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AimTo examine individual variability between perceived physical features and hormones of pubertal maturation in 9-10-year-old children as a function of sociodemographic characteristics.MethodsCross-sectional metrics of puberty were utilized from the baseline assessment of the Adolescent Brain Cognitive Development (ABCD) Study-a multi-site sample of 9-10 year-olds (n = 11,875)-and included perceived physical features via the pubertal development scale (PDS) and child salivary hormone levels (dehydroepiandrosterone and testosterone in all, and estradiol in females). Multi-level models examined the relationships among sociodemographic measures, physical features, and hormone levels. A group factor analysis (GFA) was implemented to extract latent variables of pubertal maturation that integrated both measures of perceived physical features and hormone levels.ResultsPDS summary scores indicated more males (70%) than females (31%) were prepubertal. Perceived physical features and hormone levels were significantly associated with child's weight status and income, such that more mature scores were observed among children that were overweight/obese or from households with low-income. Results from the GFA identified two latent factors that described individual differences in pubertal maturation among both females and males, with factor 1 driven by higher hormone levels, and factor 2 driven by perceived physical maturation. The correspondence between latent factor 1 scores (hormones) and latent factor 2 scores (perceived physical maturation) revealed synchronous and asynchronous relationships between hormones and concomitant physical features in this large young adolescent sample.ConclusionsSociodemographic measures were associated with both objective hormone and self-report physical measures of pubertal maturation in a large, diverse sample of 9-10 year-olds. The latent variables of pubertal maturation described a complex interplay between perceived physical changes and hormone levels that hallmark sexual maturation, which future studies can examine in relation to trajectories of brain maturation, risk/resilience to substance use, and other mental health outcomes.

AimTo examine individual variability between perceived physical features and hormones of pubertal maturation in 9-10-year-old children as a function of sociodemographic characteristics.MethodsCross-sectional metrics of puberty were utilized from the baseline assessment of the Adolescent Brain Cognitive Development (ABCD) Study-a multi-site sample of 9-10 year-olds (n = 11,875)-and included perceived physical features via the pubertal development scale (PDS) and child salivary hormone levels (dehydroepiandrosterone and testosterone in all, and estradiol in females). Multi-level models examined the relationships among sociodemographic measures, physical features, and hormone levels. A group factor analysis (GFA) was implemented to extract latent variables of pubertal maturation that integrated both measures of perceived physical features and hormone levels.ResultsPDS summary scores indicated more males (70%) than females (31%) were prepubertal. Perceived physical features and hormone levels were significantly associated with child's weight status and income, such that more mature scores were observed among children that were overweight/obese or from households with low-income. Results from the GFA identified two latent factors that described individual differences in pubertal maturation among both females and males, with factor 1 driven by higher hormone levels, and factor 2 driven by perceived physical maturation. The correspondence between latent factor 1 scores (hormones) and latent factor 2 scores (perceived physical maturation) revealed synchronous and asynchronous relationships between hormones and concomitant physical features in this large young adolescent sample.ConclusionsSociodemographic measures were associated with both objective hormone and self-report physical measures of pubertal maturation in a large, diverse sample of 9-10 year-olds. The latent variables of pubertal maturation described a complex interplay between perceived physical changes and hormone levels that hallmark sexual maturation, which future studies can examine in relation to trajectories of brain maturation, risk/resilience to substance use, and other mental health outcomes.

Atomoxetine is a non-stimulant medication with sustained benefit throughout the day, and is a useful pharmacologic treatment option for young adults with Attention-Deficit/Hyperactivity Disorder (ADHD). It is difficult to determine, however, those patients for whom atomoxetine will be both effective and advantageous. Patients may need to take the medication for several weeks before therapeutic benefit is apparent, so a biomarker that could predict atomoxetine effectiveness early in the course of treatment could be clinically useful. There has been increased interest in the study of thalamocortical oscillatory activity using quantitative electroencephalography (qEEG) as a biomarker in ADHD. In this study, we investigated qEEG absolute power, relative power, and cordance, which have been shown to predict response to reuptake inhibitor antidepressants in Major Depressive Disorder (MDD), as potential predictors of response to atomoxetine. Forty-four young adults with ADHD (ages 18-30) enrolled in a multi-site, double-blind placebo-controlled study of the effectiveness of atomoxetine and underwent serial qEEG recordings at pretreatment baseline and one week after the start of medication. qEEG measures were calculated from a subset of the sample (N = 29) that provided useable qEEG recordings. Left temporoparietal cordance in the theta frequency band after one week of treatment was associated with ADHD symptom improvement and quality of life measured at 12 weeks in atomoxetine-treated subjects, but not in those treated with placebo. Neither absolute nor relative power measures selectively predicted improvement in medication-treated subjects. Measuring theta cordance after one week of treatment could be useful in predicting atomoxetine treatment response in adult ADHD.

