bluuebunny/crossref_metadata_2025_split huggingface dataset:
Modeling the dynamics of Let-7-coupled gene regulatory networks linking cell proliferation to malignant transformation
Authors: Gérard Claude , Lemaigre Frédéric , Gonze Didier 
Published: January 2019

AbstractThe microRNA Let-7 controls the expression of proteins that belong to two distinct gene regulatory networks, namely a cyclin-dependent kinases (Cdks) network driving the cell cycle and a cell transformation network which can undergo an epigenetic switch between a non-transformed and a malignant transformed cell state.Using mathematical modeling and transcriptomic data analysis, we here investigate how Let-7 controls the cdk-dependent cell cycle network, and how it couples the latter with the transformation network. We also determine whether the two networks can be combined into a larger entity that impacts on cancer progression.Our analysis shows that the switch from a quiescent to a cycling state depends on the relative levels of Let-7 and several cell cycle activators. Numerical simulations further indicate that the Let-7-coupled cell cycle and transformation networks control each other, and our model identifies key players for this mutual control. Transcriptomic data analysis from the The Cancer Genome Atlas (TCGA) suggest that the two networks are activated in cancer, in particular in gastrointestinal cancers, and that the activation levels vary significantly among patients affected with a same cancer type. Our mathematical model, when applied to a heterogeneous cell population, suggests that heterogeneity among tumors results from stochastic switches between a non-transformed cell state with low proliferative capability and a transformed cell state with high proliferative property. The model further predicts that Let-7 may reduce tumor heterogeneity by decreasing the occurrence of stochastic switches towards a transformed, proliferative cell state.In conclusion, we identified the key components responsible for the qualitative dynamics of two GRNs interconnected by Let-7. The two GRNs are heterogeneously involved in several cancers, thereby stressing the need to consider patient’s specific GRN characteristics to optimize therapeutic strategies.
Attentional modulation of neural phase is enhanced by short-term training and linked to musical experience
Authors: Laffere Aeron , Dick Fred , Tierney Adam 
Published: January 2019

AbstractHow does the brain follow a sound that is mixed with others in a noisy environment? A possible strategy is to allocate attention to task-relevant time intervals while suppressing irrelevant intervals - a strategy that could be implemented by aligning neural modulations with critical moments in time. Here we tested whether selective attention to non-verbal sound streams is linked to shifts in the timing of attentional modulations of EEG activity, and investigated whether this neural mechanism can be enhanced by short-term training and musical experience. Participants performed a memory task on a target auditory stream presented at 4 Hz while ignoring a distractor auditory stream also presented at 4 Hz, but with a 180-degree shift in phase. The two attention conditions were linked to a roughly 180-degree shift in phase in the EEG signal at 4 Hz. Moreover, there was a strong relationship between performance on the 1-back task and the timing of the EEG modulation with respect to the attended band. EEG modulation timing was also enhanced after several days of training on the selective attention task and enhanced in experienced musicians. These results support the hypothesis that modulation of neural timing facilitates attention to particular moments in time and indicate that phase timing is a robust and reliable marker of individual differences in auditory attention. Moreover, these results suggest that nonverbal selective attention can be enhanced in the short term by only a few hours of practice and in the long term by years of musical training.
Rice annexin OsANN4 interacting with OsCDPK24, reduces root aerenchyma formation by modulating H2O2 under ABA treatment
Authors: Zhang Qian , Song Tao , Guan Can , Gao Yingjie , Ma Jianchao , Qi Zhiguang , Liu Jingze , Zhu Ying , Zhu Zhengge 
Published: January 2019

