Tracing System Research Articles

Plasmidome in mcr-1 harboring carbapenem-resistant enterobacterales isolates from human in Thailand

The emergence of the mobile colistin-resistance genes mcr-1 has attracted significant attention worldwide. This study aimed to investigate the genetic features of mcr-1-carrying plasmid among carbapenem-resistant Enterobacterales (CRE) isolates and the potential genetic basis governing transmission. Seventeen mcr-harboring isolates were analyzed based on whole genome sequencing using short-read and long-read platforms. All the mcr-1-carrying isolates could be conjugatively transferred into a recipient Escherichia coli UB1637. Among these 17 isolates, mcr-1 was located on diverse plasmid Inc types, consisting of IncX4 (11/17; 64.7%), IncI2 (4/17; 23.53%), and IncHI/IncN (2/17; 11.76%). Each of these exhibited remarkable similarity in the backbone set that is responsible for plasmid replication, maintenance, and transfer, with differences being in the upstream and downstream regions containing mcr-1. The IncHI/IncN type also carried other resistance genes (blaTEM-1B or blaTEM-135). The mcr-1-harboring IncX4 plasmids were carried in E. coli ST410 (7/11; 63.6%) and ST10 (1/11; 9.1%) and Klebsiella pneumoniae ST15 (1/11; 9.1%), ST336 (1/11; 9.1%), and ST340 (1/11; 9.1%). The IncI2-type plasmid was harbored in E. coli ST3052 (1/4; 25%) and ST1287 (1/4; 25%) and in K. pneumoniae ST336 (2/4; 50%), whereas IncHI/IncN were carried in E. coli ST6721 (1/2; 50%) and new ST (1/2; 50%). The diverse promiscuous plasmids may facilitate the spread of mcr-1 among commensal E. coli or K. pneumoniae strains in patients. These results can provide information for a surveillance system and infection control for dynamic tracing.

Abstract 14922: Combined Lineage Tracing and Scrna-Seq Reveals Unexpected First Heart Field Predominance of Human Ipsc Differentiation

Introduction: During mammalian development, the left and right ventricles arise from early populations of cardiac progenitors known as the first and second heart fields, respectively. While these populations have been studied in non-human model systems, human induced pluripotent stem cells (hiPSCs) present an exciting alternative for modeling early human embryogenesis. A major question is whether hiPSCs can model cardiac progenitor development as seen in animal systems. Hypothesis: Dual TBX5/MYL2 lineage tracing and time course single cell RNA sequencing can uncover the identity of human cardiac progenitor lineage in vitro. Methods: Using CRISPR/Cas9 genome editing we introduced a TBX5/MYL2 fluorescent reporter lineage tracing system into two independent hiPSC lines to identify FHF-progenitors and their descendants. Using a standard biphasic WNT modulation protocol, we differentiated hiPSCs into cardiomyocytes and characterized the expression of TBX5-lineage positive and MYL2 cardiomyocytes between day 3 and day 30 of differentiation. To further characterize the cell populations, present during differentiation, we conducted single cell RNA sequencing on differentiating hiPSCs across 12 timepoints with oligonucleotide-based sample multiplexing. Results: Our reporter system revealed the surprising finding that using a standard WNT modulation small molecule protocol across two cell lines yielded 93.2%±0.80% and 92.1%± 1.60% TBX5-lineage positive cardiomyocytes, respectively. Among MYL2 expressing ventricular cardiomyocytes, we observed 99.0%±0.21% and 93.2±0.84% as TBX5-lineage positive, respectively. ScRNA-seq analysis and trajectory inference revealed high expression of known FHF gene markers relative to murine developmental data and identified a clear bifurcation of cardiac progenitor populations into myocardial and epicardial lineage consistent with a FHF differentiation trajectory. Lastly, a comparison of our data with scRNA-seq data from a recently published 3D protocol reveals the identity of a SHF progenitor population within a 3D system. Conclusions: Our work provides a powerful new genetic lineage tracing approach, as well as a single cell transcriptomic atlas of hiPSCs undergoing cardiac differentiation.

Technologies monitoring and improving biosecurity compliance in barn anterooms.

