Therefore, curbing deforestation in indigenous territories must be a priority for the Brazilian government to secure these peoples' land rights, ensure the forests' protection and regulate the global climate.Studies showed that Brazilian Amazon indigenous territories (ITs) are efficient models for preserving forests by reducing deforestation, fires, and related carbon emissions. Considering the importance of ITs for conserving socio-environmental and cultural diversity and the recent climb in the Brazilian Amazon deforestation, we used official remote sensing datasets to analyze deforestation inside and outside indigenous territories within Brazil's Amazon biome during the 2013–2021 period. Deforestation has increased by 129% inside ITs since 2013, followed by an increase in illegal mining areas. In 2019–2021, deforestation was 195% higher and 30% farther from the borders towards the interior of indigenous territories than in previous years (2013–2018). Furthermore, about 59% of carbon dioxide (CO The ITs host more than one million3 square kilometers of rainforests that contribute to climate regulation5, store carbon7, and are home to unique biodiversity8 and sociocultural diversity of indigenous peoples9. Going beyond the passive benefits of a conserved area, protected areas, including ITs, can still act as shields against deforestation and forest degradation13. By ensuring land rights for the indigenous people, these territories in the Brazilian Amazon could decrease deforestation by up to 66%14. Therefore, the ITs are not only essential for maintaining indigenous ways of life, having a key role in ensuring the human rights of these peoples, but they are also a cost-effective strategy for conservation14. Furthermore, global protected areas, including ITs, could improve carbon sequestration15, highlighting the positive externality for the whole world regarding climate change mitigation these areas could impose14.About 700 thousand indigenous people live in Indigenous Territories (ITs) in Brazil16. Keeping these forests intact and protected makes it possible to avoid releasing large amounts of CO2 into the atmosphere, reducing greenhouse gas emissions. The maintenance and protection of ITs are necessary because deforestation and forest degradation in these areas can release large amounts of CO2 into the atmosphere18. In addition, the indigenous communities that inhabit these protected areas have land use practices that are often sustainable and in harmony with the environment, such as agroforestry systems, fishing, and subsistence hunting. These practices can contribute to the conservation of the forest and the reduction of carbon emissions16.ITs are crucial in reducing carbon emissions and mitigating global climate change2) from the historical peak of 200419 . This reduction resulted from multiple government initiatives, especially the Action Plan for the Prevention and Control of Deforestation in the Legal Amazon (PPCDAm)20 and international pressure, such as the soy and beef moratoria21. However, there is a scenario of gradual deforestation growth in the Brazilian Amazon since 201319, and a set of environmental setbacks25  in the last few years took 2020 to the highest deforestation rate of the decade19 . Surprisingly, in 2021, the deforestation rate surpassed 2020 by 20%, reaching 13,038 km2 of clear-cut forests26. The setback in the environmental agenda has also affected indigenous people's policies29. This scenario has imperiled the ITs31, compromising their environmental potential and role in the survival of indigenous life, culture, and knowledge.Among tropical countries, in 2012, Brazil achieved an important achievement, reducing deforestation rates in the Amazon by 84%  Was there a significant trend in deforestation rates inside and outside ILs between 2013 and 2021? (2) Within the ILs, was deforestation significantly higher in the 2019–2021 period? (3) In the 2019–2021 period, did deforestation advance to the interior of the ILs? (4) How much was carbon dioxide (CO2) emitted from deforestation between 2013 and 2021?Thus, to accurately diagnose deforestation dynamics in the Brazilian Amazon ITs, we used two official remote sensing datasets and statistical analyses to perform a space–time assessment of deforestation in indigenous territories within the Brazilian Amazon biome between 2013 and 2021; we adopted this time window because it was considered a period of a new deforestation climb in the Brazilian Amazon2 year-1 , or an increase of 137% in the 2013–2021 period , or an increase of 129% in the same period ; among the ITs with a significant increasing trend, Arara in Pará had the lowest rate (0.02 km2 year-1), while Apyterewa in the same state had the highest rate (8.58 km2 year-1). On the other hand, 11% of the ITs tended to reduce deforestation, with five being significant (p < 0.05); among the ITs with a significant decreasing trend, Alto Turiaçu in Maranhão had the lowest rate (− 0.77 km2 year-1), while Itixi Mitari in Amazonas had the highest rate (− 0.01 km2 year-1). Another 47% of the ITs did not show a trend in the analyzed period  programs.These findings suggest that the increase in deforestation inside ITs Fig. , induced2 (millions of tons of CO2) into the atmosphere between 2013 and 2021 40. About 59% of these emissions (57 Tg CO2) were concentrated between 2019 and 2021 (average of 19 Tg CO2 year-1), while the remaining 41% (39 Tg CO2) occurred in the 2013–2018 period (average of 6 Tg CO2 year-1). Thus, on average, in 2019–2021, CO2 emissions within ITs were almost three times higher than in the 2013–2018 period.Within ITs, deforestation led to the emission of 96 Tg CO42. Our findings showed that inside the ITs, deforestation notice26 classified as mining areas increased from 1% in 2016 to 14% in 2021. However, the most worrying point is that only in half of the 2022 year, mining is already equivalent to 19% of all deforestation notices26 in the analyzed ITs. Recent environmental setbacks, which include political speech, can explain the increased illegal mining activity in ITs32, besides the possibility of regulating commercial mining in these territories44. Combined with setbacks, the rise in the gold price in recent years has increased pressure on these protected areas; in the 2019–2021 period, the gold price increased more than 30%, reaching up to $65 g−1, the highest value of the last ten years (https://goldprice.org). In addition to the environmental impact, the anthropic advance on indigenous territories also serves as a vector of diseases, such as malaria, associated with illegal mining activities, which may decimate indigenous peoples along with their culture and knowledge47.Although economic activities are not allowed inside ITs, these territories have recently been threatened by illegal mining2 year−1, or 195%  ignored these responsibilities, allowing the indigenous lands to be exploited by illegal mining and land grabbing, inflating the conflicts in the region56.The recent increase in deforestation in the Brazilian Amazon is a severe threat to decade-long efforts of curbing climate change. Indigenous communities have been the most effective defenders and consistent guardians of the Amazon rainforests57, including advancing deforestation within indigenous territories, its limitations must be considered. We used two official deforestation mapping datasets, PRODES (Brazilian Amazon Deforestation Monitoring Program) and DETER . Both deforestation monitoring systems can be affected by the high cloud cover59 in the Amazon—mainly in the northern region—leading to the underestimation of forest loss mapping. Furthermore, the minimum mapped area (3–6.25 hectares) in both systems may lead to underestimating small-scale deforestation. Another is related to CO2 estimates; however, we used a method widely accepted in the literature60, reproducible and which produces results that can be compared with others in the literature. In addition, for our CO2 model, we provided uncertainty analysis  as reported in Fig. While remote sensing data are valuable for studying the Earth's surface63. Improvements that increase monitoring frequency and scale may help overcome deforestation mapping limitations. Furthermore, other datasets and CO2 emission estimation models may be tested in the future.Thus, although the estimate of deforestation magnitude used here is conservative, its trends are not affected and are consistent with other datasets reported in the literature2 were emitted due deforestation between 2013 and 2021 in the analyzed ITs.Here we find a recent increase in deforestation within ITs that coincided with a decrease in the protection and rights of indigenous people. We can now answer that: (1) there was a significant trend towards an increase in the area of forests deforested inside and outside the ITs between 2013 and 2021, with an important intensification in the 2019–2021 period, (2) within the ITs, deforestation increased significantly by 195% between 2019 and 2021, (3) in the 2019–2021 period, deforestation advanced 30% from the borders to the interior of the ITs and (4) about 96 million tons of COFundação Nacional do Índio , the federal agency responsible for securing indigenous rights, including new ITs demarcation, through more funding and hiring new staff; ensuring the involvement of indigenous communities in decision-making processes and supporting their efforts to protect their lands.Strengthening of the Revocation of laws and norms that led to environmental setbacks. In the short term, it would reverse the weakening of local governance and curb further deforestation led by illegal mining and land grabbing.Strengthening of enforcement institutions that operate inside and outside indigenous territories, prioritizing the ITs with a significant trend of increased deforestation, was found in our analysis Table .64. In addition to canceling all CARs  within the ILs, including the impossibility of new registrations in these territories64.Creation of a buffer zone of 10 km between ITs and mining exploration areas or high-impact projectsSupport initiatives that promote agriculture, forestry, and other sustainable land-use practices, including ecosystem restoration projects to reduce the pressure of deforestation on surrounding ITs.Strengthening existing remote sensing monitoring and investments in developing new systems with improvements in frequency and scale. In addition, forest degradation and non-forest native vegetation must also be monitored.Based on our findings, we believe these territories will remain vulnerable without policy action to combat illegal deforestation in these territories and enforce the constitutional rights of indigenous peoples to protect their lands. For this, we present below a set of recommendations that can be adopted to achieve this objective by federal and state decision-makers:65.Finally, we highlight the need for future work considering ITs outside the Brazilian Amazon and deforestation in non-forest native vegetation. For example, the Cerrado, the second largest biome in Brazil, has a predominance of non-forest native vegetation and increasing deforestation rates19. Therefore, we adopted two analysis periods, before (2013–2018) and during the current environmental setback intensification period (2019–2021)32. Each dataset is described below.We analyzed two official remote sensing-based deforestation datasets throughout the Brazilian Amazon Biome between 2013 and 2021. This time window was adopted as it was considered a period of a new deforestation climb in the Brazilian AmazonPRODES was created in 1988 and has since provided the official annual deforestation rates of the Brazilian