bluuebunny/crossref_metadata_2025_split huggingface dataset:
Predicting metastasis with a novel biophysical cell-adhesion force technique
Authors: Gan Jessie , Zhihai Zhao , Miao Yu 
Published: April 2020

AbstractMetastasis is widely accepted to be responsible for approximately 90% of all cancer deaths. Current research on metastasis prediction often centers on gene sequencing; however, these analyses must account for the complexity of gene regulation and rely on comprehensive datasets. To investigate the process from a simpler, non-genomic angle, some studies indicate differences in cell adhesion force, an important physical process in metastasizing cells. However, cell adhesion force methods tend to focus on cell population approaches and therefore have their drawbacks in cost or efficiency, rendering them impractical outside a research setting. In this work, we test a novel and inexpensive bead-pipette assay to investigate the adhesion forces of non-metastatic NIH3T3 cells and mutated RasV12 cells, a metastatic model cell line.Control cells and RasV12 cells were evaluated with wound healing, spreading area, and focal adhesion (FA) analysis assays. Then cells were tested by the novel bead-pipette assay, which uses a fibronectin-coated bead and a glass micropipette to measure cell adhesion force using Hooke’s law.The RasV12 cells had faster migration, polarized cell shape, and smaller FA area than control cells. The RasV12 cells also exerted higher adhesion forces than control cells and a potential force threshold was determined for distinguishing metastatic cells through a Receiver Operating Characteristic (ROC) curve. An ROC curve was computed for all other assays and the bead-pipette assay was shown to perform higher as a classifier than other assays.The RasV12 cells had increased metastatic potential compared to control. The novel bead-pipette assay showed potential as a classifier for determining metastasizing cells from non-metastatic cells. With further work, it may serve as a clinical diagnostic tool for cancer patients or as a testbed to be used in the development of anti-metastatic drugs.
EllipTrack: A Global-Local Cell-Tracking Pipeline for 2D Fluorescence Time-Lapse Microscopy
Authors: Tian Chengzhe , Yang Chen , Spencer Sabrina L. 
Published: April 2020

SummaryTime-lapse microscopy provides an unprecedented opportunity to monitor single-cell dynamics. However, tracking cells for long periods of time remains a technical challenge, especially for multi-day, large-scale movies with rapid cell migration, high cell density, and drug treatments that alter cell morphology/behavior. Here, we present EllipTrack, a global-local cell-tracking pipeline optimized for tracking such movies. EllipTrack first implements a global track-linking algorithm to construct tracks that maximize the probability of cell lineages, and then corrects tracking mistakes with a local track-correction module where tracks generated by the global algorithm are systematically examined and amended if a more probable alternative can be found. Through benchmarking, we show that EllipTrack outperforms state-of-the-art cell trackers and generates nearly error-free cell lineages for multiple large-scale movies. In addition, EllipTrack can adapt to time- and cell density-dependent changes in cell migration speeds, requires minimal training datasets, and provides a user-friendly interface. EllipTrack is available atgithub.com/tianchengzhe/EllipTrack.
A Novel Model of Pancreatic Cancer Dormancy Reveals Mechanistic Insights and a Dormancy Gene Signature with Human Relevance
Authors: Dudgeon Crissy , Harris Chris R. , Chen Ying , Ghaddar Bassel , Sharma Anchal , Shah Mihir M. , Roberts Arthur I. , Casabianca Anthony , Collisson Eric A. , Balachandran Vinod P. , Vertino Paula M. , De Subhajyoti , Carpizo Darren R. 
Published: April 2020

