General Platform Research Articles

Non-invasive calibration of a Stewart platform by photogrammetry

AbstractAccurate calibration of a Stewart platform is important for their precise and efficient operation. However, the calibration of these platforms using forward kinematics is a challenge for researchers because forward kinematics normally generates multiple feasible and unfeasible solutions for any pose of the moving platform. The complex kinematic relations among the six actuator paths connecting the fixed base to the moving platform further compound the difficulty in establishing a straightforward and efficient calibration method. The authors developed a new forward kinematics-based calibration method using Denavit-Hartenberg (DH) convention and used the Stewart platform “Tiger 66.1” developed in their lab for experimenting with the photogrammetry-based calibration strategies described in this paper. This system became operational upon completion of construction, marking its inaugural use. The authors used their calibration model for estimating the errors in the system and adopted three compensation options or strategies as per least-square method to improve the accuracy of the system. These strategies leveraged a high-resolution digital camera and off-the-shelf software to capture the poses of the moving platform’s center. This process is non-invasive and does not need any additional equipment to be attached to the hexapod or any alteration of the hexapod hardware. This photogrammetry-based calibration process involves multiple high-resolution images from different angles to measure the position and orientation of the platform center in the three-dimensional space. The target poses and actual poses are then compared, and the error compensations are estimated using the least-square methods to calculate the predicted poses. Results from each of the three compensation approaches demonstrated noticeable enhancements in platform pose accuracies, suggesting room for further improvements. Given that “Tiger 66.1” is based on the general Stewart platform structure, the proposed calibration method holds promise for extension to machines operating on similar principles where non-invasive calibration is desirable. This study contributes to advancing the field of Stewart platform calibration, paving the way for more precise and efficient applications in various domains.

Low-kappa DBR grating filters on an InP generic photonic integration foundry platform

We demonstrate narrow-bandwidth, low-kappa, distributed Bragg reflector (DBR) grating filters on an indium phosphide (InP) generic foundry platform. With the varying corrugation widths of the DBR grating, we achieve flexibility in the design of the coupling coefficients from 10 to 50cm−1, which correspond to grating bandwidths of 0.68 nm to 1.28 nm, respectively. These values are experimentally observed and agree well with the theoretical analysis. The DBR grating is based on periodic rectangular grooves in quaternary material that is placed between the waveguide core and cladding region. Such configurations of the DBR grating provide a low propagation loss of ∼2dB/cm near the telecom band around 1550 nm.

Cartoonization of Images Using Generative Adversarial Network

Abstract: This research paper introduces a web-based image style conversion system powered by a trained AnimeGANv2 model. The system enables real-time conversion of images into anime-style artwork within a web application. Through the integration of TensorFlow, Protobuf, and ONNX formats, the AnimeGANv2 model is seamlessly deployed for efficient style conversion. The web application incorporates user authentication, secure image uploading, and instant style conversion features. Evaluation through user testing assesses the system's usability, performance, and user satisfaction. Results demonstrate the successful implementation of real- time image style conversion, offering a practical solution for users interested in artistic rendering. This research contributes to understanding GAN-based image style conversion applications and their integration into web platforms for creative expression and visual content generation.

Progressive Collapse Analysis of Single-Layer Latticed Domes Under Seismic Loading Using Enhanced Fiber-Based Approach

This paper presents a novel computational framework based on the Finite Particle Method (FPM) for the progressive collapse analysis of single-layer latticed domes subjected to seismic loading. The framework integrates a fiber-based approach to simulate the nonlinear behavior of steel structural members, including yielding, cyclic buckling, and fracture. For structural seismic analysis, the fiber-based approach is enhanced by incorporating a dynamic increase factor, which accounts for the impact of high strain rates during seismic events. Furthermore, the framework extends the pseudo-dynamical stiffness of the particle to consider Rayleigh damping, surpassing the limitations of the original mass damping model. The computational efficiency of the proposed framework is optimized by implementing it on a generic parallelized GPU platform. The computational framework has the advantage of elaborately simulating the response of individual structural members while efficiently capturing the complex behavior of the overall structure. Subsequently, the effectiveness of the enhanced framework is demonstrated through the analysis of the collapse behavior of a representative single-layer latticed dome under seismic loading. The study conducts a parametric analysis to evaluate the influence of two key factors, namely, rise-to-span ratios and imperfections, on the collapse behaviors. The findings contribute to a better understanding of the complex behaviors of single-layer latticed domes during seismic events, providing knowledge for improving structural safety and resilience in public buildings.

