Miracle-dz/UniFAR-FTUs huggingface dataset:
Oxidative stress, ceruloplasmin and neopterin biomarkers in dromedary camels with clinical endometritis

Abstract The purpose of this study is to investigate the role of some oxidative stress (OS), ceruloplasmin (Cp), and neopterin (NPT) as diagnostic biomarkers for dromedary camels endometritis as well as to explore the impact of ceftiofur treatment on endometritis. Camels were categorized into two groups; healthy control group (n = 20) and endometritis group (n = 60). She-camels with clinical signs of endometritis (CE) received 6.6 mg/kg BW of ceftiofur (i/m). On days 7, and 14, she-camels were evaluated and clinical cure or failure to cure was determined. The comparison of the groups for OS demonstrated that endometritis caused an increase in serum malondialdehyde (sMDA), Cp, and NPT levels (P<0.05), but decreased serum levels of superoxide dismutase (SOD), catalase (CAT), and glutathione (GSH) (P<0.05). The most prevalent pathogens involved in the etiology of CE are Arcanobacterium pyogenes, Streptococcus pyogenes, and Staphylococcus aureus. All examined biomarkers demonstrated a high degree of recognition between CE camel and healthy controls (the area under the curve (AUC) was 95.9 for NPT). A higher proportion of camels with CE that were treated with ceftiofur (90%, P<0.0001) showed clinical cure by the first dose, while 10% required a second dose. In conclusion, CE causes increased oxidative reactions and decreased antioxidant defense competence. Subsequently, the alteration in that balance that was represented by the biomarkers of OS could be beneficial for clinical practice and basic clinical research. Additionally, all trials demonstrated the efficacy of ceftiofur for the treatment of CE in she-camel.
Geometric realization of special cases of local Langlands and Jacquet-Langlands correspondences

Let F be a non-Archimedean local field and let E be an unramified extension of F of degree n>1. To each sufficiently generic multiplicative character of E (the details are explained in the body of the paper) one can associate an irreducible n-dimensional representation of the Weil group W_F of F, which corresponds to an irreducible supercuspidal representation \pi\ of GL_n(F) via the local Langlands correspondence. In turn, via the Jacquet-Langlands correspondence, \pi\ corresponds to an irreducible representation \rho\ of the multiplicative group of the central division algebra over F with invariant 1/n. In this note we give a new geometric construction of the representations \pi\ and \rho, which is simpler than the existing algebraic approaches (in particular, the use of the Weil representation over finite fields is eliminated).
Expectation-based and Quantile-based Probabilistic Support Vector Machine Classification for Histogram-Valued Data

A histogram-valued random variable represents its value by a list of pairs of bins and their corresponding probabilities or relative frequencies. This type of data is a part of the symbolic data. There are many cases such as colors in image learning where histogram-valued data are naturally found. This study focuses on classification of the histogram-valued data by extending two approaches of support vector machine (SVM), namely, the expected-based and quantile-based probabilistic SVM on histogram-valued data. In both approaches, the cases of linear and nonlinear problems as well as the least-square classification are discussed. In addition, the extension to multi-class classification is also discussed. To compare the performance of the proposed procedures a simulation study has been done based on some generated data sets. The data are generated from various distributions with various parameters to represent different cases of classification, including binary and multi-class classification. Further, the methods are applied on two different real data sets. From the results, it can be concluded that our proposed methods perform well on wide range of classification problems.
Levels of Estrogen, Progesterone and Follicle Stimulating Hormone among Nigerian Females Living with Sickle Cell Anaemia

