A variant-dependent molecular clock with anomalous diffusion models SARS-CoV-2 evolution in humans.

The evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in humans has been monitored at an unprecedented level due to the public health crisis, yet the stochastic dynamics underlying such a process is dubious. Here, considering the number of acquired mutations as the displacement of the viral particle from the origin, we performed biostatistical analyses from numerous whole genome sequences on the basis of a time-dependent probabilistic mathematical model. We showed that a model with a constant variant-dependent evolution rate and nonlinear mutational variance with time (i.e., anomalous diffusion) explained the SARS-CoV-2 evolutionary motion in humans during the first 120 wk of the pandemic in the United Kingdom. In particular, we found subdiffusion patterns for the Primal, Alpha, and Omicron variants but a weak superdiffusion pattern for the Delta variant. Our findings indicate that non-Brownian evolutionary motions occur in nature, thereby providing insight for viral phylodynamics.

Quartic Kerr cavity combs: bright and dark solitons.

We theoretically investigate the dynamics, bifurcation structure, and stability of localized states in Kerr cavities driven at the pure fourth-order dispersion point. Both the normal and anomalous group velocity dispersion regimes are analyzed, highlighting the main differences from the standard second-order dispersion case. In the anomalous regime, single and multi-peak localized states exist and are stable over a much wider region of the parameter space. In the normal dispersion regime, stable narrow bright solitons exist. Some of our findings can be understood using a new, to the best of our knowledge, scenario reported here for the spatial eigenvalues, which imposes oscillatory tails to all localized states.

Reversible Self-Replication of Spatiotemporal Kerr Cavity Patterns.

We uncover a novel and robust phenomenon that causes the gradual self-replication of spatiotemporal Kerr cavity patterns in cylindrical microresonators. These patterns are inherently synchronized multifrequency combs. Under proper conditions, the axially localized nature of the patterns leads to a fundamental drift instability that induces transitions among patterns with a different number of rows. Self-replications, thus, result in the stepwise addition or removal of individual combs along the cylinder's axis. Transitions occur in a fully reversible and, consequently, deterministic way. The phenomenon puts forward a novel paradigm for Kerr frequency comb formation and reveals important insights into the physics of multidimensional nonlinear patterns.

Subgrouping Factors Influencing Migraine Intensity in Women: A Semi-automatic Methodology Based on Machine Learning and Information Geometry.

BACKGROUND: Migraine is a heterogeneous condition with multiple clinical manifestations. Machine learning algorithms permit the identification of population groups, providing analytical advantages over other modeling techniques. OBJECTIVE: The aim of this study was to analyze critical features that permit the differentiation of subgroups of patients with migraine according to the intensity and frequency of attacks by using machine learning algorithms. METHODS: Sixty-seven women with migraine participated. Clinical features of migraine, related disability (Migraine Disability Assessment Scale), anxiety/depressive levels (Hospital Anxiety and Depression Scale), anxiety state/trait levels (State-Trait Anxiety Inventory), and pressure pain thresholds (PPTs) over the temporalis, neck, second metacarpal, and tibialis anterior were collected. Physical examination included the flexion-rotation test, cervical range of cervical motion, forward head position while sitting and standing, passive accessory intervertebral movements (PAIVMs) with headache reproduction, and joint positioning sense error. Subgrouping was based on machine learning algorithms by using the nearest neighbors algorithm, multisource variability assessment, and random forest model. RESULTS: For migraine intensity, group 2 (women with a regular migraine headache intensity score of 7 on an 11-point Numeric Pain Rating Scale [where 0 = no pain and 10 = maximum pain]) were younger and had lower joint positioning sense error in cervical rotation, greater cervical mobility in rotation and flexion, lower flexion-rotation test scores, positive PAIVMs reproducing migraine, normal PPTs over the tibialis anterior, shorter migraine history, and lower cranio-vertebral angles while standing than the remaining migraine intensity subgroups. The most discriminative variable was the flexion-rotation test score of the symptomatic side. For migraine frequency, no model was able to identify differences between groups (ie, patients with episodic or chronic migraine). CONCLUSIONS: A subgroup of women with migraine who had common migraine intensity was identified with machine learning algorithms.

