bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evaluating the implementation fidelity of a community-based intervention to promote hearing conservation among farm and rural youth
Authors: McCullagh Marjorie , Cohen Michael A , Koval Madison , Haberkorn Elizabeth , Wood Michelle 
Published: March 2019

Abstract
Noise-induced hearing loss is common among farm and rural youth. There is a lack of programs preparing this high-risk population to use hearing conservation strategies. Safety Days are 1-day workshops that teach 100,000 rural children safe farm practices in 400 sites across the USA annually. As part of a larger study for fourth graders, a hearing conservation lesson was delivered at 25 sites nationwide. However, the fidelity to the study-designed curriculum and quality of instruction was unknown. We examined whether community volunteers are capable of delivering, with adequate fidelity and quality, a planned intervention designed to increase use of hearing conservation strategies among farm and rural youth. We invited instructors (n = 22) to deliver a health lesson to fourth graders and provide lesson recordings. We developed instruments rating (i) fidelity of the teaching to the curriculum and (ii) quality of instruction. Initial instruments were modified based on the review for clarity, relevance, and conceptual fit; inter-rater reliabilities were .90 (content fidelity) and 1.00 (quality of instruction). Of seven recordings reviewed, mean fidelity to the planned curriculum was 1.51 (max score = 2), with nearly four fifths of lesson items fully addressed. Overall mean of rating of quality of instruction was 1.97 (out of 2). Fidelity to both content and quality of instruction was high. Results provide a model and support for use of community volunteers (with no special educational or experiential background) and fidelity testing as cost-effective strategies to provide sustainable programming for hearing conservation among farm and rural youth.
Valid and powerful group statistics for decoding accuracy: Information Prevalence Inference using the i-th order statistic (i-test)
Authors: Hirose Satoshi 
Published: March 2019

AbstractIn fMRI decoding studies using pattern classification, a “second-level” group statistical test is typically performed after “first-level” decoding analyses for individual participants. Neuroscientists often test the mean decoding accuracy across participants against the chance-level accuracy (e.g., one-sample Student t-test) to verify whether brain activation includes information about the label (i.e., cognitive content). However, Allefeld et al., (2016) notified that positive results for such tests only indicate that “there are some people in the population whose fMRI data carry information about the experimental condition.” Thus, such tests cannot provide inferences about the trend of the majority. They proposed an alternative method, in which prevalence inference is implemented. In this study, I extended their method and propose statistical test “information prevalence inference using the i-th order statistic (i-test)”. Compared with the method proposed in the previous study, i-test has high statistical power to provide an inference regarding major population trends. In i-test, the i-th lowest sample decoding accuracy (the i-th order statistic) is compared to the null distribution to test whether the proportion of the higher-than-chance decoding accuracy in the population (information prevalence) is higher than the threshold. Thus, a significant result of the i-test can be interpreted as “the majority of the population has information about the label in the brain.” Numerical calculation identified its high statistical power. Also, theoretical detail is provided, and the use of this method in an fMRI decoding study is demonstrated.Allefeld, C., Görgen, K., Haynes, J.D., 2016. Valid population inference for information-based imaging: From the second-level t-test to prevalence inference. Neuroimage.
High-throughput quantitative detection of basal autophagy and autophagic flux using image cytometry
Authors: SenthilKumar Gopika , Skiba Justin H. , Kimple Randall J. 
Published: March 2019

