bluuebunny/crossref_metadata_2025_split huggingface dataset:
SLIMEr: probing flexibility of lipid metabolism in yeast with an improved constraint-based modeling framework
Authors: Sánchez Benjamín J. , Li Feiran , Kerkhoven Eduard J. , Nielsen Jens 
Published: May 2018

SummaryA recurrent problem in genome-scale metabolic models (GEMs) is to correctly represent lipids as biomass requirements, due to the numerous of possible combinations of individual lipid species and the corresponding lack of fully detailed data. In this study we present SLIMEr, a formalism for correctly representing lipid requirements in GEMs using commonly available experimental data. SLIMEr enhances a GEM with mathematical constructs where we Split Lipids Into Measurable Entities (SLIME reactions), in addition to constraints on both the lipid classes and the acyl chain distribution. By implementing SLIMEr on the consensus GEM of Saccharomyces cerevisiae, we can predict accurate amounts of lipid species, analyze the flexibility of the resulting distribution, and compute the energy costs of moving from one metabolic state to another. The approach shows potential for better understanding lipid metabolism in yeast under different conditions. SLIMEr is freely available at https://github.com/SysBioChalmers/SLIMEr.
Metanalysis of genome-wide association studies for panic disorder suggest pathways and mechanisms of pathogenesis
Authors: Silva Rhayra Xavier do Carmo , Rocha Sueslene Prado , Souza Dainara Pereira dos Santos , Lima-Maximino Monica Gomes , Maximino Caio 
Published: May 2018

AbstractPanic disorder (PD) is characterized by abrupt surges of intense fear and distress. There is evidence for a genetic component in this disorder. We ran a meta-analysis of genome-wide association studies of patients with PD, and found 25 single-nucleotide polymorphisms that were associated with the disorder. Causal gene prediction based on these polymorphisms uncovered 20 hits. Exploratory analyses suggested that these genes formed interactor networks, which was enriched in signaling pathways associated with immune and inflammatory responses, as well as growth factors and other developmental mediators. A subset of genes is enriched in limbic regions of the human brain and in microglia and myelinating oligodendrocytes of mice. While these genes were not associated with relevant neurobehavioral phenotypes in mutant mice, expression levels of several causal genes in the amygdala, prefrontal cortex, hippocampus, hypothalamus, and adrenal gland of recombinant mouse strains was associated with endophenotypes of fear conditioning. Drug repositioning prediction was unsuccessful, but this does not discard these genes and pathways as targets for investigational drugs. In general,ASB3,EIF2S2, RASGRF2, andTRMT2B(and its coded proteins) emerged as interesting targets for mechanistic research on PD. These exploratory findings point towards hypotheses of pathogenesis and neuropharmacology that need to be further investigated.
Species-habitat networks: Bridging applied ecology and network theory
Authors: Marini Lorenzo , Bartomeus Ignasi , Rader Romina , Lami Francesco 
Published: May 2018

AbstractLand-use change is massively reshaping terrestrial ecosystems worldwide, and is recognized as a key driver of biodiversity loss with negative consequences on ecosystem functioning. Understanding how species use resources across landscapes is essential for the design of effective management strategies. Despite recent advances in theoretical ecology, there is still a gap between theory and applied ecological science and we lack the tools to manage entire landscapes to maximize biodiversity conservation and ecosystem service delivery. Here, we propose a new approach that uses existing bipartite networks to create species-habitat networks. Networks enable powerful visualizations via a common language that defines most processes in terms of nodes and links. This approach explicitly links multiple species and habitat resources, provides tools to estimate the importance of particular species in a given landscape, and quantifies emerging properties of entire habitat networks. Most existing metrics used to study properties of bipartite ecological networks can easily be adapted to investigate species-habitat relationships. One key advantage of this approach is that the scale of the derived ecological information will match the scale of management interventions. The flexibility of the proposed approach is that it can be easily applied across a range of ecological fields such as species conservation, habitat restoration, ecosystem services management, or invasion ecology. Network emerging properties could also be used to test the effects of large scale drivers of global change upon ecosystem structure and stability.
Regional neutrality evolves through local adaptive niche evolution
Authors: Leibold Mathew A. , Urban Mark C. , De Meester Luc , Klausmeier Christopher A. , Vanoverbeke Joost 
Published: May 2018

