Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática

Several studies have documented the significant impact of methodological choices in microbiome analyses. The myriad of methodological options available complicate the replication of results and generally limit the comparability of findings between independent studies that use differing techniques and measurement pipelines. Here we describe the Mosaic Standards Challenge (MSC), an international interlaboratory study designed to assess the impact of methodological variables on the results. The MSC did not prescribe methods but rather asked participating labs to analyze 7 shared reference samples (5 × human stool samples and 2 × mock communities) using their standard laboratory methods. To capture the array of methodological variables, each participating lab completed a metadata reporting sheet that included 100 different questions regarding the details of their protocol. The goal of this study was to survey the methodological landscape for microbiome metagenomic sequencing (MGS) analyses and the impact of methodological decisions on metagenomic sequencing results. A total of 44 labs participated in the MSC by submitting results (16S or WGS) along with accompanying metadata; thirty 16S rRNA gene amplicon datasets and 14 WGS datasets were collected. The inclusion of two types of reference materials (human stool and mock communities) enabled analysis of both MGS measurement variability between different protocols using the biologically-relevant stool samples, and MGS bias with respect to ground truth values using the DNA mixtures. Owing to the compositional nature of MGS measurements, analyses were conducted on the ratio of Firmicutes: Bacteroidetes allowing us to directly apply common statistical methods. The resulting analysis demonstrated that protocol choices have significant effects, including both bias of the MGS measurement associated with a particular methodological choices, as well as effects on measurement robustness as observed through the spread of results between labs making similar methodological choices. In the analysis of the DNA mock communities, MGS measurement bias was observed even when there was general consensus among the participating laboratories. This study was the result of a collaborative effort that included academic, commercial, and government labs. In addition to highlighting the impact of different methodological decisions on MGS result comparability, this work also provides insights for consideration in future microbiome measurement study design.

This letter deals with the problem of the estimation of the instantaneous frequencies of the modes of multicomponent signals from their linear time-frequency representations. In most approaches, such an estimation consists of extracting the ridges associated with each mode in the time-frequency plane. A major issue associated with these techniques is that ridge detection relies on some ad-hoc parameters which essentially bound the modulation of the studied modes and put some constraints on the type of filter used in the time-frequency representation. In this paper, we alternatively propose a novel fully adaptive approach for ridge detection whose relevance is shown throughout numerical simulations.

High frequency electrical stimulation (HFS) of the skin induces increased pinprick sensitivity in the surrounding unconditioned skin. The aim of the present study was to investigate the contribution of A-fiber nociceptors to this increased pinprick sensitivity. For this we assessed if the perception and brain responses elicited by low-intensity intra-epidermal electrical stimulation (IES), a method preferentially activating Aδ-fiber nociceptors, are increased in the area of HFS-induced increased pinprick sensitivity. HFS was delivered to one of the two forearms of seventeen healthy volunteers. Mechanical pinprick stimulation and IES were delivered at both arms before HFS (T0), 20 minutes after HFS (T1) and 45 minutes after HFS (T2). In all participants, HFS induced an increase in pinprick perception at the HFS-treated arm, adjacent to the site of HFS. This increase was significant at both T1 and T2. HFS did not affect the percept elicited by IES, but did enhance the magnitude of the N2 wave of IES-evoked brain potentials, both at T1 and at T2. Our results show that HFS induces a long-lasting enhancement of the N2 wave elicited by IES in the area of secondary hyperalgesia, indicating that HFS enhances the responsiveness of the central nervous system to nociceptive A-fiber input. However, we found no evidence that HFS affects the perception elicited by IES, which may suggest that the population of nociceptors that mediate the perception elicited by IES do not contribute to HFS-induced increased pinprick sensitivity.

The transfer entropy and the transfer entropy rate are closely related concepts that measure information exchange between two dynamical systems. These measures allow us to study linear and nonlinear causality relations and can be estimated through the use of different methodologies. However, some of them assume a data model and/or are computationally expensive. This article depicts a methodology to estimate the transfer entropy rate between two systems through the Lempel-Ziv complexity. This methodology offers a set of advantages: It estimates the transfer entropy rate from two single discrete series of measures, it is not computationally expensive, and it does not assume any data model. The simulation results over three different unidirectional coupled dynamical systems suggest that this methodology can be used to assess the direction and strength of the information flow between systems. Moreover, it provides good estimations for short-length time series.

