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UNLABELLED: There is growing interest in engineering Pseudomonas putida KT2440 as a microbial chassis for the conversion of renewable and waste-based feedstocks, and metabolic engineering of P. putida relies on the understanding of the functional relationships between genes. In this work, independent component analysis (ICA) was applied to a compendium of existing fitness data from randomly barcoded transposon insertion sequencing (RB-TnSeq) of P. putida KT2440 grown in 179 unique experimental conditions. ICA identified 84 independent groups of genes, which we call fModules (functional modules), where gene members displayed shared functional influence in a specific cellular process. This machine learning-based approach both successfully recapitulated previously characterized functional relationships and established hitherto unknown associations between genes. Selected gene members from fModules for hydroxycinnamate metabolism and stress resistance, acetyl coenzyme A assimilation, and nitrogen metabolism were validated with engineered mutants of P. putida. Additionally, functional gene clusters from ICA of RB-TnSeq data sets were compared with regulatory gene clusters from prior ICA of RNAseq data sets to draw connections between gene regulation and function. Because ICA profiles the functional role of several distinct gene networks simultaneously, it can reduce the time required to annotate gene function relative to manual curation of RB-TnSeq data sets. IMPORTANCE: This study demonstrates a rapid, automated approach for elucidating functional modules within complex genetic networks. While Pseudomonas putida randomly barcoded transposon insertion sequencing data were used as a proof of concept, this approach is applicable to any organism with existing functional genomics data sets and may serve as a useful tool for many valuable applications, such as guiding metabolic engineering efforts in other microbes or understanding functional relationships between virulence-a

Independent Component Analysis (ICA) is a statistical technique for decomposing information from datasets into maximally independent components. ICA allows the researcher to recover two or more independent signals that appear mixed within the same dataset. This paper shows ICA to be an extremely effective method for separating different colours found in rock paintings into discrete images or components. The comparison between the results of ICA and PCA (Principal Component Analysis) shows that ICA accurately separates panels with more than one type of colour, while PCA achieves a lower degree of separation. This study also shows that in scenes with monochrome depictions, ICA tends to be slightly more effective in separating the pigments from the rock. The ICA method has been applied successfully to several rock art panels from Northern Chile, where the use of diverse types of mineral pigments is common. Two analyses conducted at the Pampa El Muerto 11 site in the Northern Chilean highlands reveal how ICA can contribute to a more compelling interpretation of more intricate panels. The comparison between the results of ICA and PCA (Principal Components Analysis) shows that ICA correctly separates panels with more than one type of pigment, while PCA achieves a lower degree of separation. This study also shows that in scenes with monochrome depictions, ICA tends to be slightly more effective in separating the pigments from the rock. ICA algorithm has been successfully in several rock panels from Northern Chile, where the use of diverse types of mineral pigments is usual. Two panels from the Pampa El Muerto site have been analysed with the technique mentioned above, informing that its application can collaborate on a more compelling interpretation of intricate panels., Depto. de Prehistoria, Historia Antigua y Arqueología, Fac. de Geografía e Historia, TRUE, pub

There is growing interest in engineering Pseudomonas putida KT2440 as a microbial chassis for the conversion of renewable and waste-based feedstocks, and metabolic engineering of P. putida relies on the understanding of the functional relationships between genes. In this work, independent component analysis (ICA) was applied to a compendium of existing fitness data from randomly barcoded transposon insertion sequencing (RB-TnSeq) of P. putida KT2440 grown in 179 unique experimental conditions. ICA identified 84 independent groups of genes, which we call fModules (“functional modules”), where gene members displayed shared functional influence in a specific cellular process. This machine learning-based approach both successfully recapitulated previously characterized functional relationships and established hitherto unknown associations between genes. Selected gene members from fModules for hydroxycinnamate metabolism and stress resistance, acetyl coenzyme A assimilation, and nitrogen metabolism were validated with engineered mutants of P. putida. Additionally, functional gene clusters from ICA of RB-TnSeq data sets were compared with regulatory gene clusters from prior ICA of RNAseq data sets to draw connections between gene regulation and function. Because ICA profiles the functional role of several distinct gene networks simultaneously, it can reduce the time required to annotate gene function relative to manual curation of RB-TnSeq data sets.

Independent component analysis has recently become a promising data-based approach to detect structural relations in multivariate dynamic systems in cases when apriori knowledge about causal patterns are scant. This paper suggests a kernel-based ML estimation that is largely agnostic with regard to the distributional features of the structural origins of data variation and enables causal analysis under the assumption of having only a subset of independent shocks. In an empirical application to the global oil market model of Kilian (2009) we illustrate the benefits of allowing for an unmodelled higher-order dependence among the oil supply and speculative oil demand shocks.

We describe a new method of blind source separation (BSS) on a microphone array combining subband independent component analysis (ICA) and beamforming. The proposed array system consists of the following three sections: (1) subband ICA-based BSS section with estimation of the direction of arrival (DOA) of the sound source, (2) null beamforming section based on the estimated DOA, and (3) integration of (1) and (2) based on the algorithm diversity. Using this technique, we can resolve the low-convergence problem through optimization in ICA. To evaluate its effectiveness, signal-separation and speech-recognition experiments are performed under various reverberant conditions. The results of the signal-separation experiments reveal that the noise reduction rate (NRR) of about 18dB is obtained under the nonreverberant condition, and NRRs of 8dB and 6dB are obtained in the case that the reverberation times are 150 milliseconds and 300 milliseconds. These performances are superior to those of both simple ICA-based BSS and simple beamforming method. Also, fromthe speech-recognition experiments, it is evident that the performance of the proposed method in terms of the word recognition rates is superior to those of the conventional ICA-based BSS method under all reverberant conditions.

In this paper, we propose a new blind speech extraction method consisting of a minimum mean-square error short-time spectral amplitude (MMSE STSA) estimator and noise estimation based on independent component analysis (ICA). First, we perform a computer simulation using the artificial noise whose stationarity could be controlled parametrically, and the obtained results indicate that the proposed method is superior to conventional methods, such as blind spatial subtraction array (BSSA) and the original MMSE STSA estimator under the non-point-source and nonstationary noise condition. Finally, we conduct an experiment in an actual railway-station environment, and objective and subjective evaluations to confirm the advantage of the proposed method in the real world., ICASPP2010: IEEE International Conference on Acoustics, Speech, and Signal Processing, March 14-19, 2010, Dallas, Texas, USA.

We propose methods to analyze and control source localization of stereo audio signals using blind source separation (BSS) based on independent component analysis (ICA). Although an inverse system of separation compensates distortion caused by ICA as reconstruction of stereo spatial characteristics, this technique is insufficient to analyze localization because it achieves compensation of distortion and reconstruction of spatial characteristics simultaneously. Thus we analyze spatial characteristics effectively by dividing the compensation into two steps: monaural-output compensation of distortion and its reconstruction of spatial characteristics. Additionally, we control the localization of each source by modifying the analyzed spatial characteristics. It is shown that the proposed method can be applied to stereo signals consisting of more than two sources., ICASSP2008: IEEE International Conference on Acoustics, Speech, and Signal Processing, March 30 - April 4, 2008, Las Vegas, Nevada, USA.

In this paper we propose a new unsupervised training method for nonlinear spatial filter using a new independent component analysis based on kernel infomax. The nonlinearity of the spatial filter used in this paper is equivalent to the integration of beamforming and spectral subtraction, and the whole structure is optimized by independent component analysis in the reproducing kernel Hilbert space. The optimized filter is shown to be capable of achieving better quality output than the conventional method based on time-frequency binary masking., ICASSP2009: IEEE International Conference on Acoustics, Speech, and Signal Processing, April 19-24, 2009, Taipei, Taiwan.

A hands-free system with conventional independent component analysis (ICA) should update the separation filter constantly to follow the hourly environment change. However, when the separation-filter is updated in the period where the user absent, ICA often yields an undesired separation filter. In this paper, we propose a novel algorithm of kurtosis-based voice activity detection (VAD) for an appropriate ICA optimization. In this algorithm, first, closed-form 2nd-order ICA (SO-ICA) is performed for providing the roughly separated signals, and based on the kurtosis of their signals, VAD is processed. Next, when the current time period is voiced block, the higher-order ICA's re-optimization is applied using the previously obtained SO-ICA's solution as the initial filter; this results in a fast and high convergence. The effectiveness of the proposed method is shown in a simulation experiment for blind spatial subtraction array with the proposed method., IWAENC2008: the 11th International Workshop on Acoustic Echo and Noise Control, September 14-17, 2008, Seattle, Washington USA.

SAAB has developed an autonomous underwater vehicle that can mimic a conventional submarine for military fleets to exercise anti-submarine warfare. The AUV actively emits amplified versions of received sonar pulses to create the illusion of being a larger object. To prevent acoustic feedback, the AUV must distinguish between the sound to be actively responded to and its emitted signal. This master thesis has examined techniques aimed at preventing the AUV from responding to previously emitted signals to avoid acoustical feedback, without relying on prior knowledge of either the received signal or the signal emitted by the AUV. The two primary types of algorithms explored for this problem include blind source separation and adaptive filtering. The adaptive filters based on Leaky Least Mean Square and Kalman have shown promising results in attenuating the active response from the received signal. The adaptive filters utilize the fact that a certain hydrophone primarily receives the active response. This hydrophone serves as an estimate of the active response since the signal it captures is considered unknown and is to be removed. The techniques based on blind source separation have utilized the recordings of three hydrophones placed at various locations of the AUV to separate and estimate the received signal from the one emitted by the AUV. The results have demonstrated that neither of the reviewed methods is suitable for implementation on the AUV. The hydrophones are situated at a considerable distance from each other, resulting in distinct time delays between the reception of the two signals. This is usually referred to as a convolutive mixture. This is commonly solved using the frequency domain to transform the convolutive mixture to an instantaneous mixture. However, the fact that the signals share the same frequency spectrum and are adjacent in time has proven highly challenging.

