bluuebunny/crossref_metadata_2025_split huggingface dataset:
Learning from the physical consequences of our actions improves motor memory
Authors: Bakkum Amanda , Marigold Daniel S. 
Published: October 2021

ABSTRACTActions have consequences. Motor learning involves correcting actions that lead to movement errors and remembering these actions for future behavior. In most laboratory situations, movement errors have no physical consequences and simply indicate the progress of learning. Here we asked how experiencing a physical consequence when making a movement error affects motor learning. Two groups of participants adapted to a new, prism-induced mapping between visual input and motor output while performing a precision walking task. Importantly, one group experienced an unexpected slip perturbation when making foot-placement errors during adaptation. Because of our innate drive for safety, and the fact that balance is fundamental to movement, we hypothesized that this experience would enhance motor memory. Learning generalized to different walking tasks to a greater extent in the group who experienced the adverse physical consequence. This group also showed faster relearning one week later despite exposure to a competing mapping during initial learning—evidence of greater memory consolidation. The group differences in generalization and consolidation occurred even though they both experienced similar magnitude foot-placement errors and adapted at similar rates. Our results suggest the brain considers the potential physical consequences of movement error when learning and that balance-threatening consequences serve to enhance this process.
Modelling the effects of land use on mangroves in a RAMSAR site of Panamá
Authors: López-Angarita Juliana , Diaz Juan M , Tilley Alexander 
Published: October 2021

AbstractThe role of mangroves as pivotal providers of ecosystem services has been widely acknowledged. In Latin America, mangroves play an important role in traditional coastal livelihoods, but the growing economy of these nations demands the expansion of land for development, putting pressure in ecosystems such as mangroves. Here we examine the impact of land use activities on mangroves in the Gulf of Montijo, a RAMSAR site located in the Pacific coast of Panamá. Spatial information of land use was analysed, ground-truthed and classified into agriculture, aquaculture and coastal development, and subsequently ranked according to estimated level of impact on mangroves based on 27 interviews with local informants. We developed a spatially-referenced cumulative impact model of human activities on mangroves. Results showed that despite the protection status of the Gulf of Montijo, its mangrove forests are affected by localised human activities, dominated by agriculture. Given the importance of fishing for local livelihoods, evaluating the effects of agriculture, rice in particular, on mangroves and their associated fauna will be essential for the sustainable management of this RAMSAR site.
BDNF signaling in correlation-dependent structural plasticity in the developing visual system
Authors: Kutsarova Elena , Schohl Anne , Munz Martin , Wang Alex , Zhang Yuan Yuan , Bilash Olesia M , Ruthazer Edward S 
Published: October 2021

AbstractDuring development, patterned neural activity instructs topographic map refinement. Axons with similar patterns of neural activity, converge onto target neurons and stabilize their synapses with these postsynaptic partners, restricting exploratory branch elaboration (Hebbian structural plasticity). On the other hand, non-correlated firing in inputs leads to synapse weakening and increased exploratory growth of axons (Stentian structural plasticity). We used visual stimulation to control the correlation structure of neural activity in a few ipsilaterally projecting (ipsi) retinal ganglion cell (RGC) axons with respect to the majority contralateral eye inputs in the optic tectum of albinoXenopus laevistadpoles. Multiphoton live imaging of ipsi axons, combined with specific targeted disruptions of brain-derived neurotrophic factor (BDNF) signaling, revealed that both presynaptic p75NTRand TrkB are required for Stentian axonal branch addition, whereas presumptive postsynaptic BDNF signaling is necessary for Hebbian axon stabilization. Additionally, we found that BDNF signaling mediates local suppression of branch elimination in response to correlated firing of inputs. DailyIn vivoimaging of contralateral RGC axons demonstrated that p75NTRknockdown reduces axon branch elongation and arbor spanning field volume.
Effects of culture medium and temperature on fungal growth of Mycena and marasmioid isolates and in vitro symbiotic culture in mycoheterotrophic orchid, Gastrodia pubilabiata
Authors: Shimazaki Akane , Higaki Kana , Rammitsu Kento , Ogura-Tsujita Yuki 
Published: October 2021

