P < 0.001)) and higher overall costs. Very few patients received any genetic testing (4.2–8.4% depending on cohort criteria). These results highlight the substantial proportion of the population with clinical features associated with genetic disorders and underutilization of genetic testing in these populations.We characterized US pediatric patients with clinical indicators of genetic diseases, focusing on the burden of disease, utilization of genetic testing, and cost of care. Curated lists of diagnosis, procedure, and billing codes were used to identify patients with clinical indicators of genetic disease in healthcare claims from Optum’s de-identified Clinformatics® Database . Distinct cohorts were defined to represent permissive and conservative estimates of the number of patients. Clinical phenotypes suggestive of genetic diseases were observed in up to 9.4% of pediatric patients and up to 44.7% of critically-ill infants. Compared with controls, patients with indicators of genetic diseases had higher utilization of services (e.g., mean NICU length of stay of 31.6d in a cohort defined by multiple congenital anomalies or neurological presentations compared with 10.1d for patients in the control population ( Our understanding of the genetic etiologies of many rare diseases has grown substantially in recent years2. Although most diseases have complex genetic determinants, many rare diseases are caused by pathogenic variants in single genes or by rare DNA copy number abnormalities. For example, in two recent analyses of the Orphanet database, 70–80% of rare diseases were found to be genetic or have genetic subtypes3. The Online Mendelian Inheritance in Man (OMIM) compendium lists over 6500 clinical phenotypes for which a molecular basis is known and 4200 genes with phenotype-causing pathogenic variants4. These numbers have continued to grow as information on genetic variation and gene–phenotype relationships emerge from various research projects including those utilizing comprehensive genomic sequencing approaches10. Furthermore, as our understanding of the genetic etiology of diseases has grown, our ability to treat many of these conditions has improved, including with targeted pharmaceuticals, vitamin supplementation or dietary restrictions, protein and enzyme replacement therapies, stem cell and organ transplantation, and most recently novel precision medicine approaches such as gene replacement therapy17.Rare diseases represent a significant clinical and economic burden, with >25 million Americans estimated to be living with a rare disease18. A key challenge, however, is that many genetic diseases are difficult to diagnose owing to nonspecific clinical presentations and physician unfamiliarity—leading to delays in diagnosis and initiation of precision care. These challenges are compounded by traditional diagnostic approaches, which focused on subjective assessment, non-genetic tests and imaging, and iterative application of single-gene tests or panels with limited diagnostic yield. In recent years, genome-wide approaches using whole-exome and whole-genome sequencing have demonstrated substantial improvement in diagnostic yield compared with traditional approaches23. These improvements have led to policy changes related to care delivery. For example, the National Health Service (NHS) in England has announced the commission of genome sequencing as part of routine care for patients with undiagnosed rare diseases. In the US, the majority of pediatric lives are covered for exome sequencing (ES) by commercial and public health insurance programs, with some also covering genome sequencing (Data on file from https://www.policyreporter.com/).The American College of Medical Genetics and Genomics (ACMG) has highlighted that there is “great clinical value in arriving at a precise medical diagnosis, enabling, among other things, identification of a disorder’s cause and prognosis, as well as frequently informing preventive and treatment modalities.”24. Given the burgeoning opportunity to improve the care of patients with genetic diseases, understanding their clinical and diagnostic assessment will be important to improving care delivery. Data are needed to inform our understanding of the potential impact of new interventions such as whole-genome sequencing. The clinical features that guide such testing, which are individually uncommon, are often present in patients with rare and ultrarare genetic diseases and are utilized by specialists to guide genetic testing. However, testing is not uniformly applied to owe to differences in physician practice, delays in access to specialist consultation, complex and non-standardized laboratory platforms, regional and urban/rural disparities in access, and non-uniform health insurance coverage policies. The objective of this study was to characterize the population of patients with clinical features of genetic diseases in a large US commercial healthcare claims database, with a focus on the burden of disease, utilization of genetic testing, and cost of care. Separate analyses were performed for critically-ill infants and the general pediatric population.Although the pace of research and understanding of genetic diseases has accelerated, there has been little real-world evidence published on population-based epidemiology and the care of patients with clinical indicators of genetic diseases. In a recent study of pediatric inpatients in the US using the 2012 version of the Healthcare Cost and Utilization Project (HCUP) Kid’s Inpatient Database (KID), we estimated that up to 14% of pediatric inpatient stays had indications of genetic diseaseA total of 13,076,038 unique patients were included in the source data set selected. Of these, 11,036,263 subjects were identified and retained as a control for the pediatric population, and 1,148,695 subjects that met all birth and enrollment criteria were retained as controls for the newborn population . A total of 94,714 (0.8% of eligible patients) had ICD-9/10 codes for MCA/ID/DD/E. Finally, 28,348 (0.2% of eligible patients) met the Conservative cohort definition which required at least one genetic test  and 44.7% of the total eligible population with a NICU stay . A total of 8723 critically-ill newborns  had ICD-9/10 codes for MCA/ID/DD/E (primarily MCA and E in this cohort). Finally, 1,924 critically-ill newborns  met the Conservative cohort definition which required at least one genetic test  and were therefore excluded from this population and incorporated into the pediatric population.In the newborn population, 29,348 critically-ill newborns met the Broad cohort criteria. This represented 2.3% of the total eligible newborn population n = ,303,762 P < 0.01 for all comparisons to the Control cohort). Patients in all three cohorts with the suspected genetic disease generally had higher rates of comorbidities, including respiratory failure (up to three times higher), asphyxia (up to 13 times higher), feeding problems (up to 10 times higher), and pneumonia (up to 2.5 times higher)  and pediatric intensive care unit (PICU) utilization in all three cohorts with indicators of suspected genetic disease compared with the control population Table . For exaP < 0.01; Table In the critically-ill newborn populations, mean NICU length of stay (LOS) was 24.6d, 31.6d, and 26.0d in the Broad, MCA/ID/DD/E, and Conservative populations, respectively, compared with 10.1d for patients in the control population that had at least one NICU stay , followed by endocrine/nutritional/metabolic disorders (14.7%), congenital anomalies (5.1%), and disorders presenting in the perinatal period (3.4%).possible or probable genetic disease categories to a primary diagnosis code in the definite genetic disease category is indicative of a conservative estimate of definitive genetic disease diagnoses. These transitions may have resulted from additional clinical investigations including specialist assessments, the results of imaging or other non-invasive procedures, or, in an extreme minority of cases per the genetic test utilization results above, molecular testing. The number of patients who moved into more definite categories of diagnosis is reported in Table Not all genetic diseases have specific ICD-9/10 codes; thus, a definitive assessment of genetic disease diagnoses in patients with initial clinical presentations that are indicative of a suspected genetic disease is not possible from an administrative claims-based analysis. However, assessing the proportion of patients transitioning from an initial index diagnosis code in the possible or probable to definite  extends to a time period prior to recommendations and broad coverage. Similar to the pediatric population, only a small proportion of critically-ill newborns with MCA or epilepsy/seizures—~8%—received even one genetic test in the entire follow-up period. Although not directly addressed in our study, the consequences of delaying diagnosis of genetic diseases among newborns and children can be devastating, resulting in missed opportunities for effective treatment, unnecessary procedures, excessive costs, in addition to patient and family anxiety42. Although our study design does not allow for an assessment of whether application of earlier diagnostic testing or more rapid diagnosis would have resulted in a decrease in the average LOS in the NICU, identifying the underlying clinical diagnosis would accelerate the ability to institute appropriate clinical management in this acutely ill population44.A third major takeaway was that critically-ill newborns with indicators of suspected genetic diseases had long NICU stays compared with a control group of newborns without evidence of a suspected genetic condition. In addition, both the critically-ill newborn and pediatric populations had high rates of PICU utilization. Several studies have demonstrated that rapid exome or genome approaches may lead to decreases in NICU LOS in a subset of patients24. This area of research deserves further exploration. Notably, from a health economic perspective, several published studies demonstrate that earlier incorporation of genome-wide approaches such as exome or genome sequencing is cost-effective by preventing long expensive diagnostic odysseys46. A recent study in the Netherlands found that medical costs decreased substantially following ES—regardless of whether there was a diagnosis—a finding they termed an “end of trajectory” effect47. Future studies should interrogate the services contributing to costs and how the mix of services changes over time.Finally, patients with clinical indicators of genetic disease in all of the cohorts evaluated had substantially higher costs compared with controls–particularly in the first 12–24 months post index. These data build on a previous study focused on costs associated with pediatric hospitalizationsOur study has strengths and limitations. Strengths include the size of the population evaluated—which included over 13 million unique patients from a large commercial health plan population, representative of commercially-insured patients more generally. Our methodology, which included a detailed curation of indicators of genetic disease, allowed us to evaluate several different populations of patients based on different levels of specificity. Finally, our administrative claims-based approach allowed for a comprehensive assessment of direct healthcare-related utilization and costs. Several limitations should be considered in the interpretation of our results. First, as our methodology