bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Subset of Olfactory Sensory Neurons Express Forkhead Box J1-Driven eGFP
Authors: Pathak Shivani , Larson Eric D. , Ramakrishnan Vijay R. , Finger Thomas E 
Published: May 2019

AbstractForkhead box protein J1 (Foxj1), a member of the forkhead family transcription factors, is a transcriptional regulator of motile ciliogenesis. The nasal respiratory epithelium, but not olfactory epithelium, is lined with FOXJ1-expressing multiciliated epithelial cells with motile cilia. Using a Foxj1-eGFP reporter mouse, we find robust eGFP expression not only in the multi-ciliated cells of the respiratory epithelium, but in a distinctive small subset of olfactory sensory neurons in the olfactory epithelium. These eGFP-positive cells lie at the extreme apical part of the neuronal layer and are most numerous in dorsal-medial regions of olfactory epithelium. Interestingly, we observed a corresponding small number of glomeruli in the olfactory bulb wherein eGFP-labeled axons terminate, suggesting that the population of eGFP+ receptor cells expresses a limited number of olfactory receptors. Similarly, a subset of vomeronasal sensory neurons express eGFP but these distribute throughout the full height of the vomeronasal sensory epithelium. In keeping with this broad distribution of labeled vomeronasal receptor cells, eGFP labeled axons terminate in many glomeruli of the accessory olfactory bulb. These findings suggest that Foxj1-driven eGFP marks a specific population of olfactory and vomeronasal sensory neurons although neither receptor cell population possess motile cilia.
Signatures of selection in a recent invasion reveals adaptive divergence in a highly vagile invasive species
Authors: Cardilini Adam P. A. , Stuart Katarina C. , Cassey Phillip , Richardson Mark F. , Sherwin William , Rollins Lee A. , Sherman Craig D.H. 
Published: May 2019

AbstractA detailed understanding of population genetics in invasive populations helps us to identify drivers of successful introductions. Here, we investigate putative signals of selection in Australian populations of invasive common starlings,Sturnus vulgaris, and seek to understand how these have been influenced by introduction history. We use reduced representation sequencing to determine population structure, and identity Single Nucleotide Polymorphisms (SNPs) that are putatively under selection. We found that since their introduction into Australia, starling populations have become genetically differentiated despite the potential for high levels of dispersal, and that selection has facilitated their adaptation to the wide range of environmental conditions across their geographic range. Isolation by distance appears to have played a strong role in determining genetic substructure across the starling’s Australian range. Analyses of candidate SNPs that are putatively under selection indicate that aridity, precipitation, and temperature may be important factors driving adaptive variation across the starling’s invasive range in Australia. However, we also note that the historic introduction regime may leave footprints on sites flagged as being under adaptive selection, and encourage critical interpretation of selection analyses.
Genome-wide identification of long non-coding RNAs and their regulatory networks involved in Apis mellifera ligustica response to Nosema ceranae infection
Authors: Guo Rui , Chen Huazhi , Du Yu , Zhou Dingding , Geng Sihai , Wang Haipeng , Zhu Zhiwei , Shi Caiyun , Wan Jieqi , Xiong Cuiling , Zheng Yanzhen , Chen Dafu 
Published: May 2019

