UCO Physical Rehabilitation: New Dataset and Study of Human Pose Estimation Methods on Physical Rehabilitation Exercises.

Physical rehabilitation plays a crucial role in restoring motor function following injuries or surgeries. However, the challenge of overcrowded waiting lists often hampers doctors' ability to monitor patients' recovery progress in person. Deep Learning methods offer a solution by enabling doctors to optimize their time with each patient and distinguish between those requiring specific attention and those making positive progress. Doctors use the flexion angle of limbs as a cue to assess a patient's mobility level during rehabilitation. From a Computer Vision perspective, this task can be framed as automatically estimating the pose of the target body limbs in an image. The objectives of this study can be summarized as follows: (i) evaluating and comparing multiple pose estimation methods; (ii) analyzing how the subject's position and camera viewpoint impact the estimation; and (iii) determining whether 3D estimation methods are necessary or if 2D estimation suffices for this purpose. To conduct this technical study, and due to the limited availability of public datasets related to physical rehabilitation exercises, we introduced a new dataset featuring 27 individuals performing eight diverse physical rehabilitation exercises focusing on various limbs and body positions. Each exercise was recorded using five RGB cameras capturing different viewpoints of the person. An infrared tracking system named OptiTrack was utilized to establish the ground truth positions of the joints in the limbs under study. The results, supported by statistical tests, show that not all state-of-the-art pose estimators perform equally in the presented situations (e.g., patient lying on the stretcher vs. standing). Statistical differences exist between camera viewpoints, with the frontal view being the most convenient. Additionally, the study concludes that 2D pose estimators are adequate for estimating joint angles given the selected camera viewpoints.

Information Theory and Symbolic Analysis: Theory and Applications.

Symbolic analysis has been developed and used successfully in very diverse fields [...].

Selection of Embedding Dimension and Delay Time in Phase Space Reconstruction via Symbolic Dynamics.

The modeling and prediction of chaotic time series require proper reconstruction of the state space from the available data in order to successfully estimate invariant properties of the embedded attractor. Thus, one must choose appropriate time delay τ∗ and embedding dimension p for phase space reconstruction. The value of τ∗ can be estimated from the Mutual Information, but this method is rather cumbersome computationally. Additionally, some researchers have recommended that τ∗ should be chosen to be dependent on the embedding dimension p by means of an appropriate value for the time delay τw=(p-1)τ∗, which is the optimal time delay for independence of the time series. The C-C method, based on Correlation Integral, is a method simpler than Mutual Information and has been proposed to select optimally τw and τ∗. In this paper, we suggest a simple method for estimating τ∗ and τw based on symbolic analysis and symbolic entropy. As in the C-C method, τ∗ is estimated as the first local optimal time delay and τw as the time delay for independence of the time series. The method is applied to several chaotic time series that are the base of comparison for several techniques. The numerical simulations for these systems verify that the proposed symbolic-based method is useful for practitioners and, according to the studied models, has a better performance than the C-C method for the choice of the time delay and embedding dimension. In addition, the method is applied to EEG data in order to study and compare some dynamic characteristics of brain activity under epileptic episodes.

Network Dynamics in Elemental Assimilation and Metabolism.

Metabolism and physiology frequently follow non-linear rhythmic patterns which are reflected in concepts of homeostasis and circadian rhythms, yet few biomarkers are studied as dynamical systems. For instance, healthy human development depends on the assimilation and metabolism of essential elements, often accompanied by exposures to non-essential elements which may be toxic. In this study, we applied laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) to reconstruct longitudinal exposure profiles of essential and non-essential elements throughout prenatal and early post-natal development. We applied cross-recurrence quantification analysis (CRQA) to characterize dynamics involved in elemental integration, and to construct a graph-theory based analysis of elemental metabolism. Our findings show how exposure to lead, a well-characterized toxicant, perturbs the metabolism of essential elements. In particular, our findings indicate that high levels of lead exposure dysregulate global aspects of metabolic network connectivity. For example, the magnitude of each element's degree was increased in children exposed to high lead levels. Similarly, high lead exposure yielded discrete effects on specific essential elements, particularly zinc and magnesium, which showed reduced network metrics compared to other elements. In sum, this approach presents a new, systems-based perspective on the dynamics involved in elemental metabolism during critical periods of human development.

Additive Traits Lead to Feeding Advantage and Reproductive Isolation, Promoting Homoploid Hybrid Speciation.

