Analysis of biopharma raw materials by electrophoresis microchips with contactless conductivity detection

Detailed information concerning the composition of the raw materials employed in the production of biologics is important for the efficient control and optimization of bioprocesses. We demonstrate the application of electrophoresis microchips with capacitively-coupled contactless conductivity detection (C4D) to the analysis of wa-ter-soluble vitamins and metal cations in raw material solutions that are subse-quently fed into bioreactors for the production of biologics

The Calar Alto Legacy Integral Field Area Survey: extended and remastered data release

This paper describes the extended data release of the Calar Alto Legacy Integral Field Area (CALIFA) survey (eDR). It comprises science-grade quality data for 895 galaxies obtained with the PMAS/PPak instrument at the 3.5 m telescope at the Calar Alto Observatory along the last 12 years, using the V500 setup (3700-7500{\AA}, 6{\AA}/FWHM) and the CALIFA observing strategy. It includes galaxies of any morphological type, star-formation stage, a wide range of stellar masses ($\sim$10$^7$ 10$^{12}$ Msun ), at an average redshift of $\sim$0.015 (90\% within 0.005$<$z$<$0.05). Primarily selected based on the projected size and apparent magnitude, we demonstrate that it can be volume corrected resulting in a statistically limited but representative sample of the population of galaxies in the nearby Universe. All the data were homogeneous re-reduced, introducing a set of modifications to the previous reduction. The most relevant is the development and implementation of a new cube-reconstruction algorithm that provides with an (almost) seeing-limited spatial resolution (FWHM PSF $\sim$1.0").To illustrate the usability and quality of the data, we extracted two aperture spectra for each galaxy (central 1.5" and fully integrated), and analyze them using pyFIT3D. We obtain a set of observational and physical properties of both the stellar populations and the ionized gas, that have been compared for the two apertures, exploring their distributions as a function of the stellar masses and morphologies of the galaxies, comparing with recent results in the literature. DATA RELEASE: http://ifs.astroscu. unam.mx/CALIFA_WEB/public_html/Comment: 30 pages, 26 figures, accepted for publishing in the MNRA

The space density distribution of galaxies in the absolute magnitude - rotation velocity plane: a volume-complete Tully-Fisher relation from CALIFA stellar kinematics

The space density distribution of galaxies in the absolute magnitude - rotation velocity plane: a volume-complete Tully-Fisher relation from CALIFA stellar kinematicsComment: Accepted to A&

Anaphylaxis to clavulanic acid: seven-year survey

info:eu-repo/semantics/publishedVersio

Optimization of a Lambda-RED Recombination Method for Rapid Gene Deletion in Human Cytomegalovirus

Human cytomegalovirus (HCMV) continues to be a major cause of morbidity in transplant patients and newborns. However, the functions of many of the more than 282 genes encoded in the HCMV genome remain unknown. The development of bacterial artificial chromosome (BAC) technology contributes to the genetic manipulation of several organisms including HCMV. The maintenance of the HCMV BAC in E. coli cells permits the rapid generation of recombinant viral genomes that can be used to produce viral progeny in cell cultures for the study of gene function. We optimized the Lambda-Red Recombination system to construct HCMV gene deletion mutants rapidly in the complete set of tested genes. This method constitutes a useful tool that allows for the quick generation of a high number of gene deletion mutants, allowing for the analysis of the whole genome to improve our understanding of HCMV gene function. This may also facilitate the development of novel vaccines and therapeutics.This study was supported by the Spanish Ministry of Science, Innovation and University, Instituto de Salud Carlos III Grant/Award Numbers: PI17CIII-00014 (MPY110/18); PI20CIII-00009 (MPY303/20); DTS18CIII/00006 (MPY127/19). E.G.-R. is supported by the Sara Borrell Program (CD18CIII/00007), M.L.-S. is supported by the Sara Borrell Program (CD17CIIII/00017), Instituto de Salud Carlos III, Ministerio de Ciencia, Innovación y Universidades.S

Decreased metalloproteinase production as a response to mechanical pressure in human cartilage: a mechanism for homeostatic regulation

