Role of Scientific Review Officer (SRO) Designated Federal official with overall responsibility for the review process • Performs administrative and technical review of applications to ensure completeness and accuracy • Selects reviewers based on broad input • Manages study section meetings • Prepares summary statements • Provides any requested information about study section recommendations to Institutes/Centers and National Advisory Councils/Boards
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Scientific Review Administrator Designated Federal official with overall responsibility for the review process, including: Performing administrative and technical review of applications to ensure completeness and compliance  Selecting reviewers based on broad input  Managing study section meetings  Preparing summary statements  Providing any requested information about study section recommendations to Institutes and National Advisory Councils/Boards 
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The Process of A-R Mapping 1. 2. 3. 4. 5/26/2005 Initialization: The agent society  makes a request to the role space  to instantiate the major LeadingRole class defined in the role organization , and create a role instance for it. Role assignment: for each agent  in the agent society , do the following: a. When agent  receives any sensor data from its environment, it may decide to generate some new goals or subgoals based on the sensor data and agent ’s motivations. b. With its reasoning mechanisms, agent  further deduce a set  of needed roles of types defined in the role organization . If none of the roles in set  is of type LeadingRole, go to step 2.d. c. If any role in role set  is a leading role of type LeadingRole, agent  takes the corresponding role instance from the role space , if available, updates the hiring number of other roles as needed, and makes requests to the role space  to create role instances for those roles under hiring. d. Repeat the following for a period of time : Search the role space  for any role instances that match roles in role set . If there is a match, agent  takes that role instance. If all roles in role set  have been matched with some role instances in the role space , go to Step 3. e. If any role in the role set  cannot be matched with a role instance in the role space , agent  may decide to release all role instances or keep its current occupations. Marking role incompatibility: for each agent , mark its role incompatibility as the following: for any role instances r1, r2  .rolesTaken, if .relationship(r1.getClass, r2.getClass) == incompatibility, mark agent  as potential role incompatibility with a self-loop. Setting up interaction relationships: for each agent , set up the interaction relationship between agent  and other agents from the same agent society  as the following : for any agent instance   .agentInstances, where   , if  r1  .rolesTaken, r2  .rolesTaken such that .relationship(r1.getClass, r2.getClass) == association, then (, )  dom .interaction. CIS Dept., UMass Dartmouth 14
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The Process of A-R Mapping 1. 2. 3. 4. Initialization: The agent society  makes a request to the role space  to instantiate the major LeadingRole class defined in the role organization , and create a role instance for it. Role assignment: for each agent  in the agent society , do the following: a. When agent  receives any sensor data from its environment, it may decide to generate some new goals or subgoals based on the sensor data and agent ’s motivations. b. With its reasoning mechanisms, agent  further deduce a set  of needed roles of types defined in the role organization . If none of the roles in set  is of type LeadingRole, go to step 2.d. c. If any role in role set  is a leading role of type LeadingRole, agent  takes the corresponding role instance from the role space , if available, updates the hiring number of other roles as needed, and makes requests to the role space  to create role instances for those roles under hiring. d. Repeat the following for a period of time : Search the role space  for any role instances that match roles in role set . If there is a match, agent  takes that role instance. If all roles in role set  have been matched with some role instances in the role space , go to Step 3. e. If any role in the role set  cannot be matched with a role instance in the role space , agent  may decide to release all role instances or keep its current occupations. Marking role incompatibility: for each agent , mark its role incompatibility as the following: for any role instances r1, r2  .rolesTaken, if .relationship(r1.getClass, r2.getClass) == incompatibility, mark agent  as potential role incompatibility with a self-loop. Setting up interaction relationships: for each agent , set up the interaction relationship between agent  and other agents from the same agent society  as the following : for any agent instance   .agentInstances, where   , if  r1  .rolesTaken, r2  .rolesTaken such that .relationship(r1.getClass, r2.getClass) == association, then (, )  dom .interaction. 10/21/2005 CIS Dept., UMass Dartmouth 17
