Super-sample covariance approximations and partial sky coverage

Super-sample covariance (SSC) is the dominant source of statistical error on large scale structure (LSS) observables for both current and future galaxy surveys. In this work, we concentrate on the SSC of cluster counts, also known as sample variance, which is particularly useful for the self-calibration of the cluster observable-mass relation; our approach can similarly be applied to other observables, such as galaxy clustering and lensing shear. We first examined the accuracy of two analytical approximations proposed in the literature for the flat sky limit, finding that they are accurate at the 15% and 30-35% level, respectively, for covariances of counts in the same redshift bin. We then developed a harmonic expansion formalism that allows for the prediction of SSC in an arbitrary survey mask geometry, such as large sky areas of current and future surveys. We show analytically and numerically that this formalism recovers the full sky and flat sky limits present in the literature. We then present an efficient numerical implementation of the formalism, which allows fast and easy runs of covariance predictions when the survey mask is modified. We applied our method to a mask that is broadly similar to the Dark Energy Survey footprint, finding a non-negligible negative cross-z covariance, i.e. redshift bins are anti-correlated. We also examined the case of data removal from holes due to, for example bright stars, quality cuts, or systematic removals, and find that this does not have noticeable effects on the structure of the SSC matrix, only rescaling its amplitude by the effective survey area. These advances enable analytical covariances of LSS observables to be computed for current and future galaxy surveys, which cover large areas of the sky where the flat sky approximation fails.Comment: 14 pages, 10 figures. Updated to match version published in Astronomy & Astrophysic

A synthetic data generation system for myalgic encephalomyelitis/chronic fatigue syndrome questionnaires

Artificial intelligence or machine-learning-based models have proven useful for better understanding various diseases in all areas of health science. Myalgic Encephalomyelitis or chronic fatigue syndrome (ME/CFS) lacks objective diagnostic tests. Some validated questionnaires are used for diagnosis and assessment of disease progression. The availability of a sufficiently large database of these questionnaires facilitates research into new models that can predict profiles that help to understand the etiology of the disease. A synthetic data generator provides the scientific community with databases that preserve the statistical properties of the original, free of legal restrictions, for use in research and education. The initial databases came from the Vall Hebron Hospital Specialized Unit in Barcelona, Spain. 2522 patients diagnosed with ME/CFS were analyzed. Their answers to questionnaires related to the symptoms of this complex disease were used as training datasets. They have been fed for deep learning algorithms that provide models with high accuracy [0.69–0.81]. The final model requires SF-36 responses and returns responses from HAD, SCL-90R, FIS8, FIS40, and PSQI questionnaires. A highly reliable and easy-to-use synthetic data generator is offered for research and educational use in this disease, for which there is currently no approved treatment

A synthetic data generation system for myalgic encephalomyelitis/chronic fatigue syndrome questionnaires

Computational models; Data acquisitionModelos computacionales; Adquisición de datosModels computacionals; Adquisició de dadesArtificial intelligence or machine-learning-based models have proven useful for better understanding various diseases in all areas of health science. Myalgic Encephalomyelitis or chronic fatigue syndrome (ME/CFS) lacks objective diagnostic tests. Some validated questionnaires are used for diagnosis and assessment of disease progression. The availability of a sufficiently large database of these questionnaires facilitates research into new models that can predict profiles that help to understand the etiology of the disease. A synthetic data generator provides the scientific community with databases that preserve the statistical properties of the original, free of legal restrictions, for use in research and education. The initial databases came from the Vall Hebron Hospital Specialized Unit in Barcelona, Spain. 2522 patients diagnosed with ME/CFS were analyzed. Their answers to questionnaires related to the symptoms of this complex disease were used as training datasets. They have been fed for deep learning algorithms that provide models with high accuracy [0.69–0.81]. The final model requires SF-36 responses and returns responses from HAD, SCL-90R, FIS8, FIS40, and PSQI questionnaires. A highly reliable and easy-to-use synthetic data generator is offered for research and educational use in this disease, for which there is currently no approved treatment

Unsupervised cluster analysis reveals distinct subtypes of ME/CFS patients based on peak oxygen consumption and SF-36 scores