Background: Sickle cell Anaemia (SCA) is an inherited disorder characterized by abnormality in sexual development and reduced fertility which may be due to derangement in the level of sex hormones. This study aimed to evaluate the levels of estrogen, progesterone and follicle stimulating hormone (FSH) among adult females with SCA (HbSS). Methods: A cross-sectional study of adult female patients with HbSS as well as individuals with sickle cell trait (HbAS) and normal adult haemoglobin (HbAA) was done. Hemoglobin phenotype was determined using electrophoretic method. Estrogen, progesterone and FSH levels were assayed using enzyme-linked immunosorbent assay (ELISA) technique, full blood count was determined using Hematology autoanalyzer while disease severity was evaluated using an objective scoring system. Results: Seventy females with mean age of 23.8 ±4.9 years were studied. They included 30 (42.8%) HbSS participants in steady state and 40 controls [20 (28.6%) HbAS and 20 (28.6%) HbAA]. There was significant decrease in mean serum levels of estrogen in individuals with HbSS compared to those of HbAS and HbAA (P<0.05). Also, there was significant decrease in progesterone level when HbSS were compared to HbAS participants (p< 0.05), but the difference didn’t reach significance level when HbSS were compared with HbAA participants (P>0.05). There was higher mean serum level of FSH in HbSS when compared to HbAS and HbAA, though not significant (P>0.05). FSH levels was also found to be inversely correlated with disease severity in individuals with HbSS, though not significant. Conclusion: Female individuals with HbSS have significantly lower mean levels of estrogen and progesterone compared to Hb AA and HbAS individuals but higher FSH levels compared to HbAS and HbAA individuals, though not significant. Among HbSS individuals, FSH level correlated inversely with disease severity, though not significant.
Science-based, data-driven developments in plasma processing for material synthesis and device-integration technologies

Low-temperature plasma-processing technologies are essential for material synthesis and device fabrication. Not only the utilization but also the development of plasma-related products and services requires an understanding of the multiscale hierarchies of complex behaviors of plasma-related phenomena, including plasma generation in physics and chemistry, transport of energy and mass through the sheath region, and morphology- and geometry-dependent surface reactions. Low-temperature plasma science and technology play a pivotal role in the exploration of new applications and in the development and control of plasma-processing methods. Presently, science-based and data-driven approaches to control systems are progressing with the state-of-the-art deep learning, machine learning, and artificial intelligence. In this review, researchers in material science and plasma processing, review and discuss the requirements and challenges of research and development in these fields. In particular, the prediction of plasma parameters and the discovery of processing recipes are asserted by outlining the emerging science-based, data-driven approaches, which are called plasma informatics.
β-Caryophyllene Counteracts Chemoresistance Induced by Cigarette Smoke in Triple-Negative Breast Cancer MDA-MB-468 Cells

Exposure to cigarette smoke (CS) has been associated with an increased risk of fatal breast cancers and recurrence, along with chemoresistance and chemotherapy impairment. This strengthens the interest in chemopreventive agents to be exploited both in healthy and oncological subjects to prevent or repair CS damage. In the present study, we evaluated the chemopreventive properties of the natural sesquiterpene β-caryophyllene towards the damage induced by cigarette smoke condensate (CSC) in triple negative breast cancer MDA-MB-468 cells. Particularly, we assessed the ability of the sesquiterpene to interfere with the mechanisms exploited by CSC to promote cell survival and chemoresistance, including genomic instability, cell cycle progress, autophagy/apoptosis, cell migration and related pathways. β-Caryophyllene was found to be able to increase the CSC-induced death of MDA-MB-468 cells, likely triggering oxidative stress, cell cycle arrest and apoptosis; moreover, it hindered cell recovery, autophagy activation and cell migration; at last, a marked inhibition of the signal transducer and activator of transcription 3 (STAT3) activation was highlighted: this could represent a key mechanism of the chemoprevention by β-caryophyllene. Although further studies are required to confirm the in vivo efficacy of β-caryophyllene, the present results suggest a novel strategy to reduce the harmful effect of smoke in cancer patients and to improve the survival expectations in breast cancer women.
Genesis of the Wilsons Promontory batholith of southeast Victoria, Australia: heterogeneities in the Proterozoic basement