Superposed Atomic Representation for Robust High-Dimensional Data Recovery of Multiple Low-Dimensional Structures

This paper proposes a unified Superposed Atomic Representation (SAR) framework for high-dimensional data recovery with multiple low-dimensional structures. The data can be in various forms ranging from vectors to tensors. The goal of SAR is to recover different components from their sum, where each component has a low-dimensional structure, such as sparsity, low-rankness or be lying a low-dimensional subspace. Examples of SAR include, but not limited to, Robust Sparse Representation (RSR), Robust Principal Component Analysis (RPCA), Tensor RPCA (TRPCA), and Outlier Pursuit (OP). We establish the theoretical guarantee for SAR. To further improve SAR, we also develop a Weighted SAR (WSAR) framework by paying more attention and penalizing less on significant atoms of each component. An effective optimization algorithm is devised for WSAR and the convergence of the algorithm is rigorously proved. By leveraging WSAR as a general platform, several new methods are proposed for high-dimensional data recovery. The experiments on real data demonstrate the superiority of WSAR for various data recovery problems.

VeriCompress: A Tool to Streamline the Synthesis of Verified Robust Compressed Neural Networks from Scratch

AI's widespread integration has led to neural networks (NN) deployment on edge and similar limited-resource platforms for safety-critical scenarios. Yet, NN's fragility raises concerns about reliable inference. Moreover, constrained platforms demand compact networks. This study introduces VeriCompress, a tool that automates the search and training of compressed models with robustness guarantees. These models are well-suited for safety-critical applications and adhere to predefined architecture and size limitations, making them deployable on resource-restricted platforms. The method trains models 2-3 times faster than the state-of-the-art approaches, surpassing them by average accuracy and robustness gains of 15.1 and 9.8 percentage points, respectively. When deployed on a resource-restricted generic platform, these models require 5-8 times less memory and 2-4 times less inference time than models used in verified robustness literature. Our comprehensive evaluation across various model architectures and datasets, including MNIST, CIFAR, SVHN, and a relevant pedestrian detection dataset, showcases VeriCompress's capacity to identify compressed verified robust models with reduced computation overhead compared to current standards. This underscores its potential as a valuable tool for end users, such as developers of safety-critical applications on edge or Internet of Things platforms, empowering them to create suitable models for safety-critical, resource-constrained platforms in their respective domains.

Abstract 5814: EPriL macrocycles as a platform for the rapid generation of effective kinase degrader antibody conjugates (DACs)