AbstractLatent recurrences following curative-intent pancreatic cancer surgery is a major clinical problem thought to be due to the reactivation of dormant tumor cells that disseminate before the primary tumor has been removed. How dormancy is established and ultimately reversed to drive recurrence is poorly understood. Here we introduce a novel model of pancreatic cancer dormancy that mimics early and latent survival outcomes of resected patients. Using single-cell transcriptomics we compared primary, dormant, and reactivated tumor cells and found the primary and reactivated tumor cell transcriptomes clustered together with and away from the dormant tumor cells. Using a chromatin accessibility assay we found dormancy exhibits large scale changes in chromatin remodeling. Dormant tumor cells express cancer stem cell markers that are lost upon reactivation and are chemotherapy resistant. We identified a dormancy gene signature and investigated this in patients undergoing surgery for localized PC by isolating cells from the primary tumor and liver disseminated tumor cells (DTCs) for single-cell transcriptomics. We found the signature correlated with DTCs indicating that these cells are dormant at the time of surgery. The signature also identified CCL5 as a novel dormancy marker in PC. Mechanisms of PC dormancy include upregulation of the transcriptional repressor Dec2 which drives quiescence, monoallelic suppression of the mutant KRAS allele by DNA methylation, and immunoregulation. We conclude that PC dormancy is a highly plastic and heterogeneous cellular state governed by tumor cell autonomous and non-autonomous mechanisms.One Sentence SummaryA novel model of resectable pancreatic cancer reveals pancreatic cancer dormancy is characterized by significant cellular plasticity, heterogeneity and chromatin remodeling
The covariance shift (C-SHIFT) algorithm for normalizing biological data
Authors: Chunikhina Evgenia , Logan Paul , Kovchegov Yevgeniy , Yambartsev Anatoly , Mondal Debashis , Morgun Andrey 
Published: April 2020

AbstractOmics technologies are powerful tools for analyzing patterns in gene expression data for thousands of genes. Due to a number of systematic variations in experiments, the raw gene expression data is often obfuscated by undesirable technical noises. Various normalization techniques were designed in an attempt to remove these non-biological errors prior to any statistical analysis. One of the reasons for normalizing data is the need for recovering the covariance matrix used in gene network analysis. In this paper, we introduce a novel normalization technique, called the covariance shift (C-SHIFT) method. This normalization algorithm uses optimization techniques together with the blessing of dimensionality philosophy and energy minimization hypothesis for covariance matrix recovery under additive noise (in biology, known as the bias). Thus, it is perfectly suited for the analysis of logarithmic gene expression data. Numerical experiments on synthetic data demonstrate the method’s advantage over the classical normalization techniques. Namely, the comparison is made with rank, quantile, cyclic LOESS (locally estimated scatterplot smoothing), and MAD (median absolute deviation) normalization methods.
Color naming and categorization depend on distinct functional brain networks
Authors: Siuda-Krzywicka Katarzyna , Witzel Christoph , Bartolomeo Paolo , Cohen Laurent 
Published: April 2020

AbstractNaming a color can be understood as an act of categorization, i.e. identifying it as a member of category of colors that are referred to by the same name. But are naming and categorization equivalent cognitive processes, and consequently rely on same neural substrates? Here, we used task and resting-state fMRI, as well as behavioral measures to identify functional brain networks that modulated naming and categorization of colors. Color naming and categorization response times were modulated by different resting state connectivity networks spanning from the color-sensitive regions in the ventro-occipital cortex. Color naming correlated with the connectivity between the left posterior color region, the left medial temporal gyrus, and the left angular gyrus; whereas color categorization involved the connectivity between the bilateral posterior color regions, the left frontal, right temporal and bilateral parietal areas. The networks supporting naming and categorization did not overlap, suggesting that the two processes rely on different neural mechanisms.SignificanceWhen we name a color, we also identify it as a member of a color category, e.g. blue or yellow. Are neural processes underlying color categorization equivalent to those of color naming? Here, we address this question by measuring how individual differences in color categorization and naming response times relate to the strength of functional connections in the brain. Color naming speed correlated with left-hemispheric connectivity between the color-sensitive visual regions and the anterior temporal lobe. Color categorization speed was modulated by a different brain network, encompassing bilateral color-sensitive visual areas, and high-level executive and semantic regions. Thus, color categorization and naming performance involved distinct, non-overlapping brain networks, suggesting that the two processes depend on different neural mechanisms.
Mitochondrial ROS1 increases mitochondrial fission and respiration in oral squamous cancer carcinoma
Authors: Chang Yu-Jung , Chen Kuan-Wei , Chen Linyi 
Published: April 2020

