Valdeande, a dos leguas de camino de "Clunia". Novedades epigráficas

Abordamos el estudio de dos inscripciones ya conocidas, de las que damos nueva lectura e interpretación, y apuntamos su posible relación con Clunia. Asimismo, editamos otras tres piezas inéditas y tres grafitos hallados todos ellos en Valdeande (Burgos).In this paper, we present a new interpretation about the study of two known Latin inscriptions from Valdeande (Burgos), developing a new meaning of them and establishing a potential link with Clunia. In addition, we show three unpublished pieces and three graffities, all of them found in Valdeande (Burgos)

Contemplative sciences: A future beyond mindfulness

Mindfulness is a psychological technique based on Eastern meditative practices that was developed in the late 1970s by Kabat-Zinn at the University of Massachusetts. Initially, there was a debate over whether it should be considered a scientific technique or labelled as part of the new wave practices. Today, mindfulness is omnipresent in modern societies but has suffered from merchandising and banalization, which has been strongly criticized. Despite some limitations regarding methodological aspects of mindfulness research, it is considered effective for treating many physical and psychological disorders, and even it is recommended in clinical guidelines such the British National Institute for Health and Care Excellence. During the last 2500 years, mindfulness practices have moved from Northern India across most of Asia, but their mixing with Western science and culture at the end of the 20(th) century is considered a key event in recent history. For the first time in human history, due to globalization, the wisdom of all contemplative traditions can be shared with all human beings and assessed by science. Mindfulness practices, yoga included, are giving birth to a new field of knowledge, contemplative sciences, which go beyond mindfulness and is devoted to helping humanity to reach higher levels of happiness and mental peace

Comparing the Min–Max–Median/IQR Approach with the Min–Max Approach, Logistic Regression and XGBoost, maximising the Youden index

Although linearly combining multiple variables can provide adequate diagnostic performance, certain algorithms have the limitation of being computationally demanding when the number of variables is sufficiently high. Liu et al. proposed the min–max approach that linearly combines the minimum and maximum values of biomarkers, which is computationally tractable and has been shown to be optimal in certain scenarios. We developed the Min–Max–Median/IQR algorithm under Youden index optimisation which, although more computationally intensive, is still approachable and includes more information. The aim of this work is to compare the performance of these algorithms with well-known Machine Learning algorithms, namely logistic regression and XGBoost, which have proven to be efficient in various fields of applications, particularly in the health sector. This comparison is performed on a wide range of different scenarios of simulated symmetric or asymmetric data, as well as on real clinical diagnosis data sets. The results provide useful information for binary classification problems of better algorithms in terms of performance depending on the scenario

Environmental factors influence both abundance and genetic diversity in a widespread bird species.

Genetic diversity is one of the key evolutionary variables that correlate with population size, being of critical importance for population viability and the persistence of species. Genetic diversity can also have important ecological consequences within populations, and in turn, ecological factors may drive patterns of genetic diversity. However, the relationship between the genetic diversity of a population and how this interacts with ecological processes has so far only been investigated in a few studies. Here, we investigate the link between ecological factors, local population size, and allelic diversity, using a field study of a common bird species, the house sparrow (Passer domesticus). We studied sparrows outside the breeding season in a confined small valley dominated by dispersed farms and small-scale agriculture in southern France. Population surveys at 36 locations revealed that sparrows were more abundant in locations with high food availability. We then captured and genotyped 891 house sparrows at 10 microsatellite loci from a subset of these locations (N = 12). Population genetic analyses revealed weak genetic structure, where each locality represented a distinct substructure within the study area. We found that food availability was the main factor among others tested to influence the genetic structure between locations. These results suggest that ecological factors can have strong impacts on both population size per se and intrapopulation genetic variation even at a small scale. On a more general level, our data indicate that a patchy environment and low dispersal rate can result in fine-scale patterns of genetic diversity. Given the importance of genetic diversity for population viability, combining ecological and genetic data can help to identify factors limiting population size and determine the conservation potential of populations

Validation of a Spanish language version of the pain self-perception scale in patients with fibromyalgia

<p>Abstract</p> <p>Background</p> <p>The Pain Self-Perception Scale (PSPS) is a 24-item questionnaire used to assess mental defeat in chronic pain patients. The aim of this study was to develop a Spanish language version of the PSPS (PSPS-Spanish), to assess the instrument's psychometric properties in a sample of patients with fibromyalgia and to confirm a possible overlapping between mental defeat and pain catastrophizing.</p> <p>Methods</p> <p>The PSPS was translated into Spanish by three bilingual content and linguistic experts, and then back-translated into English to assess for equivalence. The final Spanish version was administered, along with the Hospital Anxiety Depression Scale (HADS), Pain Visual Analogue Scale (PVAS), Pain Catastrophizing Scale (PCS) and Fibromyalgia Impact Questionnaire (FIQ), to 250 Spanish patients with fibromyalgia.</p> <p>Results</p> <p>PSPS-Spanish was found to have high internal consistency (Cronbach's α = 0.90 and the item-total <it>r </it>correlation coefficients ranged between 0.68 and 0.86). Principal components analysis revealed a one-factor structure which explained 61.4% of the variance. The test-retest correlation assessed with the intraclass correlation coefficient, over a 1-2 weeks interval, was 0.78. The total PSPS score was significantly correlated with all the questionnaires assessed (HADS, PVAS, PCS, and FIQ).</p> <p>Conclusions</p> <p>The Spanish version of the PSPS appears to be a valid tool in assessing mental defeat in patients with fibromyalgia. In patients with fibromyalgia and Post-Traumatic Stress Disorder (PTSD), PSPS-Spanish correlates more intensely with FIQ than in patients without PTSD. Mental defeat seems to be a psychological construct different to pain catastrophizing.</p

