Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular and Biochemical Differences of the Tandem and Cold-Adapted PET Hydrolases Ple628 and Ple629, Isolated From a Marine Microbial Consortium

Polybutylene adipate terephthalate (PBAT) is a biodegradable alternative to polyethylene and can be broadly used in various applications. These polymers can be degraded by hydrolases of terrestrial and aquatic origin. In a previous study, we identified tandem PETase-like hydrolases (Ples) from the marine microbial consortium I1 that were highly expressed when a PBAT blend was supplied as the only carbon source. In this study, the tandem Ples, Ple628 and Ple629, were recombinantly expressed and characterized. Both enzymes are mesophilic and active on a wide range of oligomers. The activities of the Ples differed greatly when model substrates, PBAT-modified polymers or PET nanoparticles were supplied. Ple629 was always more active than Ple628. Crystal structures of Ple628 and Ple629 revealed a structural similarity to other PETases and can be classified as member of the PETases IIa subclass, α/β hydrolase superfamily. Our results show that the predicted functions of Ple628 and Ple629 agree with the bioinformatic predictions, and these enzymes play a significant role in the plastic degradation by the consortium.
GhCalS5 is involved in cotton response to aphid attack through mediating callose formation

Callose synthase plays an essential role in plant growth and development and in response to all sorts of stresses through regulating callose formation. However, few research about the function and mechanism of the insect resistance of callose synthase genes have been reported in cotton. In this study, a cotton callose synthase gene GhCalS5 was cloned, and its function and mechanism of resistance to cotton aphids were analyzed. The expression of GhCalS5 was significantly upregulated in both, leaves and stems of cotton plants at 48 h after cotton aphid infestation and in the leaves of cotton plants at 24 h after salicylic acid treatment. The overexpression of GhCalS5 enhanced cotton resistance to cotton aphids. Expectedly silencing of GhCalS5 reduced cotton resistance to cotton aphids. Overexpression of GhCalS5 enhanced callose formation in cotton leaves. Our results suggest that GhCalS5 is involved in cotton resistance against cotton aphids by influencing callose formation.
Modifiable Risk Factors Are Important Predictors of COVID-19-Related Mortality in Patients on Hemodialysis

Introduction Patients with end-stage kidney disease face a higher risk of severe outcomes from SARS-CoV-2 infection. Moreover, it is not well known to what extent potentially modifiable risk factors contribute to mortality risk. In this historical cohort study, we investigated the incidence and risk factors for 30-day mortality among hemodialysis patients with SARS-CoV-2 infection treated in the European Fresenius Medical Care NephroCare network using conventional and machine learning techniques. Methods We included adult hemodialysis patients with the first documented SARS-CoV-2 infection between February 1, 2020, and March 31, 2021, registered in the clinical database. The index date for the analysis was the first SARS-CoV-2 suspicion date. Patients were followed for up to 30 days until April 30, 2021. Demographics, comorbidities, and various modifiable risk factors, expressed as continuous parameters and as key performance indicators (KPIs), were considered to tap multiple dimensions including hemodynamic control, nutritional state, and mineral metabolism in the 6 months before the index date. We used logistic regression (LR) and XGBoost models to assess risk factors for 30-day mortality. Results We included 9,211 patients (age 65.4 ± 13.7 years, dialysis vintage 4.2 ± 3.7 years) eligible for the study. The 30-day mortality rate was 20.8%. In LR models, several potentially modifiable factors were associated with higher mortality: body mass index (BMI) 30–40 kg/m2 (OR: 1.28, CI: 1.10–1.50), single-pool Kt/V (OR off-target vs on-target: 1.19, CI: 1.02–1.38), overhydration (OR: 1.15, CI: 1.01–1.32), and both low (<2.5 mg/dl) and high (≥5.5 mg/dl) serum phosphate levels (OR: 1.52, CI: 1.07–2.16 and OR: 1.17, CI: 1.01–1.35). On-line hemodiafiltration was protective in the model using KPIs (OR: 0.86, CI: 0.76–0.97). SHapley Additive exPlanations analysis in XGBoost models shows a high influence on prediction for several modifiable factors as well, including inflammatory parameters, high BMI, and fluid overload. In both LR and XGBoost models, age, gender, and comorbidities were strongly associated with mortality. Conclusion Both conventional and machine learning techniques showed that KPIs and modifiable risk factors in different dimensions ascertained 6 months before the COVID-19 suspicion date were associated with 30-day COVID-19-related mortality. Our results suggest that adequate dialysis and achieving KPI targets remain of major importance during the COVID-19 pandemic as well.
Mental Health Inequities Amid the COVID-19 Pandemic: Findings From Three Rounds of a Cross-Sectional Monitoring Survey of Canadian Adults

