Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-alcoholic fatty liver disease (NAFLD) and COVID-19 outcomes: A systematic review, meta-analysis, and meta-regression

It is important to identify risk factors for poor outcomes of coronavirus disease 2019 (COVID-19) patients. Currently, the correlation between non-alcoholic fatty liver disease (NAFLD) and COVID-19 outcomes has not been established. This study was conducted to determine the association between NAFLD and in-hospital outcomes of COVID-19 patients. The systematic searches were conducted by using PubMed and the Europe PMC databases and particular keywords were used as of December 10, 2020. Further searches were conducted up to 2022. All articles that include data about COVID-19 and fatty liver disease were collected. Statistical analysis was performed by using Review Manager 5.4 and Comprehensive Meta-Analysis version 3 software. A total of 7,210 COVID-19 patients from 18 studies were included in the final analysis. Meta-analysis revealed that NAFLD increased the risk of developing poor in-hospital outcome (pooled both severe disease and death) in COVID-19 patients (RR 1.42; 95%CI: 1.17–1.73, p<0.001, I2=84%, random-effect modeling). Subgroup analysis however found that having NAFLD only increased the chance of getting severe COVID-19 (RR 1.67; 95%CI: 1.32–2.13, p<0.001, I2=86%, random-effect modeling) and not mortality (RR 1.00; 95%CI: 0.68–1.47, p=0.98, I2=80%, random-effect modeling). Meta-regression suggested that age (p=0.001) and diabetes (p=0.029) were significantly influenced the relationship between NAFLD and in-hospital outcomes of COVID-19 (pooled both severe disease and mortality). The weaker association of NAFLD and in-hospital outcomes of COVID-19 was found for studies with median age ≥45 years old (RR 1.29) when compared to studies with median age <45 years old (RR 2.96). In addition, studies with the prevalence of diabetes ≥25% (RR 1.29) had a weaker association with in-hospital outcomes when compared to studies with diabetes prevalence <25% (RR 1.85). In conclusion, NAFLD increased the risk of chance of getting severe COVID-19 and therefore it should be evaluated closely to reduce the chance of getting severe COVID-19.
Nanomaterials for Skin Cancer Photoimmunotherapy

Skin cancer is one of the most common types of cancer, and its incidence continues to increase. It is divided into two main categories, melanoma and non-melanoma. Treatments include surgery, radiation therapy, and chemotherapy. The relatively high mortality in melanoma and the existing recurrence rates, both for melanoma and non-melanoma, create the need for studying and developing new approaches for skin cancer management. Recent studies have focused on immunotherapy, photodynamic therapy, photothermal therapy, and photoimmunotherapy. Photoimmunotherapy has gained much attention due to its excellent potential outcomes. It combines the advantages of photodynamic and/or photothermal therapy with a systemic immune response, making it ideal for metastatic cancer. This review critically discusses different new nanomaterials’ properties and mechanisms of action for skin cancer photoimmunotherapy and the main results obtained in the field.
Anatomical exoskeleton for movement approximation with neural networks

A lower extremity exoskeleton for the right leg is assembled using 3D printed soft plastic parts within a semi-rigid frame and structure balancing the rigid and soft flexible components. One leg has been assembled at this time allowing us to test the function of the mechanical system’s frame and structure. The comfort and safety of the exoskeleton is important for increasing the time the exoskeleton can be tolerated by a patient. The knee is the largest and most complicated of the lower extremity joints. This exoskeleton joint accommodates the rotational and sliding movement of the femur at the knee to increase the comfort level by allowing for the inherent anatomical motion of the knee. This is often not taken into consideration for most lower extremity orthopedic braces, exoskeletons, or prosthetics. Surface EMGs and IMUs are utilized as input sensors for the exoskeleton control system. Myo-Ware EMG/IMU sensors from Thalmic labs originally designed for the upper extremity must be adapted for our lower extremity exoskeleton. Pneumatic artificial muscles and Bowdoin cables controlled by electric motors are incorporated to assist movement as needed. The EMG/IMU input data is modified and synchronized by a supervised machine learning sensor fusion algorithm. EMG and IMU characteristics help reduce noise and synchronize input data. A reinforcement unsupervised learning (RL) algorithm determines intention and control the exoskeleton actuators. This depends on adequately implementing the RL network algorithm, thus testing the activities of daily living (standing, sitting, squatting while maintaining balance) are needed.
Apparent Opportunities and Hidden Pitfalls: The Conflicting Results of Restoring NRF2-Regulated Redox Metabolism in Friedreich’s Ataxia Pre-Clinical Models and Clinical Trials

