Miracle-dz/UniFAR-FTUs huggingface dataset:
Monocytes and Macrophages Serve as Potent Prostaglandin D2 Sources during Acute, Non-Allergic Pulmonary Inflammation

Acute respiratory inflammation, most commonly resulting from bacterial or viral infection, is one of the leading causes of death and disability worldwide. The inflammatory lipid mediator prostaglandin D2 (PGD2) and its rate-limiting enzyme, hematopoietic PGD synthase (hPGDS), are well-known drivers of allergic pulmonary inflammation. Here, we sought to investigate the source and role of hPGDS-derived PGD2 in acute pulmonary inflammation. Murine bronchoalveolar monocytes/macrophages from LPS- but not OVA-induced lung inflammation released significant amounts of PGD2. Accordingly, human monocyte-derived macrophages expressed high basal levels of hPGDS and released significant levels of PGD2 after LPS/IFN-γ, but not IL-4 stimulation. Human peripheral blood monocytes secreted significantly more PGD2 than monocyte-derived macrophages. Using human precision-cut lung slices (PCLS), we observed that LPS/IFN-γ but not IL-4/IL-13 drive PGD2 production in the lung. HPGDS inhibition prevented LPS-induced PGD2 release by human monocyte-derived macrophages and PCLS. As a result of hPGDS inhibition, less TNF-α, IL-6 and IL-10 could be determined in PCLS-conditioned medium. Collectively, this dataset reflects the time-dependent release of PGD2 by human phagocytes, highlights the importance of monocytes and macrophages as PGD2 sources and suggests that hPGDS inhibition might be a potential therapeutic option for acute, non-allergic lung inflammation.
Variability of baseline vehicle control among sober young adult cannabis users: A simulator-based exploratory study

Abstract Objective: The objective of this study was to compare the variability in vehicle control for sober young adult drivers (18–23 years old) who either use cannabis but are not acutely exposed or do not use cannabis. Methods: The data analyzed in the study were from 4 prospective driving simulation studies (completed at the National Advanced Driving Simulator at the University of Iowa) that examined vehicle control metrics in cannabis users and nonusers across high-fidelity simulated urban, interstate, and rural driving environments. Data were collected for segments of consistent driving environments including urban driving, urban curves, interstate, interstate curves, dark rural, and rural straight. Dependent measures included measures of lateral and longitudinal vehicle control. Results: Thirty out of 72 (12 users and 18 nonusers) met the age requirements for inclusion in the analysis. Between the cohorts, we identified differences in lateral and longitudinal driving performance. For lateral control there were no observed effects on variability in lane keeping. Cannabis users exhibited lower frequency steering and fewer and less variable steering reversals compared to nonusers. For longitudinal control, cannabis users drove slower than nonusers and more accelerator pedal holds and a lower accelerator pedal reversal rate were observed. Conclusions: Young adult drivers who use cannabis in our study drove slower and produced significantly less frequent steering and accelerator pedal inputs than drivers who did not use cannabis. This suggests that lasting effects of cannabis use persist and may lead to detrimental driving behaviors even after intoxication has subsided. These findings have implications for legislation in support of legalizing cannabis because sober cannabis-using drivers may still be a public health concern. Further study is needed to evaluate whether these differences persist even with longer term abstinence and whether differences are found in other age demographics.
Toward More Democratic Governance: Modernizing the Administrative State in Australasia, North America and the United Kingdom

All over the world, countries are seeking to improve their gorvernance systems through political and administrative reforms, even those considered to be among the best governed and administered. These advanced democracies have rejected radical idelogies in favor of pragmatic centrist policies termed "The Third Way" that consolidates and builds upon well-established institutions and practices. But they are all moving into new territory. Australia's new doctrine of administrative responsibility seeks to hold all executives, poblic and private, accountable for any public harm occuring on their watch. Canada is enlarging its public policy making arena by involving citizens in its new policy research initiative. New Zealand is rethinking its Beveridge style welfare state to reduce its costs and give citizens a wider choice. The United Kingdom intends to put people first in public administration rather than official convenience and bureaucratic prespects. Finally, the United States attempts to prove that public goods and services are worthwhile and rewarding by more accurately measuring government performance through refined indicators. Together, these initiatives if successful promise to strengthen their democratic ethos.
Novel Insights into the Role of the Mineralocorticoid Receptor in Human Glioblastoma

