moSCminer: a cell subtype classification framework based on the attention neural network integrating the single-cell multi-omics dataset on the cloud.

Single-cell omics sequencing has rapidly advanced, enabling the quantification of diverse omics profiles at a single-cell resolution. To facilitate comprehensive biological insights, such as cellular differentiation trajectories, precise annotation of cell subtypes is essential. Conventional methods involve clustering cells and manually assigning subtypes based on canonical markers, a labor-intensive and expert-dependent process. Hence, an automated computational prediction framework is crucial. While several classification frameworks for predicting cell subtypes from single-cell RNA sequencing datasets exist, these methods solely rely on single-omics data, offering insights at a single molecular level. They often miss inter-omic correlations and a holistic understanding of cellular processes. To address this, the integration of multi-omics datasets from individual cells is essential for accurate subtype annotation. This article introduces moSCminer, a novel framework for classifying cell subtypes that harnesses the power of single-cell multi-omics sequencing datasets through an attention-based neural network operating at the omics level. By integrating three distinct omics datasets-gene expression, DNA methylation, and DNA accessibility-while accounting for their biological relationships, moSCminer excels at learning the relative significance of each omics feature. It then transforms this knowledge into a novel representation for cell subtype classification. Comparative evaluations against standard machine learning-based classifiers demonstrate moSCminer's superior performance, consistently achieving the highest average performance on real datasets. The efficacy of multi-omics integration is further corroborated through an in-depth analysis of the omics-level attention module, which identifies potential markers for cell subtype annotation. To enhance accessibility and scalability, moSCminer is accessible as a user-friendly web-based platform seamlessly connected to a cloud system, publicly accessible at http://203.252.206.118:5568. Notably, this study marks the pioneering integration of three single-cell multi-omics datasets for cell subtype identification.

An IoT System for Air Pollution Monitoring with Safe Data Transmission.

Air pollution has become a global issue due to rapid urbanization and industrialization. Bad air quality is Europe's most significant environmental health risk, causing serious health problems. External air pollution is not the only issue; internal air pollution is just as severe and can also lead to adverse health outcomes. IoT is a practical approach for monitoring and publishing real-time air quality information. Numerous IoT-based air quality monitoring systems have been proposed using micro-sensors for data collection. These systems are designed for outdoor air quality monitoring. They use sensors to measure air quality parameters such as CO2, CO, PM10, NO2, temperature, and humidity. The data are acquired with a set of sensors placed on an electric car. They are then sent to the server. Users can subscribe to the list and receive information about local pollution. This system allows real-time localized air quality monitoring and sending data to customers. The work additionally presents a secure data transmission protocol ensuring system security. This protocol provides system-wide attack resiliency and interception, which is what existing solutions do not offer.

Aerosol-Cell Exposure System Applied to Semi-Adherent Cells for Aerosolization of Lung Surfactant and Nanoparticles Followed by High Quality RNA Extraction.

Nanoparticle toxicity assessments have moved closer to physiological conditions while trying to avoid the use of animal models. An example of new in vitro exposure techniques developed is the exposure of cultured cells at the air-liquid interface (ALI), particularly in the case of respiratory airways. While the commercially available VITROCELL® Cloud System has been applied for the delivery of aerosolized substances to adherent cells under ALI conditions, it has not yet been tested on lung surfactant and semi-adherent cells such as alveolar macrophages, which are playing a pivotal role in the nanoparticle-induced immune response. OBJECTIVES: In this work, we developed a comprehensive methodology for coating semi-adherent lung cells cultured at the ALI with aerosolized surfactant and subsequent dose-controlled exposure to nanoparticles (NPs). This protocol is optimized for subsequent transcriptomic studies. METHODS: Semi-adherent rat alveolar macrophages NR8383 were grown at the ALI and coated with lung surfactant through nebulization using the VITROCELL® Cloud 6 System before being exposed to TiO2 NM105 NPs. After NP exposures, RNA was extracted and its quantity and quality were measured. RESULTS: The VITROCELL® Cloud system allowed for uniform and ultrathin coating of cells with aerosolized surfactant mimicking physiological conditions in the lung. While nebulization of 57 µL of 30 mg/mL TiO2 and 114 µL of 15 mg/mL TiO2 nanoparticles yielded identical cell delivered dose, the reproducibility of dose as well as the quality of RNA extracted were better for 114 µL.

