Publications

The issue of failed weaning is a critical concern in the intensive care unit (ICU) setting. This scenario occurs when a patient experiences difficulty maintaining spontaneous breathing and ensuring a patent airway within the first 48 hours after the withdrawal of mechanical ventilation. Approximately 20% of ICU patients experience this phenomenon, which has severe repercussions on their health. It also has a substantial impact on clinical evolution and mortality, which can increase by 25% to 50%. To address this issue, we propose a medical support system that uses a convolutional neural network (CNN) to assess a patient's suitability for disconnection from a mechanical ventilator after a spontaneous breathing test (SBT). During SBT, respiratory flow and electrocardiographic activity were recorded and after processed using time-frequency analysis (TFA) techniques. Two CNN architectures were evaluated in this study: one based on ResNet50, with parameters tuned using a Bayesian optimization algorithm, and another CNN designed from scratch, with its structure also adapted using a Bayesian optimization algorithm. The WEANDB database was used to train and evaluate both models. The results showed remarkable performance, with an average accuracy 98 +/- 1.8% when using CNN from scratch. This model has significant implications for the ICU because it provides a reliable tool to enhance patient care by assisting clinicians in making timely and accurate decisions regarding weaning. This can potentially reduce the adverse outcomes associated with failed weaning events.

JTD Keywords: Bayesian optimization algorithm (boa, Continuous wavelet transform (cwt), Convolutional, Extubation, Failur, Intensive-care-unit, Neural network (cnn) from scratch, Respiratory-distress-syndrome, Time-frequency analysis (tfa), Weaning

Only 30% of embryos from in vitro fertilized oocytes successfully implant and develop to term, leading to repeated transfer cycles. To reduce time-to-pregnancy and stress for patients, there is a need for a diagnostic tool to better select embryos and oocytes based on their physiology. The current standard employs brightfield imaging, which provides limited physiological information. Here, we introduce METAPHOR: Metabolic Evaluation through Phasor-based Hyperspectral Imaging and Organelle Recognition. This non-invasive, label-free imaging method combines two-photon illumination and AI to deliver the metabolic profile of embryos and oocytes based on intrinsic autofluorescence signals. We used it to classify i) mouse blastocysts cultured under standard conditions or with depletion of selected metabolites (glucose, pyruvate, lactate); and ii) oocytes from young and old mouse females, or in vitro-aged oocytes. The imaging process was safe for blastocysts and oocytes. The METAPHOR classification of control vs. metabolites-depleted embryos reached an area under the ROC curve (AUC) of 93.7%, compared to 51% achieved for human grading using brightfield imaging. The binary classification of young vs. old/in vitro-aged oocytes and their blastulation prediction using METAPHOR reached an AUC of 96.2% and 82.2%, respectively. Finally, organelle recognition and segmentation based on the flavin adenine dinucleotide signal revealed that quantification of mitochondria size and distribution can be used as a biomarker to classify oocytes and embryos. The performance and safety of the method highlight the accuracy of noninvasive metabolic imaging as a complementary approach to evaluate oocytes and embryos based on their physiology.

JTD Keywords: Ai, Consumption, Culture, Embryo development, Fluorescence, Hyperspectral imagin, Implantation, In vitro fertilization, Infertility, Label-free imaging, Microscopy, Morphokinetics, Oxygen concentrations, Selectio, Time-lapse

Cable bacteria are filamentous, multicellular microorganisms that display an exceptional form of biological electron transport across centimeter-scale distances. Currents are guided through a network of nickel-containing protein fibers within the cell envelope. Still, the mechanism of long-range conduction remains unresolved. Here, we characterize the conductance of the fiber network under dry and wet, physiologically relevant, conditions. Our data reveal that the fiber conductivity is high (median value: 27 S cm-1; range: 2 to 564 S cm-1), does not show any redox signature, has a low thermal activation energy (Ea = 69 +/- 23 meV), and is not affected by humidity or the presence of ions. These features set the nickel-based conduction mechanism in cable bacteria apart from other known forms of biological electron transport. As such, conduction resembles that of an organic semi-metal with a high charge carrier density. Our observation that biochemistry can synthesize an organometal-like structure opens the way for novel bio-based electronic technologies.

JTD Keywords: 'current, Activation energy, Bacteria, Bioelectronic, Bioelectronics, Cable bacteria, Cables, Centimeter-scale, Electrochemical impedance spectroscopy, Electrochemical-impedance spectroscopies, Electron transport, Electron transport properties, Electron-transport, Long -distance electron transport, Long-distance electron transport, Microbial nanowires, Nickel, Nickel containing, Protein conductivity, Protein fibers, Proteins, Sulfur

Polarized hydroxyapatite (p-HAp) has been used as a catalyst for the synthesis of urea coupling N-2, CO2 and water under mild reaction conditions when compared to classical nitrogen fixation reactions, such as the Haber-Bosch process. The reaction of 3 bar of N-2 and 3 bar of CO2 under UV illumination at 120 degrees C (for 48 h) results in a urea yield of 1.5 +/- 0.1 mmol per gram of catalyst (g(c)) with a selectivity close to 80%, whereas the reaction is not successful without UV irradiation. However, the addition of small amounts of NO (314 ppm) produces 15.2 +/- 0.6 and 4.6 +/- 0.4 mmol g(c)(-1) with and without UV illumination, respectively, with the selectivity in both cases being close to 100%. As nitrogen fixation without UV irradiation using p-HAp as a catalyst is a challenge, studies with NO have been conducted varying the reaction conditions (time, pressure and temperature). The results suggest a mechanism based on the production of NH4+ through the oxidation of N-2.

JTD Keywords: Carbon dioxide, Carbon,dinitrogen,reduction,nitrogen,ammonia,dioxid, Catalyst selectivity, Condition, Haber-bosch process, Hydroxyapatite, Irradiation, Metabolism, Mild reaction conditions, Nitrogen fixation, Pressure and temperature, Reaction conditions, Time pressures, Time-temperature, Urea, Uv illuminations, Without uv irradiations, ]+ catalyst

Surface electromyography (sEMG) can be used to measure the electrical activity of the respiratory muscles. The possible applications of sEMG span from patients suffering from acute respiratory failure to patients receiving chronic home mechanical ventilation, to evaluate muscle function, titrate ventilatory support and guide treatment. However, sEMG is mainly used as a monitoring tool for research and its use in clinical practice is still limited-in part due to a lack of standardization and transparent reporting. During this round table meeting, recommendations on data acquisition, processing, interpretation, and potential clinical applications of respiratory sEMG were discussed. This paper informs the clinical researcher interested in respiratory muscle monitoring about the current state of the art on sEMG, knowledge gaps and potential future applications for patients with respiratory failure.

JTD Keywords: Acute respiratory failure, Artificial ventilation, Asthmatic-children, Breathing muscle, Clinical monitoring, Clinical practice, Clinical research, Consensus development, Data interpretation, Disease exacerbation, Drive, Electrode positioning, Electrode removal, Electromyography, Force, Home care, Human, Human diaphragm, Humans, Information processing, Inspiratory muscle training, Inspiratory muscles, Intensive care unit, Knowledge gap, Long term care, Mechanical ventilation, Medical procedures, Muscle contraction, Muscle fatigue, Muscle function, Muscle training, Muscle, skeletal, Muscle-activity, Noninvasive ventilation, Patient monitoring, Patient-ventilator asynchrony, Physiology, Prognosis, Quality of life, Reporting and data system, Respiratory failure, Respiratory muscles, Review, Severe exacerbations, Signal processing, Skeletal muscle, Standardization, Surface electromyography, Time factor

The combination of a powerful and broadly applicable nuclear hyperpolarization technique with emerging (near-)zero-field modalities offers novel opportunities in a broad range of nuclear magnetic resonance spectroscopy and imaging applications, including biomedical diagnostics, monitoring catalytic reactions within metal reactors and many others. These are discussed along with a roadmap for future developments.

JTD Keywords: Couplings, Hyperpolarization, Nmr, Parahydrogen, Phase, Radicals, Time

In order to exploit the pharmacological potential of natural bioactive molecules with low water solubility, such as curcumin, it is necessary to develop formulations, such as amorphous polymer dispersions, which allow a constant release rate and at the same time avoid possible toxicity effects of the crystalline form of the molecule under scrutiny. In this study, polymer dispersions of curcumin were obtained in PADAS, a biodegradable semicrystalline copolymer based on 1,12-dodecanediol, sebacic acid and alanine. The dispersions were fully characterized by means of differential scanning calorimetry and broadband dielectric spectroscopy, and the drug release profile was measured in a simulated body fluid. Amorphous homogeneous binary dispersions were obtained for curcumin mass fraction between 30 and 50%. Curcumin has significantly higher glass transition temperature Tg (≈ 347 K) than the polymer matrix (≈274-277 K depending on the molecular weight), and dispersions displayed Tg's intermediate between those of the pure amorphous components, implying that curcumin acts as an effective antiplasticizer for PADAS. Dielectric spectroscopy was employed to assess the relaxation dynamics of the binary dispersion with 30 wt% curcumin, as well as that of each (amorphous) component separately. The binary dispersion was characterized by a single structural relaxation, a single Johari-Goldstein process, and two local intramolecular processes, one for each component. Interestingly, the latter processes scaled with the Tg of the sample, indicating that they are viscosity-sensitive. In addition, both the pristine polymer and the dispersion exhibited an interfacial Maxwell-Wagner relaxation, likely due to spatial heterogeneities associated with phase disproportionation in this polymer. The release of curcumin from the dispersion in a simulated body fluid followed a Fickian diffusion profile, and 51% of the initial curcumin content was released in 48 h.Copyright © 2023. Published by Elsevier B.V.

