bluuebunny/crossref_metadata_2025_split huggingface dataset:
A shared pathway of exosome biogenesis operates at plasma and endosome membranes
Authors: Fordjour Francis K. , Daaboul George G. , Gould Stephen J. 
Published: February 2019

AbstractEukaryotic cells secrete exosomes, which are small (~30-200 nm dia.), single membrane-bound organelles that transmit signals and molecules to other cells. Exosome-mediated signaling contributes to diverse physiological and disease processes, rendering their biogenesis of high biomedical importance. The prevailing hypothesis is that exosomes bud exclusively at endosome membranes and are released only upon endosome fusion with the plasma membrane. Here we tested this hypothesis by examining the intracellular sorting and exosomal secretion of the exosome cargo proteins CD63, CD9, and CD81. We report here that CD9 and CD81 are both localized to the plasma membrane and bud >5-fold more efficiently than endosome-localized CD63. Furthermore, we show that redirecting CD63 from endosomes to the plasma membrane by mutating its endocytosis signal (CD63/Y235A) increased its exosomal secretion ~6-fold, whereas redirecting CD9 to endosomes by adding an endosome targeting signal (CD9/YEVM) reduced its exosomal secretion ~5-fold. These data demonstrate that the plasma membrane is a major site of exosome biogenesis, and more importantly, that cells possess a common pathway for exosome protein budding that operates at both plasma and endosome membranes. Using a combination of single-particle interferometry reflectance (SPIR) imaging and immunofluorescence (IF) microscopy, we also show that variations in exosome composition are controlled by differential intracellular protein trafficking rather than by separate mechanisms of exosome biogenesis. This new view of exosome biogenesis offers a simple explanation for the pronounced compositional heterogeneity of exosomes and a validated roadmap for exosome engineering.SummaryThis study of exosome cargo protein budding reveals that cells use a common pathway for budding exosomes from plasma and endosome membranes, providing a new mechanistic explanation for exosome heterogeneity and a rational roadmap for exosome engineering.
Discovering dynamic functional networks in the human neonatal brain with electric source imaging
Authors: Mehrkanoon Steve 
Published: February 2019

AbstractWhen the human brain manifests the birth of organised communication among local and large-scale neuronal populations activity remains undescribed. We report, in resting-state EEG source-estimates of 100 infants at term age, the existence of macro-scale dynamic functional connectivity, which have rich topological organisations, distinct spectral fingerprints and scale-invariance temporal dynamics. These functional networks encompass the default mode, primary sensory-limbic system, thalamo-frontal, thalamo-sensorimotor and visual-limbic system confined in the delta and low-alpha frequency intervals (1-8 Hz). The temporal dynamics of these networks not only are nested within much slower timescale (¡ 0.1 Hz) but also correlated in a hierarchical leading-following organisation. We show that the anatomically constrained richly organised spatial topologies, spectral contents and temporal fluctuations of resting-state networks reflect an established intrinsic dynamic functional connectome in the human brain at term age. The graph theoretical analysis of the spatial architectures of the networks revealed small-world topology and distinct rich-club organisations of interconnected cortical hubs that exhibit rich synchronous dynamics at multiple timescales. The approach opens new avenues to advance our understanding about the early configuration organisation of dynamic networks in the human brain and offers a novel monitoring platform to investigate functional brain network development in sick preterm infants.
Draft Genome Assembly and Annotation of Red Raspberry Rubus Idaeus
Authors: Wight Haley , Zhou Junhui , Li Muzi , Hannenhalli Sridhar , Mount Stephen M. , Liu Zhongchi 
Published: February 2019

