Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of a Recombinant RBD Subunit Vaccine for SARS-CoV-2

The novel coronavirus pneumonia (COVID-19) pandemic is a great threat to human society and now is still spreading. Although several vaccines have been authorized for emergency use, only one recombinant subunit vaccine has been permitted for widespread use. More subunit vaccines for COVID-19 should be developed in the future. The receptor binding domain (RBD), located at the S protein of SARS-CoV-2, contains most of the neutralizing epitopes. However, the immunogenicity of RBD monomers is not strong enough. In this study, we fused the RBD-monomer with a modified Fc fragment of human IgG1 to form an RBD-Fc fusion protein. The recombinant vaccine candidate based on the RBD-Fc protein could induce high levels of IgG and neutralizing antibody in mice, and these could last for at least three months. The secretion of IFN-γ, IL-2 and IL-10 in the RBD-stimulated splenocytes of immunized mice also increased significantly. Our results first showed that the RBD-Fc vaccine could induce both humoral and cellular immune responses and might be an optional strategy to control COVID-19.
Subject-Specific Cognitive Workload Classification Using EEG-Based Functional Connectivity and Deep Learning

Cognitive workload is a crucial factor in tasks involving dynamic decision-making and other real-time and high-risk situations. Neuroimaging techniques have long been used for estimating cognitive workload. Given the portability, cost-effectiveness and high time-resolution of EEG as compared to fMRI and other neuroimaging modalities, an efficient method of estimating an individual’s workload using EEG is of paramount importance. Multiple cognitive, psychiatric and behavioral phenotypes have already been known to be linked with “functional connectivity”, i.e., correlations between different brain regions. In this work, we explored the possibility of using different model-free functional connectivity metrics along with deep learning in order to efficiently classify the cognitive workload of the participants. To this end, 64-channel EEG data of 19 participants were collected while they were doing the traditional n-back task. These data (after pre-processing) were used to extract the functional connectivity features, namely Phase Transfer Entropy (PTE), Mutual Information (MI) and Phase Locking Value (PLV). These three were chosen to do a comprehensive comparison of directed and non-directed model-free functional connectivity metrics (allows faster computations). Using these features, three deep learning classifiers, namely CNN, LSTM and Conv-LSTM were used for classifying the cognitive workload as low (1-back), medium (2-back) or high (3-back). With the high inter-subject variability in EEG and cognitive workload and recent research highlighting that EEG-based functional connectivity metrics are subject-specific, subject-specific classifiers were used. Results show the state-of-the-art multi-class classification accuracy with the combination of MI with CNN at 80.87%, followed by the combination of PLV with CNN (at 75.88%) and MI with LSTM (at 71.87%). The highest subject specific performance was achieved by the combinations of PLV with Conv-LSTM, and PLV with CNN with an accuracy of 97.92%, followed by the combination of MI with CNN (at 95.83%) and MI with Conv-LSTM (at 93.75%). The results highlight the efficacy of the combination of EEG-based model-free functional connectivity metrics and deep learning in order to classify cognitive workload. The work can further be extended to explore the possibility of classifying cognitive workload in real-time, dynamic and complex real-world scenarios.
Virus Diseases of Cereal and Oilseed Crops in Australia: Current Position and Future Challenges

This review summarizes research on virus diseases of cereals and oilseeds in Australia since the 1950s. All viruses known to infect the diverse range of cereal and oilseed crops grown in the continent’s temperate, Mediterranean, subtropical and tropical cropping regions are included. Viruses that occur commonly and have potential to cause the greatest seed yield and quality losses are described in detail, focusing on their biology, epidemiology and management. These are: barley yellow dwarf virus, cereal yellow dwarf virus and wheat streak mosaic virus in wheat, barley, oats, triticale and rye; Johnsongrass mosaic virus in sorghum, maize, sweet corn and pearl millet; turnip yellows virus and turnip mosaic virus in canola and Indian mustard; tobacco streak virus in sunflower; and cotton bunchy top virus in cotton. The currently less important viruses covered number nine infecting nine cereal crops and 14 infecting eight oilseed crops (none recorded for rice or linseed). Brief background information on the scope of the Australian cereal and oilseed industries, virus epidemiology and management and yield loss quantification is provided. Major future threats to managing virus diseases effectively include damaging viruses and virus vector species spreading from elsewhere, the increasing spectrum of insecticide resistance in insect and mite vectors, resistance-breaking virus strains, changes in epidemiology, virus and vectors impacts arising from climate instability and extreme weather events, and insufficient industry awareness of virus diseases. The pressing need for more resources to focus on addressing these threats is emphasized and recommendations over future research priorities provided.
DNA methylation and sexual dimorphism: New insights from mealybugs