Abstract In pandemic mitigation, strategies such as social distancing and mask-wearing are vital to prevent disease resurgence. Yet, monitoring adherence is challenging, as individuals might be reluctant to share behavioral data with public health authorities. To address this challenge and demonstrate a framework for conducting observational research with sensitive data in a privacy-conscious manner, we employ a privacy-centric epidemiological study design: the federated cohort. This approach leverages recent computational advances to allow for distributed participants to contribute to a prospective, observational research study while maintaining full control of their data. We apply this strategy here to explore pandemic intervention adherence patterns. Participants (n = 3808) were enrolled in our federated cohort via the “Google Health Studies” mobile application. Participants completed weekly surveys and contributed empirically measured mobility data from their Android devices between November 2020 to August 2021. Using federated analytics, differential privacy, and secure aggregation, we analyzed data in five 6-week periods, encompassing the pre- and post-vaccination phases. Our results showed that participants largely utilized non-pharmaceutical intervention strategies until they were fully vaccinated against COVID-19, except for individuals without plans to become vaccinated. Furthermore, this project offers a blueprint for conducting a federated cohort study and engaging in privacy-preserving research during a public health emergency.

The body-wave magnitude (mb) is a long-standing network-averaged, amplitude-based magnitude used to estimate the magnitude of seismic sources from teleseismic observations. The U.S. Geological Survey National Earthquake Information Center (NEIC) relies on mb in its global real-time earthquake monitoring mission. Although waveform modeling-based moment magnitudes are the modern standard to characterize earthquake size, mb is important because (1) in many cases, waveform modeling is not possible (e.g., low signal-to-noise events), (2) mb is applicable over a broad range of magnitudes, ∼M 4–7, and (3) there is a many decades-long history of estimating mb magnitudes. We use the NEIC Preliminary Determination of Epicenters earthquake catalog to investigate the uncertainty in NEIC station mb measurements. We show that mb measurements are spatially correlated, which can bias event mb, and we describe an empirical relation between this spatial correlation and station-to-station distance. We further describe an approach to mitigate bias from the spatial correlation. Accounting for the spatial covariance of observations can change the event mb from −0.15 to 0.07 mb units (10th to 90th percentile) for smaller events (mb≤4.5). These smaller events have the largest mb standard deviations ranging from 0.05 to 0.15 mb units (10th to 90th percentile).

The success of Open Science in addressing the replication crisis and restoring credibility in psychology can be understood more completely by examining the successes and challenges of adapting the recommended best practices by researchers at different types of institutions, different career stages, and from different subfields within social and personality psychology. In this article, we offer personal reflections about the impact and future of the Open Science movement in a conversational form between three researchers at varying career stages who focus on different subdisciplines (relationship science, diversity science, and social neuroscience and social cognition) and work at universities that place a different emphasis on research (relative to teaching and service). We see many successes of the open science movement, but we also note that implementation has trailed behind its full potential because (a) the incentive structures of our existing rigid system remain misaligned with some open-science goals, and (b) some open science solutions were designed by researchers with certain types of scientific practices in mind. We all feel encouraged by the focus on larger samples, greater data sharing, and pre-registration both for experimental design and analytical decisions. However, there are areas that need attention. Our perspective is that the open science movement has not been as strong of an ally as it could be to another goal of psychological science: increasing diversity, equity, and inclusion. Additionally, more careful consideration of how to facilitate data sharing and pre-registration is needed and may necessitate a shift in the incentive structure of our field.

