Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of Five Decontamination Methods for Filtering Facepiece Respirators

Abstract Concerns have been raised regarding the availability of National Institute for Occupational Safety and Health (NIOSH)-certified N95 filtering facepiece respirators (FFRs) during an influenza pandemic. One possible strategy to mitigate a respirator shortage is to reuse FFRs following a biological decontamination process to render infectious material on the FFR inactive. However, little data exist on the effects of decontamination methods on respirator integrity and performance. This study evaluated five decontamination methods [ultraviolet germicidal irradiation (UVGI), ethylene oxide, vaporized hydrogen peroxide (VHP), microwave oven irradiation, and bleach] using nine models of NIOSH-certified respirators (three models each of N95 FFRs, surgical N95 respirators, and P100 FFRs) to determine which methods should be considered for future research studies. Following treatment by each decontamination method, the FFRs were evaluated for changes in physical appearance, odor, and laboratory performance (filter aerosol penetration and filter airflow resistance). Additional experiments (dry heat laboratory oven exposures, off-gassing, and FFR hydrophobicity) were subsequently conducted to better understand material properties and possible health risks to the respirator user following decontamination. However, this study did not assess the efficiency of the decontamination methods to inactivate viable microorganisms. Microwave oven irradiation melted samples from two FFR models. The remainder of the FFR samples that had been decontaminated had expected levels of filter aerosol penetration and filter airflow resistance. The scent of bleach remained noticeable following overnight drying and low levels of chlorine gas were found to off-gas from bleach-decontaminated FFRs when rehydrated with deionized water. UVGI, ethylene oxide (EtO), and VHP were found to be the most promising decontamination methods; however, concerns remain about the throughput capabilities for EtO and VHP. Further research is needed before any specific decontamination methods can be recommended.
The basal ganglia and semantic engagement: Potential insights from semantic priming in individuals with subcortical vascular lesions, Parkinson's disease, and cortical lesions

The impact of basal ganglia dysfunction on semantic processing was investigated by comparing the performance of individuals with nonthalamic subcortical (NS) vascular lesions, Parkinson's disease (PD), cortical lesions, and matched controls on a semantic priming task. Unequibiased lexical ambiguity primes were used in auditory prime-target pairs comprising 4 critical conditions; dominant related (e.g., bank–money), subordinate related (e.g., bank–river), dominant unrelated (e.g., foot–money) and subordinate unrelated (e.g., bat–river). Participants made speeded lexical decisions (word/nonword) on targets using a go–no-go response. When a short prime–target interstimulus interval (ISI) of 200 ms was employed, all groups demonstrated priming for dominant and subordinate conditions, indicating nonselective meaning facilitation and intact automatic lexical processing. Differences emerged at the long ISI (1250 ms), where control and cortical lesion participants evidenced selective facilitation of the dominant meaning, whereas NS and PD groups demonstrated a protracted period of nonselective meaning facilitation. This finding suggests a circumscribed deficit in the selective attentional engagement of the semantic network on the basis of meaning frequency, possibly implicating a disturbance of frontal–subcortical systems influencing inhibitory semantic mechanisms. (JINS, 2003, 9, 1041–1052.)
HilbertCurve: an R/Bioconductor package for high-resolution visualization of genomic data

UNLABELLED : Hilbert curves enable high-resolution visualization of genomic data on a chromosome- or genome-wide scale. Here we present the HilbertCurve package that provides an easy-to-use interface for mapping genomic data to Hilbert curves. The package transforms the curve as a virtual axis, thereby hiding the details of the curve construction from the user. HilbertCurve supports multiple-layer overlay that makes it a powerful tool to correlate the spatial distribution of multiple feature types. AVAILABILITY AND IMPLEMENTATION The HilbertCurve package and documentation are freely available from the Bioconductor project: http://www.bioconductor. org/packages/devel/bioc/html/HilbertCurve.html CONTACT m.schlesner@dkfz.de SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Expert Systems in Document Delivery: The Feasibility of Learning Capabilities

To solve the problem of document delivery in Mexico, the authors developed SEADO (Expert System for Document Supply). SEADO consists of three main components: a knowledge base, an expert system shell, and the database. The knowledge base was built through fault tree analysis and through structured flowcharts. The shell was developed with EXSYS, a generalized expert system development package. The database was based on information sources of various kinds: printed material, local databases, public databases, etc. To evaluate the impact of different learning capabilities, the authors decided to test alternative ways of achieving a predictor for the system to perform in a dynamic and adaptive way. Learning by a weighted-based scheme was compared with a probability-based scheme.
Making Sense of Spatio-Temporal Preserving Representations for EEG-Based Human Intention Recognition

