Complex Manipulation Research Articles

The ‘Oma’s of the Gammas—Cancerogenesis by γ-Herpesviruses

Epstein–Barr virus (EBV) and Kaposi’s sarcoma-associated herpesvirus (KSHV), which are the only members of the gamma(γ) herpesviruses, are oncogenic viruses that significantly contribute to the development of various human cancers, such as Burkitt’s lymphoma, nasopharyngeal carcinoma, Hodgkin’s lymphoma, Kaposi’s sarcoma, and primary effusion lymphoma. Oncogenesis triggered by γ-herpesviruses involves complex interactions between viral genetics, host cellular mechanisms, and immune evasion strategies. At the genetic level, crucial viral oncogenes participate in the disruption of cell signaling, leading to uncontrolled proliferation and inhibition of apoptosis. These viral proteins can modulate several cellular pathways, including the NF-κB and JAK/STAT pathways, which play essential roles in cell survival and inflammation. Epigenetic modifications further contribute to EBV- and KSHV-mediated cancerogenesis. Both EBV and KSHV manipulate host cell DNA methylation, histone modification, and chromatin remodeling, the interplay of which contribute to the elevation of oncogene expression and the silencing of the tumor suppressor genes. Immune factors also play a pivotal role in the development of cancer. The γ-herpesviruses have evolved intricate immune evasion strategies, including the manipulation of the major histocompatibility complex (MHC) and the release of cytokines, allowing infected cells to evade immune detection and destruction. In addition, a compromised immune system, such as in HIV/AIDS patients, significantly increases the risk of cancers associated with EBV and KSHV. This review aims to provide a comprehensive overview of the genetic, epigenetic, and immune mechanisms by which γ-herpesviruses drive cancerogenesis, highlighting key molecular pathways and potential therapeutic targets.

Misalignment of Biostatistics Content Between Licensing Exam Study Aids and Contemporary Medical Research

Background and Objectives: Medical trainees express difficulty with interpreting statistics in clinical literature. To elucidate educational gaps, we compared statistical methodologies in biomedical literature with biostatistical content in licensing exam study materials. Methods: In this bibliographic content analysis, we compiled a stratified random sample of articles involving original data analysis published during 2023 in 72 issues of three major medical journals. We recorded all discrete statistical methods and concepts detailed in the methods section of the articles and in three commercial licensing exam study resources. We created a unified list of discrete methods or concepts to define overarching domains and mapped each method to a domain to determine that domain’s presence in each resource or article. Results: In a sample of 273 journal articles and three study resources, we identified 1,057 unique key words mapped onto 20 domains. Statistical error, significance, power analysis, and group comparisons of categorical data were high-frequency domains among the articles. Overall, 63% of articles included methods from domains not covered in any study resource. Conclusions: Medical licensing exam preparation does not reflect the breadth of contemporary statistics in biomedical research. Future interventions should expand medical students’ understanding of research protocols and complex data manipulation.

A connectome manipulation framework for the systematic and reproducible study of structure–function relationships through simulations

Abstract Synaptic connectivity at the neuronal level is characterized by highly non-random features. Hypotheses about their role can be developed by correlating structural metrics to functional features. But to prove causation, manipulations of connectivity would have to be studied. However, the fine-grained scale at which non-random trends are expressed makes this approach challenging to pursue experimentally. Simulations of neuronal networks provide an alternative route to study arbitrarily complex manipulations in morphologically and biophysically detailed models. Here, we present Connectome-Manipulator, a Python framework for rapid connectome manipulations of large-scale network models in SONATA format. In addition to creating or manipulating the connectome of a model, it provides tools to fit parameters of stochastic connectivity models against existing connectomes. This enables rapid replacement of any existing connectome with equivalent connectomes at different levels of complexity, or transplantation of connectivity features from one connectome to another, for systematic study. We employed the framework in a detailed model of rat somatosensory cortex in two exemplary use cases: transplanting interneuron connectivity trends from electron microscopy data and creating simplified connectomes of excitatory connectivity. We ran a series of network simulations and found diverse shifts in the activity of individual neuron populations causally linked to these manipulations.

Unveiling the potential: implications of successful somatic cell-to-ganglion organoid reprogramming.

