Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental Characterization and Modeling Multifunctional Properties of Epoxy/Graphene Oxide Nanocomposites

Thermomechanical modeling of epoxy/graphene oxide under quasi-static and dynamic loading requires thermo-mechanical properties such as Young’s modulus, Poisson’s ratio, thermal conductivity, and frequency-temperature dependent viscoelastic properties. In this study, the effects of different graphene oxide (GO) concentrations (0.05, 0.1, and 0.2 wt%) within an epoxy matrix on several mechanical and thermal properties were investigated. The distribution of GO fillers in the epoxy was investigated using transmission electron microscopy (TEM). The digital image correlation (DIC) technique was employed during the tensile testing to determine Young’s modulus and Poisson’s ratio. Analytical models were used to predict Young’s modulus and thermal conductivity, with an error of less than 13% and 9%, respectively. Frequency–temperature dependent phenomenological models were proposed to predict the storage moduli and loss tangent, with a reasonable agreement with experimental data. A relatively high storage modulus, heat-resistance index (THRI), and thermal conductivity were observed in 0.2 wt% nanocomposite samples compared with pure epoxy and other lower concentration GO nanocomposites. A high THRI and derivative of thermogravimetric analysis peak temperatures (Tm1 and Tm2) were exhibited by adding nano-fillers in the epoxy, which confirms higher thermal stability of nanocomposites than that of pristine epoxy.
Antigenic Diversity of Human Norovirus Capsid Proteins Based on the Cross-Reactivities of Their Antisera

Human norovirus (HuNoV), which is the major causative agent of acute gastroenteritis, has broad antigenic diversity; thus, the development of a broad-spectrum vaccine is challenging. To establish the relationship between viral genetic diversity and antigenic diversity, capsid P proteins and antisera of seven GI and 16 GII HuNoV genotypes were analyzed. Enzyme-linked immunosorbent assays showed that HuNoV antisera strongly reacted with the homologous capsid P proteins (with titers > 5 × 104). However, 17 (73.9%) antisera had weak or no cross-reactivity with heterologous genotypes. Interestingly, the GII.5 antiserum cross-reacted with seven (30.4%) capsid P proteins (including pandemic genotypes GII.4 and GII.17), indicating its potential use for HuNoV vaccine development. Moreover, GI.2 and GI.6 antigens reacted widely with heterologous antisera (n ≥ 5). Sequence alignment and phylogenetic analyses of the P proteins revealed conserved regions, which may be responsible for the immune crossover reactivity observed. These findings may be helpful in identifying broad-spectrum epitopes with clinical value for the development of a future vaccine.
Physical Activity, Sitting Time, and Quality of Life among Breast and Gynaecology Cancer Survivors

Background: Increasing physical activity and reducing sitting time was recommended to cancer survivors after cancer treatment for sustained health and to enhance the quality of life. This study aimed to determine the association of physical activity and sitting time with quality of life among the Malay breast and gynaecological cancer survivors. Methods: A cross-sectional study was conducted among 95 breast and gynaecology cancer survivor subjects. The Malay International Physical Activity Questionnaire (IPAQ) was used to assess physical activity and sitting time. Quality of life was assessed using the Malay EORTC QLQ-C30 questionnaire. Sociodemographic, clinical characteristics and anthropometric measurements were also obtained in this study. Results: The mean age of the subject was 51.8 ± 7.7 years old and the duration of survivorship was 4.3 ± 3.4 years. A total of 76.8% of subjects were categorized as having low physical activity level with a mean MET 403.5 ± 332.7 minutes/week and sitting time of 416.9 ± 151.0 minutes/day. Overall, subjects aged 50 years and above (p=0.006), widowed (p=0.032), retired (p=0.029) and had other non-communicable diseases (p=0.005) showed lower levels of physical activity. Increased physical activity had a positive effect on physical function (r=0.2, p=0.038), reduced insomnia (r=-0.3, p<0.001) and constipation symptoms (r=-0.3, p=0.012) domains of quality of life. The longer the sitting period showed more severe insomnia symptoms (r=0.2, p=0.03) but improved social function (r=0.2, p=0.012). Conclusions: Increasing physical activity and reducing sitting time have a positive effect on the quality of life of cancer survivors. The focus of health education should be prioritized to older adults (50 years and above), widows, retirees, and those with other comorbidities as they are at risk of being not physically active.
The Pathogenic Role of Smooth Muscle Cell-Derived Wnt5a in a Murine Model of Lung Fibrosis

