Miracle-dz/UniFAR-FTUs huggingface dataset:
Liposomal siRNA Formulations for the Treatment of Herpes Simplex Virus-1: In Vitro Characterization of Physicochemical Properties and Activity, and In Vivo Biodistribution and Toxicity Studies

Herpes simplex virus-1 (HSV-1) is highly contagious, and there is a need for a therapeutic means to eradicate it. We have identified an siRNA (siHSV) that knocks down gene expression of the infected cell protein 0 (ICP0), which is important in the regulation of HSV infection. The selected siHSV was encapsulated in liposomes to overcome its poor stability, increase cell permeability, and prolonging siRNA circulation time. Several siRNAs against ICP0 have been designed and identified. We examined the role of various parameters, including formulation technique, lipids composition, and ratio. An optimal liposomal siHSV formulation (LipDOPE-siHSV) was characterized with desirable physiochemical properties, in terms of nano-size, low polydispersity index (PDI), neutral surface charge, high siHSV loading, spherical shape, high stability in physiologic conditions in vitro, and long-term shelf-life stability (>1 year, 4 °C). The liposomes exhibited profound internalization by human keratinocytes, no cytotoxicity in cell cultures, no detrimental effect on mice liver enzymes, and a gradual endo-lysosomal escape. Mice biodistribution studies in intact mice revealed accumulation, mainly in visceral organs but also in the trigeminal ganglion. The therapeutic potential of siHSV liposomes was demonstrated by significant antiviral activity both in the plaque reduction assay and in the 3D epidermis model, and the mechanism of action was validated by the reduction of ICP0 expression levels.
Multi-body vibration modelling of ball bearing–rotor system considering single and compound multi-defects

Vibration modelling is a key link to explore the cognitive rules in fault diagnosis. In order to foresee the vibration characteristics accurately, the dynamic contact among joint interfaces and vibration transmission path of ball bearing are well studied. A novel methodology for multi-body vibration modelling of ball bearing–rotor system with bearing inner race–balls–outer race–pedestal is presented. Additionally, the mathematical formulation is derived with consideration of the nonlinear spring at the contact point between ball and raceway. Then, the progressive processes of single and compound multi-defects are described by using bearing practical kinematics and the vibration responses are systematically analysed. Compared with the experimental results, the fault frequency and its harmonic components are clearly observed and achieved better consistency. Moreover, for compound fault, an important observation is that the strength imbalance and mutual restraint of the vibration amplitude due to the vectors superposition of different frequency components. Further, the Lempel–Ziv complexity measure is also employed to investigate the response of signature variations. The results show that the Lempel–Ziv complexity measure used as a quantitative criterion can effectively evaluate the single and compound fault of ball bearings.
Variable Window Mechanism of Candidates for Map Matching

The ubiquitous popularization and deployment of mobile sensor technology has enabled GPS to observe and record vehicle states. Digital road maps as well as transport information of vehicles also has brought great convenience to intelligent transportation systems (ITS). However, raw GPS locations are usually too noisy and thus cannot be used directly by downstream applications. To alleviate the issue of location noise, map matching has been investigated to chronologically map a series of geographical GPS locations onto the actual road segments. In this paper, a variable window size for the candidate selection mechanism is introduced, which can confine the search region to optimize map matching process. We conducted experiments with a real-world road transport dataset and evaluated the performance to validate the effectiveness of the proposed method.
Scenario Understanding and Motion Prediction for Autonomous Vehicles—Review and Comparison

