by specific variables after adjusting for other variables. For example, after controlling for other variables, the estimated community diversity is 5.7 if the community is dominated by Streptococcus and 2.6 if dominated by Burkholderia.

    Figure 2: Estimated community diversity by each predictor. Figure 2

    Pseu: Pseudomonas, Burk: Burkholderia, Strep: Streptococcus. The predicted values by each predictor were calculated by controlling for other predictors based on the “standardized” CF patient profile: 57%, 12%, 14%, and 17% chance of being dominated by Pseudomonas, Burkholderia, Streptococcus or other bacteria, respectively; 18%, 49%, and 33% chance of being in early, intermediate or late disease stage, respectively; 22%, 30%, and 48% chance of having a mild, moderate or severe disease phenotype; 64% chance of being male; 44%, 44% and 12% chance of being delta F508 homozygous, delta F508 heterozygous or another CFTR genotype, respectively; average age = 28.13 years and an antibiotic load = 0.11.

    Multi-Site Expansion of an Early Childhood Intervention and School Readiness | killexams.com real questions and Pass4sure dumps

    Multi-Site Expansion of an Early Childhood Intervention and School Readiness | Articles | Pediatrics

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    Box.net : https://app.box.com/s/pcgn7wb3ucgqcouylys0v7doyf1ydp52
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