Organoids have a wide range of potential applications in areas such as organ development, precision medicine, regenerative medicine, drug screening, disease modeling, and gene editing. Currently, most organoids are generated through three-dimensional (3D) in vitro culture of adult stem cells or pluripotent stem cells. However, this method of generating organoids still has several limitations and challenges, including complex manipulations, costly culturing materials, extended time requirements, and certain heterogeneity. Recently, we have found that fibroblasts, when overexpressing several key regulatory transcription factors, are able to directly and rapidly generate two types of ganglion organoids: sensory ganglion (SG) and autonomic ganglion (AG) organoids. They have structures and electrophysiological properties similar to those of endogenous organs in the body. Here, we provide a brief overview of organoid development, focusing on direct reprogramming of SG and AG organoids and their transplantation and regeneration. Finally, the advantages and prospects of direct reprogramming of organoids are discussed.

Anomaly Pattern Detection in High-Frequency Trading Using Graph Neural Networks

This paper presents a new method for detecting abnormal patterns in high-frequency trading (HFT) using graph neural networks (GNNs). The increasing sophistication of trading algorithms and the large volume of data have often created unprecedented challenges for traditional market analysis. Our framework addresses these challenges by introducing a GNN-based architecture that takes advantage of the physical and structural properties of business data. The proposed method transforms HFT data into graphical models where the nodes represent market conditions and the edges capture their physical and price relationships. A specialized GNN architecture, incorporating attention mechanisms and temporal convolution modules, is developed to learn complex trading patterns and identify potential anomalies. The model is evaluated on high-frequency trading data from five major stocks listed on NASDAQ, spanning six months of trading activity with over 10 million events. Experimental results demonstrate superior performance compared to existing approaches, achieving a 15% improvement in detection accuracy and maintaining robust performance across different market conditions. The framework exhibits particular strength in identifying complex manipulation patterns while maintaining low false positive rates. Our approach processes large volumes of trading data in real time with significantly reduced computational requirements compared to traditional methods. This research contributes to the development of more effective market surveillance systems and provides valuable insights for regulatory authorities in maintaining market integrity.

Nonlinear Parameter Identification of the Franka Emika PANDA Robot

This research addresses the problem of dynamic parameter identification for robot manipulators. As the complex manipulation of tasks increases, traditional control methods become insufficient, necessitating accurate model-based control. Previous studies have explored various parameter identification methods for robot manipulators. Still, the impact of different friction models on dynamic parameter identification, particularly for the Franka Emika PANDA robot, is yet to be comprehensively investigated. The linear and nonlinear parameter identification methods and nonlinear friction models (Lugre, Stribeck, and Sigmoidal) were studied on the PANDA robot. A linear inverse dynamic model was developed using the Newton-Euler method. Identification and validation trajectories were designed. Nonlinear constraints and bounds were applied for easier convergence, ensuring a positive definite inertia tensor in the center of mass (COM) frame. Using constrained least squares with Lasso regularisation, model-estimated torques comparison to measurements was done to estimate the physical parameters, considering the PANDA robot’s hand as a payload. We obtained the PANDA robot’s physical parameters, including the hand’s mass, without a friction model where discrepancies in joint torques were observed. Incorporating friction models reduced these discrepancies, validated by another trajectory comparison between the measured and the model’s predicted joint torques. Stribeck friction model exhibited the best performance in estimating parameters from its Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), indicating its effectiveness in capturing nonlinearities in the PANDA robot.

Alkynyl Ligands Templated Assemblies of Silver Nanoclusters with Exceptional Electrochemiluminescence Activity for Pancreatic Cancer Specific tsRNAs Measurement.

