ComPRePS: An Automated Cloud-based Image Analysis tool to democratize AI in Digital Pathology.

Artificial intelligence (AI) has extensive applications in a wide range of disciplines including healthcare and clinical practice. Advances in high-resolution whole-slide brightfield microscopy allow for the digitization of histologically stained tissue sections, producing gigapixel-scale whole-slide images (WSI). The significant improvement in computing and revolution of deep neural network (DNN)-based AI technologies over the last decade allow us to integrate massively parallelized computational power, cutting-edge AI algorithms, and big data storage, management, and processing. Applied to WSIs, AI has created opportunities for improved disease diagnostics and prognostics with the ultimate goal of enhancing precision medicine and resulting patient care. The National Institutes of Health (NIH) has recognized the importance of developing standardized principles for data management and discovery for the advancement of science and proposed the Findable, Accessible, Interoperable, Reusable, (FAIR) Data Principles1 with the goal of building a modernized biomedical data resource ecosystem to establish collaborative research communities. In line with this mission and to democratize AI-based image analysis in digital pathology, we propose ComPRePS: an end-to-end automated Computational Renal Pathology Suite which combines massive scalability, on-demand cloud computing, and an easy-to-use web-based user interface for data upload, storage, management, slide-level visualization, and domain expert interaction. Moreover, our platform is equipped with both in-house and collaborator developed sophisticated AI algorithms in the back-end server for image analysis to identify clinically relevant micro-anatomic functional tissue units (FTU) and to extract image features.

[Design and Development of ECG Monitoring Cloud Platform Based on the Internet of Things Electrocardiograph].

The design and development of electrocardiogram（ECG） monitoring cloud platform based on the Internet of Things（IoT） electrocardiograph is introduced. The platform is mainly composed of ECG acquisition module, algorithm module, diagnostic model comparison module, warning module, positioning module and medical aid system. The ECG acquisition module collects ECG signals of patients and displays them in real time on the mobile terminals. Then they are uploaded to the ECG monitoring cloud platform through the IoT. The algorithm module and the diagnostic model comparison module mark, process, analyze and diagnose the ECG. Meanwhile, the ECG diagnosis and warning results are pushed to patients and 120 emergency centers through the IoT. Furthermore, the cloud platform will guide patients to self-rescue and the emergency platform will open the green channel to save patients in time.The platform realizes from the onset to diagnosis and treatment in one step, and saves lives against time.

ComPRePS: An Automated Cloud-based Image Analysis tool to democratize AI in Digital Pathology.

Artificial intelligence (AI) has extensive applications in a wide range of disciplines including healthcare and clinical practice. Advances in high-resolution whole-slide brightfield microscopy allow for the digitization of histologically stained tissue sections, producing gigapixel-scale whole-slide images (WSI). The significant improvement in computing and revolution of deep neural network (DNN)-based AI technologies over the last decade allow us to integrate massively parallelized computational power, cutting-edge AI algorithms, and big data storage, management, and processing. Applied to WSIs, AI has created opportunities for improved disease diagnostics and prognostics with the ultimate goal of enhancing precision medicine and resulting patient care. The National Institutes of Health (NIH) has recognized the importance of developing standardized principles for data management and discovery for the advancement of science and proposed the Findable, Accessible, Interoperable, Reusable, (FAIR) Data Principles1 with the goal of building a modernized biomedical data resource ecosystem to establish collaborative research communities. In line with this mission and to democratize AI-based image analysis in digital pathology, we propose ComPRePS: an end-to-end automated Computational Renal Pathology Suite which combines massive scalability, on-demand cloud computing, and an easy-to-use web-based user interface for data upload, storage, management, slide-level visualization, and domain expert interaction. Moreover, our platform is equipped with both in-house and collaborator developed sophisticated AI algorithms in the back-end server for image analysis to identify clinically relevant micro-anatomic functional tissue units (FTU) and to extract image features.

Real-Time Three-Dimensional Pedestrian Localization System Using Smartphones.