Feature Extraction and Similarity of Movement Detection during Sleep, Based on Higher Order Spectra and Entropy of the Actigraphy Signal: Results of the Hispanic Community Health Study/Study of Latinos.

The aim of this work was to develop a new unsupervised exploratory method of characterizing feature extraction and detecting similarity of movement during sleep through actigraphy signals. We here propose some algorithms, based on signal bispectrum and bispectral entropy, to determine the unique features of independent actigraphy signals. Experiments were carried out on 20 randomly chosen actigraphy samples of the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) database, with no information other than their aperiodicity. The Pearson correlation coefficient matrix and the histogram correlation matrix were computed to study the similarity of movements during sleep. The results obtained allowed us to explore the connections between certain sleep actigraphy patterns and certain pathologies.

Age slowing down in detection and visual discrimination under varying presentation times.

The reaction time has been described as a measure of perception, decision making, and other cognitive processes. The aim of this work is to examine age-related changes in executive functions in terms of demand load under varying presentation times. Two tasks were employed where a signal detection and a discrimination task were performed by young and older university students. Furthermore, a characterization of the response time distribution by an ex-Gaussian fit was carried out. The results indicated that the older participants were slower than the younger ones in signal detection and discrimination. Moreover, the differences between both processes for the older participants were higher, and they also showed a higher distribution average except for the lower and higher presentation time. The results suggest a general slowdown in both tasks for age under different presentation times, except for the cases where presentation times were lower and higher. Moreover, if these parameters are understood to be a reflection of executive functions, these findings are consistent with the common view that age-related cognitive deficits show a decline in this function.

Optimization of the protocols for the use of contrast agents in PET/CT studies. / Optimización de los protocolos y del uso de contrastes en tomografía computarizada de los equipos de tomografía por emisión de positrones.

The introduction of PET/CT scanners in clinical practice in 1998 has improved care for oncologic patients throughout the clinical pathway, from the initial diagnosis of disease through the evaluation of the response to treatment to screening for possible recurrence. The CT component of a PET/CT study is used to correct the attenuation of PET studies; CT also provides anatomic information about the distribution of the radiotracer. CT is especially useful in situations where PET alone can lead to false positives and false negatives, and CT thereby improves the diagnostic performance of PET. The use of intravenous or oral contrast agents and optimal CT protocols have improved the detection and characterization of lesions. However, there are circumstances in which the systematic use of contrast agents is not justified. The standard acquisition in PET/CT scanners is the whole body protocol, but this can lead to artifacts due to the position of patients and respiratory movements between the CT and PET acquisitions. This article discusses these aspects from a constructive perspective with the aim of maximizing the diagnostic potential of PET/CT and providing better care for patients.

18F-FDG positron emission tomography in oncology: main indications. / Tomografía por emisión de positrones con 18F-FDG en oncología: principales indicaciones.

The development of molecular and functional imaging with new imaging techniques such as computed tomography, magnetic resonance imaging, and positron emission tomography (PET) among others, has greatly improved the detection of tumors, tumor staging, and the detection of possible recurrences. Furthermore, the combination of these different imaging modalities and the continual development of radiotracers for PET have advanced our understanding and knowledge of the different pathophysiological processes in cancer, thereby helping to make treatment more efficacious, improving patients' quality of life, and increasing survival. PET is one of the imaging techniques that has attracted the most interest in recent years for its diagnostic capabilities. Its ability to anatomically locate pathologic foci of metabolic activity has revolutionized the detection and staging of many tumors, exponentially broadening its potential indications not only in oncology but also in other fields such as cardiology, neurology, and inflammatory and infectious diseases.

Transcriptome of early embryonic invasion at implantation sites in a murine model.

