bluuebunny/crossref_metadata_2025_split huggingface dataset:
Shared understanding is correlated with shared neural responses in the default mode network
Authors: Nguyen Mai , Vanderwal Tamara , Hasson Uri 
Published: December 2017

AbstractHumans have a striking ability to infer meaning from even the sparsest and most abstract forms of narratives. At the same time, flexibility in the form of a narrative is matched by inherent ambiguity in the interpretation of it. How does the brain represent subtle, idiosyncratic differences in the interpretation of abstract and ambiguous narratives? In this fMRI study, we scanned subjects watching a 7-min original animation that depicts a complex narrative through the movement of simple geometric shapes. We additionally scanned two separate groups listening to concrete verbal descriptions of either the social narrative or the physical properties of the movie. After scanning, all subjects freely recalled their interpretation of the stimulus. Using an intersubject representational similarity analysis, we compared the similarity of narrative interpretation across subjects, as measured using text analysis, with the similarity of neural responses, as measured using intersubject correlation (ISC). We found that the more similar two people’s interpretations of the abstract shape movie were, the more similar their neural responses were in the default mode network (DMN). Moreover, these shared responses were modality invariant: despite vast differences in stimulus properties, we found that the shapes movie and the verbal interpretation of the movie elicited shared responses in linguistic areas and a subset of the DMN when subjects shared interpretations. Together, these results suggest that the DMN is not only sensitive to subtle individual differences in narrative interpretation, but also resilient to large differences in the modality of the narrative.Significance statementThe same narrative can be both communicated in different ways and interpreted in different ways. How are subtle, idiosyncratic differences in the interpretation of complex narratives presented in different forms represented in the brain? In this fMRI study, we show that the more similarly two people interpreted an ambiguous animation composed of moving shapes, the more similar their neural responses were in the Default Mode Network. In addition, by presenting the same narrative in a different form, we found shared responses across modalities when subjects shared interpretations despite the vast differences in modality of the stimuli. Together, these results suggest that the DMN is at once sensitive to individual differences in narrative interpretation and resilient to differences narrative modality.
Extended logistic growth model for heterogeneous populations
Authors: Jin Wang , McCue Scott W , Simpson Matthew J 
Published: December 2017

AbstractCell proliferation is the most important cellular-level mechanism responsible for regulating cell population dynamics in living tissues. Modern experimental procedures show that the proliferation rates of individual cells can vary significantly within the same cell line. However, in the mathematical biology literature, cell proliferation is typically modelled using a classical logistic equation which neglects variations in the proliferation rate. In this work, we consider a discrete mathematical model of cell migration and cell proliferation, modulated by volume exclusion (crowding) effects, with variable rates of proliferation across the total population. We refer to this variability asheterogeneity.Constructing the continuum limit of the discrete model leads to a generalisation of the classical logistic growth model. Comparing numerical solutions of the model to averaged data from discrete simulations shows that the new model captures the key features of the discrete process. Applying the extended logistic model to simulate a proliferation assay using rates from recent experimental literature shows that neglecting the role of heterogeneity can, at times, lead to misleading results.
Efficacy ofAedes aegypticontrol by indoor Ultra Low Volume (ULV) insecticide spraying in Iquitos, Peru
Authors: Gunning Christian , Okamoto Kenichi , Astete Helvio , Vasquez Gissella M. , Erhardt Erik , Aguila Clara Del , Pinedo Raul , Cardenas Roldan , Pacheco Carlos , Chalco Enrique , Rodriguez-Ferruci Hugo , Scott Thomas W. , Lloyd Alun L. , Gould Fred , Morrison Amy C. 
Published: December 2017

