FMR1 gene deletion of 5′ UTR and exon 1. Her second fetus was a female carrying a FM allele as well. This is so far the largest study of the FXS carrier screening in Chinese women. The prevalence of premutation allele for FXS in normal asymptomatic Taiwanese women was found to be as high as 0.13% (1 in 777) in this study. The empirical evidence suggests that reproductive FXS carrier screening in Taiwan might be cost-effective.Fragile X syndrome (FXS) is the most frequent genetic cause of intellectual disability (ID). It was previously believed that the FXS prevalence was low in Chinese population, and the cost-efficiency of FXS carrier screening was questioned. This retrospective observational study was conducted between September 2014 and May 2017 to determine the prevalence of FXS carriers in a large Chinese cohort of pregnant women. The FMR1 CGG repeat status was determined in 20,188 pregnant Taiwanese women and we identified 26 women with premutation (PM). The PM allele was transmitted to the fetus in 17 pregnancies (56.6%), and six of 17 expanded to full mutation (FM). One asymptomatic woman had a FM allele with 280 CGG repeats. Prenatal genetic diagnosis of her first fetus revealed a male carrying a  In the case of full mutation (FM), with over 200 repeats, there would be abnormal methylation of the gene, and FMRP, which is crucial for brain development, cannot be produced2. If the repeat number falls between 55 and 200 , the carriers would have normal gene expression, but their children would have a higher risk of developing FXS3. Moreover, it is reported that PM carriers may present with fragile X-associated tremor/ataxia syndrome (FXTAS) and fragile X-associated primary ovarian insufficiency (FXPOI) in their late adulthood5.One of the major genetic causes of intellectual disability (ID), the fragile X syndrome (FXS) is an X-linked dominant disorder, where there is a defect in the fragile X mental retardation1 (FMR1) gene. Normally, in the 5′ untranslated region of the FMR1 gene, the cytosine-guanine-guanine (CGG) trinucleotide repeat is under 446. With the advancement of PCR technology , however, the entire CGG repeat sequence in the FMR1 promoter region can be amplified to allow a confident detection of the trinucleotide repeats over 9007.Traditionally, Fragile X testing is performed by use of a laboratory-developed FMR1-specific PCR often followed by capillary electrophoresis. However, amplifying the entire CGG-rich template beyond about 100–130 repeats is challenging. In addition, differentiating full mutation from homozygous normal female samples has historically required a Southern blot reflex test, which is expensive, labor intensive and requires a large amount of high quality DNA9. However, recent data has shown that the prevalence of PM and FM alleles was 1 in 883 in Hong Kong, similar to the prevalence found in Korea11. The objectives of this retrospective study were to determine the prevalence of FXS PM and FM carriers and the cost-effectiveness of the reproductive FXS carrier screening in a large Han Chinese female population.It was regarded that the FXS prevalence was low in Chinese population, and a routine screening of FXS carrier was unwarrantedThis was a retrospective observational study conducted between September 2014 and May 2017. This study was approved by the Institutional Review Boards of national Taiwan University Hospital (201706008RIND) since June 13, 2017. All samples were collected with informed written consent for the genetic test and all methods were carried out in accordance with relevant guidelines and regulations. Pregnant women at or above the age of 20 years were eligible for the study. Women with a known family history of FXS were excluded to avoid an overestimation of the premutation carrier rate in the general population. Women were tested on their own initiative and self-paid for the testing, which was not covered by the National Health Insurance of Taiwan. Pre-test counseling was given by obstetric clinicians at the first trimester, usually upon the first prenatal visit. A printed pamphlet containing information about fragile X carrier testing was provided. If the participants have more than 55 CGG repeats, genetic counseling by well-trained geneticists would be offered. The EDTA tube was used to draw two milliliters of peripheral blood from each participant.7. In this assay, full-length and CGG repeat primed amplicons were produced using two gene-specific primers and were analyzed by capillary electrophoresis. Southern blot analysis was performed as described previously6.The CGG repeat status of the FMR1 gene was determined using commercialized CGG repeat primed PCR . Testing was carried out based on manufacturer’s recommendations as previously mentioned12.Participants with positive test results (more than 55 CGG repeats) would be referred for genetic counseling. The post-test genetic counseling was provided to the pregnant women and her family members by a well-trained genetic counselor. The prognosis of a full-mutation or premutation carrier as well as the possible phenotypes of FXTAS and FXPOI was explained. After genetic counseling, prenatal and postnatal diagnosis could be made by via samples from amniocentesis, cord blood or neonatal blood. After the consultation, the patients arrived at their own decisions on whether to continue or terminate the pregnancies. We followed the ACMG Standards and Guidelines for fragile X testing. If the CGG repeats fall in the range from 45 to 54, it is regarded as intermediate and is not reported to the patientsThe cost of the test from the society is estimated based on the expense under the National Health Insurance of Taiwan in this cohort. The method to calculate cost-effectiveness of the screening program is not a validated method.Of the 20,188 pregnant Taiwanese women who underwent fragile X carrier testing, 19,982 of them (98.9%) were deemed normal (<45 repeats). The average age of the enrolled women was 31.7 years old, ranging from 20 to 54 years old. The CGG repeat ranged from 5 to 44 in normal FMR1 alleles, with the most prevalent alleles having 29 repeats (39.26%), followed by 30 repeats (25.62%), 28 repeats (8.60%) and 36 repeats (6.0%) . The reported PM carrier frequency in Korea and mainland China were 1 in 781 (0.12%), and 1 in 1113 (0.09%), respectively13. Our research results also show that FXS carriers are not at all rare in Chinese9. This study reported the highest female carrier frequency among Asian populations but still much lower compared to data from western countries such as Israel , Finland , Canada  and Australia 18. According to the ACMG position statement on prenatal/preconception expanded carrier screening, the mutation frequencies should be known in the population being tested for each screened disorder12. Therefore, the determination of FXS carrier rate in Taiwan is important before considering universal carrier screening.Population-based prevalence studies have been conducted in various countries. This is the largest study of the FXS carrier screening in Chinese women to date. The prevalence of premutation for FXS in normal asymptomatic Taiwanese women was as high as 0.13% (1 in 777) in this study. Our discovery corresponds with the recent study of Cheng 19. It is a disease with a well-documented phenotype, an early-onset, and has detrimental effects on the quality of life, causing cognitive and physical impairment. Most women can accept screening for FXS carriers, as it is a simple maternal blood test. Meanwhile, after reproductive FXS carrier screening, the prenatal genetic diagnosis is available for FXS using the fetal DNA isolated from the amniotic fluid. Furthermore, once a carrier is identified, a family screening can be conducted to identify other carriers. Therefore, this screening has promising prospects, especially in modern society with fewer children born. Accordingly, the Royal Australian and New Zealand College of Obstetricians and Gynecologists recommends that the information on carrier screening for fragile X syndrome should be offered to all women planning pregnancy or in early pregnancy20.Fragile X premutation carrier screening is now recommended for women with a family history of fragile X-related disorders or intellectual disabilities suggestive of fragile X syndrome, or women with a personal history of ovarian insufficiencyet al. reviewed 23 published cases with FMR1 gene deletions and reported an adolescent male with typical fragile X phenotype in whom a 300–400 bp deletion within exon 1 of FMR1 was found21. The patient’s clinically normal mother also has a full mutation with 700–900 CGG repeats. Our case may represent the second documented instance of a deletion in FMR1 arising from a full mutation so far. Our second case with FMR1 gene deletion inherited from his mother. The asymptomatic mother had a normal allele with 29 CGG repeats and another allele with a 70 bp deletion at 5′UTR end and nine CGG repeats  are caused by mutation in the coding region or deletion of the FMR1 gene22. Point, missense, nonsense, frameshift, and UTR region mutations have all been described26. These mutations account for approximately 4% of FXS patients meeting the clinical criteria for FXS but with a normal range of CGG repeats23. Therefore, detailed molecular analysis of the FMR1 gene may help to identify the exact mutation attributing to the FXS phenotype.There were two male fetuses born with FMR1 gene deletions in this cohort. The mother of the first fetus was clinically normal but had a family history of intellectual disability. She was identified to have a full FMR1 mutation with 283 CGG repeats. Hommond 11. In our study, five of the six maternal alleles that exceed 69 repeats expanded to FM, while in the Korean report, all of the maternal alleles over 70 repeats expanded to FM11. This information is important for genetic counselling of PM women picked up by the carrier screening programs, although an even larger sample size may be needed to confirm and to change clinical practice. Our finding is also compatible with previous literatures that an increased number of CGG repeats confer increased instability of alleles from one generation to the next, resulting in alleles with increased number of repeats in the progenies. 