Magnetic resonance relaxation measurements using open-geometry sensors to assess the clog state of constructed wetlands

Monitoring the T1 relaxation of wetland clog matter has previously been identified as a gauge of its clogged state [1]. Magnetic resonance MR)sensors explored in other work have typically been of a bore-whole configuration, which may not be ideal in a wetland environment where the sensitive volume of the sensor may become physically clogged and therefore inoperable. This work investigates two open-geometry sensor designs and a short study is presented to determine the suitability of the sensors for monitoring the clog state of wetlands. It was shown that a bar magnet geometry has a higher stray field than that of the four magnet surface sensor also presented, leading to a prohibitively short T2eff. This means that the T1 values collected are notably shorter and not useful for distinguishing between clog state for the single magnet sensor. By contrast the four magnet surface sensor has a longer T2eff, making it more suitable for T1 measurements; where T1 = 915 ± 212 ms for a very thinly clogged sample, and T1 = 127 ± 27 ms for a heavily clogged sample. This offers a clearly resolvable difference in the T1 values allowing the clogging state to be easily determined and making this sensor the desirable choice for long-term embedding

Calculating the sample size required for developing a clinical prediction model.

Clinical prediction models aim to predict outcomes in individuals, to inform diagnosis or prognosis in healthcare. Hundreds of prediction models are published in the medical literature each year, yet many are developed using a dataset that is too small for the total number of participants or outcome events. This leads to inaccurate predictions and consequently incorrect healthcare decisions for some individuals. In this article, the authors provide guidance on how to calculate the sample size required to develop a clinical prediction model

Toward Human-Carnivore Coexistence: Understanding Tolerance for Tigers in Bangladesh

Fostering local community tolerance for endangered carnivores, such as tigers (Panthera tigris), is a core component of many conservation strategies. Identification of antecedents of tolerance will facilitate the development of effective tolerance-building conservation action and secure local community support for, and involvement in, conservation initiatives. We use a stated preference approach for measuring tolerance, based on the ‘Wildlife Stakeholder Acceptance Capacity’ concept, to explore villagers’ tolerance levels for tigers in the Bangladesh Sundarbans, an area where, at the time of the research, human-tiger conflict was severe. We apply structural equation modeling to test an a priori defined theoretical model of tolerance and identify the experiential and psychological basis of tolerance in this community. Our results indicate that beliefs about tigers and about the perceived current tiger population trend are predictors of tolerance for tigers. Positive beliefs about tigers and a belief that the tiger population is not currently increasing are both associated with greater stated tolerance for the species. Contrary to commonly-held notions, negative experiences with tigers do not directly affect tolerance levels; instead, their effect is mediated by villagers’ beliefs about tigers and risk perceptions concerning human-tiger conflict incidents. These findings highlight a need to explore and understand the socio-psychological factors that encourage tolerance towards endangered species. Our research also demonstrates the applicability of this approach to tolerance research to a wide range of socio-economic and cultural contexts and reveals its capacity to enhance carnivore conservation efforts worldwide

Cooperative secretions facilitate host range expansion in bacteria

The majority of emergent human pathogens are zoonotic in origin, that is, they can transmit to humans from other animals. Understanding the factors underlying the evolution of pathogen host range is therefore of critical importance in protecting human health. There are two main evolutionary routes to generalism: organisms can tolerate multiple environments or they can modify their environments to forms to which they are adapted. Here we use a combination of theory and a phylogenetic comparative analysis of 191 pathogenic bacterial species to show that bacteria use cooperative secretions that modify their environment to extend their host range and infect multiple host species. Our results suggest that cooperative secretions are key determinants of host range in bacteria, and that monitoring for the acquisition of secreted proteins by horizontal gene transfer can help predict emerging zoonoses

Amelioration of bleomycin-induced lung fibrosis in hamsters by dietary supplementation with taurine and niacin: biochemical mechanisms.

Interstitial pulmonary fibrosis induced by intratracheal instillation of bleomycin (BL) involves an excess production of reactive oxygen species, unavailability of adequate levels of NAD and ATP to repair the injured pulmonary epithelium, and an overexuberant lung collagen reactivity followed by deposition of highly cross-linked mature collagen fibrils resistant to enzymatic degradation. In the present study, we have demonstrated that dietary supplementation with taurine and niacin offered almost complete protection against the lung fibrosis in a multidose BL hamster model. The mechanisms for the protective effect of taurine and niacin are multifaceted. These include the ability of taurine to scavenge HOCl and stabilize the biomembrane; niacin's ability to replenish the BL-induced depletion of NAD and ATP; and the combined effect of taurine and niacin to suppress all aspects of BL-induced increases in the lung collagen reactivity, a hallmark of interstitial pulmonary fibrosis. It was concluded from the data presented at this Conference that the combined treatment with taurine and niacin, which offers a multipronged approach, will have great therapeutic potential in the intervention of the development of chemically induced interstitial lung fibrosis in animals and humans

Transparent reporting of multivariable prediction models developed or validated using clustered data (TRIPOD-Cluster): explanation and elaboration.

