Metabolic profiling of zebrafish embryo development from blastula period to early larval stages

The zebrafish embryo is a popular model for drug screening, disease modelling and molecular genetics. In this study, samples were obtained from zebrafish at different developmental stages. The stages that were chosen were 3/4, 4/5, 24, 48, 72 and 96 hours post fertilization (hpf). Each sample included fifty embryos. The samples were analysed using gas chromatography time-of-flight mass spectrometry (GC-TOF-MS). Principle component analysis (PCA) was applied to get an overview of the data and orthogonal projection to latent structure discriminant analysis (OPLS-DA) was utilised to discriminate between the developmental stages. In this way, changes in metabolite profiles during vertebrate development could be identified. Using a GC-TOF-MS metabolomics approach it was found that nucleotides and metabolic fuel (glucose) were elevated at early stages of embryogenesis, whereas at later stages amino acids and intermediates in the Krebs cycle were abundant. This agrees with zebrafish developmental biology, as organs such as the liver and pancreas develop at later stages. Thus, metabolomics of zebrafish embryos offers a unique opportunity to investigate large scale changes in metabolic processes during important developmental stages in vertebrate development. In terms of stability of the metabolic profile and viability of the embryos, it was concluded at 72 hpf was a suitable time point for the use of zebrafish as a model system in numerous scientific applications