DNA methylation is an ancient epigenetic pathway found across eukaryotes. Nevertheless, the targets of DNA methylation within genomes evolve extremely rapidly. Arthropods display many such examples. The mealybug Planococcus citri has evolved methylation at promoter sequences, associated with gene silencing just as in mammals. In this issue of Molecular Ecology, Bain et al. (2021), thoroughly characterise mealybug methylation, exploring its potential functions in gene expression and the spectacular sexual dimorphism that is a characteristic of this species. Their results provide new insights into the complex relationship between DNA methylation and gene expression and highlight how rapidly different methylation systems can evolve.
UR@NLP_A_Team @ GermEval 2021: Ensemble-based Classification of Toxic, Engaging and Fact-Claiming Comments

In this paper, we report on our approach to addressing the GermEval 2021 Shared Task on the Identification of Toxic, Engaging, and Fact-Claiming Comments for the German language. We submitted three runs for each subtask based on ensembles of three models each using contextual embeddings from pre-trained language models using SVM and neural-network-based classifiers. We include language-specific as well as language-agnostic language models – both with and without fine-tuning. We observe that for the runs we submitted that the SVM models overfitted the training data and this affected the aggregation method (simple majority voting) of the ensembles. The model records a lower performance on the test set than on the training set. Exploring the issue of overfitting we uncovered that due to a bug in the pipeline the runs we submitted had not been trained on the full set but only on a small training set. Therefore in this paper we also include the results we get when trained on the full training set which demonstrate the power of ensembles.
IRCologne at GermEval 2021: Toxicity Classification

In this paper, we describe the TH Köln’s submission for the Shared Task on the Identification of Toxic Comments at GermEval 2021. Toxicity is a severe and latent problem in comments in online discussions. Complex language model based methods have shown the most success in identifying toxicity. However, these approaches lack explainability and might be insensitive to domain-specific renditions of toxicity. In the scope of the GermEval 2021 toxic comment classification task (Risch et al., 2021), we employed a simple but promising combination of term-frequency-based classification and rule-based labeling to produce effective but to no lesser degree explainable toxicity predictions.
Optimal ECG-lead selection increases generalizability of deep learning on ECG abnormality classification

Deep learning (DL) has achieved promising performance in detecting common abnormalities from the 12-lead electrocardiogram (ECG). However, diagnostic redundancy exists in the 12-lead ECG, which could impose a systematic overfitting on DL, causing poor generalization. We, therefore, hypothesized that finding an optimal lead subset of the 12-lead ECG to eliminate the redundancy would help improve the generalizability of DL-based models. In this study, we developed and evaluated a DL-based model that has a feature extraction stage, an ECG-lead subset selection stage and a decision-making stage to automatically interpret multiple common ECG abnormality types. The data analysed in this study consisted of 6877 12-lead ECG recordings from CPSC 2018 (labelled as normal rhythm or eight types of ECG abnormalities, split into training (approx. 80%), validation (approx. 10%) and test (approx. 10%) sets) and 3998 12-lead ECG recordings from PhysioNet/CinC 2020 (labelled as normal rhythm or four types of ECG abnormalities, used as external text set). The ECG-lead subset selection module was introduced within the proposed model to efficiently constrain model complexity. It detected an optimal 4-lead ECG subset consisting of leads II, aVR, V1 and V4. The proposed model using the optimal 4-lead subset significantly outperformed the model using the complete 12-lead ECG on the validation set and on the external test dataset. The results demonstrated that our proposed model successfully identified an optimal subset of 12-lead ECG; the resulting 4-lead ECG subset improves the generalizability of the DL model in ECG abnormality interpretation. This study provides an outlook on what channels are necessary to keep and which ones may be ignored when considering an automated detection system for cardiac ECG abnormalities. This article is part of the theme issue ‘Advanced computation in cardiovascular physiology: new challenges and opportunities’.
A novel multi-branch architecture for state of the art robust detection of pathological phonocardiograms

