Miracle-dz/UniFAR-FTUs huggingface dataset:
CNN-based Approach for Prediction of Periodontally Teeth

Periodontal disease (PD) is characterized by gingival inflammation, connective tissue loss, or alveolar bone degradation. It's the world's sixth most common inflammatory disease. Clinical examination and radiograph analysis are two of the most popular approaches used nowadays to diagnose periodontal disease. Due to the constraints of the standard diagnostic paradigm, inaccuracy due to human error, and conflicting judgements by various examiners. As a result, a more consistent method of identifying periodontal disease is required. The main contribution of this paper is to propose a deep learning approach based on CNN-architectur to diagnose and predict periodontally teeth from X-ray images. There are several types of deep convolution networks, such as Vgg16, Alexnet, Inception, Resnet50, Resnet101 and Densenet169 are used. The experimental results, on combined datasets of different resources, shows that VGG16, VGG19, and Xception are the best three models, with an accuracy score of 95%, 88%, and 84% respectively. Additionally, in terms of F-Score, that VGG16 is the best model with an F1-Score of 89.5%.
Multi-agent Game Collaborative Planning of Complex Distribution Network Considering the Bounded Rationality of Wind Power Output

A high proportion of new energy dominated by wind energy is connected to the distribution network, and its output uncertainty poses a threat to the safe and stable operation of the distribution network. This paper presents a multi-agent game collaborative planning method for complex distribution networks considering the bounded rationality of wind power output. Firstly, sort out the game relationship between nature and distribution network source-network-load side interest subjects, and establish the income models of different subjects. Then, a multi-agent game collaborative planning model of complex distribution network considering the bounded rationality of wind power output is constructed. The inner layer is the evolutionary game between nature and distribution network operators. Under the condition of considering the demand response on the user side, the best planning scheme of distribution network frame and energy storage considering the bounded rationality of wind power output is decided. The outer layer is the game between distributed generation operators and distribution network operators. Under the assumption that the game subject is completely rational, the configuration strategy of distributed generation is decided. On this basis, the idea of artificial potential field and information entropy is introduced, and an evolutionary game solution method based on stage adaptive step size is proposed. Finally, it is verified by a simulation example. The results show that the planning model constructed in this paper effectively reduces the conservatism of the traditional planning method considering the complete rationality of wind power output, and is closer to the actual situation. At the same time, the evolutionary game solution method proposed in this paper greatly reduces the number of iterations of the game and improves the convergence speed on the basis of ensuring the reliable convergence of the model.
A Compact Measurement Setup for the Validation of MIMO arrays in D-band and W-band

This paper presents a measurement setup for scanning the virtual array of a Multiple Input - Multiple Output (MIMO) system which can be used to validate MIMO array concepts. Two FMCW (Frequency-Modulated Continuous Wave) radar sensors are used for this purpose, mounted on a 2D linear rail. Resulting MIMO images can be matched to corresponding radar cross section (RCS) values using the described calibration method. In order to increase the image quality, averaging is used for a larger signal-to-noise ratio (SNR). Measurements show that the achieved angular resolution matches with theoretically possible values.
A 26GHz to 34GHz Active Phase Shifter with Tunable Polyphase Filter for 5G Wireless Systems

This paper presents a vector summing Active Phase Shifter (APS) for 5G and 6G wireless systems. The circuit is fabricated using 45nm CMOS SOI technology and exploits 10bit control to set the desired amplitude and phase. The design uses a high-precision tunable polyphase filter (PPF) whose center frequency can be tuned from 23GHz to 40GHz. By utilizing a tunable PPF and independent gain tuning of the quadrature signals along with a simple calibration, a square phase constellation is achieved over the full operating band, resulting in an easy phase control with a mean resolution of 0.5 degree and 7dB of amplitude control to facilitate amplitude tapering. The measured RMS phase and amplitude errors are as small as 0.6 degree and 0.08dB, respectively. Active area occupied is 0.19 sq. mm, allowing a high degree of integration in a phased array system.
S2worm: A Fast-Moving Untethered Insect-Scale Robot With 2-DoF Transmission Mechanism

