Miracle-dz/UniFAR-FTUs huggingface dataset:
Prognostic value of elevated lactate dehydrogenase in patients with COVID-19: a systematic review and meta-analysis

Purpose This meta-analysis aimed to evaluate the prognostic performance of elevated lactate dehydrogenase (LDH) in patients with COVID-19. Methods A systematic literature search was performed using PubMed, Embase and EuropePMC on 19 November 2020. The outcome of interest was composite poor outcome, defined as a combined endpoint of mortality, severity, need for invasive mechanical ventilation and need for intensive care unit care. Severity followed the included studies’ criteria. Results There are 10 399 patients from 21 studies. Elevated LDH was present in 44% (34%–53%) of the patients. Meta-regression analysis showed that diabetes was correlated with elevated LDH (OR 1.01 (95% CI 1.00 to 1.02), p=0.038), but not age (p=0.710), male (p=0.068) and hypertension (p=0.969). Meta-analysis showed that elevated LDH was associated with composite poor outcome (OR 5.33 (95% CI 3.90 to 7.31), p<0.001; I2: 77.5%). Subgroup analysis showed that elevated LDH increased mortality (OR 4.22 (95% CI 2.49 to 7.14), p<0.001; I2: 89%). Elevated LDH has a sensitivity of 0.74 (95% CI 0.60 to 0.85), specificity of 0.69 (95% CI 0.58 to 0.78), positive likelihood ratio of 2.4 (95% CI 1.9 to 2.9), negative likelihood ratio of 0.38 (95% CI 0.26 to 0.55), diagnostic OR of 6 (95% CI 4 to 9) and area under curve of 0.77 (95% CI 0.73 to 0.80). Elevated LDH would indicate a 44% posterior probability and non-elevated LDH would in indicate 11% posterior probability for poor prognosis. Meta-regression analysis showed that age, male, hypertension and diabetes did not contribute to the heterogeneity of the analyses. Conclusion LDH was associated with poor prognosis in patients with COVID-19. PROSPERO registration number CRD42020221594.
THE MISSING TERM IN EFFECTIVE PAIR POTENTIALS

on carbon. These particles can interact with the active sites of the carbon to form a species a t the metal-carbon interface which is stable against high-temperature reduction. This interaction may occur via oxygen atoms bonding with both the carbon surface and iron atoms contacting the carbon. This oxygen could be provided by dissociation of the C O ligands during decomposition. The remainder of the surface Fe can exist in either a reduced or carburized state, but the small size of these particles results in either case in a superparamagnetically collapsed Mossbauer spectrum. For carbons with fewer active sites, more of the particles are unbound, and these particles sinter and carburize in a manner more similar to that of Fe particles supported on graphite.
Pre-harvest ethephon (2-chloroethyl phosphonic acid) on berry quality of 'Beidaneh Ghermez' grape

Pre-harvest ethephon (2-chloroethyl phosphonic acid) sprays at four levels (0, 100, 200 and 300 ppm), at 20% to 30% veraison enhanced berry maturation and other quality of (Vitis vinifera cv. Beidaneh Ghermez). ‘Beidaneh Ghermez’ is one of the most popular red seedless table grapes in the Middle East, but is usually has poor berry color. Ethephon application did not significantly affect the yield as compared to non-treated control, but fruit maturation (color) and berry size significantly (P = 0.05) increased as the rate of ethephon increased. Total berry skin anthocyanins significantly (P = 0.05) increased with every incremental increase in ethephon rate. The treatments did not significantly affect soluble solids concentrations (SSC), titratable acidity (TA) or juice pH. The highest SSC and TA were recorded in the control. The relationships between anthocynins and berry skin-color was linear. The grape berries receiving ethephon were less firm, but they had better berry quality (appearance) than those with no treatment (control).
Title: Nucleus accumbens neurons encode initiation and vigor of reward approach behavior • Abbreviated title: NAc neurons encode spontaneous reward approach

