Search

The NSF Office of Advanced Cyberinfrastructure has recognized the emerging and evolving need for platforms that fully integrate data and computing workflows, and is calling for research to deliver systems that provide a full spectrum of data services and also offer a coherent coupling with computing software. The Digital Environment to Enable Data-driven Science (DEEDS) project has created a cross-domain, self-serve platform for data and computing that supports the entire end-to-end research investigation process. DEEDS offers interactive interfaces to 1) collect, manage, and explore data, 2) define and launch tools, 3) track computational workflows, 4) access toolkits for ad-hoc analytics, and 5) publish and share the investigations with broader research community. All interfaces are available from a single dashboard so that the workflow between data and tools is smooth and intuitive. This paper highlights the DEEDS platform capabilities for providing integrated data and computing services throughout the entire research investigation lifecycle and presents some examples from use cases that contributed to the platform development.

The latitudinal diversity gradient (LDG) is one of the most recognized global patterns of species richness exhibited across a wide range of taxa. Numerous hypotheses have been proposed in the past two centuries to explain LDG, but rigorous tests of the drivers of LDGs have been limited by a lack of high-quality global species richness data. Here we produce a high-resolution (0.025° × 0.025°) map of local tree species richness using a global forest inventory database with individual tree information and local biophysical characteristics from ~1.3 million sample plots. We then quantify drivers of local tree species richness patterns across latitudes. Generally, annual mean temperature was a dominant predictor of tree species richness, which is most consistent with the metabolic theory of biodiversity (MTB). However, MTB underestimated LDG in the tropics, where high species richness was also moderated by topographic, soil and anthropogenic factors operating at local scales. Given that local landscape variables operate synergistically with bioclimatic factors in shaping the global LDG pattern, we suggest that MTB be extended to account for co-limitation by subordinate drivers. The team collaboration and manuscript development are supported by the web-based team science platform: science-i.org, with the project number 202205GFB2. We thank the following initiatives, agencies, teams and individuals for data collection and other technical support: the Global Forest Biodiversity Initiative (GFBI) for establishing the data standards and collaborative framework; United States Department of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) Program; University of Alaska Fairbanks; the SODEFOR, Ivory Coast; University Félix Houphouët-Boigny (UFHB, Ivory Coast); the Queensland Herbarium and past Queensland Government Forestry and Natural Resource Management departments and staff for data collection for over seven decades; and the National Forestry Commission of Mexico (CONAFOR). We thank M. Baker (Carbon Tanzania), together with a team of field assistants (Valentine and Lawrence); all persons who made the Third Spanish Forest Inventory possible, especially the main coordinator, J. A. Villanueva (IFN3); the French National Forest Inventory (NFI campaigns (raw data 2005 and following annual surveys, were downloaded by GFBI at https://inventaire-forestier.ign.fr/spip.php?rubrique159; site accessed on 1 January 2015)); the Italian Forest Inventory (NFI campaigns raw data 2005 and following surveys were downloaded by GFBI at https://inventarioforestale.org/; site accessed on 27 April 2019); Swiss National Forest Inventory, Swiss Federal Institute for Forest, Snow and Landscape Research WSL and Federal Office for the Environment FOEN, Switzerland; the Swedish NFI, Department of Forest Resource Management, Swedish University of Agricultural Sciences SLU; the National Research Foundation (NRF) of South Africa (89967 and 109244) and the South African Research Chair Initiative; the Danish National Forestry, Department of Geosciences and Natural Resource Management, UCPH; Coordination for the Improvement of Higher Education Personnel of Brazil (CAPES, grant number 88881.064976/2014-01); R. Ávila and S. van Tuylen, Instituto Nacional de Bosques (INAB), Guatemala, for facilitating Guatemalan data; the National Focal Center for Forest condition monitoring of Serbia (NFC), Institute of Forestry, Belgrade, Serbia; the Thünen Institute of Forest Ecosystems (Germany) for providing National Forest Inventory data; the FAO and the United Nations High Commissioner for Refugees (UNHCR) for undertaking the SAFE (Safe Access to Fuel and Energy) and CBIT-Forest projects; and the Amazon Forest Inventory Network (RAINFOR), the African Tropical Rainforest Observation Network (AfriTRON) and the ForestPlots.net initiative for their contributions from Amazonian and African forests. The Natural Forest plot data collected between January 2009 and March 2014 by the LUCAS programme for the New Zealand Ministry for the Environment are provided by the New Zealand National Vegetation Survey Databank https://nvs.landcareresearch.co.nz/. We thank the International Boreal Forest Research Association (IBFRA); the Forestry Corporation of New South Wales, Australia; the National Forest Directory of the Ministry of Environment and Sustainable Development of the Argentine Republic (MAyDS) for the plot data of the Second National Forest Inventory (INBN2); the National Forestry Authority and Ministry of Water and Environment of Uganda for their National Biomass Survey (NBS) dataset; and the Sabah Biodiversity Council and the staff from Sabah Forest