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Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 54,615 bioRxiv papers from 252,242 authors.

Most downloaded bioRxiv papers, since beginning of last month

51,512 results found. For more information, click each entry to expand.

101: Genotype-free demultiplexing of pooled single-cell RNA-seq
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Posted to bioRxiv 07 Mar 2019

Genotype-free demultiplexing of pooled single-cell RNA-seq
483 downloads bioinformatics

Jun Xu, Caitlin Falconer, Quan H. Nguyen, Joanna Crawford, Brett D. McKinnon, Sally-Anne Mortlock, Alice Pebay, Alex Hewitt, Anne Senabouth, Stacey Andersen, Nathan Palpant, Han Sheng Chiu, Grant W. Montgomery, Joseph E Powell, Lachlan J. M. Coin

A variety of experimental and computational methods have been developed to demultiplex samples from pooled individuals in a single-cell RNA sequencing (scRNA-Seq) experiment which either require adding information (such as hashtag barcodes) or measuring information (such as genotypes) prior to pooling. We introduce scSplit which utilises genetic differences inferred from scRNA-Seq data alone to demultiplex pooled samples. scSplit also extracts a minimal set of high confidence presence/absence genotypes in each cluster which can be used to map clusters to original samples. Using a range of simulated, merged individual-sample as well as pooled multi-individual scRNA-Seq datasets, we show that scSplit is highly accurate and concordant with demuxlet predictions. Furthermore, scSplit predictions are highly consistent with the known truth in cell-hashing dataset. We also show that multiplexed-scRNA-Seq can be used to reduce batch effects caused by technical biases. scSplit is ideally suited to samples for which external genome-wide genotype data cannot be obtained (for example non-model organisms), or for which it is impossible to obtain unmixed samples directly, such as mixtures of genetically distinct tumour cells, or mixed infections. scSplit is available at: <https://github.com/jon-xu/scSplit>

102: Fully Interpretable Deep Learning Model of Transcriptional Control
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Posted to bioRxiv 31 May 2019

Fully Interpretable Deep Learning Model of Transcriptional Control
482 downloads systems biology

Yi Liu, Kenneth Barr, John Reinitz

The universal expressibility assumption of Deep Neural Networks (DNN) is the key motivation behind recent work in the system biology community to employ DNNs to solve important problems in functional genomics and molecular genetics. Because of the black-box nature of DNN, such assumptions, while useful in practice, are unsatisfactory for scientific analysis. In this paper, we given an example of a DNN in which every layer is interpretable. Moreover, this DNN is biologically validated and predictive. We derive our DNN from a System Biology model that was not previously recognized as having a DNN structure. The DNN is concerned with a key unsolved problem in Biology: To understand the DNA regulatory code which controls how genes in multicellular organisms are turned on and off. Although we apply our DNN to data from the early embryo of the fruit fly Drosophila, this system serves as a testbed for the analysis of much larger data sets obtained by System Biology studies on Genomic scale.

103: A simple pressure-assisted method for cryo-EM specimen preparation
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Posted to bioRxiv 10 Jun 2019

A simple pressure-assisted method for cryo-EM specimen preparation
482 downloads molecular biology

Jingjing Zhao, Hongyi Xu, Marta Carroni, Hugo Lebrette, Karin Wallden, Agnes Moe, Rei Matsuoka, Martin Hogbom, Xiaodong Zou

Cryo-electron microscopy (cryo-EM) has made great impacts on structural biology. However, specimen preparation remains a major bottleneck. Here, we report a simple method for preparing cryo-EM specimens, named Preassis, in which the excess liquid is removed by introducing a pressure gradient through the EM grid. We show the unique advantages of Preassis in handling samples with low concentrations of protein single particles and micro-crystals in a wide range of buffer conditions.

