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Monitoring signals such as fetal heart rate (FHR) are important indicators of fetal well-being. Computer-assisted analysis of FHR patterns has been successfully used as a decision support tool. However, the absence of a gold standard for the building blocks decision-making in the systems design process impairs the development of new solutions. Here we propose a prognostic model based on advanced signal processing techniques and machine learning algorithms for the fetal state assessment within a comprehensive evaluation process. Feature-engineering-based and time-series-based machine learning classifiers were modeled into three data segmentation schemas for CTU-UHB, HUFA, and DB-TRIUM datasets and the generalization performance was assessed by a two-way cross-dataset evaluation. It has been shown that the feature-based algorithms outperformed the time-series ones on data-limited scenarios. The Support Vector Machines (SVM) obtained the best results on the datasets individually: specificity (85.6% ) and sensitivity (67.5%). On the other hand, the most effective generalization results were achieved by the Multi-layer perceptron (MLP) with a specificity of 71.6% and sensitivity of 61.7%. The overall process provided a combination of techniques and methods that increased the final prognostic model performance, achieving relevant results and requiring a smaller amount of data when compared to the state-of-the-art fetal status assessment solutions.
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Microbial and hydrothermal venting activities on the seafloor are important for the formation of sediment-hosted stratiform sulfide (SHSS) deposits. Fe isotopic compositions are sensitive to both microbial and hydrothermal activities and may be used to investigate the formation of these deposits. However, to the best of our knowledge, no Fe isotopic studies have been conducted on SHSS deposits. In the Devonian Dajiangping SHSS-type pyrite deposit (389 Ma), South China, laminated pyrite ores were precipitated from exhalative hydrothermal fluids, whereas black shales were deposited during intervals with no exhalation. Pyrite grains from black shales mostly display positive δ56Fe-py (0.01–0.73‰), higher than marine sediments (ca. 0‰), due to pyrite deriving Fe from basinal shuttled Fe(III) (hydr-)oxides and slowly crystallizing in pores of sediments with equilibrium fractionation, except for negative δ56Fe-py (−0.17‰ to −0.24‰) of two samples caused by mixing of Fe from underlain laminated ores. The positive δ34S-py (3.50–24.5‰) of black shales reflect that sulfur of pyrite originated from quantitative reduction of sulfate in closed pores of sediments. In contrast, pyrite grains of laminated ores have negative δ56Fe-py (−0.60‰ to −0.21‰), which were not only inherited from the negative δ56Fe of hydrothermal fluids but also caused by kinetic fractionation during rapid precipitation of a pyrite precursor (FeS) in hydrothermal plumes. These ores have negative δ34S-py (−28.7‰ to −1.82‰), because H2S for pyrite mineralization was produced by bacterial sulfate reduction (BSR) in a sulfate-rich seawater column or shallow sediments. The δ56Fe-py values of laminated ores co-vary positively with δ34S-py and δ13C-carbonate along the ore stratigraphy, with δ13C-carbonate values ranging from −12.0‰ to −2.50‰. However, they correlate negatively with aluminum-normalized total organic carbon (TOC/Al2O3). Organic carbon is thus considered to enhance the production of H2S by BSR activities, increase pyrite precipitation rates and promote the expression of kinetic fractionation of Fe isotopes. Intriguingly, in the ore units with vigorous hydrothermal venting activities, δ56Fe-py, δ34S-py and δ13C-carbonate values display a consistently increasing trend. Such results suggest that venting hydrothermal fluids significantly inhibited the H2S production of BSR, which then reduced the pyrite crystallization rate and decreased the kinetic fractionation of Fe isotopes. Our study reveals that the formation of SHSS deposits relies on H2S from microbial activities and metals from hydrothermal exhalation on the seafloor, but that vigorous exhalation can inhibit microbial activities and thus sulfide precipitation rates. The integrated use of Fe, S, and C isotopes can effectively elucidate these dynamic interactions between hydrothermal venting and microbial activities during the formation of SHSS deposits.
