Miracle-dz/UniFAR-FTUs huggingface dataset:
Natural Products from Actinomycetes Associated with Marine Organisms

The actinomycetes have proven to be a rich source of bioactive secondary metabolites and play a critical role in the development of pharmaceutical researches. With interactions of host organisms and having special ecological status, the actinomycetes associated with marine animals, marine plants, macroalgae, cyanobacteria, and lichens have more potential to produce active metabolites acting as chemical defenses to protect the host from predators as well as microbial infection. This review focuses on 536 secondary metabolites (SMs) from actinomycetes associated with these marine organisms covering the literature to mid-2021, which will highlight the taxonomic diversity of actinomycetes and the structural classes, biological activities of SMs. Among all the actinomycetes listed, members of Streptomyces (68%), Micromonospora (6%), and Nocardiopsis (3%) are dominant producers of secondary metabolites. Additionally, alkaloids (37%), polyketides (33%), and peptides (15%) comprise the largest proportion of natural products with mostly antimicrobial activity and cytotoxicity. Furthermore, the data analysis and clinical information of SMs have been summarized in this article, suggesting that some of these actinomycetes with multiple host organisms deserve more attention to their special ecological status and genetic factors.
Further Investigations of High-Order Ambisonics and Wavefield Synthesis for Holophonic Sound Imaging

Ambisonics and Wavefield Synthesis are two ways of rendering 3D audio, which both aim at physically reconstructing the sound field. Though they derive from distinct theoretical fundamentals, they have already been shown as equivalent under given assumptions. This paper further discusses their relationship by introducing new results regarding the coding and rendering of finite distance and enclosed sources. An updated view of the current knowledge is first given. A unified analysis of sound pickup and reproduction by mean of concentric transducer arrays then provides an insight into the spatial encoding and decoding properties. While merging the analysis tools of both techniques and investigating them on a common ground, general compromises are highlighted in terms of spatial aliasing, error and noise amplification.
PLL Control of Photovoltaic Power Grid System Based on DSP

It is important role that technique of PLL in the photovoltaic power generation system, which determine the electrical energy output of photovoltaic systems and networks important technical ability. PLL control of mathematical model is studied primarily by the phase detector, a loop filter, a voltage controlled oscillator model. For zero lock-in technique several times to reset the integrator causing lock system instability, designed by the counter effectively eliminates it. Finally, it is verified by improved simulation and experimental studies.
Verdinexor, a Selective Inhibitor of Nuclear Exportin 1, Inhibits the Proliferation and Migration of Esophageal Cancer via XPO1/c-Myc/FOSL1 Axis

Esophageal carcinoma (EC) ranks sixth among cancers in mortality worldwide and effective drugs to reduce EC incidence and mortality are lacking. To explore potential anti-esophageal cancer drugs, we conducted drug screening and discovered that verdinexor, a selective inhibitor of nuclear exportin 1 (XPO1/CRM1), has anti-esophageal cancer effects both in vivo and in vitro. However, the mechanism and role of verdinexor in esophageal cancer remain unknown. In the present study, we observed that verdinexor inhibited the proliferation and migration of EC cells in vitro and suppressed tumor growth in vivo. Additionally, we found that verdinexor induced cleavage of PARP and downregulated XPO1, c-Myc, and FOSL1 expression. RNA-sequence analysis and protein-protein interaction (PPI) analysis revealed that verdinexor regulated the XPO1/c-Myc/FOSL1 axis. The results of immunoprecipitation and proximity ligation assays confirmed that verdinexor disrupted the interaction between XPO1 and c-Myc. Overexpression of c-Myc rescued the inhibition of cell proliferation and cell migration caused by verdinexor. Overexpressed FOSL1 restored the inhibited migration by verdinexor. Taken together, verdinexor inhibited cell proliferation and migration of esophageal cancer via XPO1/c-Myc/FOSL1 axis. Our findings provide a new option for the development of anti-esophageal cancer drugs.
Effects of Grazing Intensity on Plant-Soil C: N: P Stoichiometry with Precipitation Changes During the Growing Season in Desert Grassland

