Transmission chains of the first local outbreak cause by Delta VariantB.1.617.2 COVID-19 in Guangzhou, Southern China.

BACKGROUND: The first local outbreak of Delta Variant B.1.617.2 COVID-19 of China occurred in Guangzhou city, south China, in May 2021. This study analyzed the transmission chains and local cluster characteristics of this outbreak, intended to provide information support for the development and adjustment of local prevention and control strategies. METHODS: The transmission chains and local cluster characteristics of 161 local cases in the outbreak were described and analyzed. Incubation period, serial interval and generation time were calculated using the exact time of exposure and symptom onset date of the cases. The daily number of reported cases and the estimated generation time were used to estimate the effective reproduction number (Rt). RESULTS: We identified 7 superspreading events who had more than 5 next generation cases and their infected cases infected 70.81%(114/161) of all the cases transmission. Dining and family exposure were the main transmission routes in the outbreak, with 29.19% exposed through dining and 32.30% exposed through family places. Through further analysis of the outbreak, the estimated mean incubation period was 4.22 (95%CI: 3.66-4.94) days, the estimated mean generation time was 2.60 (95%CI: 1.96-3.11) days, and the estimated Rt was 3.29 (95%CI: 2.25-5.07). CONCLUSIONS: Classification and dynamically adjusted prevention and control measures had been carried out according to analysis of transmission chains and epidemical risk levels, including promoting nucleic acid screening at different regions and different risk levels, dividing closed-off area, controlled area according to the risk of infection, raising the requirements of leaving Guangzhou. By the above control measures, Guangzhou effectively control the outbreak within 28 days without implementing a large-scale lockdown policy.

Diffusion and enrichment of high-risk antibiotic resistance genes (ARGs) via the transmission chain (mulberry leave, guts and feces of silkworm, and soil) in an ecological restoration area of manganese mining, China: Role of heavy metals.

This study investigated the diffusion and enrichment of antibiotic resistance genes (ARGs) and pathogens via the transmission chain (mulberry leaves - silkworm guts - silkworm feces - soil) near a manganese mine restoration area (RA) and control area (CA, away from RA). Horizontal gene transfer (HGT) of ARGs was testified by an IncP a-type broad host range plasmid RP4 harboring ARGs (tetA) and conjugative genes (e.g., korB, trbA, and trbB) as an indicator. Compared to leaves, the abundances of ARGs and pathogens in feces after silkworms ingested leaves from RA increased by 10.8% and 52.3%, respectively, whereas their abundance in feces from CA dropped by 17.1% and 97.7%, respectively. The predominant ARG types in feces involved the resistances to ß-lactam, quinolone, multidrug, peptide, and rifamycin. Therein, several high-risk ARGs (e.g., qnrB, oqxA, and rpoB) carried by pathogens were more enriched in feces. However, HGT mediated by plasmid RP4 in this transmission chain was not a main factor to promote the enrichment of ARGs due to the harsh survival environment of silkworm guts for the plasmid RP4 host E. coli. Notably, Zn, Mn, and As in feces and guts promoted the enrichment of qnrB and oqxA. Worriedly, the abundance of qnrB and oqxA in soil increased by over 4-fold after feces from RA were added into soil for 30 days regardless of feces with or without E. coli RP4. Overall, ARGs and pathogens could diffuse and enrich in environment via the sericulture transmission chain developed at RA, especially some high-risk ARGs carried by pathogens. Thus, greater attentions should be paid to dispel such high-risk ARGs to support benign development of sericulture industry in the safe utilization of some RAs.

Hybrid social learning in human-algorithm cultural transmission.

Humans are impressive social learners. Researchers of cultural evolution have studied the many biases shaping cultural transmission by selecting who we copy from and what we copy. One hypothesis is that with the advent of superhuman algorithms a hybrid type of cultural transmission, namely from algorithms to humans, may have long-lasting effects on human culture. We suggest that algorithms might show (either by learning or by design) different behaviours, biases and problem-solving abilities than their human counterparts. In turn, algorithmic-human hybrid problem solving could foster better decisions in environments where diversity in problem-solving strategies is beneficial. This study asks whether algorithms with complementary biases to humans can boost performance in a carefully controlled planning task, and whether humans further transmit algorithmic behaviours to other humans. We conducted a large behavioural study and an agent-based simulation to test the performance of transmission chains with human and algorithmic players. We show that the algorithm boosts the performance of immediately following participants but this gain is quickly lost for participants further down the chain. Our findings suggest that algorithms can improve performance, but human bias may hinder algorithmic solutions from being preserved. This article is part of the theme issue 'Emergent phenomena in complex physical and socio-technical systems: from cells to societies'.