utilized insurance claims data as opposed to electronic health records, we are limited in our ability to interpret both diagnostic thinking and clinical decision-making. For example, while we observe substantial delays in genetic testing in many patients, the reasons for these delays are unclear and may be clinically warranted based on the unique circumstances of individual patients. Similarly, ICD-9/10 codes do not capture the granularity required to distinguish diagnosed genetic conditions. Human Phenotype Ontology (HPO) provides a deep ontology that relates detailed clinical features to both diseases and genes. Availability of HPO terms in a large data set like this would have improved our specificity in defining populations and HPO coding (or improvements in ICD coding informed by HPO) could contribute to future studies of genetic disease in populations. In addition, while some genetic diseases have specific ICD-9/10 codes, many do not; thus, we are unable to definitively determine the end of a diagnostic odyssey for many individual patients. Second, our definitions of possible, probable and definite genetic disease are based on expert opinion and manual curation. Not all patients with these features would have had a genetic diagnosis even if optimal testing had been performed. This is likely due to additional undescribed disease-causing genes, oligogenic and complex inheritance, and environmental phenocopies. How ICD-9/10 codes are used for insurance billing purposes may not be aligned with the diagnostic thinking of clinicians. Coding systems, including diagnosis and procedure codes, are used for billing purposes and are likely to fail to reflect the variability observed in clinical practice. Furthermore, a level of miscoding is presumed to occur as is inherent in claims database studies . Third, given the substantial health conditions of patients with genetic diseases, patients may transition to public insurance programs, switch between private insurance plans, or for other reasons stopped being observed in the database. Although our continuous enrollment requirements help control for some of this variability, there are inherent limitations to the comprehensiveness of the follow-up, which could also be exacerbated by the severity of the disease. Finally, in our selection of patients, we found a large number of newborns with codes indicating a possible genetic condition but no stay in a NICU. Because we required a stay in a NICU, these patients were excluded from analysis in the critically-ill infant population and were instead included in the pediatric population.The results of our study highlight the substantial patient population with clinical features that may lead a physician to suspect genetic disease, and notably, that there is significant underutilization of genetic testing. Earlier and more comprehensive genomic testing has the potential to reduce the burden on children, families, and the healthcare system. As the availability of new therapies and approaches expands, accurate and rapid diagnosis becomes even more critical. Integrating novel diagnostic approaches early in the life of children with clinical symptoms will improve the long-term prospects of these children and reduce unnecessary costs to the healthcare system.We performed a retrospective administrative claims analysis using Optum’s de-identified Clinformatics® Data Mart Database 7.1 database which contains linked eligibility, medical claims, and pharmacy claims. Data for all patients 0–18 years of age were used to identify patients with clinical features that may lead a physician to suspect genetic disease based on the date of birth between 1 January 2007 through 31 December 2017. Age was determined by the patient’s earliest eligibility effective date. To protect patient privacy, actual dates of service were not provided in the data; instead, the database contained “derived dates”, which indicated the days since birth that an event occurred. For example, if a date was 0 this would be the day the patient was born. If the date was 365, this would indicate that the event was 1-year post birth. Data were provided in de-identified format and met all requirements for patient privacy .Institutional review board approval was not required as the Optum database has been evaluated and approved in compliance with HIPAA standards for data privacy.24, though the current study the code sets were expanded to allow for more detailed classification of patient cohorts and utilization of genetic and non-genetic testing and procedures (by including CPT/HCPCS codes) and to allow for additional timeframe analyses (by adding ICD-10 codes). The code curation process resulted in identification of 1269 ICD-9 codes, 2155 ICD-10 codes, and 781 CPT/HCPCS codes. These codes were further curated with respect to the age of presentation as it is well known that some clinical diagnoses are highly suggestive of a genetic disorder within certain age groups but not others .Our methodology to define the study populations was developed with the goal of identifying pediatric (<18 years of age) patients with indicators of genetic diseases. Diagnostic codes typically do not classify by underlying disease mechanism, and we therefore manually curated lists of International Statistical Classification of Diseases and Related Health Problems (ICD)-9 and ICD-10 diagnosis codes, Current Procedural Technology (CPT), and Healthcare Common Procedure Coding System (HCPCS) codes, and other relevant billing codes . A detailed list of all codes used in this study can be found in the 24, ICD-9/10 codes were first categorized into three sets: (1) definite genetic disease; (2) probable genetic disease; and (3) possible genetic disease. The definite category was defined by ICD-9/10 codes that identify specific genetic diseases . Probable genetic diseases were defined as ICD-9/10 codes that describe clinical presentations wherein a genetic etiology is likely to be achieved with appropriate genetic testing in >30% of patients with these codes . Finally, possible genetic diseases were defined as ICD-9/10 codes that describe clinical presentations wherein a genetic etiology is likely to be achieved with appropriate testing in ~10–30% of patients . We further identified ICD-9/10 codes where there are established and specific clinical and/or laboratory diagnostic criteria and availability of established and commonly administered targeted diagnostic tests , and therefore patients with these codes would be less likely to present clinically as an undiagnosed genetic disease. CPT and HCPCS codes were categorized into two categories: (1) genetic tests , ES); and (2) other procedures that are commonly used in the workup of patients with suspected genetic diseases , though we acknowledge this second category includes codes that are not specific to the workup of patients with suspected genetic diseases.By definition, ICD-9/10 codes are intended to classify diseases or conditions; thus, some ICD-9/10 codes definitively identify specific genetic diseases whereas others indicate clinical presentations that may be an accepted indication to test for a genetic disease. As such, as described previouslyWe used combinations of this curated code sets to group patients into three non-mutually exclusive cohorts for analysis [a complete list of codes and the algorithms for identifying each cohort  and an apossible, probable, or definite ICD-9/10 code  or ≥1 curated genetic or non-genetic CPT/HCPCS code.The Broad cohort was intended to be the broadest cohort of patients. We defined this cohort as patients with a possible or probable ICD-9/10 diagnosis code as the index code and ≥1 genetic test CPT/HCPCS code at any time. Thus, patients with definite ICD-9/10 codes as the index code were excluded.The Conservative cohort was intended to be a specific cohort of patients with indicators of undiagnosed disease and evidence of genetic testing. We defined this cohort as patients with a 54. We defined this cohort as patients with ICD-9/10 diagnosis codes for MCA, moderate or severe ID, DD, or E. For patients with MCA, patients were required to have either a code specific for multiple anomalies or codes for single anomalies in two or more organ systems.This cohort was intended to identify a specific subset of clinical presentations that are broadly accepted indications for genetic testing, as suggested by clinical guidelinesAn index date was set on the service date of the first appearing qualifying code for a diagnosis for the Conservative and MCA/ID/DD/E cohorts, or on the first appearing diagnosis or test date for the Broad cohort. Identification of the index date included all ICD codes, regardless of whether the code was principal or secondary, with a hierarchy of definite>probable>possible for the index code for claims with more than one associated diagnosis code. Patients were required to be continuously enrolled for 6 months prior to the index diagnosis, with any child <6 months of age having a reduced continuous enrollment requirement equal to their age on index.Finally, we divided the sample into two non-overlapping populations based on age and patient acuity: pediatric patients and critically-ill infants. Pediatric patients were either >28 days of age and ≤18 years on the index date, or were ≤28 days of age on the index date but had no observed NICU stay at any time during the observation period. Critically-ill newborns were ≤28 days of age on the index date and had a stay in a NICU at any time during the observation period. Thus our final analyses were completed on six groups: within both critically-ill infants and within pediatric patients we evaluated Broad, Conservative, and MCA/ID/DD/E cohorts. Fig. Fig. 3Poppossible, definite, or probable ICD-9/10 diagnosis or CPT codes used to select the study populations. The first continuous enrollment period for control was identified and the index date was set as of the start date of enrollment. For descriptive analyses, all subjects were included. Each of the six cohorts (the three definition categories for each of the two patient age groups) were matched to a control population generated by the use of propensity score technique matching on ethnicity/race, gender, Census Division, age at index (pediatric only), year of birth, and a number of children in subscriber’s household. A NICU stay was not required for inclusion in the newborn control cohort. One way of measuring the balance is with a Multivariate Imbalance Measure. In this context, an L1 of 1.0 means completely unmatched, and an L1 of 0.0 means perfectly matched. We then used three procedures for matching to determine the best methodological approach to achieve balanced cohorts. For the evaluation of survival, controls were matched using the Coarsened Exact Matching (CEM) method55 with k-to-k (k2k)—i.e., where the matching procedure reduces the samples to a 1 to 1 match rather than many to 1 match. The CEM with k2k is almost as good as CEM without k2k, but has the computational benefit of reducing the number of records in the control population, so these results were used for all matching procedures.We defined control populations with none of the 56.Variables assessed included clinical presentation, demographics , genetic test utilization, time in the NICU and PICU, and overall costs. Clinical presentations and comorbidities were identified based on ICD-9/10 diagnosis codes as described above and in the All medical and pharmacy claims were used in the calculation of costs using the Optum database standard allowable charges field. Costs were calculated as total costs , inpatient costs (identified from the inpatient confinement claims), and outpatient costs . More information on variable definitions and data limitations can be found in Supplementary Notest test. Other primary analyses performed were descriptive—to avoid introducing the possibility of error based on multiple hypothesis testing.Statistical comparisons to the control group were conducted for demographic variables, including age at index, length of enrollment, age at enrollment, % male, and % white using independent samples, two-sided, Further information on research design is available in the Supplementary InformationReporting Summary