AbstractLong non-coding RNAs (lncRNAs) are a diverse class of transcripts that structurally resemble mRNAs but do not encode proteins, and lncRNAs have been proved to play pivotal roles in a wide range of biological processes in animals and plants. However, knowledge of expression pattern and potential role of honeybee lncRNAs response to Nosema ceranae infection is completely unknown. Here, we performed whole transcriptome strand-specific RNA sequencing of normal midguts of Apis mellifera ligustica workers (Am7CK, Am10CK) and N. ceranae-inoculated midguts (Am7T, Am10T), followed by comprehensive analyses using bioinformatic and molecular approaches. A total of 6353 A. m. ligustica lncRNAs were identified, including 4749 conserved lncRNAs and 1604 novel lncRNAs. These lncRNAs had low sequence similarities with other known lncRNAs in other species; however, their structural features were similar with counterparts in mammals and plants, including shorter exon and intron length, lower exon number, and lower expression level, compared with protein-coding transcripts. Further, 111 and 146 N. ceranae-responsive lncRNAs were identified from midguts at 7 day post inoculation (dpi) and 10 dpi compared with control midguts. 12 differentially expressed lncRNAs (DElncRNAs) were shared by Am7CK vs Am7T and Am10CK vs Am10T comparison groups, while the numbers of unique ones were 99 and 134, respectively. Functional annotation and pathway analysis showed the DElncRNAs may regulate the expression of neighboring genes by acting in cis. Moreover, we discovered 27 lncRNAs harboring eight known miRNA precursors and 513 lncRNAs harboring 2257 novel miRNA precursors. Additionally, hundreds of DElncRNAs and their target miRNAs were found to form complex competitive endogenous RNA (ceRNA) networks, suggesting these DElncRNAs may act as miRNA sponges. Furthermore, DElncRNA-miRNA-mRNA networks were constructed and investigated, the result demonstrated that part of DElncRNAs were likely to participate in regulating the material and energy metabolism as well as cellular and humoral immune during host responses to N. ceranae invasion. Finally, the expression pattern of 10 DElncRNAs was validated using RT-qPCR, confirming the reliability of our sequencing data. Our findings revealed here offer not only a rich genetic resource for further investigation of the functional roles of lncRNAs involved in A. m. ligustica response to N. ceranae infection, but also a novel insight into understanding host-pathogen interaction during microsporidiosis of honeybee.
Isolation of a novel strain of Lactic Acid Bacteria from traditionally fermented common lime (Citrus aurantifolia) of Assam, India and analysis of exopolymeric substances produced by the strain
Authors: Borah Nabajyoti , Barman Arindam , Baishya Debabrat 
Published: May 2019

AbstractA gram positive, rod shaped and catalase negative strain of Lactic Acid Bacteria was isolated from traditionally fermented common lime (Citrus aurantifolia) of Assam, North-East India. Bacterial identification was done by using conventional morphological and biochemical methods as well as advanced molecular technique. Traditionally fermented lime juice was serially diluted on selective culture medium and growth of translucent, ropy bacterial colony was observed in the culture plate. Isolated bacteria were identified up to species level by using ribosomal RNA (rRNA) gene sequencing technique. Based on nucleotide homology and phylogenetic analysis the isolate was found to be a strain ofLactobacillus delbrueckii. This is the first report of finding this sub species of Lactic Acid Bacteria in citrus fruit product. The sequence determined in this study has been deposited in the GenBank database with sequential accession numberKT198973.The bacterial isolate also produced exopolysaccharide when grown in chemically defined medium. Fourier Transform Infrared Spectroscopy (FTIR) was done for chemical and compositional characterization of partially purified exopolysaccharide.
Reading-out task variables as a low-dimensional reconstruction of neural spike trains in single trials
Authors: Koren Veronika , Andrei Ariana R. , Hu Ming , Dragoi Valentin , Obermayer Klaus 
Published: May 2019

AbstractWe propose a new model of the read-out of spike trains that exploits the multivariate structure of responses of neural ensembles. Assuming the point of view of a read-out neuron that receives synaptic inputs from a population of projecting neurons, synaptic inputs are weighted with a heterogeneous set of weights. We propose that synaptic weights reflect the role of each neuron within the population for the computational task that the network has to solve. In our case, the computational task is discrimination of binary classes of stimuli, and weights are such as to maximize the discrimination capacity of the network. We compute synaptic weights as the feature weights of an optimal linear classifier. Once weights have been learned, they weight spike trains and allow to compute the post-synaptic current that modulates the spiking probability of the read-out unit in real time. We apply the model on parallel spike trains from V1 and V4 areas in the behaving monkey macaca mulatta, while the animal is engaged in a visual discrimination task with binary classes of stimuli. The read-out of spike trains with our model allows to discriminate the two classes of stimuli, while population PSTH entirely fails to do so. Splitting neurons in two subpopulations according to the sign of the weight, we show that population signals of the two functional subnetworks are negatively correlated. Disentangling the superficial, the middle and the deep layer of the cortex, we show that in both V1 and V4, superficial layers are the most important in discriminating binary classes of stimuli.
A prospectively validated machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma
Authors: Kaissis Georgios , Ziegelmayer Sebastian , Lohöfer Fabian , Algül Hana , Eiber Matthias , Weichert Wilko , Schmid Roland , Friess Helmut , Rummeny Ernst , Ankerst Donna , Siveke Jens , Braren Rickmer 
Published: May 2019