Successful implantation relies on the interaction between a competent embryo and a receptive endometrium. The aim of the present study was to investigate genes differentially expressed in early invasive embryonic tissue versus decidual tissue in mice. Samples were obtained from the ectoplacental cone, the immediately surrounding deciduas and from deciduas from interimplantation sites. Microarray analysis showed that 817 genes were differentially expressed between extra-embryonic tissue and the surrounding decidua and that 360 genes were differentially expressed between the different deciduas, with a high representation of developmental processes. Genes differentially expressed in the maternal compartment included chemokines, lipoproteins, growth factors and transcription factors, whereas the embryonic invasive tissue expressed genes commonly observed in invasive tumour-like processes. These results provide information about genes involved in early embryonic invasion and the control exerted by the surrounding decidua. This information may be useful to find targets involved in pathologies associated with implantation failure and early pregnancy loss.

Visualizing Academic Contributions to Achieving the Sustainable Development Goals through AI: The Case of Universitat Politècnica de València.

How are you contributing to SDGs and measuring sustainable improvements? AI solutions can help you to quantify it. This pilot experience shows the case of the university's scientific contributions.

[Neuroanatomical mapping of inhibitory attention and working memory with functional magnetic resonance imaging in healthy children]. / Mapeo neuroanatómico de atención inhibitoria y memoria de trabajo con resonancia magnética funcional en niños sanos.

INTRODUCTION: The objective is to produce an average brain activation mapping template in healthy children using functional magnetic resonance imaging (fMRI), with specific paradigms for activating inhibitory attention and working memory functions. SUBJECTS AND METHODS: A nutritional and neuropsychological evaluation was performed on 87 right-handed children. The inclusion criteria were met by 30 children (15 boys and 15 girls) between 9 and 11 years old, who were studied with fMRI in two inhibitory attention tests (Go/No Go), with food cues, a working memory test (Continuous Performance Test Identical Pairs) and measurement of anatomical volumes. These data were subsequently processed with the FSL-v5 program, with a threshold of p < 0.05 (cluster-wise). The brain areas activated were located using a standard Montreal Neurological Institute brain template and the Harvard-Oxford structural cortical atlas. RESULTS: The inhibitory attention tests showed activation frontal areas predominantly on the right, and the cingulate, parietal and occipital areas, with preponderance in occipital areas in the food cues test. In the Continuous Performance Test-Identical Pairs test, activation was obtained predominantly in the occipital, frontal and parietal areas. CONCLUSIONS: Brain activity mapping templates are obtained in healthy children with tests for inhibitory attention, food cues and working memory. The activation areas are mostly those reported in the literature. This provides baseline brain activation patterns for studying pathologies related to inhibitory attention, impulsivity and working memory.

TITLE: Mapeo neuroanatómico de atención inhibitoria y memoria de trabajo con resonancia magnética funcional en niños sanos.Introducción. El objetivo es lograr una plantilla de mapeo de activación cerebral promedio en niños sanos usando la resonancia magnética funcional (RMf), con paradigmas específicos para activar funciones de atención inhibitoria y de memoria de trabajo. Sujetos y métodos. Se realizó una evaluación nutricional y neuropsicológica a 87 niños diestros. Cumplieron los criterios de inclusión 30 (15 niños y 15 niñas) entre 9 y 11 años, a quienes se estudió con RMf con realización de dos pruebas de atención inhibitoria (Go/No Go), con letras e imágenes de alimentos, un test de memoria de trabajo (Test de Atención Continua-Pares Idénticos) y obtención de volúmenes anatómicos. Los datos posteriormente se procesaron con el programa FSL-v5 con un umbral de p < 0,05 (cluster-wise). Las áreas cerebrales activadas se localizaron utilizando una plantilla cerebral estándar del Montreal Neurological Institute y el atlas cortical estructural de Harvard-Oxford. Resultados. En las pruebas de atención inhibitoria hay activación en áreas frontales de predominio derecho, cíngulo, parietales y occipitales, con preponderancia en áreas occipitales en la prueba con alimentos. En la prueba Test de Atención Continua-Pares Idénticos se obtuvo activación de predominio en áreas occipitales, frontales y parietales. Conclusiones. Se obtienen plantillas de mapeo de actividad cerebral en niños sanos con test de atención inhibitoria, de alimentos y de memoria de trabajo. Las áreas de activación corresponden mayoritariamente a las descritas en la bibliografía. Esto nos permite tener patrones basales de activación cerebral para estudiar patologías relacionadas con la atención inhibitoria, la impulsividad y la memoria de trabajo.

