Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling the Heterodimer Interfaces of Melatonin Receptors

Melatonin receptors are Class A G protein-coupled receptors (GPCRs) that regulate a plethora of physiological activities in response to the rhythmic secretion of melatonin from the pineal gland. Melatonin is a key regulator in the control of circadian rhythm and has multiple functional roles in retinal physiology, memory, immunomodulation and tumorigenesis. The two subtypes of human melatonin receptors, termed MT1 and MT2, utilize overlapping signaling pathways although biased signaling properties have been reported in some cellular systems. With the emerging concept of GPCR dimerization, melatonin receptor heterodimers have been proposed to participate in system-biased signaling. Here, we used computational approaches to map the dimerization interfaces of known heterodimers of melatonin receptors, including MT1/MT2, MT1/GPR50, MT2/GPR50, and MT2/5-HT2C. By homology modeling and membrane protein docking analyses, we have identified putative preferred interface interactions within the different pairs of melatonin receptor dimers and provided plausible structural explanations for some of the unique pharmacological features of specific heterodimers previously reported. A thorough understanding of the molecular basis of melatonin receptor heterodimers may enable the development of new therapeutic approaches against aliments involving these heterodimeric receptors.
Immunomodulatory Effects of Perioperative Dexmedetomidine in Ovarian Cancer: An In Vitro and Xenograft Mouse Model Study

Background The surgical stress response (SSR) causes immunosuppression which may cause residual tumor growth and micrometastasis after cancer surgery. We investigated whether dexmedetomidine affects cancer cell behavior and immune function in an ovarian cancer xenograft mouse model. Methods The effect of dexmedetomidine on cell viability and cell cycle was assessed using SK-OV-3 cells at drug concentrations of 0.5, 0.1, 5, and 10 µg mL-1. BALB/c nude mice were used for the ovarian cancer model with the Dexmedetomidine group (n=6) undergoing surgery with dexmedetomidine infusion and the Control group (n=6) with saline infusion for 4 weeks. Natural killer (NK) cell activity, serum proinflammatory cytokines, and cortisol were measured at predetermined time points and tumor burden was assessed 4 weeks after surgery. Results Dexmedetomidine had no effect on cell viability or cell cycle. Following a sharp decrease on postoperative day (POD) 1, NK cell activity recovered faster in the Dexmedetomidine group with significant difference vs. the Control group on POD 3 (P=0.028). In the Dexmedetomidine group, cortisol levels were lower on POD 3 (P=0.004) and TNF-α levels were lower at 4 weeks after surgery (P<0.001) compared to the Control group. The Dexmedetomidine group showed lower tumor burden at 4 weeks vs. the Control group as observed by both tumor weight (P<0.001) and the in vivo imaging system (P=0.03). Conclusions Dexmedetomidine infusion may improve ovarian cancer surgery outcome by suppressing the SSR and stress mediator release. Further studies are needed to elucidate the mechanisms by which dexmedetomidine acts on cancer and immune cells.
Reduction of Pulmonary Hypertension After Transition to Sacubitril/Valsartan in Patients With Heart Failure With Preserved Ejection Fraction

Objectives: Although the PARAGON-HF trial failed to reach its primary endpoint, subgroups of patients with heart failure with preserved ejection fraction (HFpEF) still appear to benefit from Sacubitril/Valsartan therapy. As HFpEF patients with pulmonary hypertension display a specifically high mortality and morbidity, we evaluated the effect of Sacubitril/Valsartan in this subgroup of HFpEF patients. Methods: In this retrospective case-series of 18 patients with HFpEF and pulmonary hypertension, right heart catheterisation (RHC) for determination of invasive pulmonary pressure were performed at baseline (pre-Sacubitril/Valsartan) and 99 (71–156) days after transition from angiotensin-converting enzyme inhibitors and angiotensin receptor blockers to Sacubitril/Valsartan (post-Sacubitril/Valsartan). Results are given as median and interquartile range. Results: After conversion to Sacubitril/Valsartan, RHC showed significantly reduced pulmonary artery pressure (PAP) and mean pulmonary capillary wedge pressure (PCWP) compared to pre-Sacubitril/Valsartan [PAP systolic/diastolic/mean 44 (38–55)/15 (11–20)/27 (23–33) mm Hg vs. 51 (41–82)/22 (13–29)/33 (28–52) mm Hg, p < 0.05 and p < 0.01, respectively; PCWP 16 (12–20) mm Hg vs. 22 (15–27) mm Hg, p < 0.05]. Median Sacubitril/Valsartan dosage was 24/26 mg BID (24/26 BID−49/51 mg BID). Clinically, New York Heart Association functional class improved in 12 of the 18 patients (p < 0.01) after conversion to Sacubitril/Valsartan. Echocardiographic parameters of left ventricular function and cardiovascular co-medication did not differ markedly between pre- and post-Sacubitril/Valsartan. Conclusion: Sacubitril/Valsartan therapy is associated with an improvement of pulmonary hypertension in HFpEF patients.
Deep Reinforcement Learning-Based Backstepping Control of Air-Breathing Hypersonic Vehicles with Actuator Constraints

