Continuous regression: a functional regression approach to facial landmark tracking

Facial Landmark Tracking (Face Tracking) is a key step for many Face Analysis systems, such as Face Recognition, Facial Expression Recognition, or Age and Gender Recognition, among others. The goal of Facial Landmark Tracking is to locate a sparse set of points defining a facial shape in a video sequence. These typically include the mouth, the eyes, the contour, or the nose tip. The state of the art method for Face Tracking builds on Cascaded Regression, in which a set of linear regressors are used in a cascaded fashion, each receiving as input the output of the previous one, subsequently reducing the error with respect to the target locations. Despite its impressive results, Cascaded Regression suffers from several drawbacks, which are basically caused by the theoretical and practical implications of using Linear Regression. Under the context of Face Alignment, Linear Regression is used to predict shape displacements from image features through a linear mapping. This linear mapping is learnt through the typical least-squares problem, in which a set of random perturbations is given. This means that, each time a new regressor is to be trained, Cascaded Regression needs to generate perturbations and apply the sampling again. Moreover, existing solutions are not capable of incorporating incremental learning in real time. It is well-known that person-specific models perform better than generic ones, and thus the possibility of personalising generic models whilst tracking is ongoing is a desired property, yet to be addressed. This thesis proposes Continuous Regression, a Functional Regression solution to the least-squares problem, resulting in the first real-time incremental face tracker. Briefly speaking, Continuous Regression approximates the samples by an estimation based on a first-order Taylor expansion yielding a closed-form solution for the infinite set of shape displacements. This way, it is possible to model the space of shape displacements as a continuum, without the need of using complex bases. Further, this thesis introduces a novel measure that allows Continuous Regression to be extended to spaces of correlated variables. This novel solution is incorporated into the Cascaded Regression framework, and its computational benefits for training under different configurations are shown. Then, it presents an approach for incremental learning within Cascaded Regression, and shows its complexity allows for real-time implementation. To the best of my knowledge, this is the first incremental face tracker that is shown to operate in real-time. The tracker is tested in an extensive benchmark, attaining state of the art results, thanks to the incremental learning capabilities

Producción en plantas de nanopartículas recombinantes derivadas del virus del grabado del tabaco

[ES] El uso biotecnológico de nanopartículas virales (VNPs) suscita un gran interés debido a sus potenciales aplicaciones biotecnológicas en campos como la medicina o la nanotecnología. Las VNPs permiten la presentación de epítopes de interés fusionados a las proteínas de cubierta (CP) virales. En este trabajo se describe la generación de nanopartículas virales recombinantes derivadas del virus del grabado del tabaco (TEV) en plantas de Nicotiana benthamiana. Por un lado, hemos producido VNPs genéticamente modificadas capaces de presentar en su superficie la proteína fluorescente verde (GFP) o la roja mCherry. De forma similar a lo reportado para otros virus, la inserción de la secuencia de escape ribosomal F2A derivada de picornavirus fue necesaria para generar una población mixta de CPs decoradas y sin modificar que permitan el correcto ensamblaje de la particula viral. Por otro lado, hemos comenzado con el desarrollo de VNPs híbridas capaces de exponer en su superficie dos proteínas distintas. Para ello generamos un sistema de coinfección de TEV junto a un vector viral derivado del virus X de la patata (PVX), capaz de producir una segunda CP de TEV en trans. La capacidad infectiva de los clones virales recombinantes y la presencia de las proteínas de interés en la progenie viral fue analizada por ensayos de RT-PCR y de Western-blot, así como por microscopía de fluorescencia. Finalmente, con el objetivo de en un próximo paso analizar el correcto ensamblaje de las VNPs recombinantes generadas, optimizamos un protocolo eficiente de purificación de partículas virales de TEV, las cuales pudieron ser observadas por microscopía electrónica de transmisión. Estas VNPs recombinantes presentando una o más proteínas de interés sobre su superficie constituyen una novedosa herramienta biotecnológica con potenciales aplicaciones en la medicina, tales como su uso como vacunas, biosensores o en marcaje celular.[EN] The biotechnological use of viral nanoparticles (VNP) arouses great interest due to their potencial biotechnological applications in fields such as medicine or nanotechnology. VNPs allow the presentation of epitopes of interest fused to viral coat proteins (CP). In this work, the generation of recombinant viral nanoparticles derived from tobacco etch virus (TEV) in Nicotiana benthamiana plants is described. On the one hand, we have produced genetically modified VNPs capable of presenting green fluorescent protein (GFP) or red fluorescent protein (mCherry) on their surface. Acording to previous reports in other virus, insertion of the F2A ribosomal escape sequence derived from picornavirus was necessary to generate a mixed population of decorated and unmodified CPs that allowed the correct assembly of the viral particles. On the other hand, we have started the development of hybrid VNPs capable of exposing two different proteins on their surface. To do this, we generated a coinfectation system based on TEV together with a viral vector derived from potato virus X (PVX), capable of producing a second TEV CP in trans. The infective capacity of the recombinant viral clones and the presence of the interest proteins in the viral progeny were analyzed by RT-PCR and Western-blot assays, as well as by fluorescence microscopy. Finally, whit the aim of analyzing the correct assembly of the generated recombinant VNPs in a next step, we optimize an efficient protocol for the purification of TEV viral particles, which can be observed by transmission electron microscopy. These recombinant VNPs presenting one or more proteins of interest on their surface, are a novel biotechnological tool with potential applications in medicine, such as their use as vaccines, biosensors, or cell imaging.Lozano Sánchez, E. (2022). Producción en plantas de nanopartículas recombinantes derivadas del virus del grabado del tabaco. Universitat Politècnica de València. http://hdl.handle.net/10251/181672TFG