JTD Keywords: antioxidant, bioavailability, dielectric spectroscopy, domain havriliak-negami, glass transition temperature, kinetic stability, molecular mobility, nm pores, phase-behavior, physical stability, release kinetics, temperature, thermodynamic quantities, time, Amorphous formulations, Dielectric spectroscopy, Glass transition temperature, Kinetic stability, Kohlrausch-williams-watts, Molecular mobility, Release kinetics

JTD Keywords: Bacterial biofilms, Device, Growth, In-vitro, Infections, Laboratory method, Lung model, Responses, Temperature, Time

Autonomous circadian clocks exist in nearly every mammalian cell type. These cellular clocks are subjected to a multilayered regulation sensitive to the mechanochemical cell microenvironment. Whereas the biochemical signaling that controls the cellular circadian clock is increasingly well understood, mechanisms underlying regulation by mechanical cues are largely unknown. Here we show that the fibroblast circadian clock is mechanically regulated through YAP/TAZ nuclear levels. We use high-throughput analysis of single-cell circadian rhythms and apply controlled mechanical, biochemical, and genetic perturbations to study the expression of the clock gene Rev-erbα. We observe that Rev-erbα circadian oscillations are disrupted with YAP/TAZ nuclear translocation. By targeted mutations and overexpression of YAP/TAZ, we show that this mechanobiological regulation, which also impacts core components of the clock such as Bmal1 and Cry1, depends on the binding of YAP/TAZ to the transcriptional effector TEAD. This mechanism could explain the impairment of circadian rhythms observed when YAP/TAZ activity is upregulated, as in cancer and aging.© 2023 Abenza et al.

JTD Keywords: activation, dynamics, forces, growth, hippo pathway, liver, platform, time, transcription, Animals, Circadian clocks, Circadian rhythm, Gene-expression, Mammals, Signal transduction, Tea domain transcription factors, Transcriptional coactivator with pdz-binding motif proteins, Yap-signaling proteins

We introduce a method for nondestructive quantification of nuclear spin polarization, of relevance to hyperpolarized spin tracers widely used in magnetic resonance from spectroscopy to in vivo imaging. In a bias field of around 30 nT we use a high-sensitivity miniaturized 87Rb-vapor magnetometer to measure the field generated by the sample, as it is driven by a windowed dynamical decoupling pulse sequence that both maximizes the nuclear spin lifetime and modulates the polarization for easy detection. We demonstrate the procedure applied to a 0.08 M hyperpolarized [1-13C]-pyruvate solution produced by dissolution dynamic nuclear polarization, measuring polarization repeatedly during natural decay at Earth's field. Application to real-time and continuous quality monitoring of hyperpolarized substances is discussed.

JTD Keywords: performance, polarization, Atomic magnetometers, Bias field, High sensitivity, Hyperpolarized, In-vivo imaging, Magnetic resonance, Magnetic-resonance, Magnetic-resonance,polarizatio, Magnetic-resonance,polarization,performanc, Magnetometers, Non destructive, Nuclear spins, Nuclear-spin polarization, Performance, Polarization, Rb vapors, Real- time, Spin dynamics, Spin polarization

Zero- to ultralow-field nuclear magnetic resonance is a modality of magnetic resonance experiment which does not require strong superconducting magnets. Contrary to conventional high-field nuclear magnetic resonance, it has the advantage of allowing high-resolution detection of nuclear magnetism through metal as well as within heterogeneous media. To achieve high sensitivity, it is common to couple zero-field nuclear magnetic resonance with hyperpolarization techniques. To date, the most common technique is parahydrogen-induced polarization, which is only compatible with a small number of compounds. In this article, we establish dissolution dynamic nuclear polarization as a versatile method to enhance signals in zero-field nuclear magnetic resonance experiments on sample mixtures of [13C]sodium formate, [1-13C]glycine, and [2-13C]sodium acetate, and our technique is immediately extendable to a broad range of molecules with >1 s relaxation times. We find signal enhancements of up to 11,000 compared with thermal prepolarization in a 2 T permanent magnet. To increase the signal in future experiments, we investigate the relaxation effects of the TEMPOL radicals used for the hyperpolarization process at zero- and ultralow-fields.

JTD Keywords: injection, liquids, relaxation, times, Spectroscopy

Biosensors represent a powerful analytical tool for analyzing biomolecular interactions with the potential to achieve real-time quantitative analysis with high accuracy using low sample volumes, minimum sample pretreatment with high potential for the development of in situ and highly integrated monitoring platforms. Considering these advantages, their use in cell-culture systems has increased over the last few years. Between the different technologies for cell culture, organs-on-a-chip (OOCs) represent a novel technology that tries to mimic an organ's functionality by combining tissue engineering/organoid with microfluidics. Although there are still challenges to achieving OOC models with high organ mimicking relevance, these devices can offer effective models for drug treatment development by identifying drug targets, screening toxicity, and determining the potential effects of drugs in living beings. Consequently, in the future, we might replace animal studies by offering more ethical test models. Considering the relevance that different physiological and biochemical parameters have in the correct functionality of cells, sensing and biosensing platforms can offer an effective way for the real-time monitoring of physiological parameters and, in our opinion, more relevant, the secretion of biomarkers such as cytokines, growth factors, and others related with the influence of drugs or other types of stimulus in cell metabolism. Keeping this concept in mind, in this chapter, we focus on describing the potential use of sensors and biosensors in OOC devices to achieve fully integrated platforms that monitor physiological parameters and cell metabolism.© 2022. The Author(s), under exclusive license to Springer Nature Switzerland AG.

JTD Keywords: alignment, biosensors, cell, crystal microbalance biosensor, electrochemical biosensors, future, graphene oxide, label-free detection, organ-on-a-chip, oxygen, pre-clinical platforms, real-time analysis, screening, Biosensors, Organ-on-a-chip, Pre-clinical platforms, Screening, Sensors, Surface-plasmon resonance

Objective: To investigate the effects of single-session premotor and primary motor tDCS in chronic stroke patients with relation to possible inter-hemispheric interactions. Methods: Anodal tDCS of either M1 or premotor cortex of the side contralateral to the paretic hand, cathodal tDCS of the premotor cortex of the side ipsilateral to the paretic hand and sham stimulation were performed in 12 chronic stroke patients with mild hand paresis in a balanced cross-over design. The Jebsen-Taylor Hand Function test, evaluating the time required for performance of everyday motor tasks, was employed. Results: The repeated-measure ANOVA with Greenhouse-Geisser correction showed significant influence of the stimulation type (factor SESSION; F(2.6, 28.4) = 47.3, p < 0.001), the test performance time relative to stimulation (during or after tDCS; factor TIME, F(1.0, 11.0) = 234.5, p < 0.001) with higher effect after the stimulation and the interaction SESSION*TIME (F(1.7, 1.2) = 30.5, p < 0.001). All active conditions were effective for the modulation of JTT performance, though the highest effect was observed after anodal tDCS of M1, followed by effects after anodal stimulation of the premotor cortex contralateral to the paretic hand. Based on the correlation patterns, the inhibitory input to M1 from premotor cortex of another hemisphere and an excitatory input from the ipsilesional premotor cortex were suggested. Conclusion: The premotor cortex is a promising candidate area for transcranial non-invasive stimulation of chronic stroke patients. © 2022 The Author(s)

JTD Keywords: areas, contralateral primary motor, dorsal premotor, excitability, jtt, lateral premotor, object manipulation, premotor cortex, recovery, stroke, tdcs, time-course, transcranial direct current stimulation, Jtt, Noninvasive brain-stimulation, Premotor cortex, Stroke, Tdcs, Transcranial direct current stimulation

The severe neurological disorder epilepsy affects almost 1% of the world population. For patients who suffer from pharmacoresistant focal-onset epilepsy, electroencephalographic (EEG) recordings are essential for the localization of the brain area where seizures start. Apart from the visual inspection of the recordings, quantitative EEG signal analysis techniques proved to be useful for this purpose. Among other features, regularity versus irregularity and phase coherence versus phase independence allowed characterizing brain dynamics from the measured EEG signals. Can phase irregularities also characterize brain dynamics? To address this question, we use the univariate coefficient of phase velocity variation, defined as the ratio of phase velocity standard deviation and the mean phase velocity. Beyond that, as a bivariate measure we use the classical mean phase coherence to quantify the degree of phase locking. All phase-based measures are combined with surrogates to test null hypotheses about the dynamics underlying the signals. In the first part of our analysis, we use the Rössler model system to study our approach under controlled conditions. In the second part, we use the Bern-Barcelona EEG database which consists of focal and nonfocal signals extracted from seizure-free recordings. Focal signals are recorded from brain areas where the first seizure EEG signal changes can be detected, and nonfocal signals are recorded from areas that are not involved in the seizure at its onset. Our results show that focal signals have less phase variability and more phase coherence than nonfocal signals. Once combined with surrogates, the mean phase velocity proved to have the highest discriminative power between focal and nonfocal signals. In conclusion, conceptually simple and easy to compute phase-based measures can help to detect features induced by epilepsy from EEG signals. This holds not only for the classical mean phase coherence but even more so for univariate measures of phase irregularity. © 2022 American Physical Society.