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As safety is one of the most important properties of drugs, chemical toxicology prediction has received increasing attentions in the drug discovery research. Traditionally, researchers rely on in vitro and in vivo experiments to test the toxicity of chemical compounds. However, not only are these experiments time consuming and costly, but experiments that involve animal testing are increasingly subject to ethical concerns. While traditional machine learning (ML) methods have been used in the field with some success, the limited availability of annotated toxicity data is the major hurdle for further improving model performance. Inspired by the success of semi-supervised learning (SSL) algorithms, we propose a Graph Convolution Neural Network (GCN) to predict chemical toxicity and trained the network by the Mean Teacher (MT) SSL algorithm. Using the Tox21 data, our optimal SSL-GCN models for predicting the twelve toxicological endpoints achieve an average ROC-AUC score of 0.757 in the test set, which is a 6% improvement over GCN models trained by supervised learning and conventional ML methods. Our SSL-GCN models also exhibit superior performance when compared to models constructed using the built-in DeepChem ML methods. This study demonstrates that SSL can increase the prediction power of models by learning from unannotated data. The optimal unannotated to annotated data ratio ranges between 1:1 and 4:1. This study demonstrates the success of SSL in chemical toxicity prediction; the same technique is expected to be beneficial to other chemical property prediction tasks by utilizing existing large chemical databases. Our optimal model SSL-GCN is hosted on an online server accessible through: https://app.cbbio.online/ssl-gcn/home.
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The adoption of IoT for smart health applications is a relevant tool for distributed and intelligent automatic diagnostic systems. This work proposes the development of an integrated solution to monitor maternal and fetal signals for high-risk pregnancies based on IoT sensors, feature extraction based on data analytics, and an intelligent diagnostic aid system based on a 1-D convolutional neural network (CNN) classifier. The fetal heart rate and a group of maternal clinical indicators, such as the uterine tonus activity, blood pressure, heart rate, temperature, and oxygen saturation are monitored. Multiple data sources generate a significant amount of data in different formats and rates. An emergency diagnostic subsystem is proposed based on a fog computing layer and the best accuracy was 92.59% for both maternal and fetal emergency. A smart health analytics system is proposed for multiple feature extraction and the calculation of linear and nonlinear measures. Finally, a classification technique is proposed as a prediction system for maternal, fetal, and simultaneous health status classification, considering six possible outputs. Different classifiers are evaluated and a proposed CNN presented the best results, with the F1-score ranging from 0.74 to 0.91. The results are validated based on the diagnosis provided by two specialists. The results show that the proposed system is a viable solution for maternal and fetal ambulatory monitoring based on IoT.
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Classification of electroencephalogram (EEG) is a key approach to measure the rhythmic oscillations of neural activity, which is one of the core technologies of brain-computer interface systems (BCIs). However, extraction of the features from non-linear and non-stationary EEG signals is still a challenging task in current algorithms. With the development of artificial intelligence, various advanced algorithms have been proposed for signal classification in recent years. Among them, deep neural networks (DNNs) have become the most attractive type of method due to their end-to-end structure and powerful ability of automatic feature extraction. However, it is difficult to collect large-scale datasets in practical applications of BCIs, which may lead to overfitting or weak generalizability of the classifier. To address these issues, a promising technique has been proposed to improve the performance of the decoding model based on data augmentation (DA). In this article, we investigate recent studies and development of various DA strategies for EEG classification based on DNNs. The review consists of three parts: what kind of paradigms of EEG-based on BCIs are used, what types of DA methods are adopted to improve the DNN models, and what kind of accuracy can be obtained. Our survey summarizes the current practices and performance outcomes that aim to promote or guide the deployment of DA to EEG classification in future research and development.
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With the fifth generation (5G) communication technology, the mobile multiuser networks have developed rapidly. In this paper, the performance analysis of mobile multiuser networks which utilize decode-and-forward (DF) relaying is considered. We derive novel outage probability (OP) expressions. To improve the OP performance, we study the power allocation optimization problem. To solve the optimization problem, we propose an intelligent power allocation optimization algorithm based on grey wolf optimization (GWO). We compare the proposed GWO approach with three existing algorithms. The experimental results reveal that the proposed GWO algorithm can achieve a smaller OP, thus improving system efficiency. Also, compared with other channel models, the OP values of the 2-Rayleigh model are increased by 81.2% and 66.6%, respectively.
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The doctrine of original sin gives people the impression that the goodness of human nature is under-evaluated in the Christian theological tradition. The Chinese philosopher Mencius is famous for his teaching on the goodness of human nature. Reading Mencius and Thomas Aquinas side by side, this article argues that the Mencian teaching on human nature brings us to affirm the goodness of human nature by recovering the significance of the image of God for the Christian doctrine of human nature. If we seek the goodness of human nature in the possibilities to become good, it is natural to see that even in the fallen state the possibilities of becoming like God remain in human nature imprinted with the image of God. It is open to the culmination of a gradual progression to its perfection.