AbstractPurposeTo develop a supervised machine learning algorithm capable of predicting above vs. below-median overall survival from medical imaging-derived radiomic features in a cohort of patients with pancreatic ductal adenocarcinoma (PDAC).Materials and Methods102 patients with histopathologically proven PDAC were retrospectively assessed as the training cohort and 30 prospectively enrolled patients served as the external validation cohort. Tumors were segmented in pre-operative diffusion weighted-(DW)-MRI derived ADC maps and radiomic features were extracted. A Random Forest machine learning algorithm was fit to the training cohort and tested in the external validation cohort. The histopathological subtype of the tumor samples was assessed by immunohistochemistry in 21/30 patients of the external validation cohort. Individual radiomic feature importance was evaluated.ResultsThe machine learning algorithm achieved a sensitivity of 87% and a specificity of 80% (ROC-AUC 90%) for the prediction of above- vs. below-median survival on the unseen data of the external validation cohort. Heterogeneity-related features were highly ranked by the model. Of the 21 patients for whom the histopathological subtype was determined, 8/9 patients predicted by the model to experience below-median overall survival exhibited the quasi-mesenchymal subtype, while 11/12 patients predicted to experience above-median survival exhibited a non-quasi-mesenchymal subtype (Fisher’s exact test P<0.001).ConclusionThe application of machine-learning to the radiomic analysis of DW-MRI-derived ADC maps allowed the prediction of overall survival with high diagnostic accuracy in a prospectively collected cohort. The high overlap of clinically relevant histopathological subtypes with model predictions underlines the potential of quantitative imaging workflows in pre-operative subtyping and risk assessment in PDAC.
Morphology of the nervous system of monogonont rotiferEpiphanes sentawith focus on sexual dimorphism between feeding females and dwarfed males
Authors: Gąsiorowski Ludwik , Furu Anlaug , Hejnol Andreas 
Published: May 2019

AbstractBackgroundMonogononta is a large clade of rotifers comprised of diverse morphological forms found in a wide range of ecological habitats. Most of the monogonont species display a cyclical parthenogenesis, where generations of asexually reproducing females are interspaced by mixis events when sexual reproduction occurs between mictic females and dwarfed, haploid males. The morphology of monogonont feeding females is relatively well described, however data on male anatomy are very limited. Thus far, male musculature of only two species has been described with confocal laser scanning microscopy (CLSM) and it remained unknown how dwarfism influences neuroanatomy of males.ResultsHere, we provide a CLSM-based description of the nervous system of both sexes ofEpiphanes senta, a freshwater monogonont rotifer. The general nervous system architecture is similar between males and females and shows same level of complexity. However, the nervous system in males is more compact and lacks its stomatogastric part.ConclusionComparison of the neuroanatomy between male and normal-sized feeding females provides better understanding of the nature of male dwarfism in Monogononta. We propose that dwarfism of monogonont non-feeding males is a specific case of progenesis as they, due to their inability to feed, retain a juvenile body size. Reduction of the stomatogastric nervous system in the males correlates with the loss of entire digestive tract and associated morphological structures.
Environmental correlates of genetic variation in the invasive and largely panmictic European starling in North America
Authors: Hofmeister Natalie R. , Werner Scott J. , Lovette Irby J. 
Published: May 2019

