Conventional Platforms Research Articles

Definition and implementation of the Cloud Infrastructure for the integration of the Human Digital Twin in the Social Internet of Things

With the integration of virtualization technologies, the Internet of Things (IoT) is expanding its capabilities and quickly becoming a complex ecosystem of networked devices. The Social Internet of Things (SIoT), where intelligent things include social properties that improve functioning and user engagement, is the result of this progress. The SIoT still has issues with scalability, data management, and user-centric operations, despite tremendous progress. In order to overcome these obstacles, a strong architecture is needed that can handle the enormous number of IoT devices while simultaneously streamlining the user interface.This study provides a unique architecture for the IoT that uses containerization to efficiently deploy and manage services while integrating Virtual Users (VUs) and Social Virtual Objects (SVOs) into a scalable Cloud/Edge infrastructure. These innovative aspects collectively advance previous works presented in literature and focused on novel SIoT architectures and implementations, by addressing key challenges in scalability, efficiency, and automation within the SIoT. The proposed method presents an extensible, modular architecture that lets VUs self-manage IoT services, making user administration easier and improving system security and scalability. Important parts of the design include a host controller for container orchestration, a deployer for automated service deployment, and user clusters for aggregating VUs, SVOs, and apps to provide secured and efficient data sharing. We show through experimental assessment that the architecture can manage high-volume installations and operating needs, exceeding the conventional platform based on Google App Engine in terms of system overhead and deployment timeframes. The obtained results highlight how our suggested architecture, which provides an easy-to-use, scalable, and secure foundation for IoT deployments, has the potential to advance the SIoT landscape.

Nerve-sparing deep endometriosis surgery with rectal discoid resection using da Vinci SP

ObjectiveTo demonstrate the anatomical and technical highlights of nerve-sparing deep endometriosis (DE) surgery with rectal discoid resection using a newer single-port robotic system. DesignStep-by-step demonstration of this method with narrated video footage. SettingAn urban general hospital. Single-port laparoscopic surgery is a useful surgical approach in gynecology because of the excellent cosmetic results, but shows challenges including reduced intracorporeal triangulation and conflict with non-articulating instruments. The range of indications has thus been limited. PatientA 46-year-old woman was referred with severe pelvic pain, dysmenorrhea and pain on defecation. Magnetic resonance imaging revealed uterine adenomyosis, bilateral ovarian endometriomas, and 3 cm of rectal endometriosis. CT colonography confirmed 38% stenosis of the rectum. InterventionA newer single-port robotic system. Main Outcome MeasuresThe technical safety and feasibility of intrapelvic complex DE surgery using a newer single-port robotic platform. ResultsThe procedure was performed using 9 steps with a da Vinci SP surgical system (Intuitive Surgical, Sunnyvale, California). Importantly, the surgical steps were completely identical to conventional multiport laparoscopic or robotic surgery. This suggests that conventional laparoscopic or robotic skills are highly transferrable to the newer system: Step 1, adhesiolysis and adnexal surgery; Step 2, separation of the nerve plane; Step 3, dissection of the ureter; Step 4, reopening of the pouch of Douglas; Step 5, complete removal of DE lesions while avoiding injury to the nerve plane; Step 6, hysterectomy (if the patient desires non-fertility-sparing surgery); Step 7, rectal discoid resection with circular stapler; Step 8, checking for rectal injury using an air leakage test and tissue perfusion; and Step 9, application of barrier agents for adhesion prevention. The newer single-port system offered several advantages, including high-resolution three-dimensional visualization, articulating instruments (intracorporeal instrument triangulation), and improved dexterity and range of motion. These advantages allow precise dissection even in difficult situations such as deep endometriosis. ConclusionsThis appears to be the first reported use of the da Vinci SP for nerve-sparing DE surgery or rectal discoid resection. The newer single-port robotic system can provide the same quality of surgery as conventional multi-port laparoscopic and robotic platforms with cosmetic advantages for the treatment of complex pelvic pathologies.

