Scalable Platform Research Articles

A single-cell mass cytometry-based atlas of the developing mouse brain.

Development of the mammalian brain requires precise molecular changes across diverse cell lineages. While single-cell RNA abundances in the developing brain have been characterized by single-cell RNA sequencing (scRNA-seq), single-cell protein abundances have not been characterized. To address this gap, we performed mass cytometry on the whole brain at embryonic day (E)11.5-E12.5 and the telencephalon, the diencephalon, the mesencephalon and the rhombencephalon at E13.5-postnatal day (P)4 from C57/BL6 mice. Using a 40-antibody panel to analyze 24,290,787 cells from two to four biological replicates per sample, we identify 85 molecularly distinct cell clusters from distinct lineages. Our analyses confirm canonical molecular pathways of neurogenesis and gliogenesis, and predict two distinct trajectories for cortical oligodendrogenesis. Differences in protein versus RNA expression from mass cytometry and scRNA-seq, validated by immunohistochemistry and RNAscope in situ hybridization (ISH), demonstrate the value of protein-level measurements for identifying functional cell states. Our findings show the utility of mass cytometry as a scalable platform for single-cell profiling of brain tissues.

Image Text to Speech Conversion in Desired Language and Summarization with Raspberry PI

In recent years, the integration of artificial intelligence and embedded systems has gained significant traction, enabling the development of efficient and user-friendly solutions. ourproject focuses on building a system capable of extracting text from images, converting the extracted content into speech in a desired language, and providing concise summaries, all powered by a Raspberry Pi. The system employs Optical Character Recognition (OCR) for text extraction, a text-to-speech engine for audio synthesis, and natural language processing techniques for summarization. Designed with accessibility and versatility in mind, the system can assist individuals with visual impairments, language barriers, or those seeking quick comprehension of extensive information. By leveraging the computational efficiency and affordability of the Raspberry Pi, the proposed solution aims to deliver a portable, cost- effective, and scalable platform for text and audio processing applications. Advancements in embedded systems and artificial intelligence have enabled the development of innovative solutions that enhance accessibility and improve user experience. ourproject presents a multifunctional system designed to process images, extract text, convert the text into speech in the desired language, and summarize the content, utilizing the Raspberry Pi as a cost-effective and portable platform. The system integrates Optical Character Recognition (OCR) for accurately identifying text within images, a text-to-speech synthesis engine for converting text to audio output, and natural language processing (NLP) algorithms to generate concise and meaningful summaries. Our approach offers significant benefits, including aiding visually impaired individuals, breaking down language barriers, and streamlining content consumption for users seeking quick comprehension of extensive material. The choice of the Raspberry Pi ensures affordability, energy efficiency, and scalability, making it suitable for various real-world applications. The proposed solution incorporates multilingual support, enabling audio output in a language of the user’s choice, thereby enhancing its versatility. The project also explores optimization techniques to ensure real-time performance, despite the hardware constraints of the Raspberry Pi. Applications of INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT (IJSREM) VOLUME: 08 ISSUE: 12 | DEC - 2024 SJIF RATING: 8.448 ISSN: 2582-3930 © 2024, IJSREM | www.ijsrem.com DOI: 10.55041/IJSREM39818 | Page 2 oursystem span education, accessibility tools, document digitization, and language learning. By leveraging open- source tools and libraries, the project underscores the potential of embedded systems in democratizing technology for a broader audience. Keywords Artificial Intelligence (AI),Embedded Systems,Raspberry Pi,Optical Character Recognition (OCR),Text-to-Speech (TTS),Natural Language Processing (NLP),Multilingual Support,Accessibility,Real-timeOptimization,Image Processing,ContentSummarization,Cost-effective Solutions,Scalable Systems,Open-source Tools,Document Digitization,Language Learning

HealthSphere: Integrated AI-Powered and Blockchain Based Health Management and Support System