Abstract The monzogranites and syenogranites of the Early Devonian (Emsian), post-tectonic, high-level, S-type Wilsons Promontory batholith contain magmatic garnet and cordierite, in addition to biotite, as well as biotite–quartz pseudomorphs after early orthopyroxene. The batholith forms the southern tip of the Australian mainland and consists of four penecontemporaneous plutons, emplaced at shallow crustal depth, intruding Ordovician to Lower Devonian lower greenschist-facies, quartz-rich metaflysch rocks in the Bassian tectonic zone. The magmas were initially H2O-undersaturated and at T > 800 °C, having been generated by partial melting of greywacke-dominated, Paleoproterozoic to Mesoproterozoic (ca 1.6 Ga) basement rocks in the Selwyn Block (the northern tip of the VanDieland microcontinent). However, the specific source rocks differ from those of the S-type granitic plutons in the neighbouring Melbourne tectonic zone. This probably reflects compositional variations within the Selwyn Block, which has been proposed to form the basement terrane beneath both the Melbourne and Bassian zones. The compositional zonation in the batholith is due to sequential intrusion of separate magma batches rather than in situ differentiation, and the main mechanism responsible for primary chemical variations in the rocks appears to have been peritectic assemblage entrainment. Some local differentiation also occurred, probably by filter pressing, to produce aplites and tourmaline-bearing pegmatites. Normal granitic rocks (i.e. not aplites or pegmatites) with SiO2 >76 wt% were not produced through differentiation of other granitic magmas in the batholith. At the global scale, this suggests that fractionation models for the origins of high-silica granitic and rhyolitic magmas may need revision. KEY POINTS S-type magmas of the Wilsons Promontory batholith were H2O-undersaturated and at T > 800 °C. They were generated by partial melting of greywacke-dominated, Paleoproterozoic to Mesoproterozoic (ca 1.6 Ga) basement. The sources differ from those of the plutons in the Melbourne Zone, suggesting heterogeneity in the Selwyn Block. Most compositional variation is due to intrusion of separate magma batches and peritectic assemblage entrainment, not differentiation.
Dynamic recombinant relatedness and its role for regional innovation

ABSTRACT Previous research has argued that related variety enhances regional innovation as inter-industry knowledge spillovers occur more easily between cognitively similar industries. In this study, we engage with empirical operationalization of what is ‘related’ in related variety. We argue, based on theoretical grounds, that estimating regional knowledge production functions requires related variety measures that capture the recombination of knowledge explicitly. To test this proposition, we develop a set of related variety indicators that account for indirect linkages between industries and allow these linkages to vary over time. Empirically, we estimate the relationship between regional innovation output and regional industry mix in Swedish regions between 1991 and 2010. Our results suggest that related variety measures based on dynamic recombinant relatedness are superior in predicting regional innovation output.
Leisure as self-care in the times of the pandemic: Insights from a time-use diary study in India

ABSTRACT In this research note, we discuss how the pandemic forged a renewed interest in self-care among urban older Indians. A reflexive thematic analysis of time-use diaries (N = 15) allows us to examine leisure patterns and everyday subjectivities of middle-class older Indians. In particular, time-diaries reveal a heightened focus on leisure-based enacted self-care practices including meditation, online activity/learning, and socialising. Consistent with previous scholarship of an unequivocal gender inequality in leisure as self-care, we observe distinct differences among men and women in their engagement with self-care. Specifically, while men engaged in outdoor activities as a way to cope with the stress and uncertainties of the pandemic, women’s everyday lives continued to be defined by domesticity and household management. Additionally, we show that while immediately uplifting, the ethics of self-care embodies the neoliberal logic of the entrepreneurial subject that makes self-reliance a necessity to practice responsible citizenship in times of the pandemic. Overall, by shifting the logics of care to the self, we depart from the more commonly held notion of older adults being recipients of care to the crafting of autonomous subjects through the pandemic-led public health practices of committed citizenship and civic virtue.
Mechanism of Salvia miltiorrhiza Bge. for the Treatment of Ischemic Stroke Based on Bioinformatics and Network Pharmacology