Robust and accurate three-dimensional localization is essential for personal navigation, emergency rescue, and worker monitoring in indoor environments. For localization technology to be employed in various applications, it is necessary to reduce infrastructure dependence and limit the maximum error bound. This study aims to accurately estimate the location of various people using smartphones in a building with a cloud platform-based localization system. The proposed technology is modularized in a hierarchical structure to sequentially estimate the floor and location. This system comprises four localization modules: course level detection, fine level detection (FLD), fine location tracking (FLT), and level change detection (LCD). Each module operates organically according to the current user status. The position estimation range is defined as a total of three phases, and an appropriate location estimation module suitable for the corresponding phase operates to estimate the user's location gradually and precisely. When the user's floor is determined by an FLD, the two-dimensional position of the user is estimated by an FLT module that tracks the user's position by comparing the received signal strength indicator vector sequence and radio map. Also, LCD recognizes the user's floor change and converts the user's phase. To verify the proposed technology, various experiments were conducted in a six-story building, and an average accuracy of less than 2 m was obtained.

Construction of pediatric pharmaceutical care cloud platform based on Western Pediatric Development Union / 中国药房

OBJECTIVE To explore the construction of a pediatric pharmaceutical care cloud platform based on Western Pediatric Development Union， to provide a reference for narrowing the difference of pediatric pharmaceutical care in western regions and medical institutions. METHODS Based on the Western Pediatric Development Union， the “1+3+3” pediatric pharmaceutical care cloud platform had been built by relying on the alliance telemedicine service network. That was， with Children’s Hospital of Chongqing Medical University as one center， three core pharmaceutical services， including prescription dispensing， pharmaceutical guidance and health education， were carried out in the union through standardized 3 aspects of management of resource information， service process and component interface. RESULTS & CONCLUSIONS Established “1+3+ 3” pediatric pharmaceutical care cloud platform based on the remote service network of the Western Pediatric Development Union registered 1 208 thousands registrations since its operation， with 112 thousands online prescriptions and 44 thousands circulation prescriptions completed； the total number of medication consultation cases was 10 694， and the number of online training people exceeded 15 thousands. However， during the operation， there are also problems such as limited clinical medication data and insufficient coverage of the region. With the accumulation of clinical medication data on the platform， the artificial intelligence technology will be used to extract the data of prescriptions， medication behavior， and physical indicators after medication， the correlation analysis of data will be conducted under the conditions of different geographical environments， different age groups， different heights and weights in the region to obtain clinical medication characteristics for children in the region， providing decision support for further guiding rational and safe medication in pediatric clinical practice.

ICPUTRD: Image Cloud Platform for use in tagging and research on decomposition.

Human decomposition studies aim to understand the various factors influencing human decay to assess the deceased and develop postmortem interval (PMI) estimation methods. These types of studies are typically conducted through physical experiments examining the deceased; however, big data systems have the potential to transform how large-scale forensic anthropology research questions can be addressed with curated images of donors with known demographic, climatic, and postmortem historical data. This study introduces ICPUTRD (Image Cloud Platform for Use in Tagging and Research on Decomposition), a web-based software system, which enables forensic scientists to easily access, enhance (or curate), and analyze very large photographic collections documenting the longitudinal process of human decomposition. ICPUTRD, a JavaScript-based application, was designed and built through a combination of the Waterfall and Agile software development life-cycle methods and provides an image search and tagging features with a predefined nomenclature of forensic-related keywords. To evaluate the system, a user study was conducted, involving 27 participants who completed pre- and post-study surveys and three research tasks. Analysis of the study results confirmed the feasibility and practicality of ICPUTRD to facilitate aspects of forensic research and casework involving large collections of digital photographs of human decomposition. It was observed that the nomenclature lacked certain law enforcement keywords, so future work will focus on expanding it to ensure ICPUTRD is suited for all its intended users.

ReIPS: A Secure Cloud-Based Reputation Evaluation System for IoT-Enabled Pumped Storage Power Stations.

Reputation evaluation is an effective measure for maintaining secure Internet of Things (IoT) ecosystems, but there are still several challenges when applied in IoT-enabled pumped storage power stations (PSPSs), such as the limited resources of intelligent inspection devices and the threat of single-point and collusion attacks. To address these challenges, in this paper we present ReIPS, a secure cloud-based reputation evaluation system designed to manage intelligent inspection devices' reputations in IoT-enabled PSPSs. Our ReIPS incorporates a resource-rich cloud platform to collect various reputation evaluation indexes and perform complex evaluation operations. To resist single-point attacks, we present a novel reputation evaluation model that combines backpropagation neural networks (BPNNs) with a point reputation-weighted directed network model (PR-WDNM). The BPNNs objectively evaluate device point reputations, which are further integrated into PR-WDNM to detect malicious devices and obtain corrective global reputations. To resist collusion attacks, we introduce a knowledge graph-based collusion device identification method that calculates behavioral and semantic similarities to accurately identify collusion devices. Simulation results show that our ReIPS outperforms existing systems regarding reputation evaluation performance, particularly in single-point and collusion attack scenarios.