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Comparison with other methods Recently, Tjong and Zhou (2007) developed a neural network method for predicting DNA-binding sites. In their method, for each surface residue, the PSSM and solvent accessibilities of the residue and its 14 neighbors were used as input to a neural network in the form of vectors. In their publication, Tjong and Zhou showed that their method achieved better performance than other previously published methods. In the current study, the 13 test proteins were obtained from the study of Tjong and Zhou. Thus, we can compare the method proposed in the current study with Tjong and Zhou’s neural network method using the 13 proteins. Figure 1. Tradeoff between coverage and accuracy In their publication, Tjong and Zhou also used coverage and accuracy to evaluate the predictions. However, they defined accuracy using a loosened criterion of “true positive” such that if a predicted interface residue is within four nearest neighbors of an actual interface residue, then it is counted as a true positive. Here, in the comparison of the two methods, the strict definition of true positive is used, i.e., a predicted interface residue is counted as true positive only when it is a true interface residue. The original data were obtained from table 1 of Tjong and Zhou (2007), the accuracy for the neural network method was recalculated using this strict definition (Table 3). The coverage of the neural network was directly taken from Tjong and Zhou (2007). For each protein, Tjong and Zhou’s method reported one coverage and one accuracy. In contrast, the method proposed this study allows the users to tradeoff between coverage and accuracy based on their actual need. For the purpose of comparison, for each test protein, topranking patches are included into the set of predicted interface residues one by one in the decreasing order of ranks until coverage is the same as or higher than the coverage that the neural network method achieved on that protein. Then the coverage and accuracy of the two methods are compared. On a test protein, method A is better than B, if accuracy(A)>accuracy(B) and coverage (A)≥coverage(B). Table 3 shows that the graph kernel method proposed in this study achieves better results than the neural network method on 7 proteins (in bold font in table 3). On 4 proteins (shown in gray shading in table 3), the neural network method is better than the graph kernel method. On the remaining 2 proteins (in italic font in table 3), conclusions can be drawn because the two conditions, accuracy(A)>accuracy(B) and coverage (A)≥coverage(B), never become true at the same time, i.e., when coverage (graph kernel)>coverage(neural network), we have accuracy(graph kernel)accuracy(neural network). Note that the coverage of the graph kernel method increases in a discontinuous fashion as we use more patches to predict DNA-binding sites. One these two proteins, we were not able to reach at a point where the two methods have identical coverage. Given these situations, we consider that the two methods tie on these 2 proteins. Thus, these comparisons show that the graph kernel method can achieves better results than the neural network on 7 of the 13 proteins (shown in bold font in Table 3). Additionally, on another 4 proteins (shown in Italic font in Table 3), the graph kernel method ties with the neural network method. When averaged over the 13 proteins, the coverage and accuracy for the graph kernel method are 59% and 64%. It is worth to point out that, in the current study, the predictions are made using the protein structures that are unbound with DNA. In contrast, the data we obtained from Tjong and Zhou’s study were obtained using proteins structures bound with DNA. In their study, Tjong and Zhou showed that when unbound structures were used, the average coverage decreased by 6.3% and average accuracy by 4.7% for the 14 proteins (but the data for each protein was not shown).
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UA Contacts Know Your Security and Privacy Officer: • University-wide Privacy Officer: Jan Chaisson • University-wide Security Officer: Ashley Ewing • University Medical Center Privacy Officer is Jan Chaisson • University Medical Center Security Officer is Amy Sherwood • Brewer Porch Privacy/Security Officer is Warren Williams • Speech and Hearing Privacy/Security Officer is JoAnne Payne • Autism Spectrum Disorders Clinic Privacy/Security Officer is Sarah Ryan • UA Group Health Plan/FSA Privacy Officer is Emily Marbutt • UA Group Health Plan/FSA Security Officer is Greg Gaddis • WellBAMA Program Privacy/Security Officer is Heather Clayton • Working on Womanhood Program (WOW) Privacy/Security Officer is Jill Beck • Center for Advanced Public Safety (CAPS) Privacy/Security Officer is Vaughn Poe • Institutional Review Board Compliance Officer is Tanta Myles • College of Education Alabama Medicaid Agency Project Privacy/Security Officer: Rick Houser INTERNAL USE ONLY 32
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The NIH Peer Review Process Scientific Review Officer (SRO) • First level of peer review – Designated Federal Official – Extramural scientist administrator – Identifies and recruits reviewers – Manages conflicts of interest – Oversees arrangements for review meetings – Presides at review committee meetings – Prepares and releases summary statements 7 7
