Lockdowns have been fundamental to decreasing disease transmission during the COVID-19 pandemic even after vaccines were available. We aimed to evaluate and compare changes in air quality during the first year of the pandemic in different cities around the world, investigate how these changes correlate with changes in mobility, and analyse how lockdowns affected air pollutants’ annual means.2, PM2.5, and PM10 in 42 cities around the world in the first months of the pandemic in 2020 to data from 2016-2019 and correlated them with changes in mobility using Human Development Indexes (HDIs). Cities with the highest decreases in air pollutants during this period were evaluated for the whole year 2020. We calculated the annual means for these cities and compared them to the new World Health Organization (WHO) Air Quality Guidelines. A Student’s t-test  was used to evaluate significant changes.We compared the concentrations of NO2, PM2.5, and PM10 were between -50 and -70%. Cities evaluated for the whole year 2020 generally showed a recovery in air pollution levels after the initial months of the pandemic, except for London. These changes positively correlated with year-long mobility indexes for NO2 and PM2.5 for some cities. The highest reductions in air pollutants’ annual means were from -20 to -35%. In general, decreases were higher for NO2, compared to PM2.5 and PM10. All analysed cities showed annual means incompliant with the new WHO Air Quality Guidelines for NO2 of 10 μg/m3, with values 1.7 and 4.3 times higher. For PM2.5, all cities showed values 1.3 to 7.6 times higher than the WHO Guidelines of 5 μg/m3, except for New Delhi, with a value 18 times higher. For PM10, only New York complied with the new guidelines of 15 μg/m3 and all the other cities were 1.1 to 4.2 times higher, except for New Delhi, which was 11 times higher.Highest decreases in NOThese data show that even during a pandemic that highly affected mobility and economic activities and decreased air pollution around the world, complying with the new WHO Guidelines will demand a global strategical effort in the way we generate energy, move in and around the cities, and manufacture products. Lowernd India , São Paund India , Milan [nd India , London,nd India , New Delnd India , and cound India ,12, Indind India ,14, Westnd India , and thend India .2.5 and 9.4% were linked to NO2 [2.5 and PM10 as the major components, are mainly derived from combustion processes, including traffic-related and industrial activities. PM is associated with increased risk of death by ischaemic heart disease, cerebrovascular disease, lung cancer, lower respiratory infections, and chronic obstructive pulmonary disease [2 is also associated to combustion processes, and calls for more attention about its effects on human health have been issued in countries with large diesel vehicle fleets, like many European countries and India, since these vehicles are the main NO2 source in urban environments [Air pollution is responsible for more than 7 million deaths each year . From thd to NO2 . Particu disease . NO2 is ronments .2, PM2.5, and PM10 among 42 cities on six continents during the first months of the pandemic, compare them with 2016-2019 data, and check how they would correlate with changes in city mobility indexes. We also aimed to study selected cities with higher changes in air pollution for the whole year 2020, compare their NO2, PM2.5, and PM10 annual means with the 2016-2020 period and analyse them according to the new World Health Organization (WHO) air quality guidelines [Despite other studies describing the effects of lockdowns on air pollution levels, most of them compared either a few cities or many cities in the same country/region of the world. Only a few studies investigated air quality changes over longer periods in 2020, generally focusing on the February-May period. This study aimed to compare changes in NOidelines .2.5 ranging from low to high  and for 2020 was checked, as well as the consistency of these data.The 42 cities analysed were chosen based on several criteria: 1) air pollution levels of PM2.5 air pollution data for ten cities in China, including Wuhan, Beijing, and Shanghai; three in India, including New Delhi and Mumbai; cities in other Asian countries, such as Seoul, Jakarta, and Hanoi; cities in Europe such as London, Paris, Milan, and Berlin; cities in the Middle East, such as Dubai and Abu Dhabi; cities in Africa, such as Kampala and Addis Ababa; cities in Australia, such as Sydney and Adelaide; cities in North America, such as Los Angeles, New York, and Mexico City; and South America, such as São Paulo and Lima. PM10 and NO2 data were analysed for 28 cities around the world.We analysed PM2, PM2.5 and PM10 were first analysed for the first months of the pandemic, defined as February-April. The choice of this period considered the spread of the pandemic and the consequent lockdowns which happened first in China and then in Europe, USA and all the other regions analysed, according to WHO (21). After these initial months, cities which showed the highest changes in the levels of air pollutants were chosen for a further analysis covering the whole year 2020.Data for NO2.5 data for 2020 was kindly provided by Dr Paul Herenz . Some of the data for Venice were kindly provided by Patti Salvatore and Luca Zagolin from ARPAV – Dipartimento Regionale Qualità dell'Ambiente-Veneto. NO2, PM10, and PM2.5 data were generally obtained as daily mean values expressed in μg/m3. 2016-2019-monthly means were calculated and compared to 2020 to generate % changes. 2.5 annual means were obtained from the 2018 WHO Global Ambient Air Quality Database [Air pollution data were obtained from official environmental agencies, websites that reproduce these data, the EEA, US Environmental Protection Agency or US State Department (US Embassies and Consulates). For Berlin, PMDatabase , except Anonymous Google Community Mobility data were obtained from Google Inc. (USA) . Changesadj) adds other variables such as health, education, income, inequality, poverty, employment, environmental sustainability and was adjusted for the city limits in our study. The HDIadj was then corrected by the City Development Index Method [The Human Development Index (HDI) is composed of indicators of three dimensions of human development: longevity, education, and income. The adjusted Human Development Index , PM2.5 (from 2016-2019 to 2020), Workplaces Mobility Changes (% change from baseline), and Transit Stations Mobility Changes (% change from baseline) were used in the analysis. The assumptions of discriminant analysis were observed  [2R>0.80, p<0.05, ɛ<0.05), first, between HDIadj (x) and PM2.5 annual mean (y), and second, between PM2.5 (from 2016-2019 to 2020) (x) and Workplaces, and Transit stations (y). In the second situation, PM2.5 data were normalized by (ix + x̅)2−1, according to the DA assumptions [p>0.01) [t test . All variables were on a normal distribution.Discriminant analysis was performed to classify cities within groups by HDIendence) . Regressumptions ,27. Statumptions . The dat[p>0.01) . The dat[p>0.01) . The dat2R) was used to indicate trends between NO2 and PM2.5 levels and mobility for the same city in a time series through the year 2020. We prioritized linear regression to standardize the trend between the cities (Y ^ = β_0+β_1 · X_i ±ɛ i). In this work, the objective of this analysis was not a perfect fit (>2R possible), but the trend (p<0.05 and ε<0.05) [Regression analysis ( ε<0.05) .2 reductions varied widely among different cities , London (-53.0%), Paris (-59.7%), Wuhan , Madrid (-61.3%), and New Delhi (-68.2%). The results confirmed the statistical difference in NO2 levels (p<0.05) between the studied periods (2016-2019 and 2020) for 19 out 28 cities with statistically significant changes in NO2 (p<0.05). No statistically significant NO2 changes were found in Moscow, Milan, Rome, Los Angeles, and New York.We first compared air pollution levels for the first months of the pandemic (February-April) with previous years (2016-2019) to identify the cities most affected by lockdown measures during the first Covid-19 wave. The extent of NO10  due to extensive wildfires [10 levels (p<0.05) between the studied periods (2016-2019 and 2020) for 16 out of 28 cities. No statistically significant changes in PM10 were observed in Berlin, Rome, Milan, Los Angeles, and New York.New Delhi (-61.4%), Nanjing (-52.9%), and Wuhan (-52.7%) were the cities with highest decreases in PMildfires . The res2.5 . Trends for PM2.5 in these cities were similar to those for PM10, with significantly lower reduction in April. Among other cities with highest reductions on PM2.5 levels were Abu Dhabi (-54%) and New Delhi (-49.6%). Many Chinese cities showed highest decreases on PM2.5 levels in February, including Hangzhou and Wuhan  and March, including Beijing and Shanghai . The results confirmed the statistical difference in PM2.5 levels (p<0.05) between the studied periods (2016-2019 and 2020) for 18 out of the 41 cities analysed. PM2.5 changes were not statistically significant for Rome, Madrid, Los Angeles, Addis Ababa, Jakarta, Kathmandu, and Ulaanbaatar.More cities were analysed for PM2 and PM10, and 28 cities, for PM2.5. Discriminant analysis was used to classify cities by HDIadj, according to NO2 and PM2.5 changes, as shown in 2.5 and NO2 data grouped into cities with similar HDIadj values, forming four different groups. City development is differentially associated to air pollution since pollutants emission varies in developed and emerging countries [2.5 annual means and HDIadj was also evaluated . Among Italian cities, NO2 levels were lower during most of 2020. However, changes in PM2.5 and PM10 were not statistically significant  . However, changes in PM2 and PM10 in February-May (-36% to -68%), in the case of New Delhi, Beijing and Wuhan . For Abu Dhabi, highest reductions in PM2.5 were before the pandemic, in January (-62%) and May-October (-41% to -61%). The pattern of changes in PM2.5 levels was similar for Dubai, with highest reductions in January (-60%), and April-October (-26% to -43%).All the cities in Asia and Middle East showed decreased levels of all air pollutants throughout most 2020, with highest decreases for NO2.5 (+82% to +144%) and PM10 (+44% to +64%) were observed in September-October due to wildfires [2, PM2.5 and PM10 were lower most of the year, with highest decreases in March-May (-27% to -49%). In the case of Santiago, NO2 changes were higher than other pollutants with highest decreases spread over several months (March-August: -29 to -49%), while changes in PM10 were not statistically significant. The same was also the case for NO2 changes for Buenos Aires, while PM10 was decreased most of the year, similarly to what was observed