Research Centre. All TEAM data are provided by the Tropical Ecology Assessment and Monitoring (TEAM) Network, a collaboration between Conservation International, the Missouri Botanical Garden, the Smithsonian Institution and the Wildlife Conservation Society, and partially funded by these institutions, the Gordon and Betty Moore Foundation and other donors, with thanks to all current and previous TEAM site manager and other collaborators that helped collect data. We thank the people of the Redidoti, Pierrekondre and Cassipora village who were instrumental in assisting with the collection of data and sharing local knowledge of their forest and the dedicated members of the field crew of Kabo 2012 census. We are also thankful to FAPESC, SFB, FAO and IMA/SC for supporting the IFFSC. This research was supported in part through computational resources provided by Information Technology at Purdue, West Lafayette, Indiana.This work is supported in part by the NASA grant number 12000401 ‘Multi-sensor biodiversity framework developed from bioacoustic and space based sensor platforms’ (J. Liang, B.P.); the USDA National Institute of Food and Agriculture McIntire Stennis projects 1017711 (J. Liang) and 1016676 (M.Z.); the US National Science Foundation Biological Integration Institutes grant NSF‐DBI‐2021898 (P.B.R.); the funding by H2020 VERIFY (contract 776810) and H2020 Resonate (contract 101000574) (G.-J.N.); the TEAM project in Uganda supported by the Moore foundation and Buffett Foundation through Conservation International (CI) and Wildlife Conservation Society (WCS); the Danish Council for Independent Research | Natural Sciences (TREECHANGE, grant 6108- 00078B) and VILLUM FONDEN grant number 16549 (J.-C.S.); the Natural Environment Research Council of the UK (NERC) project NE/T011084/1 awarded to J.A.-G. and NE/S011811/1; ERC Advanced Grant 291585 (‘T-FORCES’) and a Royal Society-Wolfson Research Merit Award (O.L.P.); RAINFOR plots supported by the Gordon and Betty Moore Foundation and the UK Natural Environment Research Council, notably NERC Consortium Grants ‘AMAZONICA’ (NE/F005806/1), ‘TROBIT’ (NE/D005590/1) and ‘BIO-RED’ (NE/N012542/1); CIFOR’s Global Comparative Study on REDD+ funded by the Norwegian Agency for Development Cooperation, the Australian Department of Foreign Affairs and Trade, the European Union, the International Climate Initiative (IKI) of the German Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety and the CGIAR Research Program on Forests, Trees and Agroforestry (CRP-FTA) and donors to the CGIAR Fund; AfriTRON network plots funded by the local communities and NERC, ERC, European Union, Royal Society and Leverhume Trust; a grant from the Royal Society and the Natural Environment Research Council, UK (S.L.L.); National Science Foundation CIF21 DIBBs: EI: number 1724728 (A.C.C.); National Natural Science Foundation of China (31800374) and Shandong Provincial Natural Science Foundation (ZR2019BC083) (H.L.). UK NERC Independent Research Fellowship (grant code: NE/S01537X/1) (T.J.); a Serra-Húnter Fellowship provided by the Government of Catalonia (Spain) (S.d.-M.); the Brazilian National Council for Scientific and Technological Development (CNPq, grant 442640/2018-8, CNPq/Prevfogo-Ibama number 33/2018) (C.A.S.); a grant from the Franklinia Foundation (D.A.C.); Russian Science Foundation project number 19-77-300-12 (R.V.); the Takenaka Scholarship Foundation (A.O.A.); the German Research Foundation (DFG), grant number Am 149/16-4 (C.A.); the Romania National Council for Higher Education Funding, CNFIS, project number CNFIS-FDI-2022-0259 (O.B.); Natural Sciences and Engineering Research Council of Canada (RGPIN-2019-05109 and STPGP506284) and the Canadian Foundation for Innovation (36014) (H.Y.H.C.); the project SustES—Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/0000797) (E.C.); Consejo de Ciencia y Tecnología del estado de Durango (2019-01-155) (J.J.C.-R.); Science and Engineering Research Board (SERB), New Delhi, Government of India (file number PDF/2015/000447)— ‘Assessing the carbon sequestration potential of different forest types in Central India in response to climate change’ (J.A.D.); Investissement d’avenir grant of the ANR (CEBA: ANR-10-LABEX-0025) (G.D.); National Foundation for Science & Technology Development of Vietnam, 106-NN.06-2013.01 (T.V.D.); Queensland government, Department of Environment and Science (T.J.E.); a Czech Science Foundation Standard grant (19-14620S) (T.M.F.); European Union Seventh Framework Program (FP7/2007– 2013) under grant agreement number 265171 (L. Finer, M. Pollastrini, F. Selvi); grants from the Swedish National Forest Inventory, Swedish University of Agricultural Sciences (J.F.); CNPq productivity grant number 311303/2020-0 (A.L.d.G.); DFG grant HE 2719/11-1,2,3; HE 2719/14-1 (A. Hemp); European Union’s Horizon Europe research project OpenEarthMonitor grant number 101059548, CGIAR Fund INIT-32-MItigation and Transformation Initiative for GHG reductions of Agrifood systems RelaTed Emissions (MITIGATE+) (M.H.); General Directorate of the State Forests, Poland (1/07; OR-2717/3/11; OR.271.3.3.2017) and the National Centre for Research and Development, Poland (BIOSTRATEG1/267755/4/NCBR/2015) (A.M.J.); Czech Science Foundation 18-10781 S (S.J.); Danish of Ministry of Environment, the Danish Environmental Protection Agency, Integrated Forest Monitoring Program—NFI (V.K.J.); State of São Paulo Research Foundation/FAPESP as part of the BIOTA/FAPESP Program Project Functional Gradient-PELD/BIOTA-ECOFOR 2003/12595-7 & 2012/51872-5 (C.A.J.); Danish Council for Independent Research—social sciences—grant DFF 6109– 00296 (G.A.K.); Russian Science Foundation project 21-46-07002 for the plot data collected in the Krasnoyarsk region (V.K.); BOLFOR (D.K.K.); Department