AbstractThe red raspberry, Rubus idaeus, is widely distributed in all temperate regions of Europe, Asia, and North America and is a major commercial fruit valued for its taste, high antioxidant and vitamin content. However, Rubus breeding is a long and slow process hampered by limited genomic and molecular resources. Genomic resources such as a complete genome sequencing and transcriptome will be of exceptional value to improve research and breeding of this high value crop. Using a hybrid sequence assembly approach including data from both long and short sequence reads, we present the first assembly of the Rubus idaeus genome (Joan J. variety). The de novo assembled genome consists of 2,145 scaffolds with a genome completeness of 95.3% and an N50 score of 638 KB. Leveraging a linkage map, we anchored 80.1% of the genome onto seven chromosomes. Using over 1 billion paired-end RNAseq reads, we annotated 35,566 protein coding genes with a transcriptome completeness score of 97.2%. The Rubus idaeus genome provides an important new resource for researchers and breeders.
Implementations of the chemical structural and compositional similarity metric in R and Python
Authors: Brejnrod Asker , Ernst Madeleine , Dworzynski Piotr , Rasmussen Lasse Buur , Dorrestein Pieter C. , van der Hooft Justin J.J. , Arumugam Manimozhiyan 
Published: February 2019

AbstractMotivationTandem mass spectrometry (MS/MS) has the potential to substantially improve metabolomics by acquiring spectra of fragmented ions. These fragmentation spectra can be represented as a molecular network, by measuring cosine distances between them, thus identifying signals from the same or similar molecules. Metrics that enable comparison between pairs of samples based on their metabolite profiles are in great need. Taking inspiration from the successful phylogeny-aware beta-diversity measures used in microbiome research, integrating chemical similarity information about the features in addition to their abundances could lead to better insights when comparing metabolite profiles. Chemical Structural and Compositional Similarity (CSCS) is a recently published similarity metric comparing the full set of signals and their chemical similarity between two samples. Efficient, scalable and easily accessible implementations of this algorithm is currently lacking. Here, we present an easily accessible and scalable implementation of CSCS in both python and R, including a version not weighted by intensity information.ResultsWe provide a new implementation of the CSCS algorithm that is over 300 times faster than the published implementation in R, making the algorithm suitable for large-scale metabolomics applications. We also show that adding chemical information enriches existing methods. Furthermore, the R implementation includes functions for exporting molecular networks directly from the mass spectral molecular networking platform GNPS for ease of use for downstream applications.Contactbrejnrod@sund.ku.dkAvailabilitygithub.com/askerdb/rCSCS,github.com/askerdb/pyCSCS
Optimality of the spontaneous prophage induction rate
Authors: Cortes Michael , Krog Jonathan , Balázsi Gábor 
Published: February 2019

AbstractLysogens are bacterial cells that have survived after being infected by bacterial viruses called bacteriophages. Instead of being killed by the virus, the infected cell survives by integrating the viral DNA into its own genome. This is only possible with “temperate” bacteriophages which do not always lyse their host to reproduce, but sometimes replicate passively using the lysogenic pathway. After an infection resulting in lysogeny, the lysogen continues to grow and divide normally, seemingly unaffected by the integrated viral genome which is now referred to as a prophage. However, the prophage can have an impact on the host’s phenotype and overall fitness in certain environments. This makes competition between the lysogen and its nonlysogen counterpart possible because both cells have different genomes and potentially different growth rates. Additionally, the prophages within the lysogens are capable of spontaneously reverting back to the lytic pathway via spontaneous prophage induction (SPI), causing death of the lysogen and the release of new progeny phages. These new phages can then lyse or lysogenize other susceptible nonlysogens, thereby impacting the competition between lysogens and nonlysogens. In a scenario with differing growth rates, it is not clear whether SPI would be beneficial or detrimental to the lysogens since it directly causes cell death but also attacks nonlysogenic competitiors, either lysing or lysogenizing them. In this work we study the evolutionary dynamics of a mixture of lysogens and nonlysogens and derive general conditions on the rate of SPI resulting in lysogens displacing nonlysogens. We show that there exists an optimal SPI rate, and apply the model to bacteriophageλ. We find that the model can explain why the experimentally measured SPI rate for phageλis so low. We also investigate the impact of stochasticity and conclude that even at low copy numbers the SPI rate can still be fairly low while still providing an advantage to the lysogens.
Novel Methodology to Detect Physical Interaction of Inositol Phospholipid with Potassium Ion Channel Using Luminescence Resonance Energy Transfer
Authors: Chin Felix , Cheng Ping-Chin 
Published: February 2019