Brain–computer interface (BCI) is a system empowering humans to communicate with or control the outside world with exclusively brain intentions. Electroencephalography (EEG)-based BCI is one of the promising solutions due to its convenient and portable instruments. Despite the extensive research of EEG in recent years, it is still challenging to interpret EEG signals effectively due to its nature of noise and difficulties in capturing the inconspicuous relations between EEG signals and specific brain activities. Most existing works either only consider EEG as chain-like sequences while neglecting complex dependencies between adjacent signals or requiring complex preprocessing. In this paper, we introduce two deep learning-based frameworks with novel spatio-temporal preserving representations of raw EEG streams to precisely identify human intentions. The two frameworks consist of both convolutional and recurrent neural networks effectively exploring the preserved spatial and temporal information in either a cascade or a parallel manner. Extensive experiments on a large scale movement intention EEG dataset (108 subjects, 3 145 160 EEG records) have demonstrated that the proposed frameworks achieve high accuracy of 98.3% and outperform a set of state-of-the-art and baseline models. The developed models are further evaluated with a real-world brain typing BCI and achieve a recognition accuracy of 93% over five instruction intentions suggesting good generalization over different kinds of intentions and BCI systems.
IMU-Based Automated Vehicle Slip Angle and Attitude Estimation Aided by Vehicle Dynamics †

The slip angle and attitude are vital for automated driving. In this paper, a systematic inertial measurement unit (IMU)-based vehicle slip angle and attitude estimation method aided by vehicle dynamics is proposed. This method can estimate the slip angle and attitude simultaneously and autonomously. With accurate attitude, the slip angle can be estimated precisely even though the vehicle dynamic model (VDM)-based velocity estimator diverges for a short time. First, the longitudinal velocity, pitch angle, lateral velocity, and roll angle were estimated by two estimators based on VDM considering the lever arm between the IMU and rotation center. When this information was in high fidelity, it was applied to aid the IMU-based slip angle and attitude estimators to eliminate the accumulated error correctly. Since there is a time delay in detecting the abnormal estimation results from VDM-based estimators during critical steering, a novel delay estimation and prediction structure was proposed to avoid the outlier feedback from vehicle dynamics estimators for the IMU-based slip angle and attitude estimators. Finally, the proposed estimation method was validated under large lateral excitation experimental tests including double lane change (DLC) and slalom maneuvers.
The Ubiquitin System and Kaposi’s Sarcoma-Associated Herpesvirus

Ubiquitination is a post-translational modification in which one or more ubiquitin molecules are covalently linked to lysine residues of target proteins. The ubiquitin system plays a key role in the regulation of protein degradation, which contributes to cell signaling, vesicular trafficking, apoptosis, and immune regulation. Bacterial and viral pathogens exploit the cellular ubiquitin system by encoding their own proteins to serve their survival and replication in infected cells. Recent studies have revealed that Kaposi’s sarcoma-associated herpesvirus (KSHV) manipulates the ubiquitin system of infected cells to facilitate cell proliferation, anti-apoptosis, and evasion from immunity. This review summarizes recent developments in our understanding of the molecular mechanisms used by KSHV to interact with the cellular ubiquitin machinery.
Roles for proteinases in the pathogenesis of chronic obstructive pulmonary disease

Since the early 1960s, a compelling body of evidence has accumulated to show that proteinases play critical roles in airspace enlargement in chronic obstructive pulmonary disease (COPD). However, until recently the causative enzymes and their exact roles in pathologic processes in COPD have not been clear. Recent studies of gene-targeted mice in murine models of COPD have confirmed roles for proteinases not only in airspace enlargement, but also in airway pathologies in COPD. These studies have also shed light on the specific proteinases involved in COPD pathogenesis, and the mechanisms by which these proteinases injure the lung. They have also identified important interactions between different classes of proteinases, and between proteinases and other molecules that amplify lung inflammation and injury. This review will discuss the biology of proteinases and the mechanisms by which they contribute to the pathogenesis of COPD. In addition, I will discuss the potential of proteinase inhibitors and anti-inflammatory drugs as new treatment strategies for COPD patients.
Automatic Segmentation of the Prostate Using a Genetic Algorithm for Prostate Cancer Treatment Planning

