bluuebunny/crossref_metadata_2025_split huggingface dataset:
Reproducibility and reusability limitations in Regulatory Circuits: analysis and solutions
Authors: Louarn Marine , Siegel Anne , Fest Thierry , Dameron Olivier , Chatonnet Fabrice 
Published: August 2021

The Regulatory Circuits project is among the most recent and the most complete attempts to identify cell-type specific regulatory networks in Human. It is one of the largest efforts of public genomics data integration, based on data from the major consortia FANTOM5, ENCODE and Roadmap Epigenomics. This project is a main provider of biological data, cited more than 224 times (Google Scholar) and its resulting networks were used in at least 42 other articles.For such a general resource, reproducibility of both the outputs (regulation networks) and methods (data integration pipeline) is a major issue, since biological data are updated regularly. In addition, users may want to introduce new data into the Regulatory Circuits framework to provide networks about previously uncharacterized cell types or to add information about specific regulators, which require to re-execute the whole pipeline on the new data.In this article, we analyze the various factors limiting reproducibility of the Regulatory Circuits data and methods. Starting from a factual description of our understanding of the methods used in Regulatory Circuits, our contribution is two-fold: we propose (1) a characterization of the different levels of reusability, reproducibility and conceptual issues in the original workflow and (2) a new implementation of the workflow ensuring its consistency with the published description and allowing for an easier reuse and reproduction of the published outputs. Both are applicable beyond the case of Regulatory Circuits.
Involvement of ASIC1a channels in the spinal processing of pain information by deep projection neurons
Authors: Chafaï Magda , Delrocq Ariane , Inquimbert Perrine , Pidoux Ludivine , Delanoe Kevin , Lingueglia Eric , Veltz Romain , Deval Emmanuel 
Published: August 2021

AbstractDorsal horn of the spinal cord is an important crossroad of pain neuraxis, especially for the neuronal plasticity mechanisms that can lead to chronic pain states. Windup is a well-known spinal pain facilitation process initially described several decades ago, but which exact mechanism is still not fully understood. Here, we combine bothex vivoandin vivoelectrophysiological recordings of spinal neurons with computational modelling to demonstrate a role for ASIC1a-containing channels in the windup process. Spinal application of the ASIC1a inhibitory venom peptides mambalgin-1 and psalmotoxin-1 (PcTx1) significantly reduces the ability of deep wide dynamic range (WDR) neurons to develop windupin vivo. All deep WDR-like neurons recorded from spinal slices exhibit an ASIC current with biophysical and pharmacological characteristics consistent with functional expression of ASIC1a/ASIC2 heteromeric channels. A computational model of WDR neuron supplemented with heteromeric ASIC1a/ASIC2 channel parameters accurately reproduces the experimental data, further supporting a positive contribution of these channels to windup. It also predicts a calcium-dependent windup decrease for elevated ASIC conductances, a phenomenon that was experimentally validated using either a combination of calcium-activated potassium channel inhibitory peptides (apamin and iberiotoxin), or the Texas coral snake ASIC-activating toxin (MitTx). This study demonstrates a possible dual contribution to windup of calcium permeable ASIC1a/ASIC2 channels in deep laminae projecting neurons, promoting it upon moderate channel activity, but ultimately leading to calcium-dependent windup inhibition associated to potassium channels when activity increases.
Taxonomy of Rhizobiaceae revisited: proposal of a new framework for genus delimitation
Authors: Kuzmanović Nemanja , Fagorzi Camilla , Mengoni Alessio , Lassalle Florent , diCenzo George C 
Published: August 2021

