bluuebunny/crossref_metadata_2025_split huggingface dataset:
GM1 asymmetry in the membrane stabilizes pores
Authors: Aleksanyan Mina , Lira Rafael B. , Steinkühler Jan , Dimova Rumiana 
Published: January 2022

ABSTRACTCell membranes are highly asymmetric and their stability against poration is crucial for survival. We investigated the influence of membrane asymmetry on electroporation of giant unilamellar vesicles with membranes doped with GM1, a ganglioside asymmetrically enriched in the outer leaflet of neuronal cell membranes. Compared to symmetric membranes, the lifetimes of micronsized pores are about an order of magnitude longer suggesting that pores are stabilized by GM1. Internal membrane nanotubes caused by the GM1 asymmetry, obstruct and additionally slow down pore closure, effectively reducing pore edge tension and leading to leaky membranes. Our results point to the drastic effects this ganglioside can have on pore resealing in biotechnology applications based on poration as well as on membrane repair processes.SIGNIFICANCEMembrane pore closure is crucial for cell survival and is important for biotechnological and medicine applications based on transfer of material, e.g. drugs, genes, through pores. Electroporation is widely used as means to perforate the membrane but factors governing membrane resealing are still a matter of debate, in particular the large variations of pore lifetimes. Here, we probed the effect of bilayer asymmetry on pore dynamics employing cell-sized giant unilamellar vesicles doped with the ganglioside GM1 (asymmetrically enriched in neurons). We find that the presence of GM1 and its asymmetric distribution in the bilayer dramatically slows down pore resealing, not only by mere molecular stabilization of the pore rim, but also by generating membrane nanotubes.
Chronic demyelination of rabbit lesions is attributable to failed oligodendrocyte progenitor cell repopulation
Authors: Cooper James J. M. , Polanco Jessie J. , Saraswat Darpan , Peirick Jennifer , Seidl Anna , Li Yi , Ma Dan , Sim Fraser J. 
Published: January 2022

AbstractThe failure of remyelination in the human CNS contributes to axonal injury and disease progression in multiple sclerosis (MS). In contrast to regions of chronic demyelination in the human brain, remyelination in murine models is preceded by abundant oligodendrocyte progenitor cell (OPC) repopulation, such that OPC density within regions of demyelination far exceeds that of normal white matter. As such, we hypothesized that efficient OPC repopulation was a prerequisite of successful remyelination, and that increased lesion volume may contribute to the failure of OPC repopulation in human brain. In this study, we characterized the pattern of OPC activation and proliferation following induction of lysolecithin-induced chronic demyelination in adult rabbits. The density of OPCs never exceeded that of normal white matter and oligodendrocyte density did not recover even at 6 months post-injection. Rabbit OPC recruitment in large lesions was further characterized by chronic Sox2 expression in OPCs located in the lesion core and upregulation of quiescence-associated Prrx1 mRNA at the lesion border. Surprisingly, when small rabbit lesions of equivalent size to mouse were induced, they too exhibited reduced OPC repopulation. However, small lesions were distinct from large lesions as they displayed an almost complete lack of OPC proliferation following demyelination. These differences in the response to demyelination suggest that both volume dependent and species-specific mechanisms are critical in the regulation of OPC proliferation and lesion repopulation and suggest that alternate models will be necessary to fully understand the mechanisms that contribute to failed remyelination in MS.Main Points- Chronic demyelination in the rabbit CNS was associated with reduced OPC repopulation.- Quiescent OPCs accumulated around the edge of rabbit lesions.- OPC and oligodendrocyte repopulation was reduced in rabbit regardless of lesion volume.
Computational Model of Heterogeneity in Melanoma: Designing Therapies and Predicting Outcomes
Authors: Hodgkinson Arran , Trucu Dumitru , Lacroix Matthieu , Le Cam Laurent , Radulescu Ovidiu 
Published: January 2022