A novel spatial optimization approach for the cost-effectiveness improvement of LID practices based on SWMM-FTC.

Due to the increasingly frequent occurrence of urban waterlogging, the spatial optimization of low impact development (LID) practices has been commonly used to detain and reduce storm water runoff in the most cost-effective way. In this study, the flow transmission chain (FTC) was proposed to replace the routing portion of the Storm Water Management Model (SWMM) and was combined with the runoff component of the SWMM to simulate LID practices (SWMM-FTC). In the SWMM-FTC, the third Evolution Step of Generalized Differential Evolution (GDE3) was employed to optimize the LID layout design. The results showed that the relative error between the modified SWMM-FTC and the calibrated SWMM was less than 0.25% under various LID scenarios, and the computational efficiency of the SWMM-FTC was improved by 19.3 times. Moreover, the GDE3 outperformed the commonly used non-dominated sorting genetic algorithm (NSGA-II), the strength Pareto evolutionary algorithm (SPEA2), and the multi-objective shuffled frog leaping algorithm (MOSFLA) due to its ability to find the most cost-effective solution. The LID layout obtained from the SWMM-FTC with the GDE3 saved $210-1067 to achieve a 1% reduction in storm water runoff. This result demonstrates that the SWMM-FTC with the GDE3 can achieve higher environmental benefits than comparable models, providing better guidance for managers and stakeholders.

Snowball Sampling Study Design for Serosurveys Early in Disease Outbreaks.

Serological surveys can provide evidence of cases that were not previously detected, depict the spectrum of disease severity, and estimate the proportion of asymptomatic infections. To capture these parameters, survey sample sizes may need to be very large, especially when the overall infection rate is still low. Therefore, we propose the use of "snowball sampling" to enrich serological surveys by testing contacts of infected persons identified in the early stages of an outbreak. For future emerging pandemics, this observational study sampling design can answer many key questions, such as estimation of the asymptomatic proportion of all infected cases, the probability of a given clinical presentation for a seropositive individual, or the association between characteristics of either the host or the infection and seropositivity among contacts of index individuals. We provide examples, in the context of the coronavirus disease 2019 (COVID-19) pandemic, of studies and analysis methods that use a snowball sample and perform a simulation study that demonstrates scenarios where snowball sampling can answer these questions more efficiently than other sampling schemes. We hope such study designs can be applied to provide valuable information to slow the present pandemic as it enters its next stage and in early stages of future pandemics.

The impact of public health interventions in the Nordic countries during the first year of SARS-CoV-2 transmission and evolution.

BackgroundMany countries have attempted to mitigate and control COVID-19 through non-pharmaceutical interventions, particularly with the aim of reducing population movement and contact. However, it remains unclear how the different control strategies impacted the local phylodynamics of the causative SARS-CoV-2 virus.AimWe aimed to assess the duration of chains of virus transmission within individual countries and the extent to which countries exported viruses to their geographical neighbours.MethodsWe analysed complete SARS-CoV-2 genomes to infer the relative frequencies of virus importation and exportation, as well as virus transmission dynamics, in countries of northern Europe. We examined virus evolution and phylodynamics in Denmark, Finland, Iceland, Norway and Sweden during the first year of the COVID-19 pandemic.ResultsThe Nordic countries differed markedly in the invasiveness of control strategies, which we found reflected in transmission chain dynamics. For example, Sweden, which compared with the other Nordic countries relied more on recommendation-based rather than legislation-based mitigation interventions, had transmission chains that were more numerous and tended to have more cases. This trend increased over the first 8 months of 2020. Together with Denmark, Sweden was a net exporter of SARS-CoV-2. Norway and Finland implemented legislation-based interventions; their transmission chain dynamics were in stark contrast to their neighbouring country Sweden.ConclusionSweden constituted an epidemiological and evolutionary refugium that enabled the virus to maintain active transmission and spread to other geographical locations. Our analysis reveals the utility of genomic surveillance where monitoring of active transmission chains is a key metric.

