Mapping to underpin management of tropical littoral rainforest

[Extract] The aim of the project was to produce fine-resolution mapping of the location of the critically endangered Littoral Rainforest & Coastal Vine Thickets of Eastern Australia ecological community (LRF) between Townsville and Cooktown and the threats to its persistence and condition from the impacts of sea-level rise, storm surge and extreme weather events. A pilot study conducted in the Mission Beach area (Metcalfe et al. 2014) developed a mapping approach which accounts for the identification and distribution of Littoral rainforest consistent with the Listing Advice. This project extended that approach across the distribution of the ecological community from Townville to Cooktown. This project used coastal LiDAR data (1 m grid, 0.15 m accuracy) to compile fine-scale terrain layers to derive inundation levels for an 80 cm sea-level rise and for eight storm surge Annual Recurrence Intervals (ARIs) between 20 and 10,000 years. Spatial layers of the location of LRF and inundation were overlaid to determine the probability and magnitude of risk to the ecological community from these effects and to prioritise management interventions. The following spatial layers were derived and are available at the CSIRO data portal: • LRF vegetation that ‘wholly-equates’ to the EPBC Listing Advice • ‘Potential’ LRF delineating areas consistent with broad characteristics of the community described in the EPBC Listing Advice • Inundation statistics for each patch of wholly-equate LRF and potential LRF (patches defined by RE mapping) indicating: o the proportion of each patch inundated with 80 cm sea-level rise o the proportion of each patch inundated at each of 8 ARIs with and without sea-level rise o the ARI at which a patch first becomes inundated o the ARI at which a patch is >20% inundated o the ARI at which a patch is >50% inundated We describe the distribution and extent of LRF in the study region, the current pressures on LRF in the region and the distribution of LRF in the region with respect to the conservation estate and other tenures. Our mapping and inundation analysis can be used to define a number of different roles of LRF in the landscape on which a portfolio of management approaches can be derived which allow for the short-, medium- and long-term effects of sea-level rise and storm surge. We define ‘refugial’, ‘buffer’ and ‘leading-edge’ LRF patches by the relative frequency at which they become inundated and suggest management actions to improve resilience of the community as a whole

Dendritic silver self-assembly in molten-carbonate membranes for efficient carbon dioxide capture

Membranes for CO2 capture should offer high permeant fluxes to keep membrane surface area small and material requirements low. Ag-supported, dual-phase, molten-carbonate membranes routinely demonstrate the highest CO2 fluxes in this class of membrane. However, using Ag as a support incurs high cost. Here, the non-equilibrium conditions of permeation were exploited to stimulate the self-assembly of a percolating, dendritic network of Ag from the molten carbonate. Multiple membrane support geometries and Ag incorporation methods were employed, demonstrating the generality of the approach, while X-ray micro-computed tomography confirmed that CO2 and O2 permeation stimulated self-assembly. We report the highest flux of Ag-supported molten-salt membranes to date (1.25 ml min−1 cm−2 at 650 °C) and ultrahigh permeability (9.4 × 10−11 mol m−1 s−1 Pa−1), surpassing the permeability requirement for economically-competitive post-combustion CO2 capture, all whilst reducing the membrane-volume-normalised demand for Ag by one order of magnitude

A machine learning algorithm to differentiate bipolar disorder from major depressive disorder using an online mental health questionnaire and blood biomarker data.

The vast personal and economic burden of mood disorders is largely caused by their under- and misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. Here, we aimed to develop a diagnostic algorithm, based on an online questionnaire and blood biomarker data, to reduce the misdiagnosis of bipolar disorder (BD) as major depressive disorder (MDD). Individuals with depressive symptoms (Patient Health Questionnaire-9 score ≥5) aged 18-45 years were recruited online. After completing a purpose-built online mental health questionnaire, eligible participants provided dried blood spot samples for biomarker analysis and underwent the World Health Organization World Mental Health Composite International Diagnostic Interview via telephone, to establish their mental health diagnosis. Extreme Gradient Boosting and nested cross-validation were used to train and validate diagnostic models differentiating BD from MDD in participants who self-reported a current MDD diagnosis. Mean test area under the receiver operating characteristic curve (AUROC) for separating participants with BD diagnosed as MDD (N = 126) from those with correct MDD diagnosis (N = 187) was 0.92 (95% CI: 0.86-0.97). Core predictors included elevated mood, grandiosity, talkativeness, recklessness and risky behaviour. Additional validation in participants with no previous mood disorder diagnosis showed AUROCs of 0.89 (0.86-0.91) and 0.90 (0.87-0.91) for separating newly diagnosed BD (N = 98) from MDD (N = 112) and subclinical low mood (N = 120), respectively. Validation in participants with a previous diagnosis of BD (N = 45) demonstrated sensitivity of 0.86 (0.57-0.96). The diagnostic algorithm accurately identified patients with BD in various clinical scenarios, and could help expedite accurate clinical diagnosis and treatment of BD

A machine learning algorithm to differentiate bipolar disorder from major depressive disorder using an online mental health questionnaire and blood biomarker data.