AbstractPlant annexins are calcium- and lipid-binding proteins that have multiple functions, and a significant amount of research on plant annexins has been reported in recent years. However, the functions of annexins in diverse biological processes in rice are largely unclear. Herein, we report that OsANN4, a calcium-binding rice annexin protein, is a substrate for OsCDPK24, and the OsANN4 phosphorylation site is the 13th serine, which is a key site for phosphorylation. Most strikingly, abscisic acid (ABA) promotes the interaction between OsANN4 and OsCDPK24. Moreover, knocking down OsANN4 by RNA interference resulted in visible and invisible phenotypes with exogenous ABA treatment, such as shorter shoots of seedlings, less lateral roots, earlier root aerenchyma formation and so on. The further analyzed results showed that decreased superoxide dismutase (SOD) and catalase (CAT) activity of the RNAi lines, which control H2O2 accumulation for redox homeostasis, and further promoted earlier aerenchyma formation of the root. These results suggest that a proposed molecular mechanism exists between OsANN4 and H2O2 production to response ABA.HighlightOsANN4 enhances SOD and CAT activities to scavenge H2O2 to alleviate the formation of aerenchyma under ABA treatment. OsCDPK24 interacts with and phosphorylates OsANN4, this interaction strengthened under ABA treatment.
Early urinary protein changes in the tumor-forming process of the NuTu-19 lung metastasis
Authors: Wei Jing , Ni Na , Meng Wenshu , Huan Yuhang , Gao Youhe 
Published: January 2019

AbstractThe early detection of cancer is essential for effective intervention. Urine, which lacks homeostatic control, has been used to reflect early systemic changes in subcutaneous cancer models. However, urine has not been used to predict whether tumors will be formed in animal models. In this study, a cancer model was established by the tail-vein injection of 2 million NuTu-19 tumor cells. Approximately half of the rats formed lung metastatic carcinoma tumors. Urine samples were randomly selected from 4 tumor-forming rats and 4 non-tumor-forming rats on day 0/12/27/39/52 and analyzed by label-free proteomic quantitative analysis. Compared to day 0, a total of 180 and 118 urinary proteins in tumor-forming and non-tumor-forming rats, respectively, showed significant changes. Functional enrichment analysis of the differential proteins in tumor-forming rats revealed similar events during cancer metastasis cascades and tumor progression, such as the migration of tumor cell lines, coagulation system, TGF-β signaling, the STAT3 pathway and the adhesion of myeloid cells and alveolar macrophages. The differential proteins in non-tumor-forming rats were associated with agranulocyte/granulocyte adhesion and diapedesis, glutathione biosynthesis, IL-12 signaling and vitamin metabolism. After validation by parallel reaction monitoring (PRM) targeted proteomics quantitative analysis, a total of 22 urinary proteins showed significant changes in the early phase of the lung tumor formation process, 5 of which have been associated with the mechanisms of lung cancer, while 3 have been suggested as lung cancer biomarkers. Another 14 urinary proteins showed significant changes in the early phase in non-tumor-forming rats. Our results indicate that urine proteins could differentiate early tumor-forming and non-tumor-forming status.
Many unreported crop pests and pathogens are probably already present
Authors: Bebber Daniel P. , Field Elsa , Heng Gui , Mortimer Peter , Holmes Timothy , Gurr Sarah J. 
Published: January 2019

SummaryBiotic invasions threaten global biodiversity and ecosystem function. Such incursions present challenges to agriculture where invasive pest species cause significant production losses require major economic investment to control and can cause significant production losses. Pest Risk Analysis (PRA) is key to prioritizing agricultural biosecurity efforts, but is hampered by incomplete knowledge of current crop pest and pathogen distributions. Here we develop predictive models of current pest distributions and test these models using new observations at sub-national resolution. We apply generalized linear models (GLM) to estimate presence probabilities for 1739 crop pests in the CABI pest distribution database. We test model predictions for 100 unobserved pest occurrences in the People’s Republic of China (PRC), against observations of these pests abstracted from the Chinese literature. This resource has hitherto been omitted from databases on global pest distributions. Finally, we predict occurrences of all unobserved pests globally. Presence probability increases with host presence, presence in neighbouring regions, per capita GDP, and global prevalence. Presence probability decreases with mean distance from coast and known host number per pest. The models were good predictors of pest presence in Provinces of the PRC, with area under the ROC curve (AUC) values of 0.75 – 0.76. Large numbers of currently unobserved, but probably present pests (defined here as unreported pests with a predicted presence probability > 0.75), are predicted in China, India, southern Brazil and some countries of the former USSR. Our results shows that GLMs can predict presences of pseudo-absent pests at sub-national resolution. The Chinese scientific literature has been largely inaccessible to Western academia but contains important information that can support PRA. Prior studies have often assumed that unreported pests in a global distribution database represents a true absence. Our analysis provides a method for quantifying pseudo-absences to enable improved PRA and species distribution modelling.
Mitigating Data Scarcity in Protein Binding Prediction Using Meta-Learning
Authors: Luo Yunan , Ma Jianzhu , Zhao Xiaoming , Su Yufeng , Liu Yang , Ideker Trey , Peng Jian 
Published: January 2019