Abstract In atmospheric models with kilometer‐scale grids the resolution approaches the scale of convection. As a consequence the most energetic eddies in the atmosphere are partially resolved and partially unresolved. The modeling challenge to represent convection partially explicitly and partially as a subgrid process is called the convective gray zone problem. The gray zone issue has previously been discussed in the context of regional models, but the evolution in regional models is constrained by the lateral boundary conditions. Here we explore the convective gray zone starting from a defined global configuration of the Met Office Unified Model using initialized forecasts and comparing different model formulations to observations. The focus is on convection and turbulence, but some aspects of the model dynamics are also considered. The global model is run at nominal 5 km resolution and thus contributions from both resolved and subgrid turbulent and convective fluxes are non‐negligible. The main conclusion is that in the present assessment, the configurations which include scale‐aware turbulence and a carefully reduced and simplified mass‐flux convection scheme outperform both the configuration with fully parameterized convection as well as a configuration in which the subgrid convection parameterization is switched off completely. The results are more conclusive with regard to convective organization and tropical variability than extratropical predictability. The present study thus endorses the strategy to further develop scale‐aware physics schemes and to pursue an operational implementation of the global 5 km‐resolution model to be used alongside other ensemble forecasts to allow researchers and forecasters to further assess these simulations.

Micronutrient deficiency is a public health challenge globally, as it affects both people and the economy. In Nigeria, most micronutrients, especially minerals are lost during food processing. The study was carried out to determine the potassium, sodium, calcium and magnesium composition of foods commonly eaten by Nigerian adults and to estimate the average adults' daily intake of these macrominerals. The contents of these minerals in 141 food items collected ‘as consumed’ from 10 locations in Federal Capital Territory, Abuja and Ogun State, Nigeria, were quantified by digesting the foods through dry ashing and analysed using flame atomic absorption spectrometer. The levels (mg/100 g fresh weight) of potassium, sodium, calcium and magnesium in the various foods ranged from 2.92 to 1520, 1.46 to 30,700, 1.35 to 1280 and 1.16 to 416, respectively. Recovery values were within the range of 95–110%. Adults' mean mineral intakes (mg/person/day) of the analysed foods were 1970 ± 780, 2750 ± 1100, 423 ± 300 and 389 ± 130 for potassium, sodium, calcium and magnesium, respectively. Mean sodium intake was higher while potassium and calcium intakes were lower compared with international recommendations (mg/person/day) of 1500, 2300–3400 and 1000–1300, respectively; indicating the need to enlighten consumers. The snapshot data from this study are useful to update the Nigerian Food Composition Database.

Abstract Privacy protection is paramount in conducting health research. However, studies often rely on data stored in a centralized repository, where analysis is done with full access to the sensitive underlying content. Recent advances in federated learning enable building complex machine-learned models that are trained in a distributed fashion. These techniques facilitate the calculation of research study endpoints such that private data never leaves a given device or healthcare system. We show—on a diverse set of single and multi-site health studies—that federated models can achieve similar accuracy, precision, and generalizability, and lead to the same interpretation as standard centralized statistical models while achieving considerably stronger privacy protections and without significantly raising computational costs. This work is the first to apply modern and general federated learning methods that explicitly incorporate differential privacy to clinical and epidemiological research—across a spectrum of units of federation, model architectures, complexity of learning tasks and diseases. As a result, it enables health research participants to remain in control of their data and still contribute to advancing science—aspects that used to be at odds with each other.

Abstract Contact tracing is increasingly used to combat COVID-19, and digital implementations are now being deployed, many based on Apple and Google’s Exposure Notification System. These systems utilize non-traditional smartphone-based technology, presenting challenges in understanding possible outcomes. In this work, we create individual-based models of three Washington state counties to explore how digital exposure notifications combined with other non-pharmaceutical interventions influence COVID-19 disease spread under various adoption, compliance, and mobility scenarios. In a model with 15% participation, we found that exposure notification could reduce infections and deaths by approximately 8% and 6% and could effectively complement traditional contact tracing. We believe this can provide health authorities in Washington state and beyond with guidance on how exposure notification can complement traditional interventions to suppress the spread of COVID-19.

Human mobility plays a central role in the spread of infectious diseases and can help in forecasting incidence. Here the authors show a comparison of multiple mobility benchmarks in forecasting influenza, and demonstrate the value of a machine-learned mobility map with global coverage at multiple spatial scales.

During the COVID-19 pandemic there have been marked changes in individuals' belief systems (e.g., support for lockdowns) as a result of the threat of COVID-19. In the current study, we investigated whether these belief systems change as a function of changes in the threat of COVID-19. Specifically, we conducted a longitudinal study, with authoritarianism measured at the height of the COVID-19 pandemic in New Zealand and when the threat of COVID-19 was low (i.e., no known COVID-19 cases in the community). A total of 888 participants responded at both timepoints, completing measures of political orientation and distrust of science, in addition to the measure of authoritarianism. We had two hypotheses. First, that liberals would display a more marked reduction in authoritarian submission between Alert Level 4 and Alert Level 1 relative to conservatives. Second, that changes would be mediated by trust in science. Both hypotheses were supported, demonstrating that authoritarianism is sensitive to threat, even for those on the political left, and that trust in science helps to explain these changes. We suggest that fluctuations in authoritarianism may be different across the political spectrum due to underlying belief systems such as a distrust of science.