N staging of lung cancer patients with PET/MRI using a three-segment model attenuation correction algorithm: initial experience.

OBJECTIVES: Evaluate the performance of PET/MRI at tissue interfaces with different attenuation values for detecting lymph node (LN) metastases and for accurately measuring maximum standardised uptake values (SUVmax) in lung cancer patients. MATERIALS AND METHOD: Eleven patients underwent PET/CT and PET/MRI for staging, restaging or follow-up of suspected or known lung cancer. Four experienced readers determined the N stage of the patients for each imaging method in a randomised blinded way. Concerning metastases, SUVmax of FDG-avid LNs were measured in PET/CT and PET/MRI in all patients. A standard of reference was created with a fifth experienced independent reader in combination with a chart review. Results were analysed to determine interobserver agreement, SUVmax correlation between CT and MRI (three-segment model) attenuation correction and diagnostic performance of the two techniques. RESULTS: Overall interobserver agreement was high (κ = 0.86) for PET/CT and substantial (κ = 0.70) for PET/MRI. SUVmax showed strong positive correlation (Spearman's correlation coefficient = 0.93, P < 0.001) between the two techniques. Diagnostic performance of PET/MRI was slightly inferior to that of PET/CT, without statistical significance (P > 0.05). CONCLUSIONS: PET/MRI using three-segment model attenuation correction for LN staging in lung cancer shows a strong parallel to PET/CT in terms of SUVmax, interobserver agreement and diagnostic performance. KEY POINTS: •F18-FDG PET/MRI shows similar performance to F18-FDG PET/CT in lung cancer N staging. •PET/MRI has substantial interobserver agreement in N staging. •A three-segment model attenuation correction is reliable for assessing the mediastinum.

Gramian angular fields for leveraging pretrained computer vision models with anomalous diffusion trajectories.

Anomalous diffusion is present at all scales, from atomic to large ones. Some exemplary systems are ultracold atoms, telomeres in the nucleus of cells, moisture transport in cement-based materials, arthropods' free movement, and birds' migration patterns. The characterization of the diffusion gives critical information about the dynamics of these systems and provides an interdisciplinary framework with which to study diffusive transport. Thus, the problem of identifying underlying diffusive regimes and inferring the anomalous diffusion exponent α with high confidence is critical to physics, chemistry, biology, and ecology. Classification and analysis of raw trajectories combining machine learning techniques with statistics extracted from them have widely been studied in the Anomalous Diffusion Challenge [Muñoz-Gil et al., Nat. Commun. 12, 6253 (2021)2041-172310.1038/s41467-021-26320-w]. Here we present a new data-driven method for working with diffusive trajectories. This method utilizes Gramian angular fields (GAF) to encode one-dimensional trajectories as images (Gramian matrices), while preserving their spatiotemporal structure for input to computer-vision models. This allows us to leverage two well-established pretrained computer-vision models, ResNet and MobileNet, to characterize the underlying diffusive regime and infer the anomalous diffusion exponent α. Short raw trajectories of lengths between 10 and 50 are commonly encountered in single-particle tracking experiments and are the most difficult ones to characterize. We show that GAF images can outperform the current state-of-the-art while increasing accessibility to machine learning methods in an applied setting.

Update on iodine-refractory differentiated thyroid carcinoma.