Proper manipulation of the ligand complex on the motifs of metal nanoclusters (MNCs) to form an ordered self-assembly is an effective approach to enhance the electrochemiluminescence (ECL) emission of MNCs. We report a facile approach for the preparation of self-assembled AgNCs (AgNCsAssy) induced by alkynyl ligands with enhanced ECL and stability. The formation of these AgNCsAssy was simultaneously driven by the diverse coordination modes of alkynyl ligands with Ag and intercluster interactions, for which it was found that the para-substituted alkynyl ligands exhibited apparently irregular nanoparticles, while the monosubstituted counterparts were present in the form of ribbons. The calculations revealed that the energy gap between the highest occupied molecular orbitals (HOMOs) and the lowest unoccupied molecular orbitals (LUMOs) played a crucial role in their ECL emissions because of the substituent effects, especially, the low-lying LUMO levels could help to enhance the ECL emission. Moreover, mechanistic studies revealed that both the coreactant and alkynyl ligands made significant contributions to the ECL performance. Concurrently, the CRISPR-associated proteins (CRISPR-Cas) 12a system shows great potential in biosensing applications due to the advantages of easy design and precise targeting. As a proof of concept, we integrated the cascade amplification of catalytic hairpin assembly (CHA) circuit and the collateral cleavage activity of CRISPR-Cas12a to construct an ultrasensitive ECL biosensor for pancreatic cancer (PC)-specific tsRNAs, with a detection limit of 3.33 fM. This work is not only instructive for the synthesis of self-assembled MNCs with high ECL activities but also contributes to the understanding of the ECL mechanism of self-assembled MNCs.

Thermo-responsive microparticle swimmer as a remote tool for cell delivery in tissue engineering

Developing biocompatible and flexible cell manipulation tools is crucial for effective cell delivery in tissue engineering. Herein, thermo-responsive microparticle swimmers, composed of an alginate core and a rough gelatin surface, are developed as a tool for remote cell manipulation and the construction of patterned cell models. The microparticles are fabricated using a novel bio-friendly surface assembly strategy, which leverages morphology-induced gelatin adsorption combined with temperature-cycling crosslinking to deposit multiple gelatin layers onto microfluidic-derived rough microparticles. Gelatin molecules can adsorb onto the rough surface at 25 °C and become cross-linked at 4 °C to form a stable coating layer while preserving the coarse morphology for additional assembly cycles. The gelatin layers remain intact at room temperature for cell loading and transport, but rapidly dissipate at 37 °C, enabling cell release. Moreover, complex manipulations of living cell populations, such as remote tweezing, can be achieved by the introduction of magneto-responsive moieties. These versatile microparticles provide an efficient tool for the in vitro construction of flexible and dynamic patterned cell models, with promising applications in tissue engineering, drug screening, and organoid development.

Analysis of Splicing Manipulation in Digital Images using Dyadic Wavelet Transform (DyWT) and Scale Invariant Feature Transform (SIFT) Methods

In the digital age, image manipulation is common, often done before publication on social media. However, this can lead to negative impacts, including visual deception. This research aims to detect splicing type image manipulation using Dyadic Wavelet Transform (DyWT) and Scale Invariant Feature Transform (SIFT) methods. The process starts with image decomposition using DyWT to obtain LL sub-images, followed by local feature extraction using SIFT. An application built on desktop-based Matlab source was developed to detect splicing forgery in digital images. The test used 20 images, this image dataset was taken from canon 5d mark II camera and Vivo X80 mobile phone. Each 10 original images, and 10 edited images. These 10 original images are left as they are without making changes, editing or manipulation, while the other 10 images are changed, edited or manipulated using editing software, the results of this editing are uploaded to social media, such as Facebook and Instagram, which will later be used as datasets in testing. The results show that the splicing technique is detected accurately, and processing is faster on images with low pixel resolution. The DyWT and SIFT methods are effective in detecting post-processing attacks such as rotation and rescaling, although they have drawbacks. DyWT struggles in detecting subtle changes and noise, while SIFT is less effective on non-geometric manipulations. Overall, both methods face challenges in detecting complex manipulations and require significant computational resources, especially on high-resolution images.

Konnektor: A Framework for Using Graph Theory to Plan Networks for Free Energy Calculations.

Alchemical free energy campaigns can be planned using graph theory by building networks that contain nodes representing molecules that are connected by possible transformations as edges. We introduce Konnektor, an open-source Python package, for systematically planning, modifying, and analyzing free energy calculation networks. Konnektor is designed to aid in the drug discovery process by enabling users to easily setup free energy campaigns using complex graph manipulation methods. The package contains functions for network operations including concatenation of networks, deletion of transformations, and clustering of molecules along with a framework for combining these tools with existing network generation algorithms to enable the development of more complex methods for network generation. A comparison of the various network layout features offered is carried out using toy data sets. Additionally, Konnektor contains visualization and analysis tools, making the investigation of network features much simpler. Besides the content of the package, the paper also offers application examples, demonstrating how Konnektor can be used and how the different networks perform from a graph theory perspective. Konnektor is freely available via GitHub at https://github.com/OpenFreeEnergy/konnektor under the permissive MIT License.