ABSTRACTBackgroundAedes aegyptiis a primary vector of dengue, chikungunya, Zika, and urban yellow fever viruses. Indoor, ultra low volume (ULV) space spraying with pyrethroid insecticides is the main approach used forAe. aegyptiemergency control in many countries. Given the widespread use of this method, the lack of large-scale experiments or detailed evaluations of municipal spray programs is problematic.Methodology/Principal FindingsTwo experimental evaluations of non-residual, indoor ULV pyrethroid spraying were conducted in Iquitos, Peru. In each, a central sprayed sector was surrounded by an unsprayed buffer sector. In 2013, spray and buffer sectors included 398 and 765 houses, respectively. Spraying reduced the mean number of adults captured per house by ~83 percent relative to the pre-spray baseline survey. In the 2014 experiment, sprayed and buffer sectors included 1,117 and 1,049 houses, respectively. Here, the sprayed sector’s number of adults per house was reduced ~64 percent relative to baseline. Parity surveys in the sprayed sector during the 2014 spray period indicated an increase in the proportion of very young females. We also evaluated impacts of a 2014 citywide spray program by the local Ministry of Health, which reduced adult populations by ~60 percent. In all cases, adult densities returned to near-baseline levels within one month.Conclusions/SignificanceOur results demonstrate that densities of adultAe. aegyptican be reduced by experimental and municipal spraying programs. The finding that adult densities return to approximately pre-spray densities in less than a month is similar to results from previous, smaller scale experiments. Our results demonstrate that ULV spraying is best viewed as having a short-term entomological effect. The epidemiological impact of ULV spraying will need evaluation in future trials that measure capacity of insecticide spraying to reduce disease transmission.
Emergence of dynamically reconfigurable hippocampal responses by learning to perform probabilistic spatial reasoning
Authors: Kanitscheider Ingmar , Fiete Ila 
Published: December 2017

AbstractNavigation in natural environments is computationally difficult: Location errors from motion estimation noise accumulate over time, while landmarks can be spatially extended and often look alike, thus providing ambiguous data. The brain contains a number of spatially tuned neurons coding for various navigational variables, but current models do not explain how these circuits could implement navigational computations that involve non-trivial spatial reasoning. We show, using a function-first approach, that neural circuits trained to efficiently solve spatial reasoning problems with performance on par with sequential probabilistic strategies reproduce some key properties of hippocampal coding, including heterogeneous tuning, conjunctive tuning, and low-dimensional dynamics. In addition, the models predict the emergence of tuning to key latent variables that are neither present in the input data nor trained as the end result of the task, and exhibit a spontaneous dynamical reconfiguration of tuning across time during a task as the computational demands evolve, reminiscent of some of the more complex dynamics observed in the hippocampus including a switch between location and displacement coding modes. These results provide a new functional framework for understanding the rich phenomenology and potential capabilities of navigation codes in the hippocampus and associated brain areas.
Matrix-corrected mass spectrometry enables sensitive detection of food allergens and reveals widespread soy contamination of processed foods
Authors: Croote Derek , Braslavsky Ido , Quake Stephen R. 
Published: December 2017

AbstractThe frequent use of precautionary food allergen labeling statements such as “may contain” poses challenges to allergic individuals who rely on such labeling to determine whether a food is safe to consume. We developed a multiplexed liquid chromatography-mass spectrometry assay targeting 14 common allergens in order to survey how frequently these precautionary statements indicate allergen contamination and to assess whether variations in precautionary phrasing affect the likelihood of allergen contamination. A survey of 84 foods revealed how scheduled multiple reaction monitoring (MRM) transition interference derived from complex and heterogeneous sample matrices hinders sensitive analyte detection. As a solution, we developed MAtrix-Dependent Interference Correction (MADIC), an approach to sensitively detect trace peptide quantities through interference identification and stringent peptide quality control criteria. Applying this method, we find frequent contamination of soy in breads and corn flour, and observe additional instances of food contamination with tree nuts, wheat, milk, and egg. In some of these cases, the food had no precautionary labeling for the offending allergen. We also find that only 10% of warning labels are indicative of contamination, and that products with “same facility” precautionary labeling are not necessarily less likely to contain trace amounts of allergens than products labeled “may contain.”
A Multifactorial Model of T Cell Expansion and Durable Clinical Benefit in Response to a PD-L1 Inhibitor
Authors: Leiserson Mark DM , Syrgkanis Vasilis , Gilson Amy , Dudik Miroslav , Gillett Sharon , Chayes Jennifer , Borgs Christian , Bajorin Dean F , Rosenberg Jonathan , Funt Samuel , Snyder Alexandra , Mackey Lester 
Published: December 2017