Each sealer was applied to 10 teeth (n = 10) using LS or GP. Micro-computed tomography (micro-CT) was used to quantify the volume of root filling and voids before and after 8-week storage in a phosphate-rich medium. The percentage of root filling and voids were compared between the groups using a Mann–Whitney U test and Kruskal–Wallis test with a Bonferroni correction. Before aging, the percentages of root filling volume after obturation were comparable with no significant differences between sealers (p = 0.325) or application methods (p = 0.950). After aging, the voids’ volume increased significantly in all sealers (p ≤ 0.05). However, no significant differences were found between sealers (p = 0.302). In conclusion, voids in CSBSs may not reduce in size with aging; hence, SGPC should be carefully selected for suitable cases.During obturation, air voids are undesirable as they may provide shelter for microorganisms or passage for fluids. This study aimed to compare the occurrence of voids between three calcium silicate-based sealers (CSBSs)  and the change in their volume after aging. In addition, we aimed to compare voids when using two sealer application methods: lentulo-spiral (LS) and gutta-percha (GP) cone. Thirty extracted mandibular premolars ( Root canal treatment aims to prevent the occurrence, or further progress, of peri-radicular disease. This is achieved by disinfecting and sealing the root canal spaces striving to entomb microorganisms that survived chemo-mechanical preparation . This exIn recent years, calcium silicate-based sealers (CSBSs) have gained growing popularity . This isWith the introduction of CSBSs, the concept of single gutta-percha cone (SGPC) obturation resurfaced. With their dimensional stability, higher film thickness, and interactivity with dentin, CSBSs seemed compatible with this technique ,25,26. IIn the field of dentistry, the use of micro-computed tomography to study various aspects related to the root canal system is increasing ,37. It in = 30) were used in this study . Teeth were collected from the oral surgery clinics for patients attending for extraction of lower premolars for orthodontic purposes. Based on preliminary data, the sample size was calculated with an effect size of 10% and a 7% standard deviation using a significance level of 0.05 and a power of 80% to detect a difference. The estimated sample size for each group was required to be at least 9 ). Teeth were visually and radiographically examined to exclude those with more than one root or canal, carious lesions, unusual anatomical features, or fractures. An impression index was prepared for each tooth’s crown using 3M™ Express™ putty vinyl polysiloxane impression material  and cube-shaped molds (10 mm × 10 mm × 10 mm) to be used for mounting teeth during micro-CT imaging in reproducible positions.Freshly extracted human mandibular first premolars (Endodontic access cavities were prepared using a high-speed handpiece and round end tapered diamond bur (ISO 199/016). The working length was determined by measuring the length of a size 10 stainless steel K-File  inserted until visible through the root apex, and then deducting 0.5 mm of that length. The canals were chemo-mechanically prepared using ProTaper Universal rotary NiTi file system up to size F2  in association with 2.5% NaOCl irrigation solution applied with a 5 mL Luer-lock syringe and MonovacTM side-vented 27 ga irrigation tip . As a final rinse, canals were irrigated with 1 mL of 17% ethylenediaminetetraacetic acid solution (EDTA)  followed by 5 mL of 2.5% NaOCl along with a manual agitation using a ProTaper F2 matching GP cone . The canals were then dried with size 25 absorbent paper points .n = 10): BioRoot RCS , MTA Fillapex , and Bio-C Sealer   .n = 5) or a size 25 Lentulo spiral (LS) (n = 5) attached to a motor-driven slow-speed handpiece  operated at 2000 RPM with in-and-out motion keeping the instrument insertion 5 mm shorter than the working length. Each sealer application method was randomly used with half of the samples in each group. Using LS, the sealer was loaded and applied into the canal in three consecutive loads; then, the master GP cone was inserted to the full working length. For the GP cone method, the selected master cone was loaded with the sealer, applied into the canal, gently rotated while brushing against the canal walls, and repeated three times. The excess sealer was cleaned from the access cavity, and excess coronal GP was seared using a heated endodontic plugger. All obturated teeth were stored in an incubator at 37 °C and 100% humidity for 24 h before performing the baseline obturation micro-CT imaging. The samples were then separately stored in closed containers with 15 mL of a phosphate-rich solution for 8 weeks in an incubator at 37 °C. The storage solution was prepared by mixing the ingredients  of the mixture was adjusted to a neutral pH by mixing aliquots of 1 M of NaOH until the required pH was obtained. The solutions were replenished every week.A high-resolution micro-CT system  was used with a source voltage of 89 kV, source current of 112 µA, and 5.9 s exposure time using a 360° rotational angle with a 0.6° rotational step. Raw images in Tagged Image File Format (TIFF) were produced with a pixel size of 13.73 µm and a frame averaging of two. The images were reconstructed using NRecon software  from a total of 1333 slices. The reconstructed images were processed using a Gaussian kernel and ring artifact correction of 6 with 20% beam hardening correction. A standard binarization process was applied to identify and measure the voids volume, with any area falling below the threshold value of 96. This value was selected after evaluating different values and found to be the most suitable to differentiate between the root filling, dentin, and space. The micro-CT images of the teeth were obtained and reconstructed after canal preparation, post-obturation, and after 8-week storage in a phosphate-rich solution .From the reconstructed images, the volumes of prepared canals (C), root filling (F), and voids (V) were measured using the following equation:3) before storage, respectively, while V2 and F2 are the void and root filling volumes after storage. The change in void volume after storage (V3) was calculated as follows:V1 and F1 are the void and root filling volumes (mmWhile the percentage of change in void volume after storage (V%) was calculated as follows:p < 0.05). Hence, non-parametric tests of statistical significance were used. The Mann–Whitney U test and Kruskal–Wallis test with a Bonferroni correction (significance level set at p ≤ 0.017) for multiple comparisons (where necessary) were used for independent samples. The Wilcoxon signed-rank test was used for paired samples. A p-value of ≤0.05 was considered statistically significant.Data were analyzed using Statistical Package for Social Sciences . Descriptive analysis was performed, and the means and standard deviations are reported. The Shapiro–Wilk normality tests showed skewed distribution (The mean ± SD of the root filling percentage is presented in p = 0.325). Comparing the percentage of root filling volume between the two sealer application methods (p = 0.950).The average percentage for all the sealers was (92.94% ± 7.98). The highest percentage was achieved by BioRoot RCS (95.61% ± 3.46), followed by Bio-C Sealer (95.32% ± 5.14) and MTA Fillapex (87.87% ± 11.13). Although there were mean differences between the sealers, the Kruskal–Wallis test showed no statistically significant differences  (p < 0.05) based on the Wilcoxon signed-rank test  (The highest change in the voids volume (V3) was in BioRoot RCS (0.20 mL ± 0.15), followed by MTA Fillapex (0.07 mL ± 0.09), and the least was found in Bio-C Sealer (0.05 mL ± 0.04). These differences were statistically significant using the Kruskal–Wallis test ( < 0.05) .p < 0.017) and between Bio-C and BioRoot RCS (p < 0.017) (p > 0.05) based on the Kruskal–Wallis test.A Mann–Whitney U post hoc test with Bonferroni correction revealed statistically significant differences between MTA Fillapex and BioRoot RCS (< 0.017) . Looking< 0.017) . HoweverThe volumetric assessment of root filling materials is of great importance when evaluating their performance. This is principally due to the tight relation between their efficiency to fill and seal root canal spaces and the treatment’s successful outcome . In thisBased on our findings, no correlation was made between the sealer preparation (mixing) method and its root filling efficiency, and the occurrence of voids . In earlWhen CSBSs are immersed in phosphate-rich media, phosphate ions can precipitate in association with calcium ions produced by the sealer to form apatite minerals. Such deposition may take place within the voids, and hence is expected to reduce the voids volume over time . AlthougThe greatest change in the voids volume was in BioRoot RCS followed by MTA Fillapex and then Bio-C sealer. However, when considering the change as a percentage to the original voids volume , the greIn terms of the efficiency of sealer application methods, no significant difference was found between the LS and GP cone. This is comparable to previous studies that reported no significant differences between the two methods using a stereomicroscope to assess the quality of the root canal fillings after root sectioning ,38,58. ICompared to other sealers, CSBSs exhibit minimal dimensional changes that may allow them to provide a better seal for the root canal system . HoweverOne of the limitations of our study was it’s in vitro nature. The real in vivo oral environment is dynamic and offers more aggressive challenges, so a similar study could yield different results under in vivo settings. Another limitation was the number of samples used and the range of sealers tested. Including more types of sealers would provide a better understanding of the differences in their performance over time. The other limitation was the measurement of the voids volume by only one analytical method, i.e., the micro-CT technique. Performing volumetric measurements using micro-CT analysis should be carefully interpreted, as factors such as voxel size, image-processing software, and the radiopacity of the material may influence the measurements . AnalyziWithin the limitations of the current study, sealer preparation and application methods into the canal did not seem to affect the quality of the root filling when performed with SGPC. All tested CSBSs exhibited a significant increase in their voids volume after 2-month storage in a phosphate-rich medium. This refutes the suggestion that voids in CSBSs could reduce over time with mineral deposition. Therefore, SGPC obturation should be carefully selected, considering the shape and size of the canal to minimize the occurrence of voids.