for PM2.5 in the case of Lima. Overall, the results confirmed the statistical difference (p<0.05) in the data between the studied periods (2016-2019 and 2020) for most of the cities and pollutants analysed.Among cities in North and South America, overall changes in air pollutants in Los Angeles were not statistically significant , New Delhi (2R = 0.62 and 2R = 0.61), Madrid (2R = 0.51 and 2R = 0.63), Paris (2R = 0.46 and 0.47), and Venice (2R = 0.42 and 2R = 0.53). London and especially New York showed a weaker association between mobility and NO2 changes. Milan, Los Angeles, Santiago, and Buenos Aires did not show any association between changes in mobility and NO2. An association between mobility indexes and PM2.5 levels was observed only for a few cities  and Lima (2R = 0.56 and 2R = 0.60), and weaker associations for Milan. There was no apparent association between changes in mobility and PM2.5 for Dubai, Abu Dhabi, Paris, London, Rome, Venice, Los Angeles, Madrid, Santiago, and Buenos Aires.Google mobility data were further analysed for the whole year 2020  while most cities showed 2016-2019 decreases in annual means lower than -10%. Los Angeles was the only city with higher NO2 levels in 2020, despite the small difference (+5%). As shown for NO2, most cities showed progressive decreases in PM2.5 annual means in 2016-2019, and higher decreases in 2020. Highest decreases (-18% and -35%) were observed in London, Paris, Wuhan, New Delhi, Abu Dhabi, Dubai, and Lima. However, Rome, Milan, and Venice showed no decrease in PM2.5 annual mean, instead, there was an increase as high as +23% in the case of Milan. An increased PM2.5 annual mean in 2020 was also observed in Los Angeles due to wildfires. In the case of PM10, there was also a general decrease along the years, which was particularly higher in 2020 in the case of London, Wuhan, New Delhi, and Buenos Aires (-19% to -24%). Again, Rome, Milan, and Venice showed increases in PM10 annual mean, but smaller than the increases in PM2.5. PM10 annual mean for Los Angeles was +30% higher in 2020 compared to 2019, in agreement with increases in PM2.5.We also investigated how differences in air pollution levels in 2020 affected annual PM2, a -58.4% decrease was described for Hangzhou [Despite its serious impacts on the world, the COVID-19 pandemic had a positive effect on air pollution. Maximum decreases in air pollution levels described here were, in general, similar to other reports. For NOHangzhou , -62.0% Hangzhou . For somHangzhou , -51.5% Hangzhou .2 annual mean in Venice is similar to Paris, while PM2.5 and PM10 means are much higher. Besides this, Venice is one of the cities with worst air qualities among more than 300 cities in Europe, based on PM2.5 levels [Interestingly, even though some cities were studied in other reports, this is the first one to describe changes in air pollution levels due to Covid-19 restrictions in Venice. This is surprising, since NO5 levels , which m10, similar maximum reductions were described for New Delhi (-57%) [2.5, highest decreases close to the reported here were observed for Paris (-58%), and London (-56%) [For PMi (-57%) , London i (-57%)  and Wuhai (-57%) . For PM2n (-56%) , New Deln (-56%) , Shanghan (-56%)  and Wuhan (-56%) . Despiten (-56%) . Other a2.5 changes compared to other reports. However, the way data were compared was different among these studies, using comparison against 2015-2019 [Beijing was the city with highest discrepancies in PM015-2019 , 2018-20015-2019 , 2017-20015-2019  and 2016adj to group cities and compensate for such variables, allowing for a better analysis of associations between mobility and air pollution. Sokhi et al. [2 concentrations (2R = 0.513) but did not find a correlation for PM2.5 and argued that atmospheric chemical reactions leading to the formation of secondary aerosols might explain this. Venter et al. [2 and attributed this to the fact that, in many countries, PM2.5 is more associated with residential and regional energy generation and agriculture. Despite the reasons addressed by these authors, the approach used here was successful in also confirming an association between changes in mobility and PM2.5 levels. Besides this, the 2R values for both pollutants were higher for most HDIadj strata analysed here. Interestingly, when cities were analysed throughout 2020 and the regression analysis was performed between same city data, more cities showed a relevant association for NO2 than for PM2.5. This is probably linked to differences in the main sources of PM2.5 and NO2 in different cities.Many cities in low- and middle-income countries have poor urban planning, disorganized traffic, lack regular vehicular emission inspection and have older cars compared to high income countries. However, many cities in high-income countries stimulate dispersion of households away from the city centres, emphasize more environmentally friendly and efficient modes of transportation, and restrict the access to city centres creating low and ultralow emission zones . These ai et al.  found a r et al. , only fo2 levels were much lower, during the whole year. Other cities, like Paris and Madrid, also showed lower NO2 levels during the whole year but decreases were much smaller for some of the months. Highest reductions in NO2 were observed for some European cities such as London, Paris, and Madrid, as well as for New Delhi. The general trend for PM2.5 levels at the beginning of the pandemic was quite different compared to NO2. Cities like Paris, London, and Berlin showed decreasing reductions in PM2.5 levels from February to April while only a few cities like Lima, Abu Dhabi, Dubai, and New Delhi showed increasing reductions over these months. The major effects of lockdowns on PM2.5 levels were visible between March and April. London, Beijing, New Delhi, Abu Dhabi, and Dubai, however, showed decreased PM2.5 levels throughout most of the year. In general, decreases in PM2.5 levels were smaller when compared to NO2 which was especially the case for Italian cities. As expected, trends in PM10 were similar to PM2.5, as observed for Paris, London and Berlin, for example, on the first months of the pandemic and for most cities analysed for the whole year 2020.Data analysis for the whole year 2020 shows that, even though many of these cities extended or reapplied lockdowns after this period, as confirmed by the analysis of mobility indexes, the major effect of these measures on air pollution levels was observed on the first months of the pandemic, February-May. An interesting exception is London, where NO2 annual means, and higher decreases in 2020, have driven many of the cities below or close to WHO guidelines (2005) of 40 μg/m3. Milan and New Delhi were the only cities slightly above these guidelines. However, compared to the new stricter WHO guidelines (2021) [2 levels 1.7-4.3 times higher than the 10 μg/m3 guideline. Even after successive decreases in PM2.5 annual means and higher decreases observed in 2020, many of the cities still did not comply with the WHO guidelines (2005) of 10 μg/m3. With the new WHO guidelines (2021) of 5 μg/m3, Milan and Venice showed five times higher PM2.5 levels in 2020, Abu Dhabi, Dubai, Lima, and Santiago 4.7-6.8 times higher, Beijing and Wuhan 7.5 times higher, and New Delhi showed levels 18 times higher than the new WHO guidelines. Madrid, Paris, and New York were the only cities with PM2.5 annual means lower than WHO guidelines (2005) but were above the new guidelines (2021), with 2020 pm2.5 levels 1.3-1.9 times higher. In 2020, only London, Madrid, New York, and Buenos Aires complied with the older WHO guidelines (2005) for PM10 of 20 μg/m3. With the new guidelines (2021) of 15 μg/m3, this happened only for New York, while London, Madrid and Buenos Aires were barely above it. Milan, Rome, Venice, and Los Angeles were 1.7-2.2 times higher; Beijing, Wuhan, and Santiago, 3.8-4.2 times higher; and New Delhi, 11 times higher than WHO guidelines. Despite using different data sets, timeframes, and analysing different cities, Carvalho [2.5 levels, compared to 2018 WHO data, including Beijing, Ulaanbaatar, and New Delhi.Successive decreases in NOs (2021) , all theCarvalho  showed tIt is important to mention that this study has some limitations. Changes in meteorological variables may impact the concentration of air pollutants and a specific compensation for local meteorological factors was not carried out. Instead, we used year to year comparison to account for changes in meteorological variables. Different authors have also reported the impacts of lockdowns on air pollution using a year-to-year comparison as we have done here ,41, to aThe results reported here corroborate these conclusions and show that even with the COVID-19 pandemic, the decrease in air pollutant levels was limited if we consider the whole year 2020, since most cities showed a “recovery” in air pollution levels after the most restrictive months of the pandemic.2, PM2.5 and PM10 were much smaller than for 2016-2019, indicating that altogether, even when restrictions were suspended, the increase in the levels of these pollutants was not enough to counteract the decrease previously observed. However, even with the reductions in annual means, many cities were far for complying with the new WHO Air Quality Guidelines, especially for NO2.Lockdowns have been a valuable containment strategy for COVID-19, used even after vaccines were available, to maintain a low numbers of new cases, hospitalizations, and deaths. For many of the cities analysed, 2020 annual means for NO


This document is written on behalf of the two professional bodies in the United Kingdom that represent genetic counsellors (the Association of Genetic Nurses and Counsellors) and clinical geneticists  and aims to support multidisciplinary working of these professional groups highlighting within a quick-reference format, areas of shared practice and the distinctions between role profiles for a Consultant Clinical Geneticist, Principal/Consultant Genetic Counsellor and the new support role that we have termed ‘Genomic Associate’, see AGNC career structure . This bu In the United Kingdom clinical geneticists are medically qualified Members/Fellows of the Royal College Physicians or equivalent, where Clinical Genetics is an affiliated medical specialty. Genomic or genetic counsellors are allied health professionals with Masters level accreditation from the Genetic Counsellor Registration Board included in the Academy for Healthcare Science register and clinical scientists  accredited by the Health and Care Professions Council.We acknowledge there is currently variability in these roles between NHS trusts and exceptions where the scope of practice for one professional group exceeds what is provided below in Fig. In Fig.