ABSTRACTPopulations of invasive species that colonize and spread in novel environments may differentiate both through demographic processes and local selection. European starlings (Sturnus vulgaris) were introduced to New York in 1890 and subsequently spread throughout North America, becoming one of the most widespread and numerous bird species on the continent. Genome-wide comparisons across starling individuals and populations can identify demographic and/or selective factors that facilitated this rapid and successful expansion. We investigated patterns of genomic diversity and differentiation using reduced-representation genome sequencing (ddRADseq) of 17 winter-season starling populations. Consistent with this species’ high dispersal rate and rapid expansion history, we found low geographic differentiation and few FST outliers even at a continental scale. Despite starting from a founding population of approximately 180 individuals, North American starlings show only a moderate genetic bottleneck, and models suggest a dramatic increase in effective population size since introduction. In genotype-environment associations we found that ∼200 single-nucleotide polymorphisms are correlated with temperature and/or precipitation against a background of negligible genome- and range-wide divergence. Local adaptation in North American starlings may have evolved rapidly even in this wide-ranging and evolutionarily young population. This survey of genomic signatures of expansion in North American starlings is the most comprehensive to date and complements ongoing studies of world-wide local adaptation in these highly dispersive and invasive birds.
Delta phase resets mediate non-rhythmic temporal prediction
Authors: Daume Jonathan , Wang Peng , Maye Alexander , Zhang Dan , Engel Andreas K. 
Published: May 2019

AbstractThe phase of neural oscillatory activity aligns to the predicted onset of upcoming stimulation. Whether such phase alignments represent phase resets of underlying neural oscillations or just rhythmically evoked activity, and whether they can be observed in a rhythm-free visual context, however, remains unclear. Here, we recorded the magnetoencephalogram while participants were engaged in a temporal prediction task judging the visual or tactile reappearance of a uniformly moving stimulus. The prediction conditions were contrasted with a control condition to dissociate phase adjustments of neural oscillations from stimulus-driven activity. We observed stronger delta band inter-trial phase consistency (ITPC) in a network of sensory, parietal and frontal brain areas, but no power increase reflecting stimulus-driven or prediction-related processes. Delta ITPC further correlated with prediction performance in the cerebellum and visual cortex. Our results provide evidence that phase alignments of low-frequency neural oscillations underlie temporal predictions in a non-rhythmic visual and crossmodal context.
Influenza-specific effector memory B cells predict long-lived antibody responses to vaccination in humans
Authors: Nellore Anoma , Zumaquero Esther , Scharer Christopher D. , King Rodney G. , Tipton Christopher M. , Fucile Christopher F. , Mi Tian , Mousseau Betty , Bradley John E. , Zhou Fen , Goepfert Paul A. , Boss Jeremy M. , Randall Troy D. , Sanz Ignacio , Rosenberg Alexander F. , Lund Frances E. 
Published: May 2019

AbstractSeasonal influenza vaccination elicits hemagglutinin (HA)-specific CD27+ memory B cells (Bmem) that differ in expression of T-bet, BACH2 and TCF7. T-bethiBACH2loTCF7lo Bmem are transcriptionally similar to effector-like memory cells while T-betloBACH2+TCF7+ Bmem exhibit stem-like central memory properties. T-bethi Bmem do not express plasma cell-specific transcription factors but do exhibit transcriptional, epigenetic, metabolic and functional changes that poise the cells for antibody production. Consistent with these changes, D7 HA+ T-bethi Bmem express intracellular immunoglobulin and T-bethi Bmem differentiate more rapidly into ASCs in vitro. The T-bethi Bmem response positively correlates with long-lived humoral immunity and clonotypes from T-bethi Bmem are represented in the early secondary ASC response to repeat vaccination, suggesting that this effector-like population can be used to predict vaccine durability and recall potential.
Nanoscale dynamics of cholesterol in the cell membrane
Authors: Pinkwart Kerstin , Schneider Falk , Lukoseviciute Martyna , Sauka-Spengler Tatjana , Lyman Edward , Eggeling Christian , Sezgin Erdinc 
Published: May 2019

AbstractCholesterol constitutes approximately 30-40% of the mammalian plasma membrane — a larger fraction than any other single component. It is a major player in numerous signalling processes as well as molecular membrane architecture. However, our knowledge on dynamics of cholesterol in the plasma membrane is limited which restricts our understanding of the mechanisms regulating its involvement in cell signalling. Here, advanced fluorescence imaging and spectroscopy approaches were applied on in vitro (model membranes) and in vivo (live cells and embryos) membranes to systematically study the nanoscale dynamics of cholesterol in biological membranes. The results show that cholesterol diffuses faster than phospholipids in live membranes, but not in model membranes. The data indicate that diffusion of cholesterol and phospholipids is not correlated with membrane domain partitioning. Instead, our data show that the fast diffusion of cholesterol is due to its nanoscale interactions and localization in the membrane.
An omnidirectional visualization model of personalized gene regulatory networks
Authors: Chen Chixiang , Jiang Libo , Wang Ming , Wang Yaqun , Shen Biyi , Liu Zhenqiu , Wang Zuoheng , Hou Wei , Berceli Scott A. , Wu Rongling 
Published: May 2019