Spatial modes for transmission of chikungunya virus during a large chikungunya outbreak in Italy: a modeling analysis.

BACKGROUND: The spatial spread of many mosquito-borne diseases occurs by focal spread at the scale of a few hundred meters and over longer distances due to human mobility. The relative contributions of different spatial scales for transmission of chikungunya virus require definition to improve outbreak vector control recommendations. METHODS: We analyzed data from a large chikungunya outbreak mediated by the mosquito Aedes albopictus in the Lazio region, Italy, consisting of 414 reported human cases between June and November 2017. Using dates of symptom onset, geographic coordinates of residence, and information from epidemiological questionnaires, we reconstructed transmission chains related to that outbreak. RESULTS: Focal spread (within 1 km) accounted for 54.9% of all cases, 15.8% were transmitted at a local scale (1-15 km) and the remaining 29.3% were exported from the main areas of chikungunya circulation in Lazio to longer distances such as Rome and other geographical areas. Seventy percent of focal infections (corresponding to 38% of the total 414 cases) were transmitted within a distance of 200 m (the buffer distance adopted by the national guidelines for insecticide spraying). Two main epidemic clusters were identified, with a radius expanding at a rate of 300-600 m per month. The majority of exported cases resulted in either sporadic or no further transmission in the region. CONCLUSIONS: Evidence suggest that human mobility contributes to seeding a relevant number of secondary cases and new foci of transmission over several kilometers. Reactive vector control based on current guidelines might allow a significant number of secondary clusters in untreated areas, especially if the outbreak is not detected early. Existing policies and guidelines for control during outbreaks should recommend the prioritization of preventive measures in neighboring territories with known mobility flows to the main areas of transmission.

Motor constraints influence cultural evolution of rhythm.

While widely acknowledged in the cultural evolution literature, ecological factors-aspects of the physical environment that affect the way in which cultural productions evolve-have not been investigated experimentally. Here, we present an experimental investigation of this type of factor by using a transmission chain (iterated learning) experiment. We predicted that differences in the distance between identical tools (drums) and in the order in which they are to be used would cause the evolution of different rhythms. The evidence confirms our predictions and thus provides a proof of concept that ecological factors-here a motor constraint-can influence cultural productions and that their effects can be experimentally isolated and measured. One noteworthy finding is that ecological factors can on their own lead to more complex rhythms.

High-fidelity copying is not necessarily the key to cumulative cultural evolution: a study in monkeys and children.

The unique cumulative nature of human culture has often been explained by high-fidelity copying mechanisms found only in human social learning. However, transmission chain experiments in human and non-human primates suggest that cumulative cultural evolution (CCE) might not necessarily depend on high-fidelity copying after all. In this study, we test whether defining properties of CCE can emerge in a non-copying task. We performed transmission chain experiments in Guinea baboons and human children where individuals observed and produced visual patterns composed of four squares on touchscreen devices. In order to be rewarded, participants had to avoid touching squares that were touched by a previous participant. In other words, they were rewarded for innovation rather than copying. Results nevertheless exhibited fundamental properties of CCE: an increase over generations in task performance and the emergence of systematic structure. However, these properties arose from different mechanisms across species: children, unlike baboons, converged in behaviour over generations by copying specific patterns in a different location, thus introducing alternative copying mechanisms into the non-copying task. In children, prior biases towards specific shapes led to convergence in behaviour across chains, while baboon chains showed signs of lineage specificity. We conclude that CCE can result from mechanisms with varying degrees of fidelity in transmission and thus that high-fidelity copying is not necessarily the key to CCE.

Repeated imitation makes human vocalizations more word-like.

