Challenges in Columbia River Fisheries Conservation: A Response to Duda et al.

The salmonid fisheries of the Columbia River Basin (CRB) have enormous socioeconomic, cultural, and ecological importance to numerous diverse stakeholders (eg state, federal, tribal, nonprofit), and there are a wide array of opinions and perspectives on how these fisheries should be managed. Although we appreciate Duda et al.\u27s commentary, it offers only one perspective of many in this context. The objective of our paper (Hand et al. 2018) was to provide justification for “the importance of social–ecological perspectives when communicating conservation values and goals, and the role of independent science in guiding management policy and practice for salmonids in the CRB”. However, we did not intend to strictly advocate for a single course of action, and the available space within our paper\u27s Panel 1 limited us from engaging in a thorough ecological debate

Reproducibility of a peripheral quantitative computed tomography scan protocol to measure the material properties of the second metatarsal

Background: Peripheral quantitative computed tomography (pQCT) is an established technology that allows for the measurement of the material properties of bone. Alterations to bone architecture are associated with an increased risk of fracture. Further pQCT research is necessary to identify regions of interest that are prone to fracture risk in people with chronic diseases. The second metatarsal is a common site for the development of insufficiency fractures, and as such the aim of this study was to assess the reproducibility of a novel scanning protocol of the second metatarsal using pQCT. Methods. Eleven embalmed cadaveric leg specimens were scanned six times; three times with and without repositioning. Each foot was positioned on a custom-designed acrylic foot plate to permit unimpeded scans of the region of interest. Sixty-six scans were obtained at 15% (distal) and 50% (mid shaft) of the second metatarsal. Voxel size and scan speed were reduced to 0.40 mm and 25 mm.sec-1. The reference line was positioned at the most distal portion of the 2nd metatarsal. Repeated measurements of six key variables related to bone properties were subject to reproducibility testing. Data were log transformed and reproducibility of scans were assessed using intraclass correlation coefficients (ICC) and coefficients of variation (CV%). Results: Reproducibility of the measurements without repositioning were estimated as: trabecular area (ICC 0.95; CV% 2.4), trabecular density (ICC 0.98; CV% 3.0), Strength Strain Index (SSI) - distal (ICC 0.99; CV% 5.6), cortical area (ICC 1.0; CV% 1.5), cortical density (ICC 0.99; CV% 0.1), SSI - mid shaft (ICC 1.0; CV% 2.4). Reproducibility of the measurements after repositioning were estimated as: trabecular area (ICC 0.96; CV% 2.4), trabecular density (ICC 0.98; CV% 2.8), SSI - distal (ICC 1.0; CV% 3.5), cortical area (ICC 0.99; CV%2.4), cortical density (ICC 0.98; CV% 0.8), SSI - mid shaft (ICC 0.99; CV% 3.2). Conclusions: The scanning protocol generated excellent reproducibility for key bone properties measured at the distal and mid-shaft regions of the 2 nd metatarsal. This protocol extends the capabilities of pQCT to evaluate bone quality in people who may be at an increased risk of metatarsal insufficiency fractures

Identification, characterisation and expression analysis of natural killer receptor genes in Chlamydia pecorum infected koalas (Phascolarctos cinereus)