This paper investigates the backstepping control problem for air-breathing hypersonic vehicles (AHVs) using deep reinforcement learning (DRL). By utilising the prior knowledge of a widely-accepted AHV model, the proposed DRL-based controller has a high learning efficiency as well as keeps a simple structure. With the help of deep deterministic policy gradient, the intractable parameter adjustment in traditional backstepping designs for AHVs is well handled with full considerations on actuator magnitude and rate constraints. A Monte-Carlo simulation is provided to show the effectiveness of the proposed DRL-based control.
Defense Against Advanced Persistent Threats in Smart Grids: A Reinforcement Learning Approach

In smart girds, supervisory control and data acquisition (SCADA) systems have to protect data from advanced persistent threats (APTs), which exploit vulnerabilities of the power infrastructures to launch stealthy and targeted attacks. In this paper, we propose a reinforcement learning-based APT defense scheme for the control center to choose the detection interval and the number of Central Processing Units (CPUs) allocated to the data concentrators based on the data priority, the size of the collected meter data, the history detection delay, the previous number of allocated CPUs, and the size of the labeled compromised meter data without the knowledge of the attack interval and attack CPU allocation model. The proposed scheme combines deep learning and policy-gradient based actor-critic algorithm to accelerate the optimization speed at the control center, where an actor network uses the softmax distribution to choose the APT defense policy and the critic network updates the actor network weights to improve the computational performance. The advantage function is applied to reduce the variance of the policy gradient. Simulation results show that our proposed scheme has a performance gain over the benchmarks in terms of the detection delay, data protection level, and utility.
Elucidation of respiratory microbial communities and the feasibility of breath diagnostics based on microbial volatile organic compounds (VOCs) in adult cystic fibrosis patients

Ziel der vorliegenden Arbeit war es die mikrobiologischen Lebensgemeinschaften in den Atemwegen von adulten Patienten mit zystischer Fibrose (CF) zu analysieren. Daruber hinaus wurde zusatzlich ein diagnostisches Verfahren zur Atemluftanalytik entwickelt um mikrobielle Biomarker (fluchtige organische Verbindungen; VOCs) nachzuweisen. Die Durchfuhrbarkeit des Verfahrens wurde in der CF-Ambulanz getestet. Die molekularen Analysen identifizierten ein breites Spektrum an Mikroorganismen im Sputum und ermoglichten die Untersuchung der genauen Zusammensetzung der unterschiedlichen bakteriellen Gemeinschaften. Eine Korrelation zwischen Lungenfunktion und der relativen Haufigkeit einzelner Arten wurde nicht festgestellt. Jedoch konnten verschiedene, teilweise persistierende Untergruppen bakterieller Gemeinschaften voneinander abgegrenzt werden, jeweils klar definiert durch ihre individuelle Zusammensetzung. Bei den Pilzen lies sich eine unerwartet grose Artenvielfalt nachweisen. Deren starke Fluktuation uber die Zeit sowie zwischen verschiedenen Patienten deutet auf ein schwaches Kolonisierungspotential der Pilze hin. Fur die Atemanalytik wurden verschiedener Mikroorganismen zunachst in vitro anhand ihrer abgesonderten VOCs unterschieden. Obwohl in vivo jene Verbindungen nicht detektiert wurden, lies die individuelle Zusammensetzung der ausgeatmeten VOCs eine Differenzierung von Patienten und Kontrollen zu. In der vorliegenden Arbeit wurden zum ersten Mal die mikrobiologischen Gemeinschaften, einschlieslich Pilzen und Bakterien, in einer groseren Kohorte analysiert. Die Ergebnisse unterstutzen die Hypothese von polymikrobiellen Lungeninfektionen. Das Potential der Atemanalytik zur Unterscheidung verschiedener Krankheitserreger wurde nachgewiesen und konnte die Grundlage zur Entwicklung einer verbesserten Diagnostik bilden.
New Insights on Structures Forming the Lignin-Like Fractions of Ancestral Plants