Robot skill acquisition for precision assembly of flexible flat cable with force control

Abstract The flexible flat cable (FFC) assembly task is a prime challenge in electronic manufacturing. Its characteristics of being prone to deformation under external force, tiny assembly tolerance, and fragility impede the application of robotic assembly in this field. To achieve reliable and stable robotic automation assembly of FFC, an efficient assembly skill acquisition strategy is presented by combining a parallel robot skill learning algorithm with adaptive impedance control. The parallel robot skill learning algorithm is proposed to enhance the efficiency of FFC assembly skill acquisition, which reduces the risk of damaging FFC and tackles the uncertain influence resulting from deformation during the assembly process. Moreover, FFC assembly is also a complex contact-rich manipulation task. An adaptive impedance controller is designed to implement force tracking during the assembly process without precise environment information, and the stability is also analyzed based on the Lyapunov function. Experiments of FFC assembly are conducted to illustrate the efficiency of the proposed method. The experimental results demonstrate that the proposed method is robust and efficient.

Deep learning-based high-frequency jump test for detecting stock market manipulation: evidence from China’s securities market

PurposeThis study aims to tackle the critical issue of detecting stock market manipulation, which undermines the integrity and stability of financial markets globally. Even enhanced with machine learning, traditional statistical methods often struggle to analyze high-frequency trading data effectively due to inherent noise and the limited availability of publicly known manipulation cases. This leads to poor model generalization and a tendency toward over-fitting. Focusing on China's securities market, our study introduces an innovative approach that employs deep learning-based high-frequency jump tests to overcome these challenges and to develop a more effective method for identifying manipulative activities.Design/methodology/approachWe employed the “Jump Variation – Time-of-Day” (JV-TOD) non-parametric technique for jump tests on high-frequency data, coupled with the synthetic minority over-sampling technique (SMOTE) algorithm for re-balancing sample data. Our approach trains a deep neural network (DNN) on refined data to enhance its ability to identify manipulation patterns accurately.FindingsOur results show that the deep neural network model, calibrated with high-frequency price jump data, identifies manipulation behavior more specifically and accurately than traditional models. The model achieved an accuracy rate of 94.64%, an F1-score of 95.26% and a recall rate of 95.88%, significantly outperforming traditional models. These results demonstrate the effectiveness of our approach in mitigating over-fitting and improving the robustness of market manipulation detection.Practical implicationsThe proposed model provides regulatory entities and financial institutions with a more efficient tool to monitor and counteract market manipulation, thereby improving market fairness and investor protection.Originality/valueBy integrating the JV-TOD jump test with deep learning, this study proposed a new approach to market manipulation detection. The innovation is in its capacity to detect subtle manipulation signals that traditional methods typically overlook. Our model, which is trained on jump test data enhanced by the SMOTE algorithm, excels at learning complex manipulation patterns. This enhances both detection accuracy and robustness. In contrast to existing methods that are challenged by the noisy and intricate nature of high-frequency data, our approach shows enhanced performance in identifying nuanced market manipulations, offering a more effective and reliable method for detecting market manipulation.

Interactions between task complexity, task repetition, and task motivation in L2 writing

This study explores the interplay between task repetition and task motivation in second language (L2) writing, specifically focusing on syntactic complexity, accuracy, and lexical complexity (CAL). Aligning with Dörnyei’s call to balance cognitive and affective factors in task-based language teaching (TBLT), the research involved 100 advanced mid-level learners of English as a second language (ESL) aged between 18 and 20 years. Participants completed both simple and complex argumentative writing tasks in a counterbalanced order, with a one-week interval between tasks. A perception questionnaire was administered immediately after each task performance to validate cognitive task complexity manipulations. Subsequently, participants repeated the same tasks with the same time interval, complemented by the completion of a task motivation questionnaire after each task. Written essays were collected and analysed for CAL measures. Employing a multivariate multilevel approach, the results were triangulated with self-report data. Findings indicate that task repetition significantly impacted CAL in L2 writing, with a positive moderation effect by task complexity. Additionally, task motivation enhanced syntactic complexity in both simple and complex tasks, with negligible effects on accuracy. Notably, task motivation exerted a more substantial influence on students’ repeated task performance concerning lexical complexity. The study’s results offer theoretical and pedagogical insights for TBLT researchers and L2 writing practitioners.