JTD Keywords: brain, entropy, epileptogenic networks, functional connectivity, hilbert transform, seizure onset, surrogate data, synchronization, time-series, Biomedical signal processing, Brain areas, Brain dynamics, Dynamics, Electroencephalographic signals, Electroencephalography, Electrophysiology, Intracranial eeg signals, Localisation, Neurological disorders, Neurology, Phase based, Phase coherence, Signal detection, Simple++, Univariate, Velocity, World population

Visual impairments are a critical medical hurdle to be addressed in modern society. Müller glia (MG) have regenerative potential in the retina in lower vertebrates, but not in mammals. However, in mice, in vivo cell fusion between MG and adult stem cells forms hybrids that can partially regenerate ablated neurons.We used organotypic cultures of human retina and preparations of dissociated cells to test the hypothesis that cell fusion between human MG and adult stem cells can induce neuronal regeneration in human systems. Moreover, we established a microinjection system for transplanting human retinal organoids to demonstrate hybrid differentiation.We first found that cell fusion occurs between MG and adult stem cells, in organotypic cultures of human retina as well as in cell cultures. Next, we showed that the resulting hybrids can differentiate and acquire a proto-neural electrophysiology profile when the Wnt/beta-catenin pathway is activated in the adult stem cells prior fusion. Finally, we demonstrated the engraftment and differentiation of these hybrids into human retinal organoids.We show fusion between human MG and adult stem cells, and demonstrate that the resulting hybrid cells can differentiate towards neural fate in human model systems. Our results suggest that cell fusion-mediated therapy is a potential regenerative approach for treating human retinal dystrophies.This work was supported by La Caixa Health (HR17-00231), Velux Stiftung (976a) and the Ministerio de Ciencia e Innovación, (BFU2017-86760-P) (AEI/FEDER, UE), AGAUR (2017 SGR 689, 2017 SGR 926).Published by Elsevier B.V.

JTD Keywords: cell fusion, expression, fusion, ganglion-cells, in-vitro, mouse, müller glia, neural differentiation, organoids, regeneration, retina regeneration, stem cells, stromal cells, transplantation, 4',6 diamidino 2 phenylindole, 5' nucleotidase, Agarose, Alcohol, Arpe-19 cell line, Article, Beta catenin, Beta tubulin, Bone-marrow-cells, Bromophenol blue, Buffer, Calcium cell level, Calcium phosphate, Calretinin, Canonical wnt signaling, Cd34 antigen, Cell culture, Cell fusion, Cell viability, Coculture, Complementary dna, Confocal microscopy, Cornea transplantation, Cryopreservation, Cryoprotection, Crystal structure, Current clamp technique, Dimethyl sulfoxide, Dodecyl sulfate sodium, Edetic acid, Electrophysiology, Endoglin, Fetal bovine serum, Fibroblast growth factor 2, Flow cytometry, Fluorescence activated cell sorting, Fluorescence intensity, Glyceraldehyde 3 phosphate dehydrogenase, Glycerol, Glycine, Hoe 33342, Immunofluorescence, Immunohistochemistry, Incubation time, Interleukin 1beta, Lentivirus vector, Matrigel, Mercaptoethanol, Microinjection, Mueller cell, Müller glia, N methyl dextro aspartic acid, Nerve cell differentiation, Neural differentiation, Nitrogen, Nonhuman, Organoids, Paraffin, Paraffin embedding, Paraformaldehyde, Patch clamp technique, Penicillin derivative, Phenolsulfonphthalein, Phenotype, Phosphate buffered saline, Phosphoprotein phosphatase inhibitor, Polyacrylamide gel electrophoresis, Potassium chloride, Povidone iodine, Promoter region, Proteinase inhibitor, Real time polymerase chain reaction, Receptor type tyrosine protein phosphatase c, Restriction endonuclease, Retina, Retina dystrophy, Retina regeneration, Retinol, Rhodopsin, Rna extraction, Stem cell, Stem cells, Subcutaneous fat, Tunel assay, Visual impairment, Western blotting

Mycobacterium bovis bacillus Calmette-Guérin (BCG) efficacy as an immunotherapy tool can be influenced by the genetic background or immune status of the treated population and by the BCG substrain used. BCG comprises several substrains with genetic differences that elicit diverse phenotypic characteristics. Moreover, modifications of phenotypic characteristics can be influenced by culture conditions. However, several culture media formulations are used worldwide to produce BCG. To elucidate the influence of growth conditions on BCG characteristics, five different substrains were grown on two culture media, and the lipidic profile and physico-chemical properties were evaluated. Our results show that each BCG substrain displays a variety of lipidic profiles on the outermost surface depending on the growth conditions. These modifications lead to a breadth of hydrophobicity patterns and a different ability to reduce neutral red dye within the same BCG substrain, suggesting the influence of BCG growth conditions on the interaction between BCG cells and host cells.

JTD Keywords: cell wall, efficacy, glycerol, hydrophobicity, lipid, neutral red, pdim, pgl, protein, strains, viability, virulence, Acylglycerol, Albumin, Article, Asparagine, Bacterial cell wall, Bacterial gene, Bacterium culture, Bcg vaccine, Catalase, Cell wall, Chloroform, Controlled study, Escherichia coli, Gene expression, Genomic dna, Glycerol, Glycerol monomycolate, Hexadecane, Housekeeping gene, Hydrophobicity, Immune response, Immunogenicity, Immunotherapy, Lipid, Lipid fingerprinting, Magnesium sulfate, Mercaptoethanol, Methanol, Methylglyoxal, Molybdatophosphoric acid, Mycobacterium bovis bcg, Neutral red, Nonhuman, Pdim, Petroleum ether, Pgl, Phenotype, Physical chemistry, Real time reverse transcription polymerase chain reaction, Rna 16s, Rna extraction, Rv0577, Staining, Thin layer chromatography, Unclassified drug

Patients with spinal cord injury (SCI) have an increased risk of sleep-disordered breathing (SDB), which can lead to serious comorbidities and impact patients’ recovery and quality of life. However, sleep tests are rarely performed on SCI patients, given their multiple health needs and the cost and complexity of diagnostic equipment. The objective of this study was to use a novel smartphone system as a simple non-invasive tool to monitor SDB in SCI patients. We recorded pulse oximetry, acoustic, and accelerometer data using a smartphone during overnight tests in 19 SCI patients and 19 able-bodied controls. Then, we analyzed these signals with automatic algorithms to detect desaturation, apnea, and hypopnea events and monitor sleep position. The apnea–hypopnea index (AHI) was significantly higher in SCI patients than controls (25 ± 15 vs. 9 ± 7, p < 0.001). We found that 63% of SCI patients had moderate-to-severe SDB (AHI ? 15) in contrast to 21% of control subjects. Most SCI patients slept predominantly in supine position, but an increased occurrence of events in supine position was only observed for eight patients. This study highlights the problem of SDB in SCI and provides simple cost-effective sleep monitoring tools to facilitate the detection, understanding, and management of SDB in SCI patients.

JTD Keywords: apnea syndrome, biomedical signal processing, individuals, mhealth, monitoring, nasal resistance, people, position, prevalence, questionnaire, sample, sleep apnea, sleep position, sleep-disordered breathing, smartphone, time, Apnea-hypopnea indices, Biomedical signal processing, Biomedical signals processing, Cost effectiveness, Diagnosis, Mhealth, Monitoring, Noninvasive medical procedures, Oximeters, Oxygen-saturation, Patient rehabilitation, Simple++, Sleep apnea, Sleep position, Sleep research, Sleep-disordered breathing, Smart phones, Smartphone, Smartphones, Spinal cord injury, Spinal cord injury patients

Objective. Impaired trunk stability is frequent in spinal cord injury (SCI), but there is a lack of quantitative measures for assessing trunk function. Our objectives were to: (a) evaluate trunk muscle activity and movement patterns during a reaching task in SCI patients, (b) compare the impact of cervical (cSCI) and thoracic (tSCI) injuries in trunk function, and (c) investigate the effects of a startling acoustic stimulus (SAS) in these patients. Approach. Electromyographic (EMG) and smartphone accelerometer data were recorded from 15 cSCI patients, nine tSCI patients, and 24 healthy controls, during a reaching task requiring trunk tilting. We calculated the response time (RespT) until pressing a target button, EMG onset latencies and amplitudes, and trunk tilt, lateral deviation, and other movement features from accelerometry. Statistical analysis was applied to analyze the effects of group (cSCI, tSCI, control) and condition (SAS, non-SAS) in each outcome measure. Main results. SCI patients, especially those with cSCI, presented significantly longer RespT and EMG onset latencies than controls. Moreover, in SCI patients, forward trunk tilt was accompanied by significant lateral deviation. RespT and EMG latencies were remarkably shortened by the SAS (the so-called StartReact effect) in tSCI patients and controls, but not in cSCI patients, who also showed higher variability. Significance. The combination of EMG and smartphone accelerometer data can provide quantitative measures for the assessment of trunk function in SCI. Our results show deficits in postural control and compensatory strategies employed by SCI patients, including delayed responses and higher lateral deviations, possibly to improve sitting balance. This is the first study investigating the StartReact responses in trunk muscles in SCI patients and shows that the SAS significantly accelerates RespT in tSCI, but not in cSCI, suggesting an increased cortical control exerted by these patients.

JTD Keywords: accelerometer, electromyography, impairment, individuals, movements, postural stability, reaction-time, reliability, sitting balance, smartphone, spinal cord injury, startle, startreact, strategies, stroke, trunk, Accelerometer, Electromyography, Sitting balance, Smartphone, Spinal cord injury, Startreact, Trunk

Poor sleep quality or disturbed sleep is associated with multiple health conditions. Sleep position affects the severity and occurrence of these complications, and positional therapy is one of the less invasive treatments to deal with them. Sleep positions can be self-reported, which is unreliable, or determined by using specific devices, such as polysomnography, polygraphy or cameras, that can be expensive and difficult to employ at home. The aim of this study is to determine how smartphones could be used to monitor and treat sleep position at home. We divided our research into three tasks: (1) develop an Android smartphone application (‘SleepPos’ app) which monitors angle-based high-resolution sleep position and allows to simultaneously apply positional treatment; (2) test the smartphone application at home coupled with a pulse oximeter; and (3) explore the potential of this tool to detect the positional occurrence of desaturation events. The results show how the ‘SleepPos’ app successfully determined the sleep position and revealed positional patterns of occurrence of desaturation events. The ‘SleepPos’ app also succeeded in applying positional therapy and preventing the subjects from sleeping in the supine sleep position. This study demonstrates how smartphones are capable of reliably monitoring high-resolution sleep position and provide useful clinical information about the positional occurrence of desaturation events.