Bilinear modeling of batch processes. Part III: Parameter Stability

A paramount aspect in the development of a model for a monitoring system is the so-called parameter stability. This is inversely related to the uncertainty, i.e., the variance in the parameters estimates. Noise affects the performance of the monitoring system, reducing its fault detection capability. Low parameters uncertainty is desired to ensure a reduced amount of noise in the model. Nonetheless, there is no sound study on the parameter stability in batch multivariate statistical process control (BMSPC). The aim of this paper is to investigate the parameter stability associated to the most used synchronization and principal component analysis-based BMSPC methods. The synchronization methods included in this study are the following: indicator variable, dynamic time warping, relaxed greedy time warping, and time linear expanding/compressing-based. In addition, different arrangements of the three-way batch data into two-way matrices are considered, namely single-model, K-models, and hierarchicalmodel approaches. Results are discussed in connection with previous conclusions in the first two papers of the series.This research work was partially supported by the Spanish Ministry of Economy and Competitiveness under the project DPI2011-28112-C04-02. Authors also acknowledge the anonymous reviewers for their comments to improve the article.González Martínez, JM.; Camacho Páez, J.; Ferrer, A. (2014). Bilinear modeling of batch processes. Part III: Parameter Stability. Journal of Chemometrics. 28(1):10-27. https://doi.org/10.1002/cem.2562S1027281Process analysis and abnormal situation detection: from theory to practice. (2002). IEEE Control Systems, 22(5), 10-25. doi:10.1109/mcs.2002.1035214Statistical monitoring of multistage, multiphase batch processes. (2002). IEEE Control Systems, 22(5), 40-52. doi:10.1109/mcs.2002.1035216Kourti, 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.859Wold, S., Kettaneh-Wold, N., MacGregor, J. F., & Dunn, K. G. (2009). Batch Process Modeling and MSPC. Comprehensive Chemometrics, 163-197. doi:10.1016/b978-044452701-1.00108-3Camacho, J., Picó, J., & Ferrer, A. (2008). Bilinear modelling of batch processes. Part I: theoretical discussion. Journal of Chemometrics, 22(5), 299-308. doi:10.1002/cem.1113Camacho, J., Picó, J., & Ferrer, A. (2008). Bilinear modelling of batch processes. Part II: a comparison of PLS soft-sensors. Journal of Chemometrics, 22(10), 533-547. doi:10.1002/cem.1179González-Martínez J Vitale R de Noord O Ferrer A Does synchronization matter in bilinear batch process monitoring?García-Muñoz, S., Kourti, T., MacGregor, J. F., Mateos, A. G., & Murphy, G. (2003). Troubleshooting of an Industrial Batch Process Using Multivariate Methods. Industrial & Engineering Chemistry Research, 42(15), 3592-3601. doi:10.1021/ie0300023Zarzo, M., & Ferrer, A. (2004). Batch process diagnosis: PLS with variable selection versus block-wise PCR. Chemometrics and Intelligent Laboratory Systems, 73(1), 15-27. doi:10.1016/j.chemolab.2003.11.009Wallace D Prosensus multivariate v12. 02 2010Louwerse, D. J., Tates, A. A., Smilde, A. K., Koot, G. L. M., & Berndt, H. (1999). PLS discriminant analysis with contribution plots to determine differences between parallel batch reactors in the process industry. Chemometrics and Intelligent Laboratory Systems, 46(2), 197-206. doi:10.1016/s0169-7439(98)00185-3Nomikos, P., & MacGregor, J. F. (1994). Monitoring batch processes using multiway principal component analysis. AIChE Journal, 40(8), 1361-1375. doi:10.1002/aic.690400809Kaistha, N., & Moore, C. F. (2001). Extraction of Event Times in Batch Profiles for Time Synchronization and Quality Predictions. Industrial & Engineering Chemistry Research, 40(1), 252-260. doi:10.1021/ie990937cRamsay, J. O., & Silverman, B. W. (1997). Functional Data Analysis. Springer Series in Statistics. doi:10.1007/978-1-4757-7107-7Andersen, S. W., & Runger, G. C. (2012). Automated feature extraction from profiles with application to a batch fermentation process. Journal of the Royal Statistical Society: Series C (Applied Statistics), 61(2), 