The growing availability of human mobility data can help assess the structure and dynamics of urban environments and their relation to the performance of cities. Here the authors introduce a metric of hierarchy in urban travel and find correlations between levels of hierarchy and other urban indicators.

Griffithsin is an algae-derived lectin with strong anti-viral activity against HIV and a positive safety profile. Multiple clinical studies are investigating griffithsin's utility as topical HIV microbicide. HIV microbicides are an extremely cost-sensitive market and plant-based griffithsin protein expression has the potential to meet those demands. The griffithsin product used in the clinic has been expressed and purified in N. benthamiana, using a TMV-based viral vector system, Geneware®. Outdoor pharming of biopharmaceuticals would further alleviate startup costs for biotechnology firms and may allow broader product accessibility. Therefore, this study assessed expression in a hybrid tobacco line, N. excelsiana, that is susceptible to TMV-based viral vectors and can be grown outdoors. In addition to using this hybrid line we expand on methods for in planta storage of griffithsin in leafy plants by ensiling kilogram quantities of griffithsin. The ensiling process allows year-round biomanufacturing, minimal environmental-controlled storage, and reduces the industry need for multiple growth areas to maintain multi-product manufacturing of plant-based pharmaceuticals. This study shows that griffithsin can be expressed in N. excelsiana and is stable, recoverable, and active from ensiled tissue. These studies can pave the way for future plant-based pharmaceuticals to be expressed and stored in this manner.

Moral vitalism refers to a tendency to view good and evil as actual forces that can influence people and events. The Moral Vitalism Scale had been designed to assess moral vitalism in a brief survey form. Previous studies established the reliability and validity of the scale in US-American and Australian samples. In this study, the cross-cultural comparability of the scale was tested across 28 different cultural groups worldwide through measurement invariance tests. A series of exact invariance tests marginally supported partial metric invariance, however, an approximate invariance approach provided evidence of partial scalar invariance for a 5-item measure. The established level of measurement invariance allows for comparisons of latent means across cultures. We conclude that the brief measure of moral vitalism is invariant across 28 cultures and can be used to estimate levels of moral vitalism with the same precision across very different cultural settings.

People with schizophrenia have an increased risk of metabolic syndrome, with consequent elevated morbidity and mortality, largely due to cardiovascular disease. Metabolic disorders comprise obesity, dyslipidemia and elevated levels of triglycerides, hypertension, and disturbed insulin and glucose metabolism. The elevated risk of metabolic syndrome in individuals suffering from schizophrenia is believed to be multifactorial, related to a genetic predisposition, lifestyle characteristics and treatment with antipsychotic medications. Relaxin 3 (RLN3, also known as INSL7) is a recently identified member of the insulin/relaxin superfamily that plays a role in the regulation of appetite and body weight control. RLN3 stimulates relaxin-3 receptor 1 (relaxin/insulin-like family peptide receptor 3, RXFP3) and relaxin receptor 2 (relaxin/insulin-like family peptide receptor 4, RXFP4). We have investigated the role of ten polymorphisms in these genes (RLN3 rs12327666, rs1982632, and rs7249702, RLN3R1 rs42868, rs6861957, rs7702361, and rs35399, and RLN3R2 rs11264422, rs1018730 and rs12124383) in the occurrence of metabolic syndrome phenotypes (obesity, diabetes, hypercholesterolemia, hypertrigyceridemia, and hypertension) in a cross-sectional cohort of 419 US Caucasian patients treated with antipsychotic drugs. We found several associations between relaxin polymorphisms and hypecholesterolemia, obesity and diabetes, suggesting a role for the relaxin/insulin pathway in the development of metabolic disturbance observed in patients treated with antipsychotics. [ABSTRACT FROM PUBLISHER]

