Review of nomenclature for Actinidiaceae in Australia

A lectotype is chosen for Australia’s only native species of Actinidiaceae, Dillenia andreana F.Muell. A case is made for Saurauia andreana (F.Muell.) Oliv. ex F.Muell. to be treated as a new combination based on Dillenia andreana rather than as the name of a new taxon. Notes are provided on the classification of Yang-tao (Chinese Gooseberry or Kiwifruit), Actinidia chinensis var. deliciosa (A.Chev.) A.Chev., a taxon occasionally naturalised in Australia, for use on the online Flora of Australia

Topaz : Indian Summer (November)

https://digitalcommons.library.umaine.edu/mmb-ps/1949/thumbnail.jp

Visual Distraction: An Altered Aiming Spatial Response in Dementia

Background/Aims: Healthy individuals demonstrate leftward bias on visuospatial tasks such as line bisection, which has been attributed to right brain dominance. We investigated whether this asymmetry occurred in patients with probable dementia of the Alzheimer type (pAD) which is associated with neurodegenerative changes affecting temporoparietal regions. Methods: Subjects with pAD and matched controls performed a line bisection task in near and far space under conditions of no distraction, left-sided visual distraction and right-sided visual distraction. Results: Participants with pAD manifested different motor-preparatory ‘aiming’ spatial bias than matched controls. There were significantly greater rightward ‘aiming’ motor-intentional errors both without distraction and with right-sided distraction. Conclusion: ‘Aiming’ motor-preparatory brain activity may be induced by distraction in pAD subjects as compared to typical visual-motor function in controls

Probabilistic Random Forest improves bioactivity predictions close to the classification threshold by taking into account experimental uncertainty.

Measurements of protein-ligand interactions have reproducibility limits due to experimental errors. Any model based on such assays will consequentially have such unavoidable errors influencing their performance which should ideally be factored into modelling and output predictions, such as the actual standard deviation of experimental measurements (σ) or the associated comparability of activity values between the aggregated heterogenous activity units (i.e., Ki versus IC50 values) during dataset assimilation. However, experimental errors are usually a neglected aspect of model generation. In order to improve upon the current state-of-the-art, we herein present a novel approach toward predicting protein-ligand interactions using a Probabilistic Random Forest (PRF) classifier. The PRF algorithm was applied toward in silico protein target prediction across ~ 550 tasks from ChEMBL and PubChem. Predictions were evaluated by taking into account various scenarios of experimental standard deviations in both training and test sets and performance was assessed using fivefold stratified shuffled splits for validation. The largest benefit in incorporating the experimental deviation in PRF was observed for data points close to the binary threshold boundary, when such information was not considered in any way in the original RF algorithm. For example, in cases when σ ranged between 0.4-0.6 log units and when ideal probability estimates between 0.4-0.6, the PRF outperformed RF with a median absolute error margin of ~ 17%. In comparison, the baseline RF outperformed PRF for cases with high confidence to belong to the active class (far from the binary decision threshold), although the RF models gave errors smaller than the experimental uncertainty, which could indicate that they were overtrained and/or over-confident. Finally, the PRF models trained with putative inactives decreased the performance compared to PRF models without putative inactives and this could be because putative inactives were not assigned an experimental pXC50 value, and therefore they were considered inactives with a low uncertainty (which in practice might not be true). In conclusion, PRF can be useful for target prediction models in particular for data where class boundaries overlap with the measurement uncertainty, and where a substantial part of the training data is located close to the classification threshold

SARS-CoV-2 infection in the first trimester and the risk of early miscarriage: a UK population-based prospective cohort study of 3041 pregnancies conceived during the pandemic