of Biotechnology, New Delhi, Government of India (grant number BT/PR7928/ NDB/52/9/2006, dated 29 September 2006) (M.L.K.); grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (J.N.K.); Korea Forest Service (2018113A00-1820-BB01, 2013069A00-1819-AA03, and 2020185D10- 2022-AA02) and Seoul National University Big Data Institute through the Data Science Research Project 2016 (H.S.K.); the Brazilian National Council for Scientific and Technological Development (CNPq, grant 442640/2018-8, CNPq/Prevfogo-Ibama number 33/2018) (C.K.); CSIR, New Delhi, government of India (grant number 38(1318)12/EMR-II, dated: 3 April 2012) (S.K.); Department of Biotechnology, New Delhi, government of India (grant number BT/ PR12899/ NDB/39/506/2015 dated 20 June 2017) (A.K.); Coordination for the Improvement of Higher Education Personnel (CAPES) #88887.463733/2019-00 (R.V.L.); National Natural Science Foundation of China (31800374) (H.L.); project of CEPF RAS ‘Methodological approaches to assessing the structural organization and functioning of forest ecosystems’ (AAAA-A18-118052590019-7) funded by the Ministry of Science and Higher Education of Russia (N.V.L.); Leverhulme Trust grant to Andrew Balmford, Simon Lewis and Jon Lovett (A.R.M.); Russian Science Foundation, project 19-77-30015 for European Russia data processing (O.M.); grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (M.T.E.M.); the National Centre for Research and Development, Poland (BIOSTRATEG1/267755/4/NCBR/2015) (S.M.); the Secretariat for Universities and of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund (A. Morera); Queensland government, Department of Environment and Science (V.J.N.); Pinnacle Group Cameroon PLC (L.N.N.); Queensland government, Department of Environment and Science (M.R.N.); the Natural Sciences and Engineering Research Council of Canada (RGPIN-2018-05201) (A.P.); the Russian Foundation for Basic Research, project number 20-05-00540 (E.I.P.); European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement number 778322 (H.P.); Science and Engineering Research Board, New Delhi, government of India (grant number YSS/2015/000479, dated 12 January 2016) (P.S.); the Chilean Government research grants Fondecyt number 1191816 and FONDEF number ID19 10421 (C.S.-E.); the Deutsche Forschungsgemeinschaft (DFG) Priority Program 1374 Biodiversity Exploratories (P.S.); European Space Agency projects IFBN (4000114425/15/NL/FF/gp) and CCI Biomass (4000123662/18/I-NB) (D. Schepaschenko); FunDivEUROPE, European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement number 265171 (M.S.-L.); APVV 20-0168 from the Slovak Research and Development Agency (V.S.); Manchester Metropolitan University’s Environmental Science Research Centre (G.S.); the project ‘LIFE+ ForBioSensing PL Comprehensive monitoring of stand dynamics in Białowieża Forest supported with remote sensing techniques’ which is co-funded by the EU Life Plus programme (contract number LIFE13 ENV/PL/000048) and the National Fund for Environmental Protection and Water Management in Poland (contract number 485/2014/WN10/OP-NM-LF/D) (K.J.S.); Global Challenges Research Fund (QR allocation, MMU) (M.J.P.S.); Czech Science Foundation project 21-27454S (M.S.); the Russian Foundation for Basic Research, project number 20-05-00540 (N. Tchebakova); Botanical Research Fund, Coalbourn Trust, Bentham Moxon Trust, Emily Holmes scholarship (L.A.T.); the programmes of the current scientific research of the Botanical Garden of the Ural Branch of Russian Academy of Sciences (V.A.U.); FCT—Portuguese Foundation for Science and Technology—Project UIDB/04033/2020. Inventário Florestal Nacional—ICNF (H. Viana); Grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (C.W.); grants from the Swedish National Forest Inventory, Swedish University of Agricultural Sciences (B.W.); ATTO project (grant number MCTI-FINEP 1759/10 and BMBF 01LB1001A, 01LK1602F) (F.W.); ReVaTene/ PReSeD-CI 2 is funded by the Education and Research Ministry of Côte d’Ivoire, as part of the Debt Reduction-Development Contracts (C2Ds) managed by IRD (I.C.Z.-B.); the National Research Foundation of South Africa (NRF, grant 89967) (C.H.). The Tropical Plant Exploration Group 70 1 ha plots in Continental Cameroon Mountains are supported by Rufford Small Grant Foundation, UK and 4 ha in Sierra Leone are supported by the Global Challenge Research Fund through Manchester Metropolitan University, UK; the National Geographic Explorer Grant, NGS-53344R-18 (A.C.-S.); University of KwaZulu-Natal Research Office grant (M.J.L.); Universidad Nacional Autónoma de México, Dirección General de Asuntos de Personal Académico, Grant PAPIIT IN-217620 (J.A.M.). Czech Science Foundation project 21-24186M (R.T., S. Delabye). Czech Science Foundation project 20-05840Y, the Czech Ministry of Education, Youth and Sports (LTAUSA19137) and the long-term research development project of the Czech Academy of Sciences no. RVO 67985939 (J.A.). The American Society of Primatologists, the Duke University Graduate School, the L.S.B. Leakey Foundation, the National Science Foundation (grant number 0452995) and the Wenner-Gren Foundation for Anthropological Research (grant number 7330) (M.B.). Research grants from Conselho Nacional de Desenvolvimento Científico e Tecnologico (CNPq, Brazil) (309764/2019; 311303/2020) (A.C.V., A.L.G.). The Project of Sanya Yazhou Bay Science and Technology City (grant number CKJ-JYRC-2022-83) (H.-F.W.). The Ugandan NBS was supported with funds from the Forest Carbon Partnership Facility (FCPF), the Austrian Development Agency (ADC) and FAO. FAO’s UN-REDD Program, together with the project on ‘Native Forests and Community’ Loan BIRF number 8493-AR UNDP ARG/15/004 and the National Program for the Protection of Native Forests under UNDP funded Argentina’s INBN2.