Articular cartilage is optimised for bearing mechanical loads. Chondrocytes are the only cells present in mature cartilage and are responsible for the synthesis and integrity of the extracellular matrix. Appropriate joint loads stimulate chondrocytes to maintain healthy cartilage with a concrete protein composition according to loading demands. In contrast, inappropriate loads alter the composition of cartilage, leading to osteoarthritis (OA). Matrix metalloproteinases (MMPs) are involved in degradation of cartilage matrix components and have been implicated in OA, but their role in loading response is unclear. With this study, we aimed to elucidate the role of MMP-1 and MMP-3 in cartilage composition in response to mechanical load and to analyse the differences in aggrecan and type II collagen content in articular cartilage from maximum- and minimum-weight-bearing regions of human healthy and OA hips. In parallel, we analyse the apoptosis of chondrocytes in maximal and minimal load areas. Because human femoral heads are subjected to different loads at defined sites, both areas were obtained from the same hip and subsequently evaluated for differences in aggrecan, type II collagen, MMP-1, and MMP-3 content (enzyme-linked immunosorbent assay) and gene expression (real-time polymerase chain reaction) and for chondrocyte apoptosis (flow cytometry, bcl-2 Western blot, and mitochondrial membrane potential analysis). The results showed that the load reduced the MMP-1 and MMP-3 synthesis (p < 0.05) in healthy but not in OA cartilage. No significant differences between pressure areas were found for aggrecan and type II collagen gene expression levels. However, a trend toward significance, in the aggrecan/collagen II ratio, was found for healthy hips (p = 0.057) upon comparison of pressure areas (loaded areas > non-loaded areas). Moreover, compared with normal cartilage, OA cartilage showed a 10- to 20-fold lower ratio of aggrecan to type II collagen, suggesting that the balance between the major structural proteins is crucial to the integrity and function of the tissue. Alternatively, no differences in apoptosis levels between loading areas were found – evidence that mechanical load regulates cartilage matrix composition but does not affect chondrocyte viability. The results suggest that MMPs play a key role in regulating the balance of structural proteins of the articular cartilage matrix according to local mechanical demands

Computational modelling of epithelial cell monolayers during infection with Listeria monocytogenes

Intracellular bacterial infections alter the normal functionality of human host cells and tissues. Infection can also modify the mechanical properties of host cells, altering the mechanical equilibrium of tissues. In order to advance our understanding of host–pathogen interactions, simplified in vitro models are normally used. However, in vitro studies present certain limitations that can be alleviated by the use of computer-based models. As complementary tools these computational models, in conjunction with in vitro experiments, can enhance our understanding of the mechanisms of action underlying infection processes. In this work, we extend our previous computer-based model to simulate infection of epithelial cells with the intracellular bacterial pathogen Listeria monocytogenes. We found that forces generated by host cells play a regulatory role in the mechanobiological response to infection. After infection, in silico cells alter their mechanical properties in order to achieve a new mechanical equilibrium. The model pointed the key role of cell–cell and cell–extracellular matrix interactions in the mechanical competition of bacterial infection. The obtained results provide a more detailed description of cell and tissue responses to infection, and could help inform future studies focused on controlling bacterial dissemination and the outcome of infection processes. © 2022 The Author(s

Resolving galaxies in time and space: II: Uncertainties in the spectral synthesis of datacubes

In a companion paper we have presented many products derived from the application of the spectral synthesis code STARLIGHT to datacubes from the CALIFA survey, including 2D maps of stellar population properties and 1D averages in the temporal and spatial dimensions. Here we evaluate the uncertainties in these products. Uncertainties due to noise and spectral shape calibration errors and to the synthesis method are investigated by means of a suite of simulations based on 1638 CALIFA spectra for NGC 2916, with perturbations amplitudes gauged in terms of the expected errors. A separate study was conducted to assess uncertainties related to the choice of evolutionary synthesis models. We compare results obtained with the Bruzual & Charlot models, a preliminary update of them, and a combination of spectra derived from the Granada and MILES models. About 100k CALIFA spectra are used in this comparison. Noise and shape-related errors at the level expected for CALIFA propagate to 0.10-0.15 dex uncertainties in stellar masses, mean ages and metallicities. Uncertainties in A_V increase from 0.06 mag in the case of random noise to 0.16 mag for shape errors. Higher order products such as SFHs are more uncertain, but still relatively stable. Due to the large number statistics of datacubes, spatial averaging reduces uncertainties while preserving information on the history and structure of stellar populations. Radial profiles of global properties, as well as SFHs averaged over different regions are much more stable than for individual spaxels. Uncertainties related to the choice of base models are larger than those associated with data and method. Differences in mean age, mass and metallicity are ~ 0.15 to 0.25 dex, and 0.1 mag in A_V. Spectral residuals are ~ 1% on average, but with systematic features of up to 4%. The origin of these features is discussed. (Abridged)Comment: A&A, accepte

Temporal variability analysis reveals biases in electronic health records due to hospital process reengineering interventions over seven years