The majority of glioblastoma (GBM) patients require the administration of dexamethasone (DEXA) to reduce brain inflammation. DEXA activates the glucocorticoid receptor (GR), which can consequently crosstalk with the mineralocorticoid receptor (MR). However, while GR signaling is well studied in GBM, little is known about the MR in brain tumors. We examined the implication of the MR in GBM considering its interplay with DEXA. Together with gene expression studies in patient cohorts, we used human GBM cell lines and patient-derived glioma stem cells (GSCs) to assess the impact of MR activation and inhibition on cell proliferation, response to radiotherapy, and self-renewal capacity. We show that in glioma patients, MR expression inversely correlates with tumor grade. Furthermore, low MR expression correlates with poorer survival in low grade glioma while in GBM the same applies to classical and mesenchymal subtypes, but not proneural tumors. MR activation by aldosterone suppresses the growth of some GBM cell lines and GSC self-renewal. In GBM cells, the MR antagonist spironolactone (SPI) can promote proliferation, radioprotection and cooperate with DEXA. In summary, we propose that MR signaling is anti-proliferative in GBM cells and blocks the self-renewal of GSCs. Contrary to previous evidence obtained in other cancer types, our results suggest that SPI has no compelling anti-neoplastic potential in GBM.
Indoor/Outdoor Semantic Segmentation Using Deep Learning for Visually Impaired Wheelchair Users

Electrical Powered Wheelchair (EPW) users may find navigation through indoor and outdoor environments a significant challenge due to their disabilities. Moreover, they may suffer from near-sightedness or cognitive problems that limit their driving experience. Developing a system that can help EPW users to navigate safely by providing visual feedback and further assistance when needed can have a significant impact on the user’s wellbeing. This paper presents computer vision systems based on deep learning, with an architecture based on residual blocks that can semantically segment high-resolution images. The systems are modified versions of DeepLab version 3 plus that can process high-resolution input images. Besides, they can simultaneously process images from indoor and outdoor environments, which is challenging due to the difference in data distribution and context. The proposed systems replace the base network with a smaller one and modify the encoder-decoder architecture. Nevertheless, they produce high-quality outputs with fast inference speed compared to the systems with deeper base networks. Two datasets are used to train the semantic segmentation systems: an indoor application-based dataset that has been collected and annotated manually and an outdoor dataset to cover both environments. The user can toggle between the two individual systems depending on the situation. Moreover, we proposed shared systems that automatically use a specific semantic segmentation system depending on the pixels’ confidence scores. The annotated output scene is presented to the EPW user, which can aid with the user’s independent navigation. State-of-the-art semantic segmentation techniques are discussed and compared. Results show the ability of the proposed systems to detect objects with sharp edges and high accuracy for indoor and outdoor environments. The developed systems are deployed on a GPU based board and then integrated on an EPW for practical usage and evaluation. The used indoor dataset is made publicly available online.
Election forensics: Using machine learning and synthetic data for possible election anomaly detection

Assuring election integrity is essential for the legitimacy of elected representative democratic government. Until recently, other than in-person election observation, there have been few quantitative methods for determining the integrity of a democratic election. Here we present a machine learning methodology for identifying polling places at risk of election fraud and estimating the extent of potential electoral manipulation, using synthetic training data. We apply this methodology to mesa-level data from Argentina’s 2015 national elections.
Edible Flowers’ Antioxidant Properties and Polyphenols Content Reflect Their Applicability for Household and Craft Tincture Production