The overall percentage yield change from 1970 until 2020 for winter rice was 40.96%, of which 13.91% was genetic trend and 27.05% was non-genetic. For the monsoon season, the overall percentage change from 1973 until 2020 was 38.39%, of which genetic and non-genetic increases were 8.36% and 30.03%, respectively. Overall, the contribution of non-genetic trend is larger than genetic trend both for winter and monsoon seasons. These results suggest that limited progress has been made in improving yield in Bangladeshi rice breeding programs over the last 50 years. Breeding programs need to be modernized to deliver sufficient genetic gains in the future to sustain Bangladeshi food security.To assess the efficiency of genetic improvement programs, it is essential to assess the genetic trend in long-term data. The present study estimates the genetic trends for grain yield of rice varieties released between 1970 and 2020 by the Bangladesh Rice Research Institute. The yield of the varieties was assessed from 2001–2002 to 2020–2021 in multi-locations trials. In such a series of trials, yield may increase over time due to (i) genetic improvement (genetic trend) and (ii) improved management or favorable climate change (agronomic/non-genetic trend). In both the winter and monsoon seasons, we observed positive genetic and non-genetic trends. The annual genetic trend for grain yield in both winter and monsoon rice varieties was 0.01 t haThe online version contains supplementary material available at 10.1007/s00122-023-04260-x. Rice and food security are synonymous in Bangladesh Brolley . For mor−1 day−1, which provides approximately 60% of total calories and 50% of total protein for adults , of which, in 2019–20, about 88% was planted to modern varieties, with traditional landrace varieties covering 12%  of milled rice will be required to feed the increasing population of the country  has developed and released new varieties and conducted variety trials to assess their performance. Within this period, yield has increased due to improved management (agronomic trend) and plant breeding efforts (genetic trend)  and winter (November -April) seasons. The seasons differ greatly in growing conditions and consequently require different varieties, however, both seasons are equally important. Winter rice, grown in the dry season, is irrigated. During the winter, evaporative demand and solar radiation are high, and relative humidity and disease pressure are low. However, rice grown in the monsoon season is rainfed . BRRI operates large, separate breeding pipelines for each season. BRRI has been conducting multi-location rice variety trials since 2001 for both seasons. One of the major breeding objectives in connection with farmers’ needs is that varieties developed for favorable environments should also have the capabilities of performing well in less favorable environments. Similarly, stress-tolerant varieties should also perform well under non-stress conditions. For example, a flash flood tolerant variety should exhibit high yield whenever there is no flash flooding in a particular area of a farmer’s land. BRRI has 11 regional stations, all having favorable lands where the trials were performed. Conducting trials under on-station conditions has generated good quality data. All trials were managed for optimum yield, without any stresses deliberately imposed, and under generally favorable conditions (Supplementary Table S1). These trials are called stability trials and comprise a fairly consistent set of varieties over the years (strong carry-over from year to year), with newly released varieties added each year and hardly any varieties dropping out. The varieties included in the trials were developed by BRRI and representatives of all varieties that are grown in Bangladesh for both seasons. Thus, these trials form an ideal basis for assessing long-term genetic trends for yield achieved by BRRI breeding programs. This paper reports genetic trend in favorable environments of varieties developed both for favorable and less favorable environments.The monsoon rice dataset contains the results of trials through 2020, while the winter rice dataset contains results through the growing season of 2020–2021. For each season, trials were conducted each year at nine locations for winter rice and eight locations for monsoon rice  data were assessed discarding borders, amounting to a harvested area of about 2.72 m2 plot−1 and adjusted to a moisture content of 14%. The trial data are complete for all year-location combinations.The experiments were set up as randomized complete block designs (RCBD) with three replicates in each location. For all varieties, breeder’s seed was used. In each location, 40-day-old seedlings for winter rice and 25-day-old seedlings for monsoon rice were transplanted. All transplanting was conducted manually, and any damaged seedlings were retransplanted by the same aged seedlings within 3–7 days of the first transplanting  was fitted to the data of each year-location (environment) combination to estimate the genotype means and their associated standard errors:ith genotype in the jth location and kth year, ith variety, kth year, ith genotype, jth location, kth year, jkth random location-year interaction effect, ijth random genotype-location interaction effect, ikth random genotype-year interaction effect, ijkth random genotype-location-year interaction effect, and In the second stage, following Piepho et al. , linear To estimate the genetic trend of yield, we computed the difference between the expected performance of the oldest variety in the first year of the trial series (2001 for both seasons) and the expected performance of the youngest variety in the last year of the trial series (2020 in winter season and 2020–2021 in monsoon season), μ is the overall intercept, β and γ are the regression coefficients for genetic and non-genetic trend, respectively, and the start year  corresponds to the start of the period over which gain was assessed. To assess the gain for the last 50 years for both seasons, the value of tmin and rmin are 1970 for the winter season, and 1973 for the monsoon season, which are the release year of first variety for that season.The gains per year due to genetic and non-genetic causes were estimated asFor each variance component of a model, its contribution to the average variance was evaluated by the ratio of a variance component to the mean total variance of the data. Due to the two-stage analysis, the second stage is ideally done treating the variances of the genotype means estimated in the first stage as known in the second stage. With these settings, the average marginal variance of the data isHeritability is often used by plant breeders as a measure of precision of a series of trials  are plotted against the trial year and the release year of a variety in Figs. a−1 Fig. . The newThe estimate of heritability for the winter rice and monsoon rice is 0.866 and 0.915, respectively. The high heritability estimates indicate the good reliability of the data.α = 0.05). The genetic trend for monsoon rice was also 0.01 t/ha  per year  per year for winter rice  per year for winter rice, and 0.02 t/ha  per year for monsoon rice.The overall percentage change from 1970 till 2020 for winter rice was 40.96%, of which 13.91% was genetic gain and 27.05% non-genetic gain. Overall percentage change for monsoon rice was 38.39%, slightly less than for winter rice. The genetic gain was 8.36%, while non-genetic gain was 30.03% for monsoon rice  per year. The overall genetic improvement in yield over fifty years was 13.91%. The overall non-genetic percentage change in 50 years is 27.05%. Non-genetic gain for winter rice was 0.02 t ha−1 per year (0.54% per year), which is larger than genetic gain . The overall non-genetic gain in 47 years is 30.03%, which is larger than the genetic gain . When only IRRI developed cultivars were considered in analysis, the rate rose to 0.02 t ha−1 year−1 (0.46%). Peng et al.  will be needed to meet Bangladesh’s requirements through 2050. Achieving this rate of genetic gain will be a challenging job for breeders, requiring a new strategy that involves accelerating the breeding cycle and shifting away from use of a few popular varieties and non-elite breeding pools as parents.The current rate of genetic gain per year in rice yield of the BRRI released varieties of monsoon and winter season is only 0.01 t har et al. , geneticBRRI’s breeding programs are being reorganized and accelerated based on optimization strategies derived from application of the breeder’s equation to breeding pipeline design  and are less than needed to maintain rice food security in the face of population growth, climate change, and land loss to urbanization. The low rates of genetic gain observed in this study appear to be broadly representative of those achieved in the favorable rice production environments in South Asia Below is the link to the electronic supplementary material.