3. It was reported that the proportion of female adults with at least one “low normal” CGG repeat allele  was 35.0% in the adult carrier-screening cohort in Australia18. In our Chinese cohort, however, the proportion of the pregnant women with at least one allele with CGG repeats ≤26 was only 3.25%, and those carrying an allele with ≤20 repeats were only 0.83%. This discrepancy may highlight the importance of ethnicity in FMR1 allele distributions and frequencies.The CGG repeat distribution varies among different population. In our cohort, the most prevalent allele was 29 repeats (39.26%), followed by 30 repeats (25.62%), 28 repeats (8.60%) and 36 repeats (6.01%). Similar to the reports from Hong Kong and Korean, the most prevalent alleles were 29 and 30 repeats while the minor allele was 31 and 36 repeats, respectively28. It costs about 125 US dollars to do a FXS test in Taiwan. In this cohort, it took about 118,885 US dollars to identify a PM or a FM woman. Taking the costs of amniocentesis and prenatal FXS genetic test into consideration, the total cost of identifying a fetus with FM is approximately 410,091 US dollars. The empirical evidence suggests that reproductive FXS carrier screening might be cost-effective in Taiwan.There is no available data regarding the expenses of FXS treatment in Taiwan. In the United States and United Kingdom, the estimated life-cycle cost for an FXS patient is 615,397 US dollars and 510,900 US dollars, respectively29. X chromosome inactivation (XCI) is the phenomenon of the silencing of one of the two X chromosomes in mammalian females. Normally, XCI occurs randomly. However, preferential XCI can be observed under particular conditions. In the case of FMR1 mutation carriers, if the mutated chromosome is preferentially inactivated, FMRP can still be produced by the remaining normal allele, and the resulting phenotype would be less severe30. If a female fetus with FM is identified through prenatal diagnosis, Southern blot analysis should be applied to predict whether the fetus would be affected by FXS. In this cohort, only two female fetuses with FM were terminated, while the other two female fetuses with FM and seven female fetuses with PM were born. The second concern is that identification of a PM carrier may lead to anxiety regarding to the development of FXPOI and FXTAS7. The symptoms of FXTAS usually manifest in carriers over fifty years old, especially males31. Among female PM carriers, about 20% may experience early menopause before they turn 40, therefore targeted reproductive interventions are required32. Obstetricians, midwives, genetic counselors, and high-risk pregnant women should also be provided with medical information regarding this condition. Further referrals to psychiatric and neurologic specialists should be arranged as necessary.Last but not the least, pre-testing and post-testing prenatal genetic counseling can be challenging. The first difficulty in counseling is the variable phenotypes associated with FXS, especially in female fetuses with PM or FM33. Although testing for the AGG triplets is available clinically, it is not routinely performed in Taiwan and its clinical usefulness is yet to be determined according to the ACMG guideline34. Secondly, the enrolled pregnant women in this cohort were from local clinics and regional hospitals. As this a self-paid test not covered by the National Health Insurance, we could not exclude bias with respect to socioeconomic status.There were some limitations in this study. Firstly, this study did not analyze the presence of AGG interruptions. The lack of AGG interruptions might aid in risk prediction of FM expansions from premutations with repeats <100This is by far the largest study of the reproductive FXS carrier screening in Chinese women. The carrier rate of premutation for FXS in normal asymptomatic Taiwanese women was found to be 0.13% (1 in 777) in this study. One of the most impressive findings is that no PM mothers with CGG repeats in the low PM range  have expanded to a full mutation. This could be important for genetic counselling of PM women picked up in the carrier screening programs. Larger sample size may be needed to confirm this before it could change clinical practice. The reported FXS carrier rate in Taiwan is important for prenatal counseling and for the implementation of universal screening as a public health policy.Supplementary table S1


The aim of the present cross-sectional study was to assess the impact of malocclusion on the quality of life of children aged 8 to 10 years attending public elementary schools in Belo Horizonte, State of Minas Gerais, Brazil. The Brazilian version of the Child Perceptions Questionnaire 8-10 (CPQ8-10) was used to evaluate oral health-related quality of life. The children were examined for the diagnosis of malocclusion using the Dental Aesthetic Index (DAI). The data were analyzed by bivariate and multivariate descriptive statistics using Poisson regression at a 5% significance level. A total of 270 children participated in the study. p= 0.003) to have their quality of life affected compared with children diagnosed with extremely severe malocclusion (DAI ≥ 36). Children with a maxillary anterior overjet ≥ 3 mm had higher CPQ8-10 mean scores  than those with an overjet < 3 mm .  Children with normal occlusion or mild malocclusion (DAI ≤ 25) were 56% less likely (95%CI: 0.258-0.758; Extremely severe malocclusion and pronounced maxillary anterior overjet were associated with a negative impact on quality of life.  Clinical measures are important to determine a patient’s normative need for treatment and associating them with information on the impact of oral changes in people’s daily lives turns out to be useful.Normative indices - which are quite useful for the planning of public health services in orthodontics - have been used for assessing the severity of malocclusion. These indices allow determining the priority of care among extremely severe cases by the Brazilian Unified Health System, known as SUS.,Malocclusion is a health problem that has received rapt attention, being the third most prevalent oral disease, outranked only by dental caries and periodontal disease.--Numerous studies have assessed the impact of malocclusion on quality of life.,12,13Previous studies involving children aged 8 to 10 years revealed that those children with malocclusion were more prone to have a negative impact on their quality of life than malocclusion-free individuals.Accordingly, the aim of this study was to contribute to the formulation of public health policies in orthodontics. To achieve such goal, the study assessed the impact of malocclusion on the quality of life of children aged 8 to 10 years enrolled in public schools of Belo Horizonte, State of Minas Gerais, BrazilThis cross-sectional study was conducted in Belo Horizonte, State of Minas Gerais, Brazil. A total of 270 children aged 8 to 10 years, of both sexes, were included in the study. Children with special needs or cognitive impairments reported by parents and teachers were excluded for the sample. Children with traumatic dental injuries or enamel development defects in permanent teeth were also excluded, as well as those who were wearing orthodontic appliances or with history of previous orthodontic treatment. All of the participants were regularly enrolled in the city’s public elementary schools, each of which belonged to one of the nine regional administrative units. Schools were selected from the list of all elementary schools in Belo Horizonte, provided by the State of Minas Gerais Department of Education. First, one public elementary school was randomly selected in each administrative district in Belo Horizonte. After that, the classes within the schools were selected. All 8-to-10-year-old students attending the selected classes were asked to participate. The sample was completed when the target number was reached.8-10 mean=13.5; SD=11.7); children with definite, severe and extremely severe malocclusion, n=113 . Using theses parameters, the power of the study was 96.9%.The power of the sample was calculated based on the impact of malocclusion on quality of life, using EpiInfo software. The following parameters were used: 95% confidence interval; children with normal occlusion or mild malocclusion, n=157 (CPQ8-10) was applied to assess oral health-related quality of life. The responses to the questionnaire follow the 5-point Likert scale, with scores 0 to 4 for each item. The sum of scores can vary from 0 to 100. Score zero (0) indicates no impact of children’s oral health on their quality of life, while Score 100 indicates the opposite, i.e., maximum impact of oral health on children’s quality of life.The Brazilian version of the Child Perceptions Questionnaire 8-10  periodontal probes. The CPI periodontal probes were used to measure occlusal characteristics in millimeters. The children were evaluated by one calibrated dentist, who used the Dental Aesthetic Index (DAI) for malocclusion and the DMFT/deft  for dental caries, both recommended by the World Health Organization (WHO).Calibration for DAI  and DMFT/deft  included theoretical and clinical stages. The theoretical stage consisted of discussions about the diagnostic criteria for malocclusion and dental caries by means of photographs and plaster models. The calibration process was coordinated by experts in pediatric dentistry and orthodontics as golden standard. The clinical stage was conducted with 20 children. Children were evaluated by the golden standard and the examiner. These children were not included in the total study sample. The same children were reexamined one month afterwards. The Cohen’s kappa coefficient for interrater reliability was 0.81 for dental caries and 0.90 for malocclusion, whereas for intrarater reliability, it was 0.87 for dental caries and 0.91 for malocclusion (agreement between the examiner and the gold standard). The coefficients showed good and excellent agreement.DAI scores less than or equal to 25 indicate normal occlusion or mild malocclusion with little or no orthodontic treatment need. Scores between 26 and 30 indicate definite malocclusion requiring elective treatment. Scores between 31 and 35 indicate severe malocclusion with highly desirable treatment. Scores equal to or greater than 36 indicate extremely severe or handicapping malocclusion with mandatory treatment.Dental caries was included in the clinical examination and treated as confounding variable; its assessment was based on the DMFT/deft indices. DMFT/deft was used as a quantitative variable. And for an additional statistical analysis, this variable was dichotomized according to the presence or absence of cavitation caused by dental caries.The pilot study was undertaken in a school that did not participate in the main study so as to test the methods and understand the tools used for data collection. ,The Social Vulnerability Index (SVI) was used for the socioeconomic classification. The SVI is an area-based measure drafted for the city of Belo Horizonte and determines to what extent the population of each region of the city is vulnerable to social exclusion. The index is made up of five dimensions: environmental, cultural, economic, legal and security/survival. There are five different classes: Class I comprises the most socially vulnerable families and Class V comprises the least socially vulnerable families. In this study, the SVI was grouped into two categories for statistical purposes: Classes I and II were grouped in the category ‘high social vulnerability’, and Classes III