Heart auscultation is an inexpensive and fundamental technique to effectively diagnose cardiovascular disease. However, due to relatively high human error rates even when auscultation is performed by an experienced physician, and due to the not universal availability of qualified personnel, e.g. in developing countries, many efforts are made worldwide to propose computational tools for detecting abnormalities in heart sounds. The large heterogeneity of achievable data quality and devices, the variety of possible heart pathologies, and a generally poor signal-to-noise ratio make this problem very challenging. We present an accurate classification strategy for diagnosing heart sounds based on (1) automatic heart phase segmentation, (2) state-of-the art filters drawn from the field of speech synthesis (mel-frequency cepstral representation) and (3) an ad hoc multi-branch, multi-instance artificial neural network based on convolutional layers and fully connected neuronal ensembles which separately learns from each heart phase hence implicitly leveraging their different physiological significance. We demonstrate that it is possible to train our architecture to reach very high performances, e.g. an area under the curve of 0.87 or a sensitivity of 0.97. Our machine-learning-based tool could be employed for heartsound classification, especially as a screening tool in a variety of situations including telemedicine applications. This article is part of the theme issue ‘Advanced computation in cardiovascular physiology: new challenges and opportunities’.
Simulating thermal stratification and modeling outlet water temperature in reservoirs with a data-mining method

This paper simulates the thermal stratification of the Karkhe Reservoir, Iran, with the CE-QUAL-W2 model for the period 1981–1995. The simulation of reservoir water quality requires meteorological, hydrological, chemical, and discharge time series to accurately predict the temperature of water releases from the reservoir. Outlet water temperature of the Karkhe Reservoir is calculated using the CE-QUAL-W2 model and the simulated outlet water temperature is thereafter modeled with the library for support vector machines (LIBSVM) data-mining model. Simulation results show thermal stratification in the Karkhe Reservoir occurs once a year. In addition, the data-mining model is a good surrogate model for the CE-QUAL-W2 model for estimating water temperature at different outlet levels in the reservoir. The root-mean square, mean absolute error and Nash-Sutcliffe criteria are used to assess the performance of the data-mining method. The LIBSVM model was found to be a suitable surrogate model for the main simulation model, and can be linked to optimization models with which to calculate reservoir operational rules for thermal control. doi: 10.2166/aqua.2018. 036 s://iwaponline.com/aqua/article-pdf/68/1/7/526450/jws0680007.pdf Shima Soleimani Omid Bozorg-Haddad (corresponding author) Department of Irrigation and Reclamation, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Tehran 31587-77871, Iran E-mail: obhaddad@ut.ac. ir Motahareh Saadatpour School of Civil Engineering, Iran University of Science and Technology, 16846-13114 Tehran, Iran Hugo A. Loáiciga Department of Geography, University of California, Santa Barbara, California 93106, USA
Tenure transitions at the edges of ownership: Reinforcing or challenging the status quo?

This paper provides an empirical overview of housing tenure transitions in Australia, the UK and the USA during a period of unprecedented economic instability in 2001–2017. Focusing on the neglected theme of episodic homeownership, we profile those who straddle the tenure divide by moving into and out of renting from time to time. Using panel data we model this ‘churn’ in three jurisdictions, showing that even the dislocation of a global financial crisis does not eclipse the independent impact of life events during rental spells. We find that whatever individuals bring from prior ownership, shocks occurring during a rental spell – unemployment, loss of a partner, additional dependent children – can be sufficient to prevent return. Churning is also health- and age-selective, adding ‘drop-out’ among the old to ‘lock-out’ for the young as a policy concern. Even those who successfully regain owner-occupation increase their credit and investment risks without necessarily improving their housing position. Overall ‘churners’ are a diverse constituency whose life chances are powerfully shaped by episodic ownership: what they share is time spent in an unacknowledged, under-instituted space between tenures where there is latent demand for innovative financial services and untapped potential for radical policy shifts.
Application of periodic Bernoulli polynomials in approximating the solution of linear time-delay optimal control problems

In this paper, a numerical method based on application of periodic Bernoulli polynomials for solving optimal control of linear time-delay systems with quadratic performance index is presented. Some important properties of Bernoulli polynomials are reviewed and a new operational matrix of delay based on these functions is introduced. Also an error bound for approximating a function with periodic Bernoulli polynomials is presented. Using operational matrices of differentiation, delay and the integration of the cross product of two Bernoulli polynomials, the linear time delay optimal control problem with quadratic objective function is converted to a nonlinear optimization problem. Finally, numerous illustrative examples are studied to show the efficiency and accuracy of the proposed method compared with some other methods.
Multi-transitivity of semigroup actions

Moothathu [Colloq. Math. 120 (2010), pp. 127–138] proved that multi-transitivity, weakly mixing and Δ-transitivity are equivalent for minimal homeomorphisms. In this paper, we extend this result to Abelian group actions. Moreover, we point out that a multi-transitive system of Abelian group action is Li–Yorke ε-chaotic for some .
Cellular Size, Gap Junctions, and Sodium Channel Properties Govern Developmental Changes in Cardiac Conduction

