Miracle-dz/UniFAR-FTUs huggingface dataset:
A Strategy for Interpretation of Microearthquake Tomography Results in the Salton Sea Geothermal Field Based upon Rock Physics Interpretations of State 2-14 Borehole Logs

We devise a strategy for analysis of Vp and Vs microearthquake tomography results in the Salton Sea geothermal field to identify important features of the geothermal reservoir. We first interpret rock properties in State 2-14 borehole based upon logged core through the reservoir. Then, we interpret seismic recordings in the well (Daley et al., 1988) to develop the strategy. We hypothesize that mapping Poisson's ratio has two applications for the Salton Sea geothermal reservoir: (1) to map the top of the reservoir, and (2) as a diagnostic for permeable zones. Poisson's ratio can be obtained from Vp and Vs. In the State 2-14 borehole, Poisson's ratio calculated from large scale averages ({approx} 150 m) shows a monotonic decrease with depth to about 1300 m, at which point it increases with depth. Our model is that the monotonic decrease is due to compaction, and the increase below 1300 m is due to the rocks being hydrothermally altered. We hypothesize we can map the depth to alteration by identifying the transition from decreasing to increasing values; and thus, map the top of the reservoir, which is associated with a known increase in sulfite, chlorite, and epidote alteration that may be indicative ofmore » hydrothermal activity. We also observe (from Daley et. al. plots) an anomalous drop in Poisson's ratio at a depth of about 900 m, within a sandstone formation. The sandstone has a P-wave velocity significantly higher than the siltstone above it but a lower velocity in the lower half of the formation relative to the upper half. We interpret the relative decrease in velocity to be due to fracturing and chemical alteration caused by permeability. We conclude that using Vp and Vs tomography results to obtain images of Poisson's ratio has the potential to identify significant features in the geothermal reservoir in this geologic setting. Seismic attenuation tomography results (mapped as Qp and Qs) should also be useful for evaluating geothermal reservoirs, but that is not addressed at this time. « less
Combining the Physical–Chemical Approach with Standard Base Excess to Understand the Compensation of Respiratory Acid–Base Derangements: An Individual Participant Meta-analysis Approach to Data from Multiple Canine and Human Experiments

Background: Several studies explored the interdependence between Paco2 and bicarbonate during respiratory acid–base derangements. The authors aimed to reframe the bicarbonate adaptation to respiratory disorders according to the physical–chemical approach, hypothesizing that (1) bicarbonate concentration during respiratory derangements is associated with strong ion difference; and (2) during acute respiratory disorders, strong ion difference changes are not associated with standard base excess. Methods: This is an individual participant data meta-analysis from multiple canine and human experiments published up to April 29, 2021. Studies testing the effect of acute or chronic respiratory derangements and reporting the variations of Paco2, bicarbonate, and electrolytes were analyzed. Strong ion difference and standard base excess were calculated. Results: Eleven studies were included. Paco2 ranged between 21 and 142 mmHg, while bicarbonate and strong ion difference ranged between 12.3 and 43.8 mM, and 32.6 and 60.0 mEq/l, respectively. Bicarbonate changes were linearly associated with the strong ion difference variation in acute and chronic respiratory derangement (β-coefficient, 1.2; 95% CI, 1.2 to 1.3; P < 0.001). In the acute setting, sodium variations justified approximately 80% of strong ion difference change, while a similar percentage of chloride variation was responsible for chronic adaptations. In the acute setting, strong ion difference variation was not associated with standard base excess changes (β-coefficient, –0.02; 95% CI, –0.11 to 0.07; P = 0.719), while a positive linear association was present in chronic studies (β-coefficient, 1.04; 95% CI, 0.84 to 1.24; P < 0.001). Conclusions: The bicarbonate adaptation that follows primary respiratory alterations is associated with variations of strong ion difference. In the acute phase, the variation in strong ion difference is mainly due to sodium variations and is not paralleled by modifications of standard base excess. In the chronic setting, strong ion difference changes are due to chloride variations and are mirrored by standard base excess. The authors take an individual participant meta-analysis approach to data from multiple canine and human experiments, combining the physical–chemical approach with standard base excess to better understand the compensation of respiratory acid–base derangements. They find that bicarbonate adaptation that follows primary respiratory alterations is associated with variations of strong ion difference. In the acute phase, the variation in strong ion difference is mainly due to sodium variations and is not paralleled by modifications of standard base excess. In the chronic setting, strong ion difference changes are due to chloride variations and are mirrored by standard base excess.
The Therapeutic Potential of Angeli’s Salt in Mitigating Acute Trypanosoma cruzi Infection in Mice