to V were grouped in the category ‘low social vulnerability’. As children usually live near their schools and study in a social environment similar to that of their homes, school districts were used for this classification.8-10 scores were not normally distributed. The data were analyzed by descriptive analysis using absolute and relative frequencies, means, and standard deviation. Mann-Whitney and Kruskal-Wallis tests were used for comparison of CPQ8-10 means with the independent variables. Bivariate Poisson regression was used to compare the CPQ8-10 means between the DAI categories . Poisson regression with robust error variance was used for the multivariate analysis. The variables were inserted into the regression model according to their statistical significance (p< 0.20). The significance level was set at 5%.The statistical analyses were made by Statistical Package for the Social Sciences . The Kolmogorov-Smirnov test demonstrated that CPQUniversidade Federal de Minas Gerais (protocol #40521114.9.0000.5149). Only those children who granted authorization and obtained it from their parents/legal guardians participated in the study. Parents/guardians and children read and signed an informed consent form prior to their participation in the study.This study was approved by the Research Ethics Committee of n= 135) were male and 67.8% had non-cavitated caries lesions. From these, 175 (58.1%) children had normal occlusion or mild malocclusion; 75 (27.8%) children presented with definite malocclusion (DAI = 26 to 30); 31 (11.5%) children were diagnosed with severe malocclusion (DAI = 31 to 35), and seven (2.6%) children had extremely severe malocclusion (DAI ≥ 36)  . 8-10 scores. Children without malocclusion or with mild malocclusion (DAI ≤ 25) were 56% less likely  to have a negative impact on their quality of life, compared with those children diagnosed with extremely severe malocclusion (DAI ≥ 36). Presence of untreated dental caries was associated with negative impact on quality of life (p= 0.010).Higher DMFT index was associated with higher CPQ8-10 scores (p< 0.001). Social vulnerability was not associated with quality of life (p= 0.327).8-10 mean values according to the independent variables. Children with maxillary anterior overjet ≥ 3 mm had higher CPQ8-10 means  than those with an overjet < 3 mm . The other independent variables were not significantly associated with CPQ8-10 (p> 0.05).p= 0.028) to have a negative impact on their quality of life than those with a maxillary anterior overjet < 3 mm. ,There is a growing interest among researchers in the influence of facial esthetics on the quality of life of children and adolescents. This study found that children with extremely severe malocclusion were more likely to have a negative impact on quality of life, corroborating the findings of other studies on the effect of malocclusion on quality of life conducted with children in the same age group (8 to 10 years).,12,13 One of this studies demonstrated that anterior segment spacing and anterior mandibular overjet were associated with worse quality of life.13 These differences may be due to sample size and studied population. The present study was conducted with 270 children only from public schools, and one of these previous studies was carried out with a larger sample size  from public and private schools.12 The other previous study was conducted with a smaller sample (n=102) only from public schools.The bivariate analysis demonstrated that most types of malocclusion were not statistically associated with oral health-related quality of life. Studies report that tooth decay is associated with an impact on the quality of life,The analysis of the results revealed that malocclusion (DAI >25) affected 41,9% of the children examined, while previous studies carried out with children of the same age group found that malocclusions affected 32,2%It was not possible to compare the present results with many others carried out with children of the same age group since there is a paucity of studies on the impact of malocclusion on the mixed dentition,-,,Previous studies described the presence of several occlusal characteristics associated with a negative impact on quality of life,,The presence of malocclusion, mainly in the anterior region, may interfere with children’s and adolescents’ psychosocial well-being. DAI was the criterion for the diagnosis of malocclusion in this study as it uses a single score, being practical for epidemiologists,DAI was designed for permanent dentition. There is no orthodontic index that is specific to mixed dentition. DAI could thus overestimate the need for orthodontic treatment, and this tends to occur more often with mixed dentition because of transient occlusal changes such as midline diastema, spacing between incisors, edge-to-edge molar relationship, among others.A previous study asserts that malocclusion does not self-correct from deciduous to mixed dentition, nor from mixed to permanent dentition,An earlier study emphasized the importance of classifying malocclusion into severity levels in order to prioritize the treatment of more severe cases at healthcare units affiliated with SUS.,One of the advantages of combining children’s self-perception with the clinical assessment made by the health professional lies with the definition of cases that are more likely to have a negative impact on quality of life, given that, according to some studies,» Children diagnosed with extremely severe malocclusion and those with a pronounced maxillary anterior overjet experienced a larger impact on their quality of life. » Malocclusion, especially in the anterior teeth, can compromise a child’s psychosocial well-being.» Further studies are needed with children addressing the impact of malocclusion in the mixed dentition.


Drosophila, 6mA has been shown to be important for enforcing the tissue specificity of neuronal genes in the brain and suppressing transposable element expression in the ovaries. In this study, we have analyzed the raw signal data from nanopore sequencing to identify 6mA positions in the D. melanogaster genome at single-base resolution. We find that this modification is enriched upstream from transcription start sites, within the introns and 3′ UTRs of genes, as well as in simple repeats. These 6mA positions are enriched for sequence motifs that are recognized by known transcriptional activators involved in development, such as Bicoid and Caudal, and the genes that carry this modification are enriched for functions involved in development, regulation of transcription, and neuronal activity. These genes show high expression specificity in a variety of tissues besides the brain, suggesting that this modification may play a more general role in enforcing the specificity of gene expression across many tissues, throughout development, and between the sexes.N6-methyladenine (6mA or m6dA) is a DNA modification that has long been known to play an important role in a variety of biological functions in prokaryotes. This modification has only recently been described in eukaryotes, where it seems to have evolved species-specific functions ranging from nucleosome positioning to transposon repression. In  Tetrahymena thermophila and the green alga Chlamydomonas, 6mA is associated with active transcription and may play a role in nucleosome positioning  protein, DMAD, acts as a demethylase that facilitates the removal of 6mA (The DNA modification N6-methyladenine (6mA) is common among prokaryotes and is known to play a role in the restriction/modification systems involved in defense against bacteriophage infection , as wellDrosophila melanogaster adult females, to identify a set of high-confidence genomic positions where the 6mA modification is present in the majority of cells in the adult fly. We find that these positions are enriched at simple repeats and within the introns, 3′ UTRs, and upstream regions of genes. The genes that carry these modifications are highly enriched for developmental, regulatory, and neuronal functions, they are expressed in a variety of tissues, and their expression is significantly more tissue specific than unmethylated genes.Based on these results, we hypothesized that the 6mA modification might play a more general role in enforcing the tissue-specific expression of a much larger set of genes across multiple tissues in the adult fly. To test this hypothesis, we analyzed the raw signal data generated by nanopore sequencing of 1D Genomic DNA by Ligation protocol and the PCR-based library was constructed using the ONT 1D Low Input Genomic DNA with PCR protocol. Libraries were sequenced on the MinION Mk1B device using version r9.4 flow cells and basecalled using the ONT Albacore software package (version 2.1.10).DNA was extracted from 30 adult females using the Qiagen DNeasy Blood & Tissue Kit and prepped for sequencing using the Oxford Nanopore Technologies (ONT) SQK-LSK108 library preparation kit. The PCR-free libraries were constructed using the ONT Canu  Illumina reads from our Hi-C data, which we used as single-end reads to polish the assembly with Pilon  to identify A/T positions whose signal level matched a 6mA model better than the canonical base. We did this for each of the two PCR-free sequencing replicates and retained positions where at least 70% of reads were inferred to carry the 6mA modification in replicate 1 and whose percentage estimate was replicated in the second sequencing sample (within +/− 10%). We also ran Tombo with a control dataset using the sample_compare algorithm to identify all positions whose signal deviated from the expected level (determined by the control library)(command: tombo detect_modifications sample_compare) and retained positions with at least 70% modified reads. We used bedtools  and tantan  without replacement and counted the number of features for each annotation category that they overlapped. We repeated this resampling procedure 10,000 times and calculated the p-value using the number of times the counts from the random set were greater than or equal to the counts from our high-confidence positions.We transferred the coordinates of our high-confidence positions from the DGRP732 assembly to the Mercator  to creatd tantan  and detebedtools to extract +/− 25 bp of sequence surrounding each high confidence position and the findMotifs.pl script from Homer  and Whole Genome Shotgun (WGS) databases under Bioproject PRJNA515844. Supplemental material available at FigShare: Drosophila Genetic Reference Panel (DGRP) and used the 6mA model in the  package .sample compare model in Tombo to identify all genomic positions whose PCR-free current levels deviated from those in the control library. We intersected these positions with those identified using the 6mA model, to create a final set of 10,467 high-confidence 6mA positions (not carry the 6mA modification (6mA-free positions). As expected, we find a much larger