Overview of the CCP4 suite and current developments.

The CCP4 (Collaborative Computational Project, Number 4) software suite is a collection of programs and associated data and software libraries which can be used for macromolecular structure determination by X-ray crystallography. The suite is designed to be flexible, allowing users a number of methods of achieving their aims. The programs are from a wide variety of sources but are connected by a common infrastructure provided by standard file formats, data objects and graphical interfaces. Structure solution by macromolecular crystallography is becoming increasingly automated and the CCP4 suite includes several automation pipelines. After giving a brief description of the evolution of CCP4 over the last 30 years, an overview of the current suite is given. While detailed descriptions are given in the accompanying articles, here it is shown how the individual programs contribute to a complete software package

Infrared absorption spectra, radiative efficiencies, and global warming potentials of perfluorocarbons: Comparison between experiment and theory

Experimentally and theoretically determined infrared spectra are reported for a series of straight-chain perfluorocarbons: C2F6, C3F8, C4F10, C5F12, C6F14, and C8F18. Theoretical spectra were determined using both density functional (DFT) and ab initio methods. Radiative efficiencies (REs) were determined using the method of Pinnock et al. (1995) and combined with atmospheric lifetimes from the literature to determine global warming potentials (GWPs). Theoretically determined absorption cross sections were within 10% of experimentally determined values. Despite being much less computationally expensive, DFT calculations were generally found to perform better than ab initio methods. There is a strong wavenumber dependence of radiative forcing in the region of the fundamental C-F vibration, and small differences in wavelength between band positions determined by theory and experiment have a significant impact on the REs. We apply an empirical correction to the theoretical spectra and then test this correction on a number of branched chain and cyclic perfluoroalkanes. We then compute absorption cross sections, REs, and GWPs for an additional set of perfluoroalkenes

Inhaled Nitric Oxide in preterm infants: a systematic review and individual patient data meta-analysis

BACKGROUND: Preterm infants requiring assisted ventilation are at significant risk of both pulmonary and cerebral injury. Inhaled Nitric Oxide, an effective therapy for pulmonary hypertension and hypoxic respiratory failure in the full term infant, has also been studied in preterm infants. The most recent Cochrane review of preterm infants includes 11 studies and 3,370 participants. The results show a statistically significant reduction in the combined outcome of death or chronic lung disease (CLD) in two studies with routine use of iNO in intubated preterm infants. However, uncertainty remains as a larger study (Kinsella 2006) showed no significant benefit for iNO for this combined outcome. Also, trials that included very ill infants do not demonstrate significant benefit. One trial of iNO treatment at a later postnatal age reported a decrease in the incidence of CLD. The aim of this individual patient meta-analysis is to confirm or refute these potentially conflicting results and to determine the extent to which patient or treatment characteristics may explain the results and/or may predict benefit from inhaled Nitric Oxide in preterm infants. METHODS/DESIGN: The Meta-Analysis of Preterm Patients on inhaled Nitric Oxide (MAPPiNO) Collaboration will perform an individual patient data meta-analysis to answer these important clinical questions. Studies will be included if preterm infants receiving assisted ventilation are randomized to receive inhaled Nitric Oxide or to a control group. The individual patient data provided by the Collaborators will be analyzed on an intention-to-treat basis where possible. Binary outcomes will be analyzed using log-binomial regression models and continuous outcomes will be analyzed using linear fixed effects models. Adjustments for trial differences will be made by including the trial variable in the model specification. DISCUSSION: Thirteen (13) trials, with a total of 3567 infants are eligible for inclusion in the MAPPiNO systematic review. To date 11 trials (n = 3298, 92% of available patients) have agreed to participate. Funding was successfully granted from Ikaria Inc as an unrestricted grant. A collaborative group was formed in 2006 with data collection commencing in 2007. It is anticipated that data analysis will commence in late 2009 with results being publicly available in 2010

