Target System Research Articles

Targeting the Antibody Fab Region Using Light-Induced Indole-3-Butyric Acid Functionalized Magnetic Microspheres.

A novel light-controlled adsorption system for direct targeting of antibody Fab fragments was developed by utilizing indole-3-butyric acid functionalized magnetic microspheres. Indole-3-butyric acid, serving as a specific small molecule ligand, was successfully conjugated to amine-functionalized magnetic microspheres via a 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide/N-hydroxysuccinimide activation strategy. Under illumination at a particular wavelength, the indole-3-butyric acid ligand generated reactive radicals that interacted with the nucleotide-binding sites of antibody Fab fragments, enabling effective affinity adsorption. Static adsorption experiments demonstrated that the system's adsorption behavior obeys the Langmuir model (KF=0.122, R2=0.996), indicating a homogeneous adsorption process. Kinetic studies further revealed that the adsorption process follows a second-order kinetic model (k2=0.0257, R2=0.989). When compared with conventional antibody adsorption systems, this new system exhibited specific targeting of Fab fragments, enhanced selectivity, and adjustable properties. In particular, at pH 7.0, effective elution was achieved by increasing the salt concentration, with the eluted product retaining antigen-binding activity. The purification recovery rate exceeded 98%, and the system maintained effective adsorption and elution of Fab fragments across various pH conditions. Besides, even after 10 reuse cycles, the system retained more than 96% of its efficiency, thus presenting excellent regenerability and reusability. In summary, the developed light-controlled antibody Fab region adsorption system offers a highly efficient, stable, and cost-effective approach. It is also expected to become one of the most effective methods for antibody Fab purification in the future.

Recent advances in lateral flow assays for MicroRNA detection.

Lateral flow assays (LFAs) have emerged as pivotal tools for the rapid and reliable detection of microRNAs (miRNAs). It is believed that these biomarkers are crucial for the diagnosis and prognosis of various diseases, particularly cancer. Traditional miRNA detection techniques, such as quantitative PCR, are highly sensitive but have limited efficacy due to their complexity, high cost, and technical requirements. LFAs are valuable due to their simplicity, affordability, and portability, making them ideal for point-of-care testing in low-resource environments. However, challenges remain in developing highly sensitive and accurate LFA devices for miRNA detection. This review explores recent advancements in LFAs to improve miRNA detection sensitivity and specificity. Key innovations include signal amplification using isothermal methods, the application of CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats)/Cas systems for direct targeting of miRNAs, and the incorporation of nanomaterials, such as gold nanoparticles and nanorods, to enhance signal intensity. Using artificial intelligence (AI) algorithms enables precise, automated, and rapid quantification of miRNAs. Moreover, this review examines the ability of LFA-based devices to detect multiple miRNAs simultaneously. One of the most significant advancements is the detection of miR-21 levels as low as 20 pM and let-7a levels as low as 40 pM within ten minutes. This highlights the potential of these devices for clinical diagnostics.

Flooding distributed denial of service detection in software-defined networking using k-means and naïve Bayes

Software-defined networking (SDN) is a network architecture that enables the separation of the control plane and data plane, facilitating centralized management of the network. While centralized control offers numerous benefits, it also comes with certain drawbacks. Flooding distributed denial of service (DDoS) attacks pose a significant threat in SDN environments. These attacks involve overwhelming a target system with a large volume of packets, aiming to disrupt its functionality. In this paper, we propose a new approach for detecting DDoS attacks based on multiple k-means models and the naive Bayes algorithm. Our methodology involves training multiple k-means models to cluster each data point within every column of the dataset, where each column represents a feature. This process results in a new dataset with the same shape, containing only clusters, except the column containing the target variable (labels). These clusters are then used as input by naïve Bayes to perform binary classification. We assessed our approach using the InSDN and CIC-DDoS2017 datasets. The results underscore the impressive accuracy of our model, achieving 99.9839% on the InSDN dataset and 99.7030% on the CIC-DDoS2017 dataset. This performance was achieved by optimizing the desired number of clusters.