difference in current level between the PCR and PCR-free libraries for the 6mA positions compared to the 6mA-free positions . Overall, ∼90% of the high-confidence positions from DGRP732 are within 100bp of a putatively methylated position in DGRP379, suggesting that, between strains, the methylation status of a given genomic region is more conserved than that of specific basepairs.These two sequencing libraries were generated using a PCR-free protocol in order to preserve the 6mA modifications. We also generated a third library as a control using a PCR-based protocol in order to produce sequences from DNA molecules lacking the 6mA modification. We sequenced this library using a single flowcell and then used the ositions , File S1Wolbachia that is common among Drosophila . For DGRP379, we found only three reads that mapped to Wolbachia (average coverage = 0.004x) and for DGRP739, which has more sequencing data, we found 44 reads (average coverage = 0.19x). Based on these results, we conclude that Wolbachia is either absent from these strains, as previously reported, or at very low abundance.Both DGRP379 and DGRP732 were previously reported to lack the endosymbiotic bacterium P = 0.0079, P < 0.0001 in all other cases), we found that our 6mA positions are significantly enriched at introns, 3′ UTRs, upstream from transcription start sites, and simple repeats, whereas they are significantly depleted from coding sequence, intergenic sequence, and transposable elements (TEs)(3′ UTR: er cases). A similP = 3.2e-13).Previous work identified gain-of-6mA genomic regions in the brain that showed an accumulation of 6mA methylation in a DMAD null mutant. We found 3% (318 positions) of our high-confidence positions overlap these gain-of-6mA locations, which is significantly more than expected by chance (Drosophila brain (AGAAGGAG) have been shown to play a role in eye and thorax development (MEME (Homervs.MEME enriched motifs using STAMP (Homer motifs show significant similarity to one or more motifs identified by MEME (Table S2). The only motif that was not identified by MEME is Homer motif #8 . We alsoAGAAGGAG). In Drosng STAMP  and founmotif #8 . TogetheDrosophila . We performed a Gene Ontology (GO) enrichment analysis on this set of genes and found a total of 549 significantly enriched terms with FDR q-value <= 0.02 across the three GO categories (Table S3). The enriched terms are heavily biased toward categories involved in development, regulation of gene expression, and neuron-specific functions , in agreD. melanogaster tissues as well as in the whole fly. Part of the project involved the calculation of enrichment scores that measure the tissue specificity of each gene by comparing its expression level in a given tissue to its expression level in the whole fly (D. melanogaster genome (Wilcoxon test P < 2.2e-16). While mDrosophila genome. These positions tend to be located upstream from transcription start sites and within introns, 3′ UTRs, and simple repeats and they are associated with genes whose functions are related to transcriptional regulation, development, and neuronal activity. These findings corroborate those from previous studies where ChIP-seq was used to identify 6mA in Drosophila brains and ovaries  when DMAD, in concert with one or more activation proteins, removes the modification and activates the gene. Future work involving nanopore sequencing of different Drosophila tissues would provide insight into this model.Our results also suggest that, rather than only regulating the expression of neuronal genes, the 6mA modification may play a more general role in enforcing the tissue specificity of gene expression across many different tissues, and throughout development. Given that our sequencing data were from DNA extracted from whole flies, the 6mA positions that we identify are those that carry the 6mA modification in the majority of cells in the organism. The fact that our 6mA-containing genes show high expression specificity is consistent with a model where 6mA is involved in the repression of these genes in the majority of cells. Such genes would only be expressed in a minority of cells (


The standard approaches to diagnosing autism spectrum disorder (ASD) evaluate between 20 and 100 behaviors and take several hours to complete. This has in part contributed to long wait times for a diagnosis and subsequent delays in access to therapy. We hypothesize that the use of machine learning analysis on home video can speed the diagnosis without compromising accuracy. We have analyzed item-level records from 2 standard diagnostic instruments to construct machine learning classifiers optimized for sparsity, interpretability, and accuracy. In the present study, we prospectively test whether the features from these optimized models can be extracted by blinded nonexpert raters from 3-minute home videos of children with and without ASD to arrive at a rapid and accurate machine learning autism classification.We created a mobile web portal for video raters to assess 30 behavioral features  that are used by 8 independent machine learning models for identifying ASD, each with >94% accuracy in cross-validation testing and subsequent independent validation from previous work. We then collected 116 short home videos of children with autism  and 46 videos of typically developing children . Three raters blind to the diagnosis independently measured each of the 30 features from the 8 models, with a median time to completion of 4 minutes. Although several models , radial kernel, and linear SVM) performed well, a sparse 5-feature LR classifier (LR5) yielded the highest accuracy (area under the curve [AUC]: 92% [95% CI 88%–97%]) across all ages tested. We used a prospectively collected independent validation set of 66 videos (33 ASD and 33 non-ASD) and 3 independent rater measurements to validate the outcome, achieving lower but comparable accuracy (AUC: 89% [95% CI 81%–95%]). Finally, we applied LR to the 162-video-feature matrix to construct an 8-feature model, which achieved 0.93 AUC (95% CI 0.90–0.97) on the held-out test set and 0.86 on the validation set of 66 videos. Validation on children with an existing diagnosis limited the ability to generalize the performance to undiagnosed populations.These results support the hypothesis that feature tagging of home videos for machine learning classification of autism can yield accurate outcomes in short time frames, using mobile devices. Further work will be needed to confirm that this approach can accelerate autism diagnosis at scale. Dennis P. Wall and colleagues demonstrate that machine learning algorithms can classify children with and without autism spectrum disorder using nonexpert tagging of home videos. Autism has risen in incidence by approximately 700% since 1996 and now impacts at least 1 in 59 children in the United States.The current standard for diagnosis requires a direct clinician-to-child observation and takes hours to administer.The sharp rise in incidence of autism, coupled with the un-scalable nature of the standard of care (SOC), has created strain on the healthcare system, and the average age of diagnosis remains around 4.5 years, 2 years past the time when it could be reliably diagnosed.Mobile measures that scale could help to alleviate this strain on the healthcare system, reduce waiting times for access to therapy and treatment, and reach underserved populations.We applied 8 machine learning models to 162 two-minute home videos of children with and without autism diagnosis to test the ability to reliably detect autism on mobile platforms.Three nonexpert raters measured 30 behavioral features needed for machine learning classification by the 8 models in approximately 4 minutes.Leveraging video ratings, a machine learning model with only 5 features achieved 86% unweighted average recall (UAR) on 162 videos and UAR = 80% on a different and independently evaluated set of 66 videos, with UAR = 83% on children at or under 4.The above machine learning process of rendering a mobile video diagnosis quickly created a novel collection of labeled video features and a new video feature–based model with >90% accuracy.Short home videos can provide sufficient information to run machine learning classifiers trained to detect children with autism from those with either typical or atypical development. Features needed by machine learning models designed to detect autism can be identified and measured in home videos on mobile devices by nonexperts in timeframes close to the total video length and under 6 minutes.The machine learning models provide a quantitative indication of autism risk that provides more granularity than a binary outcome to flag inconclusive cases, potentially adding value for use in clinical settings, e.g., for triage.The process of mobile video analysis for autism detection generates a growing matrix of video features that can be used to construct new machine learning models that may have higher accuracy for autism detection in home video.Clinical prospective testing in general pediatric settings on populations not yet diagnosed will be needed. However, these results support the possibility that mobile video analysis with machine learning may enable rapid autism detection outside of clinics to reduce waiting periods for access to care and reach underserved populations in regions with limited healthcare infrastructure. Neuropsychiatric disorders are the single greatest cause of disability due to noncommunicable disease worldwide, accounting for 14% of the global burden of disease ,3 and noLike most mental health conditions, autism has a complex array of symptoms  that areAs a step toward enabling fast and accurate access to care for ASD, we have used supervised machine learning approaches to identify minimal sets of behaviors that align with clinical diagnoses of ASD –30. We aThese preliminary studies focused on building models optimized for accuracy, sparsity, and interpretability that differentiate autism from non-autism while managing class imbalance. We chose models with small numbers of features, with performance at or no more than 1 standard error away from best test performance, and with interpretable outcomes—for example, scores generated by a boosted decision tree or logistic regression (LR) approach. In all, these studies have used score data from 11,298 individuals with autism  and 1,356 controls (including some children for whom autism may have been suspected but was ruled out) and have identified the following 8 classifiers: a 7-feature alternating decision tree (ADTree7) , a 12-feNASD = 69; Ncontrols = 153; median age = 5.8 years), the performance, measured as the unweighted average recall  on 222 children  with 8 features developed from medical data from the administration of the diagnostic instrument ADOS Module 1, and LR5 to refer to the LR with 5 behavioral features developed from analysis of ADOS Module 2 medical