This paper presents a genetic algorithm (GA) for combining representations of learned priors such as shape, regional properties and relative location of organs into a single framework in order to perform automated segmentation of the prostate. Prostate segmentation is typically performed manually by an expert physician and is used to determine the locations for radioactive seed placement during radiotherapy treatment planning. The GA accounts for the uncertainty in the definitions of tumor margins by combining known representations of shape, texture and relative location of organs to perform automatic segmentation in two (2D) as well as three dimensions (3D).
DEVELOPMENT AND VALIDATION OF COMPUTATIONAL MODELS FORSAND EROSION AND CORROSION PREDICTION IN PIPES AND FITTINGS

The pnmary objective of this research is to develop computational models for predicting sand erosion and C02 corrosion and their co-action (erosion-corrosion) in pipelines and pipe components (elbows and tees). The motivation behind this objective is to replace the sophisticated and time-consuming computational fluid dynamics (CFD) software used for erosion simulation with an accessible, faster, and cheaper predictive tool while maintaining the prediction accuracy, and to group the erosion prediction model with C02 corrosion prediction model in one package to serve as a comprehensive erosion-corrosion predictive tool. Two models have been used to build the erosion prediction code. The Salama model, a simple empirical model, was used to predict erosion rate in elbows and tees, assuming that sand particles velocity is the same as fluid velocity. This assumption makes the model more applicable to gas flow and high gas-liquid-ratio flow. An attempt has been made to increase the accuracy of the Salama model using a set of experimental data from a number of publications. As a result, three sand erosion models according to gasliquid- ratio have been proposed . Direct Impingement Model (DIM) from University of Tulsa, a semi-empirical sand erosion model, was selected for application in all kinds of fluids due to its account of particle velocity instead of fluid velocity. In employing DIM model, a numerical algorithm was used to solve the simplified equation of particles motion, proposed by University of Tulsa, to track sand particles within a predetermined length (so-called stagnation zone). The particle impingement velocity was calculated, accordingly, and substituted in an empirical sand erosion equation to calculate erosion rate. An ad hoc equation was used with the DIM model to model temperature dependency of particle impingement velocity and erosion rate.
Vertical distribution of fungal communities in tallgrass prairie soil

We used 454 sequencing of the internal transcribed spacer region to characterize fungal communities in tallgrass prairie soils subdivided into strata 0–10, 10–20, 30–40 and 50–60 cm deep. The dataset included more than 14 000 fungal sequences distributed across Basidiomycota, Ascomycota, basal fungal lineages and Glomeromycota in order of decreasing frequency. As expected the community richness and diversity estimators tended to decrease with increasing depth. Although species richness was significantly reduced for samples from the deeper profiles, even the deepest stratum sampled contained richness of more than a third of that in the topmost stratum. More importantly, nonparametric multidimensional scaling (NMS) ordination analyses indicated that the fungal communities differed across vertical profiles, although only the topmost and deepest strata were significantly different when the NMS axis scores were compared by ANOVA. These results emphasize the importance of considering the fungal communities across the vertical strata because the deeper soil horizons might maintain a distinct community composition and thus contribute greatly to overall richness. The majority of operational taxonomic units (OTUs) declined in frequency with increasing depth, although a linear regression analysis indicated that some increased with increasing depth. The OTUs and BLAST-assigned taxa that showed increasing frequencies were mainly unculturable fungi, but some showed likely affinities to families Nectriaceae and Venturiaceae or to genus Pachnocybe. Although the ecological roles of the fungi in the deeper strata remain uncertain, we hypothesize that the fungi with preferences for deeper soil have adequate access to substrates and possess environmental tolerances that enable their persistence in those environments.
Atmospheric loading effects on free‐draining lysimeters

Temporal atmospheric pressure variations introduce an artifact into percolation measurements in free‐draining lysimeters. This anomaly is associated with transient pressure gradients that occur as fluid and gas pressures within the lysimeter equilibrate with atmospheric pressure changes at the drain. On the basis of harmonic (Fourier) analysis of a large (20 m × 10 m × 1.8 m) free‐draining lysimeter, hourly percolation was maximally affected by pressure variations of ∼2 cycles per day, with the effect decreasing with decreasing frequency. Compared to the calculated pressure differences at the drain, the measured phase delay between percolation and the atmospheric pressure was less by ∼45°, which we attribute to an additional phase lag existing between the percolation and pressure difference at the drain. These pressure‐induced changes in percolation rate can influence the outcome of water balances conducted via free‐draining lysimeters.
Serum concentration of heart-type fatty acid-binding protein in children and adolescents with congenital heart disease.