Since animal domestication, breeders have been selecting candidates for breeding based on phenotypic performance. Estimating breeding values through the best linear unbiased prediction method represents a revolutionary shift in animal breeding. On this basis, selection and mating are utilized to improve the production level of animals. The application of genomic selection has once again revolutionized animal breeding methods. However, although this kind of truncated selection based on breeding values can significantly improve genetic gain, the genetic relationship between individuals with a high breeding value is usually closed, and the probability of being co-selected is greater, which will lead to a rapid increase in the rate of inbreeding in the population. Reduced genetic variation is not conducive to long-term sustainable breeding, so a trade-off between genetic gain and inbreeding is required. Genomic mating is the use of candidate individuals’ genomic information to implement optimized breeding and mating, which can effectively control the rate of inbreeding in the population and achieve long-term and sustainable genetic gain. It is more suitable for modern animal breeding, especially for conservation and genetic improvement of local domestic animal breeds.Selection is a continuous process that can influence the distribution of target traits in a population. From the perspective of breeding, elite individuals are selected for breeding, which is called truncated selection. With the introduction and application of the best linear unbiased prediction (BLUP) method, breeders began to use pedigree-based estimated breeding values (EBV) to select candidates for the genetic improvement of complex traits. Although truncated selection based on EBV can significantly improve the genetic progress, the genetic relationships between individuals with a high breeding value are usually closed, and the probability of being co-selected is greater, which will lead to a rapid increase in the level of inbreeding in the population. Reduced genetic variation is not conducive to long-term sustainable breeding, so a trade-off between genetic progress and inbreeding is required. As livestock and poultry breeding enters the genomic era, using genomic information to obtain optimal mating plans has formally been proposed by Akdemir et al., a method called genomic mating (GM). GM is more accurate and reliable than using pedigree information. Moreover, it can effectively control the inbreeding level of the population and achieve long-term and sustainable genetic gain. Hence, GM is more suitable for modern animal breeding, especially for local livestock and poultry breed conservation and genetic improvement. This review mainly summarized the principle of genomic mating, the methodology and usage of genomic mating, and the progress of its application in livestock and poultry. Selection and mating are two important components of domestic animal breeding programs. The goal of such programs is to maximize the genetic gain, while restricting the rate of inbreeding in the nucleus breeding population. Selection is used to screen out superior individuals with higher breeding values for breeding in the next generation, which can lead to an increase in the frequencies of favored genes in the population. Mating is used to consciously set an optimal mating plan that realizes a combination of favored genes to produce elite offspring. Therefore, selection is the premise of mating, and both are complementary; selection and mating can improve the genetic performance of livestock and poultry.Recently, domestic animal breeding has entered the genomic era. Numerous genomic data have been generated, which provides a new path to estimating the breeding value and inbreeding coefficient of an individual. Using genomic data, the realized relationship of selected candidates can be directly calculated. Traditionally, the relationships are computed from pedigree information, which comprises the statistical expectation of the probability of identity by descent (IBD) ,2, and iThe question of how to effectively balance increasing genetic gain and controlling the rate of inbreeding in the nucleus breeding population, while maintaining genetic diversity is always important in animal breeding. In the late 20th century, numerous algorithms to implement optimal mating schemes were proposed on the basis of pedigree relationships ,7,8. TheIn this review, we provide a broad overview of the developments in the basic theories, methodology, and usage of genomic mating. Moreover, the current status of application of genomic mating in livestock and poultry are summarized. In 2016, genomic mating was proposed by Akdemir et al. . It is aIn terms of whether there is human intervention, mating plans can be classified into random and non-random mating . On the Controlling inbreeding is one of the most important aspects of mating programs. Traditionally, the expected inbreeding of offspring from a given mating scheme is calculated based on pedigree relationships between parents . HoweverThe goals of breed conservation or breeding programs are affected by many factors, which include economic value, historical bottlenecks, and maintenance of genetic diversity . BreedinHowever, current conservation methods do not combine the conservation of genetic resources with the genetic improvement of productive performance. In protected domestic animal breeds, it is also very important to select their specific traits, which will help to further consolidate their dominance and maintain their uniqueness. From a long-term sustainability perspective, the issue of how to combine conservation with selective breeding in the conservation field is crucial. While maintaining the overall genetic diversity of local domestic breeds, great attention should be paid to the genetic improvement of economically important traits to adapt to the sustainable development. The current study demonstrated that genomic mating was an effective method for balancing inbreeding and genetic gain, maintaining high genetic diversity across the genome. Zhao et al.  showed tIn multi-breed livestock populations, the magnitudes of dominance effects differed between and within breeds for many economically important traits. Dominant effects are not inherited from a single animal but through pairs of animals because these effects are caused by the interaction of pairs of genes at the same locus, and only one gene of each pair is passed on to individual offspring . The domHowever, selecting parents based on additive merit only before mate assignment may preclude the selection of parents that, when used in particular mating, would produce offspring with high overall genetic values, which include additive effects and dominance effects . By usingij is the expected genetic value of the progeny of male i and female j; xij is a binary decision variable. When xij is 0, it means that this mate pair is not adopted, and when xij is 1, it means this pairing will be kept. Usually, the number of uses of male and female animals is limited: for each male animal i, ix1 + ix2 +...+ ijx ≤ n means that, for each male animal i, the maximum number of uses is n; ix1 + ix2 +...+ ijx =1, which means that, for each female j, there needs to be one and only one breeding male paired with it.Linear programming was one of the earliest methods used for optimal mate allocation . Linear A be the pedigree additive genetic relationship matrix, and c be the vector of the proportion of the genetic contribution of individuals to the next generation in the random mating scheme. Given the value of the c vector, the average additive genetic correlation can be calculated as r = (1/2) c′Ac. Let b be the EBV vector of the backup candidate individuals ; then, the expected genetic gain of the next generation  is β = c’b. If the breeding goal is to obtain the expected genetic gain while keeping the genetic relationship increments within a minimum, the mating problem can be expressed as: In 1994, Woolliams et al.  proposedA) with the genomic genetic relationship matrix (G) in the OCS method. The EBV estimated vector b is also replaced by the GEBV vector g, and the genomic mating can be implemented as follows: Sonesson et al.  replacedA matrix is the expected value of true genetic relationships. The additive genetic relationship calculated based on pedigree differs from the actual relatedness due to Mendelian sampling [G matrix is the realized genetic relationship, which reflects the actual Mendelian sampling and is, therefore, more accurate. Moreover, the use of the G matrix avoids problems, such as pedigree errors or absence, as well as ignoring biases caused by older generations when computing the A matrix [A matrix in OCS seems to target autosomal genes, probably with  equal weighting to all genes, while G matrix can take account of variation in the level of relationship not only between animals within the same family but also between genomic regions [In theory, a pedigree-based sampling . G matriA matrix . In addi regions , sex chr regions .1 and λ2 are the tuning regulator, and P is the mating matrix. λ1 controls allele heterozygosity weighted by the marker effects, λ2 is the parameter whose magnitude regulated the amount of co-ancestry in the progeny. In this sense, the efficient mating problem can be stated as an optimization problem: D is the diagonal variance of founder effects, and Nc is the number of offspring.Genetic algorithms are particularly suitable for obtaining the optimal solution of combinatorial problems . The optGenomic mating focuses on mate allocation rather than truncated selection. Although both GM and GS need estimate marker effects, in GM, genetic information and estimated marker effects are used to decide which kinds of genotypes should be crossed to obtain the optimal genotypic combination in the following generation. The biggest difference between genomic mating and genomic selection is that the former directly selects the optimal mating pairs from the candidates, instead of truncated selection, which is based on breeding values. Furthermore, genomic mating takes account of the complementary information of the mating parents. Therefore, genomic mating uses genomic information more comprehensively than genomic selection. Akdemir et al.  showed tMinimum co-ancestry and minimizing the covariance between ancestral contributions (MCAC) have also been proposed based on long-term genetic contribution theory. Minimum-co-ancestry mating has long been recommended in practical breeding schemes because it minimizes the variation of each ancestor’s contribution across allocated mating ,37. ThisKinghorn published a series of papers ,41,42 abClark et al.  investigHe et al.  performeNe) was significantly increased when calculating the minimum-co-ancestry matings conditioned on markers, but the observed early levels of genetic diversity  were comparable to the diversity obtained with pedigrees alone. Toro et al. [Raoul et al.  developeo et al. , using so et al.  comparedGenomic mating took account of more factors than previous approaches to achieve optimal selection and mating of breeding animals. Compared with genomic selection, genomic mating can consider complementary relationships between mating pairs, as well as some breed characteristics, reduce the level of population inbreeding, and alter population gene frequencies. Current research on genomic mating is mainly aimed at the additive genetic effect of a single trait. At present, most GM studies are still computer simulation studies, and not widely used in practical animal production. Therefore, the issue of how to effectively apply the GM method to the genetic improvement of domestic animals still needs further exploration. Briefly, genomic mating is still at the preliminary research stage, and many problems remain to be addressed, such as the optimization algorithm of GM. The high-throughput computing of GM is also an issue, especially for multi-trait systems that include non-additive genetic effects.