AbstractA plethora of biological functions are performed through various types of protein-peptide binding. Prime examples include the protein kinase phosphorylation on peptide substrates and the binding of major histocompatibility complex to neoantigens in the immune system. Understanding the specificity of protein-peptide interactions is critical for unraveling the architectures of functional pathways and the mechanisms of cellular processes in human cells. Despite mass-spectrometric techniques were developed for the identification of protein-peptide interactions, our understanding of the preferences of proteins on their binding peptides is still rudimentary. As a complementary direction, a line of computational prediction methods has been recently proposed to predict protein-peptide bindings which efficiently provide rich functional annotations on a large scale. To achieve a high prediction accuracy, these computational methods require a sufficient amount of data to build the prediction model. However, the number of experimentally verified protein-peptide bindings is often limited in real cases. For example, a majority of protein kinases have very few experimentally verified phosphorylation sites (e.g., less than 30 sites) in existing databases. These methods are thus limited to building accurate prediction models for only well-characterized proteins with a large volume of known binding peptides and cannot be extended to predict new binding peptides for less-studied proteins. In this paper, we introduce a generic framework to address this issue of data scarcity in protein binding prediction. We demonstrate the applicability of our framework in predicting kinase-specific phosphorylation sites. Our method uses an effective training strategy to build a prediction model with robust transferability. The model is able to predict the phosphorylation sites of a less-studied kinase, even if there is only a small number of phosphorylation sites known for this kinase. To achieve this, we train the model via a meta-learning phase followed by a few-shot learning phase. We demonstrate our framework has better transferability than state-of-the-art methods and is effective in utilizing limited data to accurately predict phosphorylation sites for less-characterized kinases. The implementation of our framework is available athttps://github.com/luoyunan/MetaKinase.
Stereotyped large amplitude cortical LFP events can be clustered and reveal precisely ordered phase-locking in neuronal populations
Authors: Mitelut Catalin C. , Spacek Martin A. , Chan Allen W. , Murphy Tim H. , Swindale Nicholas V. 
Published: January 2019

AbstractDuring quiet wakefulness, slow-wave sleep and anesthesia, mammalian cortex exhibits a synchronised state during which transient changes in the local field potential (LFP) accompany periods of increased single neuron firing, known as UP-states. While UP-state genesis is still debated (Crunelli and Hughes, 2010) such transitions may constitute the default activity pattern of the entire cortex (Neske, 2016). Recent findings of preserved firing order between UP-state transitions and stimulus processing in high-firing rate (>2Hz) rat auditory and barrel cortex neurons (Luczak et al., 2015) support this hypothesis. Yet it is unknown whether UP-states are homogeneous and whether neurons with firing rates <2Hz in visual cortex or other species exhibit spiking order. Using extracellular recordings during anesthetized states in cat visual cortex and mouse visual, auditory and barrel cortex, we show that UP-states can be tracked and clustered based on the shape of the LFP waveform. We show that LFP event clusters (LECs) have current-source-density profiles that are common across different recordings or animals and using simultaneous electrophysiology and widefield voltage and calcium imaging in mouse we confirm that LEC transitions are cortex-wide phenomena. Individual LEC events can be resolved in time to within 1 – 4 ms and they elicit synchronous firing of over 75% of recorded neurons with most neurons synchronizing their firing to within ±5 – 15 ms relative LECs. Firing order of different neurons during LEC events was preserved over periods of ~30 minutes enabling future studies of UP-state transitions and firing order with near millisecond precision.Significant StatementDuring sleep and anesthetic states mammalian cortex undergoes substantial changes from awake active states. Recent studies show that single neurons in some cortical areas in rats undergo increased spiking during sleep and anesthetic states (called UP-state transitions) with some neurons firing in an order similar to awake states. This suggests that sensory processing may be similar across all states and that firing order is important for stimulus processing. Yet UP-state transitions remain poorly understood and it is unclear whether firing order is present in other cortical areas or species. Here we describe multiple classes of UP-state transitions and show most neurons in visual cortex in cats and visual, barrel and auditory cortex in mice exhibit firing order during such transitions.
A statistical modeling framework to assess cross-frequency coupling while accounting for confounding effects
Authors: Nadalin Jessica , Martinet Louis-Emmanuel , Blackwood Ethan , Lo Meng-Chen , Widge Alik S. , Cash Sydney S. , Eden Uri T. , Kramer Mark A. 
Published: January 2019