AbstractGene regulatory networks (GRNs) have been widely used as a fundamental tool to reveal the genomic mechanisms that underlie the organism’s response to environmental and developmental cues. Standard approaches infer GRNs as holistic graphs of gene co-expression, but such graphs cannot quantify how gene-gene interactions differentiate among organisms and how they alter structurally across spatiotemporal gradients. Here, we develop a generalized framework for inferring informative, dynamic, omnidirectional, and personalized GRNs (idopGRNs) from routine transcriptional experiments. This framework is constructed by a system of quasi-dynamic ordinary differential equations (qdODEs) derived from the combination of ecological and evolutionary theories. We reconstruct idopGRNs from a clinical genomic study and illustrate how network structure and organization affect surgical response to infrainguinal vein bypass grafting and the outcome of grafting. idopGNRs may shed light on genotype-phenotype relationships and provide valuable information for personalized medicine.
A fast microfluidic device to measure the deformability of red blood cells
Authors: Mehendale Ninad , Mitra Dhrubaditya , Paul Debjani 
Published: May 2019

AbstractWe report a microfluidic device to determine the shear elastic modulus and the Young’s modulus of red blood cells (RBCs). Our device consists of a single channel opening into a funnel, with a semi-circular obstacle placed at the mouth of the funnel. As a RBC passes the obstacle, it deflects from its original path. Using populations of artificially-stiffened RBCs, we show that the stiffer RBCs deflect more compared to the normal RBCs. We use calibration curves obtained from numerical simulations to map a trajectory of each RBC to its elastic constants. Our estimates of the shear elastic modulus and the Young’s modulus of normal RBCs are within the same range of values reported in the literature using AFM, optical tweezers and micropipette measurements. We also estimate indirectly the elongation index of normal and artificially hardened RBCs from their tracks, without any direct observation of their shapes. Finally, we sort a mixed population of RBCs based on their deformability alone. Our device could potentially be further miniaturized to sort and obtain the elastic constants of nanoscale objects, whose shape change is difficult to monitor by optical microscopy.
Algorithmic differentiation improves the computational efficiency of OpenSim-based optimal control simulations of movement
Authors: Falisse Antoine , Serrancolí Gil , Dembia Christopher L. , Gillis Joris , De Groote Friedl 
Published: May 2019

AbstractAlgorithmic differentiation (AD) is an alternative to finite differences (FD) for evaluating function derivatives. The primarily aim of this study was to demonstrate the computational benefits of using AD instead of FD in OpenSim-based optimal control simulations. The secondary aim was to evaluate computational choices including different AD tools, different linear solvers, and the use of first- or second-order derivatives. First, we enabled the use of AD in OpenSim through a custom source code transformation tool and through the operator overloading tool ADOL-C. Second, we developed an interface between OpenSim and CasADi to perform optimal control simulations. Third, we evaluated computational choices through simulations of perturbed balance, two-dimensional predictive simulations of walking, and three-dimensional tracking simulations of walking. We performed all simulations using direct collocation and implicit differential equations. Using AD through our custom tool was between 1.8 ± 0.1 and 17.8 ± 4.9 times faster than using FD, and between 3.6 ± 0.3 and 12.3 ± 1.3 times faster than using AD through ADOL-C. The linear solver efficiency was problem-dependent and no solver was consistently more efficient. Using second-order derivatives was more efficient for balance simulations but less efficient for walking simulations. The walking simulations were physiologically realistic. These results highlight how the use of AD drastically decreases computational time of optimal control simulations as compared to more common FD. Overall, combining AD with direct collocation and implicit differential equations decreases the computational burden of optimal control simulations, which will facilitate their use for biomechanical applications.