Chagas disease (CD), caused by Trypanosoma cruzi, is a neglected tropical disease prevalent in Latin America. Infected patients are treated to eliminate the parasite, reduce the cardiomyopathy risk, and interrupt the disease transmission cycle. The World Health Organization recognizes benznidazole (BZ) and nifurtimox as effective drugs for CD treatment. In the chronic phase, both drugs have low cure rates and serious side effects. T. cruzi infection causes intense tissue inflammation that controls parasite proliferation and CD evolution. Compounds that liberate nitric oxide (NO) (NO donors) have been used as anti-T. cruzi therapeutics. Currently, there is no evidence that nitroxyl (HNO) affects T. cruzi infection outcomes. This study investigated the effects of the HNO donor Angeli’s salt (AS) on C57BL/6 mice infected with T. cruzi (Y strain, 5 × 103 trypomastigotes, intraperitoneally). AS reduced the number of parasites in the bloodstream and heart nests and increased the protective antioxidant capacity of erythrocytes in infected animals, reducing disease severity. Furthermore, in vitro experiments showed that AS treatment reduced parasite uptake and trypomastigote release by macrophages. Taken together, these findings from the murine model and in vitro testing suggest that AS could be a promising therapy for CD.
Behavioural strategies to reduce obesity among lower socio-economic adults living in high-income countries: a Grades of Recommendation, Assessment, Development and Evaluation-assessed systematic review and meta-analysis of randomised controlled trials

Abstract Adult obesity disproportionately affects lower socio-economic groups in high-income countries and perpetuates health inequalities, imposing health and socio-economic burden. This review evaluates the effectiveness of behavioural strategies in reducing weight and cardiovascular disease (CVD) risks among low-income groups based in high-income countries. We searched major databases for randomised controlled trials published between 1 November 2011 and 1 May 2023. Meta-analyses and subgroup analyses were undertaken to analyse the pooled and individual effects of behavioural strategies. Cochrane Risk of bias (RoB 2·0) tool and Grades of Recommendation, Assessment, Development and Evaluation (GRADE) criteria were used to assess the quality and certainty of evidence. Fourteen trials (3618 adults, aged 40·2 ± 9·7 years with BMI 33·6 ± 2·8 kg/m2) and nine unique interventions were identified. Three trials with high RoB were omitted. Meta-analysis favoured interventions, demonstrating significant reductions in body weight (MD: −1·56 kg, (95 % CI –2·09, −1·03)) and HbA1c (MD: −0·05 %, (95 % CI − 0·10, −0·001)) at intervention end. Sub-group analysis showed no differences in waist circumference, blood pressure or serum lipids. Financial incentives and interactive feedback produced greatest amounts of weight losses ≥ 2 kg (GRADE: moderate). Behavioural strategies are effective weight loss interventions among lower socio-economic groups living in high-income nations. However, the impact on CVD risk remains unclear.
Nasal discharge: A unique enhanced alternative for the detection of respiratory pathogens in adults

