Phylogenomics reveals extensive misidentification of fungal strains from the genus Aspergillus.

Modern taxonomic classification is often based on phylogenetic analyses of a few molecular markers, although single-gene studies are still common. Here, we leverage genome-scale molecular phylogenetics (phylogenomics) of species and populations to reconstruct evolutionary relationships in a dense data set of 710 fungal genomes from the biomedically and technologically important genus Aspergillus. To do so, we generated a novel set of 1,362 high-quality molecular markers specific for Aspergillus and provided profile Hidden Markov Models for each, facilitating their use by others. Examining the resulting phylogeny helped resolve ongoing taxonomic controversies, identified new ones, and revealed extensive strain misidentification (7.59% of strains were previously misidentified), underscoring the importance of population-level sampling in species classification. These findings were corroborated using the current standard, taxonomically informative loci. These findings suggest that phylogenomics of species and populations can facilitate accurate taxonomic classifications and reconstructions of the Tree of Life.IMPORTANCEIdentification of fungal species relies on the use of molecular markers. Advances in genomic technologies have made it possible to sequence the genome of any fungal strain, making it possible to use genomic data for the accurate assignment of strains to fungal species (and for the discovery of new ones). We examined the usefulness and current limitations of genomic data using a large data set of 710 publicly available genomes from multiple strains and species of the biomedically, agriculturally, and industrially important genus Aspergillus. Our evolutionary genomic analyses revealed that nearly 8% of publicly available Aspergillus genomes are misidentified. Our work highlights the usefulness of genomic data for fungal systematic biology and suggests that systematic genome sequencing of multiple strains, including reference strains (e.g., type strains), of fungal species will be required to reduce misidentification errors in public databases.

Beet Soil-Borne Virus Is a Helper Virus for the Novel Beta vulgaris Satellite Virus 1A.

Sugar beet (Beta vulgaris) is grown in temperate regions around the world as a source of sucrose used for natural sweetening. Sugar beet is susceptible to a number of viral diseases, but identification of the causal agent(s) under field conditions is often difficult due to mixtures of viruses that may be responsible for disease symptoms. In this study, the application of RNAseq to RNA extracted from diseased sugar beet roots obtained from the field and from greenhouse-reared plants grown in soil infested with the virus disease rhizomania (causal agent beet necrotic yellow vein virus; BNYVV) yielded genome-length sequences from BNYVV, as well as beet soil-borne virus (BSBV). The nucleotide identities of the derived consensus sequence of BSBV RNAs ranged from 99.4 to 96.7% (RNA1), 99.3 to 95.3% (RNA2), and 98.3 to 95.9% (RNA3) compared with published BSBV sequences. Based on the BSBV genome consensus sequence, clones of the genomic RNAs 1, 2, and 3 were obtained to produce RNA copies of the genome through in vitro transcription. Capped RNA produced from the clones was infectious when inoculated into leaves of Chenopodium quinoa and B. vulgaris, and extracts from transcript-infected C. quinoa leaves could infect sugar beet seedling roots through a vortex inoculation method. Subsequent exposure of these infected sugar beet seedling roots to aviruliferous Polymyxa betae, the protist vector of both BNYVV and BSBV, confirmed that BSBV derived from the infectious clones could be transmitted by the vector. Co-inoculation of BSBV synthetic transcripts with transcripts of a cloned putative satellite virus designated Beta vulgaris satellite virus 1A (BvSat1A) resulted in the production of lesions on leaves of C. quinoa similar to those produced by inoculation with BSBV alone. Nevertheless, accumulation of genomic RNA and the encoded protein of the satellite virus in co-inoculated leaves was readily detected on Northern and Western blots, respectively, whereas no accumulation of satellite virus products occurred when satellite virus RNA was inoculated alone. The predicted sequence of the detected protein encoded by BvSat1A bears hallmarks of coat proteins of other satellite viruses, and virions of a size consistent with a satellite virus were observed in samples testing positive for the virus. The results demonstrate that BSBV is a helper virus for the novel satellite virus BvSat1A.

Open Access and Reproducibility in Plant Pathology Research: Guidelines and Best Practices.