The most important index of obstructive sleep apnea/hypopnea syndrome (OSAHS) is the apnea/hyponea index (AHI). The AHI is the number of apnea/hypopnea events per hour of sleep. Algorithms for the screening of OSAHS from pulse oximetry estimate an approximation to AHI counting the desaturation events without consider the sleep stage of the patient. This paper presents an automatic system to determine if a patient is awake or asleep using heart rate (HR) signals provided by pulse oximetry. In this study, 70 features are estimated using entropy and complexity measures, frequency domain and time-scale domain methods, and classical statistics. The dimension of feature space is reduced from 70 to 40 using three different schemes based on forward feature selection with support vector machine and feature importance with random forest. The algorithms were designed, trained and tested with 5000 patients from the Sleep Heart Health Study database. In the test stage, 10-fold cross validation method was applied obtaining performances up to 85.2% accuracy, 88.3% specificity, 79.0% sensitivity, 67.0% positive predictive value, and 91.3% negative predictive value. The results are encouraging, showing the possibility of using HR signals obtained from the same oximeter to determine the sleep stage of the patient, and thus potentially improving the estimation of AHI based on only pulse oximetry.

This paper addresses the problem of estimating the instantaneous frequency (IF) and amplitude of the modes composing a non-stationary multi-component signal in the presence of noise. A novel observation model for the signal spectrogram is developed within a Bayesian framework to handle intricate configurations involving noise or overlapping components. The model parameters are estimated using a stochastic variant of the Expectation-Maximization algorithm, bypassing the computationally expensive joint parameter estimation from the posterior distribution. We then design an algorithm for instantaneous amplitude and frequency estimation that accounts for overlap and amplitude variations of the components. To assess the performance of the proposed method, we conduct experiments on both real-world and simulated signals, involving separated or crossing modes. The benefits of our method in terms of efficiency compared with several state-of-the art techniques appear to be significant in that latter case, but also when the amplitude of the components are varying across time.

Voice jitter is defined as a random perturbation of the glottal cycle duration which can be useful for voice parametrization and that usually depends on finding fiducial points in this signal. In this paper, a novel application of the Fourier-based high-order synchrosqueezing (FSSTN) operators on voice signals is introduced for voice jitter estimation without period-segmentation. To this end, an innovative interpretation of the relative jitter formula in terms of the total variation of the sequence of periods is proposed. This allows us to derive an algorithm for jitter estimation that uses the (continuous) instantaneous fundamental frequency of the signal and its first derivative (chirp rate) which can be obtained, respectively, from the local complex frequency and the local complex modulation FSSTN operators. Numerical experiments using synthetic signals with known true jitter show that this novel approach yields similar results to other state-of-the-art method, PRAAT, for true jitter within the range [0.2%, 1.2%], and that it outperforms PRAAT for true jitter values in the range [1%, 15%]. The here proposed method seems a promising tool for voice jitter estimation and constitutes a novel application of the high-order synchrosqueezing operators for voice signals with potential impact on jitter modeling and on the clinical field.

Phasic pain stimuli are inhibited when they are applied concomitantly with a conditioning tonic stimulus at another body location (heterotopic noxious conditioning stimulation, HNCS). While the effects of HNCS are thought to rely on a spino-bulbo-spinal mechanism in animals (termed diffuse noxious inhibitory controls, DNIC), the underlying neurophysiology in humans may involve other pathways. In this study, we investigated the role of concomitant supraspinal mechanisms during HNCS by presenting auditory stimuli during a conditioning tonic painful stimulus (the cold pressor test, CPT). Considering that auditory stimuli are not conveyed through the spinal cord, any changes in brain responses to auditory stimuli during HNCS can be ascribed entirely to supraspinal mechanisms. Electroencephalography (EEG) was recorded during HNCS, and auditory stimuli were administered in three blocks, before, during and after HNCS. Nociceptive withdrawal reflexes (NWRs) were recorded at the same time points to investigate spinal processing. Our results showed that AEPs were significantly reduced during HNCS. Moreover, the amplitude of the NWR was significantly diminished during HNCS in most participants. Given that spinal and supraspinal mechanisms operate concomitantly during HNCS, the possibility of isolating their individual contributions in humans is questionable. We conclude that the net effects of HCNS are not independent from attentional/cognitive influences.