DDOS Protection System for Cloud: Architecture and Tool

Distributed Denial of Service (DDoS) attacks, in the realm of cloud computing, have become the most serious threats to the availability and reliability of services. However, these attacks become the direct cause of the target system slowdown or shutdown by saturating the servers with a massive amount of traffic. This harms the cloud-based applications' performance. As cloud infrastructure has become the backbone of every company, the development of effective and scalable DDoS protection mechanisms to ensure the continuity of services is a must. This paper gives a clear overview of the architecture and equipment involved in the cloud-based DDoS protection system. We look at the different layers of protection such as traffic filtering, rate-limiting, anomaly detection, and the application of security services native to the cloud. Examples of such Web Application Firewalls (WAFs), Content Delivery Networks (CDNs), and Cloud Security Posture Management (CSPM) systems. The architecture employs distributed and multi-layered security solutions for detecting and mitigating the attack in real-time whilst keeping the legitimate users safe from any effects

TAEFuzz : Automatic Fuzzing for Image-based Deep Learning Systems via Transferable Adversarial Examples

Deep learning (DL) components have been broadly applied in diverse applications. Similar to traditional software engineering, effective test case generation methods are needed by industry to enhance the quality and robustness of these deep learning components. To this end, we propose a novel automatic software testing technique, TAEFuzz (Automatic Fuzz -Testing via T ransferable A dversarial E xamples), which aims to automatically assess and enhance the robustness of image-based deep learning (DL) systems based on test cases generated by transferable adversarial examples. TAEFuzz alleviates the over-fitting problem during optimized test case generation and prevents test cases from prematurely falling into local optima. In addition, TAEFuzz enhances the visual quality of test cases through constraining perturbations inserted into sensitive areas of the images. For a system with low robustness, TAEFuzz trains a low-cost denoising module to reduce the impact of perturbations in transferable adversarial examples on the system. Experimental results demonstrate that the test cases generated by TAEFuzz can discover up to 46.1% more errors in the targeted systems, and ensure the visual quality of test cases. Compared to existing techniques, TAEFuzz also enhances the robustness of the target systems against transferable adversarial examples with the perturbation denoising module.

An Extended Study of the Performance of Flexible Controllers Composed of Micro-controllers

Generic controllers for self-adaptive systems can be configured parametrically according to system needs, even though their reuse is restricted because of the wide range of services that may be provided by each stage of a feedback control loop, like MAPE-K. Rainbow is a typical example of such a generic, monolithic controller. This article revisits and extends prior work that advocates structurally flexible controllers, as ensembles of micro-controllers each providing specific services. We experimented with our approach with three different architectural configurations for the controller: monolithic, decentralised, and decentralised with a meta-controller. Our results indicate that despite the decentralised configuration with a meta-controller demanding more computational resources, it performed comparatively well compared to the other configurations, including when measuring the target system’s response time. Moreover, we found that variations of the control loop timing at the different layers of the controller impact the stability of the target system. We have also evolved the controller by adding a new micro-controller, which caused no impact on the other micro-controllers, and mostly kept the target system’s performance. We conclude that a multi-layered controller design, based on micro-controllers, provides the basis for defining structurally flexible controllers at operational-time, and may promote reuse at development-time.

Screening and identification of host signaling pathways for alleviating influenza-induced production of pro-inflammatory cytokines, IP-10, IL-8, and MCP-1, using a U937 cell-based influenza model

Influenza virus infection initiates an exaggerated inflammatory response, which may culminate in a fatal cytokine storm characterized by the excessive production of pro-inflammatory cytokines. Prior research indicates that IP-10, IL-8, and MCP-1, primarily produced by monocytes and macrophages, play a crucial role in influenza-induced inflammation. The lung injury from influenza virus infection can be mitigated by suppressing or inhibiting these cytokines through knockout, knockdown, or targeted intervention approaches. To identify the key host signaling pathways responsible for producing pro-inflammatory cytokines, we utilized a U937 cell model that secretes IP-10, IL-8, and MCP-1 in response to influenza infection. This model has been previously validated in our laboratory as an appropriate system for screening anti-inflammatory agents and potential drug targets. We conducted a screening assay employing an inhibitor library consisting of 2,138 compounds that target various known pathways and host factors. Our findings indicated that inhibitors targeting protein tyrosine kinases and mitogen-activated protein kinases demonstrated superior efficacy in suppressing cytokine production induced by influenza A virus infection compared to inhibitors aimed at other host factors. Notably, a substantial proportion of the identified hits capable of inhibiting the expression of all three cytokines in the secondary screening were classified as tyrosine kinase inhibitors. Validation experiments further established that Janus kinase/signal transducers and activators of transcription (JAK/STAT) pathways, along with p38 MAPK and Raf–MEK–ERK pathways, are the principal regulators of pro-inflammatory cytokine expression in monocytes and macrophages. Moreover, our results suggest that TKIs present promising opportunities as novel therapeutic agents against influenza-induced pneumonia.

Achieving Pressure Consistency in Mechanochemical Simulations of Chemical Reactions Under Pressure.