Objectives: Adverse mental health impacts of the COVID-19 pandemic are well documented; however, there remains limited data detailing trends in mental health at different points in time and across population sub-groups most impacted. This paper draws on data from three rounds of a nationally representative cross-sectional monitoring survey to characterize the mental health impacts of COVID-19 on adults living in Canada (N = 9,061). Methods: Descriptive statistics were used to examine the mental health impacts of the pandemic using a range of self-reported measures. Multivariate logistic regression models were then used to quantify the independent risks of experiencing adverse mental health outcomes for priority population sub-groups, adjusting for age, gender, and survey round. Results: Data illustrate significant disparities in the mental health consequences of the pandemic, with inequitable impacts for sub-groups who experience structural vulnerability related to pre-existing mental health conditions, disability, LGBTQ2+ identity, and Indigenous identity. Conclusion: There is immediate need for population-based approaches to support mental health in Canada and globally. Approaches should attend to the root causes of mental health inequities through promotion and prevention, in addition to treatment.
Development and validation of a hypoxia-stemness-based prognostic signature in pancreatic adenocarcinoma

Background: Pancreatic adenocarcinoma (PAAD) is one of the most aggressive and fatal gastrointestinal malignancies with high morbidity and mortality worldwide. Accumulating evidence has revealed the clinical significance of the interaction between the hypoxic microenvironment and cancer stemness in pancreatic cancer progression and therapies. This study aims to identify a hypoxia-stemness index-related gene signature for risk stratification and prognosis prediction in PAAD. Methods: The mRNA expression-based stemness index (mRNAsi) data of PAAD samples from The Cancer Genome Atlas (TCGA) database were calculated based on the one-class logistic regression (OCLR) machine learning algorithm. Univariate Cox regression and LASSO regression analyses were then performed to establish a hypoxia-mRNAsi-related gene signature, and its prognostic performance was verified in both the TCGA-PAAD and GSE62452 corhorts by Kaplan-Meier and receiver operating characteristic (ROC) analyses. Additionally, we further validated the expression levels of signature genes using the TCGA, GTEx and HPA databases as well as qPCR experiments. Moreover, we constructed a prognostic nomogram incorporating the eight-gene signature and traditional clinical factors and analyzed the correlations of the risk score with immune infiltrates and immune checkpoint genes. Results: The mRNAsi values of PAAD samples were significantly higher than those of normal samples (p < 0.001), and PAAD patients with high mRNAsi values exhibited worse overall survival (OS). A novel prognostic risk model was successfully constructed based on the eight-gene signature comprising JMJD6, NDST1, ENO3, LDHA, TES, ANKZF1, CITED, and SIAH2, which could accurately predict the 1-, 3-, and 5-year OS of PAAD patients in both the training and external validation datasets. Additionally, the eight-gene signature could distinguish PAAD samples from normal samples and stratify PAAD patients into low- and high-risk groups with distinct OS. The risk score was closely correlated with immune cell infiltration patterns and immune checkpoint molecules. Moreover, calibration analysis showed the excellent predictive ability of the nomogram incorporating the eight-gene signature and traditional clinical factors. Conclusion: We developed a hypoxia-stemness-related prognostic signature that reliably predicts the OS of PAAD. Our findings may aid in the risk stratification and individual treatment of PAAD patients.
Optimizing Colloidal Stability and Transport of Polysaccharide-Coated Magnetic Nanoparticles for Reservoir Management: Effects of Ion Specificity