People have long pondered the evolution of language and the origin of words. Here, we investigate how conventional spoken words might emerge from imitations of environmental sounds. Does the repeated imitation of an environmental sound gradually give rise to more word-like forms? In what ways do these forms resemble the original sounds that motivated them (i.e. exhibit iconicity)? Participants played a version of the children's game 'Telephone'. The first generation of participants imitated recognizable environmental sounds (e.g. glass breaking, water splashing). Subsequent generations imitated the previous generation of imitations for a maximum of eight generations. The results showed that the imitations became more stable and word-like, and later imitations were easier to learn as category labels. At the same time, even after eight generations, both spoken imitations and their written transcriptions could be matched above chance to the category of environmental sound that motivated them. These results show how repeated imitation can create progressively more word-like forms while continuing to retain a resemblance to the original sound that motivated them, and speak to the possible role of human vocal imitation in explaining the origins of at least some spoken words.

Petting zoos as sources of Shiga toxin-producing Escherichia coli (STEC) infections.

Despite their general low incidence, Shiga toxin-producing Escherichia (E.) coli (STEC) infections are considered an important public health issue due to the severity of illness that can develop, particularly in young children. We report on two Austrian petting zoos, one in Tyrol (2015) and one in Vorarlberg (2016), which were identified as highly likely infection sources of STEC infections. The petting zoo related cases involved a case of hemolytic uremic syndrome (HUS) due to STEC O157:HNM in 2015 and an outbreak of STEC O157:H7 infections affecting five young children and two adults in 2016. The HUS case accounted for 2.8% of the 36 STEC O157:HNM/H7 infections notified in Austria in 2015 (5,9% of 17 HUS cases). The seven cases described for 2016 accounted for 4.0% of the 177 human STEC infections documented for Austria in 2016, and for 19.4% of the 36 STEC O157:HNM/H7 infections notified that year. The evaluation of the STEC infections described here clearly underlines the potential of sequence-based typing methods to offer suitable resolutions for public health applications. Furthermore, we give a state-of-the-art mini-review on the risks of petting zoos concerning exposure to the zoonotic hazard STEC and on proper measures of risk-prevention.

Unsupervised Fault Diagnosis of a Gear Transmission Chain Using a Deep Belief Network.

Artificial intelligence (AI) techniques, which can effectively analyze massive amounts of fault data and automatically provide accurate diagnosis results, have been widely applied to fault diagnosis of rotating machinery. Conventional AI methods are applied using features selected by a human operator, which are manually extracted based on diagnostic techniques and field expertise. However, developing robust features for each diagnostic purpose is often labour-intensive and time-consuming, and the features extracted for one specific task may be unsuitable for others. In this paper, a novel AI method based on a deep belief network (DBN) is proposed for the unsupervised fault diagnosis of a gear transmission chain, and the genetic algorithm is used to optimize the structural parameters of the network. Compared to the conventional AI methods, the proposed method can adaptively exploit robust features related to the faults by unsupervised feature learning, thus requires less prior knowledge about signal processing techniques and diagnostic expertise. Besides, it is more powerful at modelling complex structured data. The effectiveness of the proposed method is validated using datasets from rolling bearings and gearbox. To show the superiority of the proposed method, its performance is compared with two well-known classifiers, i.e., back propagation neural network (BPNN) and support vector machine (SVM). The fault classification accuracies are 99.26% for rolling bearings and 100% for gearbox when using the proposed method, which are much higher than that of the other two methods.

Elucidating Transmission Patterns From Internet Reports: Ebola and Middle East Respiratory Syndrome as Case Studies.

The paucity of traditional epidemiological data during epidemic emergencies calls for alternative data streams to characterize the key features of an outbreak, including the nature of risky exposures, the reproduction number, and transmission heterogeneities. We illustrate the potential of Internet data streams to improve preparedness and response in outbreak situations by drawing from recent work on the 2014-2015 Ebola epidemic in West Africa and the 2015 Middle East respiratory syndrome (MERS) outbreak in South Korea. We show that Internet reports providing detailed accounts of epidemiological clusters are particularly useful to characterize time trends in the reproduction number. Moreover, exposure patterns based on Internet reports align with those derived from epidemiological surveillance data on MERS and Ebola, underscoring the importance of disease amplification in hospitals and during funeral rituals (associated with Ebola), prior to the implementation of control interventions. Finally, we discuss future developments needed to generalize Internet-based approaches to study transmission dynamics.

Middle East respiratory syndrome coronavirus "MERS-CoV": current knowledge gaps.