Friedreich’s ataxia (FRDA) is an autosomal, recessive, inherited neurodegenerative disease caused by the loss of activity of the mitochondrial protein frataxin (FXN), which primarily affects dorsal root ganglia, cerebellum, and spinal cord neurons. The genetic defect consists of the trinucleotide GAA expansion in the first intron of FXN gene, which impedes its transcription. The resulting FXN deficiency perturbs iron homeostasis and metabolism, determining mitochondrial dysfunctions and leading to reduced ATP production, increased reactive oxygen species (ROS) formation, and lipid peroxidation. These alterations are exacerbated by the defective functionality of the nuclear factor erythroid 2-related factor 2 (NRF2), a transcription factor acting as a key mediator of the cellular redox signalling and antioxidant response. Because oxidative stress represents a major pathophysiological contributor to FRDA onset and progression, a great effort has been dedicated to the attempt to restore the NRF2 signalling axis. Despite this, the beneficial effects of antioxidant therapies in clinical trials only partly reflect the promising results obtained in preclinical studies conducted in cell cultures and animal models. For these reasons, in this critical review, we overview the outcomes obtained with the administration of various antioxidant compounds and critically analyse the aspects that may have contributed to the conflicting results of preclinical and clinical studies.
Prefigurative Legality

Since the early 2000s, many of the left groups that spurred the alt-globalization movement have embraced directly democratic organizing and the creation of ethical relationships and subjectivities far more than they have pursued projects to reform legal and political institutions. These practices are often described as prefigurative because people are working to build alternative possible futures in the here-and-now outside of dominant statist and capitalist rationalities. In this essay, we ask if prefiguration can also involve imagining legal forms anew. Drawing on Amelia Thorpe, Owning the Street: The Everyday Life of Property (2020), we discuss contemporary efforts to use the language, form, and legitimacy of law to imagine it otherwise, efforts that occur through various kinds of direct actions rather than primarily through appeals to courts, legislators, or other state officials. In so doing, we point to an emergent field of critical and sociolegal scholarship that we call prefigurative legality.
Third molar impaction prevalence and pattern: a panoramic radiography investigation

Objectives: The most commonly encountered impacted tooth is the third molar, and potential problems include oral cysts and tumors as well as caries, periapical lesions, periodontal disease, TMJ disorders, and root resorption of adjacent teeth. The aim of this investigation was to determine the third molar impaction prevalence and pattern. Materials and Methods: 530 panoramic radiographs of patients who visited Hasanuddin University Dental Hospital from January to December 2020 were collected. Data were recorded based on third molars region, eruption level, angulation, and impaction class from radiographs that met the criteria on Excel sheets and analyzed using SPSS 26. Chi square test was performed to assess relationship between impaction patterns with age and gender. The level of significance was fixed at p<0.05. Results: 115 radiographs were included and analyzed (45.2% male and 54.8% female). Third molar impaction prevalence was higher in females, in the age group 21-30 years, with level A eruption and vertical angulation. Most #38 and #48 demonstrated class 2 impaction. Apart from angulation and impaction class on #38, there was no significant difference between impaction patterns by age and gender group (p>0.05). Conclusion: Third molar impaction was more common in women aged 21-30 years with a dominant impaction pattern at level A with vertical angulation. In the mandible, class 2 of the Pell and Gregory classification are known to be dominant in third molar impaction.
Black phosphorous/graphene van der Waals heterostructure-based plasmonic metasurfaces for advanced infrared photodetectors