Upper respiratory tract infections are a significant cause of social‐ and disease burden worldwide. Currently, invasive and uncomfortable molecular detection methods are used for respiratory pathogen detection. We aimed to assess the ability and bearability of a rhinorrhea swab (RS) to detect respiratory pathogens in comparison to the combined nasopharyngeal and oropharyngeal swab (NP/OP). This study was performed at a Public Health Service severe acute respiratory syndrome coronavirus‐2 (SARS‐CoV‐2) testing facility between November and December 2022 in the Netherlands. Adults aged 16 years and older, being subjected to a standard of care NP/OP swab with nasal discharge, were included and received an additional RS. Respiratory pathogen detection was evaluated using SARS‐CoV‐2 polymerase chain reaction (PCR) and multiplex ligation‐dependent probe amplification (MLPA) PCR. Bearability was evaluated using visual analog scale (VAS) scores and a questionnaire. A total of 100 adults with a mean age ± SD of 46 ± 16 years were included. The NP/OP swab detected 104 pathogens, the RS 83 pathogens (p < 0.001), and in total 108 respiratory pathogens were identified in 89 adults (89%). The ability to detect respiratory pathogens compared between the RS and the combined NP/OP swab revealed a sensitivity of 82% (95% CI 73%–89%) and specificity of 100% (95% CI 72%–100%). RS were significantly more bearable than the combined NP/OP swab (p value < 0.001). Therefore, nasal discharge found in adults can be used as an adequate reliable medium for respiratory pathogen detection using SARS‐CoV‐2 PCR and MLPA PCR.
Container-based Virtualization for Real-time Industrial Systems—A Systematic Review

Industrial Automation and Control systems have matured into a stable infrastructure model that has been kept fundamentally unchanged, using discrete embedded systems (such as Programmable Logic Controllers) to implement the first line of sensorization, actuation, and process control and stations and servers providing monitoring, supervision, logging/database and data-sharing capabilities, among others. More recently, with the emergence of the Industry 4.0 paradigm and the need for more flexibility, there has been a steady trend towards virtualizing some of the automation station/server components, first by using virtual machines and, more recently, by using container technology. This trend is pushing for better support for real-time requirements on enabling virtualization technologies such as virtual machines and containers. This article provides a systematic review on the use of container virtualization in real-time environments such as cyber-physical systems, assessing how existing and emerging technologies can fulfill the associated requirements. Starting by reviewing fundamental concepts related to container technology and real-time requirements, it goes on to present the methodology and results of a systematic study of 37 selected papers covering aspects related to the enforcement of real-time constrains within container hosts and the expected task latency on such environments, as well as an overview of container platforms and orchestration mechanisms for RT systems.
Factores determinantes del salario relativo de los trabajadores cualificados en la industria manufacturera española 1989-1998

Dos son las teorias mas extendidas que tratan de explicar la evolucion del salario relativo de los trabajadores cualificados respecto de los no cualificados: la laboral y la comercial. En este trabajo se revisan los determinantes que cada postura sostiene que influyen en el salario relativo. A continuacion se observa la evolucion de los mismos en las ramas manufactureras espanolas entre 1989 y 1998 y se realizan previsiones de los signos de los determinantes segun las teorias anteriormente senaladas. Finalmente se realiza, mediante las tecnicas econometricas de datos de panel, la contrastacion de la capacidad de cada una de las dos teorias para explicar el salario relativo en el sector de la industria manufacturera espanola.
Single nuclei transcriptomics in human and non-human primate striatum implicates neuronal DNA damage and proinflammatory signaling in opioid use disorder

