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La mise en abyme, definida como relación especular entre la obra entera y un fragmento que refleja características importantes de ésta, se basa en un desdoblamiento (o una multiplicación) interno de una parte del texto, una repetición que puede ser figurada o literal. Mientras que la mise en abyme por sustitución metonímica y metafórica ha sido objeto de numerosos estudios, la autocita en cuanto reiteración de pasajes idénticos en la misma obra no ha atraído mucho la atención crítica y su carácter de mise en abyme resulta dudoso en muchos casos. En este artículo se examinan tres textos en que se repiten, con funciones y significados variados y en contextos diferentes, frases o incluso páginas enteras: 1. las variaciones de una conversación telefónica en la pieza teatral Cartas de amor a Stalin de Juan Mayorga; 2. la recreación de ficciones y recuerdos en la novela Remake de Bruno Galindo; 3. la autocita anticipadora del final en El Maestro y Margarita de Mijaíl Bulgákov. El análisis de estas obras permite afirmar que la repetición textual, y no sólo en su forma de autocita con cambio del nivel de enunciación, puede ser una especie de fractal especular que comparte algunas características y funciones con la mise en abyme.

Juan Goytisolo observó con entusiasmo, pero también con cierta preocu­pación, las noticias sobre las revoluciones que, desde comienzos de 2011, derrocaron varios regímenes autocráticos (en Túnez, Egipto y Libia), provocaron reformas en otros países (Marruecos) y condujeron a una guerra civil aún no terminada (Siria). Formuló sus opiniones sobre este proceso histórico en reportajes, ensayos y, sobre todo, artículos periodísticos publicados en El País. A partir de estos textos, me propongo comparar sus comentarios actuales con ideas expresadas desde finales del siglo xx y analizar las figuras argumentativas características de la visión goytisoleana de la historia reciente de África del Norte. Abstract Juan Goytisolo observed with enthusiasm, though with certain concern, the news about the revolutions that, since early 2011, overthrew several autocratical regimes (in Tunisia, Egypt and Libya), entailed reforms in other countries (Morocco) and caused a yet unfinished civil war (Syria). He gave his opinions on this historical process in repor­tages, essays and, mainly, articles published in El País. Based on these texts, I intend to compare his current comments with ideas he expressed in the late 20th century and to analize argumentation figures that are characteristic of Goytisolo’s vision of the recent history of North Africa.

Los grandes acontecimientos históricos sitúan al productor cultural (escritor, cineasta, artista, etc.) ante un dilema que se manifiesta en la tensión entre, por un lado, la inclinación a ceder ante la espectacularidad estética del suceso y usarlo como fuente de inspiración para el propio trabajo creativo y, por otro, los remordimientos de conciencia de quien se siente culpable al aprovechar como materia prima de su creación o como fuente de un placer contemplativo un suceso que causó inmen­so dolor y muerte masiva. A medida que crece la distancia entre el acontecimiento y el producto cultural, disminuye la fuerza inhibitoria del tabú y la ficción se emancipa de la historia factual. A modo de ejemplificar el proceso de destabuización, se comentan tres obras mexicanas inspiradas en la masacre de la Plaza de las Tres Culturas en Tlatelolco, 1968.

La obra de Borges ha inspirado numerosos textos de autores españoles contemporáneos como Juan Bonilla, Pedro Zarraluki y, sobre todo, Carlos Cañeque en su metanovela Quién (1997). El artículo comenta la intertextualidad borgeana (citas, temas y motivos, técnica narrativa, rasgos estilísticos) en Quién, explica la complicada construcción de la novela diferenciando los distintos niveles del texto –las dos tramas novelescas incompatibles, los paratextos intraficcionales y la ficcionalización de la historia editorial– y analiza los procedimientos utilizados para crear un problema narrativo irresoluble de condicionamiento y dependencia mutua de dos novelas dentro de la novela.