As genomic and personalized medicine is integrated into healthcare, the need for patients to understand and make decisions about their own genetic makeup increases. Genetic literacy, or one’s knowledge of genetic principles and their applications, measures an individual’s ability to apply genetic information to their own treatment. Increased genetic literacy can improve comprehension of genetic tests and therefore increase participation in testing to detect and treat genetic disorders. It can also help providers understand and explain genetic information to their patients. However, current research indicates that the population’s genetic literacy is generally low. Because many medical students, providers, and patients cannot adequately apply genetic information to their health, new and beneficial genetic technologies can be underused. More specifically, though genetic testing is recommended at the time of diagnosis for those affected by autism spectrum disorder (ASD), as few as 22% of families undergo genetic testing after diagnosis. While ASD, a neurodevelopmental condition characterized by impaired social communication and restricted interests, has both genetic and environmental risk, genetic testing can give clinicians useful information and help families avoid potentially painful and costly tests, even when many families do not receive a “positive” genetic result through microarrays or gene panels. Improving genetic literacy in populations affected by ASD can also improve attitudes toward genetic testing, thereby ensuring access to genetic health risk information. In this mini review, we discuss the current literature describing genetic literacy and genetic testing rates for ASD. Since the Human Genome Project completed in 2003, the use of genetic information in healthcare, as well as everyday life, has increased exponentially. In fact, leaders at the National Human Genome Research Institute predict that within the next decade, genetic testing will become a mainstream in healthcare, potentially becoming as commonplace as a complete blood count test . There aAs genetic research expands, there is little doubt that our genes contribute to a variety of common and rare conditions . With thFor those without a background in biology, understanding interactions between human health and chromosomal variants can be confusing and overwhelming. At a national level in the US, public understanding of how genetic information contributes to disease risk is generally low. In a survey distributed to 5,404 participants with secondary education, only 1.2% of the sample answered all of the basic genetic knowledge questions correctly . AnotherGenetic literacy, defined as “sufficient knowledge and understanding of genetic principles to make decisions that sustain personal well-being and effective participation in social decisions on genetic issues,” is one tool used to measure this phenomenon . ImportaBRCA1/2 genes  is a particular clinical example in which improving genetic literacy is important. Because the diagnosis process can be lengthy and grueling, children are often not diagnosed until years after displaying symptoms, which can impact functioning later in life. As detailed below, genetic testing can be useful in diagnosing ASD by shortening and improving the diagnostic process. However, the uptake of genetic testing in ASD is much lower than it could be, likely due to many factors including insufficient genetic literacy on all sides.ASD is a neurodevelopmental condition characterized by restricted and repetitive interests as well as impairments in socialization and communication. ASD’s etiology is complex as it is influenced by a mix of genetic, epigenetic, and environmental factors. In the US, approximately 1 in 54 children reach the threshold for an ASD diagnosis and the average age of diagnosis is 4.25 years of age . The potBecause there is a clear genetic link to ASD, genetic testing is recommended by both the American College of Medical Genetic and Genomics and the American Academy of Pediatrics following an ASD diagnosis . ASD is Though genetic testing can provide many psychosocial benefits to families, it does not always yield a conclusive result. The first-tier test for ASD, chromosomal microarray or CMA, yields a diagnostic result in only 15–20% of cases ; in addiIn a large Turkish sample, 87% of parents stated that they would pursue genetic testing if it could help identify the cause of their child’s ASD, and 84% believed that genetic testing referral is a key step in the diagnostic process . HoweverDespite guidelines recommending genetic testing following an ASD diagnosis, referral rates from medical and genetics professionals are low . IdeallyFor individuals and families affected by ASD, high genetic literacy indicates that one understands the genetic and environmental risk factors for ASD and can use this information to determine whether to pursue genetic testing. Parents with a positive association with genetic research are also more likely to support ASD genetic testing for their child . In addihttps://www.autismspeaks.org/expert-opinion/should-i-or-we-have-genetic-testing-autism or https://www.spectrumnews.org/news/genetic-testing-autism-explained/, or animated explainer videos such as https://youtu.be/LGQUE8fTx_A. Because the current level of genetic literacy is not sufficient to ensure individuals are educated to make informed decisions about their genetic information and health, we also recommend further research investigating genetic literacy and its relationship to attitudes toward genetic testing. Understanding the barriers to genetic literacy and genetic testing will help ensure equitable access to these rapidly expanding genetic technologies.We thus believe that healthcare will be improved by future research investigating genetic literacy rates in multiple population samples, and suggest that ASD is an illustrative test case where more research would be beneficial. Sufficient research can be followed by development of targeted genetic education resources addressing populations with lower genetic literacy. Examples include explainer websites targeted to families looking for resources at the time of diagnosis, like IL wrote the first draft of the manuscript. CG wrote sections and edited the manuscript. Both authors edited, read, and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Resistance to Fusarium head blight (FHB) of spelt wheat was investigated in field trials carried out at three European locations between 2016 and 2018. Resistance was assessed after artificial inoculation by visual scoring of symptoms and the determination of the contamination of grains and glumes with the mycotoxin deoxynivalenol (DON). It was found that typical spelt traits such as tall plant height, lax spikes, and tough glumes play a role as passive resistance factors. Across all test environments, modern spelt varieties with a significantly reduced plant height showed a significantly higher susceptibility to FHB and a higher contamination of the grains with DON compared to old landraces/varieties and plant genetic resources. Similarly, the lowest mycotoxin levels in grains were found only in old landraces and varieties, while the highest DON concentration was observed mainly in modern varieties. The results obtained can be used for the selection of suitable parental material for breeding spelt with improved FHB resistance. Fusarium species (Fusarium head blight (FHB) is a devastating fungal disease of wheat provoked by various  species . Since t species . In the  species . An inte species . Resista species . Resista species , whereas species . A few s species , wild em species , cultiva species , Georgia species , spelt  Thell.] is a hulled hexaploid wheat with a long cultivation tradition in southwest Germany, Switzerland, Austrian Alpine valleys, and a few other remote areas of Europe , Agroscope , Crop Research Institute , Institute of Agrobotany , IPK , LSA Hohenheim , and NordGen . The diversity panel contains landraces, old and obsolete varieties, as well as modern varieties and breeding lines, which were classified into three groups in order to investigate the effect of breeding: landraces and old varieties from (i) Switzerland and Germany; (ii) modern varieties (post 1970) from Switzerland, Germany, and Belgium; and (iii) plant genetic resources and genebank accessions of uncertain breeding status (PGR). The Czech variety “Rubiota” (released 1981), Austrian “Ebners Rotkorn” (released 1999), and Hungarian “Öko 10” (released 2000) were treated as landraces/old varieties, as they were selected from landrace accessions.A spelt diversity panel including 80 genotypes of winter spelt was established within the FP7 project HealthyMinorCerealsField experiments with artificial inoculation of the HMC spelt diversity panel were conducted in Austria, Czechia, and Switzerland from 2016 to 2018, resulting in nine test environments. Artificial inoculation was performed at full flowering of the spikes (BBCH 65) by spraying a spore suspension. FHB symptoms were scored several times after inoculation by