The landscape of scientific publishing is experiencing a transformative shift toward open access, a paradigm that mandates the availability of research outputs such as data, code, materials, and publications. Open access provides increased reproducibility and allows for reuse of these resources. This article provides guidance for best publishing practices of scientific research, data, and associated resources, including code, in The American Phytopathological Society journals. Key areas such as diagnostic assays, experimental design, data sharing, and code deposition are explored in detail. This guidance aligns with that observed by other leading journals. We hope the information assembled in this paper will raise awareness of best practices and enable greater appraisal of the true effects of biological phenomena in plant pathology.

Development of a Large-Scale Soil DNA Extraction Method for Molecular Quantification of Fusarium oxysporum f. sp. fragariae in Soil.

The most common soilborne diseases affecting the strawberry industry in California include Verticillium wilt due to Verticillium dahliae, charcoal root rot due to Macrophomina phaseolina, and Fusarium wilt due to Fusarium oxysporum f. sp. fragariae. Detection of these pathogens in soil is an important facet of disease management and fumigation recommendations. Whereas the soil populations of both M. phaseolina and V. dahliae can be readily quantified with quantitative PCR (qPCR) assays using DNA extractions with 500 mg of soil, the single-cell nature of the F. oxysporum chlamydospore does not provide enough pathogen DNA from 500-mg extractions to be reliably quantified. Here, we describe an improved DNA extraction protocol from 10 to 15 g of soil that allows for the quantification of F. oxysporum f. sp. fragariae populations below 10 CFU/g. The relationship between results from the TaqMan qPCR assay and pathogen population density in soil was determined by using this extraction method in pathogen-free soils artificially infested with a hygromycin-resistant strain of F. oxysporum f. sp. fragariae to facilitate accurate colony counts when plated on a selective medium. Although the protocol was developed for F. oxysporum f. sp. fragariae, it is applicable for detection and quantification of other soilborne pathogens.

Head and neck cancers survival in Europe, Taiwan, and Japan: results from RARECAREnet Asia based on a privacy-preserving federated infrastructure.

Background: The head and neck cancers (HNCs) incidence differs between Europe and East Asia. Our objective was to determine whether survival of HNC also differs between European and Asian countries. Methods: We used population-based cancer registry data to calculate 5-year relative survival (RS) for the oral cavity, hypopharynx, larynx, nasal cavity, and major salivary gland in Europe, Taiwan, and Japan. We modeled RS with a generalized linear model adjusting for time since diagnosis, sex, age, subsite, and histological grouping. Analyses were performed using federated learning, which enables analyses without sharing sensitive data. Findings: Five-year RS for HNC varied between geographical areas. For each HNC site, Europe had a lower RS than both Japan and Taiwan. HNC subsites and histologies distribution and survival differed between the three areas. Differences between Europe and both Asian countries persisted even after adjustments for all HNC sites but nasal cavity and paranasal sinuses, when comparing Europe and Taiwan. Interpretation: Survival differences can be attributed to different factors including different period of diagnosis, more advanced stage at diagnosis, or different availability/access of treatment. Cancer registries did not have stage and treatment information to further explore the reasons of the observed survival differences. Our analyses have confirmed federated learning as a feasible approach for data analyses that addresses the challenges of data sharing and urge for further collaborative studies including relevant prognostic factors.

Reliability and Validity of Different Lower-Limb Strength Tests to Determine 1RM in the Keiser A300 Leg Press.

ABSTRACT: Larsen, F, Loturco, I, Sigvaldsen, E, Strand, MF, Kalhovde, JM, and Haugen, T. Reliability and validity of different lower-limb strength tests to determine 1RM in the Keiser A300 leg press. J Strength Cond Res 37(10): 1963-1968, 2023-The aim of this study was to explore the reliability and validity of different lower-limb strength tests to determine the one-repetition maximum (1RM) value in the Keiser A300 leg press. Twenty-eight recreationally active subjects performed load-velocity (L-V) relationship, 1RM, isometric midthigh pull (IMTP), and maximal repetitions to failure (MRF) tests on 3 separated sessions. Predicted 1RMs for the L-V relationship were estimated from a linear regression equation, correlating movement velocity and relative loads. The number of repetitions from the MRF tests (at loads relative to bodyweight) and peak force from the IMTP tests were used in regression equations to predict 1RM. The level of significance was set to ρ ≤ 0.05. All 1RM prediction methods were highly comparable with the traditional 1RM test, as only trivial and nonsignificant differences were observed. Furthermore, the L-V relationship was the most reliable (intraclass correlation coefficient [± 95% confidence interval] = 0.99 [0.98, 0.996]; effect size = -0.01 [-0.38, 0.36], standard error of the measurement = 6.4 kg; coefficient of variation = 3.0 [2.2-3.8]% and valid (r = 0.95 [0.89, 0.98], effect size = 0.08 [-0.29, 0.45], standard error of the estimate = 20.4 kg; coefficient of variation = 7.4 [5.5-9.3]%) when compared with direct 1RM measurements. The L-V relationship test showed a significant change score relationship (r = 0.41 [0.04, 0.68]) against the direct 1RM measurements. In conclusion, the tests used in this study cannot be used interchangeably, but they represent a good alternative in training settings where 1RM testing is not feasible.