Culex pipiens complex. Members of this complex transmit damaging arboviruses and filariae worms to humans, as well as other pathogens such as avian malaria parasites that are detrimental to birds. Here we describe the development of a targeted, gene-based assay for surveying genetic diversity and population structure in this mosquito complex. To test the utility of this assay, we sequenced samples from several members of the complex, as well as from distinct populations of the relatively under-studied Culex quinquefasciatus. The data generated was then used to examine taxonomic divergence and population clustering between and within these mosquitoes. We also used this data to investigate genetic variants present in our samples that had previously been shown to correlate with insecticide-resistance. Broadly, our gene capture approach successfully enriched the genomic regions of interest, and proved effective for facilitating examinations of taxonomic divergence and geographic clustering within the Cx. pipiens complex. It also allowed us to successfully survey genetic variation associated with insecticide resistance in Culex mosquitoes. This enrichment protocol will be useful for future studies that aim to understand the genetic mechanisms underlying the evolution of these ubiquitous and increasingly damaging disease vectors.Understanding patterns of diversification, genetic exchange, and pesticide resistance in arthropod disease vectors is necessary for effective population management. With the availability of next-generation sequencing technologies, one of the best approaches for surveying such patterns involves the simultaneous genotyping of many samples for a large number of genetic markers. To this end, the targeting of gene sequences of known function can be a cost-effective strategy. One insect group of substantial health concern are the mosquito taxa that make up the  Culex pipiens complex are important vectors of the agents of several human diseases such as West Nile and St. Louis encephalitides, and lymphatic filariasis. They are also important vectors of avian malaria, which impacts livestock and wildlife. The development of effective strategies for the control of these mosquitoes requires knowledge of their origins, distribution, dispersal patterns, and the extent to which discreet taxonomic entities within the complex interbreed. To achieve these objectives, it is necessary to compare patterns of genetic diversity across many mosquito samples, which can be cost-prohibitive. To address this limitation, we developed a targeted, gene-based assay that allowed us to cost-effectively genotype a large number of genetic variants from a representative global sampling of individual Cx. pipiens complex mosquitoes. We show that this assay is a powerful tool for examining genetic structure and hybridization among populations. We also explore its utility for surveying alleles previously shown to be associated with insecticide resistance. Future use of this enrichment assay and the bioinformatics methods described here will allow researchers to study evolutionary patterns across the Cx. pipiens complex as well as monitor the presence of genetic variation that could affect control efforts.The mosquito taxa that make up the  Culex pipiens complex (Diptera: Culicidae), include two globally distributed invasive species, the temperate Culex pipiens, and the tropical Cx. quinquefasciatus, along with several additional taxa with more restricted distributions [Wuchereria bancrofti), and several epidemic encephalitides such as West Nile virus [The brown, dusk-biting mosquitoes collectively classified within the ibutions . Specifile virus  and Usutle virus . These mle virus –7.Cx. pipiens complex, and accordingly these distributions are a relatively recent phenomenon [Aedes aegypti. Outside its source location in Africa, populations of Ae. aegypti all share the same basic genotype, revealing its rapid, human-facilitated expansion [Cx. pipiens and Cx. quinquefasciatus from across the world have uncovered unexpectedly high levels of genetic diversity. For example, continental populations of Cx. quinquefasciatus flanking the Pacific Ocean are highly differentiated [Cx. quinquefasciatus into the Hawaiian Islands from the Americas [Cx. quinquefasciatus have a distinct Australasian signature [Rapid human movements around the globe likely facilitated the spread of many now cosmopolitan mosquito species such as several in the enomenon . One of xpansion . Interesentiated . FurtherAmericas , currentignature . The mecCx. pipiens complex is the extent to which genetic exchange (hybridization) has contributed to ecological divergence and patterns of disease transmission. For example, inter-taxonomic hybridization between the two forms of Cx. pipiens may have significant negative consequences for arboviral transmission to humans [Cx. pipiens or Cx. pipiens pallens (a subspecies limited to northeastern Asia) and tropical Cx. quinquefasciatus [Cx. pipiens pallens was unidirectional which is surprising since patterns of hybridization of contemporary Cx. p. pallens with Cx. quinquefasciatus appear bidirectional [Another important aspect of the o humans . Severalasciatus ,14. Finaectional .Cx. pipiens complex, it will be necessary to extensively survey population and taxonomic samples at a large number of independently segregating molecular markers. Such an analysis would provide greater clarity to patterns of evolutionary divergence, global movement, and genetic exchange within these mosquitoes. Next-generation sequencing (NGS) has enabled vast amounts of genetic data to be collected at relatively low cost [Wolbachia and endogenous viral elements in these mosquitoes [Culex are often riddled with repetitive DNA [Cx. pipiens pallens genome, 60.63% (344.11 Mb) was found to consist of repetitive elements [To address these and other questions specific to the low cost ,16. Howesquitoes . Furthertive DNA . Of a reelements . Such elCapitalizing on recent technological advancements, a capture approach where DNA or RNA probes designed to match known genes are hybridized to DNA libraries of individual specimens and sequenced has been gaining traction –22. BecaCx. quinquefasciatus genome including regions that have been shown to harbor genetic variation that correlates with insecticide resistance. We examined the utility of these baits for taxonomic differentiation and patterns of admixture by sequencing samples from four taxa of the Cx. pipiens species complex, samples of known hybrid origin, and one sample of a closely related, outgroup taxon, Culex torrentium. To further examine the potential of these baits for exploring finer scale, intra-taxonomic population structure and differentiation, we included samples of Cx. quinquefasciatus from multiple geographic sources. Finally, within our samples we investigated the presence and frequency of alleles previously found to correlate with insecticide resistance. This was done to test the utility of these baits for surveying genetic variation that may contribute to a reduced efficacy of chemical control efforts. Such information can be critical for developing effective strategies to mitigate disease transmission by these mosquitoes [Here we describe the design and use of a genetic baits assay targeting 512 genes annotated in the squitoes .Culex genes obtained from a previous comparison of de novo-assembled transcriptomes from multiple samples of Cx. pipiens f. pipiens and Cx. pipiens f. molestus [Cx. quinquefasciatus genome (v. CpipJ2.5) [https://vectorbase.org/vectorbase/app) [We designed an in-solution capture assay targeting 131 rapidly evolving molestus . These ‘molestus . This inmolestus . To thesmolestus . In totapipJ2.5)  using thase/app) .https://arborbiosci.com/) to design 39,953 120 bp baits with ~1.5x flexible tiling density (~80bp probe spacing) across our targeted genes. These baits covered the complete exonic and intronic regions for each gene, allowing for simultaneous investigation of both adaptive and neutral evolution. These candidates were then assessed using BLAST v. 2.12.0 [m) above 60°C, b) no more than two hits at Tm 62.5–65°C, or 10 hits in the same interval and at least one neighbor candidate being rejected. c) no more than 2 hits at Tm 65–67.5°C and 10 hits at Tm 62.5–65°C and two neighbor candidates on at least one side being rejected. d) no more than a single hit at or above Tm 70°C or e) no more than one hit at Tm 65–67.5°C and 2 hits at Tm 62.5–65°C and two neighbor candidates on at least one side being rejected. The baits were synthesized as a myBaits version 3 kit. After stringent filtration, 29,992 baits were retained, covering all 512 target genes with at least one bait. The targeted sequences total 2,524,269 bp in length, and are well distributed across the three Cx. quinquefasciatus chromosomes , specimens of the subspecies Cx. pipiens pallens from the Republic of Korea and, to assess the power of the assay to discern intraspecific patterns of diversity, specimens of Cx. quinquefasciatus from six distinct geographic regions: east-southeast Asia, Samoa, Hawaii, North America (including the Caribbean), Brazil and Nigeria. We also included known hybrids of Cx. pipiens and Cx. quinquefasciatus from California and North Carolina. Most specimens had previously been examined using a panel of microsatellite loci [Cx. torrentium for outgroup comparisons.To test our targeted enrichment approach, we chose specimens representative of the genetic diversity observed across the complex . Specifiite loci ,12,13,31We extracted DNA from individual mosquitoes using a phenol-chloroform method previously described . We thenCx. quinquefasciatus reference genome (v. CpipJ2.5) [After sequencing, we first used Trim Galore v. 0.4.1  to trim pipJ2.5)  using BWpipJ2.5) . Next, wpipJ2.5) . We thenpipJ2.5) . FinallypipJ2.5)  (specifiWith the resulting raw VCF files (one per sample), we used GATK’s GenotypeGVCFs function to produce a single, multi-sample VCF containing all identified variants observed across all samples. This file was filtered to retain only single nucleotide polymorphisms (SNPs), using the SelectVariants tool in GATK v. 4.0.8.1 . This toCx. pipiens f. pipiens, two Cx. pipiens f. molestus, and one Cx. pipiens pallens  across samples derived from the o be low , we wantelection –48, the Cx. torrentium (henceforth ‘Cx. pipiens complex’ dataset), and a second with only the Cx. quinquefasciatus samples (henceforth ‘Cx. quinquefasciatus’ dataset). We then removed any variant from both datasets that was not in Hardy-Weinberg equilibrium (p < 0.0001), and any variant in which the minor allele was represented at less than 5% frequency. Both filtering steps were carried out using VCFtools v. 0.1.17 [2) greater than 50% within sliding windows of 50 SNPs at 10 SNP increments between windows [We used GATK’s ‘SelectVariants’ tool to generate two new VCFs from our VCF database of high quality synonymous and intronic SNPs, one with all samples except the outgroup . 0.1.17 . Finally. 