104: Flowers respond to pollinator sound within minutes by increasing nectar sugar concentration.
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Posted to bioRxiv 28 Dec 2018

Flowers respond to pollinator sound within minutes by increasing nectar sugar concentration.
480 downloads ecology

Marine Veits, Itzhak Khait, Uri Obolski, Eyal Zinger, Arjan Boonman, Aya Goldshtein, Kfir Saban, Udi Ben-Dor, Paz Estlein, Areej Kabat, Dor Peretz, Ittai Ratzersdorfer, Slava Krylov, Daniel Chamovitz, Yuval Sapir, Yossi Yovel, Lilach Hadany

Can plants hear? That is, can they sense airborne sounds and respond to them? Here we show that Oenothera drummondii flowers, exposed to the playback sound of a flying bee or to synthetic sound-signals at similar frequencies, produced sweeter nectar within 3 minutes, potentially increasing the chances of cross pollination. We found that the flowers vibrated mechanically in response to these sounds, suggesting a plausible mechanism where the flower serves as the plant's auditory sensory organ. Both the vibration and the nectar response were frequency-specific: the flowers responded to pollinator sounds, but not to higher frequency sound. Our results document for the first time that plants can rapidly respond to pollinator sounds in an ecologically relevant way. Sensitivity of plants to pollinator sound can affect plant-pollinator interactions in a wide range of ways: Plants could allocate their resources more adequately, focusing on the time of pollinator activity; pollinators would then be better rewarded per time unit; flower shape may be selected for its effect on hearing ability, and not only on signaling; and pollinators may evolve to make sounds that the flowers can hear. Finally, our results suggest that plants may be affected by other sounds as well, including antropogenic ones.

105: Modern human origins: multiregional evolution of autosomes and East Asia origin of Y and mtDNA
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Posted to bioRxiv 18 Jan 2017

Modern human origins: multiregional evolution of autosomes and East Asia origin of Y and mtDNA
471 downloads evolutionary biology

Dejian Yuan, Xiaoyun Lei, Yuanyuan Gui, Mingrui Wang, Ye Zhang, Zuobin Zhu, Dapeng Wang, Jun Yu, Shi Huang

The neutral theory has been used as a null model for interpreting nature and produced the Recent Out of Africa model of anatomically modern humans. Recent studies, however, have established that genetic diversities are mostly at maximum saturation levels maintained by selection, therefore challenging the explanatory power of the neutral theory and rendering the present molecular model of human origins untenable. Using improved methods and public data, we have revisited human evolution and found sharing of genetic variations among racial groups to be largely a result of parallel mutations rather than recent common ancestry and admixture as commonly assumed. We derived an age of 1.86-1.92 million years for the first split in modern human populations based on autosomal diversity data. We found evidence of modern Y and mtDNA originating in East Asia and dispersing via hybridization with archaic humans. Analyses of autosomes, Y and mtDNA all suggest that Denisovan and Neanderthal were archaic Africans with Eurasian admixtures and ancestors of South Asia Negritos and Aboriginal Australians. Verifying our model, we found more ancestry of Southern Chinese from Hunan in Africans relative to other East Asian groups examined. These results suggest multiregional evolution of autosomes and replacements of archaic Y and mtDNA by modern ones originating in East Asia, thereby leading to a coherent account of modern human origins.

106: PAM recognition by miniature CRISPR-Cas14 triggers programmable double-stranded DNA cleavage
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Posted to bioRxiv 30 May 2019

PAM recognition by miniature CRISPR-Cas14 triggers programmable double-stranded DNA cleavage
466 downloads biochemistry

Tautvydas Karvelis, Greta Bigelyte, Joshua K. Young, Zhenglin Hou, Rimante Zedaveinyte, Karolina Pociute, Arunas Silanskas, Ceslovas Venclovas, Virginijus Siksnys

Small and robust CRISPR-Cas nucleases are highly desirable for genome editing applications. Being guided by a RNA to cleave targets near a short sequence termed a protospacer adjacent motif (PAM), Cas9 and Cas12 offer unprecedented flexibility, however, smaller more compact versions would simplify delivery and extend application. Recently, a new class 2 system encoding a miniature (529 amino acids) effector, Cas14a1, has been shown to exclusively function as a PAM-independent single stranded DNA nuclease. Using biochemical methods, we show that a T-rich PAM sequence triggers Cas14 proteins to also cut double-stranded DNA generating staggered ends. Finally, we demonstrate the ability of Cas14a1 to target and cleave cellular human chromosomal DNA paving the way for genome editing applications with Cas14s.

