This multimethods study aimed to: (1) compare the prevalence of intimate partner violence (IPV) during pregnancy pre-COVID-19 and during the COVID-19 pandemic using quantitative data and (2) contextualise pregnant women’s IPV experiences during the COVID-19 pandemic through supplemental interviews.Quantitative analyses use data from Performance Monitoring for Action-Ethiopia, a cohort of 2868 pregnant women that collects data at pregnancy, 6 weeks, 6 months and 1-year postpartum. Following 6-week postpartum survey, in-depth semistructured interviews contextualised experiences of IPV during pregnancy with a subset of participants (n=24).All pregnant women residing within six regions of Ethiopia, covering 91% of the population, were eligible for the cohort study (n=2868 completed baseline survey). Quantitative analyses were restricted to the 2388 women with complete 6-week survey data (retention=82.7%). A purposive sampling frame was used to select qualitative participants on baseline survey data, with inclusion criteria specifying completion of quantitative 6-week interview after the onset of the COVID-19 pandemic, and indication of IPV experience.A State of Emergency in Ethiopia was declared in response to the COVID-19 pandemic approximately halfway through 6-week postpartum interview, enabling a natural experiment .IPV during pregnancy was assessed via the 10-item Revised Conflict and Tactics Scale.1-in-10 women experienced any IPV during pregnancy prior to COVID-19 (10.5%), and prevalence of IPV during pregnancy increased to 15.1% during the COVID-19 pandemic . Stratified by residence, odds of IPV during the pandemic increased for urban women only , however, IPV prevalence was higher in rural regions at both time points. Qualitative data reveal COVID-19-related stressors, namely loss of household income and increased time spent within the household, exacerbated IPV.These multimethods results highlight the prevalent, severe violence that pregnant Ethiopian women experience, with pandemic-related increases concentrated in urban areas. Integration of IPV response and safety planning across the continuum of care can mitigate impact. Natural experiment based on split of pre-existing cohort to examine differences in intimate partner violence (IPV) during pregnancy pre-COVID-19/post-COVID-19 restrictions.Large quantitative sample inclusive of all pregnant women residing within six regions of Ethiopia, covering 91% of the population.In-depth interviews to contextualise women’s experiences with IPV during pregnancy during the COVID-19 pandemic.Groupings may not exactly estimate pre/post exposure and no women who were truly only exposed to IPV during the COVID-19 pandemic.Qualitative interviews only occurred within two regions and are not transferable to all pregnant women experiencing IPV during pregnancy.Pandemics exacerbate gender inequities, including intimate partner violence (IPV).4The pregnancy and postpartum period are pivotal time points for assessing and responding to IPV, given health impacts to mother and baby, and multiple points of contact with healthcare providers. Global estimates indicate that 2%–14% of women experience violence during pregnancy.10Violence prevention and response is critical within the Ethiopian context. Ethiopia Demographic and Health Survey (DHS) data indicate that 27% of ever-married women age 15–49 have experienced IPV within the past year and 4% experienced IPV during pregnancy.While global evidence indicates increases in IPV, to date, no studies aimed at understanding the impact of the COVID-19 pandemic or related response measures have examined violence during pregnancy within the Ethiopian context.This multimethods analysis is situated within the Performance Monitoring for Action (PMA)-Ethiopia cohort study, a collaboration between Johns Hopkins Bloomberg School of Public Health (JHSPH), Addis Ababa University (AAU) and the Ethiopian Federal Ministry of Health (FMoH). PMA-Ethiopia collects quantitative data on a cohort of 2879 pregnant women at pregnancy (any gestation), 6 weeks, 6 months and 1-year post partum. Enrolment into the cohort began in October 2019. The full protocol for PMA Ethiopia is detailed elsewhere.18All pregnant women residing within six regions of Ethiopia that covers 91% of the population were eligible for the cohort study (n=2868 completed baseline survey). Quantitative analyses were restricted to the 2388 women with complete 6-week survey data (retention=82.7%). A State of Emergency in Ethiopia was declared in response to the COVID-19 pandemic approximately halfway through fielding the 6-week postpartum interview (8 April 2020), thus enabling a natural experiment. Specifically, 1405, 6-week interviews were conducted before the onset of the COVID-19 pandemic; the remaining 983 occurred after COVID-19 emergency lockdown procedures eased in early June and data collection was able to resume.IPV, the primary outcome of interest, was measured via the 10-item Revised Conflict and Tactics Scale,The primary exposure variable of interest measures pre/post exposure to COVID-19 restrictions, a binary variable, indicating whether a woman completed her 6-week postpartum survey before (prelockdown) or after (postlockdown) 8 April, when the State of Emergency was declared and data collection paused.Analyses were stratified by urban/rural residence given the differences in COVID-19 lockdown measures. Sociodemographic variables explored as adjustment variables were chosen on a conceptual basis and included region, household wealth, age, parity and education; all were examined in categorical form.Descriptive statistics examined the distribution of sociodemographic characteristics by pre/post exposure; design-based F statistics assessed whether distributions were similar. Venn diagrams classified violence experiences during each time point. Next, bivariate distributions of each type of violence were examined by exposure, overall and stratified by urban/rural residence. Bivariate (not presented) and multivariable logistic regression models were used to examine differences in violence experience pre/during the COVID-19 pandemic and residence; for adjusted models, correlations between covariates were examined for multi-collinearity, with only residence, age and education retained within final models. All analyses were conducted in STATA V.16, with statistical significance set at p<0.05, and accounting for complex-survey design.To contextualise the impact of the COVID-19 pandemic on women’s experiences of violence during pregnancy, an explanatory qualitative phase was conducted following principles for qualitative descriptive design.21Training for the qualitative phase preceded data collection with focus on probing, ethical principles for IPV research, and research team protections. Semistructured interview guides focused on women’s experiences with IPV and IPV services. Participants were called prior to interview for scheduling considerations. All interviews lasted approximately 25–30 min. Four trained interviewers used a structured note-taking tool to allow probing of experiences, while permitting rapid analysis for timely results.Two researchers trained in qualitative analysis coded 24 structured notes using Atlas.ti software. Inductive thematic analysis was used to identify themes and subthemes and to create an initial set of codes. Dual coding and retrocoding were used to enhance agreement between coders. Coders also met and collaborated regularly with the field research team to discuss and clarify interpretation. Coding was complete when saturation of themes was achievedCommunity members are not directly involved in PMA-Ethiopia research, however, a project advisory board, including members of the FMoH, health providers and key stakeholders, are consulted about country-specific priorities, including IPV, during survey development. Results are disseminated to the advisory board to inform action.Demographic characteristics of study participants by pre/post exposure are presented in pre-COVID-19; 9.8%during-COVID-19) was higher than physical IPV  and often occurred in isolation.Bivariate and logistic regression results comparing pre-COVID-19 IPV to during COVID-19 IPV, overall and by residence, are presented in In examining violence subtypes, physical IPV increased 60% during COVID-19, compared with pre-COVID-19, in the overall sample ; this increase was greater than twofold in urban areas . Statistically significant changes by pre-exposure/post-exposure were not observed for physical violence in rural areas or for sexual violence.Qualitative data indicate that the COVID-19 pandemic and implementation of local lockdown measures affected IPV within relationships differentially based on husband’s job status. Specifically, women whose husbands experienced loss of income or employment due to the pandemic often reported that the COVID-19 pandemic put stress on their relationship and increased instances of physical violence. These stressors were described by an 18-year-old woman in Oromiya who had just given birth to her first child:Since he was not working, he didn’t have money; it was difficult for us to pay the house rent, it was difficult to buy food. These problems created conflict, then he insulted and hit me.Further, many women experienced an increase in verbal abuse due to COVID-19-related stressors. Several participants shared that the main reason for conflict prior to the pandemic was disagreement regarding household responsibilities, suggesting that the COVID-19 pandemic exacerbated this tension. This increase in household tension due to restrictions on movement was discussed by a 36-year-old woman with four children in rural SNNP:The violence increased because we stayed together at our house because there was movement restriction in our village so he couldn’t go to the market to sell goats and sheep. So, our income decreased a lot and because of that we had arguments most of the time.The pandemic also changed the amount of time that spouses spent at home together, most often due to a husband’s loss of employment. One 29-year-old participant from rural Oromiya disclosed that this gave her husband an opportunity to further control her activities and to engage in sexual violence at a high frequency:During corona time, he stays at home so he is happy with that because he can control all my day-to-day activities. No physical and psychological violence happened but sometimes there is sexual violence. It might be four times a week.It should be noted that several participants stated that the pandemic had little or no impact on their relationships with their husbands, however, previous violence persisted throughout. An example of this endemic violence is described by a 37-year-old IPV survivor from rural Oromiya:We had a disagreement during this pregnancy. It was during coronavirus. But the cause was not related with corona. We disagreed due to children. The corona pandemic has nothing to do with our relationship.In many instances, the violence described by women during pregnancy was severe— examples of violence severity, particularly in relation to physical violence, are described below.When I became pregnant, the part of my body he hits was changed but not the frequency. He used to beat every part of my body but when I became pregnant, he especially didn’t hit me around the belly. Because he cares for the baby. He slaps my face with my own hand.