Bacteriorhodopsin folds through a poorly organized transition state.

The folding mechanisms of helical membrane proteins remain largely uncharted. Here we characterize the kinetics of bacteriorhodopsin folding and employ φ-value analysis to explore the folding transition state. First, we developed and confirmed a kinetic model that allowed us to assess the rate of folding from SDS-denatured bacteriorhodopsin (bRU) and provides accurate thermodynamic information even under influence of retinal hydrolysis. Next, we obtained reliable φ-values for 16 mutants of bacteriorhodopsin with good coverage across the protein. Every φ-value was less than 0.4, indicating the transition state is not uniquely structured. We suggest that the transition state is a loosely organized ensemble of conformations

Author Correction: A cell-free platform for the prenylation of natural products and application to cannabinoid production.

In the original version of this Article, the genotype of the M30 mutant presented in Fig. 3b was given incorrectly as Y288V/A232S, and the M31 mutant was given incorrectly as M1/A232S. The correct genotype of the M30 mutant is Y288A/A232S and for M31 it is Y288V/A232S. In addition, to keep consistency in genotype formatting, the genotype of the M27 mutant should be Y288V/G286S. The errors have been corrected in both the PDF and HTML versions of the Article

The significance of 'the visit' in an English category-B prison: Views from prisoners, prisoners' families and prison staff

A number of claims have been made regarding the importance of prisoners staying in touch with their family through prison visits, firstly from a humanitarian perspective of enabling family members to see each other, but also regarding the impact of maintaining family ties for successful rehabilitation, reintegration into society and reduced re-offending. This growing evidence base has resulted in increased support by the Prison Service for encouraging the family unit to remain intact during a prisoner’s incarceration. Despite its importance however, there has been a distinct lack of research examining the dynamics of families visiting relatives in prison. This paper explores perceptions of the same event – the visit – from the families’, prisoners’ and prison staffs' viewpoints in a category-B local prison in England. Qualitative data was collected with 30 prisoners’ families, 16 prisoners and 14 prison staff, as part of a broader evaluation of the visitors’ centre. The findings suggest that the three parties frame their perspective of visiting very differently. Prisoners’ families often see visits as an emotional minefield fraught with practical difficulties. Prisoners can view the visit as the highlight of their time in prison and often have many complaints about how visits are handled. Finally, prison staff see visits as potential security breaches and a major organisational operation. The paper addresses the current gap in our understanding of the prison visit and has implications for the Prison Service and wider social policy

Exploring concepts of health with male prisoners in three category-C English prisons

Lay understandings of health and illness have a well established track record and a plethora of research now exists which has examined these issues. However, there is a dearth of research which has examined the perspectives of those who are imprisoned. This paper attempts to address this research gap. The paper is timely given that calls have been made to examine lay perspectives in different geographical locations and a need to re-examine health promotion approaches in prison settings. Qualitative data from thirty-six male sentenced prisoners from three prisons in England were collected. The data was analysed in accordance with Attride-Stirling's (2001) thematic network approach. Although the men's perceptions of health were broadly similar to the general population, some interesting findings emerged which were directly related to prison life and its associated structures. These included access to the outdoors and time out of their prison cell, as well as maintaining relationships with family members through visits. The paper proposes that prisoners' lay views should be given higher priority given that prison health has traditionally been associated with medical treatment and the bio-medical paradigm more generally. It also suggests that in order to fulfil the World Health Organization's (WHO) vision of viewing prisons as health promoting settings, lay views should be recognised to shape future health promotion policy and practice

Latent Structures based-Multivariate Statistical Process Control: a paradigm shift

