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Abstract Large language models (LLMs) with retrieval-augmented generation (RAG) have improved information extraction over previous methods, yet their reliance on embeddings often leads to inefficient retrieval. We introduce CLinical Entity Augmented Retrieval (CLEAR), a RAG pipeline that retrieves information using entities. We compared CLEAR to embedding RAG and full-note approaches for extracting 18 variables using six LLMs across 20,000 clinical notes. Average F1 scores were 0.90, 0.86, and 0.79; inference times were 4.95, 17.41, and 20.08 s per note; average model queries were 1.68, 4.94, and 4.18 per note; and average input tokens were 1.1k, 3.8k, and 6.1k per note for CLEAR, embedding RAG, and full-note approaches, respectively. In conclusion, CLEAR utilizes clinical entities for information retrieval and achieves >70% reduction in token usage and inference time with improved performance compared to modern methods.

Conflict of interest: No Introduction and Objectives: Transient elastography (TE) is an alternative diagnostic tool for estimating liver steatosis and fibrosis. Two ET devices, iLivTouch®/UAP and Fibroscan®/CAP, have shown similar diagnostic accuracy; however, no studies have evaluated the correlation of liver steatosis and fibrosis measurements between both devices in the Mexican population. Aim: To evaluate the correlation of measurement values of liver steatosis and liver fibrosis using iLivTouch®/UAP and Fibroscan®/CAP. Patients / Materials and Methods: This prospective study included adult patients who attended a check-up unit. The evaluation of liver steatosis and liver fibrosis was performed using TE with two devices (iLivTouch® FT100 and Fibroscan® 502 Touch), meeting the reliability parameters. The correlation of measurements was evaluated with the Pearson correlation coefficient; meanwhile, a Student's t-test was performed to determine differences in dB/m and kPa means. Results and Discussion: A total of 69 patients were included; 57% (n=40) were men, with a mean age and body mass index of 45±10 years and 22.4±4.5 kg/m2, respectively. The prevalence of diabetes mellitus was 7.2% (n=5). The mean dB/m and kPa were 266±42 dB/m and 5.8±1.2 kPa with iLivTouch® and 243±56 dB/m and 4.0±0.8 kPa with Fibroscan®. The prevalence of liver steatosis was 29% (n=20) by iLivTouch® and 20% (n=14) by Fibroscan® (p=0.32), while fibrosis was 49.3% (n=34) and 2.9%. (n=2), respectively, (p=0.0001). The mean difference for dB/m was -22.6, p≤0.0001, while for kPa, the difference was 1.72, p≤0.0001. According to the correlation analysis, this was r=0.73 (strong), p≤0.0001 for the estimation of liver steatosis, and r=0.35 (moderate), p=0.003 for the estimation of liver fibrosis. Conclusions: TE devices iLivTouch® and Fibroscan® show a strong correlation for estimating liver steatosis and a moderate correlation for estimating liver fibrosis.

Abstract Silicon carbide (SiC) has outstanding physical properties therefore, diodes based on SiC are being considered for many radiation detection applications such as particle accelerator experiments and medical dosimetry. Moreover, by reducing the metal on the surface of the diode there is the potential to enhance its performance in some fields where the presence of metal is detrimental. To this end, SiC detectors with an epitaxially-grown graphene layer (EG), that substitutes the metallic contact, in the sensitive region were produced at IMB-CNM, profiting from the conductivity of the mono-atomic layer material. To isolate the effect of the graphene on the charge collection, samples without graphene were produced in parallel. In this paper, the effect of EG on Silicon Carbide p-in-n radiation detectors is studied in terms of charge collection with a radioactive source and by means of the transient current technique (TCT), which allows for position-dependent signal formation analysis. As a result of the former, we show the capability of the EG-SiC sensor for charge collection after signal integration, to a resolution close to that of a sensor fully metallised. Moreover, from the TCT studies, we observe uniform charge collection across the active region, as well as an up-to $$\sim $$ ∼ 40% transient amplitude damping which, compared with the $$\sim $$ ∼ 90% on the sample containing no metallic contact, proves that the presence of graphene benefits the performance of the device and that the technology is viable for radiation detection as an alternative to metal.

Spectrep (Spectral Representation) implements a text representation framework for obtaining a multi-view representation that consolidates lexical, syntactic, and semantic features by structuring a workflow with popular text processing tools such as NTLK, Gensim, and Self-Organizing Maps. This framework is intended to be used in text pre-processing for its application in more robust machine learning applications. This software is open-source and implemented in Python. It has been successfully tested with several tasks to prove its functionality.