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.

Precise mode control of laser-written waveguides for broadband, low-dispersion 3D integrated optics

Three-dimensional (3D) glass chips are promising waveguide platforms for building hybrid 3D photonic circuits due to their 3D topological capabilities, large transparent windows, and low coupling dispersion. At present, the key challenge in scaling down a benchtop optical system to a glass chip is the lack of precise methods for controlling the mode field and optical coupling of 3D waveguide circuits. Here, we propose an overlap-controlled multi-scan (OCMS) method based on laser-direct lithography that allows customizing the refractive index profile of 3D waveguides with high spatial precision in a variety of glasses. On the basis of this method, we achieve variable mode-field distribution, robust and broadband coupling, and thereby demonstrate dispersionless LP21-mode conversion of supercontinuum pulses with the largest deviation of <0.1 dB in coupling ratios on 210 nm broadband. This approach provides a route to achieve ultra-broadband and low-dispersion coupling in 3D photonic circuits, with overwhelming advantages over conventional planar waveguide-optic platforms for on-chip transmission and manipulation of ultrashort laser pulses and broadband supercontinuum.

Meta Land: Redefining Virtual Communities through Centralized Governance, Inclusivity and Innovation

Within the dynamic realm of virtual communities, Meta Land presents itself as a welcome deviation from the decentralised standard. It presents a centralised platform with an emphasis on user participation, creativity, and inclusion. In contrast to conventional metaverse platforms, ML priori- tises the development of significant relationships via its sub- community platform, serving a variety of demographics including independent contractors and students. Decentralised governance gives people the ability to control machine learning, making sure that it stays in line with the community’s overall goals.By means of its virtual economy and engagement mechanisms that provide incentives, ML facilitates equal participation and cultivates a lively community culture. By providing a dynamic and engaging experience for people globally, ML aims to change the virtual environment with a dedication to ongoing innovation and sustainability.

Asymmetric interactions among cutting-edge technologies and pioneering conventional and Islamic cryptocurrencies: fresh evidence from intra-day-based good and bad volatilities

This study examines the nexus between the good and bad volatilities of three technological revolutions—financial technology (FinTech), the Internet of Things, and artificial intelligence and technology—as well as the two main conventional and Islamic cryptocurrency platforms, Bitcoin and Stellar, via three approaches: quantile cross-spectral coherence, quantile-VAR connectedness, and quantile-based non-linear causality-in-mean and variance analysis. The results are as follows: (1) under normal market conditions, in long-run horizons there is a significant positive cross-spectral relationship between FinTech's positive volatilities and Stellar’s negative volatilities; (2) Stellar’s negative and positive volatilities exhibit the highest net spillovers at the lower and upper tails, respectively; and (3) the quantile-based causality results indicate that Bitcoin’s good (bad) volatilities can lead to bad (good) volatilities in all three smart technologies operating between normal and bull market conditions. Moreover, the Bitcoin industry’s negative volatilities have a bilateral cause-and-effect relationship with FinTech’s positive volatilities. By analyzing the second moment, we found that Bitcoin's negative volatilities are the only cause variable that generates FinTech's good volatility in a unidirectional manner. As for Stellar, only bad volatilities have the potential to signal good volatilities for cutting-edge technologies in some middle quantiles, whereas good volatilities have no significant effect. Hence, the trade-off between Bitcoin and cutting-edge technologies, especially FinTech-related advancements, appear more broadly and randomly compared with the Stellar-innovative technologies nexus. The findings provide valuable insights for FinTech companies, blockchain developers, crypto-asset regulators, portfolio managers, and high-tech investors.

Plasmonic sensor combined with a microcuvette device for monitoring molecule binding processes at ultra-low concentrations

A novel sensing strategy is presented to monitor receptor-target pair interactions at ultra-low concentrations without functionalization processes. The biosensing is made by a sensitive chip and a surface plasmon resonance (SPR) probe connected in series, both built up on multimode plastic optical fibers (POFs). The SPR probe is a conventional D-shaped POF platform, while the capture platform consists of three microholes, containing a few hundred nanoliters each, made in the core of a modified POF. The microholes are filled with a specific receptor solution, which selectively captures the target present in the samples dropped over the filled microholes. Any variation occurring in the microholes due to the molecule binding processes changes the mode profile of the propagated light in the POF's core, modifying the plasmonic effects proportionally to the phenomena under scrutiny. As a proof of concept, the interactions of two receptor-target pairs, estradiol and cortisol to the respective receptors, namely Estrogen Receptor alpha protein (ER) and Glucocorticoid Receptor (GR), were monitored over time in the attomolar range. The receptor-target pair interactions are observed at ultra-low concentrations (order of aM) by monitoring the resonance wavelength shift over time. The proposed sensing approach stands for a novel class of laboratory instrumentation with unprecedented capabilities in terms of compactness, ultra-high sensitivity and low-cost.

Firing feature-driven neural circuits with scalable memristive neurons for robotic obstacle avoidance

Neural circuits with specific structures and diverse neuronal firing features are the foundation for supporting intelligent tasks in biology and are regarded as the driver for catalyzing next-generation artificial intelligence. Emulating neural circuits in hardware underpins engineering highly efficient neuromorphic chips, however, implementing a firing features-driven functional neural circuit is still an open question. In this work, inspired by avoidance neural circuits of crickets, we construct a spiking feature-driven sensorimotor control neural circuit consisting of three memristive Hodgkin-Huxley neurons. The ascending neurons exhibit mixed tonic spiking and bursting features, which are used for encoding sensing input. Additionally, we innovatively introduce a selective communication scheme in biology to decode mixed firing features using two descending neurons. We proceed to integrate such a neural circuit with a robot for avoidance control and achieve lower latency than conventional platforms. These results provide a foundation for implementing real brain-like systems driven by firing features with memristive neurons and put constructing high-order intelligent machines on the agenda.

AI student success predictor: Enhancing personalized learning in campus management systems

Campus Management Systems (CMSs) are vital tools in managing educational institutions, handling tasks like student enrollment, scheduling, and resource allocation. The increasing adoption of CMS for online and mixed-learning environments highlights their importance. However, inherent limitations in conventional CMS platforms hinder personalized student guidance and effective identification of academic challenges. Addressing this crucial gap, our study introduces an AI Student Success Predictor empowered by advanced machine learning algorithms, capable of automating grading processes, predicting student risks, and forecasting retention or dropout outcomes. Central to our approach is the creation of a standardized dataset, meticulously curated by integrating student information from diverse relational databases. A Convolutional Neural Network (CNN) feature learning block is developed the extract the hidden patterns in the student data. This classification model stands as an ensemble masterpiece, incorporating SVM, Random Forest, and KNN classifiers, subsequently refined by a Bayesian averaging model. The proposed ensemble model shows the ability to predict the student grades, retention, and risk levels of dropout. The accuracy achieved by the proposed model is assessed using test data, culminating in a commendable 93% accuracy for student grade prediction and student risk prediction, and a solid 92% accuracy for the complex domain of retention and dropout forecasting. The proposed AI system seamlessly integrates with existing CMS infrastructure, enabling real-time data retrieval and swift, accurate predictions, enhancing academic decision-making efficiency. Our study's pioneering AI Student Success Predictor bridges the chasm between traditional CMS limitations and the growing demands of modern education.

Design and application of small-diameter closed thoracic drainage tube fixation following lung wedge resection: A randomised prospective study.