Legal Amazon. The system annually identifies clear-cut deforestation of old-growth forests. Once identified as deforested, such an area is not revisited in the subsequent year mapped. The monitoring system uses medium spatial resolution images (20–30 m) in a combination that seeks to minimize cloud cover interference and ensure interoperability criteria. Landsat-5 satellite images have historically been the most used, but CBERS (Sino Brazilian remote sensing program) satellite images have been widely used. PRODES also used the Indian IRS-1 satellite and UK-DMC2 United Kingdom satellite.The reference year comprises August 1st of the prior year to July 31st of the reported year. The annual rate is first estimated using a subset of images from the deforestation hotspots, and the result is published in December of each year. Subsequently, the analysis of the entire dataset, corresponding to the consolidated data, is made available in the first half of the following year. Currently, the system uses the Landsat-8, CBERS-4, and IRS-2 images. PRODES's method encompasses the visual interpretation of images by a team of experts with extensive experience.http://terrabrasilis.dpi.inpe.br/en/download-2.The PRODES dataset was obtained in vector format directly from: 66. Until 2015, this system used satellite images from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor with 250-m spatial resolution. Currently, DETER uses images from the Wide Field Imager (WFI) and Advanced Wide Field Sensor (AWIFS) sensors, with 64 and 56-m spatial resolution, respectively67.DETER was developed in 2004 to support the environmental enforcement of the Brazilian Amazon, generating daily notices of deforestation67In addition, environmental agencies of Brazilian states can also access daily information. DETER's method encompasses the visual interpretation of images by a team of experts with extensive experience.The improved spatial resolution from 2006 allows the monitoring to discriminate alerts between deforestation (including mining areas) and forest degradation. These alerts are sent daily to the Brazilian Institute of Environment and Renewable Natural Resources (IBAMA), the country's environmental enforcement agency in the Brazilian Amazonhttp://terrabrasilis.dpi.inpe.br/en/download-2.The DETER dataset was obtained in vector format directly from: ® 10.8 software . For area and distance calculations, we consider a plane coordinate system under the Earth surface representation model (Datum) "South America Albers Equal Area Conic ".At this stage, all procedures were performed using ArcGIS68 292 legally approved indigenous territories69. However, after overlapping the deforestation polygons for the 2013–2021 period, only 232 (about 79%) were considered to have deforestation within their territorial domains.Here we adopt the Brazilian Amazon Biome delimitationFirst, we calculated the area in square kilometers of PRODES deforestation outside (including protected areas) and inside all the analyzed ITs annually. Then we calculated the area of deforestation within each indigenous land each year. Finally, we calculated the distance from the centroid of each deforestation polygon to the nearest indigenous land border.From the DETER deforestation notices, the mining contribution was calculated in all analyzed ITs for 2016, 2021 and 2022 (January to June).70 . In all analyses, we adopted a significance level of 95% (p < 0.05).Here we used nonparametric statistical approaches that do not assume that the data has a known distribution and are lowly sensitive to discrepant values. All analyses were performed using the R statistical software72, and Sen's Slope estimator73, respectively. For both methods, we used the "wq" R package74.To analyze the significance and magnitude of temporal deforestation trends inside and outside  indigenous territories, we used Mann–Kendall testx1, x2, x3, …, xn) are independent and identically distributed. For this, the hypotheses considered are H0, which states that observations are independent and identically distributed (there is no trend), and H1, which states that observations have a monotonic trend in time (there is a trend). About H0, the S statistic is given by Eq. , it is possible to test whether a positive or negative trend significantly differs from zero. For S to be significant and different from zero, H0 should be rejected considering the level of significance adopted, pointing to the existence of a trend in the time series, thus accepting H1.The positive values of S [Eq. ] indicatn by Eq. :3\docume75 to estimate the magnitude of trends over time series. The Sen statistic is given by the median of the slopes of each pair of points in the data set75. To calculate the Sen's Slope Estimator (Q), the data should be ranked in ascending order as a function of time, and then Eq. (xi and xj are pairs at given times i and j (j > i), respectively.After identifying trends in the time series, it is also important to estimate the magnitude. In most of the methods used for this purpose, the normality of the data is a prerequisite, being highly sensitive to outliers. To resolve this limitation, a non-parametric and robust method was developed by Senthen Eq.  is appli76. The Wilcoxon test is equivalent to the student's t-test, which compares two independent groups to test the hypothesis that they have the same median. In addition, we used Fisher's least significant difference criterion with Bonferroni adjustment methods correction77. For this method, we used the "agricolae" R package78.In addition, to compare the annual deforestation and annual deforestation distance from borders within indigenous territories before and during the current environmental setback intensification period, we used the Wilcoxon test® (Microsoft Corporation) for tabulation and linear regression analysis. To build the linear regression model, we used the Data Analysis Tool from the software Excel®.In this stage, we used the statistical software Excel2 emissions due to deforestation, we adopted the approach developed by Aragão et al. (2018)60. We regressed the reported deforestation gross CO2 emissions from Brazil's Amazon Forest Reference Emission Level (FREL-C)79 for the 2008–2015 period (Table 79) against PRODES deforestation increment area (within Brazilian Amazon biome) resulting in Eq. .2 emission from deforestation given in Tg and 2. The number in parentheses is the 95% confidence interval for the equation parameter. The chart of the regression line, including the confidence interval and the raw values used (deforestation increment and CO2 emissions) are shown in Fig. To estimate the gross COet al. 20860. We rSupplementary Information.


Aedes mosquito-bornevirus that has caused large epidemics linked to acute, chronic, and severeclinical outcomes. Currently, Brazil has the highest number of chikungunyacases in the Americas. We aimed to investigate the spatiotemporal dynamicsand recurrence pattern of chikungunya in Brazil since its introduction in2013.Chikungunya virus (CHIKV) is an In this epidemiological study, we used CHIKV genomic sequencing data,CHIKV vector information, and aggregate clinical data on chikungunya casesfrom Brazil. The genomic data comprised 241 Brazilian CHIKV genome sequencesfrom GenBank (n=180) and the 2022 CHIKV outbreak in Ceará state(n=61). The vector data (Breteau index and House index) were obtained fromthe Brazilian Ministry of Health for all 184 municipalities in Cearástate and 116 municipalities in Tocantins state in 2022. Epidemiologicaldata on laboratory-confirmed cases of chikungunya between 2013 and 2022 wereobtained from the Brazilian Ministry of Health and Laboratory of PublicHealth of Ceará. We assessed the spatiotemporal dynamics ofchikungunya in Brazil via time series, mapping, age–sex distribution,cumulative case-fatality, linear correlation, logistic regression, andphylogenetic analyses.Aedes aegypti, were not correlatedspatially with locations of chikungunya recurrence in Ceará andTocantins.Between March 3, 2013, and June 4, 2022, 253 545 laboratory-confirmedchikungunya cases were reported in 3316 (59·5%) of 5570municipalities, mainly distributed in seven epidemic waves from 2016 to2022. To date, Ceará in the northeast has been the most affectedstate, with 77 418 cases during the two largest epidemic waves in 2016 and2017 and the third wave in 2022. From 2016 to 2022 in Ceará, the oddsof being CHIKV-positive were higher in females than in males ,and the cumulative case-fatality ratio was 1·3 deaths per 1000 cases.Chikungunya recurrences in the states of Ceará, Tocantins (recurrencein 2022), and Pernambuco (recurrence in 2021) were limited to municipalitieswith few or no previously reported cases in the previous epidemic waves. Therecurrence of chikungunya in Ceará in 2022 was associated with a newEast-Central-South-African lineage. Population density metrics of the mainCHIKV vector in Brazil, Spatial heterogeneity of CHIKV spread and population immunity mightexplain the recurrence pattern of chikungunya in Brazil. These results canbe used to inform public health interventions to prevent future chikungunyaepidemic waves in urban settings.Global Virus Network, Burroughs Wellcome Fund, Wellcome Trust, USNational Institutes of Health, São Paulo Research Foundation, BrazilMinistry of Education, UK Medical Research Council, Brazilian NationalCouncil for Scientific and Technological Development, and UK RoyalSociety. Aedes aegypti and Aedesalbopictus mosquitoes.1 During the past 20 years, over 10 million cases of chikungunyahave been reported in more than 125 countries or territories.2 Chikungunya is a disease characterised byacute and chronic signs and symptoms, typically with severe and often chronicarthralgia, and can sometimes lead to some neurological complications and fataloutcomes.3 1·3 billion people have been estimatedto live in areas at risk of CHIKV transmission.4 Modelling suggests that many more parts of the world mightbecome suitable for CHIKV transmission due to climate change increasing thedistribution of Ae aegypti.5 No licensed vaccines or antiviral drugs are available toprevent CHIKV infection or treat chikungunya.Chikungunya virus (CHIKV) is a major threat to global public health, and ismainly transmitted among humans by 1 TheECSA genotype gave rise to the Indian Ocean lineage (IOL), which has beenresponsible for epidemics since 2005 in the Indian Ocean islands, south andsoutheast Asia, and Europe.2 The expansion of the ECSA-IOLepidemic has been partly attributed to adaptive mutations in the E1 and E2 virusenvelope glycoproteins, which facilitated adaptation for Aealbopictus infection and transmission.7 CHIKVinfection appears to promote life-long immunity, in which neutralising antibodiesprevent disease recurrence and potentially reinfection.8 Chikungunya outbreak recurrences in Africaand Asia are often preceded by long periods spanning several years or decades withminimal or no cases. Recurrence can be explained by several factors, including theabsence of neutralising antibodies in younger age groups after periods ofepidemiological silence.10 Additionally, recurrences of chikungunya inwest Africa have been attributed to enzootic CHIKV periodically causing spilloverinfections in people who enter or live near forests, causing individual cases andsmall outbreaks.10 The periodicityof these spillover cases appears to be driven by changes in herd immunity amongnon-human primate enzootic hosts.11 However, the dynamics and drivers associated with chikungunyarecurrence in urban environments are poorly understood.CHIKV is currently classified into West African, East-Central-South-African(ECSA), and Asian genotypes.Ae aegypti and