Aripiprazole is a dopamine D2 receptor partial agonist undergoing evaluation as a pharmacotherapy for stimulant-use disorders. Acutely administered aripiprazole attenuates the discriminative stimulus and other behavioral effects of d-amphetamine in humans; however, whether aripiprazole attenuates the effects of more commonly abused stimulants is unknown. The aim of this experiment was to assess the discriminative stimulus, subject-rated and cardiovascular effects of oral cocaine alone and following acute administration of aripiprazole in humans. Eight cocaine-dependent subjects learned to discriminate 150 mg cocaine from placebo. After acquiring the discrimination, the effects of cocaine (0, 25, 50, 100 and 200 mg) administered alone and in combination with aripiprazole (15 mg) were determined. Significant effects of cocaine were observed for the drug discrimination task, stimulant-like subject-rated effects and heart rate. Limited effects of aripiprazole were revealed. However, for most measures, fewer doses of cocaine were significantly greater than placebo when combined with aripiprazole, suggesting a reduction in the discriminative stimulus, self-reported and cardiovascular effects of cocaine. These data are consistent with previous studies that have tested acutely administered aripiprazole in combination with d-amphetamine and suggest that the ability of aripiprazole to modify stimulant effects is a function of the duration of treatment (acute vs. chronic). [ABSTRACT FROM PUBLISHER]

Garden waste arising from private households represents a major component of the biodegradable municipal waste stream. To design effective waste valorisation schemes, detailed information about garden waste is a prerequisite. While the biochemical composition of this material is well documented, there is a lack of knowledge regarding both the quantities arising, and quantities entering the services operated by waste management authorities. This work studied the quantities of garden waste arisings at urban and rural households along with the disposal methods used. A door-to-door interview survey, an analysis of kerbside collections of garden waste, and an assessment of materials brought by citizens to a waste recycling site were carried out in Hampshire, UK. If extrapolated nationally, the results indicate that households in England produce an average of 0.79 kg of garden waste per day, or 288 kg per year. On a per capita basis, this corresponds to an annual arising of 120 kg per person, out of which around 70% enters the collection schemes of the waste management authorities. The quantity generated by rural and urban households differed substantially, with rural households producing 1.96 ± 1.35 kg per day and urban households 0.64 ± 0.46 kg per day. Rural households adopted self-sufficient methods of garden waste management such as home composting or backyard burning to a much greater extent compared with urban households. Less than half of the generated rural garden waste entered services operated by the waste collection authorities, while urban households strongly relied on these services. A detailed breakdown of the disposal routes chosen by urban and rural householders can support authorities in tailoring more effective waste management schemes.

Thymic stromal lymphopoietin (TSLP) is a cytokine released by human lung epithelium in response to external insult. Considered as a master switch in T helper 2 lymphocyte (Th2) mediated responses, TSLP is believed to play a key role in allergic diseases including asthma. The aim of this study was to use a phenotypic approach to identify new biological and chemical starting points for inhibition of TSLP production in human bronchial epithelial cells (NHBE), with the objective of reducing Th2-mediated airway inflammation. To this end, a phenotypic screen was performed using poly I:C / IL-4 stimulated NHBE cells interrogated with a 44,974 compound library. As a result, 85 hits which downregulated TSLP protein and mRNA levels were identified and a representative subset of 7 hits was selected for further characterization. These molecules inhibited the activity of several members of the MAPK, PI3K and tyrosine kinase families and some of them have been reported as modulators of cellular phenotypic endpoints like cell-cell contacts, microtubule polymerization and caspase activation. Characterization of the biological profile of the hits suggested that mTOR could be a key activity involved in the regulation of TSLP production in NHBE cells. Among other targeted kinases, inhibition of p38 MAPK and JAK kinases showed different degrees of correlation with TSLP downregulation, while Syk kinase did not seem to be related. Overall, inhibition of TSLP production by the selected hits, rather than resulting from inhibition of single isolated targets, appeared to be due to a combination of activities with different levels of relevance. Finally, a hit expansion exercise yielded additional active compounds that could be amenable to further optimization, providing an opportunity to dissociate TSLP inhibition from other non-desired activities. This study illustrates the potential of phenotypic drug discovery to complement target based approaches by providing new chemistry and biology leads.