visual evaluation of the percentage of diseased spikelets of whole plots. Plant height was measured from the ground to the top of the spike.F. culmorum isolate IFA-91015 at a concentration of 2.5 × 104 conidia per ml in tap water at a rate of 100 ml m–2. Inoculations were repeated four times in 2 day intervals in order to inoculate each plot at least once at maximum flowering. After each inoculation, the crop stand was mist irrigated for 21 h with a total of 4–5 mm water. FHB severity scoring (% diseased spikelets) started on day 10 after anthesis and was repeated four times in 4 day intervals. Anther retention was recorded in the 2017 trial, as described by In Austria, the field trials were carried out in Tulln . The field experiments were performed similar as described in F. culmorum  on a 1–9 scale, as described . The spelt genotypes were sown manually as spikelets in hill plots with three replications. At the flowering period, bunches of 10 spikes were inoculated with the highly pathogenic isolate B of culmorum  at a conescribed .F. culmorum spores. Spores were kindly provided by Agroscope, Changins. Inoculation started at the beginning of flowering and was repeated three times in 2016 and 2017 and two times in 2018 in a 2 day interval. Scoring of visual symptoms  was started 10 days after the last inoculation and repeated three times in a 7 day interval.In Switzerland, the trial was sown under organic conditions in Feldbach . The genotypes were sown in 50 cm-long double rows with two replications. The inoculum was a conidial suspension of ® FAST DON enzyme immunoassay . Additionally, the DON content of the glumes was also determined in the Czech samples from 2017 and 2018. A representative grain sample was ground by a Yellowline IKA A10 Analytical Grinder Universal Mill  and thoroughly mixed. Afterward, 100 ml of distilled water were added to 5 g ground sample and shaken for 3 min in a horizontal rotary shaker at 200 rpm. After filtration, 50 μl of the filtrate were used for the ELISA test together with standards according to the manufacturer’s instructions. The absorption of the final solution was measured at 450 nm using a SunriseTM spectrophotometer. Data were processed with RIDASOFT®Win.NET software (R-Biopharm AG).Dehulled seed samples from all sites were analyzed for deoxynivalenol (DON) content by the RIDASCREENPi for each individual genotype according to Pi is defined as the distance mean square between a genotype’s response and each environment’s best response averaged over all environments. The Pi values for FHB and DON were further used to calculate a Fusarium rating as devised by FHB scoring values of the last evaluation date from each test site were converted to the 1–9 scale as described by p < 0.0001) for all years in Austria  and Czechia in 2016 (ρ = 0.54–0.57), however, not significant for Switzerland and Czechia in 2016 and 2017, respectively. In the other environments, the correlation between field replications was significant (p < 0.05) but weak (ρ = 0.21–0.34).The genotypic variation in the FHB score across environments is displayed in 2oh = 0.77. Considering the single countries, the highest heritability was observed for Austria (2oh = 0.79), where the genotypic variance component was 1.4 times the variance due to the residual error. Heritabilities for Czechia (2oh = 0.54) and Switzerland (2oh = 0.43) were medium. In these cases, the variance component for the residual error was four to five times higher than the variance explained by the genotypes.Heritability for FHB score was high with 2oh = 0.61 lower than for FHB and did not change if the two before mentioned environments were excluded from analysis. Similarly, to FHB score, the heritability was highest for the Austrian trials (2oh = 0.67) and significantly lower for Switzerland and Czechia . Similarly, to the visual scoring of FHB symptoms, the correlations between the single field replications vary between countries and years. For Austria, the correlations are significant (p < 0.0001) for each year ; for Czechia (ρ = 0.61–0.75) and Switzerland (ρ = 0.58), similar correlations were observed in 2016, whereas the field replications were not correlated in Czechia 2017 (ρ = 0.15–0.27) and weak (ρ = 0.35–0.47) but significant (p < 0.01) in the other environments.For DON contamination of the grains, genetic variation was negligible in Austria 2017 and Switzerland 2016 . HeritabPi = 0.18. Considering a cutoff of 10%, the other genotypes with a low Pi were “Toess 6D” (Pi = 0.24), “Fuggers Babenhauser Zuchtvesen” (Pi = 0.28), “Sofia 1” (Pi = 0.45), “Gugg 4H” (Pi = 0.48), “Roter Schlegeldinkel” (Pi = 0.57), “Riniken Weißkorn” (Pi = 0.71), and “Strickhof” (Pi = 0.75). Hence, all genotypes with low disease severity were from the group of old landraces/varieties and plant genetic resources, respectively. The best modern variety was not ranked before 25 . In contrary, among the 10% most susceptible genotypes with the highest Pi values, six out of eight were modern varieties, i.e., “Alkor” (Pi = 5.31), “Schwabenspelz” (Pi = 5.46), “Titan” (Pi = 5.6), “Badenkrone” (Pi = 5.96), “Zollernspelz” (Pi = 6.02), and “Cosmos” (Pi = 6.09). “Thürig Rotkorn Nr. 4” (Pi = 5.84) and “Waggershauser Hohenheimer Weißer Kolben” (Pi = 6.33) were the only old varieties within the group of highly susceptible spelt varieties , “Farnsburger Rotkorn Nr. 6” (Pi = 10.4), “Gugg 4E” (Pi = 13.8), “Ebners Rotkorn” (Pi = 20.4), “Gugg 9F” (Pi = 27.9), “Toess 6D” (Pi = 32.7), “Roter Schlegeldinkel” (Pi = 35.2), and “Ostro” (Pi = 36.2), whereas the highest DON concentration was observed mainly in modern varieties, i.e., “Badenkrone” , “Titan” , “Cosmos” , “Alkor” , and “Goldir” . In this case, however, also a few old landraces showed high DON contents, i.e., “Zuzgen Nr. 15A” , “Schnotwiler Weißkorn Nr. 35” , and “Thürig Rotkorn Nr. 4”   .P(FHB) = 0.36; P(DON) = 9.5] from which “Rubiota” was selected. On the other hand, “Franckenkorn” showed a similar medium resistance level as the abovementioned varieties but having a significantly reduced plant height. High disease severity and mycotoxin contamination were observed for “Titan” and “Zollernspelz,” two varieties released in the early 2000s. Due to its reduced plant height and therefore increased lodging tolerance, “Zollernspelz” is the most popular variety in Germany and is also widely grown in Austria and Czechia.In The Fusarium rating based on the ranks of the superiority indices for both FHB and DON summarizes the results obviously. Only 16 genotypes were rated as “resistant” , and allp < 0.05). The closest relationship was observed between FHB scores and DON content of grains (r = 0.66) . Considering correlations only within the three test countries, higher correlation coefficients were observed between FHB scores and grain DON content for Austria (r = 0.80) and between FHB scores and plant height for Czechia (r = −0.43)  , whereas = 0.66)  and/or g= −0.43) .r = 0.56 (p < 0.001)  correlated to both FHB scores (r = 0.65) and grain DON content (r = 0.63).In two environments, i.e., CZ17 and CZ18, also the mycotoxin content of the hulls was determined, which was, on average, three to five times higher than in the grain . The cor< 0.001) . In one severity  and ρ = Heritage varieties and “ancient grains” gained attention and market in the last decade. Often, they are promoted without reasonable knowledge about their performance under current agronomic and processing practices . Spelt, r = −0.35 was observed between plant height and FHB severity, which is similar  and thereby changes in the canopy microclimate. Similar conclusions were reached by Rht genes is available. It can only be assumed that, in modern varieties released after 1970, major Rht genes are present as a result of crosses with common wheat and selection for lodging tolerance . Considering the limited presence of the two major dwarfing genes in Central European common wheat germplasm  was significantly lower than the reported r = −0.59 to −0.70 by In common wheat, dwarfing genes and their effect on plant height were shown to be also associated with anther extrusion/retention , which iFusarium species in glumes and grains of naturally infected field samples. On the other hand, Alternaria infection: the contamination with the mycotoxins alternariol and alternariol monomethyl ether was fourfold higher in the hulls than in the grains of spelt.The higher anther extrusion in spelt is surprising in view of the tough, sharply keeled glumes that are firmly enclosing the floret and/or grain. The tough glumes (hulls) may constitute physical barriers to pathogens and thereby protect the developing grain. The protective effect of spelt glumes against colonization by soilborne fungi and therefore increased germination under unfavorable conditions were demonstrated by Fusarium strain, etc.) on especially the mycotoxin accumulation but also FHB severity is well documented .The original contributions presented in the study are included in the article/JC, VW, and HB conceived the field experiments. VW, MB, JP, JK, MT, QN, and JM collected the data. HG and JC