The next generation of wearable biosensors comes with the latest advancements in biosensor technology. Soft and stretchable electrode materials like hydrogels with the similar functionalities of human tissue including stretchability, self-healability, and responsiveness to different stimuli have emerged as the most versatile materials in wearable electronics. The incorporation of conductive nanofillers is found to enhance the sensitivity of the electrochemical biosensors significantly. Microfluidic technology has reduced the volume of samples and reagents required for the analysis, allowing continuous biomedical monitoring from a drop of biofluid. In this paper, the most advanced progress in electrochemical wearable platforms that can noninvasively and continuously monitor the biochemical markers in body fluids for the diagnosis and health management is reviewed. Innovation in microelectronics, modification, fabrication technologies, and detection methods are the main focus of the discussion. In particular, hydrogel-based sensors and microfluidic systems as the latest technology trends in wearable detection are discussed in detail. Integration of miniaturized electrochemical wearable biosensors with wireless technology as a great promise for real-time healthcare monitoring and point-of-care (POC) diagnostics is also summarized. Finally, we outline the most advanced wearable biosensors with optimized material and design as well as key challenges that need to be addressed to improve sensing performance (accuracy, sensitivity, selectivity, stability), portability (miniaturized size and light weight), and flexibility of the wearable biosensors.

Abstract Silicon Carbide (SiC) is a radiation hard wide bandgap semiconductor, which makes it an interesting alternative for radiation detector fabrication, with potential applications such as High Energy Physics, synchrotron and radiotherapy instrumentation. In addition, by reducing the amount of metal over the active area of said detectors (typically used for electrical connectivity with the implant of the pn-junction) unwanted effects from secondary interactions which can affect the accuracy of the measurement can be diminished, essential to meet the medical standards of precision. In this article, the use of epitaxially-grown graphene is explored as an alternative to metallic contacts with these prototypes. To this end, the first prototypes of SiC diodes with epitaxial graphene contacts were produced at IMB-CNM for radiation detection,along with reference devices. In order to characterise the feasibility of the technology in the medical application, the dose rate linearity of the SiC device with graphene was measured in a radiotherapy Linac in the dose rate range of 1–6 Gy/min. The response of the device was compared to that observed on devices with similar geometries reported elsewhere. To fully characterise the devices, the same exercise was repeated in a laboratory X-ray tube. Under the later set-up, the prototype is compared against a device with a fully metallised active region.

Abstract Purpose Data-driven diabetes research has increased its interest in exploring the heterogeneity of the disease, aiming to support in the development of more specific prognoses and treatments within the so-called precision medicine. Recently, one of these studies found five diabetes subgroups with varying risks of complications and treatment responses. Here, we tackle the development and assessment of different models for classifying Type 2 Diabetes (T2DM) subtypes through machine learning approaches, with the aim of providing a performance comparison and new insights on the matter. Methods We developed a three-stage methodology starting with the preprocessing of public databases NHANES (USA) and ENSANUT (Mexico) to construct a dataset with N = 10,077 adult diabetes patient records. We used N = 2,768 records for training/validation of models and left the remaining (N = 7,309) for testing. In the second stage, groups of observations –each one representing a T2DM subtype– were identified. We tested different clustering techniques and strategies and validated them by using internal and external clustering indices; obtaining two annotated datasets Dset A and Dset B. In the third stage, we developed different classification models assaying four algorithms, seven input-data schemes, and two validation settings on each annotated dataset. We also tested the obtained models using a majority-vote approach for classifying unseen patient records in the hold-out dataset. Results From the independently obtained bootstrap validation for Dset A and Dset B, mean accuracies across all seven data schemes were $$85.3\%$$ 85.3 % ( $$\pm 9.2\%$$ ± 9.2 % ) and $$97.1\%$$ 97.1 % ( $$\pm 3.4\%$$ ± 3.4 % ), respectively. Best accuracies were $$98.8\%$$ 98.8 % and $$98.9\%$$ 98.9 % . Both validation setting results were consistent. For the hold-out dataset, results were consonant with most of those obtained in the literature in terms of class proportions. Conclusion The development of machine learning systems for the classification of diabetes subtypes constitutes an important task to support physicians for fast and timely decision-making. We expect to deploy this methodology in a data analysis platform to conduct studies for identifying T2DM subtypes in patient records from hospitals.

