To determine the prevalence of trachoma in each of the 21 local government areas (LGAs) of Adamawa State, Nigeria.A population-based cross-sectional survey was conducted in each of the 21 LGAs of Adamawa State between 2017 and 2019. With the support of Tropical Data (TD), surveys were planned and implemented in accordance with World Health Organization (WHO) recommendations. A two-stage cluster sampling technique was used in each LGA, 25 or 30 clusters were selected with a probability of selection proportionate to cluster size, and in each of these clusters, 25 or 30 households were enrolled for the survey. All residents aged 1 year and older within selected households were examined by TD-certified graders for trachomatous inflammation – follicular (TF) and trachomatous trichiasis (TT) using the WHO simplified grading scheme. Additionally, data were collected on household water and sanitation access.All 21 LGAs had TF prevalence in 1–9-year-olds below 5%. The prevalence of TT unknown to the health system in people aged ≥15 years was ≥0.2% in three of the 21 LGAs. Access to improved water and sanitation facilities was <80% in the majority of the surveyed LGAs. Only 12 of the 21 LGAs had ≥50% household-level improved latrine access, and only Yola North had ≥80% household-level improved latrine access.There is no need for mass treatment with antibiotics for trachoma elimination purposes in any of these LGAs. There is a need for active TT case finding and provision of community-based TT surgical services in three LGAs. Furthermore, engagement with water and sanitation agencies is needed to augment access to improved water and sanitation facilities across the State; this will help to avoid the recrudescence of active trachoma in the State. The decision to implement the SAFE strategy and two of the three criteria to declare elimination of trachoma as a public health problem  <5% in 1–9-year-olds and prevalence of TT < 0.2% in ≥15-year-olds), are all based on district-level, population-based prevalence data.7Trachoma is the leading infectious cause of blindness worldwide.8 Community-based surveys are needed to accurately estimate prevalence. Prior to this series of surveys, Adamawa State had not been mapped for trachoma, largely due to security challenges, and it did not have an existing trachoma programme. Surrounding administrative areas in both Nigeria and Cameroon had previously been mapped and documented to have trachoma requiring interventions.9–12Adamawa State is located in the north-eastern zone of Nigeria; it has 21 LGAs and shares boundaries with Gombe, Taraba, and Borno States while on its eastern border lies the Far Northern and Adamaoua Regions of Cameroon. Trachoma is a notifiable disease in Adamawa State, although cases are seldom reported.Chlamydia trachomatis transmission intensity and (a) limited access to water for hygiene purposes13 and (b) increased availability of breeding sites for the human-faeces-breeding trachoma vector Musca sorbens,14–16 household-level access to water, sanitation and hygiene (WASH) facilities was also assessed.This study sought to determine the LGA-level prevalence of TF and TT in Adamawa so that the government and its partners could determine whether a trachoma elimination program would be required. Additionally, given the association between ocular 17 and standardised implementation guidelines from Tropical Data (TD) were followed, including TD’s standardised trachoma grading certification. Versions 1 (2017) and 2 (2019) of the TD training system19 were used. We implemented standard quality assurance and quality control measures for trachoma surveys that have been described in detail elsewhere.20From 2017 to 2019, we conducted LGA-level, baseline, and cross-sectional population-based prevalence surveys for trachoma in Adamawa State. Fourteen LGAs were surveyed in 2017 and seven in 2019. WHO protocol recommendationsAs is customary for trachoma surveys, the TF prevalence in 1–9-year-olds was considered the primary outcome measure, and the number of households that should be included in each constituent survey was determined on the basis of the number needed to have reasonable confidence in enrolling and examining sufficient children to estimate TF prevalence with acceptable precision. TT prevalence in ≥15-year-olds was estimated amongst ≥15-year-olds examined in the same households.21 whereby sample size (n) is equal to Deff × NRIF × Z × (p(1 − p))/(d2). For our calculations, the design effect (Deff) was set as 2.65; the non-response inflation factor (NRIF) was set as 1.2; the z-score for the risk of alpha-error (Z) was 1.96; the expected proportion of children with TF (p) was 10% and the absolute precision required (d) was ±3%.21 The calculated target sample size was therefore 1225 children.The sample size of 1–9-year-olds for estimating the TF prevalence in each LGA was determined using the single-population-proportion-for-precision formula,5 In 2017, the cluster number was determined based on each survey team being able to reliably survey 25 households in 1 day, and there being about 2 children aged 1–9 years in each rural household in northern Nigeria (1225/(25 × 2) = 24.5).22 In 2019, the number of households per day was expanded to 30, and the cluster number was increased to 30 to be more certain of meeting the sample size requirement for estimating both TF and TT prevalence.17 The second-stage clusters were households. These were selected using a compact segment sampling method. Briefly, villages were divided into segments that contained approximately 25 or 30 households  and one segment was selected by drawing lots. All the households in the selected segment were invited to take part in the survey.The evaluation unit in this survey series was the LGA. For each of the 21 LGAs, a two-stage cluster sampling strategy was used to select the study population. Villages were first-stage clusters. A list of the villages in each LGA was used as the sampling frame, from which 25 clusters (in 14 LGAs – for the 2017 surveys), and 30 clusters (in 7 LGAs – for the 2019 surveys) were selected using systematic sampling with a probability-proportional-to-village-size methodology.23 was used to grade trachoma. All graders were ophthalmic nurses who had participated in a TD grader qualifying workshop and passed both the slide-based and live patient inter-grader agreement tests with kappa statistics ≥0.70 for the diagnosis of TF.19 Data recorders also completed analogous TD processes of standardised training, testing, and certification.The WHO simplified grading scheme, as set out in 1987,Within each household, all the residents aged ≥1 year who had lived in the household for at least 6 months at the time of survey, including those absent at the time of the survey team’s visit, were enumerated and invited to be examined. Household members present at the time of the survey and willing to participate were examined for TT and TF. The teams returned at the end of the day to examine consenting persons who had been absent earlier.24 For the purposes of this paper, we defined TT as the presence of trichiasis in either the upper or lower eyelid, with or without TS. Those with TT were asked whether they had been offered and/or received management for their TT. We defined a case of TT unknown to the health system as someone with TT who had not yet been offered management or who could not remember being offered management in at least one eye. In 2019 surveys, follicle size guides25 were used to help graders consistently make a distinction between follicular inflammation that met or did not meet the definition of TF.23Each grader used a 2.5 × magnifying loupe to examine participants under daylight illumination. In any eye graded as having TT, the grader also assessed whether trachomatous scarring (TS) was present.Data were collected, stored, and managed using the TD Android phone-based data collection app, and were encrypted and transferred in near real-time to TD servers for cleaning, analysis, and storage.21 – to the household head or their proxy. Household latrine and handwashing facilities (if present) were inspected to capture details of their type (latrines) or presence/absence of water (hand washing facility). Water sources and sanitation facilities were categorised as improved or unimproved, as per the WHO/UNICEF JMP definitions used for monitoring progress towards the Sustainable Development Goals.26 A household was defined as a compound head together with all individuals normally resident in the compound and eating from the same pot.GPS coordinates for each selected household were recorded. Additional data on water and sanitation facilities were collected by administering a questionnaire – adapted from the WHO/United Nations Children’s Fund (UNICEF) Joint Monitoring Program (JMP) for Water Supply and Sanitation household questionnaireSurvey protocols were approved in advance by the Ethics Committee of the Ministry of Health of Adamawa State, the National Health Research Ethics Committee of Nigeria (NHREC/01/01/2007) and the Observational/Interventions Research Ethics Committee of the London School of Hygiene & Tropical Medicine (16105). After field teams explained the examination protocol to each adult in an appropriate language, informed verbal consent for enrolment and examination was obtained. Verbal consent was agreed by the responsible ethics committees to be culturally appropriate in Adamawa, where adult literacy rates are low. Individuals aged ≥18 years consented for themselves; heads of households gave consent for the participation of minors (<18 years). Consent  was formally documented in the TD data collection app by the data recorder, who had a unique identification number. Individuals with active (inflammatory) trachoma were given two tubes of 1% tetracycline eye ointment, and they or their carer instructed to apply it to the lower conjunctival fornix of both eyes twice daily for 6 weeks; persons with TT were referred for eyelid surgery at the nearest facility to the village. Examiners cleaned their hands with an alcohol-based skin cleaning agent after the examination of each participant.27 Cluster-level TF prevalence was adjusted for age in one-year bands using the most recent census data.28 LGA-level TF prevalence estimates were generated as the mean of the adjusted cluster-level TF prevalence estimates. Similarly, cluster-level TT prevalence was adjusted for age and gender in five-year age bands using the most recent census data, and LGA-level TT prevalence generated as the mean of adjusted cluster-level prevalence estimates. Confidence intervals (CIs) were calculated by taking the 2.5th and 97.5th centiles of the distribution of prevalence estimates generated by repeatedly resampling cluster-level estimates over 10,000 iterations.Data were analysed in R .Surveys were conducted in 21 LGAs; in February 2017 (14 LGAs) and March 2019 (7 LGAs). A total of 76,096 people were examined; 54% were female. The ages of persons examined ranged from 1 to 100 years. A total of 35,731 children aged 1–9 years were enumerated, of whom 35,338 (99%) were examined, and 36,517 adults aged ≥15 years were enumerated, of whom 33,040 (90%) were examined. The age and gender distribution of the study population for all LGAs combined is shown in 28, was calculated29 to be 2,008  and highest in Hong LGA (87%) . Only tw5 This is comparable to findings reported in neighbouring Gombe State,10 where none of the 11 LGAs surveyed required antibiotic MDA; Taraba State,9 where only one out of 13 surveyed LGAs required antibiotic MDA; and Northern Cameroon,11 where only 3 of 15 surveyed health districts required antibiotic MDA for trachoma elimination purposes. However, Madagali LGA does border some suspected endemic LGAs in Borno State, in addition to some previously trachoma-endemic districts in Cameroon, and therefore cross-border partnership is important to maintain these LGAs below the elimination prevalence threshold.30This study suggests that TF is below the target for elimination as a public health problem in all LGAs in Adamawa State. According to current guidelines, no LGA in the State requires mass drug administration (MDA) of antibiotics for trachoma elimination.31–33 Water and sanitation agencies and other partners should engage with community leaders on this issue and determine ways to