Abstract The therapeutic success of antibody drug conjugates (ADCs) drives a continuous search for novel payloads that can increase therapeutic window and thereby widen the applications for ADCs. Recently, heterobifunctional degraders have gained great interest as payloads, and degrader antibody conjugates (DACs) are seen as a novel therapeutic modality. Heterobifunctional degraders consist of a small molecule ligand that binds a target (protein of interest or POI), a spacer and an E3 ligase ligand, which can catalyze target degradation. Owing to their catalytic activity, degraders can have better potency than the equivalent inhibitors, making them suitable as ADC payload [1]. Degrader payloads can target a wider variety of mechanisms than classic payloads, which use the same antitumor strategies as chemotherapy, such as tubulin binding or topoisomerase inhibition. Since many FDA-approved targeted therapies are based on the inhibition of protein kinases, we investigated degraders of these kinases as DAC payloads. Kinase DACs could bring enhanced targeting and therefore better therapeutic window to a field where classic ADC payloads have often shown substantial toxicities. As many heterobifunctional degraders show poor cell membrane penetration, their inhibitory potential could be increased as part of a DAC, where endocytic uptake is followed by intracellular release of the degrader payload. To identify kinase degrader payloads, we present a workflow based on a platform called Energetically Privileged Ligands (EPriLs). EPriLs are macrocycle scaffolds that bind non-covalently in the kinase ATP pocket. Their unique binding mode avoids contacts with amino acid positions where resistance to kinase inhibitors frequently occurs. EPriL macrocycles can be decorated appropriately to rationally design specific inhibitors for many therapeutically relevant kinases, and provide synthetic handles to couple them to VHL or CRBN ligands to generate effective kinase degraders. Here we describe how EPriL kinase degraders can be developed into effective DACs, using consecutive libraries of EPriL ligands, spacers, E3 ligase ligands and linkers. First, suitable degraders are identified, based on rapid and deep target degradation and potent antiproliferative activity on target cell lines. Degraders are then transformed into maleimide-linked degraders using convenient attachment of enzymatically cleavable linkers. In a medium throughput fashion, these maleimides are coupled to antibodies to generate DACs, which are tested for stability and biological potency. Applying this workflow to various well-validated kinase targets in oncology resulted in a promising kinase targeting DAC with favorable ADME properties, clear potentiation compared to the parent degrader, and increased selectivity for tumor cell lines. 1] Dragovich et al., Chem. Soc. Rev. (2022) 51, 3886-3897. Citation Format: Joost C. Uitdehaag, Jos e Man, Michelle Muller, Freek an Cauter, Sander an Gemert, Milan Hoffmann, Yvonne G. an Mil, Winfried R. Mulder, Martine B. Prinsen, Jan Gerard Sterrenburg, Diep Vu, Joeri e Wit, Erik Ensing, Rogier C. Buijsman. EPriL macrocycles as a platform for the rapid generation of effective kinase degrader antibody conjugates (DACs) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5814.

Process design and economic evaluation for methane and electricity production from Agave bagasse using different pretreatments

Agave bagasse is an agricultural residue that has the potential to be used as feedstock biorefineries. However, its complex structure requires a pretreatment step to facilitate the accessibility to carbohydrates for its subsequent conversion. In this study, the capital and operative expenses (CAPEX and OPEX) were simulated for industrial plants processing 500 ton/d of Agave bagasse using different pretreatment platforms for methane production and power generation on experimental data. The scenarios consisted of i) biological, ii) enzymatic, iii) dilute acid, and iv) steam explosion pretreatments and a case-based scenario without pretreatment. All these scenarios included milling, pretreatment, anaerobic digestion, and cogeneration stages. The results showed that those pretreatments carried out under soft conditions (biological and enzymatic) decreased between 2.1 and 2.8 times CAPEX compared to the highest value obtained in the diluted acid and case-based scenarios. The steam explosion, a pretreatment carried out under severe conditions, was less effective reducing CAPEX by only 1.2 times compared to the highest value. Furthermore, compared to the case-base scenario, only the biological pretreatment reduced the OPEX by a factor of 2, whereas the other pretreatments resulted in OPEX that were 2.0 to 3.5 times higher. Among the utilities (cooling water, steam generation, and energy) the steam generation contributed 47% to 76% of the total OPEX. The energy produced (kW⋅h/yr) was approximately twice the energy consumed for all scenarios. A sensitivity analysis demonstrated the robustness of the platforms since fluctuations in the prices of feedstock and raw materials did not impact the total production cost of methane, except for biological and enzymatic pretreatments. For all platforms, the solid loading was the process parameter with the highest impact on the production cost of methane. Overall, the study showed that biological and enzymatic pretreatments had the best economic performance. However, low substrate conversion and the high cost of enzymes and equipment, respectively, remained the main challenges to address in future research.

Modifications of the 5' region of the CASPONTM tag's mRNA further enhance soluble recombinant protein production in Escherichia coli.

