Timing of exposure is critical in a highly sensitive model of SARS-CoV-2 transmission

Transmission efficiency is a critical factor determining the size of an outbreak of infectious disease. Indeed, the propensity of SARS-CoV-2 to transmit among humans precipitated and continues to sustain the COVID-19 pandemic. Nevertheless, the number of new cases among contacts is highly variable and underlying reasons for wide-ranging transmission outcomes remain unclear. Here, we evaluated viral spread in golden Syrian hamsters to define the impact of temporal and environmental conditions on the efficiency of SARS-CoV-2 transmission through the air. Our data show that exposure periods as brief as one hour are sufficient to support robust transmission. However, the timing after infection is critical for transmission success, with the highest frequency of transmission to contacts occurring at times of peak viral load in the donor animals. Relative humidity and temperature had no detectable impact on transmission when exposures were carried out with optimal timing. However, contrary to expectation, trends observed with sub-optimal exposure timing suggest improved transmission at high relative humidity or high temperature. In sum, among the conditions tested, our data reveal the timing of exposure to be the strongest determinant of SARS-CoV-2 transmission success and implicate viral load as an important driver of transmission.

Wireless Network of Silver Film Lysozyme Aptasensor Based on Fractal Measurement

In this work, we reported a wireless network composed of silver film-based graphene oxide-fluorescence resonance energy transfer (GO-FRET) lysozyme aptasensor nodes. At the sensor node level, we optimized silver substrate structure, concentrations of the aptamers, and graphene oxide and tested lysozyme detection performance with a model analyte. At the network level, we analyzed the complexity and transmission success rate using fractal measurements. We implemented the wireless network composed of the aptasensor with a portable Wi-Fi fluorescent reader. Transmission success rate testing results show that an increase in node hops can promote the rate of transmission success dramatically. When the hop count is larger than 6, the rate of transmission success can reach more than 90% if the transmission failure probability and sleep probability are 0.1 and 0.5, respectively.

Analysis and Implementation of Reinforcement Learning on a GNU Radio Cognitive Radio Platform

<p>Spectrum today is regulated based on fixed licensees. In the past radio operators have been allocated a frequency band for exclusive use. This has become problem for new users and the modern explosion in wireless services that, having arrived late find there is a scarcity in the remaining available spectrum. Cognitive radio (CR) presents a solution. CRs combine intelligence, spectrum sensing and software reconfigurable radio capabilities. This allows them to opportunistically transmit among several licensed bands for seamless communications, switching to another channel when a licensee is sensed in the original band without causing interference. Enabling this is an intelligent dynamic channel selection strategy capable of finding the best quality channel to transmit on that suffers from the least licensee interruption. This thesis evaluates a Q-learning channel selection scheme using an experimental approach. A cognitive radio deploying the scheme is implemented on GNU Radio and its performance is measured among channels with different utilizations in terms of its packet transmission success rate, goodput and interference caused. We derive similar analytical expressions in the general case of large-scale networks. Our results show that using the Q-learning scheme for channel selection significantly improves the goodput and packet transmission success rate of the system.</p>

Analysis and Implementation of Reinforcement Learning on a GNU Radio Cognitive Radio Platform

<p>Spectrum today is regulated based on fixed licensees. In the past radio operators have been allocated a frequency band for exclusive use. This has become problem for new users and the modern explosion in wireless services that, having arrived late find there is a scarcity in the remaining available spectrum. Cognitive radio (CR) presents a solution. CRs combine intelligence, spectrum sensing and software reconfigurable radio capabilities. This allows them to opportunistically transmit among several licensed bands for seamless communications, switching to another channel when a licensee is sensed in the original band without causing interference. Enabling this is an intelligent dynamic channel selection strategy capable of finding the best quality channel to transmit on that suffers from the least licensee interruption. This thesis evaluates a Q-learning channel selection scheme using an experimental approach. A cognitive radio deploying the scheme is implemented on GNU Radio and its performance is measured among channels with different utilizations in terms of its packet transmission success rate, goodput and interference caused. We derive similar analytical expressions in the general case of large-scale networks. Our results show that using the Q-learning scheme for channel selection significantly improves the goodput and packet transmission success rate of the system.</p>

A novel gateway node reconfiguration method of IOT based on hierarchical coding particle swarm optimization

In order to overcome the problems of low channel utilization, low transmission success rate and high data transmission delay in current gateway node reconfiguration methods of IOT, this paper proposes a novel gateway node reconfiguration method of IOT based on hierarchical coding particle swarm optimization. Based on the IOT network model, this paper analyzes the delay characteristics of the IOT, and constructs the object function of the gateway node reconfiguration of IOT. By monotone decreasing inertia weight strategy, the coding particle swarm optimization is optimized, and the reconfiguration objective function of the gateway node of IOT by using the optimized particle swarm optimization algorithm is solved. Experimental results show that the channel utilization ratio of the proposed method is higher than 90%, the success rate of information transmission is more than 80%, and the data transmission delay is less than 0.5 s, which indicates that the proposed method has high channel utilization, high transmission success rate and low data transmission delay.