327-344. doi:10.1111/j.1467-9876.2011.01032.xKassidas, A., MacGregor, J. F., & Taylor, P. A. (1998). Synchronization of batch trajectories using dynamic time warping. AIChE Journal, 44(4), 864-875. doi:10.1002/aic.690440412González-Martínez, J. M., Ferrer, A., & Westerhuis, J. A. (2011). Real-time synchronization of batch trajectories for on-line multivariate statistical process control using Dynamic Time Warping. Chemometrics and Intelligent Laboratory Systems, 105(2), 195-206. doi:10.1016/j.chemolab.2011.01.003Zhang Y Edgar TF A robust dynamic time warping algorithm for batch trajectory synchronization 2008 2864 2860Gins, G., Van den Kerkhof, P., & Van Impe, J. F. M. (2012). Hybrid Derivative Dynamic Time Warping for Online Industrial Batch-End Quality Estimation. Industrial & Engineering Chemistry Research, 51(17), 6071-6084. doi:10.1021/ie2019068Gurden, S. P., Westerhuis, J. A., Bijlsma, S., & Smilde, A. K. (2000). Modelling of spectroscopic batch process data using grey models to incorporate external information. Journal of Chemometrics, 15(2), 101-121. doi:10.1002/1099-128x(200102)15:23.0.co;2-vKourti, T. (2003). Multivariate dynamic data modeling for analysis and statistical process control of batch processes, start-ups and grade transitions. Journal of Chemometrics, 17(1), 93-109. doi:10.1002/cem.778Westerhuis, J. A., Kourti, T., & MacGregor, J. F. (1999). Comparing alternative approaches for multivariate statistical analysis of batch process data. Journal of Chemometrics, 13(3-4), 397-413. doi:10.1002/(sici)1099-128x(199905/08)13:3/43.0.co;2-iNomikos, P., & MacGregor, J. F. (1995). Multivariate SPC Charts for Monitoring Batch Processes. Technometrics, 37(1), 41-59. doi:10.1080/00401706.1995.10485888Wold, S., Kettaneh, N., Fridén, H., & Holmberg, A. (1998). Modelling and diagnostics of batch processes and analogous kinetic experiments. Chemometrics and Intelligent Laboratory Systems, 44(1-2), 331-340. doi:10.1016/s0169-7439(98)00162-2Chen, J., & Liu, K.-C. (2002). On-line batch process monitoring using dynamic PCA and dynamic PLS models. Chemical Engineering Science, 57(1), 63-75. doi:10.1016/s0009-2509(01)00366-9Ramaker, H.-J., van Sprang, E. N. M., Westerhuis, J. A., & Smilde, A. K. (2005). Fault detection properties of global, local and time evolving models for batch process monitoring. Journal of Process Control, 15(7), 799-805. doi:10.1016/j.jprocont.2005.02.001Lennox, B., Montague, G. A., Hiden, H. G., Kornfeld, G., & Goulding, P. R. (2001). Process monitoring of an industrial fed-batch fermentation. Biotechnology and Bioengineering, 74(2), 125-135. doi:10.1002/bit.1102Ündey, C., Ertunç, S., & Çınar, A. (2003). Online Batch/Fed-Batch Process Performance Monitoring, Quality Prediction, and Variable-Contribution Analysis for Diagnosis. Industrial & Engineering Chemistry Research, 42(20), 4645-4658. doi:10.1021/ie0208218Camacho, J., & Picó, J. (2006). Multi-phase principal component analysis for batch processes modelling. Chemometrics and Intelligent Laboratory Systems, 81(2), 127-136. doi:10.1016/j.chemolab.2005.11.003Rännar, S., MacGregor, J. F., & Wold, S. (1998). Adaptive batch monitoring using hierarchical PCA. Chemometrics and Intelligent Laboratory Systems, 41(1), 73-81. doi:10.1016/s0169-7439(98)00024-0Camacho, J., Picó, J., & Ferrer, A. (2009). The best approaches in the on-line monitoring of batch processes based on PCA: Does the modelling structure matter? Analytica Chimica Acta, 642(1-2), 59-68. doi:10.1016/j.aca.2009.02.001Van Sprang, E. N. ., Ramaker, H.-J., Westerhuis, J. A., Gurden, S. P., & Smilde, A. K. (2002). Critical evaluation of approaches for on-line batch process monitoring. Chemical Engineering Science, 57(18), 3979-3991. doi:10.1016/s0009-2509(02)00338-xLei, F., Rotbøll, M., & Jørgensen, S. B. (2001). A biochemically structured model for Saccharomyces cerevisiae. Journal of Biotechnology, 88(3), 205-221. doi:10.1016/s0168-1656(01)00269-3Camacho J González-Martínez J Ferrer A Multi-phase (MP) toolbox 2013 http://mseg.webs.upv.es/Software.htmlCamacho, J., Picó, J., & Ferrer, A. (2008). Multi-phase analysis framework for handling batch process data. Journal of Chemometrics, 22(11-12), 632-643. doi:10.1002/cem.115