sustainably improve access.34 Such improvements would be likely to have substantial health benefits beyond trachoma35 and contribute towards the attainment of Sustainable Development Goals three  and six .37To reduce the possibility of active trachoma recrudescence and maintain the low TF prevalence seen in this survey series, access to water and sanitation should be sustained and improved upon in all of Adamawa’s LGAs. In these surveys, inadequate access to improved water and sanitation facilities was recorded. The exception to this was Yola North, where 95% of households had access to improved sanitation. Community awareness should be raised on the health benefits of using improved sanitation; higher levels of access are associated with lower risk of trachoma.The prevalence of TT unknown to the health system was above the target for elimination of trachoma as a public health problem in Fufore, Numan, and Toungo LGAs. This finding is intriguing, given the low TF prevalence. Possible explanations could be due to the inclusion of lower eyelid trichiasis in the TT definition or that these present-day TT prevalences reflect past high TF prevalences, which have since been reduced to below elimination thresholds as a result of MDA. Even though only a few hundred people are in need of TT surgery in each one, the prevalence of TT meets the definition of a public health problem in these LGAs. It is therefore important for Adamawa State to undertake active TT case finding, train, and equip TT surgeons and deploy them to provide community-based services for these LGAs. These services should be durable: assuming that the trichiasis is due to trachoma, it is likely that people will continue to develop incident TT for some years.38 has faced security challenges for over a decade. During the period of the survey, several modifications and safety measures were undertaken; (i) In the second phase of the survey (2019) involving seven LGAs, the number of selected clusters was increased from 25 to 30 as the number of children encountered in the initial LGAs was too low. (ii) In some instances, no more than five villages had to be changed (another random selection of villages from a predefined list) as preselected villages came under attack by insurgents just as the survey team was due to deploy. To circumvent these security challenges and complete the surveys, the survey coordinator had to adopt a fluid itinerary that changed according to the security situation; survey teams were trained on local security conditions; satellite phones were carried; and a dedicated Security and Safety Officer was placed in charge of gathering and disseminating security-related information. We also worked with residents who advised field teams on the safest routes to take to selected villages and used local vehicles to avoid being marked out as visitors.This study was undertaken in an area that, like neighbouring Borno State,th Global Scientific Meeting on Trachoma, November 2018.40There were limitations to this study. Reliance on self-reporting for determining access to water could have led to inaccuracies, although direct observation was employed where possible to minimise this. Further, estimates of TT surgical backlog from survey data like these are acknowledged to be inaccurate because of the relatively small number of people examined in the age groups most at risk of TT. It is also worth noting that upper and lower eyelid trichiasis were not recorded separately during these surveys, precluding an analysis of the results in line with the new definition of TT agreed at the 4These surveys show that in Adamawa State, prevalence of trachoma is generally low. No MDA intervention is required, and active TT case finding and other targeted surgical interventions are warranted in only three of the 21 surveyed LGAs. Behavioural practices that encourage facial cleanliness and infrastructure improvements in water access and human waste management should continue to guard against an increase in active trachoma prevalence in the future.


This paper presents the use of deep Reinforcement Learning (RL) for autonomous navigation of an Unmanned Ground Vehicle (UGV) with an onboard three-dimensional (3D) Light Detection and Ranging (LiDAR) sensor in off-road environments. For training, both the robotic simulator Gazebo and the Curriculum Learning paradigm are applied. Furthermore, an Actor–Critic Neural Network (NN) scheme is chosen with a suitable state and a custom reward function. To employ the 3D LiDAR data as part of the input state of the NNs, a virtual two-dimensional (2D) traversability scanner is developed. The resulting Actor NN has been successfully tested in both real and simulated experiments and favorably compared with a previous reactive navigation approach on the same UGV. Off-road navigation of Unmanned Ground Vehicles (UGVs) is a challenging task that requires the ability to avoid obstacles below and above the surface level, including overhangs . This caReinforcement Learning (RL) is an ML approach that focuses on directly mapping situations to actions to maximize a numerical reward . In geneTo apply RL to autonomous navigation, it is necessary to employ vehicle simulators , as traiIn this paper, we propose a deep RL scheme, that makes use of the robotic simulator Gazebo  and the Generation of virtual 2D traversability ranges from a 3D laser scan, using a Random Forest (RF) classifier trained with synthetic data.Implementation of an Actor–Critic RL scheme, which has been trained in simulated scenarios of increasing difficulty with CL.Testing autonomous navigation on natural environments with the Actor NN in both simulated and real experiments.In this way, the main contributions of the paper can be summarized as follows:The rest of the paper is organized as follows. The next section presents some related work. Then, For end-to-end navigation, it is generally required a method to estimate the traversability of the surroundings using onboard sensors of the UGV, such as LiDARs and cameras . This taLearning-based techniques have been successfully implemented for end-to-end navigation . In 21]21], the One step further in data-trained algorithms for autonomous navigation is the use of RL. In this case, the training data is generated while practicing, and the behavior of the vehicle is continually adjusted . Thus, iWhen facing complicated tasks, it is often beneficial to employ the CL paradigm, where the training process is done in different stages of increasing difficulty . Thus, iFinally, it is relevant to mention that most of the previously cited papers about RL adopt an Actor–Critic scheme ,36,37,38Modern 3D LiDAR sensors produce a variable and large amount of 3D points, making this kind of data unsuitable for being used as input for RL, which requires a small-size constant-length vector. For this reason, 3D point clouds will be processed to emulate the output of a virtual 2D traversability scanner.Andabata is a wheeled skid-steer UGV designed for off-road navigation that weighs 41 ight see a. It has battery . The locFor outdoor localization, Andabata employs the IMU of a smartphone  and a GNSS receiver with a horizontal resolution of 1 2D LiDAR .All motors and sensors are connected to the onboard computer , it is labeled as empty in white.If at least 15% of points are non-traversable, the cell is classified as non-traversable in red.In another case, i.e., with more than 85% of traversable points, the cell is classified as traversable in green.A new RF classifier is trained to assess the traversability of each cell see . This isThe same fifteen synthetic 3D point clouds used in  have beeOnce trained the RF classifier, can be employed for predicting the traversability label of the 2D cells of a 3D point cloud see . This cot with 32 maximum navigable distances along every sector. An example of this processing on the simulated environment is shown in X and Y axes of Andabata on the 2D horizontal grid, respectively.Finally, with the traversability grid, the virtual 2D scanner will produce a vector For deep RL, it has been chosen a model-free, on-policy and online scheme, called Actor–Critic ,15, thatning see .The state goal see , which ihttps://pytorch.org, accessed on 14 March 2023). The dimensions of the input layers for Actor and Critic NNs are 35 × 200 and 36 × 200, respectively. Both have as input The Actor and Critic linear NNs each contain two hidden square layers (200 × 200 dimension) and an output layer (200 × 1 dimension) which have been implemented by using the ML library PyTorch  node generates the mobile robot localization by using the IMU, the compass and wheel odometry produced by the direct kinematics node. The 3D laser scanner node builds leveled 3D point clouds by combining vertical 2D laser scans with the angular position of the LiDAR head and UKF localization. To simulate the data generated by the real sensors and the motor drivers, several Gazebo plugins were introduced. All these nodes have been previously employed in .While a new 3D point cloud is forming, the current one is continually transformed into the current robot pose. Then, these relative point clouds are processed to generate the 2D traversability data that the Actor NN node employs to produce angular speed commands. Finally, the inverse kinematics node computes the wheel speeds of each side for skid-steering assuming that the vehicle navigates at a constant longitudinal speed.Following the CL strategy, three simulated scenarios of increasing complexity have been used to accelerate the training process and to increase its convergence. In this way, Andabata begins with a horizontal 2D laser scanner in an indoor scenario, continues with its 3D LiDAR and ends in a natural environment. In each stage, 200 navigation episodes are performed. Each one can be aborted either by reaching the goal (i.e., if At this stage, a squared maze-like environment of 80  NNs see . The iniIn addition, the 3D LiDAR has been replaced by a 2D laser scanner with 32 horizontally mounted beams with a maximum range of 10 nner see a. The ma−1 between initial and goal points.At this stage, virtual 2D traversability scans are generated from the simulation of the 3D laser scanner of Andabata, which combines successive vertical 2D scans acquired in motion see b. The reThe main difference from the previous stage is that the ranges of the 2D virtual scanner are discontinuous rather than continuous. Three tests were done to examine the resulting Actor NN. At the last stage, the maze is substituted by a natural environment modeled with a Gazebo in a square of 120 14 m see .The NN training process continues with navigation episodes along four pairs of feasible initial and end positions with a maximum allowed time of 300 The moving average with 1000 time steps of the reward function along the 200 training episodes Using the trained Actor NN, a navigation test with Andabata was performed in a trail inside a hollow as in . Figure An experiment has been performed on the natural environment already modeled in , which c16 m see .Following is a comparison between our previous reactive navigation method  and the To compare the success ratio between both approaches, a simulated test has been performed in the Gazebo environment of The paper has presented the RL training process for off-road UGV navigation with an Actor–Critic scheme. This has been accomplished with CL by using realistic robotic simulations on scenarios of increasing difficulty. Virtual 2D traversability ranges, generated by an RF classifier from a leveled 3D laser scan, have been employed as input data for both NNs.In this way, reactive navigation has been implemented for the Andabata mobile robot with its 3D laser scanner using ROS topics and nodes. One issue of the proposed deep RL method is that it requires time to retrain in case any parameter is changed, for example, on the reward function.The resulting Actor NN has been successfully tested in both simulated and real experiments. In comparison with our previous navigation method on the same UGV , the resFuture work includes the employment of a commercial portable 3D LiDAR to improve the data acquisition rate. It is also of interest to generate more 2D traversability ranges and to consider the navigation time in the reward function, which is a limiting factor in battery-operated mobile robots.