Black phosphorous (BP) is a promising material for infrared (IR) photodetectors. We theoretically investigated and proposed BP/graphene van der Waals (vdW) heterostructure-based metasurface wavelength-selective IR photodetectors that operate on the basis of plasmonic resonance. The proposed structure consists of a top layer consisting of periodic monolayers of BP, a graphene-based vdW heterostructure layer on a middle dielectric layer, and a bottom reflector. The periodic BP produces polarization-dependent localized surface plasmon resonance (LSPR) according to its armchair or zigzag edge, resulting in strong wavelength-selective absorption. The graphene layer can enhance the LSPR of BP, inducing propagating surface plasmon resonance on graphene. These results will contribute to the realization high-performance BP/graphene-based IR photodetectors.
Cross-Kingdom Regulation of Plant-Derived miRNAs in Modulating Insect Development

MicroRNAs (miRNAs), a class of non-coding small RNAs, are crucial regulatory factors in plants and animals at the post-transcriptional level. These tiny molecules suppress gene expression by complementary oligonucleotide binding to sites in the target messenger. Recently, the discovery of plant-derived miRNAs with cross-kingdom abilities to regulate gene expression in insects has promoted exciting discussion, although some controversies exist regarding the modulation of insect development by plant-derived miRNAs. Here, we review current knowledge about the mechanisms of miRNA biogenesis, the roles of miRNAs in coevolution between insects and plants, the regulation of insect development by plant-derived miRNAs, the cross-kingdom transport mechanisms of plant-derived miRNAs, and cross-kingdom regulation. In addition, the controversy regarding the modulation of insect development by plant-derived miRNAs also was discussed. Our review provides new insights for understanding complex plant–insect interactions and discovering new strategies for pest management and even crop genetic improvement.
Fault-Diagnosis and Fault-Recovery System of Hall Sensors in Brushless DC Motor Based on Neural Networks

This paper proposes a neural-network-based framework using Convolutional Neural Network and Long-Short Term Memory (CNN-LSTM) for detecting faults and recovering signals from Hall sensors in brushless DC motors. Hall sensors are critical components in determining the position and speed of motors, and faults in these sensors can disrupt their normal operation. Traditional fault-diagnosis methods, such as state-sensitive and transition-sensitive approaches, and fault-recovery methods, such as vector tracking observer, have been widely used in the industry but can be inflexible when applied to different models. The proposed fault diagnosis using the CNN-LSTM model was trained on the signal sequences of Hall sensors and can effectively distinguish between normal and faulty signals, achieving an accuracy of the fault-diagnosis system of around 99.3% for identifying the type of fault. Additionally, the proposed fault recovery using the CNN-LSTM model was trained on the signal sequences of Hall sensors and the output of the fault-detection system, achieving an efficiency of determining the position of the phase in the sequence of the Hall sensor signal at around 97%. This work has three main contributions: (1) a CNN-LSTM neural network structure is proposed to be implemented in both the fault-diagnosis and fault-recovery systems for efficient learning and feature extraction from the Hall sensor data. (2) The proposed fault-diagnosis system is equipped with a sensitive and accurate fault-diagnosis system that can achieve an accuracy exceeding 98%. (3) The proposed fault-recovery system is capable of recovering the position in the sequence states of the Hall sensors, achieving an accuracy of 95% or higher.
Hydrophobic Drug Carrier from Polycaprolactone-b-Poly(Ethylene Glycol) Star-Shaped Polymers Hydrogel Blend as Potential for Wound Healing Application