Idiopathic pulmonary fibrosis (IPF) is a disease characterized by extensive fibrosis of the lung tissue. Wnt5a expression was observed to be upregulated in IPF and suggested to be involved in the progression of the disease. Interestingly, smooth muscle cells (SMC) are a major source of Wnt5a in IPF patients. However, no study has been conducted until now to investigate the precise role of smooth muscle-derived Wnt5a in IPF. Here, we used the bleomycin-induced lung fibrosis model in a conditional gene-deficient mouse, where the Wnt5a gene was excised from SMC. We show here that the excision of the Wnt5a gene in SMC led to significantly improved health conditions with minimized weight loss and improved lung function. This improvement was based on a significantly lower deposition of collagen in the lung with a reduced number of fibrotic foci in lung parenchyma. Furthermore, the bleomycin-induced cellular infiltration into the airways was not altered in the gene-deficient mice compared with wild-type mice. Thus, we demonstrate that the Wnt5a expression of SMC of the airways leads to aggravated fibrosis of the lung with poor clinical conditions. This aggravation was not an influence in the bleomycin-induced inflammatory processes but on the development of fibrotic foci in lung parenchyma and the deposition of collagen.
Comparison of Two Real-Time PCR Assays Targeting Ribosomal Sequences for the Identification of Cystoisospora belli in Human Stool Samples

Cystoisospora (C.) belli is a coccidian parasite associated with acute or chronic gastroenteritis in immunocompromised patients. Dissatisfactory sensitivity of microscopy as the diagnostic standard approach has been described. Here, we comparatively evaluated two real-time PCRs targeting ribosomal RNA gene sequences of C. belli in stool in a test comparison without a reference standard applying latent class analysis. Therefore, 1000 stool samples from Ghanaian HIV (human immunodeficiency virus) patients (n = 905) as well as military returnees from the tropics (n = 95) were assessed by both assays in parallel. After the exclusion of 33 samples showing PCR inhibition, 29 and 33 positive results were recorded with the 5.8S rRNA gene/ITS-2 sequence PCR and the ITS-2 sequence PCR, respectively, resulting in an accuracy-adjusted prevalence of 3.2%. Nearly perfect agreement between both assays was indicated by Fleiss’ kappa of 0.933 with sensitivity and specificity of 92.8% and 100% as well as 100% and 99.8% for the 5.8S rRNA gene/ITS-2 sequence PCR and the ITS-2 sequence PCR, respectively. Both assays proved to be suitable for the diagnosis of C. belli in human stool samples with slightly better sensitivity of the ITS-2 sequence assay, while the 5.8S rRNA gene/ITS-2 sequence PCR may be considered for confirmatory testing.
The Chloroplast Phylogenomics and Systematics of Zoysia (Poaceae)

The genus Zoysia Willd. (Chloridoideae) is widely distributed from the temperate regions of Northeast Asia—including China, Japan, and Korea—to the tropical regions of Southeast Asia. Among these, four species—Zoysia japonica Steud. , Zoysia sinica Hance, Zoysia tenuifolia Thiele, and Zoysia macrostachya Franch. & Sav. —are naturally distributed in the Korean Peninsula. In this study, we report the complete plastome sequences of these Korean Zoysia species (NCBI acc. nos. MF953592, MF967579~MF967581). The length of Zoysia plastomes ranges from 135,854 to 135,904 bp, and the plastomes have a typical quadripartite structure, which consists of a pair of inverted repeat regions (20,962~20,966 bp) separated by a large (81,348~81,392 bp) and a small (12,582~12,586 bp) single-copy region. In terms of gene order and structure, Zoysia plastomes are similar to the typical plastomes of Poaceae. The plastomes encode 110 genes, of which 76 are protein-coding genes, 30 are tRNA genes, and four are rRNA genes. Fourteen genes contain single introns and one gene has two introns. Three evolutionary hotspot spacer regions—atpB~rbcL, rps16~rps3, and rpl32~trnL-UAG—were recognized among six analyzed Zoysia species. The high divergences in the atpB~rbcL spacer and rpl16~rpl3 region are primarily due to the differences in base substitutions and indels. In contrast, the high divergence between rpl32~trnL-UAG spacers is due to a small inversion with a pair of 22 bp stem and an 11 bp loop. Simple sequence repeats (SSRs) were identified in 59 different locations in Z. japonica, 63 in Z. sinica, 62 in Z. macrostachya, and 63 in Z. tenuifolia plastomes. Phylogenetic analysis showed that the Zoysia (Zoysiinae) forms a monophyletic group, which is sister to Sporobolus (Sporobolinae), with 100% bootstrap support. Within the Zoysia clade, the relationship of (Z. sinica, Z japonica), (Z. tenuifolia, Z. matrella), (Z. macrostachya, Z. macrantha) was suggested.
Structural basis for the activation of the DEAD-box RNA helicase DbpA by the nascent ribosome