JTD Keywords: accelerometry, android, apnea patients, app, association, biomedical signal processing, management, mhealth, monitoring, pathophysiology, pilot mhealth, questionnaire, sleep position, smartphone, supine position, time, Accelerometry, Android, App, Biomedical signal processing, Mhealth, Monitoring, Sleep position, Smart-phone, Smartphone, Tennis ball technique

Poor sleep quality is a risk factor for multiple mental, cardiovascular, and cerebrovascular diseases. Certain sleep positions or excessive position changes can be related to some diseases and poor sleep quality. Nevertheless, sleep position is usually classified into four discrete values: supine, prone, left and right. An increase in sleep position resolution is necessary to better assess sleep position dynamics and to interpret more accurately intermediate sleep positions. This research aims to study the feasibility of smartphones as sleep position monitors by (1) developing algorithms to retrieve the sleep position angle from smartphone accelerometry; (2) monitoring the sleep position angle in patients with obstructive sleep apnea (OSA); (3) comparing the discretized sleep angle versus the four classic sleep positions obtained by the video-validated polysomnography (PSG); and (4) analyzing the presence of positional OSA (pOSA) related to its sleep angle of occurrence. Results from 19 OSA patients reveal that a higher resolution sleep position would help to better diagnose and treat patients with position-dependent diseases such as pOSA. They also show that smartphones are promising mHealth tools for enhanced position monitoring at hospitals and home, as they can provide sleep position with higher resolution than the gold-standard video-validated PSG.

JTD Keywords: accelerometry, actigraphy, association, biomedical signal processing, index, latency, mhealth, monitoring, pathophysiology, quality, questionnaire, score, sleep apnea, sleep position, smartphone, time, Accelerometry, Biomedical signal processing, Mhealth, Monitoring, Sleep apnea, Sleep position, Smartphone, Supine position

Heart diseases are the leading cause of death in developed countries. Ascertaining the etiology of cardiomyopathies is still a challenge. The objective of this study was to classify cardiomyopathy patients through cardio, respiratory and vascular variability analysis, considering the vascular activity as the input and output of the baroreflex response. Forty-one cardiomyopathy patients (CMP) classified as ischemic (ICM, 24 patients) and dilated (DCM, 17 patients) were analyzed. Thirty-nine elderly control subjects (CON) were used as reference. From the electrocardiographic, respiratory flow, and blood pressure signals, following temporal series were extracted: beat-to-beat intervals (BBI), total respiratory cycle time series (TT), and end– systolic (SBP) and diastolic (DBP) blood pressure amplitudes, respectively. Three-dimensional representation of the cardiorespiratory and vascular activities was characterized geometrically, by fitting a polygon that contains 95% of data, and by statistical descriptive indices. The best classifiers were used to build support vector machine models. The optimal model to classify ICM versus DCM patients achieved 92.7% accuracy, 94.1% sensitivity, and 91.7% specificity. When comparing CMP patients and CON subjects, the best model achieved 86.2% accuracy, 82.9% sensitivity, and 89.7% specificity. These results suggest a limited ability of cardiac and respiratory systems response to regulate the vascular variability in these patients.

JTD Keywords: Time series analysis, Support vector machines, Blood pressure, Sensitivity, Indexes, Electrocardiography, Kernel

Modern optical sensors coupled with time-lapse microscopy devices and dedicated software tools allow the miniaturization of laboratories for biological experiments leading to the Organ-On-Chip (OoC) framework. OoCs allow performing massive measurements on a large number of cells under the assumption of reproducibility conditions, permitting to investigate the cell dynamics in terms of motility and shape changes over time. In this work, we present the OoC platform used in a preliminary study of the Rare Haemolytic Anaemia (RHA) disease, a group of rare diseases characterized by haemolysis, which is the premature loss of red blood cells (RBCs). Preliminary results demonstrate the effectiveness of shape measurement for the diagnosis of RHA.

JTD Keywords: Anaemia diagnosis, Cell tracking, Plasticity measurement, Time-lapse microscopy

At high temperatures, isoprene-emitting plants display a higher photosynthetic rate and a lower nonphotochemical quenching (NPQ) compared with nonemitting plants. The mechanism of this phenomenon, which may be very important under current climate warming, is still elusive. NPQ was dissected into its components, and chlorophyll fluorescence lifetime imaging microscopy (FLIM) was used to analyse the dynamics of excited chlorophyll relaxation in isoprene-emitting and nonemitting plants. Thylakoid membrane stiffness was also measured using atomic force microscope (AFM) to identify a possible mode of action of isoprene in improving photochemical efficiency and photosynthetic stability. We show that, when compared with nonemitters, isoprene-emitting tobacco plants exposed at high temperatures display a reduced increase of the NPQ energy-dependent component (qE) and stable (1) chlorophyll fluorescence lifetime; (2) amplitude of the fluorescence decay components; and (3) thylakoid membrane stiffness. Our study shows for the first time that isoprene maintains PSII stability at high temperatures by preventing the modifications of the surrounding environment, namely providing a more steady and homogeneous distribution of the light-absorbing centres and a stable thylakoid membrane stiffness. Isoprene photoprotects leaves with a mechanism alternative to NPQ, enabling plants to maintain a high photosynthetic rate at rising temperatures.

JTD Keywords: (High) temperature, Atomic force microscopy (AFM), Chlorophyll fluorescence (quenching and lifetime), Fluorescence lifetime imaging microscopy (FLIM), Isoprene, Nonphotochemical quenching (NPQ), Photosynthesis

The intermittency of the instantaneous concentration of a turbulent chemical plume is a fundamental cue for estimating the chemical source distance using chemical sensors. Such estimate is useful in applications such as environmental monitoring or localization of fugitive gas emissions by mobile robots or sensor networks. However, the inherent low-pass filtering of metal oxide (MOX) gas sensors-typically used in odor-guided robots and dense sensor networks due to their low cost, weight and size-hinders the quantification of concentration intermittency. In this paper, we design a digital differentiator to invert the low-pass dynamics of the sensor response, thus obtaining a much faster signal from which the concentration intermittency can be effectively computed. Using a fast photo-ionization detector as a reference instrument, we demonstrate that the filtered signal is a good approximation of the instantaneous concentration in a real turbulent plume. We then extract transient features from the filtered signal-the so-called “bouts”-to predict the chemical source distance, focusing on the optimization of the filter parameters and the noise threshold to make the predictions robust against changing wind conditions. This represents an advantage over previous bout-based models which require wind measurements-typically taken with expensive and bulky anemometers-to produce accurate predictions. The proposed methodology is demonstrated in a wind tunnel scenario where a MOX sensor is placed at various distances downwind of an emitting chemical source and the wind speed varies in the range 10-34 cm/s. The results demonstrate that models optimized with our methodology can provide accurate source distance predictions at different wind speeds.

JTD Keywords: Gas detectors, Chemical sensors, Signal processing, Machine learning, Time series analysis

Continuous adventitious sounds (CAS) are commonly observed in obstructive pulmonary diseases and are of great clinical interest. However, their evaluation is generally subjective. We have previously developed an automatic CAS segmentation and classification algorithm for CAS recorded on the chest surface. The aim of this study is to establish whether these pulmonary CAS can be identified in a similar way using a tracheal microphone. Respiratory sounds were originally recorded from 25 participants using five contact microphones, four on the chest and one on the trachea, during three progressive respiratory maneuvers. In this work CAS component detection was performed on the tracheal channel using our automatic algorithm based on the Hilbert spectrum. The tracheal CAS detected were then compared to the previously analyzed pulmonary CAS. The sensitivity of CAS identification was lower at the tracheal microphone, with CAS that appeared simultaneously in all four pulmonary recordings more likely to be identified in the tracheal recordings. These observations could be due to the CAS being obscured by the lower SNR present in the tracheal recordings or not being transmitted through the airways to the trachea. Further work to optimize the algorithm for the tracheal recordings will be conducted in the future.

JTD Keywords: Microphones, Lung, Diseases, Time-frequency analysis, Spectrogram, Sensitivity

Cardiovascular diseases are one of the most common causes of death in elderly patients. The etiology of cardiomyopathies is difficult to discern clinically. The objective of this study was to classify cardiomyopathy patients using coupling analysis, through their cardiovascular behavior and the baroreflex response. A total of thirty-eight cardiomyopathy patients (CMP) classified as ischemic (ICM, 25 patients) and dilated (DCM, 13 patients) were analyzed. Thirty elderly control subjects (CON) were used as reference. Their electrocardiographic (ECG) and blood pressure (BP) signals were studied. To characterize the cardiovascular activity, the following temporal series were extracted: beat-to-beat intervals (from the ECG signal), and end- systolic and diastolic blood pressure amplitudes (from the BP signal). Non-linear characterization techniques like high resolution joint symbolic dynamics, segmented Poincaré plot analysis, normalized shorttime partial directed coherence, and dual sequence method were used to characterize these times series. The best indices were used to build support vector machine models for classification. The optimal model for ICM versus DCM patients achieved 84.2% accuracy, 76.9% sensitivity, and 88% specificity. When CMP patients and CON subjects were compared, the best model achieved 95.5% accuracy, 97.3% sensitivity, and 93.3% specificity. These results suggest a disfunction in the baroreflex mechanism in cardiomyopathies patients.

JTD Keywords: Couplings, Time series analysis, Support vector machines, Electrocardiography, Baroreflex, Coherence, Sensitivity

The low bandwidth of metal oxide semiconductor (MOX) sensors (<0.1 Hz) is a major hurdle to gas source localization (GSL) in turbulent environments where detection of intermittent odor patches is key. We present a fast-response miniaturized electronic nose (Fast-eNose) composed of four naked MOX sensors and a digital band-pass filter that can boost the bandwidth of the system close to 1 Hz. The device was attached to a fast photo-ionization detector (330 Hz) to quantify the response time during exposure to turbulent gas plumes. The results indicate that the digital filter can improve the response time by at least a factor of 4, bringing new possibilities to mobile robot olfaction.