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National Institutes of Health Office Officeof ofthe theDirector Director National NationalInstitute Institute on onAging Aging National NationalInstitute Institute on onAlcohol AlcoholAbuse Abuse and andAlcoholism Alcoholism National NationalInstitute Institute of ofAllergy Allergyand and Infectious InfectiousDiseases Diseases National NationalInstitute Institute of ofArthritis Arthritisand and Musculoskeletal Musculoskeletal and andSkin SkinDiseases Diseases National NationalCancer Cancer Institute Institute National NationalInstitute Institute of ofChild ChildHealth Health and andHuman Human Development Development National NationalInstitute Instituteon on Deafness Deafnessand andOther Other Communication Communication Disorders Disorders National NationalInstitute Institute of ofDental Dentaland and Craniofacial Craniofacial Research Research National NationalInstitute Institute of ofDiabetes Diabetesand and Digestive Digestiveand and Kidney KidneyDiseases Diseases National NationalInstitute Institute on onDrug DrugAbuse Abuse National NationalInstitute Institute of ofEnvironmental Environmental Health HealthSciences Sciences National NationalEye Eye Institute Institute National NationalInstitute Institute of ofGeneral General Medical MedicalSciences Sciences National NationalHeart, Heart, Lung, Lung,and andBlood Blood Institute Institute National NationalHuman Human Genome GenomeResearch Research Institute Institute National NationalInstitute Institute of ofMental MentalHealth Health National NationalInstitute Institute of ofNeurological Neurological Disorders Disordersand and Stroke Stroke National NationalInstitute Institute of ofNursing NursingResearch Research National NationalInstitute Instituteof of Biomedical BiomedicalImaging Imaging and andBioengineering Bioengineering National NationalCenter Center for forComplementary Complementary and andAlternative Alternative Medicine Medicine Fogarty Fogarty International International Center Center National NationalCenter Center for forResearch Research Resources Resources National NationalLibrary Library of ofMedicine Medicine National NationalCenter Centeron on Minority Health Minority Healthand and Health HealthDisparities Disparities Clinical ClinicalCenter Center Center Centerfor for Information Information Technology Technology Center for Scientific Review
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National Institutes of Health Office of Extramural Research Office of the Director National Institute on Aging National Institute on Alcohol Abuse and Alcoholism National Institute of Allergy and Infectious Diseases National Institute of Arthritis and Musculoskeletal and Skin Diseases National Cancer Institute National Institute of Child Health and Human Development National Institute on Deafness and Other Communication Disorders National Institute of Dental and Craniofacial Research National Institute of Diabetes and Digestive and Kidney Diseases National Institute on Drug Abuse National Institute of Environmental Health Sciences National Eye Institute National Institute of General Medical Sciences National Heart, Lung, and Blood Institute National Human Genome Research Institute National Institute of Mental Health National Institute of Neurological Disorders and Stroke Fogarty International Center National Center for Research Resources National Center on Minority Health and Health Disparities National Center for Complementary and Alternative Medicine NIH Clinical Center Center for Information Technology National Library of Medicine Center for Scientific Review National Institute of Nursing Research National Institute of Biomedical Imaging and Bioengineering No funding authority3
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UA Contacts Know Your Security and Privacy Officer • University-wide Privacy Officer: Jan Chaisson • University-wide Security Officer: Ashley Ewing • University Medical Center Privacy Officer: Jan Chaisson • University Medical Center Security Officer: Amy Sherwood • Brewer Porch Privacy/Security Officer: Warren Williams • Speech and Hearing Privacy/Security Officer: JoAnne Payne • Autism Spectrum Disorders Clinic Privacy/Security Officer: Sarah Ryan • UA Group Health Plan/FSA/WellBama Privacy Officer: Emily Marbutt • UA Group Health Plan/FSA/WellBama Security Officer: Greg Gaddis • Working on Womanhood Program (WOW) Privacy/Security Officer: Jill Beck • Center for Advanced Public Safety (CAPS) Privacy/Security Officer: Vaughn Poe • Institutional Review Board Compliance Officer: Tanta Myles • College of Education Alabama Medicaid Agency Project Privacy/Security Officer: Rick Houser INTERNAL USE ONLY
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What should you do if ICE seeks access to your campus • Request for Access to Campus: If a federal immigration enforcement official, such as an ICE agent, requests access to a campus, LACCD personnel should immediately refer the federal official and his/her request to the Office of the President/Chancellor. The Office of the President/Chancellor will, in turn, work with the LACCD’s General Counsel to make a final determination on whether the request is lawful. • Request Access to Student Information/Record: Similarly, if a federal immigration enforcement official requests access to a student’s information/record, LACCD personnel should immediately refer the federal official and his/her request to the Office of the President/Chancellor. The Office of the President/Chancellor will, in turn, work with LACCD’s General Counsel to make a final determination on whether the request is lawful. • Communication with the Sheriff’s Department: When deemed appropriate, the Office of the President/Chancellor will contact the Los Angeles County Sheriff’s Department to act as a liaison. • Refusal to Follow Protocol by Federal Immigration Enforcement Official: If a federal immigration enforcement official refuses to follow the above-mentioned instruction, the LACCD personnel should immediately call 213-891-2025. • Reporting the Presence of Federal Immigration Enforcement Officials on Campus: Even where no direct request is made by a federal immigration enforcement official, the LACCD personnel, in addition to students, should immediately call 213-891-2025 to report federal immigration enforcement officials on any LACCD college.