The striatum in the brain is involved in various behavioral functions, including reward, and disease processes, such as opioid use disorder (OUD). Further understanding of the role of striatal subregions in reward behaviors and their potential associations with OUD requires molecular identification of specific striatal cell types in human brain. The human striatum contains subregions based on different anatomical, functional, and physiological properties, with the dorsal striatum further divided into caudate and putamen. Both caudate and putamen are involved in altered reward processing, formation of habits, and development of negative affect states associated with OUD. Using single nuclei RNA-sequencing of human postmortem caudate and putamen, we identified canonical neuronal cell types in striatum (e.g., dopamine receptor 1 or 2 expressing neurons, D1 or D2) and less abundant subpopulations, including D1/D2-hybrid neurons and multiple classes of interneurons. By comparing unaffected subjects to subjects with OUD, we found neuronal-specific differences in pathways related to neurodegeneration, interferon response, and DNA damage. DNA damage markers were also elevated in striatal neurons of rhesus macaques following chronic opioid administration. We also identified sex-dependent differences in the expression of stress-induced transcripts among astrocytes and oligodendrocytes from female subjects with OUD. Thus, we describe striatal cell types and leverage these data to gain insights into molecular alterations in human striatum associated with opioid addiction.
Online Learning for Dynamic Impending Collision Prediction using FMCW Radar

Radar collision prediction systems can play a crucial role in safety critical applications, such as autonomous vehicles and smart helmets for contact sports, by predicting an impending collision just before it will occur. Collision prediction algorithms use the velocity and range measurements provided by radar to calculate time to collision. However, radar measurements used in such systems contain significant clutter, noise, and inaccuracies which hamper reliability. Existing solutions to reduce clutter are based on static filtering methods. In this paper, we present a deep learning approach using frequency modulated continuous wave (FMCW) radar and inertial sensing that learns the environmental and user-specific conditions that lead to future collisions. We present a process of converting raw radar samples to range-Doppler matrices (RDMs) and then training a deep convolutional neural network that outputs predictions (impending collision vs. none) for any measured RDM. The system is retrained to work in dynamically changing environments and maintain prediction accuracy. We demonstrate the effectiveness of our approach of using the information from radar data to predict impending collisions in real-time via real-world experiments, and show that our method achieves an F1-score of 0.91 and outperforms a traditional approach in accuracy and adaptability.
Data Quality in Data Streams by Modular Change Point Detection

Sensors that collect data from complex systems generate a stream of measurements, for example, measuring CPU utilization of machines in a data center, gathering meteorological data like atmospheric pressure and humidity levels across the USA, or tracking the occupancy of taxis in a large city. Downstream systems use the streamed data in a variety of applications, including training machine learning models and making data-driven decisions as part of automation. This makes data quality critical and requires detecting significant, unexpected, and rapid changes in indicative features of the streaming data. This can be done by detecting change points in the stream – points where the underlying distribution of a statistical feature of the stream fundamentally changes. In this paper, we discuss different types of change points in the data stream – changes that indicate a potential data quality problem. We present a modular method for combining operations on data streams to examine data quality in a flexible and adaptable way. Experiments over real-world and synthetic data streams show the effectiveness of the modular approach in comparison to traditional anomaly detection methods.
RETAIN: Interpretable Predictive Model in Healthcare using Reverse Time Attention Mechanism

Accuracy and interpretation are two goals of any successful predictive models. Most existing works have to suffer the tradeoff between the two by either picking complex black box models such as recurrent neural networks (RNN) or relying on less accurate traditional models with better interpretation such as logistic regression. To address this dilemma, we present REverse Time AttentIoN model (RETAIN) for analyzing Electronic Health Records (EHR) data that achieves high accuracy while remaining clinically interpretable. RETAIN is a two-level neural attention model that can find influential past visits and significant clinical variables within those visits (e.g,. key diagnoses). RETAIN mimics physician practice by attending the EHR data in a reverse time order so that more recent clinical visits will likely get higher attention. Experiments on a large real EHR dataset of 14 million visits from 263K patients over 8 years confirmed the comparable predictive accuracy and computational scalability to the state-of-the-art methods such as RNN. Finally, we demonstrate the clinical interpretation with concrete examples from RETAIN.
El éxito de la estrategia internacional de Fluidra: análisis del modelo de negocio