PURPOSE: Myalgic encephalomyelitis, commonly referred to as chronic fatigue syndrome (ME/CFS), is a severe, disabling chronic disease and an objective assessment of prognosis is crucial to evaluate the efficacy of future drugs. Attempts are ongoing to find a biomarker to objectively assess the health status of (ME/CFS), patients. This study therefore aims to demonstrate that oxygen consumption is a biomarker of ME/CFS provides a method to classify patients diagnosed with ME/CFS based on their responses to the Short Form-36 (SF-36) questionnaire, which can predict oxygen consumption using cardiopulmonary exercise testing (CPET). METHODS: Two datasets were used in the study. The first contained SF-36 responses from 2,347 validated records of ME/CFS diagnosed participants, and an unsupervised machine learning model was developed to cluster the data. The second dataset was used as a validation set and included the cardiopulmonary exercise test (CPET) results of 239 participants diagnosed with ME/CFS. Participants from this dataset were grouped by peak oxygen consumption according to Weber's classification. The SF-36 questionnaire was correctly completed by only 92 patients, who were clustered using the machine learning model. Two categorical variables were then entered into a contingency table: the cluster with values {0,1} and Weber classification {A, B, C, D} were assigned. Finally, the Chi-square test of independence was used to assess the statistical significance of the relationship between the two parameters. FINDINGS: The results indicate that the Weber classification is directly linked to the score on the SF-36 questionnaire. Furthermore, the 36-response matrix in the machine learning model was shown to give more reliable results than the subscale matrix (p - value < 0.05) for classifying patients with ME/CFS. IMPLICATIONS: Low oxygen consumption on CPET can be considered a biomarker in patients with ME/CFS. Our analysis showed a close relationship between the cluster based on their SF-36 questionnaire score and the Weber classification, which was based on peak oxygen consumption during CPET. The dataset for the training model comprised raw responses from the SF-36 questionnaire, which is proven to better preserve the original information, thus improving the quality of the model

Análisis de la repercusión de la reforma del IRPF a diferentes perfiles de contribuyentes

En el presente trabajo de fin de grado se busca estudiar el impacto que tiene la reciente reforma del Impuesto sobre la Renta de las Personas Físicas sobre los ciudadanos españoles. Para explicarlo se utilizarán como ejemplo a seis hipotéticos ciudadanos españoles con unas características tanto en renta como en sus circunstancias personales y familiares muy heterogéneas entre sí. Con ello, se pretende comprobar cómo ha afectado la reforma fiscal a los ciudadanos y el grado de beneficio o perjuicio que les ha supuesto. Para ello, se liquidan las declaraciones de la renta de estos individuos en los años 2014 y 2015

Unsupervised Cluster Analysis Reveals Distinct Subtypes of ME/CFS Patients Based on Peak Oxygen Consumption and SF-36 Scores

Biomarker; Cardiopulmonary exercise test; Chronic fatigue syndromeBiomarcador; Prova d'esforç cardiopulmonar; Síndrome de fatiga crònicaBiomarcador; Prueba de esfuerzo cardiopulmonar; Síndrome de fatiga crónicaPurpose Myalgic encephalomyelitis, commonly referred to as chronic fatigue syndrome (ME/CFS), is a severe, disabling chronic disease and an objective assessment of prognosis is crucial to evaluate the efficacy of future drugs. Attempts are ongoing to find a biomarker to objectively assess the health status of (ME/CFS), patients. This study therefore aims to demonstrate that oxygen consumption is a biomarker of ME/CFS provides a method to classify patients diagnosed with ME/CFS based on their responses to the Short Form-36 (SF-36) questionnaire, which can predict oxygen consumption using cardiopulmonary exercise testing (CPET). Methods Two datasets were used in the study. The first contained SF-36 responses from 2,347 validated records of ME/CFS diagnosed participants, and an unsupervised machine learning model was developed to cluster the data. The second dataset was used as a validation set and included the cardiopulmonary exercise test (CPET) results of 239 participants diagnosed with ME/CFS. Participants from this dataset were grouped by peak oxygen consumption according to Weber's classification. The SF-36 questionnaire was correctly completed by only 92 patients, who were clustered using the machine learning model. Two categorical variables were then entered into a contingency table: the cluster with values {0,1} and Weber classification {A, B, C, D} were assigned. Finally, the Chi-square test of independence was used to assess the statistical significance of the relationship between the two parameters. Findings The results indicate that the Weber classification is directly linked to the score on the SF-36 questionnaire. Furthermore, the 36-response matrix in the machine learning model was shown to give more reliable results than the subscale matrix (p − value < 0.05) for classifying patients with ME/CFS. Implications Low oxygen consumption on CPET can be considered a biomarker in patients with ME/CFS. Our analysis showed a close relationship between the cluster based on their SF-36 questionnaire score and the Weber classification, which was based on peak oxygen consumption during CPET. The dataset for the training model comprised raw responses from the SF-36 questionnaire, which is proven to better preserve the original information, thus improving the quality of the model

Yeast Beta-Glucan Supplementation with Multivitamins Attenuates Cognitive Impairments in Individuals with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: A Randomized, Double-Blind, Placebo-Controlled Trial