Aealbopictus are widely distributed,12 autochthonous chikungunya cases caused by the Asiangenotype were first detected in December, 2013, and those caused by the ECSAgenotype were first detected in September, 2014.13 Subsequent genomic investigations indicated thepredominance of the ECSA genotype across Brazil’s five geographic regions. Currently, Brazil hasthe highest number of chikungunya cases in the Americas. In this study, wecontextualise the spread of CHIKV in Brazil from 2013 to 2022 and combineepidemiological, genomic, and vector density population analyses to describe andinvestigate chikungunya recurrence.In Brazil, where This epidemiological study combined CHIKV genome sequencing data, CHIKVvector information, and aggregate clinical data on chikungunya cases fromBrazil. National epidemiological data on laboratory-confirmed cases ofchikungunya were obtained from the Brazilian Ministry of Health. This datasetincluded the aggregate number of cases of chikungunya per epidemiological weekfrom all municipalities of Brazil from epidemiological week 10 (March3–9) in 2013 to epidemiological week 22 in 2022 (May 29 to June 4).Epidemiological data on laboratory-confirmed cases of chikungunya and dengue inthe state of Ceará, from Jan 1, 2015, to May 31, 2022, were obtained fromthe Central Laboratory of Public Health of Ceará. Individualised anddeidentified data were collected on all patients tested for CHIKV and denguevirus (DENV) in Ceará. These data included patient age, sex, andmunicipality residence, date of symptoms, date of sample collection, diagnosismethod, and clinical outcome . Additionally, for sequencing analysis,we used residual serum samples from patients who tested positive for CHIKV byquantitative RT-PCR (RT-qPCR) between Feb 7 and May 21, 2022, in Ceará. All serumsamples were obtained from patients cared for in the public health system, forwhom samples were submitted to the Central Laboratory of Public Health ofCeará as part of the surveillance system of Ceará. All studyprocedures followed the ethical standards of the responsible committee on humanexperimentation and were approved by the ethics committee of the University ofCampinas, Campinas, Brazil . Individualpatient-informed consent was not required for this retrospective study ofanonymised samples that were submitted for diagnostic and surveillancetesting.14 RNA samples positive for CHIKV byRT-qPCR with cycle threshold (Ct) values less than 30 were submitted for CHIKVgenome sequencing by a nanopore sequencing approach, as described in For the patients sampled in our genome sequencing analysis, basicclinical and demographic data were collected from the Brazilian LaboratorialEnvironment Management System. Anonymised patient information data for allsamples used in the current study are provided in 15 and manual adjustment was conducted with Geneious Prime(version 2020.2.3). The dataset was screened for recombination events with useof all available methods  in Recombination Detection Program (version 4).16 No evidence of recombinationwas found. We subsequently performed maximum likelihood phylogenetics usingIQ-TREE (version 2) under a GTR + I + γ model as determined byModelFinder, where GTR  is the variable basefrequencies , I is the proportion of invariablesites, and γ is the γ-distributed rate variation amongsites.18 Statistical support for nodes of themaximum likelihood phylogeny was assessed by an ultrafast-bootstrap approachwith 1000 replicates. We then regressed root-to-tip genetic divergence againstsampling dates to investigate the temporal signal and identify sequences withlow data quality in our dataset, caused by assembling issues, samplecontamination, data annotation errors, sequencing, and alignment errors. Noobvious outliers were identified in this step. Dated phylogenetic trees wereestimated with use of BEAST (version 1.10.4) under a GTR + I + γ model,strict molecular clock model, and a Skygrid tree prior, and with use of BEAGLE(version 3.1.0) to enhance computation speed. Markov chain Monte Carlo chainswere run for over 50 million generations and sampled every 1000 steps, withconvergence assessed with Tracer (version 1.7).19 Maximum clade credibility summary treeswere summarised with TreeAnnotator (version 1.10).We included CHIKV sequences with greater than 85% genome coverage in thefinal analysis. The new sequences were appended to 180 other Brazilian CHIKVcomplete coding sequences available in GenBank .20 The indices data were provided by the BrazilianMinistry of Health. The Breteau index is the number of water containerscontaining Ae aegypti larvae per 100 houses, and the Houseindex is the percentage of houses with infested containers. Additionally, weused digital surveillance data via the Google Trends tool to compile the monthlyfraction of online searches for the term “chikungunya” thatoriginated from Brazil from Jan 1, 2013, to May 31, 2022, and plotted these dataas a time series.We also analysed Breteau index and House index for all 184municipalities in Ceará state and for 116 of 139 municipalities inTocantins state, which were assessed between Jan 3 and Feb 21, 2022, as part ofthe 21 In addition, using data from the Central Laboratory ofPublic Health of Ceará, we analysed the temporal distribution ofchikungunya case numbers, incidence, and deaths at the state level and acrossall 184 Ceará state municipalities, with incidence during epidemic wavesstratified by sex  and 10-year age group. Data were alsosummarised for the states of Pernambuco and Tocantins. Logistic regression wasused to calculate odds ratios (ORs) with 95% CIs to assess statisticaldifferences in the probability of chikungunya  or probability of death  by sex and age group, incorporating both variables as covariates. Thecorrelation between laboratory-confirmed chikungunya-related deaths per monthand chikungunya cases per month in Ceará was determined byPearson’s correlation coefficients. Cumulative case-fatality ratio by sexand 10-year age group was also calculated. Case data, correlation analyses, andcase-fatality ratio are presented for dengue cases in Ceará forcomparison.All analyses were done in R studio (version 1.3.1073). Incidence ofchikungunya was calculated per 100 000 inhabitants on the basis of the estimatedpopulations of Brazilian states from 2013 to 2022, as reported by the BrazilianInstitute of Geography and Statistics. Based on national data, epidemiologicaldynamics in terms of chikungunya case numbers and incidence were presentedannually and by state to identify epidemic waves, defining the start and end ofwaves by upward or downward periods in case numbers that were substantialthrough being sustained over time.We assessed correlations between measures of surveillance ofchikungunya. We evaluated the correlation between Google Trends data forchikungunya and laboratory-confirmed chikungunya cases per Brazilian federalunits using Spearman’s rank correlation test. Correlation coefficients ofBreteau and House indices per chikungunya incidence in 2022 in Cearástate municipalities and Tocantins state municipalities were also calculatedwith Spearman’s rank correlation test.Differences by sex and age group  in Ct values for chikungunya cases in Ceará in 2022(n=638), and in the interval between symptom onset and sample collection, werecalculated by one-way ANOVA with Tukey’s honestly significant differencetest.22 In all tests, significance was defined as a p value ofless than 0·05.Figures and maps were coloured according to recommendations forscientifically derived colour maps.The funders of the study had no role in study design, data collection,data analysis, data interpretation, or writing of the report.r=0·74, p<0·0001) between Google Trendsdata and the number of chikungunya cases in the states most affected  of 5570 municipalities in all 26 states and the Federal District.Chikungunya cases were mainly distributed across seven large epidemic waves between2016 and 2022, which resulted in 24 097 to 44 604 confirmed cases annually  p 8. Theaffected  p 2. Sincted  was significant  in the Central Laboratory of Public Health of Ceará from Jan 1, 2015, toMay 31, 2022. Ceará had three chikungunya waves since the first autochthonouscase was reported on March 6, 2015, in Fortaleza, the state’s capital andmost populous city . In the nificant  p 3.We subsequently analysed the spatial distribution and incidence ofchikungunya cases across the 184 municipalities in all seven mesoregions ofCeará from 2015 to 2022. We observed that chikungunya recurrence in 2022predominantly affected municipalities in the south of Ceará . The thir=0·83, p<0·0001; From Feb 10, 2016, to May 31, 2022, 103 deaths due to laboratory-confirmedchikungunya were reported across 28 municipalities in Ceará, with 55(53·4%) of 103 deaths reported in Fortaleza . The pea24 associated with enhanced transmission potential for Aealbopictus mosquitoes  in theCHIKV strains circulating in 2022, consistent with a scarcity of incrimination ofthis species in transmission in the Americas.25 Additionally, we did not find epistatic interactions thatcontrol penetrance of the E1–A226V mutation , which are associated with adaptation to a new vector, and thus canrestrict epidemic emergence.24We investigated the genetic diversity of CHIKV in Ceará in 2022. Wesequenced 61 genomes from patients who tested positive for CHIKV during the recentoutbreak between Feb 7 and March 9, 2022. The samples were from three mesoregions of 139 Tocantins state municipalities from Jan 3, to Feb21, 2022. In Ceará, we found that 72 (39·1%) of 184municipalities had a Breteau index or House index higher than 1, which triggers arisk alert for Ae aegypti-borne virus outbreaks according to theNational Dengue Control Programme in Brazil26  and dengue incidence in Ceará in 2022  with a high sensitivitysurveillance system,29 which wasalso consistent with iterative external studies and serosurveys.30 Collectively, our results indicate thatchikungunya outbreaks can be rapid and result in a large number of cases, and acase-fatality ratio similar to that seen in dengue epidemics . Therefore, mechanisms andpotential biomarkers associated with severe chikungunya outcomes need to beinvestigated to support the development of antiviral therapies, improve clinicalmanagement, and reduce chikungunya burden.Our results show that chikungunya affected females more than males,consistent with previous findings that suggested females are 1·5-times morelikely to become infected with CHIKV than males.31Consequently, these conditions can increase the risk and dynamics of arbovirustransmission, such as for CHIKV and DENV. However, our results also indicate thatCHIKV circulation is stable during other parts of the year. Overwintering of vectorsis probably explained by variable rainy and warm seasons throughout Brazil, combinedwith the absence of cold winters and high vector endemicity in all states.Additionally, in our analysis, Ae aegypti population densitymetrics were not correlated spatially with locations of chikungunya recurrence inCeará and Tocantins. These findings highlight that vector population densitythresholds  need to be improved by incorporatingherd immunity data to improve predictions of outbreaks transmitted by Aeaegypti and Ae albopictus. Robust modelling thataccounts for mosquito population dynamics, climate data, immunity to CHIKV, and thedistribution of Ae aegypti control interventions across the countrycould help to quantify chikungunya burden and evaluate the effect of vector densityon chikungunya recurrence.We found that the peaks of chikungunya cases in epidemic waves occurredmainly between February and June in Brazil, coinciding with the rainy season andincreased