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UA Contacts Know Your Security and Privacy Officer • University-wide Privacy Officer: Jan Chaisson • University-wide Security Officer: Ashley Ewing • University Medical Center Privacy Officer: Jan Chaisson • University Medical Center Security Officer: Amy Sherwood • Brewer Porch Privacy/Security Officer: Warren Williams • Speech and Hearing Privacy/Security Officer: JoAnne Payne • Autism Spectrum Disorders Clinic Privacy/Security Officer: JoAnne Payne • UA Group Health Plan/FSA Privacy Officer: Emily Marbutt • UA Group Health Plan/FSA Security Officer: Greg Gaddis • Working on Womanhood Program (WOW) Privacy/Security Officer: Jill Beck • Center for Advanced Public Safety (CAPS) Privacy/Security Officer: Vaughn Poe • Institutional Review Board Compliance Officer: Tanta Myles INTERNAL USE ONLY 32
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03/18/19 18:35 15 Traffic Mining Results 1: TCP,INPUT,129.110.96.117,ANY,*.*.*.*,80,DENY 2: TCP,INPUT,*.*.*.*,ANY,*.*.*.*,80,ACCEPT 3: TCP,INPUT,*.*.*.*,ANY,*.*.*.*,443,DENY 4: TCP,INPUT,129.110.96.117,ANY,*.*.*.*,22,DENY 5: TCP,INPUT,*.*.*.*,ANY,*.*.*.*,22,ACCEPT 6: TCP,OUTPUT,129.110.96.80,ANY,*.*.*.*,22,DENY 7: UDP,OUTPUT,*.*.*.*,ANY,*.*.*.*,53,ACCEPT 8: UDP,INPUT,*.*.*.*,53,*.*.*.*,ANY,ACCEPT 9: UDP,OUTPUT,*.*.*.*,ANY,*.*.*.*,ANY,DENY 10: UDP,INPUT,*.*.*.*,ANY,*.*.*.*,ANY,DENY 11: TCP,INPUT,129.110.96.117,ANY,129.110.96.80,22,DEN Y 12: TCP,INPUT,129.110.96.117,ANY,129.110.96.80,80,DEN Y 13: UDP,INPUT,*.*.*.*,ANY,129.110.96.80,ANY,DENY 14: UDP,OUTPUT,129.110.96.80,ANY,129.110.10.*,ANY,DE NY 15: TCP,INPUT,*.*.*.*,ANY,129.110.96.80,22,ACCEPT 16: TCP,INPUT,*.*.*.*,ANY,129.110.96.80,80,ACCEPT 17: UDP,INPUT,129.110.*.*,53,129.110.96.80,ANY,ACCEPT 18: Rule 1, Rule 2: ==> GENRERALIZATION Rule 1, Rule 16: ==> CORRELATED Rule 2, Rule 12: ==> SHADOWED Rule 4, Rule 5: ==> GENRERALIZATION Rule 4, Rule 15: ==> CORRELATED Rule 5, Rule 11: ==> SHADOWED Anomaly Discovery Result
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It’s really not this bad! Office Officeofofthe theDirector Director National NationalInstitute Institute on onAging Aging National NationalInstitute Institute on onAlcohol AlcoholAbuse Abuse and andAlcoholism Alcoholism National NationalInstitute Institute ofofAllergy Allergyand and Infectious InfectiousDiseases Diseases National NationalInstitute Institute ofofArthritis Arthritisand and Musculoskeletal Musculoskeletal and andSkin SkinDiseases Diseases National NationalCancer Cancer Institute Institute Eunice EuniceKennedy Kennedy Shriver ShriverNational NationalInstitute Institute ofofChild ChildHealth Healthand and Human HumanDevelopment Development National NationalInstitute Instituteon on Deafness Deafnessand andOther Other Communication Communication Disorders Disorders National NationalInstitute Institute ofofDental Dentaland and Craniofacial Craniofacial Research Research National NationalInstitute Institute ofofDiabetes Diabetesand and Digestive Digestiveand and Kidney Diseases Kidney Diseases National NationalInstitute Institute on onDrug DrugAbuse Abuse National NationalInstitute Institute ofofEnvironmental Environmental Health Sciences Health Sciences National NationalEye Eye Institute Institute National NationalInstitute Institute ofofGeneral General Medical Sciences Medical Sciences National NationalHeart, Heart, Lung, Lung,and andBlood Blood Institute Institute National NationalHuman Human Genome GenomeResearch Research Institute Institute National NationalInstitute Institute ofofMental MentalHealth Health National NationalInstitute Institute ofofNeurological Neurological Disorders Disordersand and Stroke Stroke National NationalInstitute Institute ofofNursing NursingResearch Research National NationalCenter Center for forComplementary Complementary and Alternative and Alternative Medicine Medicine John JohnE. E.Fogarty Fogarty International International Center Center National NationalCenter Center for forResearch Research Resources Resources National NationalLibrary Library ofofMedicine Medicine National NationalInstitute Instituteofof Biomedical BiomedicalImaging Imaging and andBioengineering Bioengineering Clinical ClinicalCenter Center Understanding NIH Center Centerfor for Information Information Technology Technology Slide 10 National NationalInstitute Instituteon on Minority MinorityHealth Healthand and Health Disparities Health Disparities Center Centerfor for Scientific ScientificReview Review 8 November 2017