STUDY QUESTION: Does maternal infection with severe acute respiratory syndrome coronavirus (SARS-CoV-2) in the first trimester affect the risk of miscarriage before 13 week's gestation? SUMMARY ANSWER: Pregnant women with self-reported diagnosis of SARS-CoV-2 in the first trimester had a higher risk of early miscarriage. WHAT IS KNOWN ALREADY: Viral infections during pregnancy have a broad spectrum of placental and neonatal pathology. Data on the effects of the SARS-CoV-2 infection in pregnancy are still emerging. Two systematic reviews and meta-analyses reported an increased risk of preterm birth, caesarean delivery, maternal morbidity and stillbirth. Data on the impact of first trimester infection on early pregnancy outcomes are scarce. This is the first study, to our knowledge, to investigate the rates of early pregnancy loss during the SARS-CoV-2 outbreak among women with self-reported infection. STUDY DESIGN, SIZE, DURATION: This was a nationwide prospective cohort study of pregnant women in the community recruited using social media between 21st May and 31st December, 2020. We recruited 3545 women who conceived during the SARS-CoV-2 pandemic who were less than 13 week's gestation at the time of recruitment. PARTICIPANTS/MATERIALS, SETTING, METHODS: The COVID-19 Contraception and Pregnancy Study (CAP-COVID) was an on-line survey study collecting longitudinal data from pregnant women in the UK aged 18 years or older. Women who were pregnant during the pandemic were asked to complete on-line surveys at the end of each trimester. We collected data on current and past pregnancy complications, their medical history and whether they or anyone in their household had symptoms or been diagnosed with SARS-CoV-2 infection during each trimester of their pregnancy. RT-PCR-based SARS-CoV-2 RNA detection from respiratory samples (e.g., nasopharynx) is the standard practice for diagnosis of SARS-CoV-2 in the UK. We compared rate of self-reported miscarriage in three groups: 'presumed infected' i.e those who reported a diagnosis with SARS-CoV-2 infection in the first trimester; 'uncertain' i.e those who did not report a diagnosis but had symptoms/household contacts with symptoms/diagnosis; and 'presumed uninfected' i.e., those who did not report any symptoms/diagnosis and had no household contacts with symptoms/diagnosis of SARS-CoV-2. MAIN RESULTS AND THE ROLE OF CHANCE: A total of 3545 women registered for the CAP-COVID study at less than 13 weeks gestation and were eligible for this analysis. Data for the primary outcome were available from 3041 women (86%). In the overall sample, the rate of self-reported miscarriage was 7.8% (238/3041 [95% CI, 7-9]). The median gestational age at miscarriage was 9 weeks (interquartile range 8-11). Seventy-seven women were in the 'presumed infected' group (77/3041, 2.5% [95% CI 2 - 3]), 295/3041 were in the uncertain group (9.7%, [95% CI 9-11]) and the rest in the 'presumed uninfected' (87.8%, 2669/3041, [95% CI 87-89]). The rate of early miscarriage was 14% in the 'presumed infected' group, 5% in the 'uncertain' and 8% in the 'presumed uninfected' (11/77 [95% CI 6-22] versus15/295, [95% CI 3-8] versus 212/2669 [95% CI 7-9], p = 0.02). After adjusting for age, BMI, ethnicity, smoking status, gestational age at registration and the number of previous miscarriages, the risk of early miscarriage appears to be higher in the 'presumed infected' group (relative rate 1.7, 95% CI 1.0-3.0, p = 0.06). LIMITATIONS, REASONS FOR CAUTION: We relied on self-reported data on early pregnancy loss and SARS-CoV-2 infection without any means of checking validity. Some women in the 'presumed uninfected' and 'uncertain' groups may have had asymptomatic infections. The number of 'presumed infected' in our study was low and therefore the study was relatively underpowered. WIDER IMPLICATIONS OF THE FINDINGS: This was a national study from the UK, where infection rates were one of the highest in the world. Based on the evidence presented here, women who are infected with SARS-CoV-2 in their first trimester may be at an increased risk of a miscarriage. However, the overall rate of miscarriage in our study population was 8%. This is reassuring and suggests that if there is an effect of SARS-CoV-2 on the risk of miscarriage, this may be limited to those with symptoms substantial enough to lead to a diagnostic test. Further studies are warranted to evaluate a causal association between SARS-CoV-2 infection in early pregnancy and miscarriage risk. Although we did not see an overall increase in the risk of miscarriage, the observed comparative increase in the presumed infected group reinforces the message that pregnant women should continue to exercise social distancing measures and good hygiene throughout their pregnancy to limit their risk of infection. STUDY FUNDING/COMPETING INTEREST(S): This study was supported by a grant from the Elizabeth Garrett Anderson Hospital Charity, (G13-559194). The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. JAH is supported by an NIHR Advanced Fellowship. ALD is supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre. All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare: support to JAH and ALD as above; no financial relationships with any organisations that might have an interest in the submitted work in the previous 3 years; no other relationships or activities that could appear to have influenced the submitted work. TRIAL REGISTRATION NUMBER: n/a