Speciation through homoploid hybridization (HHS) is considered extremely rare in animals. This is mainly because the establishment of reproductive isolation as a product of hybridization is uncommon. Additionally, many traits are underpinned by polygeny and/or incomplete dominance, where the hybrid phenotype is an additive blend of parental characteristics. Phenotypically intermediate hybrids are usually at a fitness disadvantage compared with parental species and tend to vanish through backcrossing with parental population(s). It is therefore unknown whether the additive nature of hybrid traits in itself could lead successfully to HHS. Using a multi-marker genetic data set and a meta-analysis of diet and morphology, we investigated a potential case of HHS in the prions (Pachyptila spp.), seabirds distinguished by their bills, prey choice, and timing of breeding. Using approximate Bayesian computation, we show that the medium-billed Salvin's prion (Pachyptila salvini) could be a hybrid between the narrow-billed Antarctic prion (Pachyptila desolata) and broad-billed prion (Pachyptila vittata). Remarkably, P. salvini's intermediate bill width has given it a feeding advantage with respect to the other Pachyptila species, allowing it to consume a broader range of prey, potentially increasing its fitness. Available metadata showed that P. salvini is also intermediate in breeding phenology and, with no overlap in breeding times, it is effectively reproductively isolated from either parental species through allochrony. These results provide evidence for a case of HHS in nature, and show for the first time that additivity of divergent parental traits alone can lead directly to increased hybrid fitness and reproductive isolation.

Social distancing at health care centers early in the pandemic helps to protect population from COVID-19.

The impact of COVID-19 outbreak has been unequal across Spanish regions. The epidemic wave has been smoother in the Region of Murcia (RM) (6 deaths/100,000 residents). Physical distance from health centers from day 0 is an additional social distancing measure that confers an advantageous starting position in the fight against COVID-19. Late healthcare distancing measures are not as powerful as the early ones.

A Prototype Framework Design for Assisting the Detection of Atrial Fibrillation Using a Generic Low-Cost Biomedical Sensor.

Cardiovascular diseases are the leading cause of death around the world. As a result, low-cost biomedical sensors have been gaining importance in business and research over the last few decades. Their main benefits include their small size, light weight, portability and low power consumption. Despite these advantages, they are not generally used for clinical monitoring mainly because of their low accuracy in data acquisition. In this emerging technological context, this paper contributes by discussing a methodology to help practitioners build a prototype framework based on a low-cost commercial sensor. The resulting application consists of four modules; namely, a digitalization module whose input is an electrocardiograph signal in portable document format (PDF) or joint photographic expert group format (JPEG), a module to further process and filter the digitalized signal, a selectable data calibration module and, finally, a module implementing a classification algorithm to distinguish between individuals with normal sinus rhythms and those with atrial fibrillation. This last module employs our recently published symbolic recurrence quantification analysis (SRQA) algorithm on a time series of RR intervals. Moreover, we show that the algorithm applies to any biomedical low-cost sensor, achieving good results without requiring.

Phylogeography, Population Structure, and Species Delimitation in Rockhopper Penguins (Eudyptes chrysocome and Eudyptes moseleyi).

Rockhopper penguins are delimited as 2 species, the northern rockhopper (Eudyptes moseleyi) and the southern rockhopper (Eudyptes chrysocome), with the latter comprising 2 subspecies, the western rockhopper (Eudyptes chrysocome chrysocome) and the eastern rockhopper (Eudyptes chrysocome filholi). We conducted a phylogeographic study using multilocus data from 114 individuals sampled across 12 colonies from the entire range of the northern/southern rockhopper complex to assess potential population structure, gene flow, and species limits. Bayesian and likelihood methods with nuclear and mitochondrial DNA, including model testing and heuristic approaches, support E. moseleyi and E. chrysocome as distinct species lineages with a divergence time of 0.97 Ma. However, these analyses also indicated the presence of gene flow between these species. Among southern rockhopper subspecies, we found evidence of significant gene flow and heuristic approaches to species delimitation based on the genealogical diversity index failed to delimit them as species. The best-supported population models for the southern rockhoppers were those where E. c. chrysocome and E. c. filholi were combined into a single lineage or 2 lineages with bidirectional gene flow. Additionally, we found that E. c. filholi has the highest effective population size while E. c. chrysocome showed similar effective population size to that of the endangered E. moseleyi. We suggest that the current taxonomic definitions within rockhopper penguins be upheld and that E. chrysocome populations, all found south of the subtropical front, should be treated as a single taxon with distinct management units for E. c. chrysocome and E. c. filholi.