The eXtended Hydrostatic Compression Force Field (X-HCFF) is a mechanochemical approach in which a cavity is used to exert hydrostatic pressure on a target system. The cavity used in this method is set up to represent the van der Waals (VDW) surface of the system by joining spheres sized according to the respective atomic VDW radii. The size of this surface can be varied via a scaling factor, and it can be shown that the compression forces exerted in X-HCFF in its current implementation depend on this factor. To address this dependency, we have developed a rescaling formalism for the applied forces, allowing us to drastically reduce the dependency of the compression forces on the chosen scaling factor. Independency from the scaling factor is important, as the scaling of the VDW spheres is often used to ensure an overlap of cavities in supramolecular complexes, which is necessary for the simulation of chemical reactions. Our rescaling formalism reduces the empiricism of the X-HCFF approach and boosts its applicability in the field of computational high-pressure chemistry.

Super-resolution imaging of proteins inside live mammalian cells with mLIVE-PAINT.

Super-resolution microscopy has revolutionized biological imaging, enabling the visualization of structures at the nanometer length scale. Its application in live cells, however, has remained challenging. To address this, we adapted LIVE-PAINT, an approach we established in yeast, for application in live mammalian cells. Using the 101A/101B coiled-coil peptide pair as a peptide-based targeting system, we successfully demonstrate the super-resolution imaging of two distinct proteins in mammalian cells, one localized in the nucleus, and the second in the cytoplasm. This study highlights the versatility of LIVE-PAINT, suggesting its potential for live-cell super-resolution imaging across a range of protein targets in mammalian cells. We name the mammalian cell version of our original method mLIVE-PAINT.

Theoretical Investigation of Bond Dissociation Energies of exo-Polyhedral B–H and B–F Bonds of closo-Borate Anions [BnHn−1X]2− (n = 6, 10, 12; X = H, F)

This paper reports on a theoretical investigation of the bond dissociation energies of B–H and B–F interactions of closo-borate anions [BnHn−1X]2− (n = 6, 10 and 12; X = H and F), in which homolytic and heterolytic bond breaking cases were considered, and the main trends in bond dissociation energy values were analysed. The wB97X-D3/TZVPP level of theory was applied for geometry optimisation of the molecular species under consideration. DLPNO-CCSDT/CBS single-point calculations were made to ensure an accurate estimation of the target systems’ electronic energy. The correlations between the value of the bond dissociation energy and variables such as electron density descriptors of B–H and B–F interactions and frontier orbital energies (HOMO, SOMO and LUMO) were established.

On the "direct detection" of gravitational waves.

In this paper, I provide an account of direct (vs. indirect) detection in gravitational-wave astrophysics. In doing so, I highlight the epistemic considerations that lurk behind existing debates over the application of the term "direct". According to my analysis, there is an epistemically significant distinction between direct and indirect detections in this context. Roughly, our justification for trusting a direct detection depends mainly on the reliability of instruments that are under our control, rather than on the reliability of our models of separate target systems. In contrast, indirect detections rely on confidence in such models. Overall, this paper solves a puzzle about what counts as a "direct" detection of gravitational waves in a way that is true to scientific usage, and (more importantly) both philosophically precise and epistemically perspicuous. Having done so, this paper provides a foundation for a broader project of analyzing the epistemic situation of (gravitational-wave) astrophysics.

Proficiency-chasing and goalodicy: In prioritising checklists, are we gambling with the future of mental health nursing?

In this discussion paper, I take a critical approach to the use of standardised checklists in practice assessment documents as a valid method of assessing mental health nursing students in the UK. The game Bingo is applied here as a metaphor, highlighting the folly of using standardised cross-field checklists to assess mental health nursing students in practice. Such practices, I argue, amount to little more than a game of proficiency-chasing at the expense of seeking more meaningful learning experiences, especially where practice assessment documents currently prioritise physical health care skills above those required for successful mental health nursing. Furthermore, where the current path to qualification as a mental health nurse in the UK is determined by the navigation of a complex system of checklists and targets, I also argue that goalodicy (as in the goal and actions taken to achieve this becoming the focus, over the very reason the goal exists in the first place) becomes an inevitability; shortcuts justified in the name of achieving broader goals of passing a practice module and eventual qualification as a mental health nurse. This situation, I suggest serves neoliberal, capitalist systems, reinforcing the mechanisation of care while undermining the deeper relational, ethical and philosophical focus of what it means to be a mental health nurse. Alternative methods of practice-based assessments for mental health nursing are considered.