Joint subarray acoustic tweezers enable controllable cell translation, rotation, and deformation

Contactless microscale tweezers are highly effective tools for manipulating, patterning, and assembling bioparticles. However, current tweezers are limited in their ability to comprehensively manipulate bioparticles, providing only partial control over the six fundamental motions (three translational and three rotational motions). This study presents a joint subarray acoustic tweezers platform that leverages acoustic radiation force and viscous torque to control the six fundamental motions of single bioparticles. This breakthrough is significant as our manipulation mechanism allows for controlling the three translational and three rotational motions of single cells, as well as enabling complex manipulation that combines controlled translational and rotational motions. Moreover, our tweezers can gradually increase the load on an acoustically trapped cell to achieve controllable cell deformation critical for characterizing cell mechanical properties. Furthermore, our platform allows for three-dimensional (3D) imaging of bioparticles without using complex confocal microscopy by rotating bioparticles with acoustic tweezers and taking images of each orientation using a standard microscope. With these capabilities, we anticipate the JSAT platform to play a pivotal role in various applications, including 3D imaging, tissue engineering, disease diagnostics, and drug testing.

ИСКУССТВЕННЫЙ ИНТЕЛЛЕКТ В ОБРАЗОВАНИИ

The article is devoted to the problem of using robots in the educational process in the training of specialists. The article describes training robots based on the Robot Operating System, i.e. based on a set of software libraries and tools that help create applications for robots. Robot Integration with ROS (Robot Operating System) is the process of combining the hardware and software components of a robot with the ROS infrastructure. This allows the robot to interact with other devices and systems, as well as use a variety of ready-made tools and libraries available within the ecosystem. Robot Integration with ROS (Robot Operating System) is the process of combining the hardware and software components of a robot with the ROS infrastructure. This allows the robot to interact with other devices and systems, as well as use a variety of ready-made tools and libraries available within the ecosystem. Project Development: Using ROS, students can develop their own robotics projects, ranging from simple mobile robots for indoor navigation to complex manipulators for performing tasks in industry. Project Development: Using ROS, students can develop their own robotics projects, ranging from simple mobile robots for indoor navigation to complex manipulators for performing tasks in industry.

Optimizing phage-based mutant recovery and minimizing heat effect in the construction of transposon libraries in Staphylococcus aureus

Staphylococcus aureus (S. aureus), particularly Methicillin-resistant S. aureus (MRSA), poses a significant global public health threat, necessitating advanced methodologies to enhance our understanding of this organism at the omics levels. This study introduces a refined protocol for constructing and curing high-density transposon mutant (tn-mutant) libraries in S. aureus, addressing the challenges associated with low transductant yields, and the complex genetic manipulation mechanism in Gram-positive bacteria. Our methodology employs a Himar1 transposon based on a two-plasmid system, leveraging Himar1’s high insertional efficiency in AT-rich organisms. Enhanced transduction efficiency was achieved through chloramphenicol pre-treatment and the use of modified enriched media. Complementing this, an optimized plasmid curing procedure ensured a representative and stable tn-mutant library. The protocol was successfully applied to multiple S. aureus strains, demonstrating an increase in mutant recovery and reduced post-curing impact. The method offers a robust approach for Transposon Insertion Sequencing (TIS) applications in S. aureus, enabling deeper insights into survival, resistance, and pathogenicity mechanisms. This protocol holds a significant potential for accelerating the construction of tn-mutant libraries in various S. aureus strains.

All-Optical Trapping and Programmable Transport of Gold Nanorods with Simultaneous Orientation and Spinning Control.