AbstractCutaneous melanoma is a highly invasive tumor and, despite the development of recent therapies, most patients with advanced metastatic melanoma have a poor clinical outcome. The most frequent mutations in melanoma affect the BRAF oncogene, a protein kinase of the MAPK signaling pathway. Therapies targeting both BRAF and MEK are effective for only 50% of patients and, almost systematically, generate drug resistance. Genetic and non-genetic mechanisms associated with the strong heterogeneity and plasticity of melanoma cells have been suggested to favor drug resistance but are still poorly understood. Recently, we have introduced a novel mathematical formalism allowing the representation of the relation between tumor heterogeneity and drug resistance and proposed several models for the development of resistance of melanoma treated with BRAF/MEK inhibitors. In this paper, we further investigate this relationship by using a new computational model that copes with multiple cell states identified by single cell mRNA sequencing data in melanoma treated with BRAF/MEK inhibitors. We use this model to predict the outcome of different therapeutic strategies. The reference therapy, referred to as “continuous” consists in applying one drug (or several drugs) without disruption. In “combination therapy”, several drugs are used sequentially. In “adaptive therapy” drug application is interrupted when the tumor size is below a lower threshold and resumed when the size goes over an upper threshold. We show that, counter-intuitively, the optimal protocol in combination therapy of BRAF/MEK inhibitors with a hypothetical drug targeting cell states that develop later during the tumor response to kinase inhibitors, is to treat first with this hypothetical drug. Also, even though there is little difference in the timing of emergence of the resistance between continuous and adaptive therapies, the spatial distribution of the different melanoma subpopulations is more zonated in the case of adaptive therapy.
DelaySSAToolkit.jl: stochastic simulation of reaction systems with time delays in Julia
Authors: Fu Xiaoming , Zhou Xinyi , Gu Dongyang , Cao Zhixing , Grima Ramon 
Published: January 2022

AbstractSummaryDelaySSAToolkit.jl is a Julia package for modelling reaction systems with non-Markovian dynamics, specifically those with time delays. These delays implicitly capture multiple intermediate reaction steps and hence serve as an effective model reduction technique for complex systems in biology, chemistry, ecology and genetics. The package implements a variety of exact formulations of the delay stochastic simulation algorithm.Contactxmfu@ecust.edu.cn, zcao@ecust.edu.cn, or ramon.grima@ed.ac.ukAvailability and ImplementationThe source code and documentation of DelaySSAToolkit.jl are available at https://github.com/palmtree2013/DelaySSAToolkit.jl.
Coherence of Terrestrial Vertebrate Species Richness with External Drivers Across Scales and Taxonomic Groups
Authors: O’Malley Conor P. B. , Roberts Gareth G. , Mannion Philip D. , Hackel Jan , Wang Yanghua 
Published: January 2022

AbstractAimUnderstanding connections between environment and biodiversity is crucial for conservation, identifying causes of ecosystem stress, and predicting population responses to changing environments. Explaining biodiversity requires an understanding of how species richness and environment co-vary across scales. Here, we identify scales and locations at which biodiversity is generated and correlates with environment.LocationFull latitudinal range per continent.Time periodPresent-day.Major taxa studiedTerrestrial vertebrates: all mammals, carnivorans, bats, songbirds, humming-birds, amphibians.MethodsWe describe the use of wavelet power spectra, cross-power and coherence for identifying scale-dependent trends across Earth’s surface. Spectra reveal scale- and location-dependent coherence between species richness and topography (E), mean annual precipitation (Pn), temperature (Tm) and annual temperature range (∆T).Results>97% of species richness of taxa studied is generated at large scales, i.e. wavelengths 103km, with 30–69% generated at scales 104km. At these scales, richness tends to be highly coherent and anti-correlated withEand ∆T, and positively correlated withPnandTm. Coherence between carnivoran richness and ∆Tis low across scales, implying insensitivity to seasonal temperature variations. Conversely, amphibian richness is strongly anti-correlated with ∆Tat large scales. At scales 103km, examined taxa, except carnivorans, show highest richness within the tropics. Terrestrial plateaux exhibit high coherence between carnivorans andEat scales∼103km, consistent with contribution of large-scale tectonic processes to biodiversity. Results are similar across different continents and for global latitudinal averages. Spectral admittance permits derivation of rules-of-thumb relating long-wavelength environmental and species richness trends.Main conclusionsSensitivities of mammal, bird and amphibian populations to environment are highly scale-dependent. At large scales, carnivoran richness is largely independent of temperature and precipitation, whereas amphibian richness correlates strongly with precipitation and temperature, and anti-correlates with temperature range. These results pave the way for spectral-based calibration of models that predict biodiversity response to climate change scenarios.
Time-resolved parameterization of aperiodic and periodic brain activity
Authors: Wilson Luc E. , da Silva Castanheira Jason , Baillet Sylvain 
Published: January 2022