107: BigTop: A Three-Dimensional Virtual Reality Tool for GWAS Visualization
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Posted to bioRxiv 03 Jun 2019

BigTop: A Three-Dimensional Virtual Reality Tool for GWAS Visualization
466 downloads bioinformatics

Samuel T Westreich, Christopher Meyer, Maria Nattestad

Background: Genome-wide association studies (GWAS) are typically visualized using a two-dimensional Manhattan plot, displaying chromosomal location of SNPs along the x-axis and the negative log-10 of their p-value on the y-axis. This traditional plot provides a broad overview of the results, but offers little opportunity for interaction or expansion of specific regions, and is unable to show additional dimensions of the dataset. Results: We created BigTop, a visualization framework in virtual reality (VR), designed to render a Manhattan plot in three dimensions, wrapping the graph around the user in a simulated cylindrical room. BigTop uses the z-axis to display minor allele frequency of each SNP, allowing for the identification of allelic variants of genes. BigTop also offers additional interactivity, allowing users to select any individual SNP and receive expanded information, including SNP name, exact values, and gene location, if applicable. BigTop is built in JavaScript using the React and A-Frame frameworks, and can be rendered using commercially available VR headsets or in a two-dimensional web browser such as Google Chrome. Data is read into BigTop in JSON format, and can be provided as either JSON or a tab-separated text file. Conclusions: Using additional dimensions and interactivity options offered through VR, we provide a new, interactive, three-dimensional representation of the traditional Manhattan plot for displaying and exploring GWAS data.

108: The Spatio-Temporal Control of Zygotic Genome Activation
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Posted to bioRxiv 06 Dec 2018

The Spatio-Temporal Control of Zygotic Genome Activation
463 downloads developmental biology

George Gentsch, Nick D L Owens, James C Smith

One of the earliest and most significant events in embryonic development is zygotic genome activation (ZGA). In several species, bulk transcription begins at the mid-blastula transition (MBT) when, after a certain number of cleavages, the embryo attains a particular nuclear-to-cytoplasmic (N/C) ratio, maternal repressors become sufficiently diluted, and the cell cycle slows down. Here we resolve the frog ZGA in time and space by profiling RNA polymerase II (RNAPII) engagement and its transcriptional readout. We detect a gradual increase in both the quantity and the length of RNAPII elongation before the MBT, revealing that >1,000 zygotic genes disregard the N/C timer for their activation, and that the sizes of newly transcribed genes are not necessarily constrained by cell cycle duration. We also find that Wnt, Nodal and BMP signaling together generate most of the spatio-temporal dynamics of regional ZGA, directing the formation of orthogonal body axes and proportionate germ layers.

109: MOBN: an interactive database of multi-omics biological networks
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Posted to bioRxiv 08 Jun 2019

MOBN: an interactive database of multi-omics biological networks
458 downloads bioinformatics

Cheng Zhang, Muhammad Arif, Xiangyu Li, Sunjae Lee, Abdellah Tebani, Wenyu Zhou, Brian Piening, Linn Fagerberg, Nathan Price, Leroy Hood, Michael Snyder, Jens Nielsen, Mathias Uhlen, Adil Mardinoglu

The associations among different omics are essential to understand human wellness and disease. Howev-er, very few studies have focused on collecting and exhibiting multi-omics associations in a single database. Here, we present an interactive database of multi-omics biological networks (MOBN) and describe associations between clinical chemistry, anthropometrics, plasma proteome, plasma metabolome and gut microbiome obtained from the same indi-viduals. MOBN allows the user to interactively explore the association of a single feature with other omics data and customize its specific context (e.g. male/female specific). MOBN is designed for users who may not have a formal bio-informatics background to facilitate research in human wellness and diseases.

110: A mechanistic model for the negative binomial distribution of single-cell mRNA counts
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Posted to bioRxiv 03 Jun 2019

A mechanistic model for the negative binomial distribution of single-cell mRNA counts
456 downloads bioinformatics

Lisa Amrhein, Kumar Harsha, Christiane Fuchs

Several tools analyze the outcome of single-cell RNA-seq experiments, and they often assume a probability distribution for the observed sequencing counts. It is an open question of which is the most appropriate discrete distribution, not only in terms of model estimation, but also regarding interpretability, complexity and biological plausibility of inherent assumptions. To address the question of interpretability, we investigate mechanistic transcription and degradation models underlying commonly used discrete probability distributions. Known bottom-up approaches infer steady-state probability distributions such as Poisson or Poisson-beta distributions from different underlying transcription-degradation models. By turning this procedure upside down, we show how to infer a corresponding biological model from a given probability distribution, here the negative binomial distribution. Realistic mechanistic models underlying this distributional assumption are unknown so far. Our results indicate that the negative binomial distribution arises as steady-state distribution from a mechanistic model that produces mRNA molecules in bursts. We empirically show that it provides a convenient trade-off between computational complexity and biological simplicity.