Scenario understanding and motion prediction are essential components for completely replacing human drivers and for enabling highly and fully automated driving (SAE-Level 4/5). In deeply stochastic and uncertain traffic scenarios, autonomous driving software must act beyond existing traffic rules and must predict critical situations in advance to provide safe and comfortable rides. In addition, comprehensive prediction models intend not just to reproduce, but rather to encode the human driver behavior, which requires profound scenario understanding. Hence, research in the field of scenario understanding and motion prediction also contributes to enable intelligent driver behavior models in general. This paper aims to review the state of research and outline common methods. A classification of these models is proposed according to their underlying investigation methodology. Based on this classification, a comparison is drawn between three specific prediction methods, which considers specific functional aspects and general requirements of applicability. The results of the comparison reveal a trade-off between holism and explainability in the state of the art. In conclusion, suggestions for future research objectives to solve this conflict are proposed.
High Compliance to Mediterranean Diet Associates with Lower Platelet Activation and Liver Collagen Deposition in Patients with Nonalcoholic Fatty Liver Disease

The Mediterranean diet (Med-Diet) is considered the most effective dietary patterns to obtain weight loss in NAFLD patients. Previous evidence suggested that Med-Diet adherence could reduce cardiovascular risk and have a beneficial effect on NAFLD severity. Aim of the study was to investigate the relationship between Med-Diet adherence, platelet activation (PA), and liver collagen deposition. The study was performed in 655 consecutive NAFLD outpatients from the PLINIO study, a prospective observational cohort study aimed to identify non-conventional predictors of liver fibrosis progression in NAFLD. PA was measured by the serum thromboxane B2 (TxB2), and liver collagen deposition by N-terminal propeptide of type III collagen (Pro-C3). Adherence to the Med-diet was investigated by a short nine-item validated dietary questionnaire. Patients with high Med-Diet adherence were older and had less metabolic syndrome and lower serum triglycerides, GGT, TxB2, and Pro-C3. At multivariate regression analyses, in the linear model, the Med-Diet score negatively correlated with both TxB2 (Beta = −0.106; p = 0.009) and Pro-C3 (Beta = −0.121; p = 0.002) and in the logistic model high adherence inversely correlated with higher TxB2 tertiles (II tertile: OR = 0.576, p = 0.044; III tertile: OR = 0.556, p = 0.026) and Pro-C3 tertile (III tertile: OR = 0.488, p = 0.013). Low consumption of red meat inversely correlated with higher TxB2 tertile (II tertile: OR = 0.448, p < 0.001, III tertile: OR = 0.567, p = 0.004). In conclusion, NAFLD patients with high adherence to the Med-Diet show lower PA and liver collagen deposition, suggesting a protective role of the Med-Diet against NAFLD progression and cardiovascular risk. In addition, the correlation between TxB2 and Pro-C3 suggests a link between NAFLD severity and cardiovascular risk.
Exponential Stability and H∞ Performance Analysis for Spatially Interconnected Systems with Time-varying Delay

The problems of exponential stability and H∞ performance analysis for spatially interconnected continuous-time time-varying delay (SICTD) systems are considered in this paper. An appropriate Lyapunov-Krasovskii (L-K) functional is constructed for proving the exponential stability of SICTD system. A delay-dependent sufficient condition is then proposed to ensure the exponential stability and H∞ performance of the SICTD system. Meanwhile, we also obtain a sufficient condition ensuring the exponential stability and H∞ performance for the spatially interconnected systems (SISs) with constant time delay via the L-K method. A simulation of the vehicle platoon model is carried out to illustrate the validity of our deduced results.
A Hybrid Control Scheme In Gas Turbines

Gas turbines have been widely applicated in industry. In the control of gas turbines, an advanced control algorithm contributes to the stability, safety, and economic benefits. However, it is well known that transition state control methods in gas turbines are intractable to design. In this paper, a hybrid control scheme is constructed and utilized in transition state control of gas turbines. First, a component-level model of gas turbines is established via Toolbox for Modeling and Analysis of Thermodynamic Systems (T-MATS). Second, we introduce the notions of active disturbance rejection control (ADRC) and model predictive control (MPC). Based on the introduced control methods, a soft switch approach is constructed by combining the advantages of ADRC-PID controller and MPC. Finally, in order to demonstrate the effectiveness of the designed control scheme, comparisons between different control schemes and our proposed hybrid control scheme are given.
Coverage Enhancement Algorithm for WSNs Based on Vampire Bat and Improved Virtual Force