Escherichia coli is one of the most commonly used host organisms for the production of biopharmaceuticals, as it allows for cost-efficient and fast recombinant protein expression. However, challenging proteins are often produced with low titres or as inclusion bodies, and the manufacturing process needs to be developed individually for each protein. Recently, we developed the CASPONTM technology, a generic fusion tag-based platform process for high-titer soluble expression including a standardized downstream processing and highly specific enzymatic cleavage of the fusion tag. To assess potential strategies for further improvement of the N-terminally fused CASPONTM tag, we modified the 5'UTR and 5' region of the tag-coding mRNA to optimize the ribosome-mRNA interactions. In the present work, we found that by modifying the 5'UTR sequence of a pET30acer plasmid-based system, expression of the fusion protein CASPONTM-tumour necrosis factor α was altered in laboratory-scale carbon-limited fed-batch cultivations, but no significant increase in expression titre was achieved. Translation efficiency was highest for a construct carrying an expression enhancer element and additionally possessing a very favourable interaction energy between ribosome and mRNA (∆Gtotal). However, a construct with comparatively low transcriptional efficiency, which lacked the expression enhancer sequence and carried the most favourable ∆Gtotal tested, led to the highest recombinant protein formation alongside the reference pET30a construct. Furthermore, we found, that by introducing synonymous mutations within the nucleotide sequence of the T7AC element of the CASPONTM tag, utilizing a combination of rare and non-rare codons, the free folding energy of the nucleotides at the 5' end (-4 to + 37) of the transcript encoding the CASPONTM tag increased by 6kcal/mol. Surprisingly, this new T7ACrare variant led to improved recombinant protein titres by 1.3-fold up to 5.3-fold, shown with three industry-relevant proteins in lab-scale carbon limited fed-batch fermentations under industrially relevant conditions. This study reveals some of the complex interdependencies between the ribosome and mRNA that govern recombinant protein expression. By modifying the 5'UTR to obtain an optimized interaction energy between the mRNA and the ribosome (ΔGtotal), transcript levels were changed, highlighting the different translation efficiencies of individual transcripts. It was shown that the highest recombinant titre was not obtained by the construct with the most efficient translation but by a construct with a generally high transcript amount coupled with a favourable ΔGtotal. Furthermore, an unexpectedly high potential to enhance expression by introducing silent mutations including multiple rare codons into the 5'end of the CAPONTM tag's mRNA was identified. Although the titres of the fusion proteins were dramatically increased, no formation of inclusion bodies or negative impact on cell growth was observed. We hypothesize that the drastic increase in titre is most likely caused by better ribosomal binding site accessibility. Our study, which demonstrates the influence of changes in ribosome-mRNA interactions on protein expression under industrially relevant production conditions, opens the door to the applicability of the new T7ACrare tag in biopharmaceutical industry using the CASPONTM platform process.

Leveraging spark-based machine learning algorithm for audience sentiment analysis in youtube content

In today’s digital era, the generation and sharing of information are rapidly expanding. The increased volume of complex data is big data. YouTube is the primary source of big data. The proliferation of the internet and smart devices has led to a significant increase in content creators on social media platforms, with YouTube being a prominent example. There has been a substantial increase in content creators across various social media platforms, with YouTube emerging as one of the foremost platforms for content generation and sharing. YouTubers face challenges in enhancing content strategies due to the growing number of comments, such as big data on shared videos. Reading and finding viewers’ opinions of such a large amount of data through manual methods is time-consuming and challenging and makes it hard to understand people’s sentiments. To address this, spark-based machine learning algorithms have emerged as a transformative tool for content creators to understand the audience. The Improved Novel Ensemble Method (INEM) algorithm is designed to predict viewers’ sentiments and emotional responses based on the content they interact through the comments. The proposed results provide valuable insights for content creators, helping them refine the strategies to optimize the channel’s revenue and performance. Fit Tuber Channel is analyzed to perform the sentiment of user comments.

DANCE: a deep learning library and benchmark platform for single-cell analysis

DANCE is the first standard, generic, and extensible benchmark platform for accessing and evaluating computational methods across the spectrum of benchmark datasets for numerous single-cell analysis tasks. Currently, DANCE supports 3 modules and 8 popular tasks with 32 state-of-art methods on 21 benchmark datasets. People can easily reproduce the results of supported algorithms across major benchmark datasets via minimal efforts, such as using only one command line. In addition, DANCE provides an ecosystem of deep learning architectures and tools for researchers to facilitate their own model development. DANCE is an open-source Python package that welcomes all kinds of contributions.

Arabic Web-Based Information on Oral Lichen Planus: Content Analysis.