The growing interest of consumers in regional and traditional products drew our attention to innovative products manufactured at home and using craft methods, which include, among others, alcohol tinctures of edible flowers. The aim of this paper is to present selected tinctures of edible flowers from home and craft production, their phenol content, antioxidant properties and colour. Novel alcoholic beverages obtained from edible flowers are characterized. The tinctures from wild rose flowers, elderberry, marigold and cornflower were studied. The content of phenolic compounds, the antioxidant properties and the colour of tinctures in the CIE L*a*b* system were analysed. The study showed that edible flower tinctures are characterized by an intense colour, which is not adversely affected by the maceration process. The determined parameters were influenced by the form of flowers (fresh or dried). The total content of polyphenolic compounds and antioxidant activity in the studied tinctures were lower than in the fresh flowers. Edible flower tinctures can be an interesting alternative for both consumers looking for product innovations and alcohol connoisseurs.
Atomically thin telluride multiheterostructures: toward spatial modulation of bandgaps.

Lateral multiheterostructures with spatially modulated bandgaps have great potential for applications in high-performance electronic, optoelectronic and thermoelectric devices. Multiheterostructures based on transition metal tellurides are especially promising due to their tunable bandgap in a wide range and the rich variety of structural phases. However, the synthesis of telluride-based multiheterostructures remains a challenge due to the low activity of tellurium and the poor thermal stability of tellurium alloys. In this work, we synthesized monolayer WSe2-2xTe2x/WSe2-2yTe2y (x > y) multiheterostructures in situ using chemical vapor deposition (CVD). Photoluminescence analysis and Raman mapping confirm the spatial modulation of the bandgap in the radial direction. Furthermore, field-effect transistors with the channels parallel (type I) and perpendicular (type II) to the multiheterostructure rings were fabricated. Type I transistors exhibit enhanced ambipolar transport, due to the low energy bridges between the source and drain. Remarkably, the photocurrents in type I transistors are two orders of magnitude higher than those in type II transistors, which can be attributed to the fact that the photovoltaic photocurrents generated at the two heterojunctions are summed together in type I transistors, but they cancel each other in type II transistors. These multiheterostructures will provide a new platform for novel electronic/photonic devices with potential applications in broadband light sensing, highly sensitive imaging and ultrafast optoelectronic integrated circuits.
Modelling Electricity Consumption Forecasting Using the Markov Process and Hybrid Features Selection

Given the problem of electrical energy storage, it is critical to predict the amount of load required in order to have a reliable and stable power distribution network. Predicting electricity consumption of subscribers and analyzing their consumption behavior under the influence of various factors and time variables is important. Given the large volume of subscriber consumption data and the effective factors, it is only possible to analyze the data using new information technology tools such as data mining. In this paper, feature selection, clustering and Markov process techniques are used to model and predict the power consumption data of subscribers. First, the selection of a subset of effective features is based on the combined PCA approach and the Firefly algorithm. Subscribers are then clustered based on the features selected by the K-means. Finally, subscriber behavior patterns are modeled to predict consumption using the Markov process on high-risk clusters. This study is simulated based on the data of electricity subscribers in Bushehr-Iran Power Distribution Company. The simulation results show the superiority of the proposed model over other similar algorithms such as LASSO-QRNN and HyFIS. The accuracy of power consumption prediction in the proposed method is about 1% compared to LASSO-QRNN and about 0.5% compared to HyFIS.
Data Mining for Cyberbullying and Harassment Detection in Arabic Texts

Broadly cyberbullying is viewed as a severe social danger that influences many individuals around the globe, particularly young people and teenagers. The Arabic world has embraced technology and continues using it in different ways to communicate inside social media platforms. However, the Arabic text has drawbacks for its complexity, challenges, and scarcity of its resources. This paper investigates several questions related to the content of how to protect an Arabic text from cyberbullying/harassment through the information posted on Twitter. To answer this question, we collected the Arab corpus covering the topics with specific words, which will explain in detail. We devised experiments in which we investigated several learning approaches. Our results suggest that deep learning models like LSTM achieve better performance compared to other traditional yberbullying classifiers with an accuracy of 72%.
Thoracic Aorta Calcium Detection and Quantification Using Convolutional Neural Networks in a Large Cohort of Intermediate-Risk Patients