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Understanding the Equity Summary Score Methodology 2 3. Calculate – The normalized analysts’ recommendations and the accuracy weightings are combined to create a single score. For the largest 1,500 stocks by market capitalization, these scores are then forcibly ranked against all the other scores to create a standardized Equity Summary Score on a scale of 0.1 to 10.0 for the 1,500 stocks. This means that there will be a uniform distribution of scores provided by the model thereby assisting investors in evaluating the largest stocks (in terms of Understanding the Equity Summary Score Methodology Provided By 2 capitalization), which typically make up the majority of individual investors’ portfolios. Finally, smaller cap stocks are then slotted into this distribution without a force ranking, and may not exhibit the same balanced distribution. The Equity Summary Score and associated sentiment ratings by StarMine are: 0.1 to 1.0 ‐ very bearish 1.1 to 3.0 ‐ bearish 3.1 to 7.0 ‐ neutral 7.1 to 9.0 ‐ bullish 9.1 to 10.0 ‐ very bullish Other Important Model Factors:  An Equity Summary Score is only provided for stocks with ratings from four or more independent research providers.  New research providers are ramped in slowly by StarMine to avoid rapid fluctuations in Equity Summary Scores. Indep. research providers that are removed from Fidelity.com will similarly be ramped out slowly to avoid rapid fluctuations. Notes on Using the Equity Summary Score: The Equity Summary Score and sentiment ratings are ratings of relative, not absolute forecasted performance. The StarMine model anticipates that the highest rated stocks, those labeled “Very Bullish” as a group, may outperform lower rated groups of stocks. In a rising market, most stocks may experience price increases, and in a declining market, most stocks may experience price declines  Proper diversification within a portfolio is critical to the effective use of the Equity Summary Score. Individual company performance is subject to a broad range of factors that cannot be adequately captured in any rating system.  Larger differences in Equity Summary Scores may lead to differences in future performance. The sentiment rating labels should only be used for quick categorization. An 8.9 Bullish is closer to a 9.1 Very Bullish than a 7.1 Bullish.  For a customer holding a stock with a lower Equity Summary Score, there are many important considerations (for example, taxes) that may be much more important than the Score.  The Equity Summary Score by StarMine does not predict future performance of underlying stocks. The Equity Summary Score model has only been in production since August 2009 and therefore no assumptions should be made about how the model will perform in differing market conditions. Understanding the Equity Summary Score Methodology Provided By 3 How has the Equity Summary Score performed? Transparency is a core value at Fidelity, and that is why StarMine provides Fidelity with a view of the historical aggregate performance of the Equity Summary Score across all covered stocks each month. You can use this to obtain insight into the performance and composition of the Equity Summary Score. In addition, the individual stock price performance during each period of the Equity Summary
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