The dissertation consists of three peer-reviewed publications and is related to the basic research of autism spectrum disorder (ASD), especially the assessment of changes in brain function using functional magnetic resonance imaging (fMRI). The purpose was to discover possible differences in cued and spontaneous brain activity in autistic child and adolescent participants compared to typically developing controls. We used blood oxygen level dependent (BOLD) fMRI imaging of the brain, with which the participants were examined at rest and while looking at facial expressions. The resting state (RS) fMRI data artifacts were reduced, and brain networks were identified using independent component analysis. In addition, the RS was analyzed 1) over the entire measurement period using the regional homogeneity (ReHo) method, which measures local connectivity, and 2) based on the states of different brain networks grouped into shorter periods using the co-activation patterns (CAP) method. Statistically significant differences between groups were found in RS, more clearly with the CAP method. Also, significant differences in brain activity were found between the groups regarding the observation of facial expressions. The dissertation increases the understanding of changes in brain networks related to the autism spectrum, strengthening and supplementing previous research results. Based on our results, analyses of brain networks grouped into similar activation phases of shorter duration are worth further development. The new information can help develop earlier and more accurate imaging diagnostics, tentatively recognizing possible intervention target brain networks and evaluating therapeutic effects., Tiivistelmä Väitöskirja koostuu kolmesta vertaisarvioidusta julkaisusta ja liittyy autismikirjon kehityshäiriön perustutkimukseen, erityisesti aivotoiminnan muutosten arviointiin toiminnallisen magneettikuvauksen (functional MRI, fMRI) avulla. Tutkimuksen tarkoituksena oli selvittää stimuloidun ja spontaanin aivotoiminnan mahdollisia eroavaisuuksia lasten ja nuorten autismikirjossa neurotyypillisiin verrokkeihin nähden. Tutkimusmenetelmänä käytettiin veren happipitoisuudesta riippuvaista aivojen fMRI-kuvausta, jolla osallistujia tutkittiin levossa sekä heidän katsellessaan kasvojen ilmeitä. Itsenäisten komponenttien analyysilla (ICA) vähennettiin lepotilan fMRI-datan häiriöitä ja tunnistettiin aivoverkostoja. Lisäksi lepotilaa analysoitiin 1) koko mittausjakson ajalta signaalien alueellista homogeenisuutta ts. aivojen paikallista kytkennällisyyttä mittaavalla regional homogeneity (ReHo) -menetelmällä ja 2) eri aivoverkostojen tilojen perusteella lyhyemmiksi ajanjaksoiksi ns. yhtäaikaisten aktivaatioiden kuvioihin (co-activation patterns; CAP) ryhmiteltyinä. Näissä löydettiin tilastollisesti merkittäviä ryhmien välisiä eroja, selkeämmin CAP-menetelmällä. Myös kasvojen ilmeiden tarkkailuun liittyen havaittiin tilastollisesti merkittäviä aivotoiminnan eroja ryhmien välillä. Väitöskirja lisää ymmärrystä autismikirjoon liittyvistä aivoverkostojen muutoksista vahvistaen ja täydentäen aiempia tutkimustuloksia. Sen perusteella samankaltaisiin lyhempikestoisiin aktivaatiovaiheisiin ryhmiteltyjen aivoverkostojen analyyseja kannattaa kehittää. Uusi tieto voi auttaa varhaisemman ja tarkemman kuvantamisdiagnostiikan kehittämisessä, tarvittaessa oikeisiin aivoverkostoihin kohdennetuissa interventioissa ja niiden vaikutusten arvioinnissa ja seurannassa.

SAAB has developed an autonomous underwater vehicle that can mimic a conventional submarine for military fleets to exercise anti-submarine warfare. The AUV actively emits amplified versions of received sonar pulses to create the illusion of being a larger object. To prevent acoustic feedback, the AUV must distinguish between the sound to be actively responded to and its emitted signal. This master thesis has examined techniques aimed at preventing the AUV from responding to previously emitted signals to avoid acoustical feedback, without relying on prior knowledge of either the received signal or the signal emitted by the AUV. The two primary types of algorithms explored for this problem include blind source separation and adaptive filtering. The adaptive filters based on Leaky Least Mean Square and Kalman have shown promising results in attenuating the active response from the received signal. The adaptive filters utilize the fact that a certain hydrophone primarily receives the active response. This hydrophone serves as an estimate of the active response since the signal it captures is considered unknown and is to be removed. The techniques based on blind source separation have utilized the recordings of three hydrophones placed at various locations of the AUV to separate and estimate the received signal from the one emitted by the AUV. The results have demonstrated that neither of the reviewed methods is suitable for implementation on the AUV. The hydrophones are situated at a considerable distance from each other, resulting in distinct time delays between the reception of the two signals. This is usually referred to as a convolutive mixture. This is commonly solved using the frequency domain to transform the convolutive mixture to an instantaneous mixture. However, the fact that the signals share the same frequency spectrum and are adjacent in time has proven highly challenging.

Alaşehir-Sarıgöl sub-basin (ASSB) in Türkiye faces severe water stress mainly due to intensive agricultural irrigation. This has led to declining groundwater levels, aquifer compaction, and subsequent land subsidence. This study aims to evaluate the roles of tectonic activity and groundwater withdrawal in land-subsidence and investigate additional factors like faults and soft soil thickness. The P-SBAS algorithm was applied, using 98 and 123 Sentinel-1 SAR images in ascending and descending orbits, respectively, from 2016 to 2020. Independent Component Analysis separated long-term displacements from seasonal variations in the InSAR time series data. InSAR analysis showed displacement rates up to −6.40 cm/year. Results reveal a direct correlation between InSAR displacement and soft soil thickness, highlighting aquitard layer compaction due to groundwater withdrawal and piezometric head depletion as the primary causes of land subsidence. It was not possible to disentangle the multi-year displacements with different rates, i.e., to isolate the displacements caused by tectonic activity from displacements due to the soft soil compaction induced by groundwater withdrawal. Nonetheless, the analysis identified two spatiotemporal displacement trends: one linked to long-term, linear plastic compaction of the aquitard due to groundwater withdrawal and the other involving long-term displacements with seasonal rebounds caused by yearly water level fluctuations.

Independent component analysis (ICA) has been proven useful for suppression of artifacts in EEG recordings. It involves separation of measured signals into statistically independent components or sources, followed by rejection of those deemed artificial. We show that a "leak" of cerebral activity of interest into components marked as artificial means that one is going to lost that activity. To overcome this problem we propose a novel wavelet enhanced ICA method (wICA) that applies a wavelet thresholding not to the observed raw EEG but to the demixed independent components as an intermediate step. It allows recovering the neural activity present in "artificial" components. Employing semi-simulated and real EEG recordings we quantify the distortions of the cerebral part of EEGs introduced by the ICA and wICA artifact suppressions in the time and frequency domains. In the context of studying cortical circuitry we also evaluate spectral and partial spectral coherences over ICA/wICA-corrected EEGs. Our results suggest that ICA may lead to an underestimation of the neural power spectrum and to an overestimation of the coherence between different cortical sites. wICA artifact suppression preserves both spectral (amplitude) and coherence (phase) characteristics of the underlying neural activity. (c) 2006 Elsevier B.V. All rights reserved., Spanish Ministry of Education and Science (program Ramón y Cajal), MAPFRE, Depto. de Análisis Matemático y Matemática Aplicada, Fac. de Ciencias Matemáticas, TRUE, pub

© 2006 Pascual-Montano et a. This work has been partially funded by the Spanish grants CICYT BFU2004-00217/BMC, GEN2003-20235-c05-05, CYTED-505PI0058, TIN2005-5619, PR27/05-13964-BSCH and a collaborative grant between the Spanish CSIC and the Canadian NRC (CSIC-050402040003). PCS is recipient of a grant from CAM. APM acknowledges the support of the Spanish Ramón y Cajal program., Medical imaging has become an absolutely essential diagnostic tool for clinical practices; at present, pathologies can be detected with an earliness never before known. Its use has not only been relegated to the field of radiology but also, increasingly, to computer-based imaging processes prior to surgery. Motion analysis, in particular, plays an important role in analyzing activities or behaviors of live objects in medicine. This short paper presents several low-cost hardware implementation approaches for the new generation of tablets and/or smartphones for estimating motion compensation and segmentation in medical images. These systems have been optimized for breast cancer diagnosis using magnetic resonance imaging technology with several advantages over traditional X-ray mammography, for example, obtaining patient information during a short period. This paper also addresses the challenge of offering a medical tool that runs on widespread portable devices, both on tablets and/or smartphones to aid in patient diagnostics., CICYT (Spain), CSIC (Spain), Canadian NRC, CAM, Spanish Ramón y Cajal program, Sección Deptal. de Arquitectura de Computadores y Automática (Físicas), Fac. de Ciencias Físicas, TRUE, pub

The transcriptional regulatory network (TRN) of the phytopathogen Pseudomonas syringae pv. tomato DC3000 regulates its response to environmental stimuli, including interactions with hosts and neighboring bacteria. Despite the importance of transcriptional regulation during these agriculturally significant interactions, a comprehensive understanding of the TRN of P. syringae is yet to be achieved. Here, we collected and decomposed a compendium of public RNA-seq data from P. syringae to obtain 45 independently modulated gene sets (iModulons) that quantitatively describe the TRN and its activity state across diverse conditions. Through iModulon analysis, we (i) untangle the complex interspecies interactions between P. syringae and other terrestrial bacteria in cocultures, (ii) expand the current understanding of the Arabidopsis thaliana-P. syringae interaction, and (iii) elucidate the AlgU-dependent regulation of flagellar gene expression. The modularized TRN yields a unique understanding of interaction-specific transcriptional regulation in P. syringae.