Coverage enhancement is an important strategy to avoid coverage holes caused by the random deployment of sensor nodes. Given the harsh physical environments of wireless sensor networks (WSNs), which impede the energy supplement and recovery of sensor nodes, the motivation of our research is to repair the coverage holes and reduce the energy consumption during the deployment of sensor nodes. Firstly, by using cellular grids to stack the monitoring area, a bipartite graph model between the nodes and the cellular grids is constructed to minimize and balance the moving distance during the redeployment. Secondly, the vampire bat algorithm is introduced for bipartite graph matching. Finally, the coverage effect and moving distance are further optimized based on the improved virtual force. The simulation results show that the coverage rate is increased by 5.43%, 3.25%, and 1.63%, and the moving distance is reduced by 33.64%, 3.66%, and 2.01% when compared with COSH, VF-IALO, and HA, respectively. In addition, the proposed algorithm shows good performance in terms of uniformity of moving distance and maximum moving distance.
FoG Recognition Based on Time-frequency Analysis and Convolutional Neural Network Combined with Attention Mechanism

Freezing of gait is one of the most common movement disorders in Parkinson’s disease. In this paper, with the use of attention mechanism, a neural network detection method is proposed for time-frequency data of FoG events. The acceleration signal collected by the IMU is firstly converted into a two-dimensional image representing the time-frequency information through continuous wavelet transform, and then sent to the convolutional neural network combined with the attention module for feature extracting and model training. Finally, the effectiveness is demonstrated with an accuracy of 91.37% and geometric mean of 88.73% on the test set.
An Empirical Risk and Return Analysis of Select Stocks in NASDAQ 100

Stock market indices are considered to be a powerful economic indicator. These indices can be classified based on the methodology of weight allocation for each stock and the rules governing the entry, retention and exit criteria of various stocks in the index. This paper presents a descriptive and an exploratory analysis carried out on the daily returns data of NASDAQ 100 (^NDX) index and shortlist of 20 stocks in the index. Random sampling was conducted at the sector level strata of all stocks that make up the index. This approach was followed to avoid selection bias and that stocks from the varied sectors are represented equally for this analysis. R-squared values and correlation coefficients were used to determine the explain-ability and relationship between the stock returns and the index returns respectively. The paper applied descriptive univariate analysis on daily returns at an individual stock level and at an aggregated sector level. Inter-relationship between stocks and the index returns was carried out by computing Pearson’s correlation coefficient across the different combinations of stocks and index return values. Linear regression was carried out identify the explain ability of the variance in the returns of from the index to the returns from the stocks. All analysis was carried out using the python and the stats-models library. As anticipated, the returns of randomly picked 20 stocks were able to explain ~85 % of the variance of the returns of index. One of the primary focus of the paper was to explore whether NASDAQ-100 index can explain the variability of the technology stocks relatively more than the stocks that belong to other sectors in its portfolio owing to the nature of most stocks that make up the index.
A Real-time Football Goalkeeper Robot System Based on Fuzzy Logic Control

This paper develops a football goalkeeper robot system that can entertain people or assist routine training in indoor or outdoor environments. The system uses an Intel RealSense D435 depth camera to provide image data and the yolov3-tiny Net to locate the football. In the process of coordinate transformation, the least square method was utilised to obtain the seven parameters, so that the error can be kept within an acceptable range (within the pre-specified range). Using the fuzzy logic control algorithm to predict the hitting points on the football baffle and PID algorithm to control motor rotation to prevent the football from entering crossbar. In the experiments, comparison between the fuzzy logic control algorithm and the Kalman filter algorithm was implemented. The results shows that the fuzzy logic control has a better performance.
Status of social capital and its relationship with the success rate of smoking cessation in patients referred to the smoking cessation clinic of Farshchian Cardiovascular Hospital in Hamadan