The use of web-based health information (WBHI) is on the rise, serving as a valuable tool for educating the public about health concerns and enhancing treatment adherence. Consequently, evaluating the availability and quality of context-specific WBHI is crucial to tackle disparities in health literacy and advance population health outcomes. This study aims to explore and assess the quality of the WBHI available and accessible to the public on oral lichen planus (OLP) in Arabic. The Arabic translation of the term OLP and its derivatives were searched in three general search platforms, and each platform's first few hundred results were reviewed for inclusion. We excluded content related to cutaneous LP, content not readily accessible to the public (eg, requiring subscription fees or directed to health care providers), and content not created by health care providers or organizations (ie, community forums, blogs, and social media). We assessed the quality of the Arabic WBHI with three standardized and validated tools: DISCERN, Journal of the American Medical Association (JAMA) benchmarks, and Health On the Net (HON). Of the 911 resources of WBHI reviewed for eligibility, 49 were included in this study. Most WBHI resources were provided by commercial affiliations (n=28, 57.1%), with the remainder from academic or not-for-profit affiliations. WBHI were often presented with visual aids (ie, images; n=33, 67.4%). DISCERN scores were highest for WBHI resources that explicitly stated their aim, while the lowest scores were for providing the effect of OLP (or OLP treatment) on the quality of life. One-quarter of the resources (n=11, 22.4%) met all 4 JAMA benchmarks, indicating the high quality of the WBHI, while the remainder of the WBHI failed to meet one or more of the JAMA benchmarks. HON scores showed that one-third of WBHI sources had scores above 75%, indicating higher reliability and credibility of the WBHI source, while one-fifth of the sources scored below 50%. Only 1 in 7 WBHI resources scored simultaneously high on all three quality instruments. Generally, WBHI from academic affiliations had higher quality scores than content provided by commercial affiliations. There are considerable variations in the quality of WBHI on OLP in Arabic. Most WBHI resources were deemed to be of moderate quality at best. Providers of WBHI could benefit from increasing collaboration between commercial and academic institutions in creating WBHI and integrating guidance from international quality assessment tools to improve the quality and, hopefully, the utility of these valuable WBHI resources.

Spin Unlocked Vortex Beam Generation on Nonlinear Chiroptical Metasurfaces.

Optical vortices with spin and orbital angular momentum (SAM and OAM) states offer multiple degrees of freedom for manipulating optical fields and thus enable great potentials in optical information processing. Recently, the optical metasurface has become an important platform for vortex beam generation and steering. However, the strong spin-orbit interaction on such metasurfaces usually leads to spin locked OAM generation, which limits the complete control of the angular momentum state of light. Here, we propose to solve this constraint using geometric phase controlled nonlinear chiroptical metasurfaces. The metasurface consists of two types of plasmonic meta-atoms which have opposite handedness and exhibit a strong spin-dependent circular dichroism effect. By encoding specific phase singularities and phase gradients to different channels, we experimentally demonstrate the spin unlocked second harmonic beam steering. The proposed nonlinear chiroptical metasurfaces may have important applications in developing multifunctional nonlinear optical devices.

Development of label-free triboelectric nanosensors as screening platforms for anti-tumor drugs

This study introduces a general label-free drug screening platform with potential to support high-throughput drug screening for any protein target. Its innovation lies in quantifying the affinity of molecule-molecule interactions via voltage output variation, which achieves unprecedented selectivity and sensitivity. It combines computational analysis complemented by experimental substantiation with a solid-liquid triboelectric nanosensor. Owing to its high binding affinity, FKBP-rapamycin is used as a model system to demonstrate the platform's sensitivity. Subsequently, the research is extended to study drug interactions with an oncogenic protein ATG4B. The findings unveil the binding of S130 and Tioconazole to ATG4B, a critical cysteine protease involved in autophagy, while revealing that Dexamethasone does not bind ATG4B. This binding exerts a specific inhibitory effect on autophagic flux and triggers cancer cell apoptosis. The results support the previously verified inhibitory effects of these drugs and the effectiveness of a newly developed self-powered drug screening platform, leveraging the principles of solid-liquid contact electrification. This approach adeptly confronts enduring challenges in traditional drug development methods where biochemical assays need to be designed for each individual protein target or only time-consuming and concentration-demanding molecular interaction measurements were made available.

From Fluorescence-Transfer-Lightening-Printing-Assisted Conductive Adhesive Nanocomposite Hydrogels toward Wearable Interactive Optical Information-Electronic Strain Sensors.