AbstractCross frequency coupling (CFC) is emerging as a fundamental feature of brain activity, correlated with brain function and dysfunction. Many different types of CFC have been identified through application of numerous data analysis methods, each developed to characterize a specific CFC type. Choosing an inappropriate method weakens statistical power and introduces opportunities for confounding effects. To address this, we propose a statistical modeling framework to estimate high frequency amplitude as a function of both the low frequency amplitude and low frequency phase; the result is a measure of phase-amplitude coupling that accounts for changes in the low frequency amplitude. We show in simulations that the proposed method successfully detects CFC between the low frequency phase or amplitude and the high frequency amplitude, and outperforms an existing method in biologically-motivated examples. Applying the method to in vivo data, we illustrate how CFC evolves during seizures and is affected by electrical stimuli.
Age-related kinematic performance must be considered during fast head-neck rotation target task in individuals aged from 8 to 85 years old
Authors: Hage Renaud , Dierick Frédéric , Roussel Nathalie , Pitance Laurent , Detrembleur Christine 
Published: January 2019

AbstractRationaleKinematic behavior during fast cervical rotations is a useful parameter for assessing sensorimotor control performances in neck-pain patients. However, in asymptomatic individuals from children to seniors, the influence of age still needs to be explored.Aim and methodWe assessed the impact of age on sensorimotor control performance of the head-neck with execution time and kinematic variables (time of task, mean speed/acceleration/deceleration, overshoots, minimum/maximum speed) during standardized fast rotation target task using the DidRen Laser test. Eighty volunteers were stratified in four different age-groups: Children [8-14y]: n=16; Young Adults [18-35y]: n=29; Old Adults [36-64y]: n=18; Seniors [65-85y]: n=17.ResultsTo perform the test, Children were slower compared to Young Adults (p<0.001) and Old Adults (p<0.001). It was also slower in Seniors compared to Young Adults (p<0.013). Mean speed was slower in Children and Seniors compared to Young Adults (p<0.001) and Old Adults (p<0.001). Mean acceleration was slower for Children compared to Young Adults (p<0.016) and Old Adults (p<0.015). Mean deceleration was slower for Children compared to Young Adults (p<0.001) and Old Adults (p<0.003).ConclusionThe DidRen Laser test allows us to discriminate age-specific performances for mean speed, acceleration and deceleration. Seniors and Children needed to be slower to become as precise as Young and Old people, no difference was observed for overshoots which assesses accuracy of movement. Age must therefore be considered as a key parameter when analyzing execution time and kinematic results during DidRen Laser test.
Alveolar macrophage chromatin is modified to orchestrate host response toMycobacterium bovisinfection
Authors: Hall Thomas Jonathan , Vernimmen Douglas , Browne John Andrew , Mullen Michael P. , Gordon Stephen Vincent , MacHugh David Evan , O’Doherty Alan Mark 
Published: January 2019