—36-year-old IPV survivor with four children from rural SNNPHe doesn’t care if I am pregnant or not. When he is very angry, he gets very emotional and throws at me whatever he finds, whether it is a chair or anything. I had stillbirth, the fetus died in the belly, though I had a spontaneous vaginal delivery. When he heard the baby died in my uterus, he got angry and thought I was the one who killed the baby. −18-year-old nulliparous IPV survivor from rural SNNPWhen I was nine months pregnant; he hit me and threw me on the stone, then I went to my father’s home. Immediately, my father’s neighbors took me to hospital. I was admitted there for 15 days.—18-year-old primiparous IPV survivor from urban OromiyaThese multimethods results highlight the prevalent, severe violence that women experience at a critical, and often vulnerable, period during their reproductive lives. Using a natural experiment embedded in cohort of recently pregnant women in the six largest regions of Ethiopia, we found heightened IPV during the COVID-19 pandemic, compared with the pre-COVID-19 period, with greatest between-time impact concentrated in urban areas. Discrepancies by residence corroborate heightened cases and more COVID-19 preventive measures within urban areas.pre-COVID-19)—IPV during pregnancy reported by recently pregnant women in this study was substantially higher than 2016 DHS lifetime estimates (3.7%).More than 1-in-10 women experienced violence during pregnancy within Ethiopia . Heightened prevalence within rural settings likely speaks to cohesive, patriarchal community norms promoting violence against womenGiven distinct patterns of IPV by urban and rural residence, interventions must be tailored by locality and women’s circumstances. The impact of the COVID-19 pandemic on violence during pregnancy was concentrated primarily within urban settings. Immediate intervention and connection to IPV referral services should focus on urban settings, prone to closures and economic impact. As time and household finances were indicated as stressors, temporary separation and seeking support from family members and neighbours may be a feasible strategy to minimise impact of violence—this safety strategy has been useful for women experiencing IPV in urban informal settlements of Nairobi and other low- and middle-income contexts where leaving the relationship is not feasible or socially acceptable.This study is not without limitations—namely, these groupings may not exactly estimate pre/post exposure and we have no women who were truly only exposed to IPV during the COVID-19 pandemic. Further, our sample size was limited for additional subgroup analysis to examine women most at risk for IPV during pregnancy by pre-COVID-19 and during-COVID-19 time points. To maximise women’s confidentiality and bolster safety in line with best practices for violence-related research,While IPV during pregnancy increased overall during the COVID-19 pandemic, violence and related gender-based power disparities were prominent prior to the onset of the pandemic—accordingly, IPV prevention and response efforts in Ethiopia must be sustained in the postpandemic era. We offer two concrete recommendations for maternal health providers. First, while antenatal care is a critical intervention point for identifying and providing care for women experiencing IPV during pregnancy, providers across the maternal and neonatal continuum of care must similarly provide support. Integration of IPV screening and psychological care into postnatal care is critical given links to postpartum depression.


We present an exploration of the spatial determinants of reported COVID-19 incidence across India’s 641 urban and rural districts, comparing two waves (2020–2021). Three key results emerge using three COVID-19 incidence metrics: cumulative incidence proportion (aggregate risk), cumulative temporal incidence rate, and severity ratio. First, in the same district, characteristics of COVID-19 incidences are similar across waves, with the second wave over four times more severe than the first. Second, after controlling for state-level effects, urbanization (urban population share), living standards, and population age emerge as positive determinants of both risk and rates across waves. Third, keeping all else constant, lower shares of workers working from home correlate with greater infection risk during the second wave. While much attention has focused on intra-urban disease spread, our findings suggest that understanding spatial determinants  The physical proximity of people, as well as infrastructure provisioning in urban areas (such as water and sanitation), have historically shaped endemics, epidemics, and pandemics2. Increased physical interactions between people due to higher mobility within and across urban areas nationally and globally have furthered the influence of these urban characteristics on infectious diseases5. Against this backdrop, understanding the spatial determinants of infectious diseases at urban and intra-urban scales becomes particularly important. Recent scientific studies suggest a significant role of urban infrastructure, socioeconomic stratifications, population density, and other key urban characteristics on COVID-19 transmission8. Analyzing several types of human settlements—populated places and regions—in a country can yield insights into how human settlement variations shape COVID-19 transmission.With over 55% of the global population currently living in urban areas10. Significant spatial variations in population density, infrastructure, built environments, and socioeconomic features characterize the country’s transitory phase. Some of these variations contribute to comorbidities and shape health disparities within and across regions and urban areas. Third, COVID-19 transmission in India is particularly understudied from a sub-national perspective, with most studies focusing on national- or state-level data14. Some studies have reported significant spatial and temporal heterogeneities underlying COVID-19 transmission in India, which necessitate understanding disease progression and associated spatial determinants across human settlements along the rural-urban continuum. COVID-19 transmission waves observed in India15 present a significant opportunity to compare the two dominant waves in the context of their spatial determinants. Such comparisons can advance our understanding of how urbanization and associated variations shaped COVID-19 incidence across waves.This study focuses on COVID-19 incidence in India across its urban and rural districts, motivated by three main reasons. First, India is the second-most populous country in the world, with over 1.3 billion people. Second, India is at the cusp of urbanization, i.e., expected to significantly urbanize between now and the middle of this century, with its demographic base rapidly shifting from rural to urban via natural urban population growth, rural-to-urban population migration, rural land reclassification, and urban land expansion17. Studies focusing on finer spatial scales (district-level) identified several key determinants of, and vulnerabilities to, COVID-19 transmission. For instance, population density is closely associated with the spatial distribution of COVID-19 cases in India20. This finding contrasted with the distribution of COVID-19 cases in the United States, where population density had no association with COVID-19 incidence rates in the initial phases of disease spread when the size of the metropolitan areas had been controlled for21. Besides density, urbanization rates  also determine COVID-19 transmission risk in India22, owing to greater social interactions in more urbanized areas compared to others. Similarly, out-migration from cities also explains the spread of COVID-1923. These associations are consistent with basic predictions from meta-population epidemiological models5.Recent studies conducted in India have highlighted place-based characteristics, which may have made some parts of India more vulnerable to COVID-19 transmission than others. The greater relative severity of the second wave in India compared to the first wave—in terms of reported incidences and mortalities—demonstrated several additional vulnerabilities, such as a low level of health care access and overall health care infrastructure deficit24 and sustainability science25.The overarching goal of this paper is to examine if there are a common set of spatial determinants, i.e., characteristics of a place, whose variations across districts shaped disease incidence across the two waves of COVID-19 transmission in India. Specifically, we focus on urbanization and attendant spatial variations in social-infrastructural-environmental dimensions, as a determinant of COVID-19 incidence characteristics across districts in India. Furthermore, we examine COVID-19 incidence severity during the second wave relative to the first wave across districts (Table CIP), and their overall impacts on disease risk across waves. In addition, to explore the spatial determinants underlying rapid COVID-19 transmission, we use cumulative temporal incidence rate (CIR) as a second metric. Lastly, we evaluate severity ratios (SR) comparing these metrics between the second (more severe) wave relative to the first wave: SRCIPand SRCIR. We control for state-level effects  that might impact CIP, CIR, and SR at the district level.Our analysis complements compartmental models—such as the Susceptible, Exposed, Infected, and Recovered (SEIR) model—that focus on epidemic curves under different model parameters. These compartmental models focus primarily on temporal dynamics but can be integrated with spatially explicit demographic, socioeconomic, infrastructural, and environmental data to understand disease progression across space and time. This requires an understanding of the key determinants of disease incidence, which can complement, inform, and help validate such integrative models. This study seeks to evaluate whether there are spatial determinants whose variations across districts shape cumulative incidence patterns based on four metrics