The interactive flexible device, which monitors the human motion in optical and electrical synergistic modes, has attracted growing attention recently. The incorporation of information attribute within the optical signal is deemed advantageous for improving the interactive efficiency. Therefore, the development of wearable optical information-electronic strain sensors holds substantial promise, but integrating and synergizing various functions and realizing strain-mediated information transformation keep challenging. Herein, an amylopectin (AP) modified nanoclay/polyacrylamide-based nanocomposite (NC) hydrogel and an aggregation-induced-emission-active ink are fabricated. Through the fluorescence-transfer printing of the ink onto the hydrogel film in different strains with nested multiple symbolic information, a wearable interactive fluorescent information-electronic strain sensor is developed. In the sensor, the nanoclay plays a synergistic "one-stone-three-birds" role, contributing to "lightening" fluorescence (≈80 times emission intensity enhancement), ionic conductivity, and excellent stretchability (>1000%). The sensor has high biocompatibility, resilience (elastic recovery ratio: 97.8%), and strain sensitivity (gauge factor (GF): 10.9). Additionally, the AP endows the sensor with skin adhesiveness. The sensor can achieve electrical monitoring of human joint movements while displaying interactive fluorescent information transformation. This research poses an efficient strategy to develop multifunctional materials and provides a general platform for achieving next-generation interactive devices with prospective applications in wearable devices, human-machine interfaces, and artificial intelligence.

EPHA: An Energy-efficient Parallel Hybrid Architecture for ANNs and SNNs

Artificial neural networks (ANNs) and spiking neural networks (SNNs) are two general approaches to achieve artificial intelligence (AI). The former have been widely used in academia and industry fields; the latter, SNNs, are more similar to biological neural networks and can realize ultra-low power consumption, thus have received widespread research attention. However, due to their fundamental differences in computation formula and information coding, the two methods often require different and incompatible platforms. Alongside the development of AI, a general platform that can support both ANNs and SNNs is necessary. Moreover, there are some similarities between ANNs and SNNs, which leaves room to deploy different networks on the same architecture. However, there is little related research on this topic. Accordingly, this article presents an energy-efficient, scalable, and non-Von Neumann architecture (EPHA) for ANNs and SNNs. Our study combines device-, circuit-, architecture-, and algorithm-level innovations to achieve a parallel architecture with ultra-low power consumption. We use the compensated ferrimagnet to act as both synapses and neurons to store weights and perform dot-product operations, respectively. Moreover, we propose a novel computing flow to reduce the operations across multiple crossbar arrays, which enables our design to conduct large and complex tasks. On a suite of ANN and SNN workloads, the EPHA is 1.6× more power-efficient than a state-of-the-art design, NEBULA, in the ANN mode. In the SNN mode, our design is 4 orders of magnitude more than the Loihi in power efficiency.

Airborne Short-Baseline Millimeter Wave InSAR System Analysis and Experimental Results

For the challenges of high-precision mapping in complex terrain, a novel airborne Interferometric Synthetic Aperture Radar (InSAR) system is designed. This system, named ASMIS (Airborne Short-Baseline Millimeter-Wave InSAR System), adopts the coplanar antenna and a pod-type structure. This design makes the system lightweight and highly integrated. It can be compatible with small general aviation flight platforms. The baseline is millimeters in size, which greatly simplifies the unwrapping process. The coplanar antennas have two advantages: they maximize the baseline utilization and minimize the Doppler decorrelation and the motion error inconsistency. Acquisition campaigns of the system have been carried out in Boao, Bayannur, and Chengde, China. In the Chengde experimental area, we designed an antiparallel flight experiment to account for the topographic relief. High-precision Digital Orthophoto Maps (DOMs) and Digital Surface Models (DSMs) at a scale of 1:5000 were obtained. The coordinate Root Mean Square Error (RMSE) of the checkpoints within the obtained DSM is less than 0.82 m in altitude and 3 m horizontally. The RMSE of the Sparse Ground Control Points (GCPs) within the obtained DSM is less than 0.3 m in altitude. Experimental results from different areas, including plains, mountains, and coastlines, demonstrate the system’s performance.

