Long-term shifts in water quality show scale-dependent bioindicator responses across Russia – Insights from 40 year-long bioindicator monitoring program

Scale-related assessment strategies are important contributions to successful ecosystem management. With varying impact of environmental drivers from local to regional scales, a focal task is to understand scale-de- pendent responses when assessing the state of an ecosystem. In this study we use large-scale monitoring data, spanning 40 years and including four aquatic bioindicator groups (phytoplankton, zooplankton, periphyton, zoobenthos) to expose the long-term changes of water quality across Russia. We include four hierarchical spatial scales (region, basin, waterbody and observation point) to identify the relative importance of different spatio- temporal scales for the variation of each bioindicator and patterns of co-variation among the bioindicators at different hierarchical levels. We analysed the data with Hierarchical Modelling of Species Communities (HMSC), an approach that belongs to the framework of joint species distribution models. We performed a cross validation to reveal the predictive power of modelled bioindicator variation, partitioned explained variance among the fixed effects (waterbody type, and influence of human population density) and the random effects (spatial and spatio-temporal variation at the four hierarchical scales), and examined the co-variation among bioindicators at each spatio-temporal scale. We detected generally decreasing water quality across Russian freshwaters, yet with region and bioindicator specific trends. For all bioindicators, the dominating part of the variation was attributed the largest (region) and smallest (observation point) hierarchical scales, the region particularly important for benthic and the observation point for pelagic bioindicators. All bioindicators captured the same spatial variation in water quality at the smallest scale of observation point, with phytoplankton, zooplankton and periphyton being associated positively to each other and negatively to zoobenthos. However, at larger spatial scales and at spatio-temporal scales, the associations among the bioindicators became more complex, with phytoplankton and zooplankton showing opposite trends over time. Our study reveals the sensitivity of bioindicators to spatial and temporal scales. While delivering unidirectional robust water quality assessments at the local scale, bioindicator co-variation is more complex over larger geographic scales and over time.Peer reviewe

Water-Blown Polyurethane Foams Showing a Reversible Shape-Memory Effect

Water-blown polyurethane (PU) foams are of enormous technological interest as they are widely applied in various fields, i.e., consumer goods, medicine, automotive or aerospace industries. The discovery of the one-way shape-memory effect in PU foams provided a fresh impetus for extensive investigations on porous polymeric actuators over the past decades. High expansion ratios during the shape-recovery are of special interest when big volume changes are required, for example to fill an aneurysm during micro-invasive surgery or save space during transportation. However, the need to program the foams before each operation cycle could be a drawback impeding the entry of shape- memory polymeric (SMP) foams to our daily life. Here, we showed that a reversible shape-memory effect (rSME) is achievable for polyurethane water- blown semicrystalline foams. We selected commercially available crystallizable poly(ε-caprolactone)-diols of different molecular weight for foams synthesis, followed by investigations of morphology, thermal, thermomechanical and shape- memory properties of obtained compositions. Densities of synthesized foams varied from 110 to 180 kg∙m−3, while peak melting temperatures were composition-dependent and changed from 36 to 47 °C, while the melting temperature interval was around 15 K. All semicrystalline foams exhibited excellent one-way SME with shape-fixity ratios slightly above 100% and shape- recovery ratios from the second cycle of 99%. The composition with broad distribution of molecular weights of poly(ε-caprolactone)-diols exhibited an rSME of about 12% upon cyclic heating and cooling from Tlow = 10 °C and Thigh = 47 °C. We anticipate that our experimental study opens a field of systematic investigation of rSMEs in porous polymeric materials on macro and micro scale and extend the application of water-blown polyurethane foams to, e.g., protective covers with zero thermal expansion or even cushions adjustable to a certain body shape. View Full-Tex

BK virus-induced nephritis and cystitis after matched unrelated donor stem cell transplantation: A case report

Currently, there is no standard therapy for a BK virus infection of the urogenital tract in immunocompromised, stem cell transplanted patients, so that early diagnosis and introduction of supportive measures have the highest response rates to date. © 2020 The Authors. Clinical Case Reports published by John Wiley & Sons Ltd

Investigation of a Second Exhaust Valve Lift to Improve Combustion in a Methane - Diesel Dual-Fuel Engine

In recent years, the utilization of dual-fuel combustion has gained popularity in order to improve engine efficiency and emissions. With its high knock resistance, methane allows operation in high compression diesel engines with lower risk of knocking. With the use of diesel fuel as an ignition source, it is possible to exploit the advantages of lean combustion without facing problems to provide the high amount of ignition energy necessary to burn methane under such operating conditions. Another advantage is the variety of sources from which the primary fuel can be obtained. In addition to fossil sources, methane can also be produced from biomass or electrical energy. As the rate of substitution of diesel by methane increases, the trade- off between nitrogen oxide and soot is mitigated. However, emissions of carbon monoxide and unburned methane increase. Since carbon monoxide is toxic and methane has 25 times the global warming potential of carbon dioxide, these emission components pose a problem. Because of the stability of the molecule, methane catalysts require an exhaust gas temperature of over 500 °C in order to work effectively. In this work, the effect of conventional cooled external exhaust gas recirculation (EGR) and additional hot internal EGR are investigated for different substitution rates in a nonroad tractor engine converted to dual-fuel operation. The internal EGR rate is controlled by a variable second exhaust valve lift during the intake stroke – an approach which promises to benefit dual-fuel engines by increasing the in-cylinder gas temperature, thus favoring more complete combustion. A simulation model of the engine is used to determine the internal EGR rates and in-cylinder temperatures based on the experimental data. When internal EGR is used in combination with external EGR, the resulting emissions show additional reductions in nitrogen oxide (up to -51 %), carbon monoxide (up to -18 %) and methane (up to -28 %) with increasing internal EGR, while still maintaining low soot levels due to the substitution of diesel fuel for methane