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AimsVascular calcification is a common clinical complication of chronic kidney disease (CKD), atherosclerosis (AS), and diabetes, which is associated with increased cardiovascular morbidity and mortality in patients. The transdifferentiation of vascular smooth muscle cells (VSMCs) to an osteochondrogenic phenotype is a crucial step during vascular calcification. The transcription factor CCAAT/enhancer-binding protein alpha (C/EBPα) plays an important role in regulating cell proliferation and differentiation, but whether it regulates the calcification of arteries and VSMCs remains unclear. Therefore, this study aims to understand the role of C/EBPα in the regulation of vascular calcification.Methods and ResultsBoth mRNA and protein expression levels of C/EBPα were significantly increased in calcified arteries from mice treated with a high dose of vitamin D3 (vD3). Upregulation of C/EBPα was also observed in the high phosphate- and calcium-induced VSMC calcification process. The siRNA-mediated knockdown of C/EBPα significantly attenuated VSMC calcification in vitro. Moreover, C/EBPα depletion in VSMCs significantly reduced the mRNA expression of the osteochondrogenic genes, e.g., sex-determining region Y-box 9 (Sox9). C/EBPα overexpression can induce SOX9 overexpression. Similar changes in the protein expression of SOX9 were also observed in VSMCs after C/EBPα depletion or overexpression. In addition, silencing of Sox9 expression significantly inhibited the phosphate- and calcium-induced VSMC calcification in vitro.ConclusionFindings in this study indicate that C/EBPα is a key regulator of the osteochondrogenic transdifferentiation of VSMCs and vascular calcification, which may represent a novel therapeutic target for vascular calcification.
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The research explores the perceptions of five secondary school students with special education needs (SEN) about their participation in learning, group membership, and agency within an inclusive school in Macau SAR. This goal is achieved by using students' voices documented in open-ended interviews and is underpinned by the conceptual framework of heutagogy. The aim is to shed light on students' perceptions on school effectiveness in supporting their needs through successful participation and agentic possibilities. Findings showed that students were more prone to social rejection and being isolated or bullied than their peers. They were struggling to feel included or participate, their needs were only partially being met, and they had few opportunities to exert influence on their educational trajectories. Recommendations are provided to assist educators and schools in enhancing students with SEN to connect to the learning process and community, with the provision of appropriate learning adjustments and more active approaches to ensure their acceptance by mainstream students, including the formation of coaching peers to assist in developing social and academic skills under teacher's scaffolding practices. This study highlights the contribution of the heutagogical perspective to advance research on the participation and agency of students with SEN in mainstream schools.
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A growing focus on God’s mercy and forgiveness emerged in the wake of the recent Pontificates of John Paul II, Benedict XVI, and Francis. Our time with its multiple crises cries for healing, forgiveness, and the experience of God’s mercy. In social, political, and global terms, humanity craves for “lasting peace, born of the marriage of justice and mercy” (John Paul II, 2001, no. 15). The experience of God’s forgiveness, merciful healing and new life has been expressed many times in the Bible. But, theologically, it has never been formulated as directly as in Hosea 11:8, when God’s own heart becomes “turned over”, “converted” following the blaze of his own overwhelming compassion, paving the way for a fundamental spiritual transformation, rooted in forgiveness and mercy, that opens wellsprings of dignity, healing, and new life for all.
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In this essay we argue that, based on current scientific data, the most prudential course of future actions that an American conservative can take, is one that assumes what we call climate change alarmism. In order to establish this thesis, we first provide a basic overview of the relevant climate change science, as well as give an analysis of the alarmist and lukewarming dialectic (the two primary interpretations of the data). We then move to develop our environmental wager. Finally, following Roger Scruton, we end this work by proposing what sort of policies conservatives should endorse going further.
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Launonen and Mullins argue that if Classical Theism is true, human cognition is likely not theism-tracking, at least, given what we know from cognitive science of religion. In this essay, we develop a model for how classical theists can make sense of the findings from cognitive science, without abandoning their Classical Theist commitments. We also provide an argument for how our model aligns well with the Christian doctrine of general revelation.
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It is argued that the role of the Chinese government to support the cross-border operations of Chinese firms is to assist these firms in overcoming their limited established brands, and their disadvantages in technology and managerial resources, which were also the reasons why such firms decided to enter emerging markets instead of developed markets. This strategic choice is preferred to avoid direct confrontation with established firms from developed countries endowed with superior ownership advantages. Therefore, Chinese resources seeking firms innovate by increasing investment in developing and emerging markets to develop unique ownership advantages for sustainable market development and competitive advantage. This research investigates the ownership advantages of resources seeking Chinese firms in these markets using the OLI theory. The paper contributes to explaining the specific advantages of Chinese MNEs when entering emerging markets. The study applied a two-stage qualitative methodology to examine Chinese firms operating in Nigeria. The first stage included an exploratory study based on interviews with key informants and experts while the second stage included a case study methodology. The study focused on resources seeking Chinese MNEs operating in Nigeria.
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