AbstractBackgroundBovine tuberculosis is caused by infection withMycobacterium bovis, which can also cause disease in a range of other mammals, including humans. Alveolar macrophages are the key immune effector cells that first encounterM. bovisand how the macrophage epigenome responds to mycobacterial pathogens is currently not well understood.ResultsHere, we have used chromatin immunoprecipitation sequencing (ChIP-seq), RNA-seq and miRNA-seq to examine the effect ofM. bovisinfection on the bovine alveolar macrophage (bAM) epigenome. We show that H3K4me3 is more prevalent, at a genome-wide level, in chromatin fromM. bovis-infected bAM compared to control non-infected bAM; this was particularly evident at the transcriptional start sites of genes that determine programmed macrophage responses to mycobacterial infection (e.g. M1/M2 macrophage polarisation). This pattern was also supported by the distribution of RNA Polymerase II (PolII) ChIP-seq results, which highlighted significantly increased transcriptional activity at genes demarcated by permissive chromatin. Identification of these genes enabled integration of high-density GWAS data, which revealed genomic regions associated with resilience to infection withM. bovisin cattle.ConclusionsThrough integration of these data, we show that bAM transcriptional reprogramming occurs through differential distribution of H3K4me3 and PolII at key immune genes. Furthermore, this subset of genes can be used to prioritise genomic variants from a relevant GWAS data set.
Inferring wildlife poaching in Southeast Asia with multispecies dynamic occupancy models
Authors: Marescot Lucile , Lyet Arnaud , Singh Rohit , Carter Neil , Gimenez Olivier 
Published: January 2019

AbstractDetermining the ‘space race’ between sympatric species is crucial to understand the effects of interspecific interactions on the extinction risk of species threatened by poachers, predators, pathogens, and invasive competitors. Dynamic two-species occupancy models provide a flexible framework to decompose complex species interaction patterns while accounting for imperfect detection. In particular, these models can describe poachers-wildlife interactions by considering the occupancy, the extinction and colonisation probabilities of wildlife conditional on the presence or absence of poachers and vice versa. We apply our model to a case study on wildlife poaching in the Eastern plains of Cambodia. We used co-occurrence data extracted from the database of the SMART partnership to study the distribution dynamics between poachers and six ungulate species regarded as main prey of tigers. We used 4 years of survey data reporting the locations of ranger patrols on the detection of snares with visual detections or presence signs of the ungulates. Our results showed that a substantial proportion of the sites occupied by ungulate species went extinct over the years of the study while the proportion of sites colonised by poachers increased. We also showed, for the first time, that spatio-temporal heterogeneity in the patrolling effort explains a great deal of the variation in the detection of poachers and ungulates. Our approach provides practitioners with a flexible and robust tool to assess conservation status of species and extinction risk of wildlife populations. It can assist managers in better evaluating, learning and adapting the patrolling strategies of rangers.
Follicle-stimulating hormone and luteinizing hormone increase Ca2+ in the granulosa cells of mouse ovarian follicles1
Authors: Egbert Jeremy R. , Fahey Paul G. , Reimer Jacob , Owen Corie M. , Evsikov Alexei V. , Nikolaev Viacheslav O. , Griesbeck Oliver , Ray Russell S. , Tolias Andreas S. , Jaffe Laurinda A. 
Published: January 2019

ABSTRACTIn mammalian ovarian follicles, follicle stimulating hormone (FSH) and luteinizing hormone (LH) signal primarily through the G-protein Gs to elevate cAMP, but both of these hormones can also elevate Ca2+ under some conditions. Here we investigate FSH- and LH-induced Ca2+ signaling in intact follicles of mice expressing genetically encoded Ca2+ sensors, Twitch-2B and GCaMP6s. At a physiological concentration (1 nM), FSH elevates Ca2+ within the granulosa cells of preantral and antral follicles. The Ca2+ rise begins several minutes after FSH application, peaks at ~10 minutes, remains above baseline for another ~10 minutes, and depends on extracellular Ca2+. However, suppression of the FSH-induced Ca2+ increase by reducing extracellular Ca2+ does not inhibit FSH-induced phosphorylation of MAP kinase, estradiol production, or the acquisition of LH responsiveness. Like FSH, LH also increases Ca2+, when applied to preovulatory follicles. At a physiological concentration (10 nM), LH elicits Ca2+ oscillations in a subset of cells in the outer mural granulosa layer. These oscillations continue for at least 6 hours and depend on the activity of Gq family G-proteins. Suppression of the oscillations by Gq inhibition does not inhibit meiotic resumption, but does delay the time to 50% ovulation by about 3 hours. In summary, both FSH and LH increase Ca2+ in the granulosa cells of intact follicles, but the functions of these Ca2+ rises are only starting to be identified.Summary sentenceBoth FSH and LH increase Ca2+ in the granulosa cells of intact ovarian follicles from mice expressing genetically encoded sensors.