The use of indwelling closed thoracic drainage tubes in the wedge resection of the lungs is of great significance to postoperative recovery. However, there are potential risks. To explore the design feasibility and application effect of triple-buffer-system-fixed small-diameter (18 F) thoracic closed drainage tubes following lung wedge resection. A total of 136 patients with indwelling thoracic drainage tubes following pulmonary wedge resection were recruited, with 70 patients allocated to the control group and 66 to the experimental group. The drainage tube in the experimental group was fixed with the triple-buffer system, while that in the control group was fixed using the conventional lifting platform method. The incidence of unplanned extubation, the indwelling time of the drainage tube and the time and material costs, as well as information regarding any subcutaneous emphysema and skin tension blisters, were recorded following the operation. The pain and degree of comfort were assessed using a chi-square test and a rank sum t-test to compare the differences between the two groups. There were no statistically significant differences in terms of age, gender and sweating between the two groups. Compared with the control group, the unplanned extubation rate of the experimental group was lower (χ2= 8.513; P= 0.004), the indwelling time of the drainage tube was shorter (t= 2.108; P= 0.037), the cumulative material cost was lower (t= 3.778; P< 0.001), the time cost was also lower (Z= 2.717; P= 0.008), the degree of comfort was higher (Z= 2.752; P= 0.006), and the degree of pain was lower (Z= 4.019; P< 0.001). The incidence of subcutaneous emphysema was significantly lower in the experimental group than in the control group (χ2= 8.513; P= 0.004). The use of the triple-buffer system to fix small-diameter (18 F) thoracic closed drainage tubes can reduce the unplanned extubation rate, indwelling time of the drainage tube and the incidence of adverse reactions.

Advances in Cell-Rich Inks for Biofabricating Living Architectures.

Advancing biofabrication toward manufacturing living constructs with well-defined architectures and increasingly biologically relevant cell densities is highly desired to mimic the biofunctionality of native human tissues. The formulation of tissue-like, cell-dense inks for biofabrication remains, however, challenging at various levels of the bioprinting process. Promising advances have been made toward this goal, achieving relatively high cell densities that surpass those found in conventional platforms, pushing the current boundaries closer to achieving tissue-like cell densities. On this focus, herein the overarching challenges in the bioprocessing of cell-rich living inks into clinically grade engineered tissues are discussed, as well as the most recent advances in cell-rich living ink formulations and their processing technologies are highlighted. Additionally, an overview of the foreseen developments in the field is provided and critically discussed.

Phylogenomics and genetic analysis of solvent-producing Clostridium species.

The genus Clostridium is a large and diverse group within the Bacillota (formerly Firmicutes), whose members can encode useful complex traits such as solvent production, gas-fermentation, and lignocellulose breakdown. We describe 270 genome sequences of solventogenic clostridia from a comprehensive industrial strain collection assembled by Professor David Jones that includes 194 C. beijerinckii, 57 C. saccharobutylicum, 4 C. saccharoperbutylacetonicum, 5 C. butyricum, 7 C. acetobutylicum, and 3 C. tetanomorphum genomes. We report methods, analyses and characterization for phylogeny, key attributes, core biosynthetic genes, secondary metabolites, plasmids, prophage/CRISPR diversity, cellulosomes and quorum sensing for the 6 species. The expanded genomic data described here will facilitate engineering of solvent-producing clostridia as well as non-model microorganisms with innately desirable traits. Sequences could be applied in conventional platform biocatalysts such as yeast or Escherichia coli for enhanced chemical production. Recently, gene sequences from this collection were used to engineer Clostridium autoethanogenum, a gas-fermenting autotrophic acetogen, for continuous acetone or isopropanol production, as well as butanol, butanoic acid, hexanol and hexanoic acid production.

Ultimately Adaptive Fluid Interfacial Phospholipid Membranes Unveiled Unanticipated High Cellular Mechanical Work.