Designing terrestrial insect-scale robot with high maneuverability and autonomy is becoming an essential challenge in the field of robotics research. Previous work has indicated that compact transmission and integrated control devices can improve the application potential. In this work, an untethered insect-scale inchworm robot is designed and fabricated by using screw theory and smart composite microstructure method, and is named S2worm. The robot weighs 4.34 g and spans 4.1 cm in length. The robot is equipped with custom designed onboard control system and high voltage boost converter to provide the driving signal for the piezoelectric bending actuator. Attributing to the novel transmission mechanism with two degrees of freedom designed based on screw theory, the S2worm can be fabricated through the smart composite microstructure method easily and shows high mobility such as high crawling speed (27.4 cm/s, 6.7 body length/s) and small turning radius (1.7 cm, 0.4 body length/s). The S2worm holds the following advantages: small size, untethered, high mobility and low energy consumption. The robot is promising for application in planetary exploration, earthquake search and constructing insect-scale multi-robot system.
Sparse component analysis-based under-determined blind source separation for bearing fault feature extraction in wind turbine gearbox

The signal processing-based bearing fault diagnosis in wind turbine gearbox is considered challenging as the vibration signals collected from acceleration transducers are, in general, a mixture of signals originating from an unknown number of sources. Even worse, the source number is often larger than the number of installed sensors, and hence the fault characterisation is effectively an under-determined blind source separation problem. In this study, a novel sparse component analysis-based algorithmic solution is proposed to address this technical challenge from two aspects: source number estimation and source signal recovery, to enable accurate and efficient bearing fault diagnosis. The source number estimation is implemented based on the empirical mode decomposition and singular value decomposition joint approach. The observed signals are transformed to the time-frequency domain using short-time Fourier transform to obtain the sparse representation of the signals. The fuzzy C-means clustering and l 1 norm decomposition methods are used to estimate the mixing matrix and recover the source signals, respectively. The proposed solution is assessed through simulation experiments for scenarios of linearly and non-linearly mixed bearing vibration signals, and the numerical result confirms the effectiveness of the proposed algorithmic solution.
A Proposed Ensemble Voting Model for Fake News Detection

Fake news or rumors are a phenomenon that significantly influences our social lives. Politicians in the political world usually rely on fake news as a powerful mechanism to change public opinion. Fake news spread through the media poses a real threat to the credibility of information, and the detection of fake news has attracted increased attention in recent years. Therefore, it becomes highly necessary to develop a method to identify fake news. This paper proposes a new ensemble voting model for detecting fake news in online text using a hybrid of machine learning and deep learning algorithms. Our ensemble model consists of three algorithms, namely, Convolution Neural Network (CNN) Gated Recurrent Unit (GRU) model of Recurrent Neural Network (RNN) and Random Forest. We relied on Natural language processing to extract statistical and representative features from the LIAR dataset. We experimented with the extracted features with our ensemble model. Experimental evaluation showed that our model achieves the best performance on the LIAR dataset with an accuracy of 0.410.
Optimization of Jet Reconstruction Settings and Parton-Level Corrections for the tt̄H Channel

The typical final state for tt̄H associated production where the Higgs decays into a pair of b quark is an event with high jet multiplicity. The expected jet number for one (two) semi-leptonic top decay or fully hadronic top decay is 6(4) and 8 respectively. The ch oi e of the jet algorithm is therefore crucial to improve the chance to detect a light Higgs boson in the top-associated H production. Previous studies both with fast and full simulated events show t hat different jet algorithms give different results in the selection procedure. To fully investigate th e effect of different jet algorithm however is mandatory to use jets calibrated for detector effects and co rre ted for effects related to the parton-jet formation. Different jet finders and Monte-Carlo jet calibr ation parameters are studied relatively to the best performance for the tt̄H channel observability. The set of algorithm chosen is the it erative cone algorithm with cone size going from 0.30 up to 0.50 (with 0.05 step) and the inclusive K⊥ algorithm withr=0.4. Different calibration parameters as a function of η, ET and jet flavour are calculated. Finally, as an example, the jet-to-parton pairing efficiency for the fully hadronictt̄H decay is computed.
Two Pathways of Subsurface Spiciness Anomalies in the Subtropical South Pacific