Penalized Partial Least Squares Based on B-Splines Transformations

We propose a novel method to model nonlinear regression problems by adapting the principle of penalization to Partial Least Squares (PLS). Starting with a generalized additive model, we expand the additive component of each variable in terms of a generous amount of B-Splines basis functions. In order to prevent overfitting and to obtain smooth functions, we estimate the regression model by applying a penalized version of PLS. Although our motivation for penalized PLS stems from its use for B-Splines transformed data, the proposed approach is very general and can be applied to other penalty terms or to other dimension reduction techniques. It turns out that penalized PLS can be computed virtually as fast as PLS. We prove a close connection of penalized PLS to the solutions of preconditioned linear systems. In the case of high-dimensional data, the new method is shown to be an attractive competitor to other techniques for estimating generalized additive models. If the number of predictor variables is high compared to the number of examples, traditional techniques often suffer from overfitting. We illustrate that penalized PLS performs well in these situations

Array of Hall Effect Sensors for Linear Positioning of a Magnet Independently of Its Strength Variation. A Case Study: Monitoring Milk Yield during Milking in Goats

In this study we propose an electronic system for linear positioning of a magnet independent of its modulus, which could vary because of aging, different fabrication process, etc. The system comprises a linear array of 24 Hall Effect sensors of proportional response. The data from all sensors are subject to a pretreatment (normalization) by row (position) making them independent on the temporary variation of its magnetic field strength. We analyze the particular case of the individual flow in milking of goats. The multiple regression analysis allowed us to calibrate the electronic system with a percentage of explanation R-2 = 99.96%. In our case, the uncertainty in the linear position of the magnet is 0.51 mm that represents 0.019 L of goat milk. The test in farm compared the results obtained by direct reading of the volume with those obtained by the proposed electronic calibrated system, achieving a percentage of explanation of 99.05%.This work was partially supported by the Spanish Government (Ministerio de Ciencia e Innovacion) under projects HAR2010-21944-C02-01 and HAR2010-21944-C02-02. A. Sanchez-Quinche acknowledges the support received from SENESCYT (Secretaria Nacional de Educacion Superior, Ciencia, Tecnologia e Innovacion) from the Equator Republic.García Diego, FJ.; Sanchez-Quinche, A.; Merello Giménez, P.; Beltrán Medina, P.; Peris Ribera, CJ. (2013). Array of Hall Effect Sensors for Linear Positioning of a Magnet Independently of Its Strength Variation. A Case Study: Monitoring Milk Yield during Milking in Goats. Sensors. 13(6):8000-8012. doi:10.3390/s130608000S80008012136RICORDEAU, G., MARTINET, J., DENAMUR, R., PETREQUIN, P., & CARPENTIER, M. (1963). TRAITE A LA MACHINE DES BREBIS PRÉALPES DU SUD. IMPORTANCE DES DIFFÉRENTES OPÉRATIONS DE LA TRAITE. Annales de Zootechnie, 12(3), 203-223. doi:10.1051/animres:19630305Barillet, F. (2007). Genetic improvement for dairy production in sheep and goats. Small Ruminant Research, 70(1), 60-75. doi:10.1016/j.smallrumres.2007.01.004Ilahi, H., Chastin, P., Bouvier, F., Arhainx, J., Ricard, E., & Manfredi, E. (1999). Milking characteristics of dairy goats. Small Ruminant Research, 34(2), 97-102. doi:10.1016/s0921-4488(99)00057-7Ait-Saidi, A., Caja, G., Carné, S., Salama, A. A. K., & Ghirardi, J. J. (2008). Short Communication: Comparison of Manual Versus Semiautomatic Milk Recording Systems in Dairy Goats. Journal of Dairy Science, 91(4), 1438-1442. doi:10.3168/jds.2007-0815Nishio, T., Chen, Q., Gillijns, W., De Keyser, K., Vervaeke, K., & Moshchalkov, V. V. (2008). Scanning Hall probe microscopy of vortex patterns in a superconducting microsquare. Physical Review B, 77(1). doi:10.1103/physrevb.77.012502Kim, J., Lee, J., Jun, J., Le, M., & Cho, C. (2012). Integration of Hall and Giant Magnetoresistive Sensor Arrays for Real-Time 2-D Visualization of Magnetic Field Vectors. IEEE Transactions on Magnetics, 48(11), 3708-3711. doi:10.1109/tmag.2012.2200662Benitez, D., Gaydecki, P., Quek, S., & Torres, V. (2007). Development of a solid-state multi-sensor array camera for real time imaging of magnetic fields. Journal of Physics: Conference Series, 76, 012030. doi:10.1088/1742-6596/76/1/012030Misron, N., Shin, N. W., Shafie, S., Marhaban, M. H., & Mailah, N. F. (2011). A Mobile Ferromagnetic Shape Detection Sensor Using a Hall Sensor Array and Magnetic Imaging. Sensors, 11(11), 10474-10489. doi:10.3390/s111110474http://www.allegromicro.com/˜/media/Files/Datasheets/A1301–2-Datasheet.ashxhttp://www.Arduino.cchttp://www.supermagnete.es/R-27–16–05-NRavaud, R., Lemarquand, G., Lemarquand, V., & Depollier, C. (2008). THE THREE EXACT COMPONENTS OF THE MAGNETIC FIELD CREATED BY A RADIALLY MAGNETIZED TILE PERMANENT MAGNET. Progress In Electromagnetics Research, 88, 307-319. doi:10.2528/pier08112708Gallagher, N. B., Wise, B. M., & Stewart, C. W. (1996). Application of multi-way principal components analysis to nuclear waste storage tank monitoring. Computers & Chemical Engineering, 20, S739-S744. doi:10.1016/0098-1354(96)00131-7Rännar, S., MacGregor, J. F., & Wold, S. (1998). Adaptive batch monitoring using hierarchical PCA. Chemometrics and Intelligent Laboratory Systems, 41(1), 73-81. doi:10.1016/s0169-7439(98)00024-0Zarzo, M., & Ferrer, A. (2004). Batch process diagnosis: PLS with variable selection versus block-wise PCR. Chemometrics and Intelligent Laboratory Systems, 73(1), 15-27. doi:10.1016/j.chemolab.2003.11.009García-Diego, F.-J., & Zarzo, M. (2010). Microclimate monitoring by multivariate statistical control: The renaissance frescoes of the Cathedral of Valencia (Spain). Journal of Cultural Heritage, 11(3), 339-344. doi:10.1016/j.culher.2009.06.002Zarzo, M., Fernández-Navajas, A., & García-Diego, F.-J. (2011). Long-Term Monitoring of Fresco Paintings in the Cathedral of Valencia (Spain) Through Humidity and Temperature Sensors in Various Locations for Preventive Conservation. Sensors, 11(9), 8685-8710. doi:10.3390/s110908685Statgraphics 5.1http://www.statgraphics.ne