Utilizing Flexible Magnetic-Alloy Sheet with Rolling-to-Attaching Method to Fabricate a Smart Nail for Electromagnetic Screw-Hole Targeting in Intramedullary Interlocking-Nail Surgery

Abstract In this paper, we present a novel method to fabricate a smart nail for electromagnetic targeting of distal screw-holes in intramedullary interlocking-nail surgery. The nail is fabricated by using a flexible magnetic-alloy Metglas sheet with a rolling-to-attaching method. That is, among most high permeable magnetic materials, the Metglas sheet has superior features in flexibility and bendability and therefore can be rolled and inserted inside the nail to accordingly conform to curvature of inner surface of the nail. Regarding the targeting system, we used a conventional c-shaped electromagnet (consisting of a c-shaped silicon steel core, emitting coil, and receiving coil) with measurement electronics as the targeting system. At first, an AC input voltage is applied to the emitting coil to generate magnetic flux in the air-gap between emitting coil and receiving coil. Consequently, by electromagnetic induction, an AC voltage is induced in the receiving coil. As the nail is axially moved or rotated within the air-gap between the emitting and receiving coils, the magnetic flux between the coils is influenced, leading to a change in the voltage output of the receiving coil. This voltage change is further analyzed to determine location and orientation of the Metglas sheet (as well as screw holes). Based on this targeting principle, we conduct location and orientation targeting tests. Results show that our method not only can successfully achieve these targeting, but also significantly simplifies the complexity of both targeting system and surgical procedure, in practical perspective.

Lethal empowerment and electronic crime: A focus on radio-frequency interference capabilities

This article focuses on the capabilities of criminals in using radiofrequency interference (RFI) devices to target systems that use the Global Positioning System (GPS). Surveying over a 22-year period during which GPS has been widely used by many industries, it seeks to understand how the electronic threat has evolved and changed. Focusing on the accessibility, usability, effectiveness, versatility, transportability, and concealability of RFI devices, and utilising a number of sources from engineering disciplines, hacker events, and media pieces, it argues that the more reliant we are on GPS, the more threat actors’ target choices and means, ends, and, indeed, motivations for targeting systems will expand, elevating the risks to GPS users. This article finds that arguably some of the most disagreeable actors have elevated from unsophisticated to semi-sophisticated in the space of 20 years, and can target systems cheaply, easily, and effectively. In the space of two decades, the combination of war, the expansion of digitalisation, the commercialisation of military systems, and the demand and supply that feeds technological innovations, have left us with an entirely different threat picture.

Modulating motion event categorization through brief training: Meaning-focused versus form-focused instructional conditions

Abstract There is evidence that learning a second language (L2) can shift cognition toward that predicted for the L2 and that this effect might vary with L2 proficiency, age of acquisition, length of immersion, etc. Here we explore the previously neglected variable of language instructional conditions. Participants categorized motion events in a triads-matching task after being trained on two novel linguistic labels highlighting (in)transitivity through one of three instructional conditions. Participants who learned the relevant knowledge under a meaning-focused instructional condition (memorizing meanings of exemplar sentences) showed a higher likelihood of categorizing based on motion (in)transitivity immediately after training than a control group; those who learned under a required rule search instructional condition showed this effect only after additional practice; while those who learned through another type of form-focused instructional condition (direct metalinguistic explanation) did not show this effect even after such practice. These differences were obtained despite the fact that the three groups were matched on awareness of the target system at the level of understanding and near-perfect performance on a grammaticality judgment task. The findings are discussed in terms of the depth of processing in instructed SLA and models of language–cognition interactions.

A Susceptible Cell-Selective Delivery (SCSD) of mRNA-Encoded Cas13d Against Influenza Infection.

To bolster the capacity for managing potential infectious diseases in the future, it is critical to develop specific antiviral drugs that can be rapidly designed and delivered precisely. Herein, a CRISPR/Cas13d system for broad-spectrum targeting of influenza A virus (IAV) from human, avian, and swine sources is designed, incorporating Cas13d mRNA and a tandem CRISPR RNA (crRNA) specific for the highly conserved regions of viral polymerase acidic (PA), nucleoprotein (NP), and matrix (M) gene segments, respectively. Given that the virus targets cells with specific receptors but is not limited to a single organ, a Susceptible Cell Selective Delivery (SCSD) system is developed by modifying a lipid nanoparticle with a peptide mimicking the function of the hemagglutinin of influenza virus to target sialic acid receptors. The SCSD system can precisely deliver an all-RNA-based CRISPR/Cas13d system into potentially infected cells. This drug is shown to reduce the viral load in the lungs by 2.37 log10 TCID50mL-1 and protect 100% of mice from lethal influenza infection. The SCSD-based CRISPR/Cas13d system shows promise for the flexible and efficient therapy of infections caused by rapidly evolving and novel viruses.

