Modular Platform Research Articles

Automated Continuous Crystallization Platform with Real-Time Particle Size Analysis via Laser Diffraction.

The fourth industrial revolution is gaining momentum in the pharmaceutical industry. However, particulate processes and suspension handling remain big challenges for automation and the implementation of real-time particle size analysis. Moreover, the development of antisolvent crystallization processes is often limited by the associated time-intensive experimental screenings. This work demonstrates a fully automated modular crystallization platform that overcomes these bottlenecks. The system combines automated crystallization, sample preparation, and immediate crystal size analysis via online laser diffraction (LD) and provides a technology for rapidly screening crystallization process parameters and crystallizer design spaces with minimal experimental effort. During the LD measurements, to avoid multiple scattering events, crystal suspension samples are diluted automatically. Multiple software tools, i.e., LabVIEW, Python, and PharmaMV, and logic algorithms are integrated in the platform to facilitate automated control of all the sensors and equipment, enabling fully automated operation. A customized graphical user interface is provided to operate the crystallization platform automatically and to visualize the measured crystal size and the crystal size distribution of the suspension. Antisolvent crystallization of ibuprofen, with ethanol as solvent and water with Soluplus (an additive) as antisolvent, is used as a case study. The platform is demonstrated for antisolvent crystallization of small ibuprofen crystals in a confined impinging jet crystallizer, performing automated preplanned user-defined experiments with online LD analysis.

Delineating redox cooperativity in water-soluble and membrane multiheme cytochromes through protein design.

Nature has evolved diverse electron transport proteins and multiprotein assemblies essential to the generation and transduction of biological energy. However, substantially modifying or adapting these proteins for user-defined applications or to gain fundamental mechanistic insight can be hindered by their inherent complexity. De novo protein design offers an attractive route to stripping away this confounding complexity, enabling us to probe the fundamental workings of these bioenergetic proteins and systems, while providing robust, modular platforms for constructing completely artificial electron-conducting circuitry. Here, we use a set of de novo designed mono-heme and di-heme soluble and membrane proteins to delineate the contributions of electrostatic micro-environments and dielectric properties of the surrounding protein medium on the inter-heme redox cooperativity that we have previously reported. Experimentally, we find that the two heme sites in both the water-soluble and membrane constructs have broadly equivalent redox potentials in isolation, in agreement with Poisson-Boltzmann Continuum Electrostatics calculations. BioDC, a Python program for the estimation of electron transfer energetics and kinetics within multiheme cytochromes, also predicts equivalent heme sites, and reports that burial within the low dielectric environment of the membrane strengthens heme-heme electrostatic coupling. We conclude that redox cooperativity in our diheme cytochromes is largely driven by heme electrostatic coupling and confirm that this effect is greatly strengthened by burial in the membrane. These results demonstrate that while our de novo proteins present minimalist, new-to-nature constructs, they enable the dissection and microscopic examination of processes fundamental to the function of vital, yet complex, bioenergetic assemblies.

Position control of a soft pneumatic actuator based on the pressure parameter feedback model (PPFM)

Abstract. Soft pneumatic actuators have been one of the cores of soft robotics research and play a key role in driving the development of soft robots. Due to its high degree of internal nonlinearity and unpredictable deformation caused by environmental influences, the control model established for soft robots is still a difficult problem in terms of improving accuracy. This paper proposes a new positional control method for soft pneumatic actuators that are suitable for independent 3D deformation at any position and are the core units of continuous robots. The pressure parameter feedback model (PPFM) of the airbag is obtained by adjusting the pressure input through a proportional valve, collecting the air pressure inside the airbag and obtaining the airbag expansion height. The pressure input signal is changed according to the PPFM of the airbag to control the position of the soft pneumatic actuator. A modular experimental platform is built to validate the PPFM-based control strategy, which is able to adjust the position of the end center point of the soft pneumatic actuator in space with the discussed characteristics. It is demonstrated that the theoretical model can significantly improve the stability and accuracy of the soft pneumatic actuator motion.

3D- printed modular spr platform integrated with ellipsometer for in-situ label-free optical sensing applications

The present work focuses on advancing optical sensing abilities through the instrumentation of Surface Plasmon Resonance Spectroscopy (SPR) with a commercial ellipsometer. This approach involves the incorporation of an in-house-built 3D-printed SPR module to circumvent the complexity of optical alignments with the ellipsometer. Currently, no commercially available SPR-enhanced Ellipsometry (SPRE) system exists, and previous systems are mostly custom-designed. Our work addresses this gap by introducing an in-house-built SPR module compatible with Ellipsometry and constructed using 3D printing technology. The 3D-printed SPR module is user-friendly, durable, lightweight, and cost-effective, incorporating a flow cell crucial for liquid media operation. This integrated tool provides a highly surface-sensitive, non-invasive, and label-free optical sensing platform. The developed SPR-Ellipsometry platform demonstrates excellent performance in detecting biochemical analytes and multilayer adsorption of poly-electrolytes. The bulk refractive index sensitivity measurement was performed to calibrate the Au sensor chip. The Wavelength (SPR mode), amplitude (Ψ), and phase interrogation (∆) exhibit bulk refractive index sensitivities of 3098 ± 177 nm/RIU, 835 ± 22 °/RIU and 5839 ± 229 °/RIU, respectively. We obtained the refractive index resolution around 1.712 × 10−7 RIU. Protein-protein (BSA/Anti-BSA) interactions were also conducted, achieving a detection limit of 3 pM for Anti-BSA. This work will significantly expand the functionality of SPRE sensors by offering enhanced sensitivity, rich information, and high precision, as well as opening avenues for more efficient and accessible optical sensing technologies. Moreover, the study establishes a foundational framework for researchers, shedding light on the potential of 3D printers in laboratories and expediting the development of cost-effective integrated sensing platforms.

Site‐Selective Distal C(sp3)−H Bromination of Aliphatic Amines as a Gateway for Forging Nitrogen‐Containing sp3 Architectures

AbstractHerein, we disclose a new strategy that rapidly and reliably incorporates bromine atoms at distal, secondary C(sp3)−H sites in aliphatic amines with an excellent and predictable site‐selectivity pattern. The resulting halogenated building blocks serve as versatile linchpins to enable a series of carbon‐carbon and carbon‐heteroatom bond‐formations at remote C(sp3) sites, thus offering a new modular and unified platform that expediates the access to advanced sp3 architectures possessing valuable nitrogen‐containing saturated heterocycles of interest in medicinal chemistry settings.

Site-Selective Distal C(sp3)-H Bromination of Aliphatic Amines as a Gateway for Forging Nitrogen-Containing sp3 Architectures.

Herein, we disclose a new strategy that rapidly and reliably incorporates bromine atoms at distal, secondary C(sp3)-H sites in aliphatic amines with an excellent and predictable site-selectivity pattern. The resulting halogenated building blocks serve as versatile linchpins to enable a series of carbon-carbon and carbon-heteroatom bond-formations at remote C(sp3) sites, thus offering a new modular and unified platform that expediates the access to advanced sp3 architectures possessing valuable nitrogen-containing saturated heterocycles of interest in medicinal chemistry settings.

NeuroMotion: Open-source platform with neuromechanical and deep network modules to generate surface EMG signals during voluntary movement.

Neuromechanical studies investigate how the nervous system interacts with the musculoskeletal (MSK) system to generate volitional movements. Such studies have been supported by simulation models that provide insights into variables that cannot be measured experimentally and allow a large number of conditions to be tested before the experimental analysis. However, current simulation models of electromyography (EMG), a core physiological signal in neuromechanical analyses, remain either limited in accuracy and conditions or are computationally heavy to apply. Here, we provide a computational platform to enable future work to overcome these limitations by presenting NeuroMotion, an open-source simulator that can modularly test a variety of approaches to the full-spectrum synthesis of EMG signals during voluntary movements. We demonstrate NeuroMotion using three sample modules. The first module is an upper-limb MSK model with OpenSim API to estimate the muscle fibre lengths and muscle activations during movements. The second module is BioMime, a deep neural network-based EMG generator that receives nonstationary physiological parameter inputs, like the afore-estimated muscle fibre lengths, and efficiently outputs motor unit action potentials (MUAPs). The third module is a motor unit pool model that transforms the muscle activations into discharge timings of motor units. The discharge timings are convolved with the output of BioMime to simulate EMG signals during the movement. We first show how MUAP waveforms change during different levels of physiological parameter variations and different movements. We then show that the synthetic EMG signals during two-degree-of-freedom hand and wrist movements can be used to augment experimental data for regressing joint angles. Ridge regressors trained on the synthetic dataset were directly used to predict joint angles from experimental data. In this way, NeuroMotion was able to generate full-spectrum EMG for the first use-case of human forearm electrophysiology during voluntary hand, wrist, and forearm movements. All intermediate variables are available, which allows the user to study cause-effect relationships in the complex neuromechanical system, fast iterate algorithms before collecting experimental data, and validate algorithms that estimate non-measurable parameters in experiments. We expect this modular platform will enable validation of generative EMG models, complement experimental approaches and empower neuromechanical research.