Developing IoT Sensing System for Construction-Induced Vibration Monitoring and Impact Assessment.

Construction activities often generate intensive ground-borne vibrations that may adversely affect structure safety, human comfort, and equipment functionality. Vibration monitoring systems are commonly deployed to assess the vibration impact on the surrounding environment during the construction period. However, traditional vibration monitoring systems are associated with limitations such as expensive devices, difficult installation, complex operation, etc. Few of these monitoring systems have integrated functions such as in situ data processing and remote data transmission and access. By leveraging the recent advances in information technology, an Internet of Things (IoT) sensing system has been developed to provide a promising alternative to the traditional vibration monitoring system. A microcomputer (Raspberry Pi) and a microelectromechanical systems (MEMS) accelerometer are adopted to minimize the system cost and size. A USB internet dongle is used to provide 4G communication with cloud. Time synchronization and different operation modes have been designed to achieve energy efficiency. The whole system is powered by a rechargeable solar battery, which completely avoids cabling work on construction sites. Various alarm functions, MySQL database for measurement data storage, and webpage-based user interface are built on a public cloud platform. The architecture of the IoT vibration sensing system and its working mechanism are introduced in detail. The performance of the developed IoT vibration sensing system has been successfully validated by a series of tests in the laboratory and on a selected construction site.

Cloud-Based System for Effective Surveillance and Control of COVID-19: Useful Experiences From Hubei, China.

BACKGROUND: Coronavirus disease (COVID-19) has been an unprecedented challenge to the global health care system. Tools that can improve the focus of surveillance efforts and clinical decision support are of paramount importance. OBJECTIVE: The aim of this study was to illustrate how new medical informatics technologies may enable effective control of the pandemic through the development and successful 72-hour deployment of the Honghu Hybrid System (HHS) for COVID-19 in the city of Honghu in Hubei, China. METHODS: The HHS was designed for the collection, integration, standardization, and analysis of COVID-19-related data from multiple sources, which includes a case reporting system, diagnostic labs, electronic medical records, and social media on mobile devices. RESULTS: HHS supports four main features: syndromic surveillance on mobile devices, policy-making decision support, clinical decision support and prioritization of resources, and follow-up of discharged patients. The syndromic surveillance component in HHS covered over 95% of the population of over 900,000 people and provided near real time evidence for the control of epidemic emergencies. The clinical decision support component in HHS was also provided to improve patient care and prioritize the limited medical resources. However, the statistical methods still require further evaluations to confirm clinical effectiveness and appropriateness of disposition assigned in this study, which warrants further investigation. CONCLUSIONS: The facilitating factors and challenges are discussed to provide useful insights to other cities to build suitable solutions based on cloud technologies. The HHS for COVID-19 was shown to be feasible and effective in this real-world field study, and has the potential to be migrated.

A Novel Cost-Efficient Framework for Critical Heartbeat Task Scheduling Using the Internet of Medical Things in a Fog Cloud System.