AbstractMembrane proteins like ion channels are located within a lipid environment. While the bulk lipids provide a supporting architecture for proteins, some lipids also play important signaling roles in modulating protein activity. This modulation can occur through direct interaction with proteins without involving second messenger pathways. Phosphoinositides are one such lipids, which have been implicated in interactions with a diverse range of proteins and such interactions mediate a variety of cellular functions. Towards understanding how these lipids play their roles in regulating protein functions It is necessary to detect interactions between proteins and lipids and to identify binding sites on proteins. However, detection of protein-lipid interactions has been difficult especially in their native environment because only a fraction of lipids are bound to proteins while the bulk is freely diffusing. The purpose of this study is to develop an experimental method capable of identifying protein-lipid interactions in membrane environments. The strategy involves luminescence energy transfer to locally illuminate lipids around proteins thereby suppressing the large background of light from unbound lipid molecules. The approach is applied to an inward rectifier potassium ion channel for its interactions with phosphoinositide lipids. Experiments show that it can successfully detect phosphoinositides bound to proteins, and when combined with mutagenic approaches, can further identify binding site. The approach thus provides a valuable methodology for probing protein-lipid interactions in a native-like lipid environment.
Neuronal memory for language processing
Authors: Fitz Hartmut , Uhlmann Marvin , van den Broek Dick , Duarte Renato , Hagoort Peter , Petersson Karl Magnus 
Published: February 2019

AbstractIn language processing, an interpretation is computed incrementally within memory while utterances unfold in time. Here, we investigate the nature of this processing memory in a spiking network model of sentence comprehension. We show that the history dependence of neuronal responses endows circuits of biological neurons with adequate memory to assign semantic roles and resolve binding relations between words in a stream of language input. A neurobiological read-write memory is proposed where short-lived spiking activity encodes information into coupled dynamic variables that move at slower timescales. This state-dependent network does not rely on persistent activity, excitatory feedback, or synaptic plasticity for storage. Instead, information is maintained in adaptive neuronal conductances and can be accessed directly during comprehension without cued retrieval of previous input words. This work provides a step towards a computational neurobiology of language.
Resourceful mice do not starve: feeding efforts and decision-making process under a restricted unknown food source
Authors: Hernández Ma Carmen , Navarro-Castilla Álvaro , Barja Isabel 
Published: February 2019

AbstractForaging decisions must balance the energy gained, the time investment and the influence of key environmental factors. In our work, we aimed to examine the importance of predation risk cues and experience in the feeding efforts and decision-making process when a novel food resource is presented. To achieve this, free ranging wood miceApodemus sylvaticuswere live-trapped in “Monte de Valdelatas” (Madrid) by setting 80 Sherman traps in 4 plots. Traps were subjected to two food access difficulties three-night consecutive treatments: open plastic bottles and closed bottles, both using corn as bait. To generate predation risk, we set fox faeces in half of the traps in each plot. Also, we considered indirect predator cues as the moon phase. We analyse whether mice had bitten the bottles and the area gnawed of each bottle was measured. We discovered that mice feeding decisions and efforts were driven by food access difficulty, experience and predation risk. The ability of mice to properly balance their energy budget was probed since they bit and performed bigger orifices in the closed bottles, hence, individuals can adapt the feeding effort when a new food source is available. Moreover, experience was determinant in the use of this new resource since recaptured mice gnawed the bottles more successfully and the skill was improved each time an individual was recaptured. Additionally, direct predation risk cues prompt mice to bite the bottles whereas the effect of different moon phases varied among the treatments. This is the first study that provides direct evidence of wild mice formidable efficacy to exploit a new nutrient resource while deepening in crucial environmental factors that shape decision-making procedure.
Assemblage of Focal Species Recognizers - AFSR: A technique for decreasing false positive rates of acoustic automatic identification in a multiple species context
Authors: Campos Ivan Braga , Landers Todd J. , Lee Kate D. , Lee William George , Friesen Megan R. , Gaskett Anne C. , Ranjard Louis 
Published: February 2019