AbstractMacroscopic neural dynamics comprise both aperiodic and periodic signal components. Recent advances in parameterizing neural power spectra offer practical tools for evaluating these features separately. Although neural signals vary dynamically and express non-stationarity in relation to ongoing behaviour and perception, current methods yield static spectral decompositions. Here, we introduce Spectral Parameterization Resolved in Time (SPRiNT) as a novel method for decomposing complex neural dynamics into periodic and aperiodic spectral elements in a time- resolved manner. First, we demonstrate with naturalistic synthetic data SPRiNT’s capacity to reliably recover time-varying spectral features. We emphasize SPRiNT’s specific strengths compared to other time-frequency parameterization approaches based on wavelets. Second, we use SPRiNT to illustrate how aperiodic spectral features fluctuate across time in empirical resting-state electroencephalography data (n = 178), and relate the observed changes in aperiodic parameters over time to participants’ demographics and behaviour. Lastly, we use SPRiNT to demonstrate how aperiodic dynamics relate to movement behaviour in intracranial recordings in rodents. We foresee SPRiNT responding to growing neuroscientific interests in the parameterization of time-varying neural power spectra and advancing the quantitation of complex neural dynamics at the natural time scales of behaviour.Significance StatementThe new method and reported findings address a growing interest in neuroscience for research tools that can reliably decompose brain activity at the mesoscopic scale into interpretable components. We show that the new approach proposed is capable of tracking transient, dynamic spectral (aperiodic and periodic) components across time, both in synthetic data and in in vivo experimental data. We anticipate that this novel technique, SPRiNT, will enable new neuroscience inquiries that reconcile multifaceted neural dynamics with complex behaviour.
chromMAGMA: regulatory element-centric interrogation of risk variants
Authors: Nameki Robbin , Shetty Anamay , Dareng Eileen , Tyrer Jonathan , Lin Xianzhi ,   the Ovarian Cancer Association Consortium, Pharoah Paul , Corona Rosario I. , Kar Siddhartha , Lawrenson Kate 
Published: January 2022

ABSTRACTGenome-wide association studies (GWASs) have identified thousands of genetic variants associated with common polygenic traits. The candidate causal risk variants reside almost exclusively in noncoding regions of the genome and the underlying mechanisms remain elusive for most. Innovative approaches are necessary to understand their biological function. Multimarker analysis of genomic annotation (MAGMA) is a widely used program that nominates candidate risk genes by mapping single-nucleotide polymorphism (SNP) summary statistics from genome-wide association studies to gene bodies. We augmented MAGMA into chromatin-MAGMA (chromMAGMA), a novel method to nominate candidate risk genes based on the presence of risk variants within noncoding regulatory elements (REs). We applied chromMAGMA to a genetic susceptibility dataset for epithelial ovarian cancer (EOC), a rare gynecologic malignancy characterized by high mortality. Disease-specific RE landscapes were defined using H3K27ac chromatin immunoprecipitation-sequence data. This identified 155 unique candidate EOC risk genes across five EOC histotypes; 83% (105/127) of high-grade serous ovarian cancer risk genes had not previously been implicated in this EOC histotype. Risk genes nominated by chromMAGMA converged on mRNA splicing and transcriptional dysregulation pathways. chromMAGMA is a pipeline that nominates candidate risk genes through a gene regulation-focused approach and helps interpret the biological mechanism of noncoding risk variants in complex diseases.
Transcriptional mapping of the macaque retina and RPE-choroid reveals conserved inter-tissue transcription drivers and signaling pathways
Authors: Mungale Ameera , McGaughey David M. , Zhang Congxiao , Yousaf Sairah , Liu James , Brooks Brian P. , Maminishkis Arvydas , Fufa Temesgen D. , Hufnagel Robert B. 
Published: January 2022

