Beef productivity of steers of Kalmyk breed of different breeding

The success of the development of beef cattle breeding in the Republic of Buryatia mostly depends on the effectiveness of using animals of Kalmyk breed of different origins in order to increase beef production. The study of the productive traits and expediency of using the gene pool of Kalmyk breed from other regions in comparison with the animals of local selection when breeding beef herds has practical and scientifi c significance. The purpose of the research was to study the productive traits of cattle of Kalmyk breed imported from various climatic zones of Russia. For the experiment, 3 groups of newborn calves of Kalmyk breed of different breeding of 15 heads in each have been selected according to the principle of analogues. The 1st group consisted of steers Kalmyk breed of Buryat breeding, the 2nd – of Kalmyk breeding, the 3rd group – of Rostov breeding. During rearing and feeding, the steers were in the same feeding and housing conditions. During the growth process, the largest live weight at the age of 7 months has been observed in the 1st group of steers received from parents of local reproduction. They surpassed the herdmates of the 2nd group by 3,1 kg or 1,7 %, and the 3rd group by 4,8 kg or 2,7 %. With age the differences in live weight increased at 14 and 18 months the steers of Buryat breeding exceeded their herdmates of the 2nd group by 10,6 kg or 3,4 % and 15,7 kg or 3,8 % (P > 0,95) and the 3rd group by 16,8 kg or 5,4 % (P > 0,95) and 23,5 kg or 5,8 % (Р > 0,99), respectively. A comparative study of the beef productivity of steers of Kalmyk breed obtained under the conditions of the Republic of Buryatia and imported from the Republic of Kalmykia and the Rostov region has shown the advantage of the animals of Buryat breeding, while the herdmates of Rostov selection were the worst, and Kalmyk breeding steers occupied an intermediate position.

Mitigating COVID-19 outbreaks in workplaces and schools by hybrid telecommuting

The COVID-19 epidemic has forced most countries to impose contact-limiting restrictions at workplaces, universities, schools, and more broadly in our societies. Yet, the effectiveness of these unprecedented interventions in containing the virus spread remain largely unquantified. Here, we develop a simulation study to analyze COVID-19 outbreaks on three real-life contact networks stemming from a workplace, a primary school and a high school in France. Our study provides a fine-grained analysis of the impact of contact-limiting strategies at workplaces, schools and high schools, including: (1) Rotating strategies, in which workers are evenly split into two shifts that alternate on a daily or weekly basis; and (2) On-Off strategies, where the whole group alternates periods of normal work interactions with complete telecommuting. We model epidemics spread in these different setups using a stochastic discrete-time agent-based transmission model that includes the coronavirus most salient features: super-spreaders, infectious asymptomatic individuals, and pre-symptomatic infectious periods. Our study yields clear results: the ranking of the strategies, based on their ability to mitigate epidemic propagation in the network from a first index case, is the same for all network topologies (workplace, primary school and high school). Namely, from best to worst: Rotating week-by-week, Rotating day-by-day, On-Off week-by-week, and On-Off day-by-day. Moreover, our results show that below a certain threshold for the original local reproduction number R 0 l o c a l within the network (< 1.52 for primary schools, < 1.30 for the workplace, < 1.38 for the high school, and < 1.55 for the random graph), all four strategies efficiently control outbreak by decreasing effective local reproduction number to R 0 l o c a l < 1. These results can provide guidance for public health decisions related to telecommuting.

Effects of B.1.1.7 and B.1.351 on COVID-19 Dynamics: A Campus Reopening Study

The timing and sequence of safe campus reopening has remained the most controversial topic in higher education since the outbreak of the COVID-19 pandemic. By the end of March 2020, almost all colleges and universities in the United States had transitioned to an all online education and many institutions have not yet fully reopened to date. For a residential campus like Stanford University, the major challenge of reopening is to estimate the number of incoming infectious students at the first day of class. Here we learn the number of incoming infectious students using Bayesian inference and perform a series of retrospective and projective simulations to quantify the risk of campus reopening. We create a physics-based probabilistic model to infer the local reproduction dynamics for each state and adopt a network SEIR model to simulate the return of all undergraduates, broken down by their year of enrollment and state of origin. From these returning student populations, we predict the outbreak dynamics throughout the spring, summer, fall, and winter quarters using the inferred reproduction dynamics of Santa Clara County. We compare three different scenarios: the true outbreak dynamics under the wild-type SARS-CoV-2, and the hypothetical outbreak dynamics under the new COVID-19 variants B.1.1.7 and B.1.351 with 56% and 50% increased transmissibility. Our study reveals that even small changes in transmissibility can have an enormous impact on the overall case numbers. With no additional countermeasures, during the most affected quarter, the fall of 2020, there would have been 203 cases under baseline reproduction, compared to 4727 and 4256 cases for the B.1.1.7 and B.1.351 variants. Our results suggest that population mixing presents an increased risk for local outbreaks, especially with new and more infectious variants emerging across the globe. Tight outbreak control through mandatory quarantine and test-trace-isolate strategies will be critical in successfully managing these local outbreak dynamics.