AbstractQuantitative assessment of changes in macro-autophagy is often performed through manual quantification of the number of LC3B foci in immunofluorescence microscopy images. This method is highly laborious, subject to image-field selection and foci-counting bias, and is not sensitive for analyzing changes in basal autophagy. Alternative methods such as flow cytometry and transmission electron microscopy require highly specialized, expensive instruments and time-consuming sample preparation. Immunoblots are prone to exposure-related variations and noise that prevent accurate quantification. We report a high-throughput, inexpensive, reliable, and objective method for studying basal level and flux changes in late-stage autophagy using image cytometry and acridine orange staining.Methods summaryA high-throughput, inexpensive, reliable, and objective method for studying both basal autophagy and autophagic flux is reported. This approach uses acridine orange staining of late-stage autophagy and image cytometry to quantify autophagy.
Lateral pressure equalisation as a principle for designing support surfaces to prevent deep tissue pressure ulcers
Authors: Boyle Colin J. , Carpanen Diagarajen , Pandelani Thanyani , Higgins Claire A. , Masen Marc A. , Masouros Spyros D. 
Published: March 2019

AbstractWhen immobile or neuropathic patients are supported by beds or chairs, their soft tissues undergo deformations that can cause pressure ulcers. Current support surfaces that redistribute under-body pressures at vulnerable body sites have not succeeded in reducing pressure ulcer prevalence. Here we show that adding a supporting lateral pressure can counter-act the deformations induced by under-body pressure, and that this ‘pressure equalisation’ approach is a more effective way to reduce ulcer-inducing deformations than current approaches based on redistributing under-body pressure.A finite element model of the seated pelvis predicts that applying a lateral pressure to the soft tissue reduces peak von Mises stress in the deep tissue by a factor of 2.4 relative to a standard cushion — a greater effect than that achieved by using a more conformable cushion. The ratio of peak lateral pressure to peak under-body pressure was shown to regulate deep tissue stress better than under-body pressure alone. By optimising the magnitude and position of lateral pressure, tissue deformations can be reduced to that induced when suspended in a fluid.Our results explain the lack of efficacy in current support surfaces, and suggest a new approach to designing and evaluating support surfaces: ensuring sufficient lateral pressure is applied to counter-act under-body pressure.
Cell-type and region-specific nucleus accumbens AMPAR plasticity associated with morphine reward, reinstatement, and spontaneous withdrawal
Authors: Madayag Aric C. , Gomez Devan , Anderson Eden M. , Ingebretson Anna E. , Thomas Mark J. , Hearing Matthew 
Published: March 2019

ABSTRACTDespite evidence that morphine-related pathologies reflect adaptations in NAc glutamate signaling, substantial gaps in basic information remain. The current study examines the impact of non-contingent acute, repeated, and withdrawal-inducing morphine dosing regimens on glutamate transmission in D1- or D2-MSNs in the NAcSh and NAcC sub-regions in hopes of identifying excitatory plasticity that may contribute to differing facets of opioid addiction-related behavior. Three hours following an acute morphine injection (10 mg/kg), average miniature excitatory postsynaptic current (mEPSC) amplitude mediated by AMPA-type glutamate receptors was increased at D1-MSNs in the both the shell and core regions, whereas only the frequency of events was elevated at D2-MSNs in the shell. In contrast, somatic withdrawal induced by escalating dose of repeated morphine twice per day (20, 40, 60, 80, 100mg/kg) only enhanced mEPSC frequency at D2-MSNs in the shell 24 hrs following the final drug exposure. Further, drug re-exposure 10-14 days following a preference-inducing regimen of morphine produced a rapid and enduring endocytosis of GluA2-containing AMPARs at D1-MSNs in the shell, that when blocked by an intra-NAc shell infusion of the Tat-GluA23Y peptide, increased reinstatement of morphine place preference – a phenomenon distinctly different than effects previously found with cocaine. The present study is the first to directly identify unique circuit specific adaptations in NAc glutamate synaptic transmission associated with morphine-related acute reward and somatic withdrawal as well as post-abstinence short-term plasticity. While differing classes of abused drugs (i.e., psychostimulants and opioids) produce seemingly similar bidirectional plasticity in the NAc following exposure to relapse-linked stimuli, our findings indicate this plasticity has distinct behavioral consequences.Compliance with Ethical StandardsThe authors have no conflicts of interest to disclose. All authors have given their consent for manuscript submission. The research in the current study used mice single- or group-housed on a 12 h light/dark cycle with food and water available ad libitum with experiments run during the light portion. All experiments were approved by the University of Minnesota and Marquette University Institutional Animal Care and Use Committee. The following funding sources made the study possible: National Institute for Neurological Disorders and Stroke (P30 NS062158); National Institute on Drug Abuse grant K99 DA038706 (to M.H.), R00DA038706 (M.H.), R00DA038706-04S1 (A.C.M), R01DA019666 (M.J.T.), K02DA035459 (M.J.T.) and T32 DA007234 (A.E.I.).
ezTrack: An open-source video analysis pipeline for the investigation of animal behavior
Authors: Pennington Zachary T. , Dong Zhe , Bowler Regina , Feng Yu , Vetere Lauren M , Shuman Tristan , Cai Denise J. 
Published: March 2019