Background and aimsHigh-intensity grazing in the Mongolian grassland has led to the general deterioration of biodiversity and ecosystem functioning. Although abundant evidence shows that grazing affects the structure and function of grassland ecosystems, research on the impact of precipitation, especially under drought and overgrazing. MethodsWe examined the effects of heavy grazing, moderate grazing and no grazing on plant communities; plant and soil C, N and P contents; and plant and soil C:N:P stoichiometry in the desert grassland in different years with different amounts of precipitation. ResultsThere was no significant difference in the species diversity between the grazing and no grazing treatment, while the no grazing treatment was significantly higher than the heavy grazing treatment. Compared with the amounts in the no grazing and moderate grazing treatments, the N and P contents of the plants in the heavy grazing treatment were the highest, and the N content of the soil increased. There was a positive correlation between precipitation and the N and P contents of plants and the C and N contents of the soil at 0-10 cm and 10-20 cm. ConclusionsOur study suggest that a large amount of precipitation of plant growth will drive changes in the community species diversity. Grazing promoted the flow of N between plants and the soil, especially under heavy grazing. Under grazing stress, plants maintain the potential of compensatory growth, and precipitation in the peak season of plant growth induces rapid growth, suggesting that precipitation is an important factor driving grazing ecosystems.
Research on Fast Target Positioning Method of Self-Calibration Manipulator

Hand-eye calibration and three-dimensional space target positioning are the keys to realize the automatic grasping of the manipulator. Aiming at the problems of a difficult camera manipulator calibration and poor real-time segmentation and positioning of stacked targets in industrial environment, a rapid target positioning method of self-calibration manipulator is proposed. Firstly, the spatial spherical autonomous path planning is carried out based on the quaternion linear interpolation method to calculate spatially symmetric path trajectory of the calibrator. The RGB-D camera mounted at the end of the manipulator is used to obtain multiple groups of RGB and depth images of the calibration plate. Combined with the position and attitude of the manipulator end, the internal and external parameters of the camera and the hand-eye conversion matrix are automatically calibrated. Then, based on KD tree algorithm, the holes in the point cloud are extracted to plan the shooting pose of the complementary image, and the target object is photographed from multiple symmetric angles. Combined with the iterative shooting pose of the manipulator, the rapid registration of the point cloud is realized and the complete outer surface model of the target object is obtained. Finally, the improved double pyramid feature fusion depth image is used to segment the RGB image through Mask R-CNN and mapped to the point cloud space, to achieve fast target segmentation of end-to-end 3D point cloud. The experimental results show that the eye-in-hand manipulator system can be self-calibrated to greatly simplify the calibration process and achieve the calibration accuracy of the traditional calibration method. The average error in each direction of the calibration result is less than 2 mm, which can achieve the acquisition accuracy of the vision sensor. It can also register and reconstruct point clouds for complex scenes in 1 s. The improved Mask-RCNN increases the segmentation accuracy of stacking objects by 8%. Compared with the physical error of hardware, the positioning error is no more than 0.89% and can meet the requirements of practical applications.
Antibody binding affinity ligands

The present application discloses a solid support material having covalently immobilized thereon an affinity ligand, said ligand comprising one or more hydrophobic functional group(s) and one or more cationic functional group(s) or one or more heteroaromatic functional group(s), wherein at least one hydrophobic functional group is separated from at least one cationic/heteroaromatic functional group by a through bond distance of from 5 A to 20 A wherein said ligand has a molecular weight of from 120 Da to 5,000 Da. Typically, the affinity resin has a binding capacity larger than 5 mg monoclonal antibody per mL of affinity resin. A method for the isolation of biomolecules, such as proteins, in particular antibodies, such as monoclonal antibodies, or derivatives thereof, is also disclosed.
Dynamics of a beam and a moving two-axle system with separation

The dynamics of a two-axle system travelling along an elastic beam is studied in this paper. A particular aspect that concerns the dynamics of the two structures is that the travelling structure can separate partially or totally from the supporting structure. This phenomenon may induce changes to the response of both structures. In this paper, the separation phenomenon that occurs for a two-axle system moving along a beam is considered. Its influence on the dynamic response of the beam is demonstrated by numerical examples that show the influence of certain parameters on the separation of the travelling structure from the beam. It can be seen that separation may take place at moderate speeds.
Chronobiology, the Future of Medicine