Table . We explOur results suggest that COVID-19 transmission across waves in India may be associated with a common set of spatial determinants, whose variations across districts shape disease incidence. Results show that the spatial variations of cumulative incidence proportions and rates are consistent across waves, on average  spatial autocorrelation in relative second-wave severity across districts with a Moran’s I index of 0.31, suggesting an underlying spatial structuring. The severity ratios also emphasize the role of geography in infection spread at the regional scale . The lower physical access in northeastern states and associated limited inter-district population movement in relation to other states can explain the lower COVID-19 incidence during the first wave and delayed COVID-19 spread, i.e., higher infections in the second wave.Comparing COVID-19 cumulative temporal incidence rates between the two dominant waves and across districts, we find that the second COVID-19 wave was over four times more severe than the first wave, on average. We estimate an average .85 Fig. . FurtherR0R0 and P value > 0.1).Consistent with reports of greater infection risk in the second wave than the first wave due to the Delta variant, we find that P value < 0.01) spatial autocorrelation in COVID-19 incidence proportions  correlate positively with COVID-19 incidence proportion and rate. Global Moran’s I indices indicate significant  decrease in incidence proportion (rate) during the second wave. We find this association insignificant for the first wave, at 0.05 significance level, suggesting the role of strict nationwide lockdown during the first wave in shaping these associations as well as differences between the waves. Furthermore, we consistently find insignificant associations with average commute distance and inter-district population movement . These results align with weak (ρ ≤ 0.2) or insignificant correlations  between incidence metrics and in-degree metrics  increase in incidence proportion and 4.5% (8.1%) increase in incidence rates, during the first (second) wave  and insignificant (during the first wave) association between population density and incidence proportion and rates emphasized rethinking urban form and function. Statistically significant associations between the spatial determinants and COVID-19 incidence identified in the present study underscore the importance of urbanization and associated spatial characteristics vis-à-vis public health in India. Here, our results suggest that planning a pandemic-proof urban transition in India will require a spatial understanding of its inter-urban characteristics (across human settlements) in addition to an intra-urban focus.As highlighted in a recent United Nations report28. However, our results show spatial structuring in incidence proportions and temporal incidence rates across the two waves and in relative second-wave severity. This significant spatial structuring underscores COVID-19 transmission as also a regional process in India.Consistent with the first law of geography, results show a significant spatial structuring across districts in India, i.e., COVID-19 cumulative incidence in each district is associated with the incidence in adjoining districts. This finding is consistent with previous reports concerning cumulative COVID-19 cases and mortalities across districtsIn the context of spatial determinants, we find similar COVID-19 incidence characteristics between waves and across districts. This suggests that some geographies are inherently more vulnerable to COVID-19 incidences than others, which has implications for future COVID-19 waves and other infectious diseases of similar characteristics. Prioritizing these geographies for disease control and prevention can reduce the disease burden more effectively. To this end, our findings put forth urbanization, wealth, population age, and population density as the significant spatial determinants of COVID-19 incidence. Disease control and prevention nationally, as a result, can be more effective by prioritizing districts based on these characteristics. With the impending urban transition, our results suggest that such prioritization may become even more critical in the context of future epidemics and pandemics of similar characteristics. In the contemporary context, results suggest that advancing epidemiological modeling on COVID-19 in India can benefit from incorporating geographic variations in social, economic, and infrastructural dimensions. In this direction, a shift from aspatial to spatial modeling will be necessary. However, our findings with respect to the negative association of population density need further investigation, considering proxies of super spreader events or population activity densities.29 and more transmissible viral variants30. Across districts, we find significant differences in the severity ratio between states, but only average wealth and population age explained district-level variations. Second, we find characteristic differences in the leading centers of disease spread across the two waves. These differences suggest that the spatial patterns of disease onset tend to be less predictable and emphasize the importance of continued disease surveillance across districts in India. Third, we find that % workers working from home only explain spatial variations in COVID-19 incidence proportions and rates during the second wave, when lockdowns were less stringent compared to those imposed during the first wave. In contrast, commute distance had an insignificant association once other factors were controlled for. Existing studies have reported associations between the temporal patterns of human mobility and COVID-19 outcomes33. However, our analysis did not find inter-district population movement as a key spatial determinant of incidence proportion and rate.Our results also highlight some differences in incidence characteristics across waves. First, the second wave in India was over four times more severe on average than the first wave, based on temporal incidence rate-based severity ratio. This greater second-wave severity can be attributed to less-stringent lockdown in the second wave than the first wave34, it is yet unclear how data quality varies across districts. Third, while we limited the scope of the present study to reported COVID-19 incidences owing to reporting concerns with respect to mortality36, it is unclear whether similar associations exist with COVID-related mortalities in India. Fourth, our study shows an insignificant association between social media platform-derived inter-district population movement data and COVID-19 incidences, suggesting inter-district population movement as an insignificant spatial determinant across districts in India. Still, these datasets could help us understand COVID-19 incidences vis-à-vis human mobility within districts and across time37, requiring an in-depth spatiotemporal analysis of movement data vis-à-vis COVID-19 incidences. Fifth, our study focuses on basic epidemiological metrics that capture patterns of disease incidence but contributes little towards a detailed understanding of transmission dynamics over time and under different COVID-19 variants.Our study highlights several areas that need further investigation. First, our study design is sensitive to baseline population data. In the absence of a recent census dataset, future studies can analyze the implications of using different population data sources, each generated with a unique set of assumptions, on incidence risk assessment. Second, our interpretation of the associations assumes that state-level differences control for potential bias in COVID-19 incidence reporting. Although this assumption is consistent with significant disparities in data quality across states in IndiaOverall, our study emphasizes that our understanding of the spatial determinants of COVID-19 incidence will benefit substantially from investigations into the geographic characteristics and scale of human settlements. While much attention has focused on intra-urban disease spread, our findings suggest that understanding spatial determinants across human settlements is also important in managing current and future pandemics. Databases such as the one developed and analyzed here can advance a foundational understanding of the spatial determinants of human health relevant for future pandemics.40.We use daily district-level COVID-19 reported incidence data  from the COVID19India.org platform, a citizen-science-based data collection and validation effort, which has compiled incidences, recoveries, deaths, and vaccination data from various government and media reports. We preprocess the data to ensure nationwide coverage and spatial consistency with Census data. District-level data is unavailable for a few States and Union Territories . Consequently, we disaggregate the data for these regions using a population-weighted allocation of daily incidence, informed by our observation of significant correlations between (log) cumulative incidence and (log) population size (Pearson’s correlation coefficient = 0.73) and recent scaling analyses applied to COVID-19 incidences41 compared to other candidate datasets such as, Landscan42 and GPW43. We aggregate this dataset  to estimate total population, population density, and average population age, at the district level. In addition, we use 2018 Global Human Settlement Layer Dataset  to identify urban areas (probability threshold = 0.1) and derive urban population shares (% urban) by combining with the WorldPop dataset.We use the population dataset for 2020 from WorldPop with age groupings, owing to the absence of a recent Census dataset and given higher spatial resolution (~100 m)https://censusindia.gov.in) as the data source for modes of travel to work (as a share of the working population commuting by a specific mode of transportation) and commuting patterns across districts in India. We focus on two variables for commuting patterns: the share of the working population with no commute. We calculate % workers working from home for each district as a percentage share of the number of workers with “No Travel” to the total working population. For the average commuting distance to work, we take the mid-point of the distance bins with maximum distance of 60 km). Owing to the absence of a recent dataset, we assume that the cross-sectional distributions of these characteristics remain invariant between 2011 and the temporal coverage of the COVID-19 time series.We use the 2011 Census  for each edge, i.e., district (j), of the network with V vertices (Eq. We use Facebook’s population movement dataices Eq. ).1\documhttps://dhsprogram.com/), we estimate weighted district-level average wealth index, using