Miracle-dz/UniFAR-FTUs huggingface dataset:
Ethical considerations in utilizing artificial intelligence for analyzing the NHGRI’s History of Genomics and Human Genome Project archives

Understanding “how to optimize the production of scientific knowledge” is paramount to those who support scientific research—funders as well as research institutions—to the communities served, and to researchers. Structured archives can help all involved to learn what decisions and processes help or hinder the production of new knowledge. Using artificial intelligence (AI) and large language models (LLMs), we recently created the first structured digital representation of the historic archives of the National Human Genome Research Institute (NHGRI), part of the National Institutes of Health.
Evaluation of Produced Vaccines against Pseudomonas aeruginosa

Pseudomonas aeruginosa is the leader among gram-negative bacteria in causing burn wound infections. Exotoxin A (ETA) is a major virulence factor that produced by this organism. Specifically infecting strains tend to elaborate a smooth lipopolysaccharide (LPS) and they are resistant to the bactericidal effects of serum. It is important that antitoxin A antibody and anti-LPS antibody may provide protection through an independent and additive mechanisms.Howeverappears that optimal protection against P. aeruginosa would be obtained by use of a vaccine capable of engendering both anti-LPS and antitoxin A antibodies. P.aeruginosa is motile with a single polar flagellum that has played an important role in the pathogenesis. Promising results in researchs show prevention of the acquisition of P. aeruginosa infection in CF patients immunized with a bivalent type a and b flagellum vaccines. One of the most component of colonization is the adhesion of type IV pili to asialo-GM1 receptors on the surface of epithelial cells. The toxin-pilin protein can candidate vaccine to prevent Pseudomonas colonization in CF. P. aeruginosa has an outer membrane which contains Protein F (OprF). The outer membrane protein F gene (oprF) of Pseudomonas aeruginosa was recently showed protect in mice against P. aeruginosa chronic pulmonary infection. P. aeruginosa is an opportunist pathogen that causes acute life-threatening infections, including pneumonia and bacteremia, individuals with immunocompromised. The control of infectious diseases requires multiple approaches and vaccination is an extremely attractive way that can induce long-term protection.
Enhancing the Predictability of Seasonal Streamflow With a Statistical‐Dynamical Approach

Seasonal streamflow forecasts facilitate water allocation, reservoir operation, flood risk management, and crop forecasting. They are generally computed by forcing hydrological models with outputs from general circulation models (GCMs) or using large‐scale climate indices as predictors in statistical models. In contrast, hybrid statistical‐dynamical forecasts (combining statistical methods with dynamical climate predictions) are still uncommon, and their skill is largely unknown. Here we conduct systematic forecasting of seasonal streamflow using eight GCMs from the North American Multi‐Model Ensemble, 0.5–9.5 months ahead, at 290 stream gauges in the U.S. Midwest. Probabilistic forecasts are developed for low to high streamflow using predictors that reflect climatic and anthropogenic influences. Results indicate that GCM forecasts of climate and antecedent climatic conditions enhance seasonal streamflow predictability; while land cover and population density predictors decrease biases or enhance skill in certain catchments. This paper paves the way for novel forecasting approaches using dynamical GCM predictions within statistical frameworks.
The toxicity of Copper Sulphate and Atrazine to the diatom Aulacoseira Granulata (Ehrenberg) Simmons.

: The toxicity of copper sulphate and atrazine to the diatom Aulacoseira granulata (Ehrenberg) Simmons. The use of pesticides in modern agriculture, frequently applied incorrectly and in exaggerated amounts, has grave consequences for the rivers, lakes and reservoirs in which the residues accumulate. Among herbicides, atrazine has been widely employed throughout the world, including in Brazil, to combat weeds among sugar cane, pineapple, soy bean and many other crops. Algae, being physiologically similar to higher plants, are potential targets for these herbicide residues. In the present study, ecotoxicological tests were carried out on the diatom Aulacoseira granulata (Ehrenberg) Simmons, with the aim of determining its sensitivity to atrazine and also to copper sulfate, a reference substance. The results obtained with A. granulata were compared with literature data for other species of algae and macrophytes. The comparison indicated that although A. granulata has low sensitivity to copper, very low concentrations of atrazine are sufficient to inhibit greatly the growth of this diatom.
NRC Publications Archive Archives des publications du CNRC

