Miracle-dz/UniFAR-FTUs huggingface dataset:
Medical Directors, Facilities, and Finances: Resource Deficiencies in Accredited Paramedic Programs.

BACKGROUND Initial paramedic education must have sufficient rigor and appropriate resources to prepare graduates to provide lifesaving prehospital care. Despite required national paramedic accreditation, there is substantial variability in paramedic pass rates that may be related to program infrastructure and clinical support. Our objective was to evaluate US paramedic program resources and identify common deficiencies that may affect program completion. METHODS We conducted a cross-sectional mixed methods analysis of the 2018 Committee on Accreditation of Educational Programs for the Emergency Medical Services Professions annual report, focusing on program Resource Assessment Matrices (RAM). The RAM is a 360-degree evaluation completed by program personnel, advisory committee members, and currently enrolled students to identify program resource deficiencies affecting educational delivery. The analysis included all paramedic programs that reported graduating students in 2018. Resource deficiencies were categorized into ten categories: faculty, medical director, support personnel, curriculum, financial resources, facilities, clinical resources, field resources, learning resources, and physician interaction. Descriptive statistics of resource deficiency categories were conducted, followed by a thematic analysis of deficiencies to identify commonalities. Themes were generated from evaluating individual deficiencies, paired with program-reported analysis and action plans for each entry. RESULTS Data from 626 programs were included (response rate = 100%), with 143 programs reporting at least one resource deficiency (23%). A total of 406 deficiencies were identified in the ten categories. The largest categories (n = 406) were medical director (14%), facilities (13%), financial resources (13%), support personnel (11%), and physician interaction (11%). The thematic analysis demonstrated that a lack of medical director engagement in educational activities, inadequate facility resources, and a lack of available financial resources affected the educational environment. Additionally, programs reported poor data collection due to program director turnover. CONCLUSION Resource deficiencies were frequent for programs graduating paramedic students in 2018. Common themes identified were a need for medical director engagement, facility problems, and financial resources. Considering the pivotal role of EMS physicians in prehospital care, a consistent theme throughout the analysis involved challenges with medical director and physician interactions. Future work is needed to determine best practices for paramedic programs to ensure adequate resource availability for initial paramedic education.
Profiles of COVID-19 Impact on Informal Caregivers of Older Mexican Americans

Objectives: This manuscript is a descriptive exploratory study of the impact of the COVID-19 pandemic for caregivers of very old Mexican Americans. Methods: Using data from Wave 10 of the Hispanic Established Population for the Epidemiological Study of Elderly (2021), we examined the characteristics of caregivers of very old Mexican Americans by their perceived level of impact from the pandemic. We examined sociodemographic characteristics, COVID-19 testing and vaccination, stress and depression, and health of caregivers and care recipients. Results: Caregivers who reported the most impact were more likely to report financial and emotional stress. They reported more depressive symptoms and high levels of distress related to neuropsychological behavior symptoms of their care recipient. Discussion: High levels of depressive symptoms, financial stress, and neuropsychological behavior symptoms illustrate that the concerns of caregivers before the pandemic were heightened during the pandemic.
How exceptional are the classic adaptive radiations of passerine birds?

Significance Adaptive radiations—species descended from a common ancestor that are adapted to a wide variety of ecological niches—occur frequently on remote islands where a colonist finds few competitors in an unexploited ecological landscape. We examined recognized cases of island radiations among passerine birds, asking whether the morphological diversity exceeds that in a set of clades including some composed in part or wholly by continental species. Island clades were slightly more diverse than the other clades, but the focal adaptive radiations were not exceptional. Clade variation conformed to a normal distribution, suggesting that special circumstances, such as evolution of key innovations, are unnecessary to explain the evolutionary diversity of diverse clades.
Pro-arrhythmic role of adrenergic spatial densities in the human atria: An in-silico study