temperatures. These wet and warm periods have been described as crucialdrivers of the magnitude and seasonality of mosquito-borne virus transmission, byaffecting mosquito reproduction, survival, biting rates, and the adult vectorpopulation density.32 Bycontrast, our data show that no more than 7% of the populations of municipalitiesmost affected received a laboratory diagnosis of chikungunya in the three waves inCeará. Several factors could explain these discrepancies, such asoligosymptomatic and asymptomatic cases (up to 80% of cases),33 challenges of the syndromic surveillancesystems due to the co-circulation of ZIKV and DENV, which cause similar symptoms tochikungunya,34 andvariable health-care-seeking behaviours. Third, the shortage of diagnostic kits forarboviruses during the epidemic (due to case numbers exceeding expected demand whencompared with the same period in 202123) might underestimate the chikungunya burden in 2022 becauseendemic DENV and ZIKV infections are easily confused. Fourth, information was absenton individual-level factors related to socioeconomic status and deprivation that canbe important determinants of arboviral risk,35 which should be explored in the context of Brazilian CHIKVoutbreaks in future research. Finally, comparisons with the epidemiology of DENV andZIKV in Brazil are needed to improve understanding of their apparent differences inoutbreak dynamics.There are several limitations to our study. First, the absence of CHIKVgenomic sequences from other Brazilian states prevented us from accuratelyconcluding on the date of introduction and geographical origins of the new lineagein Ceará. The implementation of screening of blood donors combined with CHIKVgenome sequencing could provide unique data to understand the epidemiological andevolutionary dynamics of CHIKV in Brazil. Second, the absence of CHIKVseroprevalence studies at the state and national levels in Brazil limits estimatesof susceptibility and herd immunity in the population. For example, someCHIKV-endemic countries, such as India, have an overall prevalence of IgG antibodiesagainst CHIKV of 18%.In conclusion, our findings provide important context about the dynamics anddrivers of chikungunya in Brazil, and might inform future studies and public healthpolicy on strategies to mitigate the effects of new epidemic waves in urban settingsand immunisation programmes. For example, subsequent modelling studies based on thepresent data could predict potential hotspots for new chikungunya epidemic waves.Such studies could be followed by optimised and focused public healthcountermeasures such as mosquito control in identified hotspots, and could informfurther vaccine prioritisation strategies. Additionally, chikungunya should beconsidered as a possible death diagnosis in endemic countries to improveunderstanding of the burden of fatal disease.https://github.com/wmarciel/Chikungunya-in-Brazil-2013-2022.git).The deidentified individualised data provided by the Brazilian Ministry of Healthand the Central Laboratory of Public Health of Ceará can be made availablefor research purposes; any future research should be approved by a committee onhuman experimentation. The data can be provided upon request to the correspondingauthor. The Breteau index and House index data were obtained from the BrazilianMinistry of Health through the Right to Information Law  underprocess number 25072.015694/2022–81.All statistical computing analyses were conducted with use of the R project.R packages used in this study for time series and age–sex distributionanalysis were tidyverse, ggpubr, and scico, and spatiotemporal analysis wasperformed with raster, tmap, sf, and scico. No custom code was developed. Newchikungunya virus sequences have been deposited in GenBank with accession numbersOP964932 to OP964992. All clinical and laboratory datasets aggregated and presentedin the present study are available on GitHub (12


Assessing genetic distinctness and redundancy is an important part of plant germplasm characterization. Over the last decade, such assessment has become more feasible and informative, thanks to the advances in genomic analysis. An attempt was made here to search for genebank germplasm with published genomic data and to assess their genetic distinctness and redundancy based on average pairwise dissimilarity (APD). The effort acquired 12 published genomic data sets from CIMMYT, IPK, USDA-ARS, IRRI, and ICRISAT genebanks. The characterized collections consisted of 661 to 55,879 accessions with up to 2.4 million genome-wide SNPs. The assessment generated an APD estimate for each sample. As a higher or lower APD is indicative of more genetic distinctness or redundance for an accession, respectively, these APD estimates helped to identify the most genetically distinct and redundant groups of 100 accessions each and a genetic outlier group with APD estimates larger than five standard deviations in each data set. An APD-based grouping of the conserved germplasm in each data set revealed among-group variances ranging from 1.5 to 53.4% across all data sets. Additional analyses showed that these APD estimations were more sensitive to SNP number, minor allele frequency, and missing data. Generally, 5000 to 10,000 genome-wide SNPs were required for an effective APD analysis. These findings together are encouraging and useful for germplasm management, utilization, and conservation, particularly in the genetic categorization of conserved germplasm. There are more than seven million plant germplasm accessions of more than 16,500 plant species currently conserved in 1750 genebanks worldwide , thanks To facilitate germplasm characterization, we previously developed a genetic marker-based approach using an average pairwise dissimilarity (APD) of an accession against the other assayed accessions to assess genetic distinctness and genetic redundancy in a plant germplasm collection . The APDGenomic characterization of conserved germplasm has become more feasible than before , T. durum (2n = 4x), T. aethiopicum (2n = 4x), Aegilops tauschii (2n = 2x) and A. triuncialis (2n = 2x)   can be calculated based on SNP genotypes following the simple matching coefficient of Sokal and Michener [S. The average pairwise dissimilarity (or APD) for the given sample can be obtained by averaging all n − 1 pairwise dissimilarity estimates. The higher the APD estimate obtained for the given sample, the more genetically distinct the sample representing the accession is in the collection.Each cleaned data set was first analyzed with respect to allelic frequency, minor allelic frequency, and missing SNP data, allowing for a better understanding of the variability and heterogeneity of these data sets. For each data set, APD and its standard deviation were obtained for each sample using the APD.r script in an R v 4.1.2 environment . The R sMichener , and theThe APD.r script was specifically modified for data input to analyze each of the 12 cleaned data sets . Table SThe acquired APD estimates in each data set were further analyzed for their variations with basic statistics and distribution. Grouping was made on all the samples in a data set based on the distribution (M for mean and SD for standard deviation) of ADP estimates: 1 (M + 3SD), 2 (M + 2SD), 3 (M + SD), 4 (M), 5 (M − SD), and 6 (M − 2SD). Analysis of molecular variance  was alsTo facilitate plant germplasm management and utilization, we identified a genetic outlier group of samples with APD estimates larger than five standard deviations and generated genetically distinct and redundant groups with 100 samples in each data set by selecting the samples with the largest or smallest APD estimates, respectively. These two distinct and redundant groups were further characterized for APD variation, along with the published passport data such as origin country and biological status , relative to the whole cleaned data set.This was the first time that APD estimation was applied to such a large genomic data set, and the APD properties were largely unknown. Efforts were made to assess the impacts of sample size, SNP number, minor allelic frequency, and missing data on APD estimation. Specifically, an APD correlation analysis was performed using a custom R script between the original APD estimates and new APD estimates of the same samples, in which the former was obtained from the whole original data set, and the latter was generated under a scenario with respect to each of the four mentioned variables. A higher correlation means that the APD estimates in the analyzed scenario would provide an APD-based sample ranking more consistent with those from the APD estimates of the original genomic data set.For sample size, we focused on two large data sets, Hordeum vulgare and Glycine max, and randomly selected 1000, 2000, 5000, and 10,000 samples out of the original SNP data sets for APD analysis. We also created two new Hordeum vulgare and Glycine max data sets with the original samples, albeit having only 1000 randomly selected SNPs. The data sets with reduced SNP numbers served as the new whole data sets and were subjected to the same sampling scenarios as the original whole data sets, facilitating a comparative assessment of the effects of sample size and SNP number. For the number of SNPs, we generated new data sets with 2000 to 10,000 SNPs from Hordeum vulgare, Triticum aestivum-f20K, and Cicer arietinum-f300k data sets. For minor allelic frequency, we used Hordeum vulgare and Cicer arietinum data sets with minor allelic frequencies as low as 0.001 and generated new data sets by removing SNPs with minor allelic frequencies from 0.001 to 0.05 in these three original data sets. For missing SNP data, we selected Hordeum vulgare, Triticum aestivum-f20k, and Cicer arietinum-f300k data sets with different missing SNP data profiles and generated new data sets by removing SNPs with missing rates up to 0.07. For the APD analysis in each scenario, the APD.r script was modified as described in the The 12 germplasm SNP data sets  represenTriticum spp. and two Aegilops spp. data sets, and there were APD estimates larger than three standard deviations (SD) on the right. Typically, the distribution for the Triticum durum data set revealed many estimates of less than two SDs on the left and larger than three SDs on the right, and the distribution for the Cicer arietinum-f300k data set displayed many estimates smaller or larger than two SDs on both the right and left. For specific data sets, a wide range of APD estimates was observed , 2 (M + 2SD), 3 (M + SD), 4 (M), 5 (M − SD), and 6 (M − 2SD). This APD-based grouping helped to group samples with different levels of genetic diversity in each data set . SpecifiThe genetic outliers were identified and presented in each APD excel file  . HoweverFor variable SNP minor allelic frequencies. The impacts of missing SNP data on APD estimation shown in  varied gThis assessment revealed several significant findings for plant germplasm characterization and management, particularly in the genetic categorization of conserved germplasm. First, an APD assessment of large germplasm collections with published large genomic data sets was technically and practically feasible. Second, our assessment generated an APD estimate for each sample, a genetic outlier group, and genetically distinct and redundant groups of 100 samples each in the 12 germplasm APD data sets. The APD-based grouping revealed among-groups variances from 1.52 to 53.37% in various data sets. Third, the APD estimation was found to be more sensitive to SNP number, minor allele frequency, and missing data. An accurate sample APD estimation required 5000 to 10,000 genome-wide SNPs.The assessment of