BACKGROUND: Koalas (Phascolarctos cinereus), an iconic Australian marsupial, are being heavily impacted by the spread of Chlamydia pecorum, an obligate intracellular bacterial pathogen. Koalas vary in their response to this pathogen, with some showing no symptoms, while others suffer severe symptoms leading to infertility, blindness or death. Little is known about the pathology of this disease and the immune response against it in this host. Studies have demonstrated that natural killer (NK) cells, key components of the innate immune system, are involved in the immune response to chlamydial infections in humans. These cells can directly lyse cells infected by intracellular pathogens and their ability to recognise these infected cells is mediated through NK receptors on their surface. These are encoded in two regions of the genome, the leukocyte receptor complex (LRC) and the natural killer complex (NKC). These two families evolve rapidly and different repertoires of genes, which have evolved by gene duplication, are seen in different species. METHODS: In this study we aimed to characterise genes belonging to the NK receptor clusters in the koala by searching available koala transcriptomes using a combination of search methods. We developed a qPCR assay to quantify relative expression of four genes, two encoded within the NK receptor cluster (CLEC1B, CLEC4E) and two known to play a role in NK response to Chalmydia in humans (NCR3, PRF1). RESULTS: We found that the NK receptor repertoire of the koala closely resembles that of the Tasmanian devil, with minimal genes in the NKC, but with lineage specific expansions in the LRC. Additional genes important for NK cell activity, NCR3 and PRF1, were also identified and characterised. In a preliminary study to investigate whether these genes are involved in the koala immune response to infection by its chlamydial pathogen, C. pecorum, we investigated the expression of four genes in koalas with active chlamydia infection, those with past infection and those without infection using qPCR. This analysis revealed that one of these four, CLEC4E, may be upregulated in response to chlamydia infection. CONCLUSION: We have characterised genes of the NKC and LRC in koalas and have discovered evidence that one of these genes may be upregulated in koalas with chlamydia, suggesting that these receptors may play a role in the immune response of koalas to chlamydia infection

Unplanned readmission rates, length of hospital stay, mortality, and medical costs of ten common medical conditions: a retrospective analysis of Hong Kong hospital data

<p>Abstract</p> <p>Background</p> <p>Studies on readmissions attributed to particular medical conditions, especially heart failure, have generally not addressed the factors associated with readmissions and the implications for health outcomes and costs. This study aimed to investigate the factors associated with 30-day unplanned readmission for 10 common conditions and to determine the cost implications.</p> <p>Methods</p> <p>This population-based retrospective cohort study included patients admitted to all public hospitals in Hong Kong in 2007. The sample consisted of 337,694 hospitalizations in Internal Medicine. The disease-specific risk-adjusted odd ratio (OR), length of stay (LOS), mortality and attributable medical costs for the year were examined for unplanned readmissions for 10 medical conditions, namely malignant neoplasms, heart diseases, cerebrovascular diseases, pneumonia, injury and poisoning, nephritis and nephrosis, diabetes mellitus, chronic liver disease and cirrhosis, septicaemia, and aortic aneurysm.</p> <p>Results</p> <p>The overall unplanned readmission rate was 16.7%. Chronic liver disease and cirrhosis had the highest OR (1.62, 95% confidence interval (CI) 1.39-1.87). Patients with cerebrovascular disease had the longest LOS, with mean acute and rehabilitation stays of 6.9 and 3.0 days, respectively. Malignant neoplasms had the highest mortality rate (30.8%) followed by aortic aneurysm and pneumonia. The attributed medical cost of readmission was highest for heart disease (US$3 199 418, 95$2 579 443-803 393).</p> <p>Conclusions</p> <p>Our findings showed variations in readmission rates and mortality for different medical conditions which may suggest differences in the quality of care provided for various medical conditions. In-hospital care, comprehensive discharge planning, and post-discharge community support for patients need to be reviewed to improve the quality of care and patient health outcomes.</p

Neuroimaging-Based Classification of PTSD Using Data-Driven Computational Approaches:A Multisite Big Data Study from the ENIGMA-PGC PTSD Consortium

BACKGROUND: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group.METHODS: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality.RESULTS: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60% test AUC for s-MRI, 59% for rs-fMRI and 56% for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75% AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance.CONCLUSION: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.</p

Clinical Utility of Random Anti–Tumor Necrosis Factor Drug–Level Testing and Measurement of Antidrug Antibodies on the Long-Term Treatment Response in Rheumatoid Arthritis