ABSTRACTCheckpoint inhibitor immunotherapies have had major success in treating patients with late-stage cancers, yet the minority of patients benefit [1]. Mutation load and PD-L1 staining are leading biomarkers associated with response, but each is an imperfect predictor. A key challenge to predicting response is modeling the interaction between the tumor and immune system. We begin to address this challenge with a multifactorial model for response to anti-PD-L1 therapy. We train a model to predict immune response in patients after treatment based on 36 clinical, tumor, and circulating features collected prior to treatment. We analyze data from 21 bladder cancer patients [2] using the elastic net high-dimensional regression procedure [3] and, as training set error is a biased and overly optimistic measure of prediction error, we use leave-one-out cross-validation to obtain unbiased estimates of accuracy on held-out patients. In held-out patients, the model explains 79% of the variance in T cell clonal expansion. This predicted immune response is multifactorial, as the variance explained is at most 23% if clinical, tumor, or circulating features are excluded. Moreover, if patients are triaged according to predicted expansion, only 38% of non-durable clinical benefit (DCB) patients need be treated to ensure that 100% of DCB patients are treated. In contrast, using mutation load or PD-L1 staining alone, one must treat at least 77% of non-DCB patients to ensure that all DCB patients receive treatment. Thus, integrative models of immune response may improve our ability to anticipate clinical benefit of immunotherapy.
BRUTUS-LIKE proteins moderate the transcriptional response to iron deficiency in roots
Authors: Rodríguez-Celma Jorge , Green Robert T. , Connorton James M. , Kruse Inga , Cui Yan , Ling Hong Qing , Balk Janneke 
Published: December 2017

ABSTRACTIron is an essential micronutrient but in excess is toxic inside cells. Under iron deficiency, the expression of iron uptake genes is increased, but it is not known how the transcriptional response is controlled to avoid uptake of too much iron. The hemerythrin E3 ligases BRUTUS (BTS) and BTS-LIKE (BTSL) have previously been identified as negative regulators of the iron deficiency response. Our phylogenetic analysis indicated that BTSL proteins are present in dicotyledonous plants only and form a separate clade from BTS homologs.BTSL1andBTSL2inArabidopsis thalianaare in a network with nearly all iron uptake genes, whereasBTSis in a shoot-specific network.BTSL1andBTSL2are expressed predominantly in the root epidermis and cortex, separate fromBTSin the root stele, shoot and embryos. Mutant analysis identifiedBTSL2as the dominant paralog of the otherwise redundantBTSLgenes. Thebtsldouble mutant had increased protein levels of FIT, the FER-like Iron deficiency-induced Transcription factor, and failed to switch off the transcriptional response upon iron resupply, leading to dramatic iron accumulation in roots and shoots. Protein interaction between the C-terminus of BTSL proteins and FIT indicate that FIT is a direct target for degradation. Taken together, our studies show thatBTSL1andBTSL2control iron uptake in the epidermis and cortex, upstream of BTS in the vasculature and leaves.
Cell-type specific eQTL of primary melanocytes facilitates identification of melanoma susceptibility genes
Authors: Zhang Tongwu , Choi Jiyeon , Kovacs Michael A. , Shi Jianxin , Xu Mai ,   NISC Comparative Sequencing Program,   Melanoma, Meta-Analysis Consortium, Goldstein Alisa M. , Iles Mark M. , Duffy David , MacGregor Stuart , Amundadottir Laufey T. , Law Matthew H. , Loftus Stacie K. , Pavan William J. , Brown Kevin M. 
Published: December 2017