Improving protein therapeutic development through cloud-based data integration.

Pharma 4.0 is a digital evolution of the pharmaceutical industry that automates scientists' traditional workflows with the implementation of modern technologies like cloud pipelines, artificial intelligence, robotic platforms, and augmented reality. Lab data capture (LDC) is an essential strategy for initiating Pharma 4.0 that aggregates and harmonizes siloed lab data from analytical instruments, reporting systems, and operational platforms. This publication describes the execution of LDC within a quantitative PCR (qPCR) workflow using the Tetra Data Platform (TDP). We selected this workflow because the qPCR instrument, the ViiA7, generates discrete file-based data that documents execution of individual assays for quantifying residual DNA throughout biologics process development and product profiling. TDP executes LDC through the deployment of file scanning software agents, scanning and ingestion processes, and a cloud-based parsing pipeline that harmonizes source data. Web applications were developed to query, visualize, and interpret harmonized qPCR data for automated experiment data processing and process control charting from the TDP platform. Our implementation of LDC enables analytical researchers to harness FAIR (Findable, Accessible, Interoperable, Reproducible) data practices across the organization and establishes a "compliance-by-code" culture in development labs.

Accelerating genomic workflows using NVIDIA Parabricks.

BACKGROUND: As genome sequencing becomes better integrated into scientific research, government policy, and personalized medicine, the primary challenge for researchers is shifting from generating raw data to analyzing these vast datasets. Although much work has been done to reduce compute times using various configurations of traditional CPU computing infrastructures, Graphics Processing Units (GPUs) offer opportunities to accelerate genomic workflows by orders of magnitude. Here we benchmark one GPU-accelerated software suite called NVIDIA Parabricks on Amazon Web Services (AWS), Google Cloud Platform (GCP), and an NVIDIA DGX cluster. We benchmarked six variant calling pipelines, including two germline callers (HaplotypeCaller and DeepVariant) and four somatic callers (Mutect2, Muse, LoFreq, SomaticSniper). RESULTS: We achieved up to 65 × acceleration with germline variant callers, bringing HaplotypeCaller runtimes down from 36 h to 33 min on AWS, 35 min on GCP, and 24 min on the NVIDIA DGX. Somatic callers exhibited more variation between the number of GPUs and computing platforms. On cloud platforms, GPU-accelerated germline callers resulted in cost savings compared with CPU runs, whereas some somatic callers were more expensive than CPU runs because their GPU acceleration was not sufficient to overcome the increased GPU cost. CONCLUSIONS: Germline variant callers scaled well with the number of GPUs across platforms, whereas somatic variant callers exhibited more variation in the number of GPUs with the fastest runtimes, suggesting that, at least with the version of Parabricks used here, these workflows are less GPU optimized and require benchmarking on the platform of choice before being deployed at production scales. Our study demonstrates that GPUs can be used to greatly accelerate genomic workflows, thus bringing closer to grasp urgent societal advances in the areas of biosurveillance and personalized medicine.

MiSurv: an Integrative Web Cloud Platform for User-Friendly Microbiome Data Analysis with Survival Responses.

Investigators have studied the treatment effects on human health or disease, the treatment effects on human microbiome, and the roles of the microbiome on human health or disease. Especially, in a clinical trial, investigators commonly trace disease status over a lengthy period to survey the sequential disease progression for different treatment groups (e.g., treatment versus placebo, new treatment versus old treatment). Hence, disease responses are often available in the form of survival (i.e., time-to-event) responses stratified by treatment groups. While the recent web cloud platforms have enabled user-friendly microbiome data processing and analytics, there is currently no web cloud platform to analyze microbiome data with survival responses. Therefore, we introduce here an integrative web cloud platform, called MiSurv, for comprehensive microbiome data analysis with survival responses. IMPORTANCE MiSurv consists of a data processing module and its following four data analytic modules: (i) Module 1: Comparative survival analysis between treatment groups, (ii) Module 2: Comparative analysis in microbial composition between treatment groups, (iii) Module 3: Association testing between microbial composition and survival responses, (iv) Module 4: Prediction modeling using microbial taxa on survival responses. We demonstrate its use through an example trial on the effects of antibiotic use on the survival rate against type 1 diabetes (T1D) onset and gut microbiome composition, respectively, and the effects of the gut microbiome on the survival rate against T1D onset. MiSurv is freely available on our web server (http://misurv.micloud.kr) or can alternatively run on the user's local computer (https://github.com/wg99526/MiSurvGit).