Laboratory-based x-ray phase contrast microscopy system for targeting in unstained soft-tissue samples

Laboratory-based x-ray phase contrast microscopy system for targeting in unstained soft-tissue samples

On-Demand Controlled Release Multi-Drugs Delivery System for Spatiotemporally Synergizing Antitumor Immunotherapy.

Although cytotoxic T lymphocytes (CTLs) activation combined with programmed cell death-1 (PD-1)/programmed cell death ligand-1 (PD-L1) axis blockade have emerged as an effective strategy to improve immunotherapeutic potency, it remains challenging to realize the spatiotemporal synergy of these two components. Herein, the study reports an engineered bacterial-based delivery system that can simultaneously promote CTLs infiltration and control PD-L1 binding protein (PD-L1 trap) release on demand at tumor site. The drug release button of this tumor targeting system is the specific temperature, which is accomplished by dual-modified melanin nanoparticles with photothermal conversion capacity on the engineered bacterial. These dual-modified nanoparticles can form in situ reservoir of heat supplier and antitumor immunity activator once arriving at tumor microenvironment (TME). Importantly, the study establishes the personalized administration regimen according to TME changes, and perform local laser irradiation to trigger PD-L1 trap production only in TME when infiltrated CTLs reach the highest level. This work provides a flexible platform for optimizing cancer immunotherapy.

Advanced shooting target with bullet collector, semi-automatic bulls-eye paper positioning and automatic shooting score

Shooting exercises in Indonesia typically use simple bulls-eye targets on wooden boards with sand backstops, requiring manual setup and score calculation. This setup is inefficient, especially for long-range shooting, as operators must walk far to retrieve targets, and bullets embedded in sand are hard to recycle. This project developed an advanced shooting target featuring a bullet collector, semi-automatic target setup, automatic scoring, and target monitoring. A system with such complete features is not available in the market. This target system has a roll of bulls-eye paper and the roller is powered by a servo motor controlled by a switch to command a fresh new page of bulls-eye its positioning is helped by an infrared sensor to detect markers in the paper for correct positioning. This system is equipped with a bullet collector system by directing the bullet to a container using 450 angled armor and a layer of sand in the container to stop the bullet. This system is also equipped with a camera pointing to the bulls-eye paper and its output is transmitted to a monitor close to the shooter to identify bullet tracks for evaluating his shooting performance and to improve his shooting strategy. The image from the same camera is used for image processing with the OpenCV library and Python scripts to calculate the shooting score automatically. Several physical tests have been conducted and the system proves to perform reasonably well in the tests with some errors of around 3% for single bullet holes and simple multiple bullet holes. Based on test results, the pistol bullets have quite different properties from the rifle bullets. Pistol bullets follow the impact deflection with a coefficient of restitution e = 0 while rifle bullets follow the impact deflection with e ≈ 0.5. The pistol bullets are completely disintegrated after impact while the rifle bullets are just distorted.

Development of a comprehensive sustainability index for extractive and mining companies: Integrating the Sustainable Development Goals.

Sustainability in the extractive and mining industry has become a key global concern, necessitating alignment with the United Nations Sustainable Development Goals (SDGs). This research introduces an innovative, scientific index-based assessment methodology designed to quantify the contribution of companies in these sectors to the SDGs. The methodology leverages a metric index scoring system to evaluate and enhance their sustainability performance. The methodology involves scoring a series of parameters associated with each SDG, resulting in individual indices that are rescaled for comparability. This comprehensive approach also integrates environmental, social, and economic factors. The practical application of this index is critical, providing a step-by-step guide for extractive and mining companies to adopt and utilize the sustainability index, aiding in compliance with future regulations. The results of this study show that the system of SDGs, targets, aspects, and metrics is naturally weighted towards social issues. It has also been observed that the SDG system, with respect to the targets, is not balanced. This same heterogeneous behavior occurs for aspects. This implies that the system is naturally weighted, which directly affects the comparison of contribution. The proposed methodology provides a process of normalization of indices that avoids this natural weighting, allowing the comparison of results. This study advances a comprehensive methodology for evaluating extractive companies' contributions to the SDGs, incorporating multiple dimensions and expert opinions, and its adaptability allows for continuous improvement by incorporating new parameters. The Susceptibility Index helps management assess contributions on a scale of 0-10, facilitating ongoing improvements and anticipating regulatory changes. This research enhances the assessment of extractive companies' SDG performance, promoting responsible practices. The open-access nature of the methodology enables any organization to conduct a sustainability analysis, fostering transparency and collaboration.