Applying federated learning to combat food fraud in food supply chains.

Ensuring safe and healthy food is a big challenge due to the complexity of food supply chains and their vulnerability to many internal and external factors, including food fraud. Recent research has shown that Artificial Intelligence (AI) based algorithms, in particularly data driven Bayesian Network (BN) models, are very suitable as a tool to predict future food fraud and hence allowing food producers to take proper actions to avoid that such problems occur. Such models become even more powerful when data can be used from all actors in the supply chain, but data sharing is hampered by different interests, data security and data privacy. Federated learning (FL) may circumvent these issues as demonstrated in various areas of the life sciences. In this research, we demonstrate the potential of the FL technology for food fraud using a data driven BN, integrating data from different data owners without the data leaving the database of the data owners. To this end, a framework was constructed consisting of three geographically different data stations hosting different datasets on food fraud. Using this framework, a BN algorithm was implemented that was trained on the data of different data stations while the data remained at its physical location abiding by privacy principles. We demonstrated the applicability of the federated BN in food fraud and anticipate that such framework may support stakeholders in the food supply chain for better decision-making regarding food fraud control while still preserving the privacy and confidentiality nature of these data.

Disease progression joint model predicts time to type 1 diabetes onset: Optimizing future type 1 diabetes prevention studies.

Clinical trials seeking type 1 diabetes prevention are challenging in terms of identifying patient populations likely to progress to type 1 diabetes within limited (i.e., short-term) trial durations. Hence, we sought to improve such efforts by developing a quantitative disease progression model for type 1 diabetes. Individual-level data obtained from the TrialNet Pathway to Prevention and The Environmental Determinants of Diabetes in the Young natural history studies were used to develop a joint model that links the longitudinal glycemic measure to the timing of type 1 diabetes diagnosis. Baseline covariates were assessed using a stepwise covariate modeling approach. Our study focused on individuals at risk of developing type 1 diabetes with the presence of two or more diabetes-related autoantibodies (AAbs). The developed model successfully quantified how patient features measured at baseline, including HbA1c and the presence of different AAbs, alter the timing of type 1 diabetes diagnosis with reasonable accuracy and precision (<30% RSE). In addition, selected covariates were statistically significant (p < 0.0001 Wald test). The Weibull model best captured the timing to type 1 diabetes diagnosis. The 2-h oral glucose tolerance values assessed at each visit were included as a time-varying biomarker, which was best quantified using the sigmoid maximum effect function. This model provides a framework to quantitatively predict and simulate the time to type 1 diabetes diagnosis in individuals at risk of developing the disease and thus, aligns with the needs of pharmaceutical companies and scientists seeking to advance therapies aimed at interdicting the disease process.

A federated approach to identify women with early-stage cervical cancer at low risk of lymph node metastases.