RuleML is a family of XML languages whose modular system of schemas permits high-precision (Web) rule interchange. The family’s top-level distinction is deliberation rules vs. reaction rules. In this paper we address the Reaction RuleML subfamily of RuleML and survey re-lated work. Reaction RuleML is a standardized rule markup/serialization language and semantic interchange format for reaction rules and rule-based event processing. Reaction rules include distributed Complex Event Processing (CEP), Knowledge Representation (KR) calculi, as well as Event-Condition-Action (ECA) rules, Production (CA) rules, and Trigger (EA) rules. Reaction RuleML 1.0 incorporates this reactive spectrum of rules into RuleML employing a system of step-wise extensions of the Deliberation RuleML 1.0 foundation.
May-Thurner Syndrome in a Geriatric Patient

A 79-year-old male presented with worsening asymmetric leg swelling, worse on the left. His has end-stage renal disease on hemodialysis with chronic venous insufficiency of bilateral lower extremities and chronic edema despite prior venous stripping surgery. Other medical problems include hypertension, diastolic dysfunction and monoclonal gammopathy of undetermined significance (MGUS).
ANNALES DE L’INSTITUT FOURIER

— We give necessary and suﬃcient conditions for the existence of a BMO solution to the quasilinear equation − ∆ p u = µ in R n , u (cid:62) 0, where µ is a locally ﬁnite Radon measure, and ∆ p u = div( |∇ u | p − 2 ∇ u ) is the p -Laplacian ( p > 1). Wealso characterize BMO solutions to equations − ∆ p u = σu q + µ in R n , u (cid:62) 0, with q > 0, where both µ and σ are locally ﬁnite Radon measures. Our main results hold for a class of more general quasilinear operators div( A ( x, ∇· )) in place of ∆ p . Résumé. — Nous donnons les conditions nécessaires et suﬃsantes pour l’exis-tence d’une solution BMO de l’équation quasi-linéaire − ∆ p u = µ dans R n , u (cid:62) 0, où µ est une mesure de Radon localement ﬁnie, et ∆ p u = div( |∇ u | p − 2 ∇ u ) est le p -Laplacien ( p > 1). Nous caractérisons également les solutions BMO de l’équation − ∆ p u = σu q + µ dans R n , u (cid:62) 0, avec q > 0, où µ et σ sont des mesures de Radon localement ﬁnies. Nos principaux résultats sont valables pour la classe plus générale des opérateurs quasi-linéaires plus généraux div( A ( x, ∇· )) à la place de ∆ p .
ORIGINAL RESEARCH Learners’ Determinants of Learning Environment Preferences: Online vs. Blended

In recent years, the education sector has witnessed a significant shift towards online and blended learning, further accelerated by global events like the COVID-19 pandemic. Understanding what influences learners' preferences for these modes is vital for educational institutions adapting to changing pedagogical needs. This study focuses on exploring the characteristics that shape medical students' choices between online-only and blended learning.
Boundedness of Maximal Calder\'on-Zygmund Operators on Non-homogeneous Metric Measure Spaces