analyzed the data and drafted the manuscript. DJ contributed to manuscript editing and organized funding. All authors reviewed and approved the manuscript for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Vulpes lagopus). By combining a genetically verified pedigree covering almost 20 years with a long-term dataset on individual fitness , we found no evidence for elevated fitness in immigrant F2 and F3 compared to native inbred foxes. Population inbreeding levels showed a fluctuating increasing trend and emergence of inbreeding within immigrant lineages shortly after immigration. Between 0–5 and 6–9 years post immigration, the average number of breeding adults decreased by almost 22% and the average proportion of immigrant ancestry rose from 14% to 27%. Y chromosome analysis revealed that 2 out of 3 native male lineages were lost from the gene pool, but all founders represented at the time of immigration were still contributing to the population at the end of the study period through female descendants. The results highlight the complexity of genetic rescue and suggest that beneficial effects can be brief. Continuous gene flow may be needed for small and threatened populations to recover and persist in a longer time perspective.Genetic rescue can facilitate the recovery of small and isolated populations suffering from inbreeding depression. Long-term effects are however complex, and examples spanning over multiple generations under natural conditions are scarce. The aim of this study was to test for long-term effects of natural genetic rescue in a small population of Scandinavian Arctic foxes ( Inbreeding can result in lowered fitness  by increased expression of deleterious recessive alleles, or through loss of heterozygosity at loci with heterozygote advantage . This isIn times of accelerated population declines, an in-depth understanding about the impact of inbreeding depression and genetic rescue over longer time frames is crucial. Genetic rescue under natural conditions have been documented in a number of mammal populations and despite empirical support for genetic rescue effects in first-generation immigrant offspring , an area located in the most southern part of the Swedish mountain tundra. The population was close to extinction in the 1990s, but since 2001 reproductions have occurred on a regular basis following return of regular prey cycles and conservation actions (f = 0.125 (The study population resides in Helagsfjällen (Helags), Jämtland county  and Norwegian lemmings  are visited each summer to document survival and reproductive success, and cubs are captured and ear-tagged with unique colour-coded tags with individual numerical codes. An average of 93% (±0.039) of all observed litters were sampled each year. Of all individuals, 68% were sampled during 2001–2013  were derived for litters with at least 2 full generations of known parentage  in GenAlEx v.6.5  of monitoring was needed to properly evaluate the foxes’ survival. Individuals born after 2015 were thus excluded from the analysis due to a lack of fitness data available at the time.n = 144) was genotyped in 3 Y-chromosome-linked microsatellite loci: VVY15, VVY16, VVY17. Primers were originally developed for the red fox .Y chromosome alleles were manually assembled into haplotypes and visualized through a median-joining network constructed in NETWORK 5.0  could be derived for 25 out of 48 sampled litters born during the same time period. A total of 213 litters were recorded since monitoring began in 2001, of which 700 individuals from 140 litters (66%) were included in the final pedigree (2001–2019). A majority of these litters (78%) could be traced to at least one founder. The probability of identity was PID= 7 × 10–4 assuming a full-sibling relationship and PID = 5.8 × 10–8 assuming they were unrelated.Of the 146 Arctic foxes that were ear-tagged in 2016–2019, 144 were successfully genotyped in 10 loci. Inbreeding coefficients (f) of cubs peaked at f = 0.155  and other 2 cases were matings between half-siblings (N3-F1 × N3-F1) and full-siblings (and N2-F1 × N2-F1), where N1-N3 represent the 3 immigrants. The remaining 8 cases were between more distantly related individuals. An average of 29 adults reproduced in 2016–2019, which equals a 22% decline from the average of 37 adults that reproduced in 2010–2015.In 2010, when the first gene flow event occurred, the average inbreeding coefficient  and remained significantly higher at 5.38 (±0.91) in 2016–2019 . There was no significant change in allelic richness between the 2 post-immigration periods . Further, heterozygosity (sMLH) was significantly higher in immigrant F1  compared to both native offspring  and immigrant F2+F3 . There was no significant difference in sMLH between native offspring and immigrant F2+F3  or between any of the 3 time periods .Allelic richness increased significantly from 3.78 (±0.39) in 2001–2009 to 4.90 (±0.74) in 2010–2015 (χ Prior to immigration (2001–2009), 5 loci displayed significant heterozygote excess . After iK = 2–10, the Evanno approach demonstrated that K = 3 was the most likely number of clusters over 3 replicates . There was however no difference in survival between native offspring and immigrant F2+F3 (P = 0.996). Irrespective of their ancestry, 26% of cubs survived their first year during increasing rodent densities compared to 19.6% during decreasing rodent densities . Further, there was a trend for lower survival for males compared to females . Male first-year survival was 18.4% and female survival was 26.7%.The analysis included 145 native offspring, 53 immigrant F1 and 166 immigrant F2+F3 born in 2010–2015 . First-yY chromosome genotypes could be determined for 133 out of 144 males from the study population. Loci VVY15 and VVY17 were polymorphic before immigration occurred . Gene flFour out of six constructed haplotypes were recorded in our study population and the remaining 2 were observed in Canadian samples . H1 was When combining Y chromosome genotypes with pedigree data it was however established that all male founders were still genetically represented in the population gene pool in 2019 . FurtherBased on the updated pedigree, it was established that all except one litter with known parents born in 2016–2019 were descendants of the immigrants . GeneticGenetic rescue has the potential to facilitate recovery of small and isolated populations suffering from inbreeding depression . Our knoAn important question was whether the positive fitness effect in immigrant offspring remained across generations. Evidence for genetic rescue in immigrant F1 following gene flow was documented by Initial positive fitness effects following gene flow is thought to be due to heterosis of immigrant offspring, which occurs when immigrant alleles mask deleterious recessive alleles present in the native population, or when heterozygosity is brought back to loci with heterozygous advantage . HeterozShortly after gene flow, we recorded inbreeding events between the immigrant lineages, which suggests that continued inbreeding is a likely future scenario, especially if gene flow is not continuous e.g., . The conA cornerstone of genetic rescue is increased population growth . GeneticPrevious studies have demonstrated that genetic sweep following gene flow may under specific circumstances have a profound impact on both individual fitness and population demography . We founOur analysis of founder contribution demonstrated that all male founders were still genetically represented in 2019. The pedigree analysis revealed that one of the native male founders who was previously thought to have been genetically lost in 2008  re-appeaWe demonstrate that the genetic contribution from immigrants almost doubled over time, from an average of 14% in 2010–2015 to 27% in 2016–2019. Related to this, we recorded a gradual change in individual genetic composition after the immigration events in 2010, which likely is a consequence of high genetic impact of immigrants . GeneticThis study highlights the complex nature of genetic rescue and shows that even though initial effects on fitness and demography are strong, they may not persist across generations. This supports the background theory of genetic rescue being more pronounced in the F1 generation and adds to empirical evidence from other species. Inbreeding levels can rapidly increase in absence of continuous gene flow and from initial high success of immigrant lineages. The risk of inbreeding depression may thus return through increased homozygosity of deleterious alleles. High success of immigrants and their descendants can come at the cost of reduced native genetic diversity, but we have shown that native founders are not necessarily lost in this process. Together, our results suggest that a singular pulse of gene flow is not enough to mitigate inbreeding depression in a longer time perspective. Absence of additional gene flow into the population will likely result in a downward spiral of further intensified inbreeding depression over time.esab011_suppl_Supplementary_MaterialClick here for additional data file.