Mounting evidence suggests that geographic ranges of tree species worldwide are shifting under global environmental change, but little is known about forest migration—the shift in the geographic ranges of forest types—and how it differs from individual tree species migration. Here, based on in situ records of more than 9 million trees from 596,282 sample plots, we quantified and compared the migration patterns of forests and tree species across North America between 1970 and 2019. On average, forests migrated at a mean velocity of 205.2 km per decade, which is twice as fast as species-level migration (95.6 km per decade), and 12 times faster than the average of previous estimates (16.3 km per decade). Our findings suggest that as subtle perturbations in species abundance can aggregate to change an entire forest from one type to another, failing to see the forest for the trees may result in a gross underestimation of the impacts of global change on forest ecosystem functioning and services. With the first forest classification and quantification of forest migration patterns at a continental level, this study provides an urgently needed scientific basis for a new paradigm of adaptive forest management and conservation under a rapid forest migration.

An ever-expanding photovoltaics (PV) community has been publishing enormous amounts of data regarding all aspects of PV technology. The data generated via these studies and reports range from experiments, simulation, and standard tests to policies and economics of PV. These data require organization, systematic storage, and analyses. Previous works have focused on research-specific data environments and repositories. However, a comprehensive discipline-neutral platform for preserving, sharing, and analyzing the data has not been built. Digital Environment for Enabling Data-Driven Science (DEEDS) provides a unique solution to this problem. DEEDS enables a user to create datasets (projects), cases, and tools; and store data which can be structured, compared, and numerically analyzed, all on a single holistic online platform. In this paper, we demonstrate the capabilities of DEEDS using an example research study called the Solar PV Diagnosis. DEEDS platform has the potential to be used by the entire PV community to preserve various PV projects, interpret their performance and reliability, and to facilitate worldwide collaboration.

Background Positron emission tomography may have a role in the pretreatment workup of patients with thymic malignancies. This study was undertaken to determine the utility of the maximum standardized uptake value (SUVmax) in predicting histologic type and tumor stage in a large cohort of thymic epithelial tumors. Methods The large, multiinstitutional, prospective database of The International Thymic Malignancy Interest Group (ITMIG) was queried for the use of positron emission tomography in the pretreatment workup of patients with thymic tumors. Data analyzed included demographics, SUVmax, histologic tumor type, and tumor stage. The distribution of SUVmax according to histologic type and Masaoka-Koga pathologic stage was determined, and the ability of SUVmax to predict these two variables was calculated using analysis of receiver operating characteristic curves. Results Since 2012, data from 926 patients with thymic malignancies were entered into the ITMIG prospective database, of which 154 had a reported value for SUVmax. The area under the receiver operating characteristic curve for SUVmax in predicting histologic type and pathologic stage was 0.79 (95% confidence interval, 0.70 to 0.88; p Conclusions Positron emission tomography has utility in predicting clinicopathologic features of thymic malignancies. These results may have clinical application in the pretreatment workup of patients with these rare tumors.

Emerging technologies and big data influence the role of nurses, calling for new ways of thinking and teaching. Innovative educational methods are needed to prepare students for providing evidence-based care in today's complex healthcare environment. Active learning methods appeal to tech-savvy, self-directed learners who desire instant results during the learning process. The aim of this pretest/posttest study was to evaluate the impact of active learning methods on student attitudes and feelings, using the Grasha-Riechmann Student Learning Style Scale. Results were used to tailor active learning interventions using Twitter and Federal Adverse Event Reporting System data, for a research and evidence-based practice nursing course. Participants (N = 126) evaluated tweets describing adverse drug events and their concordance with federal reporting system data. Paired-samples t test results revealed significant differences (P < .05) between pretest/posttest for five of the six learning style preferences. Active learning methods resulted in high levels of student engagement and satisfaction. Data mining as an active learning intervention is popular with learners and offers a quick, valuable way to reveal real-world adverse drug event experiences while introducing basic research principles.

Smart infusion pump technology is a mainstay in health care, and the integration and use of those pumps is crucial for patient safety. An institution purchasing smart infusion pumps has the ability to trial the various vendors before purchase, however literature that documents a conversion from one pump to another is lacking. This article describes the conversion from one smart infusion pump platform to another at a government institution and a large multisite facility. The differences in 2 smart infusion pumps are described as well as lessons learned following the conversion in both organizations.