ABSTRACTIncreasedROS1oncogene expression has been implicated in the invasiveness of human oral squamous cell carcinoma (OSCC). The cellular distribution of ROS1 has long been assumed at the plasma membrane. However, a previous work reported a differential cellular distribution of mutant ROS1 derived from chromosomal translocation, resulting in increased carcinogenesis. We thus hypothesized that cellular distribution of up-regulated ROS1 in OSCC may correlate with invasiveness. We found that ROS1 can localize to mitochondria in the highly invasive OSCC and identified a mitochondria-targeting signal sequence in ROS1. We also demonstrated that ROS1 targeting to mitochondria is required for mitochondrial fission phenotype in the highly invasive OSCC cells. OSCC cells expressing high levels of ROS1 consumed more oxygen and had increased levels of cellular ATP levels. Our results also revealed that ROS1 regulates mitochondrial biogenesis and cellular metabolic plasticity. Together, these findings demonstrate that ROS1 targeting to mitochondria enhances OSCC invasion through regulating mitochondrial morphogenesis and cellular respiratory.Summary StatementThis study discovers a new role for the ROS1 in mitochondrial fission and metabolic activities.
sureLDA: A Multi-Disease Automated Phenotyping Method for the Electronic Health Record
Authors: Ahuja Yuri , Zhou Doudou , He Zeling , Sun Jiehuan , Castro Victor M. , Gainer Vivian , Murphy Shawn N. , Hong Chuan , Cai Tianxi 
Published: April 2020

ABSTRACTObjectiveA major bottleneck hindering utilization of electronic health record (EHR) data for translational research is the lack of precise phenotype labels. Chart review as well as rule-based and supervised phenotyping approaches require laborious expert input, hampering applicability to studies that require many phenotypes to be defined and labeledde novo. Though ICD codes are often used as surrogates for true labels in this setting, these sometimes suffer from poor specificity. We propose a fully automated topic modeling algorithm to simultaneously annotate multiple phenotypes.MethodssureLDA is a label-free multidimensional phenotyping method. It first uses the PheNorm algorithm to initialize probabilities based on two surrogate features for each target phenotype, and then leverages these probabilities to constrain the Latent Dirichlet Allocation (LDA) topic model to generate phenotype-specific topics. Finally, it combines phenotype-feature counts with surrogates via clustering ensemble to yield final phenotype probabilities.ResultssureLDA achieves reliably high accuracy and precision across a range of simulated and real-world phenotypes. Its performance is robust to phenotype prevalence and relative informativeness of surogate versus non-surrogate features. It also exhibits powerful feature selection properties.DiscussionsureLDA combines attractive properties of PheNorm and LDA to achieve high accuracy and precision robust to diverse phenotype characteristics. It offers particular improvement for phenotypes insufficiently captured by a few surrogate features. Moreover, sureLDA’s feature selection ability enables it to handle high feature dimensions and produce interpretable computational phenotypes.ConclusionsureLDA is well suited toward large-scale EHR phenotyping for highly multi-phenotype applications such as PheWAS.
An approach for long-term, multi-probe Neuropixels recordings in unrestrained rats
Authors: Luo Thomas Zhihao , Bondy Adrian G. , Gupta Diksha , Elliott Verity A. , Kopec Charles D. , Brody Carlos D. 
Published: April 2020

AbstractThe use of Neuropixels probes for chronic neural recordings is in its infancy and initial studies leave questions about long-term stability and probe reusability unaddressed. Here we demonstrate a new approach for chronic Neuropixels recordings over a period of months in freely moving rats. Our approach allows multiple probes per rat and multiple cycles of probe reuse. We found that hundreds of units could be recorded for multiple months, but that yields depended systematically on anatomical position. Explanted probes displayed a small increase in noise compared to unimplanted probes, but this was insufficient to impair future single-unit recordings. We conclude that cost-effective, multi-region, and multi-probe Neuropixels recordings can be carried out with high yields over multiple months in rats or other similarly sized animals. Our methods and observations may facilitate the standardization of chronic recording from Neuropixels probes in freely moving animals.
SpiderSeqR: an R package for crawling the web of high-throughput multi-omic data repositories for data-sets and annotation
Authors: Sozanska Anna M. , Fletcher Charles , Bihary Dóra , Samarajiwa Shamith A. 
Published: April 2020