This paper presents Kawak, a GIS-bigdata model for merging retrospective meteorological data with ground-based observations to achieve comprehensive territorial coverage. This model identifies areas without active ground-based stations and creates virtual stations for these areas. Kawak incorporates AI algorithms for conducting spatio-temporal studies, enabling seamless merging and exploration of climate patterns. We implemented Kawak in a prototype and conducted an exploratory case study by fusing temperature records from MERRA-2 with EMAS ground-based observations in Mexico over 33 years. The findings include: (i) Missing data and outliers in ground-based observations increased over time; (ii) Ground-station territorial coverage gradually reduced; (iii) Differences between ground-based and reanalysis observations were 1.95 and 1.91 Celsius degrees for maximum and minimum yearly temperatures; (iv) Kawak replaces inactive ground stations with virtual stations based on MERRA data, ensuring complete territorial coverage. The implementation of this GIS-bigdata fusion model is a viable solution for achieving whole coverage and supporting decision-making processes based on temperature territory observations, such as defining plans for allocating or replacing ground stations, climate data quality assessment, refining climate models and enhancing the accuracy and reliability of temperature predictions, ultimately supporting effective climate monitoring, adaptation, and mitigation strategies.

The fabrication of macrostructures based on carbon nanostructures (CNS), such as CNS sheets, while maintaining the nanoscale mechanical properties of CNS, poses a significant technological and fundamental challenge. The presence of voids in the structure of CNS sheets significantly impacts their mechanical and electrical reliabilities. This study presents the fabrication of high-strength CNS (carbon nanostructures) sheets achieved through the mechanical densification of CNS sheets produced via the filtration process. Utilizing the filtration process as a potentially scalable and cost-efficient method reliably enhances the mechanical properties of CNS sheets. Within these CNS sheets, carbon nanostructures are densely stacked into macroscopically layered films, reducing voids, and enhancing mechanical properties. These CNS sheets hold promise for various applications, including electromagnetic interference shielding, electronics, energy storage, heat dissipation, among others. The CNS sheets obtained through the densification of CNS layers are analyzed to investigate how surfactant concentration and sonication time impact their mechanical properties. These high-strength CNS sheets, with improved mechanical properties, open doors to diverse applications ranging from electronics to energy storage, promising innovation in multiple fields.

The selection of an appropriate transducer is a key element in biosensor development. Currently, a wide variety of substrates and working electrode materials utilizing different fabrication techniques are used in the field of biosensors. In the frame of this study, the following three specific material configurations with gold-finish layers were investigated regarding their efficacy to be used as electrochemical (EC) biosensors: (I) a silicone-based sensor substrate with a layer configuration of 50 nm SiO/50 nm SiN/100 nm Au/30–50 nm WTi/140 nm SiO/bulk Si); (II) polyethylene naphthalate (PEN) with a gold inkjet-printed layer; and (III) polyethylene terephthalate (PET) with a screen-printed gold layer. Electrodes were characterized using electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV) to evaluate their performance as electrochemical transducers in an aptamer-based biosensor for the detection of cardiac troponin I using the redox molecule hexacyanoferrade/hexacyaniferrade (K3[Fe (CN)6]/K4[Fe (CN)6]. Baseline signals were obtained from clean electrodes after a specific cleaning procedure and after functionalization with the thiolate cardiac troponin I aptamers “Tro4” and “Tro6”. With the goal of improving the PEN-based and PET-based performance, sintered PEN-based samples and PET-based samples with a carbon or silver layer under the gold were studied. The effect of a high number of immobilized aptamers will be tested in further work using the PEN-based sample. In this study, the charge-transfer resistance (Rct), anodic peak height (Ipa), cathodic peak height (Ipc) and peak separation (∆E) were determined. The PEN-based electrodes demonstrated better biosensor properties such as lower initial Rct values, a greater change in Rct after the immobilization of the Tro4 aptamer on its surface, higher Ipc and Ipa values and lower ∆E, which correlated with a higher number of immobilized aptamers compared with the other two types of samples functionalized using the same procedure.