Comparison of the inhomogeneous SEPIR model and data from the COVID-19 outbreak in South Carolina

During the COVID-19 pandemic authorities have been striving to obtain reliable predictions for the spreading dynamics of disease. We recently developed an in-homogeneous multi-"sub-populations" (multi-compartments: susceptible, exposed, pre-symptomatic, infectious, recovered) model, that accounts for the spatial in-homogeneous spreading of the infection and shown, for a variety of examples, how the epidemic curves are highly sensitive to location of epicenters, non-uniform population density, and local restrictions. In the present work we tested our model against real-life data from South Carolina during the period May 22 to July 22 (2020), that was available in the form of infection heat-maps and conventional epidemic curves. During this period, minimal restrictions have been employed, which allowed us to assume that the local reproduction number is constant in time. We accounted for the non-uniform population density in South Carolina using data from NASA, and predicted the evolution of infection heat-maps during the studied period. Comparing the predicted heat-maps with those observed, we find high qualitative resemblance. Moreover, the Pearson's correlation coefficient is relatively high and does not get lower than 0.8, thus validating our model against real-world data. We conclude that our model accounts for the major effects controlling spatial in-homogeneous spreading of the disease. Inclusion of additional sub-populations (compartments), in the spirit of several recently developed models for COVID-19, can be easily performed within our mathematical framework.

Effects of B.1.1.7 and B.1.351 on COVID-19 dynamics. A campus reopening study

The timing and sequence of safe campus reopening has remained the most controversial topic in higher education since the outbreak of the COVID-19 pandemic. By the end of March 2020, almost all colleges and universities in the United States had transitioned to an all online education and many institutions have not yet fully reopened to date. For a residential campus like Stanford University, the major challenge of reopening is to estimate the number of incoming infectious students at the first day of class. Here we learn the number of incoming infectious students using Bayesian inference and perform a series of retrospective and projective simulations to quantify the risk of campus reopening. We create a physics-based probabilistic model to infer the local reproduction dynamics for each state and adopt a network SEIR model to simulate the return of all undergraduates, broken down by their year of enrollment and state of origin. From these returning student populations, we predict the outbreak dynamics throughout the spring, summer, fall, and winter quarters using the inferred reproduction dynamics of Santa Clara County. We compare three different scenarios: the true outbreak dynamics under the wild-type SARS-CoV-2, and the hypothetical outbreak dynamics under the new COVID-19 variants B.1.1.7 and B.1.351 with 56% and 50% increased transmissibility. Our study reveals that even small changes in transmissibility can have an enormous impact on the overall case numbers. With no additional countermeasures, during the most affected quarter, the fall of 2020, there would have been 203 cases under base-line reproduction, compared to 4727 and 4256 cases for the B.1.1.7 and B.1.351 variants. Our results suggest that population mixing presents an increased risk for local outbreaks, especially with new and more infectious variants emerging across the globe. Tight outbreak control through mandatory quarantine and test-trace-isolate strategies will be critical in successfully managing these local outbreak dynamics.

Sensitivity analysis of the infection transmissibility in the UK during the COVID-19 pandemic

The coronavirus (COVID-19) outbreak started in December 2019 and rapidly spread around the world affecting millions of people. With the growth of infection rate, many countries adopted different policies to control the spread of the disease. The UK implemented strict rules instructing individuals to stay at home except in some special circumstances starting from 23 March 2020. Accordingly, this study focuses on sensitivity analysis of transmissibility of the infection as the effects of removing restrictions, for example by returning different occupational groups to their normal working environment and its effect on the reproduction number in the UK. For this reason, available social contact matrices are adopted for the population of UK to account for the average number of contacts. Different scenarios are then considered to analyse the variability of total contacts on the reproduction number in the UK as a whole and each of its four nations. Our data-driven retrospective analysis shows that if more than 38.5% of UK working-age population return to their normal working environment, the reproduction number in the UK is expected to be higher than 1. However, analysis of each nation, separately, shows that local reproduction number in each nation may be different and requires more adequate analysis. Accordingly, we believe that using statistical methods and historical data can provide good estimation of local transmissibility and reproduction number in any region. As a consequence of this analysis, efforts to reduce the restrictions should be implemented locally via different control policies. It is important that these policies consider the social contacts, population density, and the occupational groups that are specific to each region.