ABSTRACTMost expression quantitative trait loci (eQTL) studies to date have been performed in heterogeneous tissues as opposed to specific cell types. To better understand the cell-type specific regulatory landscape of human melanocytes, which give rise to melanoma but account for <5% of typical human skin biopsies, we performed an eQTL analysis in primary melanocyte cultures from 106 newborn males. We identified 597,335cis-eQTL SNPs prior to LD-pruning and 4,997 eGenes (FDR<0.05), which are higher numbers than in any GTEx tissue type with a similar sample size. Melanocyte eQTLs differed considerably from those identified in the 44 GTEx tissues, including skin. Over a third of melanocyte eGenes, including key genes in melanin synthesis pathways, were not observed to be eGenes in two types of GTEx skin tissues or TCGA melanoma samples. The melanocyte dataset also identified cell-type specifictrans-eQTLs with a pigmentation-associated SNP for four genes, likely through itscis-regulation ofIRF4, encoding a transcription factor implicated in human pigmentation phenotypes. Melanocyte eQTLs are enriched incis-regulatory signatures found in melanocytes as well as melanoma-associated variants identified through genome-wide association studies (GWAS). Co-localization of melanoma GWAS variants and eQTLs from melanocyte and skin eQTL datasets identified candidate melanoma susceptibility genes for six known GWAS loci including unique genes identified by the melanocyte dataset. Further, a transcriptome-wide association study using published melanoma GWAS data uncovered four new loci, where imputed expression levels of five genes (ZFP90, HEBP1, MSC, CBWD1, andRP11-383H13.1) were associated with melanoma at genome-wide significantP-values. Our data highlight the utility of lineage-specific eQTL resources for annotating GWAS findings and present a robust database for genomic research of melanoma risk and melanocyte biology.
Statistical Approaches to Decreasing the Discrepancy of Non-detects in qPCR Data
Authors: Sherina Valeriia , McMurray Helene R. , Powers Winslow , Land Hartmut , Love Tanzy M.T. , McCall Matthew N. 
Published: December 2017

AbstractQuantitative real-time PCR (qPCR) is one of the most widely used methods to measure gene expression. Despite extensive research in qPCR laboratory protocols, normalization, and statistical analysis, little attention has been given to qPCR non-detects – those reactions failing to produce a minimum amount of signal. While most current software replaces these non-detects with a value representing the limit of detection, recent work suggests that this introduces substantial bias in estimation of both absolute and differential expression. Recently developed single imputation procedures, while better than previously used methods, underestimate residual variance, which can lead to anti-conservative inference. We propose to treat non-detects as non-random missing data, model the missing data mechanism, and use this model to impute missing values or obtain direct estimates of relevant model parameters. To account for the uncertainty inherent in the imputation, we propose a multiple imputation procedure, which provides a set of plausible values for each non-detect. In the proposed modeling framework, there are three sources of uncertainty: parameter estimation, the missing data mechanism, and measurement error. All three sources of variability are incorporated in the multiple imputation and direct estimation algorithms. We demonstrate the applicability of these methods on three real qPCR data sets and perform an extensive simulation study to assess model sensitivity to misspecification of the missing data mechanism, to the number of replicates within the sample, and to the overall size of the data set. The proposed methods result in unbiased estimates of the model parameters; therefore, these approaches may be beneficial when estimating both absolute and differential gene expression. The developed methods are implemented in the R/Bioconductor package nondetects. The statistical methods introduced here reduce discrepancies in gene expression values derived from qPCR experiments, providing more confidence in generating scientific hypotheses and performing downstream analysis.
Linked-Read sequencing resolves complex structural variants
Authors: Garcia Sarah , Williams Stephen , Xu Andrew Wei , Herschleb Jill , Marks Patrick , Stafford David , Church Deanna M. 
Published: December 2017