Objective: To investigate whether antidrug antibodies and/or drug non-trough levels predict the long-term treatment response in a large cohort of patients with rheumatoid arthritis (RA) treated with adalimumab or etanercept and to identify factors influencing antidrug antibody and drug levels to optimize future treatment decisions.  Methods: A total of 331 patients from an observational prospective cohort were selected (160 patients treated with adalimumab and 171 treated with etanercept). Antidrug antibody levels were measured by radioimmunoassay, and drug levels were measured by enzyme-linked immunosorbent assay in 835 serial serum samples obtained 3, 6, and 12 months after initiation of therapy. The association between antidrug antibodies and drug non-trough levels and the treatment response (change in the Disease Activity Score in 28 joints) was evaluated.  Results: Among patients who completed 12 months of followup, antidrug antibodies were detected in 24.8% of those receiving adalimumab (31 of 125) and in none of those receiving etanercept. At 3 months, antidrug antibody formation and low adalimumab levels were significant predictors of no response according to the European League Against Rheumatism (EULAR) criteria at 12 months (area under the receiver operating characteristic curve 0.71 [95% confidence interval (95% CI) 0.57, 0.85]). Antidrug antibody–positive patients received lower median dosages of methotrexate compared with antidrug antibody–negative patients (15 mg/week versus 20 mg/week; P = 0.01) and had a longer disease duration (14.0 versus 7.7 years; P = 0.03). The adalimumab level was the best predictor of change in the DAS28 at 12 months, after adjustment for confounders (regression coefficient 0.060 [95% CI 0.015, 0.10], P = 0.009). Etanercept levels were associated with the EULAR response at 12 months (regression coefficient 0.088 [95% CI 0.019, 0.16], P = 0.012); however, this difference was not significant after adjustment. A body mass index of ≥30 kg/m2 and poor adherence were associated with lower drug levels.  Conclusion: Pharmacologic testing in anti–tumor necrosis factor–treated patients is clinically useful even in the absence of trough levels. At 3 months, antidrug antibodies and low adalimumab levels are significant predictors of no response according to the EULAR criteria at 12 months

Dietary Pectin Increases Intestinal Crypt Stem Cell Survival following Radiation Injury

This research was performed as a project of the Intestinal Stem Cell Consortium, a collaborative research project funded by the National Institute of Diabetes and Digestive and Kidney Diseases (NIH U01 DK-085508 to CWH), and a grant from Oklahoma Center for the Advancement of Science and Technology to CWH.Gastrointestinal (GI) mucosal damage is a devastating adverse effect of radiation therapy. We have recently reported that expression of Dclk1, a Tuft cell and tumor stem cell (TSC) marker, 24h after high dose total-body gamma-IR (TBI) can be used as a surrogate marker for crypt survival. Dietary pectin has been demonstrated to possess chemopreventive properties, whereas its radioprotective property has not been studied. The aim of this study was to determine the effects of dietary pectin on ionizing radiation (IR)-induced intestinal stem cell (ISC) deletion, crypt and overall survival following lethal TBI. C57BL/6 mice received a 6% pectin diet and 0.5% pectin drinking water (pre-IR mice received pectin one week before TBI until death; post-IR mice received pectin after TBI until death). Animals were exposed to TBI (14 Gy) and euthanized at 24 and 84h post-IR to assess ISC deletion and crypt survival respectively. Animals were also subjected to overall survival studies following TBI. In pre-IR treatment group, we observed a three-fold increase in ISC/crypt survival, a two-fold increase in Dclk1+ stem cells, increased overall survival (median 10d vs. 7d), and increased expression of Dclk1, Msi1, Lgr5, Bmi1, and Notch1 (in small intestine) post-TBI in pectin treated mice compared to controls. We also observed increased survival of mice treated with pectin (post-IR) compared to controls. Dietary pectin is a radioprotective agent; prevents IR-induced deletion of potential reserve ISCs; facilitates crypt regeneration; and ultimately promotes overall survival. Given the anti-cancer activity of pectin, our data support a potential role for dietary pectin as an agent that can be administered to patients receiving radiation therapy to protect against radiation-induces mucositis.Yeshttp://www.plosone.org/static/editorial#pee

Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies

Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner’s ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person’s own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships