Phylogenetic affiliation and quantification of psychrophilic sulfate-reducing isolates in marine Arctic sediments

Thirteen psychrophilic sulfate-reducing isolates from two permanently cold fjords of the Arctic island Spitsbergen (Hornsund and Storfjord) were phylogenetically analyzed. They all belonged to the delta subclass of Proteobacteria. and were widely distributed within this group, indicating that psychrophily is a polyphyletic property. A new 16S rRNA-directed oligonucleotide probe was designed against the largest coherent cluster of these isolates. The new probe, as well as a set of available probes,was applied in rRNA slot blot hybridization to investigate the composition of the sulfate-reducing :bacterial community in the sediments. rRNA related to the new cluster of incompletely oxidizing, psychrophilic isolates made up 1.4 to 20.9% of eubacterial rRNA at Storfjord and 0.6 to 3.5% of eubacterial rRNA at Hornsund. This group was the second-most-abundant group of sulfate reducers at these sites. Denaturing gradient gel electrophoresis and hybridization analysis showed bands identical to those produced by our isolates. The data indicate that the psychrophilic isolates are quantitatively important in Svalbard sediments

Estimates of tree root water uptake from soil moisture profile dynamics

Root water uptake (RWU), as an important process in the terrestrial water cycle, can help us to better understand the interactions in the soil–plant–atmosphere continuum. We conducted a field study monitoring soil moisture profiles in the rhizosphere of beech trees at two sites with different soil conditions. We present an algorithm to infer RWU from step-shaped, diurnal changes in soil moisture. While this approach is a feasible, easily implemented method for moderately moist and homogeneously textured soil conditions, limitations were identified during drier states and for more heterogeneous soil settings. A comparison with the time series of xylem sap velocity underlines that RWU and sap flow (SF) are complementary measures in the transpiration process. The high correlation between the SF time series of the two sites, but lower correlation between the RWU time series, suggests that soil characteristics affect RWU of the trees but not SF

Focused Bayesian Prediction

We propose a new method for conducting Bayesian prediction that delivers accurate predictions without correctly specifying the unknown true data generating process. A prior is defined over a class of plausible predictive models. After observing data, we update the prior to a posterior over these models, via a criterion that captures a user-specified measure of predictive accuracy. Under regularity, this update yields posterior concentration onto the element of the predictive class that maximizes the expectation of the accuracy measure. In a series of simulation experiments and empirical examples we find notable gains in predictive accuracy relative to conventional likelihood-based prediction

Changes in socioeconomic determinants of health in a copper mine development area, northwestern Zambia

In 2011, an industrial copper mine was developed in northwestern Zambia. A health impact assessment was conducted to anticipate and address potential health impacts. To monitor these impacts, three community-based surveys were conducted in the area (2011, 2015 and 2019). We analysed these data to determine how household socioeconomic indicators - considered determinants of health - have changed in the area over time. In mine-impacted communities, between 2011 (pre-construction) and 2019, significant changes were observed for: (i) average household size (-0.6 members); (ii) proportion of mothers that have not completed primary school (+20.4%); (iii) ownership of economic assets (e.g. phones +29.3%; televisions +15.6%); (iv) access to safe drinking water (+27.4%); and (v) improved housing structures (e.g. finished roof +58.6%). When comparing changes between 2015 and 2019 in impacted communities to nearby comparison communities, there was (i) an increased proportion of mothers that had not completed primary school in comparison communities vs. no change in impacted communities; and (ii) increased ownership of economic assets in impacted vs. comparison communities in 2019. This study found generally positive changes in the socioeconomic development of impacted compared to comparison communities, with the most pronounced improvements in the early phases of mine development

Two stages of parafoveal processing during reading: Evidence from a display change detection task

We used a display change detection paradigm (Slattery, Angele, & Rayner Human Perception and Performance, 37, 1924–1938 2011) to investigate whether display change detection uses orthographic regularity and whether detection is affected by the processing difficulty of the word preceding the boundary that triggers the display change. Subjects were significantly more sensitive to display changes when the change was from a nonwordlike preview than when the change was from a wordlike preview, but the preview benefit effect on the target word was not affected by whether the preview was wordlike or nonwordlike. Additionally, we did not find any influence of preboundary word frequency on display change detection performance. Our results suggest that display change detection and lexical processing do not use the same cognitive mechanisms. We propose that parafoveal processing takes place in two stages: an early, orthography-based, preattentional stage, and a late, attention-dependent lexical access stage

Binary Willshaw learning yields high synaptic capacity for long-term familiarity memory

We investigate from a computational perspective the efficiency of the Willshaw synaptic update rule in the context of familiarity discrimination, a binary-answer, memory-related task that has been linked through psychophysical experiments with modified neural activity patterns in the prefrontal and perirhinal cortex regions. Our motivation for recovering this well-known learning prescription is two-fold: first, the switch-like nature of the induced synaptic bonds, as there is evidence that biological synaptic transitions might occur in a discrete stepwise fashion. Second, the possibility that in the mammalian brain, unused, silent synapses might be pruned in the long-term. Besides the usual pattern and network capacities, we calculate the synaptic capacity of the model, a recently proposed measure where only the functional subset of synapses is taken into account. We find that in terms of network capacity, Willshaw learning is strongly affected by the pattern coding rates, which have to be kept fixed and very low at any time to achieve a non-zero capacity in the large network limit. The information carried per functional synapse, however, diverges and is comparable to that of the pattern association case, even for more realistic moderately low activity levels that are a function of network size.Comment: 20 pages, 4 figure

Uncertainty-aware deep learning methods for robust diabetic retinopathy classification

Automatic classification of diabetic retinopathy from retinal images has been increasingly studied using deep neural networks with impressive results. However, there is clinical need for estimating uncertainty in the classifications, a shortcoming of modern neural networks. Recently, approximate Bayesian neural networks (BNNs) have been proposed for this task, but previous studies have only considered the binary referable/non-referable diabetic retinopathy classification applied to benchmark datasets. We present novel results for 9 BNNs by systematically investigating a clinical dataset and 5-class classification scheme, together with benchmark datasets and binary classification scheme. Moreover, we derive a connection between entropy-based uncertainty measure and classifier risk, from which we develop a novel uncertainty measure. We observe that the previously proposed entropy-based uncertainty measure improves performance on the clinical dataset for the binary classification scheme, but not to such an extent as on the benchmark datasets. It improves performance in the clinical 5-class classification scheme for the benchmark datasets, but not for the clinical dataset. Our novel uncertainty measure generalizes to the clinical dataset and to one benchmark dataset. Our findings suggest that BNNs can be utilized for uncertainty estimation in classifying diabetic retinopathy on clinical data, though proper uncertainty measures are needed to optimize the desired performance measure. In addition, methods developed for benchmark datasets might not generalize to clinical datasets

Participating together: dialogic space for children and architects in the design process

© 2016 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis GroupTypically enmeshed in the ‘voice’ perspective within children’s participation debates, there are currently sporadic insights into designer–child creative dialogue. Drawing on the findings of a Leverhulme Trust-funded research project, this paper articulates moments of dialogue between architects and children in spatial design processes, whose spatial and symbolic qualities help to understand the interactions and meeting of cultures. Several authors have discussed the transformational potential for adults and children to ‘co-author’ identities in dialogical contexts. The paper builds on this body of research to suggest that design dialogue offers the space, literally and metaphorically, for children and architects to participate together. Identifying the qualities of the dialogic design space as potentially present in children’s and adults’ everyday cultures and interdependent relations, it is proposed that this dialogical framework might diversify architects’ and children’s roles in the design process and enrich practices and perceptions of design participation