APD variation yielded some encouraging findings for plant germplasm characterization. First, the custom APD.r script was capable of handling large genomic SNP data sets such as Triticum aestivum-f20k with 55,879 samples with 20,000 SNPs and Hordeum vulgare with 19,778 samples with 76,102 SNPs and had computation time up to a few days in a high-performance Linux server with up to 30 threads . Such peOur empirical assessment of APD variation was preliminary and further explorations still need to enhance the application of the APD approach to large and new genomic data. For example, we considered only the extent of the minor allelic frequency and missing SNP data, but not the variation patterns across the 12 data sets . It is hThe APD approach can assist in the assessment of genetic distinctness and/or redundancy in plant germplasm characterization. The assessment outputs (as listed in 12 APD excel files) can facilitate germplasm management at the five genebanks for uses such as the development of germplasm core subsets and accession identification for safety backup. However, the APD approach per se is of limited resolution in defining genetically redundant or distinct groups, as clearly stated in Fu . There iThe revealed APD profiles in these 12 germplasm APD data sets helped to identify a genetic outlier group of samples with APD estimates larger than five standard deviations in each data set. Biologically, these outliers may suggest the presence of species’ misclassification, introgressed or hybrid germplasm. For example, the only Cicer arietinum sample identified as a breeding line from East Africa had an APD estimate of 0.2649, while the mean APD for the whole data set was 0.0806. This result strongly suggests that this sample may be misclassified as Cicer arietinum. There were 291 of 55,879 samples identified as outliers for the Triticum aestivum data set, and it is possible that these “outliers” may represent introgressed or hybrid germplasm. However, it is important for a genebank manager to assess the biological identification and status of these outliers, as our APD analysis can reveal only their extreme SNP variations but not verify their biological identity and status.With APD estimates acquired at the individual sample level, it is feasible to perform the genetic categorization of conserved germplasm within a crop gene pool. An APD estimate represents another genetic diversity indicator associated with genetic distance and a sample with a higher APD estimate may harbor more rare and/or unique alleles than other samples with lower APD estimates. Thus, the APD estimate per sample can be used to genetically compare and group germplasm. The APD-based germplasm grouping following the APD distribution performed in this assessment provided an objective genetic tool to categorize conserved germplasm with up to six groups in each germplasm APD data set. As shown in This assessment generated an APD estimate per sample, identified a genetic outlier group, and produced genetically distinct and redundant groups of 100 samples each in 12 germplasm APD data sets. The APD-based germplasm grouping revealed among-group variances ranging from 1.52 to 53.37% across these data sets. Further assessments revealed that these APD estimations were more sensitive to SNP number, minor allele frequency, and missing data. An APD assessment of large germplasm collections with published large genomic data sets was demonstrated to be technically and practically feasible. These findings together are useful for plant germplasm characterization and management, particularly in the genetic categorization of conserved germplasm.


In plant breeding programs, multiple traits are recorded in each trial, and the traits are often correlated. Correlated traits can be incorporated into genomic selection models, especially for traits with low heritability, to improve prediction accuracy. In this study, we investigated the genetic correlation between important agronomic traits in safflower. We observed the moderate genetic correlations between grain yield (GY) and plant height , and low correlations between grain yield and days to flowering . A 4%–20% prediction accuracy improvement for grain yield was achieved when plant height was included in both training and validation sets with multivariate models. We further explored the selection responses for grain yield by selecting the top 20% of lines based on different selection indices. Selection responses for grain yield varied across sites. Simultaneous selection for grain yield and seed oil content (OL) showed positive gains across all sites with equal weights for both grain yield and oil content. Combining g×E interaction into genomic selection (GS) led to more balanced selection responses across sites. In conclusion, genomic selection is a valuable breeding tool for breeding high grain yield, oil content, and highly adaptable safflower varieties. Genomic selection (GS), as an efficient breeding tool, was first implemented in animal genetic improvements . With thImplementing GS in the breeding program heavily depends on the prediction accuracy of GEBVs. Multivariate models showed higher prediction accuracy than univariate models in GS studies . The addGS can improve the genetic gain of target traits in crop breeding programs mainly by increasing selection accuracy, shortening breeding cycles, etc. . StudiesGS can simultaneously select multiple traits with the genomic selection index (GSI), a linear combination of ‘traits’ GEBVs. Both simulation and real data showed that GSI was more efficient than the phenotypic selection index (PSI) per unit of time . A studyAlternaria Carthami) and root rot (Macrophomina phaseolina), respectively, and most varieties grown today in Australia were developed overseas . Six seeds per accession were crushed, and genomic DNA was extracted, digested, amplified, purified, and sequenced with Illumina Hiseq 3,000 sequencer with in-house GBS protocols. SNPs were filtered with a missing data rate of <50% and minor allele frequency (MAF) > 0.01 for this study. The resulting 6,911 SNPs were imputed with LinkImpute  and showy1 and y2 are the vectors of two traits, b1 and b2 are the vectors of two traits’ mean and replications, g1 and g2 are the vectors of random genetic effects following a variance-covariance matrix of two traits, as r1, r2, c1, and c2 are the field design row and column vectors of two traits, ε1 and ε2 are the residuals of two traits also following a variance-covariance matrix as X, Zg, Zr, and Zc are the incidence matrices associating phenotypes with fixed and random effects, and I is the identity matrix. Phenotypic variance 2) was calculated as follow:The phenotypic correlation between traits was calculated as:σPx2 and σPy2 are the phenotypic variances of traits x and y, G) replaced 2) was calculated as the proportion of additive genetic variance A multivariate linear mixed model with the variance-covariance matrix was used to estimate the genetic parameters  at each site. The model could be illustrated as follows:σAx2, σAy2) and covariance , GY with PH/DF, and GY, PH, and DF (multivariate), were fitted respectively, different prediction models were developed for estimating the prediction accuracy of GY.The best linear unbiased estimates for each safflower accession (BLUEs) were estimated for each trial the same as the previous study  by fittiThe prediction accuracy was defined as ‘Pearson’s correlation coefficient between GEBVs and BLUEs for GY by a five-fold cross-validation method. All accessions were randomly divided into five equal subsets. Each subset was, in turn, chosen as the validation set and was subsequently predicted by using the other four subsets as the training set. The prediction accuracies of multivariate models were compared from two cross-validation prediction scenarios. The first cross-validation scenario (MT-CV1) predicted accessions in the validation set that have been phenotyped with PH and DF but not GY, while the training set had phenotypes of GY, PH, and DF. The second cross-validation scenario (MT-CV2) predicted the performance of accessions in the validation set that have not been evaluated with GY, PH, and DF, while the training set had been phenotyped with GY, PH, and DF. The whole process was repeated five times. We calculated the mean prediction accuracy and standard deviation (SD) across all 25 validation sets at each site.Aside from assessing the genomic prediction accuracy for GY, we further explored the selection responses of two breeding strategies with different indices, which 1) aimed to select high-yield genotypes and 2) to select simultaneously high-yielding and high-oil content genotypes. For selecting GY, we used BLUEs as the phenotypic selection index to perform phenotypic selection (PS). GEBVs estimated from the univariate model as the first GS index (SGS) and GEBVs estimated from the multivariate model (PH and GY) as the second GS index (MGS) were used for the genomic selection (GS), respectively.b is a vector of index weight for each trait, g is a vector of the additive genetic variance of the traits, P−1 is the inverse of the phenotypic variance-covariance matrix . The GEBVs estimated for GY and OL with univariate GBLUP models were standardized before applying the weights. The second multi-traits GS index (MTGS2) was calculated with unequal weights, calculated following the formula: e matrix :σp12σp1Z2 being the incidence matrix. The combined GEBVs were used as GSI for GY selection (g×EGS) or combined with equal weights to simultaneously select for GY and OL (g×EMTGS1).All selections with different indices were conducted at each site. The selection responses at the other two sites were also calculated with the same selected candidates to compare the response across sites. We further estimated GEBVs for GY and OL with a g×E GBLUP model, which combined three sites and described by  as follo2S, where h2 is the narrow-sense heritability, and S is the selection differential. The percentage of the gain increased at each site was calculated to facilitate the comparison.We selected the top 20% of the accessions based on indices as selected candidates. The selection differential (S) is calculated as the difference between the mean GY or OL of the selected candidates and the mean GY or OL of the diverse population at each site. We used the selection differential to calculate the selection response (R) according to R = h2) for all traits in each site. The results are averaged from pairwise traits combination and shown in 2A) was higher than the error variance (σ2e) for most traits at all sites, suggesting SNPs used in the study were able to capture the additive genetic variations in both the optimal site and stressed sites. SW and OL showed high estimated h2, ranging from 0.63 to 0.81, while PC, FT, and DM all showed moderate h2, around 0.5 at all sites. We observed large variations between sites for h2 for DF, PH, and GY. DF had a relatively low estimated h2 at site RF, 0.4, with high h2 at the other three sites. The estimated h2 for GY and PH were lower at sites LR and HR compared with sites IR and RF. The boxplot of BLUEs for each trait at each site was shown in We estimated the variance components and the narrow sense heritability  at each site was predicted with univariate and multivariate models by fitting BLUEs as the “phenotypes”. All models showed higher prediction accuracy at sites IR and RF than at sites LR and HR in both scenarios . The preThe percentage of gain increase for each site was plotted in When the selection was performed for GY and OL simultaneously, the equal weight GS (MTGS1) and unequal weight GS (MTGS2) indices showed