Significance DEAD-box RNA helicases are essential cellular enzymes that remodel misfolded RNA structures in an adenosine triphosphate (ATP)-dependent process. The DEAD-box helicase DbpA is involved in the complex and highly regulated process of ribosome maturation. To prevent wasteful hydrolysis of ATP by DbpA, the enzyme is only active when bound to maturing ribosomes. Here, we elucidate the structural basis behind this important regulatory mechanism and find that the recruited ribosome substrate is able to stabilize the catalytically important closed state of the helicase. In addition, our data identify the natural site of action for DbpA in the maturing ribosome and provide a molecular explanation for the observed ribosome maturation defects that result from the overexpression of a DbpA mutant form. The adenosine triphosphate (ATP)-dependent DEAD-box RNA helicase DbpA from Escherichia coli functions in ribosome biogenesis. DbpA is targeted to the nascent 50S subunit by an ancillary, carboxyl-terminal RNA recognition motif (RRM) that specifically binds to hairpin 92 (HP92) of the 23S ribosomal RNA (rRNA). The interaction between HP92 and the RRM is required for the helicase activity of the RecA-like core domains of DbpA. Here, we elucidate the structural basis by which DbpA activity is endorsed when the enzyme interacts with the maturing ribosome. We used nuclear magnetic resonance (NMR) spectroscopy to show that the RRM and the carboxyl-terminal RecA-like domain tightly interact. This orients HP92 such that this RNA hairpin can form electrostatic interactions with a positively charged patch in the N-terminal RecA-like domain. Consequently, the enzyme can stably adopt the catalytically important, closed conformation. The substrate binding mode in this complex reveals that a region 5′ to helix 90 in the maturing ribosome is specifically targeted by DbpA. Finally, our results indicate that the ribosome maturation defects induced by a dominant negative DbpA mutation are caused by a delayed dissociation of DbpA from the nascent ribosome. Taken together, our findings provide unique insights into the important regulatory mechanism that modulates the activity of DbpA.
Selective Laser Sintering (SLS), a New Chapter in the Production of Solid Oral Forms (SOFs) by 3D Printing

3D printing is a new emerging technology in the pharmaceutical manufacturing landscape. Its potential advantages for personalized medicine have been widely explored and commented on in the literature over recent years. More recently, the selective laser sintering (SLS) technique has been investigated for oral drug-delivery applications. Thus, this article reviews the work that has been conducted on SLS 3D printing for the preparation of solid oral forms (SOFs) from 2017 to 2020 and discusses the opportunities and challenges for this state-of-the-art technology in precision medicine. Overall, the 14 research articles reviewed report the use of SLS printers equipped with a blue diode laser (445–450 nm). The review highlights that the printability of pharmaceutical materials, although an important aspect for understanding the sintering process has only been properly explored in one article. The modulation of the porosity of printed materials appears to be the most interesting outcome of this technology for pharmaceutical applications. Generally, SLS shows great potential to improve compliance within fragile populations. The inclusion of “Quality by Design” tools in studies could facilitate the deployment of SLS in clinical practice, particularly where Good Manufacturing Practices (GMPs) for 3D-printing processes do not currently exist. Nevertheless, drug stability and powder recycling remain particularly challenging in SLS. These hurdles could be overcome by collaboration between pharmaceutical industries and compounding pharmacies.
Automatic detection of oral cancer in smartphone-based images using deep learning for early diagnosis