Living cells actively interact biochemically and mechanically with the surrounding extracellular matrices (ECMs) and undergo dramatic morphological and dimensional transitions, concomitantly remodeling ECMs. However, there is no suitable method to quantitatively discuss the contribution of mechanical interactions in such mutually adaptive processes. Herein, a highly deformable "living" cellular scaffold is developed to evaluate overall mechanical energy transfer between cell and ECMs. It is based on the water-perfluorocarbon interface decorated with phospholipids bearing a cell-adhesive ligand and fluorescent tag. The bioinert nature of the phospholipid membranes prevents the formation of solid-like protein nanofilms at the fluid interface, enabling to visualize and quantify cellular mechanical work against the ultimately adaptive model ECM. A new cellular wetting regime is identified, wherein interface deformation proceeds to cell flattening, followed by its eventual restoration. The cellular mechanical work during this adaptive wetting process is one order of magnitude higher than those reported withconventional elastic platforms. The behavior of viscous liquid drops at the air-water interface can simulate cellular adaptive wetting, suggesting that overall viscoelasticity of the cell body predominates the emergent wetting regime and regulates mechanical output. Cellular-force-driven high-energy states on the adaptive platform can be useful for cell fate manipulation.

Comparison of Costs in Teledermatology Using PC and Camera Versus Smartphone.

Introduction: One of the challenges faced by the Colombian Health System is to improve access to health services for the dispersed and isolated rural population, particularly in the field of dermatology. This article examines the implementation of a teledermatology service using a PC and camera versus smartphone technology. Methods: A total of 542 teledermatology visits were conducted, involving 478 patients, in addition to 64 visits for clinical follow-up for patients as per the dermatologist's recommendation. Out of the 478 patients, 461 met the inclusion criteria and agreed to participate in the study. The data collection instrument from the general practitioner or referring provider covered three consultation moments: (1) sending an initial consultation, (2) providing a response to the patient, and (3) sending a follow-up consultation. Seven hundred forty-seven records were completed by the general practitioner for the three consultation moments. Furthermore, 372 consultations were documented by the dermatologist or referring provider for two moments: (1) response to the initial consultation by the dermatologist, and (2) response to the follow-up consultation by the dermatologist. After validating the information reported in the instruments, a descriptive analysis of the data was conducted, utilizing absolute frequencies and percentages for qualitative variables and measures of central tendency (mean, median, standard deviation, and interquartile range) for quantitative variables. The data were analyzed from 747 records of the referring provider instrument related to 461 patients, between 18 and 98 years of age, with a predominantly female representation. Results: The results indicated that for teleconsultations conducted using a mobile device, the average total duration of the teleconsultation was longer on the traditional platform compared with the mobile device (13.03 vs. 8.27 min). Additionally, it was observed that the time taken to store, send, and capture a single image (clinical or dermoscopic) using the mobile device was three times lower than that on the conventional platform (25 vs. 75 s). Similar findings were noted for teleconsultations carried out by the dermatologist, predominantly utilizing a mobile device. The average consultation time was shorter for the mobile device compared with the traditional platform (8.14 vs. 12 min). Conclusions: The cost reduction suggests that the operation of the service is more efficient with smartphone technology in comparison to the use of a PC and camera. Teledermatology with smartphones provides a streamlined, efficient, and technically sound process for obtaining clinical and dermoscopic images.

An ultra energy-efficient hardware platform for neuromorphic computing enabled by 2D-TMD tunnel-FETs

Brain-like energy-efficient computing has remained elusive for neuromorphic (NM) circuits and hardware platform implementations despite decades of research. In this work we reveal the opportunity to significantly improve the energy efficiency of digital neuromorphic hardware by introducing NM circuits employing two-dimensional (2D) transition metal dichalcogenide (TMD) layered channel material-based tunnel-field-effect transistors (TFETs). Our novel leaky-integrate-fire (LIF) based digital NM circuit along with its Hebbian learning circuitry operates at a wide range of supply voltages, frequencies, and activity factors, enabling two orders of magnitude higher energy-efficient computing that is difficult to achieve with conventional material and/or device platforms, specifically the silicon-based 7 nm low-standby-power FinFET technology. Our innovative 2D-TFET based NM circuit paves the way toward brain-like energy-efficient computing that can unleash major transformations in future AI and data analytics platforms.