In the present work, lignin-like fractions were isolated from several ancestral plants –including moss (Hypnum cupressiforme and Polytrichum commune), lycophyte (Selaginella kraussiana), horsetail (Equisetum palustre), fern (Nephrolepis cordifolia and Pteridium aquilinum), cycad (Cycas revoluta), and gnetophyte (Ephedra fragilis) species– and structurally characterized by pyrolysis-gas chromatography-mass spectrometry (Py-GC/MS) and two-dimensional nuclear magnetic resonance (2D-NMR) spectroscopy. Py-GC/MS yielded marker compounds characteristic of lignin units, except in the H. cupressiforme, P. commune and E. palustre “lignins,” where they were practically absent. Additional structural information on the other five samples was obtained from 2D-NMR experiments displaying intense correlations signals of guaiacyl (G) units in the fern and cycad lignins, along with smaller amounts of p-hydroxyphenyl (H) units. Interestingly, the lignins from the lycophyte S. kraussiana and the gnetophyte E. fragilis were not only composed of G- and H-lignin units but they also incorporated significant amounts of the syringyl (S) units characteristic of angiosperms, which appeared much later in plant evolution, most probably due to convergent evolution. The latter finding is also supported by the abundance of syringol derivatives after the Py-GC/MS analyses of these two samples. Regarding lignin structure, β−O−4′ alkyl-aryl ethers were the most abundant substructures, followed by condensed β−5′ phenylcoumarans and β−β′ resinols (and dibenzodioxocins in the fern and cycad lignins). The highest percentages of alkyl-aryl ether structures correlated with the higher S/G ratio in the S. Kraussiana and E. fragilis lignin-like fractions. More interestingly, apart from the typical monolignol-derived lignin units (H, G and S), other structures, assigned to flavonoid compounds never reported before in natural lignins (such as amentoflavone, apigenin, hypnogenol B, kaempferol, and naringenin), could also be identified in the HSQC spectra of all the lignin-like fractions analyzed. With this purpose, in vitro synthesized coniferyl-naringenin and coniferyl-apigenin dehydrogenation polymers were used as standards. These flavonoids were abundant in H. cupressiforme appearing as the only constituents of the moss lignin-like fraction (including 84% of dimeric hypnogenol B) and their abundance decreased in those of S. Kraussiana (with amentoflavone and naringenin representing 14% of the total aromatic units), and the two ancient gymnosperms (0.4–1.2%) and ferns (0–0.7%).
EEG Alpha and Beta Band Functional Connectivity and Network Structure Mark Hub Overload in Mild Cognitive Impairment During Memory Maintenance

Background: While decreased alpha and beta-band functional connectivity (FC) and changes in network topology have been reported in Alzheimer’s disease, it is not yet entirely known whether these differences can mark cognitive decline in the early stages of the disease. Our study aimed to analyze electroencephalography (EEG) FC and network differences in the alpha and beta frequency band during visuospatial memory maintenance between Mild Cognitive Impairment (MCI) patients and healthy elderly with subjective memory complaints. Methods: Functional connectivity and network structure of 17 MCI patients and 20 control participants were studied with 128-channel EEG during a visuospatial memory task with varying memory load. FC between EEG channels was measured by amplitude envelope correlation with leakage correction (AEC-c), while network analysis was performed by applying the Minimum Spanning Tree (MST) approach, which reconstructs the critical backbone of the original network. Results: Memory load (increasing number of to-be-learned items) enhanced the mean AEC-c in the control group in both frequency bands. In contrast to that, after an initial increase, the MCI group showed significantly (p < 0.05) diminished FC in the alpha band in the highest memory load condition, while in the beta band this modulation was absent. Moreover, mean alpha and beta AEC-c correlated significantly with the size of medial temporal lobe structures in the entire sample. The network analysis revealed increased maximum degree, betweenness centrality, and degree divergence, and decreased diameter and eccentricity in the MCI group compared to the control group in both frequency bands independently of the memory load. This suggests a rerouted network in the MCI group with a more centralized topology and a more unequal traffic load distribution. Conclusion: Alpha- and beta-band FC measured by AEC-c correlates with cognitive load-related modulation, with subtle medial temporal lobe atrophy, and with the disruption of hippocampal fiber integrity in the earliest stages of cognitive decline. The more integrated network topology of the MCI group is in line with the “hub overload and failure” framework and might be part of a compensatory mechanism or a consequence of neural disinhibition.
Functional Identification and Characterization of Leucokinin and Its Receptor in the Fall Webworm, Hyphantria cunea