Micro-Doppler (µDoppler) ultrasound imaging is a high frame rate ultrasound imaging modality that provides high spatiotemporal resolution ultrasound images of blood flow. It is sensitive to slow blood flow and particularly suitable for capturing fast-changing phenomena like rapid blood flow. Clutter filtering is an essential step in µDoppler data processing to reject tissue clutter signals and keep blood flow information as much as possible. 3D freehand µDoppler imaging is an emerging ultrasound technique that can construct full spatial vasculature images with a panoramic view that conventional 2D ultrasound is not able to provide. As freehand implies the continuous and nonuniform movement of the probe, it becomes more challenging for clutter filtering to acquire high-quality images. This thesis explores and compares different state-of-art clutter filtering techniques on freehand in-vivo µDoppler imaging of the human brain. Specifically, Singular Value Decomposition (SVD), Robust Principle Component Analysis (Robust PCA), and Independent Component Analysis (ICA) clutter filtering techniques have been investigated. The aim is to test and compare their performance on in-vivo µDoppler ultrasound data with freehand probe movement and understand how freehand motion affects the threshold selection criteria. Besides that, a newly proposed method that combines ICA clutter filtering and clustering is included in this thesis to bring another perspective for sorting independent components corresponding to blood flow and rejecting unwanted ones consisting mostly of tissue clutter signals., Electrical Engineering | Signals and Systems

Recorded Electroencephalogram (EEG) signals are typically affected by interference and artifacts, which can both impact eye reading and computer analysis of the data. Artifacts are induced by physiological (noncerebral) activities of the patient, such as muscular activities of the eyes, or the heart, or the body, while interference may be of external or internal origin. External interference can be induced by electrical machines if the latter are in the same room as the patients, while internal interference can be caused by abnormal breathing, or body movement, or electrode malfunction, or headset movements. Interference may cause severe distortion of EEG signals, resulting in loss of some segments of brain signals, while artifacts are additive signals to brain signals. Therefore, in order to analyze the brain activity signals of a patient, we need to identify and eliminate interference and isolate artifacts. In this paper, we analyze the EEG signals that were recorded using a headset with fourteen channels placed on the heads of comatose patients at the National Institute of Neurology in Tunis, Tunisia. We identify the interference using a robust statistical method known as projection statistics and we separate the brain signals from the artifacts cited above by applying an independent component analysis method. Finally, we show the multifractal behavior of the EEG signals without interference by applying the wavelet leader method and analyze their properties using the singularity spectrum.

Staphylococcus aureus is a versatile pathogen and a leading urgent threat to human health. The clinical burden of this organism is predicted to steadily grow worldwide as it becomes resistant to an increasing number of currently available antibiotics. At the same time, development of new effective antibiotics have dropped precipitously in the past decades. The confluence of these two factors is setting the stage for a “post-antibiotic” era where a great portion of S. aureus infection may not be treatable by the existing regimen. In order to stay ahead of this emerging resistance wave, a deeper understanding of the fundamental biology underlying S. aureus resistance and pathogenesis is necessary. Emerging works have demonstrated that resistance and virulence are deeply linked to other aspects of physiology such as metabolism and stress response by criss crossing gene regulation. However, untangling these complex regulatory systems from bottom up approaches can be challenging. This dissertation focuses on resolving these complexities in transcriptional regulation by applying Independent Component Analysis (ICA) to RNA sequencing data. ICA recovers the underlying signals from regulators that combine together to shape the expression profile of the cell, creating a scalable and operable model of the TRN. We utilized ICA to describe the structure and composition of the TRN in S. aureus USA300 strains. Next, we used the TRN model in conjunction with metabolic models to understand how metabolic and regulatory cross-talks coordinate carbon and nitrogen metabolism to direct protein synthesis. Finally, we modeled TRNs from multiple strains and revealed how gene-regulator interactions have evolved during the emergence of the endemic USA300 strains. Together, this demonstrates the utility of ICA in studying the TRN of S. aureus to rapidly discover its organization and its evolution over time.

Background The "dual syndrome hypothesis" distinguished two subtypes in mild cognitive impairment (MCI) in Parkinson's disease: frontostriatal, characterized by attentional and executive deficits; and posterior cortical, characterized by visuospatial, memory, and language deficits. Objective The aim was to identify resting-state functional modifications associated with these subtypes. Methods Ninety-five nondemented patients categorized as having normal cognition (n = 31), frontostriatal (n = 14), posterior cortical (n = 20), or mixed (n = 30) cognitive subtype had a 3 T resting-state functional magnetic resonance imaging scan. Twenty-four age-matched healthy controls (HCs) were also included. A group-level independent component analysis was performed to identify resting-state networks, and the selected components were subdivided into 564 cortical regions in addition to 26 basal ganglia regions. Global intra- and inter-network connectivity along with global and local efficiencies was compared between groups. The network-based statistics approach was used to identify connections significantly different between groups. Results Patients with posterior cortical deficits had increased intra-network functional connectivity (FC) within the basal ganglia network compared with patients with frontostriatal deficits. Patients with frontostriatal deficits had reduced inter-network FC between several networks, including the visual, default-mode, sensorimotor, salience, dorsal attentional, basal ganglia, and frontoparietal networks, compared with HCs, patients with normal cognition, and patients with a posterior cortical subtype. Similar results were also found between patients with a mixed subtype and HCs. Conclusion MCI subtypes are associated with specific changes in resting-state FC. Longitudinal studies are needed to determine the predictive potential of these markers regarding the risk of developing dementia. (c) 2021 International Parkinson and Movement Disorder Society

Resting-state functional magnetic resonance imaging is currently the mainstay of functional neuroimaging and has allowed researchers to identify intrinsic connectivity networks (aka functional networks) at different spatial scales. However, little is known about the temporal profiles of these networks and whether it is best to model them as continuous phenomena in both space and time or, rather, as a set of temporally discrete events. Both categories have been supported by series of studies with promising findings. However, a critical question is whether focusing only on time points presumed to contain isolated neural events and disregarding the rest of the data is missing important information, potentially leading to misleading conclusions. In this work, we argue that brain networks identified within the spontaneous blood oxygenation level-dependent (BOLD) signal are not limited to temporally sparse burst moments and that these event present time points (EPTs) contain valuable but incomplete information about the underlying functional patterns. We focus on the default mode and show evidence that is consistent with its continuous presence in the BOLD signal, including during the event absent time points (EATs), i.e., time points that exhibit minimum activity and are the least likely to contain an event. Moreover, our findings suggest that EPTs may not contain all the available information about their corresponding networks. We observe distinct default mode connectivity patterns obtained from all time points (AllTPs), EPTs, and EATs. We show evidence of robust relationships with schizophrenia symptoms that are both common and unique to each of the sets of time points (AllTPs, EPTs, EATs), likely related to transient patterns of connectivity. Together, these findings indicate the importance of leveraging the full temporal data in functional studies, including those using event-detection approaches.

This study focuses on the automatic decoding of inner speech using noninvasive methods, such as Electroencephalography (EEG). While inner speech has been a research topic in philosophy and psychology for half a century, recent attempts have been made to decode nonvoiced spoken words by using various brain–computer interfaces. The main shortcomings of existing work are reproducibility and the availability of data and code. In this work, we investigate various methods (using Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), Long Short-Term Memory Networks (LSTM)) for the detection task of five vowels and six words on a publicly available EEG dataset. The main contributions of this work are (1) subject dependent vs. subject-independent approaches, (2) the effect of different preprocessing steps (Independent Component Analysis (ICA), down-sampling and filtering), and (3) word classification (where we achieve state-of-the-art performance on a publicly available dataset). Overall we achieve a performance accuracy of 35.20% and 29.21% when classifying five vowels and six words, respectively, in a publicly available dataset, using our tuned iSpeech-CNN architecture. All of our code and processed data are publicly available to ensure reproducibility. As such, this work contributes to a deeper understanding and reproducibility of experiments in the area of inner speech detection., Godkänd;2022;Nivå 0;2022-08-18 (hanlid)

This study focuses on the automatic decoding of inner speech using noninvasive methods, such as Electroencephalography (EEG). While inner speech has been a research topic in philosophy and psychology for half a century, recent attempts have been made to decode nonvoiced spoken words by using various brain–computer interfaces. The main shortcomings of existing work are reproducibility and the availability of data and code. In this work, we investigate various methods (using Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), Long Short-Term Memory Networks (LSTM)) for the detection task of five vowels and six words on a publicly available EEG dataset. The main contributions of this work are (1) subject dependent vs. subject-independent approaches, (2) the effect of different preprocessing steps (Independent Component Analysis (ICA), down-sampling and filtering), and (3) word classification (where we achieve state-of-the-art performance on a publicly available dataset). Overall we achieve a performance accuracy of 35.20% and 29.21% when classifying five vowels and six words, respectively, in a publicly available dataset, using our tuned iSpeech-CNN architecture. All of our code and processed data are publicly available to ensure reproducibility. As such, this work contributes to a deeper understanding and reproducibility of experiments in the area of inner speech detection., Godkänd;2022;Nivå 0;2022-08-18 (hanlid)

Staphylococcus aureus is a versatile pathogen and a leading urgent threat to human health. The clinical burden of this organism is predicted to steadily grow worldwide as it becomes resistant to an increasing number of currently available antibiotics. At the same time, development of new effective antibiotics have dropped precipitously in the past decades. The confluence of these two factors is setting the stage for a “post-antibiotic” era where a great portion of S. aureus infection may not be treatable by the existing regimen. In order to stay ahead of this emerging resistance wave, a deeper understanding of the fundamental biology underlying S. aureus resistance and pathogenesis is necessary. Emerging works have demonstrated that resistance and virulence are deeply linked to other aspects of physiology such as metabolism and stress response by criss crossing gene regulation. However, untangling these complex regulatory systems from bottom up approaches can be challenging. This dissertation focuses on resolving these complexities in transcriptional regulation by applying Independent Component Analysis (ICA) to RNA sequencing data. ICA recovers the underlying signals from regulators that combine together to shape the expression profile of the cell, creating a scalable and operable model of the TRN. We utilized ICA to describe the structure and composition of the TRN in S. aureus USA300 strains. Next, we used the TRN model in conjunction with metabolic models to understand how metabolic and regulatory cross-talks coordinate carbon and nitrogen metabolism to direct protein synthesis. Finally, we modeled TRNs from multiple strains and revealed how gene-regulator interactions have evolved during the emergence of the endemic USA300 strains. Together, this demonstrates the utility of ICA in studying the TRN of S. aureus to rapidly discover its organization and its evolution over time.