Determining Contingencies in the Management of Construction Projects

[EN] This research describes the managerial approaches that contractors follow to determine different types of contingencies in construction project management. Two large Spanish general contractors were selected for an in-depth analysis. Interviews and surveys were conducted with six additional companies to explore the external validity of the findings. Managers constrain time and cost buffers through project objectives, applying heuristics to determine inventory buffers. The management of capacity buffers is entrusted to subcontractors. The contractors take advantage of scope and quality buffers to meet project objectives but rarely share these buffers with the owner, unless the owner is an internal client.Ortiz-González, JI.; Pellicer, E.; Molenaar, KR. (2019). Determining Contingencies in the Management of Construction Projects. Project Management Journal. 50(2):226-242. https://doi.org/10.1177/8756972819827389S226242502Adafin, J., Wilkinson, S., Rotimi, J. O. B., & Odeyinka, H. (2014). Accuracy in Design Stage Cost Estimating through Risk-contingency Analysis: A Theoretical Exploration. Construction Research Congress 2014. doi:10.1061/9780784413517.151Ballard, G., & Howell, G. (1998). Shielding Production: Essential Step in Production Control. Journal of Construction Engineering and Management, 124(1), 11-17. doi:10.1061/(asce)0733-9364(1998)124:1(11)Barraza, G. A. (2011). Probabilistic Estimation and Allocation of Project Time Contingency. Journal of Construction Engineering and Management, 137(4), 259-265. doi:10.1061/(asce)co.1943-7862.0000280Blomquist, T., Hällgren, M., Nilsson, A., & Söderholm, A. (2010). Project-as-Practice: In Search of Project Management Research that Matters. Project Management Journal, 41(1), 5-16. doi:10.1002/pmj.20141Chan, E. H., & Au, M. C. (2009). Factors Influencing Building Contractors’ Pricing for Time-Related Risks in Tenders. Journal of Construction Engineering and Management, 135(3), 135-145. doi:10.1061/(asce)0733-9364(2009)135:3(135)De la Cruz, M. P., del Caño, A., & de la Cruz, E. (2006). Downside Risks in Construction Projects Developed by the Civil Service: The Case of Spain. Journal of Construction Engineering and Management, 132(8), 844-852. doi:10.1061/(asce)0733-9364(2006)132:8(844)Ford, D. N. (2002). Achieving Multiple Project Objectives through Contingency Management. Journal of Construction Engineering and Management, 128(1), 30-39. doi:10.1061/(asce)0733-9364(2002)128:1(30)González, V., Alarcón, L. F., & Molenaar, K. (2009). Multiobjective design of Work-In-Process buffer for scheduling repetitive building projects. Automation in Construction, 18(2), 95-108. doi:10.1016/j.autcon.2008.05.005Guest, G., Bunce, A., & Johnson, L. (2006). How Many Interviews Are Enough? Field Methods, 18(1), 59-82. doi:10.1177/1525822x05279903Günhan, S., & Arditi, D. (2007). Budgeting Owner’s Construction Contingency. Journal of Construction Engineering and Management, 133(7), 492-497. doi:10.1061/(asce)0733-9364(2007)133:7(492)Hällgren, M., & Wilson, T. L. (2008). The nature and management of crises in construction projects: Projects-as-practice observations. International Journal of Project Management, 26(8), 830-838. doi:10.1016/j.ijproman.2007.10.005Harbuck R. H. (2004). Competitive bidding for highway construction projects (pp. ES91–ES94). Morgantown, WV: AACE International Transactions.HORMAN, M., & KENLEY, R. (1998). Process Dynamics: Identifying a Strategy for the Deployment of Buffers in Building Projects. International Journal of Logistics Research and Applications, 1(3), 221-237. doi:10.1080/13675569808962049Horman, M. J., & Thomas, H. R. (2005). Role of Inventory Buffers in Construction Labor Performance. Journal of Construction Engineering and Management, 131(7), 834-843. doi:10.1061/(asce)0733-9364(2005)131:7(834)Howell, G., Laufer, A., & Ballard, G. (1993). Interaction between Subcycles: One Key to Improved Methods. Journal of Construction Engineering and Management, 119(4), 714-728. doi:10.1061/(asce)0733-9364(1993)119:4(714)Howell, G., Laufer, A., & Ballard, G. (1993). Uncertainty and project objectives. Project Appraisal, 8(1), 37-43. doi:10.1080/02688867.1993.9726884Idrus, A., Fadhil Nuruddin, M., & Rohman, M. A. (2011). Development of project cost contingency estimation model using risk analysis and fuzzy expert system. Expert Systems with Applications, 38(3), 1501-1508. doi:10.1016/j.eswa.2010.07.061Laryea, S., & Hughes, W. (2011). Risk and Price in the Bidding Process of Contractors. Journal of Construction Engineering and Management, 137(4), 248-258. doi:10.1061/(asce)co.1943-7862.0000293Leach, L. (2003). Schedule and Cost Buffer Sizing: How to Account for the Bias between Project Performance and Your Model. Project Management Journal, 34(2), 34-47. doi:10.1177/875697280303400205Lee, S., Peña-Mora, F., & Park, M. (2006). Reliability and Stability Buffering Approach: Focusing on the Issues of Errors and Changes in Concurrent Design and Construction Projects. Journal of Construction Engineering and Management, 132(5), 452-464. doi:10.1061/(asce)0733-9364(2006)132:5(452)Oviedo-Haito, R. J., Jiménez, J., Cardoso, F. F., & Pellicer, E. (2014). Survival Factors for Subcontractors in Economic Downturns. Journal of Construction Engineering and Management, 140(3), 04013056. doi:10.1061/(asce)co.1943-7862.0000811Pellicer, E., Sanz, M. A., Esmaeili, B., & Molenaar, K. R. (2016). Exploration of Team Integration in Spanish Multifamily Residential Building Construction. Journal of Management in Engineering, 32(5), 05016012. doi:10.1061/(asce)me.1943-5479.0000438Pellicer, E., & Victory, R. (2006). IMPLEMENTATION OF PROJECT MANAGEMENT PRINCIPLES IN SPANISH RESIDENTIAL DEVELOPMENTS. International Journal of Strategic Property Management, 10(4), 233-248. doi:10.3846/1648715x.2006.9637555Rooke, J., Seymour, D., & Fellows, R. (2004). Planning for claims: an ethnography of industry culture. Construction Management and Economics, 22(6), 655-662. doi:10.1080/014461904200026324Slauson N. P. (2005). The effectiveness of the construction contract (pp. PM121–PM127). Morgantown, WV: AACE International Transactions.Tah, J. H. M., Thorpe, A., & McCaffer, R. (1993). Contractor project risks contingency allocation using linguistic approximation. Computing Systems in Engineering, 4(2-3), 281-293. doi:10.1016/0956-0521(93)90052-xTaylor, J. E., Dossick, C. S., & Garvin, M. (2011). Meeting the Burden of Proof with Case-Study Research. Journal of Construction Engineering and Management, 137(4), 303-311. doi:10.1061/(asce)co.1943-7862.0000283Thal, A. E., Cook, J. J., & White, E. D. (2010). Estimation of Cost Contingency for Air Force Construction Projects. Journal of Construction Engineering and Management, 136(11), 1181-1188. doi:10.1061/(asce)co.1943-7862.0000227Thamhain, H. (2013). Managing Risks in Complex Projects. Project Management Journal, 44(2), 20-35. doi:10.1002/pmj.21325Yeo, K. T. (1990). Risks, Classification of Estimates, and Contingency Management. Journal of Management in Engineering, 6(4), 458-470. doi:10.1061/(asce)9742-597x(1990)6:4(458