AbstractTracking small animal behavior by video is one of the most common tasks in the fields of neuroscience and psychology. Although commercial software exists for the execution of this task, commercial software often presents enormous cost to the researcher, and can also entail purchasing specific hardware setups that are not only expensive but lack adaptability. Moreover, the inaccessibility of the code underlying this software renders them inflexible. Alternatively, available open source options frequently require extensive model training and can be challenging for those inexperienced with programming. Here we present an open source and platform independent set of behavior analysis pipelines using interactive Python (iPython/Jupyter Notebook) that researchers with no prior programming experience can use. Two modules are described. One module can be used for the positional analysis of an individual animal across a session (i.e., location tracking), amenable to a wide range of behavioral tasks including conditioned place preference, water maze, light-dark box, open field, and elevated plus maze, to name but a few. A second module is described for the analysis of conditioned freezing behavior. For both modules, a range of interactive plots and visualizations are available to confirm that chosen parameters produce results that conform to the user’s approval. In addition, batch processing tools for the fast analysis of multiple videos is provided, and frame-by-frame output makes aligning the data with neural recording data simple. Lastly, options for cropping video frames to mitigate the influence of fiberoptic/electrophysiology cables, analyzing specified portions of time in a video, and defining regions of interest, can be implemented with ease.
Common DNA sequence variation influences 3-dimensional conformation of the human genome
Authors: Gorkin David U. , Qiu Yunjiang , Hu Ming , Fletez-Brant Kipper , Liu Tristin , Schmitt Anthony D. , Noor Amina , Chiou Joshua , Gaulton Kyle J , Sebat Jonathan , Li Yun , Hansen Kasper D. , Ren Bing 
Published: March 2019

ABSTRACTThe 3-dimensional (3D) conformation of chromatin inside the nucleus is integral to a variety of nuclear processes including transcriptional regulation, DNA replication, and DNA damage repair. Aberrations in 3D chromatin conformation have been implicated in developmental abnormalities and cancer. Despite the importance of 3D chromatin conformation to cellular function and human health, little is known about how 3D chromatin conformation varies in the human population, or whether DNA sequence variation between individuals influences 3D chromatin conformation. To address these questions, we performed Hi-C on Lymphoblastoid Cell Lines (LCLs) from 20 individuals. We identified thousands of regions across the genome where 3D chromatin conformation varies between individuals and found that this conformational variation is often accompanied by variation in gene expression, histone modifications, and transcription factor (TF) binding. Moreover, we found that DNA sequence variation influences several features of 3D chromatin conformation including loop strength, contact insulation, contact directionality and density of localciscontacts. We mapped hundreds of Quantitative Trait Loci (QTLs) associated with 3D chromatin features and found evidence that some of these same variants are associated at modest levels with other molecular phenotypes as well as complex disease risk. Our results demonstrate that common DNA sequence variants can influence 3D chromatin conformation, pointing to a more pervasive role for 3D chromatin conformation in human phenotypic variation than previously recognized.
FIND: Identifying Functionally and Structurally Important Features in Protein Sequences with Deep Neural Networks
Authors: Murali Ranjani , Hemp James , Orphan Victoria , Bisk Yonatan 
Published: March 2019