Blending hydrogel with an amphiphilic polymer can increase the hydrophobic drug loading and entrapment efficiency of hydrogel-based formulations. In this study, a hydrogel formulation with star-shaped polycaprolactone-b-poly(ethylene glycol) (PCL-b-PEG) as the hydrophobic drug cargo is produced. The 4-arm and 6-arm star-shaped PCL are synthesized with different molecular weights (5000, 10,000, 15,000 g/mol) via ROP and MPEG as the hydrophilic segment is attached via the Steglich esterification. FTIR and 1H-NMR analysis showed the presence of all functional groups for homopolymers and copolymers. Mn for all synthesized polymers is close to the theoretical value while GPC spectra showed a monomodal peak with narrow molecular weight distribution (PDI:1.01–1.25). The thermal degradation temperature and crystalline melting point of synthesized polymers increase with the increase in molecular weight and number of arms. All formulations possess high drug loading and entrapment efficiency (>99%) and increase with increasing molecular weight, number of arms, and amount of polymer in the formulations. All formulations showed a sustained drug release pattern with no initial burst, which follows the Korsmeyer–Peppas kinetic model. The polymer hydrogel formulations showed antibacterial activity against E. coli and S. aureus. The hydrogel containing 4-arm PCL15k-PEG is chosen as the best formulation due to its high drug release, good antimicrobial activity, and morphology.
Cadmium Absorption in Various Genotypes of Rice under Cadmium Stress

Cadmium (Cd) is a kind of heavy metal. Cadmium pollution in paddy fields will accumulate a large amount of cadmium in rice, which will affect the growth and development of rice. In addition, long-term consumption of rice contaminated with Cd can harm human health. In this study, four rice varieties with high Cd accumulation (S4699, TLY619, JHY1586, QLY155) and four varieties with low Cd accumulation (YY4949, CYJ-7, G8YXSM, MXZ-2) were screened through field experiments for two consecutive years (2021 and 2022) and differences in antioxidant enzyme systems and expression of genes in their organs were analyzed. The total Cd content showed as follows: indica rice > japonica rice, high-Cd-accumulation variety > low-Cd-accumulation variety, and the total Cd content of each organ of rice showed root > stem > leaf > grain. The results of the antioxidant enzyme system showed that the contents of malondialdehyde (MAD), reduced glutathione (GSH), oxidized glutathione (GSSH), and peroxidase (POD) were positively correlated with the total Cd content in rice, and superoxide dismutase (SOD) showed the opposite performance in the leaves. There was no correlation between catalase (CAT) and Cd content, but CAT content decreased in leaves and grains and increased in roots and stems with increasing fertility. Based on this study, RT-qPCR was used to further validate the expression of Cd-uptake-related genes in different rice varieties. It was found that high expression of OsHMA3, OsCCX2, OsNRAMP5, and OsHMA9 genes promoted Cd uptake and translocation in rice, especially in rice varieties with high Cd accumulation. The high expression of OslRT1, OsPCR1, and OsMTP1 genes hindered Cd uptake by rice plants, which was especially evident in low-accumulating Cd rice varieties. These results provide an important theoretical reference and scientific basis for our in-depth study and understanding of the mechanism of cadmium stress tolerance in rice.
Analysis and Prediction of Terrestrial Water Storage Anomalies in the Lower Yangtze River Basin Based on an Improved Grey-Markov Model

ABSTRACT Li, X. ; Wang, B. ; Tong, Z. ; Jiang, Z. , and Cai, C. , 2023. Analysis and prediction of terrestrial water storage anomalies in the lower Yangtze River Basin based on an improved Grey-Markov model. Journal of Coastal Research, 39(3), 544–552. Charlotte (North Carolina), ISSN 0749-0208. The data from Gravity Recovery and Climate Experiment (GRACE) satellites have been used to estimate terrestrial water storage anomalies (TWSAs) in many areas. From the former studies, the TWSAs from GRACE have been proven as a sensitive indicator to extreme hydrological events, which means it is possible to forecast the extreme hydrological events through the prediction of TWSA. However, because of the limited time series and missing data of the GRACE satellite, it is still very difficult to analyze the long-term TWSAs and predict the future changes of TWSAs. In this paper, the TWSAs of the lower Yangtze River basin from 2002 to 2021 estimated by the GRACE are compared with the TWS from Global Land Data Assimilation System (GLDAS) with the aspects of trend and correlation. The TWSAs from GRACE and the TWS from GLDAS in the study area are both on a slight upward trend. The Pearson correlation coefficient and the spectral analysis present a strong correlation between the two elements. According to the high correlation between the two elements, both linear and nonlinear methods are used to reconstruct a long-term GRACE data, and an improved Grey-Markov model is proposed to predict the future TWSA in the study area. The results show that a linear relationship occurs between the two elements. Moreover, the forecast results show that the TWSA of the lower Yangtze River basin will exceed 0.35 cm in 2021 and 2022, indicating continuous wet conditions in the basin. The basin should be prepared for the occurrence of floods.
Vibration and Buckling of Lattice Sandwich Structures