Connecting species’ geographical distributions to environmental variables: range maps versus observed points of occurrence

Connecting the geographical occurrence of a species with underlying environmental variables is fundamental for many analyses of life history evolution and for modeling species distributions for both basic and practical ends. However, raw distributional information comes principally in two forms: points of occurrence (specific geographical coordinates where a species has been observed), and expert-prepared range maps. Each form has potential short-comings: range maps tend to overestimate the true occurrence of a species, whereas occurrence points (because of their frequent non-random spatial distribution) tend to underestimate it. Whereas previous comparisons of the two forms have focused on how they may differ when estimating species richness, less attention has been paid to the extent to which the two forms actually differ in their representation of a species’ environmental associations. We assess such differences using the globally distributed avian order Galliformes (294 species). For each species we overlaid range maps obtained from IUCN and point-of-occurrence data obtained from GBIF on global maps of four climate variables and elevation. Over all species, the median difference in distribution centroids was 234 km, and median values of all five environmental variables were highly correlated, although there were a few species outliers for each variable. We also acquired species’ elevational distribution mid-points (mid-point between minimum and maximum elevational extent) from the literature; median elevations from point occurrences and ranges were consistently lower (median −420 m) than mid-points. We concluded that in most cases occurrence points were likely to produce better estimates of underlying environmental variables than range maps, although differences were often slight. We also concluded that elevational range mid-points were biased high, and that elevation distributions based on either points or range maps provided better estimates

Evidence-based selection on the appropriate FIT cut-off point in CRC screening programs in the COVID pandemic

Background: The COVID pandemic has forced the closure of many colorectal cancer (CRC) screening programs. Resuming these programs is a priority, but fewer colonoscopies may be available. We developed an evidence-based tool for decision-making in CRC screening programs, based on a fecal hemoglobin immunological test (FIT), to optimize the strategy for screening a population for CRC. Methods: We retrospectively analyzed data collected at a regional CRC screening program between February/2014 and November/2018. We investigated two different scenarios: not modifying vs. modifying the FIT cut-off value. We estimated program outcomes in the two scenarios by evaluating the numbers of cancers and adenomas missed or not diagnosed in due time (delayed). Results: The current FIT cut-off (20-mu g hemoglobin/g feces) led to 6, 606 colonoscopies per 100, 000 people invited annually. Without modifying this FIT cut-off value, when the optimal number of individuals invited for colonoscopies was reduced by 10-40%, a high number of CRCs and high-risk adenomas (34-135 and 73-288/100.000-people invited, respectively) will be undetected every year. When the FIT cut-off value was increased to where the colonoscopy demand matched the colonoscopy availability, the number of missed lesions per year was remarkably reduced (9-36 and 29-145/100.000 people, respectively). Moreover, the unmodified FIT scenario outcome was improved by prioritizing the selection process based on sex (males) and age, rather than randomly reducing the number invited. Conclusions: Assuming a mismatch between the availability and demand for annual colonoscopies, increasing the FIT cut-off point was more effective than randomly reducing the number of people invited. Using specific risk factors to prioritize access to colonoscopies should be also considered

A stepwise algorithm for linearly combining biomakers under Youden Index maximisation

Combining multiple biomarkers to provide predictive models with a greater discriminatory ability is a discipline that has received attention in recent years. Choosing the probability threshold that corresponds to the highest combined marker accuracy is key in disease diagnosis. The Youden index is a statistical metric that provides an appropriate synthetic index for diagnostic accuracy and a good criterion for choosing a cut-off point to dichotomize a biomarker. In this study, we present a new stepwise algorithm for linearly combining continuous biomarkers to maximize the Youden index. To investigate the performance of our algorithm, we analyzed a wide range of simulated scenarios and compared its performance with that of five other linear combination methods in the literature (a stepwise approach introduced by Yin and Tian, the min-max approach, logistic regression, a parametric approach under multivariate normality and a non-parametric kernel smoothing approach). The obtained results show that our proposed stepwise approach showed similar results to other algorithms in normal simulated scenarios and outperforms all other algorithms in non-normal simulated scenarios. In scenarios of biomarkers with the same means and a different covariance matrix for the diseased and non-diseased population, the min-max approach outperforms the rest. The methods were also applied on two real datasets (to discriminate Duchenne muscular dystrophy and prostate cancer), whose results also showed a higher predictive ability in our algorithm in the prostate cancer databas