Abstract Surface cleaning of the working electrode has a key role in improved electrochemical and physicochemical properties of the biosensors. Herein, chemical oxidation in piranha, chemical cleaning in potassium hydroxide-hydrogen peroxide, combined (electro-) chemical alkaline treatment, and potential cycling in sulfuric acid were applied to gold finish electrode surfaces deposited onto three different substrates; low temperature co-fired ceramics (LTCC), polyethylene naphthalate (PEN), and polyimide (PI), using three different deposition technologies; screen printing, inkjet printing, and electroplating (printed circuit board technology, PCB) accordingly. The effects of the (electro-) chemical treatments on the gold content and electrochemical responses of LTCC, PEN, and PCB applicable for aptamer-based sensors are discussed. In order to assess the gold surface and to compare the efficiency of the respective cleaning procedures; cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), X-ray photoelectron spectroscopy (XPS), and scanning electron microscopy (SEM) were employed. LTCC sensors electrochemically cycled in sulfuric acid resulted in the most gold content on the electrode surface, the lowest peak potential difference, and the highest charge transfer ability. While, for PEN, the highest elemental gold and the lowest peak-to-peak separation were achieved by a combined (electro-) chemical alkaline treatment. Gold content and electrochemical characteristics on the PCB surface with extremely thin gold layer could be slightly optimized with the chemical cleaning in KOH + H2O2. The proposed cleaning procedures might be generally applied to various kinds of Au electrodes fabricated with the same conditions comparable with those are introduced in this study.

Abstract Knowledge graphs (KGs) are useful data structures for the integration, retrieval, dissemination, and inference of information in various information domains. One of the main challenges in building KGs is the extraction of named entities (nodes) and their relations (edges), particularly when processing unstructured text as it has no semantic descriptions. Generating KGs from texts written in Spanish represents a research challenge as the existing structures, models, and strategies designed for other languages are not compatible in this scenario. This paper proposes a method to design and construct KGs from unstructured text in Spanish. We defined lexical patterns to extract named entities and (non) taxonomic, equivalence, and composition relations. Next, named entities are linked and enriched with DBpedia resources through a strategy based on SPARQL queries. Finally, OWL properties are defined from the predicate relations for creating resource description framework (RDF) triples. We evaluated the performance of the proposed method to determine the degree of elements extracted from the input text and to assess their quality through standard information retrieval measures. The evaluation revealed the feasibility of the proposed method to extract RDF triples from datasets in general and computer science domains. Competitive results were observed by comparing our method regarding an existing approach from the literature.

A high-level control strategy for a quad rotorcraft Unmanned Aircraft System to perform trajectory tracking tasks is presented, which is based on a regressor-based adaptive approach. The high-level control is designed to interact with a low-level (internal) control loop that cannot be modified to suit the needs of academic researchers. Hence, the proposed control framework computes the appropriate high-level inputs for the inner controller, enabling the trajectory tracking task. The controller includes an integral action to overcome steady-state errors that may occur due to parameter estimation errors or constant disturbances. The stability of the equilibrium point is analyzed using Lyapunov theory, which shows that the tracking errors converge to zero and the parameter estimation errors remain bounded. The proposed control framework was tested on a real-time quad rotorcraft platform, and its performance was compared with four different control strategies. The results indicate that the proposed controller exhibits high accuracy and has better performance with respect to the other controllers.

Abstract Cerebral autoregulation (CA) is impaired during acute high-altitude (HA) exposure, however, effects of temporarily living high and working higher on CA require further investigation. In 18 healthy lowlanders (11 women), we hypothesized that the cerebral autoregulation index (ARI) assessed by the percentage change in middle cerebral artery peak blood velocity (Δ%MCAv)/percentage change in mean arterial blood pressure (Δ%MAP) induced by a sit-to-stand maneuver, is (i) reduced on Day1 at 5050 m compared to 520 m, (ii) is improved after 6 days at 5050 m, and (iii) is less impaired during re-exposure to 5050 m after 7 days at 520 m compared to Cycle1. Participants spent 4-8 h/day at 5050 m and slept at 2900 m similar to real-life working shifts. High/low ARI indicate impaired/intact CA, respectively. With the sit-to-stand at 520 m, mean (95% CI) in ΔMAP and ΔMCAv were − 26% (− 41 to − 10) and − 13% (− 19 to − 7), P