ABSTRACT Background and Objective Several studies showed the effect of social capital on smoking cessation. The present study examined this issue. Materials and Methods One hundred eleven smokers admitted to the smoking cessation clinic were consecutively included in a cross-sectional study in 2019. The data were collected using a checklist of demographic characteristics, nicotine dependency (Fagerström questionnaire) and social capital scale (Bullen). The success rate for smoking cessation was determined for smokers at 1-, 3- and 6-month intervals. Data were analyzed by SPSS 22 software. Result Of the 111 participants, 107 were males. The mean age of participants was 47.42 ± 12.66 years. The minimum score of social capital was 63 and the maximum was 172. Of all the patients admitted to the clinic, only 43 (38.7%) completed the cessation sessions (at least 4 sessions), with the mean score of social capital having no significant differences between the two groups (107.97 ± 17.93, 107.58 ± 19.95, respectively, p: NS) Conclusion Given the lack of a significant relationship between social capital and successful smoking cessation in the study, studying other factors like socioeconomic conditions and social norms that affect social capital and smoking cessation seems to be advisable to consider in future studies.
Mice with Targeted Knockout of Tetraspanin 3 Exhibit Reduced Trabecular Bone Mass Caused by Decreased Osteoblast Functions

Tetraspanin3 (TSPAN3) was identified as a binding partner of claudin11 (CLDN11) in osteoblasts and other cell types. Mice with targeted disruption of Cldn11 exhibited trabecular bone mass deficit caused by reduced bone formation and osteoblast function. To determine if the disruption of CLDN11 interacting protein gene Tspan3 results in a similar skeletal phenotype as that of Cldn11 knockout (KO) mice, we generated homozygous Tspan3 KO and heterozygous control mice and characterized their skeletal phenotypes at 13 weeks of age. Micro-CT measurements of the secondary spongiosa of the distal femur revealed 17% and 29% reduction in trabecular bone volume adjusted for tissue volume (BV/TV) in the male and female mice, respectively. Similarly, trabecular BV/TV of the proximal tibia was reduced by 19% and 20% in the male and female mice, respectively. The reduced trabecular bone mass was caused primarily by reduced trabecular thickness and number, and increased trabecular spacing. Consistent with the reduced bone formation as confirmed by histomorphometry analyses, serum alkaline phosphatase was reduced by 11% in the KO mice as compared with controls. Our findings indicate that TSPAN3 is an important positive regulator of osteoblast function and trabecular bone mass, and the interaction of TSPAN3 with CLDN11 could contribute in part to the bone forming effects of Cldn11 in mice.
Reference Database Expansion for Deep Belief Network based CSI Device-Free Localization

Device-free localization (DFL) based on Channel State Information (CSI) has received widespread attention for its non-invasiveness and easy deployment as a new indoor localization technology. The machine learning is one of the main research methods for DFL, while it constructs a fine-grained reference database laboriously. In this paper, we propose a CSI reference database expansion approach based on Kriging Interpolation to address the problem. Concretely, we collect CSI amplitude of specific locations, then estimate all sub-carriers information for other locations through Kriging Interpolation to expand database, and finally use Deep Belief Networks (DBN) as the localization model. The experiment is carried out in real environment. The experiment result verifies the proposed approach addresses the difficulty of insufficient database and collecting data for a long time efficiently, as well as improves the localization accuracy.
Assessment of Blood Biophysical Properties Using Pressure Sensing with Micropump and Microfluidic Comparator