The Lakehouse: State of the Art on Concepts and Technologies

In the context of data analytics, so-called lakehouses refer to novel variants of data platforms that attempt to combine characteristics of data warehouses and data lakes. In this way, lakehouses promise to simplify enterprise analytics architectures, which often suffer from high operational costs, slow analytical processes and further shortcomings resulting from data replication. However, different views and notions on the lakehouse paradigm exist, which are commonly driven by individual technologies and varying analytical use cases. Therefore, it remains unclear what challenges lakehouses address, how they can be characterized and which technologies can be leveraged to implement them. This paper addresses these issues by providing an extensive overview of concepts and technologies that are related to the lakehouse paradigm and by outlining lakehouses as a distinct architectural approach for data platforms. Concepts and technologies from literature with regard to lakehouses are discussed, based on which a conceptual foundation for lakehouses is established. In addition, several popular technologies are evaluated regarding their suitability for the building of lakehouses. All findings are supported and demonstrated with the help of a representative analytics scenario. Typical challenges of conventional data platforms are identified, a new, sharper definition for lakehouses is proposed and technical requirements for lakehouses are derived. As part of an evaluation, these requirements are applied to several popular technologies, of which frameworks for data lakes turn out to be particularly helpful for the construction of lakehouses. Our work provides an overview of the state of the art and a conceptual foundation for the lakehouse paradigm, which can support future research.

Exciton polariton condensation from bound states in the continuum at room temperature

Exciton–polaritons (polaritons) resulting from the strong exciton–photon interaction stimulates the development of novel low-threshold coherent light sources to circumvent the ever-increasing energy demands of optical communications1–3. Polaritons from bound states in the continuum (BICs) are promising for Bose–Einstein condensation owing to their theoretically infinite quality factors, which provide prolonged lifetimes and benefit the polariton accumulations4–7. However, BIC polariton condensation remains limited to cryogenic temperatures ascribed to the small exciton binding energies of conventional material platforms. Herein, we demonstrated room-temperature BIC polariton condensation in perovskite photonic crystal lattices. BIC polariton condensation was demonstrated at the vicinity of the saddle point of polariton dispersion that generates directional vortex beam emission with long-range coherence. We also explore the peculiar switching effect among the miniaturized BIC polariton modes through effective polariton−polariton scattering. Our work paves the way for the practical implementation of BIC polariton condensates for integrated photonic and topological circuits.

Whole urine-based multiple cancer diagnosis and metabolite profiling using 3D evolutionary gold nanoarchitecture combined with machine learning-assisted SERS

To develop onsite applicable cancer diagnosis technologies, a noninvasive human biofluid detection method with high sensitivity and specificity is required, available for classifying cancer from the normal group. Herein, a three-dimensional evolutionary gold nanoarchitecture (3D-EGN) is developed by forming Au nanosponge (AuS) on a 96-well plate, followed by a decoration of Au nanoparticles (AuNPs) evolved with Au nanolamination (AuNL) for high-throughput urine sensing in liquid phase. The 3D-EGN exhibits not only strong electromagnetic field generated from numerous hotspot regions between AuNPs and further enhanced light scattering from multigrain boundaries after lamination process, but also highly volumetric field due to nanoporous structure of AuS, which is advantageous for sensitive liquid-phase SERS detection. SERS activity of the 3D-EGN platform is characterized using malachite green, showing a limit detection of 1.23 × 10−9 M in liquid phase, and excellent uniformities both within single well and well-to-well with relative standard deviation (RSD) values of about 10%. The 3D-EGN platform has been demonstrated for the detection of whole clinical human urine samples, proving effective molecular sensing in the presence of Brownian motion from liquid medium. Subsequently, cancer metabolite candidates are investigated to verify the metabolic alternations of multicancer, including pancreatic, prostate, lung, and colorectal cancers, simultaneously classifying them into five different groups, including normal with an accuracy of 95.6%, using machine-learning methods. The integration of nanomaterials with the conventional clinical platform provides rapid and high-throughput multicancer diagnostic system and opens a new era for noninvasive diseases diagnosis using clinical human biofluids.