People can act as mechanical vectors, and introduce and spread infectious diseases on farms. Preventive measures, such as changing boots and washing hands, need systematic implementation to manage this risk. Unfortunately, biosecurity compliance regarding biosecurity measures in barn anterooms has been shown to be generally low in all animal production systems. Indeed, the main challenge with biosecurity is maintaining compliance. The development of an effective on-farm biosecurity program requires several elements. These include farm and barn designs facilitating implementation of biosecurity measures; consistently communicating with all personnel and visitors informing them about threats and biosecurity; training programs for all farm personnel, explaining why biosecurity is effective in preventing infectious disease transmission, which measures are needed, and how to best implement them. All these components would be further optimized if automated monitoring systems were implemented with feedback mechanisms. Technologies are now available and are being adapted to the farm context to monitor biosecurity compliance. Two pilot projects using radio-frequency-identification-based (RFID) real-time continuous automated monitoring system quantifying hand sanitizing and boot compliance were conducted. The first one (MediHand Trace system) was a system designed to monitor and provide real-time feedback for handwashing in a hospital environment. It was functional for this task, although not sturdy enough for long-term use in a farm environment. The second system was a prototype designed for barns and with foot mats allowing the monitoring of footwear management as well as handwashing. These pilot studies have shown that real-time feedback helps improve compliance. However, the efficacy of the systems was very dependent on the physical set-up of the anteroom.

Design of Automatic Correction System for UAV’s Smoke Trajectory Angle Based on KNN Algorithm

Unmanned aerial vehicles (UAVs) have evolved with the progress of science and technology in recent years. They combine high-tech, such as information and communications technology, mechanical power, remote control, and electric power storage. In the past, drones could be flown only via remote control, and the mounted cameras captured images from the air. Now, UAVs integrate new technologies such as 5G, AI, and IoT in Taiwan. They have a great application value in a high-altitude data acquisition, entertainment performances (such as night light shows and UAV shows with smoke), agriculture, and 3D modeling. UAVs are susceptible to the natural wind when spraying smoke into the air, which leads to a smoke track offset. This study developed an autocorrect system for UAV smoke tracing. An AI model was used to calculate smoke tube angle corrections so that smoke tube angles could be immediately corrected when smoke is sprayed. This led to smoke tracks being consistent with flight tracks.

CoAvoid: Secure, Privacy-Preserved Tracing of Contacts for Infectious Diseases

To fight against infectious diseases (e.g., SARS, COVID-19, Ebola, etc.), government agencies, technology companies and health institutes have launched various contact tracing approaches to identify and notify the people exposed to infection sources. However, existing tracing approaches can lead to severe privacy and security concerns, thereby preventing their secure and widespread use among communities. To tackle these problems, this paper proposes CoAvoid, a decentralized, privacy-preserved contact tracing system that features good dependability and usability. CoAvoid leverages the Google/Apple Exposure Notification (GAEN) API to achieve decent device compatibility and operating efficiency. It utilizes GPS along with Bluetooth Low Energy (BLE) to dependably verify user information. In addition, to enhance privacy protection, CoAvoid applies fuzzification and obfuscation measures to shelter sensitive data, making both servers and users agnostic to information of both low and high-risk populations. The evaluation demonstrates good efficacy and security of CoAvoid. Compared with four state-of-art contact tracing applications, CoAvoid can reduce upload data by at least 90% and simultaneously resist wormhole and replay attacks in various scenarios.

Confidentiality-Preserving Edge-Based Wireless Communications for Contact Tracing Systems

This paper addresses two issues of a contact-tracing system with edge-based wireless communication techniques: to locate close contacts and to provide confidentiality-preserving communications. In this paper, we propose the Confidentiality-Preserving Contact-Tracing (CPCT) system with multiple wireless edge-based nodes. The CPCT system consists of three stages: registration, authentication, and confidentiality-preserving communication stages. We propose two Physical-Layer Authentication (PLA) schemes for the authentication stage of the CPCT system: the Coordinate-based Location PLA (CLP) scheme using the estimated coordinates and the Time-of-Arrival (ToA) based Location PLA (TLP) scheme using the estimated ToAs. We propose a distributed encryption scheme for the confidentiality-preserving communication stage of the CPCT system named the Distributed Channel Impulse Response (CIR)-based Encryption (DCE) scheme. We provide the theoretical analysis of the proposed schemes and derive their closed-form expressions. We implement the proposed schemes and conduct extensive performance comparisons through simulations. We observe that the theoretical results perfectly match the corresponding simulation results. Moreover, the proposed PLA schemes provide higher authentication performance than the prior PLA scheme, while the proposed encryption scheme provides higher confidentiality performance than the prior encryption scheme.