The Virgin River Basin Study: A Regional Approach to Multiobjective Planning for Water and Related Resources

A joint research effort by the Utah Water Research Laboratory and the Nevado Center for Water Resources Research applied two multiobjective planning models to the Virgin River Basin in order to test the efficiency and practicality of applying such tools in water resrouces planning. The surrogate Worth Trade-off (SWT) method couples mathematical optimization to quantify trade offs among noncommensurable objectives with interviews to compare public preferences with respect to these trade offs. PROPDEMM uses information on interest group objectives, balues, willingness to pay, influence, lebel of information, etc. to compare the political feasibility of alternative courses of action. Both models were applied to assess the difficulties in doing so and the usefulness of the results. The trade offs identified by the SWT method showed agricultural water use to be so dominant in the basin that slight adjustments in irrigation efficiency could supply all foreseeable needs for additional water for other uses, such trade offs to be too inconsequential to identify and compare public preferences, and other trade offs to be impossible because of the position taken by ecologists that any environmental change would destroy a rare species of minnow. Prior to analyzing a situation by the SWT method, the planner should make sure that the trade offs will be of a magnitude meaningful to the public and that the model selected will be sufficiently refined in analyzing small units in time and space to identify locally significant trade offs. PROPDEMM showed the politically most controversial trade off to be between construction of energy generating facilities and life support for the minnow, a controbersy that would probably be decided in favor of the environmentalist because of their power and non-openness to change. Improvements to the model to do a better job of interfacing environmental with social data were recommended. Social modeling in such low population areas was found to be restricted by laws against disclosure of private information because of the very small numbers of individuals living in many evaluation units

Optimised Anaesthesia to Reduce Post Operative Cognitive Decline (POCD) in Older Patients Undergoing Elective Surgery, a Randomised Controlled Trial