Let $(\cx,\,d,\,\mu)$ be a metric measure space and satisfy the so-called upper doubling condition and the geometrically doubling condition. In this paper, the authors show that for the maximal Calder\'on-Zygmund operator associated with a singular integral whose kernel satisfies the standard size condition and the H\"ormander condition, its $L^p(\mu)$ boundedness with $p\in(1,\infty)$ is equivalent to its boundedness from $L^1(\mu)$ into $L^{1,\infty}(\mu)$. Moreover, applying this, together with a new Cotlar type inequality, the authors show that if the Calder\'on-Zygmund operator $T$ is bounded on $L^2(\mu)$, then the corresponding maximal Calder\'on-Zygmund is bounded on $L^p(\mu)$ for all $p\in(1,\infty)$, and bounded from $L^1(\mu)$ into $L^{1,\infty}(\mu)$. These results essentially improve the existing results.
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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.  Abstract— Reservoir computing (RC) is a computational framework for neural network based information processing. Little work, however, has been conducted on adapting the structure of the neural reservoir. In this paper, we propose a developmental approach to structural self-organization in reservoir computing. More specifically, a recurrent spiking neural network is adopted for building up the reservoir, whose synaptic and structural plasticity are regulated by a gene regulatory network (GRN). Meanwhile, the expression dynamics of the GRN is directly influenced by the activity of the neurons in the reservoir. We term this proposed model as GRN-regulated self-organizing RC (GRN-SO-RC). Contrary to a randomly initialized and fixed structure used in most existing RC models, the structure of the reservoir in the GRN-SO-RC model is self-organized to adapt to the specific task using the GRN-based mechanism. To evaluate the proposed model, experiments have been conducted on several benchmark problems widely used in RC models, such as memory capacity and nonlinear auto-regressive moving average. In addition, we apply the GRN-SO-RC model to solving complex real-world problems, including speech recognition and human action recognition. Our experimental results on both the benchmark and real-world problems demonstrate that the GRN-SO-RC model is effective and robust in solving different types of problems.
Integrating LiDAR and high-resolution imagery for object-based mapping of forest habitats in a heterogeneous temperate forest landscape

ABSTRACT Land-cover mapping (LCM) at a fine scale would be useful for forest management across heterogeneous natural landscapes. However, the heterogeneity of land covers at such scales results in complex spectral and textural properties that hinder the applicability of LCM. Besides, the method suffers from, e.g. inconsistent representation of different land-cover types, lack of sufficient and balanced training samples, and instability of classifiers trained by a high number of predictor variables. Even well-known object-based classification approaches are challenged with an objective evaluation of segmentation outputs. Here we classified partially ambiguous land-cover types across heterogeneous forest landscapes in the Bavarian Forest National Park (Germany) by combining metrics from airborne light detection and ranging (LiDAR) and colour infrared (CIR) imagery data and a random forest classifier implemented in an object-based paradigm. We evaluated the segmentation results by creating a global quality score based on inter- and intra-measurements of variance and the number of segments. Selected segmentation outputs were combined with balanced training samples to run the classification algorithm based on representative blocks within the national park. The entire processing chain was implemented in an open-source domain. The final segmentation consisted of LiDAR-based height, image-based Normalized Difference Vegetation Index (NDVI) and red band, with 20 cluster seeds and a minimum segment size of 40 pixels. In the classification, the most important variables included the height of the top layer, NDVI, Enhanced Vegetation Index (EVI) and Green–Red Vegetation Index (GRVI). The average values of 500 random forest runs indicated an overall accuracy of 86.6% and an estimated Cohen’s kappa coefficient of 85.2%, with different probabilities of correct classification for land-cover classes. Mature deciduous, standing deadwood, fallen deadwood, meadow, and bare soil classes were classified most accurately, whereas classification of young coniferous, intermediate-age coniferous, mature coniferous, young deciduous, and intermediate-age deciduous were associated with the highest uncertainties. Our methodology is sufficiently robust to be applied to other similarly structured sites across temperate forested landscapes. The versatility of the method is partially guaranteed by the proposed segmentation quality score, which satisfactorily corrects under- and over-segmentation.
Super-Rapid Scan Satellite Imagery Analysis of Two Hailstorms Sampled by HailSTONE

Over the last several years, the Geostationary Operational Environmental Satellite (GOES)-14 Super Rapid Scan Operations for GOES-R (SRSOR) has occasionally operated in an experimental 1-minute mode to serve testbed and proving ground evaluations, as well as research efforts, leading up to the launch of GOES-R in November of 2016. These SRSOR periods were also coincident with operations of the field campaign A Hail Spatial and Temporal Observing Network Effort (HailSTONE) on two occasions: 10 May 2014 and 25 May 2015, both in northeast Kansas. The high spatiotemporal resolution of the hail observations collected by HailSTONE and the high temporal resolution of SRSOR provides a unique opportunity to compare satellite and hail observations on similar scales in severe convective weather environments. Products that were evaluated and compared to the reports collected by HailSTONE include infrared and visible satellite imagery, cloud-
Seasonal streamflow forecasts using mixture-kernel GPR and advanced methods of input variable selection