The vast personal and economic burden of mood disorders is largely caused by their under- and misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. Here, we aimed to develop a diagnostic algorithm, based on an online questionnaire and blood biomarker data, to reduce the misdiagnosis of bipolar disorder (BD) as major depressive disorder (MDD). Individuals with depressive symptoms (Patient Health Questionnaire-9 score ≥5) aged 18-45 years were recruited online. After completing a purpose-built online mental health questionnaire, eligible participants provided dried blood spot samples for biomarker analysis and underwent the World Health Organization World Mental Health Composite International Diagnostic Interview via telephone, to establish their mental health diagnosis. Extreme Gradient Boosting and nested cross-validation were used to train and validate diagnostic models differentiating BD from MDD in participants who self-reported a current MDD diagnosis. Mean test area under the receiver operating characteristic curve (AUROC) for separating participants with BD diagnosed as MDD (N = 126) from those with correct MDD diagnosis (N = 187) was 0.92 (95% CI: 0.86-0.97). Core predictors included elevated mood, grandiosity, talkativeness, recklessness and risky behaviour. Additional validation in participants with no previous mood disorder diagnosis showed AUROCs of 0.89 (0.86-0.91) and 0.90 (0.87-0.91) for separating newly diagnosed BD (N = 98) from MDD (N = 112) and subclinical low mood (N = 120), respectively. Validation in participants with a previous diagnosis of BD (N = 45) demonstrated sensitivity of 0.86 (0.57-0.96). The diagnostic algorithm accurately identified patients with BD in various clinical scenarios, and could help expedite accurate clinical diagnosis and treatment of BD

A machine learning algorithm to differentiate bipolar disorder from major depressive disorder using an online mental health questionnaire and blood biomarker data

The vast personal and economic burden of mood disorders is largely caused by their under- and misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. Here, we aimed to develop a diagnostic algorithm, based on an online questionnaire and blood biomarker data, to reduce the misdiagnosis of bipolar disorder (BD) as major depressive disorder (MDD). Individuals with depressive symptoms (Patient Health Questionnaire-9 score >= 5) aged 18-45 years were recruited online. After completing a purpose-built online mental health questionnaire, eligible participants provided dried blood spot samples for biomarker analysis and underwent the World Health Organization World Mental Health Composite International Diagnostic Interview via telephone, to establish their mental health diagnosis. Extreme Gradient Boosting and nested cross-validation were used to train and validate diagnostic models differentiating BD from MDD in participants who self-reported a current MDD diagnosis. Mean test area under the receiver operating characteristic curve (AUROC) for separating participants with BD diagnosed as MDD (N = 126) from those with correct MDD diagnosis (N = 187) was 0.92 (95% CI: 0.86-0.97). Core predictors included elevated mood, grandiosity, talkativeness, recklessness and risky behaviour. Additional validation in participants with no previous mood disorder diagnosis showed AUROCs of 0.89 (0.86-0.91) and 0.90 (0.87-0.91) for separating newly diagnosed BD (N = 98) from MDD (N = 112) and subclinical low mood (N = 120), respectively. Validation in participants with a previous diagnosis of BD (N = 45) demonstrated sensitivity of 0.86 (0.57-0.96). The diagnostic algorithm accurately identified patients with BD in various clinical scenarios, and could help expedite accurate clinical diagnosis and treatment of BD

Identification of a Ruminant Origin Group B Rotavirus Associated with Diarrhea Outbreaks in Foals

Equine rotavirus group A (ERVA) is one of the most common causes of foal diarrhea. Starting in February 2021, there was an increase in the frequency of severe watery to hemorrhagic diarrhea cases in neonatal foals in Central Kentucky. Diagnostic investigation of fecal samples failed to detect evidence of diarrhea-causing pathogens including ERVA. Based on Illumina-based metagenomic sequencing, we identified a novel equine rotavirus group B (ERVB) in fecal specimens from the affected foals in the absence of any other known enteric pathogens. Interestingly, the protein sequence of all 11 segments had greater than 96% identity with group B rotaviruses previously found in ruminants. Furthermore, phylogenetic analysis demonstrated clustering of the ERVB with group B rotaviruses of caprine and bovine strains from the USA. Subsequent analysis of 33 foal diarrheic samples by RT-qPCR identified 23 rotavirus B-positive cases (69.69%). These observations suggest that the ERVB originated from ruminants and was associated with outbreaks of neonatal foal diarrhea in the 2021 foaling season in Kentucky. Emergence of the ruminant-like group B rotavirus in foals clearly warrants further investigation due to the significant impact of the disease in neonatal foals and its economic impact on the equine industry