[EN] Objective To evaluate the effects of Process-Reengineering interventions on the Electronic Health Records (EHR) of a hospital over 7 years. Materials and methods Temporal Variability Assessment (TVA) based on probabilistic data quality assessment was applied to the historic monthly-batched admission data of Hospital La Fe Valencia, Spain from 2010 to 2016. Routine healthcare data with a complete EHR was expanded by processed variables such as the Charlson Comorbidity Index. Results Four Process-Reengineering interventions were detected by quantifiable effects on the EHR: (1) the hospital relocation in 2011 involved progressive reduction of admissions during the next four months, (2) the hospital services re-configuration incremented the number of inter-services transfers, (3) the care-services re-distribution led to transfers between facilities (4) the assignment to the hospital of a new area with 80,000 patients in 2015 inspired the discharge to home for follow up and the update of the pre-surgery planned admissions protocol that produced a significant decrease of the patient length of stay. Discussion TVA provides an indicator of the effect of process re-engineering interventions on healthcare practice. Evaluating the effect of facilities¿ relocation and increment of citizens (findings 1, 3¿4), the impact of strategies (findings 2¿3), and gradual changes in protocols (finding 4) may help on the hospital management by optimizing interventions based on their effect on EHRs or on data reuse. Conclusions The effects on hospitals EHR due to process re-engineering interventions can be evaluated using the TVA methodology. Being aware of conditioned variations in EHR is of the utmost importance for the reliable reuse of routine hospitalization data.F.J.P.B, C.S., J.M.G.G. and J.A.C. were funded Universitat Politecnica de Valencia, project "ANALISIS DE LA CALIDAD Y VARIABILIDAD DE DATOS MEDICOS". www.upv.es. J.M.G.G.is also partially supported by: Ministerio de Economia y Competitividad of Spain through MTS4up project (National Plan for Scientific and Technical Research and Innovation 2013-2016, No. DPI2016-80054-R); and European Commission projects H2020-SC1-2016-CNECT Project (No. 727560) and H2020-SC1-BHC-2018-2020 (No. 825750). The funders did not play any role in the study design, data collection and analysis, decision to publish, nor preparation of the manuscript.Perez-Benito, FJ.; Sáez Silvestre, C.; Conejero, JA.; Tortajada, S.; Valdivieso, B.; Garcia-Gomez, JM. (2019). Temporal variability analysis reveals biases in electronic health records due to hospital process reengineering interventions over seven years. PLoS ONE. 14(8):1-19. https://doi.org/10.1371/journal.pone.0220369S119148Aguilar-Savén, R. S. (2004). Business process modelling: Review and framework. International Journal of Production Economics, 90(2), 129-149. doi:10.1016/s0925-5273(03)00102-6Poulymenopoulou, M. (2003). Journal of Medical Systems, 27(4), 325-335. doi:10.1023/a:1023701219563Dadam P, Reichert M, Kuhn K. Clinical Workflows -The Killer Application for Process-oriented Information Systems? Proceedings of the 4th International Conference on Business Information Systems. London: Springer London; 2000. pp. 36–59. doi: https://doi.org/10.1007/978-1-4471-0761-3Lenz, R., & Reichert, M. (2007). IT support for healthcare processes – premises, challenges, perspectives. Data & Knowledge Engineering, 61(1), 39-58. doi:10.1016/j.datak.2006.04.007Rebuge, Á., & Ferreira, D. R. (2012). Business process analysis in healthcare environments: A methodology based on process mining. Information Systems, 37(2), 99-116. doi:10.1016/j.is.2011.01.003Amour EAEH, Ghannouchi SA. Applying Data Mining Techniques to Discover KPIs Relationships in Business Process Context. 2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT). IEEE; 2017. pp. 230–237. doi: https://doi.org/10.1109/PDCAT.2017.00045Chou, Y.-C., Chen, B.-Y., Tang, Y.-Y., Qiu, Z.-J., Wu, M.-F., Wang, S.-C., … Chuang, W.-C. (2010). Prescription-Filling Process Reengineering of an Outpatient Pharmacy. Journal of Medical Systems, 36(2), 893-902. doi:10.1007/s10916-010-9553-5Leu, J.