In our MuLB scoring system, score of >8 was associated with high risk of mortality and <5 was at mild risk of mortality (P < 0.001). The interpretation was that The MuLB risk score model could help to predict survival in patients with severe COVID-19 infection and to guide further clinical research on risk-based treatment.COVID-19pandemic was started in December 2019. It has variable presentation from mild sore throat to severe respiratory distress. It is important to identify individuals who are likely to worsen. The Research question is how to identify patients with COVID-19 who are at high risk and to predict patient outcome based on a risk stratification model? We evaluated 251 patients with COVID-19 in this prospective inception study. We used a multi-variable Cox proportional hazards model to identify the independent prognostic risk factors and created a risk score model on the basis of available MuLBSTA score. The model was validated in an independent group of patients from October2020 to December 2021. We developed a combined risk score, the MuLBA score that included the following values and scores: Multi lobar infiltrates , lymphopenia (lymphocytes of <0.8x10 The novel corona virus disease 2019 (COVID-19) has affected the public across the globe and continue to be an important and urgent threat to global health. Since the outbreak in early December 2019 in the Hubei province of the People’s Republic of China, the number of patients confirmed to have the disease has exceeded 5,507,376 in more than 160 countries, and the number of people infected is 308,034,154 as of 10-1-22 [The “Multi-lobar infiltration, hypo lymphocytosis, bacterial co-infection, smoking history, hyper-tension and age score” abbreviated as the MuLBSTA score is one such proposed model which is likely to assist medical professional in arriving at a clinical decision . The resThis study was aimed at validating MuLBSTA score , 4 in prPrimary objective of the current study was to identify and validate MuLBSTA score as clinical indicator tool that could predict the prognosis of patients with severe COVID-19 during admission in the study cohort. We also aimed to establish and validate MulBSTA score and to derive modified score if possible.Secondary objective of the study was to identify other prognostic markers by studying the association of various laboratory parameters and the comorbidities with mortality in severe COVID 19 infection in a tertiary setting.This was an analytical cohort study to validate the role of MuLBSTA score in estimating 28-day mortality. The study was commenced after receiving an authorization certificate from both the scientific and ethical committee of Amrita Institute of Medical Sciences and Research Centre, Kochi. Informed written consent was taken from all patients before enrolling them in the study. The study included analysis of the clinical characteristics and laboratory parameters during the time of hospitalization between the survivor and the non-survivor groups. Patients admitted with severe COVID 19 infection between October 2020 and December 2021 were considered in our study. Based on the proportion of greater than or equal to 12 of MuLBSTA score in alive patients (3.6%) and odds ratio of 22.29 observed in an earlier study on “MuLBSTA score in COVID-19 pneumonia and prediction of 14-day mortality risk “by Preetham and et al.  and withStudy definitions: COVID-19 illness was categorized as mild, moderate or severe as per following symptomatology.Mild COVID-19 Illness: Individuals who have any of the various signs and symptoms of COVID-19  but who do not have shortness of breath, dyspnoea, or abnormal chest imaging.2) ≥94% on room air at sea level.Moderate COVID-19 Illness: Individuals who show evidence of lower respiratory disease during clinical assessment or imaging and who have an oxygen saturation  <300 mm Hg, a respiratory rate >30 breaths/min, or lung infiltrates >50%.Inclusion criteria- All consecutive patients admitted with severe COVID-19 infection from October 2020 and December 2021 were included. All patients with diagnosis of COVID-19 with RT PCR/antigen/Xpert who required admission either in high dependency unit (HDU) or intensive care unit were included in the study.Exclusion criteria: Patients <18 yrs. of age, 2) explicit refusal to participate in the study and 3) patients with mild or moderate COVID-19 infection that did not require admission in ICU or HDU were excluded.Demographics characteristics, history, clinical presentation laboratory findings, and radiological features were noted. The MuLBSTA score was calculated as showed in 2 test or Fisher exact test to compare the categorical variables between different groups and the Mann-Whitney U test to compare median differences between the two groups for continuous variables. Uni variate and multivariate Cox regression models served as the main statistical methods for identification of prognostic factors for 28-day mortality. All the variables found to have P value of <0.05 in Uni variate analysis were subjected to multivariate analysis. We used a multi variable logistic regression analysis model to identify independent prognostic risk factors and calculate their HR’s, 95% CI, and beta regression coefficients. To estimate the overall survival time of samples, Kaplan Meier curves was used. The optimal cut-off value for MuLBSTA score was determined with the use of receiver operating characteristic curve analysis, then each continuous parameter was converted into a classification variable. For all analyses, statistical significance was calculated with the use of two-tailed probability values; P < .05 was considered significant.Statistical analysis was done using IBM SPSS 20. . For all the continuous variables, the results are given in Mean ± SD, and for categorical variables as percentage. We used the xDuring the study period 26,854 total hospital admissions occurred and among them 2,081 were COVID-19 related admissions. Out of them 251 had severe COIVD-19 infection requiring ICU or HDU admission and formed the study cohort . The meaUni-variate analysis of baseline demographics and co-morbidities among survivors and non survivors are shown in The variables which were showing P value <0.005 were subjected to multivariate analysis.On multivariate analysis of the individual parameters in the scoring system, Multi lobar infiltrate , Absolute lymphocyte count , and Bacterial co infection  were found to be independent risk factors associated with mortality as shown in On multivariate analysis, Multi lobar infiltrate , Absolute lymphocyte count , and Bacterial co-infection  were found to be independent risk factors associated with mortality.The area under the receiver operating characteristic curve (ROC) of MuLBSTA for predicting mortality at the time of admission was 0.799 (SE 0.028) as shown in The median  value of MuLBSTA score among survivors was 5 (2–8), and the median  value of MuLBSTA score among non-survivors was 11(7–15). The P value was found to be statistically significant (<0.001) as shown in Out of the 116 patients with MuLBSTA score ≥8, 78 expired (67.2%) whereas, out of the 135 patients with MuLBSTA score < 8, 27 expired (20%) during the study period. Kaplan Meier curve was plotted for MuLBSTA score of >8 and <8 days as shown in For validation and deriving modified score linear regression analysis was done and the results are shown in COVID-19 virus has thrown up a multitude of challenges since its first detection. One of the foremost challenges has been the identification of at-risk individuals and appropriate targeting of resources for effective management so as to limit the accompanying mortality. Even though the condition has many similarities with other viral pneumonia’s, we are yet to identify a definite tool to ascertain the disease’s outcome immediately upon infection. It may be noted that the association of various risk factors assists in better prediction of the worse outcomes .Out of t 251 patients, 169 were men and 82 women with a mean age group of 61 years and the male gender was found to be a risk factor associated with mortality. It is well known fact that male gender is an independent risk factor of mortality as found in many studies earlier [Although, the virus infects people of all ages alike, poorer outcomes have been noted in older age groups, and especially those persons above sixty years of age. However, in our study age more than sixty years was not associated with mortality. Patients with smoking history were more prone to die and this must probably be because tobacco smoking likely alters the expression of “angiotensin converting enzyme receptors” making it more likely to be associated with mortality . It is aMost patients in our study with multi lobar infiltrates on the chest X-ray and/or lymphopenia during the time of admission had worse outcomes; the finding similar to previously published study .It has been proven by various studies that DM, CKD, CLD, CAD and cancer are comorbidities associated with poor outcome in COVID-19 infection –22. In cPrevious studies have shown that there is no impact of Hypothyroidism on COVID 19 mortality . Howevermmol/L , Platelet count<150x 109/L  were found to be independent risk factor associated with mortality. On multi-variable cox regression analysis after adjusting for all the factors that were obtained during the time of admission in our study, platelet counts<150109/L , Serum LDH ≥ 245 units/Litre , and D Dimer ≥1 mcg/mL  were found to be statistically significant predictors of mortality in patients with COVID 19 infection. The finding was similar to other studies [The laboratory parameters that were obtained at the time of admission were analyzed and it was observed that Serum Albumin <3.5g/dl , CRP > 100 mg/mL , LDH ≥245 units/Litre , Serum Ferritin≥300 ng/mL , D Dimer ≥1 mcg/mL , Serum sodium<135  studies . Hypoalb studies . Many of studies , 35. TheMuLBSTA scoring system was proven to be a good model for predicting mortality and risk stratification of the patients in a study by Rong Xu et al.  PrognosiWe also did validation of MuLBSTA score in the study cohort and derived the new score for each variable as shown in The strength of our study was that we were able to include two hundred fifty-one patients which attributed to a good sample size.The limitations of our study are that it was single center study and the intrinsic bias associated while collecting some data retrospectively could not be avoided. The MuLBSTA score was used to predict ninety-day mortality in all the previous studies and our study was limited to following up patients only for a period of 28 days. The laboratory values of patients who developed the viral infection as a part of hospital acquired infection was taken during the date of positivity and not at the time of admission. The new MuLB score is very promising but it needs further validation by other studies.Our study validated the MuLBSTA score with ROC analysis and score of ≥ 8 was dependable tool to assess 28-day mortality in seriously ill COVID-19 positive patients. We also derived the new MuLB score which was better score than existing score to identify the high risk COVID-19 patients. It was also noted that high levels of C-reactive protein, Serum Ferritin, Serum Lactate dehydrogenase, D Dimer and low levels of serum albumin, sodium, platelet and absolute lymphocyte counts were associated with increased mortality. Co -morbidities such as CKD, CLD and cancer were also associated with poor outcome. It may hence be concluded that such a tool may help primary care medical doctors in reallocating patients to centers with better care and facilities and as a consequent thereof even aid in containing the disease and its mortality rates.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S1 Data(XLSX)Click here for additional data file.S2 Data(XLSX)Click here for additional data file.