OBJECTIVE: Lymph node metastases (pN+) in presumed early-stage cervical cancer negatively impact prognosis. Using federated learning, we aimed to develop a tool to identify a group of women at low risk of pN+, to guide the shared decision-making process concerning the extent of lymph node dissection. METHODS: Women with cervical cancer between 2005 and 2020 were identified retrospectively from population-based registries: the Danish Gynaecological Cancer Database, Swedish Quality Registry for Gynaecologic Cancer and Netherlands Cancer Registry. Inclusion criteria were: squamous cell carcinoma, adenocarcinoma or adenosquamous carcinoma; The International Federation of Gynecology and Obstetrics 2009 IA2, IB1 and IIA1; treatment with radical hysterectomy and pelvic lymph node assessment. We applied privacy-preserving federated logistic regression to identify risk factors of pN+. Significant factors were used to stratify the risk of pN+. RESULTS: We included 3606 women (pN+ 11%). The most important risk factors of pN+ were lymphovascular space invasion (LVSI) (odds ratio [OR] 5.16, 95% confidence interval [CI], 4.59-5.79), tumour size 21-40 mm (OR 2.14, 95% CI, 1.89-2.43) and depth of invasion>10 mm (OR 1.81, 95% CI, 1.59-2.08). A group of 1469 women (41%)-with tumours without LVSI, tumour size ≤20 mm, and depth of invasion ≤10 mm-had a very low risk of pN+ (2.4%, 95% CI, 1.7-3.3%). CONCLUSION: Early-stage cervical cancer without LVSI, a tumour size ≤20 mm and depth of invasion ≤10 mm, confers a low risk of pN+. Based on an international privacy-preserving analysis, we developed a useful tool to guide the shared decision-making process regarding lymph node dissection.

Standard Intein Gene Expression Ramps (SIGER) for Protein-Independent Expression Control.

Coordination of multigene expression is one of the key challenges of metabolic engineering for the development of cell factories. Constraints on translation initiation and early ribosome kinetics of mRNA are imposed by features of the 5'UTR in combination with the start of the gene, referred to as the "gene ramp", such as rare codons and mRNA secondary structures. These features strongly influence the translation yield and protein quality by regulating the ribosome distribution on mRNA strands. The utilization of genetic expression sequences, such as promoters and 5'UTRs in combination with different target genes, leads to a wide variety of gene ramp compositions with irregular translation rates, leading to unpredictable levels of protein yield and quality. Here, we present the Standard Intein Gene Expression Ramp (SIGER) system for controlling protein expression. The SIGER system makes use of inteins to decouple the translation initiation features from the gene of a target protein. We generated sequence-specific gene expression sequences for two inteins (DnaB and DnaX) that display defined levels of protein expression. Additionally, we used inteins that possess the ability to release the C-terminal fusion protein in vivo to avoid the impairment of protein functionality by the fused intein. Overall, our results show that SIGER systems are unique tools to mitigate the undesirable effects of gene ramp variation and to control the relative ratios of enzymes involved in molecular pathways. As a proof of concept of the potential of the system, we also used a SIGER system to express two difficult-to-produce proteins, GumM and CBM73.

The Effect of Flying Sprints at 90% to 95% of Maximal Velocity on Sprint Performance.

PURPOSE: Submaximal sprinting allows for larger accumulated work to be reached before the onset of fatigue, compared with maximal efforts. The aim of this study was to investigate the effect of sprint running at 90% to 95% of maximal velocity (Vmax) on sprint performance. METHODS: Recreationally active adults were randomly assigned into a control group (n = 12, 27 [5] y, 172 [9] cm, 72 [15] kg) and a training group (n = 14, 26 [4] y, 171 [9] cm, 69 [11] kg). Both groups completed pretesting and posttesting in form of a 30-m sprint separated by a 6-week period. The training group performed a weekly sprint-training session consisting of 30-m flying sprints at 90% to 95% of Vmax, while the control group performed no intervention training. RESULTS: Significant improvements in the training group were observed for 10- (P = .003), 20- (P = .001), and 30-m sprint time (P = .002). These improvements were accompanied by higher step rate (P = .006) and theoretical Vmax (P = .007) and maximal power (P = .004). Significant between-groups differences were observed for 10- (P = .008), 20- (P < .001), and 30-m sprint time (P < .001), as well as for step rate (P = .015), theoretical Vmax (P = .016), and maximal power (P = .008). All within- and between-groups differences were in the range of trivial to small. CONCLUSION: Sprint running at 90% to 95% of Vmax can enhance 10- to 30-m sprint performance in recreationally active adults.

Islet autoantibody screening in at-risk adolescents to predict type 1 diabetes until young adulthood: a prospective cohort study.