-D., & Huang, Y.-T. (2009). An Application of Business Process Method to the Clinical Efficiency of Hospital. Journal of Medical Systems, 35(3), 409-421. doi:10.1007/s10916-009-9376-4Gand K. Investigating on Requirements for Business Model Representations: The Case of Information Technology in Healthcare. 2017 IEEE 19th Conference on Business Informatics (CBI). IEEE; 2017. pp. 471–480. doi: https://doi.org/10.1109/CBI.2017.36Ferreira, G. S. A., Silva, U. R., Costa, A. L., & Pádua, S. I. D. de D. (2018). The promotion of BPM and lean in the health sector: main results. Business Process Management Journal, 24(2), 400-424. doi:10.1108/bpmj-06-2016-0115Abdulrahman Jabour RM. Cancer Reporting: Timeliness Analysis and Process. 2016; Available: https://search.proquest.com/openview/4ecf737c5ef6d2d503e948df8031fe54/1?pq-origsite=gscholar&amp;cbl=18750&amp;diss=yHewitt M, Simone J V. Enhancing Data Systems to Improve the Quality of Cancer Care [Internet]. National Academy Press; 2000. Available: http://www.nap.edu/catalog/9970.htmlWeiskopf, N. G., & Weng, C. (2013). Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. Journal of the American Medical Informatics Association, 20(1), 144-151. doi:10.1136/amiajnl-2011-000681Saez C, Robles M, Garcia-Gomez JM. Comparative study of probability distribution distances to define a metric for the stability of multi-source biomedical research data. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. IEEE; 2013. pp. 3226–3229. doi: https://doi.org/10.1109/EMBC.2013.6610228Sáez, C., Rodrigues, P. P., Gama, J., Robles, M., & García-Gómez, J. M. (2014). Probabilistic change detection and visualization methods for the assessment of temporal stability in biomedical data quality. Data Mining and Knowledge Discovery, 29(4), 950-975. doi:10.1007/s10618-014-0378-6Sáez, C., Zurriaga, O., Pérez-Panadés, J., Melchor, I., Robles, M., & García-Gómez, J. M. (2016). Applying probabilistic temporal and multisite data quality control methods to a public health mortality registry in Spain: a systematic approach to quality control of repositories. Journal of the American Medical Informatics Association, 23(6), 1085-1095. doi:10.1093/jamia/ocw010International Ethical Guidelines for Epidemiological Studies [Internet]. Geneva: Council for International Organizations of Medical Sciences (CIOMS) in collaboration with the World Health Organization; 2009. Available: https://cioms.ch/wp-content/uploads/2017/01/International_Ethical_Guidelines_LR.pdfResearch Ethics Committee of the Universitari i Politècnic La Fe Hospital [Internet]. Available: https://www.iislafe.es/en/research/ethics-committees/Charlson, M. E., Pompei, P., Ales, K. L., & MacKenzie, C. R. (1987). A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. Journal of Chronic Diseases, 40(5), 373-383. doi:10.1016/0021-9681(87)90171-8Schneeweiss, S., Wang, P. S., Avorn, J., & Glynn, R. J. (2003). Improved Comorbidity Adjustment for Predicting Mortality in Medicare Populations. Health Services Research, 38(4), 1103-1120. doi:10.1111/1475-6773.00165Quan, H., Sundararajan, V., Halfon, P., Fong, A., Burnand, B., Luthi, J.-C., … Ghali, W. A. (2005). Coding Algorithms for Defining Comorbidities in ICD-9-CM and ICD-10 Administrative Data. Medical Care, 43(11), 1130-1139. doi:10.1097/01.mlr.0000182534.19832.83Sáez Silvestre C. Probabilistic methods for multi-source and temporal biomedical data quality assessment [Internet]. Thesis. Universitat Politècnica de València. 2016. doi: https://doi.org/10.4995/Thesis/10251/62188Amari S, Nagaoka H. Methods of Information Geometry [Internet]. Amer. Math. Soc. and Oxford Univ. Press. American Mathematical Society; 2000. Available: https://books.google.es/books?hl=es&amp;lr=&amp;id=vc2FWSo7wLUC&amp;oi=fnd&amp;pg=PR7&amp;dq=Methods+of+Information+geometry&amp;ots=4HmyCCY4PX&amp;sig=2-dpCuwMQvEC1iREjxdfIX0yEls#v=onepage&amp;q=MethodsofInformationgeometry&amp;f=falseCsiszár, I., & Shields, P. C. (2004). Information Theory and Statistics: A Tutorial. Foundations and Trends™ in Communications and Information Theory, 1(4), 417-528. doi:10.1561/0100000004Lin, J. (1991). Divergence measures based on the Shannon entropy. IEEE Transactions on Information Theory, 37(1), 145-151. doi:10.1109/18.61115M.Cover T. Elements Of Information Theory Notes [Internet]. 