record data, and so on.We  appliedWe  used a dWe  performeWe  used scoIn this experiment, we  used medIn this experiment, we  used medAccounting for overlap in the features selected, these 8 models measure 23 unique features in total. The test accuracy for each model was >90%. All models contain approximately 90% fewer questions than the ADI-R and 70%–84% fewer questions than the total features measured within the ADOS. An additional 7 features were chosen for their potential diagnostic value and scored by video raters to assess their suitability for scoring home videos, creating a total of 30 features for the mobile video rating process described below .Under an approved Stanford University IRB protocol, we developed a mobile portal to facilitate the collection of videos of children with ASD, from which participants electronically consented to participate and upload their videos. Participants were recruited via crowdsourcing methods –41 targeWe relied on self-reported information provided by the parents concerning the child’s official diagnosis of autism or non-autism, the age of the child when the video was submitted, and additional demographic information for videos that were submitted directly to the web portal. For videos that were provided via YouTube URLs, we used YouTube metatags to confirm the age and diagnosis of the child in the video. If a video did not include a metatag for the age of the child in the video, the age was assigned following full agreement among the estimates made by 3 clinical practitioners in pediatrics. To evaluate the accuracy of the parents’ self-report and to safeguard against reporting biases, we commissioned a practicing pediatric specialist certified to administer the ADOS to review a random selection of 20 videos. We also commissioned a developmental pediatrician to review a nonoverlapping random selection of 10 additional videos. These clinical experts classified each video as “ASD” or “non-ASD.”We employed a total of 9 video raters who were either students  or working professionals. None had training or certification for detection or diagnosis of autism. All were given instructions on how to tag the 30 questions and were asked to score 10 example videos before performing independent feature tagging of new videos. After training, we provided the raters with unique usernames and passwords to access the secure online portal to watch videos and answer 30 questions for each video needed by the feature vectors to run the 8 machine learning classifiers . FeatureTo test the viability of feature tagging videos for rapid machine learning detection and diagnosis of autism, we empirically identified a minimum number of video raters needed to score parent-provided home videos. We selected a random subset of videos from the full set of videos collected through our crowdsourced portal and ran the ADTree8  model onAfter determining the minimally viable number of video raters, we used that minimum to generate the full set of 30-feature vectors on all videos. Seven of the models were written in Python 3 using the package scikit-learn, and one was written in R. We ran these 8 models on our feature matrices after feature tagging on videos. We measured the model accuracy through comparison of the raters’ majority classification result with the true diagnosis. We evaluated model performance further by age categories: ≤2 years, >2 to ≤4 years, >4 years to ≤6 years, and >6 years. For each category, we calculated accuracy, sensitivity, and specificity.We collected timed data from each rater for each video, which began when a video rater pressed “play” on the video and concluded when a video rater finished scoring by clicking “submit” on the video portal. We used these time stamps to calculate the time spent annotating each video. We approximated the time taken to answer the questions by excluding the length of the video from the total time spent to score a video.The process of video feature tagging provides an opportunity to generate a crowdsourced collection of independent feature measurements that are specific to the video of the child as well as independent rater impressions of that child’s behaviors. This in turn has the ability to generate a valuable feature matrix to develop models that include video-specific features rather than features identified through analysis on archived data generated through administration of the SOC  and were trained for video tagging through the same procedures.This study was conducted under approval by Stanford University’s IRB under protocol IRB-31099. Informed and written consent was obtained from all study participants who submitted videos to the study.All classifiers used for testing the time and accuracy of mobile video rating had accuracies above 90% . The unin = 25) or inadequate video quality (n = 6), leaving 162 videos (116 with ASD and 46 non-ASD) which were loaded into our mobile video rating portal for the primary analysis. To validate self-reporting of the presence or absence of an ASD diagnosis, 2 clinical staff trained and certified in autism diagnosis evaluated a random selection of 30 videos (15 with ASD and 15 non-ASD) from the 162 videos. Their classifications had perfect correspondence with the diagnoses provided through self-report by the primary caregiver.We collected a total of 193 videos  with aveWe randomly selected 50 videos (25 ASD and 25 non-ASD) from the total 162 collected videos and had 9 raters feature tag all in an effort to evaluate the potential for an optimal number of raters, with optimal being defined through a balance of scalability and information content. The average video length of this random subset was 1 minute 54 seconds (SD = 46 seconds) for the ASD class and 2 minutes 36 seconds (SD = 1 minute 15 seconds) for the non-ASD class. We then ran the ADTree8  model onThree raters performed video screening and feature tagging to generate vectors for each of the 8 machine learning models for comparative evaluation of performance . All clan = 1] class in this range). This model performed best on children between the ages of 4 and 6 years, with sensitivity and specificity both above 90%  between the average time spent to score the videos of children with ASD and the average time spent to score the non-ASD videos (6 minutes 36 seconds compared with 5 minutes 8 seconds).The median time for the 3 raters to watch and score a video was 4 minutes . Excludin = 14) of the children with ASD were male and 45% (n = 15) of the non-ASD children were male. The average video length was 3 minutes 24 seconds, with an SD of 45 seconds. For this independent replication, we used 3 different raters, each with no official training or experience with developmental pediatrics. The raters required a median time of 6 minutes 48 seconds for complete feature tagging. LR5 again yielded the highest accuracy, with a sensitivity of 87.8% and a specificity of 72.7%. A total of 13 of the 66 videos were misclassified, with 4 false negatives.To validate the feasibility and accuracy of rapid feature tagging and machine learning on short home videos, we launched a second effort for crowdsourcing videos of children with and without autism to generate an independent replication dataset. We collected 66 videos, 33 of children with autism and 33 non-ASD. This set of videos was comparable to the initial set of 162 videos in terms of gender, age, and video length. The average age for children with ASD was 4 years 5 months (SD = 1 year 9 months), and the average age for non-ASD children was 3 years 11 months (SD = 1 year 7 months). Forty-two percent  novel measures of the 30 video features used to distinguish the autism class from the neurotypical cohort. Out of these 176 videos , 162  were from the analysis set, and 14 videos  were from the set of 66 validation videos. Model hyperparameters  identified through 10-fold cross-validation were 0.01 and 0.6, respectively. We used a high L1 ratio to enforce sparsity and to decrease model complexity and the number of features. We had similar proportions (0.60) for non-ASD and ASD measures in the training set and held-out test set, which allowed us to create a model that generalizes well without a significant change in sensitivity or specificity on novel data. The model had an area under the receiver operating characteristic curve (AUC-ROC) of 93.3% and accuracy of 87.7% on the held-out test set. A comparison of LR-EN-VF with LR L2 penalty (no feature reduction) revealed similar results  . The topPrevious work –29 has sBy using a mobile format that can be accessed online, we showed that it is possible to get multiple independent feature vectors for classification. This has the potential to elevate confidence in classification outcome at the time of diagnosis  while fostering the growth of a novel matrix of features from short home videos. In the second part of our study, we tested the ability for this video feature matrix to enable development of a new model that can generalize to the task of video-based classification of autism. We found that an 8-feature LR model could achieve an AUC of 0.93 on the held-out subset and 0.86 on the prospective independent validation set. One of the features used by this model, sensory seeking, was not used by the instruments on which the original models were trained, suggesting the possibility that alternative features may provide added power for video classification.These results support the hypothesis that the detection of autism can be done effectively at scale through mobile video analysis and machine learning classification to produce a quantified indicator of autism risk quickly. Such a process could streamline autism diagnosis to enable earlier detection and earlier access to therapy that has the highest impact during earlier windows of social development. Further, this approach could help to reduce the geographic and financial burdens associated with access to diagnostic resources and provide more equal opportunity to underserved populations, including those in developing countries. Further testing and refinement should be conducted to identify the most viable method(s) of crowdsourcing video acquisition and feature tagging. In addition, prospective trials in undiagnosed and in larger, more-balanced cohorts including examples of children with non-autism developmental delays will be needed to better understand the approach’s potential for use in autism diagnosis.S1 TableLR10, LR5, and ADTree7 are the top-3 best-performing classifiers on the validation set, which falls in line with the results observed on the test dataset of 162 videos used earlier. LR5 still performs with the highest specificity out of the 8 models. ADTree7, 7-feature alternating decision tree; LR5, 5-feature logistic regression classifier; LR10, 10-feature logistic regression classifier.(DOCX)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.S1 Checklist(DOCX)Click here for additional data file.