Saccharomyces cerevisiae lineages growing together under varying environmental conditions applied across multiple generations, revealing fitness differences and lineage-specific adaptations. Then, we demonstrate how gUMI-BEAR can be used to perform parallel screening of a huge number of randomly generated variants of the Hsp82 gene. We further show how our method allows isolation of variants, even if their frequency in the population is low, thus enabling unsupervised identification of modifications that lead to a behaviour of interest.Cellular lineage tracking provides a means to observe population makeup at the clonal level, allowing exploration of heterogeneity, evolutionary and developmental processes and individual clones’ relative fitness. It has thus contributed significantly to understanding microbial evolution, organ differentiation and cancer heterogeneity, among others. Its use, however, is limited because existing methods are highly specific, expensive, labour-intensive, and, critically, do not allow the repetition of experiments. To address these issues, we developed gUMI-BEAR (genomic Unique Molecular Identifier Barcoded Enriched Associated Regions), a modular, cost-effective method for tracking populations at high resolution. We first demonstrate the system’s application and resolution by applying it to track tens of thousands of  HowevThe study of heterogeneity need not be limited to individuals within a population but could also be considered in the context of a particular gene or pathway. Quantifying the effect of gene-variants allows mapping the fitness landscape of a gene towards the understanding of the biophysical and biochemical requirements of particular regions. Likewise, it can aid in designing disease treatments. Here, the conjugation of DNA barcodes to specific gene variants creates a one-to-one association between barcode and variant and allows to use barcode incidence as a proxy for the abundance of each variant in a population. Thus, the fitness of many variants can be tested together, in one experiment, in the evolutionary setting in which they compete with each other.Cellular barcoding has thus become invaluable for understanding core evolutionary processes such as clonal interference , differedirectly single out specific clones for further study. Currently, if particular clones are found of interest, based on the dynamics of the population, the procedure for distinguishing them for further study requires first separating single clones using other methods such as streaking or sorting and then sequencing the barcode of each colony or well to identify the variant. This procedure can be particularly cumbersome if one is looking for clones that are present at low frequencies, as the probability of finding a clone goes as its percentage in the population. Thus, a clone present below 1% requires (on average) more than 100 colonies to be sampled and sequenced, a feat that is both formidable and expensive.Another ability lacking from current barcoding techniques, which prevents them from being used to study particular adaptations, is the ability to To tackle some of the above limitations, we set out to simplify and streamline the barcoding process so that it can be applied to a broader range of studies by incorporating the following capabilities, (1) generation of identical library duplicates, (2) direct variant identification, (3) precision and modularity in the insertion point of the barcode and in the number of participating lineages and cells and, (4) potential for integration into different organisms. In this paper, we describe the method we devised to achieve these goals, which we named gUMI-BEAR (genomic Unique Molecular Identifier-Barcode Enriched Associated Regions) and show how it can be utilised to accomplish high-resolution lineage tracking at a relatively low cost as well as to directly test millions of variants of a gene in the context of a growing population. While it can be adapted to other systems, the setup we describe here is particularly applicable to asexual populations. We note that this method can be applied by any lab versed in standard molecular biology techniques and thus hope that it will be utilised by labs specialising in different areas.gUMI-BEAR is based on the unsupervised barcoding of each cell in an initial population with a construct, the gUMI-box, that is inserted into the genome using CRISPR-Cas9. The construct includes a barcode, along with auxiliary elements required for successful transformation and ones that simplify downstream preparation of the samples for sequencing see .The barcode (gUMI) region of the gUMI-box we used contains a 24 bp random DNA segment preceded by five non-guanine nucleotides to allow efficient cluster generation by Illumina sequencers. This length of the random segment can be changed according to the requirements of each experiment, provided that the minimum of 25 base-pairs for cluster generation is kept. The barcode region is flanked by elements that direct the construct to its proper genomic locus upon transformation as well as components that shorten the preparation of samples for sequencing after the experiment see . Thus, uThe full donor that is transformed into yeast cells is composed of the gUMI-box, together with the hygromycin cassette under the TEF1 promoter, and the 3’ homology sequence for genomic insertion . It is formed by PCR amplification using the gUMI-box as the forward primer together with a reverse primer that consists a 20bp region complementary to the Hygromycin resistance cassette box together with the RHA sequence .As stated, we designed the gUMI-box to also aid the process of preparing the library for sequencing. We used the linker in a short-cycle PCR step to attach a UMI and a READ2 sequence to each amplicon  and the percentage of dead cells in the culture at several time points up to 40 hours post-transformation see . We founOne of the important prerequisites we set for the system was to be able to produce identical copies of libraries to allow comparisons between experiments. This was achieved using the following procedure. First, we sorted out the desired number of live cells at the time point we identified previously as the one at which single cells produce distinct lineages. Then we allowed the required amount of time for the cell population to expand sufficiently to produce 100 copies of each barcoded yeast (approximately 32 hours) and, finally, divided the culture into 100 aliquots and froze them in glycerol stock for the experiments. We note that the number of library copies produced can be changed by modulating the time the culture containing the sorted cells is allowed to expand and the number of aliquots accordingly. However, care must be taken to keep the expansion time short enough for the probability of acquiring mutations before the start of the experiment to be small. A detailed description and protocol of the transformation process can be found in the methods section.3, 55×103, 110×103) and the expansion times after transformation (31h or 34 h) (p < 0.001).To assess the effect of various library preparation parameters on lineage makeup, we created seven mini-libraries that varied in the following parameters: the number of transformations performed (1 or 10), the number of cells sorted (10×10or 34 h) . The comor 34 h) , demonstNext, we wanted to verify that our experimental procedures (as described in Methods) produce an accurate description of the population. We, therefore, constructed two mock communities of known composition and subjected them to our analysis pipeline. To generate the mock populations, we first plated our library, picked eight colonies, and used generic primers spanning the barcode region to amplify and sequence (Sanger) the barcode of each colony. DNA was then extracted from the identified colonies and was mixed at two different ratios to construct two mock mixtures simulating different lineage proportions. Mock1 included equal proportions of all eight lineages, and Mock2 was composed of a non-uniform distribution, 50% of which is made up of lineage F and the remaining by equal proportions of the residual seven lineages. We first tested our system in a microbial evolution experiment setting, which is an area in which cellular barcoding has provided considerable insights ,24. ThesCells were grown in the turbidostats in YPD media for 44 days while temperatures were varied three times to induce changes in the structure of the population. These were expected to occur due to differential fitness changes in individual lineages in the population ,14,25,26We tested the sequencing depth used in the analyses to rule out an inadequate one, which would make the results inaccurate, or oversampling, which would increase sampling costs unnecessarily. To this end, we constructed rarefaction curves for both the number of unique lineages detected and their distribution using an increasing number of reads at different time points along the experiment. As seen in Using our analyses, we were able to follow a population that initially consisted of 26,000 distinct lineages  distribuHsp82 to demonstrate the method’s capability to screen genes that are longer than the NGS read length and because the protein it encodes affects fitness gain and loss via variability control through chaperone-like activity  Please clarify whether this [conference proceeding or publication] was peer-reviewed and formally published. If this work was previously peer-reviewed and published, in the cover letter please provide the reason that this work does not constitute dual publication and should be included in the current manuscript.2. We noted in your submission details that a portion of your manuscript may have been presented or published elsewhere. Reviewers' comments:Reviewer's Responses to Questions  Comments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?  The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: YesReviewer #2: Yes**********   2. Has the statistical analysis been performed appropriately and rigorously?   Reviewer #1: YesReviewer #2: Yes**********   3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.  The Reviewer #1: YesReviewer #2: Yes**********   4. Is the manuscript presented in an intelligible fashion and written in standard English?  PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********   5. Review Comments to the Author  Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: I have reviewed the paper titled "gUMI-BEAR, a modular, unsupervised population barcoding method to track variants and evolution at high resolution" and I find it well-written and carefully executed study.The authors have presented a versatile and cost-effective method for generating and screening libraries of yeast mutants using CRISPR/Cas9 and homology-directed repair to introduce a unique molecular barcode (gUMI-box) into the yeast genome. The study demonstrates that the method can generate large libraries of yeast mutants and that the resulting populations can be tracked and analyzed using the gUMI-BEAR method. The authors have also shown that the system can identify biologically relevant mutations and variants of interest.However, I have some minor comments for the authors which I believe would improve the paper:1-The purpose of using homology model of HSP82 is not very clear to me. Additionally, I was unable to locate the discussion for Fig. 4c and Supplementary Figure 5 in the text.2- I suggest that the authors consider using the HSP82 model from Alphafold2 as it would improve the accuracy of their protein-protein interaction models.3- The authors could elaborate on how they generated the protein-protein interaction models of HSP82 with Cdc37 and Cdk4.4- It would be informative for readers if the authors could provide information on the HSP82 mutant highlighted in the homology model in table format, along with its consequences if known.5- In lines 450-470, the authors describe that the gUMI-BEAR method can be used for variant screening in complex systems with multiple genes by adding a short sequence to identify each ORF. I suggest that the authors provide a few examples of complex enzymatic pathways to illustrate this.Overall, I believe that this study provides a valuable contribution to the field and I recommend its publication in PLOS ONE after the authors have addressed the minor comments above.Reviewer #2: The author introduces a novel method for tracking variant populations using gUMI-BEAR (genomic Unique Molecular Identifier Barcoded Enriched Associated Regions), a modular system with promising applications in variant tracking.The method described in the paper is technically well-explained, and the author provides two examples of its use in tracking evolutionary dynamics and gene variants. I believe that these examples can be replicated in other labs. However, it is worth noting that there are some minor typographical errors in the paper which needs to be corrected.Typographical mistakesPage line 237: Full stop missing.Page 24 line 551: "analysis we centrifuged each sample at 8.6g for 2 minutes" define speed clearly.Additionally, the paper would benefit from a more comprehensive comparative analysis with existing methods. Despite these limitations, I believe that this work is a valuable contribution to the field, and I recommend it for your consideration.**********   6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.If you choose “no”, your identity will remain anonymous but your review may still be made public.Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.  Yes: Badri N. DubeyReviewer #1: Reviewer #2: No**********https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool,  15 May 2023Reviewer #1:We thank the reviewer for his helpful comments and suggestions. Below please find a detailed response to each point raised, along with the changes we made in the text to address it. 1-The purpose of using homology model of HSP82 is not very clear to me. Additionally, I was unable to locate the discussion for Fig. 4c and Supplementary Figure 5 in the text.• Following the reviewer’s suggestion, we have added the following explanation for the use of the homology model in the main text : “To gain insights into the structural role of the different Hsp82 mutations, we utilise the deep learning algorithms implemented in the AlphaFold2 software  system). The AlphaFold2-multimer was used to predict the Hsp82 homodimer of 28 different constructs from the highest ranking variants, each containing between one to four different mutations and some with deletion of the C-terminal segment (701ADTEMEEVD709). The WT Hsp82 homodimer model (residues 1-709) is shown in cyan and purple, and all mutations identified in all the variants are shown as blue and pink balls for the two Hsp82 monomers, respectively . Most mutations do not involve the dimer interface and are unlikely to explain the relatively higher fitness of successful lineages. This demonstrates the utility of applying our system, together with random mutagenesis, to reveal variants of interest whose advantage could not have been predicted. “• Reference to Figure 4c was made in the added text above (row 406) in the main text file). • We thank the reviewer for pointing out that the analysis presented in Figure 5 of the supplementary is beyond the scope of this text, and we have therefore omitted it.2- I suggest that the authors consider using the HSP82 model from Alphafold2 as it would improve the accuracy of their protein-protein interaction models.• As the reviewer suggested, we implemented the Alphafold2 model on 28 sequenced variants and found several mutations on the flexible residues that Phyre2 lacked the resolution to assign. We have replaced the model in Fig 4c with the new one and edited the figure caption (rows 341-345) to reflect the change. The caption to Fig 4c now reads: “Cartoon representation of the Hsp82 protein. The AlphaFold2-multimer was utilised to predict the structural model of the Hsp82 homodimer shown in cyan and purple. All mutations found by long-read sequencing are shown as blue and pink balls in the Hsp82 dimer. The image was produced using the PyMOL software package .• We added a paragraph describing the implementation of AlphaFold2 in the text on page 18, lines 399-410 that also addresses this (see point 1 above)• We have also revised the description of the model in the second paragraph of the Supplementary results (page 7) to depict the new model obtained using AlphaFold2. The new description reads: “The crystal structure of the Hsp82 homodimer from Saccharomyces cerevisiae is known (PDB-ID 2CG9). The Hsp82 structure is missing two segments 217V-K261 and the C-terminal 678I-D709. To gain insights into the structural role of the different Hsp82 mutations we utilize the deep learning algorithms implemented in the AlphaFold2 software . The AlphaFold2-multimer was used to predict the Hsp82 homodimer of all 28 different constructs, obtained by Loop genomics long read sequencing, each containing between 1 to 4 different mutations and some with deletion of the C-terminal segment (701ADTEMEEVD709). The different constructs contain 50 different mutations . The WT Hsp82 homodimer model (residues 1-709) is shown in cyan and purple, and all mutations found by long-read sequencing are shown as blue and pink balls for the two Hsp82 monomers, respectively . The dimer interface of the various constructs of the Hsp82 model contains between 130-260 contacts up to 3.5 Å involving 60-80 different amino acid residues. Approximately half of them appear in the dimer interface of each of the 28 different constructs and 8 are involved in salt bridge contacts . Only 4 of the residues involved in the dimer interface are mutated residues found in the following constructs; I505V (Hsp-11), A595V (Hsp-4), M603I (Hsp-20), and L598M (Hsp-29). Since the dimer interface of each of the Hsp82 constructs involves between 60-80 different residues, it seems reasonable to assume that a single point mutation in a residue involved in the dimer interface will not prevent dimer formation.” 3- The authors could elaborate on how they generated the protein-protein interaction models of HSP82 with Cdc37 and Cdk4.Following the reviewer’s first comment above we omitted the figure that showed these models (see reply to comment 1)4- It would be informative for readers if the authors could provide information on the HSP82 mutant highlighted in the homology model in table format, along with its consequences if known.We agree with the reviewer about the importance of such data, but we could not find an explanation for the advantage provided by the mutations. We believe this demonstrates the strength of the method in revealing variants whose advantage could not have been predicted based on known structures. The last line in the paragraph added in response to the first comment above also addresses this point.“To gain insights into the structural role of the different Hsp82 mutations, we utilise the deep learning algorithms implemented in the AlphaFold2 software  system). The AlphaFold2-multimer was used to predict the Hsp82 homodimer of 28 different constructs from the highest ranking variants, each containing between one to four different mutations and some with deletion of the C-terminal segment (701ADTEMEEVD709). The WT Hsp82 homodimer model (residues 1-709) is shown in cyan and purple, and all mutations identified in all the variants are shown as blue and pink balls for the two Hsp82 monomers, respectively . Most mutations do not involve the dimer interface and are unlikely to explain the relatively higher fitness of successful lineages. This demonstrates the utility of applying our system, together with random mutagenesis, to reveal variants of interest whose advantage could not have been predicted. 5- In lines 450-470, the authors describe that the gUMI-BEAR method can be used for variant screening in complex systems with multiple genes by adding a short sequence to identify each ORF. I suggest that the authors provide a few examples of complex enzymatic pathways to illustrate this.We have added a few sentences at the end of the paragraph that mentions complex enzymatic pathways to illustrate such an application. Specifically, we have added the following to the text :“For example, in order to optimise the glycolysis pathway, one could produce randomly generated, gUMI-barcoded variants of each of the enzymes that participate in it and follow the fitness of yeast harbouring different combinations of variants thus optimising the entire pathway. The system also has the potential to optimise industrial and medicinal production pathways by, for example, improving protein secretory pathways to increase recombinant protein expression.”Reviewer #2Page line 237: Full stop missing.We thank the reviewer and have added a full stop on page 11, line 237 of the revised manuscriptPage 24 line 551: "analysis we centrifuged each sample at 8.6g for 2 minutes" define speed clearly.We thank the reviewer for the comment and have added the relevant numbers to the text on page 25, line 570 which now reads “To prepare the DNA/RNA samples for later analysis we centrifuged each sample at 8600g (9500 RPM) for 2 minutes, removed the supernatant, and flash frozen them in liquid nitrogen at -80°C.”Minor stylistic changes:Page 18, line 397: For clarity, we modified the sentence “Having identified variants of interest, one can go on to test them in isolation by inserting the relevant ORF into wild-type strains.” and it now reads: “We note that identifying variants of interest allows one to go on to test them in isolation by inserting the relevant ORF into wild-type strains. “ Supplementary Figure 1 caption:The beginning of the first line in the caption to Supplementary Figure 1 was changed from:“Donor DNA prior to transformation ….” to:“gUMI region of the donor DNA prior to transformation …”Supplementary Figure 2 caption:We edited the caption slightly from:“GC content was measured based on sequences derived from all lineages at three steps during library construction.  Donor DNA prior to transformation.  Lineages at the beginning of the experiment.  Lineages at the end of the experiment. All data are presented in histograms to reveal the GC content distribution in the populations.”to:“GC content was measured based on sequences derived from all lineages at three steps during library construction.  Donor DNA prior to transformation,  from lineages at the beginning of the experiment,  from lineages at the end of the experiment. All data are presented in histograms to reveal the GC content distribution in the populations.” Supplementary results page 7:The title was edited to concur with S. Cerevisiae protein nomenclature from:Proteomics analysis of hsp82 variantsto :Proteomics analysis of Hsp82 variantsA spelling mistake in the second line of the second paragraph was corrected.Supplementary methods, page 8, First paragraph on Restriction Free cloning :We corrected the reference of CHOP-CHOP to reference 3. First line in last paragraph on page 8:We replaced “For use with tracking” to “For use in tracking”.Page 10, title below All primer sequences used for the donor construction process The title was changed from “No initial fitness differences experiment” to “Tracking evolutionary dynamics experiment” to match the name of the experiment in the main text.Throughout the supplementary methods:All “hsp82 gene” were replaced with “HSP82 gene” to concur with S. Cerevisiae nomenclatureAttachmentResponse to Reviewers .docxSubmitted filename: Click here for additional data file. 22 May 2023gUMI-BEAR, a modular, unsupervised population barcoding method to track variants and evolution at high resolutionPONE-D-23-06106R1Dear Dr. Ruti Kapon,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at onepress@plos.org.If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact Kind regards,Sudhir Kumar Rai, Ph.DAcademic EditorPLOS ONE 25 May 2023PONE-D-23-06106R1 gUMI-BEAR, a modular, unsupervised population barcoding method to track variants and evolution at high resolution Dear Dr. Kapon:I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact plosone@plos.org. If we can help with anything else, please email us at Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staffon behalf ofDr. Sudhir Kumar Rai Academic EditorPLOS ONE