Archaeological cobs from Paredones and Huaca Prieta (Peru) represent some of the oldest maize known to date, yet they present relevant phenotypic traits corresponding to domesticated maize. This contrasts with the earliest Mexican macro-specimens from Guila Naquitz and San Marcos, which are phenotypically intermediate for these traits, even though they date more recently in time. To gain insights into the origins of ancient Peruvian maize, we sequenced DNA from three Paredones specimens dating ~6700–5000 calibrated years before present (BP), conducting comparative analyses with two teosinte subspecies (Zea mays ssp. mexicana and parviglumis) and extant maize, that include highland and lowland landraces from Mesoamerica and South America. We show that Paredones maize originated from the same domestication event as Mexican maize and was domesticated by ~6700 BP, implying rapid dispersal followed by improvement. Paredones maize shows no relevant gene flow from mexicana, smaller than that observed in teosinte parviglumis. Thus, Paredones samples represent the only maize without confounding mexicana variation found to date. It also harbors significantly fewer alleles previously found to be adaptive to highlands, but not of alleles adaptive to lowlands, supporting a lowland migration route. Our overall results imply that Paredones maize originated in Mesoamerica, arrived in Peru without mexicana introgression through a rapid lowland migration route, and underwent improvements in both Mesoamerica and South America.

Spoken keyword spotting (KWS) deals with the identification of keywords in audio streams and has become a fast-growing technology thanks to the paradigm shift introduced by deep learning a few years ago. This has allowed the rapid embedding of deep KWS in a myriad of small electronic devices with different purposes like the activation of voice assistants. Prospects suggest a sustained growth in terms of social use of this technology. Thus, it is not surprising that deep KWS has become a hot research topic among speech scientists, who constantly look for KWS performance improvement and computational complexity reduction. This context motivates this paper, in which we conduct a literature review into deep spoken KWS to assist practitioners and researchers who are interested in this technology. Specifically, this overview has a comprehensive nature by covering a thorough analysis of deep KWS systems (which includes speech features, acoustic modeling and posterior handling), robustness methods, applications, datasets, evaluation metrics, performance of deep KWS systems and audio-visual KWS. The analysis performed in this paper allows us to identify a number of directions for future research, including directions adopted from automatic speech recognition research and directions that are unique to the problem of spoken KWS.

Long-term follow-up studies are crucial to ensure surveillance and intervention for late complications after allogeneic stem cell transplantation, but they are scarce on the pediatric population. This study aims to analyze risk factors for long-term transplant outcomes. We report a landmark analysis of 162 pediatric patients who underwent allogeneic transplantation between 1991 and 2016, and survived for at least 12 months after the transplant. With a median follow-up time of 10 years for the survivors, the probability of disease-free survival (DFS) and overall survival (OS) is 81 ± 3 and 88 ± 2%, respectively. Variables that influenced DFS in the univariate analysis were: disease phase (early phase 87 ± 3% vs. advanced phase 74 ± 5%; p = 0.04), acute graft vs. host disease (aGvHD; yes 73 ± 5% vs. no 87 ± 3%; p = 0.038), severe chronic GvHD (cGvHD; yes 41 ± 13% vs. no 85 ± 3%; p = 0.0001), and CD4+ lymphocytes 2 years after the transplant (above the median of 837/μl 98 ± 2% vs. below the median 82 ± 6%, p = 0.026). However, in the multivariate analysis, the only variable that influenced DFS was presence of severe chronic GvHD (yes vs. no, HR 6.25; 95% CI, 1.35–34.48; p = 0.02). Transplant strategies should aim to reduce the risk of severe cGvHD. Immune reconstitution surveillance may help clinicians to better deal with late transplant complications.