Although currently recommended antibiotics for Lyme disease such as doxycycline or amoxicillin cure the majority of the patients, about 10-20% of patients treated for Lyme disease may experience lingering symptoms including fatigue, pain, or joint and muscle aches. Under stress conditions such as starvation or antibiotic exposure, Borrelia burgdorferi can develop round body forms, which are a type of persister bacteria that are not killed by current Lyme antibiotics. To identify more effective drugs that are active against the round bodies of B. burgdorferi, we established a round body persister model induced by amoxicillin and screened the Food and Drug Administration (FDA) drug library consisting of 1581 drug compounds and also 22 drug combinations using the SYBR Green I/propidium iodide (PI) viability assay. We identified 23 drug candidates that have higher activity against the round bodies of B. burgdorferi than either amoxicillin or doxycycline. Eleven of these scored better than metronidazole and tinidazole which have been previously described to be active against round bodies. While some drug candidates such as daptomycin and clofazimine overlapped with a previous screen against stationary phase B. burgdorferi persisters, additional drug candidates active against round bodies we identified include artemisinin, ciprofloxacin, nifuroxime, fosfomycin, chlortetracycline, sulfacetamide, sulfamethoxypyridazine and sulfathiozole. Two triple drug combinations had the highest activity against round bodies and stationary phase B. burgdorferi persisters: artemisinin/cefoperazone/doxycycline and sulfachlorpyridazine/daptomycin/doxycycline. These findings confirm and extend previous findings that certain drug combinations have superior activity against B. burgdorferi persisters in vitro, even if pre-treated with amoxicillin. These findings may have implications for improved treatment of Lyme disease.

Background. An etiological role for immune factors operating during early brain development in children with autism spectrum disorders (ASD) has not yet been established. A major obstacle has been the lack of early biologic specimens that can be linked to later diagnosis. In a prior study, we found lower risk of ASD associated with higher levels of maternally-derived total IgG and Toxoplasmosis gondii (Toxo) IgG in newborn blood spot specimens from children later diagnosed with ASD compared to population controls.Methods. We obtained maternal mid-gestational serum specimens and newborn screening blood spots from the California Genetics Disease Screening Program (GDSP) for linked mother-baby pairs for 84 children with ASD and 49 children with developmental delay but not ASD (DD) identified from California Department of Developmental Services records and for 159 population controls sampled from birth certificates. Immunoglobulin levels in maternal and newborn specimens were measured by solid phase immunoassays and analyzed in logistic regression models for total IgG, total IgM, and Toxo IgG, and, for maternal specimens only, Toxo IgM. Correlations between maternal and newborn ranked values were evaluated.Results. In both maternal and newborn specimens, we found significantly lower risk of ASD associated with higher levels of Toxo IgG. In addition, point estimates for all comparisons were

The identification of individuals at high risk of developing psychosis is entirely based on clinical assessment, associated with limited predictive potential. There is therefore increasing interest in the development of biological markers that could be used in clinical practice for this purpose. We studied 25 individuals with an At Risk Mental State for psychosis and 25 healthy controls using structural MRI, and functional MRI in conjunction with a verbal memory task. Data were analysed using a standard univariate analysis, and with Support Vector Machine (SVM), a multivariate pattern recognition technique that enables statistical inferences to be made at the level of the individual, yielding results with high translational potential. The application of SVM to structural MRI data permitted the identification of individuals at high risk of psychosis with a sensitivity of 68% and a specificity of 76%, resulting in an accuracy of 72% (p

For the ornamental crop Gerbera hybrida, breeding at the moment is done using conventional methods. As this has drawbacks in breeding speed and efficiency, especially for complex traits like disease resistance, we set out to develop genomic resources. The leaf and flower bud transcriptomes of four parents, used to generate two gerbera populations, were sequenced using Illumina paired-end sequencing. In total, 36,770 contigs with an average length of 1397 bp were generated and these have been the starting point for SNP identification and annotation. The consensus contig sequences were used to map reads of individual parents, to identify genotype specific SNPs, and to assess the presence of common SNPs between genotypes.Comparison with the non-redundant protein database (nr) showed that 29,146 contigs gave BLAST hits. Of sequences with blast results, 73.3% obtained a clear gene ontology (GO) annotation. EST contigs coding for enzymes were found in Kyoto Encyclopedia of Genes and Genomes maps (KEGG). Through these annotated data and KEGG molecular interaction network, transcripts associated with the phenylpropanoid metabolism, other secondary metabolite biosynthesis pathways, phytohormone biosynthesis and signal transduction were analysed in more detail. Identifying genes involved in these processes could provide genetic and genomic resources for studying the mechanism of disease resistance in gerbera.