Chronobiology, the study of biological rhythms, has made a significant contribution to the development of medicine in recent years. It is now clear that the circadian clock not only affects the body, but also has a significant impact on the mind and behavior. The discovery of the molecular basis of the circadian rhythm by Jeffrey Hall, Michael Rosbash, and Michael Young was a seminal contribution to the field of medicine and was recognized by the Nobel Prize in Physiology or Medicine, 2017 [1]. Chronobiology finds itself at the epicenter of future medical advances. In the past, clinical medicine did not pay much attention to the circadian rhythm as related to the human body and mind. Diagnoses were made based on the physical examination, laboratory tests, or imaging tests at cross-sectional time points. There was an underlying assumption that the test performed at a certain time did not yield results different from the test conducted at other times. However, it soon became apparent that the test results may vary depending on the time of day. In addition, it was also seen that disruption or misalignment of the circadian rhythm may play an important role in the pathogenesis and treatment of diseases. Circadian clock is an internal timekeeping mechanism that controls most of the body’s functions. The suprachiasmatic nucleus is the master regulator of biological clock that regulates the function of various organs such as brain, liver, kidney, and heart, to name a few. Disruption of circadian clock either due to shift work, jet lag, or severely delayed sleep phase significantly increases the risk of developing various diseases such as metabolic disorders, cardiovascular diseases, cancer, neurodegenerative disorders, and mental disorders [2-4]. This suggests that circadian clock-controlled actions play essential roles in human physiology and behavior. The liver as an independent circadian oscillator, possibly entrained by food timing. There is a vast need for additional research in this area, especially as it impacts shift work. Since disruption in the biological clock is associated with a variety of human diseases, restoring the circadian clock can help with the treatment or prevention of several diseases. Restoration of circadian clock has been shown to improve the clinical symptoms of Chronobiology, the Future of Medicine
Characterization of diagnostic and prognostic significance of cell cycle-linked genes in hepatocellular carcinoma

Background The high degree of heterogeneity of hepatocellular carcinoma (HCC) imposes a significant challenge to predict the prognosis. Currently, increasing evidence has indicated that cell cycle-linked genes are strongly linked to occurrence and progress of HCC. Herein, we purposed to create a prediction model on the basis of cell cycle-linked genes. Methods The transcriptome along with clinicopathological data abstracted from The Cancer Genome Atlas (TCGA) were used as a training cohort. Lasso regression analysis was employed to create a prediction model in TCGA cohort. The data of samples obtained from the International Cancer Genome Consortium (ICGC) data resource were applied in the verification of the model. A series of bioinformatics analyzed the relationship of the risk signature with overall survival (OS), biological function, and clinicopathological features. Results Six cell cycle-linked genes (PLK1, CDC20, HSP90AA1, CHEK1, HDAC1, and NDC80) were chosen to create the prognostic model, demonstrating a good prognostic capacity. Further analyses indicated that the model could independently assess the OS of HCC patients. A single-sample gene set enrichment analysis (ssGSEA) indicated that the risk signature was remarkably linked to immune status. Additionally, there was a remarkable association of the risk signature with TP53 mutation frequency, as well as immune checkpoint molecule expression levels. Conclusions We created a prediction model using six cell cycle-linked genes to predict HCC prognosis. The six genes are expected to be novel markers for HCC diagnosis, as well as treatment.
Influence of Magnetron Sputtering and Annealing Treatment on the Organizational Properties of ZnO/SiO2 Composite Films

ZnO/SiO2 composite films were deposited on Si substrate by RF magnetron sputtering technique and annealed in air . The influence of sputtering and annealing treatment conditions on the composition,the surface morphology and the wettability of the composite films was investigated by XRD, EDS, SEM and contact angle measurement. The results show that ZnO, SiO2 and Zn2SiO4 are main phases in the composite films existing by hexagonal wurtzite structure ,amorphous structure and willemite structure respectively. In this experimental conditions we get superior crystallization property of ZnO nanometer tissue with hexagonal wurtzite structure. The tissue shows bamboo leaf-like shape.The composite films has super hydrophobic,the contact angle is 146°.
Actuation stress modelling of piezoceramic transducers under variable temperature field

The article presents a two-dimensional temperature-dependent model of piezoceramic transducers used for Lamb wave–based damage detection applications. The effect of temperature on shear stress and normal stress components influencing wave actuation is analysed. Three models are analysed and compared, that is, static theoretical model and two numerical models, that is, static and dynamic. These models are analysed for two different piezoceramic transducers’ thicknesses. The results demonstrate that the effective transducer’s length is smaller than its physical in-plane dimension. The influence of the normal actuation stress component is significant for wave actuation. This component is dependent on temperature and transducer’s dimension.
Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization.