Decentralized multi-client functional encryption for set intersection with improved efficiency

Functional encryption (FE) is a new paradigm of public key encryption that can control the exposed information of plaintexts by supporting computation on encrypted data. In this paper, we propose efficient multi-client FE (MCFE) schemes that compute the set intersection of ciphertexts generated by two clients. First, we propose an MCFE scheme that calculates the set intersection cardinality (MCFE-SIC) and prove its static security under dynamic assumptions. Next, we extend our MCFE-SIC scheme to an MCFE scheme for set intersection (MCFE-SI) and prove its static security under dynamic assumptions. The decryption algorithm of our MCFE-SI scheme is more efficient than the existing MCFE-SI scheme because it requires fewer pairing operations to calculate the intersection of two clients. Finally, we propose a decentralized MCFE scheme for set intersection (DMCFE-SI) that decentralizes the generation of function keys. Our MCFE schemes can be effectively applied to a privacy-preserving contact tracing system to prevent the spread of recent infectious diseases.

ExtHT: A hybrid tracing method for cyber-attacks in power industrial control systems

Tracing the sources of cyber-attacks in Power Industrial Control Systems (PICS) can help the defense systems to block the attacks, and support the decision of the grid control policies. However, there has been no work on the cyber-attack source traceback for PICS, and the methods for the Internet are not suitable for PICS in terms of fineness, real-time performance, and supporting communication protocols. Therefore, a method for tracing cyber-attacks in PICS is proposed. First, the communication network architecture of PICS and the cyber security threats to PICS are analyzed. Then, an extended hybrid tracing method (ExtHT) based on packet marking and packet logging is proposed. This method involves all the devices working at the data link layer and upper layers to achieve more fine-grained attack tracing. At the same time, taking the costs of attack tracing into consideration, a coarse-grained tracing mode is presented to improve the tracing speed. In addition, a log database optimization scheme is provided to reduce storage costs. To facilitate the application of this method in practice, a cyber-attack source tracing system and its deployment architecture are designed for PICS. Further, the applicability and limitations of ExtHT are analyzed, theory ratiocinations are given to justify our ExtHT, and the performance of our ExtHT is compared with that of existing mainstream methods. Finally, two cyber-attack scenarios against PICS are constructed and the feasibility of ExtHT is verified on them.

Network analysis of a COVID-19 outbreak in the west of Ireland in February 2021

Abstract Notification and contact tracing systems of COVID-19 hold a vast amount of information on transmission chains of the virus. It can be hard to gain an understanding of these due to the number of individuals involved. Network analysis can be used as a method of visualising these systems, gaining understanding of transmission chains and as a potential tool for monitoring outbreaks. It can link cases together and show how far reaching initial infections were. Here, a system developed in the programming language R links the Irish infectious disease notification system and contact tracing system together and creates a network representation of the result. The system finds any cases or close contacts linked in any manner to the known cases. The result is a network from the earliest found case to the latest found case or contact related to the outbreak. A large outbreak of COVID-19 occurred in the student population in the West of Ireland in February 2021 with 449 cases linked to it by the Department of Public Health at the time. Using the system, 192 further positive cases were found to be linked to the outbreak. A total of 1,431 individuals were linked in some manner to it with 68% in the 19-24 age group and less than 1% in the 65+ age group. This takes a matter of seconds to run and highlights clusters within the outbreak, the largest of which had 96 cases and 121 not detected close contacts. Visualising the transmission chains also showed that there were no other large clusters outside of the outbreak at the time. A system such as this can link cases to outbreaks not previously linked, dramatically reduce the time taken to link cases together and visualise transmission chains to gain a deeper understanding of what is happening. This automated system frees up resources to allow for deeper investigation into cases and situations of concern. It also has the potential to link outbreaks together and spot previously unnoticed situations of concern. Key messages • Network analysis is beneficial for the monitoring of the spread of an infectious disease like COVID-19. • Here, 192 cases were found to be linked to an outbreak that was thought to have 449 cases in it.