Optimal nutrition is important for Norwegian-farmed Atlantic salmon in the challenging early seawater phase, which shows a higher mortality leading to significant economic losses. Phospholipids are reported to enhance growth, survival, and health in the early stages of the fish life. Atlantic salmon (74 to 158 g) were fed six test diets to evaluate alternative phospholipid (PL) sources in freshwater and were transferred to a common seawater tank with crowding stress after being fed the same commercial diet up to 787 g. Krill meal (KM) was evaluated using dose response with the highest 12% KM diet compared against 2.7% fluid soy lecithin and 4.2% marine PL  diets, which were formulated to provide the same level of added 1.3% PL in the diet similar to base diets with 10% fishmeal in the freshwater period. A trend showing increased weight gain with high variability was associated with an increased KM dose in the freshwater period but not during the whole trial, whereas the 2.7% soy lecithin diet tended to decrease growth during the whole trial. No major differences were observed in liver histology between the salmon that were fed different PL sources during transfer. However, a minor positive trend in gill health based on two gill histology parameters was associated with the 12% KM and control diets versus the soy lecithin and marine PL diets during transfer.Growth and histological parameters were evaluated in Atlantic salmon (74 g) that were fed alternative phospholipid (PL) sources in freshwater (FW) up to 158 g and were transferred to a common seawater (SW) tank with crowding stress after being fed the same commercial diet up to 787 g. There were six test diets in the FW phase: three diets with different doses of krill meal , a diet with soy lecithin, a diet with marine PL , and a control diet. The fish were fed a common commercial feed in the SW phase. The 12% KM diet was compared against the 2.7% fluid soy lecithin and 4.2% marine PL diets, which were formulated to provide the same level of added 1.3% PL in the diet similar to base diets with 10% fishmeal in the FW period. A trend for increased weight gain with high variability was associated with an increased KM dose in the FW period but not during the whole trial, whereas the 2.7% soy lecithin diet tended to decrease growth during the whole trial. A trend for decreased hepatosomatic index (HSI) was associated with an increased KM dose during transfer but not during the whole trial. The soy lecithin and marine PL diets showed similar HSI in relation to the control diet during the whole trial. No major differences were observed in liver histology between the control, 12% KM, soy lecithin, and marine PL diets during transfer. However, a minor positive trend in gill health (lamella inflammation and hyperplasia histology scores) was associated with the 12% KM and control diets versus the soy lecithin and marine PL diets during transfer. Farmed salmon are typically transferred from early phase production in tanks on land to seawater cages that constitutes a challenging environment, where fish can experience significant mortality before reaching harvest size. For example, mortality in Atlantic salmon ranged from 15 to 16% from 2017 to 2021 in Norway, with approximately 35% of sea cage mortality occurring in the first 0–3 months at sea for the 2010–11 salmon generations in the Norwegian-farmed Atlantic salmon . AntarctTM Aqua; Aker BioMarine Antarctic ASA) at four levels for dose response , (ii) fluid soy lecithin as a vegetable PL source, and (iii) marine phospholipid-rich oil sourced from North Atlantic fish species from Triple 9 ). , and a control diet. The trial diets are referred to as Control, KM4, KM8, KM12, VegPL, and MarPL, respectively. Trial feeds were formulated using a commercial formulation program with external oil mix calculations and produced by extrusion at Cargill Innovation Center  for ca. 74 g fish with lipid nutrients and then adjusted for purposes of the trial. The 4-mm pre-transfer freshwater trial feeds were formulated and analyzed to have similar digestible energy (22.1–23.6 MJ/kg gross energy), protein (46–49% range), and fat (22–24% range) (Three different sources of PL were tested in pre-transfer freshwater feeds: (i) krill meal (Qrill% range)  and withThe experiment was performed according to the guidelines and protocols approved by the European Union  and by the National Guidelines for Animal Care and Welfare published by the Norwegian Ministry of Education and Research.Atlantic salmon  with an initial weight of ca. 67 g were used for the trial. The fish were pit-tagged and randomly distributed into 24 freshwater flow-through tanks (1 m diameter and 0.45 m3 volume) to have 40 fish per tank at the start of trial diet feeding. These fish after 15 days of tank acclimation were 74 ± 12 g  and then were fed the freshwater pre-transfer trial diets  with a water temperature drop at transfer (ca. 14 to 9 °C) and crowding stress after transfer  in the common seawater tank after all 17–20 fish per tank from the 24 freshwater tanks fish were transferred over and then were fed a common commercial extruded salmon diet (EWOS AS) for a further 98 days. Daily water temperature was lower during the seawater phase averaging 9.4 °C (8.5–11.1 °C range).al diets  over a 5The 40 fish per tank were weighed individually with pit-tag identification on acclimation to the freshwater tanks (Day 0), at the start of trial diet feeding (Day 15), at intermediate weighing (Day 33), and after 53 days of trial feeding in the freshwater (Day 68). The fish weight gain in the freshwater pre-transfer period from Day 15  to 68 were compared statistically between diets. A total of 17–20 fish from each of the 24 freshwater tanks were transferred to the common seawater tank on Day 68 with fish weighing performed on Days 35, 73, and 98 after transfer to seawater. There were 9 to 17 fish representing the original tanks in the freshwater period with 50 to 58 fish representing each of the test diets from the freshwater period at final weighing in seawater at 98 days after transfer to the common seawater tank. The fish weight gain over the whole trial period in freshwater and seawater from Day 15 to 166 days were statistically compared between diets.Hepatosomatic index (HSI) is the liver weight percent of the whole body weight. HSI was measured on 10 fish randomly sampled per tank (four tank replicates per diet) to study 40 fish per diet at the end of the freshwater pre-transfer period when fed test diets and 40 fish per diet (identified by pit-tag) at the end of the seawater phase when fed the common commercial diet.Gill and liver histology were performed on the fish involved in the dietary phospholipid source comparison  and on fish fed the Control diet at the end of the freshwater pre-transfer period. Liver  and gill (left gill arch 2) tissues were randomly sampled from five fish per tank to give a total of 20 liver and 20 gill tissues per diet group for histological analysis. The tissues were fixed in formalin  and stored at room temperature until sent to Pharmaq Analytiq AS  for histological analysis.The weight gain for the different periods was modelled by computing the weight gain of each tagged individual and then using a hierarchical generalized additive model (GAM) with the spline function to describe the possibly non-linear dose-response. A random effect of tank was added to the model to account for the multiple individual observations per experimental unit. The total feed intake over the periods of interest was modelled with a single level GAM with a spline function describing the dose-response function. Hepatosomatic index (HSI) was modelled by a hierarchical GAM model using a spline function to describe the dose-response function, mean-centered round weight of the fish as a covariate, and a random effect of tank to account for the multiple individual observations per tank. From this model the expected liver weight was solved for an average-sized sampled fish and expressed as HSI by dividing the expected liver weight with the mean round weight of the sample. Gill and liver histology scores are ordinal variables for which common arithmetic operations, such as sum or mean, are not defined and therefore scores require an ordinal model returning the score probability for evaluation. A hierarchical GAM for ordinal data was set up by using a spline function to describe the dose-response function, and a random effect of tank was included to account for multiple individuals observed per tank. The models for weight gain, feed intake, and HSI assumed the error distribution is the normal distribution, and the model for gill and liver scores assumed the model is ordinal and the errors followed the ordered categorical family. All data processing and statistical modelling was conducted with the R language . The GAMThe outcomes from the fitted statistical models are presented graphically by showing the mean response and the 95% credible intervals. The mean (median) response and the 95% credible intervals were computed with the help of a parametric bootstrap  by taking the 25%, 50%, and 97.5% quantiles of the computed response vector. In the case of a categorical predictor variable (for comparing the different PL sources), the graphs show the mean and an error bar of the 95% credible interval. In the case of a continuous predictor , the mean response is shown as a median dose-response curve and the 95% credible interval is shown as a confidence band around the mean curve. This way both the magnitude of any potential effect  and the uncertainty of any effect estimate  can be shown in the same graph for all the results independent of the response following the normal, binomial, or ordered categorical distribution.Atlantic salmon of 74 g  were fed the six test diets up to 158 g , growing 2.1-times the initial fish weight to the end of the freshwater pre-transfer period. There was no clear trend for increased