GAMER-MRIL identifies Disability-Related Brain Changes in Multiple Sclerosis

Objective: Identifying disability-related brain changes is important for multiple sclerosis (MS) patients. Currently, there is no clear understanding about which pathological features drive disability in single MS patients. In this work, we propose a novel comprehensive approach, GAMER-MRIL, leveraging whole-brain quantitative MRI (qMRI), convolutional neural network (CNN), and an interpretability method from classifying MS patients with severe disability to investigating relevant pathological brain changes. Methods: One-hundred-sixty-six MS patients underwent 3T MRI acquisitions. qMRI informative of microstructural brain properties was reconstructed, including quantitative T1 (qT1), myelin water fraction (MWF), and neurite density index (NDI). To fully utilize the qMRI, GAMER-MRIL extended a gated-attention-based CNN (GAMER-MRI), which was developed to select patch-based qMRI important for a given task/question, to the whole-brain image. To find out disability-related brain regions, GAMER-MRIL modified a structure-aware interpretability method, Layer-wise Relevance Propagation (LRP), to incorporate qMRI. Results: The test performance was AUC=0.885. qT1 was the most sensitive measure related to disability, followed by NDI. The proposed LRP approach obtained more specifically relevant regions than other interpretability methods, including the saliency map, the integrated gradients, and the original LRP. The relevant regions included the corticospinal tract, where average qT1 and NDI significantly correlated with patients' disability scores ($\rho$=-0.37 and 0.44). Conclusion: These results demonstrated that GAMER-MRIL can classify patients with severe disability using qMRI and subsequently identify brain regions potentially important to the integrity of the mobile function. Significance: GAMER-MRIL holds promise for developing biomarkers and increasing clinicians' trust in NN

Early mortality and loss to follow-up in HIV-infected children starting antiretroviral therapy in Southern Africa.

BACKGROUND: Many HIV-infected children in Southern Africa have been started on antiretroviral therapy (ART), but loss to follow up (LTFU) can be substantial. We analyzed mortality in children retained in care and in all children starting ART, taking LTFU into account. PATIENTS AND METHODS: Children who started ART before the age of 16 years in 10 ART programs in South Africa, Malawi, Mozambique, and Zimbabwe were included. Risk factors for death in the first year of ART were identified in Weibull models. A meta-analytic approach was used to estimate cumulative mortality at 1 year. RESULTS: Eight thousand two hundred twenty-five children (median age 49 months, median CD4 cell percent 11.6%) were included; 391 (4.8%) died and 523 (7.0%) were LTFU in the first year. Mortality at 1 year was 4.5% [95% confidence interval (CI): 2.8% to 7.4%] in children remaining in care, but 8.7% (5.4% to 12.1%) at the program level, after taking mortality in children and LTFU into account. Factors associated with mortality in children remaining in care included age [adjusted hazard ratio (HR) 0.37; 95% CI: 0.25 to 0.54 comparing > or =120 months with <18 months], CD4 cell percent (HR: 0.56; 95% CI: 0.39 to 0.78 comparing > or =20% with <10%), and clinical stage (HR: 0.12; 95% CI: 0.03 to 0.45 comparing World Health Organization stage I with III/IV). CONCLUSIONS: In children starting ART and remaining in care in Southern Africa mortality at 1 year is <5% but almost twice as high at the program level, when taking LTFU into account. Age, CD4 percentage, and clinical stage are important predictors of mortality at the individual level

Model-Informed Machine Learning for Multi-component T2 Relaxometry

Recovering the T2 distribution from multi-echo T2 magnetic resonance (MR) signals is challenging but has high potential as it provides biomarkers characterizing the tissue micro-structure, such as the myelin water fraction (MWF). In this work, we propose to combine machine learning and aspects of parametric (fitting from the MRI signal using biophysical models) and non-parametric (model-free fitting of the T2 distribution from the signal) approaches to T2 relaxometry in brain tissue by using a multi-layer perceptron (MLP) for the distribution reconstruction. For training our network, we construct an extensive synthetic dataset derived from biophysical models in order to constrain the outputs with \textit{a priori} knowledge of \textit{in vivo} distributions. The proposed approach, called Model-Informed Machine Learning (MIML), takes as input the MR signal and directly outputs the associated T2 distribution. We evaluate MIML in comparison to non-parametric and parametric approaches on synthetic data, an ex vivo scan, and high-resolution scans of healthy subjects and a subject with Multiple Sclerosis. In synthetic data, MIML provides more accurate and noise-robust distributions. In real data, MWF maps derived from MIML exhibit the greatest conformity to anatomical scans, have the highest correlation to a histological map of myelin volume, and the best unambiguous lesion visualization and localization, with superior contrast between lesions and normal appearing tissue. In whole-brain analysis, MIML is 22 to 4980 times faster than non-parametric and parametric methods, respectively.Comment: Preprint submitted to Medical Image Analysis (July 14, 2020

Adjusting Mortality for Loss to Follow-Up: Analysis of Five ART Programmes in Sub-Saharan Africa

Evaluation of antiretroviral treatment (ART) programmes in sub-Saharan Africa is difficult because many patients are lost to follow-up. Outcomes in these patients are generally unknown but studies tracing patients have shown mortality to be high. We adjusted programme-level mortality in the first year of antiretroviral treatment (ART) for excess mortality in patients lost to follow-up