SCP4ssd: A Serverless Platform for Nucleotide Sequence Synthesis Difficulty Prediction Using an AutoML Model.

DNA synthesis is widely used in synthetic biology to construct and assemble sequences ranging from short RBS to ultra-long synthetic genomes. Many sequence features, such as the GC content and repeat sequences, are known to affect the synthesis difficulty and subsequently the synthesis cost. In addition, there are latent sequence features, especially local characteristics of the sequence, which might affect the DNA synthesis process as well. Reliable prediction of the synthesis difficulty for a given sequence is important for reducing the cost, but this remains a challenge. In this study, we propose a new automated machine learning (AutoML) approach to predict the DNA synthesis difficulty, which achieves an F1 score of 0.930 and outperforms the current state-of-the-art model. We found local sequence features that were neglected in previous methods, which might also affect the difficulty of DNA synthesis. Moreover, experimental validation based on ten genes of Escherichia coli strain MG1655 shows that our model can achieve an 80% accuracy, which is also better than the state of art. Moreover, we developed the cloud platform SCP4SSD using an entirely cloud-based serverless architecture for the convenience of the end users.

A Novel Intelligent Rebound Hammer System Based on Internet of Things.

In order to improve the test efficiency of concrete strength and ensure measured data reliability, we present a novel intelligent rebound hammer system which is based on the Internet of Things (IoT) and speech recognition technology. The system uses a STM32F103C8T6 microcontroller as the Main Control Unit (MCU), and one BC26 module as the communication unit, combined with a LD3320 voice recognition module and TOF050H laser ranging sensor to achieve the function of phonetic transcription and laser ranging. Without the need for traditional multi-person collaboration and burdensome data transfer, the system can collect the data of rebound value and location information and send them to the remote cloud information management system automatically in real time. The test results show that the system has high measuring accuracy, good data transmission stability and convenient operation, which could provide guidance for other types of non-destructive testing equipment designs.

Application of 3D virtual reconstruction and printing technology based on thin-slice CT images in network cloud+dual-track teaching / 中华医学教育探索杂志

Objective:To investigate the application prospect of 3D virtual reconstruction and printing technology based on thin-slice CT images in network cloud+dual-track teaching.Methods:A total of 120 medical students who were on probation in The Second Affiliated Hospital of Air Force Medical University from April 2021 to April 2022 were selected as subjects and were randomly divided into experimental group and control group, with 60 students in each group. The students in the experimental group received 3D virtual reconstruction and printing technology combined with network cloud+dual-track teaching, and those in the control group received network cloud+dual-track teaching alone. After 6 months of learning, the teaching effect was compared by closed-book examination, skill operation, speech defense, and questionnaire survey. SPSS 23.0 was used for the t-test and the chi-square test. Results:Compared with the control group in terms of department examination, the experimental group had significantly better scores of theoretical knowledge [(84.25±5.53) vs. (79.43±6.69), P<0.001] and operational skills [(87.68±5.72) vs. (82.97±5.32), P<0.001]. The experimental group had significantly higher scores than the control group in speech [(44.90±2.56) vs. (41.88±2.71), P<0.001] and defense [(45.83±2.62) vs. (43.85±2.56), P<0.001]. The questionnaire survey showed that compared with the control group, the experimental group had significantly higher scores of practical ability, active learning ability, expression ability, practice enthusiasm, and information acquisition ability ( P<0.001). Conclusion:The network cloud+dual-track teaching model assisted by 3D virtual reconstruction and printing technology can significantly improve the objective learning effect and subjective learning initiative of students and has a relatively high value of teaching application and promotion.