Resting-state functional magnetic resonance imaging is currently the mainstay of functional neuroimaging and has allowed researchers to identify intrinsic connectivity networks (aka functional networks) at different spatial scales. However, little is known about the temporal profiles of these networks and whether it is best to model them as continuous phenomena in both space and time or, rather, as a set of temporally discrete events. Both categories have been supported by series of studies with promising findings. However, a critical question is whether focusing only on time points presumed to contain isolated neural events and disregarding the rest of the data is missing important information, potentially leading to misleading conclusions. In this work, we argue that brain networks identified within the spontaneous blood oxygenation level-dependent (BOLD) signal are not limited to temporally sparse burst moments and that these event present time points (EPTs) contain valuable but incomplete information about the underlying functional patterns. We focus on the default mode and show evidence that is consistent with its continuous presence in the BOLD signal, including during the event absent time points (EATs), i.e., time points that exhibit minimum activity and are the least likely to contain an event. Moreover, our findings suggest that EPTs may not contain all the available information about their corresponding networks. We observe distinct default mode connectivity patterns obtained from all time points (AllTPs), EPTs, and EATs. We show evidence of robust relationships with schizophrenia symptoms that are both common and unique to each of the sets of time points (AllTPs, EPTs, EATs), likely related to transient patterns of connectivity. Together, these findings indicate the importance of leveraging the full temporal data in functional studies, including those using event-detection approaches.

This study applies adaptive mixture independent component analysis (AMICA) to learn a set of ICA models, each optimized by fitting a distributional model for each identified component process while maximizing component process independence within some subsets of time points of a multi-channel EEG dataset. Here, we applied 20-model AMICA decomposition to long-duration (1-2 h), high-density (128-channel) EEG data recorded while participants used guided imagination to imagine situations stimulating the experience of 15 specified emotions. These decompositions tended to return models identifying spatiotemporal EEG patterns or states within single emotion imagination periods. Model probability transitions reflected time-courses of EEG dynamics during emotion imagination, which varied across emotions. Transitions between models accounting for imagined "grief" and "happiness" were more abrupt and better aligned with participant reports, while transitions for imagined "contentment" extended into adjoining "relaxation" periods. The spatial distributions of brain-localizable independent component processes (ICs) were more similar within participants (across emotions) than emotions (across participants). Across participants, brain regions with differences in IC spatial distributions (i.e., dipole density) between emotion imagination versus relaxation were identified in or near the left rostrolateral prefrontal, posterior cingulate cortex, right insula, bilateral sensorimotor, premotor, and associative visual cortex. No difference in dipole density was found between positive versus negative emotions. AMICA models of changes in high-density EEG dynamics may allow data-driven insights into brain dynamics during emotional experience, possibly enabling the improved performance of EEG-based emotion decoding and advancing our understanding of emotion.

Background: While functional connectivity is widely studied, there has been little work studying functional connectivity at different spatial scales. Likewise, the relationship of functional connectivity between spatial scales is unknown. Methods: We proposed an independent component analysis (ICA)-based approach to capture information at multiple-model orders (component numbers), and to evaluate functional network connectivity (FNC) both within and between model orders. We evaluated the approach by studying group differences in the context of a study of resting-state functional magnetic resonance imaging (rsfMRI) data collected from schizophrenia (SZ) individuals and healthy controls (HC). The predictive ability of FNC at multiple spatial scales was assessed using support vector machine-based classification. Results: In addition to consistent predictive patterns at both multiple-model orders and single-model orders, unique predictive information was seen at multiple-model orders and in the interaction between model orders. We observed that the FNC between model orders 25 and 50 maintained the highest predictive information between HC and SZ. Results highlighted the predictive ability of the somatomotor and visual domains both within and between model orders compared with other functional domains. Also, subcortical-somatomotor, temporal-somatomotor, and temporal-subcortical FNCs had relatively high weights in predicting SZ. Conclusions: In sum, multimodel order ICA provides a more comprehensive way to study FNC, produces meaningful and interesting results, which are applicable to future studies. We shared the spatial templates from this work at different model orders to provide a reference for the community, which can be leveraged in regression-based or fully automated (spatially constrained) ICA approaches. Impact statement Multimodel order independent component analysis (ICA) provides a comprehensive way to study brain functional network connectivity within and be

"Several technical challenges are related to data collection, inverse modeling, model fusion, and integrated interpretations in the exploration of geophysics. A fundamental problem in integrated geophysical interpretation is the proper geological understanding of multiple inverted physical property images. Tackling this problem requires high-dimensional techniques for extracting geological information from modeled physical property images. In this study, we developed a 3D statistical tool to extract geological features from inverted physical property models based on a synergy between independent component analysis and continuous wavelet transform. An automated interpretation of multiple 3D geophysical images is also presented through a hybrid spectral feature subset selection (SFSS) algorithm based on a generalized supervised neural network algorithm to rebuild limited geological targets from 3D geophysical images. Our self-proposed algorithm is tested on an Au/Ag epithermal system in British Columbia (Canada), where layered volcano-sedimentary sequences, particularly felsic volcanic rocks, are associated with mineralization. Geophysical images of the epithermal system were obtained from 3D cooperative inversion of aeromagnetic, direct current resistivity, and induced polarization data sets. The recovered cooperative susceptibilities allowed locating a magnetite destructive zone associated with porphyritic intrusions and felsic volcanoes (Au host rocks). The practical implementation of the SFSS algorithm in the study area shows that the proposed spectral learning scheme can efficiently learn the lithotypes and Au grade patterns and makes predictions based on 3D physical property inputs. The SFSS also minimizes the number of extracted spectral features and tries to pick the best representative features for each target learning case. This approach allows interpreters to understand the relevant and irrelevant spectral features in addition to the 3D predictive models. C

Radiosonde data are important for understanding and monitoring the upper troposphere and lower stratosphere (UTLS) region. Over much of Africa, however, such data are lacking; consequently, the African UTLS is understudied, and potential proxies such as climate models and reanalysis products fail to fully capture the behavior of the UTLS. This study pioneers the use of Global Navigation Satellite System-Radio Occultation (GNSS-RO) data from 2001 to 2020 to address the radiosonde data gap over Africa and contributes to a better understanding of the tropopause (TP) characteristics under the influence of global and regional climate drivers over the continent. As a first step to using GNSS-RO for infilling the radiosonde data gap over Africa, we analyzed the performance of GNSS-RO (2001–2020) and reanalysis products (European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) and Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2)) against radiosonde observations applying the Kling-Gupta Efficiency metric. The analyses show that GNSS-RO data from Challenging Mini-satellite Payload, Gravity Recovery and Climate Experiment, Meteorological Operational, Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC), and COSMIC-2 are in good agreement with radiosonde measurements with differences being smaller than 1 K in the UTLS, thereby enabling infilling of missing radiosonde data in Africa during 2001–2020. By contrast, the smoothed vertical temperature profiles of reanalysis products lead to a warm bias of +0.8 K in ERA5 and +1.2 K in MERRA-2 and these biases alter some vertical and temporal structure details, with possible implications on climate change detection and attribution. Furthermore, the analysis of GNSS-RO data over Africa revealed: (a) the teleconnections of El Niño-Southern Oscillation (ENSO), Quasi-Biennial Oscillation (QBO), Indian Ocean Dipole (IOD), Madden-Julian Oscillation (MJO), North Atla

Mycobacterium tuberculosis is one of the most consequential human bacterial pathogens, posing a serious challenge to 21st century medicine. A key feature of its pathogenicity is its ability to adapt its transcriptional response to environmental stresses through its transcriptional regulatory network (TRN). While many studies have sought to characterize specific portions of the M. tuberculosis TRN, and some studies have performed system-level analysis, few have been able to provide a network-based model of the TRN that also provides the relative shifts in transcriptional regulator activity triggered by changing environments. Here, we compiled a compendium of nearly 650 publicly available, high quality M. tuberculosis RNA-sequencing data sets and applied an unsupervised machine learning method to obtain a quantitative, top-down TRN. It consists of 80 independently modulated gene sets known as “iModulons,” 41 of which correspond to known regulons. These iModulons explain 61% of the variance in the organism’s transcriptional response. We show that iModulons (i) reveal the function of poorly characterized regulons, (ii) describe the transcriptional shifts that occur during environmental changes such as shifting carbon sources, oxidative stress, and infection events, and (iii) identify intrinsic clusters of regulons that link several important metabolic systems, including lipid, cholesterol, and sulfur metabolism. This transcriptome-wide analysis of the M. tuberculosis TRN informs future research on effective ways to study and manipulate its transcriptional regulation and presents a knowledge-enhanced database of all published high-quality RNA-seq data for this organism to date. IMPORTANCE Mycobacterium tuberculosis H37Rv is one of the world's most impactful pathogens, and a large part of the success of the organism relies on the differential expression of its genes to adapt to its environment. The expression of the organism's genes is driven primarily by its transcrip