BackgroundT-cell depleted (TCD) haploidentical transplantation using CD3+/CD19+ and TCRαβ+/CD19+ depletion techniques has been increasingly used in children with hematological malignancies. We present a retrospective study aimed to compare transplant outcomes in children with leukemia receiving a TCD haploidentical transplant using either CD3+/CD19+ or TCRαβ+/CD19+ platforms.MethodsA total of 159 children with leukemia (ALL=80) (AML=79) that received a TCD haploidentical transplantation using either CD3+/CD19+ (n=79) or TCRαβ+/CD19+ (n=80) platforms between 2005 and 2020 were included. Median age was 9 years in both groups. There were no differences in patient, donor, and transplant characteristics between groups except for donor KIR B genotype more frequent in the TCRαβ+/CD19+ group (91%) than in the CD3+/CD19+ group (76%) (p=0.009) and a high number of NK+ cells and lower CD19+ cells infused in the TCRαβ+/CD19+ group (35.32x106/kg and 0.06 x106/Kg) than in the CD3+/CD19 group (24.6x106/Kg and 0.25 x106/Kg) (p=0.04 and p=0.0001), respectively. Conditioning was based on TBF. Median follow-up for survivors was 11 years (range; 8-16 y) in CD3+/CD19+ group and 5 years (range; 2-9 y) in the TCRαβ+/CD19+ group.ResultsEngraftment kinetics were similar in both groups (13 days for neutrophils and 10 days for platelets). There was no difference in the incidence of acute GvHD II-IV (29 ± 5% in the CD3+/CD19+ group vs 38 ± 5% in the TCRαβ+/CD19+ group) and chronic GvHD (32 ± 5% vs 23 ± 4%, respectively). NRM was 23 ± 5% in the CD3+/CD19+group vs 21 ± 4% in the TCRαβ+/CD19+group. Relapse incidence was also similar, 32 ± 5% vs 34 ± 6%, respectively. DFS and OS were not different (45 ± 5% vs 45 ± 6% and 53 ± 6% vs 58 ± 6% respectively). As there were no differences on transplant outcomes between groups, we further analyzed all patients together for risk factors associated with transplant outcomes. On multivariate analysis, we identified that early disease status at transplant (HR: 0.16; 95%CI (0.07-0.35) (p=0.0001), presence of cGvHD (HR: 0.38; 95%CI (0.20-0.70) (p= 0.002), and donor KIR-B genotype (HR: 0.50; 95%CI (0.32-0.90) (p=0.04) were associated with better DFS.ConclusionsOur data suggest that there are no advantages in transplant outcomes between TCD platforms. Risk factors for survival are dependent on disease characteristic, donor KIR genotype, and chronic GvHD rather than the TCD platform used.

Proteins and peptides are major components of snake venom. Venom protein transcriptomes and proteomes of many snake species have been reported; however, snake venom complexity (i.e., the venom protein-protein interactions, PPIs) remains largely unknown. To detect the venom protein interactions, we used the most common snake venom component, phospholipase A2s (PLA2s) as a “bait” to identify the interactions between PLA2s and 14 of the most common proteins in Western diamondback rattlesnake (Crotalus atrox) venom by using yeast two-hybrid (Y2H) analysis, a technique used to detect PPIs. As a result, we identified PLA2s interacting with themselves, and lysing-49 PLA2 (Lys49 PLA2) interacting with venom cysteine-rich secretory protein (CRISP). To reveal the complex structure of Lys49 PLA2-CRISP interaction at the structural level, we first built the three-dimensional (3D) structures of Lys49 PLA2 and CRISP by a widely used computational program-MODELLER. The binding modes of Lys49 PLA2-CRISP interaction were then predicted through three different docking programs including ClusPro, ZDOCK and HADDOCK. Furthermore, the most likely complex structure of Lys49 PLA2-CRISP was inferred by molecular dynamic (MD) simulations with GROMACS software. The techniques used and results obtained from this study strengthen the understanding of snake venom protein interactions and pave the way for the study of animal venom complexity.

The Chilean workforce has over 200,000 people that are intermittently exposed to altitudes over 4,000 m. In 2012, the Ministry of Health provided a technical guide for high-altitude workers that included a series of actions to mitigate the effects of hypoxia. Previous studies have shown the positive effect of oxygen enrichment at high altitudes. The Atacama Large Millimeter/submillimeter Array (ALMA) radiotelescope operates at 5,050 m [Array Operations Site (AOS)] and is the only place in the world where pressure swing adsorption (PSA) and liquid oxygen technologies have been installed at a large scale. These technologies reduce the equivalent altitude by increasing oxygen availability. This study aims to perform a retrospective comparison between the use of both technologies during operation in ALMA at 5,050 m. In each condition, variables such as oxygen (O2), temperature, and humidity were continuously recorded in each AOS rooms, and cardiorespiratory variables were registered. In addition, we compared portable O2 by using continuous or demand flow during outdoor activities at very high altitudes. The outcomes showed no differences between production procedures (PSA or liquid oxygen) in regulating oxygen availability at AOS facilities. As a result, big-scale installations have difficulties reaching the appropriate O2 concentration due to leaks in high mobility areas. In addition, the PSA plant requires adequacy and maintenance to operate at a very high altitude. A continuous flow of 2–3 l/min of portable O2 is recommended at 5,050 m.