Abstract. Significance: Oral cancer is a quite common global health issue. Early diagnosis of cancerous and potentially malignant disorders in the oral cavity would significantly increase the survival rate of oral cancer. Previously reported smartphone-based images detection methods for oral cancer mainly focus on demonstrating the effectiveness of their methodology, yet it still lacks systematic study on how to improve the diagnosis accuracy on oral disease using hand-held smartphone photographic images. Aim: We present an effective smartphone-based imaging diagnosis method, powered by a deep learning algorithm, to address the challenges of automatic detection of oral diseases. Approach: We conducted a retrospective study. First, a simple yet effective centered rule image-capturing approach was proposed for collecting oral cavity images. Then, based on this method, a medium-sized oral dataset with five categories of diseases was created, and a resampling method was presented to alleviate the effect of image variability from hand-held smartphone cameras. Finally, a recent deep learning network (HRNet) was introduced to evaluate the performance of our method for oral cancer detection. Results: The performance of the proposed method achieved a sensitivity of 83.0%, specificity of 96.6%, precision of 84.3%, and F1 of 83.6% on 455 test images. The proposed “center positioning” method was about 8% higher than that of a simulated “random positioning” method in terms of F1 score, the resampling method had additional 6% of performance improvement, and the introduced HRNet achieved slightly better performance than VGG16, ResNet50, and DenseNet169, with respect to the metrics of sensitivity, specificity, precision, and F1. Conclusions: Capturing oral images centered on the lesion, resampling the cases in training set, and using the HRNet can effectively improve the performance of deep learning algorithm on oral cancer detection. The smartphone-based imaging with deep learning method has good potential for primary oral cancer diagnosis.
A Repeated Time-to-Positive Symptoms Improvement among Malaysian Patients with Schizophrenia Spectrum Disorders Treated with Clozapine

Clozapine remains the drug of choice for resistant schizophrenia. However, its dose-response relationship is still controversial. The current investigation aimed to develop a repeated time-to-positive symptoms improvement following the onset of clozapine treatment in Malaysian schizophrenia spectrum disorder patients. Data from patients’ medical records in the Psychiatric Clinic, Penang General Hospital, were retrospectively analyzed. Several parametric survival models were evaluated using nonlinear mixed-effect modeling software (NONMEM 7.3.0). Kaplan–Meier-visual predictive check (KM-VPC) and sampling-importance resampling (SIR) methods were used to validate the final model. A total of 116 patients were included in the study, with a mean follow-up of 306 weeks. Weibull hazard function best fitted the data. The hazard of positive symptoms improvement decreased 4% for every one-year increase in age over the median of 41 years (adjusted hazard ratio (aHR), 0.96; 95% confidence intervals (95% CI), (0.93–0.98)). However, patients receiving a second atypical antipsychotic agent had four-folds higher hazard (aHR, 4.01; 95% CI, (1.97–7.17)). The hazard increased 2% (aHR, 1.02; 95% CI, (1.01–1.03)) for every 1 g increase in the clozapine six months cumulative dose over the median of 34 g. The developed model provides essential information on the hazard of positive symptoms improvement after the first clozapine dose administration, including modifiable predictors of high clinical importance.
Association of Polymorphisms in the Glutathione S-Transferase Theta-1 Gene with Cirrhosis and Hepatocellular Carcinoma in Brazilian Patients with Chronic Hepatitis C