Prior studies show an association between caregiver depression and child health outcomes. There has been little examination of depression among caregivers of HIV-infected children in sub-Saharan countries where pediatric HIV is concentrated. Using baseline data collected in the pediatric HIV disclosure intervention trial, Sankofa, we examined the prevalence and factors associated with depression among caregivers (N = 446) of children infected with HIV in Ghana. Data were analyzed with descriptive and regression analyses. The mean age of the caregivers was 42.2 ± 10.4 years. Eighty percent of the caregivers were female and 59% were HIV-infected. Twenty-eight percent (n = 126) of the caregivers were found to have mild to severe depression. In the adjusted model, factors significantly associated with caregiver depression included: HIV-positive caregiver status (P = 0.04), low income (P = 0.02), lower social support, (P = 0.01), lower HIV knowledge, (P = 0.01), worse HIV illness perceptions (P≤0.001), and greater perceived HIV stigma (P≤0.001). Although we found a high prevalence of depression among our study participants, several of the risks factors identified are modifiable and amenable to interventions that are locally available and affordable.

Adherence to antiretroviral therapy (ART) remains one of the greatest obstacles in pediatric HIV care. We sought to determine the prevalence of adherence to ART among undisclosed HIV-infected children and adolescents in Ghana. We analyzed baseline data from HIV-infected children and adolescents aged 7 to 18 years old enrolled in the SANKOFA Pediatric HIV disclosure intervention study in Ghana. Antiretroviral medication adherence was measured using caregiver 3-day recall; child 3-day recall; and pharmacy records for antiretroviral time-to-refill. Four hundred and twenty child-caregiver dyads were enrolled from January 2013 to June 2016. The median adherence (interquartile range), as measured by time-to-refill, was 93.2% (68.0% – 100.0%). However, only 47.5% of children had ≥95% adherence (“good adherence”) using time-to-refill data. Children of caregivers who had received secondary or higher level of education versus no school (aOR, 2.90, 95% Confidence Interval, CI 1.29 to 6.56), p = 0.010) or elementary education only (aOR, 2.20, CI, 1.24 to 3.88, p = 0.007) were more likely to have “good adherence” (≥95%). In this cohort of children unaware of their HIV positive status, median ART adherence rate was sub-optimal (by World Health Organization definition) while 38% had poor adherence (

The infusion pump informatics (IPI) system represents an innovative collaboration among a community of practice and researchers at Purdue University; the Rosen Center for Advanced Computing (RCAC) and the Regenstrief Center for Healthcare Engineering (RCHE). This six-year-old decision support system was started to address needs for better management of smart infusion pumps. IPI supports a systems approach to continuous improvement in the safety of medications administered with smart infusion pumps. Its design has been user driven and includes two essential components. The first component, developed by a RCAC team led by a senior research scientist (A.C.C.), receives data uploaded from smart infusion pumps, provides secure Internet access for clinicians, and enables analyses requested by its users. Clinicians using IPI are able to navigate using intuitive, point-and-click technology to execute complex analytics and receive real-time online reports that can be configured to their specification and downloaded in a CSV file for customized analyses offline (Figure 1). The second component of IPI is CatalyzeCare, which is a web-based hub supporting communities of practice (i.e., groups of people who share a concern, set of problems, or passion about a topic and who deepen their knowledge and expertise in this area by interacting on an ongoing basis).1 The IPI community shares infusion pump alert data, drug libraries, and domain knowledge to enable interhospital comparative analyses and exchange of expertise. Since its inception in 2008, the community has grown to include representatives from more than 50 hospitals. The following cases, which were provided by community members, illustrate how IPI is being used to improve performance in areas recommended as priorities for smart infusion pump implementation and use.2,3 Using Informatics to Improve Medical Device Safety And Systems Thinking

Information technology and high-performance computing play a critical role in the development of virtual organizations for science and engineering disciplines. In this paper, we report on our efforts to design and develop pharmaHUB.org, a prototype virtual organization that intends to promote the joint creation and sharing of tools, knowledge, and educational materials across the various science and engineering disciplines related to pharmaceutical product development and manufacturing. pharmaHUB has been brought online using middleware from HUBzero which provides content management and community building capabilities and also makes it possible to deliver simulation tools through the web. By leveraging the HUBzero middleware and forming partnerships with existing communities of researchers, it has been possible to rapidly develop a prototype virtual organization with a significant offering of educational and simulation tool resources. Prototype tools to support the knowledge management needs of pharmaceutical development have also been integrated into the hub environment.

Prevalence of pediatric HIV disclosure is low in resource-limited settings. Innovative, culturally sensitive, and patient-centered disclosure approaches are needed. Conducting such studies in resource-limited settings is not trivial considering the challenges of capturing, cleaning, and storing clinical research data. To overcome some of these challenges, the Sankofa pediatric disclosure intervention adopted an interactive cyber infrastructure for data capture and analysis. The Sankofa Project database system is built on the HUBzero cyber infrastructure ( https://hubzero.org ), an open source software platform. The hub database components support: (1) data management - the "databases" component creates, configures, and manages database access, backup, repositories, applications, and access control; (2) data collection - the "forms" component is used to build customized web case report forms that incorporate common data elements and include tailored form submit processing to handle error checking, data validation, and data linkage as the data are stored to the database; and (3) data exploration - the "dataviewer" component provides powerful methods for users to view, search, sort, navigate, explore, map, graph, visualize, aggregate, drill-down, compute, and export data from the database. The Sankofa cyber data management tool supports a user-friendly, secure, and systematic collection of all data. We have screened more than 400 child-caregiver dyads and enrolled nearly 300 dyads, with tens of thousands of data elements. The dataviews have successfully supported all data exploration and analysis needs of the Sankofa Project. Moreover, the ability of the sites to query and view data summaries has proven to be an incentive for collecting complete and accurate data. The data system has all the desirable attributes of an electronic data capture tool. It also provides an added advantage of building data management capacity in resource-limited settings due to its innovative data query and summary views and availability of real-time support by the data management team.