feed intake with KM dose in the FW pre-transfer phase . A trendA trend for decreased hepatosomatic index  was indicated for the fish that were fed increased KM dose from 0 to 12% of diet at the end of the freshwater pre-transfer feeding phase . There wAn increased probability for very mild to mild gill lamella inflammation and hyperplasia score was indicated for the salmon that were fed the VegPL and MarPL diets compared to the Control and 12% KM diets at the end of the freshwater pre-transfer phase after 53 d of feeding the trial diets a,b. OtheNo major differences were observed in liver histology between the control, 12% KM, soy lecithin, and marine PL diets at the end of the FW pre-transfer phase after 53 d of feeding the trial diets (data not shown). The following liver histology responses were evaluated: total amount of abnormal tissue, inflammation, necrosis, inflammation in liver tissue or capsule (peritonitis), peribiliary or perivascular inflammation, neoplasia, fibrosis, lipid deposition, other degenerative changes, vascular lesions, and other lesions noted as absent or present.The present study evaluated the effect of different phospholipid sources fed over 53 d in the freshwater pre-transfer phase followed by feeding the same commercial diet over 98 d in a common seawater tank on growth performance and health parameters of Atlantic salmon. KM was evaluated in dose response , and diets with 2.7% fluid soy lecithin (VegPL) and 4.2% MarPL as alternative PL sources were formulated to provide the same level of added 1.3% PL in diet as 12% KM. All the test diets contained 10% fishmeal in the FW phase. A trend was indicated for increased fish weight gain (high variability) with increased KM dose in the FW pre-transfer phase but a carry-over effect on growth was not observed for the same salmon fed the same commercial diet after seawater transfer. Salmon  that were fed krill meal at 7.5 and 15% of diet for higher fishmeal diets (40–52% of diet range) than the current trial had increased growth after transfer to sea cage . FishmeaA trend for decreased fish weight gain was indicated for the VegPL diet in the FW phase and over the whole trial compared with the control diet, whereas the MarPL diet showed more similar growth to the control diet over the whole trial, noting that only one PL level tested for MarPL and fluid soy lecithin matched that provided by KM12, so optimal dose was not evaluated. The choline level was formulated to be the same for the control and VegPL diets with KM12 and MarPL providing additional choline to these diets in the form of phosphatidylcholine (PC). Formulated choline levels for the control diet and fluid VegPL diets were in excess of the NRC 2011 requirements for salmonids and in excess of the lowest choline level used by Hansen et al. in 2020 with no growth differences observed  . PL fromAddition of KM did not give a clear increase in feed intake compared to the control diet and there was an indicated trend of decreased feed intake for the MarPL and VegPL diets, but strong conclusions cannot be made due to the high variability. Feed intake can only be measured on a tank basis, so it was not possible to estimate feed intake of fish with different pre-transfer freshwater feeding histories in a common tank that were fed the same diet in the seawater phase.A trend for decreased hepatosomatic index (HSI) was indicated with increased KM inclusion and for the 12% KM diet versus the other PL sources added to provide the same PL level in the pre-transfer phase, but the effect of KM on decreasing HSI was not carried over into the seawater phase with fish that were fed the same diet in a common tank . There wGills are one of the most vital organs of fish, due to their function in respiration, osmoregulation, excretion of nitrogenous waste, pH regulation, and hormone production . Gill heOverall, increased KM tended to increase growth (high variability), whereas the VegPL diet tended to decrease growth compared to the control diet in the FW pre-transfer phase. The positive growth trend indicated for KM fed pre-transfer was not carried over into the seawater phase for fish fed the same diet. A minor positive trend in gill health (lamella inflammation and hyperplasia histology scores) was indicated for the 12% KM and Control diets compared with the VegPL and MarPL diets in the FW pre-transfer phase. Hepatosomatic index tended to decrease with KM fed in the pre-transfer phase, noting that all livers evaluated by histology were considered normal for lipid droplet accumulation. Only one VegPL and MarPL dose was tested, so dose effect of these PL sources and comparison with krill oil to better isolate the PL effect from other nutrients in KM as well as a post-transfer feeding comparison of these PL sources could be areas to research further in transfer diets for salmon.


Many forces influence genetic variation across the genome including mutation, recombination, selection, and demography. Increased mutation and recombination both lead to increases in genetic diversity in a region-specific manner, while complex demographic patterns shape patterns of diversity on a more global scale. While these processes act across the entire genome, the X chromosome is particularly interesting because it contains several distinct regions that are subject to different combinations and strengths of these forces: the pseudoautosomal regions (PARs) and the X-transposed region (XTR). The X chromosome thus can serve as a unique model for studying how genetic and demographic forces act in different contexts to shape patterns of observed variation. We therefore sought to explore diversity, divergence, and linkage disequilibrium in each region of the X chromosome using genomic data from 26 human populations. Across populations, we find that both diversity and substitution rate are consistently elevated in PAR1 and the XTR compared to the rest of the X chromosome. In contrast, linkage disequilibrium is lowest in PAR1, consistent with the high recombination rate in this region, and highest in the region of the X chromosome that does not recombine in males. However, linkage disequilibrium in the XTR is intermediate between PAR1 and the autosomes, and much lower than the non-recombining X. Finally, in addition to these global patterns, we also observed variation in ratios of X versus autosomal diversity consistent with population-specific evolutionary history as well. While our results were generally consistent with previous work, two unexpected observations emerged. First, our results suggest that the XTR does not behave like the rest of the recombining X and may need to be evaluated separately in future studies. Second, the different regions of the X chromosome appear to exhibit unique patterns of linked selection across different human populations. Together, our results highlight profound regional differences across the X chromosome, simultaneously making it an ideal system for exploring the action of evolutionary forces as well as necessitating its careful consideration and treatment in genomic analyses. Genetic variation is influenced by factors that vary across genomic regions. Mutation rate –5 and reeμ [e is the effective population size and μ is the mutation rate. Because diversity is a function of population size, regions of the genome that have a lower Ne are expected to have proportionally lower genetic diversity. The X chromosome, in particular, is composed of multiple regions that differ in Ne. The pseudoautosomal regions exist on both the X and the Y and therefore have a similar Ne to that of the autosomes, while the non-pseudoautosomal regions of the X chromosome exist in two copies in females and one copy in males, effectively resulting in ¾ the effective size of the autosomes, assuming random mating. Thus, one would expect differences in genetic diversity across the X chromosome simply due to differences in Ne.Some regions of the genome  differ in genetic diversity due to differences in effective population size . Under teμ , where NSelection can also shape genetic diversity across the genome. Linked selection reduces diversity in neutral regions that are closely linked to genes , 18 and In addition to the processes described above, patterns of human demography strongly affect patterns of genetic variation –27. For The X chromosome contains several distinct regions that have different evolutionary histories, and which operate under various combinations of the above processes. The sex chromosomes (X and Y) in mammals diverged from a pair of autosomes approximately 180–210 million years ago . Over tiThe evolutionary history of the pseudoautosomal regions has been well studied , 53. VarBecause of its unique structure, inheritance, and evolutionary history, the X chromosome serves as a unique model for studying how genetic and demographic forces act in different contexts to shape patterns of observed variation. For example, departures from neutral equilibrium expectations of X/A diversity have been used to study sex biases in processes such as migration, admixture, generation time, and reproductive success , 55, 56.We measured nucleotide diversity in 26 human populations  from theTo further explore differences in diversity among regions on the X chromosome, we divided diversity values calculated in PAR1, XTR, and chrX by those from an autosome approximately the same size as the X, chr8 (referred to here as autosome or A). We do this for each of the 26 1000 genomes populations . The nonSurprisingly, our observations of XTR did not match our expectation that it would behave similarly to chrX because both are present in one copy in genetic males and two copies in genetic females. Across populations, XTR/A diversity was consistently greater than observed chrX/A diversity  and variMutation rate, which is known to vary across the genome –65, inflWhile we did not find an association between human-dog divergence and distance from genes, we observed striking differences in substitution rates across the different regions of the X chromosome and chromosome 8 . PAR1 har2 across the X chromosome and chromosome 8 to characterize linkage disequilibrium (LD) as a proxy for recombination rate —the population in our dataset exhibiting the greatest diversity across most regions of the X chromosome . If pattFor PAR/A and XTR/A ratios, the “flattening” effect of normalizing to MSL was less pronounced . AlthougTo look at the effect of different outgroup populations on normalizing X/A ratios, we divided diversity ratios for the 26 populations by the populations with the highest diversity in each of the four remaining superpopulations . When weThere are many processes that shape the landscape of genetic variation across the genome. Patterns of genetic variation across the X chromosome are especially complex because its unique structure and pattern of inheritance have the potential to interact with these processes in different ways. In this study, we examined 26 diverse human populations and found remarkable variation in genetic diversity on the X chromosome, both among populations and across different regions of the X chromosome itself. More specifically, we found that the landscape of genetic variation across the X chromosome was structured by mutation, recombination, and population history, which differentially affected major regions of the X chromosome—the PAR, XTR, and nonPAR—and led to substantial variation