Chronic stress among young patients (≤ 45 years old) could result in autonomic dysfunction. Autonomic dysfunction could be exhibited via sympathetic hyperactivity, sympathetic nerve sprouting, and diffuse adrenergic stimulation in the atria. Adrenergic spatial densities could alter atrial electrophysiology and increase arrhythmic susceptibility. Therefore, we examined the role of adrenergic spatial densities in creating arrhythmogenic substrates in silico. We simulated three 25 cm2 atrial sheets with varying adrenergic spatial densities (ASD), activation rates, and external transmembrane currents. We measured their effects on spatial and temporal heterogeneity of action potential durations (APD) at 50% and 20%. Increasing ASD shortens overall APD, and maximum spatial heterogeneity (31%) is achieved at 15% ASD. The addition of a few (5% to 10%) adrenergic elements decreases the excitation threshold, below 18 μA/cm2, while ASDs greater than 10% increase their excitation threshold up to 22 μA/cm2. Increase in ASD during rapid activation increases APD50 and APD20 by 21% and 41%, respectively. Activation times of captured beats during rapid activation could change by as much as 120 ms from the baseline cycle length. Rapidly activated atrial sheets with high ASDs significantly increase temporal heterogeneity of APD50 and APD20. Rapidly activated atrial sheets with 10% ASD have a high likelihood (0.7 ± 0.06) of fragmenting otherwise uniform wavefronts due to the transient inexcitability of adrenergically stimulated elements, producing an effective functional block. The likelihood of wave fragmentation due to ASD highly correlates with the spatial variations of APD20 (ρ = 0.90, p = 0.04). Our simulations provide a novel insight into the contributions of ASD to spatial and temporal heterogeneities of APDs, changes in excitation thresholds, and a potential explanation for wave fragmentation in the human atria due to sympathetic hyperactivity. Our work may aid in elucidating an electrophysiological link to arrhythmia initiation due to chronic stress among young patients.
ST-CopulaGNN : A Multi-View Spatio-Temporal Graph Neural Network for Traffic Forecasting

Modern cities heavily rely on complex transportation, making accurate traffic speed prediction crucial for traffic management authorities. Classical methods, including statistical techniques and traditional machine learning techniques, fail to capture complex relationships, while deep learning approaches may have weaknesses such as error accumulation, difficulty in handling long sequences, and overlooking spatial correlations. Graph neural networks (GNNs) have shown promise in extracting spatial features from non-Euclidean graph structures, but they usually initialize the adjacency matrix based on distance and may fail to detect hidden statistical correlations. The choice of correlation measure can have a significant impact on the resulting adjacency matrix and the effectiveness of graph-based models. This paper proposes a novel approach for accurately forecasting traffic patterns by utilizing a multi-view spatio-temporal graph neural network that captures data from both realistic and statistical domains. Unlike traditional correlation measures such as Pearson correlation, copula models are utilized to extract hidden statistical correlations and construct multivariate distribution functions to obtain the correlation relationship among traffic nodes. A two-step approach is adopted, which involves selecting and testing different types of bivariate copulas to identify the ones that best fit the traffic data, and utilizing these copulas to create multi-weight adjacency matrices. The second step involves utilizing a graph convolutional network to extract spatial information and capturing temporal trends using dilated causal convolutions. The proposed ST-CopulaGNN model outperforms other models in spatio-temporal traffic forecasting that solely rely on distance-based adjacency matrices, such as DCRNN and Graph WaveNet. It also achieves the lowest MAE for 30 and 60 minutes ahead and the lowest MAPE for 15 minutes ahead on the PEMS-BAY dataset. The model incorporates copulas, and the study explores copula function selection and the impact of using paired time-series with a time lag. The findings suggest that using copula-based adjacency matrix configurations, particularly those including Clayton and Gumbel copulas, can enhance traffic forecasting accuracy.
Evaluating Autoencoders for Dimensionality Reduction of MRI-derived Radiomics and Classification of Malignant Brain Tumors

Malignant brain tumors including parenchymal metastatic (MET) lesions, glioblastomas (GBM), and lymphomas (LYM) account for 29.7% of brain cancers. However, the characterization of these tumors from MRI imaging is difficult due to the similarity of their radiologically observed image features. Radiomics is the extraction of quantitative imaging features to characterize tumor intensity, shape, and texture. Applying machine learning over radiomic features could aid diagnostics by improving the classification of these common brain tumors. However, since the number of radiomic features is typically larger than the number of patients in the study, dimensionality reduction is needed to balance feature dimensionality and model complexity. Autoencoders are a form of unsupervised representation learning that can be used for dimensionality reduction. It is similar to PCA but uses a more complex and non-linear model to learn a compact latent space. In this work, we examine the effectiveness of autoencoders for dimensionality reduction on the radiomic feature space of multiparametric MRI images and the classification of malignant brain tumors: GBM, LYM, and MET. We further aim to address the class imbalances imposed by the rarity of lymphomas by examining different approaches to increase overall predictive performance through multiclass decomposition strategies.
TGSLN : Time-aware Graph Structure Learning Network for Multi-variates Stock Sector Ranking Recommendation