With the scale-up of effective antiretroviral therapy in resource-limited settings, many HIV-infected children are now able to survive into adulthood. To achieve this potential, children must navigate normative developmental processes and challenges while living with an unusually complex, stigmatizing, potentially fatal chronic illness and meeting the demands of treatment.Yet many of these children, especially preadolescents, do not know they are HIV-infected. Despite compelling evidence supporting the merits of informing children of their HIV status, there has been little emphasis on equipping the child's caregiver with information and skills to promote disclosure, particularly, when the caregiver faces a variety of sociocultural barriers and is reluctant to do so. In this study, we present the background, process and methods for a first of its kind collaboration that is examining the efficacy of an intervention developed to facilitate the engagement of caregivers in the process of disclosure in a manner suitable to the sociocultural context and developmental age and needs of the child in Ghana. We also report preliminary data that supported the design of the intervention approach and currently available domains of the data system. Finally, we discuss challenges and implications for future research.

Hierarchical video browsing and feature-based video retrieval are two standard methods for accessing video content. Very little research, however, has addressed the benefits of integrating these two methods for more effective and efficient video content access. In this paper, we introduce Insight Video, a video analysis and retrieval system, which joins video content hierarchy, hierarchical browsing and retrieval for efficient video access. We propose several video processing techniques to organize the content hierarchy of the video. We first apply a camera motion classification and key-frame extraction strategy that operates in the compressed domain to extract video features. Then, shot grouping, scene detection and pairwise scene clustering strategies are applied to construct the video content hierarchy. We introduce a video similarity evaluation scheme at different levels (key-frame, shot, group, scene, and video.) By integrating the video content hierarchy and the video similarity evaluation scheme, hierarchical video browsing and retrieval are seamlessly integrated for efficient content access. We construct a progressive video retrieval scheme to refine user queries through the interactions of browsing and retrieval. Experimental results and comparisons of camera motion classification, key-frame extraction, scene detection, and video retrieval are presented to validate the effectiveness and efficiency of the proposed algorithms and the performance of the system. © 2005 IEEE.

Real-world video-based applications require database technology that is capable of storing digital video in the form of video databases and providing content-based video search and retrieval. Methods for handling traditional data storage, query, search, retrieval, and presentation cannot be extended to provide this functionality. The VDBMS research initiative is motivated by the requirements of video-based applications to search and retrieve portions of video data based on content and by the need for testbed facilities to facilitate research in the area of video database management. In this paper we describe the VDBMS video database research platform, a system that supports comprehensive and efficient database management for digital video. Our fundamental concept is to provide a full range of functionality for video as a well-defined abstract database data type, with its own description, parameters, and applicable methods. Research problems that are addressed by VDBMS to support the handling of video data include MPEG7 standard multimedia content representation, algorithms for image-based shot detection, image processing techniques for extracting low-level visual features, a high-dimensional indexing technique to access the high-dimensional feature vectors extracted by image preprocessing, multimedia query processing and optimization, new query operators, real-time stream management, a search-based buffer management policy, and an access control model for selective, content-based access to streaming video. VDBMS also provides an environment for testing the correctness and scope of new video processing techniques, measuring the performance of algorithms in a standardized way, and comparing the performance of different implementations of an algorithm or component. We are currently developing video component wrappers with well-defined interfaces to facilitate the modification or replacement of video processing components. The ultimate goal of the VDBMS project is a flexible, extensible framework that can be used by the research community for developing, testing, and benchmarking video database technologies.

Purpose A Web-based analytics system for conducting inhouse evaluations and cross-facility comparisons of alert data generated by smart infusion pumps is described. Summary The Infusion Pump Informatics (IPI) project, a collaborative effort led by research scientists at Purdue University, was launched in 2009 to provide advanced analytics and tools for workflow analyses to assist hospitals in determining the significance of smart-pump alerts and reducing nuisance alerts. The IPI system allows facility-specific analyses of alert patterns and trends, as well as cross-facility comparisons of alert data uploaded by more than 55 participating institutions using different types of smart pumps. Tools accessible through the IPI portal include (1) charts displaying aggregated or breakout data on the top drugs associated with alerts, numbers of alerts per device or care area, and override-to-alert ratios, (2) investigative reports that can be used to characterize and analyze pump-programming errors in a variety of ways (e.g., by drug, by infusion type, by time of day), and (3) “drill-down” workflow analytics enabling users to evaluate alert patterns—both internally and in relation to patterns at other hospitals—in a quick and efficient stepwise fashion. Conclusion The formation of the IPI analytics system to support a community of hospitals has been successful in providing sophisticated tools for member facilities to review, investigate, and efficiently analyze smart-pump alert data, not only within a member facility but also across other member facilities, to further enhance smart pump drug library design.