Methods In this study, SymMap was used to screen the 50 bioactive scored components and 65 putative targets of Salvia miltiorrhiza Bge. , and their targets were standardized using the UniProt platform. The disease targets related to stroke were collected by comparative toxicogenomics database (CTD), GeneCards, and quantitative structure-activity relationships-TargetNet (QSAR-TargetNet). Thereafter, the protein-protein interaction (PPI) network was constructed using the STRING platform and visualized by Cytoscape (3.8.2) software. Then, the Metascape platform was used to analyze the Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. Cytoscape (3.7.2) software was also used to construct the network of the “herb-component-target-pathway. ” We found that Tanshinol B, Tanshinol A, Przewaquinone C, Tanshinone II, and other main components of Salvia miltiorrhiza Bge. may regulate neurotransmitters and neurological function. Therefore, we speculate Salvia miltiorrhiza Bge. has a neuroprotective effect. For further verification, potential core targets (STAT3, MMP2, ESR1, TERT, and MMP9 proteins) for ischemic stroke and core active ingredients (Tanshinol A, Tanshinol B, Tanshinone II A, and Przewaquinone C) for Salvia miltiorrhiza Bge. were further verified by molecular docking. Results Our findings revealed that Tanshinol A, Tanshinol B, Tanshinone II A, and Przewaquinone C as the main component of Salvia miltiorrhiza Bge. may have a neuroprotective effect against ischemic stroke, which provides a new understanding for the development of therapies for the prevention and treatment of ischemic stroke.
A distributed object-oriented simulator framework for marine power plants with weak power grids

ABSTRACT In this work, we discuss and demonstrate how multi-engine marine power plants with weak power grids efficiently can be set up and simulated in a distributed co-simulation framework. To facilitate configuration switching such as starting and stopping, connecting and disconnecting arbitrary gensets online, the generator models are modelled as hybrid causality component models. This implementation enables seamless and energy conservative model switching. Also, the proposed simulator framework is scalable such that the number of gensets in the power plant can be set by a single parameter, which automatically scales the power management system and the tailored simulator master algorithm accordingly. To control the number of active gensets being connected to the power grid while running the simulation, a simple mixed integer linear programming formulation is proposed. A simulation case study including a marine power plant configuration with four equal-sized gensets is conducted in the end to demonstrate the features of the proposed simulator framework, which also can be applied to, e.g. a small wind farm, or an isolated number of islands with interconnected power generators.
Assessing the quality of bibliographic data sources for measuring international research collaboration

Abstract Measuring international research collaboration (IRC) is essential to various research assessment tasks but the effect of various measurement decisions, including which data sources to use, has not been thoroughly studied. To better understand the effect of data source choice on IRC measurement, we design and implement a data quality assessment framework specifically for bibliographic data by reviewing and selecting available dimensions and designing appropriate computable metrics, and then validate the framework by applying it to four popular sources of bibliographic data: Microsoft Academic Graph, Web of Science (WoS), Dimensions, and the ACM Digital Library. Successful validation of the framework suggests it is consistent with the popular conceptual framework of information quality proposed by Wang and Strong (1996) and adequately identifies the differences in quality in the sources examined. Application of the framework reveals that WoS has the highest overall quality among the sets considered; and that the differences in quality can be explained primarily by how the data sources are organized. Our study comprises a methodological contribution that enables researchers to apply this IRC measurement tool in their studies and makes an empirical contribution by further characterizing four popular sources of bibliographic data and their impact on IRC measurement.
Power Tracking and Energy Balancing of Energy Storage Systems under Unreliable Communication Network