0.1.17  to remov windows . This wa windows .adegenet v. 2.1.5 [adegenet, with sample assignments determined in the initial clustering analysis. We used 75% of the data for a training set and the remaining 25% for data confirmation. This was repeated for 100 replicates. We used the ‘dapc’ function to probabilistically assign each sample to a cluster. From the information on discriminant functions, a genotype composition plot (Compoplot) was generated indicating the attributed probabilities of each sample to a cluster [We first used principal component analyses (PCAs) to investigate non-parametric clustering among the samples in both datasets. These PCAs were conducted with the program PLINK v. 1.90b6.6 , and thev. 2.1.5  in R. Wev. 2.1.5 . If our  cluster .With ADMIXTURE, we examined potential clusters (K) from one to seven in both datasets. Each K value was run 20 independent times with a different seed value for each run. Across K values, we compared the means observed for the standard error of the 10-fold cross-validation (CV) error estimate to determine the number of clusters best supported by the data . We deteCx. quinquefasciatus only) means and standard errors of the means. Although these estimates do not give us absolute estimates of genetic diversity (because they only include known segregating sites), they do allow us to make relative comparisons between groups of samples .To examine the amount of genetic diversity harbored within individual samples, populations, and taxa, we used GATK v. 4.0.8.1  to desigst) [Cx. torrentium. We also compared the Cx. quinquefasciatus populations from the six designated geographic regions. All sample taxonomic and population designations were based on their prior assignments , we used VCFtools v. 0.1.17 and our larger clustering dataset to calculate the pairwise fixation index (Fst) . Compariignments . We repoCx. torrentium, we performed a maximum likelihood phylogenetic analysis. For this analysis, we focused on neutral variants that are likely to have similar mutation probabilities. Therefore, from our annotated variants dataset, we used BCFtools v. 1.9 [To further examine sample clustering as well as taxonomic relationships amongst all samples, including the outgroup s v. 1.9  to selecs v. 1.9 . We thens v. 1.9 ,68 with s v. 1.9  to carrys v. 1.9 .Cx. pipiens complex mosquitoes. Specifically, we examined publications that reported the gene and position of a segregating variant that correlated with resistance to one or more active insecticidal products . These were exclusively missense mutations that changed the amino acid sequence and likely protein interactions with the insecticide. With their genome coordinates (chromosome and base position), we used VCFtools v. 0.1.17 to calculate the frequencies of the susceptible and resistant alleles across all our samples. We also used VCFtools to examine the sample-specific presence of these variants to compare taxa and populations.To assess the utility of our capture assay for surveying genetic polymorphism that may contribute to insecticide resistance, we first conducted a literature survey to identify known single nucleotide variants that have been shown to be associated with insecticide resistance in The average percentage of total reads mapped to the full genome was very similar between our enriched libraries prepared with a capture assay and unenriched libraries  and insertions/deletions (INDELs). After removing all INDELs and any additional variants not located in our designated baits, we were left with 315,512 SNPs. Quality filtering further reduced this to 132,185 SNPs.Culex pipiens complex, we generated a dataset consisting of 14,303 unlinked SNPs annotated as either ‘synonymous’ or ‘intronic’. A principal component analysis with this dataset revealed that the greatest genetic divergence  occurred between the samples designated as Cx. quinquefasciatus and all the other samples , suggesting possible taxonomic-specific genetic divergence. From the DAPC using all complex samples, the most likely number of clusters was K = 2  further divide the Cx. quinquefasciatus samples and revealed samples with varying degrees of admixture.The analysis of clustering using ADMIXTURE also indicated that a K value of 2 was best supported . Populat samples . HoweverCx. quinquefasciatus samples. This dataset consisted of 9,829 unlinked, segregating variants annotated as ‘synonymous’ or ‘intronic’. All samples clustered within their known geographic region  in all samples. The mean number of heterozygous sites and the mean sample pairwise heterozygosity (π) for all taxa are given in Cx. pipiens pallens at 0.091 (SE = 0.006). This value means that among the Cx. pipiens pallens, on average 9.1% of the 916 SNPs were found in a heterozygous state. The next highest value of π was observed in the Cx. torrentium sample with 0.084. The known hybrids had an average π of 0.066 (SE = 0.009). The lowest mean π value was observed in the various Cx. quinquefasciatus samples .To examine relative genetic diversity within all Cx. quinquefasciatus samples, we used 540 SNPs that were determined to be biallelic and had a depth of at least 15 reads (15×) in the samples under consideration. These SNPs were also considered most likely to be evolving neutrally by virtue of being annotated as ‘synonymous’ or ‘intronic’. The mean number of heterozygous sites and the mean sample pairwise heterozygosity (π) for the six geographic designations of Cx. quinquefasciatus are given in To examine relative genetic diversity within just the st) [Cx. pipiens complex taxa examined here as well as the outgroup, Cx. torrentium. Weighted estimates were always larger than unweighted estimates. Not surprisingly, the highest values were observed between the Cx. pipiens complex taxa and the Cx. torrentium sample. Among the taxa within the Cx. pipiens complex, the highest unweighted Fst value was between Cx. quinquefasciatus and Cx. pipiens f. pipiens (0.2967). With weighted Fst values, the highest was between Cx. quinquefasciatus and Cx. pipiens f. molestus (0.6415). The lowest estimated values were between the two Cx. pipiens forms .st) , betweenst between the six designated geographic populations of Cx. quinquefasciatus are given in st values, the lowest estimate was between samples from Brazil and North America, including the Caribbean (-0.2329). The lowest estimated weighted Fst value was between Brazilian and east Asian samples (-0.0668).Pairwise unweighted and weighted estimates of FCx. torrentium was clearly distinct from the samples of the Cx. pipiens complex  . As expe complex . The Cx.Cx. pipiens complex . We also show that this data can be used to survey a large number of segregating genetic sites from across the genomes of several Culex pipiens complex samples. Use of these sites allowed us to successfully examine taxonomic relationships, population structure, and patterns of admixture in these mosquitoes, and recovered similar patterns of population differentiation observed after the analyses of thousands of specimens at 7–12 microsatellite loci [We present evidence that targeted gene enrichment in ite loci ,12–14,31Cx. quinquefasciatus samples mapped the best to the reference genome, while the outgroup sample, Cx. torrentium, mapped the poorest , should have minimized such effects [st may not accurately reflect the levels of genetic differentiation which exists between specific populations within the Cx. pipiens mosquito complex. In the future, analysis of more samples could address this question.In both examinations of taxonomic differentiation using Fl Tables  and 3. Sstimates ,77. Howe effects . NonetheCx. torrentium was distinct from the other samples (Cx. pipiens complex there are two major clades comprised of the Cx. quinquefasciatus specimens and everything else. The Cx. quinquefasciatus clade was well supported (99/100 bootstrap support), whereas the second clade was more poorly supported (52/100 bootstrap support). This was likely due to the presence of the hybrid specimens from North America. Of note, the samples of Cx. pipiens f. pipiens and Cx. pipiens f. molestus do not form monophyletic clades in this analysis. This may reflect the low level of genetic differentiation between the two taxa, combined with documented genetic exchange between them [As expected, in the taxonomic analysis the outgroup sample  samples . Within een them ,25,42,79Cx. pipiens complex and underscores the importance of careful insecticide resistance management [Our assessment of insecticide resistance-associated alleles revealed the presence of all identified variants in at least one of the sequenced samples. This points to the ubiquity and maintenance of these alleles in the nagement . HoweverAnother consideration regarding resistance-associated alleles (and observed genetic variation more broadly), is the extent to which the same derived mutation may have arisen independently in multiple complex populations (‘genetic homoplasy’). We have assumed a single origin for all examined genetic variation, but such an assumption is unlikely to be true across such a large number of segregating sites. If there is extensive homoplasy in the data examined here, this would likely obscure patterns of population clustering and taxonomic differentiation . ConsideInterestingly, all but one of the resistance-associated variants we surveyed were segregating at low frequencies Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S1 FigCulex mosquitoes have three chromosomes, which do not have centromeres. The gene classification is indicated in the key at the lower right corner of the figure.(PDF)Click here for additional data file.S2 FigFiltering thresholds are indicated by the red vertical bars. See text for more details.(PDF)Click here for additional data file.S3 FigResults of our DAPC a) BIC scores for K values 1–18, for all Cx. pipiens complex samples. b) Density plot considering the first discriminant function. The Cx. quinquefasciatus sample cluster is indicated on the right in red, and the cluster for all other complex samples is indicated on the left in blue. Each hash along the horizontal axis represents one sample. c) Genotype composition plot (compoplot) indicating the attributed probabilities of each sample to a cluster. Cx. quinquefasciatus samples are indicated in red and all other complex samples are indicated in blue. d) BIC scores for K values 1–12, for just Cx. quinquefasciatus samples.(PDF)Click here for additional data file.S4 FigCx. pipiens complex samples b) just Cx. quinquefasciatus samplesa) all (PDF)Click here for additional data file.S5 Fig(PDF)Click here for additional data file.


M = 22.93; SD = 5.24), with sport experience ranging between 2 and 20 years , took part in our study. Participants listened to each previously recorded script and then were asked to imagine the scene for a minute. During the task SCL was monitored using the Biofeedback Expert 2000. Machine learning predictive models based on artificial neural networks have been trained for prediction of physiological response, as a function of selected psychological tests. We found an association among neuroticism, prestart anxiety, and general tendency to use imagery with SCL. From a practical point of view our results may help athletes, coaches, and psychologists to be more aware of the role of individual differences in sport.Imagery is a well-known technique in mental training which improves performance efficiency and influences physiological arousal. One of the biomarkers indicating the amount of physiological arousal is skin conductance level (SCL). The aim of our study is to understand how individual differences in personality (e.g. neuroticism), general imagery and situational sport anxiety are linked to arousal measuring with SCL in situational imagery. Thirty participants aged between 14 and 42 years ( In his bioinformational theory, Lang6 emphasized the role of stimuli, that is, characteristics of the imagined situation , in eliciting certain physiological responses such as increased heart rate or skin conductance level (SCL) in individuals. This autonomic response2 depends on the activation of coded information stored in long-term memory, where abstract units of information organized into three categories of information are stored . Among the possible predictors of the level of psychophysiological changes during imagery7 we can list factors like individual higher levels of imagery ability or the extent to which scenes are personally relevant and known to the subject, as well as an image script of the situation and direct instructions to experience the image physiologically.Imagery is a multimodal cognitive simulation process that enables us to represent perceptual information in our minds in the absence of actual sensory input. It is also a well-known technique in mental training that, aside from its behavioral effect on performance, can also influence physiological arousal8. A general measure of EDA is skin conductance (SC), which shows fluctuations due to the appearance of sweat on the skin, secreted in response to activation of the autonomic nervous system (ANS). Two main components of SC can be distinguished, which differ in their dynamics of fluctuation. The first component is the skin conductance level (SCL), presented as the average of all SC measurement data recorded over a given signal time window. Changes in SCL can become apparent within several dozen seconds of the measurement duration and are thought to reflect the overall changes in autonomic arousal. SCL is usually recommended when the monitoring lasts longer. The second component is the phase component, the Skin Conductance Response (SCR), and refers to the faster changing components of the signal, which is arousal from a given stimulus. The change in SCR is recorded each time the participant experiences an emotional change9.A reliable biomarker of psychophysiological activation is electrodermal activity (EDA), both for actual and imagined situations. The term EDA is used to describe the whole range of phenomena closely related to autonomic emotional and cognitive processing, which are manifested by changes in the electrical properties of the skin. The independence of EDA from parasympathetic activity makes it widely used, as an indicator of emotional processing and sympathetic activity6 could be a cognitive explanation for the psychophysiological mechanisms of mental imagery, but without a direct reference to individual differences. Moving forward, in their revised applied model of deliberate imagery use (RAMDIU)10 Cumming and Williams included the component ‘who ’, which embraced individual characteristics like gender, competitive