sampling weights.District-level income or wealth data in India has limited availability. The existing national consumer expenditure surveys (CES) measure consumption-expenditure as an income proxy, which is less reliable at the district level due to the limited sample size. This study uses a household assets-based measure of wealth or living standards from the 2015–16 demographic and health survey (DHS) with an approximated six times greater sample size than CES, with 601,509 sampled households. Available as a composite index at the household level (1) and 90% (P2) percentile range (PR): PR = P2 – P1. We define outliers as values falling below P1 – 3PR or above P2 + 3PR and linearly interpolate the outliers and other missing values within the 14-day window , start date (SDi), and end date (EDi) across i waves. We detect PDi based on the following criterion: a peak’s amplitude is greater than the third quartile of the incidence time series and any two local peaks are three months apart. Following the peak date detection, we estimate the starting and ending dates for the waves. For the first wave, we identify the start date as to when the changes in the number of confirmed cases turn to a positive integer. For the subsequent waves, we identify the start date such that its amplitude h2 meets the following condition .2\documH2 is the peak’s amplitude of the non-first wave  is equal to or less than 5% of (H2–h) searching backward from this wave’s peak. The end date of any wave was determined using the same heuristic as the starting date of a non-first wave (Eq. h1–h) is equal to or less than 5% of (H1–h) searching forward in time from this wave’s peak.ave Fig. . h is theak Fig. . The stawave Eq. . The difOur wave detection and characterization algorithm yielded two (three) COVID-19 waves for 610 (26) districts, out of 639 districts  cumulative incidence proportion, (2) temporal incidence rate, and (3) severity ratio. Cumulative incidence proportion (CIPij) is a measure of disease risk defined here as a ratio of cumulative COVID-19 incidence (Iij) during a wave (i) over total population (Popj) for a district (j)  Eq. ).4\documCIRij) is a measure of the rapidity of disease incidence amongst a population and over a fixed time period (t days) per wave (i)  Eq. ).5\documSRj) to measure the severity of the second COVID-19 wave in India compared to the first wave .6\documCIPij, CIRij, and SRj. First, we compare CIPij, and CIRij, between the two dominant waves and across districts to examine spatial consistency in disease risk and rates of disease spread. Next, we analyze the spatial patterns of COVID-19 wave start date for the two waves to identify leading centers. Finally, we analyze SRj metric across districts and estimate the average second-wave severity (relative to the first wave) and associated 95% confidence intervals based on bootstrap estimation with 100,000 replications46. We noted a significant correlation between severity ratio metrics calculated with incidence proportions and incidence rates with a Pearson’s correlation coefficient of 0.91. Consequently, we use incidence rates to examine second-wave severity here and in our subsequent correlation and regression analysis.As a preliminary analysis, we analyze spatial variations in 0 based on exponential growth rate48, with a gamma distribution for the serial interval, assuming a mean and standard deviation of 4.4 and 3 days, respectively, for the generation time49. Using the same serial interval assumptions, we estimate Rt using a maximum-likelihood estimation50, but based on incidence curves based on start and peak dates of the wave, yielding an average estimate per wave 0 and Rt, computed with a functional form consistent with Eq. 0 and Rt estimations, and our focus on COVID-19 incidences in India, we restrict our detailed analysis to CIPij, CIRij, and SRj , i.e., spatial lag models (SLM) and spatial error models (SEM). These spatial models account for potential bias and inconsistencies in OLS estimates due to spatial autocorrelations52  metric between OLS, SLM, and SEM. Finally, we report Bayesian Markov Chain Monte Carlo estimates51 for the selected spatial models, as a robustness check.Wy spatially lagged outcome variable for spatial weights matrix W, ε is the error term, and ρ and β are the model parameters.ε is the spatial autocorrelated error terms, υ are independently and identically distributed errors, and λ and β are the model parameters.We use correlation and regression analysis to characterize general features of COVID-19 incidence across districts, spanning the entire rural-to-urban gradient. Specifically, we focus on the associations between COVID-19 incidence metrics and related key spatial characteristics, i.e., urbanization, population density, wealth, and mobility (Supplementary Table s52 Eqs.  and 8)))spatialrIn consultation with the office of the Institutional Review Board (IRB), Human Research Protection Program at Princeton University, we determined that an ethical approval was not required for our research based on the human subjects research definition and scope.Supplementary Information


During any critical health care situation as COVID-19 pandemic, it is expected that the medical staff will be under a high level of stress. However, nurses specifically are under both physical and psychological pressure during this pandemic, with a risk of mental health problems, such as anxiety and depression. Accordingly, nurses exposed to patients with COVID-19 infection are expected to suffer from a high level of depressive symptoms. This cross-sectional study was applied on 456 nurses with age ranges from 18 to 60. They were divided into two groups: group 1 were composed of 228 nurses who were directly exposed to suspected or confirmed cases of COVID-19 patients, while group 2 were composed of 228 who were less exposed to suspected or confirmed cases of COVID-19 patients for comparison. Data were collected by personal interviews with nurses using Patient Health Questionnaire 9 (PHQ 9) scale for assessment of presence of depressive symptoms and its severity. We aimed to assess the both the prevalence and the predictors of depressive symptoms among nurses exposed to COVID-19 patients.We found a statistically significant higher percentage of depressive symptoms among nurses directly exposed to COVID-19 patients (61.8%) versus the less exposed group (18%). There was a statistically and significantly higher specific COVID-19 associated stressors score (SCAS) among nurses directly exposed to suspected or confirmed cases of COVID-19 patients compared to those who were less exposed; likewise, the PHQ-9 score was a statistically and significantly higher among directly exposed group compared to less-exposed group. Moderate and severe depressive symptoms were present in 23.2% and 22.4%, respectively, within the group of nurses with direct exposure; meanwhile, the less-exposed group showed 7.5% and 3.9%, respectively, with statistically higher significant difference. On doing a linear regression analysis, all the following predictors were significantly independently associated with higher PHQ-9 scores (with higher depressive symptoms severity) among nurses exposed to COVID-19: physical isolation , exposure to a new COVID-19 patient, developing COVID-19-like symptoms, displaying COVID-19-like symptoms by colleagues, knowing that COVID mortality rate exceeds influenza, possible separation from family, concern about family members, fears about infection for patients, family, and friends.COVID-19 pandemic has serious effects on the psychological well-being of nurses exposed to COVID-19 patients. There was an increased rate of depressive symptoms among them during the pandemic with its subsequent burden. Therefore, nurses exposed to COVID-19 patients are in a high need of care and support during the pandemic. Specialized workplace issues such as excessive stress, overworked clinical job, and occupational stress pose a severe threat to nurses' mental health. Furthermore, nurses frequently encounter a variety of life experiences, including illness, trauma, and even death, which have additional physical and psychological impacts on them. In addition to having an immediate impact on their own health, nurses' psychological well-being has also an impact on the quality of medical care they deliver to the patients in a hospital setting .For example, depressive symptoms can reduce the cognitive performance that can affect the work performance, resulting in a decreased work efficiency which may ultimately have an impact on the standard of medical care . CoronavAl Amer et al. and Arafa et al., found that depression, anxiety, and stress are comorbidities that are highly prevalent among both nurses and those who contacted COVID-19 patients , 12. AnoAccordingly, the present study aimed to estimate the prevalence and correlates of depressive symptoms among nurses working at Zagazig University hospital in Egypt during COVID-19 pandemic. Knowing that nurses are a cornerstone in the health care system, it is an important issue to care for their psychological well-being to raise the quality of medical care.A consecutive sample of 456 nurses were divided into two groups: group 1 (exposed group) was composed of 228 nurses who were directly exposed to suspected or confirmed cases of COVID-19 patients, and group 2 (less-exposed group) was composed of 228 who were less exposed to suspected or confirmed cases of COVID-19 patients. The sample size was calculated using open epi software. This cross-sectional study that was conducted from June 2021 to December 2021. The exposed group was recruited from the Isolation Department from inpatient (chest) wards and outpatient clinics ; participants in this group were directly exposed to suspected or confirmed cases of COVID-19 patients, in Zagazig University Hospitals, Zagazig, Egypt. While the less-exposed group was recruited from Intensive Care Units, inpatient wards  and outpatient clinics ; participants in this group were less exposed to suspected or confirmed cases of COVID-19 patients, in Zagazig University Hospitals, Zagazig, Egypt. The data were collected by the study team. Eligibility of subjects for participation in the study was determined according to the following specified inclusion and exclusion criteria: the inclusion criteria: nurses who are currently working at Zagazig University Hospitals in a full-time work for at least 1 year of either sex, and age ranges from 18 to 60. The exclusion criteria: past history of any psychiatric disorder or substance abuse, or presence of any severe medical illness that could affect psychological condition (as cancer or autoimmune disorders). The exclusion was done by history taking and revision of the medical files of