Recognition of prokaryotic promoters based on a novel variable-window Z-curve method

Transcription is the first step in gene expression, and it is the step at which most of the regulation of expression occurs. Although sequenced prokaryotic genomes provide a wealth of information, transcriptional regulatory networks are still poorly understood using the available genomic information, largely because accurate prediction of promoters is difficult. To improve promoter recognition performance, a novel variable-window Z-curve method is developed to extract general features of prokaryotic promoters. The features are used for further classification by the partial least squares technique. To verify the prediction performance, the proposed method is applied to predict promoter fragments of two representative prokaryotic model organisms (Escherichia coli and Bacillus subtilis). Depending on the feature extraction and selection power of the proposed method, the promoter prediction accuracies are improved markedly over most existing approaches: for E. coli, the accuracies are 96.05% (σ70 promoters, coding negative samples), 90.44% (σ70 promoters, non-coding negative samples), 92.13% (known sigma-factor promoters, coding negative samples), 92.50% (known sigma-factor promoters, non-coding negative samples), respectively; for B. subtilis, the accuracies are 95.83% (known sigma-factor promoters, coding negative samples) and 99.09% (known sigma-factor promoters, non-coding negative samples). Additionally, being a linear technique, the computational simplicity of the proposed method makes it easy to run in a matter of minutes on ordinary personal computers or even laptops. More importantly, there is no need to optimize parameters, so it is very practical for predicting other species promoters without any prior knowledge or prior information of the statistical properties of the samples

On Optimization of Injection Molding Cooling

This thesis is devoted to analysis and optimization of the injection molding process with a focus on the mold. In the analysis, both process parameters and the design of the mold are taken in consideration. A procedure has been developed, i.e. a method and a program code, which enables optimization of different quantities, not only restricted to injection molding simulation, by altering different variables. There are many ways to interpret the word “optimization”. In this work, “optimization” means the use of mathematical algorithms in order to maximize or minimize any given quantity. This code, called VerOpt, is written in Matlab. It is versatile since it has the functionality of choosing different optimization routines, and it can make use of parallelization over TCP/IP and different external solvers. The software and different applications are further described in Paper A. There is a pocketful of softwares on the commercial market today, which enables the analysis of the injection molding process. One example is the software Moldex3D by Coretech System. By using simulation in the product development process, much can be gained since the software allows one to make most of the tedious and cost-consuming trial-and-errors in the virtual world, instead of on the shop floor. In Paper B, Moldex3D is used in order to compare the efficiency, in terms of the dimensional accuracy, between two different cooling channel layouts. One conventional layout uses straight holes and a baffle, and another layout makes use of conformal cooling channels manufactured by Free-Form Fabrication (FFF). In Paper C, a comparative study is presented where numerical results are compared with corresponding experimental results using these two types of cooling channel design. For the FFF layout, the Electron Beam Melting (EBM) method was used to manufacture the core insert to the mold. In Paper D, the surface heat distribution of conventional and FFF inserts was investigated, and the influence of the coolant temperature on the surface of the insert was studied. FFF, or Rapid Prototyping (RP), has been commercially available since the late eighties, but the method is not that well known within the injection molding industry. The first available material was plastics. Today, FFF has evolved and fully dense forms in different metal alloys can be manufactured which are suitable for mold inserts. Studies have shown that by using FFF mold inserts in injection molding, both that the part quality can be increased and that the costs for manufacturing the insert can be decreased. The effects of different process parameters on the warpage of a plastic part are however rather complicated and the introduction of new types of inserts manufactured by FFF makes these effects even harder to predict. In Paper E, Design of Experiments (DOE) is used in order to investigate the influence of different process parameter on the critical dimensional accuracy of a test part. The same part was used for two different studies: one using conventional cooling and the other one using an FFF layout. In Paper F, four process parameters were chosen after considering the results obtained from the DOE in Paper E, as variables in an optimization study where the warpage of the test part was minimized using the VerOpt code. In order to measure the efficiency of the FFF mold insert, the optimization was performed on the conventional layout as well. In conclusion, this work has demonstrated the feasibility of including a versatile optimization environment to a commercial injection molding software, and it has also pointed out some important differences in the influence of different process parameters on the warpage of a plastic part when conventional and FFF cooling channel layouts are used

