Miracle-dz/UniFAR-FTUs huggingface dataset:
Hierarchical fusion detection algorithm for sleep spindle detection

Background Sleep spindles are a vital sign implying that human beings have entered the second stage of sleep. In addition, they can effectively reflect a person’s learning and memory ability, and clinical research has shown that their quantity and density are crucial markers of brain function. The “gold standard” of spindle detection is based on expert experience; however, the detection cost is high, and the detection time is long. Additionally, the accuracy of detection is influenced by subjectivity. Methods To improve detection accuracy and speed, reduce the cost, and improve efficiency, this paper proposes a layered spindle detection algorithm. The first layer used the Morlet wavelet and RMS method to detect spindles, and the second layer employed an improved k-means algorithm to improve spindle detection efficiency. The fusion algorithm was compared with other spindle detection algorithms to prove its effectiveness. Results The hierarchical fusion spindle detection algorithm showed good performance stability, and the fluctuation range of detection accuracy was minimal. The average value of precision was 91.6%, at least five percentage points higher than other methods. The average value of recall could reach 89.1%, and the average value of specificity was close to 95%. The mean values of accuracy and F1-score in the subject sample data were 90.4 and 90.3%, respectively. Compared with other methods, the method proposed in this paper achieved significant improvement in terms of precision, recall, specificity, accuracy, and F1-score. Conclusion A spindle detection method with high steady-state accuracy and fast detection speed is proposed, which combines the Morlet wavelet with window RMS and an improved k-means algorithm. This method provides a powerful tool for the automatic detection of spindles and improves the efficiency of spindle detection. Through simulation experiments, the sampled data were analyzed and verified to prove the feasibility and effectiveness of this method.
More eyes on the prize: open-source data, software and hardware for advancing plant science through collaboration

Abstract Automating the analysis of plants using image processing would help remove barriers to phenotyping and large-scale precision agricultural technologies, such as site-specific weed control. The combination of accessible hardware and high-performance deep learning (DL) tools for plant analysis is becoming widely recognised as a path forward for both plant science and applied precision agricultural purposes. Yet, a lack of collaboration in image analysis for plant science, despite the open-source origins of much of the technology, is hindering development. Here, we show how tools developed for specific attributes of phenotyping or weed recognition for precision weed control have substantial overlapping data structure, software/hardware requirements and outputs. An open-source approach to these tools facilitates interdisciplinary collaboration, avoiding unnecessary repetition and allowing research groups in both basic and applied sciences to capitalise on advancements and resolve respective bottlenecks. The approach mimics that of machine learning in its nascence. Three areas of collaboration are identified as critical for improving efficiency, (1) standardized, open-source, annotated dataset development with consistent metadata reporting; (2) establishment of accessible and reliable training and testing platforms for DL algorithms; and (3) sharing of all source code used in the research process. The complexity of imaging plants and cost of annotating image datasets means that collaboration from typically distinct fields will be necessary to capitalize on the benefits of DL for both applied and basic science purposes.
AN APPROACH TO SPHERICAL COILS DESIGN BASED ON THE TARGET FIELD APPROACH

Using the method of separation of variables and expanding the magnetic field generated by currents flowing on a sphere by means of Fourier-Legendre expansion, we can obtain the inverse relationship between the current density and magnetic field.The current density required to generate a specified target field B_Z inside the sphere may be evaluated. This approach develops Turner's target field method and can generate all kinds of z-axis specified field in large usable volumes inside the sphere, which can satisfy the requirement of high-energy physics,medical treatment,space detection etc. In this paper the surface current density that can generate uniform z-axis field and gradient field that vasty linearly along the z direction is calculated by this method.The validation of this approach is also verified by commercial finite element method software.
Microfabrication approaches for oral research and clinical dentistry

Currently, there is a variety of laboratory tools and strategies that have been developed to investigate in-vivo processes using in-vitro models. Amongst these, microfabrication represents a disruptive technology that is currently enabling next-generation biomedical research through the development of complex laboratory approaches (e.g., microfluidics), engineering of micrometer scale sensors and actuators (micropillars for traction force microscopy), and the creation of environments mimicking cell, tissue, and organ-specific contexts. Although microfabrication has been around for some time, its application in dental and oral research is still incipient. Nevertheless, in recent years multiple lines of research have emerged that use microfabrication-based approaches for the study of oral diseases and conditions with micro- and nano-scale sensitivities. Furthermore, many investigations are aiming to develop clinically relevant microfabrication-based applications for diagnostics, screening, and oral biomaterial manufacturing. Therefore, the objective of this review is to summarize the current application of microfabrication techniques in oral sciences, both in research and clinics, and to discuss possible future applications of these technologies for in-vitro studies and practical patient care. Initially, this review provides an overview of the most employed microfabrication methods utilized in biomedicine and dentistry. Subsequently, the use of micro- and nano-fabrication approaches in relevant fields of dental research such as endodontic and periodontal regeneration, biomaterials research, dental implantology, oral pathology, and biofilms was discussed. Finally, the current and future uses of microfabrication technology for clinical dentistry and how these approaches may soon be widely available in clinics for the diagnosis, prevention, and treatment of relevant pathologies are presented.
Utility of quantitative MRI metrics in brain ageing research