Abstract : This thesis incorporates a mixed discrete/continuous parameter genetic algorithm optimization capability into the Design Optimization/Markov Evaluation (DOME) program developed by the Charles Stark Draper Laboratory of Cambridge, Massachusetts. DOME combines the merits of Markov modeling and the Optimal Design Process to generate a systematic framework for system design with realistic reliability and cost analyses. The addition of genetic algorithms expands the design problem domain to include discrete parameter problems, which current optimization methods continue to struggle with. A new variant of the genetic algorithm called the steady-state genetic algorithm is introduced to eliminate the idea of distinct generations. Functional constraints are dealt with by ingenious use of the function information contained in the genetic algorithm population. The optimal genetic algorithm parameter settings are investigated, and the genetic algorithm is compared to the Monte Carlo method and the Branch and Bound method to show its relative utility in optimization. This research shows that a single criterion genetic algorithm can be expected to outperform other methods in efficiency, accuracy, and speed on problems of moderate to high complexity. The work then extends to multicriteria optimization, as applied to fault tolerant system design. A multicriteria genetic algorithm is created as a competitive means of generating the efficient (Pareto) set. Method parameters such as cloning, sharing, domination pressure, and population variability are investigated. The method is compared to the epsilon-constraint multi criteria method with a steady-state genetic algorithm performing the underlying single-criterion optimization.
Supporting Co-Design of Physical and Control Architectures of Mechatronic Systems

There is a rather recent tendency to define the physical structure and the control structure of a system concurrently when designing the architecture of a product, i.e., to perform codesign. We argue that co-design can only be enabled when the mutual influence between physical system and control is made evident to the designer at an early stage. Though the idea of design integration is not new, to the best of our knowledge, there is no computer tooling that explicitly supports this activity by enabling co-design as stated before. In this paper the authors propose a method for co-design of physical and control architectures as a better approach to design mechatronic systems, allowing to exploit the synergy between software and hardware and detecting certain design problems at an early stage of design. The proposed approach is supported by a set of tools and demonstrated through an example case.Copyright © 2011 by ASME
Support vector machine for hand geometry-based identity verification system

A new approach to classifying the hand geometry features for personal verification is presented. This paper attempts to improve the performance of hand geometry-based systems by applying the Support Vector Machine (SVM) to the template classification task. We also compare the SVM-based approach to vector distance-based and neural network-based approaches as well as to other systems described in the literature and to the popular commercial systems. The testing results show that our system is competitive to the other known solutions.
An Enhanced Pedestrian Visual-Inertial SLAM System Aided with Vanishing Point in Indoor Environments

The visual-inertial simultaneous localization and mapping (SLAM) is a feasible indoor positioning system that combines the visual SLAM with inertial navigation. There are accumulated drift errors in inertial navigation due to the state propagation and the bias of the inertial measurement unit (IMU) sensor. The visual-inertial SLAM can correct the drift errors via loop detection and local pose optimization. However, if the trajectory is not a closed loop, the drift error might not be significantly reduced. This paper presents a novel pedestrian dead reckoning (PDR)-aided visual-inertial SLAM, taking advantage of the enhanced vanishing point (VP) observation. The VP is integrated into the visual-inertial SLAM as an external observation without drift error to correct the system drift error. Additionally, the estimated trajectory’s scale is affected by the IMU measurement errors in visual-inertial SLAM. Pedestrian dead reckoning (PDR) velocity is employed to constrain the double integration result of acceleration measurement from the IMU. Furthermore, to enhance the proposed system’s robustness and the positioning accuracy, the local optimization based on the sliding window and the global optimization based on the segmentation window are proposed. A series of experiments are conducted using the public ADVIO dataset and a self-collected dataset to compare the proposed system with the visual-inertial SLAM. Finally, the results demonstrate that the proposed optimization method can effectively correct the accumulated drift error in the proposed visual-inertial SLAM system.
Multi-Robot Routing Problem with Min-Max Objective

In this paper, we study the “Multi-Robot Routing problem” with min–max objective (MRR-MM) in detail. It involves the assignment of sequentially ordered tasks to robots such that the maximum cost of the slowest robot is minimized. The problem description, the different types of formulations, and the methods used across various research communities are discussed in this paper. We propose a new problem formulation by treating this problem as a bipartite graph with a permutation matrix to solve it. A comparative study is done between three methods: Stochastic simulated annealing, deterministic mean-field annealing, and a heuristic-based graph search method. Each method is investigated in detail with several data sets (simulation and real-world), and the results are analysed and compared with respect to scalability, computational complexity, optimality, and its application to real-world scenarios. The paper shows that the heuristic method produces results very quickly with good scalability. However, the solution quality is sub-optimal. On the other hand, when optimal or near-optimal results are required with considerable computational resources, the simulated annealing method proves to be more efficient. However, the results show that the optimal choice of algorithm depends on the dataset size and the available computational budget. The contribution of the paper is three-fold: We study the MRR-MM problem in detail across various research communities. This study also shows the lack of inter-research terminology that has led to different names for the same problem. Secondly, formulating the task allocation problem as a permutation matrix formulation (bipartite graph) has opened up new approaches to solve this problem. Thirdly, we applied our problem formulation to three different methods and conducted a detailed comparative study using real-world and simulation data.