El texto literario provoca lecturas que no buscan primordialmente la información, sino que la integran en una hipótesis hermenéutica que está también al servicio del placer del lector. Este tiende a favorecer la lectura que le parece la más probable debido a su conformidad con una serie de criterios, como la literalidad (vs. sentido figurado), la normalidad de la experiencia o la frecuencia empíricamente más alta de ocurrencia de un fenómeno (vs. excepcionalidad), el menor esfuerzo hermenéutico (vs. interpretación compleja, sofisticada), la suspensión de la desconfianza (vs. escrutinio escéptico), el antropocentrismo del enfoque (vs. perspectiva insólita), etc.…

¿Quién mata mujeres en Ciudad Juárez? Nadie lo sabe o nadie lo dice y la única certeza que queda es la larga lista de las víctimas. El asunto, empero, ha influido fuertemente en la producción artística de los últimos tiempos. En las novelas (y películas) que estudia este trabajo se observa una mezcla de sentimientos de impotencia ante los crímenes y el deseo de encontrar una solución clara, sencilla y conforme con una cosmovisión propia, que haga salir a la luz a un grupo identificable de culpables y así poner fin a la serie de asesinatos. Con el análisis, se trata de probar que Ciudad Juárez no es el caso excepcional que excita tanto la fantasía de escritores, cineastas y cazadores de conspiraciones, sino la punta más visible de un iceberg cuya causa principal reside en la brutalización de la sociedad como consecuencia de la paulatina pauperización.

Abstract Multivariate interactions between process parameters can heavily impact product quality and process performance in biopharmaceutical manufacturing processes. Thus, multivariate interactions should be identified and appropriately controlled. This article describes an in-silico approach to establish multivariate acceptable ranges; these ranges help to illustrate the combined impact of multiple input variables on product quality and process performance. Additionally, this article includes a case study for a monoclonal antibody polishing application. Proven acceptable ranges are set by changing only one input parameter at a time while keeping all others constant to understand the impact of process variability on product quality or process performance, but the impact of synergistic variables are not evaluated. Within multivariate acceptable ranges, any combination of input parameters of a unit operation yields the desired product quality and process performance. The layered approach applied in this article is based on risk assessment and statistical models to leverage prior knowledge and existing data. The risk assessment is specific for a manufacturing facility but is applicable to multiple products manufactured in the same facility. No additional wet-lab experiments are required for building the statistical models when development and process characterization are executed using a design of experiments approach, compared to a univariate evaluation of data. The established multivariate acceptable range justifies revised normal operating ranges to ensure process control. Further, the determination of multivariate acceptable ranges adds to overall process knowledge, ultimately supporting the implementation of a more effective control strategy.

Within process development, numerous experimental studies are undertaken to establish, optimize and characterize individual bioprocess unit operations. These studies pursue diverse objectives such as enhancing titer or minimizing impurities. Consequently, Design of Experiment (DoE) studies are planned and analyzed independently from each other, making it challenging to interlink individual data sets to form a comprehensive overview at the conclusion of the development process. This paper elucidates the methodology for constructing a Life-Cycle-DoE (LDoE), which integrates data-driven process knowledge through design augmentations. It delves into the strategy, highlights the challenges encountered and provides solutions for overcoming them. The LDoE approach facilitates the augmentation of an existing model with new experiments in a unified design. It allows for flexible design adaptations as per the requirements of subject matter experts (SME) during process development, concurrently enhancing model predictions by utilizing all available data. The LDoE boasts a broad application spectrum as it consolidates all data generated within bioprocess development into a single file and model. The study demonstrates that the LDoE approach enables a process characterization study (PCS) to be performed solely with development data. Furthermore, it identifies potentially critical process parameters (pCPPs) early, allowing for timely adaptations in process development to address these challenges.