The basic fundamentals of statistical process control (SPC) were proposed by Walter Shewhart for data-starved production environments typical in the 1920s and 1930s. In the 21st century, the traditional scarcity of data has given way to a data-rich environment typical of highly automated and computerized modern processes. These data often exhibit high correlation, rank deficiency, low signal-to-noise ratio, multistage and multiway structures, and missing values. Conventional univariate and multivariate SPC techniques are not suitable in these environments. This article discusses the paradigm shift to which those working in the quality improvement field should pay keen attention. We advocate the use of latent structure based multivariate statistical process control methods as efficient quality improvement tools in these massive data contexts. This is a strategic issue for industrial success in the tremendously competitive global market.This research work was partially supported by the Spanish Ministry of Economy and Competitiveness under the project DPI2011-28112-C04-02.Ferrer, A. (2014). Latent Structures based-Multivariate Statistical Process Control: a paradigm shift. Quality Engineering. 26(1):72-91. https://doi.org/10.1080/08982112.2013.846093S7291261Aparisi, F., Jabaioyes, J., & Carrion, A. (1999). Statistical properties of the lsi multivariate control chart. Communications in Statistics - Theory and Methods, 28(11), 2671-2686. doi:10.1080/03610929908832445Arteaga, F., & Ferrer, A. (2002). Dealing with missing data in MSPC: several methods, different interpretations, some examples. Journal of Chemometrics, 16(8-10), 408-418. doi:10.1002/cem.750Bersimis, S., Psarakis, S., & Panaretos, J. (2007). Multivariate statistical process control charts: an overview. Quality and Reliability Engineering International, 23(5), 517-543. doi:10.1002/qre.829Bharati, M. H., & MacGregor, J. F. (1998). Multivariate Image Analysis for Real-Time Process Monitoring and Control. Industrial & Engineering Chemistry Research, 37(12), 4715-4724. doi:10.1021/ie980334lBharati, M. H., MacGregor, J. F., & Tropper, W. (2003). Softwood Lumber Grading through On-line Multivariate Image Analysis Techniques. Industrial & Engineering Chemistry Research, 42(21), 5345-5353. doi:10.1021/ie0210560Bisgaard, S. (2012). The Future of Quality Technology: From a Manufacturing to a Knowledge Economy & From Defects to Innovations. Quality Engineering, 24(1), 30-36. doi:10.1080/08982112.2011.627010Box, G. E. P. (1954). Some Theorems on Quadratic Forms Applied in the Study of Analysis of Variance Problems, I. Effect of Inequality of Variance in the One-Way Classification. The Annals of Mathematical Statistics, 25(2), 290-302. doi:10.1214/aoms/1177728786Camacho, J., & Ferrer, A. (2012). Cross-validation in PCA models with the element-wise k-fold (ekf) algorithm: theoretical aspects. Journal of Chemometrics, 26(7), 361-373. doi:10.1002/cem.2440Duchesne, C., Liu, J. J., & MacGregor, J. F. (2012). Multivariate image analysis in the process industries: A review. Chemometrics and Intelligent Laboratory Systems, 117, 116-128. doi:10.1016/j.chemolab.2012.04.003Efron, B., & Gong, G. (1983). A Leisurely Look at the Bootstrap, the Jackknife, and Cross-Validation. The American Statistician, 37(1), 36-48. doi:10.1080/00031305.1983.10483087Ferrer, A. (2007). Multivariate Statistical Process Control Based on Principal Component Analysis (MSPC-PCA): Some Reflections and a Case Study in an Autobody Assembly Process. Quality Engineering, 19(4), 311-325. doi:10.1080/08982110701621304Fuchs, C. (1998). Multivariate Quality Control. doi:10.1201/9781482273731Geladi, P., & Kowalski, B. R. (1986). Partial least-squares regression: a tutorial. Analytica Chimica Acta, 185, 1-17. doi:10.1016/0003-2670(86)80028-9Helland, I. S. (1988). On the structure of partial least squares regression. Communications in Statistics - Simulation and Computation, 17(2), 581-607. doi:10.1080/03610918808812681Höskuldsson, A. (1988). PLS regression methods. Journal of Chemometrics, 2(3), 211-228. doi:10.1002/cem.1180020306Hunter, J. S. (1986). The Exponentially Weighted Moving Average. Journal of Quality Technology, 18(4), 203-210. doi:10.1080/00224065.1986.11979014Edward Jackson, J. (1985). Multivariate quality control. Communications in Statistics - Theory and Methods, 14(11), 2657-2688. doi:10.1080/03610928508829069Jackson, J. E., & Mudholkar, G. S. (1979). Control Procedures for Residuals Associated With Principal Component Analysis. Technometrics, 21(3), 341-349. doi:10.1080/00401706.1979.10489779Process analysis and abnormal situation detection: from theory to practice. (2002). IEEE Control Systems, 22(5), 10-25. doi:10.1109/mcs.2002.1035214Kourti, T. (2005). Application of latent variable methods to process control and