AbstractIn vitro symbiotic culture of Gastrodia pubilabiata seeds were conducted with the fungal isolates from G. pubilabiata roots. We obtained five fungal isolates, which belonged to Mycena, Marasmiaceae and Omphalotaceae. Firstly, optimal temperature and culture medium for subculture of these fungal isolates were examined. All five isolates grew the fastest on malt extract agar medium. Mycelial growth rate was highest at 25 °C between 10 °C and 40 °C. Secondly, we evaluated suitable culture vessels and organic materials for symbiotic culture. Seeds germinated well in petri dishes with Quercus leaf disc on water agar medium, and the seed germination process was well observed without dense mycelium. The most developed seedlings were found in glass bottles filled with Japanese cedar leaves, but densely grew mycelium prevent accurate seedling counts. Leaves of Quercus, Japanese cedar and bamboo were used as organic materials for symbiotic culture. All three leaves induced seed germination with Mycena and Marasmiaceae fungi, but material types affected subsequent seedling growth. Our method will contribute to understanding the mycorrhizal association of Gastrodia species and also other mycoheterotrophic plants.
Mining hidden knowledge: Embedding models of cause-effect relationships curated from the biomedical literature
Authors: Krämer Andreas , Green Jeff , Billaud Jean-Noël , Pasare Nicoleta Andreea , Jones Martin , Tugendreich Stuart 
Published: October 2021

We explore the use of literature-curated signed causal gene expression and gene-function relationships to construct un-supervised embeddings of genes, biological functions, and diseases. Our goal is to prioritize and predict activating and inhibiting functional associations of genes, and to discover hidden relationships between functions. As an application, we are particularly interested in the automatic construction of networks that capture relevant biology in a given disease context.We evaluated several unsupervised gene embedding models leveraging literature-curated signed causal gene expression findings. Using linear regression, it is shown that, based on these gene embeddings, gene-function relationships can be predicted with about 95% precision for the highest scoring genes. Function embedding vectors, derived from parameters of the linear regression model, allow to infer relationships between different functions or diseases. We show for several diseases that gene and function embeddings can be used to recover key drivers of pathogenesis, as well as underlying cellular and physiological processes. These results are presented as disease-centric networks of genes and functions. To illustrate the applicability of the computed gene and function embeddings to other machine learning tasks we expanded the embedding approach to drug molecules, and used a simple neural network to predict drug-disease associations.
COVID-19 Vaccine Effectiveness by Product and Timing in New York State
Authors: Rosenberg Eli S. , Dorabawila Vajeera , Easton Delia , Bauer Ursula E. , Kumar Jessica , Hoen Rebecca , Hoefer Dina , Wu Meng , Lutterloh Emily , Conroy Mary Beth , Greene Danielle , Zucker Howard A. 
Published: October 2021

AbstractBackgroundUS population-based data on COVID-19 vaccine effectiveness (VE) for the 3 currently FDA-authorized products is limited. Whether declines in VE are due to waning immunity, the Delta variant, or other causes, is debated.MethodsWe conducted a prospective study of 8,834,604 New York adults, comparing vaccine cohorts defined by product, age, and month of full-vaccination to age-specific unvaccinated cohorts, by linking statewide testing, hospital, and vaccine registry databases. VE was estimated from May 1, 2021 for incident laboratory-confirmed COVID-19 cases (weekly life-table hazard rates through September 3) and hospitalizations (monthly incidence rates through August 31).Results155,092 COVID-19 cases and 14,862 hospitalizations occurred. Estimated VE for cases declined contemporaneously across age, products, and time-cohorts, from high levels beginning May 1 (1.8% Delta variant prevalence), to a nadir around July 10 (85.3% Delta), with limited changes thereafter (>95% Delta). Decreases were greatest for Pfizer-BioNTech (−24.6%, −19.1%, −14.1% for 18-49, 50-64 years, and ≥65 years, respectively), and similar for Moderna (−18.0%, −11.6%, −9.0%, respectively) and Janssen (−19.2%, −10.8, −10.9%, respectively). VE for hospitalization for adults 18-64 years was >86% across cohorts, without time trend. Among persons ≥65 years, VE declined from May to August for Pfizer-BioNTech (95.0% to 89.2%) and Moderna (97.2% to 94.1%). VE was lower for Janssen, without trend, ranging 85.5%-82.8%.ConclusionsDeclines in VE for cases may have been primarily driven by factors other than waning. VE for hospitalizations remained high, with modest declines limited to Pfizer-BioNTech and Moderna recipients ≥65 years, supporting targeted booster dosing recommendations.
The seasonality of three childhood infections in a pre-industrial society without schools
Authors: Briga Michael , Ukonaho Susanna , Pettay Jenni E , Taylor Robert J , Ketola Tarmo , Lummaa Virpi 
Published: October 2021