ABSTRACTThe alphaproteobacterial family Rhizobiaceae is highly diverse, with 168 species with validly published names classified into 17 genera with validly published names. Most named genera in this family are delineated based on genomic relatedness and phylogenetic relationships, but some historically named genera show inconsistent distribution and phylogenetic breadth. Most problematic is Rhizobium, which is notorious for being highly paraphyletic, as most newly described species in the family being assigned to this genus without consideration for their proximity to existing genera, or the need to create novel genera. In addition, many Rhizobiaceae genera lack synapomorphic traits that would give them biological and ecological significance. We propose a common framework for genus delimitation within the family Rhizobiaceae. We propose that genera in this family should be defined as monophyletic groups in a core-genome gene phylogeny, that are separated from related species using a pairwise core-proteome average amino acid identity (cpAAI) threshold of approximately 86%. We further propose that the presence of additional genomic or phenotypic evidence can justify the division of species into separate genera even if they all share greater than 86% cpAAI. Applying this framework, we propose to reclassify Rhizobium rhizosphaerae and Rhizobium oryzae into the new genus Xaviernesmea gen. nov. Data is also provided to support the recently proposed genus “Peteryoungia”, and the reclassifications of Rhizobium yantingense as Endobacterium yantingense comb. nov., Rhizobium petrolearium as Neorhizobium petrolearium comb. nov., Rhizobium arenae as Pararhizobium arenae comb. nov., Rhizobium tarimense as Pseudorhizobium tarimense comb. nov., and Rhizobium azooxidefex as Mycoplana azooxidifex comb. nov. Lastly, we present arguments that the unification of the genera Ensifer and Sinorhizobium in Opinion 84 of the Judicial Commission is no longer justified by current genomic and phenotypic data. We thus argue that the genus Sinorhizobium is not illegitimate and now encompasses 17 species.
Prediction performance of linear models and gradient boosting machine on complex phenotypes in outbred mice
Authors: Perez B.C. , Bink M.C.A.M. , Churchill G.A. , Svenson K.L. , Calus M.P.L. 
Published: August 2021

ABSTRACTRecent literature suggests machine learning methods can capture interactions between loci and therefore could outperform linear models when predicting traits with relevant epistatic effects. However, investigating this empirically requires data with high mapping resolution and phenotypes for traits with known non-additive gene action. The objective of the present study was to compare the performance of linear (GBLUP, BayesB and elastic net [ENET]) methods to a non-parametric tree-based ensemble (gradient boosting machine – GBM) method for genomic prediction of complex traits in mice. The dataset used contained phenotypic and genotypic information for 835 animals from 6 non-overlapping generations. Traits analyzed were bone mineral density (BMD), body weight at 10, 15 and 20 weeks (BW10, BW15 and BW20), fat percentage (FAT%), circulating cholesterol (CHOL), glucose (GLUC), insulin (INS) and triglycerides (TGL), and urine creatinine (UCRT). After quality control, the genotype dataset contained 50,112 SNP markers. Animals from older generations were considered as a reference subset, while animals in the latest generation as candidates for the validation subset. We also evaluated the impact of different levels of connectedness between reference and validation sets. Model performance was measured as the Pearson’s correlation coefficient and mean squared error (MSE) between adjusted phenotypes and the model’s prediction for animals in the validation subset. Outcomes were also compared across models by checking the overlapping top markers and animals. Linear models outperformed GBM for seven out of ten traits. For these models, accuracy was proportional to the trait’s heritability. For traits BMD, CHOL and GLU, the GBM model showed better prediction accuracy and lower MSE. Interestingly, for these three traits there is evidence in literature of a relevant portion of phenotypic variance being explained by epistatic effects. We noticed that for lower connectedness, i.e., imposing a gap of one to two generations between reference and validation populations, the superior performance of GBM was only maintained for GLU. Using a subset of top markers selected from a GBM model helped for some of the traits to improve accuracy of prediction when these were fitted into linear and GBM models. The GBM model showed consistently fewer markers and animals in common among the top ranked than linear models. Our results indicate that GBM is more strongly affected by data size and decreased connectedness between reference and validation sets than the linear models. Nevertheless, our results indicate that GBM is a competitive method to predict complex traits in an outbred mice population, especially for traits with assumed epistatic effects.
From fitting the average to fitting the individual: A cautionary tale for mathematical modelers
Authors: Luo Michael C. , Nikolopoulou Elpiniki , Gevertz Jana L. 
Published: August 2021