The healthcare sector is undergoing a significant transformation as it embraces advanced technologies to address some of the most pressing challenges related to accessibility, data security, and operational efficiency. Among these innovations, Artificial Intelligence (AI) and Blockchain technology have emerged as key enablers of enhanced healthcare delivery. AI-powered chatbots are being increasingly utilized to provide immediate, real-time assistance to patients, helping them assess symptoms, obtain basic medical advice, and access information without the need for in-person consultations. These chatbots, powered by Natural Language Processing (NLP) and Machine Learning (ML) algorithms, can efficiently handle a wide range of patient inquiries, improving accessibility and reducing the burden on healthcare providers. On the other hand, Blockchain technology is revolutionizing the way health records are managed. It offers a decentralized, secure, and transparent method of storing and sharing health data, ensuring data integrity, and empowering patients with control over their own medical information. By enabling smart contracts and secure data transactions, Blockchain ensures that health records are easily shared among authorized entities, reducing the risks of unauthorized access and data breaches. This paper provides an in-depth survey of the integration of these technologies into a unified healthcare management system. It explores the practical applications of AI chatbots in improving patient engagement, symptom management, and appointment scheduling, while also discussing how Blockchain can enhance the security, transparency, and interoperability of health records. The paper further addresses the challenges that come with implementing these technologies, including issues related to scalability, data privacy, and regulatory compliance. Finally, it proposes a scalable, secure, and user-friendly platform that combines the strengths of both AI chatbots and Blockchain to create an efficient and reliable healthcare system, poised to improve the overall delivery of healthcare services..

Appization™ @ Neurahub™: Leveraging The App Store Model For AI Functions On Edge Devices_ Case Study Of Indoai AI Camera

The “ Appization™ @ NeuraHub™ ” framework applies the successful AI app store model for mobile handsets to AI functions on edge devices, aiming to create an adaptable, scalable and customizable platform for deploying, managing, and updating AI models on specialized devices like IndoAI’s Edge Cameras. This paper provides a comprehensive examination of the Appization™ @ NeuraHub™ platform, proposing it as an ecosystem where AI models can be installed, managed, and updated independently. NeuraHub is a comprehensive ecosystem where AI models can be deployed, managed and updated independently for developers. The framework introduces standardized protocols, a developer-friendly marketplace and a microservices architecture that streamlines AI model deployment on edge devices, overcoming current challenges and scaling edge AI for specialized applications

Practical SERS Substrates by Spray Coating of Silver Solutions for Deep Learning-Assisted Sensitive Antigen Identification

Surface-enhanced Raman spectroscopy (SERS) has long been recognized for its rapid and sensitive detection capabilities; however, challenges persist in practical fabrication of the substrates and interpreting complex data. Herein, we propose a deep learning (DL) assisted SERS approach to enable rapid and sensitive detection of analytes on a practical yet highly effective substrates prepared by direct spray-coating of a nanoparticle-free true solution of a reactive Ag ink and on-site thermal annealing mediated generation of nanostructures. This design ensured homogeneous distribution of Ag nanostructures throughout the entire substrate, significantly increasing the number of hotspots and enhancing the Raman signals, thereby achieving an impressive analytical enhancement factor of ~1010 in a reproducible and consistent manner. The diagnostic utility of this platform was demonstrated by detecting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike (S) protein in both buffer and saliva, with detection limits of 74.3pg/mL and 7.43ng/mL, respectively. The DL-assisted SERS not only accurately identified the presence or absence of viral antigen, but also automatically quantified the viral load. This automatic identification achieved an outstanding accuracy of ~99.9%, highlighting the exceptional performance of the proposed platform. This simple, cost-effective, scalable, and ultra-sensitive DL-assisted SERS platform offers significant opportunities for early and precise detection in a range of analytical scenarios.

Detecting Missing Persons Using Face Recognition

In response to the challenge of finding missing people, we developed an advanced facial recognition model integrated with the TensorFlow machine learning platform. Traditional methods are often inefficient and slow, but our innovative solution uses facial recognition technology to optimize the search process. Missing people's facial information is fed into the system, which searches social media, CCTV footage, and public places for matches. Once a match is identified, carers and local authorities are immediately notified, enabling fast and accurate searches. Through advanced algorithms and machine learning, our solutions significantly increase your chances of success while ensuring a high level of privacy and security. Integration with the Django framework provides a powerful and scalable platform that improves the overall effectiveness and efficiency of locating missing people and reuniting them with their families.

INTERACTIVE CYBERSECURITY TRAINING SYSTEM BASED ON SIMULATION ENVIRONMENTS

Rapid progress in the development of information technology has led to a significant increase in the number and complexity of cyber threats. Traditional methods of cybersecurity training based on theoretical knowledge do not provide a sufficient level of practical skills to effectively counter real threats. The article explores the possibilities of integrating simulation environments into the cybersecurity training process as an effective approach to improving the quality of training. The article presents the architecture of a simulation environment based on a cluster of KVM hypervisors, which allows creating scalable and flexible platforms at minimal cost. The article describes the implementation of various scenarios using open source software tools such as pfSense, OPNsense, Security Onion, Kali Linux, Parrot Security OS, Ubuntu Linux, Oracle Linux, FreeBSD, and others, which create realistic conditions for practical training.