Abstract Background OnabotulinumtoxinA is effective in preventing chronic migraine (CM); however, the benefit of onabotulinumtoxinA in patients with CM with daily headache is unknown because these patients are typically excluded from clinical trials. This subanalysis of the COMPEL Study assessed the efficacy and safety of onabotulinumtoxinA in people with CM with and without daily headache. Methods In total, 715 patients received onabotulinumtoxinA 155 U with or without concomitant oral preventive treatment. Patients who had complete daily diary records for the 28 days of the baseline period were stratified based on daily headache status. The primary outcome variable was reduction in headache-day frequency per 28-day period at 108 weeks (after 9 treatment cycles) relative to baseline. Exploratory outcomes included moderate to severe headache days, migraine disability (using the Migraine Disability Assessment [MIDAS] questionnaire), and health-related quality of life (Migraine-Specific Quality-of-Life Questionnaire v2 [MSQ]). Adverse events and their relatedness were recorded. Results Overall, 641 patients had complete daily diary records at baseline. In patients with daily headache (n = 138) versus without (n = 503), treatment with onabotulinumtoxinA was associated with a significant mean (SD) reduction in 28-day headache-day frequency relative to baseline at week 108 (− 10.5 [9.2] vs − 12.2 [6.7], respectively; both P

An experiment evaluated the effects of reciprocal behavior of a confederate on the behavior of a participant under concurrent individual and shared contingencies. The experimental situation consisted of solving a puzzle on two synchronized computer screens. Sixteen female and male university students were assigned to the same number of dyads, in which the other member was a confederate. They were divided into four groups of four dyads each. Groups 1 and 3 were exposed to an ascending percentage of reciprocal behavior by the confederate (0, 50, and 100%) whereas groups 2 and 4 were exposed to a descending percentage (100, 50, and 0%). All groups were exposed to two shared contingency situations: partial altruism, in which no points or tokens were provided, and unequal, and asymmetric labor exchange, in which different amounts of valuables were exchanged for earned points. Groups 1 and 2 were first exposed to the partial altruism situation whereas groups 3 and 4 were first exposed to the labor exchange situation. Results show that participants in all groups matched the reciprocal behavior of confederates, resulting in partial altruism during percentages larger than 0 in both conditions, with and without points and earnings. These results confirm previous findings suggesting that reciprocity is a function of the behavior of at least one of the members of the dyad. Results are also discussed in relation to the functional relevance for the occurrence of cultural and economic interactions. Keywords Labor exchange * Partial altruism * Reciprocity * Asymmetry * Inequity, A distinction has recently been made between two general types of shared contingencies (Ribes, Pulido, Rangel, & SanchezGatell, 2016). One of them is concerned with interactions that affect the behavior [...]

Introduction and Objectives: Liver function tests (LFT) abnormalities are reported in up to 50% of COVID-19 patients, and metabolic comorbidities are associated with poorer outcomes. The aim of the study was to determine the prevalence of liver steatosis and fibrosis in patients with COVID-19 and their association with clinical outcomes. Material and methods: Retrospective study in hospitalized COVID-19 patients was conducted. The risk for liver steatosis was estimated by HSI > 36, and risk for advanced liver fibrosis with APRI > 1.0, NAFLD FS > 0.675 and/or FIB-4 > 3.25. Clinical outcomes were admission to Intensive Care Unit (ICU) and mortality. Results: Of 155 patients, 71.6% were male (n = 111), and 28.4% (n = 44) were obese. Abnormal LFT were present in 96.8% (n = 150), prevalence of steatosis was 42.6% (n = 66) and of significative liver fibrosis was 44.5% (n = 69). Liver fibrosis by FIB-4 was associated with risk of ICU admission (OR 1.74 [95%CI 1.74–2.68; p = 0.023]) and mortality (OR 6.45 [95%CI 2.01−20.83, p = 0.002]); no independent associations were found. Conclusions: The prevalence of steatosis and significant liver fibrosis was high in COVID-19 patients but was not associated with clinical outcomes.

Abstract Background OnabotulinumtoxinA is effective in treating chronic migraine (CM), but there are limited data assessing how allodynia affects preventive treatment responses. This subanalysis of the 108-week, multicenter, open-label COMPEL Study assessed the efficacy and safety of onabotulinumtoxinA in people with CM with and without allodynia. Methods Patients (n = 715) were treated with onabotulinumtoxinA 155 U every 12 weeks for 9 treatment cycles. The Allodynia Symptom Checklist was used to identify patients with allodynia (scores ≥3). The primary outcome for this subanalysis was reduction in monthly headache days from baseline for weeks 105 to 108 in groups with and without allodynia. Other outcomes included assessments of moderate to severe headache days, disability (using the Migraine Disability Assessment [MIDAS] questionnaire), and health-related quality of life (Migraine-Specific Quality-of-Life Questionnaire [MSQ] v2). Adverse events and their relation to treatment were recorded. Results OnabotulinumtoxinA was associated with a significant mean (SD) reduction in headache day frequency at week 108 relative to baseline in patients with (n = 289) and without (n = 426) allodynia (− 10.8 [7.1] and − 12.5 [7.4], respectively; both P