AbstractBackgroundThe burden of many infectious diseases varies seasonally and a better understanding of the drivers of infectious disease seasonality would help to improve public health interventions. For directly transmitted highly-immunizing childhood infections, the leading hypothesis is that seasonality is strongly driven by social gatherings imposed by schools, with maxima and minima during school terms and holidays respectively. However, we currently have a poor understanding of the seasonality of childhood infections in societies without schools and whether these are driven by human social gatherings. Here, we used unique nationwide data consisting of >40 epidemics over 100 years in 18th and 19th century Finland, an agricultural pre-health care society without schools, to (i) quantify the seasonality of three easily identifiable childhood infections, smallpox, pertussis and measles and (ii) test the extent to which seasonality of these diseases is driven by seasonal social gatherings.MethodsWe quantified the seasonality of transmission using time series Susceptible-Infected-Recovery models, wavelet analyses and general additive mixed models.ResultsWe found that all three infections were seasonal and the seasonality patterns differed from those in industrialized societies with schools. Smallpox and measles showed high transmission in the first half of the year, but we could not associate this with seasonal human gatherings events. For pertussis, however, transmission was higher during social gathering events such as New Year and Easter.ConclusionsOur results show that the seasonality of childhood infections is more variable than previously described in other populations and indicate a pathogen-specific role of human social aggregation in driving the infectious disease dynamics.FundingAcademy of Finland (278751, 292368), Nordforsk (104910), the Ehrnrooth Foundation, the Finnish Cultural Foundation, the University of Turku Foundation and the Doctoral Programme in Biology, Geography and Geology, University of Turku.Clinical trial numberNA
COVIDNearTerm: A Simple Method to Forecast COVID-19 Hospitalizations
Authors: Olshen Adam B. , Garcia Ariadna , Kapphahn Kristopher I. , Weng Yingjie , Wesson Paul D. , Rutherford George W. , Gonen Mithat , Desai Manisha 
Published: October 2021

1AbstractCOVID-19 has caused tremendous death and suffering since it first emerged in 2019. In response, models were developed to help predict the course of various disease metrics, and these models have been relied upon to help guide public health policy. Here we present a method called COVIDNearTerm to “forecast” hospitalizations in the short term, two to four weeks from the time of prediction. COVIDNearTerm is based on an autoregressive model and utilizes a parametric bootstrap approach to make predictions. We evaluated COVIDNearTerm on San Francisco Bay Area hospitalizations and compared it to models from the California COVID Assessment Tool (CalCAT). We found that that COVIDNearTerm pre-dictions were more accurate than the CalCAT ensemble predictions for all comparisons and any CalCAT component for a majority of comparisons. For instance, at the county level our 14-day hospitalization median absolute percentage errors ranged from 16% to 36%. For those same comparisons the CalCAT ensemble errors were between 30% and 59%. COVIDNearT-erm is also easier to use than some other methods. It requires only previous hospitalization data and there is an open source R package that implements the algorithm.
Automated skull stripping in mouse fMRI analysis using 3D U-Net
Authors: Ruan Guohui , Liu Jiaming , An Ziqi , Wu Kaiibin , Tong Chuanjun , Liu Qiang , Liang Ping , Liang Zhifeng , Chen Wufan , Zhang Xinyuan , Feng Yanqiu 
Published: October 2021