111: Brain Network Mechanisms of General Intelligence
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Posted to bioRxiv 03 Jun 2019

Brain Network Mechanisms of General Intelligence
454 downloads neuroscience

Chandra Sripada, Mike Angstadt, Saige Rutherford, Aman Taxali

We identify novel mechanisms of general intelligence involving activation patterns of large-scale brain networks. During hard, cognitively demanding tasks, the fronto-parietal network differentially activates relative to the default mode network, creating greater separation between the networks, while during easy tasks, network separation is reduced. In 920 adults in the Human Connectome Project dataset, we demonstrate that these network separation patterns across hard and easy task conditions are strongly associated with general intelligence, accounting for 21% of the variance in intelligence scores across individuals. Moreover, we identify the presence of a crossover relationship in which FPN-DMN separation profiles that strongly predict higher intelligence in hard task conditions reverse direction and strongly predict lower intelligence in easy conditions, helping to resolve conflicting findings in the literature. We further clarify key properties of FPN-DMN separation: It is a mediator, and not just a marker, of general intelligence, and FPN-DMN separation profiles during the task state can be reliably predicted from connectivity patterns during rest. We demonstrate the robustness of our results by replicating them in a second task and in an independent large sample of youth. Overall, our results establish FPN-DMN separation as a major locus of individual differences in general intelligence, and raise intriguing new questions about how FPN-DMN separation is regulated in different cognitive tasks, across the lifespan, and in health and disease.

112: Layer 6 ensembles can selectively regulate the behavioral impact and layer-specific representation of sensory deviants
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Posted to bioRxiv 03 Jun 2019

Layer 6 ensembles can selectively regulate the behavioral impact and layer-specific representation of sensory deviants
453 downloads neuroscience

Jakob Voigts, Christopher A Deister, Christopher I Moore

Predictive models can enhance the salience of unanticipated input, and the neocortical laminar architecture is believed to be central to this computation. Here, we examined the role of a key potential node in model formation, layer (L) 6, using behavioral, electrophysiological and imaging methods in mouse somatosensory cortex. To test the contribution of L6, we applied weak optogenetic drive that changed which L6 neurons were sensory-responsive, without affecting overall firing rates in L6 or L2/3. This stimulation suppressed L2/3 deviance encoding, but maintained other stimulus encoding. The stimulation also selectively suppressed behavioral sensitivity to deviant stimuli without impacting baseline performance. In contrast, stronger L6 drive inhibited firing and suppressed overall sensory function. These findings indicate that, despite their sparse activity, specific ensembles of stimulus-driven L6 neurons are required to form neocortical predictions, and for their behavioral benefit.

113: Maternal brain gain: enlarged representation of the peripersonal space in pregnancy
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Posted to bioRxiv 10 Dec 2018

Maternal brain gain: enlarged representation of the peripersonal space in pregnancy
453 downloads neuroscience

Flavia Cardini, Natalie Fatemi-Ghomi, Katarzyna Gajewska-Knapik, Victoria Gooch, Jane Elizabeth Aspell

Our ability to maintain a coherent bodily self despite continuous changes within and outside our body relies on the highly flexible multisensory representation of the body, and of the space surrounding it: the peripersonal space (PPS). The aim of our study was to investigate whether during pregnancy - when extremely rapid changes in body size and shape occur - a likewise rapid plastic reorganization of the neural representation of the PPS occurs. We used an audio-tactile integration task to measure the PPS boundary at different stages of pregnancy. We found that in the second trimester of pregnancy and postpartum women did not show differences in their PPS size as compared to the control group (non-pregnant women). However, in the third trimester the PPS was larger than the controls' PPS and the shift between representation of near and far space was more gradual. We therefore conclude that during pregnancy the brain adapts to the sudden bodily changes, by expanding the representation of the space around the body. This may represent a mechanism to protect the vulnerable abdomen from injury from surrounding objects.