Beta-glucan; Myalgic encephalomyelitis; Vitamin B6Betaglucano; Encefalomielitis miálgica; Vitamina B6Betaglucà; Encefalomielitis miàlgica; Vitamina B6This research aimed to examine the potential alleviative effects of beta-glucan administration on fatigue, unrefreshing sleep, anxiety/depression symptoms and health-related quality of life in ME/CFS. A 36-week unicenter, randomized, double-blind, placebo-controlled trial was conducted in 65 ME/CFS patients, who were randomly allocated to one of two arms to receive four capsules each one of 250 mg beta-glucan, 3.75 µg vitamin D3, 1.05 mg vitamin B6, and 7.5 mg zinc (n = 35), or matching placebo including only microcrystalline cellulose as an excipient (n = 30) once daily. The findings showed that the beta-glucan supplementation significantly improved cognitive fatigue (assessed with FIS-40 scores) after the 36-week treatment compared to the baseline (p = 0.0338). Taken together, this study presents the novel finding that yeast-derived beta-glucan may alleviate cognitive fatigue symptoms in ME/CFS. Thus, it offers valuable scientific insights into the potential use of yeast beta-glucan as a nutritional supplement and/or functional food to prevent or reduce cognitive dysfunction in patients with ME/CFS. Further interventions are warranted to validate these findings and also to delve deeper into the possible immunometabolic pathomechanisms of beta-glucans in ME/CFS.J.C.-M. received financial support and honoraria from Vitae Health Innovation S.L. (Montmeló, Barcelona, Spain). This study was supported by Vitae Health Innovation S.L. (Montmeló, Spain) which supplied both treatments (funding number: 2020-0445)

Effect of Dietary Coenzyme Q10 Plus NADH Supplementation on Fatigue Perception and Health-Related Quality of Life in Individuals with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: A Prospective, Randomized, Double-Blind, Placebo-Controlled Trial

Coenzima Q10; Mitocondrias; Encefalomielitis miálgicaCoenzyme Q10; Mitochondria; Myalgic encephalomyelitisCoenzim Q10; Mitocondris; Encefalomielitis miàlgicaMyalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex, multisystem, and profoundly debilitating neuroimmune disease, probably of post-viral multifactorial etiology. Unfortunately, no accurate diagnostic or laboratory tests have been established, nor are any universally effective approved drugs currently available for its treatment. This study aimed to examine whether oral coenzyme Q10 and NADH (reduced form of nicotinamide adenine dinucleotide) co-supplementation could improve perceived fatigue, unrefreshing sleep, and health-related quality of life in ME/CFS patients. A 12-week prospective, randomized, double-blind, placebo-controlled trial was conducted in 207 patients with ME/CFS, who were randomly allocated to one of two groups to receive either 200 mg of CoQ10 and 20 mg of NADH (n = 104) or matching placebo (n = 103) once daily. Endpoints were simultaneously evaluated at baseline, and then reassessed at 4- and 8-week treatment visits and four weeks after treatment cessation, using validated patient-reported outcome measures. A significant reduction in cognitive fatigue perception and overall FIS-40 score (p < 0.001 and p = 0.022, respectively) and an improvement in HRQoL (health-related quality of life (SF-36)) (p < 0.05) from baseline were observed within the experimental group over time. Statistically significant differences were also shown for sleep duration at 4 weeks and habitual sleep efficiency at 8 weeks in follow-up visits from baseline within the experimental group (p = 0.018 and p = 0.038, respectively). Overall, these findings support the use of CoQ10 plus NADH supplementation as a potentially safe therapeutic option for reducing perceived cognitive fatigue and improving the health-related quality of life in ME/CFS patients. Future interventions are needed to corroborate these clinical benefits and also explore the underlying pathomechanisms of CoQ10 and NADH administration in ME/CFS.J.C.-M. received financial support and honoraria from Vitae Health Innovation Co., S.L. (Montmeló, Barcelona, Spain). This study was partially supported by the Vall d’Hebron Hospital Research Institute (Barcelona, Spain). Vitae Health Innovation Co. supplied both treatments (Coenzyme Q10 and NADH supplement tablets and placebo)

The Conners Continuous Performance Test CPT3™: Is it a reliable marker to predict neurocognitive dysfunction in Myalgic encephalomyelitis/chronic fatigue syndrome?

Chronic fatigue syndrome; Continuous performance test; Neurocognitive dysfunctionSíndrome de fatiga crónica; Prueba de rendimiento continuo; Disfunción neurocognitivaSíndrome de fatiga crònica; Prova de rendiment contínua; Disfunció neurocognitivaIntroduction: The main objective is to delimit the cognitive dysfunction associated with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) in adult patients by applying the Continuous Performance Test (CPT3™). Additionally, provide empirical evidence on the usefulness of this computerized neuropsychological test to assess ME/CFS. Method: The final sample (n = 225; 158 Patients/67 Healthy controls) were recruited in a Central Sensitization Syndromes (CSS) specialized unit in a tertiary hospital. All participants were administered this neuropsychological test. Results: There were significant differences between ME/CFS and healthy controls in all the main measures of CPT3™. Mainly, patients had a worse indicator of inattentiveness, sustained attention, vigilance, impulsivity, slow reaction time, and more atypical T-scores, which is associated with a likelihood of having a disorder characterized by attention deficits, such as Attention Deficit Hyperactivity Disorder (ADHD). In addition, relevant correlations were obtained between the CPT3™ variables in the patient's group. The most discriminative indicators of ME/CFS patients were Variability and Hit Reaction Time, both measures of response speed. Conclusion: The CPT3™ is a helpful tool to discriminate neurocognitive impairments from attention and response speed in ME/CFS patients, and it could be used as a marker of ME/CFS severity for diagnosing or monitoring this disease