the nurses.Sociodemographic form that is composed of questions related to personal characteristics of the nurses including age, gender, marital status, education, residence, years of work, and smoking status.PHQ 9 :The Patient Health Questionnaire (PHQ 9) was used to assess depressive symptoms . The PHQSpecific COVID-19 Associated Stressors Score (SCAS) questionnaire:It is derived and modified from the US National Center for Posttraumatic Stress Disorder 2020 and MERS-CoV staff questionnaire . It consT test and Mann Whitney test were used as a test of significance for the differences among groups. Multiple regression analysis was used to assess the predictors of depression. We tested the normality of the data regarding the sample size using Kolmogorov–Smirnov (K–S) test with a p value > 0.05 that proves the normality. A p value < 0.05 was used to indicate statistical significance.The data analysis and sample size calculations (with 80% power) were performed using the statistical package for social sciences (SPSS version 20). The categorical data were presented in the form of number and percentage with Chi square was used as a test of significance. Continuous data were expressed as mean ± SD (Standard deviation) and Median with the Interquartile range (IQR). The Independent We found a statistically significant higher percentage of depression among nurses directly exposed to COVID-19 patients (61.8%) versus those not directly exposed (18%).Table Table Table β = 3.34, p = 0.02), every time being exposed to a new COVID-19 patient , developing COVID-19-like symptoms , colleagues displaying COVID-19-like symptoms , a relatively higher mortality rate compared to influenza , possible separation from family, and concern about family members , and fears about infection for patients, family and friends   among nurses exposed to COVID-19: physical isolation   versus less-exposed group (18%). In addition, mild, moderate, and severe depression among directly exposed group were 16.2%, 23.2%, 22.4% versus 6.6%, 7.5%, 3.9% in less-exposed group, respectively, with a statistically significant higher difference. Few studies have investigated depression among exposed and less exposed nurses, and most of them were consistent with our results about the increased prevalence of depression among the directly exposed group; nevertheless, there may be varying percentages. This could be explained by the severe stress of the COVID-19 pandemic suffered by the nurses. In line with our results, Cai et al. showed a statistically significant higher depression level in directly exposed nurses versus less exposed nurses during the outbreak period; moderate to severe depression represented 21.8% in directly exposed nurses versus 9.1% in less exposed . Further% versus All these predictors for depressive symptoms in directly exposed nurses to patients with COVID 19 during the pandemic should be taken into account when developing a supportive program to decrease the burden of the depressive symptoms among those nurses.Our study revealed a high prevalence of depression among nurses exposed to suspected or confirmed cases of COVID-19 patients. All the following predictors were significantly independently associated with increased risk of depression: physical isolation , exposure to a new COVID-19 patient, developing COVID-19-like symptoms, displaying COVID-19-like symptoms by colleagues, knowing the higher mortality rate of COVID compared to influenza, possible separation from, concern about family members and fears about infection for patients, family, and friends. Therefore, implementation of targeted interventions aiming to enhance the psychological support of nurses exposed to COVID-19 patients with reduction of their stressors are mandatory; this will result in not only on the improvement of depression among those nurses but also a better proper care for the patients.Our study has certain limitations, because exposures and outcomes were assessed simultaneously in a cross-sectional study; longitudinal investigations are advised, because there is no evidence of a causal relationship between exposure and outcome. However, we have many strengths in our study such as focusing on nurses exposed to patients with COVID-19 infection who are one of the highly important sectors among the health system in Egypt, and they were already under severe stress. Our study was performed by direct interview, not online questionnaires, which guarantees correct understanding of the patients to the questions and good interpretation of the results.


Endometrial cancer (EC) is the most common gynaecological malignancy in the developed world, and concerningly incidence is rising, particularly in younger people. Therefore, there is increased interest in novel diagnostic and prognostic biomarkers. Extracellular vesicles (EVs) are membrane-bound particles present in bodily fluids that have the potential to facilitate non-invasive, early diagnosis of EC and could aid with monitoring of recurrence and treatment response. EV cargo provides molecular insight into the tumor, with the lipid bilayer providing stability for RNA species usually prone to degradation. miRNAs have recently become a focus for EV biomarker research due to their ability to regulate cancer related pathways and influence cancer development and progression. This review evaluates the current literature on EV miRNA biomarkers with a focus on EC, and discusses the challenges facing this research. This review finally highlights areas of focus for EV miRNA biomarker research going forward, such as standardization of normalization approaches, sample storage and processing, extensive reporting of methodologies and moving away from single miRNA biomarkers.  Endometrial cancer (EC) is the most common gynaecological cancer worldwide, and is one of the few cancers with increasing incidence ,2. PatieEC diagnosis is normally through pipelle biopsy, an invasive and painful procedure that is often repeated due to poor tissue collection Extracellular vesicles (EVs) are membrane-bound particles that cannot replicate and are actively released from most cell types. EV is a general term used to describe a range of particles which differ by biogenesis This review discusses the current literature of EV miRNA biomarkers in EC diagnosis, highlights the current limitations in general EV miRNA research and provides recommendations for research in this overall field going forward.miRNAs have the potential to act as diagnostic and prognostic biomarkers of EC, as demonstrated in a multitude of studies . EVs facMultiple studies have identified EV miRNAs that are differentially abundant in EVs between EC patients and healthy controls. 3), suggesting miR-15a-5p may be a marker of more aggressive cancers. As such, miR-15a-5p may have potential as a prognostic marker but has yet to be studied more extensively with patient outcome data.Two studies stand out for evaluating the abundance of a large number of miRNAs within EC EVs. Zhou et al. Zhou et al. utilized serum and precipitation-based EV isolation while Roman-Canal et al. investigated peritoneal lavage EVs isolated using differential ultracentrifugation. Despite these differences, there were eight miRNAs that showed consistent directional change with miR-21-5p increased and miR-101-5p, miR-130a-3p, miR-139, miR-200b-3p, miR-219a-5p, miR-222-3p and miR-885 reduced in EC EVs. Four miRNAs miR-126-5p, miR-194-5p, miR-451a, miR-1180-3p had opposite expression patterns observed, showing increase in one study and decrease in the other. miR-451a levels are significantly altered by the degree of haemolysis of blood samples n = 10 in their pilot study is a strong limitation. Further contradictory results include Zheng et al. Of the differently abundant miRNAs present in both of these studies discussed prior, levels of miR-21-5p and miR-200b-3p had been previously investigated in urine EVs with no difference found between EC and controls p-value presented for the fold change data. Resultingly, the lack of statistical comparisons between expression in cases and controls and the unavailability of the data limits the utility of the study. Of the other miRNAs most abundant in EC EVs in the Srivastava et al. study, increased abundance was also found in one other study for miR-21-5p, while miR-100-5p, miR-26a-5p, miR-26b-5p and miR-125a were found to be reduced in other studies [Using a PCR microarray, Srivastava et al.  studies ,26. TherThe absence of standardized methodologies and heterogeneity in study design  Another important source of study heterogeneity is the EC cohort, particularly the stage of the cancers. EC cohorts in studies by Zhou et al. (1)Determine standard normalization approachesVariations in study design, normalization strategies and EV isolation methodologies have contributed to the conflicting results found between EC EV miRNA biomarker studies. These factors, along with other challenges associated with EV-based biomarkers, have hindered the validation and clinical implementation of candidate EV miRNA biomarkers for all disease states, including EC. As such, we subsequently highlight areas that require attention to improve reproducibility of future research and subsequent advancement into clinical practice.