Radioiodine therapy represents a fundamental pillar in the routine adjuvant therapy of patients with high-risk differentiated thyroid carcinoma. However, a non-negligible percentage of these patients will develop iodine refractoriness, showing a worse prognosis, as well a lower survival, which demonstrates a clear need to explore different therapeutic approaches. Iodine refractory patient treatment continues to be a challenge, currently having different novel therapeutic options that should be known by the different specialties related to differentiated thyroid carcinoma (DTC). The aim of this work is to review iodine refractory thyroid carcinoma treatment, focusing especially on the definition of iodine refractoriness, highlighting its importance due to its high mortality, and introducing the different therapeutic options available for these patients.

Predicting COVID-19 pandemic waves including vaccination data with deep learning.

Introduction: During the recent COVID-19 pandemics, many models were developed to predict the number of new infections. After almost a year, models had also the challenge to include information about the waning effect of vaccines and by infection, and also how this effect start to disappear. Methods: We present a deep learning-based approach to predict the number of daily COVID-19 cases in 30 countries, considering the non-pharmaceutical interventions (NPIs) applied in those countries and including vaccination data of the most used vaccines. Results: We empirically validate the proposed approach for 4 months between January and April 2021, once vaccination was available and applied to the population and the COVID-19 variants were closer to the one considered for developing the vaccines. With the predictions of new cases, we can prescribe NPIs plans that present the best trade-off between the expected number of COVID-19 cases and the social and economic cost of applying such interventions. Discussion: Whereas, mathematical models which include the effect of vaccines in the spread of the SARS-COV-2 pandemic are available, to the best of our knowledge we are the first to propose a data driven method based on recurrent neural networks that considers the waning effect of the immunization acquired either by vaccine administration or by recovering from the illness. This work contributes with an accurate, scalable, data-driven approach to modeling the pandemic curves of cases when vaccination data is available.

COVID-19 outbreaks analysis in the Valencian Region of Spain in the prelude of the third wave.

Introduction: The COVID-19 pandemic has led to unprecedented social and mobility restrictions on a global scale. Since its start in the spring of 2020, numerous scientific papers have been published on the characteristics of the virus, and the healthcare, economic and social consequences of the pandemic. However, in-depth analyses of the evolution of single coronavirus outbreaks have been rarely reported. Methods: In this paper, we analyze the main properties of all the tracked COVID-19 outbreaks in the Valencian Region between September and December of 2020. Our analysis includes the evaluation of the origin, dynamic evolution, duration, and spatial distribution of the outbreaks. Results: We find that the duration of the outbreaks follows a power-law distribution: most outbreaks are controlled within 2 weeks of their onset, and only a few last more than 2 months. We do not identify any significant differences in the outbreak properties with respect to the geographical location across the entire region. Finally, we also determine the cluster size distribution of each infection origin through a Bayesian statistical model. Discussion: We hope that our work will assist in optimizing and planning the resource assignment for future pandemic tracking efforts.

Subphenotyping of Mexican Patients With COVID-19 at Preadmission To Anticipate Severity Stratification: Age-Sex Unbiased Meta-Clustering Technique.