AbstractAn outstanding challenge in the clinical care of cancer is moving from a one-size-fits-all approach that relies on population-level statistics towards personalized therapeutic design. Mathematical modeling is a powerful tool in treatment personalization, as it allows for the incorporation of patient-specific data so that treatment can be tailor-designed to the individual. Herein, we work with a mathematical model of murine cancer immunotherapy that has been previously-validated against the average of an experimental dataset. We ask the question: what happens if we try to use this same model to perform personalized fits, and therefore make individualized treatment recommendations? Typically, this would be done by choosing a single fitting methodology, and a single cost function, identifying the individualized best-fit parameters, and extrapolating from there to make personalized treatment recommendations. Our analyses show the potentially problematic nature of this approach, as predicted personalized treatment response proved to be sensitive to the fitting methodology utilized. We also demonstrate how a small amount of the right additional experimental measurements could go a long way to improve consistency in personalized fits. Finally, we show how quantifying the robustness of the average response could also help improve confidence in personalized treatment recommendations.Author summaryAs we enter the era of healthcare where personalized medicine becomes a more common approach to treating cancer patients, harnessing the power of mathematical models will only become more essential. Using a preclinical dataset on cancer immunotherapy, we explore the challenges and limitations that arise when trying to move from fitting and making predictions for the population-level average, to fitting and making predictions for an individual. We find that the standard of approach of picking a single fitting methodology and a single cost function may end up having limited predictive value when applied to individual data. We also show how having a small amount of the right additional experimental data, and establishing the robustness of average treatment response, can help improve confidence in personalized model predictions.
High Level of Interaction between Phages and Bacteria in an Artisanal Raw Milk Cheese Microbial Community
Authors: Queiroz Luciano Lopes , Lacorte Gustavo Augusto , Isidorio William Ricardo , Landgraf Mariza , de Melo Franco Bernadette Dora Gombossy , Pinto Uelinton Manoel , Hoffmann Christian 
Published: August 2021

AbstractEndogenous starter cultures are used in the production of several cheeses around the world, such as Parmigiano-Reggiano, in Italy, Époisses, in France, and Canastra, in Brazil. These microbial communities are responsible for many of the intrinsic characteristics of each of these cheeses. Bacteriophages are ubiquitous around the world, well known to be involved in the modulation of complex microbiological processes. However, little is known about phage–bacteria growth dynamics in cheese production systems, where phages are normally treated as problems, as the viral infections can negatively affect or even eliminate the starter culture during production. Furthermore, a recent metagenomic based meta-analysis has reported that cheeses contain a high abundance of phage-associated sequences. Here, we analyse the viral and bacterial metagenomes of Canastra cheese, a tradition artisanal cheese produced using an endogenous starter culture. We observe a very high phage diversity level, mostly composed of novel sequences. We detect several metagenomic assembled bacterial genomes at strain level resolution, and several putative phage-bacteria interactions, evidenced by the recovered viral and bacterial genomic signatures. We postulate that at least one bacterial strain detected could be endogenous to the Canastra region, in Brazil, and that its growth seems to be modulated by native phages present in this artisanal production system. This relationship is likely to influence the fermentation dynamics and ultimately the sensorial profile of these cheeses, with implication for all cheeses that employ similar production processes around the world.
Mnemonic content and hippocampal patterns shape judgments of time
Authors: Sherman Brynn E. , DuBrow Sarah , Winawer Jonathan , Davachi Lila 
Published: August 2021