Trends and challenges in organoid modeling and expansion with pluripotent stem cells and somatic tissue.

The increasing demand for disease modeling, preclinical drug testing, and long waiting lists for alternative organ substitutes has posed significant challenges to current limitations in organoid technology. Consequently, organoid technology has emerged as a cutting-edge tool capable of accurately recapitulating the complexity of actual organs in physiology and functionality. To bridge the gaps between basic research and pharmaceutical as well as clinical applications, efforts have been made to develop organoids from tissue-derived stem cells or pluripotent stem cells. These developments include optimizing starting cells, refining culture systems, and introducing genetic modifications. With the rapid development of organoid technology, organoid composition has evolved from single-cell to multi-cell types, enhancing their level of biomimicry. Tissue structure has become more refined, and core challenges like vascularization are being addressed actively. These improvements are expected to pave the way for the construction of organoid atlases, automated large-scale cultivation, and universally compatible organoid biobanks. However, major obstacles remain to be overcome before urgently proof-of-concept organoids can be readily converted to practical applications. These obstacles include achieving structural and functional summarily to native tissue, remodeling the microenvironment, and scaling up production. This review aims to summarize the status of organoid development and applications, highlight recent progress, acknowledge existing limitations and challenges, and provide insights into future advancements. It is expected that this will contribute to the establishment of a reliable, scalable, and practical platform for organoid production and translation, further promoting their use in the pharmaceutical industry and regenerative medicine.

Advancements in Single-Cell Proteomics and Mass Spectrometry-Based Techniques for Unmasking Cellular Diversity in Triple Negative Breast Cancer.

Triple-negative breast cancer (TNBC) is an aggressive and complex subtype of breast cancer characterized by a lack of targeted treatment options. Intratumoral heterogeneity significantly drives disease progression and complicates therapeutic responses, necessitating advanced analytical approaches to understand its underlying biology. This review aims to explore the advancements in single-cell proteomics and their application in uncovering cellular diversity in TNBC. It highlights innovations in sample preparation, mass spectrometry-based techniques, and the potential for integrating proteomics into multi-omics platforms. The review discusses the combination of improved sample preparation methods and cutting-edge mass spectrometry techniques in single-cell proteomics. It emphasizes the challenges associated with protein analysis, such as the inability to amplify proteins akin to transcripts, and examines strategies to overcome these limitations. Single-cell proteomics provides a direct link to phenotype and cell behavior, complementing transcriptomic approaches and offering new insights into the mechanisms driving TNBC. The integration of advanced techniques has enabled deeper exploration of cellular heterogeneity and disease mechanisms. Despite the challenges, single-cell proteomics holds immense potential to evolve into a high-throughput and scalable multi-omics platform. Addressing existing hurdles will enable deeper biological insights, ultimately enhancing the diagnosis and treatment of TNBC.

High-efficiency electrocatalytic hydrogen generation under harsh acidic condition by commercially viable Pt nanocluster-decorated non-polar faceted GaN nanowires

Electrochemical water splitting is vital for green hydrogen production and clean energy. This study introduces a novel approach: platinum nanoclusters (Pt NCs) decorated GaN nanowires (GNWs) on p++-Si substrates to enhance hydrogen generation efficiency. Highly-crystalline GNWs synthesized via commercial metal-organic chemical vapor deposition provide a scalable platform for hydrogen evolution. To address the cost limitations of Pt-based electrocatalysts, we developed a method for loading ultralow Pt NCs via photoelectrochemical deposition. Investigations underscore the Pt–Ga sites' crucial role in promoting efficient H2 production. The Pt NCs/GNWs/p++-Si electrode achieved −10 mA/cm2 current density at +50 mV vs. the RHE and sustained −20 mA/cm2 for 90 h under harsh acidic conditions at room temperature and atmospheric pressure with nearly 100% retention. This study offers insights into efficient and stable electrodes for electrochemical H2 generation.