AbstractPassive acoustic monitoring (PAM) coupled with automated species identification is a promising tool for species monitoring and conservation worldwide. However, high false positive rates are still an important limitation and a crucial factor for acceptance of these techniques in wildlife surveys. Here we present the Assemblage of Focal Species Recognizers - AFSR, a novel approach for decreasing false positives and increasing models’ precision in multispecies contexts. AFSR focusses on decreasing false positives by excluding unreliable sound file segments that are prone to misidentification. We used MatlabHTK, a hidden Markov models interface for bioacoustics analyses, for illustrating AFSR technique by comparing two approaches, 1) a multispecies recognizer where all species are identified simultaneously, and 2) an assemblage of focal species recognizers (AFSR), where several recognizers that each prioritise a single focal species are then summarised into a single output, according to a set of rules designed to exclude unreliable segments. Both approaches (the multispecies recognizer and AFSR) used the same sound files training dataset, but different processing workflow. We applied these recognisers to PAM recordings from a remote island colony with five seabird species and compared their outputs with manual species identifications. False positive rates and precision improved for all the five species when using AFSR, achieving remarkable 0% false positives and 100% precision for three of five seabird species, and < 6% false positive rates, and >90% precision for the other two species. AFSR’ output was also used to generate daily calling activity patterns for each species. Instead of attempting to withdraw useful information from every fragment in a sound recording, AFSR prioritises more trustworthy information from sections with better quality data. AFSR can be applied to automated species identification from multispecies PAM recordings worldwide.
Predictor species: Improving assessments of rare species occurrence by modelling environmental co-responses
Authors: Thompson Peter R. , Fagan William F. , Staniczenko Phillip P.A. 
Published: February 2019

ABSTRACTDesigning an effective conservation strategy requires understanding where rare species are located. Although species distribution models are primarily used to identify patterns at large spatial scales, their general methodology is relevant for predicting the occurrence of individual species at specific locations. Here we present a new approach that uses Bayesian networks to improve predictions by modelling environmental co-responses among species. For species from a European peat bog community, our approach consistently performs better than single-species models, and better than conventional multi-species models for rare species when calibration data are limited. Furthermore, we identify a group of “predictor species” that are relatively common, insensitive to the presence of other species, and can be used to improve occurrence predictions of rare species. Predictor species are distinct from other categories of conservation surrogates such as umbrella or indicator species, which motivates focused data collection of predictor species to enhance conservation practices.
Ordered patterning of the sensory system is susceptible to stochastic features of gene expression
Authors: Giri Ritika , Papadopoulos Dimitrios K. , Posadas Diana M. , Potluri Hemanth K. , Tomancak Pavel , Mani Madhav , Carthew Richard W. 
Published: February 2019

AbstractSensory neuron numbers and positions are precisely organized to accurately map environmental signals in the brain. However, this precision must emerge from biochemical processes within and between cells that are stochastic. We measured intrinsic noise insenselessprotein output, a key determinant of sensory fate, duringDrosophiladevelopment. Perturbing microRNA regulation or genomic locus ofsenselesstranscription produced distinct noise signatures. Genomic location altered protein stochasticity in an allelic-pairing dependent manner (transvection). This generated sensory pattern disorder without perturbing protein abundance. In contrast, loss of microRNA repression ofsenselessincreased protein abundance but not sensory pattern disorder. This suggests that gene expression stochasticity is a critical feature that must be constrained during development to allow rapid yet accurate cell fate resolution.One Sentence SummaryLife on the Margin: balancing speed and accuracy during animal development.
Assessment of kinship detection using RNA-seq data
Authors: Blay Natalia , Casas Eduard , Galván-Femenía Iván , Graffelman Jan , de Cid Rafael , Vavouri Tanya 
Published: February 2019

