Double hydroxide solids precipitated homogeneously from three laboratory-synthesized aqueous solutions that simulated mildly contaminated surface or groundwater. Over a limited pH range, precipitates formed rapidly from dissolved ions, and more slowly by incorporating ions dissolving from other solids, including highly soluble aluminous solids. The precipitates were characterized by size and shape via transmission electron microscopy (TEM), by composition via inductively coupled plasma-mass spectrometry (ICP-MS) of mother solutions and analytical electron microscopy (AEM) of precipitates, and by structure via powder X-ray diffraction (XRD), TEM, and extended X-ray absorption fine structure (EXAFS) spectroscopy. They were identified as nanocrystalline cobalt hydrotalcite (CoHT) of the form [Co(II)1-xAl(III)x(OH)2]x+(An−x/n)·mH2O, with x = 0.17–0.25, A = CO32−, NO3−, or H3SiO4−n = anion charge and m undetermined. Complete solid solution may exist at the macroscopic level for the range of stoichiometrics reported, but clustering of Co atoms within hydroxide layers indicates a degree of immiscibility at the molecular scale. Composition evolved toward the Co-rich endmember with time for at least one precipitate. The small layer charge in the x = 0.17 precipitate caused anionic interlayers to be incomplete, producing interstratification of hydrotalcite and brucite-like layers. Solubility products estimated from solution measurements for the observed final CoHT stoichiometries suggest that CoHT is less soluble than the inactive forms of Co(OH)2 and CoCO3 near neutral pH. Low solubility and rapid formation suggest that CoHT solids may be important sinks for Co in contact with near neutral pH waters. Because hydrotalcite can incorporate a range of transition metals, precipitation of hydrotalcite may be similarly effective for removing other trace metals from natural waters.

X-ray absorption spectroscopy (XAS) was used to determine the local molecular environment of Co(II) surface complexes sorbed on three different kaolinites at ambient temperature and pressure in contact with an aqueous solution. Interatomic distances and types and numbers of backscattering atoms have been derived from analysis of the extended X-ray absorption fine structure (EXAFS). These data show that, at the lowest amounts of Co uptake on kaolinite (0.20–0.32 µmol m−2), Co is surrounded by ≈6 O atoms at 2.04–2.08 Å and a small number or Al or Si atoms (N = 0.6–1.5) at two distinct distances, 2.67–2.72 Å and 3.38–3.43 Å. These results indicate that Co bonds to the kaolinite surface as octahedrally coordinated, bidentate inner-sphere mononuclear complexes at low surface coverages, confirming indirect evidence from solution studies that a fraction of sorbed Co forms strongly bound complexes on kaolinite. In addition to inner-sphere complexes identified by EXAFS spectroscopy, solution studies provide evidence for the presence of weakly bound, outer-sphere Co complexes that cannot be detected directly by EXAFS. One orientation for inner-sphere complexes indicated by XAS is bidentate bonding of Co to oxygen atoms at two Al-O-Si edge sites or an Al-O-Si and Al-OH (inner hydroxyl) edge site, i.e., corner-sharing between Co octahedra and Al and Si polyhedra. At slightly higher surface sorption densities (0.51–0.57/ µmol m−2), the presence of a small number of second-neighbor Co atoms (average NCo < 1) at 3.10–3.13 Å indicates the formation of oxy- or hydroxy-bridged, multinuclear surface complexes in addition to mononuclear complexes. At these surface coverages, Co-Co and Co-Al/Si distances derived from EXAFS are consistent with edge-sharing between Co and Al octahedra on either edges or (001) faces of the aluminol sheet in kaolinite. Multinuclear complexes form on kaolinite at low surface sorption densities equivalent to <5% coverage by a monolayer of oxygen-ligated Co octahedra over the N2-BET surface area. These spectroscopic results have several implications for macroscopic modeling of metal ion uptake on kaolinite: 1) Primary binding sites on the kaolinite surface at low uptake are edge, non-bridging Al-OH inner hydroxyl sites and edge Al-O-Si bridging oxygen sites, not Si-OH sites typically assumed in sorption models; 2) specific adsorption of Co is via bidentate, inner-sphere complexation; and 3) at slightly higher uptake but still a small fraction of monolayer coverage, formation of Co multinuclear complexes, primarily edge-sharing with Al-OH octahedra, begins to dominate sorption.