The TRIPOD-Cluster (transparent reporting of multivariable prediction models developed or validated using clustered data) statement comprises a 19 item checklist, which aims to improve the reporting of studies developing or validating a prediction model in clustered data, such as individual participant data meta-analyses (clustering by study) and electronic health records (clustering by practice or hospital). This explanation and elaboration document describes the rationale; clarifies the meaning of each item; and discusses why transparent reporting is important, with a view to assessing risk of bias and clinical usefulness of the prediction model. Each checklist item of the TRIPOD-Cluster statement is explained in detail and accompanied by published examples of good reporting. The document also serves as a reference of factors to consider when designing, conducting, and analysing prediction model development or validation studies in clustered data. To aid the editorial process and help peer reviewers and, ultimately, readers and systematic reviewers of prediction model studies, authors are recommended to include a completed checklist in their submission

The CTLA-4 and PD-1/PD-L1 inhibitory pathways independently regulate host resistance to Plasmodium-induced acute immune pathology.

The balance between pro-inflammatory and regulatory immune responses in determining optimal T cell activation is vital for the successful resolution of microbial infections. This balance is maintained in part by the negative regulators of T cell activation, CTLA-4 and PD-1/PD-L, which dampen effector responses during chronic infections. However, their role in acute infections, such as malaria, remains less clear. In this study, we determined the contribution of CTLA-4 and PD-1/PD-L to the regulation of T cell responses during Plasmodium berghei ANKA (PbA)-induced experimental cerebral malaria (ECM) in susceptible (C57BL/6) and resistant (BALB/c) mice. We found that the expression of CTLA-4 and PD-1 on T cells correlates with the extent of pro-inflammatory responses induced during PbA infection, being higher in C57BL/6 than in BALB/c mice. Thus, ECM develops despite high levels of expression of these inhibitory receptors. However, antibody-mediated blockade of either the CTLA-4 or PD-1/PD-L1, but not the PD-1/PD-L2, pathways during PbA-infection in ECM-resistant BALB/c mice resulted in higher levels of T cell activation, enhanced IFN-γ production, increased intravascular arrest of both parasitised erythrocytes and CD8(+) T cells to the brain, and augmented incidence of ECM. Thus, in ECM-resistant BALB/c mice, CTLA-4 and PD-1/PD-L1 represent essential, independent and non-redundant pathways for maintaining T cell homeostasis during a virulent malaria infection. Moreover, neutralisation of IFN-γ or depletion of CD8(+) T cells during PbA infection was shown to reverse the pathologic effects of regulatory pathway blockade, highlighting that the aetiology of ECM in the BALB/c mice is similar to that in C57BL/6 mice. In summary, our results underscore the differential and complex regulation that governs immune responses to malaria parasites

Minimum sample size for external validation of a clinical prediction model with a binary outcome

In prediction model research, external validation is needed to examine an existing model's performance using data independent to that for model development. Current external validation studies often suffer from small sample sizes and consequently imprecise predictive performance estimates. To address this, we propose how to determine the minimum sample size needed for a new external validation study of a prediction model for a binary outcome. Our calculations aim to precisely estimate calibration (Observed/Expected and calibration slope), discrimination (C-statistic), and clinical utility (net benefit). For each measure, we propose closed-form and iterative solutions for calculating the minimum sample size required. These require specifying: (i) target SEs (confidence interval widths) for each estimate of interest, (ii) the anticipated outcome event proportion in the validation population, (iii) the prediction model's anticipated (mis)calibration and variance of linear predictor values in the validation population, and (iv) potential risk thresholds for clinical decision-making. The calculations can also be used to inform whether the sample size of an existing (already collected) dataset is adequate for external validation. We illustrate our proposal for external validation of a prediction model for mechanical heart valve failure with an expected outcome event proportion of 0.018. Calculations suggest at least 9835 participants (177 events) are required to precisely estimate the calibration and discrimination measures, with this number driven by the calibration slope criterion, which we anticipate will often be the case. Also, 6443 participants (116 events) are required to precisely estimate net benefit at a risk threshold of 8%. Software code is provided.</p