Continuous regression: a functional regression approach to facial landmark tracking

Facial Landmark Tracking (Face Tracking) is a key step for many Face Analysis systems, such as Face Recognition, Facial Expression Recognition, or Age and Gender Recognition, among others. The goal of Facial Landmark Tracking is to locate a sparse set of points defining a facial shape in a video sequence. These typically include the mouth, the eyes, the contour, or the nose tip. The state of the art method for Face Tracking builds on Cascaded Regression, in which a set of linear regressors are used in a cascaded fashion, each receiving as input the output of the previous one, subsequently reducing the error with respect to the target locations. Despite its impressive results, Cascaded Regression suffers from several drawbacks, which are basically caused by the theoretical and practical implications of using Linear Regression. Under the context of Face Alignment, Linear Regression is used to predict shape displacements from image features through a linear mapping. This linear mapping is learnt through the typical least-squares problem, in which a set of random perturbations is given. This means that, each time a new regressor is to be trained, Cascaded Regression needs to generate perturbations and apply the sampling again. Moreover, existing solutions are not capable of incorporating incremental learning in real time. It is well-known that person-specific models perform better than generic ones, and thus the possibility of personalising generic models whilst tracking is ongoing is a desired property, yet to be addressed. This thesis proposes Continuous Regression, a Functional Regression solution to the least-squares problem, resulting in the first real-time incremental face tracker. Briefly speaking, Continuous Regression approximates the samples by an estimation based on a first-order Taylor expansion yielding a closed-form solution for the infinite set of shape displacements. This way, it is possible to model the space of shape displacements as a continuum, without the need of using complex bases. Further, this thesis introduces a novel measure that allows Continuous Regression to be extended to spaces of correlated variables. This novel solution is incorporated into the Cascaded Regression framework, and its computational benefits for training under different configurations are shown. Then, it presents an approach for incremental learning within Cascaded Regression, and shows its complexity allows for real-time implementation. To the best of my knowledge, this is the first incremental face tracker that is shown to operate in real-time. The tracker is tested in an extensive benchmark, attaining state of the art results, thanks to the incremental learning capabilities

Cascaded regression with sparsified feature covariance matrix for facial landmark detection

This paper explores the use of context on regression-based methods for facial landmarking. Regression based methods have revolutionised facial landmarking solutions. In particular those that implicitly infer the whole shape of a structured object have quickly become the state-of-the-art. The most notable exemplar is the Supervised Descent Method (SDM). Its main characteristics are the use of the cascaded regression approach, the use of the full appearance as the inference input, and the aforementioned aim to directly predict the full shape. In this article we argue that the key aspects responsible for the success of SDM are the use of cascaded regression and the avoidance of the constrained optimisation problem that characterised most of the previous approaches.We show that, surprisingly, it is possible to achieve comparable or superior performance using only landmark-specific predictors, which are linearly combined. We reason that augmenting the input with too much context (of which using the full appearance is the extreme case) can be harmful. In fact, we experimentally found that there is a relation between the data variance and the benefits of adding context to the input. We finally devise a simple greedy procedure that makes use of this fact to obtain superior performance to the SDM, while maintaining the simplicity of the algorithm. We show extensive results both for intermediate stages devised to prove the main aspects of the argumentative line, and to validate the overall performance of two models constructed based on these considerations

Dynamical analysis on cubic polynomials of Damped Traub s method for approximating multiple roots

[EN] In this paper, the performance of a parametric family including Newton¿s and Traub¿s schemes on multiple roots is analyzed. The local order of convergence on nonlinear equations with multiple roots is studied as well as the dynamical behavior in terms of the damping parameter on cubic polynomials with multiple roots. The fixed and critical points, and the associated parameter plane are some of the characteristic dynamical features of the family which are obtained in this work. From the analysis of these elements we identify members of the family of methods with good numerical properties in terms of stability and efficiency both for finding the simple and multiple roots, and also other ones with very unstable behavior.This research was partially supported by Ministerio de Economia y Competitividad MTM2014-52016-C2-2-P Spain and Generalitat Valenciana PROMETEO/2016/089 SpainVázquez-Lozano, JE.; Cordero Barbero, A.; Torregrosa Sánchez, JR. (2018). Dynamical analysis on cubic polynomials of Damped Traub s method for approximating multiple roots. Applied Mathematics and Computation. 328:82-99. https://doi.org/10.1016/j.amc.2018.01.043S829932