multivariate statistical process control in industry. International Journal of Adaptive Control and Signal Processing, 19(4), 213-246. doi:10.1002/acs.859Kourti, T. (2006). Process Analytical Technology Beyond Real-Time Analyzers: The Role of Multivariate Analysis. Critical Reviews in Analytical Chemistry, 36(3-4), 257-278. doi:10.1080/10408340600969957Kourti, T., & MacGregor, J. F. (1996). Multivariate SPC Methods for Process and Product Monitoring. Journal of Quality Technology, 28(4), 409-428. doi:10.1080/00224065.1996.11979699Liu, R. Y. (1995). Control Charts for Multivariate Processes. Journal of the American Statistical Association, 90(432), 1380-1387. doi:10.1080/01621459.1995.10476643Liu, R. Y., Singh, K., & Teng*, J. H. (2004). DDMA-charts: Nonparametric multivariate moving average control charts based on data depth. Allgemeines Statistisches Archiv, 88(2), 235-258. doi:10.1007/s101820400170Liu, R. Y., & Tang, J. (1996). Control Charts for Dependent and Independent Measurements Based on Bootstrap Methods. Journal of the American Statistical Association, 91(436), 1694-1700. doi:10.1080/01621459.1996.10476740LOWRY, C. A., & MONTGOMERY, D. C. (1995). A review of multivariate control charts. IIE Transactions, 27(6), 800-810. doi:10.1080/07408179508936797MacGregor, J. F. (1997). Using On-Line Process Data to Improve Quality: Challenges for Statisticians. International Statistical Review, 65(3), 309-323. doi:10.1111/j.1751-5823.1997.tb00311.xMason, R. L., Champ, C. W., Tracy, N. D., Wierda, S. J., & Young, J. C. (1997). Assessment of Multivariate Process Control Techniques. Journal of Quality Technology, 29(2), 140-143. doi:10.1080/00224065.1997.11979743Montgomery, D. C., & Woodall, W. H. (1997). A Discussion on Statistically-Based Process Monitoring and Control. Journal of Quality Technology, 29(2), 121-121. doi:10.1080/00224065.1997.11979738Nelson, P. R. C., Taylor, P. A., & MacGregor, J. F. (1996). Missing data methods in PCA and PLS: Score calculations with incomplete observations. Chemometrics and Intelligent Laboratory Systems, 35(1), 45-65. doi:10.1016/s0169-7439(96)00007-xNomikos, P., & MacGregor, J. F. (1995). Multivariate SPC Charts for Monitoring Batch Processes. Technometrics, 37(1), 41-59. doi:10.1080/00401706.1995.10485888Prats-Montalbán, J. M., de Juan, A., & Ferrer, A. (2011). Multivariate image analysis: A review with applications. Chemometrics and Intelligent Laboratory Systems, 107(1), 1-23. doi:10.1016/j.chemolab.2011.03.002Prats-Montalbán, J. M., Ferrer, A., Malo, J. L., & Gorbeña, J. (2006). A comparison of different discriminant analysis techniques in a steel industry welding process. Chemometrics and Intelligent Laboratory Systems, 80(1), 109-119. doi:10.1016/j.chemolab.2005.08.005Prats-Montalbán, J. M., & Ferrer, A. (2007). Integration of colour and textural information in multivariate image analysis: defect detection and classification issues. Journal of Chemometrics, 21(1-2), 10-23. doi:10.1002/cem.1026Bisgaard, S., Doganaksoy, N., Fisher, N., Gunter, B., Hahn, G., Keller-McNulty, S., … Wu, C. F. J. (2008). The Future of Industrial Statistics: A Panel Discussion. Technometrics, 50(2), 103-127. doi:10.1198/004017008000000136Stoumbos, Z. G., Reynolds, M. R., Ryan, T. P., & Woodall, W. H. (2000). The State of Statistical Process Control as We Proceed into the 21st Century. Journal of the American Statistical Association, 95(451), 992-998. doi:10.1080/01621459.2000.10474292Tracy, N. D., Young, J. C., & Mason, R. L. (1992). Multivariate Control Charts for Individual Observations. Journal of Quality Technology, 24(2), 88-95. doi:10.1080/00224065.1992.12015232Wierda, S. J. (1994). Multivariate statistical process control—recent results and directions for future research. Statistica Neerlandica, 48(2), 147-168. doi:10.1111/j.1467-9574.1994.tb01439.xWold, S. (1978). Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models. Technometrics, 20(4), 397-405. doi:10.1080/00401706.1978.10489693Woodall, W. H. (2000). Controversies and Contradictions in Statistical Process Control. Journal of Quality Technology, 32(4), 341-350. doi:10.1080/00224065.2000.11980013Woodall, W. H., & Montgomery, D. C. (1999). Research Issues and Ideas in Statistical Process Control. Journal of Quality Technology, 31(4), 376-386. doi:10.1080/00224065.1999.11979944Yu, H., & MacGregor, J. F. (2003). Multivariate image analysis and regression for prediction of coating content and distribution in the production of snack foods. Chemometrics and Intelligent Laboratory Systems, 67(2), 125-144. doi:10.1016/s0169-7439(03)00065-0Yu, H., MacGregor, J. F., Haarsma, G., & Bourg, W. (2003). Digital Imaging for Online Monitoring and Control of Industrial Snack Food Processes. Industrial & Engineering Chemistry Research, 42(13), 3036-3044. doi:10.1021/ie020941