114: Modulation of RNA condensation by the eIF4A RNA helicase
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Posted to bioRxiv 02 Jul 2019

Modulation of RNA condensation by the eIF4A RNA helicase
450 downloads biochemistry

Devin Tauber, Gabriel Tauber, Anthony Khong, Briana Van Treeck, Jerry Pelletier, Roy Parker

Stress granules are condensates of non-translating mRNAs and proteins involved in the stress response and neurodegenerative diseases. Stress granules are proposed to form in part through intermolecular RNA-RNA interactions, although the process of RNA condensation is not well understood. In vitro , we demonstrate that the minimization of surface free energy promotes the recruitment and interaction of RNAs on RNA or RNP condensate surfaces. We demonstrate that the ATPase activity of the DEAD-box RNA helicase eIF4A reduces RNA recruitment to RNA condensates in vitro and in cells, as well as limiting stress granule formation. This defines a new function for eIF4A, and potentially other RNA helicases, to limit thermodynamically favored intermolecular RNA-RNA interactions in cells, thereby allowing for proper RNP function. Highlights

115: Combinatorial prediction of gene-marker panels from single-cell transcriptomic data.
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Posted to bioRxiv 30 May 2019

Combinatorial prediction of gene-marker panels from single-cell transcriptomic data.
449 downloads bioinformatics

Conor Delaney, Alexandra Schnell, Louis Cammarata, Aaron Yao-Smith, Aviv Regev, Vijay K Kuchroo, Meromit Singer

Single-cell transcriptomic studies are identifying novel cell populations with exciting functional roles in various in vivo contexts, but identification of succinct gene-marker panels for such populations remains a challenge. In this work we introduce COMET, a computational framework for the identification of candidate marker panels consisting of one or more genes for cell populations of interest identified with single-cell RNA-seq data. We show that COMET outperforms other methods for the identification of single-gene panels, and enables, for the first time, prediction of multi-gene marker panels ranked by relevance. Staining by flow-cytometry assay confirmed the accuracy of COMET's predictions in identifying marker-panels for cellular subtypes, at both the single- and multi-gene levels, validating COMET's applicability and accuracy in predicting favorable marker-panels from transcriptomic input. COMET is a general non-parametric statistical framework and can be used as-is on various high-throughput datasets in addition to single-cell RNA-sequencing data. COMET is available for use via a web interface (http://www.cometsc.com/) or a standalone software package (https://github.com/MSingerLab/COMETSC).

116: CRISPR interference-based platform for multimodal genetic screens in human iPSC-derived neurons
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Posted to bioRxiv 07 Jan 2019

CRISPR interference-based platform for multimodal genetic screens in human iPSC-derived neurons
447 downloads cell biology

Ruilin Tian, Mariam A Gachechiladze, Connor H Ludwig, Matthew T. Laurie, Jason Y Hong, Diane Nathaniel, Anika V Prabhu, Michael S Fernandopulle, Rajan Patel, Mehrnoosh Abshari, Michael E Ward, Martin Kampmann

CRISPR/Cas9-based functional genomics have transformed our ability to elucidate mammalian cell biology. However, most previous CRISPR-based screens were conducted in cancer cell lines, rather than healthy, differentiated cells. Here, we describe a CRISPR interference (CRISPRi)-based platform for genetic screens in human neurons derived from induced pluripotent stem cells (iPSCs). We demonstrate robust and durable knockdown of endogenous genes in such neurons, and present results from three complementary genetic screens. First, a survival-based screen revealed neuron-specific essential genes and genes that improved neuronal survival upon knockdown. Second, a screen with a single-cell transcriptomic readout uncovered several examples of genes whose knockdown had strikingly cell-type specific consequences. Third, a longitudinal imaging screen detected distinct consequences of gene knockdown on neuronal morphology. Our results highlight the power of unbiased genetic screens in iPSC-derived differentiated cell types and provide a platform for systematic interrogation of normal and disease states of neurons.

117: General and robust covalently linked graphene oxide affinity grids for high-resolution cryo-EM
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Posted to bioRxiv 02 Jun 2019

General and robust covalently linked graphene oxide affinity grids for high-resolution cryo-EM
447 downloads biophysics

Feng Wang, Yanxin Liu, Zanlin Yu, Sam Li, Yifan Cheng, David Agard

Despite their great potential to facilitate rapid preparation of quite impure samples, affinity grids have not yet been widely employed in single particle cryo-EM. Here, we chemically functionalize graphene oxide coated grids and use a highly specific covalent affinity tag system. Importantly, our polyethylene glycol spacer keeps particles away from the air-water interface and graphene oxide surface, protecting them from denaturation or aggregation and permits high-resolution reconstructions of small particles.