The Middle East respiratory syndrome coronavirus (MERS-CoV) that causes a severe lower respiratory tract infection in humans is now considered a pandemic threat to the Gulf region. Since its discovery in 2012, MERS-CoV has reached 23 countries affecting about 1100 people, including a dozen children, and claiming over 400 lives. Compared to SARS (severe acute respiratory syndrome), MERS-CoV appears to kill more people (40% versus 10%), more quickly, and is especially more severe in those with pre-existing medical conditions. Most MERS-CoV cases (>85%) reported thus far have a history of residence in, or travel to the Middle East. The current epidemiology is characterised by slow and sustained transmission with occasional sparks. The dromedary camel is the intermediate host of MERS-CoV, but the transmission cycle is not fully understood. In this current review, we have briefly summarised the latest information on the epidemiology, clinical features, diagnosis, treatment and prevention of MERS-CoV especially highlighting the knowledge gaps in its transmission dynamics, diagnosis and preventive strategy.

Exploring new technologies for the future generation: exploration-exploitation trade-off in an intergenerational framework.

Decision making on exploring or exploiting technology was studied by means of a laboratory experiment with a two-generation framework. In this framework, the design of a virtual tool is transmitted from the first to second generation, and hence, the former can help the latter by frequently exploring better tool designs but at the cost of reduced opportunities to exploit the existing tool to increase its own benefits. We set two experimental conditions ('repaid' and 'unrepaid') as well as a control condition (asocial), in which the second generation is absent. In the 'repaid' experimental condition, participants received an extra payment proportional to the score gained by the second generation, such that they were monetarily incentivized to help the second generation. Such an incentive was not given in the 'unrepaid' condition. An analysis of a formal model and computer simulations predicted that rational participants should increase investment in exploration only in the repaid condition when compared with the asocial control. The prediction was confirmed by the results of the experiment. These findings together suggest that humans may not have a propensity to invest in costly exploration of new technologies solely to help future generations.

[Adaptation of the electron transport chain improves the biocatalytic efficiency of progesterone 17α hydroxylation].

17α hydroxylase is a key enzyme for the conversion of progesterone to prepare various progestational drug intermediates. To improve the specific hydroxylation capability of this enzyme in steroid biocatalysis, the CYP260A1 derived from cellulose-mucilaginous bacteria Sorangium cellulosum Soce56 and the Fpr and bovine adrenal-derived Adx4-108 derived from Escherichia coli str. K-12 were used to construct a new electron transfer system for the conversion of progesterone. Selective mutation of CYP260A1 resulted in a mutant S276I with significantly enhanced 17α hydroxylase activity, and the yield of 17α-OH progesterone reached 58% after optimization of the catalytic system in vitro. In addition, the effect of phosphorylation of the ferredoxin Adx4-108 on 17α hydroxyl activity was evaluated using a targeted mutation technique, and the results showed that the mutation Adx4-108T69E transferred electrons to S276I more efficiently, which further enhanced the catalytic specificity in the C17 position of progesterone, and the yield of 17α-OH progesterone was eventually increased to 74%. This study provides a new option for the production of 17α-OH progesterone by specific transformation of bacterial-derived 17α hydroxylase, and lays a theoretical foundation for the industrial production of progesterone analogs using biotransformation method.

Reconstructing early transmission networks of SARS-CoV-2 using a genomic mutation model.

The coronavirus disease 2019 (COVID-19) pandemic has greatly damaged human society, but the origins and early transmission patterns of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pathogen remain unclear. Here, we reconstructed the transmission networks of SARS-CoV-2 during the first three and six months since its first report based on ancestor-offspring relationships using BANAL-52-referenced mutations. We explored the position (i.e., root, middle, or tip) of early detected samples in the evolutionary tree of SARS-CoV-2. In total, 6 799 transmission chains and 1 766 transmission networks were reconstructed, with chain lengths ranging from 1-9 nodes. The root node samples of the 1 766 transmission networks were from 58 countries or regions and showed no common ancestor, indicating the occurrence of many independent or parallel transmissions of SARS-CoV-2 when first detected (i.e., all samples were located at the tip position of the evolutionary tree). No root node sample was found in any sample ( n=31, all from the Chinese mainland) collected in the first 15 days from 24 December 2019. Results using six-month data or RaTG13-referenced mutation data were similar. The reconstruction method was verified using a simulation approach. Our results suggest that SARS-CoV-2 may have already been spreading independently worldwide before the outbreak of COVID-19 in Wuhan, China. Thus, a comprehensive global survey of human and animal samples is essential to explore the origins of SARS-CoV-2 and its natural reservoirs and hosts.