JTD Keywords: CFD, Gas plume, Gas sensors, MOX, Response time, Signal processing

In this paper, a non-linear HRV analysis was performed to assess fragmentation signatures observed in heartbeat time series after intermittent hypoxia (IH). Three markers quantifying short-term fragmentation levels, PIP, IALS and PSS, were evaluated on R-R interval series obtained in a rat model of recurrent apnea. Through airway obstructions, apnea episodes were periodically simulated in six anesthetized Sprague-Dawley rats. The number of apnea events per hour (AHI index) was varied during the first half of the experiment while apnea episodes lasted 15 s. For the second part, apnea episodes lasted 5, 10 or 15 s, but the AHI index was fixed. Recurrent apnea was repeated for 15-min time intervals in all cases, alternating with basal periods of the same duration. The fragmentation markers were evaluated in segments of 5 minutes, selected at the beginning and end of the experiment. The impact of the heart and breathing rates (HR and BR, respectively) on the parameter estimates was also investigated. The results obtained show a significant increase (from 5 to 10%, p <; 0.05) in fragmentation measures of heartbeat time series after IH, indicating a clear deterioration of the initial conditions. Moreover, there was a strong linear relationship (r > 0.9) between these markers and BR, as well as with the ratio given by HR/BR. Although fragmentation may be impacted by IH, we found that it is highly dependent on HR and BR values and thus, they should be considered during its calculation or used to normalize the fragmentation estimates.

JTD Keywords: Rats, Time series analysis, Radio access technologies, Protocols, Heart beat

This study evaluates the onset and offset of neural inspiratory time estimated from surface diaphragm electromyographic (EMGdi) recordings. EMGdi and airflow signals were recorded in ten healthy subjects according to two respiratory protocols based on respiratory rate (RR) increments, from 15 to 40 breaths per minute (bpm), and fractional inspiratory time (Ti/Ttot) decrements, from 0.54 to 0.18. The analysis of diaphragm electromyographic (EMGdi) signal amplitude is an alternative approach for the quantification of neural respiratory drive (NRD). The EMGdi amplitude was estimated using the fixed sample entropy computed over a 250 ms moving window of the EMGdi signal (EMGdifse). The neural onset was detected through a dynamic threshold over the EMGdifse using the kernel density estimation method, while neural offset was detected by finding when the EMGdifse had decreased to 70 % of the peak value reached during inspiration. The Bland-Altman analysis between airflow and neural onsets showed a global bias of 46 ms in the RR protocol and 22 ms in the Ti/Ttot protocol. The Bland-Altman analysis between airflow and neural offsets reveals a global bias of 11 ms in the RR protocol and -2 ms in the Ti/Ttot protocol. The relationship between pairs of RR values (Pearson’s correlation coefficient of 0.99, Bland- Altman limits of -2.39 to 2.41 bpm, and mean bias of 0.01 bpm) and between pairs of Ti/Ttot values (Pearson’s correlation coefficient of 0.86, Bland-Altman limits of -0.11 to 0.10, and mean bias of -0.01) showed a good agreement. In conclusion, we propose a method for determining neural onset and neural offset based on non-invasive recordings of the electrical activity of the diaphragm that requires no filtering of cardiac muscle interference.

JTD Keywords: Kernel density estimation (KDE),, Surface diaphragm electromyographic,, (EMGdi) signal,, Inspiratory time,, Neural respiratory drive (NRD),, Neural inspiratory time,, Fixed sample entropy (fSampEn)

The time delay estimation problem associated with an ensemble of misaligned, repetitive signals is revisited. Each observed signal is assumed to be composed of an unknown, deterministic signal corrupted by Gaussian, white noise. This paper shows that maximum likelihood (ML) time delay estimation can be viewed as the maximization of an eigenvalue ratio, where the eigenvalues are obtained from the ensemble correlation matrix. A suboptimal, one-step time delay estimate is proposed for initialization of the ML estimator, based on one of the eigenvectors of the inter-signal correlation matrix. With this approach, the ML estimates can be determined without the need for an intermediate estimate of the underlying, unknown signal. Based on respiratory flow signals, simulations show that the variance of the time delay estimation error for the eigenvalue-based method is almost the same as that of the ML estimator. Initializing the maximization with the one-step estimates, rather than using the ML estimator alone, the computation time is reduced by a factor of 5M when using brute force maximization (M denoting the number of signals in the ensemble), and a factor of about 1.5 when using particle swarm maximization. It is concluded that eigenanalysis of the ensemble correlation matrix not only provides valuable insight on how signal energy, jitter, and noise influence the estimation process, but it also leads to a one-step estimator which can make the way for a substantial reduction in computation time.

JTD Keywords: Biomedical signals, Time delay estimation, Eigenanalysis, Ensemble analysis

Cardiac death risk is still a big problem by an important part of the population, especially in elderly patients. In this study, we propose to characterize and analyze the cardiovascular and cardiorespiratory systems using the Poincaré plot. A total of 46 cardiomyopathy patients and 36 healthy subjets were analyzed. Left ventricular ejection fraction (LVEF) was used to stratify patients with low risk (LR: LVEF > 35%, 16 patients), and high risk (HR: LVEF ≤ 35%, 30 patients) of heart attack. RR, SBP and TTot time series were extracted from the ECG, blood pressure and respiratory flow signals, respectively. Parameters that describe the scatterplott of Poincaré method, related to short- and long-term variabilities, acceleration and deceleration of the dynamic system, and the complex correlation index were extracted. The linear discriminant analysis (LDA) and the support vector machines (SVM) classification methods were used to analyze the results of the extracted parameters. The results showed that cardiac parameters were the best to discriminate between HR and LR groups, especially the complex correlation index (p = 0.009). Analising the interaction, the best result was obtained with the relation between the difference of the standard deviation of the cardiac and respiratory system (p = 0.003). When comparing HR vs LR groups, the best classification was obtained applying SVM method, using an ANOVA kernel, with an accuracy of 98.12%. An accuracy of 97.01% was obtained by comparing patients versus healthy, with a SVM classifier and Laplacian kernel. The morphology of Poincaré plot introduces parameters that allow the characterization of the cardiorespiratory system dynamics.

JTD Keywords: Time series analysis, Electrocardiography, Support vector machines, Kernel, Standards, Correlation, RF signals

Obstructive sleep apnea (OSA) is a highly prevalent chronic disease, especially in elderly and obese populations. Despite constituting a serious health, social and economic problem, most patients remain undiagnosed and untreated due to limitations in current equipment. In this work, we propose a novel method to diagnose OSA and monitor therapy adherence and effectiveness at home in a non-invasive and inexpensive way: combining acoustic analysis of breathing and snoring sounds with oral appliance therapy (OA). Audiodontics has introduced a new sensor, a tooth microphone coupled to an OA device, which is the main pillar of this system. The objective of this work is to characterize the response of this sensor, comparing it with a commercial tracheal microphone (Biopac transducer). Signals containing OSA-related sounds were acquired simultaneously with the two microphones for that purpose. They were processed and analyzed in time, frequency and time-frequency domains, in a custom MATLAB interface. We carried out a single-event approach focused on breaths, snores and apnea episodes. We found that the quality of the signals obtained by both microphones was quite similar, although the tooth microphone spectrum concentrated more energy at the high-frequency band. This opens a new field of study about high-frequency components of snores and breathing sounds. These characteristics, together with its intraoral position, wireless option and combination with customizable OAs, give the tooth microphone a great potential to reduce the impact of sleep disorders, by enabling prompt detection and continuous monitoring of patients at home.

JTD Keywords: Microphones, Teeth, Sleep apnea, Time-frequency analysis, Signal to noise ratio, Monitoring, Acoustics

Synchronization is a central key feature of neural information processing and communication between different brain areas. Disturbance of oscillatory brain rhythms and decreased synchronization have been associated with different disorders including schizophrenia. The aim of this study was to investigate whether synchronization (in relaxed conditions with no stimuli) between different brain areas within the delta, theta, alpha (alpha1, alpha2), beta (beta1, beta2), and gamma bands is altered in patients with a neurological disorder in order to generate significant cortical enhancements. To achieve this, we investigated schizophrenic patients (SZO; N=17, 37.5±10.4 years, 15 males) and compared them to healthy subjects (CON; N=21, 36.7±13.4 years, 15 males) applying the phase locking value (PLV). We found significant differences between SZO and CON in different brain areas of the theta, alpha1, beta2 and gamma bands. These areas are related to the central and parietal lobes for the theta band, the parietal lobe for the alpha1, the parietal and frontal for the beta2 and the frontal-central for the gamma band. The gamma band revealed the most significant differences between CON and SZO. PLV were 61.7% higher on average in SZO in most of the clusters when compared to CON. The related brain areas are directly related to cognition skills which are proved to be impaired in SZO. The results of this study suggest that synchronization in SZO is also altered when the patients were not asked to perform a task that requires their cognitive skills (i.e., no stimuli are applied - in contrast to other findings).

JTD Keywords: Synchronization, Electroencephalography, Electrodes, Brain, Time series analysis, Oscillators, Frequency synchronization

In this work, the cardiorespiratory pattern of patients undergoing extubation process is studied. First, the respiratory and cardiac signals were resampled, next the Symbolic Dynamics (SD) technique was implemented, followed of a dimensionality reduction applying Forward Selection (FS) and Moving Window with Variance Analysis (MWVA) methods. Finally, the Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM) classifiers were used. The study analyzed 153 patients undergoing weaning process, classified into 3 groups: Successful Group (SG: 94 patients), Failed Group (FG: 39 patients), and patients who had been successful during the extubation and had to be reintubated before 48 hours, Reintubated Group (RG: 21 patients). According to the results, the best classification present an accuracy higher than 88.98 ± 0.013% in all proposed combinations.