level, age, experience, personality and temperament. This model incorporated characteristics of the individual (“who”), and distinguishes between “what” is imaged (i.e. the content) and “why” (i.e. the function) it is imaged by recognizing the importance of personal meaning. RAMDIU also expands how imagery ability is likely to influence the relationship between imagery use and outcomes.The question arises whether personal or contextual factors can explain the link between imagery and physiological responses. Bioinformational theory11 or illness, with fearful and joyful imagery compared to neutral imagery12, suggesting high levels of arousal as a manifestation of mental and behavioral reactions to stimuli. Neutral imagery tended to reduce arousal below the original baseline levels11. However, mental imagery might be viewed as the emotional amplifier of the arousal response13. The inability to visualize imagery scenarios, such as in people without visual imagery (aphantasia), was also linked to a lower SCL than in the control group while imaging frightening stimuli15. A study by Hägni et al.16 confirmed that audio-visual stimulation combined with mental imagery induced higher skin conductance after a sudden threat to a virtual limb, compared to audio-visual stimulation alone.Previous research showed that SCL increased during positive and negative imagery18 should be explained. In referring to temperamental traits, Eysenck20 compared extraverts and emotionally stable individuals with introverted and neurotic individuals, and concluded that the latter showed increased cortical and autonomic arousal to stimuli21. More precisely neurotic individuals generally showed increased reactivity of the limbic system and strong but also long-lasting reactions to emotional experiences, which were reflected in physiological responses like heart rate or electrodermal activity. The biological theory of personality also evidenced that people are characterized by high and low arousability, which is treated as a trait. More arousable individuals might thus be more reactive to external stimuli and invest more imagery and emotions in the internal processing of their own experiences22.Since the aforementioned ‘who’ components also referred to individual differences in temperament, the concept of general arousal to stimuli or trait arousability23.Eysenck claimed that high neuroticism was linked to increased reactivity of the limbic system and a strong but also long-lasting reaction to emotional experiences which can also be observed in physiological responses like heart rate or electrodermal activity. Moreover, in Costa and McCrae’s personality theory, neuroticism is associated with negative emotionality and a higher predisposition to anxiety23. Individuals with higher levels of neuroticism exhibit both a greater skin conductance reactivity to emotional (and particularly aversive) pictures and more extended reactivity than emotionally stable individuals24. A study by Cruz and Larsen25 found relationships between personality factors and the spontaneous fluctuation rate of electrodermal activity (EDA lability) in a sample of 62 undergraduate students. The respondents filled in personality questionnaires and during this time the skin conductance responses were monitored. They found that the EDA lability strongest correlated with neuroticism. Brumbaugh et al.26 measured association between personality (Big Five) and physiological responses in a group of 169 participants, who watched video clips with negatively emotionally saturated scenes. The results confirmed that extraversion and neuroticism were mostly associated with physiological outcomes.What is highly evidenced is the relationship between emotions and personality, with a specific focus mainly on extraversion and neuroticism27, examined the physiological correlates of behavioral inhibition and trait anxiety in speech anxiety. They monitored heart rate and skin conductance level during the experiment on a group of 55 male college students. There were also self-report measures for assessing social anxiety, behavioral inhibition, and trait anxiety. SCL was a better autonomic indicator of trait anxiety than heart rate, while hierarchical multiple regressions revealed that trait anxiety predicted SCL. Neither behavioral inhibition nor trait anxiety predicted heart rate reactivity.Different kinds of situational performance are often linked to anxiety, which in turn reveals higher physiological responses. Hofmann and Kim28 concerned the moderated influence of anxiety sensitivity on the physiological and subjective experience of acute psychosocial stress ) on a group of fifty-eight undergraduate students. The results revealed that heart rate and skin conductance increased in response to the TSST. However, anxiety sensitivity did not moderate any of the physiological outcomes of the TSST.Another study by Wearne et al.29 on pilots where the capacity to cope with anxiety during a flight was similar to the prestart situation when the stressful situation occurred, the results confirmed that neuroticism was linked to cognitive and somatic anxiety. Even when the neuroticism scores were moderate, a positive correlation with the pilots’ subjective anxiety was found while they were carrying out the tasks under the social stressor. Other studies are congruent with these findings. In sport and music performance neuroticism and trait anxiety are predictors of anxiety31. The first one investigated the relationship between personality traits, anxiety and physiological arousal in athletes, and revealed that an athlete’s neurotic personality could influence their cognitive and physiological responses in a competition. Moreover, a study by Dal et al.32 revealed that neuroticism could moderate the relationship between anxiety and autonomic activity (respiration) before and during an archery competition.The linkage between personality, anxiety and responses to different challenging situations has been previously confirmed. In the study by Hidalgo-Munoz et al.These studies concerned personality and anxiety in some form of realistic circumstances. However, there is a lack of such studies where the association among these dimensions has been investigated in imagined situations that reflect sport performance familiar to athletes. Therefore, the aim of our study is to understand how individual differences in personality , general imagery and situational sport anxiety are linked to arousal  in situational imagery (as scripted for sport-related scenes).By relying on the theoretical premises, we constructed a predictive regressive model, based on artificial intelligence methods. We planned to use artificial neural networks to explain the relationship among the tested factors.Taking into account previous investigations, we linked higher skin conductance level to higher neuroticism  and somatic anxiety (examined in the context of sport), and a general tendency to use imagery in life (H1). Moreover, we explored whether three dimensional aspects of anxiety level in sport diversifies the results (H2). From a practical point of view, we also tested whether different kinds of sport anxiety required a different mental training approach (Q).mSCL), as functions of psychological factors. Those models are able to reproduce nonlinear relation between predictors and dependent variable. Example results are shown in Figs. mSCL values greater than 1, "moderate" for those between − 4 and 1, and "low" for values less than − 4. These thresholds represent the 33.3rd and 66.7th percentiles, so each of the chosen groups covered a third of the data, for which the total range changed from -9 to 6. Moreover, we presented the results using colors, where red and orange meant a high level of SCL, yellow and green a moderate level, and blue a low level. A specific legend for the results was included in the figures.Machine learning models based on artificial neural networks, trained in a supervised way, have been used to perform predictions of the mean values of normalized SCL  or narrower (anxiety in sport) level, in the general tendency to use imagery and in the psychophysiological response (SCL) during imagery tasks in the sports domain.35, thus indicating that intergroup differences among athletes have already been explored. This is also the reason why in our study we focused on individual differences in the context of physiological activation. Our results confirmed the first hypothesis that skin conductance level is positively associated with neuroticism , somatic anxiety (examined in the context of sport), and a general tendency to use imagery in life. In other words, respondents who reveal higher physiological activation during imagery tasks are more likely to experience emotional instability, stress, and anxiety, express more physiological changes as a result of anxiety before and during a competition but also present a higher tendency to use the imagery in daily life. Our findings also confirmed that high neuroticism is linked to high SCL, regardless of the level of anxiety and imagery, which means that neuroticism is mainly the core aspect, and therefore the main predictor of high SCL. Indeed, individuals with higher levels of neuroticism exhibited greater arousal. These results are also in line with previous studies26, in which neuroticism was connected to greater autonomic arousal responses to emotional scenes. In our study the linkage between neuroticism and SCL was the clearest within the somatic anxiety subscale .Previous research has indeed reported that sports champions had lower levels of neuroticism36 or adversely too stimulating and therefore they were disengaged from continuing the task. The last explanation is in line with Pavlov’s protective inhibition theory or Hobfoll's37 conservation of resources theories. Therefore, it is worth exploring the role of neuroticism as a general higher reactivity to stimuli alone or with the occurrence of different ranges of general imagery scores. Additionally lower, moderate, and higher levels of anxiety may facilitate or be an obstacle where imagery is concerned. The role of general imagery should be verified.We observed that a moderate level of SCL was mainly linked to a moderate level of at least one predictor. However, the results were not so conclusive. Some lower levels of arousal measured by SCL were linked to high levels of other pre-start anxiety dimensions (concentration disruption and worry) similarly to somatic anxiety, except when neuroticism was high, sometimes alongside high general imagery. Since these athletes showed high sport anxiety levels, we could argue that they were underactivated, or disengaged. This finding may be because the imagined situations were insufficient for them to be physiologically aroused according to Yerkes-Dodson’s first law39, which distinguishes four performance categories  derived from a hypothesised interaction of optimal/suboptimal and automatic/controlled performance dimensions. Specifically, we could refer to type 2  or type 3  performances which are both linked to high energy consumption.For the second hypothesis we explored whether the three-dimensional aspects of sport anxiety levels diversify the results. We observed that the highest level of SCL was linked to a moderate/high level of general imagery, a high level of neuroticism and a moderate level of somatic anxiety. Although we did not collect data on the athletes’ performance, we could refer to the framework of the MAP Model38 found that electrodermal activity is more elevated during type 2 and type 3 performances than during type 1 and type 4 performances, and suggested the existence of different mechanisms of energy mobilization and regulation as well as different levels of stress and fatigue. Both mechanisms are associated with an increased sympathetic activation.The highest SCL might indicate that the participants were imagining performances in which a high level of control was required, with various possible outcomes. Type 2 is characterized by high performance effectiveness and low processing efficiency, but in type 3 both performance effectiveness and processing efficiency are low. For instance, Bertollo et al.40.Regarding the high level of SCL and neuroticism, we also could ask whether or in what kind of circumstances athletes with that profile are able to achieve the type 1 performance type (high performance with high processing efficiency—flow like state). These results could also be an extension of the MAP model with individual differences included, analyzing the transitions between mental states in different types of performanceOn the contrary, low SCL was observed in the configuration of somatic anxiety with other predictors, mainly when neuroticism was low or moderate. Once again, referring to the MAP model and taking the individual differences