different gains for GY and OL . MTGS1 mSafflower is an underutilized oil seed crop. Breeding efforts for genetic improvement of target traits were modest. With limited genetic and genomic resources and limited research funds, the datasets we used in our study have been used for genetic diversity study, genetic characterization, and genomic parameters estimation of important agronomic traits in safflower. All those studies enhanced our knowledge of safflower and paved the way to transfer conventional safflower breeding into a highly targeted and more efficient modern crop breeding scheme. In this study, we further investigated the genomic prediction accuracy for grain yield with multivariate models and compared grain yield selection responses to different selection strategies. Combining GS into the conventional breeding program and optimizing the breeding strategies would facilitate and fast-track the genetic improvement for safflower.via the multivariate model in our study showed consistent trends with the univariate GBLUP models (2 could be estimated more accurately compared with the univariate model. Therefore, our study indicates that the multivariate models may perform as well as the univariate models in genetic parameters estimation.Genetic parameters are important estimates for quantitative traits. In this study, we nearly doubled the SNPs number compared to our previous study . A high P models . With thCombining the correlated trait with high heritability would improve the prediction accuracy for the low heritability traits by the multivariate model. The correlation between traits could be a key factor determining the multivariate model’s advantage over the univariate model . In our With the limited genetic and genomic resources for the safflower, taking advantage of the modern breeding tool, such as GS, could fast-track the genetic improvement of the crop. Tradition safflower breeding program depends on the phenotypic selection of the elite parental lines to initiate the crossing. In our study, we have chosen the top 20% of individuals according to the BLUEs and GEBVs, respectively, and the best gain achieved was 0.7 t/ha, about a 23% yield increase in the optimal site. This indicated the high yield potential for safflower genetic improvement. We expected to find the highest response by PS for within site selection because the selection differential was used to predict selection response in the next-generation. However, the selection responses in PS and GS were very similar, which could be associated with the fact that most of the top 20% of the accessions with the highest phenotypes had the highest GEBVs as well . SelectiSelection indices have been used efficiently for multi-trait breeding programs for animals and crops . If seleIn summary, we estimated the heritability and genetic correlations as well as selection responses to different selection indices for a diverse safflower Genebank collection. We achieved higher genetic prediction accuracy for grain yield by combining the correlated trait into the multivariate model. High selection responses for GY were achieved at the selected sites with both PS and GS, but the gains varied across the sites. Combining the g×E model with the equal weighted selection index GS model could simultaneously improve GY and OL in all studied sites.


Butyrate is an established feed additive for supporting gut health in pigs and poultry and also young ruminants where it is used to promote rumen development. However, the effect of supplementing dairy cows with butyrate ahead of calving has been less extensively studied, and it is not known what effect precalving butyrate supplementation has on the calf. The purpose of this on-farm study was to investigate the effect of magnesium butyrate (MgB) fed daily for the last 3 weeks before the expected time of calving on dairy cow and calf-related parameters. The study findings showed that prepartum MgB enhanced colostrum yield and the total yield of immunglobulin G, protein and lactose. Improved early lactation performance, calving ease, fertility and body condition score were observed in the cows of the MgB-supplemented group, and their calves had increased vitality at birth. These findings highlight that prepartum MgB supplementation of dairy cows gives a wide range of benefits, which also extend to the calf.n = 107) and Control groups (n = 112). Colostrum yield and the total yield of IgG, protein and lactose were higher (p ≤ 0.05) in the supplemented group. The calving assistance rate was lower (p ≤ 0.012), and the neonatal vitality score was higher (p ≤ 0.001) in the MgB group. Improved parameters related to cow health and fertility were observed in the supplemented group. The MgB group also had higher milk yield during the first week of lactation (p ≤ 0.001), and a higher (p ≤ 0.05) body condition score from 3 to 9 weeks after calving. In conclusion, prepartum MgB supplementation provides a wide range of benefits for dairy cows, as well as their newborn calves.Butyrate promotes rumen epithelium growth and function; however, the effect of prepartum butyrate supplementation on dairy cow productivity, health and their offspring has not been extensively studied. Furthermore, no studies have investigated the effect of magnesium butyrate (MgB), which is also a source of magnesium. A trial was performed to test the hypothesis that prepartum MgB supplementation (105 g/cow/day) would increase colostrum quality and improve calving, newborn calf vitality and cow health. Multiparous Holstein cows were randomly assigned to MgB supplemented ( Rumen development is stimulated by volatile fatty acids (VFA) which increase cell proliferation in rumen wall tissue . Unlike Building on this and the known benefits of butyrate for rumen development in young calves ,5, KowalAs rumen wall surface area decreases during the dry period of cows by more than 50% , the effDietary strategy may be an important aspect to consider when testing the effect of prepartum butyrate supplementation on dairy cows . For exaRelative to calcium management strategies of transition cows, prepartum feeding of butyrate in the form of calcium or sodium salts also needs consideration. For example, calcium butyrate would not be advisable for cows fed low calcium content prepartum rations, and sodium butyrate has implications relative to the use of dietary cation-anion difference (DCAD). Magnesium butyrate (MgB) could be an interesting alternative, particularly as it would safeguard against the risk of magnesium deficiency adversely affecting calcium mobilization rates ,11. ThisColostrum is also key to giving newborn calves the best start in life. In colostrum, immunoglobulins make up 70–80% of the total protein , and thiBased on the above literature, it was hypothesized that prepartum supplementation of MgB can increase colostrum quality and positively influence factors linked to calving, newborn calf vitality and cow health. In order to test this hypothesis, multiparous dairy cows with or without prepartum  MgB supplementation were studied. At partum, cows were monitored in terms of calving ease, colostrum quality and quantity. Newborn calves were examined at birth in terms of vitality, and monitored until weaning in terms of health. As the effect of prepartum MgB supplementation on postpartum performance and health has not been previously assessed in dairy cows, milk yield, body condition score (BCS), health disorders and fertility were also studied during the first 70 days in milk (DIM).All methods and the applied procedures on the animals were performed in accordance with the relevant guidelines and regulations of the Pest County Government Office, Department of Animal Health (Permit Number: PE/EA/1973-6/2016) that approved the study.n = 116) or the regular diet with no supplementation  in the prepartum period.The experiment was carried out on a large-scale dairy farm in Hungary (47°18′191″ N 18°48′336″ E), which has a herd of over 900 lactating Holstein Friesian cows. The farm was visited for a 9-month period between January and September 2021. A matched pair design was applied to control for confounders and to balance control and experimental groups for previous lactational milk yield, parity, body condition and time of expected calving. Two hundred-and-thirty-two multiparous Holstein-Friesian cows that were expected to calve between February and June were enrolled in the study. Forty days before the start of the trial, cows were blocked by the expected calving date and randomly assigned to receive diets containing MgB supplementation  was added to the prepartum total mixed ration (TMR) of MgB cows at a dosage level of 150 g/cow/day . Rumen-Ready® is formulated so that the MgB is fully released in the rumen in a gradual manner. The MgB supplement was mixed into the prepartum TMR before morning feeding according to the number of cows in the MgB group. The prepartum TMR was pushed in towards the pen eight times daily. The amount of feed refusal was checked before evening feeding, in order to monitor MgB intake, and no feed refusals were observed.Before 23 days of the expected time of calving until parturition, 70% MgB encapsulated in a fat matrix , previous lactational milk yield = 9681 ± 143, BCS = 3.61 ± 0.02] and 107 MgB cows participated in the trial . At the start of the trial, 10 Control cows had a locomotion score of 2, and 102 Control cows had a locomotion of score of 1, whereas 8 MgB cows had a locomotion score of 2, and 99 MgB cows had a locomotion score of 1 at the trial start.Directly before enrollment, each animal was examined clinically and cows with a suboptimal BCS (<3 or >4.25), locomotion scores higher than 2  or exhibThe monthly average temperature humidity index (THI), the number of Control and MgB animals enrolled monthly in the trial, and changes in average and maximal daily THI in the barns of Control and MgB cows are presented in From 28 d before expected calving, dry cows were housed in a prepartum pen (measuring 35 m × 20 m), which included 50–60 animals and was bedded with deep straw. Before calving, cows were fed a prepartum TMR ad libitum containing a dietary forage-to-concentrate ratio of 81.7:18.3 on a dry matter (DM) basis. The prepartum TMR was fed twice daily (early morning and late evening). Calvings took place in the prepartum pen or, if continuous supervision or obstetrical assistance was required at calving, in a separate maternity pen. Assistance by well-trained farm personnel was performed at the latest within 90 min after the appearance of the amniotic sac in the vulva as described previously by Kovács et al. . NewbornDuring the first 5 DIM, cows were housed in postpartum pens, each including four animals, and were milked twice daily at 0400 and 1400 h in a four-stall herringbone milking parlor operated with DeLaval Control Valve bucket milking machines . After 5 DIM, cows were introduced to the fresh lactation group and milked twice daily at 0500 and 1500 h in a 2 × 28-stall parallel Bosmark milking parlor . Cows were fed a postpartum TMR twice daily with a 57.7:42.3 forage to concentrate ratio on a DM basis until 90 DIM and water was available ad libitum. The composition of prepartum and postpartum TMRs did not change during the study period. The ingredient and chemical composition of prepartum and postpartum TMRs are shown in Within 1 h after calving, all four udder quarters of the dams were completely milked by a portable milking machine into a steel churn. Colostrum was then transferred into plastic graded milk buckets and the volume (L) was recorded (hereinafter referred to as colostrum yield). Colostrum from each cow was agitated for at least 2 min, to ensure