Oxidative stress contributes to hepatitis C virus (HCV)–induced liver damage. Host genetic factors may be involved in progression of HCV infection. The present study was conducted to determine the influence of glutathione S-transferase (GST)-M1 and T1 gene polymorphisms during different stages of HCV infection, including chronic hepatitis, cirrhosis, and hepatocellular carcinoma (HCC). The study population comprised 190 patients (47 with chronic hepatitis, 83 with cirrhosis (without HCC), and 60 with HCC). GSTM1 and GSTT1 gene polymorphisms were analyzed via multiplex polymerase chain reaction. The GSTT1-null genotype was more commonly detected in patients with cirrhosis (n = 17; 20.5%) and HCC (n = 13; 21.7%) than those with chronic hepatitis (n = 3; 6.4%). The differences in GSTT1-null genotype frequencies were significant for cirrhosis vs. chronic hepatitis (odds ratio, OR, 3.778 (95% confidence interval, CI, 1.045–13.659); p = 0.043) and HCC vs. chronic hepatitis (OR, 4.057 (95% CI, 1.083–15.201); p = 0.038) groups. However, the incidence of individual GSTM1-null or combined GSTM1/GSTT1 double-null genotypes did not vary significantly between the groups. Our collective findings support the utility of the GSTT1-null genotype as a useful biomarker for liver disease progression in Brazilian patients with chronic hepatitis C.
Low-Bandwidth and Compute-Bound RGB-D Planar Semantic SLAM

Visual simultaneous location and mapping (SLAM) using RGB-D cameras has been a necessary capability for intelligent mobile robots. However, when using point-cloud map representations as most RGB-D SLAM systems do, limitations in onboard compute resources, and especially communication bandwidth can significantly limit the quantity of data processed and shared. This article proposes techniques that help address these challenges by mapping point clouds to parametric models in order to reduce computation and bandwidth load on agents. This contribution is coupled with a convolutional neural network (CNN) that extracts semantic information. Semantics provide guidance in object modeling which can reduce the geometric complexity of the environment. Pairing a parametric model with a semantic label allows agents to share the knowledge of the world with much less complexity, opening a door for multi-agent systems to perform complex tasking, and human–robot cooperation. This article takes the first step towards a generalized parametric model by limiting the geometric primitives to a planar surface and providing semantic labels when appropriate. Two novel compression algorithms for depth data and a method to independently fit planes to RGB-D data are provided, so that plane data can be used for real-time odometry estimation and mapping. Additionally, we extend maps with semantic information predicted from sparse geometries (planes) by a CNN. In experiments, the advantages of our approach in terms of computational and bandwidth resources savings are demonstrated and compared with other state-of-the-art SLAM systems.
Automated Flood Depth Estimates from Online Traffic Sign Images: Explorations of a Convolutional Neural Network-Based Method

Flood depth monitoring is crucial for flood warning systems and damage control, especially in the event of an urban flood. Existing gauge station data and remote sensing data still has limited spatial and temporal resolution and coverage. Therefore, to expand flood depth data source taking use of online image resources in an efficient manner, an automated, low-cost, and real-time working frame called FloodMask was developed to obtain flood depth from online images containing flooded traffic signs. The method was built on the deep learning framework of Mask R-CNN (regional convolutional neural network), trained by collected and manually annotated traffic sign images. Following further the proposed image processing frame, flood depth data were retrieved more efficiently than manual estimations. As the main results, the flood depth estimates from images (without any mirror reflection and other inference problems) have an average error of 0.11 m, when compared to human visual inspection measurements. This developed method can be further coupled with street CCTV cameras, social media photos, and on-board vehicle cameras to facilitate the development of a smart city with a prompt and efficient flood monitoring system. In future studies, distortion and mirror reflection should be tackled properly to increase the quality of the flood depth estimates.
Lead Reconstruction Using Artificial Neural Networks for Ambulatory ECG Acquisition

One of the most powerful techniques to diagnose cardiovascular diseases is to analyze the electrocardiogram (ECG). To increase diagnostic sensitivity, the ECG might need to be acquired using an ambulatory system, as symptoms may occur during a patient’s daily life. In this paper, we propose using an ambulatory ECG (aECG) recording device with a low number of leads and then estimating the views that would have been obtained with a standard ECG location, reconstructing the complete Standard 12-Lead System, the most widely used system for diagnosis by cardiologists. Four approaches have been explored, including Linear Regression with ECG segmentation and Artificial Neural Networks (ANN). The best reconstruction algorithm is based on ANN, which reconstructs the actual ECG signal with high precision, as the results bring a high accuracy (RMS Error < 13 μV and CC > 99.7%) for the set of patients analyzed in this paper. This study supports the hypothesis that it is possible to reconstruct the Standard 12-Lead System using an aECG recording device with less leads.