Tuning Structural Organization via Molecular Design and Hierarchical Assembly to Develop Supramolecular Thermoresponsive Hydrogels.

The cellular microenvironment is composed of a dynamic hierarchical fibrillar architecture providing a variety of physical and bioactive signals to the surrounding cells. This dynamicity, although common in biology, is a challenge to control in synthetic matrices. Here, responsive synthetic supramolecular monomers were designed that are able to assemble into hierarchical fibrous structures, combining supramolecular fiber formation via hydrogen bonding interactions, with a temperature-responsive hydrophobic collapse, resulting in cross-linking and hydrogel formation. Therefore, amphiphilic molecules were synthesized, composed of a hydrogen bonding ureido-pyrimidinone (UPy) unit, a hydrophobic alkyl spacer, and a hydrophilic oligo(ethylene glycol) tail. The temperature responsive behavior was introduced by functionalizing these supramolecular amphiphiles with a relatively short poly(N-isopropylacrylamide) (PNIPAM) chain (M n ∼ 2.5 or 5.5 kg/mol). To precisely control the assembly of these monomers, the length of the alkyl spacer between the UPy moiety and PNIPAM was varied in length. A robust sol-gel transition, with the dodecyl UPy-PNIPAM molecule, was obtained, with a network elasticity enhancing over 2000 times upon heating above room temperature. The UPy-PNIPAM compounds with shorter alkyl spacers were already hydrogels at room temperature. The sol-gel transition of the dodecyl UPy-PNIPAM hydrogelator could be tuned by the incorporation of different UPy-functionalized monomers. Furthermore, we demonstrated the suitability of this system for microfluidic cell encapsulation through a convenient temperature sol-gel transition. Our results indicate that this novel thermoresponsive supramolecular system offers a modular platform to study and guide single-cell behavior.

Modular Synthetic Platform for Interior-Functionalized Dendritic Macromolecules Enabled by the Palladium/Norbornene Catalysis.

Synthesis of interior-functionalized dendritic macromolecules is generally tedious and labor-intensive, which has been a key factor hampering their practical applications. Here, we have revisited this long-standing challenge and devised a modular and convergent platform to synthesize multifunctional dendrons with all-carbon backbones up to four generations via an in situ functionalization strategy. Enabled by the palladium/norbornene cooperative catalysis, different functional groups can be introduced to each generation of dendrons during the dendron growth, making it convenient for systematic comparison of their properties. The utility of this versatile platform is further showcased in the internal-functionalization-dependent properties of dendrons as organogels and aggregation-induced emission materials.

Truly Modular and Open System Design is Difficult

AbstractIn the United States, major defense acquisition programs must implement a modular open systems approach (MOSA) as required by U.S. law. (Defense Standardization Program, 2016). Some in the defense community have focused on MOSA as a checklist compliance activity. However, designing economically and operationally competitive system platforms that are truly modular is extremely challenging. Many such modular system platform efforts fail to meet early expectations for convenience, cost, and community uptake. Open standards are challenging as well. If a highly successful, stable, well‐documented, appropriate open standard is available for an interface, then leveraging such a standard can be fairly straightforward. However, often there is no obvious or adequate open standard available and the system platform owners have to organize a community to bring such a standard into existence.This paper will review three concrete examples of such MOSA efforts. The three cases cover both commercial and defense applications as well as covering both hardware and software. The two systems that focused on hardware modularity did eventually go into production but did not completely fulfill early expectations for their programs. The software system used an out‐of‐the‐box approach and achieved a best‐in‐class result in terms of economic effectiveness, time to market, and stakeholder satisfaction. This paper will discuss underlying challenges that seem to be common to all such efforts, review the results of the three cases, and offer some simple guidelines for increasing the probability of success for such a program.