Droplet-Based Microfluidic Photobioreactor as a Growth Optimization Tool for Cyanobacteria and Microalgae

Microalgae and cyanobacteria are photosynthetic microorganisms with significant biotechnological potential for the production of bioactive compounds, making them a promising resource for diverse industrial applications. This study presents the development and validation of a modular, droplet-based microfluidic photobioreactor (µPBR) designed for high-throughput screening and cultivation under controlled light conditions. The µPBR, based on polytetrafluoroethylene (PTFE) tubing and a 4-channel LED illumination system, enables precise modulation of light intensity, wavelength, and photoperiod, facilitating dose–response experiments. Synechococcus elongatus UTEX 2973 and Chlorella vulgaris were used to demonstrate the system’s capacity to support photosynthetic growth under various conditions. The results indicate that continuous illumination, particularly under blue and mixed blue-red light, promotes higher autofluorescence and chlorophyll a content in cyanobacteria Synechococcus elongatus UTEX2973, while Chlorella vulgaris achieved optimal growth under a 16:8 light-dark cycle with moderate light intensity. This µPBR offers not only a flexible, scalable platform for optimizing growth parameters but also allows for the investigation of highly resolved dose response screenings of environmental stressors such as salinity. The presented findings highlight its potential for advancing microalgal biotechnology research and applications.

Leveraging Cloud Native Development for Ecosystem Strategy

This article explores the intersection of cloud-native development and business ecosystem strategies, highlighting how cloud-native technologies create scalable, flexible, and interoperable platforms that foster collaboration and innovation. It examines the composition and dynamics of business ecosystems, the key advantages of cloud-native development for ecosystem strategies, and practical approaches for implementing cloud-native ecosystem platforms. The paper provides insights into the transformative potential of these technologies in shaping modern business ecosystems, supported by market data, case studies, and industry trends.

Defect-insensitive cylindrical surface lattice resonance array and its batch replication for enhanced immunoassay

Surface lattice resonances (SLR) have been demonstrated to enhance the sensitivity and reduce the full width at half maximum (FWHM) of the plasmonic resonances. However, their widespread application in immunoassays has been hindered by limitations of high structural defect sensitivity and fabrication costs. Here, we design a novel three-layer cylindrical SLR array that exhibits high tolerance against structural defects, which would facilitate straightforward fabrication. By integrating metal evaporation and nanoimprint lithography, we demonstrate the replication of the SLR array with exceptional quality. Theoretical simulations indicate that the resonance dips of these arrays exhibit are not sensitive to various structural defects. The experimental results reveal that the FWHM of these arrays can be as low as 5.1 nm while maintaining robust resonance characteristics. Furthermore, we demonstrated the high spectral sensitivity of the SLR array, which enabled the detection of immunoglobulin G (IgG) at concentrations as low as 609 pg/mL. These findings emphasize the potential of the defect-insensitive SLR array as a highly scalable immunoassay platform with exceptional performance.

From Grapes to Clicks : A Cloud-Based Wine-Selling Solution for Single Vendors

This document outlines the design and creation of a robust wine vending application for a single vendor, which combines a web platform, an Android mobile app, and a cloud-based framework. This solution addresses the increasing demand for online wine sales while delivering a smooth, secure, and adaptable shopping experience. Essential features of the system include an intuitive web interface and an application focused on effective product searching, ordering, and payment processing. Cloud infrastructure is implemented to guarantee high availability, scalability, and secure data management. The paper discusses various architectural alternatives, such as utilizing cloud services for load balancing, data handling, and disaster recovery. It also emphasizes the necessity of strong security protocols, including encryption, user authentication, and adherence to industry standards for online transactions. Additionally, the document addresses challenges related to regulatory compliance, including age verification mandates and data protection laws. Lastly, it explores strategies for performance enhancement, improvements to user experience, and potential avenues for future expansion. The proposed solution aims to deliver a dependable, secure, and scalable platform tailored to the requirements of individual wine retailers while accommodating future growth.

Doubling photon-processing power for MKID readout

Scalable platform based on Radio-Frequency System-on-Chip technology can read out twice as many Microwave Kinetic Inductance Detectors as previous systems.

Hybrid III-V/Silicon Quantum Photonic Device Generating Broadband Entangled Photon Pairs

The demand for integrated photonic chips combining the generation and manipulation of quantum states of light is steadily increasing, driven by the need for compact and scalable platforms for quantum information technologies. While photonic circuits with diverse functionalities are being developed in different single material platforms, it has become crucial to realize hybrid photonic circuits that harness the advantages of multiple materials while mitigating their respective weaknesses, resulting in enhanced capabilities. Here, we demonstrate a hybrid III-V/silicon quantum photonic device combining the strong second-order nonlinearity and direct band gap of the III-V semiconductor platform with the high maturity and CMOS compatibility of the silicon photonic platform. Our device embeds the spontaneous parametric down-conversion (SPDC) of photon pairs into an AlGaAs source and their vertical routing to an adhesively bonded silicon-on-insulator circuitry, within an evanescent coupling scheme managing both polarization states. This enables the on-chip generation of broadband (>40 nm) telecom photons by type-0 and type-2 SPDC from the hybrid device, at room temperature and with internal pair generation rates exceeding 105s−1 for both types, while the pump beam is strongly rejected. Two-photon interference with 92% visibility (and up to 99% upon 5-nm spectral filtering) proves the high energy-time entanglement quality of the produced quantum state, thereby enabling a wide range of quantum information applications on chip, within a hybrid architecture compliant with electrical pumping and merging the assets of two mature and highly complementary platforms in view of out-of-the-lab deployment of quantum technologies. Published by the American Physical Society 2024