Recently, there has been a cloud-based Internet of Medical Things (IoMT) solution offering different healthcare services to wearable sensor devices for patients. These services are global, and can be invoked anywhere at any place. Especially, electrocardiogram (ECG) sensors, such as Lead I and Lead II, demands continuous cloud services for real-time execution. However, these services are paid and need a lower cost-efficient process for the users. In this paper, this study considered critical heartbeat cost-efficient task scheduling problems for healthcare applications in the fog cloud system. The objective was to offer omnipresent cloud services to the generated data with minimum cost. This study proposed a novel health care based fog cloud system (HCBFS) to collect, analyze, and determine the process of critical tasks of the heartbeat medical application for the purpose of minimizing the total cost. This study devised a health care awareness cost-efficient task scheduling (HCCETS) algorithm framework, which not only schedule all tasks with minimum cost, but also executes them on their deadlines. Performance evaluation shows that the proposed task scheduling algorithm framework outperformed the existing algorithm methods in terms of cost.

A Fine-Grained Video Encryption Service Based on the Cloud-Fog-Local Architecture for Public and Private Videos.

With the advancement of cloud computing and fog computing, more and more services and data are being moved from local servers to the fog and cloud for processing and storage. Videos are an important part of this movement. However, security issues involved in video moving have drawn wide attention. Although many video-encryption algorithms have been developed to protect local videos, these algorithms fail to solve the new problems faced on the media cloud, such as how to provide a video encryption service to devices with low computing power, how to meet the different encryption requirements for different type of videos, and how to ensure massive video encryption efficiency. To solve these three problems, we propose a cloud-fog-local video encryption framework which consists of a three-layer service model and corresponding key management strategies, a fine-grain video encryption algorithm based on the network abstract layer unit (NALU), and a massive video encryption framework based on Spark. The experiment proves that our proposed solution can meet the different encryption requirements for public videos and private videos. Moreover, in the experiment environment, our encryption algorithm for public videos reaches a speed of 1708 Mbps, and can provide a real-time encryption service for at least 42 channels of 4K-resolution videos.

In situ production and characterization of cloud forming dextrans in fruit-juices.

Turbidity in beverages is typically achieved by addition of emulsion based cloud systems. Their intrinsic instability necessitates the widespread use of technological measures and use of food additives to prevent emulsion decay. In this work, we explored the possibility to establish a new generation of natural, stable clouding systems based on bacterial dextrans. Lactobacillus hordei TMW 1.1907 originating from water kefir was used to produce dextrans in sucrose supplemented apple or grape juices. By varying the fermentation conditions, two distinct types of dextran molecules could be produced at yields ranging from 2.5 to 8.5 g/L. The dextran-containing fermentates showed an unchanged turbidity after pasteurization at acidic pH and subsequent storage for three months. No sedimentation of particles occurred upon storage. Neutralization of the acidic fruit juices to pH 7 prior to fermentation significantly increased the dextran yields. The molecular weight, rms radii and turbidity of dextrans produced at 20 °C were higher than those produced at 30 °C. Characterization of the isolated dextrans by asymmetric flow field-flow fractionation coupled to multi-angle laser light scattering revealed a random-coil like structure and rms radii ranging from 66.0 to 87.4 nm. The averaged molar masses of the cloud forming dextrans were in the approximate range of 103.1 to 141.6 MDa. In conclusion, our results demonstrate the possibility to ferment fruit juices for in situ production of dextrans exhibiting novel techno-functional properties beyond gelling and thickening.

Security of Separated Data in Cloud Systems with Competing Attack Detection and Data Theft Processes.

Empowered by virtualization technology, service requests from cloud users can be honored through creating and running virtual machines. Virtual machines established for different users may be allocated to the same physical server, making the cloud vulnerable to co-residence attacks where a malicious attacker can steal a user's data through co-residing their virtual machines on the same server. For protecting data against the theft, the data partition technique is applied to divide the user's data into multiple blocks with each being handled by a separate virtual machine. Moreover, early warning agents (EWAs) are deployed to possibly detect and prevent co-residence attacks at a nascent stage. This article models and analyzes the attack success probability (complement of data security) in cloud systems subject to competing attack detection process (by EWAs) and data theft process (by co-residence attackers). Based on the suggested probabilistic model, the optimal data partition and protection policy is determined with the objective of minimizing the user's cost subject to providing a desired level of data security. Examples are presented to illustrate effects of different model parameters (attack rate, number of cloud servers, number of data blocks, attack detection time, and data theft time distribution parameters) on the attack success probability and optimization solutions.