In this paper, we consider the distributed power tracking and energy balancing problem of a general energy storage system subject to unreliable switching communication network. In order to deal with the uncertainty of the network topology, a distributed observer-based approach has been proposed. First, an adaptive distributed observer is employed to recover the reference power for the entire energy storage system for each energy storage unit. Second, based on the estimated reference power, a certainty equivalent control law is synthesized to simultaneously achieve power tracking and energy balancing. Numerical simulations are provided to validate the proposed control approach.
Coordinated Planning of Wind Power Generation and Energy Storage With Decision-Dependent Uncertainty Induced by Spatial Correlation

The global experience on wind farm development reveals that due to the spatial correlation, the prediction error of wind power is related to the scale of wind farms. This evidence indicates that the uncertainty features of wind power output from large-scale wind farms are not fixed but dependent on expansion decisions. The decision-dependent uncertainty (DDU) will alter the traditional optimization process and pose solution challenges. This article proposes a coordinated planning model for large-scale wind farms and energy storage considering DDU. First, a DDU model, which quantifies the relationship between wind power prediction errors and the wind farm size, is established based on historical data. The proposed DDU model for a single wind farm is extended to multiple wind farms with their spatial correlation captured by a Gaussian Mixture Model. Then, tackling the coupling relationship between decisions and uncertainty, an affine function-based solution method for the stochastic model with decision-dependent probability distributions is proposed. The constructed affine function maps planning decisions to decision-dependent wind power scenario sets via linear transformation. The difference between the planning model with and without the DDU in wind power is compared and discussed. Case studies verify the proposed model and solution method.
Challenges and research gap in project-based learning – A review

The 21st-century learning requirements are different from the 20th-century learning requirements. Critical thinking skills are requirements for learning, and to achieve critical thinking skills, various new learning techniques and strategies have been developed and applied. By applying new learning techniques and strategies, various challenges have been faced by researchers and instructors. The objectives of this study were to highlight the project-based learning (PjBL) used at different educational levels; to discuss the challenges and their solutions; and to present the research gap in the PjBL strategy. This was achieved using the literature review methods, with resources from previous studies. This study will help the researchers and instructors to carry out more research on the challenges and solutions of PjBL. Keywords: Critical thinking skills, instructors, learners, learning strategies, problems;
Galaxy and Mass Assembly (GAMA): Selection of the Most Massive Clusters

Abstract We have developed a galaxy cluster finding technique based on the Delaunay Tessellation Field Estimator (DTFE) combined with caustic analysis. Our method allows us to recover clusters of galaxies within the mass range of $10^{12}$ to $10^{16}\ \mathcal{M}_{\odot}$. We have found a total of 113 galaxy clusters in the Galaxy and Mass Assembly survey (GAMA). In the corresponding mass range, the density of clusters found in this work is comparable to the density traced by clusters selected by the thermal Sunyaev Zel'dovich Effect; however, we are able to cover a wider mass range. We present the analysis of the two-point correlation function for our cluster sample.
Image Quality Assessment for Effective Ear Recognition

Due to the recent challenges in access control, surveillance and security, there is an increased need for efficient human authentication solutions. Ear recognition is an appealing choice where the data acquisition procedure is contactless, non-intrusive, and covert. This article proposes a deep learning-based solution for effective ear recognition. First, we explore multiple strategies to enhance learning using alternative ear datasets with a wide range of ear poses. Second, we investigate the performance of the proposed deep ear models in the presence of various image artifacts, which commonly occur in real-life recognition applications, to identify the robustness of the proposed ear recognition models in controlled and uncontrolled conditions (dataset dependent). Finally, we propose an efficient ear image quality assessment tool designed to guide the proposed ear recognition system. By performing a set of experiments on extended degraded ear datasets, we determine that the employment of the proposed ear image quality assessment tool improves ear identification performance from 58.72% to 97.25% for the USTB degraded dataset and from 45.80% to 75.11% for the degraded FERET dataset.