Transfer entropy on symbolic recurrences.

Recurrence quantification analysis offers a powerful framework to investigate complexity in dynamical systems. While several studies have demonstrated the possibility of multivariate recurrence quantification analysis, information-theoretic tools for the discovery of causal links remain elusive. Particularly enticing is to formulate information-theoretic tools on symbolic recurrence plots, which alleviate some of the methodological challenges of traditional recurrence plots and offer a richer representation of recurrences. Toward this aim, we establish a probability space in which we ground a theory of information that encodes information in the recurrences of the symbols. We introduce transfer entropy on symbolic recurrences as a tool to guide the inference of the strength and direction of the interaction between dynamical systems. We demonstrate statistically reliable discovery of causal links on synthetic and experimental time series, from only two time series or a larger dataset with multiple realizations. The proposed approach brings together recurrence plots, information theory, and symbolic dynamics to empower researchers and practitioners with effective means to visualize and quantify interactions in dynamical systems.

Application of symbolic recurrence to experimental data, from firearm prevalence to fish swimming.

Recurrence plots and recurrence quantification analysis are powerful tools to study the behavior of dynamical systems. What we learn through these tools is typically determined by the choice of a distance threshold in the phase space, which introduces arbitrariness in the definition of recurrence. Not only does symbolic recurrence overcome this difficulty, but also it offers a richer representation that book-keeps the recurrent portions of the phase space. Using symbolic recurrences, we can construct recurrence plots, perform quantification analysis, and examine causal links between dynamical systems from their time-series. Although previous efforts have demonstrated the feasibility of such a symbolic framework on synthetic data, the study of real time-series remains elusive. Here, we seek to bridge this gap by systematically examining a wide range of experimental datasets, from firearm prevalence and media coverage in the United States to the effect of sex on the interaction of swimming fish. This work offers a compelling demonstration of the potential of symbolic recurrence in the study of real-world applications across different research fields while providing a computer code for researchers to perform their own time-series explorations.

Inference of time-varying networks through transfer entropy, the case of a Boolean network model.

Inferring network topologies from the time series of individual units is of paramount importance in the study of biological and social networks. Despite considerable progress, our success in network inference is largely limited to static networks and autonomous node dynamics, which are often inadequate to describe complex systems. Here, we explore the possibility of reconstructing time-varying weighted topologies through the information-theoretic notion of transfer entropy. We focus on a Boolean network model in which the weight of the links and the spontaneous activity periodically vary in time. For slowly-varying dynamics, we establish closed-form expressions for the stationary periodic distribution and transfer entropy between each pair of nodes. Our results indicate that the instantaneous weight of each link is mapped into a corresponding transfer entropy value, thereby affording the possibility of pinpointing the dominant weights at each time. However, comparing transfer entropy readings at different times may provide erroneous estimates of the strength of the links in time, due to a counterintuitive modulation of the information flow by the non-autonomous dynamics. In fact, this time variation should be used to scale transfer entropy values toward the correct inference of the time evolution of the network weights. This study constitutes a necessary step toward a mathematically-principled use of transfer entropy to reconstruct time-varying networks.

Symbolic recurrence plots to analyze dynamical systems.

This paper, based on the concept of symbolic correlation integral, introduces a set of symbolic recurrence plots and associated invariant measures, which are independent of the distance parameter, serving as a tool for quantifying the dynamic structure. These new measures allow the study of transient behavior, coexistence of attractors, bifurcations, and structural change. The final user does not have to choose the free distance parameter. An empirical application to electrocardiography data illustrates the use of symbolic recurrence measures.

Symbolic dynamics of animal interaction.

Since its introduction nearly two decades ago, transfer entropy has contributed to an improved understanding of cause-and-effect relationships in coupled dynamical systems from raw time series. In the context of animal behavior, transfer entropy might help explain the determinants of leadership in social groups and elucidate escape response to predator attacks. Despite its promise, the potential of transfer entropy in animal behavior is yet to be fully tested, and a number of technical challenges in information theory and statistics remain open. Here, we examine an alternative approach to the computation of transfer entropy based on symbolic dynamics. In this context, a symbol is associated with a specific locomotory bout across two or more consecutive time instants, such as reversing the swimming direction. Symbols encapsulate salient locomotory patterns and the associated permutation transfer entropy quantifies the ability to predict the patterns of an individual given those of another individual. We demonstrate this framework on an existing dataset on fish, for which we have knowledge of the underlying cause-and-effect relationship between the focal subject and the stimulus. Symbolic dynamics offers an intuitive and robust approach to study animal behavior, which could enable the inference of causal relationship from noisy experimental observations of limited duration.