There is evidence that social norm messages can be used to promote the selection of fruit and vegetables in low habitual consumers of these foods but it is unclear whether this effect is sustained over time. It is also unclear whether information about others’ liking for a food (liking norm) could have the same effect. Using a 2 x 5 x 2 experimental design we investigated the effects of exposure to various messages on later intake from a food buffet and whether any effects were sustained 24 hours after exposure in both low and high consumers of vegetables. There were three factors: delay (immediate food selection versus food selection 24 hours after exposure), message type (liking norm, descriptive norm, health message, food-based vegetable variety control, and neutral control message) and habitual consumption (low versus high). The buffet consisted of three raw vegetables, three energy-dense foods and two dips. For vegetables and non-vegetables there were no main effects of message type, nor any main effect of delay. There was a significant message x habitual vegetable consumption interaction for vegetable consumption; however, follow up tests did not yield any significant effects. Examining each food individually, there were no main effects of message type, nor any main effect of delay, for any of the foods; however, there was a message x habitual vegetable consumption interaction for broccoli. Consumption of broccoli in the health message and descriptive norm conditions did not differ from the control neutral condition. However, habitually low consumers of vegetables increased their consumption of broccoli in the food-based vegetable variety control and liking norm conditions relative to habitual low vegetable consumers in the neutral control condition (p < 0.05). Further investigation of the effects of the liking norm and vegetable variety message on vegetable intake is warranted. This trial is listed as NCT02618174 at clinicaltrials.gov.

To gain further knowledge on the preclinical phase of AD, we sought to characterize cognitive performance, volumetric MRI, amyloid PET, FDG PET, retinal amyloid, and plasma biomarkers in a cohort of non-demented adults with Down Syndrome (DS). The goal of the Down Syndrome Biomarker Initiative (DSBI) pilot is to test feasibility of this approach for future multicenter studies. We enrolled 12 non-demented participants with DS between the ages of 30-60 years old. Participants underwent extensive cognitive testing, volumetric MRI, amyloid PET 18F-florbetapir, 18F-fluorodeoxyglucose (18F-FDG) PET, and retinal amyloid imaging. In addition, plasma beta-amyloid species were measured and ApoE genotyping was performed. Consistent with previous autopsy studies, subjects demonstrated amyloid PET positivity reflecting fibrillar amyloid plaque deposition. Results from our multimodal analysis also suggest greater hippocampal atrophy with amyloid load. Additionally, we identified an inverse relationship between amyloid load and regional glucose metabolism. Cognitive and functional measures did not correlate with amyloid load in DS but did correlate with regional FDG PET measures. Retinal amyloid imaging demonstrated presence of plaques. Biomarkers of AD can be readily studied in adults with DS as in other preclinical AD populations. Importantly, all subjects in this feasibility study were able to complete all test procedures. The data indicate that a large, multicenter longitudinal study is feasible to better understand the trajectories of AD biomarkers in this enriched population. This trial is registered with ClinicalTrials.gov, number NCT02141971.

In order to develop a versatile and large SNP array for rose, we set out to mine ESTs from diverse sets of rose germplasm. For this RNA-Seq libraries containing about 700 million reads were generated from tetraploid cut and garden roses using Illumina paired-end sequencing, and from diploid Rosa multiflora using 454 sequencing. Separate de novo assemblies were performed in order to identify single nucleotide polymorphisms (SNPs) within and between rose varieties. SNPs among tetraploid roses were selected for constructing a genotyping array that can be employed for genetic mapping and marker-trait association discovery in breeding programs based on tetraploid germplasm, both from cut roses and from garden roses. In total 68,893 SNPs were included on the WagRhSNP Axiom array.Next, an orthology-guided assembly was performed for the construction of a non-redundant rose transcriptome database. A total of 21,740 transcripts had significant hits with orthologous genes in the strawberry (Fragaria vesca L.) genome. Of these 13,390 appeared to contain the full-length coding regions. This newly established transcriptome resource adds considerably to the currently available sequence resources for the Rosaceae family in general and the genus Rosa in particular.

Next Generation Sequencing (NGS) may enable estimating relationships among genotypes using allelic variation of multiple nuclear genes simultaneously. We explored the potential and caveats of this strategy in four genetically distant Lilium cultivars to estimate their genetic divergence from transcriptome sequences using three approaches: POFAD (Phylogeny of Organisms from Allelic Data, uses allelic information of sequence data), RAxML (Randomized Accelerated Maximum Likelihood, tree building based on concatenated consensus sequences) and Consensus Network (constructing a network summarizing among gene tree conflicts). Twenty six gene contigs were chosen based on the presence of orthologous sequences in all cultivars, seven of which also had an orthologous sequence in Tulipa, used as out-group. The three approaches generated the same topology. Although the resolution offered by these approaches is high, in this case there was no extra benefit in using allelic information. We conclude that these 26 genes can be widely applied to construct a species tree for the genus Lilium.