BACKGROUND Serum heart-type fatty acid-binding protein (H-FABP) is widely applied as a marker of cardiac myocyte injury. Recently, it has been reported that levels of H-FABP are elevated in adult patients with chronic heart failure and thus provide useful prognostic information. The aim of the present study was to examine the relationships between serum H-FABP levels and pathophysiological characteristics in children and adolescents with congenital heart disease (CHD). METHODS AND RESULTS Serum H-FABP levels were preoperatively and postoperatively measured in 238 consecutive patients with CHD aged 1-31 years. The relationships between H-FABP levels and severity of heart failure, circulatory status and laboratory data were cross-sectionally analyzed. Multivariate regression analysis indicated that serum H-FABP levels are independently affected by age, New York Heart Association functional class, creatine kinase MB, creatinine and arterial oxygen saturation (standard regression coefficients, -0.378, 0.237, 0.422, 0.615, and -0.210, respectively). Neither left ventricular ejection fraction nor B-type natriuretic peptide correlated with H-FABP levels. CONCLUSIONS H-FABP could serve as a new monitoring tool to provide information that will guide the optimal therapy and management of CHD patients.
Tumor treating fields: a novel treatment modality and its use in brain tumors

Tumor treating fields (TTFields) are low-intensity electric fields alternating at an intermediate frequency (200kHz), which have been demonstrated to block cell division and interfere with organelle assembly. This novel treatment modality has shown promise in a variety of tumor types. It has been evaluated in randomized phase 3 trials in glioblastoma (GBM) and demonstrated to prolong progression-free survival (PFS) and overall survival (OS) when administered together with standard maintenance temozolomide (TMZ) chemotherapy in patients with newly diagnosed GBM. TTFields are continuously delivered by 4 transducer arrays consisting each of 9 insulated electrodes that are placed on the patient’s shaved scalp and connected to a portable device. Here we summarize the preclinical data and mechanism of action, the available clinical data, and further outlook of this treatment modality in brain tumors and other cancer indications.
Selectionist and Evolutionary Approaches to Brain Function: A Critical Appraisal

We consider approaches to brain dynamics and function that have been claimed to be Darwinian. These include Edelman’s theory of neuronal group selection, Changeux’s theory of synaptic selection and selective stabilization of pre-representations, Seung’s Darwinian synapse, Loewenstein’s synaptic melioration, Adam’s selfish synapse, and Calvin’s replicating activity patterns. Except for the last two, the proposed mechanisms are selectionist but not truly Darwinian, because no replicators with information transfer to copies and hereditary variation can be identified in them. All of them fit, however, a generalized selectionist framework conforming to the picture of Price’s covariance formulation, which deliberately was not specific even to selection in biology, and therefore does not imply an algorithmic picture of biological evolution. Bayesian models and reinforcement learning are formally in agreement with selection dynamics. A classification of search algorithms is shown to include Darwinian replicators (evolutionary units with multiplication, heredity, and variability) as the most powerful mechanism for search in a sparsely occupied search space. Examples are given of cases where parallel competitive search with information transfer among the units is more efficient than search without information transfer between units. Finally, we review our recent attempts to construct and analyze simple models of true Darwinian evolutionary units in the brain in terms of connectivity and activity copying of neuronal groups. Although none of the proposed neuronal replicators include miraculous mechanisms, their identification remains a challenge but also a great promise.
Cardiopulmonary exercise testing machine fault

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Identification of spatially interconnected systems using neural network

This paper presents an identification technique based on linear recurrent neural network to identify spatially interconnected systems both in open and closed-loop form. The latter has not been addressed in the literature for the systems under consideration. The paper considers identification of two-dimensional (time and space) systems; the method can be easily extended to have more than one dimension in space. In this paper we consider a semi-causal (causal in time and non-causal in space) two-dimensional (2-D) system, which may be separable or non-separable but the method can also be used for 2-D systems which are causal in both dimensions. Furthermore the algorithm can handle boundary conditions. The effectiveness of the method is shown with application to simulation examples.