In the field of financial prediction, most studies focus on individual stocks or stock indices. Stock sectors are collections of stocks with similar characteristics and the indices of sectors have more stable trends and predictability compared to individual stocks. Additionally, stock sectors are subsets of stock indices, which implies that investment portfolios based on stock sectors have a greater potential to achieve excess returns. In this paper, we propose a new method, Time-aware Graph Structure Learning Network (TGSLN), to address the problem of stock sector ranking recommendation. In this model, we use an indicator called Relative Price Strength (RPS) to describe the ranking change trend of the sectors. To construct the inherent connection between sectors, we construct a multi-variable time series that consists of multi-scale RPS sequences and effective indicators filtered through the factor selector. We also build a stock sector relation graph based on authoritative stock sector classifications. Specially, we design a time-aware graph structure learner, which can mine the sector relations from time series, and enhance the initial graph through graph fusion. Our model outperforms state-of-the-art baselines in both A-share and NASDAQ markets.
Single-Cell Measurements and Modeling and Computation of Decision-Making Errors in a Molecular Signaling System with Two Output Molecules

A cell constantly receives signals and takes different fates accordingly. Given the uncertainty rendered by signal transduction noise, the cell may incorrectly perceive the signals. It may mistakenly behave as if there is a signal - although there is none, or may miss the presence of a signal that actually exists. In this paper, we consider a signaling system that has two outputs, and introduce and develop methods to model and compute key cell decision making parameters based on the two outputs, and in response to the input signal. In the considered system, the tumor necrosis factor (TNF) regulates the two transcription factors, the nuclear factor κB (NFκB) and the activating transcription factor-2 (ATF-2). These two system outputs are involved in important physiological functions such as cell death and survival, viral replication, and pathological conditions such as autoimmune diseases and different types of cancer. Using the introduced methods, we compute and show what the decision thresholds are, based on the single cell measured concentration levels of NFκB and ATF-2. We also define and compute the decision error probabilities, i.e., false alarm and miss probabilities, based on the concentration levels of the two outputs. By considering the joint response of the two outputs of a signaling system, one can learn more about complex cellular decision making processes, the corresponding decision error rates, and their possible involvement in the development of some pathological conditions.
Inducible genetic ablation of Immt induces a lethal disruption of the MICOS complex

The mitochondrial contact site and cristae organizing system (MICOS) is important for cristae junctions (CJ) formation and for maintaining inner mitochondrial membrane (IMM) architecture. As the largest member, MIC60 is the primary scaffold protein for this complex. While MIC60 has been well studied in yeast and cell culture models, its function in mammals is poorly understood. To address this, we developed a mouse model conditionally deleting Immt (which encodes MIC60) and found that global Immt deletion disrupted the MICOS complex and resulted in lethality within 9 days of tamoxifen treatment. Pathologically, these mice display intestinal defects consistent with paralytic ileus, resulting in dehydration. We also identified bone marrow hypocellularity in tamoxifen-treated mice. However, bone marrow transplants from ImmtWT mice failed to rescue survival. Altogether, this novel mouse model demonstrates the importance of MIC60 in vivo, in both hematopoietic and non-hematopoietic tissues, and provides a valuable resource for future mechanistic investigations into the MICOS complex. Such investigations could include an in vivo structure-function analysis of MIC60 functional domains, with characterizations that are relevant to human diseases.
Spectral decomposition unlocks ascidian morphogenesis

Describing morphogenesis generally consists in aggregating the multiple high resolution spatiotemporal processes involved into reproducible low dimensional morphological processes consistent across individuals of the same species or group. In order to achieve this goal, biologists often have to submit movies issued from live imaging of developing embryos either to a qualitative analysis or to basic statistical analysis. These approaches, however, present noticeable drawbacks, as they can be time consuming, hence unfit for scale, and often lack standardisation and a firm foundation. In this work, we leverage the power of a continuum mechanics approach and flexibility of spectral decompositions to propose a standardised framework for automatic detection and timing of morphological processes. First, we quantify whole-embryo scale shape changes in developing ascidian embryos by statistically estimating the strain-rate tensor field of its time-evolving surface without the requirement of cellular segmentation and tracking. We then apply to this data spectral decomposition in space using spherical harmonics and in time using wavelets transforms. These transformations result in the identification of the principal dynamical modes of ascidian embryogenesis and the automatic unveiling of its blueprint in the form of spectograms that tell the story of development in ascidian embryos.
The effects of human population density on trophic interactions are contingent upon latitude

Aim: Studies conducted at a global scale are necessary to make general conclusions on the effect of urbanization on trophic interactions and explore how these effects change along latitudinal gradients. Since biotic interactions are more intense at lower latitudes, we predict that the intensity of trophic interactions will decrease in response to urbanization (quantified by human population density), with higher impacts of urbanization at higher latitudes. Location: Global (881 study sites). Time period: 2000-2021. Major taxa studied: Birds, arthropods and woody plants. Methods: We compiled global data on insect herbivory and bird predation from individual studies using similar methodologies, and fitted generalized linear mixed models to test the effect of human population density, latitude and their interaction on these two response variables. Results: The intensity of herbivory and predation decreased with the increase of human population density at lower latitudes, remained unaffected at intermediate latitudes, and increased at higher latitudes. Main conclusions: The effect of human population density on insect herbivory and bird predation consistently varies across latitudes, with a reversal of the pattern at high versus low latitudes.
Author ' s personal copy Air quality models sensitivity to on-road traffic speed representation : Effects on air quality of 80 kmh 1 speed limit in the Barcelona