Analysis of sodium boiling initiated by unprotected loss of flow in European sodium fast reactor core with different subassembly designs

In this paper, an Unprotected Loss of Flow (ULOF) assessment has been performed on the European Sodium Fast Reactor developed in the ESFR-SMART EU project. To conduct the analysis, a simplified 42 channel thermal–hydraulic model has been established in TRACE system code, using a point kinetics model accounting for various reactivity feedback effects. The assessment reveals the core behavior of a commercial size, 3600 MWth, sodium fast reactor using a state-of-the-art low void effect reactor core design. The study focuses on the sodium boiling phenomenon and sodium reactivity feedback effect evolution during the accident with the reference subassembly (SA) design. Following this analysis, a study has been performed with a modified SA design. The boiling progression and phenomenology within the reference and the modified core have been compared, and the impact of the SA modification was described.

Linking contact tracing with genomic surveillance to deconvolute SARS-CoV-2 transmission on a university campus

Contact tracing and genomic data, approaches often used separately, have both been important tools in understanding the nature of SARS-CoV-2 transmission. Linked analysis of contact tracing and sequence relatedness of SARS-CoV-2 genomes from a regularly sampled university environment were used to build a multilevel transmission tracing and confirmation system to monitor and understand transmission on campus. Our investigation of an 18-person cluster stemming from an athletic team highlighted the importance of linking contact tracing and genomic analysis. Through these findings, it is suggestive that certain safety protocols in the athletic practice setting reduced transmission. The linking of traditional contact tracing with rapid-return genomic information is an effective approach for differentiating between multiple plausible transmission scenarios and informing subsequent public health protocols to limit disease spread in a university environment.

Contact Tracing Strategies for COVID-19 Prevention and Containment: A Scoping Review

Coronavirus Disease 2019 (COVID-19) spreads rapidly and is easily contracted by individuals who come near infected persons. With this nature and rapid spread of the contagion, different types of research have been conducted to investigate how non-pharmaceutical interventions can be employed to contain and prevent COVID-19. In this review, we analyzed the key elements of digital contact tracing strategies developed for the prevention and containment of the dreaded epidemic since its outbreak. We carried out a scoping review through relevant studies indexed in three databases, namely Google Scholar, PubMed, and ACM Digital Library. Using some carefully defined search terms, a total of 768 articles were identified. The review shows that 86.32% (n = 101) of the works focusing on contact tracing were published in 2020, suggesting there was an increased awareness that year, increased research efforts, and the fact that the pandemic was given a very high priority by most journals. We observed that many (47.86%, n = 56) of the studies were focused on design and implementation issues in the development of COVID-19 contact tracing systems. In addition, has been established that most of the studies were conducted in 41 countries and that contract tracing app development are characterized by some sensitive issues, including privacy-preserving and case-based referral characteristics.

Using next generation matrices to estimate the proportion of infections that are not detected in an outbreak

Contact tracing, where exposed individuals are followed up to break ongoing transmission chains, is a key pillar of outbreak response for infectious disease outbreaks. Unfortunately, these systems are not fully effective, and infections can still go undetected as people may not remember all their contacts or contacts may not be traced successfully. A large proportion of undetected infections suggests poor contact tracing and surveillance systems, which could be a potential area of improvement for a disease response. In this paper, we present a method for estimating the proportion of infections that are not detected during an outbreak. Our method uses next generation matrices that are parameterized by linked contact tracing data and case line-lists. We validate the method using simulated data from an individual-based model and then investigate two case studies: the proportion of undetected infections in the SARS-CoV-2 outbreak in New Zealand during 2020 and the Ebola epidemic in Guinea during 2014. We estimate that only 5.26% of SARS-CoV-2 infections were not detected in New Zealand during 2020 (95% credible interval: 0.243 – 16.0%) if 80% of contacts were under active surveillance but depending on assumptions about the ratio of contacts not under active surveillance versus contacts under active surveillance 39.0% or 37.7% of Ebola infections were not detected in Guinea (95% credible intervals: 1.69 – 87.0% or 1.70 – 80.9%).

Geographical Origin Traceability of Procambarus clarkii Based on Mineral Elements and Stable Isotopes.