Bacterial gene expression is orchestrated by numerous transcription factors (TFs). Elucidating how gene expression is regulated is fundamental to understanding bacterial physiology and engineering it for practical use. In this study, a machine-learning approach was applied to uncover the genome-scale transcriptional regulatory network (TRN) in Pseudomonas putida KT2440, an important organism for bioproduction. We performed independent component analysis of a compendium of 321 high-quality gene expression profiles, which were previously published or newly generated in this study. We identified 84 groups of independently modulated genes (iModulons) that explain 75.7% of the total variance in the compendium. With these iModulons, we (i) expand our understanding of the regulatory functions of 39 iModulon associated TFs (e.g., HexR, Zur) by systematic comparison with 1993 previously reported TF-gene interactions; (ii) outline transcriptional changes after the transition from the exponential growth to stationary phases; (iii) capture group of genes required for utilizing diverse carbon sources and increased stationary response with slower growth rates; (iv) unveil multiple evolutionary strategies of transcriptome reallocation to achieve fast growth rates; and (v) define an osmotic stimulon, which includes the Type VI secretion system, as coordination of multiple iModulon activity changes. Taken together, this study provides the first quantitative genome-scale TRN for P. putida KT2440 and a basis for a comprehensive understanding of its complex transcriptome changes in a variety of physiological states.

In the present contribution, a new approach based on mutual information (MI) is proposed for exploring the independence of feasible solutions in two component systems. Investigating how independent are different feasible solutions can be a way to bridge the gap between independent component analysis (ICA) and multivariate curve resolution (MCR) approaches and, to the best of our knowledge, has not been investigated before. For this purpose, different chromatographic and hyperspectral imaging (HSI) datasets were simulated, considering different noise levels and different degrees of overlap for two-component systems. Feasible solutions were then calculated by both grid search (GS) and Lawton-Sylvester (LS) plots. MI map which is the plot of MI vs. rotation matrix elements was used to estimate the degree of independence between different solutions. Inspection of the results showed that the different solutions in the feasible bands correspond to different MI values and that those values are lower for spectral profiles (more independent) than for concentration profiles (more dependent) as expected from the duality concept and the opposite is true. In addition, component profiles are found near more dependent solutions for concentration profiles and near less dependent solutions for spectral profiles which is due to the fact that "independence" constraint was applied to the spectral profiles in ICA algorithms. The performance of three well-known ICA algorithms (mean-field independent component analysis (MF-ICA), mutual information-based least dependent component analysis (MILCA) and joint approximate diagonalization of eigenmatrices (JADE)) as well as MCR-alternating least squares (MCR-ALS) were investigated. MI maps showed that the solutions of MF-ICA and MCR-ALS are in the feasible bands but the MILCA and JADE solutions which are just based on the independence maximization are outside the MI maps.

Vietnam, one of the three leading rice producers globally, has recently seen an increased threat to its rice production emanating from climate extremes (floods and droughts). Understanding spatio-temporal variability in precipitation and soil moisture is essential for policy formulations to adapt and cope with the impacts of climate extremes on rice production in Vietnam. Adopting a higher-order statistical method of independent component analysis (ICA), this study explores the spatio-temporal variability in the Climate Hazards Group InfraRed Precipitation Station's (CHIRPS) precipitation and the Global Land Data Assimilation System's (GLDAS) soil moisture products. The results indicate an agreement between monthly CHIRPS precipitation and monthly GLDAS soil moisture with the wetter period over the southern and South Central Coast areas that is latter than that over the northern and North Central Coast areas. However, the spatial patterns of annual mean precipitation and soil moisture disagree, likely due to factors other than precipitation affecting the amount of moisture in the soil layers, e.g., temperature, irrigation, and drainage systems, which are inconsistent between areas. The CHIRPS Standardized Precipitation Index (SPI) is useful in capturing climate extremes, and the GLDAS Standardized Soil Moisture Index (SSI) is useful in identifying the influences of climate extremes on rice production in Vietnam. During the 2016-2018 period, there existed a reduction in the residual rice yield that was consistent with a decrease in soil moisture during the same time period.

Background The "dual syndrome hypothesis" distinguished two subtypes in mild cognitive impairment (MCI) in Parkinson's disease: frontostriatal, characterized by attentional and executive deficits; and posterior cortical, characterized by visuospatial, memory, and language deficits. Objective The aim was to identify resting-state functional modifications associated with these subtypes. Methods Ninety-five nondemented patients categorized as having normal cognition (n = 31), frontostriatal (n = 14), posterior cortical (n = 20), or mixed (n = 30) cognitive subtype had a 3 T resting-state functional magnetic resonance imaging scan. Twenty-four age-matched healthy controls (HCs) were also included. A group-level independent component analysis was performed to identify resting-state networks, and the selected components were subdivided into 564 cortical regions in addition to 26 basal ganglia regions. Global intra- and inter-network connectivity along with global and local efficiencies was compared between groups. The network-based statistics approach was used to identify connections significantly different between groups. Results Patients with posterior cortical deficits had increased intra-network functional connectivity (FC) within the basal ganglia network compared with patients with frontostriatal deficits. Patients with frontostriatal deficits had reduced inter-network FC between several networks, including the visual, default-mode, sensorimotor, salience, dorsal attentional, basal ganglia, and frontoparietal networks, compared with HCs, patients with normal cognition, and patients with a posterior cortical subtype. Similar results were also found between patients with a mixed subtype and HCs. Conclusion MCI subtypes are associated with specific changes in resting-state FC. Longitudinal studies are needed to determine the predictive potential of these markers regarding the risk of developing dementia. (c) 2021 International Parkinson and Movement Disorder Society

[EN] Fault diagnosis is important for industrial processes because it permits to determine the necessity of emergency stops in a process and/or to propose a maintenance plan. Two strategies for fault diagnosis are compared in this work. On the one hand, the data are preprocessed using the independent components analysis for dimension reduction, then the wavelet transform is used in order to highlight the faulty signals, with this information an artificial neural network was fed. On the other hand, the second strategy, the main contribution of this work, is the implementation of a long short term memory. This memory is fed with the most representative variables selected by an elastic net to use both, the L1 and L2 norms. These strategies are applied in the Tennessee Eastman process, a benchmark widely used for fault diagnosis. The fault isolation had better results than those reported in the literature., [ES] El diagnóstico de fallas es importante en los procesos industriales, ya que permite determinar si es necesario detener el proceso en operación y/o proponer un plan de mantenimiento. En el presente trabajo se comparan dos estrategias para diagnosticar fallas. La primera realiza un preprocesamiento de datos usando el análisis de componentes independientes para reducir la dimensión de los datos, posteriormente, se emplea la transformada wavelet para resaltar las señales de falla, con esta información se alimenta una red neuronal artificial. Por su parte, la segunda estrategia, principal contribución de este trabajo, usa una memoria de corto y largo plazo. Esta memoria es alimentada por las variables más significativas seleccionadas mediante una red elástica para usar tanto la norma $L_1$ como la $L_2$. Como ejemplo de aplicación se utilizó el proceso químico Tennessee Eastman, un proceso ampliamente usado en el diagnóstico de fallas. El aislamiento de fallas mostró mejores resultados con respecto a los reportados en la literatura.

In vitro antibiotic susceptibility testing often fails to accurately predict in vivo drug efficacies, in part due to differences in the molecular composition between standardized bacteriologic media and physiological environments within the body. Here, we investigate the interrelationship between antibiotic susceptibility and medium composition in Escherichia coli K-12 MG1655 as contextualized through machine learning of transcriptomics data. Application of independent component analysis, a signal separation algorithm, shows that complex phenotypic changes induced by environmental conditions or antibiotic treatment are directly traced to the action of a few key transcriptional regulators, including RpoS, Fur, and Fnr. Integrating machine learning results with biochemical knowledge of transcription factor activation reveals medium-dependent shifts in respiration and iron availability that drive differential antibiotic susceptibility. By extension, the data generation and data analytics workflow used here can interrogate the regulatory state of a pathogen under any measured condition and can be applied to any strain or organism for which sufficient transcriptomics data are available. IMPORTANCE Antibiotic resistance is an imminent threat to global health. Patient treatment regimens are often selected based on results from standardized antibiotic susceptibility testing (AST) in the clinical microbiology lab, but these in vitro tests frequently misclassify drug effectiveness due to their poor resemblance to actual host conditions. Prior attempts to understand the combined effects of drugs and media on antibiotic efficacy have focused on physiological measurements but have not linked treatment outcomes to transcriptional responses on a systems level. Here, application of machine learning to transcriptomics data identified medium-dependent responses in key regulators of bacterial iron uptake and respiratory activity. The analytical workflow presented here is scalable to a