AbstractSkull stripping is an initial and critical step in the pipeline of mouse fMRI analysis. Manual labeling of the brain usually suffers from intra- and inter-rater variability and is highly time-consuming. Hence, an automatic and efficient skull-stripping method is in high demand for mouse fMRI studies. In this study, we investigated a 3D U-Net based method for automatic brain extraction in mouse fMRI studies. Two U-Net models were separately trained on T2-weighted anatomical images and T2*-weighted functional images. The trained models were tested on both interior and exterior datasets. The 3D U-Net models yielded a higher accuracy in brain extraction from both T2-weighted images (Dice > 0.984, Jaccard index > 0.968 and Hausdorff distance < 7.7) and T2*-weighted images (Dice > 0.964, Jaccard index > 0.931 and Hausdorff distance < 3.3), compared with the two widely used mouse skull-stripping methods (RATS and SHERM). The resting-state fMRI results using automatic segmentation with the 3D U-Net models are identical to those obtained by manual segmentation for both the seed-based and group independent component analysis. These results demonstrate that the 3D U-Net based method can replace manual brain extraction in mouse fMRI analysis.
Global Plant Extinction Risk Assessment Inform Novel Biodiversity Hotspots
Authors: Haevermans Thomas , Tressou Jessica , Kwon Joon , Pellens Roseli , Dubéarnès Anne , Veron Simon , Bel Liliane , Dervaux Stéphane , Dibie-Barthelemy Juliette , Gaudeul Myriam , Govaerts Rafaël , Le Bras Gwenaël , Muller Serge , Rouhan Germinal , Sarthou Corinne , Soler Lydie 
Published: October 2021

AbstractCurbing biodiversity loss and its impact on ecosystem services, resilience and Nature’s Contributions to People is one of the main challenges of our generation (IPBES, 2019b, 2019a; Secretariat of the United Nations Convention on Biological Diversity, 2020). A global baseline assessment of the threat status of all of biodiversity is crucial to monitor the progress of conservation policies worldwide (Mace & al., 2000; Secretariat of the United Nations Convention on Biological Diversity, 2021) and target priority areas for conservation (Walker & al., 2021). However, the magnitude of the task seems insurmountable, as even listing the organisms already known to science is a challenge (Nic Lughadha & al., 2016; Borsch & al., 2020; Govaerts & al., 2021). A new approach is needed to overcome this stumbling block and scale-up the assessment of extinction risk. Here we show that analyses of natural history mega-datasets using artificial intelligence allows us to predict a baseline conservation status for all vascular plants and identify target areas for conservation corresponding to hotspots optimally capturing different aspects of biodiversity. We illustrate the strong potential of AI-based methods to reliably predict extinction risk on a global scale. Our approach not only retrieved recognized biodiversity hotspots but identified new areas that may guide future global conservation action (Myers & al., 2000; Brooks & al., 2006). To further work in this area and guide the targets of the post-2020 biodiversity framework (Díaz & al., 2020a; Secretariat of the United Nations Convention on Biological Diversity, 2020; Mair & al., 2021), it will be necessary to accelerate the acquisition of fundamental data and allow inclusion of social and economic factors (Possingham & Wilson, 2005).
Detecting expressed cancer somatic mutations from single-cell RNA sequencing data
Authors: Zhang Tianyun , Shen Ning 
Published: October 2021

AbstractIdentifying expressed somatic mutations directly from single-cell RNA sequencing (scRNA-seq) data is challenging but highly valuable. Computational methods have been attempted but no reliable methods have been reported to identify somatic mutations with high fidelity. We present RESA -- Recurrently Expressed SNV Analysis, a computational framework that identifies expressed somatic mutations from scRNA-seq data with high precision. We test RESA in multiple cancer cell line datasets, where RESA demonstrates average area under the curve (AUC) of 0.9 on independently held out test sets, and achieves average precision of 0.71 when evaluated by bulk whole exome, which is substantially higher than previous approaches. In addition, RESA detects a median of 201 mutations per cell, 50 times more than what was reported in experimental technologies with simultaneous expression and mutation profiling. Furthermore, applying RESA to scRNA-seq from a melanoma patient, we demonstrate that RESA recovers the known BRAF driver mutation of the sample and melanoma dominating mutational signatures, identifies mutation associated expression signatures, reveals nondriver perturbed and stage specific cancer hallmarks, and unveils the complex relationship between genomic and transcriptomic intratumor heterogeneity. Therefore, RESA could provide novel views in the study of intratumor heterogeneity and relate genetic alterations to transcriptional changes at single cell level.
Cloacimonadota metabolisms include adaptations for engineered environments that are reflected in the evolutionary history of the phylum
Authors: Johnson Lisa A. , Hug Laura A. 
Published: October 2021