Building accurate movement decoding models from brain signals is crucial for many biomedical applications. Predicting specific movement features, such as speed and force, before movement execution may provide additional useful information at the expense of increasing the complexity of the decoding problem. Recent attempts to predict movement speed and force from the electroencephalogram (EEG) achieved classification accuracies at or slightly above chance levels, highlighting the need for more accurate prediction strategies. Thus, the aims of this study were to accurately predict hand movement speed and force from single-trial EEG signals and to decode neurophysiological information of motor preparation from the prediction strategies. To these ends, a decoding model based on convolutional neural networks (ConvNets) was implemented and compared against other state-of-the-art prediction strategies, such as support vector machines and decision trees. ConvNets outperformed the other prediction strategies, achieving an overall accuracy of 84% in the classification of two different levels of speed and force (four-class classification) from pre-movement single-trial EEG (100 ms and up to 1,600 ms prior to movement execution). Furthermore, an analysis of the ConvNet architectures suggests that the network performs a complex spatiotemporal integration of EEG data to optimize classification accuracy. These results show that movement speed and force can be accurately predicted from single-trial EEG, and that the prediction strategies may provide useful neurophysiological information about motor preparation.

In this paper, our goal is to investigate local chirp rate (CR) estimation in noisy multicomponent signals. The focus is put on improving a specific type of local CR estimator used in the second order synchrosqueezing transform, which proves to be very inaccurate in the presence of noise. More precisely, the noise creates spurious oscillations in the local CR estimate, and we first put the emphasis on the terms responsible for these oscillations. Then we propose a novel technique to filter them out, resulting in a much more accurate local CR estimator. We finally show how to use the latter to improve mode reconstruction and investigate in what way the new CR estimator is useful in the context of voice signals.

There is considerable scientific evidence revealing a decrease in pollinating insects in different ecosystems around the world. In this context, agricultural intensification and the use of phytosanitary products are likely the main causes. This problem is common to many pollinators but of particular ecosystemic, economic and bromatological significance for honey bees (Apis mellifera) since their presence in these landscapes is mainly due to the proximity of apiaries for human food production and because they are the most important biotic pollinators of agricultural crops. In this review, we present a synthesis of the results of several years of research on this topic, as well as potential solutions referenced in the bibliography that might help alleviate the effects of contamination on honey bees and their products. Additionally, we expose the possible limits of the real implementation of such solutions and conclude on the need to implement land-use planning strategies for agricultural systems. Without mitigating actions in the short term, the sustainability of agricultural ecosystems as bee-friendly habitats and the production of foods suitable for human consumption are uncertain.

In this paper, we define a new ridge detector that enables to localize strong interference in multicomponent signals in the time-frequency (TF) plane. Each mode of a multicomponent signal can usually be associated with a ridge in the TF plane, but this is no longer the case when strong interferences occur in the signal. The new ridge detector we propose is thus designed to determine when such situations happen in the TF plane. We show that this knowledge helps to determine an appropriate window length in the definition of the spectrogram, as well as the nature of the strong interference detected. An application of the proposed approach to voice signals concludes the paper.

Abstract Background There is increasing evidence for oxytocin as a neurotransmitter in spinal nociceptive processes. Hypothalamic oxytocinergic neurons project to the spinal dorsal horn, where they activate GABA‐ergic inhibitory interneurons. The present study tested whether the long‐acting oxytocin‐analogue carbetocin has anti‐nociceptive effects in multi‐modal experimental pain in humans. Methods Twenty‐five male volunteers received carbetocin 100 mcg and placebo (0.9% NaCl) on two different sessions in a randomized, double‐blinded, cross‐over design. Multi‐modal quantitative sensory testing (QST) including a model of capsaicin‐induced hyperalgesia and allodynia were performed at baseline and at 10, 60 and 120 min after drug administration. QST data were analysed using mixed linear and logistic regression models. Carbetocin plasma concentrations and oxytocin receptor genotypes were quantified and assessed in an exploratory fashion. Results An anti‐nociceptive effect of carbetocin was observed on intramuscular electrical temporal summation (estimated difference: 1.26 mA, 95% CI 1.01 to 1.56 mA, p = .04) and single‐stimulus electrical pain thresholds (estimated difference: 1.21 mA, 95% CI 1.0 to 1.47 mA, p = .05). Furthermore, the area of capsaicin‐induced allodynia was reduced after carbetocin compared to placebo (estimated difference: −6.5 cm 2 , 95% CI −9.8 to −3.2 cm 2 , p < .001). Conclusions This study provides evidence of an anti‐nociceptive effect of carbetocin on experimental pain in humans. Significance This study provides evidence of the anti‐nociceptive effect of intravenous administration of the oxytocin agonist carbetocin in healthy male volunteers.