A Feasibility Study of the Full Outpatient Conduction of Hematopoietic Transplants in Persons with Multiple Sclerosis Employing Autologous Non-Cryopreserved Peripheral Blood Stem Cells.

BACKGROUND: With the goal of achieving immune system reset, autologous hematopoietic stem cell transplantations have been performed in patients with multiple sclerosis (MS). MATERIAL AND METHODS: Two hundred and eighty-six consecutive patients with MS were autografted in a single center using non-frozen peripheral blood stem cells (PBSCs), on an outpatient basis and conditioning with cyclophosphamide and rituximab. The protocol was registered in ClinicalTrials.gov identifier NCT02674217. RESULTS: One hundred and ninety-four females and 92 males were included; the median age was 47. All procedures were started on an outpatient basis and only 8 persons needed to be admitted to the hospital during the procedure. In order to obtain at least 1 × 106/kg viable CD34 cells, 1-4 aphereses were performed (median 1). The total number of viable CD34+ cells infused ranged between 1 and 19.2 × 106/kg (median 4.6). Patients recovered above 0.5 × 109/L absolute granulocytes on median day 8 (range 0-12). Two individuals needed red blood cells but none needed platelet transfusions. There were no transplant-related deaths and the 128-month overall survival of the patients is 100%. In 82 persons followed up for 3 or more months, the Expanded Disability Status Scale diminished from a mean of 5.2-4.9, the best results being obtained in relapsing-remitting and primary progressive MS. CONCLUSIONS: It is possible to conduct autotransplants for patients with MS employing non-frozen PBSCs and outpatient conduction. Additional information is needed to assess the efficacy of these procedures in the treatment of patients with MS.

Selection of Temporal Lags When Modeling Economic and Financial Processes.

This paper suggests new nonparametric statistical tools and procedures for modeling linear and nonlinear univariate economic and financial processes. In particular, the tools presented help in selecting relevant lags in the model description of a general linear or nonlinear time series; that is, nonlinear models are not a restriction. The tests seem to be robust to the selection of free parameters. We also show that the test can be used as a diagnostic tool for well-defined models.

The Effects of Acute Deep Seawater Supplementation on Muscle Function after Triathlon.

(1) Background: Trainers and athletes have always sought to reduce the failure of muscle function during long endurance events. However, nowadays, it is a topic that is generating much debate in the scientific field. Currently, deep-sea water (DSW) intake seems to be a suitable hydration alternative for this type of endurance event. Therefore, the aim of this study was to determine whether DSW consumption during a triathlon event could preserve muscle function after exercise. (2) Methods: Nineteen trained male triathletes (age = 39.0 ± 4.25 years; BMI = 23.67 ± 1.81 kg/m2) randomly performed three triathlons, one of them consuming DSW (Totum SPORT 30 AB, Laboratories Quinton International, S.L., Spain), the other consuming isotonic placebo and the last with tap water-hydration. A vertical jump test with countermovement and an isometric muscle strength test were conducted before and after the triathlon test. (3) Results: There was a significant difference between treatment × time during the isometric muscle strength test. Based on the Tukey post hoc analysis, the peak net force decreased statistically in the placebo (p = 0.045) and control conditions (p = 0.026), but not in the experimental condition (p = 0.121). In addition, all of the conditions studied obtained similar results in the countermovement vertical jump after exercise. (4) Conclusions: As a result, consumption of DSW seems to delay the failure of muscle function specifically in isometric exercises but does not improve performance in sports. Thus, DSW does not alter muscle capacity in a negative way; therefore, its consumption may be recommended.

Evaluating the Impact of Flossing Band Integration in Conventional Physiotherapy for Patellofemoral Pain Syndrome.