Background The study determined the one year incidence of post operative cognitive decline (POCD) and evaluated the effectiveness of an intra-operative anaesthetic intervention in reducing post-operative cognitive impairment in older adults (over 60 years of age) undergoing elective orthopaedic or abdominal surgery. Methods and Trial Design The design was a prospective cohort study with a nested randomised, controlled intervention trial, using intra-operative BiSpectral index and cerebral oxygen saturation monitoring to enable optimisation of anaesthesia depth and cerebral oxygen saturation in older adults undergoing surgery. Results In the 52 week prospective cohort study (192 surgical patients and 138 controls), mild (?2 = 17.9 p<0.0001), moderate (?2 = 7.8 p = 0.005) and severe (?2 = 5.1 p = 0.02) POCD were all significantly higher after 52 weeks in the surgical patients than among the age matched controls. In the nested RCT, 81 patients were randomized, 73 contributing to the data analysis (34 intervention, 39 control). In the intervention group mild POCD was significantly reduced at 1, 12 and 52 weeks (Fisher’s Exact Test p = 0.018, ?2 = 5.1 p = 0.02 and ?2 = 5.9 p = 0.015), and moderate POCD was reduced at 1 and 52 weeks (?2 = 4.4 p = 0·037 and ?2 = 5.4 p = 0.02). In addition there was significant improvement in reaction time at all time-points (Vigilance Reaction Time MWU Z = ?2.1 p = 0.03, MWU Z = ?2.7 p = 0.004, MWU Z = ?3.0 p = 0.005), in MMSE at one and 52 weeks (MWU Z = ?2.9 p = 0.003, MWU Z = ?3.3 p = 0.001), and in executive function at 12 and 52 weeks (Trail Making MWU Z = ?2.4 p = .0.018, MWU Z = ?2.4 p = 0.019). Conclusion POCD is common and persistent in older adults following surgery. The results of the nested RCT indicate the potential benefits of intra-operative monitoring of anaesthetic depth and cerebral oxygenation as a pragmatic intervention to reduce post-operative cognitive impairment

Living in the Past: Phylogeography and Population Histories of Indo-Pacific Wrasses (Genus Halichoeres) in Shallow Lagoons versus Outer Reef Slopes

Sea level fluctuations during glacial cycles affect the distribution of shallow marine biota, exposing the continental shelf on a global scale, and displacing coral reef habitat to steep slopes on oceanic islands. In these circumstances we expect that species inhabiting lagoons should show shallow genetic architecture relative to species inhabiting more stable outer reefs. Here we test this expectation on an ocean-basin scale with four wrasses (genus Halichoeres): H. claudia (N = 194, with ocean-wide distribution) and H. ornatissimus (N = 346, a Hawaiian endemic) inhabit seaward reef slopes, whereas H. trimaculatus (N = 239) and H. margaritaceus (N = 118) inhabit lagoons and shallow habitats throughout the Pacific. Two mitochondrial markers (cytochrome oxidase I and control region) were sequenced to resolve population structure and history of each species. Haplotype and nucleotide diversity were similar among all four species. The outer reef species showed significantly less population structure, consistent with longer pelagic larval durations. Mismatch distributions and significant negative Fu’s F values indicate Pleistocene population expansion for all species, and (contrary to expectations) shallower histories in the outer slope species. We conclude that lagoonal wrasses may persist through glacial habitat disruptions, but are restricted to refugia during lower sea level stands. In contrast, outer reef slope species have homogeneous and well-connected populations through their entire ranges regardless of sea level fluctuations. These findings contradict the hypothesis that shallow species are less genetically diverse as a consequence of glacial cycles

The retrospective analysis of Antarctic tracking data project

The Retrospective Analysis of Antarctic Tracking Data (RAATD) is a Scientific Committee for Antarctic Research project led jointly by the Expert Groups on Birds and Marine Mammals and Antarctic Biodiversity Informatics, and endorsed by the Commission for the Conservation of Antarctic Marine Living Resources. RAATD consolidated tracking data for multiple species of Antarctic meso- and top-predators to identify Areas of Ecological Significance. These datasets and accompanying syntheses provide a greater understanding of fundamental ecosystem processes in the Southern Ocean, support modelling of predator distributions under future climate scenarios and create inputs that can be incorporated into decision making processes by management authorities. In this data paper, we present the compiled tracking data from research groups that have worked in the Antarctic since the 1990s. The data are publicly available through biodiversity.aq and the Ocean Biogeographic Information System. The archive includes tracking data from over 70 contributors across 12 national Antarctic programs, and includes data from 17 predator species, 4060 individual animals, and over 2.9 million observed locations