We explore the prospect of applying mineral element and stable isotope data in origin tracing Procambarus clarkii to establish an origin tracing system. Microwave digestion–atomic absorption spectrometry and stable isotope ratio mass spectrometry determined the contents of 14 mineral elements (Na, Mg, Al, K, Ca, Mn, Zn, Cu, Fe, Sr, Ba, As, Se and Cd) and the abundances of C and N stable isotopes in the muscle tissue of P. clarkii from Guangdong, Hunan and Hubei regions. The one-way ANOVA and Duncan multiple comparison results revealed Na, Sr, Ba, Cu, Mn, Fe, Al, Se, δ13C and δ15N varied significantly between the three regions (p < 0.05). A systematic clustering analysis revealed the stable isotopes combined with the mineral elements easily distinguished samples into the three different regions. Multivariate statistical analysis allowed us to establish a discriminant model for distinguishing P. clarkii from the three geographical regions. When stable isotopes were combined with mineral elements, the accuracy of the linear discriminant analysis of the samples from Guangdong, Hunan and Hubei were 95%, 95% and 100%, respectively. The initial overall discriminant accuracy was 96.7%, and the cross-validation discriminant accuracy was 93.3%. Principal component analysis identified three main components which were based on eleven major factors, including Cu, Ba, Cd, Mn, δ13C, δ15N, Al and Mg, resulting in a cumulative variance contribution rate of 78.77%. We established a three-dimensional coordinate system using the three principal components to create scatter diagrams with the samples from the three regions in the coordinate system. The results revealed the samples clearly differentiated into the three regions. Therefore, mineral elements combined with stable isotopes can distinguish the regional origin of P. clarkii.

Strengthening government's response to COVID-19 in Indonesia: A modified Delphi study of medical and health academics.

The Indonesian government has issued various policies to control COVID-19. However, COVID-19 new cases continued to increase, and there remain uncertainties as to the future trajectory. We aimed to investigate how medical and health academics view the Indonesian government's handling of COVID-19 and which areas of health systems need to be prioritized to improve the government's response to COVID-19. We conducted a modified Delphi study adapting the COVID-19 assessment scorecard (COVID-SCORE) as the measurement criteria. We invited medical and health academics from ten universities across Indonesia to take part in the two-round Delphi study. In the first round, participants were presented with 20 statements of COVID-SCORE and asked to rate their agreement with each statement using a five-point Likert scale. All participants who completed the first cycle were invited to participate in the second cycle. They had the opportunity to revise their answers based on the previous cycle's results and ranked a list of actions to improve government response. We achieved a moderate consensus level for five statements, a low consensus level for 13 statements and no consensus for two statements. The prioritization suggested that top priorities for improving the government's response to COVID-19 in Indonesia encompass: (1) strengthening capacity to ensure consistent, credible and targeted communication while adopting a more inclusive and empathic communication style to address public concerns; (2) ensuring universal access to reliable COVID-19 testing by expanding lab infrastructure, facilitating operational readiness, and scaling up implementation of proven alternative/complementary tests to RT-PCR; and (3) boosting contact tracing implementation capacity and facilitating contact tracing for all positive cases, involving key stakeholders in further development of the existing contact tracing system (i.e. PeduliLindungi) as well as its evaluation and quality assurance. Ultimately, our study highlights the importance of strengthening health system functions during the pandemic and improving health system resilience for dealing with future public health emergencies.

Internet of things (IoT) imbedded point-of-care SARS-CoV-2 testing in the pandemic and post-pandemic era

The outbreaks of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant in China have revealed a high rate of asymptomatic cases, making isolation and quarantine measures exceedingly difficult. Public health surveillance and intervention measures will require rapid and accurate testing preferably on-site using point-of-care tests (POCTs) technology for SARS-CoV-2 variants. However, delayed and/or inaccurate surveillance data is a major obstacle blocking the large-scale implementation of POCTs in curbing spread of infectious pathogens and reducing mortality during an outbreak. To determine levels of community transmission and timely strategies accordingly, highly sensitive and specific POCT embedded with the internet of things (IoT) technology could enable on-site screening and real-time data collection. A new Rapid Amplification with Sensitivity And Portability point-of-care test (RASAP-POCT) system based on thermal convection PCR is the first IoT-based isothermal nucleic acid amplification POCT, which can provide test results within 20–30 min using saliva and/or nasopharyngeal swab samples without nucleic acid extraction. With the IoT-imbedded feature, the RASAP-POCT system can be integrated easily and smoothly with China’s existing mobile-phone-based contact tracing system, which has previously proved to be highly effective in maintaining the dynamic zero-COVID policy. Current regulatory guidelines and rules should be modified to accelerate the adoption of new technologies under an emergency use authorization (EUA).