AbstractMore than three decades ago, the microarray revolution brought about high-throughput data generation capability to biology and medicine. Subsequently, the emergence of massively parallel sequencing technologies led to many big-data initiatives such as the human genome project and the encyclopedia of DNA elements (ENCODE) project. These, in combination with cheaper, faster massively parallel DNA sequencing capabilities, have democratised multi-omic (genomic, transcriptomic, translatomic and epigenomic) data generation leading to a data deluge in bio-medicine. While some of these data-sets are trapped in inaccessible silos, the vast majority of these data-sets are stored in public data resources and controlled access data repositories, enabling their wider use (or misuse). Currently, most peer reviewed publications require the deposition of the data-set associated with a study under consideration in one of these public data repositories. However, clunky and difficult to use interfaces, subpar or incomplete annotation prevent discovering, searching and filtering of these multi-omic data and hinder their re-purposing in other use cases. In addition, the proliferation of multitude of different data repositories, with partially redundant storage of similar data are yet another obstacle to their continued usefulness. Similarly, interfaces where annotation is spread across multiple web pages, use of accession identifiers with ambiguous and multiple interpretations and lack of good curation make these data-sets difficult to use. We have produced SpiderSeqR, an R package, whose main features include the integration between NCBI GEO and SRA databases, enabling an integrated unified search of SRA and GEO data-sets and associated annotations, conversion between database accessions, as well as convenient filtering of results and saving past queries for future use. All of the above features aim to promote data reuse to facilitate making new discoveries and maximising the potential of existing data-sets.Availabilityhttps://github.com/ss-lab-cancerunit/SpiderSeqR
Temporo-parietal brain regions are involved in higher order object perception
Authors: Nestmann Sophia , Wiesen Daniel , Karnath Hans-Otto , Rennig Johannes 
Published: April 2020

AbstractLesions to posterior temporo-parietal brain regions are associated with deficits in perception of global, hierarchical shapes, but also impairments in the processing of objects presented under demanding viewing conditions. Evidence from neuroimaging studies and lesion patterns observed in patients with simultanagnosia and agnosia for object orientation suggest similar brain regions to be involved in perception of global shapes and processing of objects in atypical (‘non-canonical’) orientation. In a localizer experiment, we identified individual temporoparietal brain areas involved in global shape perception and found significantly higher BOLD signals during the processing of non-canonical compared to canonical objects. In a multivariate approach, we demonstrated that posterior temporo-parietal brain areas show distinct voxel patterns for non-canonical and canonical objects and that voxel patterns of global shapes are more similar to those of objects in non-canonical compared to canonical viewing conditions. These results suggest that temporo-parietal brain areas are not only involved in global shape perception but might serve a more general mechanism of complex object perception. Our results challenge a strict attribution of object processing to the ventral visual stream by suggesting specific dorsal contributions in more demanding viewing conditions.HighlightsPosterior temporo-parietal brain areas in the TPJ region that are involved in global shape perception are significantly involved in object perceptionIndividual global shape TPJ ROIs identified with a specific localizer experiment prefer objects in non-canonical over objects in canonical orientationsUnivariate activations and multivariate voxel patterns in global shape TPJ ROIs distinguish canonical and non-canonical object presentations
The Medial Temporal Lobe Supports Mnemonic Discrimination For Event Duration
Authors: Muncy Nathan M. , Kirwan C. Brock 
Published: April 2020