Creating effective mechanisms to detect misogyny online automatically represents significant scientific and technological challenges. The complexity of recognizing misogyny through computer models lies in the fact that it is a subtle type of violence, it is not always explicitly aggressive, and it can even hide behind seemingly flattering words, jokes, parodies, and other expressions. Currently, it is even difficult to have an exact figure for the rate of misogynistic comments online because, unlike other types of violence, such as physical violence, these events are not registered by any statistical systems. This research contributes to the development of models for the automatic detection of misogynistic texts in Latin American Spanish and contributes to the design of data augmentation methodologies since the amount of data required for deep learning models is considerable.

Clustering is an important task in data analysis to find a partition on an unlabeled dataset based on similarity relationships among its elements. Typically, such similarity is determined by a proximity measure or distance. Then, the optimal partition is the one that minimizes the distance among elements belonging to the same subset and maximizes the distance among elements from different subsets. The way in which the optimal partition is found is called clustering method. The adequateness of the partition found is commonly determined in terms of a validity index. In this paper, we propose a clustering method referred to as quality-driven search for optimal partition (QDSOC) where the search process of the optimal partition is directly driven by a validity index instead of a proximity measure. Our approach allows to efficiently exploring a large solution space via a breed of genetic algorithm, the so-called eclectic genetic algorithm. Unlike existing clustering methods, the proposed QDSOC offers the optimal partition and provides the mathematical model of such partition in terms of a representation based on membership functions. This model describes the points that belong to the subsets in the partition found. Thus, by using this model, we can predict the membership of new objects without performing the search process again. As part of the experimental evaluation, our proposed QDSOC method is compared with k-means and self-organizing maps (SOMs), which are two well-known clustering approaches. The clustering methods were used to solve a wide sample of clustering problems, and using three different validity indices. From the obtained results, we demonstrate that QDSOC statistically outperforms k-means and SOMs. We also point out that our approach does not incur in excessive computational overhead with respect to such traditional clustering methods.

Indoor navigation systems incorporating augmented reality allow users to locate places within buildings and acquire more knowledge about their environment. However, although diverse works have been introduced with varied technologies, infrastructure, and functionalities, a standardization of the procedures for elaborating these systems has not been reached. Moreover, while systems usually handle contextual information of places in proprietary formats, a platform-independent model is desirable, which would encourage its access, updating, and management. This paper proposes a methodology for developing indoor navigation systems based on the integration of Augmented Reality and Semantic Web technologies to present navigation instructions and contextual information about the environment. It comprises four modules to define a spatial model, data management (supported by an ontology), positioning and navigation, and content visualization. A mobile application system was developed for testing the proposal in academic environments, modeling the structure, routes, and places of two buildings from independent institutions. The experiments cover distinct navigation tasks by participants in both scenarios, recording data such as navigation time, position tracking, system functionality, feedback (answering a survey), and a navigation comparison when the system is not used. The results demonstrate the system’s feasibility, where the participants show a positive interest in its functionalities.

The data produced by sensors of IoT devices are becoming keystones for organizations to conduct critical decision-making processes. However, delivering information to these processes in real-time represents two challenges for the organizations: the first one is achieving a constant dataflow from IoT to the cloud and the second one is enabling decision-making processes to retrieve data from dataflows in real-time. This paper presents a cloud-based Web of Things method for creating digital twins of IoT devices (named sentinels).The novelty of the proposed approach is that sentinels create an abstract window for decision-making processes to: (a) find data (e.g., properties, events, and data from sensors of IoT devices) or (b) invoke functions (e.g., actions and tasks) from physical devices (PD), as well as from virtual devices (VD). In this approach, the applications and services of decision-making processes deal with sentinels instead of managing complex details associated with the PDs, VDs, and cloud computing infrastructures. A prototype based on the proposed method was implemented to conduct a case study based on a blockchain system for verifying contract violation in sensors used in product transportation logistics. The evaluation showed the effectiveness of sentinels enabling organizations to attain data from IoT sensors and the dataflows used by decision-making processes to convert these data into useful information.