Design of Sports Training System and Motion Monitoring and Recognition under Wireless Sensor Network

In conventional sports training, coaches record and observe athletes' sports data and judge whether it is reasonable based on their own experience. This qualitative analysis method is highly subjective, has large errors, and is susceptible to interference. To solve the above problems, the design of the sports training system under the wireless sensor network and the research of movement monitoring and recognition become very important. This article aims to study the design of sports training system and the monitoring and recognition of actions under the wireless sensor network technology. This paper simulates the implementation of the proposed data collection protocol and the two basic protocols, the direct transfer algorithm and the flooding algorithm, and compares the protocol proposed in this paper with the other two algorithms in terms of average information transmission success rate and average network overhead. Among them, the average information transmission success rate represents the ratio of the number of messages successfully arriving at the base station to the total amount of information generated by all nodes, and the average network overhead represents the average number of messages sent by each node. Experimental results show that the data collection protocol proposed in this paper can dynamically provide different transmission qualities for information of different importance levels, effectively reducing network overhead, and the reduced overhead is 11% of the original.

Lightweight Microcontroller with Parallelized ECC-Based Code Memory Protection Unit for Robust Instruction Execution in Smart Sensors

Embedded systems typically operate in harsh environments, such as where there is external shock, insufficient power, or an obsolete sensor after the replacement cycle. Despite these harsh environments, embedded systems require data integrity for accurate operation. Unintended data changes can cause a serious error in reduced instruction set computer (RISC)-based small embedded systems. For instance, if communication is performed on an edge, where there is insufficient power supply, the peak threshold is not reached, resulting in data transmission failure or incorrect data transmission. To ensure data integrity, we use an error-correcting code (ECC), which can detect and correct errors. The ECC parity bit and data are stored together using additional ECC memory, and the original data are extracted through the ECC decoding process. The process of extracting the original data is executed in the instruction fetch stage, where a bottleneck appears in the RISC-based structure. When the ECC decoding process is executed in the bottleneck, the instruction fetch stage increases the instruction fetch time and significantly reduces the overall performance. In this study, we attempt to minimize the effect of ECC on the transmission speed by executing the ECC decoding process in parallel to improve speed by degrading the bottleneck. To evaluate the performance of a parallelized ECC decoding block, we applied the proposed method to the tiny processing unit (TPU) with a RISC-based von Neumann structure and compared memory usage, speed, and reliability according to different transmission success rates in each model. The experiment was conducted using a benchmark that repeatedly executed several 3*3 matrix calculations, and reliability improvement was compared by corrupting the stored random date to confirm the reliability of the transmission success rate. As a result, in the proposed model, using the additional parity bits for parallel processing, memory usage increased by 10 bits per instruction, reducing the data rate from 80 to 61%. However, it showed an improvement in overall reliability and a 7% increase in speed.

Parasite immune evasion and manipulation of host phenotype

All parasites manipulate their hosts by interference with immune defences and host behaviour. Passive evasion, for example, is by molecular mimicry or by changing surfaces. Active interference involves production of molecules that block or modify host immune defence regulation or affect metabolism and neuronal systems. All steps of the immune defence cascades are attacked by parasites, including the microbiota. Manipulation can increase the duration of infection or transmission success. The latter is particularly prominent in intermediate hosts that need to be consumed by a final host. Host fecundity reduction and gigantism provide extra resources for the parasite. Theory can predict what manipulation should be best; conflicts arise among co-infecting parasites.

Performance Estimation of Sustainable Smart Farming with Blockchain Technology

Sustainable smart agriculture with increase in signal to interference or signal to noise ratio (SIR/SNR) for selection of best relay is discussed in a wireless blockchain based network. The overall communication throughput (OCT), power splitting relaying (PSR), time switching relaying (TSR) and transmission success rate (TRS) are also derived during the selection of best relay performance with and without interference. The performance of OCT, PSR, TSR and TRS increases with the increase in the number of potential relay nodes as seen in the results of derivation. The accuracy of the theoretical values are validated by numerical simulations.

Assessment of closely related Mycobacterium tuberculosis variants with different transmission success and in vitro infection dynamics

Whole genome sequencing (WGS) is able to differentiate closely related Mycobacterium tuberculosis variants within the same transmission cluster. Our aim was to evaluate if this higher discriminatory power may help identify and characterize more actively transmitted variants and understand the factors behind their success. We selected a robust MIRU-VNTR-defined cluster from Almería, Spain (22 cases throughout 2003–2019). WGS allowed discriminating, within the same epidemiological setting, between a successfully transmitted variant and seven closely related variants that did not lead to secondary cases, or were involved in self-limiting transmission (one single secondary case). Intramacrophagic growth of representative variants was evaluated in an in vitro infection model using U937 cells. Intramacrophage multiplication ratios (CFUs at Day 4/CFUs at Day 0) were higher for the actively transmitted variant (range 5.3–10.7) than for the unsuccessfully transmitted closely related variants (1.5–3.95). Two SNPs, mapping at the DNA binding domain of DnaA and at kdpD, were found to be specific of the successful variant.