Gaussian Process Regression (GPR) is a new machine-learning method based on Bayesian theory and statistical learning theory. It provides a flexible framework for probabilistic regression and uncertainty estimation. The main effort in GPR modelling is determining the structure of the kernel function. As streamflow is composed of trend, period and random components. In this study, we constructed a mixture-kernel composed of squared exponential kernel, periodic kernel and a rational quadratic term to reflect different properties of streamflow time series to make streamflow forecasts. A relevant feature-selection wrapper algorithm was used, with a top-down search for relevant features by Random Forest, to offer a systematic factors analysis that can potentially affect basin streamflow predictability. Streamflow prediction is evaluated by putting emphasis on the degree of coincidence, the deviation on low flows, high flows and the error level. The objective of this study is to construct a seasonal streamflow forecasts model using mixture-kernel GPR and the advanced input variable selection method. Results show that the mixture-kernel GPR has good forecasting quality, and top importance predictors are streamflow at 12, 6, 5, 1, 11, 7, 8, 4 months ahead, Nino 1 + 2 at 11, 5, 12, 10 months ahead.
Analyzing, Modelling, and Designing Software Ecosystems: Towards the Danish Telemedicine Software Ecosystem

As in many western countries, Denmark’s demographic changes and the organizational and financial changes of the Danish healthcare are challenging the levels of provided healthcare and point towards the establishment of telemedicine as means of patient support, diagnosis, and treatment. However, telemedical solutions, despite the benefits they provide, are faced with a number of challenges that inhibit their adoption, development, and implementation. In this project, we examine the approach of a software ecosystem as means of addressing these issues. A software ecosystem can be roughly explained as the software development and distribution by a set of actors dependent on each other and the ecosystem. We commence on the hypothesis that the establishment of a software ecosystem on the telemedicine services of Denmark would address these issues and investigate how a software ecosystem can foster the development, implementation, and use of telemedicine services. We initially expand the theory of software ecosystems by contributing to the definition and understanding of software ecosystems, providing means of analyzing existing and designing new ecosystems, and defining and measuring the qualities of software ecosystems. We use these contributions to design a software ecosystem in the telemedicine services of Denmark with (i) a common platform that supports and promotes development from different actors, (ii) high software interaction, (iii) strong social network of actors, (iv) robust business structures, supporting actor involvement in the ecosystem, and (v) proper orchestration and governance of the ecosystem to promote and support the changes and the health of the ecosystem. Our work contributes to Net4Care, a platform to serve as the common platform in the software ecosystem under establishment. In addition, it contributes by providing input and guidelines on the role and activity of 4S organization, an organization to serve as an orchestrator in the ecosystem with the aim of managing the platform, supporting actor and software interactions, and promoting the ecosystem health. This thesis documents the groundwork towards addressing the challenges faced by telemedical technologies today and establishing telemedicine as a means of patient diagnosis and treatment. Furthermore, it serves as an empirical example of designing a software ecosystem.
PENGARUH KEPUASAN DAN KEPERCAYAAN TERHADAP LOYALITAS PELANGGAN PENGGUNA JASA EMKL

This study is useful to determine the effect of satisfaction and trust given to customer loyalty at PT. Mitra Utama Indotrans Surabaya. The population in this study amounted to 60 respondents, while the sampling technique used the census method because the population and sample were the same, and the data collection technique used was a questionnaire, while the data analysis technique used the explanatory research method using statistical analysis with regression, correlation, and determination testing. , and hypothesis testing. The results of this study that satisfaction has a significant effect on customer loyalty, hypothesis testing is obtained t count > t table or (5,273> 2.002) . Trust has a significant effect on customer loyalty, hypothesis testing is obtained t count > t table or (4.473> 2.002). Satisfaction and trust simultaneously have a significant effect on customer loyalty with the regression equation Y=0,325+0,483X1+0,381X2+0,43, hypothesis testing is obtained F count > F table (38.805>3.159).
Basic geological data from the Croatian part of the Zagorje - Mid-Transdanubian Zone