Sus scrofa) has been pronounced in DRZ, and the spread of highly contaminated wild boars into other areas is cause for concern. Understanding the population structure based on the genetic diversity of wild boars in Fukushima provides important information for the management of the animals. In this study, we carried out MIG-seq analysis to clarify the dispersal and gene flow of the local wild boar population and uncover the genetic population boundary in Fukushima. We obtained 328 single-nucleotide polymorphisms from 179 wild boars. Based on STRUCTURE analysis, we found significant genetic differences between groups of wild boars inhabiting in the east and west, divided by the Abukuma River. Since the urbanized area is concentrated along the Abukuma River in Fukushima, both the Abukuma River and the urbanized area are likely to interfere in the migration and dispersal of wild boars. Furthermore, our results indicate that the population in the western area was established by the migration from other neighboring prefectures rather than by that from the eastern group of Fukushima Prefecture.After the Fukushima Daiichi nuclear power plant accident, a highly contaminated area with radionuclide appeared and was designated a difficult-to-return zone (DRZ). After that, the increase in number of wild boars (Sus scrofa) population and find their genetic boundary in Fukushima Prefecture. After the nuclear incident in 2011, the land was considered a difficult-to-return zone, and the increase in the number of wild boars was pronounced. To provide an effective management strategy for the wild boar population, we used multiplexed inter-simple sequence repeat genotyping by sequencing (MIG-seq) and clarified the genetic structure of wild boars. We obtained 328 single-nucleotide polymorphisms from 179 samples. STRUCTURE analysis showed that the most likely number of population cluster was K = 2. Molecular analysis of variance showed significant genetic differences between groups of wild boars inhabiting in the east and west across the Abukuma River. The migration rate from the eastern population to the western population is higher than in the reverse case based on BayesAss analysis. Our study indicates that both the Abukuma River and anthropogenic urbanization along the river may affect the migration of wild boars and the population in western was established mainly by the migration from other neighboring prefectures.We aimed to reveal the dispersal and gene flow of the local wild boar ( Sus scrofa leucomystax) and the Ryukyu wild boar (Sus scrofa riukiuanus). The Japanese wild boar (hereafter referred to as “wild boar”) is widely distributed in mainland Japan, including Honshu, Shikoku, and Kyushu islands, except for the north Tohoku district [Recently, some mammals, such as deer and wild boar, have expanded their population size and their habitats expand closer to human dwellings, resulting in damage to agriculture, forestry, and ecosystems in Japan ,2. Wild district   and the reduction of hunting pressure due to the aging population ,8. In adUnderstanding the population structure based on the genetic diversity and genetic boundary of species is important for wildlife management . The genThe use of microsatellite analysis has increased linearly since the early 1990s, whereas the use of genome-wide single-nucleotide polymorphisms (SNPs) has increased exponentially since the late 1990s . Genetichttp://shop.amakusa-web.jp/cinghiale, accessed on 15 February 2022) . As in the case of wild boars in DRZ, we used muscle samples of wild boars captured by the Ministry of the Environment under “The habitat survey and capture of the wild animals project in and around the former restricted areas (areas within 20-km radius from Fukushima Daiichi NPP)”, and “The habitat survey and capture of the wild animals project in and around the former restricted areas (areas within 20-km radius from Fukushima Daiichi NPP)”. Therefore, no wild boars were killed specifically for this research and no live animals were used. One hundred and seventy-nine wild boars captured in Fukushima Prefecture from October 2013 to June 2018 were used . Informary 2022) A. Kumamory 2022) B; the otry 2022) .http://hannonlab.cshl.edu/fastx_toolkit/, accessed on 15 February 2022) was used to remove the primer sequences  = 33, Minimum percent of bases that must have “q” quality (p) = 40) from the raw reads. TagDust v2.31 [n) as 4, and then used sstacks to determine the SNP loci for each individual. We used stacks to select SNPs with a minimum percentage of samples in a population (r) as 0.5 and the minimum number of populations in a locus (p) as 2; we set up separate populations for individuals captured in Fukushima and Kumamoto prefectures. For the parameters not specifically mentioned above, all default values were used. For the candidate SNP loci obtained, loci that appeared only once in all individuals were excluded from further analysis. CLC Genomic Workbench  was used to perform pairwise similarity calculations after the alignment of sequences containing SNPs. SNPs showing more than 50% sequence similarity were considered duplicates and removed, and the remaining SNP loci were extracted. The registration number of the obtained sequences are in DNA was extracted from frozen, freeze-dried, or ethanol-fixed wild boar meat pieces. Genomic DNA was extracted using DNeasy EZ1 Kit  and BIO ROBOT EZ1 (QIAGEN). MIG-seq analysis was performed as previously described by Suyama and Matsuki . For thest v2.31  was thenst v2.31 . To creaK) was set from 1 to 6, and ten calculations were performed for each K. After STRUCTURE analysis, we calculated ΔK [K with the highest ΔK was set as the optimal number of clusters. STRUCTURE analysis was performed independently for each data set of wild boars from the six populations of Fukushima and Kumamoto prefectures or only wild boars from six populations of Fukushima Prefecture. Hierarchical cluster analysis was performed for six groups in Fukushima Prefecture via Ward’s method using “stat” program that is implemented in R package by default , then tree diagrams were created. The fixed index of genetic differentiation (Fst) among the six groups in Fukushima Prefecture was calculated using AMOVA approach in GenAlEx 6.503 [Fst values and AMOVA was assessed based on 9999 permutations and applied the “interpolate missing” option. p-Values of pairwise Fst values were corrected for multiple comparisons using the Bonferroni method [Genetic population structure estimation based on the obtained SNPs was performed using STRUCTURE ver. 2.3.4 . We set lated ΔK  using Stlated ΔK , and theEx 6.503 . AMOVA wEx 6.503  to examii method . We estii method . Prelimii method  for detei method  was usedK value was observed at K = 2 (ΔK = 687.88), followed by K = 3 (ΔK = 117.61)  was obtained when K = 2 in comparison to other K values, strongly indicating that the wild boar population in Fukushima Prefecture could be classified into two groups , and regions 3, 4 and 6 (dark red and geyser blue clusters)  . Therefo 117.61) . In addio groups . The barlusters) . To anallusters) . FurtherK = 2 data in p ≤ 0.001), and the variation between the east and west groups represented 11% of the total variation  and the geyser blue cluster as wild boars in Fukushima-West (WF-W) based on the ariation . Taken tOur study revealed that there are two genetically differentiated lineages of wild boars  in Fukushima Prefecture. Inhabitation of wild boars in the west side in Fukushima prefecture (WF-W group) has been confirmed since 2004 . With a Fst values among each site were very small, indicating that the degree of genetic differentiation among the regions tended to be small  and that both the Abukuma River and anthropogenic urbanization along the river may affect the migration and dispersal of wild boars. In addition, our results also suggest that the establishment of the western wild boar population in Fukushima Prefecture may have occurred due to the migration of wild boars from neighboring prefectures, in addition to the eastern population of Fukushima Prefecture.


Brettanomyces bruxellensis is described as a wine spoilage yeast with many mainly strain-dependent genetic characteristics, bestowing tolerance against environmental stresses and persistence during the winemaking process. Thus, it is essential to discriminate B. bruxellensis isolates at the strain level in order to predict their stress resistance capacities. Few predictive tools are available to reveal intraspecific diversity within B. bruxellensis species; also, they require expertise and can be expensive. In this study, a Random Amplified Polymorphic DNA (RAPD) adapted PCR method was used with three different primers to discriminate 74 different B. bruxellensis isolates. High correlation between the results of this method using the primer OPA-09 and those of a previous microsatellite analysis was obtained, allowing us to cluster the isolates among four genetic groups more quickly and cheaply than microsatellite analysis. To make analysis even faster, we further investigated the correlation suggested in a previous study between genetic groups and cell polymorphism using the analysis of optical microscopy images via deep learning. A Convolutional Neural Network (CNN) was trained to predict the genetic group of B. bruxellensis isolates with 96.6% accuracy. These methods make intraspecific discrimination among B. bruxellensis species faster, simpler and less costly. These results open up very promising new perspectives in oenology for the study of microbial ecosystems. Brettanomyces bruxellensis presents many strain dependent characteristics, such as volatile phenol production that contributes to the famous “Brett character” [2 resistance) [B. bruxellensis to persist in wine [2 doses) and the impact of climate change on the physicochemical characteristics of wines (like higher pH levels), make the control of B. bruxellensis more challenging [The spoilage yeast aracter” ,2,3, or istance) ,5,6,7,8. in wine ,10,11 an in wine ,13, the llenging ,15 and, llenging . ConsequB. bruxellensis, mostly by distinguishing groups of strains. All of the studies described molecular methods, except one, which used Fourier Transform Infrared Attenuated Total Reflectance (FTIR-ATR) spectroscopy to analyze the molecular composition of cells with mid-infrared radiation for B. bruxellensis strain discrimination. Despite being rapid and not requiring DNA extraction, this method cannot be applied routinely due to the need to standardize the protocol and access a database specific to the target microorganism [A large number of methods have been developed to discriminate yeasts at the strain level, but few studies have investigated intraspecific diversity among the species organism .Besides, molecular methods need a DNA extraction step and some of them used restriction enzymes like the Mitochondrial DNA Restriction Fragment Length Polymorphism (mtDNA RFLP) method ,19,20 anB. bruxellensis species [2 resistance [2 resistance and, thus, help winemakers to adapt their antimicrobial techniques [B. bruxellensis species, even if it could sometimes be combined with other methods to improve discrimination [B. bruxellensis species, even if molecular methods require advanced skills and specialized equipment.Then, the methods that followed were based only on PCR. One of them was the Intron Splice Site PCR (ISS-PCR) method ,26,27. R species ,28. Thissistance ,12,29,30chniques . In addimination ,35,36,37mination , but themination . Howevermination ,38. Thermination  to reveaAnother