The advent of new, advanced quantitative MRI metrics allows for in vivo evaluation of multiple biological processes highly relevant for ageing. The presented study combines several MRI parameters hypothesised to detect distinct biological characteristics as myelin density, cellularity, cellular membrane integrity and iron concentration. 116 healthy volunteers, continuously distributed over the whole adult age span, underwent a multi-modal MRI protocol acquisition. Scatterplots of individual MRI metrics revealed that certain MRI protocols offer much higher sensitivity to early adulthood changes while plateauing in higher age (e.g., global functional connectivity in cerebral cortex or orientation dispersion index in white matter), while other MRI metrics provided reverse ability—stable levels in young adulthood with sharp changes with rising age (e.g., T1ρ and T2ρ). Nonetheless, despite the previously published validations of specificity towards microstructural biology based on cytoarchitectonic maps in healthy population or alterations in certain pathologies, several metrics previously hypothesised to be selective to common measures failed to show similar scatterplot distributions, pointing to further confounding factors directly related to age. Furthermore, other metrics, previously shown to detect different biological characteristics, exhibited substantial intercorrelations, be it due to the nature of the MRI protocol itself or co-dependence of relevant biological microstructural processes. All in all, the presented study provides a unique basis for the design and choice of relevant MRI parameters depending on the age group of interest. Furthermore, it calls for caution in simplistic biological inferences in ageing based on one simple MRI metric, even though previously validated under other conditions. Complex multi-modal approaches combining several metrics to extract the shared subcomponent will be necessary to achieve the desired goal of histological MRI.
Hubble Space Telescope Ultraviolet and Ground-based Optical Spectropolarimetry of IRAS Quasi-stellar Objects: Dusty Scattering in Luminous Active Galactic Nuclei

We present UV and optical spectropolarimetry of two highly polarized IRAS-selected quasi-stellar objects (QSOs), IRAS 13349+2438 and the broad absorption line QSO (BALQSO) IRAS 14026+4341. The polarization in both objects rises rapidly toward the blue, peaks near 3000 Å in the rest frame and remains nearly constant for shorter wavelengths. The rest frame optical polarized flux density spectra also increase rapidly toward the blue but then decrease dramatically below 3000 Å. This distinctive wavelength dependence of polarized flux shows that the polarization is produced by dust scattering. As for many Seyfert, radio, and hyperluminous infrared galaxies (HIGs), the lower polarization of the weak [O III] λλ4959, 5007 lines in IRAS 13349+2438 suggests that the scattering grains lie interior to or mixed with the narrow-line gas. We construct full radiative transfer models of these systems consisting of a dusty sphere of modest optical depth illuminated axisymmetrically from within by a power-law QSO spectrum. We show that this simple model successfully reproduces the qualitative polarization properties of the objects. Despite similarities to other IRAS-selected BALQSOs, our faint object spectrograph spectropolarimetry of IRAS 13349+2438 does not reveal broad absorption lines. IRAS 14026+4341 has an Al III broad absorption line in both scattered and total flux density. We discuss these two objects in terms of both orientation and evolutionary unified schemes for QSOs, BALQSOs, and HIGs.
Emotional intelligence and anxiety in university students: evidence of a curvilinear relationship

ABSTRACT Emotional intelligence, or the ability to perceive, interpret, and regulate emotions, has been linked to numerous adaptive outcomes. However, recent investigations have demonstrated that emotional intelligence can exert a nonlinear impact on motivational, interpersonal, and affective factors. Although past work has provided evidence of optimal levels of emotional intelligence, many studies have relied on statistical procedures that are prone to inferential errors when used to investigate curvilinear relationships. Thus, the current study was designed to test for the presence of a nonlinear association between emotional intelligence and generalized anxiety using an inferential method designed to detect curvilinear relationships between variables. University students (N = 345; 68.79% Caucasian, 86.99% female, X ̅Age = 26.11) completed the Brief Emotional Intelligence Scale and Zung Self-Rating Anxiety Scale. Using segmented regression analysis, we determined that the relationship between emotional intelligence and anxiety follows a nonlinear trend. Specifically, our results indicate that emotional intelligence is positively related to anxiety among those with ‘low’ and ‘high’ levels of emotional intelligence. Additionally, our findings revealed emotional intelligence and anxiety were negatively related among those with ‘moderate’ levels of emotional intelligence. We believe the current work has important implications for efforts designed to support university students’ emotional growth and academic success.
Quantitative test method of Ritchey-Common test in large high precision flat measurements