Eight common genetic variants associated with serum dheas levels suggest a key role in ageing mechanisms

Dehydroepiandrosterone sulphate (DHEAS) is the most abundant circulating steroid secreted by adrenal glands-yet its function is unknown. Its serum concentration declines significantly with increasing age, which has led to speculation that a relative DHEAS deficiency may contribute to the development of common age-related diseases or diminished longevity. We conducted a meta-analysis of genome-wide association data with 14,846 individuals and identified eight independent common SNPs associated with serum DHEAS concentrations. Genes at or near the identified loci include ZKSCAN5 (rs11761528; p = 3.15×10-36), SULT2A1 (rs2637125; p = 2.61×10-19), ARPC1A (rs740160; p = 1.56×10-16), TRIM4 (rs17277546; p = 4.50×10-11), BMF (rs7181230; p = 5.44×10-11), HHEX (rs2497306; p = 4.64×10-9), BCL2L11 (rs6738028; p = 1.72×10-8), and CYP2C9 (rs2185570; p = 2.29×10-8). These genes are associated with type 2 diabetes, lymphoma, actin filament assembly, drug and xenobiotic metabolism, and zinc finger proteins. Several SNPs were associated with changes in gene expression levels, and the related genes are connected to biological pathways linking DHEAS with ageing. This study provides much needed insight into the function of DHEAS

Resistance to natural and synthetic gene drive systems

Scientists are rapidly developing synthetic gene drive elements intended for release into natural populations. These are intended to control or eradicate disease vectors and pests, or to spread useful traits through wild populations for disease control or conservation purposes. However, a crucial problem for gene drives is the evolution of resistance against them, preventing their spread. Understanding the mechanisms by which populations might evolve resistance is essential for engineering effective gene drive systems. This review summarizes our current knowledge of drive resistance in both natural and synthetic gene drives. We explore how insights from naturally occurring and synthetic drive systems can be integrated to improve the design of gene drives, better predict the outcome of releases and understand genomic conflict in general

Results from the centers for disease control and prevention's predict the 2013-2014 Influenza Season Challenge

Background: Early insights into the timing of the start, peak, and intensity of the influenza season could be useful in planning influenza prevention and control activities. To encourage development and innovation in influenza forecasting, the Centers for Disease Control and Prevention (CDC) organized a challenge to predict the 2013-14 Unites States influenza season. Methods: Challenge contestants were asked to forecast the start, peak, and intensity of the 2013-2014 influenza season at the national level and at any or all Health and Human Services (HHS) region level(s). The challenge ran from December 1, 2013-March 27, 2014; contestants were required to submit 9 biweekly forecasts at the national level to be eligible. The selection of the winner was based on expert evaluation of the methodology used to make the prediction and the accuracy of the prediction as judged against the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). Results: Nine teams submitted 13 forecasts for all required milestones. The first forecast was due on December 2, 2013; 3/13 forecasts received correctly predicted the start of the influenza season within one week, 1/13 predicted the peak within 1 week, 3/13 predicted the peak ILINet percentage within 1 %, and 4/13 predicted the season duration within 1 week. For the prediction due on December 19, 2013, the number of forecasts that correctly forecasted the peak week increased to 2/13, the peak percentage to 6/13, and the duration of the season to 6/13. As the season progressed, the forecasts became more stable and were closer to the season milestones. Conclusion: Forecasting has become technically feasible, but further efforts are needed to improve forecast accuracy so that policy makers can reliably use these predictions. CDC and challenge contestants plan to build upon the methods developed during this contest to improve the accuracy of influenza forecasts. © 2016 The Author(s)