Abstract LB289: Single-cell CRISPR screens at scale to understand T cell biology

Abstract CRISPR screens have become the primary discovery engine in modern biology and are widely used to uncover novel targets in immuno-oncology. However, these genetic screens are usually coupled to rather simplistic read-outs such as cell fitness. In contrast, CROP-Seq (or “CRISPR Droplet” Sequencing) screens combine CRISPR perturbation with single-cell transcriptomics, enabling high-content phenotyping at single-cell resolution. Briefly, cells are perturbed with a pooled sgRNA library and transcriptomic profiles of each cell are recorded using conventional single-cell RNA sequencing platforms. Currently, the scale of these screens is limited to the perturbation of a couple of hundred genes, possibly up to 1.000 genes, because of increased costs for single-cell library preparation and next generation sequencing (NGS). At Myllia, we have built a platform allowing high-content CROP-Seq screens in both CRISPR-ready cancer cell lines as well as primary immune cells, e.g., human T cells. Using CRISPR KO, CRISPR interference or CRISPR activation workflows, we can help accelerate the identification of target genes and regulatory networks involved in immuno-oncology. Primary T cells are of great interest in the immunotherapy community as they are key players in autoimmune and inflammatory disease. Engineered T-lymphocytes such as CAR-T cells are currently developed as novel cellular medicines, yet many T cell-intrinsic features involved in CAR-T potency remain elusive. To enable the discovery of novel targets, we have performed multiple customized CROP-Seq CRISPR KO screens in primary human T cells investigating T cell stemness and effector phenotypes as well as CD4+ T helper cell differentiation towards Th1, Th2, or Th17 subsets. Here, we present screens for regulators of functional T cell plasticity and Th2 commitment in which we aimed to identify genes determining critical T cell fate decisions, potentially linking gene (dys)function to either autoimmune/inflammatory disease or functional persistence of cytotoxic T cells for cancer immunotherapy. Overall, the advanced CROP-Seq platform combining large-scale perturbations and targeted sequencing (TA) read-outs will catalyse a paradigm shift for CROP-Seq enabling high-throughput functional genomic screens that support the validation of drug targets in autoimmunity, inflammation and immuno-oncology. Citation Format: Nicole Untermoser, Johanna Irnstorfer, Anatoly Vasilyev, Nikola Vinko, Sumit Pawar, Anke Loregger, Adam Krejci, Henrik Schmidt, Tilmann Bürckstümmer. Single-cell CRISPR screens at scale to understand T cell biology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB289.

Fully Additively Manufactured Counter Electrodes for Dye-Sensitized Solar Cells.

In dye-sensitized solar cells (DSSCs), the counter electrode (CE) plays a crucial role as an electron transfer agent and regenerator of the redox couple. Unlike conventional CEs that are generally made of glass-based substrates (e.g., FTO/glass), polymer substrates appear to be emerging candidates, owing to their intrinsic properties of lightweight, high durability, and low cost. Despite great promise, current manufacturing methods of CEs on polymeric substrates suffer from serious limitations, including low conductivity, scalability, process complexity, and the need for dedicated vacuum equipment. In the present study, we employ and evaluate a fully additive manufacturing route that can enable the fabrication of CEs for DSSCs in a high-throughput and eco-friendly manner with improved performance. The proposed approach sequentially comprises: (1) material extrusion 3-D printing of polymer substrate; (2) conductive surface metallization through cold spray particle deposition; and (3) over-coating of a thin-layer catalyzer with a graphite pencil. The fabricated electrodes are characterized in terms of microstructure, electrical conductivity, and photo-conversion efficiency. Owing to its promising electrical conductivity (8.5 × 104 S·m-1) and micro-rough surface structure (Ra ≈ 6.32 µm), the DSSCs with the additively manufactured CEs led to ≈2.5-times-higher photo-conversion efficiency than that of traditional CEs made of FTO/glass. The results of the study suggest that the proposed additive manufacturing approach can advance the field of DSSCs by addressing the limitations of conventional CE manufacturing platforms.