an even distribution of constituents, before a 50 mL sample was placed into a large plastic vial (50 mL). This vial was used for fat, protein, lactose, and total solids contents analysis. At the time of collecting samples for the colostrum chemical composition analysis, a 2 mL aliquot was also collected in a 2 mL plastic vial for colostral IgG measurement. All samples were frozen at −20 °C until analysis. Frozen colostrum samples (50 mL) were thawed in a water bath at 40 ± 2 °C, and vials were inverted 10 times to thoroughly mix the colostrum and secure an even distribution of constituents. Colostrum fat, protein, lactose and solids non-fat concentrations (%) were determined using the LactoScope infrared spectrometry analyzer . It should be noted that the colostrum samples met the microbiological quality requirements for bacterial contamination . Colostrum IgG concentrations (mg/mL) were determined using a Bovine IgG Radial Immunodiffusion Test Kit which had a sensitivity range of 200 to 3000 mg/dL . Manufacturer’s instructions were followed when using the kit with the exception that colostrum samples were first diluted in water before being analyzed.Calving ease  was assessed by the first author based on the recordings of a closed-circuit camera system including two day/night outdoor network bullet cameras  installed above the prepartum group pen allowing the identification of individual cows. In cases of assisted calvings, two portable video cameras  were used after placing the cows into the maternity pen. The incidence and degree of dystocia was scored using a 4-point scale as follows: (1) eutocia  was recorded as no assistance at calving ; (2) lign = 116 born to Control dams, and n = 116 born to MgB dams) including twins  were examined in the study. Calf sex was determined immediately after birth. Calf vitality was assessed immediately after birth by a trained individual who was blinded to treatment groups. The calf vitality criteria recommended by Szenci [A total of 232 calves  were visually inspected by a trained investigator (who was blinded to treatments) from birth until weaning (50 d of life) three times per week . A systematic scoring system developed for the assessment of bovine respiratory disease (BRD) in pre-weaned dairy calves was used [Live calves with adequate colostrum uptake (3.8 L) from their own dams  were recorded. Health disorders diagnosed during the trial included milk fever, puerperal metritis, abomasum displacement, mastitis, clinical ketosis, and other digestive disorders. For each individual cow, examinations were conducted daily during the first 5 DIM, and then every three days throughout the remainder of the first 70 DIM. Retained fetal membranes (RFM) were defined as the non-expulsion of fetal membranes beyond 24 h after calving ,30, and Daily milk yield (kg) was recorded during the first 70 DIM using on-farm records from the milking system. The BCS of the cows was scored weekly from 255 to 260 d of pregnancy until 70 DIM  using the 5-point USA scoring system  with 0.2p < 0.05. Data were tested for constant variance (Levene’s test) and the Shapiro–Wilk test was used for testing the normality of data. Means of colostrum-related variables were compared between groups using a two-way ANOVA with treatment (Control vs. MgB), level of parity (parity = 2 vs. parity ≥ 3) and their interaction as factor variables. As well as the colostral protein, fat, lactose and solids non-fat concentrations (g/kg), the total amount of these components was calculated (g) and compared between groups.Statistical analyses were performed in the R–4.1.2 statistical environment and language . The levt-test. Late embryonic/early fetal mortality rate, stillbirth rate, the prevalence of NCD and BRD, and the preweaning mortality rates were compared between groups by Fisher’s exact test.A calving ease score above 1 was classed as assisted calving. The relative frequency of assisted calvings was compared between groups using Fisher’s exact test. The means of calf birth weight, hip height, hip width and vitality score were compared between calves born to Control and MgB dams using Welch’s p-values were then adjusted for multiple comparisons, by controlling for false discovery rate.The SPC was compared between treatment groups separately for different levels of parity by means of the Wilcoxon–Mann–Whitney test. The interval from calving to first heat and calving to conception was considered time-to-event data, which included censored observations due to culling before first heat/conception or unobserved outcome. Median days from calving to first heat and calving to conception for different levels of parity are presented as Kaplan–Meier estimates. Medians of intervals between treatment groups adjusting for the level of parity were compared by means of the stratified log-rank test with the treatment group as the main effect and the level of parity as the stratum. The relative frequency of RFM cases, other health disorders  and culling rate before 70 DIM were compared between Control and MgB groups using the Fisher’s exact test. The distribution of the number of animals according to the lameness categories was compared between groups separately for each sampling week using the Fisher’s exact test. p < 0.05. Differences in mean average milk yield between Control and MgB groups were compared by posthoc single-step multiple comparisons based on studentized range distributions conditioning for week controlling for the family-wise error rate using Tukey’s p-value adjustment.Daily milk yields per group were averaged over weeks. Weekly average milk yields were then compared between groups (Control and MgB) and weeks by fitting a linear mixed-effects model. The response variable was the daily average milk yield and fixed effects were treatment, week as a categorical variable and their interaction term. To take into account the correlation of repeated measurements on the same animal, cow identity (ID) was included as a random term that enables separating individual variation and main effects of interest. The level of significance for fixed effects was set at p < 0.05.Mean BCS was compared across groups and weeks by fitting a linear mixed effects model, similar to milk yield results. The fixed effects were treatment, week, and their interaction. Cow ID was included in the model as a random effect. Differences in mean BCS between Control and MgB groups each week were compared by posthoc tests as described for milk yield. The level of significance for main effects and all comparisons was set at The MgB cows spent 23.2 ± 1.4 d  in the prepartum pen receiving MgB supplementation before calving, whereas Control cows spent 23.0 ± 1.3 d  in the prepartum pen before calving.Colostrum yield and chemical analysis variables of Control and MgB cows are presented in p < 0.001) in newborn calves born to MgB dams compared to calves born to Control dams  and mortality rates  were similar between Control and MgB groups, respectively, and the prevalence of NCD  and BRD  showed also no significant difference between groups. Data described in this section are not shown elsewhere.The mean colostral IgG amount fed to Control and MgB calves was calculated to be 271.7 ± 23.5 g and 269.4 ± 21.8 g, respectively. None of the parameters related to the health of the calves were significantly influenced by the treatment. Stillbirth . The weekly average of daily milk yield was higher in cows from the MgB group during the first week postpartum (p < 0.001) compared to the Control group. There were no significant differences between groups in average daily milk production for the other weeks of lactation until 70 DIM. Mean BCS was similar across groups at 3, 2 and 1 weeks prior to calving and at parturition (p < 0.001). Between weeks 3–9 of lactation, mean BCS was higher for cows in the MgB group compared to the Control group.Increases in milk yield showed a similar pattern in both groups with a group × week interaction (turition . After pFurther details of the milk yield and BCS are provided in The main objectives of the present study were to: (a) test the hypothesis that prepartum supplementation of MgB would improve colostrum quality and positively influence factors linked to calving, newborn calf vitality and health, and (b) assess the subsequent effect of prepartum MgB supplementation on postpartum milk yield, BCS, health disorders and fertility.As the study was conducted on a commercial dairy farm, the findings can be considered to be reflective of what may happen in practice when MgB is supplemented prepartum. However, as a consequence, there are certain limitations that need to be considered when interpreting the study findings. Dietary supplementation of MgB was not able to be conducted at an individual animal level, but only at a group level. This meant that the exact amount of MgB consumed by each animal could not be determined. However, when the prepartum TMR was supplemented with MgB no feed refusals were observed. This indicates that, on average, the intended dosage of 105 g MgB/cow/day was achieved. The inability to determine feed intake on the farm is also a limitation of the study, and this point is discussed elsewhere in this section.To the best of our knowledge, this is the first study showing the benefit of MgB on colostrum yield. Previous pig studies with sodium butyrate have not assessed this parameter, only colostrum composition, presumably due to practical issues with measuring colostrum yield ,19,34. HBovine colostrum quality is associated with the most abundant immunoglobulin, IgG , and hasA negative correlation exists between colostrum yield and IgG concentration ,50, inclIn earlier studies, experts have estimated a minimum mass of 100 g of IgG in the first colostrum feeding is needed to achieve successful passive transfer of immunity in an average 43 kg Holstein calf ,52, whilBetween 2 and 10% of all calves are born dead or die within 48 h after birth . The stiThe duration of retention of fetal membranes is highly dependent on uterine muscular contractions , and theCows with RFM have an increased risk of developing metritis, ketosis, mastitis, and even loss of their subsequent pregnancy . DespiteA shorter interval to first heat, lower number of SPC and the interval to conception in MgB cows indicate a possible association between improved fertility and prepartum Mg-butyrate supplementation. The number of SPC reported in the present study is higher than those reported by others for Holstein-Friesian cows ,64,65. TIn the present study, cows with MgB supplementation produced more milk during the first week of lactation compared to the Control animals. This finding is in contrast to that of other studies, with either sodium butyrate  and calc®) that enabled a much faster and complete release of the magnesium butyrate in the rumen (data not shown). As cows with RFM produce 9.79 L of milk/d less compared to their healthy counterparts [Another factor to consider is the type of butyrate supplementation used and its encapsulation. Kowalski et al.  used a sterparts , this materparts . HoweverBased on the study findings, it can be concluded that prepartum MgB supplementation has a beneficial effect on calving ease, colostrum yield, milk production, BCS, and fertility. The vitality of newborn calves born to MgB dams was also better compared to calves from Control dams. In summary, due to its numerous positive effects, MgB can be advised to be fed to dairy cows prepartum. Further studies are needed to gain additional insight into the basis by which prepartum MgB supplementation benefits post-partum cows, including assessment of metabolic parameters and rumen fermentation.