The nucleus accumbens (NAc) is considered an interface between motivation and action, with NAc neurons playing an important role in promoting reward approach. However, the encoding by NAc neurons that contribute to this role remains unknown. Here, we trained male rats to find rewards in an 8-arm radial maze. The activity of 62 neurons, mostly in the shell of the NAc, were recorded while rats ran towards each reward place. General linear model (GLM) analysis showed that variables related to the vigor of the locomotor approach, like speed and acceleration, and the fraction of the approach run completed were the best predictors of the firing rate for most NAc neurons. Nearly 23% of the recorded neurons, here named locomotion-off cells, were inhibited during the entire approach run, suggesting that reduction in firing of these neurons promotes initiation of locomotor approach. Another 24% of the neurons presented a peak of activity during acceleration followed by a valley during deceleration (peak-valley cells). Together, these neurons accounted for most of the speed and acceleration encoding identified in the GLM analysis. Cross-correlations between firing and speed indicated that the spikes of peak-valley cells were followed by increases in speed, suggesting that the activity of these neurons drives acceleration. In contrast, a further 19% of neurons presented a valley during acceleration followed by a peak just prior to or after reaching reward (valley-peak cells). These findings suggest that these three classes of NAc neurons control the initiation and vigor of the locomotor approach to reward. Significance Statement Deciphering the mechanisms by which the NAc controls the vigor of motivated behavior is critical to better understand and treat psychiatric conditions in which motivation is
Protein Ensemble Learning with Atrous Spatial Pyramid Networks for Secondary Structure Prediction

The secondary structure of proteins is significant for studying the three-dimensional structure and functions of proteins. Several models from image understanding and natural language modeling have been successfully adapted in the protein sequence study area, such as Long Short-term Memory (LSTM) network and Convolutional Neural Network (CNN). Recently, Gated Convolutional Neural Network (GCNN) has been proposed for natural language processing and reduces latency while achieving high levels of sentence scoring. Conditionally Parameterized Convolution (CondConv), which use extra sample-dependant modules to conditionally adjust the convolutional network, have achieved great success in the image processing area. In this paper, we propose a novel Conditionally Parameterized Convolutional network (CondGCNN) which utilize the power of both CondConv and GCNN, and we leverage an ensemble encoder to combine the capabilities of both LSTM and CondGCNN to encode protein sequences to obtain better sequential features from proteins. In addition, due to the similarity between the image segmentation problem and the secondary structure prediction problem, we propose an ASP network (Atrous Spatial Pyramid Pooling (ASPP) based network) as the secondary structure generator in our proposed framework. We have conducted extensive ablation studies over each component in the proposed model to verify its effectiveness. Extensive experiments show that the proposed method can achieve higher performance on protein secondary structure prediction task than existing methods on CB513, Caspll and CASP12 datasets. Our method is expected to be useful for protein structure and further protein functions prediction.
The photovoltaic properties of novel narrow band gap Cu2SnS3 films prepared by a spray pyrolysis method

The ternary compound Cu2SnS3 (CTS) was fabricated by a spray pyrolysis method. The influence of the Cu precursor concentration and substrate temperature on the properties of the prepared films was investigated in detail. X-ray diffraction spectroscopy & Raman spectroscopy, energy dispersive spectroscopy, UV-vis spectrophotometry, Four-probe Resistivity Testing and Stylus Profilometry were employed for the analysis of the structural, compositional, optical, electrical and morphological properties of the prepared CTS thin films. The characterization results showed that the CTS thin film fabricated under optimal conditions (substrate temperature 350 °C, Cu concentration 0.02 M) had an atomic ratio of Cu1.43SnS2. 59, and presented as a tetragonal structure with preferential (1, 1, 2) orientation. Optical measurements showed that the thin film had a high absorption coefficient (>104 cm−1) with a band gap value of 1.16 eV. The resistivity of the sprayed CTS thin film was 11.6 × 10−2 Ω cm. All these properties indicated that the as-fabricated CTS thin film could be a promising absorber layer material for high efficiency thin film solar cells.
Microwave Nondestructive Testing for Defect Detection in Composites Based on K-Means Clustering Algorithm