Atmospheric modeling permits to quantitatively assess the effects of emissions abatement strategies in urban areas, which are mainly oriented to reduce on-road traffic sector contributions. Nowadays the emissions inventories are one of the causes of uncertainties in air quality modeling. This work explores the improvements in urban air quality assessment by using the WRF-ARW/HERMES/CMAQ modeling system when switching from constant on-road traffic speed to hourly variable speeds taken from measurement campaigns in the emissions model. Furthermore, the effects of limiting the speed circulation in the Barcelona Metropolitan area to 80 km h 1 are assessed. A photochemical pollution episode representative of summertime conditions (corresponding to the year 2004) in the northeastern Iberian Peninsula and fitting in a usual traffic circulation pattern (working day) is selected to carry out the study. The introduction of variable speed in the HERMES emissions model affect the model evaluation in the urban area, improving specifically the O3 precursors estimates, and consequently O3 predictions in the area (reductions in mean normalized gross error (MNGE) around 1%). The speed limitation effects assessed with the modified modeling system represent improvements in air quality levels; specifically it reduces local NO2, SO2 and PM10 levels, up to 5.7%, 5.3% and 3.0% over the area affected by the speed limit, respectively. The O3 concentrations slightly increase in the urban area (up to 2.4%), due to the chemical regime (NOx emissions reduction in a VOCs limited area), but the effects in the urban plume are negligible. The need for air quality models as management tools makes essential to improve the emissions models accuracy, by introducing changes to better represent real conditions. 2008 Elsevier Ltd. All rights reserved.
Resolution of Uniform Resource Identifiers using the Domain Name System

The requirements document for URN resolution systems defines the concept of a "resolver discovery service". This document describes the first, experimental, RDS. It is implemented by a new DNS Resource Record, NAPTR (Naming Authority PoinTeR), that provides rules for mapping parts of URIs to domain names. This memo defines an Experimental Protocol for the Internet community.
Simulation of clothing with folds and wrinkles

Clothing is a fundamental part of a character's persona, a key storytelling tool used to convey an intended impression to the audience. Draping, folding, wrinkling, stretching, etc. all convey meaning, and thus each is carefully controlled when filming live actors. When making films with computer simulated cloth, these subtle but important elements must be captured. In this paper we present several methods essential to matching the behavior and look of clothing worn by digital stand-ins to their real world counterparts. Novel contributions include a mixed explicit/implicit time integration scheme, a physically correct bending model with (potentially) nonzero rest angles for pre-shaping wrinkles, an interface forecasting technique that promotes the development of detail in contact regions, a post-processing method for treating cloth-character collisions that preserves folds and wrinkles, and a dynamic constraint mechanism that helps to control large scale folding. The common goal of all these techniques is to produce a cloth simulation with many folds and wrinkles improving the realism.
A sketching interface for articulated figure animation

We introduce a new interface for rapidly creating 3D articulated figure animation, from 2D sketches of the character in the desired key frame poses. Since the exact 3D animation corresponding to a set of 2D drawings is ambiguous we first reconstruct the possible 3D configurations and then apply a set of constraints and assumptions to present the user with the most likely 3D pose. The user can refine this candidate pose by choosing among alternate poses proposed by the system. This interface is supported by pose reconstruction and optimization methods specifically designed to work with imprecise hand drawn figures. Our system provides a simple, intuitive and fast interface for creating rough animations that leverages our users' existing ability to draw. The resulting key framed sequence can be exported to commercial animation packages for interpolation and additional refinement.
An example-based approach for facial expression cloning

In this paper, we present a novel example-based approach for cloning facial expressions of a source model to a target model while reflecting the characteristic features of the target model in the resulting animation. Our approach comprises three major parts: key-model construction, parameterization, and expression blending. We first present an effective scheme for constructing key-models. Given a set of source example key-models and their corresponding target key-models created by animators, we parameterize the target key-models using the source key-models and predefine the weight functions for the parameterized target key-models based on radial basis functions. In runtime, given an input model with some facial expression, we compute the parameter vector of the corresponding output model, to evaluate the weight values for the target key-models and obtain the output model by blending the target key-models with those weights. The resulting animation preserves the facial expressions of the input model as well as the characteristic features of the target model specified by animators. Our method is not only simple and accurate but also fast enough for various real-time applications such as video games or internet broadcasting.