The possibility of obtaining sorghum grains with quality to match the standards for a diversity of end-uses is frequently hampered by the susceptibility to pre-harvest sprouting (PHS) displayed by many elite genotypes. For these reasons, obtaining resistance to PHS is considered in sorghum breeding programs, particularly when the crop is expected to approach harvest maturity under rainy or damp conditions prevalence. As in other cereals, the primary cause for sprouting susceptibility is a low dormancy prior to crop harvest; in consequence, most research has focused in understanding the mechanisms through which the duration of dormancy is differentially controlled in genotypes with contrasting sprouting behavior. With this aim two tannin-less, red-grained inbred lines were used as a model system: IS9530 (sprouting resistant) and Redland B2 (sprouting susceptible). Redland B2 grains are able to germinate well before reaching physiological maturity (PM) while IS9530 ones can start to germinate at 40–45 days after pollination, well after PM. Results show that the anticipated dormancy loss displayed by Redland B2 grains is related reduced embryo sensitivity to abscisic acid (ABA) and increased levels of GA upon imbibition. In turn, transcriptional data showed that ABA signal transduction is impaired in Redland B2, which appears to have an impact on GA catabolism, thus affecting the overall GA/ABA balance that regulates germination. QTL analyses were conducted to test whether previous candidate genes were located in a dormancy QTL, but also to identify new genes involved in dormancy. These analyses yielded several dormancy QTL and one of them located in chromosome 9 (qGI-9) was consistently detected even across environments. Fine mapping is already in progress to narrow down the number of candidate genes in qGI-9. Sorghum bicolor (L.) Moench] is a grass species cultivated for its grain which is used for feeding both humans and animals. Also known as durra, jowari, or milo, sorghum can grow under harsher conditions and therefore may be better than maize or sugarcane in some environments. Sorghum originated in Northern Africa but is now cultivated widely in tropical and subtropical environments to the point that it has become the fifth most important cereal of the world after rice, wheat, maize, and barley. Sorghum grains for human consumption are used to make flat breads and beverages and also for malting and brewery. As in many other crops, the sorghum grain is also required to perform as the propagule for a new crop. The possibility of obtaining grains with quality to match the standards for these end-uses is frequently hampered by the susceptibility to pre-harvest sprouting (PHS) displayed by many elite genotypes. Indeed, untimely germination in the mother plant promotes reserve mobilization and/or leads to either immediate loss of seed viability or to a large reduction in seed longevity  that promotes anthocyanin synthesis in the aleurone  in wheat and Hvmyb10 in barley fail to accumulate proanthocyanidins in the testa and contribute to reduce grain sensitivity to ABA and dormancy  onward] is due to a complex interplay between coat- and embryo-based dormancy, with the former playing a major role in the overall contribution to grain dormancy. Removal of the covering structures  results in a reduced expression of dormancy at a wide range of temperatures even with embryos isolated as early as 15 DAP  demonstrating that the different capacity to germinate during development is the result of dormancy differentially imposed by the presence of the seed coat tissues. The pattern of dormancy release during grain filling normally shows two phases in Redland B2 grains: an early one which is not observed in IS9530 grains, starting around 15–20 DAP and reaching a plateau prior to PM (30–35 DAP), and a second one starting after PM and coinciding with the beginning of exit from dormancy in IS9530 grains . Susceptibility to PHS in Redland B2, then, is related to precocious grain dormancy loss, while resistance in IS9530 can be associated to a more persistent dormancy , an also tannin-less, three-dwarf derivative line, resulting from the US Sorghum Conversion Program . During dormancy . It is wdormancy . For theArabidopsis and maize that are ABA-deficient or ABA-insensitive germinate precociously , was mimicked by combining low ABA content with exogenous GA supplementation. These results suggested that the anticipated dormancy release displayed by Redland B2 grains was not only related to a low embryo responsiveness to ABA, but also to a high GA content or, alternatively, to a high capacity to synthesize GA de novo upon imbibition. Similar to observations of ABA content in developing grains of these two genotypes, no correlation was found between natural endogenous GA content and dormancy  and ABA signaling appeared as the main components of the ABA/GA balance regulating the germination response in developing grains of both lines. A candidate gene approach was followed to assess and identify potential regulatory sites at the transcriptional level that would lead to the observed differences at the physiological level.It has been known for a long time that GAs promote germination of dormant seeds in many species, antagonizing the inhibitory effect of ABA. Both ABA and GA also act antagonistically in the inception of dormancy during early development. Application of paclobutrazol, a GA biosynthesis inhibitor to young sorghum panicles, reduced GA content during development and delayed dormancy release for several weeks in Redland B2 grains . Moreove2 grains . Indeed,dormancy . Insteadmination . This inite GA34  suggestiSbABI3/VP1, SbABI4, SbABI5, and SbPKABA1 (together with SbABI5 protein levels) occurred in imbibed grains of IS9530, but not in RedlandB2. This “induction” pattern in IS9530 was observed in immature grains (30 DAP), but after PM expression of these genes decreased rapidly and similarly in both lines  showed, on the other hand, a transient increase in dormant grains (IS9530) during the first 2–3 days of grain imbibition, which did not occur in Redland B2  was also increased This observation, together with a negative association between embryo content of active GA4 and its corresponding catabolite GA34, supported the notion that GA4 levels remain low in dormant IS9530 grains as a result of a prominent catabolic activity by GA2-oxidases, which, in turn, is reduced in RedlandB2. On the other hand, incubation of dormant grains in 100 μM GA3 promoted germination but did not reduce the expression of most key GA synthesis genes, ruling out the idea of a negative feedback regulatory mechanism driven by active GAs. In addition, and in contrast to other reports which show a feed-forward mechanism affecting expression of GA2-oxidase genes by active GA levels in Arabidopsis , and SbGA2-oxidase genes suggested the existence of a possible functional link between both pathways. This, together with the presence of an ABA-responsive complex (ABRC) conformed by cis-regulatory elements related to ABA  in the 5′ regulatory region of GA-2ox3, suggested a possible interaction between ABA signaling pathway and GA catabolism  and transcriptional data  support that ABA signal transduction is impaired in Redland B2. Reduced ABA signaling appears to have an impact on GA catabolism, thus affecting the overall GA/ABA balance that regulates germination.2 mapping population derived from a cross between IS9530 x Redland B2. These authors used RFLP, RAPDs, and AFLP markers, and reported a putative association between SbVP1 and a dormancy QTL in likage group “E”  in 42 DAP grains  which were successfully anchored on the S. bicolor genome assembly v2.1 (qGI-9) to 547 (qGI-3). No epistasis was detected for the identified QTL. Interestingly, SbABI3/VP1 located within qGI-3, which makes it a good candidate to be the causal gene for this QTL. Allelic variants for SbVP1 were found to exist in RedlandB2 and IS9530  and 448  analyses were conducted to investigate the genetic bases of the contrasting dormancy phenotypes in both sorghum inbred lines IS 9530 and Redland B2. This approach would allow testing whether previous candidate genes are located in a dormancy QTL, but also would lead to the identification of new genes involved in dormancy. The first genetic study was done by roup “E” . To furtroup “E” . In thisbly v2.1  through d IS9530 . The sorPro/Ser; . These rPro/Ser; . Interes reduced , while en by ABA . An alteur group .3 mapping population used by locus in qGI-9 is very close to DWARF-1, a gene regulating plant stature and recently cloned by two groups  of parental lines. Both strategies are aimed to narrow down the number of candidate genes in these regions and identify DNA polymorphisms in these genes. Future studies including transcriptomic analysis of developing grains for both parental lines are expected to help identify potential transcriptional variants (due to polymorphisms in the promoter region) among the candidate genes within each QTL. Strategies based on the combination of genetic and genomic tools have accelerated the process leading to identify a solid, causal gene for a QTL. An example of this is the identification of MOTHER OF FT (MFT) as the causal gene of a dormancy QTL (QPhs.ocs-3A.1) in wheat by Ongoing work relies on a recombinant inbred line (RIL) population derived from the Fo groups . Sorghumo groups . The genAS panel  suggestsAll authors listed above made a substantial, direct and intellectual contribution to this work. RB-A and MR discussed the contents and wrote the first version of the manuscript. MR corrected the revised version. Both authors read and approved the final version 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.