Subduction and migration of density-compensated (warm/salty or cool/fresh) temperature and salinity water-mass anomalies on isopycnals, referred to as spiciness anomalies, are examined in the subtropical gyre of the South Pacific using an observational dataset. The present results demonstrate that the spiciness anomalies are found to follow two pathways from the subtropical region to the tropical area on the 25–25. 5σθ isopycnals. The water masses of one pathway subduct south of 20°S and mainly flow westward via the mean geostrophic current to the western boundary region. The water masses of this pathway correspond to the salinity maximum on these isopycnals as well as the bottom of the South Pacific Tropical Water. Positive temperature and salinity trends were prominent along this pathway during the study period. In the other pathway, the water masses subduct north of 20°S and go directly to the tropics through the interior region. Decadal variability of the spiciness anomalies is prominent along this pathway. In both pathways, sea surface salinity variability likely plays an important role in generating the spiciness anomalies on the isopycnals. A passive tracer experiment revealed that the advection by the South Equatorial Countercurrent (SECC) divides these two pathways. Hence, SECC plays a key role in determining whether a spiciness anomaly propagates through the interior region or the western boundary region.
Obesity Accelerates Leukocyte Telomere Length Shortening in Apparently Healthy Adults: A Meta-Analysis

Background Shorter telomere length is associated with numerous comorbidities. Several studies have investigated the role of obesity in telomere shortening. In the current systematic review and meta-analysis, we summarized the results of studies that evaluated the association between obesity and telomere length. Methods A systematic search from Scopus, PubMed, Embase, and ProQuest electronic databases up to 19 March 2021 without language restriction was performed and after data extraction and screening, 19 manuscripts were eligible to be included in the final meta-synthesis. Results The highest category of telomere length was associated with an approximate 0.75 kg/m2 reduction in body mass index (BMI; WMD = −0.75 kg/m2; CI = −1.19, −0.31; p < 0.001; I2 = 99.4%). Moreover, overweight/obese individuals had 0.036 kbp shorter telomere length compared with non-overweight/obese adults (WMD = −0.036; CI = −0.05, −0.02; p = 0.030; I2 = 100%). According to the results of subgroupings, continent, age, and sample size could be possible sources of heterogeneity. Conclusion From the results, it was clear that obesity was associated with shorter telomere length. Because of the observational design of included studies, the causality inference of results should be done with caution; thus, further longitudinal studies are warranted for better inference of causal association.
The Curved Openspace Algorithm and a Spike-Latency Model for Sonar-Based Obstacle Avoidance

The rapid control of a sonar-guided vehicle to pursue a goal while avoiding obstacles has been a persistent research topic for decades. Taking into account the limited field-of-view of practical sonar systems and vehicle kinematics, we propose a neural model for obstacle avoidance that maps the 2-D sensory space into a 1-D motor space and evaluates motor actions while combining obstacles and goal information. A two-stage winner-take-all (WTA) mechanism is used to select the final steering action. To avoid excessive scanning of the environment, an attentional system is proposed to control the directions of sonar pings for efficient, task-driven, sensory data collection. A mobile robot was used to test the proposed model navigating through a cluttered environment using a narrow field-of-view sonar system. We further propose a spiking neural model using spike-timing representations, a spike-latency memory, and a “race-to-first-spike” WTA circuit.
Pomegranate Peel Extract Decreases Plaque Necrosis and Advanced Atherosclerosis Progression in Apoe -/- Mice