2006. Available: http://books.google.fr/books?id=VWq5GG6ycxMC&printsec=frontcover&dq=intitle:Elements+of+Information+Theory&hl=&cd=1&source=gbs_api%5Cnpapers2://publication/uuid/BAF426F8-5A4F-44A4-8333-FA8187160D9BBrandes, U., & Pich, C. (s. f.). Eigensolver Methods for Progressive Multidimensional Scaling of Large Data. Lecture Notes in Computer Science, 42-53. doi:10.1007/978-3-540-70904-6_6Liaw, S. T., Rahimi, A., Ray, P., Taggart, J., Dennis, S., de Lusignan, S., … Talaei-Khoei, A. (2013). Towards an ontology for data quality in integrated chronic disease management: A realist review of the literature. International Journal of Medical Informatics, 82(1), 10-24. doi:10.1016/j.ijmedinf.2012.10.001Arts, D. G. T. (2002). Defining and Improving Data Quality in Medical Registries: A Literature Review, Case Study, and Generic Framework. Journal of the American Medical Informatics Association, 9(6), 600-611. doi:10.1197/jamia.m1087Bray, F., & Parkin, D. M. (2009). Evaluation of data quality in the cancer registry: Principles and methods. Part I: Comparability, validity and timeliness. European Journal of Cancer, 45(5), 747-755. doi:10.1016/j.ejca.2008.11.032Parkin, D. M., & Bray, F. (2009). Evaluation of data quality in the cancer registry: Principles and methods Part II. Completeness. European Journal of Cancer, 45(5), 756-764. doi:10.1016/j.ejca.2008.11.033Fernandez-Llatas, C., Ibanez-Sanchez, G., Celda, A., Mandingorra, J., Aparici-Tortajada, L., Martinez-Millana, A., … Traver, V. (2019). Analyzing Medical Emergency Processes with Process Mining: The Stroke Case. Lecture Notes in Business Information Processing, 214-225. doi:10.1007/978-3-030-11641-5_17Fernandez-Llatas, C., Lizondo, A., Monton, E., Benedi, J.-M., & Traver, V. (2015). Process Mining Methodology for Health Process Tracking Using Real-Time Indoor Location Systems. Sensors, 15(12), 29821-29840. doi:10.3390/s151229769Van der Aalst, W., Weijters, T., & Maruster, L. (2004). Workflow mining: discovering process models from event logs. IEEE Transactions on Knowledge and Data Engineering, 16(9), 1128-1142. doi:10.1109/tkde.2004.47Weijters AJMM, Van Der Aalst WMP, Alves De Medeiros AK. Process Mining with the HeuristicsMiner Algorithm [Internet]. Available: https://pdfs.semanticscholar.org/1cc3/d62e27365b8d7ed6ce93b41c193d0559d086.pdfShim, S. J., & Kumar, A. (2010). Simulation for emergency care process reengineering in hospitals. Business Process Management Journal, 16(5), 795-805. doi:10.1108/14637151011076476Svolba, G., & Bauer, P. (1999). Statistical Quality Control in Clinical Trials. Controlled Clinical Trials, 20(6), 519-530. doi:10.1016/s0197-2456(99)00029-xKahn, M. G., Raebel, M. A., Glanz, J. M., Riedlinger, K., & Steiner, J. F. (2012). A Pragmatic Framework for Single-site and Multisite Data Quality Assessment in Electronic Health Record-based Clinical Research. Medical Care, 50, S21-S29. doi:10.1097/mlr.0b013e318257dd67Batini, C., Cappiello, C., Francalanci, C., & Maurino, A. (2009). Methodologies for data quality assessment and improvement. ACM Computing Surveys, 41(3), 1-52. doi:10.1145/1541880.1541883Heinrich, B., Klier, M., & Kaiser, M. (2009). A Procedure to Develop Metrics for Currency and its Application in CRM. Journal of Data and Information Quality, 1(1), 1-28. doi:10.1145/1515693.1515697Sirgo, G., Esteban, F., Gómez, J., Moreno, G., Rodríguez, A., Blanch, L., … Bodí, M. (2018). Validation of the ICU-DaMa tool for automatically extracting variables for minimum dataset and quality indicators: The importance of data quality assessment. International Journal of Medical Informatics, 112, 166-172. doi:10.1016/j.ijmedinf.2018.02.007Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504-507. doi:10.1126/science.1127647Kohn LT, Corrigan JM. To err is human: building a safer health system. A report of the Committee on Quality of Health Care in America. 2000. p. 287. National Academies Press