BACKGROUND: Screening for islet autoantibodies in children and adolescents identifies individuals who will later develop type 1 diabetes, allowing patient and family education to prevent diabetic ketoacidosis at onset and to enable consideration of preventive therapies. We aimed to assess whether islet autoantibody screening is effective for predicting type 1 diabetes in adolescents aged 10-18 years with an increased risk of developing type 1 diabetes. METHODS: Data were harmonised from prospective studies from Finland (the Diabetes Prediction and Prevention study), Germany (the BABYDIAB study), and the USA (Diabetes Autoimmunity Study in the Young and the Diabetes Evaluation in Washington study). Autoantibodies against insulin, glutamic acid decarboxylase, and insulinoma-associated protein 2 were measured at each follow-up visit. Children who were lost to follow-up or diagnosed with type 1 diabetes before 10 years of age were excluded. Inverse probability censoring weighting was used to include data from remaining participants. Sensitivity and the positive predictive value of these autoantibodies, tested at one or two ages, to predict type 1 diabetes by the age of 18 years were the main outcomes. FINDINGS: Of 20 303 children with an increased type 1 diabetes risk, 8682 were included for the analysis with inverse probability censoring weighting. 1890 were followed up to 18 years of age or developed type 1 diabetes between the ages of 10 years and 18 years, and their median follow-up was 18·3 years (IQR 14·5-20·3). 442 (23·4%) of 1890 adolescents were positive for at least one islet autoantibody, and 262 (13·9%) developed type 1 diabetes. Time from seroconversion to diabetes diagnosis increased by 0·64 years (95% CI 0·34-0·95) for each 1-year increment of diagnosis age (Pearson's correlation coefficient 0·88, 95% CI 0·50-0·97, p=0·0020). The median interval between the last prediagnostic sample and diagnosis was 0·3 years (IQR 0·1-1·3) in the 227 participants who were autoantibody positive and 6·8 years (1·6-9·9) for the 35 who were autoantibody negative. Single screening at the age of 10 years was 90% (95% CI 86-95) sensitive, with a positive predictive value of 66% (60-72) for clinical diabetes. Screening at two ages (10 years and 14 years) increased sensitivity to 93% (95% CI 89-97) but lowered the positive predictive value to 55% (49-60). INTERPRETATION: Screening of adolescents at risk for type 1 diabetes only once at 10 years of age for islet autoantibodies was highly effective to detect type 1 diabetes by the age of 18 years, which in turn could enable prevention of diabetic ketoacidosis and participation in secondary prevention trials. FUNDING: JDRF International.

The genome of the oomycete Peronosclerospora sorghi, a cosmopolitan pathogen of maize and sorghum, is inflated with dispersed pseudogenes.

Several species in the oomycete genus Peronosclerospora cause downy mildew on maize and can result in significant yield losses in Asia. Bio-surveillance of these pathogens is a high priority to prevent epidemics on maize in the United States and consequent damage to the US economy. The unresolved taxonomy and dearth of molecular resources for Peronosclerospora spp. hinder these efforts. P. sorghi is a pathogen of sorghum and maize with a global distribution, for which limited diversity has been detected in the southern USA. We characterized the genome, transcriptome, and mitogenome of an isolate, representing the US pathotype 6. The highly homozygous genome was assembled using 10× Genomics linked reads and scaffolded using Hi-C into 13 chromosomes. The total assembled length was 303.2 Mb, larger than any other oomycete previously assembled. The mitogenome was 38 kb, similar in size to other oomycetes, although it had a unique gene order. Nearly 20,000 genes were annotated in the nuclear genome, more than described for other downy mildew causing oomycetes. The 13 chromosomes of P. sorghi were highly syntenic with the 17 chromosomes of Peronospora effusa with conserved centromeric regions and distinct chromosomal fusions. The increased assembly size and gene count of P. sorghi is due to extensive retrotransposition, resulting in putative pseudogenization. Ancestral genes had higher transcript abundance and were enriched for differential expression. This study provides foundational resources for analysis of Peronosclerospora and comparisons to other oomycete genera. Further genomic studies of global Peronosclerospora spp. will determine the suitability of the mitogenome, ancestral genes, and putative pseudogenes for marker development and taxonomic relationships.

A school-based physical activity intervention on psychosocial health outcomes among 11- and 12-year-olds - HOPP-project.