(2)Extensive reporting of methodologyA factor for consideration is translating pre-clinical findings into clinically implementable tests. PCR-based assays remain the test with the greatest clinical applicability, with RNA sequencing and microarrays both costly and time-consuming. For the clinical implementation of a PCR test for EV miRNA biomarkers, the absence of standard endogenous reference sequences for normalization is an issue, as previously discussed. The impact of isolation methodology and EV source on the population of EVs isolated means stable abundance of candidate reference sequences needs to be validated for each isolation methodology and biofluid used. Thus, alternative approaches discussed earlier such as exogenous spike-ins may be more suitable for EV biomarkers. Normalization strategies for any assay, PCR-based or otherwise, should also be standardized as the approach taken can affect the results and thus impact on reproducibility.The Minimal Information for Studies of Extracellular Vesicles (MISEV) guidelines ,65 have (3)Standardize sample processing and storageFurther recommendations of a similar nature to the MISEV guidelines are needed, providing processing and reporting requirements for the various biofluids commonly used for EV isolation, and for downstream analyses such as miRNA analysis. This would benefit scientists new to EVs, while also aiding the translatability of all EV research. The International Society of Extracellular Vesicles (ISEV) have begun this work with papers providing recommendations for working with urine There are an immense number of pre-analytical factors that are recommended to be reported on, as outlined in a position paper published following the 2012 ISEV workshop (4)Move away from single miRNAs as diagnostic biomarkersThe extent of standardization must be kept reasonable without excessive collection and processing specifications, so the implementation of these requirements is feasible. However, a certain level of standardization will be required to ensure the reliability of the EV biomarker. Many pre-analytical factors which affect EV yield can be easily standardized such as storage temperature, anticoagulants, measurement of haemolysis and centrifugation speeds ,71,72. MA single EV miRNA is unlikely to have utility as a sole diagnostic biomarker of EC. A single EV miRNA is often dysregulated in several cancer types, for example miR-21-5p is dysregulated in liver EVs provide an exciting opportunity to progress miRNA biomarker research, with their stability and abundance making EVs ideal for use clinically as diagnostic biomarkers. Currently, there are only a handful of studies focusing on EV-based miRNA biomarkers in EC. Results thus far have been inconsistent, with limited reproducibility and translatability due to disparate study design and incomplete methodological reporting. Well designed and large-scale future studies with robust methodologies are required to identify and validate candidate biomarkers. An important focus for the EV field is improving reproducibility through thorough methodological reporting and standardized approaches for expression normalization and sample processing. With further research into the areas outlined, EV miRNA biomarkers have great potential to facilitate simpler, accurate and more accessible EC clinical diagnostics.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Obtaining crop above-ground biomass (AGB) information quickly and accurately is beneficial to farmland production management and the optimization of planting patterns. Many studies have confirmed that, due to canopy spectral saturation, AGB is underestimated in the multi-growth period of crops when using only optical vegetation indices. To solve this problem, this study obtains textures and crop height directly from ultrahigh-ground-resolution (GDS) red-green-blue (RGB) images to estimate the potato AGB in three key growth periods. Textures include a grayscale co-occurrence matrix texture (GLCM) and a Gabor wavelet texture. GLCM-based textures were extracted from seven-GDS  RGB images. Gabor-based textures were obtained from magnitude images on five scales . Potato crop height was extracted based on the generated crop height model. Finally, to estimate potato AGB, we used (i) GLCM-based textures from different GDS and their combinations, (ii) Gabor-based textures from different scales and their combinations, (iii) all GLCM-based textures combined with crop height, (iv) all Gabor-based textures combined with crop height, and (v) two types of textures combined with crop height by least-squares support vector machine (LSSVM), extreme learning machine, and partial least squares regression techniques. The results show that (i) potato crop height and AGB first increase and then decrease over the growth period; (ii) GDS and scales mainly affect the correlation between GLCM- and Gabor-based textures and AGB; (iii) to estimate AGB, GLCM-based textures of GDS1 and GDS30 work best when the GDS is between 1 and 5 cm and 10 and 60 cm, respectively ; (iv) the AGB estimation based on a single-type texture is not as good as estimates based on multi-resolution GLCM-based and multiscale Gabor-based textures (with the latter being the best); (v) different forms of textures combined with crop height using the LSSVM technique improved by 22.97, 14.63, 9.74, and 8.18%  compared with using only all GLCM-based textures, all Gabor-based textures, the former combined with crop height, and the latter combined with crop height, respectively. Therefore, different forms of texture features obtained from RGB images acquired from unmanned aerial vehicles and combined with crop height improve the accuracy of potato AGB estimates under high coverage. The above-ground biomass (AGB) of crops is the total dry organic mass of the above-ground vegetative organs per unit area at a particular time . AGB is Remote-sensing technology is currently the most effective non-contact method for estimating crop AGB over large scales . To estiVegetation indices (VIs) such as the normalized difference vegetation index, the renormalized difference vegetation index, and the ratio vegetation index obtained from broadband multispectral data from the visible to the near-infrared have been used with significant success to estimate crops AGB . At presHowever, numerous studies have challenged the wisdom of using VIs to estimate the crop AGB in the multi-growth periods. The main arguments brought to bear are that (i) VIs saturate easily under high crop coverage, and (ii) VIs lose their sensitivity to AGB in the multi-growth period, making it challenging to use VIs to estimate crop AGB in the multi-growth period . At presGiven the use of long-wavelength electromagnetic radiation, synthetic aperture radar remote-sensing techniques can penetrate the crop canopy and are not affected by weather conditions, which means that they overcome the problem of premature saturation of AGB estimates by optical remote-sensing VIs and are thus highly suitable for long-term monitoring of AGB for areas with high crop coverage . PreviouLiDAR remote-sensing techniques actively transmit electromagnetic wave pulses with a specific penetration ability to interact with ground objects. By statistically calculating the height or quantity of LiDAR echoes, different variables characterizing crop canopy structure (such as volume and coverage) can be obtained, which is very useful for estimating crop AGB . For exaNarrowband hyperspectral techniques have the capacity to continuously acquire crop canopy spectral reflectance data with high spectral resolution, which means that mining the hidden information in the spectrum is helpful for AGB estimation. Therefore, crop AGB estimation can be enhanced by using spectral differential analysis , band-deAlthough the above-mentioned remote-sensing techniques accurately estimate AGB and allow for real-time monitoring of crop growth, obtaining the data incurs high cost, and data processing is complex, which prevents this approach from gaining wide acceptance in the private sector . In contPrevious reports have confirmed that the CHM generated from UAV-based RGB images can be used for AGB estimation . HoweverUAV-based ultrahigh-GDS RGB images are helpful not only for estimating AGB  but alsoA literature review shows that most studies only extract single-scale GLCM-based texture features from specific GDS images to estimate crop AGB. However, the Gabor-based transformation features provide information that can be used to describe image textures and have been fully applied in the field of image processing, despite receiving little attention in crop phenotyping research . The croFurthermore, the texture features extracted from ultrahigh-GDS UAV-based RGB images can represent the high-frequency information of crop canopy photos, which provides new information  about the crop canopy. If this information can be combined with the vertical height of the crop canopy, it could be used to accurately estimate AGB. On the one hand, different texture techniques offer different potentials for extracting high-frequency information. On the other hand, the high-frequency information contained in images of different GDS will vary. After a careful literature review, no study has been found that proposes using different multiscale texture techniques to extract high-frequency information for estimating potato AGB.Therefore, the present study investigates the performance of GLCM- and Gabor-based textures and various textures combined with crop canopy height to estimate potato AGB. GLCM-based textures were extracted from seven-GDS  RGB images. Gabor-based textures were obtained from magnitude images on five scales . We estimate the potato AGB by applying a least-squares support vector machine (LSSVM), an ELM, and PLSR using, respectively, (i) GLCM-based textures from different GDS RGB images and their combinations, (ii) Gabor-based textures from different scales and their combinations, and (iii) GLCM-based textures, Gabor-based textures, and their combinations integrated with crop height.The experiment was conducted at the National Precision Agriculture Research Demonstration Base , Changping District, Beijing, China. Changping District has a typical warm temperate semi-humid continental monsoon climate, where the main crops are summer maize and winter wheat.2 and with a row spacing of 0.6 m. Eighteen plots filled the density test area with three gradients  and six repetitions. Twenty-four plots occupied the nitrogen test area with four gradients  and six repetitions. Six plots occupied the potassium fertilizer test area with two gradients  and three repetitions. The specific test plan is shown in To increase the spatial growth difference of potato crops in the field, we use Zhongshu 5 (Z5) and Zhongshu 3 (Z3) early maturing potato varieties planted with different planting densities, nitrogen, and potassium fertilizers treatments. Forty-eight plots were planted, with each plot covering 32.5 mGround crop height and AGB data were collected on 28 May 2019, 10 June 2019, and 20 June 2019, which corresponded to periods of potato tuber formation (P1), tuber growth (P2), and starch storage (P3), respectively. During each growth period, four plants representative of the overall growth level were selected from each plot. The vertical height of each plant was measured with a ruler, and the average value was taken as the measured crop height (in centimeters) for each plot.2).To obtain the AGB data, three plants representative of the overall growth level were artificially selected from each plot. After artificial field sampling, put it into a white sealed bag and quickly took it back to the laboratory. The samples were washed with running water indoors. After the sample was naturally dried, the stems and leaves were cut into small pieces by using scissors. The separated samples were killed at 105°C, and dried at 80°C in a large bake oven until reaching constant mass. For each growth period, the plant density and dry mass of the stems and leaves of each plant sample as measured by a high-precision balance were used to calculate the potato AGB of each plot  and the three critical growth periods of potatoes. The UAV system was equipped with a three-channel CMOS sensor, with 20 million effective