We outline the design of a recommendation system (MyPYTHIA) implemented as a Web portal. MyPYTHIA's design objectives include evaluating the quality and performance of scientific software on Grid platforms, creating knowledge about which software and computational services should be selected for solving particular problems, selecting parameters of software (or of computational services) based on user-specified computational objectives, providing access to performance data and knowledge bases over the Web and enabling recommendations for targeted application domains. MyPYTHIA uses a combination of statistical analysis, pattern extraction techniques and a database of software performance to map feature-based representations of problem instances to appropriate software. MyPYTHIA's open architecture allows the user to customize it for conducting individual case studies. We describe the architecture as well as several scientific domains of knowledge enabled by such case studies. Copyright © 2002 John Wiley & Sons, Ltd.

The article presents the software architecture and implementation of the problem-solving environment (PSE) PELLPACK for modeling physical objects described by partial differential equations (PDEs). The scope of this PSE is broad, as PELLPACK incorporates many PDE solving systems, and some of these, in turn, include several specific PDE solving methods. Its coverage for 1D, 2D. and 3D elliptic or parabolic problems is quite broad, and it handles some hyperbolic problems, Since a PSE should provided complete support for the problem-solving process, PELLPACK also contains a large amount of code to support graphical user interfaces, analystic tools, user help, domain or mesh partitioning, machine and data selection, visualization, and various other tasks. Its total size is well over 1 million lines code. Its open-ended software architecture consists of several software layers. The top layer is an interactive graphical interface for specifying the PDE model and its solution framework This interface saves that results of the user specification in the form of a very high level PDE language which is an alternative interface to the PELLPACK system. This language also allows a user to specify the PDE problem and its solution framework textually in a natural form. The PELLPACK language preprocessor generates a Fortran control program with the interfaces, calls to specified components and libraries of the PDE solution framework, and functions defining the PDE problem. The PELLPACK program execution is supported by a hign-level tool where the virtual parallel system is defined, where the compilation, loading, and execution are controlled. Finally, the PELLPACK PSE integrates several PDE libraries and PDE systems based on the decomposition of discrete geometric data to map sparse PDE computations to parallel machines. An instance of the system is available as aWeb server(WebPELLPACK) for public use at http://pellpack.cs.purdue.edu.

Background: Our knowledge of thymic malignancies has largely been derived from small, single-institution series. Recognition of the need for broad collaboration led to the creation of the International Thymic Malignancy Interest Group (ITMIG) and the development of a large, centralized database to advance knowledge of these rare tumors. Methods: A multidisciplinary Database Committee was convened to define a common set of data elements a priori. Retrospective data were solicited from ITMIG members and collated using standardized fields. Patients with thymoma, thymic carcinoma, or thymic carcinoid were included. Results: Over a 6-month period, 47 institutions spanning 15 countries contributed a total of 6097 cases (mean, 129 [range, 10-1209]). The sex distribution was equal for thymomas, but there was a greater proportion of men with thymic carcinoma and thymic carcinoid (p < 0.0001). Nearly all cases (99%) were treated surgically. WHO type B2 was the most frequent histologic classification among thymomas, whereas squamous was the most common among thymic carcinomas. In total, 38% of patients with thymoma had myasthenia gravis compared with less than or equal to 5% for thymic carcinoma and thymic carcinoid. Median overall survival was 18.9 years (95% confidence interval [CI], 17.4-20.3) for thymoma, 6.8 years (95% CI, 5.5-7.9) for thymic carcinoma, and 7.5 years (95% CI, 6.5-8.5) for thymic carcinoid. Conclusions: The rapid creation of the ITMIG database demonstrates the feasibility of international collaboration for this rare set of malignancies and attests to the engagement of its membership. This database represents the largest collective data set ever assembled and provides an unprecedented resource for research of these tumors.

We describe a newly established research project called SoftLab in the area of computational science and computational engineering. The SoftLab project attempts to link physical laboratory experimentation with computer control and simulation to provide a virtual laboratory for computational science. We describe the overall project objectives and then introduce the three focus projects of SoftLab : Two Chemical Engineering SoftLabs (bioseparation and computational electronics) and a Mechanical Engineering SoftLab (computational mechanics). Preliminary results of our efforts are also described.

New data for thermophysical properties of materials are being discovered and measured routinely throughout the world, but sometimes conflicts exist in the published data from various sources. We introduce an online thermal properties database intended for use as a repository for experimentally obtained thermophysical properties of materials by researchers across the globe. This online database is a part of www.thermalhub.org, which is a NSF-funded cyber infrastructure initiative aimed at serving the global heat transfer community. The relevant details associated with an experiment, such as the material type and composition, the thermophysical property measured, the test methods employed, the test conditions of the experiment, the results of the experiments, the uncertainty associated with the experimental results, and the literature where the results are published, are included as fields in the online database. It is known that one thermophysical property can be tested by various methods whose results may differ. Similarly, one thermophysical property can be experimentally measured under varying test conditions to produce different results.Copyright © 2011 by ASME

We present the demonstration of the design of "STEAM", Purdue Boiler Makers' stream database system that allows for the processing of continuous and snap-shot queries over data streams. Specifically, the demonstration focuses on the query processing engine, "Nile". Nile extends the query processor engine of an object-relational database management system, PREDATOR, to process continuous queries over data streams. Nile supports extended SQL operators that handle sliding-window execution as an approach to restrict the size of the stored state in operators such as join.