Atherosclerosis is a chronic lipid-driven inflammatory condition of the arteries and is a leading cause of stroke, myocardial infarction, and other peripheral arterial diseases. Plant products rich in polyphenols such as pomegranate juice and peel extract are known to have beneficial effects in suppressing atherogenesis. However, the mechanism of action and its effect on advanced atherosclerosis progression which results in adverse clinical outcomes are not well understood. Herein, we use a standardized hydroethanolic extract of Punica granatum (pomegranate) peel in the Apoe -/- a murine model of advanced atherosclerosis. It was observed that the pomegranate peel extract fed mice have decreased plaque necrosis and elevated lesional collagen content which was associated with a favorable metabolic profile including lowering of blood glucose, cholesterol, and triglyceride. The decrease in plaque necrosis was linked with increased lesional macrophage efferocytosis efficiency which was associated with enhanced expression of the efferocytosis receptor Mertk. Using in vitro studies, we show that pomegranate peel extract blocks the shedding of Mertk and preserves macrophage efferocytosis efficiency. These data identify a novel mechanism by which pomegranate peel extract promotes the resolution of inflammation in atherosclerosis.
A Pilot Study of Radiomics Models Combining Multi-Probe and Multi-Modality Images of 68Ga-NOTA-PRGD2 and 18F-FDG PET/CT for Differentiating Benign and Malignant Pulmonary Space-Occupying Lesions

Background This is a pilot study of radiomics based on 68Ga-NOTA-PRGD2 [NOTA-PEG4-E[c(RGDfK)]2)] and 18F-FDG PET/CT to (i) evaluate the diagnostic efficacy of radiomics features of 68Ga-NOTA-PRGD2 PET in the differential diagnosis of benign and malignant pulmonary space-occupying lesions and (ii) compare the diagnostic efficacy of multi-modality and multi-probe images. Methods We utilized a dataset of 48 patients who participated in 68Ga-NOTA-PRGD2 PET/CT and 18F-FDG PET/CT clinical trials to extract image features and evaluate their diagnostic efficacy in the differentiation of benign and malignant lesions by the Mann-Whitney U test. After feature selection with sequential forward selection, random forest models were developed with tenfold cross-validation. The diagnostic performance of models based on different image features was visualized by receiver operating characteristic (ROC) curves and compared by permutation tests. Results Fourteen of the 68Ga-NOTA-PRGD2 PET features between benign and malignant pulmonary space-occupying lesions had significant differences (P<0.05, Mann-Whitney U test). Eighteen of the 68Ga-NOTA-PRGD2 PET features demonstrated higher AUC values than all CT features in the differential diagnosis of pulmonary lesions. The AUC value (0.908) of the three-modal feature model was significantly higher (P<0.05, permutation test) than those of the single- and dual-modal models. Conclusion 68Ga-NOTA-PRGD2 PET features have better diagnostic capacity than CT features for pulmonary space-occupying lesions. The combination of multi-modality and multi-probe images can improve the diagnostic efficiency of models. Our preliminary clinical hypothesis of using radiomics based on 68Ga-NOTA-PRGD2 PET images and multimodal images as a diagnostic tool warrants further validation in a larger multicenter sample size.
Interaction Between Treatment and Age or Sex in Non-ST-Segment Elevation Acute Coronary Disease and Three-Vessel Disease

Aims To explore the effects of age and sex on the outcomes of coronary artery bypass grafting (CABG) and percutaneous coronary intervention (PCI) in non-ST-segment elevation acute coronary syndrome (NSTE-ACS) patients with the three-vessel disease (TVD). Methods and Results The study is a subanalysis of data from a prospective cohort of 8,943 patients with angiographically confirmed TVD at Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing, China. The primary end point was major adverse cardiac and cerebrovascular events (MACCEs), a composite of all-cause death, myocardial infarction, and stroke. In total, 2,819 patients with NSTE-ACS who received CABG (43.6%) or PCI (56.4%) were included, among whom 32.7% were of 65–74 years, 7.2% were ≥75 years, and 22.6% were women. The median follow-up duration was 6.8 years. The superiority of CABG relative to PCI in terms of MACCE was decreased with age (adjusted hazard ratio [HR] [95% confidence interval (CI)]: <65 years: 0.662 [0.495–0.885], p = 0.005; 65–74 years: 0.700 [0.512–0.956], p = 0.025; ≥75 years: 0.884 [0.529–1.479], p = 0.640) and was only seen in men (adjusted HR [95% CI]: men: 0.668 [0.526–0.848], p = 0.001; women: 0.713 [0.505–1.006], p = 0.054). Significant treatment-by-sex and treatment-by-age interactions were observed in patients ≥ 75 years and women, respectively, (pinteraction with sex = 0.001; pinteraction with age = 0.002). Conclusion Coronary artery bypass grafting is favorable for most NSTE-ACS patients with TVD. The preponderance of CABG over PCI disappeared in patients ≥ 75 years and women. PCI is superior in women ≥ 75 years.
Advances and Challenges in Fluorescence in situ Hybridization for Visualizing Fungal Endobacteria