AbstractJoost Vanoverbeke, Department of Biology, Katholieke Universiteit Leuven, Leuven, Belgium. Abstract:
Biodiversity in natural systems can be maintained either because niche differentiation among competitors facilitates stable coexistence or because equal fitness among neutral species allows for their long-term co-occurrence despite a slow drift toward extinction. Whereas the relative importance of these two ecological mechanisms has been well-studied in the absence of evolution, the role of local adaptive evolution in maintaining biological diversity through these processes is less clear. Here we study the contribution of local adaptive evolution to coexistence in a landscape of interconnected patches subject to disturbance. Under these conditions, early colonists to empty patches following disturbance can often adapt to novel local conditions sufficiently fast to prevent successful colonization by other pre-adapted species. Over the long term, the iteration of these local-scale priority effects results in niche convergence of species at the regional scale even though species tend to monopolize local patches. Thus, the dynamics evolve from stable coexistence through niche differentiation to neutral co-occurrence at the landscape level while still maintaining strong local niche differentiation. Our model results show that neutrality can emerge at the regional scale from local, niche-based adaptive evolution, potentially resolving why ecologists often observe neutral distribution patterns at the landscape level despite strong niche divergence among local communities. Our results also demonstrate how local adaptive evolution can shape cryptic eco-evolutionary dynamics and thus alter the regional mechanisms that determine biological diversity and resistance to disturbance.
Simple but Robust Improvement in Multivoxel Pattern Classification
Authors: Lee Sangil , Kable Joseph W. 
Published: May 2018

AbstractMultivoxel pattern analysis (MVPA) typically begins with the estimation of single trial activation levels, and several studies have examined how different procedures for estimating single trial activity affect the ultimate classification accuracy of MVPA. Here we show that the currently preferred estimation procedures impart spurious shifts in run-level means that cause the estimated activities to be misaligned across runs. These shifts are caused by positive correlations between the means of different category activity estimates within the same scanner run. In other words, if the mean of the estimates for one type of trials is high (low) in a given scanner run, then the mean of the other type of trials is also high (low) for that same scanner run, and the mean across all trials therefore shifts from run to run. Simulations show that these correlations are unavoidable whenever there is a need to deconvolve overlapping trial activities in the presence of noise. We show that subtracting the mean across all trials of a run from all the estimates within that run (i.e., run-level mean centering of estimates), by cancelling out these mean shifts, leads to robust and significant improvements in MVPA classification accuracy. These improvements are seen in both simulated and real data across a wide variety of situations and can provide significant direct benefits with no computational cost. However, we also point out that there could be cases when mean activations are expected to shift across runs and that run-level mean centering could be detrimental in some of these cases (e.g., different proportion of trial types between different runs).
A rational account of human memory search
Authors: Zhang Qiong , Anderson John R. 
Published: May 2018

AbstractPerforming everyday tasks requires the ability to search through and retrieve past memories. A central paradigm to study human memory search is the semantic fluency task, where participants are asked to retrieve as many items as possible from a category (e.g. animals). Observed responses tend to be clustered semantically. To understand when our mind decides to switch from one cluster/patch to the next, recent work has proposed two competing mechanisms. Under the first switching mechanism, people make strategic decision to switch away from a depleted patch based on marginal value theorem, similar to optimal foraging in a spatial environment. The second switching mechanism demonstrates that similar behavior patterns can emerge using a random walk on a semantic network, without necessarily involving strategic switches. In the current work, instead of comparing competing switching mechanisms over observed human data, we propose a rational account of the problem by examining what would be the optimal patch-switching policy under the framework of reinforcement learning. The reinforcement learning agent, a Deep Q-Network (DQN), is built upon the random walk model and allows strategic switches based on features of the local semantic patch. After learning from rewards, the resulted policy of the agent gives rise to a third switching mechanism, which outperforms the previous two switching mechanisms. Our results provide theoretical justification of strategies used in human memory research, and shed light on how an optimal AI agent under realistic human constraints can generate hypothesis about human strategies in the same task.
ICeD-T Provides Accurate Estimates of Immune Cell Abundance in Tumor Samples by Allowing for Aberrant Gene Expression Patterns
Authors: Wilson Douglas R. , Ibrahim Joseph G. , Sun Wei 
Published: May 2018