[ES] La disfagia es la dificultad o la incapacidad para deglutir los alimentos o bebidas como resultado de la lesión en las áreas motoras y su conexión con el tronco encefálico. Su prevalencia es del 30-40% en personas de más de 65 años. La disfagia generalmente es causada por un derrame cerebral u otras enfermedades neurodegenerativas y trastornos del sistema nervioso central. La disfagia puede conducir a una serie de consecuencias como la desnutrición, la aspiración pulmonar, la mortalidad y la mala calidad de vida. El Centro de Investigación e Innovación en Bioingeniería ha lanzado un proyecto que tiene como objeto impulsar la rehabilitación de disfagia basada en la detección de movimientos imaginarios de deglución en la señal electroencefalográfica (EEG). No obstante, las señales EEG registradas en el cuero cabelludo son de muy baja amplitud, y además presentan una fuerte interferencia de la actividad muscular durante el proceso deglutorio que dificulta la caracterización de las mismas dado que sus distribuciones espectrales se solapan parcialmente. El objetivo del presente TFG es desarrollar técnicas de minimización de interferencia de la actividad muscular de la señal EEG durante la deglución para la caracterización robusta de la actividad cerebral en las distintas bandas de frecuencia. Con ello se pretende asentar la base necesaria para diseñar un sistema de rehabilitación de disfagia basándose en el neurofeedback. Se empleará un sistema de registro simultáneo de 32-canales de EEG mediante el estándar 10-20 y de electromiograma (EMG) en 4 músculos deglutorios (suprahioideos e infrahioideos izquierdo y derecho) con marcadores de eventos. El alumno completará la base de datos hasta obtener al menos unos 20 registros en sujetos sanos. Se analizará la correlación entre la interferencia muscular embebida en la señal EEG y el registro EMG adquirido en los músculos deglutorios para determinar la posibilidad de utilizar técnicas de filtrado adaptativo para su minimiz, [EN] Dysphagia is the difficulty or inability to swallow food or drink as a result of injury to the motor areas and their connection to the brainstem. Its prevalence is 30-40% in people over 65 years of age. Dysphagia is usually caused by stroke or other neurodegenerative diseases and central nervous system disorders. Dysphagia can lead to a range of consequences such as malnutrition, pulmonary aspiration, mortality and poor quality of life. The Centre for Research and Innovation in Bioengineering has launched a project that aims to boost dysphagia rehabilitation based on the detection of imaginary swallowing movements in the electroencephalographic (EEG) signal. However, the EEG signals recorded in the scalp are of very low amplitude, and also present a strong interference of the muscular activity during the swallowing process, which makes their characterisation difficult since their spectral distributions partially overlap. The aim of this TFG is to develop techniques to minimise the interference of the muscle activity of the EEG signal during swallowing for the robust characterisation of brain activity in the different frequency bands. This is intended to provide the basis for designing a neurofeedback-based dysphagia rehabilitation system. A simultaneous 32-channel EEG recording system using standard 10-20 and electromyogram (EMG) on 4 swallowing muscles (suprahyoid and left and right infrahyoid) with event markers will be used. The student will complete the database to obtain at least 20 records in healthy subjects. The correlation between the muscle interference embedded in the EEG signal and the EMG recording acquired in the swallowing muscles will be analysed to determine the possibility of using adaptive filtering techniques to minimise it. The possibility of combining independent component analysis (ICA) with wavelet, multivariate empirical mode decomposition or multivariate variational mode decomposition for the minimisation of muscle interference in the

[ES] La disfagia es la dificultad o la incapacidad para deglutir los alimentos o bebidas como resultado de la lesión en las áreas motoras y su conexión con el tronco encefálico. Su prevalencia es del 30-40% en personas de más de 65 años. La disfagia generalmente es causada por un derrame cerebral u otras enfermedades neurodegenerativas y trastornos del sistema nervioso central. La disfagia puede conducir a una serie de consecuencias como la desnutrición, la aspiración pulmonar, la mortalidad y la mala calidad de vida. El Centro de Investigación e Innovación en Bioingeniería ha lanzado un proyecto que tiene como objeto impulsar la rehabilitación de disfagia basada en la detección de movimientos imaginarios de deglución en la señal electroencefalográfica (EEG). No obstante, las señales EEG registradas en el cuero cabelludo son de muy baja amplitud, y además presentan una fuerte interferencia de la actividad muscular durante el proceso deglutorio que dificulta la caracterización de las mismas dado que sus distribuciones espectrales se solapan parcialmente. El objetivo del presente TFG es desarrollar técnicas de minimización de interferencia de la actividad muscular de la señal EEG durante la deglución para la caracterización robusta de la actividad cerebral en las distintas bandas de frecuencia. Con ello se pretende asentar la base necesaria para diseñar un sistema de rehabilitación de disfagia basándose en el neurofeedback. Se empleará un sistema de registro simultáneo de 32-canales de EEG mediante el estándar 10-20 y de electromiograma (EMG) en 4 músculos deglutorios (suprahioideos e infrahioideos izquierdo y derecho) con marcadores de eventos. El alumno completará la base de datos hasta obtener al menos unos 20 registros en sujetos sanos. Se analizará la correlación entre la interferencia muscular embebida en la señal EEG y el registro EMG adquirido en los músculos deglutorios para determinar la posibilidad de utilizar técnicas de filtrado adaptativo para su minimiz, [EN] Dysphagia is the difficulty or inability to swallow food or drink as a result of injury to the motor areas and their connection to the brainstem. Its prevalence is 30-40% in people over 65 years of age. Dysphagia is usually caused by stroke or other neurodegenerative diseases and central nervous system disorders. Dysphagia can lead to a range of consequences such as malnutrition, pulmonary aspiration, mortality and poor quality of life. The Centre for Research and Innovation in Bioengineering has launched a project that aims to boost dysphagia rehabilitation based on the detection of imaginary swallowing movements in the electroencephalographic (EEG) signal. However, the EEG signals recorded in the scalp are of very low amplitude, and also present a strong interference of the muscular activity during the swallowing process, which makes their characterisation difficult since their spectral distributions partially overlap. The aim of this TFG is to develop techniques to minimise the interference of the muscle activity of the EEG signal during swallowing for the robust characterisation of brain activity in the different frequency bands. This is intended to provide the basis for designing a neurofeedback-based dysphagia rehabilitation system. A simultaneous 32-channel EEG recording system using standard 10-20 and electromyogram (EMG) on 4 swallowing muscles (suprahyoid and left and right infrahyoid) with event markers will be used. The student will complete the database to obtain at least 20 records in healthy subjects. The correlation between the muscle interference embedded in the EEG signal and the EMG recording acquired in the swallowing muscles will be analysed to determine the possibility of using adaptive filtering techniques to minimise it. The possibility of combining independent component analysis (ICA) with wavelet, multivariate empirical mode decomposition or multivariate variational mode decomposition for the minimisation of muscle interference in the

In vitro antibiotic susceptibility testing often fails to accurately predict in vivo drug efficacies, in part due to differences in the molecular composition between standardized bacteriologic media and physiological environments within the body. Here, we investigate the interrelationship between antibiotic susceptibility and medium composition in Escherichia coli K-12 MG1655 as contextualized through machine learning of transcriptomics data. Application of independent component analysis, a signal separation algorithm, shows that complex phenotypic changes induced by environmental conditions or antibiotic treatment are directly traced to the action of a few key transcriptional regulators, including RpoS, Fur, and Fnr. Integrating machine learning results with biochemical knowledge of transcription factor activation reveals medium-dependent shifts in respiration and iron availability that drive differential antibiotic susceptibility. By extension, the data generation and data analytics workflow used here can interrogate the regulatory state of a pathogen under any measured condition and can be applied to any strain or organism for which sufficient transcriptomics data are available. IMPORTANCE Antibiotic resistance is an imminent threat to global health. Patient treatment regimens are often selected based on results from standardized antibiotic susceptibility testing (AST) in the clinical microbiology lab, but these in vitro tests frequently misclassify drug effectiveness due to their poor resemblance to actual host conditions. Prior attempts to understand the combined effects of drugs and media on antibiotic efficacy have focused on physiological measurements but have not linked treatment outcomes to transcriptional responses on a systems level. Here, application of machine learning to transcriptomics data identified medium-dependent responses in key regulators of bacterial iron uptake and respiratory activity. The analytical workflow presented here is scalable to a

[ES] La disfagia es la dificultad o la incapacidad para deglutir los alimentos o bebidas como resultado de la lesión en las áreas motoras y su conexión con el tronco encefálico. Su prevalencia es del 30-40% en personas de más de 65 años. La disfagia generalmente es causada por un derrame cerebral u otras enfermedades neurodegenerativas y trastornos del sistema nervioso central. La disfagia puede conducir a una serie de consecuencias como la desnutrición, la aspiración pulmonar, la mortalidad y la mala calidad de vida. El Centro de Investigación e Innovación en Bioingeniería ha lanzado un proyecto que tiene como objeto impulsar la rehabilitación de disfagia basada en la detección de movimientos imaginarios de deglución en la señal electroencefalográfica (EEG). No obstante, las señales EEG registradas en el cuero cabelludo son de muy baja amplitud, y además presentan una fuerte interferencia de la actividad muscular durante el proceso deglutorio que dificulta la caracterización de las mismas dado que sus distribuciones espectrales se solapan parcialmente. El objetivo del presente TFG es desarrollar técnicas de minimización de interferencia de la actividad muscular de la señal EEG durante la deglución para la caracterización robusta de la actividad cerebral en las distintas bandas de frecuencia. Con ello se pretende asentar la base necesaria para diseñar un sistema de rehabilitación de disfagia basándose en el neurofeedback. Se empleará un sistema de registro simultáneo de 32-canales de EEG mediante el estándar 10-20 y de electromiograma (EMG) en 4 músculos deglutorios (suprahioideos e infrahioideos izquierdo y derecho) con marcadores de eventos. El alumno completará la base de datos hasta obtener al menos unos 20 registros en sujetos sanos. Se analizará la correlación entre la interferencia muscular embebida en la señal EEG y el registro EMG adquirido en los músculos deglutorios para determinar la posibilidad de utilizar técnicas de filtrado adaptativo para su minimiz, [EN] Dysphagia is the difficulty or inability to swallow food or drink as a result of injury to the motor areas and their connection to the brainstem. Its prevalence is 30-40% in people over 65 years of age. Dysphagia is usually caused by stroke or other neurodegenerative diseases and central nervous system disorders. Dysphagia can lead to a range of consequences such as malnutrition, pulmonary aspiration, mortality and poor quality of life. The Centre for Research and Innovation in Bioengineering has launched a project that aims to boost dysphagia rehabilitation based on the detection of imaginary swallowing movements in the electroencephalographic (EEG) signal. However, the EEG signals recorded in the scalp are of very low amplitude, and also present a strong interference of the muscular activity during the swallowing process, which makes their characterisation difficult since their spectral distributions partially overlap. The aim of this TFG is to develop techniques to minimise the interference of the muscle activity of the EEG signal during swallowing for the robust characterisation of brain activity in the different frequency bands. This is intended to provide the basis for designing a neurofeedback-based dysphagia rehabilitation system. A simultaneous 32-channel EEG recording system using standard 10-20 and electromyogram (EMG) on 4 swallowing muscles (suprahyoid and left and right infrahyoid) with event markers will be used. The student will complete the database to obtain at least 20 records in healthy subjects. The correlation between the muscle interference embedded in the EEG signal and the EMG recording acquired in the swallowing muscles will be analysed to determine the possibility of using adaptive filtering techniques to minimise it. The possibility of combining independent component analysis (ICA) with wavelet, multivariate empirical mode decomposition or multivariate variational mode decomposition for the minimisation of muscle interference in the