The purpose was to investigate the effect of a school-based physical activity (PA)-intervention among 11- and 12-year-olds (6th- and 7th graders) across 4 years. Seven primary schools in Horten municipality in Norway received 45 min daily extra PA as part of the curriculum. The intervention started in 2015 with follow-up in 2016-2019. The effect was measured after 1-4 years of participation, among the same children (6th to 7th grade) and among new children starting in 6th grade. Two control schools received no additional PA beyond the regular PA at school. The Self-reported Strength and Difficulties Questionnaire (SDQ-S) focusing on internalizing and externalizing difficulties were administrated. A statistical model for repeated measurements was used and adjusted for parents' educational level, sex, age, and waist-to-height ratio (WHtR). The significance level was p ≤ 0.01. In total, 1221 children completed the SDQ-S. SDQ-S scores were stable, and difficulties were relatively low. The control group had significantly lower SDQ-S scores than the intervention group at start, indicating fewer difficulties. The adjusted effect within the intervention schools showed a borderline significant increase in total difficulty scores between 2018 and 2019 (mean difference: 1.02, 95% CI: -1.82, -0.23, p ≤ 0.01). Educational level showed a weak negative correlation with total difficulty score (r = -0.1). No significant change was reported within the control schools. Few psychosocial health problems among 11- and 12-year-olds were detected. The borderline increase in total difficulty score that was seen for the intervention schools, is believed to be of limited clinical relevance.

Quantifying the utility of islet autoantibody levels in the prediction of type 1 diabetes in children.

AIMS/HYPOTHESIS: The aim of this study was to explore the utility of islet autoantibody (IAb) levels for the prediction of type 1 diabetes in autoantibody-positive children. METHODS: Prospective cohort studies in Finland, Germany, Sweden and the USA followed 24,662 children at increased genetic or familial risk of developing islet autoimmunity and diabetes. For the 1403 who developed IAbs (523 of whom developed diabetes), levels of autoantibodies against insulin (IAA), glutamic acid decarboxylase (GADA) and insulinoma-associated antigen-2 (IA-2A) were harmonised for analysis. Diabetes prediction models using multivariate logistic regression with inverse probability censored weighting (IPCW) were trained using 10-fold cross-validation. Discriminative power for disease was estimated using the IPCW concordance index (C index) with 95% CI estimated via bootstrap. RESULTS: A baseline model with covariates for data source, sex, diabetes family history, HLA risk group and age at seroconversion with a 10-year follow-up period yielded a C index of 0.61 (95% CI 0.58, 0.63). The performance improved after adding the IAb positivity status for IAA, GADA and IA-2A at seroconversion: C index 0.72 (95% CI 0.71, 0.74). Using the IAb levels instead of positivity indicators resulted in even better performance: C index 0.76 (95% CI 0.74, 0.77). The predictive power was maintained when using the IAb levels alone: C index 0.76 (95% CI 0.75, 0.76). The prediction was better for shorter follow-up periods, with a C index of 0.82 (95% CI 0.81, 0.83) at 2 years, and remained reasonable for longer follow-up periods, with a C index of 0.76 (95% CI 0.75, 0.76) at 11 years. Inclusion of the results of a third IAb test added to the predictive power, and a suitable interval between seroconversion and the third test was approximately 1.5 years, with a C index of 0.78 (95% CI 0.77, 0.78) at 10 years follow-up. CONCLUSIONS/INTERPRETATION: Consideration of quantitative patterns of IAb levels improved the predictive power for type 1 diabetes in IAb-positive children beyond qualitative IAb positivity status.

A universal approach to gene expression engineering.

In this study, we provide a universal approach to Gene Expression Engineering (GeneEE) for creating artificial expression systems. GeneEE leads to the generation of artificial 5' regulatory sequences (ARES) consisting of promoters and 5' untranslated regions. The ARES lead to the successful recruitment of RNA polymerase, related sigma factors and ribosomal proteins that result in a wide range of expression levels. We also demonstrate that by engaging native transcription regulators, GeneEE can be used to generate inducible promoters. To showcase the universality of the approach, we demonstrate that 200-nucleotide (nt)-long DNA with random composition can be used to generate functional expression systems in six bacterial species, Escherichia coli, Pseudomonas putida, Corynebacterium glutamicum, Thermus thermophilus, Streptomyces albus and Streptomyces lividans, and the eukaryote yeast Saccharomyces cerevisiae.