AbstractImmunotherapies have achieved phenomenal success in the treatment of cancer and promise even more breakthroughs in the near future. The need to understand the underlying mechanisms of immunotherapies and to develop precision immunotherapy regimens has spurred great interest in characterizing immune cell composition within the tumor microenvironment. Several methods have been developed to estimate immune cell composition using gene expression data from bulk tumor samples. However, these methods are not flexible enough to handle aberrant patterns of gene expression data, e.g., inconsistent cell type-specific gene expression between purified reference samples and this cell type in tumor samples. In this paper, we present a novel statistical model for expression deconvolution called ICeD-T (Immune Cell Deconvolution in Tumor tissues), which models gene expression by a log-normal distribution that is appropriate for both microarray and RNA-seq data. ICeD-T automatically identifies aberrant genes whose expressions are inconsistent with the deconvolution model and down-weights their contributions to cell type abundance estimates. We evaluated the performance of ICeD-T versus existing methods in simulation studies and several real data analyses. ICeD-T displayed comparable or superior performance to these competing methods. Applying these methods to assess the relationship between immunotherapy response and immune cell composition, ICeD-T is able to identify significant associations that are missed by its competitors.
The Effect of Previous Life Cycle Phase on the Growth Kinetics, Morphology and Antibiotic Resistance ofSalmonellaTyphimurium DT104 in Brain Heart Infusion and Ground Chicken Extract
Authors: Hawkins Jabari L. , Uknalis Joseph , Oscar Tom P. , Schwarz Jurgen G. , Vimini Bob , Parveen Salina 
Published: May 2018

AbstractGrowth models are predominately used in the food industry to estimate the potential growth of select microorganisms under environmental conditions. The growth kinetics, cellular morphology and antibiotic resistance were studied throughout the life cycle ofSalmonellaTyphimurium. The effect of the previous life cycle phase (late log phase [LLP], early stationary phase [ESP], late stationary phase [LSP] and early death phase [EDP]) ofSalmonellaafter reinoculation in brain heart infusion broth (BHI), ground chicken extract (GCE) and BHI at pH 5, 7 and 9 and salt concentrations 2, 3 and 4% was investigated. The growth media and previous life cycle phase had significant effects on the lag time (λ), specific growth rate (μmax) and maximum population density (Ymax). At 2% and 4% salt concentration the LLP had the significantly (P<0.05) fastest μmax(1.07 and 0.69 log CFU/mL/h, respectively). As the cells transitioned from the late log phase (LLP) to the early death phase (EDP), the λ significantly (P<0.05) increased. At pH 5 and 9 the EDP had a significantly (P<0.05) lower Ymaxthan the LLP, ESP and LSP. As the cells transitioned from a rod shape to a coccoid shape in the EDP, the cells were more susceptible to antibiotics. The cells regained their resistance as they transitioned back to a rod shape from the EDP to the log and stationary phase. Our results revealed that growth kinetics, cell’s length, shape and antibiotic resistance were significantly affected by the previous life cycle phase.
Inflammation is the main risk factor of voriconazole overdose in hematological patients
Authors: Gautier-Veyret Elodie , Truffot A , Bailly S , Fonrose X , Thiebaut-Bertrand A , Tonini J , Cahn JY , Stanke-Labesque F 
Published: May 2018