In this work we explore the mechanisms of ion-specific stabilization of a polysaccharide-based coating for colloidal nanomaterials used within the oil & gas industry. While nanotechnology has wide prevalence across multiple industries, its utility within this sector is largely undeveloped but has potential applications in areas including (but not limited to) exploration, drilling and production processes. For example, reservoir contrast agents in the form of superparamagnetic nanoparticles could be used to accurately determine the residual oil saturation distribution in a reservoir and thus advise enhanced oil recovery (EOR) efforts. However, deployment of such materials in oil reservoirs proves challenging in cases where high salinity subsurface environments induce nanoparticle aggregation, leading to loss of mobility. Here, we report the synthesis and characterization of dextran-coated superparamagnetic iron oxide nanoparticles (Dex-SPIONs), the colloidal stability of which was evaluated in various brine formulations at elevated temperatures. Initial dynamic light scattering (DLS) measurements reveal a lack of contingency between particle stability and total electrolyte concentration for samples comprised of synthetic seawater and low-salinity brine, the latter fluid of which possesses higher ionic strength yet preserves colloidal integrity to a much greater extent than its seawater counterpart. Further experiments point to a calcium (Ca2+) ion-specific stabilization effect wherein surface complexation of Ca2+ ions to the dextran periphery improves carbohydrate hydration and thus enhances colloidal stability. Ion selective electrode (ISE) measurements provide additional evidence of the Ca2+ - dextran binding interaction, the role of which also factors significantly into mitigation of polysaccharide degradation [as demonstrated through gel permeation chromatography (GPC)]. Finally, we assess the transport of Dex-SPIONs through porous media, including examination of retention properties with respect to variances in ionic composition.
Role of Cardiovascular Magnetic Resonance to Assess Cardiovascular Inflammation

Cardiovascular inflammatory diseases still represent a challenge for physicians. Inflammatory cardiomyopathy, pericarditis, and large vessels vasculitis can clinically mimic a wide spectrum of diseases. While the underlying etiologies are varied, the common physio-pathological process is characterized by vasodilation, exudation, leukocytes infiltration, cell damage, and fibrosis. Cardiovascular magnetic resonance (CMR) allows the visualization of some of these diagnostic targets. CMR provides not only morphological and functional assessment but also tissue catheterization revealing edema, hyperemia, tissue injury, and reparative fibrosis through T2 weighted images, early and late gadolinium enhancement, and parametric mapping techniques. Recent developments showed the role of CMR in the identification of ongoing inflammation also in other CV diseases like myocardial infarction, atherosclerosis, arrhythmogenic and hypertrophic cardiomyopathy. Future developments of CMR, aiming at the specific assessment of immune cell infiltration, will give deeper insight into cardiovascular inflammatory diseases.
Frequency Compensation of Three-Stage OTAs to Achieve Very Wide Capacitive Load Range

This paper proposes an optimal design approach for three-stage amplifiers driving an ultra-wide range of load capacitor. To this end, efficient state-of-the-art solutions have been combined to develop a power-efficient frequency compensation solution. High-speed feedback pathways relying on Miller capacitors and current buffers are implemented within the amplifier scheme to push the non-dominant poles to high frequencies for small to medium load capacitors. A small resistor is also shared between the two pathways to improve the stability regardless of the load capacitor. A serial R-C branch is then added to extend the lower limit of load drive capability to small load capacitors. Gain margin is, for the first time in literature, analytically evaluated and included in the design phase. A prototype of the proposed amplifier is fabricated in 65-nm CMOS process with active area of 0.0017 mm2 and 1.15 pF total compensation capacitance. It can drive the load capacitor range from 200 pF to 100 nF, while drawing a quiescent current of 7.4 μA from a 1.2-V input voltage supply. A unity-gain frequency of 1.67 MHz was measured with an average slew-rate of 1.31 V/μs, when the proposed amplifier is wired in unity-gain configuration to drive a 500-pF load capacitor.
Cost Partitioning Heuristics for Stochastic Shortest Path Problems