Although the individual has been the focus of most research into judgment and decision-making (JDM), important decisions in the real world are often made collectively rather than individually, a tendency that has increased in recent times with the opportunities for easy information exchange through the Internet. From this perspective, JDM research that factors in this social context has increased generalizability and mundane realism relative to that which ignores it. We delineate a problem-space for research within which we locate protocols that are used to study or support collective JDM, identify a common research question posed by all of these protocols—‘What are the factors leading to opinion change for the better (‘virtuous opinion change’) in individual JDM agents?’—and propose a modeling approach and research paradigm using structured groups (i.e., groups with some constraints on their interaction), for answering this question. This paradigm, based on that used in studies of judge-adviser systems, avoids the need for real interacting groups and their attendant logistical problems, lack of power, and poor experimental control. We report an experiment using our paradigm on the effects of group size and opinion diversity on judgmental forecasting performance to illustrate our approach. The study found a U-shaped effect of group size on the probability of opinion change, but no effect on the amount of virtuous opinion change. Implications of our approach for development of more externally valid empirical studies and theories of JDM, and for the design of structured-group techniques to support collective JDM, are discussed.

COVID-19 impacts population health equity. While mRNA vaccines protect against serious illness and death, little New Zealand (NZ) data exist about the impact of Omicron – and the effectiveness of vaccination – on different population groups. We aim to examine the impact of Omicron on Māori, Pacific, and Other ethnicities and how this interacts with age and vaccination status in the Te Manawa Taki Midland region of NZ. Daily COVID-19 infection and hospitalisation rates (1 February 2022 to 29 June 2022) were calculated for Māori, Pacific, and Other ethnicities for six age bands. A multivariate logistic regression model quantified the effects of ethnicity, age, and vaccination on hospitalisation rates. Per-capita Omicron cases were highest and occurred earliest among Pacific (9 per 1,000) and Māori (5 per 1,000) people and were highest among 12–24-year-olds (7 per 1,000). Hospitalisation was significantly more likely for Māori people (odds ratio (OR) = 2.03), Pacific people (OR = 1.75), over 75-year-olds (OR = 39.22), and unvaccinated people (OR = 4.64). Length of hospitalisation is strongly related to age. COVID-19 vaccination reduces hospitalisations for older individuals and Māori and Pacific populations. Omicron inequitably impacted Māori and Pacific people through higher per-capita infection and hospitalisation rates. Older people are more likely to be hospitalised and for longer.

Microscopic examination of blood smears remains the gold standard for laboratory inspection and diagnosis of malaria. Smear inspection is, however, time-consuming and dependent on trained microscopists with results varying in accuracy. We sought to develop an automated image analysis method to improve accuracy and standardization of smear inspection that retains capacity for expert confirmation and image archiving. Here, we present a machine learning method that achieves red blood cell (RBC) detection, differentiation between infected/uninfected cells, and parasite life stage categorization from unprocessed, heterogeneous smear images. Based on a pretrained Faster Region-Based Convolutional Neural Networks (R-CNN) model for RBC detection, our model performs accurately, with an average precision of 0.99 at an intersection-over-union threshold of 0.5. Application of a residual neural network-50 model to infected cells also performs accurately, with an area under the receiver operating characteristic curve of 0.98. Finally, combining our method with a regression model successfully recapitulates intraerythrocytic developmental cycle with accurate lifecycle stage categorization. Combined with a mobile-friendly web-based interface, called PlasmoCount, our method permits rapid navigation through and review of results for quality assurance. By standardizing assessment of Giemsa smears, our method markedly improves inspection reproducibility and presents a realistic route to both routine lab and future field-based automated malaria diagnosis.