direction could be explored in order to further simplify discrimination. A previous work described different cell morphologies depending on the strain, and suggested a link between the variability in yeast cell shapes and the genetic group to which they belong . To valiB. bruxellensis and discriminated in four different genetic groups  by microsatellite analysis  [w/v yeast extract , 1% w/v bactopeptone (Biokar), 2% w/v D-glucose  and 0.02% w/v chloramphenicol , containing 20% v/v glycerol.This study included a total of 74 isolates previously identified as analysis : (i) 10 LO6/036) , and (iig for 4 min at 4 °C), washed with 1 mL of sterile water and then re-suspended in 200 µL of extraction buffer , with 0.3 g glass beads  and 60 µL phenol:chloroform:isoamyl alcohol 25:24:1 (Sigma). The cells were broken for 3 × 45 s at 6500 rpm with a 60-s interval using a Precellys 24-Dual Homogenizer . After the addition of 200 µL of Tris EDTA buffer , the cultures were centrifuged at 18,200× g for 10 min at 4 °C. The aqueous phase was collected and the DNA was precipitated with 1 mL of 100% ethanol and then centrifuged at 18,200× g for 10 min at 20 °C. The pellet was washed with 70% ethanol and centrifuged at 18,200× g for 5 min at 20 °C. Then, the pellet was dried and resuspended in sterile water. Finally, the samples were stored at −20 °C. The concentration and purity of DNA was measured by Infinite M200 Pro NanoQuant .Using stock-cultures stored at −80 °C, cultures were prepared in YPD medium at 28 °C. After growth until the stationary phase, cells were harvested by centrifugation , 0.25 mM of each dNTP and 2.5 U of Go Taq polymerase . The amplification reactions were carried out in a thermocycler BioRAD T100™  under the following conditions: initial denaturation at 95 °C for 5 min, followed by 40 cycles of 94 °C for 1 min, 36 °C for 1 min, 72 °C for 2 min and a final extension at 72 °C for 10 min [RAPD profiles were obtained using 3 primers, OPA-02 (5′-TGC CGA GCT G-3′), OPA-03 (5′-AGT CAG CCA G-3′) and OPA-09 (5′-GGG TAA CGC C-3′) , as previously described ,21,31,33r 10 min ,33. The w/v agar) at 28 °C from the stock at −80 °C. From each YPD plate, one isolated colony was inoculated in 15 mL of YPD and the liquid cultures were incubated at 28 °C for 72 h.Isolates were grown on YPD plates .For each culture, 6 µL was placed between the slide and coverslip and optical microscopic transmitted light observations were performed. Images were scanned at 1280 × 960 pixels, 8-bit gray scale, at ×40 magnification and 38% brightness using the EVOSTwo data sets were built.Dataset1 was composed of 10 images per culture and 3 independent cultures for each of the 74 isolates. Six images of isolate 30 and one of isolate 56 were rejected due to artifacts. The distribution of images among genetic groups was the following: 354 images for GG1, 210 for GG2, 1499 for GG3 and 150 for GG4.Dataset2 consisted of 233 images, with 46 images for GG1 , 63 images for GG2 , 66 images for GG3  and 58 images for GG4 .l/w) ratio and the cell area were determined from the measurement of 100 single cells per isolate, amounting to 7400 cells.From dataset1, one representative  image per biological replicate was selected, amounting to 3 images per isolate. A total of 100 cells were pseudo-randomly selected (excluding buds) from these 3 images. Then, ImageJ software (1.52i) was used to measure cell morphology characteristics as described in a previous study . BrieflyThe pre-trained Convolutional Neural Network GoogleNet was chosen. This CNN was trained with over 1 million images classified into 1000 object categories. This network thus learned rich feature representations for a wide range of images. It had to be adapted by transfer learning to specialize it for the discrimination of genetic groups. Hence, the end of the CNN was customized to output 4 categories, corresponding to the 4 genetic groups instead of the 1000 initial object categories. Then, the learning rate weight of the last layers was set to 5 to accentuate the learning rate. On the contrary, the learning rate weight of the 10 first layers was frozen in order to keep the advantage of the previous training.Firstly, dataset1 was used to train the CNN. To fit the input requirement of the network and increase the number of learning cases, the microscopic images were divided into subpictures . To do tx-, y-axis translations . The training was performed on 64 epochs using a Nvidia GTX1080 GPU.Due to the low number of images available, data augmentation was performed to increase the amount of training data. The augmentation process was achieved by applying several random transformations from original images consisting of rotations , horizontal and vertical mirrors and Secondly, dataset2 was used to test the accuracy of the trained model. For each of the 233 images to be classified, the same thumbnailing process as for the dataset1 was applied. Then, each thumbnail of the image to be labeled was presented to the CNN, which associated it with one genetic group. The genetic group that was most frequently assigned among the thumbnails was then assigned to the image.B. bruxellensis isolates. RAPD-PCR assay was performed on 74 B. bruxellensis isolates with 3 different primers among those most used in the literature: OPA-02, OPA-03 and OPA-09 [RAPD-PCR was adapted here to assess its discriminating power and provide an accessible protocol to determine the genetic group of d OPA-09 ,21,31,33d OPA-09 ,31. The B. bruxellensis isolate’s adaptability to given environmental factors, such as SO2 resistance, was demonstrated by determining a genetic group of isolates using microsatellite genotyping [2 resistance. Therefore, the intraspecific diversity obtained by RAPD-PCR was compared to the discrimination in genetic groups by microsatellite analysis , corresponding to rounder cells. However, GG1 presented a lower average area (13.9 µm2) than GG4 (18.5 µm2). GG2 and GG3 presented higher average l/w ratios , corresponding to more elongated cells. Moreover, these groups could be discriminated by their average area, 11.5 µm2 for GG2 corresponding to small cells, compared to 16.9 µm2 for GG3. Differences appeared between genetic groups, but considering the spread of the curves, overlaps suggested similarities in cell morphology between genetic groups. In particular, the curves corresponding to GG3 had a large amplitude. As such, to refine the discrimination according to cell morphology, it would be relevant to explore it at the isolate level. Therefore, the average l/w ratio and the average area of 100 cells were determined for each isolate. The 2D spatial representation of these averages specified the clusters according to genetic groups  and automatic learning ,41.For the present study, the use of a CNN allows us to change the focus from studying single cells to that of the general aspect of the population. Indeed, although the different classes share common shapes, the algorithm ought to separate them according to their differences, and thus, the majority shapes and behaviors.To exploit intra- and inter-genetic group variability more efficiently, the CNN GoogleNet was used and trained to classify microscopic images in the 4 genetic groups. The detailed performance analysis is shown in l/w ratio in Results of classification for dataset validation (from dataset1) are presented in the confusion matrix . The diaB. bruxellensis, it is possible to predict the genetic group of the isolate studied with fairly high confidence. The prediction of the CNN might achieve greater accuracy by including more strains, since the larger and more representative the training database, the better the performances of the CNN. The CNN trained in this study provides a very fast initial result, which can be used as is. Nevertheless, if a higher level of confidence is required, it can be achieved with the RAPD-PCR protocol. This allows confirmation at the genetic level of the pre-determination in silico of the genetic group. The complementarity of both methods makes intraspecific discrimination within B. bruxellensis species efficient, rapid, simple and inexpensive.To test the “real-world” performance of the model, classification was performed on dataset2. With these microscopic images, the model achieved an accuracy of 96.6%, confirming its outstanding predictive power. Thus, this deep learning method stands as a prime method for the study of microbial intraspecific discrimination. Indeed, from a simple microscopic observation of a culture of B. bruxellensis species indicates that shape variation is probably linked to clusters of genes encoding for various functions, which could be related to adaptation to environmental factors, for example.The existence of a link between cell polymorphism and genetic groups in the Saccharomyces cerevisiae [B. bruxellensis species, since the genetic groups gather strains with the same morphological typology, it is likely that strains from the same genetic group share similar mutation types.In a study investigating the phenotype of a nearly complete collection of gene-deletion mutants of revisiae , 673 strB. bruxellensis were structured in genetic groups according to ploidy level [S. cerevisiae, different triggers for pseudohyphae formation have been reported for diploid (nitrogen limitation) and haploid cells (glucose deprivation) [In addition, microsatellite analysis revealed that strains of dy level . Thus, civation) ,53,54,55S. cerevisiae as protection mechanisms against environmental stresses [B. bruxellensis, it turns out that GG4, which presented this feature, is mainly composed of SO2 resistant/tolerant strains [2 resistance of the yeast.Besides the shapes, one of the genetic groups was distinguishable by a multicellular phenotype. Such phenotypes, like flocs resulting from aggregation or clumps formed by incomplete separation, have been described in stresses ,57,58,59B. bruxellensis isolates, since it was able to correctly predict the genetic group of an isolate from a simple microscopic observation with 96.6% accuracy. This CNN provides a rapid, simple and inexpensive tool for routine intraspecific discrimination in B. bruxellensis species. Pre-determination in silico of the genetic group of an isolate can be confirmed at the genetic level by the RAPD-PCR protocol, since the use of primer OPA-09 successfully discriminated B. bruxellensis isolates in the same distribution among genetic groups as microsatellite analysis, in a more accessible and less expensive manner.In this study, the CNN GoogleNet was trained to provide rapid and highly reliable screening of genetic groups of B. bruxellensis species clearly appears to be related to the genetic group of isolates. Therefore, assessing cell morphology could allow us to predict the genetic group of isolates, itself linked to their SO2 resistance. The same intraspecific discrimination study could be considered for other yeast species using a CNN. Furthermore, discrimination could be extended at the interspecies level for the identification of unknown microorganisms. CNN could be used to pre-classify species of yeast or species of bacteria from routine microscopic observations performed after isolation to optimize subsequent molecular analyses. Moreover, this tool could be useful for implantation control and population monitoring during winemaking, providing real-time results simply by microscopic observation.The present study opens new perspectives for the use of deep-learning methods in oenology to provide powerful, robust and timesaving analyses. The polymorphism of yeast cells among