AbstractTime has an integral role in episodic memory and previous work has implicated the medial temporal lobe in both representing time and discriminating between similar memory traces. Here we developed a novel paradigm to test mnemonic discrimination for temporal duration, as previous temporal work has largely focused on order or maintaining information over time. Thirty-five healthy, young adults completed a continuous-recognition temporal discrimination task in which participants were tasked with detecting a change of stimulus duration on the order of 0.5 seconds and whole-brain high-resolution fMRI data were acquired during this process. Analyses of behavioral results indicate that participants were successful at detecting whether the stimulus duration changed. Further, fMRI analyses revealed that successful trial performance was associated with differential processes in the left entorhinal and perirhinal cortices. Specifically, the left entorhinal cortex was differentially engaged during encoding trials that preceded Target detection, and the left perirhinal cortex was differentially engaged during successful test phase Target and Lure detections. These findings suggest that the entorhinal cortex is involved in the encoding of temporal context information and that the perirhinal cortex is representing the conjunction of item and context during retrieval.
Charged Gram-positive species sequester and decrease the potency of pediocin PA-1 in mixed microbial settings
Authors: Trivedi Vikas D. , Nair Nikhil U. 
Published: April 2020

AbstractAntimicrobial peptides (AMPs) have gained attention recently due to increasing antibiotic resistance amongst pathogens. Most AMPs are cationic in nature and their preliminary interactions with the negatively charged cell surface is mediated by electrostatic attraction. This is followed by pore formation, which is either receptor-dependent or -independent and leads to cell death. Typically, AMPs are characterized by their killing activity using bioactivity assays to determine host range and degree of killing. However, cell surface binding is independent from killing. Most of the studies performed to-date have attempted to quantify the peptide binding using artificial membranes. Here, we use the narrow-spectrum class IIa bacteriocin AMP pediocin PA-1 conjugated to a fluorescent dye as a probe to monitor cell surface binding. We developed a flow cytometry-based assay to quantify the strength of binding in target and non-target species. Through our binding assays, we found a strong positive correlation between cell surface charge and pediocin PA-1 binding. Interestingly, we also found inverse correlation between zeta potential and pediocin PA-1 binding, the correlation coefficient for which improved when only Gram-positives were considered. We also show the effect of the presence of protein, salt, polycationic species, and other non-target species on the binding of pediocin PA-1 to the target organism. We conclude that the of presence of highly charged non-target species, as well as solutes, can decrease the binding, and the apparent potency, of pediocin PA-1. Thus, these outcomes are highly significant to the use of pediocin PA-1 and related AMPs in mixed microbial settings such as those found in the gut microbiota.
An empirical comparison of univariate versus multivariate methods for the analysis of brain-behavior mapping
Authors: Ivanova Maria V. , Herron Timothy J. , Dronkers Nina F. , Baldo Juliana V. 
Published: April 2020

AbstractLesion symptom mapping (LSM) tools are used on brain injury data to identify the neural structures critical for a given behavior or symptom. Univariate lesion-symptom mapping (ULSM) methods provide statistical comparisons of behavioral test scores in patients with and without a lesion on a voxel by voxel basis. More recently, multivariate lesion-symptom mapping (MLSM) methods have been developed that consider the effects of all lesioned voxels in one model simultaneously. However, very little work has been done to empirically compare the advantages and disadvantages of these two different methods. In the current study, we provide a needed systematic comparison of 5 ULSM and 8 MLSM methods, using both synthetic and real data to identify the potential strengths and weaknesses of both approaches. We tested power and spatial precision of each LSM method for both single and dual (network type) anatomical target simulations across anatomical target location, sample size, noise level, and lesion smoothing. Additionally, we performed false positive simulations to identify the characteristics associated with each method’s spurious findings. Simulations showed no clear superiority of either ULSM or MLSM methods overall, but rather highlighted specific advantages of different methods. No single method produced a thresholded LSM map that exclusively delineated brain regions associated with the target behavior. Thus, different LSM methods are indicated, depending on the particular study design, specific hypotheses, and sample size. Overall, we recommend the use of both ULSM and MLSM methods in tandem to enhance confidence in the results: Brain foci identified as significant across both types of methods are unlikely to be spurious and can be confidently reported as robust results.