How much to pay for a track and trace system: a simulation model for South Africa

BackgroundThe illicit trade in tobacco reduces the effectiveness of tobacco-control policies. Independent track and trace (T&T) systems are considered one of the most effective measures available to reduce the illicit...

Efficient calculation of radiative intensity in three-dimensional atmospheres based on the Pn-IMS truncation method with a backward ray tracing system

The computational efficiency of solving the radiative transfer (RT) equation is an important issue for remote sensing and the monitoring of aerosols, clouds, and radiation, all of which involve the analysis of large volumes of data. In this study, based on the Improved Multiple and Single scattering (IMS) correction method, a backward ray tracing method was developed for the three-dimensional RT calculation (3D-IMS). We extended the IMS method to the 3D-RT problem using a backward ray tracing Monte Carlo system for calculation of both upward and downward radiance fields. In the 3D-IMS method, photons are assumed to be of two orthogonal types: one has less anisotropic scattering, while the other has highly anisotropic scattering decomposed by the delta-M method of Wiscombe (1977) to truncate a spherical harmonics expansion series. In this manner, an accurate radiance field is reconstructed by two separated ray tracings in the two independent spaces (i.e., delta-M and IMS spaces). Compared to the ordinary Monte Carlo method with the original phase function, the 3D-IMS method can better reduce the total photon number for the reconstruction of radiances. The performance of the 3D-IMS method was evaluated for plane-parallel atmospheres and for the case-4 experiment datasets of the Intercomparison of 3D Radiation Codes (I3RC) project. Among solvers included in the intercomparison, in cases with both completely backward and lateral scattering regions, the 3D-IMS method belonged to the group near the I3RC consensus mean data.

Enabling end-to-end digital carbon emission tracing with shielded NFTs

In the energy transition, there is an urgent need for decreasing overall carbon emissions. Against this background, the purposeful and verifiable tracing of emissions in the energy system is a crucial key element for promoting the deep decarbonization towards a net zero emission economy with a market-based approach. Such an effective tracing system requires end-to-end information flows that link carbon sources and sinks while keeping end consumers’ and businesses’ sensitive data confidential. In this paper, we illustrate how non-fungible tokens with fractional ownership can help to enable such a system, and how zero-knowledge proofs can address the related privacy issues associated with the fine-granular recording of stakeholders’ emission data. Thus, we contribute to designing a carbon emission tracing system that satisfies verifiability, distinguishability, fractional ownership, and privacy requirements. We implement a proof-of-concept for our approach and discuss its advantages compared to alternative centralized or decentralized architectures that have been proposed in the past. Based on a technical, data privacy, and economic analysis, we conclude that our approach is a more suitable technical backbone for end-to-end digital carbon emission tracing than previously suggested solutions.

DevCellPy is a machine learning-enabled pipeline for automated annotation of complex multilayered single-cell transcriptomic data

A major informatic challenge in single cell RNA-sequencing analysis is the precise annotation of datasets where cells exhibit complex multilayered identities or transitory states. Here, we present devCellPy a highly accurate and precise machine learning-enabled tool that enables automated prediction of cell types across complex annotation hierarchies. To demonstrate the power of devCellPy, we construct a murine cardiac developmental atlas from published datasets encompassing 104,199 cells from E6.5-E16.5 and train devCellPy to generate a cardiac prediction algorithm. Using this algorithm, we observe a high prediction accuracy (>90%) across multiple layers of annotation and across de novo murine developmental data. Furthermore, we conduct a cross-species prediction of cardiomyocyte subtypes from in vitro-derived human induced pluripotent stem cells and unexpectedly uncover a predominance of left ventricular (LV) identity that we confirmed by an LV-specific TBX5 lineage tracing system. Together, our results show devCellPy to be a useful tool for automated cell prediction across complex cellular hierarchies, species, and experimental systems.