Discussion on medication law of Shao Nianfang in the treatment of kidney deficiency and bone marrow loss in senile dementia based on ancient and modern medical case cloud platform / 国际中医中药杂志

Objective:To discuss the medication law in prescriptions of Professor Shao Nianfang in the treatment of kidney deficiency and bone marrow loss in senile dementia based on data mining.Methods:Medical cases of kidney deficiency and bone marrow loss in senile dementia in the Geriatric Hospital of Shandong University of Traditional Chinese Medicine from 1st Jan. 2014 to 31st Oct, 2019 were collected. Through hospital electronic medical records system prescription statistics, using ancient and modern medical case cloud platform (V1.2.4), medication frequency, property ans taste, efficacy analysis, correlation rule analysis, clustering analysis and complex network analysis were performed.Results:Totally 110 cases were included in medical cases, involving 238 kinds of Chinese materia medica. The top 10 Chinese materia medica with use frequency were Poria, Acori Tatarinowii Rhizoma, Corni Fructus, Dioscoreae Rhizoma, Rehmanniae Radix Praeparata, Alpiniae Oxyphyllae Fructus, Rehmanniae Radix, Astragali Radix, Chuanxiong Rhizoma, Atractylodis Macrocephalae Rhizoma; the properties were mainly mild, warm slight cold, and cold; the tastes were mainly sweet, bitter, pungent, and light; the meridians were mainly spleen, liver, lung and kidney meridians; the efficacy was clearing dampness and promoting diuresis, clearing heat and promoting blood circulation, calming mind, clearing heat and detoxification, reducing dampness and promote appetizing, tonifying spleen; the association analysis found 15 groups of drug combinations used more than 25 times, they were: Corni Fructus and Poria, Corni Fructus and Dioscoreae Rhizoma, Dioscoreae Rhizoma and Corni Fructus, Rehmanniae Radix Praeparata and Corni Fructus, Dioscoreae Rhizoma and Poria, Astragali Radix and Poria, Alismatis Rhizoma and Poria, Moutan Cortex and Poria, Rehmanniae Radix Praeparata and Poria, Rehmanniae Radix and Poria, Polygalae Radix and Acori Tatarinowii Rhizoma, Moutan Cortex and Corni Fructus, Moutan Cortex and Dioscoreae Rhizoma, Alismatis Rhizoma and Corni Fructus, Alismatis Rhizoma and Dioscoreae Rhizoma; clustering analysis identified four groups of new prescriptions, the first group: Poria, Rehmanniae Radix, Rehmanniae Radix Praeparata, Alismatis Rhizoma, Moutan Cortex, Corni Fructus, Dioscoreae Rhizoma; the second group: Acori Tatarinowii Rhizoma, Cistanches Herba, Morindae Officinalis Radix; the third group: Alpiniae Oxyphyllae Fructus, Chuanxiong Rhizoma, Glycyrrhizae Radix et Rhizoma Praeparata Cum Melle; the fourth group: Atractylodis Macrocephalae Rhizoma, Codonopsis Radix, Astragali Radix, Angelicae Sinensis Radix; the results of complex network analysis showed that the core prescription was modified Liuwei Dihuang Pills. Conclusion:This study found that in view of kidney deficiency and bone marrow loss in senile dementia, Professor Shao pays attention to strengthening the healthy qi, and focuses on tonifying deficiency, taking into account the methods of clearing dampness, clearing heat, detoxification, removing blood stasis and restoring consciousness. The four new prescriptions found in the study can provide a reference for modified medication for syndrome differentiation.

Research on the rules of Traditional Chinese Medicine prescriptions of gastroesophageal reflux disease based on Ancient and Modern Medical Records Cloud Platform / 国际中医中药杂志