An aptamer-mediated base editing platform for simultaneous knockin and multiple gene knockout for allogeneic CAR-T cells generation

Gene editing technologies hold promise for enabling the next generation of adoptive cellular therapies. Conventional gene editing platforms that rely on nuclease activity, such as Clustered regularly interspaced short palindromic repeats-CRISPR associated protein 9 (CRISPR-Cas9), allow efficient introduction of genetic modifications; however, these modifications occur via the generation of DNA double-strand breaks (DSBs) and can lead to unwanted genomic alterations and genotoxicity. Here, we apply a novel modular RNA aptamer-mediated Pin-point™ base editing platform to simultaneously introduce multiple gene knockouts and site-specific integration of a transgene in human primary T cells. We demonstrate high editing efficiency and purity at all target sites and significantly reduced frequency of chromosomal translocations compared to the conventional CRISPR-Cas9 system. Site-specific knock-in of a chimeric antigen receptor (CAR) and multiplex gene knockout are achieved within a single intervention and without the requirement for additional sequence-targeting components. The ability to perform complex genome editing efficiently and precisely highlights the potential of the Pin-point platform for application in a range of advanced cell therapies.

Real-time automatic control of multi-energy system for smart district community: A coupling ensemble prediction model and safe deep reinforcement learning

Energy system autonomous control is influenced by day-ahead forecasting, despite being carried out independently in the literature. This paper develops an energy management modular platform that integrates multi-variable timeseries prediction and autonomous energy infrastructure scheduling in real-time. Firstly, an ensemble prediction model is developed for the day-ahead multi-energy and renewable power prediction, which is implemented by coupling variants of CNN, GRU, and BiLSTM models into a global model using an ensemble approach. Secondly, a deep reinforcement learning (DRL) with a soft actor critic (SAC) algorithm that include safety-guided network to make the policy network constraint-aware is proposed. A multi-energy system with renewable energy and carbon capture technology is then anticipated as the energy infrastructure for achieving a carbon neutral community and evaluating our proposed model. The proposed models are trained and tested on a real-world dataset from Arizona, USA. The ensemble prediction model achieved the least root mean squared error (RMSE). On the other hand, the improved DRL method exhibits superior performance in reducing energy cost, minimum constraint violation, and fast deployment compared to state-of-the-art DRL methods. Finally, the two models are coupled and carry out generalization performance on the prediction and energy management scheme, including the sensitivity analysis on carbon capture price, in the case studies.

Switching-mode audio amplifier based on a ΔΣ A/D converter

The article describes the design and implementation of a modular hardware platform intended for testing and measuring various configurations of switching-mode audio amplifiers based on sigma-delta modulation. Two single-channel power amplifier modules were designed and manufactured, along with a stereophonic module serving as the basic source of modulated signals. Additionally, measurements were conducted on the fundamental parameters of the completed amplifier, such as harmonic distortion level, dynamic range, and output power. The developed platform serves as a foundation for modifications and further advancement in the technology of building switching-mode audio amplifiers.

Soft Polyethylene Glycol Hydrogels Support Human PSC Pluripotency and Morphogenesis.

Lumenogenesis within the epiblast represents a critical step in early human development, priming the embryo for future specification and patterning events. However, little is known about the specific mechanisms that drive this process due to the inability to study the early embryo in vivo. While human pluripotent stem cell (hPSC)-based models recapitulate many aspects of the human epiblast, most approaches for generating these 3D structures rely on ill-defined, reconstituted basement membrane matrices. Here, we designed synthetic, nonadhesive polyethylene glycol (PEG) hydrogel matrices to better understand the role of matrix mechanical cues in iPSC morphogenesis, specifically elastic modulus. First, we identified a narrow range of hydrogel moduli that were conducive to the hPSC viability, pluripotency, and differentiation. We then used this platform to investigate the effects of the hydrogel modulus on lumenogenesis, finding that matrices of intermediate stiffness yielded the most epiblast-like aggregates. Conversely, stiffer matrices impeded lumen formation and apico-basal polarization, while the softest matrices yielded polarized but aberrant structures. Our approach offers a simple, modular platform for modeling the human epiblast and investigating the role of matrix cues in its morphogenesis.