in genetic diversity across these regions.Of the X-chromosomal regions we studied, our results for the X-transposed region (XTR) were especially surprising because its properties were intermediate to both the pseudoautosomal regions and the nonPAR regions of the X chromosome. Though the XTR shares homology with the Y chromosome, we expected it to behave similarly to the nonPAR regions of the X chromosome because it underwent an inversion preventing recombination . Howeveret al. [The unusual pattern of diversity within the XTR could be driven, in part, by technical artifacts. We recently showed that, due to homology, the X-transposed sequences between the X and the Y are similar enough to confound the mapping of raw sequencing reads . This leet al. .Our observation of lower linkage disequilibrium (LD) in XTR, if LD serves as a good proxy for recombination in this case, is consistent with recombination in this region. This is unexpected because, despite its X-Y homology, the XTR experienced an inversion event that is proposed to have prevented further recombination from occurring . Our resWe propose that there are two additional explanations for the observed XTR/A diversity. First, is that the XTR has only recently started to diverge between the X and Y chromosomes, so may reflect a transition state between a fully recombining region with an autosomal effective population size  and a region with a lower effective population size (like the nonPAR/nonXTR X chromosome regions) that does not typically recombine in genetic males. Thus, there could be a biological expectation that diversity on the XTR relative to autosomes should be intermediate to the PAR/A and X/A regions. Alternatively, building on our previous description of technical artifacts in this region  and in aS. cerevisiae [de novo mutations and recombination hotspots [While some past studies have generally concluded that genetic divergence is not associated with recombination hotspots across the autosomes , other wrevisiae . Additiohotspots . Here, whotspots ; a resulAdditionally, it has been argued that the correlation between recombination rate and genetic diversity in the human PAR1 is driven specifically by the relationship between recombination rate and divergence . Here ouThe ratio of X chromosome to autosome diversity has long been of interest in exploring aspects of population history, particularly those that are sex-biased , 56, 77.When considering just the nonPAR X, we observe a pattern largely in line with previous studies: the highest ratios are in African populations . As popuIn contrast to the nonPAR X, when we used XTR/A or PAR1/A ratios, we observed very different patterns . Both raThere are a few possibilities to explain this trend of increasing PAR1/A ratios. We observe that the interaction of differences in recombination  and population history over human evolution  could potentially offer one explanation for both the higher-than-expected PAR1/A ratios and the gradual increase in these ratios in non-African populations. Another possibility is the effect of balancing selection, which could maintain greater genetic diversity in PAR1 relative to the autosomes if it were acting disproportionately in this region , 81. AnoWhen considering the effect of linked selection, we saw that nonPAR X/A ratios increase after filtering sequence close to genes which is consistent with the hypothesis that the X chromosome experiences more efficient diversity-reducing selection  than the autosomes, due in part to it being found in only one copy in most genetic males , 21. In In order to learn more about the differences in the X/A, PAR/A, and XTR/A ratios across the populations we studied , we consArbiza et al. (2014) found that, for two populations (from EUR and EAS), this normalization resulted in roughly equal X/A ratios near and far from genes, leading authors to conclude that the general relationship of selection between the X and autosomes was similar across human populations . We replOverall, our work builds on a growing picture that shows if we are to fully understand genomic variation and human evolutionary history, we need to look at a diversity of populations . While n20141020.strict_mask.whole_genome.bed) to assess callability and determine the number of monomorphic  sites in each region.We obtained human genetic variant data in the form of VCF files from Phase 3 of The 1000 Genomes Project mapped to the reference genome hg19 . We analWe used the UCSC Table Browser  to obtaiTo account for mutation rate variation, we corrected our diversity estimates in each region using pairwise divergence values between human and dog (hg19-canFam3) reference genomes. We used hg19-canFam3 divergence because substitution rates with more recently diverged primate species (rhesus macaque and marmoset) tended to correspond closely with the human PAR1 whereas in the dog comparison, variation in the substitution rate appeared to be independent of the human PAR1 boundary . We obtaIn addition to using these substitution rate estimates to account for variation in mutation rate, we explored how hg19-canFam3 divergence estimates within PAR1, PAR2, XTR, chrX, and chr8 change as we filter with increasing distance from genes . We additionally calculated divergence ratios between each of the X chromosome regions relative to chromosome 8 .ni is the allele count of allele i in a sample and n is the sum of ni [et al. [We estimated uncorrected and unnormalized genetic diversity as the average number of pairwise differences per site (π) among sequences in each population. We used allele frequencies of single nucleotide polymorphisms to calculate diversity for each variant site:um of ni . We calc [et al. . See S4  [et al. , 91.In each window/region we corrected for differences in mutation rate by dividing the window by the corresponding calculation of substitution rate in that window/region. For several of these windows on the long arm of the X chromosome, the divergence-corrected values are elevated as a result of high variability in the hg19-canFam3 substitution rates. We chose to include these windows because previous analyses of the X chromosome regions showed little effect of correcting for these high-diversity values on the results .After correcting for divergence in each of the 100kb windows, we used permutation tests to compare mean diversity among the X chromosome regions for each of the 26 1000 Genomes populations. We divided chrX into 100kb non-overlapping windows and we permuted these windows 10,000 times to test the significance of the difference between diversity corrected for divergence in each X chromosome region  and the rest of chrX .To explore variation across the X chromosome regions , we calculated the ratio of diversity corrected for divergence in each region relative to diversity corrected for divergence on chr8. We did this for each human population. We calculated 95% bootstrapped confidence intervals for these corrected diversity ratios .To explore the role that demography plays across these regions, we normalized diversity on the X and autosome, both corrected for divergence, by dividing by the population with the highest level of estimated diversity . Thus, we have estimates of normalized diversity for 25 of the 1000 Genomes populations.To explore the effects that linked selection has on diversity, we analyzed sequence diversity with increasing distance from genes . To visualize the effects of removing potentially linked sequences, we plotted the difference in diversity between each filter that removed flanking regions from genes  with the measurement of diversity that only excluded genes and no flanking sequence (0kb). We did this both for unnormalized diversity and for measurements of diversity normalized to MSL  as well r2 in 100 kb windows across each chromosome as well as within the X chromosome regions and all of chromosome 8. We did this separately for each superpopulation . We estimated 95% bootstrapped confidence intervals by resampling 1000 times in each region of interest. To explore the relationship between LD and diversity, we used a linear regression analysis to compare the average r2 values and diversity values calculated in 100kb windows across the X chromosome  betweenromosome . All anaS1 Figr2 values +/- 300kb from that site. Average r2 values for each site are then used to calculate mean LD for a given region. Error bars represent 95% bootstrapped confidence intervals (1000 replicates with replacement).Linkage disequilibrium (LD) is calculated in each X chromosome region and for chromosome 8 for each 1000 Genomes Population. LD is calculated for each site in a given genomic region by averaging all pairwise (PDF)Click here for additional data file.S2 FigR2 values for the negative correlation are reported on each plot.Average linkage disequilibrium was calculated in 100kb windows and plotted against corresponding average nucleotide diversity in 100kb windows (corrected for mutation rate with hg19-canFam3 divergence). This was done for a) diversity calculated by only filtering for genes and b) diversity calculated by filtering for genes +/- 10 kb flanking regions. (PDF)Click here for additional data file.S3 FigDiversity ratios between regions on the X chromosome—non-pseudoautosomal X (X), pseudoautosomal region 1 (PAR), and X-transposed region (XTR)—and autosomes for 25 1000 genomes populations. Values are reported as the difference between using a filter for only genes and a filter including 1kb, 5kb, 10kb, and 20kb of sequences flanking genes. These ratios are demography normalized by reporting each population relative to a) Toscani in Italia; b) Punjabi from Lahore, Pakistan; c) Kinh in Ho Chi Minh City, Vietnam; and d) Puerto Ricans from Puerto Rico. The order of populations is the same as reported in (PDF)Click here for additional data file.S4 FigRhesus macaque , Callithrix jacchus , and Canis lupus familiaris . Red indicates the pseudoautosomal regions  and blue indicates the X-transposed region (XTR).Substitution rates calculated in 100kb sliding windows and corrected using the Jukes-Cantor 1969 model  across t(PDF)Click here for additional data file.S1 TableThe number of male and female samples and population code used for each of the 26 1000 Genomes Project populations organized by superpopulation .(CSV)Click here for additional data file.S2 TableNucleotide diversity was calculated for each 1000 Genomes population and normalized for mutation rate using canFam3-hg19 divergence. P values are calculated using a permutation method with 10,000 replicates for the difference between a region  and nonPAR. P-values here are not multiple-test corrected.(CSV)Click here for additional data file.S3 TableThe amount of data remaining for each filter was calculated with increasing distance from genes . Callable sites, variants, uncorrected diversity measures, and diversity corrected to canFam3 divergence are reported for each X chromosome region , the Y chromosome, and chromosome 8.(CSV)Click here for additional data file.S4 TableGene length and the number of genes are reported for each region across the X chromosome  and chromosome 8. (1) nonPAR is calculated as the remaining regions after removing PAR1, PAR2 and XTR. Thus, it is split up into two non-contiguous regions. (2) PAR1 and PAR2 coordinates come from the hg19 region definitions. (3) XTR is defined between 88 and 93 Mb . It cons(CSV)Click here for additional data file.