To identify the biophysical properties of blood samples consistently, macroscopic pumps have been used to maintain constant flow rates in a microfluidic comparator. In this study, the bulk-sized and expensive pump is replaced with a cheap and portable micropump. A specific reference fluid (i.e., glycerin solution [40%]) with a small volume of red blood cell (RBC) (i.e., 1% volume fraction) as fluid tracers is supplied into the microfluidic comparator. An averaged velocity () obtained with micro-particle image velocimetry is converted into the flow rate of reference fluid (Qr) (i.e., Qr = CQ × Ac × , Ac: cross-sectional area, CQ = 1.156). Two control variables of the micropump (i.e., frequency: 400 Hz and volt: 150 au) are selected to guarantee a consistent flow rate (i.e., COV < 1%). Simultaneously, the blood sample is supplied into the microfluidic channel under specific flow patterns (i.e., constant, sinusoidal, and periodic on-off fashion). By monitoring the interface in the comparator as well as Qr, three biophysical properties (i.e., viscosity, junction pressure, and pressure-induced work) are obtained using analytical expressions derived with a discrete fluidic circuit model. According to the quantitative comparison results between the present method (i.e., micropump) and the previous method (i.e., syringe pump), the micropump provides consistent results when compared with the syringe pump. Thereafter, representative biophysical properties, including the RBC aggregation, are consistently obtained for specific blood samples prepared with dextran solutions ranging from 0 to 40 mg/mL. In conclusion, the present method could be considered as an effective method for quantifying the physical properties of blood samples, where the reference fluid is supplied with a cheap and portable micropump.
Online Learning based GA-BP Neural Network to Predict Ship Trajectory

Ship’s tracking prediction is an important part of ship navigation. By using neural network to predict ship’s track can avoid building complex mathematical model of ship motion. BP neural network is easy to fall into the trap of local error minimization, and it’s global search ability of genetic algorithm can overcome the shortcomings of BP neural network which easy to fall into local optimum. Experiment is using genetic algorithm to optimize the BP neural network on the number of hidden layer nodes, weights and thresholds. Finally, by training the neural network online learning method to real-time prediction of ship trajectory simulation experiment, through the contrast experiment, prove that GA-BP neural network online learning model has high precision by using the measured data and to verify the practicability and reliability of the GA-BP prediction model. This experimental result is helpful to the research of ship automatic control and comprehensive maneuvering.
Artificial Intelligence Applications on Restaging [18F]FDG PET/CT in Metastatic Colorectal Cancer: A Preliminary Report of Morpho-Functional Radiomics Classification for Prediction of Disease Outcome

The aim of this study was to investigate the application of [18F]FDG PET/CT images-based textural features analysis to propose radiomics models able to early predict disease progression (PD) and survival outcome in metastatic colorectal cancer (MCC) patients after first adjuvant therapy. For this purpose, 52 MCC patients who underwent [18F]FDGPET/CT during the disease restaging process after the first adjuvant therapy were analyzed. Follow-up data were recorded for a minimum of 12 months after PET/CT. Radiomics features from each avid lesion in PET and low-dose CT images were extracted. A hybrid descriptive-inferential method and the discriminant analysis (DA) were used for feature selection and for predictive model implementation, respectively. The performance of the features in predicting PD was performed for per-lesion analysis, per-patient analysis, and liver lesions analysis. All lesions were again considered to assess the diagnostic performance of the features in discriminating liver lesions. In predicting PD in the whole group of patients, on PET features radiomics analysis, among per-lesion analysis, only the GLZLM_GLNU feature was selected, while three features were selected from PET/CT images data set. The same features resulted more accurately by associating CT features with PET features (AUROC 65.22%). In per-patient analysis, three features for stand-alone PET images and one feature (i.e., HUKurtosis) for the PET/CT data set were selected. Focusing on liver metastasis, in per-lesion analysis, the same analysis recognized one PET feature (GLZLM_GLNU) from PET images and three features from PET/CT data set. Similarly, in liver lesions per-patient analysis, we found three PET features and a PET/CT feature (HUKurtosis). In discrimination of liver metastasis from the rest of the other lesions, optimal results of stand-alone PET imaging were found for one feature (SUVbwmin; AUROC 88.91%) and two features for merged PET/CT features analysis (AUROC 95.33%). In conclusion, our machine learning model on restaging [18F]FDGPET/CT was demonstrated to be feasible and potentially useful in the predictive evaluation of disease progression in MCC.