GEN-RWD Sandbox: bridging the gap between hospital data privacy and external research insights with distributed analytics

BackgroundArtificial intelligence (AI) has become a pivotal tool in advancing contemporary personalised medicine, with the goal of tailoring treatments to individual patient conditions. This has heightened the demand for access to diverse data from clinical practice and daily life for research, posing challenges due to the sensitive nature of medical information, including genetics and health conditions. Regulations like the Health Insurance Portability and Accountability Act (HIPAA) in the U.S. and the General Data Protection Regulation (GDPR) in Europe aim to strike a balance between data security, privacy, and the imperative for access.ResultsWe present the Gemelli Generator - Real World Data (GEN-RWD) Sandbox, a modular multi-agent platform designed for distributed analytics in healthcare. Its primary objective is to empower external researchers to leverage hospital data while upholding privacy and ownership, obviating the need for direct data sharing. Docker compatibility adds an extra layer of flexibility, and scalability is assured through modular design, facilitating combinations of Proxy and Processor modules with various graphical interfaces. Security and reliability are reinforced through components like Identity and Access Management (IAM) agent, and a Blockchain-based notarisation module. Certification processes verify the identities of information senders and receivers.ConclusionsThe GEN-RWD Sandbox architecture achieves a good level of usability while ensuring a blend of flexibility, scalability, and security. Featuring a user-friendly graphical interface catering to diverse technical expertise, its external accessibility enables personnel outside the hospital to use the platform. Overall, the GEN-RWD Sandbox emerges as a comprehensive solution for healthcare distributed analytics, maintaining a delicate equilibrium between accessibility, scalability, and security.

Experimental Setup for Evaluating Depth Sensors in Augmented Reality Technologies Used in Medical Devices.

This paper presents a fully automated experimental setup tailored for evaluating the effectiveness of augmented and virtual reality technologies in healthcare settings for regulatory purposes, with a focus on the characterization of depth sensors. The setup is constructed as a modular benchtop platform that enables quantitative analysis of depth cameras essential for extended reality technologies in a controlled environment. We detail a design concept and considerations for an experimental configuration aimed at simulating realistic scenarios for head-mounted displays. The system includes an observation platform equipped with a three-degree-of-freedom motorized system and a test object stage. To accurately replicate real-world scenarios, we utilized an array of sensors, including commonly available range-sensing cameras and commercial augmented reality headsets, notably the Intel RealSense L515 LiDAR camera, integrated into the motion control system. The paper elaborates on the system architecture and the automated data collection process. We discuss several evaluation studies performed with this setup, examining factors such as spatial resolution, Z-accuracy, and pixel-to-pixel correlation. These studies provide valuable insights into the precision and reliability of these technologies in simulated healthcare environments.

DELTA: Integrating Multimodal Sensing with Micromobility for Enhanced Sidewalk and Pedestrian Route Understanding.

Urban environments are undergoing significant transformations, with pedestrian areas emerging as complex hubs of diverse mobility modes. This shift demands a more nuanced approach to urban planning and navigation technologies, highlighting the limitations of traditional, road-centric datasets in capturing the detailed dynamics of pedestrian spaces. In response, we introduce the DELTA dataset, designed to improve the analysis and mapping of pedestrian zones, thereby filling the critical need for sidewalk-centric multimodal datasets. The DELTA dataset was collected in a single urban setting using a custom-designed modular multi-sensing e-scooter platform encompassing high-resolution and synchronized audio, visual, LiDAR, and GNSS/IMU data. This assembly provides a detailed, contextually varied view of urban pedestrian environments. We developed three distinct pedestrian route segmentation models for various sensors-the 4K camera, stereocamera, and LiDAR-each optimized to capitalize on the unique strengths and characteristics of the respective sensor. These models have demonstrated strong performance, with Mean Intersection over Union (IoU) values of 0.84 for the reflectivity channel, 0.96 for the 4K camera, and 0.92 for the stereocamera, underscoring their effectiveness in ensuring precise pedestrian route identification across different resolutions and sensor types. Further, we explored audio event-based classification to connect unique soundscapes with specific geolocations, enriching the spatial understanding of urban environments by associating distinctive auditory signatures with their precise geographical origins. We also discuss potential use cases for the DELTA dataset and the limitations and future possibilities of our research, aiming to expand our understanding of pedestrian environments.