espanolEl presente articulo reflexiona, utilizando la metodologia del estudio de caso, sobre cuales han sido los aspectos que han conducido al exito de la estrategia internacional de la empresa multinacional catalana Fluidra. Se trata de un buen ejemplo de rapido crecimiento e internacionalizacion, poco habitual entre las empresas manufactureras espanolas. A traves del analisis de su modelo de negocio se observa como la empresa presenta numerosos circulos virtuosos, donde su estrategia de diversificacion, tanto horizontal como vertical, y su capacidad de innovacion impulsan de manera decisiva su internacionalizacion. EnglishThe present article reflects through a case study methodology on the aspects that have driven the success of the international strategy of the catalan multinational company Fluidra. This case represents a good example of intensive growth and quick internationalization, not very common among Spanish manufacturing firms. Based on the analysis of its business model, we observe that Fluidra presents many virtuous cycles, where the diversification strategy (horizontal and vertical), and its innovation capacity boosts strongly its internationalization.
DEBBIES to compare life history strategies across ectotherms

Demographic models are used to explore how life history traits structure life history strategies across species. This study presents the DEBBIES dataset that contains estimates of eight life history traits (length at birth, puberty and maximum length, maximum reproduction rate, fraction energy allocated to respiration versus reproduction, von Bertalanffy growth rate, mortality rates) for 185 ectotherm species. The dataset can be used to parameterise dynamic energy budget integral projection models (DEB-IPMs) to calculate key demographic quantities like population growth rate and demographic resilience, but also link to conservation status or biogeographical characteristics. Our technical validation shows a satisfactory agreement between observed and predicted longevity, generation time, age at maturity across all species. Compared to existing datasets, DEBBIES accommodates (i) easy cross-taxonomical comparisons, (ii) many data-deficient species, and (iii) population forecasts to novel conditions because DEB-IPMs include a mechanistic description of the trade-off between growth and reproduction. This dataset has the potential for biologists to unlock general predictions on ectotherm population responses from only a few key life history traits.
Toward Scalable Docker-Based Emulations of Blockchain Networks

Blockchain, like any other complex technology, needs a strong testing methodology to support its evolution in both research and development contexts. Setting up meaningful tests for permissionless blockchain technology is a notoriously complex task for several reasons: software is complex, large number of nodes are involved, network is non ideal, etc. Developers usually adopt small virtual laboratories or costly real devnets, based on real software. Researchers usually prefer simulations of a large number of nodes, based on simplified models. In this paper, we aim to obtain the advantages of both approaches, i.e., performing large, realistic, inexpensive, and flexible experiments, using real blockchain software within a virtual environment. To do that, we tackle the challenge of running large blockchain networks in a single physical machine, leveraging Linux and Docker. We analyze a number of problems that arise when large blockchain networks are emulated and we provide technical solutions for all of them. Finally, we describe two experiences of emulating fairly large blockchain networks on a single machine, adopting both research oriented and production oriented software, and involving up to more than 3000 containers.
Generating Data Augmentation Queries Using Large Language Models

Users often want to augment entities in their datasets with relevant information from external data sources. As many external sources are accessible only via keyword-search interfaces, a user usually has to manually formulate a keyword query that extracts relevant information for each entity. This is challenging as many data sources contain numerous tuples, only a small fraction of which may be relevant. Moreover, different datasets may represent the same information in distinct forms and under different terms. In such cases, it is difficult to formulate a query that precisely retrieves information relevant to a specific entity. Current methods for information enrichment mainly rely on resource-intensive manual effort to formulate queries to discover relevant information. However, it is often important for users to get initial answers quickly and without substantial investment in resources (such as human attention). We propose a progressive approach to discovering entity-relevant information from external sources with minimal expert intervention. It leverages end users’ feedback to progressively learn how to retrieve information relevant to each entity in a dataset from external data sources. To bootstrap performance, we use a pre-trained large language model (LLM) to produce rich representations of entities. We evaluate the use of parameter efficient techniques for aligning the LLM’s representations with our downstream task of online query policy learning and find that even lightweight fine-tuning methods can effectively adapt encodings to domain-specific data.