AbstractAnalysis of RNA sequencing (RNA-seq) data from related individuals is widely used in clinical and molecular genetics studies. Sample labelling mistakes are estimated to affect more than 4% of published samples. Therefore, as a method of data quality control, a way to reconstruct pedigrees from RNA-seq data would be useful for confirming the expected relationships. Currently, reconstruction of pedigrees is based mainly on SNPs or microsatellites, obtained from genotyping arrays, whole genome sequencing and whole exome sequencing. Potential problems with using RNA-seq data for kinship detection are the low proportion of the genome that it covers, the highly skewed coverage of exons of different genes depending on expression level and allele-specific expression.In this study we assess the use of RNA-seq data to detect kinship between individuals, through pairwise identity-by-descent (IBD) estimates. First, we obtained high quality SNPs after successive filters to minimize the effects due to allelic imbalance as well as errors in sequencing, mapping and genotyping. Then, we used these SNPs to calculate pairwise IBD estimates. By analysing both real and simulated RNA-seq data we show that it is possible to identify up to second degree relationships using RNA-seq data of even low to moderate sequencing depth.
CXCR3-expressing metastasis-initiating cells induce and exploit a fibroblast niche in the lungs to fuel metastatic colonization
Authors: Pein Maren , Insua-Rodríguez Jacob , Meier Jasmin , Hongu Tsunaki , Wiedmann Lena , Essers Marieke A.G. , Sinn Hans-Peter , Spaich Saskia , Sütterlin Marc , Schneeweiss Andreas , Trumpp Andreas , Oskarsson Thordur 
Published: February 2019

ABSTRACTMetastatic colonization relies on interactions between disseminated cancer cells and the microenvironment in secondary organs. Here, we show that disseminated breast cancer cells evoke major phenotypic changes in lung fibroblasts to form a metastatic niche that supports malignant growth. Colonization of the lungs by cancer cells confers an inflammatory phenotype in associated fibroblasts, where IL-1α and IL-1β, secreted by breast cancer cells, induceCXCL9andCXCL10production in metastasis-associated fibroblasts via NF-κB signaling. These paracrine interactions fuel the growth of lung metastases. Notably, we find that the chemokine receptor CXCR3, that binds CXCL9/10, is specifically expressed in a small subset of breast cancer cells with stem/progenitor cell properties and high tumor-initiating ability when co-transplanted with fibroblasts. CXCR3-expressing cancer cells show high JNK signaling that drives IL-1α/β expression. Thus, CXCR3 marks a population of breast cancer cells that induces CXCL9/10 production in fibroblast, but can also respond to and benefit from these chemokines. Importantly, disruption of this intercellular JNK-IL-1-CXCL9/10-CXCR3 axis significantly reduces metastatic colonization in xenograft and syngeneic mouse models. These data mechanistically demonstrate an essential role for this molecular crosstalk between breast cancer cells and their fibroblast niche in the progression of metastasis.
World’s Fastest Brain-Computer Interface: Combining EEG2Code with Deep Learning
Authors: Nagel Sebastian , Spüler Martin 
Published: February 2019

AbstractIn this paper, we present a Brain-Computer Interface (BCI) that is able to reach an information transfer rate (ITR) of more than 1200 bit/min using non-invasively recorded EEG signals. By combining the EEG2Code method with deep learning, we present an extremely powerful approach for decoding visual information from EEG. This approach can either be used in a passive BCI setting to predict properties of a visual stimulus the person is viewing, or it can be used to actively control a BCI spelling application. The presented approach was tested in both scenarios and achieved an average ITR of 701 bit/min in the passive BCI approach with the best subject achieving an online ITR of 1237 bit/min. The presented BCI is more than three times faster than the previously fastest BCI and allows to discriminate 500,000 different visual stimuli based on 2 seconds of EEG data with an accuracy of up to 100 %. When using the approach in an asynchronous BCI for spelling, we achieved an average utility rate of 175 bit/min, which corresponds to an average of 35 error-free letters per minute. As we observe a ceiling effect where more powerful approaches for brain signal decoding do not translate into better BCI control anymore, we discuss if BCI research has reached a point where the performance of non-invasive BCI control cannot be substantially improved anymore.