Ritchey-Common test is an effective method for large flat measurement. The flat is set in divergent beam, the relation between figure errors of flat and wave-front aberrations (influence function) is complicated and difficult to derive, therefore it's usually regarded as a qualitative or semi-quantitative way. An algorithm is given, the influence function is deduced, and the quantitative test is realized.
Guessing and Forgetting: A Latent Class Model for Measuring Learning

Guessing on closed-ended knowledge items is common. Under likely-to-hold assumptions, in the presence of guessing, the most common estimator of learning, difference between pre- and postprocess scores, is negatively biased. To account for guessing-related error, we develop a latent class model of how people respond to knowledge questions and identify the model with the mild assumption that people do not lose knowledge over short periods of time. A Monte Carlo simulation over a broad range of informative processes and knowledge items shows that the simple difference score is negatively biased and the method we develop here is unbiased. To demonstrate its use, we apply our model to data from Deliberative Polls. We find that estimates of learning, once adjusted for guessing, are about 13% higher. Adjusting for guessing also eliminates the gender gap in learning, and halves the pre-deliberation gender gap on political knowledge.
Identifying Putative Resistance Genes for Barley Yellow Dwarf Virus-PAV in Wheat and Barley

Barley yellow dwarf viruses (BYDVs) are one of the most widespread and economically important plant viruses affecting many cereal crops. Growing resistant varieties remains the most promising approach to reduce the impact of BYDVs. A Recent RNA sequencing analysis has revealed potential genes that respond to BYDV infection in resistant barley genotypes. Together with a comprehensive review of the current knowledge on disease resistance in plants, we selected nine putative barley and wheat genes to investigate their involvement in resistance to BYDV-PAV infection. The target classes of genes were (i) nucleotide binding site (NBS) leucine-rich repeat (LRR), (ii) coiled-coil nucleotide-binding leucine-rich repeat (CC-NB-LRR), (iii) LRR receptor-like kinase (RLK), (iv) casein kinase, (v) protein kinase, (vi) protein phosphatase subunits and the transcription factors (TF) (vii) MYB TF, (viii) GRAS (gibberellic acid-insensitive (GAI), repressor of GAI (RGA) and scarecrow (SCR)), and (ix) the MADS-box TF family. Expression of genes was analysed for six genotypes with different levels of resistance. As in previous reports, the highest BYDV-PAV titre was found in the susceptible genotypes Graciosa in barley and Semper and SGS 27-02 in wheat, which contrast with the resistant genotypes PRS-3628 and Wysor of wheat and barley, respectively. Statistically significant changes in wheat show up-regulation of NBS-LRR, CC-NBS-LRR and RLK in the susceptible genotypes and down-regulation in the resistant genotypes in response to BYDV-PAV. Similar up-regulation of NBS-LRR, CC-NBS-LRR, RLK and MYB TF in response to BYDV-PAV was also observed in the susceptible barley genotypes. However, no significant changes in the expression of these genes were generally observed in the resistant barley genotypes, except for the down-regulation of RLK. Casein kinase and Protein phosphatase were up-regulated early, 10 days after inoculation (dai) in the susceptible wheat genotypes, while the latter was down-regulated at 30 dai in resistant genotypes. Protein kinase was down-regulated both earlier (10 dai) and later (30 dai) in the susceptible wheat genotypes, but only in the later dai in the resistant genotypes. In contrast, GRAS TF and MYB TF were up-regulated in the susceptible wheat genotypes while no significant differences in MADS TF expression was observed. Protein kinase, Casein kinase (30 dai), MYB TF and GRAS TF (10 dai) were all up-regulated in the susceptible barley genotypes. However, no significant differences were found between the resistant and susceptible barley genotypes for the Protein phosphatase and MADS FT genes. Overall, our results showed a clear differentiation of gene expression patterns in both resistant and susceptible genotypes of wheat and barley. Therefore, further research on RLK, NBS-LRR, CC-NBS-LRR, GRAS TF and MYB TF can lead to BYDV-PAV resistance in cereals.
Optimization Algorithm for Edge-Based Semantic Similarity Calculation