In classical planning, cost partitioning is a powerful method which allows to combine multiple admissible heuristics while retaining an admissible bound. In this paper, we extend the theory of cost partitioning to probabilistic planning by generalizing from deterministic transition systems to stochastic shortest path problems (SSPs). We show that fundamental results related to cost partitioning still hold in our extended theory. We also investigate how to optimally partition costs for a large class of abstraction heuristics for SSPs. Lastly, we analyze occupation measure heuristics for SSPs as well as the theory of approximate linear programming for reward-oriented Markov decision processes. All of these fit our framework and can be seen as cost-partitioned heuristics.
PSNet: LiDAR and Camera Registration using Parallel Subnetworks

The working environment of autonomous driving and robot navigation is so complex and dynamic that a single type of sensor is insufficient for performing object detection. Thus, in many perception schemes, the LiDAR-camera fusion strategy is preferred. However, the performance of a LiDAR-camera fusion heavily relies on a set of accurately calibrated extrinsic parameters. We propose PSNet, an end-to-end convolutional neural network (CNN) for calibration; this is the first calibration network to use parallel subnetworks to obtain multiresolution features and fuse them adaptively to encourage robustness against different initial error ranges. The method has three key characteristics: (i) Addition of a downsampling block to improve suitability for sparse projected depth maps; (ii) Connection of the high-to-low resolution convolution streams in parallel to obtain multiresolution features that are spatially more precise and contain richer semantic information; (iii) Fusion of multiresolution streams by the multiscale feature aggregation module. The network corrects errors from initial calibration to the ground truth online, rather than directly obtaining the accurate parameters. We evaluated our model on the KITTI datasets and it outperformed other CNN-based methods. In addition, extensive experiments evaluating the model with untrained and unfamiliar datasets demonstrated that our method exhibited good generalization ability.
Talking Trucks: Decentralized Collaborative Multi-Agent Order Scheduling for Self-Organizing Logistics

Logistics planning is a complex optimization problem involving multiple decision makers. Automated scheduling systems offer support to human planners; however state-of-the-art approaches often employ a centralized control paradigm. While these approaches have shown great value, their application is hindered in dynamic settings with no central authority. Motivated by real-world scenarios, we present a decentralized approach to collaborative multi-agent scheduling by casting the problem as a Distributed Constraint Optimization Problem (DCOP). Our model-based heuristic approach uses message passing with a novel pruning technique to allow agents to cooperate on mutual agreement, leading to a near-optimal solution while offering low computational costs and flexibility in case of disruptions. Performance is evaluated in three real-world field trials with a logistics carrier and compared against a centralized model-free Deep Q-Network (DQN)-based Reinforcement Learning (RL) approach, a Mixed-Integer Linear Programming (MILP)-based solver, and both human and heuristic baselines. The results demonstrate that it is feasible to have virtual agents make autonomous decisions using our DCOP method, leading to an efficient distributed solution. To facilitate further research in Self-Organizing Logistics (SOL), we provide a novel real-life dataset.
Multi–class Classification Performance Curve

Quality of predictive models is a critical factor. Many evaluation measures have been proposed for binary and multi–class datasets. However, less attention has been paid to graphical representation of the classification performance, where the ROC curve is extensively used for binary datasets but there is no standard method accepted by the scientific community for multi–class datasets. In this work, a multi–class classification performance (MCP) curve based on the Hellinger distance between true and prediction probabilities of the classifier is introduced. The MCP curve shows the classification performance, contributes to highlight the low or high confidence on correct predictions, and quantifies the quality by means of the area under the curve.
Pseudo Complex-Valued Deformable ConvLSTM Neural Network With Mutual Attention Learning for Hyperspectral Image Classification