pixels and a maximum pixel value of 4,000 × 3,000. To ensure the generation of RGB images with high GDS, the UAV’s flying height was manually set to 20 m, and the heading and side overlap were both 80%. By using its position and orientation system during flight, the UAV recorded the three-dimensional position of the sensor in real-time. The flights were conducted in clear, calm weather to reduce the variation of crop canopy reflection intensity caused by uneven illumination. The take-off position and flight path were essentially the same for each flight to accurately match the digital surface model (DSM) obtained in different growth periods.Before extracting crop height and multiscale texture features from UAV RGB images, we used Agisoft PhotoScan Professional software to splice together digital images from the various periods. Next, the RGB images were topographically corrected based on the measured three-dimensional coordinates of G1–G11, and the correction error for each period was less than 2 cm. Finally, the DSM and digital orthophoto map of the test location were derived for each period. The specific image-processing flow followed that of Crop height represents the growth of groups and symbolizes the vertical structure of the crop canopy, which is closely related to AGB. Therefore, the accurate acquisition of crop-height information is important for monitoring crop growth and managing farmland production . First, The GLCM is a matrix function involving pixel distance and angle. It reflects spatial variations in the gray distribution of the image by calculating the correlation between the gray levels of two points separated by a certain distance and in a specific direction in the image . The flyThe Gabor transform, also known as the windowed Fourier transform (Gaussian function as a window function), is a transformation from the time to the frequency domain and offers good characteristics for extracting local space and frequency domain information from the target . The GabTo verify the hypothesis proposed herein, repeats 2 and 3 data (32 groups) collected in each potato growth period in 2019 served as the calibration set to estimate AGB, and repeat 1 data (16 groups) served as the validation set to verify the reliability and stability of the model. p > 0.01), which means that crop height may contribute to the potato AGB estimates over multi-growth periods.p < 0.05), and that, when considering the multiple growth periods of crops, GLCM-based textures correlate positively  or negatively  with potato AGB (p < 0.01), which indicates that the use of GLCM-based textures may also improve the accuracy of AGB estimates of potatoes in multiple growth periods.Taking the RGB images of GDS1 as an example, p < 0.01). The GLCM-based textures of GDS5 and GDS10 correlate more weakly with AGB and the correlation coefficient has the opposite sign. The GLCM-based textures of GDS20 and GDS30; GDS40, GDS50, and GDS60 approximately correlate with AGB. These results show that the image GDS affects the relationship between GLCM-based textures and AGB. Therefore, we must evaluate the accuracy of GLCM-based textures at different resolutions to estimate potato AGB.The results shown in p < 0.01). Compared with the results in 2, the Gabor-based textures remain sensitive to AGB (p < 0.01), which indicates that the multiscale Gabor-based textures are more promising for AGB estimation than multi-resolution GLCM-based textures.As shown in R2 = 0.68–0.71, RMSE = 261–269 kg/hm2, MAE = 214–218 kg/hm2, NRMSE = 21.94–22.65%) than others with single ground resolution textures. In addition, for GDS1 to GDS5, the GLCM-based textures of GDS1 provide the best AGB estimates . Between GDS10 and GDS60, the GLCM-based textures of GDS30 provide the best AGB estimates .The multiscale Gabor-based textures used in this study to estimate potato AGB are listed in R2 = 0.70–0.74, RMSE = 244–271 kg/hm2, MAE = 207–232 kg/hm2, NRMSE = 20.55–22.80%). (ii) For different scales, the AGB estimation results gradually became less accurate with increasing scale. (iii) Finally, when the AGB exceeds 1,000 kg/hm2, the multiscale Gabor-based textures The results in R2 = 0.73–0.78, RMSE = 236–255 kg/hm2, MAE = 187–199 kg/hm2, NRMSE = 19.90–20.90%); (ii) combining textures of different resolutions and scales separately with the crop height enhances the accuracy of AGB estimates (and more so for the latter); (iii) different textures combined with crop height produce the most accurate estimates of potato AGB for the same regression technique . More importantly, different textures combined with crop height are not underestimated when AGB exceeds 1,000 kg/hm2. The results in To determine whether the fusion of high-frequency information and vertical crop canopy structure information is helpful for AGB estimation, we estimate potato AGB by using (i) GLCM-based textures of different resolutions and crop height; (ii) Gabor-based textures of different scales and crop height; and (iii) GLCM- and Gabor-based textures of all forms and crop height. The results in The use of different varieties, planting density, nitrogen fertilizer, and potassium fertilizer leads to significant differences in the growth of potato crops. At present, to obtain AGB information, crop-growth monitoring is done mainly by optical remote sensing. However, this approach has limitations  because Fluctuations in crop height can reflect crop health and nutritional status. At the same time, reasonable plant height is also the basis for stable and high-yield crops. Therefore, accurate acquisition of plant height information is vital for crop-growth monitoring and farmland production management . For exap < 0.01) in single or multiple growth periods, which is consistent with potato crop growth and indicates that potato crop height could be used for AGB estimation. The quantitative analysis results in R2 increases, RMSE and MAE decrease) in different models improves the accuracy of AGB estimation and reduces the underestimation of AGB, which also confirms the hypothesis that crop height may support AGB estimation in multiple growth periods of potatoes. The visualization in The results show that the correlation between GLCM-based textures and AGB remains basically unaffected by the direction and window , which mWe classified GLCM-based textures obtained with different ground resolutions into five categories: (i) GDS1, (ii) GDS5, (iii) GDS10, (iv) GDS20, GDS30, and (v) GDS40, GDS50, GDS60. As shown in Consider the Dis of the B-band of plot s04 as an example. Under the same growth period, the Dis-texture gradually increases and then decreases from GDS1- to GDS30-cm. This shows that, when the GDS exceeds half the line spacing, the information contained in the ROI changes from high frequency  to low frequency ; that is, the texture changes from nonuniform  maintainThe Gabor filter produces an effect very similar to the human visual response and is not sensitive to local illumination, making it highly suitable for extracting fine texture features. Following the work of others , we set Consider as an example the B-band of the n01 plot for the P1-P3 growth period. The results show that the amplitude images generated by the convolution of Gabor wavelet kernels of different scales with the RGB images have different spatial characteristics . As ment2, the Gabor-based textures remain sensitive to AGB, which means that using multiscale Gabor-based textures to estimate AGB over multiple potato growth periods may produce more accurate result than GLCM-based textures, which is similar to the above hypothesis. The results in The above results show that extracting multiscale Gabor-based textures more finely describes variations in potato canopy structure. The results show that the sign of the correlation coefficients of the Ent-, Cor-, Sec-texture (GLCM-based) and AGB change , which indicates that estimates of potato AGB made by using Gabor-based textures may differ from estimates of potato AGB made by using CLCM-based textures. Furthermore, the results shown in RGB images of the potato canopy typically consist of soil, stems, leaves, weeds, and shade , and theThe use of GLCM-based textures of GDS1 produces the most accurate AGB estimates when the ground resolution ranges from 1 to 5 cm. While the ground resolution ranges from 10 to 60 cm, the use of GLCM-based textures of GDS30 produces the most accurate AGB estimate . This di2 are less underestimated, which is consistent with the results of Similarly, we estimate potato AGB using single-scale Gabor-based textures . The res2 AGB sample as the splitting point, we counted the NRMSE of the different models (2) samples as benchmarks, the accuracy of the AGB estimation model improves (i) by 22.97% (NRMSE) when using different types of textures combined with crop height  with the LSSVM technique as opposed to using only GLCM-all textures, (ii) by 14.63% compared with using only Gabor-all textures, (iii) by 9.74% compared with GLCM-all textures combined with crop height , and (iv) by 8.18% compared with Gabor-all textures combined with crop height . This study used one-year data to estimate potato AGB based on crop morphological characteristics accurately, but it lacked validation analysis of multi-year experiments. In future research, we will collect potato data from different places and years to evaluate the model’s performance, enhancing the reliability of the research results.We estimated AGB using the LSSVM, ELM, and PLSR techniques by combining all GLCM-based textures, all Gabor-based textures, and the combined textures with crop height . The rest models . Using hThis work evaluates the performance of GLCM-based textures of differing resolutions and Gabor-based textures of differing scales and their combination with crop height for estimating potato AGB. The results lead to the following conclusions:The correlation between GLCM-based textures and AGB is unaffected by the direction and window, but it is more affected by the ground resolution. The correlation between Gabor-based textures and potato AGB is significantly more affected by scale than by direction.The GLCM-based textures of GDS1and GDS30 produce the most accurate AGB estimates when the ground resolution ranges from 1 to 5 cm and 10 to 60 cm, respectively. However, the accuracy of potato AGB estimates based on Gabor-based textures gradually deteriorates upon increasing the convolution kernel scale.Both multi-resolution GLCM-based textures and multiscale Gabor-based textures are better than single-type textures for estimating AGB, and multiscale Gabor-based textures are the best for estimating AGB.Gabor-based textures combined with crop height produce more accurate AGB estimates than GLCM-based textures. Optimistically, combining two different types of textures with crop height solves the problem whereby potato samples with high AGB are underestimated, which is especially important for monitoring crop growth and advancing the development of precision agriculture.The original contributions presented in the study are included in the article/HF, ZL, and GY designed the experiments. YL and HF collected the AGB, plant height and UAV digital images, analyzed the data, and wrote the manuscript. JY, XJ, and ZL provided comments and suggestions for the manuscript and checked the writing. All authors contributed to the article and approved the submitted version.This study was supported by the Key scientific and technological projects of Heilongjiang province (2021ZXJ05A05), the Key Field Research and Development Program of Guangdong Province (2019B020216001) and the National Natural Science Foundation of China (41601346).