Concept semantic similarity calculation is a problem in applications.To simplify the single ontology concept semantic similarity calculation,an optimization algorithm for edge-based similarity calculation is put forward.It utilizes hierarchical relationship between concepts to simplify calculation process.Based on the semantic similarity between a pair of ontology concepts,the optimization algorithm gives all semantic similarity between each pair of ontology concepts.Results of simulation experiments show that the calculation complexity is reduced considerably,and the similarity calculation speed is improved by a factor of two.
An Adaptive and Dynamical Neural Network for Machine Remaining Useful Life Prediction

Recently, many neural networks have been proposed for machine remaining useful life (RUL) prediction. However, most network architectures of the existing approaches are fixed. Since the sequential information depends on the input data and distributes differently, these fixed networks that cannot be dynamically adjusted according to the input data may not be able to capture this sequential information well, resulting in suboptimal performances. To mitigate this issue, we propose an adaptive and dynamical neural network (AdaNet), which can dynamically adjust its architecture according to the input data. A neural network is generally determined by kernel size, depth, and channel size. In this article, we aim to enable our proposed AdaNet to adjust its kernel size and channel size dynamically. First, we explore to adapt the deformable convolution to time-series data, which allows the convolutional kernel to change according to the feature map. With this deformable convolution, the convolutional kernels in the AdaNet become adjustable, which is beneficial to fully exploit the sequential information in time-series data, leading to accurate RUL prediction. In addition, a channel selection module is devised, which can selectively activate the feature channel according to the input, further improving the performance of our AdaNet. Extensive experiments have been carried out on the C-MAPSS dataset, demonstrating that our proposed AdaNet achieves state-of-the-art performances.
Quantifying Systemic Risk in the Presence of Unlisted Banks: Application to the European Banking Sector

We propose a credit portfolio approach for evaluating systemic risk and attributing it across institutions. We construct a model that can be estimated from high-frequency CDS data. This captures risks from publicly traded banks, privately held institutions, and coöperative banks, extending approaches that rely on information from the public equity market only. We account for correlated losses between the institutions, overcoming a modeling weakness in earlier studies. We also offer a modeling extension to account for fat tails and skewness of asset returns. The model is applied to a universe of banks where we find discrepancies between the capital adequacy of the largest contributors to systemic risk relative to less systemically important banks on a European scale.
KIT D816V Mast Cells Derived from Induced Pluripotent Stem Cells Recapitulate Systemic Mastocytosis Transcriptional Profile

Mast cells (MCs) represent a population of hematopoietic cells with a key role in innate and adaptive immunity and are well known for their detrimental role in allergic responses. Yet, MCs occur in low abundance, which hampers their detailed molecular analysis. Here, we capitalized on the potential of induced pluripotent stem (iPS) cells to give rise to all cells in the body and established a novel and robust protocol for human iPS cell differentiation toward MCs. Relying on a panel of systemic mastocytosis (SM) patient-specific iPS cell lines carrying the KIT D816V mutation, we generated functional MCs that recapitulate SM disease features: increased number of MCs, abnormal maturation kinetics and activated phenotype, CD25 and CD30 surface expression and a transcriptional signature characterized by upregulated expression of innate and inflammatory response genes. Therefore, human iPS cell-derived MCs are a reliable, inexhaustible, and close-to-human tool for disease modeling and pharmacological screening to explore novel MC therapeutics.
The Tourism Demarketing Mix: Working to Reduce Revisits to Egyptian Nature Reserves

This study tests the impact of the tourism demarketing mix (product, price, place, promotion) on tourists’ intention to reduce revisits to nature reserves in Egypt. In particular, this work focuses on the moderating role of switching cost. Data were collected from 401 tourists of the Wadi Degla and Petrified Forest nature reserves in Egypt. Path analysis is employed to test the research hypotheses using WarpPLS 6. Findings confirm that the tourism demarketing mix significantly affects tourists’ perception and intention to reduce revisits to the nature reserves. Lower switching cost partially enhanced the relationship between the tourism demarketing mix and tourists’ intention to reduce revisits. Regarding implications, managers can use findings from this study to design marketing plans that contribute to sustainable stewardship of destinations and protect nature reserves. Limitations and conclusions were also provided.
Fast color image matting by online active contour model

Compared with gray images, colorful images contain more useful information. In this paper, an online active contour model regarding color image matting is proposed. Using our model, the objects of color images are detected according to their colors. For the proposed model, the new scheme firstly identifies the objects to be segmented by setting the initial contour. Then the new energy functional, which is based on the intensities in each channel, is minimized through an efficient level set formula. Thus less iterations and little calculation time are needed. Finally, the morphological opening and closing operation is adopted for regularization. Experiments results demonstrate the efficiency and effectiveness of the proposed approach, compared with the current active contour models.