For the last two decades, high-dimensional data and methods have proliferated throughout the literature. Yet, the classical technique of linear regression has not lost its usefulness in applications. In fact, many high-dimensional estimation techniques can be seen as variable selection that leads to a smaller set of variables (a “submodel”) where classical linear regression applies. We analyze linear regression estimators resulting from model selection by proving estimation error and linear representation bounds uniformly over sets of submodels. Based on deterministic inequalities, our results provide “good” rates when applied to both independent and dependent data. These results are useful in meaningfully interpreting the linear regression estimator obtained after exploring and reducing the variables and also in justifying post-model-selection inference. All results are derived under no model assumptions and are nonasymptotic in nature.

Intrauterine preeclampsia exposure affects the lifelong cardiometabolic health of the child. Our study aimed to compare the growth (from birth to 6 months) of infants exposed to either a normotensive pregnancy or preeclampsia and explore the influence of being born small for gestational age (SGA). Participants were children of women participating in the Post-partum, Physiology, Psychology and Paediatric follow-up cohort study. Birth and 6-month weight and length z-scores were calculated for term and preterm (<37 weeks) babies, and change in weight z-score, rapid weight gain (≥0.67 increase in weight z-score) and conditional weight gain z-score were calculated. Compared with normotensive exposed infants (n = 298), preeclampsia exposed infants (n = 84) were more likely to be born SGA (7% versus 23%; P < 0.001), but weight gain from birth to 6 months, by any measure, did not differ between groups. Infants born SGA, irrespective of pregnancy exposure, were more likely to have rapid weight gain and had greater increases in weight z-score compared with those not born SGA. Preeclampsia exposed infants born SGA may benefit from interventions designed to prevent future cardiometabolic disease.

The principal aim of this study was to optimize the diagnosis of canine neuroangiostrongyliasis (NA). In total, 92 cases were seen between 2010 and 2020. Dogs were aged from 7 weeks to 14 years (median 5 months), with 73/90 (81%) less than 6 months and 1.7 times as many males as females. The disease became more common over the study period. Most cases (86%) were seen between March and July. Cerebrospinal fluid (CSF) was obtained from the cisterna magna in 77 dogs, the lumbar cistern in f5, and both sites in 3. Nucleated cell counts for 84 specimens ranged from 1 to 146 150 cells μL−1 (median 4500). Percentage eosinophils varied from 0 to 98% (median 83%). When both cisternal and lumbar CSF were collected, inflammation was more severe caudally. Seventy-three CSF specimens were subjected to enzyme-linked immunosorbent assay (ELISA) testing for antibodies against A. cantonensis; 61 (84%) tested positive, titres ranging from <100 to ⩾12 800 (median 1600). Sixty-one CSF specimens were subjected to real-time quantitative polymerase chain reaction (qPCR) testing using a new protocol targeting a bioinformatically-informed repetitive genetic target; 53/61 samples (87%) tested positive, CT values ranging from 23.4 to 39.5 (median 30.0). For 57 dogs, it was possible to compare CSF ELISA serology and qPCR. ELISA and qPCR were both positive in 40 dogs, in 5 dogs the ELISA was positive while the qPCR was negative, in 9 dogs the qPCR was positive but the ELISA was negative, while in 3 dogs both the ELISA and qPCR were negative. NA is an emerging infectious disease of dogs in Sydney, Australia.

Effective community engagement in T3–T4 research is widespread, however, similar stakeholder involvement is missing in T1–T2 research. As part of an effort to embed community stakeholders in T1–T2 research, an academic community partnered team conducted discussion groups with researchers to assess perspectives on (1) barriers/challenges to including community stakeholders in basic science, (2) skills/training required for stakeholders and researchers, and (3) potential benefits of these activities. Engaging community in basic science research was perceived as challenging but with exciting potential to incorporate “real-life” community health priorities into basic research, resulting in a new full-spectrum translational research model.