AbstractAim: Voriconazole (VRC) overdoses are frequent and expose patients at high risk of adverse effects. This case-control study performed in hematological patients who benefited from VRC therapeutic drug monitoring from January 2012 to December 2015 aimed to identify risk factors of VRC overdose.Methods: Pharmacogenetic, biological, and demographic parameters at the time of VRC trough concentration (Cmin) were retrospectively collected from medical records. Cases (VRC overdose: defined by a VRC Cmin ≥ 4 mg/l; n = 31) were compared to controls (no VRC overdose: defined by VRC Cmin < 4 mg/L; n = 31) using non-parametric or Chi-square tests followed by multivariable analysis.Results: VRC overdoses were significantly associated with high CRP and bilirubin levels, intra-venous administration, and age in univariable analysis. In contrast, the proportion of CYP genotypes (CYP2C19, CYP3A4, or CYP3A5, considered alone or combined in a genetic score1) were not significantly different between patients who experienced a VRC overdose and those who did not. In multivariable analysis, the class of CRP level (defined by median CRP levels of 96 mg/l) was the sole independent risk factor of VRC overdose (p < 0.01). Patients with CRP levels > 96 mg/l had a 27-fold (IC 95%: [6-106]) higher risk of VRC overdose than patients with CRP levels ≤ 96 mg/l.Conclusion: This study demonstrates that inflammatory status, assessed by CRP levels, is the main risk factor of VRC overdose in French hematological patients, whereas pharmacogenetic determinants do not appear to be involved.
Low frequency ultrasound enhances chemotherapy sensitivity and induces autophagy in human paclitaxel resistance prostate cancer PC-3R cells through the ERs-mediated PI3K/Akt/mTOR signaling pathway
Authors: Wu Yuqi , Liu Xiaobing , Qin Zizhen , Wang Xiangwei 
Published: May 2018

AbstractSonodynamic therapy (SDT) as an emerging tumor-assisting method has attracted a great deal of interest in tumor therapy research in recent years. However, autophagy has been observed in several cancer cells treated with SDT and its role and mechanism are not clear. In the present study, we have investigated the effect of low frequency ultrasound on paclitaxel(PTX) resistance prostate cancer PC-3R cells and demonstrated that low frequency ultrasound could induce cancer cell apoptosis, inhibit the expression of multiple drug resistance genes (MRP3, MRP7 and P-glycoprotein) and reverse drug resistance; we further found that low frequency ultrasound induced autophagy in PC-3R cells. Our results demonstrated that low frequency ultrasound enhanced chemotherapy sensitivity and induced autophagy in PC-3R cells by inhibiting the PI3K/AKT/mTOR pathway. Moreover, we observed that low frequency ultrasound-induced cell autophagy was correlated with endoplasmic reticulum stress (ERs). 4-phenylbutyric acid (4-PBA) - mediated protection against ERs clearly implicated ERs in the activation of autophagy and cell apoptosis. In addition, the results showed that ERs served as an upstream effector of the PI3K/AKT/mTOR pathway. More importantly, we observed that inhibition of low frequency ultrasound-induced autophagy enhanced ERs and improved the role of low frequency ultrasound in reversing drug resistance. Over all, our findings provide new insights into the molecular mechanisms underlying low frequency ultrasound-mediated reversal of drug resistance and autophagy in PC-3R cells and support autophagy as a potential agent for enhancing anti-cancer effect of SDT.
Social learning strategies regulate the wisdom and madness of interactive crowds
Authors: Toyokawa Wataru , Whalen Andrew , Laland Kevin N. 
Published: May 2018

AbstractWhy groups of individuals sometimes exhibit collective ‘wisdom’ and other times maladaptive ‘herding’ is an enduring conundrum. Here we show that this apparent conflict is regulated by the social learning strategies deployed. We examined the patterns of human social learning through an interactive online experiment with 699 participants, varying both task uncertainty and group size, then used hierarchical Bayesian model-ftting to identify the individual learning strategies exhibited by participants. Challenging tasks elicit greater conformity amongst individuals, with rates of copying increasing with group size, leading to high probabilities of herding amongst large groups confronted with uncertainty. Conversely, the reduced social learning of small groups, and the greater probability that social information would be accurate for less-challenging tasks, generated ‘wisdom of the crowd’ effects in other circumstances. Our model-based approach provides evidence that the likelihood of collective intelligence versus herding can be predicted, resolving a longstanding puzzle in the literature.
Sensory cheating: adversarial body patterns can fool a convolutional visual system during signaling
Authors: Laan Andres , de Polavieja Gonzalo.G 
Published: May 2018