Arterial calcification is an independent predictor of cardiovascular disease (CVD) events whereas thoracic aorta calcium (TAC) detection might anticipate extracoronary outcomes. In this work, we trained six convolutional neural networks (CNNs) to detect aortic calcifications and to automate the TAC score assessment in intermediate CVD risk patients. Cardiac computed tomography images from 1415 patients were analyzed together with their aortic geometry previously assessed. Orthogonal patches centered in each aortic candidate lesion were reconstructed and a dataset with 19,790 images (61% positives) was built. Three single-input 2D CNNs were trained using axial, coronal and sagittal patches together with two multi-input 2.5D CNNs combining the orthogonal patches and identifying their best regional combination (BRC) in terms of lesion location. Aortic calcifications were concentrated in the descending (66%) and aortic arch (26%) portions. The BRC of axial patches to detect ascending or aortic arch lesions and sagittal images for the descending portion had the best performance: 0.954 F1-Score, 98.4% sensitivity, 87% of the subjects correctly classified in their TAC category and an average false positive TAC score per patient of 30. A CNN that combined axial and sagittal patches depending on the candidate aortic location ensured an accurate TAC score prediction.
Identification of Novel Kinase–Transcription Factor–mRNA–miRNA Regulatory Network in Nasopharyngeal Carcinoma by Bioinformatics Analysis

Purpose Nasopharyngeal carcinoma (NPC) is one of the most common malignant tumors of the head and neck. This study aimed to investigate the crucial genes and regulatory networks involved in the carcinogenesis of NPC using a bioinformatics approach. Methods Five mRNA and two miRNA expression datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and miRNAs (DEMs) between NPC and normal samples were analyzed using R software. The WebGestalt tool was used for functional enrichment analysis, and protein–protein interaction (PPI) network analysis of DEGs was performed using STRING database. Transcription factors (TFs) were predicted using TRRUST and Transcriptional Regulatory Element Database (TRED). Kinases were identified using X2Kgui. The miRNAs of DEGs were predicted using miRWalk database. A kinase–TF–mRNA–miRNA integrated network was constructed, and hub nodes were selected. The hub genes were validated using NPC datasets from the GEO and Oncomine databases. Finally, candidate small-molecule agents were predicted using CMap. Results A total of 122 DEGs and 44 DEMs were identified. DEGs were associated with the immune response, leukocyte activation, endoplasmic reticulum stress in GO analysis, and the NF-κB signaling pathway in KEGG analysis. Four significant modules were identified using PPI network analysis. Subsequently, 26 TFs, 73 kinases, and 2499 miRNAs were predicted. The predicted miRNAs were cross-referenced with DEMs, and seven overlapping miRNAs were selected. In the kinase–TF–mRNA–miRNA integrated network, eight genes (PTGS2, FN1, MMP1, PLAU, MMP3, CD19, BMP2, and PIGR) were identified as hub genes. Hub genes were validated with consistent results, indicating the reliability of our findings. Finally, six candidate small-molecule agents (phenoxybenzamine, luteolin, thioguanosine, reserpine, blebbistatin, and camptothecin) were predicted. Conclusion We identified DEGs and an NPC regulatory network involving kinases, TFs, mRNAs, and miRNAs, which might provide promising insight into the pathogenesis, treatment, and prognosis of NPC.
Modeling and experimental validation of thin, tightly rolled dielectric elastomer actuators