BACKGROUND: The COVID-19 pandemic has led to an unprecedented global health care challenge for both medical institutions and researchers. Recognizing different COVID-19 subphenotypes-the division of populations of patients into more meaningful subgroups driven by clinical features-and their severity characterization may assist clinicians during the clinical course, the vaccination process, research efforts, the surveillance system, and the allocation of limited resources. OBJECTIVE: We aimed to discover age-sex unbiased COVID-19 patient subphenotypes based on easily available phenotypical data before admission, such as pre-existing comorbidities, lifestyle habits, and demographic features, to study the potential early severity stratification capabilities of the discovered subgroups through characterizing their severity patterns, including prognostic, intensive care unit (ICU), and morbimortality outcomes. METHODS: We used the Mexican Government COVID-19 open data, including 778,692 SARS-CoV-2 population-based patient-level data as of September 2020. We applied a meta-clustering technique that consists of a 2-stage clustering approach combining dimensionality reduction (ie, principal components analysis and multiple correspondence analysis) and hierarchical clustering using the Ward minimum variance method with Euclidean squared distance. RESULTS: In the independent age-sex clustering analyses, 56 clusters supported 11 clinically distinguishable meta-clusters (MCs). MCs 1-3 showed high recovery rates (90.27%-95.22%), including healthy patients of all ages, children with comorbidities and priority in receiving medical resources (ie, higher rates of hospitalization, intubation, and ICU admission) compared with other adult subgroups that have similar conditions, and young obese smokers. MCs 4-5 showed moderate recovery rates (81.30%-82.81%), including patients with hypertension or diabetes of all ages and obese patients with pneumonia, hypertension, and diabetes. MCs 6-11 showed low recovery rates (53.96%-66.94%), including immunosuppressed patients with high comorbidity rates, patients with chronic kidney disease with a poor survival length and probability of recovery, older smokers with chronic obstructive pulmonary disease, older adults with severe diabetes and hypertension, and the oldest obese smokers with chronic obstructive pulmonary disease and mild cardiovascular disease. Group outcomes conformed to the recent literature on dedicated age-sex groups. Mexican states and several types of clinical institutions showed relevant heterogeneity regarding severity, potentially linked to socioeconomic or health inequalities. CONCLUSIONS: The proposed 2-stage cluster analysis methodology produced a discriminative characterization of the sample and explainability over age and sex. These results can potentially help in understanding the clinical patient and their stratification for automated early triage before further tests and laboratory results are available and even in locations where additional tests are not available or to help decide resource allocation among vulnerable subgroups such as to prioritize vaccination or treatments.

Endometrial receptivity is affected in women with high circulating progesterone levels at the end of the follicular phase: a functional genomics analysis.

BACKGROUND: Elevated serum progesterone levels at the end of the follicular phase in controlled ovarian stimulation (COS) leads to a poorer ongoing pregnancy rate in IVF cycles due to reduced endometrial receptivity. The objective of this study was to use microarray technology to compare endometrial gene expression profiles at the window of implantation according to the levels of circulating progesterone. METHODS: For this prospective cohort study, microarray data were obtained from endometrial biopsies from 12 young healthy oocyte donors undergoing COS with pituitary suppression by either gonadotrophin-releasing hormone (GnRH) agonists or antagonists, and recombinant FSH. On the day of recombinant chorionic gonadotrophin (rCG) administration, six women had serum progesterone levels (P) >1.5 ng/ml (study group) and six had serum P levels <1.5 ng/ml (control group). Endometrial samples were collected using a Pipelle catheter 7 days after the rCG injection. RESULTS: Using the parametric test, we identified 140 genes significantly dysregulated (64 up- and 76 down-regulated) in the study group endometria compared with the control endometria, regardless of the GnRH analogue employed. These genes are related to cell adhesion, developmental processes, the immune system and others, which are all required for normal endometrial function development. Of the 25 gene targets previously proposed as markers for endometrial receptivity, 13 appeared over-regulated in the study group. CONCLUSIONS: Our results reveal that elevated progesterone levels on the day of rCG administration can induce significant alterations in the gene expression profile of the endometrium.

Potential limitations in COVID-19 machine learning due to data source variability: A case study in the nCov2019 dataset.

OBJECTIVE: The lack of representative coronavirus disease 2019 (COVID-19) data is a bottleneck for reliable and generalizable machine learning. Data sharing is insufficient without data quality, in which source variability plays an important role. We showcase and discuss potential biases from data source variability for COVID-19 machine learning. MATERIALS AND METHODS: We used the publicly available nCov2019 dataset, including patient-level data from several countries. We aimed to the discovery and classification of severity subgroups using symptoms and comorbidities. RESULTS: Cases from the 2 countries with the highest prevalence were divided into separate subgroups with distinct severity manifestations. This variability can reduce the representativeness of training data with respect the model target populations and increase model complexity at risk of overfitting. CONCLUSIONS: Data source variability is a potential contributor to bias in distributed research networks. We call for systematic assessment and reporting of data source variability and data quality in COVID-19 data sharing, as key information for reliable and generalizable machine learning.