Convolutional long short-term memory (ConvLSTM) has received much attention for hyperspectral image (HSI) classification due to its ability of modeling long-range correlations, which, however, is vulnerable to too many parameters and insufficient training, limiting its classification accuracy, especially for small samples. Different from it, traditional hand-crafted methods extract the features with basic attributes of HSIs, which can provide the lack of details and interpretability of deep semantic features. However, existing methods fail to incorporate their complementarity for HSI classification. As such, a Pseudo complex-valued (CV) Deformable ConvLSTM Neural Network with mutual Attention learning (APDCLNN) is proposed, providing a new way to realize the collaborative learning of hand-crafted and deep features for HSI classification. First, a 2-D pseudo CV deformable ConvLSTM (PDConvLSTM2D) cell is designed using deformable convolution and complex operations, with which a spatial–spectral PDConvLSTM2D neural network (SSPDCL2DNN) is built to extract scale- and spectral-enhanced deep spatial–spectral features. Then, 3-D Gabor filter is used to extract hand-crafted features, and a mutual attention-based multimodality feature learning and fusion (MAMLF) module is designed to integrate them into deep features for training and optimization of SSPDCL2DNN. Finally, an attention loss subnetwork is designed to refine the classification results. As we know, this is the first attempt to apply the idea of mutual attention learning to fuse hand-crafted and deep features for HSI classification. Extensive experiments on three widely used HSI datasets show the advantages of our model over other deep methods in terms of both quantitative and visual quality.
On Bidirectional Heuristic Search in Classical Planning: An Analysis of BAE

Heuristic search is a successful approach to cost-optimal planning. Bidirectional heuristic search algorithms have been around for a long time, but only recent advances have led to algorithms like BAE* that have the potential to outperform unidirectional heuristic search algorithms like A* in practice. In this work, we analyze BAE* for classical planning and the challenges associated with the underlying assumption of an explicit state representation. We show that it is crucial to use mutexes and reachability analysis to reduce the potentially exponential number of goal states, which makes it possible to create an explicit representation of a reversed planning task that can be used for the backward search of BAE*. Our empirical evaluation shows that BAE* solves more instances than A* in multiple domains with significantly fewer node expansions, demonstrating the usefulness of BAE* in planning.
Sensitivity analysis of C & Ku-band Scatterometers for River Water Level Estimation

Water level monitoring is essential for analyzing hydrological extremes, environmental flows, and sustainable development and management of water resources. Satellite Altimeters have operationally been used to estimate river water levels (e.g., SARAL-Altika, Jason-2/3, etc.) with poor revisit times (10-35 days). In this current study, we propose a method to estimate river water levels using scatterometer data with 2-days temporal resolution. Sensitivity analysis of radar frequency (Ku and C band) for river water level estimation was carried out. The concept of change in radar backscatter with an increase in soil wetness is used to develop a model function between Basin Water Index (BWI) and observed river stage. High-resolution OSCAT (Ku-band) and ASCAT (C-band) SIR (Scatterometer Image Reconstruction), digital elevation model, and observed water stage datasets have been used in this study. We observed that C-band scatterometer shows better results than Ku-band in terms of R, NSE, and RMSE, which can be attributed to the higher soil wetness sensitivity of the C band compared to Ku-band scatterometers. R ranged between 0.93 to 0.97 for C-band and between 0.89 to 0.95 for Ku-band data. Similarly, NSE ranged from 0.84 to 0.95 for C-band and between 0.56 to 0.95 for Ku-band data over selected study sites in the Brahmaputra river basin. Model accuracy was found to be adequate for monitoring of river stage from a remote sensing platform. This study shows that scatterometer observations can be used for operational water level estimations, which can be useful for water resource management.
DRFL-VAT: Deep Representative Feature Learning with Virtual Adversarial Training for Semi-Supervised Classification of Hyperspectral Image

While deep learning algorithms have achieved good results in hyperspectral image (HSI) classification, several supervised classification algorithms rely on a large amount of labeled samples to get adequate performance. Collecting a large amount of labeled samples is expensive in many real applications. To address this issue, a novel semi-supervised HSI classification framework called deep representative feature learning (DRFL) with virtual adversarial training (DRFL-VAT) is developed in this paper. By embedding the local manifold learning (LML) into the fully connected layers of a convolutional neural network (CNN), our newly developed DRFL can learn representative features. The VAT regularization is adopted to exploit the prediction label distribution of training samples and addresses the overfitting problem. Finally, the objective function of DRFL-VAT is solved by a customized algorithm. We test our method on three widely public HSI data sets and our results show that our method is competitive when compared to other state-of-the-art approaches.