[EN] Fault diagnosis is important for industrial processes because it permits to determine the necessity of emergency stops in a process and/or to propose a maintenance plan. Two strategies for fault diagnosis are compared in this work. On the one hand, the data are preprocessed using the independent components analysis for dimension reduction, then the wavelet transform is used in order to highlight the faulty signals, with this information an artificial neural network was fed. On the other hand, the second strategy, the main contribution of this work, is the implementation of a long short term memory. This memory is fed with the most representative variables selected by an elastic net to use both, the L1 and L2 norms. These strategies are applied in the Tennessee Eastman process, a benchmark widely used for fault diagnosis. The fault isolation had better results than those reported in the literature., [ES] El diagnóstico de fallas es importante en los procesos industriales, ya que permite determinar si es necesario detener el proceso en operación y/o proponer un plan de mantenimiento. En el presente trabajo se comparan dos estrategias para diagnosticar fallas. La primera realiza un preprocesamiento de datos usando el análisis de componentes independientes para reducir la dimensión de los datos, posteriormente, se emplea la transformada wavelet para resaltar las señales de falla, con esta información se alimenta una red neuronal artificial. Por su parte, la segunda estrategia, principal contribución de este trabajo, usa una memoria de corto y largo plazo. Esta memoria es alimentada por las variables más significativas seleccionadas mediante una red elástica para usar tanto la norma $L_1$ como la $L_2$. Como ejemplo de aplicación se utilizó el proceso químico Tennessee Eastman, un proceso ampliamente usado en el diagnóstico de fallas. El aislamiento de fallas mostró mejores resultados con respecto a los reportados en la literatura.

[EN] Fault diagnosis is important for industrial processes because it permits to determine the necessity of emergency stops in a process and/or to propose a maintenance plan. Two strategies for fault diagnosis are compared in this work. On the one hand, the data are preprocessed using the independent components analysis for dimension reduction, then the wavelet transform is used in order to highlight the faulty signals, with this information an artificial neural network was fed. On the other hand, the second strategy, the main contribution of this work, is the implementation of a long short term memory. This memory is fed with the most representative variables selected by an elastic net to use both, the L1 and L2 norms. These strategies are applied in the Tennessee Eastman process, a benchmark widely used for fault diagnosis. The fault isolation had better results than those reported in the literature., [ES] El diagnóstico de fallas es importante en los procesos industriales, ya que permite determinar si es necesario detener el proceso en operación y/o proponer un plan de mantenimiento. En el presente trabajo se comparan dos estrategias para diagnosticar fallas. La primera realiza un preprocesamiento de datos usando el análisis de componentes independientes para reducir la dimensión de los datos, posteriormente, se emplea la transformada wavelet para resaltar las señales de falla, con esta información se alimenta una red neuronal artificial. Por su parte, la segunda estrategia, principal contribución de este trabajo, usa una memoria de corto y largo plazo. Esta memoria es alimentada por las variables más significativas seleccionadas mediante una red elástica para usar tanto la norma $L_1$ como la $L_2$. Como ejemplo de aplicación se utilizó el proceso químico Tennessee Eastman, un proceso ampliamente usado en el diagnóstico de fallas. El aislamiento de fallas mostró mejores resultados con respecto a los reportados en la literatura.

[EN] Fault diagnosis is important for industrial processes because it permits to determine the necessity of emergency stops in a process and/or to propose a maintenance plan. Two strategies for fault diagnosis are compared in this work. On the one hand, the data are preprocessed using the independent components analysis for dimension reduction, then the wavelet transform is used in order to highlight the faulty signals, with this information an artificial neural network was fed. On the other hand, the second strategy, the main contribution of this work, is the implementation of a long short term memory. This memory is fed with the most representative variables selected by an elastic net to use both, the L1 and L2 norms. These strategies are applied in the Tennessee Eastman process, a benchmark widely used for fault diagnosis. The fault isolation had better results than those reported in the literature., [ES] El diagnóstico de fallas es importante en los procesos industriales, ya que permite determinar si es necesario detener el proceso en operación y/o proponer un plan de mantenimiento. En el presente trabajo se comparan dos estrategias para diagnosticar fallas. La primera realiza un preprocesamiento de datos usando el análisis de componentes independientes para reducir la dimensión de los datos, posteriormente, se emplea la transformada wavelet para resaltar las señales de falla, con esta información se alimenta una red neuronal artificial. Por su parte, la segunda estrategia, principal contribución de este trabajo, usa una memoria de corto y largo plazo. Esta memoria es alimentada por las variables más significativas seleccionadas mediante una red elástica para usar tanto la norma $L_1$ como la $L_2$. Como ejemplo de aplicación se utilizó el proceso químico Tennessee Eastman, un proceso ampliamente usado en el diagnóstico de fallas. El aislamiento de fallas mostró mejores resultados con respecto a los reportados en la literatura.

Available online 6 March 2021., Performing a BOLD functional MRI (fMRI) acquisition during breath-hold (BH) tasks is a non-invasive, robust method to estimate cerebrovascular reactivity (CVR). However, movement and breathing-related artefacts caused by the BH can substantially hinder CVR estimates due to their high temporal collinearity with the effect of interest, and attention has to be paid when choosing which analysis model should be applied to the data. In this study, we evaluate the performance of multiple analysis strategies based on lagged general linear models applied on multi- echo BOLD fMRI data, acquired in ten subjects performing a BH task during ten sessions, to obtain subject-specific CVR and haemodynamic lag estimates. The evaluated approaches range from conventional regression models, i.e. including drifts and motion timecourses as nuisance regressors, applied on single-echo or optimally-combined data, to more complex models including regressors obtained from multi-echo independent component analysis with different grades of orthogonalization in order to preserve the effect of interest, i.e. the CVR. We compare these models in terms of their ability to make signal intensity changes independent from motion, as well as the reliability as measured by voxelwise intraclass correlation coefficients of both CVR and lag maps over time. Our results reveal that a conservative independent component analysis model applied on the optimally-combined multi-echo fMRI signal offers the largest reduction of motion-related effects in the signal, while yielding reliable CVR amplitude and lag estimates, although a conventional regression model applied on the optimally-combined data results in similar estimates. This work demonstrates the usefulness of multi-echo based fMRI acquisitions and independent component analysis denoising for precision mapping of CVR in single subjects based on BH paradigms, fostering its potential as a clinically-viable neuroimaging tool for individual patients. It also prove

[ES] La disfagia es la dificultad o la incapacidad para deglutir los alimentos o bebidas como resultado de la lesión en las áreas motoras y su conexión con el tronco encefálico. Su prevalencia es del 30-40% en personas de más de 65 años. La disfagia generalmente es causada por un derrame cerebral u otras enfermedades neurodegenerativas y trastornos del sistema nervioso central. La disfagia puede conducir a una serie de consecuencias como la desnutrición, la aspiración pulmonar, la mortalidad y la mala calidad de vida. El Centro de Investigación e Innovación en Bioingeniería ha lanzado un proyecto que tiene como objeto impulsar la rehabilitación de disfagia basada en la detección de movimientos imaginarios de deglución en la señal electroencefalográfica (EEG). No obstante, las señales EEG registradas en el cuero cabelludo son de muy baja amplitud, y además presentan una fuerte interferencia de la actividad muscular durante el proceso deglutorio que dificulta la caracterización de las mismas dado que sus distribuciones espectrales se solapan parcialmente. El objetivo del presente TFG es desarrollar técnicas de minimización de interferencia de la actividad muscular de la señal EEG durante la deglución para la caracterización robusta de la actividad cerebral en las distintas bandas de frecuencia. Con ello se pretende asentar la base necesaria para diseñar un sistema de rehabilitación de disfagia basándose en el neurofeedback. Se empleará un sistema de registro simultáneo de 32-canales de EEG mediante el estándar 10-20 y de electromiograma (EMG) en 4 músculos deglutorios (suprahioideos e infrahioideos izquierdo y derecho) con marcadores de eventos. El alumno completará la base de datos hasta obtener al menos unos 20 registros en sujetos sanos. Se analizará la correlación entre la interferencia muscular embebida en la señal EEG y el registro EMG adquirido en los músculos deglutorios para determinar la posibilidad de utilizar técnicas de filtrado adaptativo para su minimiz, [EN] Dysphagia is the difficulty or inability to swallow food or drink as a result of injury to the motor areas and their connection to the brainstem. Its prevalence is 30-40% in people over 65 years of age. Dysphagia is usually caused by stroke or other neurodegenerative diseases and central nervous system disorders. Dysphagia can lead to a range of consequences such as malnutrition, pulmonary aspiration, mortality and poor quality of life. The Centre for Research and Innovation in Bioengineering has launched a project that aims to boost dysphagia rehabilitation based on the detection of imaginary swallowing movements in the electroencephalographic (EEG) signal. However, the EEG signals recorded in the scalp are of very low amplitude, and also present a strong interference of the muscular activity during the swallowing process, which makes their characterisation difficult since their spectral distributions partially overlap. The aim of this TFG is to develop techniques to minimise the interference of the muscle activity of the EEG signal during swallowing for the robust characterisation of brain activity in the different frequency bands. This is intended to provide the basis for designing a neurofeedback-based dysphagia rehabilitation system. A simultaneous 32-channel EEG recording system using standard 10-20 and electromyogram (EMG) on 4 swallowing muscles (suprahyoid and left and right infrahyoid) with event markers will be used. The student will complete the database to obtain at least 20 records in healthy subjects. The correlation between the muscle interference embedded in the EEG signal and the EMG recording acquired in the swallowing muscles will be analysed to determine the possibility of using adaptive filtering techniques to minimise it. The possibility of combining independent component analysis (ICA) with wavelet, multivariate empirical mode decomposition or multivariate variational mode decomposition for the minimisation of muscle interference in the