High‐mobility group box 1 protein (HMGB1) is extensively involved in causing ischemic stroke, pathological damage of ischemic brain injury, and neural tissue repair after ischemic brain injury. However, the precise role of HMGB1 in ischemic stroke remains to be elucidated.Comprehensive literature search and narrative review to summarize the current field of HMGB1 in cerebral ischemic based on the basic structure, structural modification, and functional roles of HMGB1 described in the literature.Studies have exhibited the crucial roles of HMGB1 in cell death, immunity and inflammation, thrombosis, and remodeling and repair. HMGB1 released after cerebral infarction is extensively involved in the pathological injury process in the early stage of cerebral infarction, whereas it is involved in the promotion of brain tissue repair and remodeling in the late stage of cerebral infarction. HMGB1 plays a neurotrophic role in acute white matter stroke, whereas it causes sustained activation of inflammation and plays a damaging role in chronic white matter ischemia.HMGB1 plays a complex role in cerebral infarction, which is related to not only the modification of HMGB1 and bound receptors but also different stages and subtypes of cerebral infarction. future studies on HMGB1 should investigate the spatial and temporal dynamics of HMGB1 after cerebral infarction. Moreover, future studies on HMGB1 should attempt to integrate different stages and infarct subtypes of cerebral infarction. The topic combines one of the most death‐causing pathological condition with the reference DAMP protein, HMGB1, that has crucial roles in the pathophysiology of many inflammation‐related diseases. In this reviews,we explained its effect on different subtypes of cerebral infarction and its key mechanism based on the Spatiotemporal dynamics and Modifications of HMGB1 after cerebral infarction. These processes interact with and promote each other, which cause severe damage to the neurons, glial cells, and endothelial cells and their interconnections, eventually leading to cellular damage and necrosis  is a member of the high‐mobility protein family and is a highly abundant nonhistone protein in the mammalian cell nuclei  and chromosome region maintenance 1 (CRM1)‐dependent mechanisms to promote HMGB1 release from the monocytes    (Suzuki et al., ONOO, an oxidant/nitrator that is produced after cerebral infarction, exerts stronger cytotoxic effects than O3+ Gr‐1+ on the cell membrane surface. The CD11b + Gr‐1+ monocyte subpopulation induces apoptosis in activated lymphocytes (Rodríguez and Ochoa, + monocyte subpopulation, which exhibits a hyporesponsive state after stroke, as evidenced by the reduced expressions of MHC‐II and costimulatory molecules and reduced secretion of the proinflammatory cytokines TNF‐α and IL‐12. Poststroke immunosuppression induced by the HMGB1/RAGE pathway is independent of the classical pathway (Liesz et al., After ischemic cerebral infarction onset, microglia and macrophages in the brain are activated by ischemia and DAMP stimulation, releasing proinflammatory factors such as IL‐1β and TNF‐α (Taylor and Sansing, 4The blood–brain barrier is composed of an interdependent cell network, which comprises capillary endothelial cells, glial cells, pericytes, and extracellular matrix. An intact blood–brain barrier plays a role in maintaining the relative stability of the intracranial environment (Stokum et al., 5The ischemic brain tissue after cerebral infarction can be divided into two regions, namely core and penumbra. The neuronal cells in the core region undergo necrosis at an early stage, whereas those in the penumbra region undergo apoptosis, necrosis, parthanatos, ferroptosis, autolysis, phagoptosis, autophagy, and necroptosis upon simulation due to pathological changes such as ischemia, oxidation, inflammatory mediators, and excitatory amino acids (Kim et al., 6In the late stage of ischemic cerebral infarction, repair processes such as neural and vascular regeneration and synaptic function remodeling occur in the injured brain tissues. Studies have demonstrated the vital role of HMGB1 in the repair of neural tissues after cerebral infarction. HMGB1 released from the activated astrocytes after cerebral infarction promotes EPC proliferation through RAGE, and HMGB1 facilitates EPC migratory aggregation toward the peri‐infarct cortex. Thus, as a signal between astrocytes and EPC, HMGB1 promotes the interaction between astrocytes and EPC, while accelerating postinfarct vascular regeneration and brain tissue repair (Hayakawa et al., 7Subcortical white matter stroke accounts for 25% of stroke incidences and is particularly common in the elderly population, in which it causes severe motor and cognitive dysfunctions (Marin and Carmichael, 8HMGB1 plays a dual role after cerebral infarction by causing both damage and repair. Several studies have exhibited that the blood HMGB1 concentration after cerebral infarction is positively correlated with cerebral infarction severity and poor prognosis (Le et al., 9HMGB1 is an important molecule released early after ischemic stroke, which plays an important role in the initiation, amplification, and dissipation of inflammation after stroke. Redox modification of HMGB1 plays an important role in this process. Currently, a few studies have explored the redox modification of HMGB1 after ischemic stroke. Studying the effect of different redox modified HMGB1 on ischemic stroke may be an important topic of research. In addition, HMGB1 seems to play different roles in different stroke subtypes, and whether these differences are related to the redox modification of HMGB1 remains unknown. Research in these areas may contribute to a deeper understanding of the pathophysiological mechanism after stroke and provide a theoretical basis for more accurate stroke treatment in the future.4HMGB1, which is located in the nucleus under physiological conditions, plays a pivotal role in genetics. When cells are subjected to various stimuli, HMGB1 is modified and translocated to the cytoplasm and in the extracellular space, wherein it plays diverse functional roles. Studies have exhibited the crucial roles of HMGB1 in cell death, immunity and inflammation, thrombosis, and remodeling and repair. HMGB1 released after cerebral infarction is extensively involved in the pathological injury process in the early stage of cerebral infarction, whereas it is involved in the promotion of brain tissue repair and remodeling in the late stage of cerebral infarction. HMGB1 plays a neurotrophic role in acute white matter stroke, whereas it causes sustained activation of inflammation and plays a damaging role in chronic white matter ischemia, indicating that it exhibits diverse functional roles in different cerebral infarction subtypes. Thus, HMGB1 plays a complex role in cerebral infarction, which is related to not only the modification of HMGB1 and bound receptors but also different stages and subtypes of cerebral infarction. The functional roles of HMGB1 in cerebral infarction are poorly understood. Thus, future studies on HMGB1 should investigate the spatial and temporal dynamics of HMGB1 after cerebral infarction. Moreover, future studies on HMGB1 should attempt to integrate different stages and infarct subtypes of cerebral infarction.Conceptualization: J.L. and B.G. Investigation: N.H. and H.G. Writing—original draft: B.G. and S.W. Writing—review & editing: G.Z. and M.Z. Validation: J.L., B.G., S.W. Visualization: J.L.All other authors declare no competing interests.The study highlights one of the death‐causing pathological conditions with reference to the DAMP protein HMGB1 that plays crucial roles in the pathophysiology of many inflammation‐related diseases. The scenario is complex as HMGB1 activates several signaling pathways and exhibits different functional features acting as a positive or a negative regulator. In this review, we explain its effect on different subtypes of cerebral infarction and its key mechanism based on the spatiotemporal dynamics and modifications of HMGB1 after cerebral infarction.https://publons.com/publon/10.1002/brb3.2987.The peer review history for this article is available at