Several bacteria have long been known to interact intimately with fungi, but molecular approaches have only recently uncovered how cosmopolitan these interactions are in nature. Currently, bacterial–fungal interactions (BFI) are inferred based on patterns of co-occurrence in amplicon sequencing investigations. However, determining the nature of these interactions, whether the bacteria are internally or externally associated, remains a grand challenge in BFI research. Fluorescence in situ hybridization (FISH) is a robust method that targets unique sequences of interest which can be employed for visualizing intra-hyphal targets, such as mitochondrial organelles or, as in this study, bacteria. We evaluate the challenges and employable strategies to resolve intra-hyphal BFI to address pertinent criteria in BFI research, such as culturing media, spatial distribution of bacteria, and abundance of bacterial 16S rRNA copies for fluorescent labeling. While these experimental factors influence labeling and detection of endobacteria, we demonstrate how to overcome these challenges thorough permeabilization, appropriate media choice, and targeted amplification using hybridization chain reaction FISH. Such microscopy imaging approaches can now be utilized by the broader research community to complement sequence-based investigations and provide more conclusive evidence on the nature of specific bacterial–fungal relationships.
Deciphering the Active Compounds and Mechanisms of HSBDF for Treating ALI via Integrating Chemical Bioinformatics Analysis

The Huashi Baidu Formula (HSBDF), a key Chinese medical drug, has a remarkable clinical efficacy in treating acute lung injury (ALI), and it has been officially approved by the National Medical Products Administration of China for drug clinical trials. Nevertheless, the regulated mechanisms of HSBDF and its active compounds in plasma against ALI were rarely studied. Based on these considerations, the key anti-inflammatory compounds of HSBDF were screened by molecular docking and binding free energy. The key compounds were further identified in plasma by LC/MS. Network pharmacology was employed to identify the potential regulatory mechanism of the key compounds in plasma. Next, the network pharmacological prediction was validated by a series of experimental assays, including CCK-8, EdU staining, test of TNF-α, IL-6, MDA, and T-SOD, and flow cytometry, to identify active compounds. Molecular dynamic simulation and binding interaction patterns were used to evaluate the stability and affinity between active compounds and target. Finally, the active compounds were subjected to predict pharmacokinetic properties. Molecular docking revealed that HSBDF had potential effects of inhibiting inflammation by acting on IL-6R and TNF-α. Piceatannol, emodin, aloe-emodin, rhein, physcion, luteolin, and quercetin were key compounds that may ameliorate ALI, and among which, there were five compounds (emodin, aloe-emodin, rhein, luteolin, and quercetin) in plasma. Network pharmacology results suggested that five key compounds in plasma likely inhibited ALI by regulating inflammation and oxidative damage. Test performed in vitro suggested that HSBDF (0.03125 mg/ml), quercetin (1.5625 μM), emodin (3.125 μM), and rhein (1.5625 μM) have anti-inflammatory function against oxidative damage and decrease apoptosis in an inflammatory environment by LPS-stimulation. In addition, active compounds (quercetin, emodin, and rhein) had good development prospects, fine affinity, and stable conformations with the target protein. In summary, this study suggested that HSBDF and its key active components in plasma (quercetin, emodin, and rhein) can decrease levels of pro-inflammatory factors (IL-6 and TNF-α), decrease expression of MDA, increase expression of T-SOD, and decrease cell apoptosis in an inflammatory environment. These data suggest that HSBDF has significant effect on anti-inflammation and anti-oxidative stress and also can decrease cell apoptosis in treating ALI. These findings provided an important strategy for developing new agents and facilitated clinical use of HSBDF against ALI.