AbstractOur experience of time can feel dilated or compressed, rather than reflecting true “clock time.” Although many contextual factors influence the subjective perception of time, it is unclear how memory accessibility plays a role in constructing our experience of and memory for time. Here, we used a combination of behavioral and fMRI measures to ask the question of how memory is incorporated into temporal duration judgments. Behaviorally, we found that event boundaries, which have been shown to disrupt ongoing memory integration processes, result in the temporal compression of duration judgments. Additionally, using a multivoxel pattern similarity analysis of fMRI data, we found that greater temporal pattern change in the left hippocampus within individual trials was associated with longer duration judgments. Together, these data suggest that mnemonic processes play a role in constructing representations of time.Statement of RelevanceOur everyday experiences convey a powerful truth: That our perception of time often diverges from the reality of time. When enjoying an active vacation with family, time moves quickly: hours go by in minutes. When sitting through an unnecessary meeting, time moves slowly: minutes go by in hours. What is the origin of these phenomenologically compelling illusions of time perception? Past research has examined how a range of specific factors, from emotions to blinking, contribute to the distortion of time. Here, in contrast, we evaluate how the content and accessibility of our memories shapes time perception. We show that context shifts, known to disrupt memory processing, also lead to robust contractions of perceived time. We discuss how both effects — memory disruptions and time distortions — may be linked via the hippocampus.
Neurotensin neurons in the central extended amygdala control energy balance
Authors: Furlan Alessandro , Corona Alberto , Boyle Sara , Sharma Radhashree , Rubino Rachel , Habel Jill , Gablenz Eva Carlotta , Giovanniello Jacqueline , Beyaz Semir , Janowitz Tobias , Shea Stephen D. , Li Bo 
Published: August 2021

SUMMARYOvereating and a sedentary life style are major causes of obesity and related metabolic disorders. Identification of the neurobiological processes that regulate energy balance will facilitate development of interventions for these disorders. Here we show that the Neurotensin-expressing neurons in the mouse IPAC (IPACNts), a nucleus of the central extended amygdala, bidirectionally coordinate hedonic feeding and physical activity, thereby regulating energy balance, metabolic processes and bodyweight. IPACNts are preferentially activated by consumption of highly palatable food or exposure to its taste and smell. Activating IPACNts promotes food intake in a palatability-dependent manner and decreases locomotion. Conversely, inhibiting IPACNts selectively reduces palatable food intake and dramatically enhances physical activity and energy expenditure, and in parallel stimulates physiological responses that oppose diet-induced obesity and metabolic dysfunctions. Thus, a single neuronal population, Neurotensin-expressing neurons in the IPAC, acts to control obesogenic and leptogenic processes by synergistically coordinating energy intake and expenditure with metabolism.
Operators on the vanishing moments subspace and Stieltjes classes for M-indeterminate probability distributions
Authors: López-García Marcos 
Published: August 2021

PurposeIn this work the author gathers several methods and techniques to construct systematically Stieltjes classes for densities defined on R+.Design/methodology/approachThe author uses complex integration to obtain integrable functions with vanishing moments sequence, and then the author considers some operators defined on the vanishing moments subspace.FindingsThe author gather several methods and techniques to construct systematically Stieltjes classes for densities defined onR+. The author constructs explicitly Stieltjes classes with center at well-known probability densities. The author gives a lot of examples, including old cases and new ones.Originality/valueThe author computes the Hilbert transform of powers of|ln⁡x|to construct Stieltjes classes by using a recent result connecting the Krein condition and the Hilbert transform.
The voice of profit: exploring the tone of Australian CEO's letters to shareholders after the global financial crisis
Authors: Alshorman Salah Aldain Abdullah , Shanahan Martin 
Published: August 2021