DESIGNING A LAB ASSIGNMENT FOR STUDYING REAL-TIME EMBEDDED SYSTEMS

Embedded systems are all around us in the modern world and continue to evolve as time passes. Therefore, it is important to keep the knowledge in the field evolving, and education is a big part of it. This thesis focuses on how to design a lab assignment for a course about embedded systems with the stress on networking. Embedded systems have reliability and timeliness requirements, which will have to be accounted for when designing the network of the system. The work started with a literature study of communication protocols and how they support the requirements imposed by the embedded systems. Using this knowledge hardware was evaluated and chosen. With the lab assignment in mind, Arduino Zero was chosen as the platform as well as three network modules: Wi-Fi, Bluetooth and Zigbee. The hardware was used to implement a simple embedded system consisting of two nodes: a sensor node and a controller node. The sensor node sends the data to the controller which then acts upon the data. Three program were written, each with its own communication solution (time-triggered, event-triggered and a hybrid solution) and then tested in different environments. From the results of the tests, guidelines were formulated about how to design an assignment and what hardware to use. A general guideline was also created describing a lab assignment step by step. We recommended switching the platform from Arduino Zero to Arduino Uno to reduce the amount of workarounds needed to get the system running. Having more than one communication protocol also proved valuable since the students could show their knowledge by argumenting for their choice of protocol

Rovdjursjakt i Västerbotten

Syftet med studien är att undersöka hur jägares sociala kapital påverkar drivkrafter och möjligheten till att jaga stora rovdjur. Den stora rovdjursjaktens vara eller icke vara är en känslig fråga i samhällsdebatten. Sveriges riksdag har beslutat att de fem stora rovdjuren varg, björn, järv, lo och kungsörn ska få finnas, reproducera sig samt ha en gynnsam bevarandestatus i den svenska naturen. Rovdjursjakten är en del av rovdjursförvaltningen och sker enbart genom licensjakt eller skyddsjakt. De senaste åren har antalet djur som får fällas på licensjakt ökat på grund av växande stammar. Det har bidragit till fler tillfällen att jaga stora rovdjur. I den här studien har sex jägare som nästan enbart jagar rovdjur intervjuats från Västerbotten. Studien använder sig av teorier om socialt kapital tillsammans med begreppen relationer och tillit för att analysera resultatet. Studien visar att jägarnas sociala kapital genom relationer och tillit tydligt påverkar drivkrafter och möjligheten att jaga stora rovdjur. Dels på grund av avsaknaden av specifika jaktmarker och jaktlag, dels på grund av jägarnas vilja att skapa och vårda relationer till jaktligt syfte.The purpose of this study is to analyze how hunters’ social capital affects their driving factors and ability to hunt large carnivores. Whether or not carnivore hunting should be or not be is an emotional debate in society. The Swedish parliament has decided that the five large carnivores wolf, brown bear, wolverine, lynx and golden eagle should exist and be able to have a favorable conservation status in the Swedish nature. The hunting of carnivores is part of the Swedish carnivore administration and take place only through license hunting or protective hunting. Due to the growing carnivore population the number of animals taken on license hunts has increased. It has contributed to more opportunities to hunt carnivores. In this study, six hunters from Västerbotten, Sweden who almost exclusively hunt carnivores have been interviewed. The study uses theories of social capital together with concept relation and trust to analyze the results. The study shows that the hunters’ social capital through relations and trust clearly influences driving factors and the ability to hunt big carnivores. Partly due to specific hunting territories and hunting groups, partly due to the hunters willingness to create and nurture relationships for hunting purposes