fertilization techniques, only one-third of these procedures are successful. New lab-on-a-chip systems that focus on spermatozoa selection require a better understanding of sperm behavior under ultra-confined conditions in order to improve outcomes. Experimental studies combined with models and simulations allow the evaluation of the efficiency of different lab-on-a-chip devices during the design process. In this work, we provide experimental evidence of the dynamics of sperm interacting with a lateral wall in a shallow chamber. We observe a decrease in average sperm velocity during initial wall interaction and partial recovery after the alignment of the trajectory of the cell. To describe this phenomenon, we propose a simple model for the sperm alignment process with a single free parameter. By incorporating experimental motility characterization into the model, we achieve an accurate description of the average velocity behavior of the sperm population close to walls. These results will contribute to the design of more efficient lab-on-a-chip devices for the treatment of human infertility.

We study numerically the appearance and number of axial vortices in the outlets of X-shaped junctions of two perpendicular channels of rectangular sections with facing inlets. We explore the effect of the aspect ratio of the cross section, AR, on the number of vortices created at the center of the junction. Direct numerical simulations (DNSs) performed for different values of the Reynolds number Re and AR demonstrate that vortices with their axis parallel to the outlets, referred to as axial vortices, appear above critical Reynolds numbers Rec. As AR increases from 1 to 11, the number of vortices observed increases from 1 to 4, independently of Re. For AR = 1, the single axial vortex induces an interpenetration of the inlet fluids in the whole section; instead, for larger AR’s for which more vortices appear, the two inlet fluids remain largely segregated in bands, except close to the vortices. The linear stability analysis demonstrates that only one leading eigenmode is unstable for a given set of values of AR and Re. This mode provides a simplified model of the flow field, reproducing its key features such as the number of vortices and their distance. Its determination with this method requires a much smaller computational load than the DNS. This approach is shown to allow one to determine quickly and precisely the critical Reynolds number Rec and the sensitivity function S, which characterizes the influence of variations of the base flow on the unstable one.

Abstract Building accurate movement decoding models from brain signals is crucial for many biomedical applications. Decoding specific movement features, such as speed and force, may provide additional useful information at the expense of increasing the complexity of the decoding problem. Recent attempts to predict movement speed and force from the electroencephalogram (EEG) achieved classification accuracy levels not better than chance, stressing the demand for more accurate prediction strategies. Thus, the aim of this study was to improve the prediction accuracy of hand movement speed and force from single-trial EEG signals recorded from healthy volunteers. A strategy based on convolutional neural networks (ConvNets) was tested, since it has previously shown good performance in the classification of EEG signals. ConvNets achieved an overall accuracy of 84% in the classification of two different levels of speed and force (4-class classification) from single-trial EEG. These results represent a substantial improvement over previously reported results, suggesting that hand movement speed and force can be accurately predicted from single-trial EEG.