Sequence- and Structure-Specific tRNA Dihydrouridylation by hDUS2.

The post-transcriptional reduction of uridine to dihydrouridine (D) by dihydrouridine synthase (DUS) enzymes is among the most ubiquitous transformations in RNA biology. D is found at multiple sites in tRNAs, and studies in yeast have proposed that each of the four eukaryotic DUS enzymes modifies a different site; however, the molecular basis for this exquisite selectivity is unknown, and human DUS enzymes have remained largely uncharacterized. Here we investigate the substrate specificity of human dihydrouridine synthase 2 (hDUS2) using mechanism-based cross-linking with 5-bromouridine (5-BrUrd)-modified oligonucleotide probes and in vitro dihydrouridylation assays. We find that hDUS2 exclusively modifies U20 across diverse tRNA substrates and identify a minimal GU sequence within the tRNA D loop that underlies selective substrate modification. Further, we use our mechanism-based platform to screen small molecule inhibitors of hDUS2, a potential anticancer target. Our work elucidates the principles of substrate modification by a conserved DUS and provides a general platform for studying RNA modifying enzymes with sequence-defined activity-based probes.

Software and Architecture Orchestration for Process Control in Industry 4.0 Enabled by Cyber-Physical Systems Technologies

In the context of Industry 4.0, this paper explores the vital role of advanced technologies, including Cyber–Physical Systems (CPS), Big Data, Internet of Things (IoT), digital twins, and Artificial Intelligence (AI), in enhancing data valorization and management within industries. These technologies are integral to addressing the challenges of producing highly customized products in mass, necessitating the complete digitization and integration of information technology (IT) and operational technology (OT) for flexible and automated manufacturing processes. The paper emphasizes the importance of interoperability through Service-Oriented Architectures (SOA), Manufacturing-as-a-Service (MaaS), and Resource-as-a-Service (RaaS) to achieve seamless integration across systems, which is critical for the Industry 4.0 vision of a fully interconnected, autonomous industry. Furthermore, it discusses the evolution towards Supply Chain 4.0, highlighting the need for Transportation Management Systems (TMS) enhanced by GPS and real-time data for efficient logistics. A guideline for implementing CPS within Industry 4.0 environments is provided, focusing on a case study of real-time data acquisition from logistics vehicles using CPS devices. The study proposes a CPS architecture and a generic platform for asset tracking to address integration challenges efficiently and facilitate the easy incorporation of new components and applications. Preliminary tests indicate the platform’s real-time performance is satisfactory, with negligible delay under test conditions, showcasing its potential for logistics applications and beyond.

Polydopamine functionalized dendritic fibrous silica nanoparticles as a generic platform for nucleic acid-based biosensing

Accurate and rapid detection of nucleic acid sequences is of utmost importance in various fields, including disease monitoring, clinical treatment, gene analysis and drug discovery. In this study, we developed a "turn-on" fluorescence biosensor that enables simple and highly efficient detection of nucleic acid biomarkers. Our approach involves the utilization of 6-carboxyfluorescein modified single-stranded DNA (FAM-ssDNA) as molecular recognition element, along with polydopamine-functionalized dendritic fibrous nanosilica (DFNS). FAM-ssDNA serves as both specific molecular recognition element for the target analyte and reporter capable of transducing a detectable signal through Watson–Crick base pairing. The polydopamine-functionalized DFNS (DFNS@DA) exhibits strong binding to FAM-ssDNA via polyvalent metal mediated coordination leading to effective quenching by fluorescence resonance energy transfer. In the presence of a complementary target sequence, FAM-ssDNA forms hybridized structure and detaches from DFNS@DA, which causes an increased fluorescence emission. The analytical system based on FAM-ssDNA and DFNS@DA demonstrates exceptional sensitivity, selectivity, and rapid response for the detection of nucleic acid sequences, leveraging the high adsorption and quenching properties of DFNS@DA. For the first proof of concept, we demonstrated the successful detection of microRNA (miR-21) in cancer cells using the FAM-ssDNA/DFNS@DA system. Our results highlight the promising capabilities of DFNS@DA and nucleic acid-based biosensors, offering a generic and cost-effective solution for the detection of nucleic acid-related biomarkers.Graphical abstract