IoT-Based Library Automation and Monitoring system: Developing an Implementation framework of Implementation / Sistema de automatización y monitoreo de bibliotecas basado en el internet de las cosas: Desarrollando un marco de implementación

Abstract Currently, the Information and Communication Technology (ICT) and related topics such as Internet of Things (IoT) have an essential influence on all elements of human life. IoT as a prevalent phenomenon is transforming daily life by the usage of the smart features of Radio Frequency Identification (RFID) and Wireless Sensor Network (WSN) technologies. As IoT progresses, it has extended in size and dimension, improving many contexts of the society; such as, the traditional library system. This research proposes an implementation framework for employing the IoT in renovating the conventional library systems to become smart online library schemes. The IoT enables connectivity of a physical object (such as a book or other text typologies) with the real-time communication technology by using the RFID tags and tiny sensors. The continuous monitoring of books in real time and the tracking of labeled objects geographically are some desirable characteristics that result from the use of the IoT tags. These characteristics of IoT allow implementing an online library supply chain, integrating it with different types of technologies such as data bases, data gathering, and cloud systems. The Internet of Things can also provide a global linking of a huge number of libraries and universities in real time, all the time. It is concluded that the IoT-based library management systems will be a promising structure that can play a vital role in the human data organization and knowledge access by helping researchers, designers, and administrators in a more efficient and smarter manner.

Resumen En la actualidad, las Tecnologías de Información y Comunicación (TIC) y asuntos relacionados, tales como el Internet de las Cosas (IoT, por sus siglas en inglés), tiene una influencia decisiva en todos los aspectos de la vida humana. IoT como fenómeno dominante es la transformación de la vida diaria mediante el uso de las funciones inteligentes como la de Identificación por Radio Frecuencia (RFID, por sus siglas en inglés) y las Redes de Tecnología de Sensores Inalámbricos (WSN, por sus siglas en inglés). Conforme el IoT avanza, se amplía en tamaño y dimensiones, mejorando muchos de los contextos de la sociedad; tales como, el sistema tradicional de biblioteca. Esta investigación propone un marco de ejecución sobre el empleo del IoT con el fin de renovar la estructura y esquema convencional de las bibliotecas a sistemas inteligentes en línea. El IoT permite la conectividad en tiempo real de un objeto físico (como un libro o cualquier otro tipo de texto) mediante el uso de las etiquetas RFID y sensores diminutos. El monitoreo continuo de los libros en tiempo real y la localización de objetos de manera geográfica son algunas de las características que se derivan del uso de las etiquetas de IoT. Estas características de IoT permiten la implementación de una línea de bibliotecas por medio de una cadena de suministros, la integración con diferentes tipos de tecnologías como la base de datos, la recopilación de datos y sistemas en la nube. El Internet de las Cosas también ofrece una panorámica de la vinculación entre el gran número de universidades y bibliotecas en el mundo en tiempo real, todo el tiempo. Se concluye que el implementar el IoT en sistemas de gestión de bibliotecas sería una estructura prometedora que puede jugar un papel vital en la organización de conocimientos del ser humano y en el acceso a informaciones para ayudar a investigadores, diseñadores y administradores en una manera más eficiente e inteligente.

Smart cloud system with image processing server in diagnosing brain diseases dedicated for hospitals with limited resources.