ՏԱՎԱՐԻ ՍԻՄԵՆԹԱԼ ՑԵՂԻ ՏԵՂԱԿԱՆ ՎԵՐԱՐՏԱԴՐՈՒԹՅԱՆ ԿՈՎԵՐԻ ԿԱԹՆԱՅԻՆ ՄԹԵՐԱՏՎՈՒԹՅԱՆ ՏԱՐԻՔԱՅԻՆ ՓՈՓՈԽՈՒԹՅՈՒՆՆԵՐԸ «ԱԳՐՈՀՈԼԴԻՆԳ ԱՐՄԵՆԻԱ» ՍՊԸ ԵՎ «ՀԻՄՆԱՏԱՎՈՒՇ» ԶԱՐԳԱՑՄԱՆ ՀԻՄՆԱԴՐԱՄԻ ՏՆՏԵՍՈՒԹՅՈՒՆՆԵՐԻ ՊԱՅՄԱՆՆԵՐՈՒՄ

The studies have shown that the Simmental cattle breed, raised from the cows of local reproduction on the farms of “Agroholding Armenia” LLC in Spitak city and “Himnatavush” development fund in Lusadzor village, has exceeded the stated breed standards in milk productivity, milk fat and live weight, while the index of protein content has fallen down the mentioned standards. Thus, the further breeding of the Simmental cattle breed from the cows of local reproduction is highly recommended in Armenia.

Kirchenpaueria halecioides: a non-native hydroid in the coast of Buenos Aires, Argentina

Hydroid colonies are among the groups frequently carried and introduced by human actions. Many species have been successfully transported as fouling organisms on ship hulls or in ballast water (pelagic stages) and the sea harbours appear as the places with high probability to detect exotic species. During routinely SCUBA diving conducted in Mar del Plata Harbour, Argentina (38°08´S – 57°31´W; May 2005, December 2006, March 2007, and December 2016) clumps of a plumularid were photographed and collected. Hydroid colonies were identified as Kirchenpaueria halecioides, a species frequently reported in tropical and subtropical water from the southwestern Atlantic, Brazil. Records of mature colonies in 2006 and 2016 suggest local reproduction of this non-native species. Monitoring will be necessary in order to analyse if species colonize neighbouring areas or remain confined to the port area.

The evolution of altruism and the serial rediscovery of the role of relatedness

The genetic evolution of altruism (i.e., a behavior resulting in a net reduction of the survival and/or reproduction of an actor to benefit a recipient) once perplexed biologists because it seemed paradoxical in a Darwinian world. More than half a century ago, W. D. Hamilton explained that when interacting individuals are genetically related, alleles for altruism can be favored by selection because they are carried by individuals more likely to interact with other individuals carrying the alleles for altruism than random individuals in the population (“kin selection”). In recent decades, a substantial number of supposedly alternative pathways to altruism have been published, leading to controversies surrounding explanations for the evolution of altruism. Here, we systematically review the 200 most impactful papers published on the evolution of altruism and identify 43 evolutionary models in which altruism evolves and where the authors attribute the evolution of altruism to a pathway other than kin selection and/or deny the role of relatedness. An analysis of these models reveals that in every case the life cycle assumptions entail local reproduction and local interactions, thereby leading to interacting individuals being genetically related. Thus, contrary to the authors’ claims, Hamilton’s relatedness drives the evolution to altruism in their models. The fact that several decades of investigating the evolution to altruism have resulted in the systematic and unwitting rediscovery of the same mechanism is testament to the fundamental importance of positive relatedness between actor and recipient for explaining the evolution of altruism.

Modeling the role of clusters and diffusion in the evolution of COVID-19 infections during lock-down

Epidemics such as the spreading of the SARS-CoV-2 virus are highly non linear, and therefore difficult to predict. In the present pandemic as time evolves, it appears more and more clearly that a clustered dynamics is a key element of description. This means that the disease rapidly evolves within spatially localized networks, that diffuse and eventually create new clusters. We improve upon the simplest possible compartmental model, the SIR model, by adding an additional compartment associated with the clustered individuals. This sophistication is compatible with more advanced compartmental models and allows, at the lowest level of complexity, to leverage the well-mixedness assumption. The so-obtained SBIR model takes into account the effect of inhomogeneity on epidemic spreading, and compares satisfactorily with results on the pandemic propagation in a number of European countries, during and immediately after lock-down. Especially, the decay exponent of the number of new cases after the first peak of the epidemic is captured without the need to vary the coefficients of the model with time. We show that this decay exponent is directly determined by the diffusion of the ensemble of clustered individuals and can be related to a global reproduction number, that overrides the classical, local reproduction number.