Composite such as Glass Fibre Reinforced Polymer (GFRP) is increasingly used as insulation in many industrial applications such as the steel pipelines in the oil and gas industry. Due to ageing and cyclic operation, many hidden defects exist under insulation, such as corrosion and delamination. If these defects are not promptly detected and restored, the growth of defects causes a catastrophic loss. Therefore, an effective inspection technique using non-destructive testing (NDT) to detect the underneath defect is required. The ability of microwave signals to penetrate and interact with the inner structure within composites makes them a promising candidate for composite inspection. In the case of GFRP, the random patterns cause permittivity variations that influence the propagation of the microwave signals, which results in a blurred spatial image making the assessment of the material’s state difficult. In this research, a novel microwave NDT technique is presented based on k-means unsupervised machine learning for defect detection in composites. At present, the defect evaluation using an unsupervised machine learning-based microwave NDT technique is not reported elsewhere. The unsupervised machine learning is employed to enhance the imaging efficiency and defect detection in GFRP. The technique is based on scanning the composite material with an open-ended rectangular waveguide operating from 18 to 26.5 GHz with 101 frequency points. The influence of the permittivity variations on the reflected coefficients due to the random patterns of GFRP is mitigated by measuring the mean of a set of the adjacent points at each operating frequency point using a small rectangular window. The measured data is converted to the time domain using a fast inverse Fourier transform (IFFT) to provide significant features and increase the signal resolution to 201-time steps. K-means algorithm is utilized to cluster the given features into the defect and defect-free regions in GFRP. The findings presented in this paper demonstrate the benefits of an unsupervised machine learning to detect a defect down to 1 mm, which is a considerable contribution over any existing defect inspection technique in composites.
Empirical Classification of Advanced Information Technology Towards Their Support of Leadership Behaviors in Virtual Project Management Settings

In times of globalization and societal change on the topic of working remotely, virtual collaboration of project teams has a high relevance. The continuous improvement of advanced information technology makes it possible to work globally distributed and in home office. This paper focuses on the usage of communication tools to support leadership behaviors and on the behavior’s requirements of media richness and media synchronicity. Managers (N=10) name in qualitative interviews communication tools, they are using for the communication with their employees concerning different leadership topics. These tools are classified in a theoretical model of leadership behavior. Building upon these results, a second study with project managers (N=75) who work in virtual teams rate these communication tools according to their usage for different leadership behaviors. Results show, that the demand on advanced information technology and their media richness and media synchronicity depends on the displayed leadership behavior. The results give implications for future investigations on the role of communication tools for the leadership of virtual teams.
Effects of Forest Canopy Structure on Forest Aboveground Biomass Estimation Using Landsat Imagery

Accurate remote sensing-based forest aboveground biomass (AGB) estimation is important for accurate understanding of carbon accounting and climate change at a large scale. However, over- and underestimation are common in the process, resulting in inaccurate AGB estimations. Here, the AGB was estimated and mapped by combining Landsat 8 images and forest inventory data in western Hunan Province, China. We used forest canopy density (FCD) mapper to quantify the forest canopy structure. The linear model (LR) and piecewise model with FCD gradients (classified by k-means clustering; sparse, medium, and dense) were developed to estimate AGB for each forest type (coniferous, broadleaf, mixed, and total forests). The piecewise model considered the following scenarios: piecewise model using the variables of LR model (PM), and piecewise model using the variables selected for different FCD gradients (PMV). The PM (R2:0.45–0.56) and PMV (R2:0.63–0.75) models showed better agreement between observed and predicted AGB than the LR (R2:0.18–0.27) models, and the PMV model was the most accurate for each forest type. The PM and PMV models performed better than LR models at different FCD gradients. The PM and PMV models can better alleviate the over- and underestimations of the LR models. At different FCD gradients, the PMV models had different variables, indicating that the correlation between the AGB and spectral variables was different. Overall, FCD is an important forest parameter that influences AGB estimation, and the piecewise model has potential to improve remote sensing-based AGB estimation.
Graph Reasoning-Based Emotion Recognition Network