118: The Reproducibility Of Research And The Misinterpretation Of P Values
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Posted to bioRxiv 31 May 2017

The Reproducibility Of Research And The Misinterpretation Of P Values
446 downloads scientific communication and education

David Colquhoun

We wish to answer this question If you observe a “significant” P value after doing a single unbiased experiment, what is the probability that your result is a false positive? The weak evidence provided by P values between 0.01 and 0.05 is explored by exact calculations of false positive risks. When you observe P = 0.05, the odds in favour of there being a real effect (given by the likelihood ratio) are about 3:1. This is far weaker evidence than the odds of 19 to 1 that might, wrongly, be inferred from the P value. And if you want to limit the false positive risk to 5%, you would have to assume that you were 87% sure that there was a real effect before the experiment was done. If you observe P = 0.001 in a well-powered experiment, it gives a likelihood ratio of almost 100:1 odds on there being a real effect. That would usually be regarded as conclusive, But the false positive risk would still be 8% if the prior probability of a real effect was only 0.1. And, in this case, if you wanted to achieve a false positive risk of 5% you would need to observe P = 0.00045. It is recommended that the terms “significant” and “non-significant” should never be used. Rather, P values should be supplemented by specifying the prior probability that would be needed to produce a specified (e.g. 5%) false positive risk. It may also be helpful to specify the minimum false positive risk associated with the observed P value. Despite decades of warnings, many areas of science still insist on labelling a result of P < 0.05 as “statistically significant”. This practice must account for a substantial part of the lack of reproducibility in some areas of science. And this is before you get to the many other well-known problems, like multiple comparisons, lack of randomisation and P-hacking. Science is endangered by statistical misunderstanding, and by university presidents and research funders who impose perverse incentives on scientists.

119: An association between sexes of successive siblings in the data from Demographic and Health Survey program
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Posted to bioRxiv 12 Nov 2015

An association between sexes of successive siblings in the data from Demographic and Health Survey program
445 downloads physiology

Mikhail Monakhov

The prediction of future child's sex is a question of keen public interest. The probability of having a child of either sex is close to 50%, although multiple factors may slightly change this value. Some demographic studies suggested that sex determination can be influenced by previous pregnancies, although this hypothesis was not commonly accepted. This paper explores the correlations between siblings' sexes using data from the Demographic and Health Survey program. In the sample of about 2,214,601 women (7,985,855 children), the frequencies of sibships with multiple siblings of the same sex were significantly higher than can be expected by chance. A formal modelling demonstrated that sexes of the children were dependent on three kinds of sex ratio variation: a variation between families (Lexian), a variation within a family (Poisson) and a variation contingent upon the sex of preceding sibling (Markovian). There was a positive correlation between the sexes of successive siblings (coefficient = 0.067, p < 0.001), i.e. a child was more likely to be of the same sex as its preceding sibling. This correlation could be caused by secondary sex ratio adjustment in utero since the effect was decreasing with the length of birth-to-birth interval, and the birth-to-birth interval was longer for siblings with unlike sex.

120: Extracellular matrix micropatterning technology for whole cell cryogenic electron microscopy studies
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Posted to bioRxiv 02 Jun 2019

Extracellular matrix micropatterning technology for whole cell cryogenic electron microscopy studies
443 downloads bioengineering

Leeya Engel, Guido Gaietta, Liam P Dow, Mark F Swift, Gaspard Pardon, Niels Volkmann, William I Weis, Dorit Hanein, Beth L. Pruitt

Cryogenic electron tomography is the highest resolution tool available for structural analysis of macromolecular organization inside cells. Micropatterning of extracellular matrix (ECM) proteins is an established in vitro cell culture technique used to control cell shape. Recent traction force microscopy studies have shown correlation between cell morphology and the regulation of force transmission. However, it remains unknown how cells sustain increased strain energy states and localized stresses at the supramolecular level. Here, we report a technology to enable direct observation of mesoscale organization in epithelial cells under morphological modulation, using a maskless protein photopatterning method to confine cells to ECM micropatterns on electron microscopy substrates. These micropatterned cell culture substrates can be used in mechanobiology research to correlate changes in nanometer-scale organization at cell-cell and cell-ECM contacts to strain energy states and traction stress distribution in the cell.

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