The use of medical imaging in diagnosing brain disease is growing. The challenges are related to the big size of data and complexity of the image processing. High standard of hardware and software are demanded, which can only be provided in big hospitals. Our purpose was to provide a smart cloud system to help diagnosing brain diseases for hospital with limited infrastructure. The expertise of neurologists was first implanted in cloud server to conduct an automatic diagnosis in real time using image processing technique developed based on ITK library and web service. Users upload images through website and the result, in this case the size of tumor was sent back immediately. A specific image compression technique was developed for this purpose. The smart cloud system was able to measure the area and location of tumors, with average size of 19.91 ± 2.38 cm2 and an average response time 7.0 ± 0.3 s. The capability of the server decreased when multiple clients accessed the system simultaneously: 14 ± 0 s (5 parallel clients) and 27 ± 0.2 s (10 parallel clients). The cloud system was successfully developed to process and analyze medical images for diagnosing brain diseases in this case for tumor.

Cross-sectional investigation of drug-related problems among adults in a medical center outpatient clinic: application of virtual medicine records in the cloud.

PURPOSE: To analyze and characterize data regarding the prevalence and types of outpatient drug-related problems (DRPs) found by clinical pharmacists after implementation of the Virtual Medicine Record in Cloud System (VMRCS). METHODS: A cross-sectional study regarding outpatient pharmaceutical care was conducted at a medical center in Taiwan. Patients aged >20 years old with multiple chronic diseases and polypharmacy were enrolled. In Stage I (1 October-31 December 2014), patients received pharmaceutical care according to prescription data accessed online in the VMRCS. In Stage II (1 June-31 August 2015), the VMRCS were pre-download and arranged to the institute's required format, facilitated DRP detection. Clinical pharmacists then reviewed and evaluated the prescription data through pre-downloaded VMRCS. Overall, 1539 and 1600 prescriptions were evaluated in these two stages, respectively. DRPs were recorded using the Pharmaceutical Care Network Europe (PCNE)-DRP. RESULTS: DRPs were found for 50.2% of patients in Stage I and 55.2% in Stage II (p < 0.05) and were most frequently encountered for "Drugs for the cardiovascular system" and caused by "Inappropriate duplication of therapeutic group or active ingredient." In terms of problems, incidence of "Unnecessary drug treatment" was highest. Duplicate medications were most frequently seen for "Drugs for acid-related disorders." The efficiency to identify DRPs was at least 2.4 times higher with pre-downloaded prescription data than with real-time online queries. CONCLUSIONS: With VMRCS, DRPs were more easily identified whether patients received medical care in the same hospital or not. DRPs could be efficiently prevented through the use of pre-downloaded patient prescription data. Copyright © 2016 John Wiley & Sons, Ltd.

An investigation of symptom burden and quality of life in Chinese chemo-naïve advanced lung cancer patients by using the Instrument-Cloud QOL System.

OBJECTIVES: This study was designed to assess the disease-related symptom burden and quality of life (QOL) in Chinese chemo-naïve advanced lung cancer patients. MATERIALS AND METHODS: Chemo-naïve patients with stage III/IV lung cancer were enrolled. 43 centers from 16 provinces of China participated in the study. Functional Assessment of Cancer Therapy-Lung (FACT-L) scale and Cloud QOL System were applied in the study. RESULTS: 376 eligible patients were analyzed. The three most common and severe symptoms were appetite loss (84.3%, scored 2.46), breathing difficulty (79.0%, scored 2.56), and cough (75.5%, scored 2.81). Significant correlation was found between QOL and symptoms. Regression analysis of QOL indicated that almost every symptom item (except shortness of breath) was the negative indicator of QOL. Moreover, pulmonary diseases, pleural metastases and brain metastases had significant negative impact on both symptoms and QOL. Relatively poor performance status affected the QOL only, while cardiovascular diseases merely affected the symptoms. And patients with EGFR mutations had less symptom burden than those with wild-type EGFR. CONCLUSION: QOL evaluation by using the Cloud QOL System was feasible. Appetite loss, breathing difficulty and cough were the three most common and severe symptoms seen in Chinese chemo-naïve advanced lung cancer patients. Almost all symptoms had negative impact on QOL. And some clinical characteristics could predict the symptoms and QOL.