Based on the continuum assumption, an equivalent vibration analysis is performed for lattice sandwich beam. The natural frequencies of the pyramidal core sandwich beam from theoretical prediction are compared with corresponding finite element results. For simply-supported sandwich plates with the tetrahedral and pyramidal cores, the modal analysis and buckling analysis are performed by FEM, and the influences of microscopic geometrical parameters on natural frequency and buckling are also investigated.
Hybrid deep learning OPC framework with generative adversarial network

OPC is a key step to improve design fidelity when people transfer patterns from the photomask to the wafer. However, to complete a traditional OPC job in advanced technology node, a huge number of CPU cores and above several days are required. In this article, we proposed a pixel based OPC and deep learning OPC hybrid optimization framework. First, the pixOPC is done with the raw training clips. The pairs of the raw training clip and post OPC clip form the training data set. The training clip pairs are fed into GAN (Generative Adversarial Network) OPC architecture and the GAN network is trained. The GAN OPC generator is then validated to ensure that it has enough accuracy and does not overfit the data. The validated GAN OPC generator is then applied to generate OPC masks for the new design clips and the generated masks are refined with traditional OPC to exclude some unexpected outliers generated by the GAN method. We design the reversed high discretion pix2pix GAN to generate OPC masks. Its runtime and performance are compared with the model based OPC, pixOPC and U-Net. The generated OPC masks, simulated lithographic contours, EPE, PVBAND and NILS are compared. We find the GAN generative models have better performance compared with the traditional OPC, and the runtime are also much shorter.
Decision-Level Fusion Filtering Algorithm Based onAdvanced D-S Theory of Evidence

A fusion filtering algorithm based on D-S theory of evidence is proposed to combat shortcomings of linear and nonlin-ear filtering techniques. It first analyzes how basic D-S theory of evidence can result in limitations and to what degree, and then takes measures to annul dependencies among evidences and adaptively assigns eontradictionary information to conflict-relevant pixels, thus improving the combination criteria of D-S theory of evidence as well as increasing its fusion capability. On this foundation, and for thedisadvantage in hybrid fdters of great risk of misjudging image edges, lower reliabihty, and had error tolerance, we add a multi-sub-source adjudgement criteria, and utilize modified combination criteria in D-S theory of evidenced to perform fusion adjudgement. Subse-quent experimental results show the validity of the algorithm, and thus providing a new way to improve image filtering techniques.
Dirichlet Lasso: A Bayesian approach to variable selection

Selection of the most important predictor variables in regression analysis is one of the key problems statistical research has been concerned with for long time. In this article, we propose the methodology, Dirichlet Lasso (abbreviated as DLASSO) to address this issue in a Bayesian framework. In many modern regression settings, large set of predictor variables are grouped and the coefficients belonging to any one of these groups are either all redundant or all important in predicting the response; we say in those cases that the predictors exhibit a group structure. We show that DLASSO is particularly useful where the group structure is not fully known. We exploit the clustering property of Dirichlet Process priors to infer the possibly missing group information. The Dirichlet Process has the advantage of simultaneously clustering the variable coefficients and selecting the best set of predictor variables. We compare the predictive performance of DLASSO to Group Lasso and ordinary Lasso with real data and simulation studies. Our results demonstrate that the predictive performance of DLASSO is almost as good as that of Group Lasso when group label information is given; and superior to the ordinary Lasso for missing group information. For high dimensional data (e.g., genetic data) with missing group information, DLASSO will be a powerful approach of variable selection since it provides a superior predictive performance and higher statistical accuracy.