Semantic information from images can be used to improve the performance of deep learning methods in recognizing human emotions. In this paper, we propose a novel framework based on the graph convolutional network for emotion recognition by utilizing the semantic relationships of different regions. First, we extract the salient image regions within video frame clips by using the bottom-up attention module to construct the node features of a graph. Then, we build the graphs containing the node features and the semantic correlations of nodes by using the graph convolutional network. For refinement, each node feature of graph vectors is enhanced via a gated recurrent unit consisting of gate and memory units to remove redundant feature information. Experimental results show that our proposed method achieves superior performance over state-of-the-art approaches for the emotion recognition on the CEAR and AFEW datasets.
Absolute line intensities in the ν1 + 3ν3 band of 12C2H2 by laser photoacoustic spectroscopy and Fourier transform spectroscopy

Line intensities and self-broadening coefficients in the ν1 + 3ν3 band of 12C2H2 near 0.8 μm at room temperature were measured by means of both laser photoacoustic and Fourier transform spectroscopy. An experimental protocol has been developed to obtain absolute intensities from the photoacoustic measurements. Namely, the spectrometer was calibrated using water vapour line intensities available in Hitran 1996 [L. S. Rothman et al. (1998) J. quant. Spectrosc. Radiat. Transfer, 60, 665–710]. These photoacoustic line intensities were found to be on average 5% higher than corresponding measurements performed using Fourier transform spectroscopy, the accuracy of the latter being estimated to better than 4%. The accuracy of the photoacoustic intensities is discussed. Previous results from the literature [F. Herregodts, D. Hurtmans, J. Vander Auwera, and M. Herman (1999) J. chem. Phys., 111, 7954—7960] are revised.
Assessing the Use of Secondary Structure Fingerprints and Deep Learning to Classify RNA Sequences

Non-coding RNAs (ncRNAs) are RNA molecules that do not code for protein, but take part in biological processes, including gene expression. Interestingly, like proteins, they can fold into complex structures to perform their wide array of biological functions. Since the folded structure of a ncRNA may be critical to its function, many studies have attempted to exploit structural data to infer information, often using machine learning techniques. For instance, they have used predicted secondary structures as input features to various machine learning techniques, in order to classify RNA sequences. However, it is known that a strand of RNA can fold into more than one possible structure, and some strands even form different structures in vivo and in vitro. Furthermore, ncRNAs often function as RNA-protein complexes, which can affect structure. We therefore hypothesized that using a single predicted secondary structure for a single sequence may discard important information, which may result in poorer classification accuracy. To investigate this claim, we propose the use of secondary structure fingerprints as features for machine learning applications, and report on a preliminary evaluation of this approach. The fingerprints comprise two categories: a higher-level (topological) representation derived from RNA-As-Graphs (RAG), and free energy fingerprints based on a novel curated repertoire of small RNA motifs. We have also evaluated our deep learning architecture with k-mers as features, alone and combined with secondary structure fingerprints; to see whether secondary structures or nucleotide composition is more useful in RNA classification, and whether or not both feature types complement each other well. The dataset, trained models, and supplemental material of this study are available at https://www.site. uottawa.ca/turcotte/bibm2020.
A Search-Based Approach to Identifying Jamming Attacks and Defense Policies in Wireless Networked Control

State-dependent attack and defense mechanisms in a wireless networked control system are investigated. The controller in this system attempts to transmit control commands to a remotely located plant by using a wireless channel that is subject to jamming attacks from an adversary. The probability of a failure on this channel depends on the powers of both the transmission signal emitted from the controller and the jamming interference signal emitted from the attacker. We show that if the power levels of transmissions from the controller are consistently above a threshold and the average power level of the jamming interference is sufficiently small, then the networked control system is guaranteed to be almost surely asymptotically stable. However, it is hard to analytically derive optimal attack and defense policies due to the complexity of the wireless channel model. To find out potentially dangerous attacks and high-performance defense policies, we propose a search-based approach and utilize genetic algorithms. Specifically, by considering Voronoi partitions of the state space, we find attack policies that achieve large quadratic costs. We use the search-based approach also for designing defense policies, where the controller adjusts the power of the transmission signal based on the plant’s state information. The efficacy of the proposed approach is demonstrated through a number of simulation-based experiments.
Self-healable polymer networks based on the cross-linking of epoxidised soybean oil by an aqueous citric acid solution