Neuropeptides function as central neuromodulators and circulating hormones that modulate insect behavior and physiology. Leucokinin (LK) is an intercellular signaling molecule that mediates many physiological and behavioral processes. However, the functions of LK associated with environmental stress and feeding behavior in the fall webworm, Hyphantria cunea, is little known. Our primary objective is to understand the function of LK and LK receptor (LKR) neuroendocrine system in H. cunea. In the present study, the results showed that LK/LKR are expressed at different developmental stages and in various tissues of H. cunea. A candidate receptor–ligand pairing for LK was identified in the larval transcriptome of H. cunea. In a heterologous expression system, the calcium assay was used to demonstrate that LKR is activated by HcLKs in a dose-dependent manner, with 50% effective concentration (EC50) values of 8.44–90.44nM. Knockdown of HcLK and HcLKR by microinjecting target-specific dsRNA leads to several effects in H. cunea, including feeding promotion, increase in resistance to desiccation and starvation stress, and regulation of water homeostasis. The transcript levels of HILP2 (except in the LK knockdown group), HILP5, and HILP8 increased, whereas those of HILP3, HILP4, and HILP6 decreased; HILP1, HILP2 (in the LK knockdown group), and HILP7 gene expression was not influenced after LK and LKR knockdown. Variations in mRNA expression levels in insulin-like peptide genes in the knockdown larvae suggest an essential role of these genes in survival in H. cunea. To our knowledge, the present study is the first comprehensive study of LK and LKR – from gene to behavior – in H. cunea.
Incorporating Generalized Momentum Method to Accelerate Clustering Analysis of Complex Networks

Many complicated systems can be represented by complex networks. Their accurate clustering analysis plays a critical role in understanding their intrinsic organizations. An effective Fuzzy-based Clustering Algorithm for Networks (FCAN) has thus been developed. However, its major disadvantage is its slow convergence to optimal or near-optimal solutions. To overcome this problem, we make use of a generalized momentum method to accelerate it and accordingly propose a fast fuzzy clustering algorithm, namely F2 CAN. Experimental results on several practical datasets demonstrate that F2 CAN performed better than FCAN in terms of efficiency while maintaining the same-level accuracy. Hence, it is more promising to conduct an accurate and fast clustering analysis for complex networks.
Multi-Robot Formation Control And Implementation

This paper develops a distributed method for formation control of ground mobile robots via partial differential equations (PDEs)-based approach. The collective dynamics of a group of mobile robots is modeled by a reaction-advection-diffusion PDE. Based the model, we design a boundary control by using of the PDE backstepping method, which results in the leader-actuating formation control scheme with the leader robots locate on the boundary. In the control scheme, each follower robot only needs to obtain its own position and the relative position to its neighbors , which reduce the cost of the followers. We verify the effectiveness of the control scheme through the mobile robots experiment. In the experiment, we use LinkTrack UWB module to realize the positioning function adding the Kalman filter algorithm to improve the positioning accuracy and we use MPC to realize the movement control of the car's forward speed, lateral speed and angle.
Prediction method of dioxin emission concentration based on PCA and deep forest regression