Due to their large deformation, high energy density, and high compliance, dielectric elastomer actuators (DEAs) have found a number of applications in several areas of mechatronics and robotics. Among the many types of DEAs proposed in the literature, rolled DEAs (RDEAs) represent one of the most popular configurations. RDEAs can be effectively used as compact muscle-like actuators for soft robots, since they allow eliminating the need for external motors or compressors while providing at the same time a flexible and lightweight structure with self-sensing capabilities. To effectively design and control complex RDEA-driven systems and robots, accurate and numerically efficient mathematical models need to be developed. In this work, we propose a novel lumped-parameter model for silicone-based, thin and tightly rolled RDEAs. The model is grounded on a free-energy approach, and permits to describe the electro-mechanically coupled response of the transducer with a set of nonlinear ordinary differential equations. After deriving the constitutive relationships, the model is validated by means of an extensive experimental campaign, conducted on three RDEA specimens having different geometries. It is shown how the developed model permits to accurately predict the effects of several parameters (external load, applied voltage, actuator geometry) on the RDEA electro-mechanical response, while maintaining an overall simple mathematical structure.
A Comprehensive Review on Applications of Polymeric Surfactants for Chemical Enhanced Oil Recovery

The decline in conventional oil reserves encouraged oil producers to exploit heavy and unconventional oils, which in many situations requires application of chemical enhanced oil recovery (CEOR) on extracting and recovering the hydrocarbons. Available CEOR methods, although they are well stablished, new challenges always there, and hence, new CEOR techniques are always emerge. Recently researchers suggest polymeric surfactants which represent a very attractive EOR fluid due to their ability of increasing the water viscosity while simultaneously decreasing the interfacial tension. This paper reviews the research conducted in the previous three decadeson testing the ability of using the polymeric surfactants solutions in EOR applications.
Surface properties and doxorubicin delivery in mixed systems comprising a natural rosin-based ester tertiary amine and an anionic surfactant

Abstract A rosin-based ester tertiary amine salt (RETAS) cationic surfactant was obtained using natural rosin as raw material. GC-MS of RETAS was detailed analyzed. The pH-responsive mechanism of rosin-based ester tertiary amine (RETA) and RETAS was confirmed by applying theoretical calculations about electrostatic potential maps of RETA and RETAS cation using Gaussian software. Mixed system surfactants were obtained by blending RETAS cationic surfactant with sodium dodecyl benzene sulfonate (SDBS) anionic surfactant. The binary mixed surfactant systems of RETAS and SDBS had obvious synergistic effect. The γcmc and CMC were 39.40 mN/m and 0.56 mmol/L at the optimum molar fraction of RETAS (α) = 0.6, respectively. The stability time of emulsion with the optimum mixed system as emulsifier increased to 309 s at α = 0.6. The emulsifying capacity of RETAS was much better than that of RETA. RETAS had pH-responsive targeted release and the optimum mixed system showed a relatively sustained drug release by using doxorubicin (DOX) as a model drug. These results indicate that RETAS surfactant and mixed system surfactant are both promising for applications in drug delivery and emulsification. Graphical Abstract
Should dental amalgams be used in dental training? The perspectives of dental academics at a university in South Africa

Dental amalgam has been used in dentistry for the last 150 years to restore posterior teeth. Concerns regarding the safety issues with amalgam due to its mercury content have lead to the introduction of composite dental material to restore posterior teeth. This has transformed the teaching and training trends of direct restorative materials for posterior teeth. This descriptive study aimed to gain insight into academic staff and clinical supervisors’ perceptions regarding the use of dental amalgams in the teaching of restorative dentistry. This study used a qualitative method of data collection. Two focus group discussions were conducted between March and May 2020. Seventeen participants were invited to participate. Fourteen responded and participated, yielding an 82% response rate. The data identified two major themes, viz. the challenges experienced with the teaching of dental amalgam and curriculum development recommendations. Clinical quota requirements, disposal of waste products, and occupational and environmental risks regarding mercury exposure were sub-themes. Some of the curriculum recommendations included the skills gained during cavity preparations, the training and quota requirements of dental materials, and teaching trends locally, nationally and internationally.This study revealed that dental amalgam is still an integral part of the restorative dentistry curriculum at the university. However, the teaching of resin composite now occupies more than fifty percent of the restorative dentistry curriculum. Academics and clinical supervisors show a greater affinity for the placement of composite restorations.