To achieve more efficient video indexing and access, we introduce a video database management framework and strategies for video content structure and events mining. The video shot segmentation and representative frame selection strategy are first utilized to parse the continuous video stream into physical units. Video shot grouping, group merging, and scene clustering schemes are then proposed to organize the video shots into a hierarchical structure using clustered scenes, scenes, groups, and shots, in increasing granularity from top to bottom. Then, audio and video processing techniques are integrated to mine event information, such as dialog, presentation and clinical operation, from the detected scenes. Finally, the acquired video content structure and events are integrated to construct a scalable video skimming tool which can be used to visualize the video content hierarchy and event information for efficient access. Experimental results are also presented to evaluate the performance of the proposed framework and algorithms.

The increased use of video data sets for multimedia-based applications has created a demand for strong video database support, including efficient methods for handling the content-based query and retrieval of video data. Video query processing presents significant research challenges, mainly associated with the size, complexity and unstructured nature of video data. A video query processor must support video operations for search by content and streaming, new query types, and the incorporation of video methods and operators in generating, optimizing and executing query plans. In this paper, we address these query processing issues in two contexts, first as applied to the video data type and then as applied to the stream data type. We first present the query processing functionality of the VDBMS video database management system as a framework designed to support the full range of functionality for video as an abstract data type. We describe two query operators for the video data type which implement the rank-join and stop-after algorithms. As videos may be considered streams of consecutive image frames, video query processing can be expressed as continuous queries over video data streams. The stream data type was therefore introduced into the VDBMS system, and system functionality was extended to support general data streams. From this viewpoint, we present an approach for defining and processing streams, including video, through the query execution engine. We describe the implementation of several algorithms for video query processing expressed as continuous queries over video streams, such as fast forward, region-based blurring and left outer join. We include a description of the window-join algorithm as a core operator for continuous query systems, and discuss shared execution as an optimization approach for stream query processing.

In our project, we are adopting a new approach for handling video data. We view the video as a well-defined data type with its own description, parameters and applicable methods. The system is based on PREDATOR, an open-source object-relational DBMS. PREDATOR uses Shore as the underlying storage manager. Supporting video operations (storing, searching-by-content and streaming) and new query types (query-by-example and multi-feature similarity searching) requires major changes in many of the traditional system components. More specifically, the storage and buffer manager has to deal with huge volumes of data with real-time constraints. Query processing has to consider the video methods and operators in generating, optimizing and executing the query plans.

The focus of this paper is the application and extension of the knowledge discovery in databases process [5] developed in PYTHIA recommender system, to analyze the behavior of a DOE ASCI application/hardware pairs in the context of POEMS project[4]. The POEMS project has built a library of models for modeling scalable architectures like those in the ASCI program. Moreover, it supports detail simulation of a variety of state-of-the-art processors and memory hierarchies and incorporates parallel evaluation of discrete-event simulation. The driver application used is SWEEP3D.

Rapid advances in modern networking technologies and commodity high performance computing systems are leading the field of computing in a new paradigm referred to as network-based computing (NC). This paradigm views a large number of geographically distributed computer resources such as PCs, workstations, Symmetric Multi-processors (SMP) and Massively Parallel Processing (MPP) systems connected through a high speed network as a single meta-computer or computational grid [2]. In this paper, we focus on the Internet (WAN) and Intranet (LAN) based computational grids and their ability to support scalable and robust “deep” computing. We present various implementations of the NC paradigm using commodity and customized software in the context of the existing PELLPACK problem solving environment [8], the ITPACK library as it has been implemented in the PELLPACK system, and a muti-physics application for the design of gas turbine engines [11]. Through this study we attempt to assess the feasibility and efficiency of several NC paradigms for scientific applications utilizing existing middleware.

The new economic realities require the rapid prototyping of manufactured artifacts and rapid solutions to problems with numerous interrelated elements. In the SoftLab project, we investigate the issues involved in the design and implementation of a virtual laboratory that simulates and controls the functionality of a wet/dry prototyping laboratory. The 21st century prototyping laboratory environments will include: i) integrated view of the design, simulation, manufacturing, and training phases of the underlying prototyping process, ii) virtual environments capable of controlling both the experimental (physical) and computational (simulation) prototypes and their interactions, iii) knowledge based systems for handling the increasing amount of knowledge and data a designer must synthesize, compare, trade-off, and optimize, iv) numerical simulation systems that support the different possible models of the underlying physical process. In this paper we present a software framework for a virtual laboratory and report on our experiences in its design and implementation.

Problem Solving Environments (PSEs) are very high level software environments that provide all the facilities needed for handling some class of problems. It is clear that building PSEs is a costly endeavor both in terms of the person-years required and the diversity of knowledge and expertise required. This paper describes the Purdue PSE Kernel (PPK), a software framework designed to assist in the development of PSEs. PPK assumes a fairly general model of PSEs, where PSEs are viewed as a collection of communicating, cooperating entities. The architecture of PPK is designed to provide the infrastructure needed to build application PSEs that adhere to this model. The model is realized in terms of an electronic notebook for user interaction with the PSE, an object manager for storing problem information, and a software bus for supporting communication between components of the PSE.