Background/Objectives: Patellofemoral Pain Syndrome (PFPS) is prevalent among physically active individuals, highlighting the need for innovative treatment strategies beyond conventional physiotherapy. This study investigates the effectiveness of integrating flossing band therapy with standard physiotherapy, anticipating improved outcomes in pain reduction, functional ability, and patient satisfaction. Methods: A double-blinded randomized controlled trial involved 50 PFPS-diagnosed participants. They were divided into two groups: Standard Physiotherapy Group (SPG) and Flossing Band and Physiotherapy Group (FBPG), each undergoing an 8-week intervention focusing on resistance training supplemented by respective therapies. Assessment metrics included pain (VAS), strength (Dynamometry), lower limb function (LEFS), and PFPS function (AKPS) measured before and after the intervention. Results: Significant enhancements in all outcome measures were noted for both groups, yet the FBPG exhibited notably superior improvements in pain, knee functionality, muscle strength, and lower extremity function. The FBPG demonstrated statistically significant greater efficacy in pain alleviation and strength enhancement. Conclusions: The addition of flossing band therapy to conventional physiotherapy presents a more effective treatment modality for PFPS, suggesting its potential to redefine therapeutic standards. Future studies should delve into the long-term impacts and mechanistic underpinnings of floss band therapy in PFPS management.

Maintenance of the response to dimethyl sulfoxide treatment using hyperbaric oxygen in interstitial cystitis/painful bladder syndrome: a prospective, randomized, comparative study.

INTRODUCTION: Interstitial cystitis (IC)/painful bladder syndrome (PBS) is a difficult disease to manage and creates critical limitations in patients' daily lives. Our objective was to determine the efficacy of hyperbaric oxygen (HBO) therapy in the maintenance of response after the administration of intravesical dimethyl sulfoxide (DMSO). MATERIALS AND METHODS: We conducted an open, prospective, randomized, comparative pilot study with women diagnosed with IC/PBS according to the European Society for the Study of Interstitial Cystitis criteria. In the first phase, DMSO was given to all patients. In the second phase, we used 1:1 randomization and administered HBO to 10 women. The evaluated variables were pain (through a visual analog scale), frequency and urgency of voids, nocturia, and quality of life using the O'Leary-Sant Interstitial Cystitis Score/Problem Index and the King's Health Questionnaire. In the second phase, we measured the length of time that clinical improvement was maintained. RESULTS: The mean age was 47.6 years (SD 18.4). Out of 20 patients, 14 experienced clinical improvement after DMSO in all of the evaluated symptoms (p < 0.05; 95% CI). After the second phase, all patients who received HBO had a more substantive and prolonged maintenance of the effects of DMSO. CONCLUSIONS: In this study, HBO improved the maintenance of the beneficial effects of DMSO among women with IC/PBS.

A method to analyse measurement invariance under uncertainty in between-subjects design.

In this research we have introduced a new test (H-test) for analyzing scale invariance in between group designs, and considering uncertainty in individual responses, in order to study the adequacy of disparate rating and visual scales for measuring abstract concepts. The H-test is easy to compute and, as a nonparametric test, does not require any a priori distribution of the data nor conditions on the variances of the distributions to be tested. We apply this test to measure perceived service quality of consumers of a sports services. Results show that, without considering uncertainty, the 1-7 scale is invariant, in line with the related works regarding this topic. However, de 1-5 scale and the 1-7 scale are invariant when adding uncertainty to the analysis. Therefore, adding uncertainty importantly change the conclusions regarding invariance analysis. Both types of visual scales are not invariant in the uncertainty scenario. Implications for the use of rating scales are discussed.

Quantifying the role of the COVID-19 pandemic in the 2020 U.S. presidential elections.

In the media, a prevalent narrative is that the incumbent United States President Donald J. Trump lost the 2020 elections because of the way he handled the COVID-19 pandemic. Quantitative evidence to support this narrative is, however, limited. We put forward a spatial, information-theoretic approach to critically examine the link between voting behavior and COVID-19 incidence in the 2020 presidential elections. The approach overcomes classical limitations of traditional regression analysis, where it does not require an underlying mathematical model and it can capture nonlinear interactions. From the analysis of county-level data, we uncovered a robust association between voting behavior and prevalence of COVID-19 cases. Surprisingly, such an association points in the opposite direction from the accepted narrative: in counties that experienced less COVID-19 cases, the incumbent President lost more ground to his opponent, now President Joseph R. Biden Jr. A tenable explanation of this observation is the different attitude of liberal and conservative voters toward the pandemic, which led to more COVID-19 spreading in counties with a larger share of republican voters.