During functional electrical stimulation (FES) tasks with able-bodied (AB) participants, spatially distributed sequential stimulation (SDSS) has demonstrated substantial improvements in power output and fatigue properties compared to conventional single electrode stimulation (SES). The aim of this study was to compare the properties of SDSS and SES in participants with spinal cord injury (SCI) in a dynamic isokinetic knee extension task simulating knee movement during recumbent cycling.PmeanL,R) was calculated from all stimulated extensions for initial, final and all extensions. Fatigue is presented as an index value with respect to initial power from 1 to 0, whereby 1 means no fatigue.Using a case-series design, m. vastus lateralis and medialis of four participants with motor and sensory complete SCI (AIS A) were stimulated for 6 min on both legs with both electrode setups. With SES, target muscles were stimulated by a pair of electrodes. In SDSS, the distal electrodes were replaced by four small electrodes giving the same overall stimulation frequency and having the same total surface area. Torque was measured during knee extension by a dynamometer at an angular velocity of 110 deg/s. Mean power of the left and right sides  and for the initial phase . Fatigue resistance was better with SDSS for P1, P2 and P4 but worse for P3 .SDSS showed higher PmeanL,R was observed for all four participants for initial and overall contractions using SDSS. This supports findings from previous studies with AB participants. Fatigue properties were better in three of the four participants. The lower fatigue resistance with SDSS in one participant may be explained by a very low muscle activation level in this case. Further investigation in a larger cohort is warranted.Consistently higher  Mobilisation and exercise play an important role during the rehabilitation of persons after a spinal cord injury (SCI). On the one hand it is important to regain strength and mobility, and on the other hand it helps to prevent secondary complications –3. Arm cTwo major factors that limit the use and effectiveness of FES are the well-known problems of rapid muscle fatigue and low power output of artificial stimulation when used for functional tasks –18. In tStimulation intensity can also be controlled using pulse width or amplitude. High intensity is usually correlated to high power output , 30but eMost investigations have shown some improvements in either power or fatigue but no study to date has demonstrated meaningful increases in both power and fatigue. Non-selective recruitment of motor units and poor intra- and inter-muscular coordination leads to an exaggerated metabolic cost of electrically evoked contractions , 17. ThiThe use of more electrodes and spatial differentiation, termed spatially distributed sequential stimulation (SDSS), has been shown to be very effective in power generation and fatigue reduction –43. WithThe increasing number of studies with positive outcomes regarding distributed electrode setups indicates that this strategy is a promising solution for the low power and the low fatigue resistance of conventional surface electrode stimulation. However, the number of studies with SCI participants is limited and the transfer of knowledge gained with able-bodied participants to SCI is another challenge . FollowiThe aim of this study is to compare the power output and fatigue properties of the quadricps femoris muscles in response to spatially distributed sequential stimulation (SDSS) versus traditional single electrode stimulation (SES) in four untrained participants with motor-complete spinal cord injury during a dynamic leg extension task simulating knee joint movement in recumbent cycling.Four participants with motor-complete spinal cord injury and an American Spinal Cord Injury Association (ASIA) impairment score (AIS) A were included Table  accordinMeasurements were conducted using an isokinetic dynamometer  with an upgrade package . The dynamometer was set up for concentric quadriceps measurement. After seating, individual adjustments were made for ergonomic knee joint movement.The dynamometer controlled the angular range of motion at the knee joint from 40 to 120 deg (180 deg means straight leg) at an angular velocity of 110 deg/s at the knee joint, which is equivalent to a cycling cadence of 50 rpm. The dynamometer was interfaced to a PC running Matlab/Simulink and the Real-Time Toolbox , which recorded raw data of the knee angle, torque and time. These data were used to control the stimulation range and the stimulation parameters. A graphical user interface was implemented for setting up and controlling the stimulation device and the timing .μs (1 μs steps) and a frequency range of 0 - 100 Hz. For the SES setup, a single pair of self-adhesive electrodes with a dimension of 9 × 5 cm  were placed on the motor points of the m. vastus lateralis and medialis. Reference electrodes with the same size were placed 10 - 15 cm proximal of the corresponding muscle motor point and the frequency was set at 35 Hz. In the SDSS setup, four small electrodes each with a size of 4.5 × 2.5 cm were placed around the previously detected motor point. Each of the four electrodes was stimulated with a frequency of 8.75 Hz and a phase shift of 90 deg, which corresponds to the stimulation frequency of the SES setup of 35 Hz and using the same total electrode area. The SDSS stimulation order was always the same, i.e. from 1 to 4, and the reference electrodes were the same as for the SES setup . Motor points and electrode positions were marked to ensure identical placement across the sessions. The investigated stimulation setup was similar to a previous study with able-bodied participants . Two difμs  μs stepsMeasurements were conducted in two sessions with at least 24 h between each session. Within each session, each leg was stimulated with only one electrode configuration. Between the two independent leg measurements participants had a break of 15 min. Stimulation and leg order  was chosen randomly. Motor point detection was conducted prior to the first measurement while participants were seated in their wheelchair. Participants where then transferred to the dynamometer system and individual adjustments to body proportions were made. Before each stimulation (leg and setup), a brief familiarization was conducted to determine stimulation parameters and to control the device setup.μs and was manually increased after every extension until the power output started to plateau. Stimulation was halted and the pulse width needed to produce the maximum power (PWmax) was noted. The pulse width used for the subsequent measurement was 80% of PWmax (PWm80=0.8∗PWmax). After a 10-min rest period following familiarisation, the actual measurement was commenced with an ns-phase of two min and a stimulation phase (st-phase) of six min. A second two min ns-phase completed the measurement.The familiarisation started with a two-minute passive phase in which the measured leg was moved by the device without stimulation . The data collected during this phase was used as a baseline measurement for the leg movement resistance. The next phase involved stimulation while the leg was being moved by the dynamometer. The pulse width was initially set at 45 Pm generated during the stimulated knee extension. The power used to move the leg during the ns-phase was defined as Pns and the net power output of one stimulation cycle, Pstim, was then obtained as Pstim=Pm−Pns. The mean power output over the stimulation angle range during one extension (Pmean), peak power output (Ppeak) and the time from onset of the stimulation to 80% of Ppeak (tpeak80) was calculated for each extension. For each of these three output parameters mean values and standard deviations were calculated for the initial 10 stimulated extensions (init), the final 20 extensions  and all 160 extenstions  for each of the four subjects. PmeanL,R is denoted as the mean power output of the left and right legs.The measured torque together with the angular speed was used to calculate the gross power output Pmean describes the loss of power between the ten initial knee extensions (Pinit) and the final 20 knee extensions  from the stimulated phase. Thus, FI=1−/Pinit. The higher the value, the higher the fatigue resistance; FI=1 means no fatigue.A fatigue index (FI) based on PmeanL,R values for all four participants overall  produced with the left leg was doubled for both setups compared to the output of the right leg  versus traditional single electrode stimulation (SES) during a dynamic leg extension task in four untrained participants with motor-complete spinal cord injury. The task was designed to simulate knee joint movement in recumbent cycling for future applications with FES.Pmean. These results suggests that SDSS is beneficial overall when compared to SES and that it may be beneficial in a wide range of FES applications. The following discussion analyses the differences between SDSS and SES in detail.In all four participants the mean power output during stimulated knee extension was higher with the SDSS setup. This outcome is in line with previous measurements with able-bodied participants with the same measurement protocol . Three oCompared to previous measurements with able-bodied participants, the power output reached in this study was very low and likely not sufficient to perform functional tasks. All four participants were using FES for the first time and their muscles were untrained and already atrophied due to reduced muscle activity following the spinal cord injury. After such an injury, morphological and contractile changes occur in the muscles below the level of the lesion. A decrease in muscle cross-sectional area and reduced enzymatic activity leads to low muscle power as observed in participants P1 and P3 , 57, 58.Eight legs of four different participants were measured and in all cases the SDSS setup produced a higher power output compared to the conventional SES setup. With the exception of participant P1’s left leg, the initial power output produced with SDSS was markedly superior to SES. Power is strongly related to the number of motor units recruited, so SDSS is probably able to recruit more motor units in SCI participants , 66. WheStimulation intensity is strongly correlated with muscular fatigue, during both voluntary contractions and in artificial muscle stimulation , 71. In Pmean was higher than initial Pmean (Figs. Pmean. Thus, the muscular load as well as the intensity were very low, which is consistent with previous observations that low intensity highly correlates with high fatigue resistance [Three out of four participants showed increased fatigue resistance with SDSS Table . Analysian Figs. d, 5d. Insistance , 30, 73.Pmean or fatigue index