A four-generation family transmission chain of COVID-19 along the China-Myanmar border in October to November 2021.

Background: Foreign imported patients and within-household transmission have been the focus and difficulty of coronavirus disease 2019 (COVID-19) prevention and control, which has also posed challenges to border areas' management. However, household transmission caused by foreign imported cases has not been reported in China's border areas. This study aimed to reveal a clear family clustering transmission chain of COVID-19 caused by contact with Myanmar refugees along the China-Myanmar border during an outbreak in October to November 2021. Methods: During the outbreak, detailed epidemiological investigations were conducted on confirmed patients with COVID-19 and their close contacts in daily activities. Patients were immediately transported to a designated hospital for treatment and quarantine, and their close contacts were quarantined at designated sites. Regular nucleic acid testing and SARS-CoV-2 antibody testing were provided to them. Results: A clear four-generation family clustering transmission involving five patients with COVID-19 was found along the China-Myanmar border. The index case (Patient A) was infected by brief conversations with Myanmar refugees across border fences during work. His wife (Patient B) and 9-month-old daughter (Patient C) were second-generation cases infected by daily contact with him. His 2-year-old daughter (Patient D) was the third-generation case infected by her mother and sister during quarantine in the same room and then transmitted the virus to her grandmother (Patient E, the fourth-generation case) who looked after her after Patients B and C were diagnosed and transported to the hospital. The household secondary attack rate was 80.0%, the average latent period was 4 days, and the generation time was 3 days. Ten of 942 close contacts (1.1%) of this family had positive IgM antibody during the medical observation period. In total 73.9% (696/942) of them were positive for IgG antibody and 8.3% (58/696) had IgG levels over 20 S/CO (optical density of the sample/cut-off value of the reagent). Conclusion: This typical transmission chain indicated that it is essential to strengthen COVID-19 prevention and control in border areas, and explore more effective children care approaches in quarantine sites.

From Storytelling to Facebook : Content Biases When Retelling or Sharing a Story.

Cultural evolution researchers use transmission chain experiments to investigate which content is more likely to survive when transmitted from one individual to another. These experiments resemble oral storytelling, wherein individuals need to understand, memorize, and reproduce the content. However, prominent contemporary forms of cultural transmission-think an online sharing-only involve the willingness to transmit the content. Here I present two fully preregistered online experiments that explicitly investigated the differences between these two modalities of transmission. The first experiment (N = 1,080 participants) examined whether negative content, information eliciting disgust, and threat-related information were better transmitted than their neutral counterpart in a traditional transmission chain setup. The second experiment (N = 1,200 participants) used the same material, but participants were asked whether or not they would share the content in two conditions: in a large anonymous social network or with their friends, in their favorite social network. Negative content was both better transmitted in transmission chain experiments and shared more than its neutral counterpart. Threat-related information was successful in transmission chain experiments but not when sharing, and finally, information eliciting disgust was not advantaged in either. Overall, the results present a composite picture, suggesting that the interactions between the specific content and the medium of transmission are important and, possibly, that content biases are stronger when memorization and reproduction are involved in the transmission-as in oral transmission-than when they are not-as in online sharing. Negative content seems to be reliably favored in both modalities of transmission.

Epidemiological Analysis of COVID-19 Cases in Native Amazonian Communities from Peru.

Despite early control measures, SARS-CoV-2 reached all regions of Peru during the first wave of the pandemic, including native communities of the Peruvian Amazon. Here, we aimed to describe the epidemiological situation of COVID-19 in the Amazonas region of Peru using an open database of 11,124 COVID-19 cases reported from 19 March to 29 July 2020, including 3278 cases from native communities. A high-incidence area in northern Amazonas (Condorcanqui) reported a cumulative incidence of 63.84/1000 inhabitants with a much lower death rate (0.95%) than the national average. Our results showed at least eight significant factors for mortality, and the Native Amazonian ethnicity as a protective factor. Molecular confirmatory tests are necessary to better explain the high incidence of antibody response reported in these communities.