Objective:To explore the rules of Traditional Chinese Medicine (TCM) prescriptions of gastroesophageal reflux disease based on Ancient and Modern Medical Records Cloud Platform.Method:The relevant medical cases from ancient medical case database, modern medical case database, shared medical case database and famous doctors' medical case database in Ancient and Modern Medical Records Cloud Platform (V2.3.8) were selected, and frequency analysis, attribute analysis, association analysis, cluster analysis and complex network analysis were performed on the herbs.Results:A total of 107 medical records were obtained, including 225 TCMs. The core medicines were Radix et Rhizoma Glycyrrhizae, Pericarpium Citri Reticulatae, Rhizoma Coptidis, Poria, and Fructus Evodiae. The drug property was mainly cold and warm, and the herbal tastes bitter and pungent. The meridian tropism of drugs mainly manifested in the spleen and stomach meridians. The core herbal pairs were Radix et Rhizoma Glycyrrhizae and Pericarpium Citri Reticulatae. The core prescription consisted of 17 herbs including Radix Glycyrrhizae, Pericarpium Citri Reticulatae, Rhizoma Coptidis, Fructus Evodiae, Poria, Endoconcha Sepiae, Herba Taraxaci, Fructus Aurantii, Radix Paeoniae Alba, Radix Bupleuri, Jiang Banxia, Rhizoma Cyperi, Radix Aucklandiae, Caulis Bambusae In Taenia, Fructus Aurantii Immaturus, Fructus Amomi, and Rhizoma Atractylodis Macrocephalae. Conclusions:Chinese medicine treatment of gastroesophageal reflux disease is mainly based on Chaihu Shugan Powder, Zuojin Pill, and Wendan Decoction. Moreover, we need to combine with clinical symptoms to add or subtract herbs.

Analysis on the medication rules of Pu Ji Fang for treating acne externally based on data mining / 国际中医中药杂志

Objective:To analyze the medication rules in the ancient book Pu Ji Fang for the external treatment of acne based on data mining method. Methods:By screening out the methods of treating acne externally in Pu Ji Fang and establishing a standardized medical record database, this paper adopted the web version of Ancient and Modern Medical Record Cloud Platform to calculate the frequency, properties, flavors, and meridians of those medicines, and conduct cluster analysis by using IBM SPSS Modeler 18.0 software to analyze the association rules. Results:A total of 87 prescriptions were selected, including 164 kinds of Chinese materia medica, among which. Radix Angelicae, Ligusticum Wallichii, Rhizoma Typhoni and lead powder are frequently appeared. The properties of those medicines are mainly warm, cold and mild; the flavors of those medicines are mainly spicy, acrid, sweet and bitter, and the meridians mainly belongs to lung, spleen, stomach and liver meridians. The medical pair and group with the strongest associationion are Ligusticum Wallichii- Radix Angelicae and Rhizoma Typhonii- Radix Angelicae- Ligusticum Wallichii. Those freuently appeared medicines could be grouped into three categories. The paste dosage that was frequently appeared has strong correlation with tallow, mercury and lead powder, while the powder dosage that was frequenctly appeared has strong correlation with Angelica Dahurica, Radix Saponicae, Gleditsia sinensis, Radices Ligustici Sinensis and Ligusticum Wallichii. Conclusions:The application of data mining method could preliminarily reveal the medication rules of Pu Ji Fang for the external treatment of acne. The main treatment method is XinSanFaYue. The three categories of Chinese materia medica are used to treat the syndrome of asthenic habitus attacked by exogenous pathogenic factors, exterior attacked by wind heat and hot blood stasis respectively, showing the rules of treating acne externally before Ming Dynasty and providing references for the clinical treatment of acne.

Cloud platform to improve efficiency and coverage of asynchronous multidisciplinary team meetings for patients with digestive tract cancer.

Background: Multidisciplinary team (MDT) meetings are the gold standard of cancer treatment. However, the limited participation of multiple medical experts and the low frequency of MDT meetings reduce the efficiency and coverage rate of MDTs. Herein, we retrospectively report the results of an asynchronous MDT based on a cloud platform (cMDT) to improve the efficiency and coverage rate of MDT meetings for digestive tract cancer. Methods: The participants and cMDT processes associated with digestive tract cancer were discussed using a cloud platform. Software programming and cMDT test runs were subsequently conducted to further improve the software and processing. cMDT for digestive tract cancer was officially launched in June 2019. The doctor response duration, cMDT time, MDT coverage rate, National Comprehensive Cancer Network guidelines compliance rate for patients with stage III rectal cancer, and uniformity rate of medical experts' opinions were collected. Results: The final cMDT software and processes used were determined. Among the 7462 digestive tract cancer patients, 3143 (control group) were diagnosed between March 2016 and February 2019, and 4319 (cMDT group) were diagnosed between June 2019 and May 2022. The average number of doctors participating in each cMDT was 3.26 ± 0.88. The average doctor response time was 27.21 ± 20.40 hours, and the average duration of cMDT was 7.68 ± 1.47 min. The coverage rates were 47.85% (1504/3143) and 79.99% (3455/4319) in the control and cMDT groups, respectively. The National Comprehensive Cancer Network guidelines compliance rates for stage III rectal cancer patients were 68.42% and 90.55% in the control and cMDT groups, respectively. The uniformity rate of medical experts' opinions was 89.75% (3101/3455), and 8.97% (310/3455) of patients needed online discussion through WeChat; only 1.28% (44/3455) of patients needed face-to-face discussion with the cMDT group members. Conclusion: A cMDT can increase the coverage rate of MDTs and the compliance rate with National Comprehensive Cancer Network guidelines for stage III rectal cancer. The uniformity rate of the medical experts' opinions was high in the cMDT group, and it reduced contact between medical experts during the COVID-19 pandemic.