Neurophysiologic correlates of motor learning that can be monitored during neurorehabilitation interventions can facilitate the development of more effective learning methods. Previous studies have focused on the role of the beta band (14–30 Hz) because of its clear response during motor activity. However, it is difficult to discriminate between beta activity related to learning a movement and performing the movement. In this study, we analysed differences in the electroencephalography (EEG) power spectra of complex and simple explicit sequential motor tasks in healthy young subjects. The complex motor task (CMT) allowed EEG measurement related to motor learning. In contrast, the simple motor task (SMT) made it possible to control for EEG activity associated with performing the movement without significant motor learning. Source reconstruction of the EEG revealed task-related activity from 5 clusters covering both primary motor cortices (M1) and 3 clusters localised to different parts of the cingulate cortex (CC). We found no association between M1 beta power and learning, but the CMT produced stronger bilateral beta suppression compared to the SMT. However, there was a positive association between contralateral M1 theta (5–8 Hz) and alpha (8–12 Hz) power and motor learning, and theta and alpha power in the posterior mid-CC and posterior CC were positively associated with greater motor learning. These findings suggest that the theta and alpha bands are more related to motor learning than the beta band, which might merely relate to the level of perceived difficulty during learning., Biomechatronics & Human-Machine Control, Biomechanical Engineering

Resting state functional magnetic resonance imaging (rs-fMRI) is one of the most relevant techniques in brain exploration. However, it is susceptible to many external factors that can occlude the signal of interest. In this order of ideas, rs-fMRI images have been studied adopting different approaches, with a particular interest in artifact removal techniques through Independent Component Analysis (ICA). Such an approach is a powerful tool for blind source separation, where elements associated with noise can be eliminated. Nevertheless, such removal is subject to the identification or classification of the components provided by the ICA. In that sense, this study focuses on finding an alternative strategy to classify the independent components. The problem was addressed in two stages. In the first one, the components (3D volumes) were reduced to images by Principal Component Analysis (PCA) and by obtaining the median planes. The methods achieved a reduction of up to two orders of magnitude in the weight of the data size, and they were shown to preserve the spatial characteristics of the independent components. In the second stage, the reductions were used to train six models of convolutional neural networks. The networks analyzed in this study reached accuracies around 98 % in classification, one of them even up to 98.82 %, which reflects the high discrimination capacity of convolutional neural networks., La resonancia magnética funcional en estado de reposo (rs-fMRI) es una de las técnicas más relevantes en exploración cerebral. No obstante, la misma es susceptible a muchos factores externos que pueden ocluir la señal de interés. En este orden de ideas, las imágenes rs-fMRI han sido estudiadas desde diferentes enfoques, existiendo un especial interés en las técnicas de eliminación de artefactos a través del Análisis de Componentes Independientes (ICA por sus siglas en inglés). El enfoque es una herramienta poderosa para la separación ciega de fuentes donde es posible eliminar los elementos asociados a ruido. Sin embargo, dicha eliminación está sujeta a la identificación o clasificación de las componentes entregadas por ICA. En ese sentido, esta investigación se centró en encontrar una estrategia alternativa para la clasificación de las componentes independientes. El problema se abordó en dos etapas. En la primera de ellas, se redujeron las componentes (volúmenes 3D) a imágenes mediante el Análisis de Componentes Principales (PCA por sus siglas en inglés) y con la obtención de los planos medios. Los métodos lograron una reducción de hasta dos órdenes de magnitud en peso de los datos y, además, demostraron conservar las características espaciales de las componentes independientes. En la segunda etapa, se usaron las reducciones para entrenar seis modelos de redes neuronales convolucionales. Las redes analizadas alcanzaron precisiones alrededor de 98 % en la clasificación e incluso se encontró una red con una precisión del 98.82 %, lo cual refleja la alta capacidad de discriminación de las redes neuronales convolucionales.

Available online 6 March 2021., Performing a BOLD functional MRI (fMRI) acquisition during breath-hold (BH) tasks is a non-invasive, robust method to estimate cerebrovascular reactivity (CVR). However, movement and breathing-related artefacts caused by the BH can substantially hinder CVR estimates due to their high temporal collinearity with the effect of interest, and attention has to be paid when choosing which analysis model should be applied to the data. In this study, we evaluate the performance of multiple analysis strategies based on lagged general linear models applied on multi- echo BOLD fMRI data, acquired in ten subjects performing a BH task during ten sessions, to obtain subject-specific CVR and haemodynamic lag estimates. The evaluated approaches range from conventional regression models, i.e. including drifts and motion timecourses as nuisance regressors, applied on single-echo or optimally-combined data, to more complex models including regressors obtained from multi-echo independent component analysis with different grades of orthogonalization in order to preserve the effect of interest, i.e. the CVR. We compare these models in terms of their ability to make signal intensity changes independent from motion, as well as the reliability as measured by voxelwise intraclass correlation coefficients of both CVR and lag maps over time. Our results reveal that a conservative independent component analysis model applied on the optimally-combined multi-echo fMRI signal offers the largest reduction of motion-related effects in the signal, while yielding reliable CVR amplitude and lag estimates, although a conventional regression model applied on the optimally-combined data results in similar estimates. This work demonstrates the usefulness of multi-echo based fMRI acquisitions and independent component analysis denoising for precision mapping of CVR in single subjects based on BH paradigms, fostering its potential as a clinically-viable neuroimaging tool for individual patients. It also prove

This paper compares the dimension reduction or feature extraction techniques, e.g., Principal Component Analysis, Factor Analysis, Independent Component Analysis and Neural Networks Principal Component Analysis, which are used as techniques for extracting the underlying systematic risk factors driving the returns on equities of the Mexican Stock Exchange, under a statistical approach to the Arbitrage Pricing Theory. We carry out our research according to two different perspectives. First, we evaluate them from a theoretical and matrix scope, making a parallelism among their particular mixing and demixing processes, as well as the attributes of the factors extracted by each method. Secondly, we accomplish an empirical study in order to measure the level of accuracy in the reconstruction of the original variables., Este artículo compara las técnicas de reducción de dimensionalidad o de extracción de características: Análisis de Componentes Principales, Análisis Factorial, Análisis de Componentes Independientes y Análisis de Componentes Principales basado en Redes Neuronales, las cuales son usadas para extraer los factores de riesgo sistemático subyacentes que generan los rendimientos de las acciones de la Bolsa Mexicana de Valores, bajo un enfoque estadístico de la Teoría de Valoración por Arbitraje. Llevamos a cabo nuestra investigación de acuerdo a dos diferentes perspectivas. Primero, las evaluamos desde una perspectiva teórica y matricial, haciendo un paralelismo entre los particulares procesos de mezcla y separación de cada método. En segundo lugar, efectuamos un estudio empírico con el fin de medir el nivel de precisión en la reconstrucción de las variables originales.

Electroconvulsive therapy (ECT) is a highly effective treatment for depression but how it achieves its clinical effects remains unclear. We set out to study the brain's response to ECT from a large-scale brain-network perspective. Using a voxelwise analysis, we looked at resting-state functional connectivity before and after a course of ECT at the whole-brain and the between- and within-network levels in 17 patients with a depressive episode. Using a group-independent component analysis approach, we focused on four networks known to be affected in depression: the salience network (SN), the default mode network (DMN), the cognitive executive network (CEN), and a subcortical network (SCN). Our clinical measures included mood, cognition, and psychomotor symptoms. We found ECT to have increased the connectivity of the left CEN with the left angular gyrus and left middle frontal gyrus as well as its within-network connectivity. Both the right CEN and the SCN showed increased connectivity with the precuneus and the anterior DMN with the left amygdala. Finally, improvement of psychomotor retardation was positively correlated with an increase of within-posterior DMN connectivity. The limitations of our study include its small sample size and the lack of a control dataset to confirm our findings. Our voxelwise data demonstrate that ECT induces a significant increase of connectivity across the whole brain and at the within-network level. Furthermore, we provide the first evidence on the association between an increase of within-posterior DMN connectivity and an improvement of psychomotor retardation, a core symptom of depression.