Gold nanorods (GNRs) are of special interest in nanotechnology and biomedical applications due to their biocompatibility, anisotropic shape, enhanced surface area, and tunable optical properties. The use of GNRs, for example, as sensors and mechanical actuators, relies on the ability to remotely control their orientation as well as their translational and rotational motion, whether individually or in groups. Achieving such particle control by using optical tools is challenging and exceeds the capabilities of conventional laser tweezers. We present a tool that addresses this complex manipulation problem by using a curve-shaped laser trap, enabling the optical capture and programmable transport of single and multiple GNRs along any trajectory. This type of laser trap combines confinement and propulsion optical forces with optical torque to transport the GNRs while simultaneously controlling their rotation (spinning) and orientation. The proposed system facilitates the light-driven control of GNRs and the quantitative characterization of their motion dynamics including transport speed, spinning frequency, orientation, and confinement strength. We experimentally demonstrate that remote control of the GNRs can be achieved both near a substrate surface (2D trapping) and deep within the sample (3D all-optical trapping). The motion dynamics of two sets of off-resonant GNRs, possessing similar aspect ratios but different resonance wavelengths, are analyzed to highlight the role played by their optical and mechanical properties in the optical manipulation process. The experimental results are supported by a theoretical model describing the observed motion dynamics of the GNRs. This optical manipulation tool can significantly facilitate applications of light-driven nanorods.

Learning human actions from complex manipulation tasks and their transfer to robots in the circular factory

Abstract Process automation is essential to establish an economically viable circular factory in high-wage locations. This involves using autonomous production technologies, such as robots, to disassemble, reprocess, and reassemble used products with unknown conditions into the original or a new generation of products. This is a complex and highly dynamic issue that involves a high degree of uncertainty. To adapt robots to these conditions, learning from humans is necessary. Humans are the most flexible resource in the circular factory and they can adapt their knowledge and skills to new tasks and changing conditions. This paper presents an interdisciplinary research framework for learning human action knowledge from complex manipulation tasks through human observation and demonstration. The acquired knowledge will be described in a machine-executable form and will be transferred to industrial automation execution by robots in a circular factory. There are two primary research objectives. First, we investigate the multi-modal capture of human behavior and the description of human action knowledge. Second, the reproduction and generalization of learned actions, such as disassembly and assembly actions on robots is studied.

U-TAG: Electromagnetic Wireless Sensing System for Robotic Hand Pre-Grasping.

In order to perform complex manipulation and grasp tasks, robotic hands require sensors that can handle increasingly demanding functionality and degrees of freedom. This research paper proposes a radiofrequency sensor that uses a wireless connection between a probe and a tag. A compact and low-profile antenna is mounted on the hand and functions as a probe to read a printed passive resonator on the plastic object being targeted, operating within a pre-touch sensing range. The grasping strategy consists of four stages that involve planar alignment in up-to-down and left-to-right directions between the probe and tag, the search for an appropriate distance from the object, and rotational (angular) alignment. The real and imaginary components of the probe-input impedance are analyzed for different orientation strategies and positioning between the resonator on the object and the probe. These data are used to deduce the orientation of the hand relative to the target object and to determine the optimal position for grasping.

Light-induced manipulation of ultra-low surface tension droplets on stable quasi-liquid surfaces

HypothesisPerfluorocarbon is commonly used as a coolant, chemical reaction carrier solvent, medical anti-hypoxic agents and blood substitutes. The realization of non-contact complex manipulation of perfluorocarbon liquids is urgently needed in human life and industrial production. However, most liquid-repellent interfaces are ineffective for the transport of ultra-low surface tension perfluorocarbon liquids, and struggle to maintain good durability due to unstable air or oil cushions in the surface. Therefore, preparing surfaces for stable non-contact complex manipulation of ultra-low surface tension droplets remains a challenge. ExperimentsIn this paper, a novel solution, a photothermal responsive droplet manipulation surface based on polydimethylsiloxane brushes, has been reported. On this surface, droplets with different surface tensions (as low as 10 mN/m) can be efficiently manipulated through induced near-infrared light. Notably, this surface maintains its effectiveness after exposure to extreme anthropogenic conditions. FindingsThe interface effect between perfluorocarbon droplets and polydimethylsiloxane brushes by near-infrared light-induced was investigated in detail. In addition, ultra-low surface tension droplets demonstrate the ability to transport solid particles. The conductive droplets exhibit sophisticated manipulation realizing the controlled switching of smart circuits. This research opens up new possibilities for advancing the capabilities and adaptability of ultralow surface tension droplets in a range of applications.