Photometric Classification of 2315 Pan-STARRS1 Supernovae with Superphot

The classification of supernovae (SNe) and its impact on our understanding of explosion physics and progenitors have traditionally been based on the presence or absence of certain spectral features. However, current and upcoming wide-field time-domain surveys have increased the transient discovery rate far beyond our capacity to obtain even a single spectrum of each new event. We must therefore rely heavily on photometric classification— connecting SN light curves back to their spectroscopically defined classes. Here, we present Superphot, an opensource Python implementation of the machine-learning classification algorithm of Villar et al., and apply it to 2315 previously unclassified transients from the Pan-STARRS1 Medium Deep Survey for which we obtained spectroscopic host-galaxy redshifts. Our classifier achieves an overall accuracy of 82%, with completenesses and purities of >80% for the best classes (SNe Ia and superluminous SNe). For the worst performing SN class (SNe Ibc), the completeness and purity fall to 37% and 21%, respectively. Our classifier provides 1257 newly classified SNe Ia, 521 SNe II, 298 SNe Ibc, 181 SNe IIn, and 58 SLSNe. These are among the largest uniformly observed samples of SNe available in the literature and will enable a wide range of statistical studies of each class

A developmental approach to diversifying neuroscience through effective mentorship practices: perspectives on cross-identity mentorship and a critical call to action.

Many early-career neuroscientists with diverse identities may not have mentors who are more advanced in the neuroscience pipeline and have a congruent identity due to historic biases, laws, and policies impacting access to education. Cross-identity mentoring relationships pose challenges and power imbalances that impact the retention of diverse early career neuroscientists, but also hold the potential for a mutually enriching and collaborative relationship that fosters the mentee\u27s success. Additionally, the barriers faced by diverse mentees and their mentorship needs may evolve with career progression and require developmental considerations. This article provides perspectives on factors that impact cross-identity mentorship from individuals participating in Diversifying the Community of Neuroscience (CNS)-a longitudinal, National Institute of Neurological Disorders and Stroke (NINDS) R25 neuroscience mentorship program developed to increase diversity in the neurosciences. Participants in Diversifying CNS were comprised of 14 graduate students, postdoctoral fellows, and early career faculty who completed an online qualitative survey on cross-identity mentorship practices that impact their experience in neuroscience fields. Qualitative survey data were analyzed using inductive thematic analysis and resulted in four themes across career levels: (1) approach to mentorship and interpersonal dynamics, (2) allyship and management of power imbalance, (3) academic sponsorship, and (4) institutional barriers impacting navigation of academia. These themes, along with identified mentorship needs by developmental stage, provide insights mentors can use to better support the success of their mentees with diverse intersectional identities. As highlighted in our discussion, a mentor\u27s awareness of systemic barriers along with active allyship are foundational for their role

Effect of Pretreatment Method on the Nanostructure and Performance of Supported Co Catalysts in Fischer−Tropsch Synthesis

ABSTRACT: Understanding precursor transformation to active catalysts is crucial to heterogeneous Fischer−Tropsch (FT) catalysis directed toward production of hydrocarbons for transportation fuels. Despite considerable literature on FT catalysis, the effect of pretreatment of supported cobalt catalysts on cobalt dispersion, dynamic atomic structure, and the activity of the catalysts is not well understood. Here we present systematic studies into the formation of active catalyst phases in supported Co catalyst precursors in FT catalysis using in situ environmental (scanning) transmission electron microscopy (E(S)TEM) with single-atom resolution under controlled reaction environments for in situ visualization, imaging, and analysis of reacting atomic species in real time, EXAFS, XAS, DRIFTS analyses, and catalytic activity measurements. We have synthesized and analyzed dried reduced (D) and dried calcined reduced (DC) Co real catalysts on reducible and nonreducible supports, such as SiO2, Al2O3, TiO2, and ZrO2. Comparisons of dynamic in situ atomic structural observations of reacting single atoms, atomic clusters, and nanoparticles of Co and DRIFTS, XAS, EXAFS, and catalytic activity data of the D and DC samples reveal in most cases better dispersion in the D samples, leading to a larger number of low-coordination Co0 sites and a higher number of active sites for CO adsorption. The experimental findings on the degree of reduction of D and 27 DC catalysts on reducible and nonreducible supports and correlations between hexagonal (hcp) Co sites and the activity of the catalysts generate structural insights into the catalyst dynamics, important to the development of efficient FT catalysts

The intergalactic propagation of ultra-high energy cosmic ray nuclei

We investigate the propagation of ultra-high energy cosmic ray nuclei (A = 1-56) from cosmologically distant sources through the cosmic radiation backgrounds. Various models for the injected composition and spectrum and of the cosmic infra-red background are studied using updated photo-disintegration cross-sections. The observational data on the spectrum and the composition of ultra-high energy cosmic rays are jointly consistent with a model where all of the injected primary cosmic rays are iron nuclei (or a mixture of heavy and light nuclei).Comment: 19 pages, 51 figures; accepted for publication in Astroparticle Physics (with minor revisions