AbstractThe ability to correctly predict the functional role of proteins from their amino acid sequences would significantly advance biological studies at the molecular level by improving our ability to understand the biochemical capability of biological organisms from their genomic sequence. Existing methods that are geared towards protein function prediction or annotation mostly use alignment-based approaches and probabilistic models such as Hidden-Markov Models. In this work we introduce a deep learning architecture (FunctionIdentification withNeuralDescriptions orFIND) which performs protein annotation from primary sequence. The accuracy of our methods matches state of the art techniques, such as protein classifiers based on Hidden Markov Models. Further, our approach allows for model introspection via a neural attention mechanism, which weights parts of the amino acid sequence proportionally to their relevance for functional assignment. In this way, the attention weights automatically uncover structurally and functionally relevant features of the classified protein and find novel functional motifs in previously uncharacterized proteins. While this model is applicable to any database of proteins, we chose to apply this model to superfamilies of homologous proteins, with the aim of extracting features inherent to divergent protein families within a larger superfamily. This provided insight into the functional diversification of an enzyme superfamily and its adaptation to different physiological contexts. We tested our approach on three families (nitrogenases, cytochromebd-type oxygen reductases and heme-copper oxygen reductases) and present a detailed analysis of the sequence characteristics identified in previously characterized proteins in the heme-copper oxygen reductase (HCO) superfamily. These are correlated with their catalytic relevance and evolutionary history. FIND was then applied to discover features in previously uncharacterized members of the HCO superfamily, providing insight into their unique sequence features. This modeling approach demonstrates the power of neural networks to recognize patterns in large datasets and can be utilized to discover biochemically and structurally important features in proteins from their amino acid sequences.Author summary
Deep learning-based imaging classification identified cingulate island sign in dementia with Lewy bodies
Authors: Iizuka Tomomichi , Fukasawa Makoto , Kameyama Masashi 
Published: March 2019

abstractThe differentiation of dementia with Lewy bodies (DLB) from Alzheimer’s disease (AD) using brain perfusion single photon emission tomography is important but has been a challenge because these conditions have common features. The cingulate island sign (CIS) is the most recently identified specific feature of DLB for a differential diagnosis. The present study aimed to examine the usefulness of deep learning-based imaging classification for the diagnoses of DLB and AD. We also investigated whether CIS was focused by the deep convolutional neural network (CNN) during differentiation.Brain perfusion single photon emission tomography images were acquired from 80 patients each with DLB and with AD and 80 individuals with normal cognition (NL). The CNN was trained on brain surface perfusion images. Gradient-weighted class activation mapping (Grad-CAM) was applied to the CNN for visualization of the features that the trained CNN focused on.Binary classifications between DLB and NL, DLB and AD and AD and NL were 94.69%, 87.81% and 94.38% accurate, respectively. The CIS ratios closely correlated with softmax output scores for DLB-AD discrimination (DLB/AD scores). The Grad-CAM highlighted CIS in the DLB discrimination. Visualization of learning process by guided Grad-CAM revealed that CIS became more focused by the CNN as the training progressed. DLB/AD score was significantly associated with three core-features of DLB.Deep learning-based imaging classification was useful not only for objective and accurate differentiation of DLB from AD but also for predicting clinical features of DLB. The CIS was identified as a specific feature during DLB classification. The visualization of specific features and learning process could have important implications for the potential of deep learning to discover new imaging features.
Population fragmentation leads to morpho-functional variation in British red squirrels (Sciurus vulgaris)
Authors: Cox Philip G. , Morris Philip J.R. , Kitchener Andrew C. 
Published: March 2019