Open-source simulation platform for air source heat pump integrated with thermal energy storage

This article introduces a modular simulation platform for assessing thermal energy storage (TES) integrated with air source heat pumps (ASHP). The Python platform is an open-source library that includes classes for modeling air-air and air-water heat pumps, TES devices, and the heating load of residential buildings. To validate the ASHP model, the study utilized experimental data obtained from a commercial heat pump evaluated at the National Renewable Energy Laboratory (NREL). The results indicate a mean deviation of 0.7% for COP across the operating range, with a maximum relative deviation of 12.6%. In terms of system heating capacity, the model had an average deviation of 4.3% compared to experimental results, with a maximum deviation of 8.2%. Three classes were implemented for modeling distinct types of TES devices: a generic TES based on energy balances, a sensible isothermal water tank, and a stratified water tank. Details of the mathematical models are provided, along with their respective strengths and limitations. An example is provided showcasing the integration of a residential 10 kWh thermal storage unit with an ASHP operating in Denver, CO. The comparison of two different discharge criteria for the TES unit highlights the importance of control strategies in the system performance.

Diagnosing viral gastro-enteritis using the fully automated sample-in, result-out STARlet All in one system (AIOS)

The STARlet All-In-One system is a modular platform that integrates the complete molecular diagnostic workflow from nucleic acid extraction of clinical samples to PCR set-up and amplification. The platform was evaluated in comparison with laboratory developed tests (LDT) on fecal samples from patients with suspected viral gastro-enteritis. In a retrospective study, 72 positive samples were analysed, including all pathogens detected by the Seegene Allplex™ GI-virus assay, adenovirus, astrovirus, norovirus GI and GII, sapovirus, and rotavirus. Concordant results were obtained for 69 samples (96 %). Three discordant results were observed, one norovirus GII positive that gave an invalid result in the AIOS and two samples that were negative in the AIOS. One adenovirus positive that was subtyped as a genotype 2 virus, which is not associated with gastro-enteritis, and a sapovirus. In the prospective part of the study, 661 fecal samples were included. A total of 61 positive samples were detected, of which 60 were also detected by the AIOS. One norovirus GII positive sample (CT 35.2) was tested negative in the AIOS. Two additional sapovirus positive samples, CT 37 and 38, were detected by the AIOS but not by the LDT. The STARlet All-In-One platforms results in an automated molecular workflow with reduced hands-on time and enables running assays during out of office hours. Application of the Seegene Allplex™ GI-virus assay showed excellent concordance to the current diagnostic LDT. In a prospective comparison, only three discordant results were observed, all with CT values over 35 and therefore unlikely of clinical relevance.

Sucrose-Powered Liposome Nanosensors for Urinary Glucometer-Based Monitoring of Cancer.

Timely detection of early-stage cancer holds immense potential in enhancing prognostic outcomes. There is an increasing desire for versatile tools to enable simple, sensitive, and cost-effective cancer detection. By exploiting the extraintestinal metabolic inertness and efficiency renal clearance of sucrose, we designed a liposome nanosensor using sucrose as a messenger to convert tumor-specific esterase activity into glucose meter readout, enabling economical and sensitive urinalysis for cancer detection in point-of-care testing (POCT). Our results demonstrate that the nanosensors exhibited significant signal differences between tumor-bearing and healthy mice in both orthotopic and metastatic tumor models. Additionally, efficient elimination of the nanosensors through the hepatobiliary pathway was observed with no significant toxicity. Such a non-invasive diagnostic modality significantly assists in personalized pharmacological treatment and follow-up efficacy assessment. We envision that this modular liposome nanosensor platform might be applied for economically detecting diverse diseases via a simple urinary test.