MRNA vaccines: A promising platform for safer, more effective next-generation Orthopoxvirus immunization

In recent work, Mucker etal.1 demonstrated that mRNA-1769 outperforms modified vaccinia Ankara (MVA), which has been deployed against recent mpox virus (MPXV) outbreaks, in reducing clinical symptoms and controlling viral replication, highlighting its potential as a scalable, safe, and effective next-generation platform for orthopoxvirus vaccination.

Revolutionizing Precision Agriculture: Advanced Seed Prescription Generation and Cloud Infrastructure

This article explores the revolutionary impact of advanced seed prescription generation engines and cloud infrastructure on precision agriculture. It delves into the technical intricacies of enhancing seed prescription systems through cutting-edge data pipelines and scalable cloud platforms. The article discusses how these sophisticated systems analyze various factors such as soil composition, topography, historical yield data, and climate patterns to determine optimal seeding rates and patterns with unprecedented accuracy. It highlights the challenges faced in implementing these systems, including scalability, data processing, reliability, and monitoring, and presents innovative cloud-based solutions to address these issues. The benefits of these advancements, including improved efficiency, enhanced accuracy, scalability, reliability, and cost-effectiveness, are examined, along with their potential to reshape the global food production landscape and promote sustainable farming practices.

Glioma immune microenvironment composition calculator (GIMiCC): a method of estimating the proportions of eighteen cell types from DNA methylation microarray data

A scalable platform for cell typing in the glioma microenvironment can improve tumor subtyping and immune landscape detection as successful immunotherapy strategies continue to be sought and evaluated. DNA methylation (DNAm) biomarkers for molecular classification of tumor subtypes have been developed for clinical use. However, tools that predict the cellular landscape of the tumor are not well-defined or readily available. We developed the Glioma Immune Microenvironment Composition Calculator (GIMiCC), an approach for deconvolution of cell types in gliomas using DNAm data. Using data from 17 isolated cell types, we describe the derivation of the deconvolution libraries in the biological context of selected genomic regions and validate deconvolution results using independent datasets. We utilize GIMiCC to illustrate that DNAm-based estimates of immune composition are clinically relevant and scalable for potential clinical implementation. In addition, we utilize GIMiCC to identify composition-independent DNAm alterations that are associated with high immune infiltration. Our future work aims to optimize GIMiCC and advance the clinical evaluation of glioma.

Screening of the biphenyl polyamides nanofilms appropriate for molecular separation in organic solution feed

Developing polymeric separation membranes that combine excellent solvent stability, high permeability, and customizable molecular weight interception remains a challenge. Herein, thin-film composite (TFC) polyamide (PA) membranes with poly(ether ether ketone) (PEEK) support and biphenyl-centered polyamides active layer were fabricated through interfacial polymerization technique for the first time. Enhanced solvent stability was exhibited by the fabricated membranes due to the intrinsic resistance to organic solvents possessed by PEEK and biphenyl polyamides. The separation performance of the obtained membranes was systematically studied by relating to the properties of PA nanofilms in terms of nano-mechanical strength, morphologies, and microporosity. The organic solvent nanofiltration (OSN) performance of the resultant membranes in terms of permeability and molecular weight cut-off (MWCO) could be regulated. The optimized membrane exhibited a pure solvent permeability as high as 21.6 L m−2 h−1 bar−1 for acetonitrile and 14.0 L m−2 h−1 bar−1 for methanol. It also showed excellent long-term durability with almost unchanged permeability as well as solute retention remained above 97.8 % after a cumulative OSN operation of N, N-Dimethylformamide (DMF) based feed for 240 h. In particular, OSN membrane with targeted MWCOs covering the range of 170–370 g·mol−1 could be fabricated by selecting appropriate biphenyl acyl chlorides as building blocks. This approach offers a scalable and versatile platform for the development of solvent-stable, highly permeable, and customizable MWCO membranes for process-oriented OSN.