Abstract Objective Random effects are often neglected when defining the control strategy for a biopharmaceutical process. In this article, we present a case study that highlights the importance of considering the variance introduced by random effects in the calculation of proven acceptable ranges (PAR), which form the basis of the control strategy. Methods Linear mixed models were used to model relations between process parameters and critical quality attributes in a set of unit operations that comprises a typical biopharmaceutical manufacturing process. Fitting such models yields estimates of fixed and random effect sizes as well as random and residual variance components. To form PARs, tolerance intervals specific to mixed models were applied that incorporate the random effect contribution to variance. Results We compared standardized fixed and random effect sizes for each unit operation and CQA. The results show that the investigated random effect is not only significant but in some unit operations even larger than the average fixed effect. A comparison between ordinary least squares and mixed models tolerance intervals shows that neglecting the contribution of the random effect can result in PARs that are too optimistic. Conclusions Uncontrollable effects such as week-to-week variability play a major role in process variability and can be modelled as a random effect. Following a workflow such as the one suggested in this article, random effects can be incorporated into a statistically sound control strategy, leading to lowered out of specification results and reduced patient risk.

Intermediate acceptance criteria are the foundation for developing control strategies in process validation stage 1 in the pharmaceutical industry. At drug substance or product level such intermediate acceptance criteria for quality are available and referred to as specification limits. However, it often remains a challenge to define acceptance criteria for intermediate process steps. Available guidelines underpin the importance of intermediate acceptance criteria, because they are an integral part for setting up a control strategy for the manufacturing process. The guidelines recommend to base the definition of acceptance criteria on the entirety of process knowledge. Nevertheless, the guidelines remain unclear on how to derive such limits. Within this contribution we aim to present a sound data science methodology for the definition of intermediate acceptance criteria by putting the guidelines recommendations into practice (ICH Q6B, 1999). By using an integrated process model approach, we leverage manufacturing data and experimental data from small scale to derive intermediate acceptance criteria. The novelty of this approach is that the acceptance criteria are based on pre-defined out-of-specification probabilities, while also considering manufacturing variability in process parameters. In a case study we compare this methodology to a conventional +/- 3 standard deviations (3SD) approach and demonstrate that the presented methodology is superior to conventional approaches and provides a solid line of reasoning for justifying them in audits and regulatory submission.

A hallmark of adaptive immunity is CD4 T cells’ ability to differentiate into specialized effectors. A long-standing question is whether T cell receptor (TCR) signal strength can dominantly instruct the development of Th1 and T follicular helper (Tfh) cells across distinct infectious contexts. We characterized the differentiation of murine CD4 TCR transgenic T cells responding to altered peptide ligand lymphocytic choriomeningitis viruses (LCMV) derived from acute and chronic parental strains. We found that TCR signal strength exerts opposite and hierarchical effects on the balance of Th1 and Tfh cells responding to acute versus persistent infection. TCR signal strength correlates positively with Th1 generation during acute but negatively during chronic infection. Weakly activated T cells express lower levels of markers associated with chronic T cell stimulation and may resist functional inactivation. We anticipate that the panel of recombinant viruses described herein will be valuable for investigating a wide range of CD4 T cell responses.

Maximizing the value of each available data point in bioprocess development is essential in order to reduce the time-to-market, lower the number of expensive wet-lab experiments, and maximize process understanding. Advanced in silico methods are increasingly being investigated to accomplish these goals. Within this contribution, we propose a novel integrated process model procedure to maximize the use of development data to optimize the Stage 1 process validation work flow. We generate an integrated process model based on available data and apply two innovative Monte Carlo simulation-based parameter sensitivity analysis linearization techniques to automate two quality by design activities: determining risk assessment severity rankings and establishing preliminary control strategies for critical process parameters. These procedures are assessed in a case study for proof of concept on a candidate monoclonal antibody bioprocess after process development, but prior to process characterization. The evaluation was successful in returning results that were used to support Stage I process validation milestones and demonstrated the potential to reduce the investigated parameters by up to 24% in process characterization, while simultaneously setting up a strategy for iterative updates of risk assessments and process controls throughout the process life-cycle to ensure a robust and efficient drug supply.