SummaryPhylum Cloacimonadota (previously Cloacimonetes, WWE1) is an understudied bacterial lineage frequently associated with engineered and wastewater systems. Cloacimonadota members were abundant and diverse in metagenomic datasets from a municipal landfill, prompting an examination of phylogenetic relationships, metabolic diversity, and pangenomic dynamics across the phylum, based on 22 publicly available genomes and 24 from landfill samples. Cloacimonadota formed two discrete clades, with one clade’s genomes principally deriving from engineered systems. A few more-divergent genomes were placed basal in the tree, and not associated with either clade. Metabolic reconstructions for metagenome-assembled genomes predict an anaerobic, acetogenic, and fermentative lifestyle for the majority of Cloacimonadota surveyed. Genomes from engineered ecosystems (first clade) encode a unique suite of genes not typically found in genomes from natural environments (second clade). These encoded functions include acetate kinase, the enzyme responsible for the formation of acetate from acetyl phosphate, and carbon utilization enzymes, suggesting different substrate degradation and energy generation strategies in these ecologically and phylogenetically distinct lineages.Originality/Significance StatementCloacimonadota is a bacterial phylum that is under-described compared to its members’ prevalence in genome repositories. Cloacimonadota are frequently associated with engineered systems, including being identified as abundant and diverse in the municipal landfill site surveyed in this study. We reconstructed twenty-four landfill-associated Cloacimonadota metagenome assembled genomes (MAGs), more than doubling the number of publicly available Cloacimonadota genomes. We combined these MAGs with available reference genomes to predict major metabolic pathways and to describe the conserved features in the lifestyle of phylum Cloacimonadota. We found that Cloacimonadota have distinct evolutionary histories associated with engineered versus natural environments. Prior studies have evaluated metabolism from individual Cloacimonadota genomes – this work is the first to examine trait distribution across a more-complete representation of the phylum, including identification of genomic features and metabolic strategies that correlate to habitat of origin.
Speech sound categorization: The contribution of non-auditory and auditory cortical regions
Authors: Preisig Basil , Riecke Lars , Hervais-Adelman Alexis 
Published: October 2021

AbstractWhich processes in the human brain lead to the categorical perception of speech sounds? Investigation of this question is hampered by the fact that categorical speech perception is normally confounded by acoustic differences in the stimulus. By using ambiguous sounds, however, it is possible to dissociate acoustic from perceptual stimulus representations. Twenty-seven normally hearing individuals took part in an fMRI study in which they were presented with an ambiguous syllable (intermediate between /da/ and /ga/) in one ear and with disambiguating acoustic feature (third formant, F3) in the other ear. Multi-voxel pattern searchlight analysis was used to identify brain areas that consistently differentiated between response patterns associated with different syllable reports. By comparing responses to different stimuli with identical syllable reports and identical stimuli with different syllable reports, we disambiguated whether these regions primarily differentiated the acoustics of the stimuli or the syllable report. We found that BOLD activity patterns in left perisylvian regions (STG, SMG), left inferior frontal regions (vMC, IFG, AI), left supplementary motor cortex (SMA/pre-SMA), and right motor and somatosensory regions (M1/S1) represent listeners’ syllable report irrespective of stimulus acoustics. Most of these regions are outside of what is traditionally regarded as auditory or phonological processing areas. Our results indicate that the process of speech sound categorization implicates decision-making mechanisms and auditory-motor transformations.HighlightsAmbiguous dichotic syllables elicit distinct percepts of identical stimuliMultivariate searchlight analysis reveals syllabic-category sensitive brain areasCategorical responses arise in non-auditory cortical areas including motor areasSMA is a possible locus for transforming sensory signals into perceptual decisions