Municipal solid waste incineration (MSWI) is a widely used technology for reducing, harmless and recycling municipal solid waste. However, dioxin (DXN), a highly toxic pollutant in the exhaust gas of MSWI process, is the main factor that causes the "NIMBY effect" in building incineration power plants. The existing DXN detection method by combining long-period online sampling and off-line testing cannot meet the requirement of direct optimization control of DXN. To solve this problem, the prediction model of DXN emission concentration based on easy-to-measure process variables has become a research hotspot. However, due to the high dimension of process variables and the complex mechanism of DXN generation, adsorption and emission, it is difficult to effectively select input features of DXN model. Therefore, this paper proposes a modeling method based on principal component analysis (PCA) and deep forest regression (DFR). At first, the dioxin emission characteristics of MSWI process are described. Then, a modeling strategy and algorithm including PCA dimension reduction and DFR modeling are proposed. Finally, the effectiveness of the proposed method is verified by using the Benchmark data set and the actual DXN data.
Electronic Components Detection for PCBA Based on A Tailored YOLOv3 Network with Image Pre-Processing

Printed Circuit Board Assembly (PCBA) plays an important role in 3C (Computer, Communication and Consumer Electronics) industry. The object detection of electronic components is the key technology for flaw inspection and quality control of PCBA. YOLOv3 has been widely used in object detection. Image pre-processing can eliminate some distractions and prominent some feature to improve the detection accuracy. The tailored YOLOv3 has a smaller computational cost and transfer learning can accelerate the training speed and improve inspection accuracy.
An adaptive tracking control of quadrotor UAV based on interference estimation

In order to solve the problems of external disturbance, parameter uncertainty and actuator failure during the flight of a quadrotor drone, a composite control strategy integrating nonlinear disturbance observer and adaptive sliding mode control is proposed. External disturbances, uncertain parameters and actuator failures can be compensated simultaneously in the proposed control strategy. Firstly, a nonlinear disturbance observer is designed to estimate the external disturbance and feed forward compensate it. Secondly, the sliding mode control parameters are adaptively adjusted through neural network online learning. And prove that the proposed control strategy can achieve stability. Finally, simulations were performed in Matlab to verify the effectiveness of the designed control strategy.
Condition Time Series Prediction of Aero-Engine Gas-Path Performance Based on Self-Attention Mechanism

The vital parameter condition prediction of aero-engine gas-path performance is increasingly becoming an important part of aero-engine fault diagnosis. The rapid and accurate parameter forecast plays a significant role in the early detection of the aero-engine faults. Comparing to the time series structure of traditional RNN or LSTM, the mechanism of self-attention and positional encoding approach are applied to avoid the long-term training process, as well as the training complexity of each layer is also improved and simplified. Moreover, the wavelet threshold denoising method is previously employed in data preprocessing, and then the Transformer model based on self-attention mechanism is constructed for time series prediction, and the prediction results are compared with the traditional regression algorithm. Finally, it is concluded that the model can accurately predict the development trend of aero-engine gas-path parameters. Therefore, the model is applied and validated in practice on site data, which implies the accuracy and efficiency of the approach. Transformer model based on self-attention mechanism has broad application prospects in the field of aero-engine parameter prediction.
Enhanced Recognition Algorithm for Facial Expressions under Deflection Angles

Facial expression recognition technology is significant to psychological analysis, polygraph technology, clinical medicine, and safe driving. However, traditional facial expression recognition technologies mainly recognize frontal facial expressions, and few studies focus on the recognition of facial expressions under deflection angles. In this situation, this paper combines Adaboost cascaded detectors, Ensemble of Regression Trees algorithm, and Random Forest algorithm to recognize facial expressions under deflection angles. After detecting faces by Adaboost cascaded detectors, the facial expression features under deflection angles are extracted by the Ensemble of Regression Trees algorithm. Finally, the Random Forest algorithm is used to output the recognition results based on expression features. Experiments show that the enhanced facial expression recognition algorithm proposed in this paper realizes the recognition of non-frontal facial expressions, and it has good real-time performance and recognition accuracy.
Classification of Travel Modes from Cellular Network Data Using Machine Learning Algorithms

Data availability in recent years has grown exponentially, allowing researchers in the transport sector to harness valuable information regarding traffic flows. In that sense, cellular network data represents valuable traffic information when dealing with spatially large areas due to its property of collecting route data using distant mobile base stations. This property enables the automatic collection of origin-destination data, which is traditionally collected using field or online questionnaires. This paper aims to present the possibility of using origin-destination data extracted from cellular network dataset to classify travel modes. A case study was performed on the dataset collected in the City of Rijeka, Croatia. Dataset is evaluated on five machine learning algorithms, which resulted in Random forest as the highest performing algorithm with an accuracy score of 99.93%.