AbstractIt is well-known that population fragmentation and isolation can lead to rapid morphological and functional divergence, with the effect being particularly well-documented in rodents. Here, we investigated whether such a phenomenon could be identified in the Eurasian red squirrel (Sciurus vulgaris), which was once widespread across the majority of Great Britain, but suffered a severe population decline across the 20thcentury, leaving a highly fragmented distribution. The aim was to test for morphological and biomechanical variation of the mandible between the remaining British red squirrel populations. Linear and geometric morphometric methods were used to analyse shape in a sample of over 250 squirrel mandibles from across the UK and Germany. Canonical variates analysis identified significant shape differences between most British red squirrel populations, but particularly between squirrels from Formby and those from other populations. Linear measurements showed that Formby red squirrels have a significantly lower mechanical advantage of the temporalis muscle, indicating that they are less efficient at gnawing. We suggest that this difference may be related to past supplemental feeding of Formby squirrels with peanuts, which are less mechanically resistant than food items that occur naturally in the diet of British red squirrels.
Relationship between faecal microbiota and plasma metabolome in rats fed NK603 and MON810 GM maize from the GMO90+ study
Authors: Mesnage Robin , Le Roy Caroline I. , Biserni Martina , Salles Bernard , Antoniou Michael N. 
Published: March 2019

ABSTRACTSafety concerns arising from the consumption of foods derived from genetically modified (GM) crops remains a highly debated and controversial subject. We report here a faecal microbiota compositional analysis in Wistar rats from the GMO90+ study, which fed glyphosate-tolerant NK603 (+/− Roundup application during cultivation) and Bt toxin MON810 GM maize for 6 months (at 11 and 33% composition of the feed) in comparison to their closest non-GM isogenic lines. We first integrated the faecal microbiota compositional data with results from plasma metabolomics to establish a baseline allowing us to understand which bacterial species can influence host metabolism.CoriobacteriaceaeandAcetatifactorsignificantly predicted plasma metabolic profile in males, whileBifidobacteriumandRuminococcuswere able to predict female plasma metabolites. We then investigated the differences in fecal microbiota composition between group of rats fed MON810 or NK603 GM maize varieties in comparison to their respective isogenic lines. Bacterial community richness was not altered by the test diets. There were no statistically significant differences in taxa abundance in the rat faecal microbiota that we could attribute to the consumption of either MON810 or NK603 GM maize varieties. In conclusion, we show that the consumption of the widely cultivated GM maize varieties NK603 and MON810 even up to 33% of the total diet had no effect on the status of the faecal microbiota compared to non-GM near isogenic lines.
Just how big is intraspecific trait variation in basidiomycete wood fungal fruit bodies?
Authors: Dawson Samantha K. , Jönsson Mari 
Published: March 2019

AbstractAs the use of functional trait approaches is growing in fungal ecology, there is a corresponding need to understand trait variation. Much of trait theory and statistical techniques are built on the assumption that interspecific variation is larger than intraspecific variation. This allows the use of mean trait values for species, which the vast majority of trait studies adopt. We examined the size of intra- vs. inter-specific variation in two wood fungal fruit body traits: size and density. Both coefficients of variation (CV) and Trait Probability Density analyses were used to quantify trait variation. We found that intraspecific variation in fruit body density was more than twice as variable as interspecific variation, and fruit body size was hugely variable (CVs averaged 190%), although interspecific variation was larger. Further, there was a very high degree of overlap in the trait space of species, indicating that there may be little niche partitioning at the species level. These findings show that intraspecific variation is highly important and should be accounted for when using trait approaches to understand fungal ecology. More data on variation of other fungal traits is also desperately needed to ascertain whether the high level of variation found here is typical for fungi. While the need to measure individuals does reduce the ability to generalise at the species level, it does not negate the usefulness of fungal trait measurements. There are two reasons for this: first, the ecology of most fungal species remains poorly known and trait measurements address this gap; and secondly, if trait overlap between species more generally is as much as we found here, then individual measurements may be more helpful than species identity for untangling fungal community dynamics.