JTD Keywords: Support vector machines, Standards, Time series analysis, Resonant frequency, Nonlinear dynamical systems, Ventilation

Synapses learn and remember by persistent modifications of their internal structures and composition but, due to their small size, it is difficult to observe these changes at the ultrastructural level in real time. Two-photon fluorescence microscopy (2PM) allows time-course live imaging of individual synapses but lacks ultrastructural resolution. Electron microscopy (EM) allows the ultrastructural imaging of subcellular components but cannot detect fluorescence and lacks temporal resolution. Here, we describe a combination of procedures designed to achieve the correlated imaging of the same individual synapse under both 2PM and EM. This technique permits the selective stimulation and live imaging of a single dendritic spine and the subsequent localization of the same spine in EM ultrathin serial sections. Landmarks created through a photomarking method based on the 2-photon-induced precipitation of an electrodense compound are used to unequivocally localize the stimulated synapse. This technique was developed to image, for the first time, the ultrastructure of the postsynaptic density in which long-term potentiation was selectively induced just seconds or minutes before, but it can be applied for the study of any biological process that requires the precise relocalization of micron-wide structures for their correlated imaging with 2PM and EM.

JTD Keywords: Correlated imaging, DAB, Dendritic spine, Photobranding, Photoetching, Photomarking, Postsynaptic density, Serial-section transmission electron microscopy, Synapse, Time-lapse live two-photon fluorescence microscopy

The use of non-invasive methods for the study of respiratory muscle signals can provide clinical information for the evaluation of the respiratory muscle function. The aim of this study was to evaluate time-frequency characteristics of the electrical activity of the sternocleidomastoid muscle recorded superficially by means of concentric ring electrodes (CREs) in a bipolar configuration. The CREs enhance the spatial resolution, attenuate interferences, as the cardiac activity, and also simplify the orientation problem associated to the electrode location. Five healthy subjects underwent a respiratory load test in which an inspiratory load was imposed during the inspiratory phase. During the test, the electromyographic signal of the sternocleidomastoid muscle (EMGsc) and the inspiratory mouth pressure (Pmouth) were acquired. Time-frequency characteristics of the EMGsc signal were analyzed by means of eight time-frequency representations (TFRs): the spectrogram (SPEC), the Morlet scalogram (SCAL), the Wigner-Ville distribution (WVD), the Choi-Williams distribution (CHWD), two generalized exponential distributions (GED1 and GED2), the Born-Jordan distribution (BJD) and the Cone-Kernel distribution (CKD). The instantaneous central frequency of the EMGsc showed an increasing behavior during the inspiratory cycle and with the increase of the inspiratory load. The bilinear TFRs (WVD, CHWD, GEDs and BJD) were less sensitive to cardiac activity interference than classical TFRs (SPEC and SCAL). The GED2 was the TFR that shown the best results for the characterization of the instantaneous central frequency of the EMGsc.

JTD Keywords: Electrodes, Interference, Kernel, Mouth, Muscles, Spectrogram, Time-frequency analysis

Metal oxide (MOX) gas sensors arrays are a predominant technological choice to perform fundamental tasks of chemical detection. Yet, their use has been mainly limited to relatively controlled instrument configurations where the sensor array is placed within a closed measurement chamber. Usually, the experimental protocol is defined beforehand and it includes three stages: the array is first exposed to a gas reference, then to the gas sample, and finally to the reference again to recover the initial state. Such sampling procedure requires signal acquisition during the complete experimental protocol and usually delays the output prediction until the predefined measurement duration is complete. Due to the slow time response of chemical sensors, the completion of the measurement typically requires minutes. In this paper we propose the use of reservoir computing (RC) algorithms to overcome the slow temporal dynamics of chemical sensor arrays, allowing identification and quantification of chemicals of interest continuously and reducing measurement delays. We generated two datasets to test the ability of RC algorithms to provide accurate and continuous prediction to fast varying gas concentrations in real time. Both datasets - one generated with synthetic data and the other acquired from actual gas sensors - provide time series of MOX sensors exposed to binary gas mixtures where concentration levels change randomly over time. Our results show that our approach improves the time response of the sensory system and provides accurate predictions in real time, making the system specifically suitable for online monitoring applications. Finally, the collected dataset and developed code are made publicly available to the research community for further studies.

JTD Keywords: Chemical sensors, Continuous gas prediction, Electronic nose, Real-time detection, Reservoir computing

This work investigates the performance of cardiorespiratory analysis detecting periodic breathing (PB) in chest wall recordings in mountaineers climbing to extreme altitude. The breathing patterns of 34 mountaineers were monitored unobtrusively by inductance plethysmography, ECG and pulse oximetry using a portable recorder during climbs at altitudes between 4497 and 7546 m on Mt. Muztagh Ata. The minute ventilation (VE) and heart rate (HR) signals were studied, to identify visually scored PB, applying time-varying spectral, coherence and entropy analysis. In 411 climbing periods, 30–120 min in duration, high values of mean power (MPVE) and slope (MSlopeVE) of the modulation frequency band of VE, accurately identified PB, with an area under the ROC curve of 88 and 89 %, respectively. Prolonged stay at altitude was associated with an increase in PB. During PB episodes, higher peak power of ventilatory (MPVE) and cardiac (MP LF HR ) oscillations and cardiorespiratory coherence (MP LF Coher ), but reduced ventilation entropy (SampEnVE), was observed. Therefore, the characterization of cardiorespiratory dynamics by the analysis of VE and HR signals accurately identifies PB and effects of altitude acclimatization, providing promising tools for investigating physiologic effects of environmental exposures and diseases.

JTD Keywords: High-altitude periodic breathing, Cardiorespiratory characterization, Time-varying spectral analysis, Acclimatization, Hypoxia

Cardiovascular diseases are the first cause of death in developed countries. Using electrocardiographic (ECG), blood pressure (BP) and respiratory flow signals, we obtained parameters for classifying cardiomyophaty patients. 42 patients with ischemic (ICM) and dilated (DCM) cardiomyophaties were studied. The left ventricular ejection fraction (LVEF) was used to stratify patients with low risk (LR: LVEF>35%, 14 patients) and high risk (HR: LVEF≤ 35%, 28 patients) of heart attack. RR, SBP and T_Tot time series were extracted from the ECG, BP and respiratory flow signals, respectively. The time series were transformed to a binary space and then analyzed using Joint Symbolic Dynamic with a word length of three, characterizing them by the probability of occurrence of the words. Extracted parameters were then reduced using correlation and statistical analysis. Principal component analysis and support vector machines methods were applied to characterize the cardiorespiratory and cardiovascular interactions in ICM and DCM cardiomyopaties, obtaining an accuracy of 85.7%.

JTD Keywords: Blood pressure, Electrocardiography, Joints, Kernel, Principal component analysis, Support vector machines, Time series analysis

The scope of our work focuses on investigating the potential use of the built-in accelerometer of the smartphones for the recording of the respiratory activity and deriving the respiratory rate. Five healthy subjects performed an inspiratory load protocol. The excursion of the right chest was recorded using the built-in triaxial accelerometer of a smartphone along the x, y and z axes and with an external uniaxial accelerometer. Simultaneously, the respiratory airflow and the inspiratory mouth pressure were recorded, as reference respiratory signals. The chest acceleration signal recorded in the z axis with the smartphone was denoised using a scheme based on the ensemble empirical mode decomposition, a noise data assisted method which decomposes nonstationary and nonlinear signals into intrinsic mode functions. To distinguish noisy oscillatory modes from the relevant modes we use the detrended fluctuation analysis. We reported a very strong correlation between the acceleration of the z axis of the smartphone and the reference accelerometer across the inspiratory load protocol (from 0.80 to 0.97). Furthermore, the evaluation of the respiratory rate showed a very strong correlation (0.98). A good agreement was observed between the respiratory rate estimated with the chest acceleration signal from the z axis of the smartphone and with the respiratory airflow signal: Bland-Altman limits of agreement between -1.44 and 1.46 breaths per minute with a mean bias of -0.01 breaths per minute. This preliminary study provides a valuable insight into the use of the smartphone and its built-in accelerometer for respiratory monitoring.

JTD Keywords: Acceleration, Accelerometers, Correlation, Empirical mode decomposition, Fluctuations, Protocols, Time series analysis

Due to the increasing elderly population and the extensive number of comorbidities that affect them, studies are required to determine future increments in admission to emergency departments. Some of these studies could focus on the relation between chronic diseases and breathing pattern in elderly patients. Variations in the fractal properties of respiratory signals can be associated with several diseases. To determine the relationship between these variations and breathing patterns, and to quantify the fractal properties of respiratory flow signals, we estimated the Hurst exponent (H). Detrended fluctuation analysis (DFA) and discrete wavelet transform-based estimation (DWTE) methods were applied. The estimation methods were analyzed using simulated data series generated by fractional Gaussian noise. 43 elderly patients (19 patients with a non-periodic breathing pattern - nPB, and 24 patients with a periodic breathing pattern - PB) were studied. The results were evaluated according to the length of data and the number of averaged data series used to obtain a good estimation. The DWTE method estimated the respiratory flow signals better than the DFA method, and obtained Hurst values clustered by group. We found significant differences in the H exponent (p = 0.002) between PB and nPB patients, which showed different behavior in the fractal properties.

JTD Keywords: Discrete wavelet transforms, Diseases, Estimation, Fractals, Modulation, Senior citizens, Time series analysis

Blood pressure carries a great deal of information about people’s physical attributes. We analyzed the blood pressure signal in healthy subjects considering two positions, supine and sitting. 44 healthy subjects were studied. Parameters extracted from the blood pressure signal, related to time and frequency domain were used to compare the effect of postural position between supine and sitting. In time domain analysis, the time systolic interval and the time of blood pressure interval were higher in supine than in sitting position (p = 0.001 in both case). Parameters related to frequency peak, interquartile range, in frequency domain presented statistically significant difference (p < 0.0005 in both case). The blood pressure variability parameters presented smaller values in supine than in sitting position (p < 0.0005). In general, the position change of supine to sitting produces an increment in the pressure gradient inside heart, reflected in the blood pressure variability.

JTD Keywords: Blood pressure variability, Systolic time intervals, Diastolic time intervals

Ischemic and dilated cardiomyopathy are associated with disorders of myocardium. Using the blood pressure (BP) signal and the values of the ventricular ejection fraction, we obtained parameters for stratifying cardiomyopathy patients as low- and high-risk. We studied 48 cardiomyopathy patients characterized by NYHA ≥2: 19 patients with dilated cardiomyopathy (DCM) and 29 patients with ischemic cardiomyopathy (ICM). The left ventricular ejection fraction (LVEF) percentage was used to classify patients in low risk (LR: LVEF > 35%, 17 patients) and high risk (HR: LVEF ≤ 35%, 31 patients) groups. From the BP signal, we extracted the upward systolic slope (BP_sl), the difference between systolic and diastolic BP (BPA), and systolic time intervals (STI). When we compared the LR and HR groups in the time domain analysis, the best parameters were standard deviation (SD) of 1=STI, kurtosis (K) of BP_sl, and K of BPA. In the frequency domain analysis, very low frequency (VLF) and high frequency (HF) bands showed statistically significant differences in comaprisons of LR and HR groups. The area under the curve of power spectral density was the best parameter in all classifications, and particularly in the very-low-and high- frequency bands (p <; 0.001). These parameters could help to improve the risk stratification of cardiomyopathy patients.

JTD Keywords: blood pressure measurement, cardiovascular system, diseases, medical disorders, medical signal processing, statistical analysis, time-domain analysis, BP signal, HR groups, LR groups, blood pressure signal analysis, cardiomyopathy patients, diastolic BP, dilated cardiomyopathy, frequency domain analysis, high-frequency bands, ischemic cardiomyopathy, left ventricular ejection fraction, low-frequency bands, myocardium disorders, patient characterization, power spectral density curve, standard deviation, statistical significant differences, systolic BP, systolic slope, systolic time intervals, time domain analysis, ventricular ejection fraction, Abstracts, Databases, Parameter extraction, Telecommunication standards, Time-frequency analysis

One of the most challenging problems in intensive care is still the process of discontinuing mechanical ventilation, called weaning process. Both an unnecessary delay in the discontinuation process and a weaning trial that is undertaken too early are undesirable. In this study, we analyzed respiratory pattern variability using the respiratory volume signal of patients submitted to two different levels of pressure support ventilation (PSV), prior to withdrawal of the mechanical ventilation. In order to characterize the respiratory pattern, we analyzed the following time series: inspiratory time, expiratory time, breath duration, tidal volume, fractional inspiratory time, mean inspiratory flow and rapid shallow breathing. Several autoregressive modeling techniques were considered: autoregressive models (AR), autoregressive moving average models (ARMA), and autoregressive models with exogenous input (ARX). The following classification methods were used: logistic regression (LR), linear discriminant analysis (LDA) and support vector machines (SVM). 20 patients on weaning trials from mechanical ventilation were analyzed. The patients, submitted to two different levels of PSV, were classified as low PSV and high PSV. The variability of the respiratory patterns of these patients were analyzed. The most relevant parameters were extracted using the classifiers methods. The best results were obtained with the interquartile range and the final prediction errors of AR, ARMA and ARX models. An accuracy of 95% (93% sensitivity and 90% specificity) was obtained when the interquartile range of the expiratory time and the breath duration time series were used a LDA model. All classifiers showed a good compromise between sensitivity and specificity.

JTD Keywords: autoregressive moving average processes, feature extraction, medical signal processing, patient care, pneumodynamics, signal classification, support vector machines, time series, ARX, autoregressive modeling techniques, autoregressive models with exogenous input, autoregressive moving average model, breath duration time series, classification method, classifier method, discontinuing mechanical ventilation, expiratory time, feature extraction, final prediction errors, fractional inspiratory time, intensive care, interquartile range, linear discriminant analysis, logistic regression analysis, mean inspiratory flow, patient respiratory volume signal, pressure support level, pressure support ventilation, rapid shallow breathing, respiratory pattern variability characterization, support vector machines, tidal volume, weaning trial, Analytical models, Autoregressive processes, Biological system modeling, Estimation, Support vector machines, Time series analysis, Ventilation

This work investigates the very low frequency (VLF) modulation of QRS slopes and heart rate variability (HRV). Electrocardiogram (ECG) and respiratory flow signal were acquired from patients with dilated cardiomyopathy and ischemic cardiomyopathy. HRV as well as the upward QRS slope (I_US) and downward QRS slope (I_DS) were extracted from the ECG. The relation between HRV and QRS slopes in the VLF band was measured using ordinary coherence in 5-minute segments. Partial coherence was then used to remove the influence that respiration simultaneously exerts on HRV and QRS slopes. A statistical threshold was determined, below which coherence values were considered not to represent a linear relation. 7 out of 276 segments belonging to 5 out of 29 patients for I_US and 10 segments belonging to 5 patients for I_DS presented a VLF modulation in QRS slopes, HRV and respiration. In these segments spectral coherence was statistically significant, while partial coherence decreased, indicating that the coupling HRV and QRS slopes was related to respiration. 4 segments had a partial coherence value below the threshold for I_US, 3 segments for I_DS. The rest of the segments also presented a notable decrease in partial coherence, but still above the threshold, which means that other non-linearly effects may also affect this modulation.

JTD Keywords: diseases, electrocardiography, feature extraction, medical signal processing, pneumodynamics, statistical analysis, ECG, QRS slopes, cardiomyopathy patients, dilated cardiomyopathy, electrocardiogram, feature extraction, heart rate variability, ischemic cardiomyopathy, ordinary coherence, partial coherence value, respiration, respiratory flow signal acquisition, spectral coherence, statistical threshold, time 5 min, very low frequency modulation, Coherence, Educational institutions, Electrocardiography, Frequency modulation, Heart rate variability

The process of weaning from mechanical ventilation is one of the challenges in intensive care units. 66 patients under extubation process (T-tube test) were studied: 33 patients with successful trials and 33 patients who failed to maintain spontaneous breathing and were reconnected. Each patient was characterized using 7 time series from respiratory signals, and for each serie was evaluated the discrete wavelet transform. It trains a neural network for discriminating between patients from the two groups.

JTD Keywords: discrete wavelet transforms, neural nets, patient treatment, pneumodynamics, time series, ventilation, T-tube test, discrete wavelet transform, extubation process, intensive care units, mechanical ventilation, moment of disconnection, neural network, patients, respiratory signals, spontaneous breathing, time series, weaning, Mechanical Ventilation, Neural Networks, Time series from respiratory signals, Wavelet Transform

Rat model of Obstructive Sleep Apnea (OSA) is a realistic approach for studying physiological mechanisms involved in sleep. Rats are usually anesthetized and autonomic nervous system (ANS) could be blocked. This study aimed to assess the effect of anesthesia on ANS activity during OSA episodes. Seven male Sprague-Dawley rats were anesthetized intraperitoneally with urethane (1g/kg). The experiments were conducted applying airway obstructions, simulating 15s-apnea episodes for 15 minutes. Five signals were acquired: respiratory pressure and flow, SaO2, ECG and photoplethysmography (PPG). In total, 210 apnea episodes were studied. Normalized power spectrum of Pulse Rate Variability (PRV) was analyzed in the Low Frequency (LF) and High Frequency (HF) bands, for each episode in consecutive 15s intervals (before, during and after the apnea). All episodes showed changes in respiratory flow and SaO₂ signal. Conversely, decreases in the amplitude fluctuations of PPG (DAP) were not observed. Normalized LF presented extremely low values during breathing (median=7,67%), suggesting inhibition of sympathetic system due to anesthetic effect. Subtle increases of LF were observed during apnea. HRV and PPG analysis during apnea could be an indirect tool to assess the effect and deep of anesthesia.

JTD Keywords: electrocardiography, fluctuations, medical disorders, medical signal detection, medical signal processing, neurophysiology, photoplethysmography, pneumodynamics, sleep, ECG, SaO₂ flow, SaO₂ signal, airway obstructions, amplitude fluctuations, anesthesia effects, anesthetized nervous system, autonomic evaluation, autonomic nervous system, breathing, heart rate variability, high-frequency bands, low-frequency bands, male Sprague-Dawley rats, normalized power spectrum, obstructive sleep apnea, photoplethysmography, physiological mechanisms, pulse rate variability, rat model, respiratory flow, respiratory pressure, signal acquisition, sympathetic system inhibition, time 15 min, time 15 s, Abstracts, Atmospheric modeling, Computational modeling, Electrocardiography, Rats, Resonant frequency

Some of the most common clinical problems in elderly patients are related to diseases of the cardiac and respiratory systems. Elderly patients often have altered breathing patterns, such as periodic breathing (PB) and Cheyne-Stokes respiration (CSR), which may coincide with chronic heart failure. In this study, we used the envelope of the respiratory flow signal to characterize respiratory patterns in elderly patients. To study different breathing patterns in the same patient, the signals were segmented into windows of 5 min. In oscillatory breathing patterns, frequency and time-frequency parameters that characterize the discriminant band were evaluated to identify periodic and non-periodic breathing (PB and nPB). In order to evaluate the accuracy of this characterization, we used a feature selection process, followed by linear discriminant analysis. 22 elderly patients (7 patients with PB and 15 with nPB pattern) were studied. The following classification problems were analyzed: patients with either PB (with and without apnea) or nPB patterns, and patients with CSR versus PB, CSR versus nPB and PB versus nPB patterns. The results showed 81.8% accuracy in the comparisons of nPB and PB patients, using the power of the modulation peak. For the segmented signal, the power of the modulation peak, the frequency variability and the interquartile ranges provided the best results with 84.8% accuracy, for classifying nPB and PB patients.