Mechanisms governing codon usage bias and the implications for protein expression in the chloroplast of Chlamydomonas reinhardtii.

Chloroplasts possess a considerably reduced genome that is decoded via an almost minimal set of tRNAs. These features make an excellent platform for gaining insights into fundamental mechanisms that govern protein expression. Here, we present a comprehensive and revised perspective of the mechanisms that drive codon selection in the chloroplast of Chlamydomonas reinhardtii and the functional consequences for protein expression. In order to extract this information, we applied several codon usage descriptors to genes with different expression levels. We show that highly expressed genes strongly favor translationally optimal codons, while genes with lower functional importance are rather affected by directional mutational bias. We demonstrate that codon optimality can be deduced from codon-anticodon pairing affinity and, for a small number of amino acids (leucine, arginine, serine, and isoleucine), tRNA concentrations. Finally, we review, analyze, and expand on the impact of codon usage on protein yield, secondary structures of mRNA, translation initiation and termination, and amino acid composition of proteins, as well as cotranslational protein folding. The comprehensive analysis of codon choice provides crucial insights into heterologous gene expression in the chloroplast of C. reinhardtii, which may also be applicable to other chloroplast-containing organisms and bacteria.

Vision Screening in Children: The New South Wales Statewide Eyesight Preschooler Screening Program.

PURPOSE: The aim was to investigate universality of access, screening rate, and outcomes from the New South Wales (NSW) Statewide Eyesight Preschooler Screening (StEPS) over the period of 2009 to 2016. DESIGN: Cross-sectional, observational study. METHODS: The StEPS program provides vision screening to 4-year-old children residing in NSW and is administered within Local Health Districts (LHDs). Visual acuity (VA) was examined by trained lay and nurse screeners using HOTV logMAR. Children who had VA <6/9-2 were referred to local practitioners while those with VA <6/18 were referred to public hospital pediatric ophthalmic outpatient clinics where available. Activity data were collected by NSW Health and screening rates determined from population projections of 4-year-olds per LHD based on adjusted 2014 Census data. To determine factors impacting screening and referral rates, a random effects panel analysis was undertaken. RESULTS: A total of 719,686 (96.4%) NSW 4-year-old children were offered StEPS vision screening between 2009 and 2016, 84% accepted and 564,825 children (75.6%) were screened. The screening rate increased from 67.3% in 2009 to 74.5% in 2016, with an 80% target reached for 3 consecutive years from 2013 to 2015. Of those screened, 19.2% were referred to an eye health professional or advised to have a vision retest in 12 months. This referral rate remained steady over the period studied, with little variation between metropolitan, and rural and regional LHDs. CONCLUSIONS: StEPS is an ideal service model for preschool vision screening providing coverage that is comparable to school-based screening programs and at an age likely to facilitate optimal treatment outcomes.

Islet Autoantibody Levels Differentiate Progression Trajectories in Individuals With Presymptomatic Type 1 Diabetes.

In our previous data-driven analysis of evolving patterns of islet autoantibodies (IAb) against insulin (IAA), GAD (GADA), and islet antigen 2 (IA-2A), we discovered three trajectories, characterized according to multiple IAb (TR1), IAA (TR2), or GADA (TR3) as the first appearing autoantibodies. Here we examined the evolution of IAb levels within these trajectories in 2,145 IAb-positive participants followed from early life and compared those who progressed to type 1 diabetes (n = 643) with those remaining undiagnosed (n = 1,502). With use of thresholds determined by 5-year diabetes risk, four levels were defined for each IAb and overlaid onto each visit. In diagnosed participants, high IAA levels were seen in TR1 and TR2 at ages <3 years, whereas IAA remained at lower levels in the undiagnosed. Proportions of dwell times (total duration of follow-up at a given level) at the four IAb levels differed between the diagnosed and undiagnosed for GADA and IA-2A in all three trajectories (P < 0.001), but for IAA dwell times differed only within TR2 (P < 0.05). Overall, undiagnosed participants more frequently had low IAb levels and later appearance of IAb than diagnosed participants. In conclusion, while it has long been appreciated that the number of autoantibodies is an important predictor of type 1 diabetes, consideration of autoantibody levels within the three autoimmune trajectories improved differentiation of IAb-positive children who progressed to type 1 diabetes from those who did not.