The most characteristic tectonostratigraphic units of the Zagorje - Mid-Transdanubian Zone are represented in outcrops of the Medvednica Mts. of northwestern Croatia. Following its present-day structural stacking order from the bottom to the top, four main tectonostratigraphic units can be distinguished: 1) Tectonized ophiolite melange; 2) Paleozoic-Triassic magmatic-sedimentary complex overprinted by Early Cretaceous metamorphism; 3) Late Cretaceous-Paleocene flysch; and 4) Triassic clastic and platform carbonate formations. Based on data available, main characteristics of each tectonostratigraphic unit are reviewed. In the Zagorje - Mid-Transdanubian Zone in Croatia units showing Alpine and Dinaridic characteristics, respectively occur. Oil-well data indicate that the previously supposed location of southern part of the Zagreb-Zemplen Fault Zone is to be expected further eastward. We propose that the area adjoining Zagreb-Zemplen Fault Zone and the southern part of the Mid-Transdanubian Zone can be taken as a northwestern boundary of the Internal Dinarides.
Temperature Response of Respiration Across the Heterogeneous Landscape of the Alaskan Arctic Tundra

Predictions of the response of ecosystem respiration to warming in the Arctic are not well constrained, partly due to the considerable spatial heterogeneity of these permafrost‐dominated areas. Accurate calculations of in situ temperature sensitivities of respiration (Q10) are vital for the prediction of future Arctic emissions. To understand the impact of spatial heterogeneity on respiration rates and Q10, we compared respiration measured from automated chambers across the main local polygonized landscape forms (high and low centers, polygon rims, polygon troughs) to estimates from the flux‐partitioned net ecosystem exchange collected in an adjacent eddy covariance tower. Microtopographic type appears to be the most important variable explaining the variability in respiration rates, and low‐center polygons and polygon troughs show the greatest cumulative respiration rates, possibly linked to their deeper thaw depth and higher plant biomass. Regardless of the differences in absolute respiration rates, Q10 is surprisingly similar across all microtopographic features, possibly indicating a similar temperature limitation to decomposition across the landscape. Q10 was higher during the colder early summer and lower during the warmer peak growing season, consistent with an elevated temperature sensitivity under colder conditions. The respiration measured by the chambers and the estimates from the daytime flux‐partitioned eddy covariance data were within uncertainties during early and peak seasons but overestimated respiration later in the growing season. Overall, this study suggests that it is possible to simplify estimates of the temperature sensitivity of respiration across heterogeneous landscapes but that seasonal changes in Q10 should be incorporated into model simulations.
A Semantic Earth Observation Data Cube for Monitoring Environmental Changes during the Syrian Conflict

Freely and openly available, remotely-sensed Earth observation (EO) data are rapidly developing in terms of increased geo-spatial resolution and temporal frequency. This type of data requires automated workflows for handling, processing and analysis, including methods to convert data into valid information. This study presents a proof-of-concept implementation of a generic, semantic EO data cube with automated daily integration and semantic enrichment of Sentinel-2 data. The paper focuses on the technical implementation of an automated dataflow that enables semantic queries in an EO data cube. It proposes a transferable analytical environment with analysis-ready data to facilitate research on replicable extraction of EO-based indicators. Application-independent semantic data cubes can facilitate monitoring of land cover changes and the development of transferable, generic EO-based indicators to support decision-makers in the humanitarian sector, and international initiatives, such as the United Nations’ Sustainable Development Goals. Syria was chosen as the study area because of the on-going conflict and humanitarian demand, paired with the relatively cloud-free climate’s suitability for optical EO time-series analysis.
Brief communication: Post-event analysis of loss of life due to hurricane Harvey

An analysis was made of the loss of life directly caused by hurricane Harvey. Information was collected for 70 fatalities that occurred directly due to the event. Most of the fatalities occurred in the greater Houston area, which was most severely affected by extreme rainfall and heavy flooding. The majority of fatalities in this area were recovered outside the designated 100 and 500 year flood zones. Most fatalities occurred due to drowning (81 %), particularly in and around vehicles. Males and people over 50 years old were overrepresented.