Epoxidised soybean oil (ESO) was cross-linked with an aqueous citric acid (CA) solution without the addition of any other catalyst or solvent. Completely bio-based polymer networks were generated. The initial system was an emulsion, but it became a homogeneous and transparent polymer network by reaction. The ability of the final materials to self-heal without adding extrinsic catalysts was assessed by stress relaxation and lap-shear tests. This was achieved by molecular rearrangements produced by thermally activated transesterification reactions of β-hydroxyester groups generated in the polymerization reaction.
Partitioning Street Lights Load on Grid-Photovoltaic System as an Alternative Solution for Illuminating Al-Bayda City Roads

Relying only on the stand-alone solar photovoltaic system for street lighting faces several barriers. The most important of them is the installation expensive cost, the low autonomy of batteries, and the efficiency of the system which is usually low too. This paper investigates the study of a hybrid system consisting of two energy sources, the national utility grid and photovoltaic panels with storage batteries. The system used to supply lighting poles in the streets of Al-Bayda city. Two software are employed to model and simulate the studied system. The first is a trial version of HOMER Pro. It is used to show the potentiality of the selected location. The second software is Matlab/Simulink. Twenty four hours have been divided into four periods in order to simulate the proposed system. The obtained results show a savings in energy available by the utility grid. In addition, the problem of the autonomy of batteries is solved by the existence of the utility grid as a component of studied system.
Aging Effects on Thermal and Dielectric Properties of Polypropylene/Magnesium Aluminate Nanocomposites

This paper reports on the effects of aging on thermal and dielectric properties of unfilled polypropylene and polypropylene nanocomposites containing 2 wt% and 5 wt% of magnesium aluminate nanofiller. These evaluated samples were prepared using a melt blending technique. A hydraulic laboratory press was employed in order to obtain 100 μm thick sample. Two different aging conditions were considered, namely, unaged (as a reference) and aged at 140 °C. The evaluated samples were then characterized using differential scanning calorimetry (DSC) for thermal analysis. Meanwhile, the dielectric strength was determined based on DC electrical breakdown tests. The experimental results show that, aging the samples at 140 °C changed the thermal behavior of all evaluated samples. Specifically, the melting temperature (Tm) increased when the samples were aged at 140 °C. Meanwhile, the DC breakdown strength of unfilled polypropylene reduced significantly after aging at 140 °C when compared to polypropylene nanocomposites.
Predicting circRNA-disease associations using meta path-based representation learning on heterogenous network

Circular RNA (circRNA) is a new class of regulatory non-coding RNAs modulating gene expression by acting as a microRNA (miRNA) sponge, RNA binding protein sponge and translational regulator. A increasing number of experimental studies have shown that circRNA plays an important role in the development of diseases, and circRNA biomarkers are helpful for the diagnosis and treatment of various human diseases. There is a pressing demand to establish an effective computational method to identify the associations between circRNAs and diseases. In this paper, we propose a new computational framework for the prediction of the circRNA-disease associations. In particular, we calculated meta path-based feature vectors for each circRNAdisease pair on a heterogeneous information network (HIN) that integrated multiple subnetworks, including circRNA similarity network, disease similarity network, protein similarity network, circRNA-disease associations, circRNA-protein associations and protein-disease associations. A positive-unlabeled learning algorithm was adopted to generate negative samples, and a random forest classifier was trained to predicted circRNA-disease associations. We conducted performance comparison with three popular methods on the CircR2Disease dataset. The experimental results show that our method outperform other existing methods by achieving AUC 0.983 on 5-fold cross-validation.