PurposeThis study examines the association between firm profitability and the “voice” of the CEO measured through tones they convey in their annual letter to shareholders. The paper examines whether the tones corresponds to a firm's profitability and the extent to which CEO tone varies with changes in profitability.Design/methodology/approachThe authors analyze 187 Australian CEOs communications in 748 annual letters to their shareholders between 2010 and 2013. Two-word lists created by previous researchers are used to assess tones for their positive-negative plurality, uncertainty and use of modal words. Firm profitability is identified using return on assets. The authors examine the relationship between profitability and tones using simple ANOVA as well as a linear mixed model and then a change (differences) model. The change model captures any inertia or genre effect in the CEO letter to shareholders.FindingsUsing both the level and change model, the authors find that firm profitability is associated with CEO's tones that are more optimistic and less pessimistic. The authors also find that the use of negative words has more communicative value than positive words or “net” positive words. The authors also observe some genre effect when CEOs use strong modal words.Research limitations/implicationsThe sample is restricted to a selection of Australian firms that had the same CEO for the fiscal years 2010–2013; which reported in each financial year and which survived the global financial crisis. Generalizing the findings to other periods, types of firms, or to CEOs with shorter tenure, might be questionable. This study was conducted in Australia, which may limit the applicability of the findings to other jurisdictions.Practical implicationsThe significant link between firm profitability and CEOs' use of positive, net positive and negative words implies that investors may place reliance on the use of these tones in the CEO's annual letter to accurately reflect the profitability of the firm.Originality/valueThe study extends the existing literature by examining whether a change in firm profitability is linked to a change in CEO tone. It concludes that even in periods of general financial stress, shareholders should be confident that CEOs' letters to shareholders provide credible information that corresponds to firm performance.
Game learning analytics of instant messaging and online discussion forums in higher education
Authors: Hernández-Lara Ana Beatriz , Perera-Lluna Alexandre , Serradell-López Enric 
Published: August 2021

PurposeWith the growth of digital education, students increasingly interact in a variety of ways. The potential effects of these interactions on their learning process are not fully understood and the outcomes may depend on the tool used. This study explores the communication patterns and learning effectiveness developed by students using two basic synchronous and asynchronous communication tools in e-learning environments, specifically business simulation games.Design/methodology/approachThe authors conduct a quasi-experiment research with 478 online business students, 267 of whom used online discussion forums and 211 interacted via an instant messaging app. The application of learning analytics and text mining on natural language processing allows us to explore the student communication patterns with each of tools and their effectiveness in terms of learning.FindingsThe results confirm the complementarity of the communication tools, asynchronous tools being especially the suitable for task-related communication and synchronous ones for speeding up and facilitating student social interactions.Originality/valueThe main value of this research lies in the use of data analytics and text mining to access and analyse the content of student interactions to assess the learning process in greater depth, comparing synchronous and asynchronous learning modes, considering that little is known about the impact of online synchronous interaction or instant messaging, and even less about the different features, content and performance that emerge when these two learner interaction modalities are compared.
CVS health faces a new wave of disruption
Authors: York John , Lugo Kaley , Jarosz Lukasz , Toscani Michael 
Published: August 2021

Purpose
The purpose of this study is to understand how Amazon’s threat may impact the Pharmacy Industry as a whole and whether traditional drugstore chains such as consumer value stores (CVS) Pharmacy will need to re-think their business strategy, especially in the digital space, to account for potential disruption.

Design/methodology/approach
This is a hypothetical case study used as a teaching exercise to guide the learner through a decision-making process. The case starts by presenting a disruption in the retail pharmacy business that the main character must navigate by using real-world data and insights, provided in the case, to formulate a recommendation.

Findings
In an extremely competitive and consolidated pharmacy market, Amazon has the potential to change the business entirely. CVS Health will potentially face strong headwinds from Amazon’s PillPack and a downward trend in prescription sales. Regardless of the new competition, CVS Health continues to be innovative in the space. Instead of being a one-trick pony, CVS has encompassed the mentality of becoming a one-stop-shop by expanding into areas such as specialty pharmacy, health clinics, pharmacy benefits management and innovative digital capabilities.

Originality/value
This paper provides the reader with existing and known information about the evolving retail pharmacy business and allows the reader to interpret the new information to make their own decision on how a digital business strategy team can account for potential disruption.