Electrical conduction in cardiac ventricular tissue is regulated via sodium (Na+) channels and gap junctions (GJs). We and others have recently shown that Na+channels preferentially localize at the site of cell-cell junctions, the intercalated disc (ID), in adult cardiac tissue, facilitating coupling via the formation of intercellular Na+nanodomains, also termed ephaptic coupling (EpC). Several properties governing EpC vary with age, including Na+channel and GJ expression and distribution and cell size. Prior work has shown that neonatal cardiomyocytes have immature IDs with Na+channels and GJs diffusively distributed throughout the sarcolemma, while adult cells have mature IDs with preferentially localized Na+channels and GJs. In this study, we perform an in silico investigation of key age-dependent properties to determine developmental regulation of cardiac conduction. Simulations predict that conduction velocity (CV) biphasically depends on cell size, depending on the strength of GJ coupling. Total cell Na+channel conductance is predictive of CV in cardiac tissue with high GJ coupling, but not correlated with CV for low GJ coupling. We find that ephaptic effects are greatest for larger cells with low GJ coupling typically associated with intermediate developmental stages. Finally, simulations illustrate how variability in cellular properties during different developmental stages can result in a range of possible CV values, with a narrow range for both neonatal and adult myocardium but a much wider range for an intermediate developmental stage. Thus, we find that developmental changes predict associated changes in cardiac conduction.
Genetic Algorithm for Feature Selection in Lower Limb Pattern Recognition

Choosing the right features is important to optimize lower limb pattern recognition, such as in prosthetic control. EMG signals are noisy in nature, which makes it more challenging to extract useful information. Many features are used in the literature, which raises the question which features are most suited for use in lower limb myoelectric control. Therefore, it is important to find combinations of best performing features. One way to achieve this is by using a genetic algorithm, a meta-heuristic capable of searching vast feature spaces. The goal of this research is to demonstrate the capabilities of a genetic algorithm and come up with a feature set that has a better performance than the state-of-the-art feature set. In this study, we collected a dataset containing ten able-bodied subjects who performed various gait-related activities while measuring EMG and kinematics. The genetic algorithm selected features based on the performance on the training partition of this dataset. The selected feature sets were evaluated on the remaining test set and on the online benchmark dataset ENABL3S, against a state-of-the-art feature set. The results show that a feature set based on the selected features of a genetic algorithm outperforms the state-of-the-art set. The overall error decreased up to 0.54% and the transitional error by 2.44%, which represent a relative decrease in overall errors up to 11.6% and transitional errors up to 14.1%, although these results were not significant. This study showed that a genetic algorithm is capable of searching a large feature space and that systematic feature selection shows promising results for lower limb myoelectric control.
Influence of the Manner of Information Presentation on Risky Choice

We are constantly faced with decisive situations in which the options are not presented simultaneously. How the information of options is presented might influence the subsequent decision-making. For instance, presenting the information of options in an alternative- or dimension-wise manner may affect searching patterns and thus lead to different choices. In this study, the effects of this manner of information presentation on risky choice according to two experiments (Experiment 1, N = 45; Experiment 2, N = 50) are systematically examined. Specifically, two tasks with different presentation are conducted. Participants could search the information of one option (alternative-wise task) or dimension (dimension-wise task) for each time. Results revealed that the participants assigned in the alternative-wise task exhibited more choices consistent with expected value theory and took a longer decision time than those in the dimension-wise task. Moreover, the effect of task on choice was mediated by the direction of information search. These findings suggest a relationship between information search pattern and risky choice and allow for a better understanding of the mechanisms and processes involved in risky choice.
The ‘Comparative Logic’ and Why We Need to Explain Interlanguage Grammars

In this paper we argue that Bley-Vroman’s Comparative Fallacy, which warns against comparisons between native speakers and learners in second-language acquisition (SLA) research, is not justified on either theoretical or methodological grounds and should be abandoned as it contravenes the explanatory nature of SLA research. We argue that for SLA to be able to provide meaningful explanations, grammatical comparisons with a baseline (usually of native speakers although not always the case) are not only justified but necessary, a position which we call the ‘Comparative Logic’. The methodological choices assumed by this position ensure that interlanguage grammars are analysed in their own right and respecting their own principles. Related issues, such as why we focus on the native speaker and why investigating deficits in linguistic-cognitive SLA is essential in our field are discussed as well.