perspective, we could argue that when neuroticism and somatic anxiety were low, and general imagery was high, the athletes were calm during the task, thus relaxed and prone to achieving a flow-like state or type 1 performance.We observed that a moderate level of SCL was related to somatic anxiety, mainly when respondents presented a rather low level of neuroticism, but also with the occurrence of the following conditions: somatic anxiety high/moderate and general imagery low or somatic anxiety low and general imagery moderate/high.When concentration disruption was taken into account, similarly to somatic anxiety, we observed the highest SCL when neuroticism was rather high (moderate/high), general imagery was high, and concentration disruption was low or moderate. Another profile with high SCL emerged, with all the levels of neuroticism, low concentration disruption and moderate general imagery, which may be indicative of the athletes’ focus on the task. We also observed high SCL when neuroticism and concentration disruption were high and general imagery was low. Athletes with such a profile may be more prone to represent suboptimal performances like type 3  or type 4 . Our results, as previously mentioned, may be interpreted considering the MAP perspective; however, the individual features may differentiate the SCL signal.We observed a moderate level of SCL with at least a moderate level in one of the three subscales, mainly neuroticism. When athletes showed low SCL with high concentration disruption, low neuroticism and moderate/high general imagery we might argue that they used imagery. However, in this case, we could not conclude whether they were not focused enough on the task, not interested in taking part in imagining or simply calm.Considering the worry subscale, similarly to previous anxiety subscales, we observed high SCL when neuroticism was also high. In addition, when neuroticism was high with high levels of imagery and worry, athletes were rather ineffective in their imagery, they used a lot of energy to follow the instructions of the task, or did not control their imagery (type 3 performance in the MAP model). However, when the general imagery and worry were moderate, athletes used some mental skill resources, but we are not aware if they could cope with the task or not (try to do something). Athletes showed high SCL also with high neuroticism, moderate worry, and low imagery, probably because they did not have imagery ability and therefore did not use imagery. When we observed moderate SCL, similarly to previous subscales, rather low or moderate neuroticism was the general tendency. Specifically, when worry was moderate, we also observed moderate levels in the other predictors. Moreover, with low worry, general imagery did not play any role; however, when worry was high then general imagery was moderate or high. These results may indicate that in such circumstances, a general tendency to use imagery could be a useful tool to diminish SCL level.36 connected with underactivation, low arousal, lack of task concentration and low performance. Low excitable athletes (who show low/moderate neuroticism) should be aware of their need for more arousal to perform optimally.Considering the relationship between worry and low level of SCL, we observed high worry, low/moderate general imagery, and low/moderate neuroticism, which could be explained using Yerkes-Dodson’s first lawOverall, we questioned whether participants with the lowest SCL were underactivated or disengaged during the imagery task. Low neuroticism is the most important for low SCL, regardless of other predictors, but low SCL might be caused by the fact that the participants were reacting to imaginary rather than real situations. The aspect of anxiety might indicate motivational aspects in this study, because while they were typically easily agitated in sport situations, here they might have been calm because they were disengaged or not committed enough to be physiologically aroused.42, but our results suggest that other factors should be considered as predictors of SCL. Therefore, we distinguish three types of profiles and suggest interpretation based on the characteristics of the predictor:does not use imagery: high worry, low or moderate general imagery, low/moderate neuroticism, low mental training skills and predispositions. This means they find it difficult to cope with starts in difficult situations,inconsistent use of imagery: high concentration disruption, low neuroticism, higher/moderate general imagery. These athletes use imagery, but they are not able to modulate their level of activation  due to a high level of cognitive anxiety,uses imagery: low somatic anxiety, low neuroticism, high general imagery, high mental training skills and predispositions. This forms a good basis for optimal concentration along with positive and controlled imagery.In our study we also tested whether different kinds of sport anxiety would require a different mental training approach (Q); therefore, the role of imagery was also considered. The results were still difficult to interpret, and indeed there is evidence that people with high imagery ability use imagery more oftenThese three profiles could form a realistic hypothesis to test in future research.A limitation of our study was undoubtedly the rather small sample, but each participant imagined 7 situations, which required complex and time-consuming procedure. Additionally, the sample consisted of athletes who took part in sport competitions. The strength of this study is the length of the scripts, because previous studies often used very short and simple scripts. We also focused on selected individual differences to design the neural network. In the future it is advisable to also check the role of other psychological factors. This is the first study in which not only psychological indices but also physiological biomarkers were used in the neural network method during complex imagery tasks, which undoubtedly constituted the main strength of this work. Due to the complexity of the model, we aimed to analyze predictors of only imagined rather than actual performance, as the latter is typically burdened with many uncontrollable distractors. In the future we would like to enlarge the sample, and include the level of sport experience, the type of sport and gender in the design of the study.To conclude, in this multidisciplinary work, we found a linkage between neuroticism, prestart anxiety and a general tendency to use imagery with SCL. We extended the aspect of “who ” from the RAMDIU model with psychophysiological parameters and discussed our results in the MAP model framework, in order to shed light on the influence of personality traits in the performance types. From a practical point of view, our results may help athletes, coaches and psychologists to become more aware of the role of individual differences in sport and how to use this information in psychological skills training.M = 22.93; SD = 5.24 ). The majority of the participants (N = 25) were emerging adults . They represented sport experience ranging between 2 and 20 years  and different sport disciplines (combat sports (N = 11), individual sports (N = 7), team sports (N = 12) and sport levels: international (N = 8), national (N = 17), regional and local (N = 5).Thirty participants took part in our study, aged between 14 and 42 years .In our study we used the following questionnaires:44 is a multidimensional 51-item measurement tool with seven subscales, i.e., physiological feelings, modalities, ease/control, perspective, affirmations, visual, and general. The participants imagined themselves before the start in a high-level competition for 60 s and after this task responded to the 51 items assessing the different aspects of the image on a scale from 1  to 5 (completely so). All the subscales  were related to the imagined situation: i.e., situational imagery. The “general” subscale consisted of six questions and was developed separately to assess the general tendency to use imagery. In our study we only used the last scale, as the most stable, the general tendency to use imagery.The Imagination in Sport Questionnaire (ISQ) formulated by Budnik-PrzybylskaSAS45 adapted into Polish by M. Krawczyński46. This is a special sport psychological tool which measures anxiety as a feature; it helps to determine somatic anxiety levels, cognitive anxiety (worrying) and concentration disruption. Somatic anxiety—experiencing physiological changes as a result of anxiety—pacing heart, shortness of breath, sweating palms, muscle tension, etc. This kind of anxiety occurs shortly before a start, grows gradually, climaxes at the beginning of a competition and then drops. Cognitive anxiety (worrying)—anxious thoughts, worrying, doubts, imagining failure and humiliation. It binds with negative expectations and cognitive concentration on oneself tied to negative self-esteem. Concentration disruption—this is an inherent part of cognitive anxiety, and determines a player’s concentration difficulty levels in terms of the start of a competition.47 of the Big Five Inventory-Short (BFI-S) was used48. It is a fifteen-item tool with a 7-point scale of answers, where 1 means definitely not, and 7 means definitely yes. This tool is used to measure personality in terms of a five-factor personality theory. Specific subscales measure the following traits: extraversion, Openness to Experience, Agreeableness, Neuroticism and Conscientiousness. Due to its short form, this scale is increasingly used in exploratory research measuring many variables50.In order to study personality, the Polish versionSubjects listened to a pre-recorded script and were then asked to imagine the scene they listened to for one minute. The study used scripts for 7 sport-related scenes—one test scene: imagining performing 10 squats and 6 situations used from two existing questionnaires on imagery in sport, the Sport Imagery Ability Measure (SIAM) questionnaire in the Polish adaptation and the Imagination in Sport Questionnaire (ISQ). During this task, their body responses were monitored at all times using multimodal device. Six scenes  with a target imagery were presented in random order. There was a brief pause between each block to activate the subject. After the session they were asked to fill out a set of questionnaires—ISQ, SAS, BFIS. As this was the first stage of the Miniatura 5 project entitled “Psychophysiology of the imagery in sport” (2021/05/X/HS6/00459) in this study we have analyzed only the SCL as a measure of arousal during imagery.51. As a result, at the individual level, selected scripts with outlying variance and/or mean values have been excluded from the dataset. The data was then normalized by recalculating against the average value of a 5 s pause before starting each script (pSCL): nSCL = 100 × (SCL − pSCL)/pSCL. As a result, the normalized SCL signal, nSCL, represents the percentage SCL change from the pre-script average. A similar normalization procedure has previously been used elsewhere53. The test scene (squats), excluded from the main analysis, has been used to calculate the statistical power of the study. The mean difference between mean SCL in the test and the 6 examined scenes (0.023), as well as the standard deviation (0.03) give the standardized mean difference of 0.77. This, with a significance level set to 5% (0.05), gives the statistical power of 91.35%.Raw SCL data, collected at a frequency of 40 Hz and expressed in units of microsiemens (μS), were cleaned according to the 1.5 IQR ruleThe results of the psychological test have also been cleaned, using the same rule as for the SCL data. No outlying values were detected. Data was normalized to the  range.54. In the present work, machine learning predictive models based on artificial neural networks (ANN) were trained in the supervised way, in frames of the Tensorflow framework56 and used for the prediction of physiological response , as a function of selected psychological tests  is presented in Fig. Hyperband hyperparameters tuning algorithm57.The ANN architecture used (3–7–9–1 k-fold (k = 10) cross-validation method58 was employed. Except for overfitting prevention59, the method allowed us to reduce the size of the validation data set in each model, which is particularly important when the amount of data is limited60. In our case, the dataset was randomly divided into 10 folds, each of 27 training and 3 validating samples. Each fold was then independently trained using Adam optimizer61. Additionally, the regularization technique for training of the particular folds was employed: the training was early-stopped62 when the loss  on the validation samples stopped improving. After that, the final predictive model was constructed by (weighted) averaging predictions from the particular folds (see Fig. To train the models, the