SummaryLarge genomic structural variants (>50bp) are important contributors to disease, yet they remain one of the most difficult types of variation to accurately ascertain, in part because they tend to cluster in duplicated and repetitive regions, but also because the various signals for these events can be challenging to detect with short reads. Clinically, aCGH and karyotype remain the most commonly used assays for genome-wide structural variant (SV) detection, though there is clear potential benefit to an NGS-based assay that accurately detects both SVs and single nucleotide variants. Linked-Read sequencing is a relatively simple, fast, and cost-effective method that is applicable to both genome and targeted assays. Linked-Reads are generated by performing haplotype-level dilution of long input DNA molecules into >1 million barcoded partitions, generating barcoded short reads within those partitions, and then performing short read sequencing in bulk. We performed 30x Linked-Read genome sequencing on a set of 23 samples with known balanced or unbalanced SVs. Twenty-seven of the 29 known events were detected and another event was called as a candidate. Sequence downsampling was performed on a subset to determine the lowest sequence depth required to detect variations. Copy-number variants can be called with as little as 1-2x sequencing depth (5-10Gb) while balanced events require on the order of 10x coverage for variant calls to be made, although specific signal is clearly present at 1-2x sequencing depth. In addition to detecting a full spectrum of variant types with a single test, Linked-Read sequencing provides base-level resolution of breakpoints, enabling complete resolution of even the most complex chromosomal rearrangements.
Teaching data science fundamentals through realistic synthetic clinical cardiovascular data
Authors: Laderas Ted , Vasilevsky Nicole , Pederson Bjorn , Haendel Melissa , McWeeney Shannon , Dorr David A. 
Published: December 2017

ABSTRACTObjectiveOur goal was to create a synthetic dataset and curricular materials to assist in teaching fundamentals of translational data science.Materials and MethodsA literature review was conducted to extract current cardiovascular risk score logic, data elements, and population characteristics. Then, clinical data elements in the models were pulled from clinical data and transformed to the Observational Medical Outcomes Partnership (OMOP) common data model; genetic data elements were added based on population rates. A hybrid Bayesian network was used to create synthetic data from the logical elements of the risk scores and the underlying population frequencies of the clinical data.ResultsA synthetic dataset of 446,000 patients was created. A two-day curriculum was created based on this synthetic data with exploratory data analysis and machine learning components. The curriculum was offered on two separate occasions; the two groups of learners were given the curriculum and data, and results were tallied, summarized, and compared. Students’ ability to complete the challenge was mixed; more experienced students achieved a range of 70%-85% in balanced accuracy, but many others did not perform better than the baseline model.DiscussionOverall, students enjoyed the course and dataset, but some struggled to consistently apply machine learning techniques. The curriculum, data set, techniques for generation, and results are available for others to use for their own training.ConclusionA realistic synthetic data with clinical and genetic components helps students learn issues in cardiovascular risk scoring, practice data science skills, and compete in a challenge to improve identification of risk.
The soft side of branding: leveraging emotional intelligence
Authors: Hutchins Jennifer , Rodriguez Darlene Xiomara 
Published: December 2017

Purpose
The purpose of this paper is to support the need for further research and theory building on emotions in business-to-business (B2B) industries. Specifically, this research conceptualizes emotional intelligence (EI) as an alternative to building brand equity for B2B companies beyond name recognition, product quality and catchy slogans. Additionally, the authors closely analyze social media content marketing by B2Bs as a channel through which to exercise EI with the goal of enhancing brand image and growing brand equity.

Design/methodology/approach
This study takes a conceptual approach, building on current literature to develop a model of functional and emotional paths to B2B brand equity. The study uses content analysis methodology to examine 11 “best in class” B2B companies recognized for their social media content marketing. The analysis reveals the level to which each company demonstrates the collective EI of the firm through content marketing.

Findings
EI is a learned skill that can be an essential behavioral asset. In addition to hard skills such as product development and process refinement, leveraging a firm’s soft skills and collective EI is also a viable route to increasing brand equity. Additionally, B2B companies that are considered the best at content marketing demonstrate EI through their online communication strategies.

Research limitations/implications
This research reveals another avenue through which B2B firms to increase brand equity, but there is a need to empirically validate the model and connections made in this paper.

Practical implications
Leveraging EI in B2B firms through content marketing is a source of or key driver of competitive advantage and increased brand equity.

Originality/value
By providing the history and importance of EI, and by connecting existing knowledge of B2B communications to concepts of improving brand equity, this study offers a unique, extended framework that urges further research to expand this field.