AbstractAnimals often assess each other by paying special attention to signals, which help to communicate the quality of each individual. When there is a conflict of interest between the signaler and the receiver, then the signaler has an incentive to cheat by producing signals which exaggerate its apparent quality. One opportunity for cheating might be to rely on sensory illusions, but it has been difficult to study sensory cheating because we have lacked quantitative models of complex visual perception. Here we address this problem by taking advantage of recent advances in modeling visual brain areas as convolutional neural networks. Given these models, we use the technique of adversarial perturbations to show how sensory cheating can shape animal appearance while nevertheless resulting in an evolutionarily stable signaling system. In our simulations, animals typically evolve exaggerated color patterns which might be analogous to the evolution of colorful body patterns in guppies.
A whole-brain and cross-diagnostic perspective on functional brain network dysfunction
Authors: Spronk Marjolein , Kulkarni Kaustubh , Ji Jie Lisa , Keane Brian P. , Anticevic Alan , Cole Michael W 
Published: May 2018

AbstractA wide variety of mental disorders have been associated with resting-state functional network alterations, which are thought to contribute to the cognitive changes underlying mental illness. These observations have seemed to support various theories postulating large-scale disruptions of brain systems in mental illness. However, existing approaches isolate differences in network organization without putting those differences in broad, whole-brain perspective. Using a graph distance measure – connectome-wide correlation – we found that whole-brain resting-state functional network organization in humans is highly similar across a variety of mental diseases and healthy controls. This similarity was observed across autism spectrum disorder, attention-deficit hyperactivity disorder, and schizophrenia. Nonetheless, subtle differences in network graph distance were predictive of diagnosis, suggesting that while functional connectomes differ little across health and disease those differences are informative. Such small network alterations may reflect the fact that most psychiatric patients maintain overall cognitive abilities similar to those of healthy individuals (relative to, e.g., the most severe schizophrenia cases), such that whole-brain functional network organization is expected to differ only subtly even for mental diseases with devastating effects on everyday life. These results suggest a need to reevaluate neurocognitive theories of mental illness, with a role for subtle functional brain network changes in the production of an array of mental diseases.
EternaBrain: Automated RNA design through move sets and strategies from an Internet-scale RNA videogame
Authors: Koodli Rohan V. , Keep Benjamin , Coppess Katherine R. , Portela Fernando ,   Eterna participants, Das Rhiju 
Published: May 2018

ABSTRACTEmerging RNA-based approaches to disease detection and gene therapy require RNA sequences that fold into specific base-pairing patterns, but computational algorithms generally remain inadequate for these secondary structure design tasks. The Eterna project has crowdsourced RNA design to human video game players in the form of puzzles that reach extraordinary difficulty. Here, we demonstrate that Eterna participants’ moves and strategies can be leveraged to improve automated computational RNA design. We present an eternamoves-large repository consisting of 1.8 million of player moves on 12 of the most-played Eterna puzzles as well as an eternamoves-select repository of 30,477 moves from the top 72 players on a select set of more advanced puzzles. On eternamoves-select, we present a multilayer convolutional neural network (CNN) EternaBrain that achieves test accuracies of 51% and 34% in base prediction and location prediction, respectively, suggesting that top players’ moves are partially stereotyped. Pipelining this CNN’s move predictions with single-action-playout (SAP) of six strategies compiled by human players solves 61 out of 100 independent puzzles in the Eterna100 benchmark. EternaBrain-SAP outperforms previously published RNA design algorithms and achieves similar or better performance than a newer generation of deep learning methods, while being largely orthogonal to these other methods. Our study provides useful lessons for future efforts to achieve human-competitive performance with automated RNA design algorithms.