Alzheimer’s disease (AD) is the most common cause of dementia worldwide. As advancing age is the greatest risk factor for developing AD, the number of those afflicted is expected to increase markedly with the aging of the world’s population. The inability to definitively diagnose AD until autopsy remains an impediment to establishing effective targeted treatments. Neuroimaging has enabled in vivo visualisation of pathological changes in the brain associated with the disease, providing a greater understanding of its pathophysiological development and progression. However, neuroimaging biomarkers do not yet offer clear advantages over current clinical diagnostic criteria for them to be accepted into routine clinical use. Nonetheless, current insights from neuroimaging combined with the elucidation of biochemical and molecular processes in AD are informing the ongoing development of new imaging techniques and their application. Much of this research has been greatly assisted by the availability of transgenic mouse models of AD. In this review we summarise the main efforts of neuroimaging in AD in humans and in mouse models, with a specific focus on β-amyloid, and discuss the potential of new applications and novel approaches.

In the pursuit of clinical utility, neuroimaging researchers of psychiatric and neurological illness are increasingly using analyses, such as support vector machine (SVM), that allow inference at the single-subject level. Recent studies employing single-modality data, however, suggest that classification accuracies must be improved for such utility to be realised. One possible solution is to integrate different data types to provide a single combined output classification; either by generating a single decision function based on an integrated kernel matrix, or, by creating an ensemble of multiple single modality classifiers and integrating their predictions. Here, we describe four integrative approaches: 1) an un-weighted sum of kernels, 2) multi-kernel learning, 3) prediction averaging, and 4) majority voting, and compare their ability to enhance classification accuracy relative to the best single-modality classification accuracy. We achieve this by integrating structural, functional and diffusion tensor magnetic resonance imaging data, in order to compare ultra-high risk (UHR; n=19), first episode psychosis (FEP; n=19) and healthy control subjects (HCs; n=19). Our results show that i) whilst integration can enhance classification accuracy by up to 13%, the frequency of such instances may be limited, ii) where classification can be enhanced, simple methods may yield greater increases relative to more computationally complex alternatives, and, iii) the potential for classification enhancement is highly influenced by the specific diagnostic comparison under consideration. In conclusion, our findings suggest that for moderately sized clinical neuroimaging datasets, combining different imaging modalities in a data-driven manner is no magic bullet for increasing classification accuracy.

Down syndrome (DS), caused by trisomy of whole or part of chromosome 21 is the most common mental impairment. All Down syndrome (DS) individuals suffer from cognitive decline and develop Alzheimer’s disease (AD) by the age of forty. The appearance of enlarged early endosomes, followed by Amyloid β peptide deposition, the appearance of tau-containing neurofibrillary tangles and basal forebrain cholinergic neuron (BFCN) degeneration are the neuropathological characteristics of this disease. In this review we will examine the role of metal ion dyshomeostasis and the genes which may be involved in these processes, and relate these back to the manifestation of age-dependant cognitive decline in DS.

Alzheimer’s disease (AD) is the most common form of dementia, which is characterized by the neuropathological accumulation of extracellular amyloid plaques and intracellular neurofibrillary tangles (NFTs). Clinically, patients will endure a gradual erosion of memory and other higher order cognitive functions. Whilst the underlying etiology of the disease remains to be definitively identified, a body of work has developed over the last two decades demonstrating that AD plasma/serum and brain are characterized by a dyshomeostasis in a number of metal ions. Furthermore, these metals (such as zinc, copper and iron) play roles in the regulation of the levels AD-related proteins, including the amyloid precursor protein (APP) and tau. It is becoming apparent that metals also interact with other proteins, including apolipoprotein E (ApoE). The Apolipoprotein E gene (APOE) is critically associated with AD, with APOE4 representing the strongest genetic risk factor for the development of late-onset AD whereas APOE2 appears to have a protective role. In this review we will summarize the evidence supporting a role for metals in the function of Apolipoprotein E (ApoE) and its consequent role in the pathogenesis of AD.