values higher than 0.8. Although a high fatigue index means good fatigue resistance, which is desirable, those high values strongly correlate with low Pmean overall, which is suboptimal but in consistent to other reports in the literature [With regard to progression of knee extension Figs. a, d, in terature , 73–75.Pmean might indicate the activation of fast-twitch fibres. Participants P2, P3 and P4 were 6 months post injury and the measurements were conducted around the time at which oxidative enzymatic activity is expected to start to decline and the proportion of fibres that co-express both fast and slow myosin heavy chain isoforms increase [The steady decrease in force might be a sign of recruitment of slow-twitch fibres, whereas a rapid decrease of the initial increase , 76. Theincrease . Based oPmean values, which, as noted above, explains the very high fatigue resistance in these measurements.In addition to changes in muscle fibre structure and loss of enzymatic activity, the whole body structure, such as fat mass and bone density, changes after a spinal cord injury . The musWith regard to power output and fatigue properties, this study revealed some major benefits of the SDSS setup compared to SES in participants with motor- and sensory-complete SCI. One limitation of the present study is the case-series design and the concomitant low number of participants. This does not mean that FES is not applicable for other persons with SCI with different impairment classification, but for homogeneity and to reduce confounding factors only persons with motor- and sensory-complete SCI were included. In a next step, the focus should be on recruiting more SCI participants with different grades of injury, and to recruit a sufficient number of participants to allow well-powered statistical analysis. The pilot data from the present study provide a basis for a sample-size and statistical-power calculation.Pmean was higher for SDSS in all measurements. These outcomes are consistent with the results of previous measurements with able-bodied participants using the same test protocol [PmeanL,R with SDSS was more than twice as high as PmeanL,R with SES. These positive results should encourage the development of more sophisticated electrode setups for use in gross motor movements, such as in leg cycling. To date, several studies have shown increased power and fatigue resistance based on multi-electrode setups; combining these approaches with modulation of stimulation parameters might be a good future strategy to further increase power and fatigue resistance [This study demonstrated higher power output for the SDSS setup in all eight leg-to-leg comparisons. Fatigue characteristics for the SDSS setup looks promising and SDSS seems to be more resistant when both setups activate the muscle at the same intensity level. Final protocol  and demoprotocol  reportedprotocol . In thissistance –83. A fu


MEIS1. The MEIS1 GWAS signals are some of the strongest genetic associations reported for any common disease. MEIS1 belongs to the homeobox containing transcriptional regulatory network (HOX). Work in C. elegans showed a link between the MEIS1 ortholog and iron homeostasis, which is in line with the fact that central nervous system (CNS) iron insufficiency is thought to be a cause of RLS. Zebrafish and mice have been used to study the MEIS1 gene identifying an RLS-associated-SNP dependent enhancer activity from the highly conserved non-coding regions (HCNR) of MEIS1. Furthermore, this gene shows a lower expression of mRNA and protein in blood and thalamus of individuals with the MEIS1 RLS risk haplotype. Simulating this reduced MEIS1 expression in mouse models resulted in circadian hyperactivity, a phenotype compatible with RLS. While MEIS1 shows a strong association with RLS, the protein's function that is directly linked to an RLS biological pathway remains to be discovered. The links to iron and the enhancer activity of the HCNRs of MEIS1 suggest promising links to RLS pathways, however more in-depth studies on this gene's function are required. One important aspect of MEIS1's role in RLS is the fact that it encodes a homeobox containing transcription factor, which is essential during development. Future studies with more focus on the transcriptional regulatory role of MEIS1 may open novel venues for RLS research.Restless legs syndrome (RLS) is a common sleep-related disorder for which the underlying biological pathways and genetic determinants are not well understood. The genetic factors so far identified explain less than 10% of the disease heritability. The first successful genome-wide association study (GWAS) of RLS was reported in 2007. This study identified multiple RLS associated risk variants including some within the non-coding regions of  Restless legs syndrome is a common neurological disorder. The prevalence of RLS cases based on the minimum diagnostic criteria of the international RLS study group (IRLSSG) was estimated between 3.9 and 14.3% of the adult population. It is more common in women than men and the prevalence increases with age in the European and North American populations . Twin stThe first attempts to identify RLS genetic risk factors used genome wide linkage (GWL) approaches in large multiplex families, most of which had an autosomal dominant mode of inheritance and one with autosomal recessive inheritance pattern. The GWL identified loci with large genomic regions, but no causative variant was identified by this approach and the results have often not been reproducible –12. ConsMEIS1 gene  (RLS is the first common sleep disorder for which genome wide association studies (GWAS) was performed and genetic risk loci identified. In 2007, the first genome-wide association study on RLS using 401 patients with familial RLS and 1,644 control individuals of German and French-Canadian origin identified common variants in three noncoding genomic regions . The strIS1 gene . This asin 2017) –17. Furtin 2017) , 19. Thiin 2017) –24.MEIS1 were also proposed to contribute to be the cause of RLS. An Arg272-to-His (p.R272H) was found in one of 71 familial probands with RLS. However, a case-control genotyping study of this mutation across a North American cohort failed to validate this variant (MEIS1 isoform 1 was observed in RLS cases compared to controls in a burden test on a German population (MEIS1 exons (and their respective splice junctions) were sequenced in 285 familial probands with a confirmed clinical diagnosis and no variants were identified  express lower levels of MEIS1 mRNA and protein in LCL and thalamus  makes it one of the 64 genes that increase the worm's life span. Unc-62 in the worms has 25% identity with MEIS1 and almost 80% identity with its homeodomain. Adding iron chelators in the culture media of the worms resulted in changes in the effect of Unc-62 inactivation on worms' lifespan, suggesting that Unc-62 is involved in iron metabolism. The link between Unc-62 and iron was also measured through its impact on the expression of ferritin. It was found that post developmental inactivation of Unc-62 using RNAi resulted in higher expression of ferritin, a protein that plays a key role in iron metabolism through its ability to store excess iron and to release it into a soluble and nontoxic form  light and heavy chains. Furthermore, DMT1 gene expression was higher in these individuals. DMT1 is a proton-coupled metal transporter that carries iron from the extracellular domain to the cytoplasm   , 36. Thesymptoms . Hypoxiasymptoms . Anotherpression , 40. TheMEIS1 locus suggests the presence of cis-regulatory elements . The effect of rs12469063 on MEIS1 enhancer function in the LGE/MGE region suggests that RLS may involve the basal ganglia because these regions give rise to the basal ganglia [also discussed in a review by Salminen et al. (A cluster of highly conserved non-coding regions (HCNRs) in the elements . ConsideS1 HCNRs . They stn et al. ]. The enn et al. . Affinitn et al. , 42.BTBD9, MAP2K5, and SKOR1  did not find changes in their levels of expression in lymphoblasts or two brain regions  of RLS patients . The risk allele in this locus reduces the binding affinity of MEIS1 to the SKOR1 promotor and results in reduced expression of SKOR1 (Expression studies of the RLS associated loci of patients . Howeverof MEIS1  43). Fo. FoBTBD9of SKOR1 .MEIS1 has the strongest association signal, suggesting MEIS1 may be a shared genetic risk factor for RLS and insomnia (MEIS1's association with insomnia, thus suggesting that MEIS1 has a pleiotropic effect on RLS and insomnia (MEIS1 with insomnia only comes from the inclusion of RLS cases (MEIS1 (MEIS1's wide expression pattern during the development (MEIS1 have been reported.Insomnia is characterized by problems in falling asleep or maintaining asleep. With a heritability estimate of 38 and 59% in men and women, respectively, genetic factors must play a crucial role in insomnia. Recent genetic studies of insomnia using cases from the UK biobank showed that insomnia –47. Someinsomnia . HoweverLS cases . Such ins (MEIS1 –53. Thiselopment . So far,MEIS1 in development make the study of its role in RLS challenging. MEIS1 establishes motor neuron pool identity and their target-muscle connectivity (MEIS1 in RLS involves, among possibly other functions, altered iron homeostasis via altered transcriptional regulatory activity in RLS pathways. More in depth follow up studies on the function of MEIS1 with more focus on its regulatory role as a transcription factor might shed more light on the underlying pathways involved in RLS. To reach this goal, it will be essential to have access to RLS patients brain material carrying different genotypes of the RLS GWAS signals and to detailed clinical data to be used as covariates in the analyses. This combination would increase the likelihood of identifying elements that are critical to the onset and progression of RLS. This clinical data includes patient iron levels, their response to medication, age at onset, familial or sporadic RLS, presence or absence of PLMS, diagnosis by a physician and information about the patients' other health conditions. The next step could involve the use of model organisms to further validate and investigate RLS related mechanisms.MEIS1 region is one of the several loci found to be associated with RLS genetic, which overall explain less than 10% of RLS heritability. The many roles for ectivity , it alsoectivity . This prectivity –24. Meanectivity –59. DataFS contributed to the original concept of the article, did the literature search, wrote the original version of the manuscript and reviewed it as it progressed. PD and GR contributed to the original concept of the manuscript and reviewed the manuscript.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.