Abstract Understanding species diversity and delineating their boundaries are crucial for effective management and conservation efforts. In the case of bats, species identification holds particular importance from an epidemiological standpoint. The genus Molossus (Chiroptera: Molossidae) encompasses 15 species distributed across the Neotropics, ranging from the southeastern United States to Argentina. This genus exhibits two contrasting patterns of variation: some species are cryptic, while others are morphologically distinct yet genetically similar. This study explores the diversity of Molossus in Argentina through a molecular phylogenetic approach. We analyzed sequences from three molecular markers (cyt b , COI, and FGB) along with morphology data obtained from a sample of 64 individuals. Uni- and multivariate analyses of external and cranial measurements were conducted, alongside comparisons of external and cranial characteristics among species. Based on molecular and morphological differences, we describe a new species within the Molossus genus. This newly discovered species exhibits a broad distribution spanning the Paraná River basin across three distinct ecoregions. It is noteworthy that this species is pseudo-cryptic with respect to similar-sized species such as M. molossus and M. melini . Additionally, it is important to mention that all species in Argentina have overlapping distribution ranges. In summary, this study provides valuable insights into the diversity and distribution of Molossus bats in Argentina, employing molecular and morphological analyses. The discovery of a new species underscores the ongoing importance of comprehensive research efforts in understanding and conserving bat populations in the Neotropics.

Abstract Background Several studies have documented the significant impact of methodological choices in microbiome analyses. The myriad of methodological options available complicate the replication of results and generally limit the comparability of findings between independent studies that use differing techniques and measurement pipelines. Here we describe the Mosaic Standards Challenge (MSC), an international interlaboratory study designed to assess the impact of methodological variables on the results. The MSC did not prescribe methods but rather asked participating labs to analyze 7 shared reference samples (5x human stool samples and 2x mock communities) using their standard laboratory methods. To capture the array of methodological variables, each participating lab completed a metadata reporting sheet that included 100 different questions regarding the details of their protocol. The goal of this study was to survey the methodological landscape for microbiome metagenomic sequencing (MGS) analyses and the impact of methodological decisions on metagenomic sequencing results. Results A total of 44 labs participated in the MSC by submitting results (16S or WGS) along with accompanying metadata; thirty 16S rRNA gene amplicon datasets and 14 WGS datasets were collected. The inclusion of two types of reference materials (human stool and mock communities) enabled analysis of both MGS measurement variability between different protocols using the biologically-relevant stool samples, and MGS bias with respect to ground truth values using the DNA mixtures. Owing to the compositional nature of MGS measurements, analyses were conducted on the ratio of Firmicutes: Bacteroidetes allowing us to directly apply common statistical methods. The resulting analysis demonstrated that protocol choices have significant effects, including both bias of the MGS measurement associated with a particular methodological choices, as well as effects on measurement robustness as observed through the spread of results between labs making similar methodological choices. In the analysis of the DNA mock communities, MGS measurement bias was observed even when there was general consensus among the participating laboratories. Conclusion This study was the result of a collaborative effort that included academic, commercial, and government labs. In addition to highlighting the impact of different methodological decisions on MGS result comparability, this work also provides insights for consideration in future microbiome measurement study design.

The use of Bayes Factors is becoming increasingly common in psychological sciences. Thus, it is important that researchers understand the logic behind the Bayes Factor in order to correctly interpret it, and the strengths of weaknesses of the Bayesian approach. As education for psychological scientists focuses on Frequentist statistics, resources are needed for researchers and students who want to learn more about this alternative approach. The aim of the current article is to provide such an overview to a psychological researcher. We cover the general logic behind Bayesian statistics, explain how the Bayes Factor is calculated, how to set the priors in popular software packages, to reflect the prior beliefs of the researcher, and finally provide a set of recommendations and caveats for interpreting Bayes Factors.

Reliability is a topic in health science in which a critical appraisal of the magnitudes of the measurements is often left aside to favour a formulaic analysis. Furthermore, the relationship between clinical relevance and reliability of measurements is often overlooked. In this context, the aim of the present article is to provide an overview of the design and analysis of reliability studies, the interpretation of the reliability of measurements and its relationship to clinical significance in the context of pain research and management. The article is divided in two sections: the first section contains a step-by-step guide with simple and straightforward recommendations for the design and analysis of a reliability study, with a relevant example involving a commonly used assessment measure in pain research. The second section provides deeper insight about the interpretation of the results of a reliability study and the association between the reliability of measurements and their experimental and clinical relevance. SIGNIFICANCE: Reliability studies quantify the measurement error in experimental or clinical setups and should be interpreted as a continuous outcome. The assessment of measurement error is useful to design and interpret future experimental studies and clinical interventions. Reliability and clinical relevance are inextricably linked, as measurement error should be considered in the interpretation of minimal detectable change and minimal clinically important differences.