JTD Keywords: cardiovascular system, diseases, feature extraction, geriatrics, medical signal processing, oscillations, pneumodynamics, signal classification, time-frequency analysis, Cheyne-Stokes respiration, apnea, cardiac systems, chronic heart failure, classification problems, discriminant band, diseases, elderly patients, feature selection process, frequency variability, interquartile ranges, linear discriminant analysis, nonperiodic breathing, oscillatory breathing pattern, periodic breathing, respiratory How signal, respiratory systems, signal segmentation, time 5 min, time-frequency parameters, Accuracy, Aging, Frequency modulation, Heart, Senior citizens, Time-frequency analysis

Sleep apnea-hypopnea syndrome (SAHS) is a serious sleep disorder, and snoring is one of its earliest and most consistent symptoms. We propose a new methodology for identifying two distinct types of snores: the so-called non-regular and regular snores. Respiratory sound signals from 34 subjects with different ranges of Apnea-Hypopnea Index (AHI = 3.7-109.9 h -1) were acquired. A total number of 74,439 snores were examined. The time interval between regular snores in short segments of the all night recordings was analyzed. Severe SAHS subjects show a shorter time interval between regular snores (p = 0.0036, AHI cp: 30 h -1) and less dispersion on the time interval features during all sleep. Conversely, lower intra-segment variability (p = 0.006, AHI cp: 30 h -1) is seen for less severe SAHS subjects. Features derived from the analysis of time interval between regular snores achieved classification accuracies of 88.2 % (with 90 % sensitivity, 75 % specificity) and 94.1 % (with 94.4 % sensitivity, 93.8 % specificity) for AHI cut-points of severity of 5 and 30 h -1, respectively. The features proved to be reliable predictors of the subjects' SAHS severity. Our proposed method, the analysis of time interval between snores, provides promising results and puts forward a valuable aid for the early screening of subjects suspected of having SAHS.

JTD Keywords: Sleep apnea, Snore sounds, Snore time interval

In this paper, we present a new Multi-Agent System for reduction of the bullwhip effect in fuzzy supply chains. First, we show that a supply chain that uses an optimal ordering policy without data sharing among echelons still suffers from the bullwhip effect. Then, we propose the multi-agent solution to manage and reduce the bullwhip effect. The proposed multi-agent system includes four different types of agents in which each agent has its own list of actions. The proposed Multi-agent System applies a new Tabu Search algorithm for fuzzy rule generation, and a new data filtering algorithm for extraction of the bullwhip-free data from supply chain data warehouse. We validate the multi-agent system under different conditions and discuss how the system responds to different factors. The results show that the proposed multi-agent system reduces the bullwhip effect significantly in a rational time.

JTD Keywords: Bullwhip effect, Bullwhip-free data, Decentralized decision making, Fuzzy rule base, Fuzzy supply chain, Fuzzy time series, Multi-agent system, Supply chain management

Robot-assisted rehabilitation therapies usually focus on physical aspects rather than on cognitive factors. However, cognitive aspects such as attention, motivation, and engagement play a critical role in motor learning and thus influence the long-term success of rehabilitation programs. This paper studies motor-related EEG activity during the execution of robot-assisted passive movements of the upper limb, while participants either: i) focused attention exclusively on the task; or ii) simultaneously performed another task. Six healthy subjects participated in the study and results showed lower desynchronization during passive movements with another task simultaneously being carried out (compared to passive movements with exclusive attention on the task). In addition, it was proved the feasibility to distinguish between the two conditions.

JTD Keywords: Electrodes, Electroencephalography, Induction motors, Medical treatment, Robot sensing systems, Time frequency analysis, Biomechanics, Cognition, Electroencephalography, Medical robotics, Medical signal detection, Medical signal processing, Patient rehabilitation, Attention, Cognitive aspects, Desynchronization, Engagement, Motivation, Motor learning, Motor task, Motor-related EEG activity, Physical aspects, Robot-assisted passive movement detection, Robot-assisted rehabilitation therapies, Upper limb

In this paper, the problem of time alignment is revisited by adopting an ensemble-based approach with all signals jointly aligned. It is shown that the maximization of an eigenvalue ratio is synonymous to maximizing the signal-to-jitter-and-noise ratio. Since optimization of this criterion is extremely time consuming, a relaxed optimization procedure is introduced which converges much more quickly. Using simulations based on respiratory flow signals, the results suggest that the time delay error variance of the new method is much lower than that obtained with the well-known Woody’s method.

JTD Keywords: Time alignment, Signal ensemble, Subsample precision, Eigenvalue decomposition

Weaning trials process of patients in intensive care units is a complex clinical procedure. 153 patients under extubation process (T-tube test) were studied: 94 patients with successful trials (group S), 38 patients who failed to maintain spontaneous breathing and were reconnected (group F), and 21 patients with successful test but that had to be reintubated before 48 hours (group R). The respiratory pattern of each patient was characterized through the following time series: inspiratory time (T_I), expiratory time (T_E), breathing cycle duration (T_Tot), tidal volume (V_T), inspiratory fraction (T_I/T_Tot), half inspired flow (V_T/T_I), and rapid shallow index (f/V_T), where f is respiratory rate. Using techniques as autoregressive models (AR), autoregressive moving average models (ARMA) and autoregressive models with exogenous input (ARX), the most relevant parameters of the respiratory pattern were obtained. We proposed the evaluation of these parameters using classifiers as logistic regression (LR), linear discriminant analysis (LDA), support vector machines (SVM) and classification and regression tree (CART) to discriminate between patients from groups S, F and R. An accuracy of 93% (98% sensitivity and 82% specificity) has been obtained using CART classification.

JTD Keywords: Accuracy, Indexes, Logistics, Regression tree analysis, Support vector machines, Time series analysis, Autoregressive moving average processes, Medical signal processing, Pattern classification, Pneumodynamics, Regression analysis, Sensitivity, Signal classification, Support vector machines, Time series, SVM, T-tube testing, Autoregressive models-with-exogenous input, Autoregressive moving average models, Breathing cycle duration, Classification-and-regression tree, Expiratory time, Extubation process, Half inspired flow, Inspiratory fraction, Inspiratory time, Intensive care units, Linear discriminant analysis, Logistic regression, Rapid shallow index, Respiratory pattern parameter performance, Sensitivity, Spontaneous breathing, Support vector machines, Tidal volume, Time 48 hr, Time series, Weaning process classifiers

Background: Epidermal Growth Factor (EGF) is a key regulatory growth factor activating many processes relevant to normal development and disease, affecting cell proliferation and survival. Here we use a combined approach to study the EGF dependent transcriptome of HeLa cells by using multiple long oligonucleotide based microarray platforms (from Agilent, Operon, and Illumina) in combination with digital gene expression profiling (DGE) with the Illumina Genome Analyzer. Results: By applying a procedure for cross-platform data meta-analysis based on RankProd and GlobalAncova tests, we establish a well validated gene set with transcript levels altered after EGF treatment. We use this robust gene list to build higher order networks of gene interaction by interconnecting associated networks, supporting and extending the important role of the EGF signaling pathway in cancer. In addition, we find an entirely new set of genes previously unrelated to the currently accepted EGF associated cellular functions. Conclusions: We propose that the use of global genomic cross-validation derived from high content technologies (microarrays or deep sequencing) can be used to generate more reliable datasets. This approach should help to improve the confidence of downstream in silico functional inference analyses based on high content data.

JTD Keywords: Gene-expression measurements, Quality-control maqc, Cancer-cell-lines, Real-time pcr, Oligonucleotide microarrays, Phosphorylation dynamics, In-vivo, Networks, Signal, Technologies

There is an increasing level of interest in non-tuberculous mycobacteria (NTM) due to the increasing reported rates of diseases caused by them. Although it is well known that NTM are widely distributed in the environment it is necessary to identify its reservoirs to prevent possible infections. In this study, we aimed to investigate the occurrence and levels of NTM in cooling towers to provide evidences for considering these settings as possible sources of respiratory infections. In the current study, we detected and quantified the presence of NTM by means of a rapid method in water samples taken from 53 cooling towers of an urban area (Barcelona, Spain). A genus-specific quantitative PCR (Q-PCR) assay with a quantification limit (QL) of 500 cells l(-1) was used. 56% (30) of samples were positive with a concentration range from 4.6 x 10(3) to 1.79 x 10(6) cells l(-1). In some cases (9/30), samples were positive but with levels below the QL. The colonization rate confirmed that cooling towers could be considered as a potential reservoir for NTM. This study also evaluated Q-PCR as a useful method to detect and quantify NTM in samples coming from environmental sources.

JTD Keywords: Real-time PCR, Disease, Identification, Tuberculosis, Pathogens, Waters

Mechanical ventilators are used to provide life support in patients with respiratory failure. Assessing autonomic control during the ventilator weaning provides information about physiopathological imbalances. Autonomic parameters can be derived and used to predict success in discontinuing from the mechanical support. Time-frequency analysis is used to derive cardiac and respiratory parameters, as well as their evolution in time, during ventilator weaning in 130 patients. Statistically significant differences have been observed in autonomic parameters between patients who are considered ready for spontaneous breathing and patients who are not. A classification based on respiratory frequency, heart rate and heart rate variability spectral components has been proposed and has been able to correctly classify more than 80% of the cases.

JTD Keywords: Automatic Data Processing, Databases, Factual, Electrocardiography, Humans, Models, Statistical, Respiration, Respiration, Artificial, Respiratory Insufficiency, Respiratory Mechanics, Respiratory Muscles, Signal Processing, Computer-Assisted, Time Factors, Ventilator Weaning, Ventilators, Mechanical, Work of Breathing