The history of degenerate (bipartite) extremal graph problems

This paper is a survey on Extremal Graph Theory, primarily focusing on the case when one of the excluded graphs is bipartite. On one hand we give an introduction to this field and also describe many important results, methods, problems, and constructions.Comment: 97 pages, 11 figures, many problems. This is the preliminary version of our survey presented in Erdos 100. In this version 2 only a citation was complete

A prospective, multicentre, observational cohort study of analgesia and outcome after pneumonectomy

Background Meta-analysis and systematic reviews of epidural compared with paravertebral blockade analgesia techniques for thoracotomy conclude that although the analgesia is comparable, paravertebral blockade has a better short-term side-effect profile. However, reduction in major complications including mortality has not been proven. Methods The UK pneumonectomy study was a prospective observational cohort study in which all UK thoracic surgical centres were invited to participate. Data presented here relate to the mode of analgesia and outcome. Data were analysed for 312 patients having pneumonectomy at 24 UK thoracic surgical centres in 2005. The primary endpoint was a major complication. Results The most common type of analgesia used was epidural (61.1%) followed by paravertebral infusion (31%). Epidural catheter use was associated with major complications (odds ratio 2.2, 95% confidence interval 1.1–3.8; P=0.02) by stepwise logistic regression analysis. Conclusions An increased incidence of clinically important major post-pneumonectomy complications was associated with thoracic epidural compared with paravertebral blockade analgesia. However, this study is unable to provide robust evidence to change clinical practice for a better clinical outcome. A large multicentre randomized controlled trial is now needed to compare the efficacy, complications, and cost-effectiveness of epidural and paravertebral blockade analgesia after major lung resection with the primary outcome of clinically important major morbidity

Diffusion of Zn into GaAs and AlGaAs from isothermal Liquid-phase epitaxy solutions

In this work we present results of zinc diffusion in GaAs using the liquid phase epitaxy technique from liquid solutions of Ga‐As‐Zn and Ga‐As‐Al‐Zn. Using silicon‐doped n‐GaAs substrates, working at a diffusion temperature of 850 °C, and introducing a dopant concentration ranging 1018–1019 cm−3, the most important findings regarding the diffusion properties are as follows: (a) zinc concentration in the solid depends on the square root of zinc atomic fraction in the liquid; (b) the diffusion is dominated by the interstitial‐substitutional process; (c) the diffusivity D varies as about C3 in the form D=2.9×10−67C3.05; (d) aluminum plays the role of the catalyst of the diffusion process, if it is introduced in the liquid solution, since it is found that D varies as (γAsXlAs)−1; (e) the zinc interstitial is mainly doubly ionized (Zn++i); (f) the zinc diffusion coefficient in Al0.85 Ga0.15 As is about four times greater than in GaAs; (g) by means of all these results, it is possible to control zinc diffusion processes in order to obtain optimized depth junctions and doping levels in semiconductor device fabrication

Transfer Matrices and Partition-Function Zeros for Antiferromagnetic Potts Models. IV. Chromatic polynomial with cyclic boundary conditions

We study the chromatic polynomial P_G(q) for m \times n square- and triangular-lattice strips of widths 2\leq m \leq 8 with cyclic boundary conditions. This polynomial gives the zero-temperature limit of the partition function for the antiferromagnetic q-state Potts model defined on the lattice G. We show how to construct the transfer matrix in the Fortuin--Kasteleyn representation for such lattices and obtain the accumulation sets of chromatic zeros in the complex q-plane in the limit n\to\infty. We find that the different phases that appear in this model can be characterized by a topological parameter. We also compute the bulk and surface free energies and the central charge.Comment: 55 pages (LaTeX2e). Includes tex file, three sty files, and 22 Postscript figures. Also included are Mathematica files transfer4_sq.m and transfer4_tri.m. Journal versio