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Clinical factors, morphology of bridge by quantitative coronary analysis and ten-year long mortality data were collected. The LAD-MBneg group was associated with younger age and decreased incidence of diabetes mellitus, as well as with increased minimal diameter to reference diameter ratio (LAD-MBneg 54.5 (13.1)% vs. LAD-MBpos 46.5 (16.4)%, p = 0.016), while there was a tendency towards longer lesions and higher vessel diameter values compared to the LAD-MBpos group. The LAD-MBpos group was associated with increased mortality compared to the LAD-MBneg group. The analysis of our data showed that morphological parameters of LAD bridge did not influence long-term mortality, either in the overall population or in the LAD-MBneg patients. Morphological parameters of LAD bridge did not influence long-term mortality outcomes; therefore, it suggests that anatomical differences might not predict long-term outcomes and should not influence therapy.Although myocardial bridging (MB) has been intensively investigated using different methods, the effect of bridge morphology on long-term outcome is still doubtful. We aimed at describing the anatomical differences in coronary angiography between symptomatic and non-symptomatic LAD myocardial bridges and to investigate the influence of clinical and morphological factors on long-term mortality. In our retrospective, long-term, single center study we found relevant MB on the left anterior descendent (LAD) coronary artery in 146 cases during a two-year period, when 11,385 patients underwent coronary angiography due to angina pectoris. Patients were divided into two groups: those with myocardial bridge only (LAD-MB Myocardial bridging (MB) is a relatively common congenital coronary anomaly, in which a segment of coronary artery takes an intramuscular course that causes vessel compression during systole . The preWhile myocardial bridging is generally considered as a benign phenomenon and related serious events are uncommon, many cases have been reported where MB was considered responsible for a wide variety of cardiovascular symptoms and complications, including angina, acute coronary syndrome, left ventricular dysfunction, arrhythmias and even sudden cardiac death ,7,8,9,10Despite these data, the prognostic factors and the clinical evaluation of MB has remained limited. Moreover, the relationship between anatomy and potentially dangerous MBs has not yet been clarified. In this retrospective, high-number population-based clinical study we aimed at describing the anatomical differences between symptomatic and non-symptomatic LAD myocardial bridges found on coronary angiography and to investigate the influence of clinical and bridge morphological factors on long-term mortality.A total of 11,385 diagnostic coronary angiographies were performed (indications according to the current European guidelines) in our cardiology center, at the Heart and Vascular Center of the Semmelweis University of Budapest between 25 March 2009 and 12 March 2011. Ethical approval (of enrolled patients) was obtained from the Central Ethics Committee of Hungary, with all of the participants completing informed consent forms, which were in conformity with the WMA Declaration of Helsinki-Ethical Principles for Medical Research Involving Human Subjects.n = 57), see n = 146) were divided into two groups according to accompanying coronary artery disease. Seventy-eight patients were referred for angiography because of typical angina pectoris, and, except for LAD MB, no other underlying, epicardial coronary disease was found (LAD-MBneg group). Sixty-eight patients were also referred for angiography because of angina pectoris, but in this group significant coronary artery disease was also revealed in addition to the LAD MB (LAD-MBpos group). In the LAD-MBpos group coronary plaque was considered significant if lumen narrowing (>50% in diameter) of significant epicardial coronary arteries (>1.5 mm diameter) was observed, and we provided therapy according to current guidelines. Ad-hoc percutaneous coronary intervention was performed in 23 (34%) of these patients, with indications according to current guidelines.We enrolled a total of 203 patients (1.78%) with a clear presence of MBs . To obtain a more consistent study group, we excluded patients with MBs affecting arteries other than the LAD and where any other obvious cause of angina pectoris was presented . Measurements were obtained with digital calipers. All of the MBs were measured in lateral view  in end-systole and end-diastole by an expert interventionist . Three mIn addition to these data, we also recorded height, weight, sex of patients and presence of main cardiovascular risk factors, such as diabetes mellitus, hypertension, and dyslipidemia.We collected information about the patients’ mortality by phone visit and we also checked data on survival status  on 1 April 2020.GraphPad Prism  and SPSS  were used for the statistical analysis.neg and LAD-MBpos groups directly. In the case of normal distribution of continuous data, the unpaired t-test was used. In the case of non-normal distribution, the Mann–Whitney U-test was utilized. To compare parameters with binomial outcomes , the Chi-square test was performed.We compared the LAD-MBMortality rate was summarized by constructing Kaplan–Meier curves, and the distributions of the groups were compared by a log-rank test. For this analysis, median values were used to dichotomize continuous variables .p value < 0.15 by single variable analysis were entered into the multiple variable Cox regression analysis with the parameters of myocardial bridge . Hazard ratio was given with 95% CI interval and p value was provided for the significance of different parameters on clinical outcomes.Single variable Cox regression analysis was used for the search of predictors of death from the data of the patients . All variables associated with a Continuous data are expressed as means with standard deviation.p value < 0.05 was considered significant for all tests.A pos and LAD-MBneg groups regarding sex. The LAD-MBpos patients were characterized by older age and increased presence of type 2 diabetes mellitus, while there was no statistical difference between the presence of hypertension or hyperlipidemia and BMI.Demographic and clinical data and LAD-MB morphological data are summarized in neg group was characterized with more severe morphological features. The shortening of MB  significantly decreased, while the length and reference diameter showed a strong tendency towards the decreased value in the LAD-MBneg group compared to the LAD-MBpos group. The minimal diameter showed no differences between our groups.MB morphological data are also presented in neg group; thus, the all-cause mortality rate in this population with isolated LAD-MB was 10.3% for this follow-up period. We searched for differences between the two prespecified subgroups: the Kaplan–Meier analysis revealed significant disparity in mortality between the LAD-MBneg and LAD-MBpos groups (Long term follow-up: The average follow-up period of this patient population was 3115 (249) days, almost ten years. When we checked survival status, we found that mortality was 16.4% in the overall LAD-MB population. Eight people died in the LAD-MBs groups .neg group. As a first step, we determined factors from the demographic and clinical data using single variable Cox regression  and influencing factors from single variable analysis. Our results show that none of the morphological parameters influence mortality  than by angiography (0.4–15.8%) ,17,18. AThe morphological parameters of these bridges, measured by quantitative coronary analysis , are also comparable with the data from literature  1,19,20,2019,20.Clinical adjudication of myocardial bridges is often difficult. On the one hand, there is an essential discrepancy between the high prevalence of the phenomenon accompanied with excellent prognosis  ,23,24,25pos patients were older and could be characterized by an increased prevalence of type 2 diabetes mellitus and were associated with higher mortality compared to the LAD-MBneg group  was also present, to examine whether in this population–where myocardial bridge might be only an accidental finding–there are different clinical factors and bridge features compared to patients with isolated LAD MB . The cuteg group . From aneg group –despite  studies . Symptom studies .neg group is comparable with 1.1% average yearly mortality in 45–59 years old men and women in Hungary . Therefore, we investigated in the LAD-MBneg group whether the anatomical parameters measured by QCA might influence long-term mortality (Isolated myocardial bridging is generally considered as a benign condition. Beside appealing, unique clinical cases ,7,8,9, sortality . We founortality , Table 2The main limitations of the study were the retrospective design of the study and the lack of non-invasive assessment of MB anatomy with MDCT and invasive imaging techniques, such as IVUS or OCT. Although these measurements, which are not routinely used, would be more precise to describe bridge morphology, they require more financial and medical effort and are associated with increased radiation time. We also emphasize that no healthy control group was investigated to compare mortality directly to these individuals.Here we provided a retrospective, single-center study using long-term follow-up. Morphological parameters of angina-associated, isolated LAD bridges were more severe compared to bridges that were accidentally found in patients with obstructive coronary disease. Our ten-year long follow-up period showed that morphological parameters measured with QCA did not influence long-term mortality outcomes; therefore, it suggests that anatomical differences might not predict long-term outcomes. Our data underline the benign nature of myocardial bridge.