A functional regression approach to facial landmark tracking

Linear regression is a fundamental building block in many face detection and tracking algorithms, typically used to predict shape displacements from image features through a linear mapping. This paper presents a Functional Regression solution to the least squares problem, which we coin Continuous Regression, resulting in the first real-time incremental face tracker. Contrary to prior work in Functional Regression, in which B-splines or Fourier series were used, we propose to approximate the input space by its first-order Taylor expansion, yielding a closed-form solution for the continuous domain of displacements. We then extend the continuous least squares problem to correlated variables, and demonstrate the generalisation of our approach. We incorporate Continuous Regression into the cascaded regression framework, and show its computational benefits for both training and testing. We then present a fast approach for incremental learning within Cascaded Continuous Regression, coined iCCR, and show that its complexity allows real-time face tracking, being 20 times faster than the state of the art. To the best of our knowledge, this is the first incremental face tracker that is shown to operate in real-time. We show that iCCR achieves state-of-the-art performance on the 300-VW dataset, the most recent, large-scale benchmark for face tracking

ReCom: A semi-supervised approach to ultra-tolerant database search for improved identification of modified peptides.

Open-search methods allow unbiased, high-throughput identification of post-translational modifications in proteins at an unprecedented scale. The performance of current open-search algorithms is diminished by experimental errors in the determination of the precursor peptide mass. In this work we propose a semi-supervised open search approach, called ReCom, that minimizes this effect by taking advantage of a priori known information from a reference database, such as Unimod or a database provided by the user. We present a proof-of-concept study using Comet-ReCom, an improved version of Comet-PTM. Comet-ReCom increased identification performance of Comet-PTM by 68%. This increased performance of Comet-ReCom to score the MS/MS spectrum comes in parallel with a significantly better assignation of the monoisotopic peak of the precursor peptide in the MS spectrum, even in cases of peptide coelution. Our data demonstrate that open searches using ultra-tolerant mass windows can benefit from using a semi-supervised approach that takes advantage from previous knowledge on the nature of protein modifications. SIGNIFICANCE: The present study introduces a novel approach to ultra-tolerant database search, which employs prior knowledge of post-translational modifications (PTMs) to improve identification of modified peptides. This method addresses the limitations related to experimental errors and precursor mass assignation of previous open-search methods. Thus, it enables the study of the biological significance of a wider variety of PTMs, including unknown or unexpected modifications that may have gone unnoticed using non-supervised search methods.This study was supported by competitive grants from the Spanish Ministry of Science, Innovation and Universities (PGC2018-097019-B-I00, PID2021-122348NB-I00, PLEC2022-009235 and PLEC2022-009298), the Instituto de Salud Carlos III (Fondo de Investigación Sanitaria grant PRB3 (PT17/0019/0003- ISCIIISGEFI / ERDF, ProteoRed), Comunidad de Madrid (IMMUNO-VAR, P2022/BMD-7333) and “la Caixa” Banking Foundation (project codes HR17-00247 and HR22-00253). The CNIC is supported by the Instituto de Salud Carlos III (ISCIII), the Ministerio de Ciencia e Innovación (MCIN) and the Pro CNIC Foundation), and is a Severo Ochoa Center of Excellence (grant CEX2020-001041-S funded by MICIN/AEI/10.13039/501100011033).S

Interaction of Mitochondrial and Epigenetic Regulation in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a pathology preceded mainly by cirrhosis of diverse etiology and is associated with uncontrolled dedifferentiation and cell proliferation processes. Many cellular functions are dependent on mitochondrial function, among which we can mention the enzymatic activity of PARP-1 and sirtuin 1, epigenetic regulation of gene expression, apoptosis, and so on. Mitochondrial dysfunction is related to liver diseases including cirrhosis and HCC; the energetic demand is not properly supplied and mitochondrial morphologic changes have been observed, resulting in an altered metabolism. There is a strong relationship between epigenetics and mitochondrion since the first one is dependent on the correct function of the last one. There is an interest to improve or to maintain mitochondrial integrity in order to prevent or reverse HCC; such is the case of IFC-305 that has a beneficial effect on mitochondrial function in a sequential model of cirrhosis-HCC. In this model, IFC-305 downregulates the expression of PCNA, thymidylate synthase, HGF and its receptor c-Met and upregulates the cell cycle inhibitor p27, thereby decreasing cell proliferation. Both effects, improvement of mitochondria function and reduction of tumor proliferation, suggest its use as HCC chemoprevention or as an adjuvant in chemotherapy