Information sharing through digitalisation in decentralised supply chains.

This study investigates the impact of private information on decision making process and how emerging technologies can facilitate information sharing and reduce misinformation in decentralised settings. Focusing on business environments, we examine if information sharing between distinct partners can be a mutually beneficial option. In principle, information affects the preferences and the actions of decision makers and usually contributes to inefficiencies for the entire system. A supply chain with two rational firms is considered; the firms have conflicting objectives and possess information that cannot be verified. Real-time communication through a cloud platform is allowed, before the firms finalise their strategies. During the communication phase, both firms are free to report whatever information optimises their individual objectives, even fake. Misinformation seems a plausible option, especially in competitive environments, since the firms may take advantages from such behaviour. We demonstrate that sharing the actual information can be beneficial for both, under the implementation of an appropriate mechanism that considers the welfare of the entire chain. Despite the individualistic behaviour of independent decision makers, it is doable to eliminate entirely information asymmetry and misinformation. This happens by including sufficient incentives on a mechanism that induce firms to reveal their information, because it is in their self-interest to do so. The value of information and the expected benefits of the voluntary information sharing are calculated, indicating the potential improvement.

An In Vitro Dosimetry Tool for the Numerical Transport Modeling of Engineered Nanomaterials Powered by the Enalos RiskGONE Cloud Platform.

A freely available "in vitro dosimetry" web application is presented enabling users to predict the concentration of nanomaterials reaching the cell surface, and therefore available for attachment and internalization, from initial dispersion concentrations. The web application is based on the distorted grid (DG) model for the dispersion of engineered nanoparticles (NPs) in culture medium used for in vitro cellular experiments, in accordance with previously published protocols for cellular dosimetry determination. A series of in vitro experiments for six different NPs, with Ag and Au cores, are performed to demonstrate the convenience of the web application for calculation of exposure concentrations of NPs. Our results show that the exposure concentrations at the cell surface can be more than 30 times higher compared to the nominal or dispersed concentrations, depending on the NPs' properties and their behavior in the cell culture medium. Therefore, the importance of calculating the exposure concentration at the bottom of the cell culture wells used for in vitro arrays, i.e., the particle concentration at the cell surface, is clearly presented, and the tool introduced here allows users easy access to such calculations. Widespread application of this web tool will increase the reliability of subsequent toxicity data, allowing improved correlation of the real exposure concentration with the observed toxicity, enabling the hazard potentials of different NPs to be compared on a more robust basis.

Watcher: Cloud-Based Coding Activity Tracker for Fair Evaluation of Programming Assignments.

Online learning has made it possible to attend programming classes regardless of the constraint that all students should be gathered in a classroom. However, it has also made it easier for students to cheat on assignments. Therefore, we need a system to deal with cheating on assignments. This study presents a Watcher system, an automated cloud-based software platform for impartial and convenient online programming hands-on education. The primary features of Watcher are as follows. First, Watcher offers a web-based integrated development environment (Web-IDE) that allows students to start programming immediately without the need for additional installation and configuration. Second, Watcher collects and monitors the coding activity of students automatically in real-time. As Watcher provides the history of the coding activity to instructors in log files, the instructors can investigate suspicious coding activities such as plagiarism, even for a short source code. Third, Watcher provides facilities to remotely manage and evaluate students' hands-on programming assignments. We evaluated Watcher in a Unix system programming class for 96 students. The results showed that Watcher improves the quality of the coding experience for students through Web-IDE, and it offers instructors valuable data that can be used to analyze the various coding activities of individual students.