The CCP4 suite: integrative software for macromolecular crystallography

The Collaborative Computational Project No. 4 (CCP4) is a UK-led international collective with a mission to develop, test, distribute and promote software for macromolecular crystallography. The CCP4 suite is a multiplatform collection of programs brought together by familiar execution routines, a set of common libraries and graphical interfaces. The CCP4 suite has experienced several considerable changes since its last reference article, involving new infrastructure, original programs and graphical interfaces. This article, which is intended as a general literature citation for the use of the CCP4 software suite in structure determination, will guide the reader through such transformations, offering a general overview of the new features and outlining future developments. As such, it aims to highlight the individual programs that comprise the suite and to provide the latest references to them for perusal by crystallographers around the world.Jon Agirre is a Royal Society University Research Fellow (UF160039 and URF\R\221006). Mihaela Atanasova is funded by the UK Engineering and Physical Sciences Research Council (EPSRC; EP/R513386/1). Haroldas Bagdonas is funded by The Royal Society (RGF/R1/181006). Jose´ Javier Burgos-Ma´rmol and Daniel J. Rigden are supported by the BBSRC (BB/S007105/1). Robbie P. Joosten is funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 871037 (iNEXTDiscovery) and by CCP4. This work was supported by the Medical Research Council as part of United Kingdom Research and Innovation, also known as UK Research and Innovation: MRC file reference No. MC_UP_A025_1012 to Garib N. Murshudov, which also funded Keitaro Yamashita, Paul Emsley and Fei Long. Robert A. Nicholls is funded by the BBSRC (BB/S007083/1). Soon Wen Hoh is funded by the BBSRC (BB/T012935/1). Kevin D. Cowtan and Paul S. Bond are funded in part by the BBSRC (BB/S005099/1). John Berrisford and Sameer Velankar thank the European Molecular Biology Laboratory–European Bioinformatics Institute, who supported this work. Andrea Thorn was supported in the development of AUSPEX by the German Federal Ministry of Education and Research (05K19WWA and 05K22GU5) and by Deutsche Forschungsgemeinschaft (TH2135/2-1). Petr Kolenko and Martin Maly´ are funded by the MEYS CR (CZ.02.1.01/0.0/0.0/16_019/0000778). Martin Maly´ is funded by the Czech Academy of Sciences (86652036) and CCP4/STFC (521862101). Anastassis Perrakis acknowledges funding from iNEXT (grant No. 653706), iNEXT-Discovery (grant No. 871037), West-Life (grant No. 675858) and EOSC-Life (grant No. 824087) funded by the Horizon 2020 program of the European Commission. Robbie P. Joosten has been the recipient of a Veni grant (722.011.011) and a Vidi grant (723.013.003) from the Netherlands Organization for Scientific Research (NWO). Maarten L. Hekkelman, Robbie P. Joosten and Anastassis Perrakis thank the Research High Performance Computing facility of the Netherlands Cancer Institute for providing and maintaining computation resources and acknowledge the institutional grant from the Dutch Cancer Society and the Dutch Ministry of Health, Welfare and Sport. Tarik R. Drevon is funded by the BBSRC (BB/S007040/1). Randy J. Read is supported by a Principal Research Fellowship from the Wellcome Trust (grant 209407/Z/17/Z). Atlanta G. Cook is supported by a Wellcome Trust SRF (200898) and a Wellcome Centre for Cell Biology core grant (203149). Isabel Uso´n acknowledges support from STFC-UK/CCP4: ‘Agreement for the integration of methods into the CCP4 software distribution, ARCIMBOLDO_LOW’ and Spanish MICINN/AEI/FEDER/UE (PID2021-128751NB-I00). Pavol Skubak and Navraj Pannu were funded by the NWO Applied Sciences and Engineering Domain and CCP4 (grant Nos. 13337 and 16219). Bernhard Lohkamp was supported by the Ro¨ntgen A˚ ngstro¨m Cluster (grant 349-2013-597). Nicholas Pearce is currently funded by the SciLifeLab and Wallenberg Data Driven Life Science Program (grant KAW 2020.0239) and has previously been funded by a Veni Fellowship (VI.Veni.192.143) from the Dutch Research Council (NWO), a Long-term EMBO fellowship (ALTF 609-2017) and EPSRC grant EP/G037280/1. David M. Lawson received funding from BBSRC Institute Strategic Programme Grants (BB/P012523/1 and BB/P012574/1). Lucrezia Catapano is the recipient of an STFC/CCP4-funded PhD studentship (Agreement No: 7920 S2 2020 007).Peer reviewe