AbstractPurposeThe macula and fovea comprise a highly sensitive visual detection tissue that is susceptible to common disease processes like age-related macular degeneration (AMD). Our understanding of the molecular determinants of high acuity vision remains unclear, as few model organisms possess a human-like fovea. We explore transcription factor networks and receptor-ligand interactions to elucidate tissue interactions in the macula and peripheral retina and concomitant changes in the underlying retinal pigment epithelium (RPE)/choroid.MethodsPoly-A selected, 100 bp paired-end RNA-sequencing (RNA-seq) was performed across the macular/foveal, perimacular, and temporal peripheral regions of the neural retina and RPE/choroid tissues of four adult Rhesus macaque eyes to characterize region- and tissue-specific gene expression. RNA-seq reads were mapped to both the macaque and human genomes for maximum alignment and analyzed for differential expression and Gene Ontology (GO) enrichment.ResultsComparison of the neural retina and RPE/choroid tissues indicated distinct, contiguously changing gene expression profiles from fovea through perimacula to periphery. Top GO enrichment of differentially expressed genes in the RPE/choroid included cell junction organization and epithelial cell development. Expression of transcriptional regulators and various disease-associated genes show distinct location-specific preference and retina-RPE/choroid tissue-tissue interactions.ConclusionsRegional gene expression changes in the macaque retina and RPE/choroid is greater than that found in previously published transcriptome analysis of the human retina and RPE/choroid. Further, conservation of human macula-specific transcription factor profiles and gene expression in macaque tissues suggest a conservation of programs required for retina and RPE/choroid function and disease susceptibility.
FAME: Fragment-based Conditional Molecular Generation for Phenotypic Drug Discovery
Authors: Pham Thai-Hoang , Xie Lei , Zhang Ping 
Published: January 2022

AbstractDe novo molecular design is a key challenge in drug discovery due to the complexity of chemical space. With the availability of molecular datasets and advances in machine learning, many deep generative models are proposed for generating novel molecules with desired properties. However, most of the existing models focus only on molecular distribution learning and target-based molecular design, thereby hindering their potentials in real-world applications. In drug discovery, phenotypic molecular design has advantages over target-based molecular design, especially in first-in-class drug discovery. In this work, we propose the first deep graph generative model (FAME) targeting phenotypic molecular design, in particular gene expression-based molecular design. FAME leverages a conditional variational autoencoder framework to learn the conditional distribution generating molecules from gene expression profiles. However, this distribution is difficult to learn due to the complexity of the molecular space and the noisy phenomenon in gene expression data. To tackle these issues, a gene expression denoising (GED) model that employs contrastive objective function is first proposed to reduce noise from gene expression data. FAME is then designed to treat molecules as the sequences of fragments and learn to generate these fragments in autoregressive manner. By leveraging this fragment-based generation strategy and the denoised gene expression profiles, FAME can generate novel molecules with a high validity rate and desired biological activity. The experimental results show that FAME outperforms existing methods including both SMILES-based and graph-based deep generative models for phenotypic molecular design. Furthermore, the effective mechanism for reducing noise in gene expression data proposed in our study can be applied to omics data modeling in general for facilitating phenotypic drug discovery.
Brain-optimized neural networks learn non-hierarchical models of representation in human visual cortex
Authors: St-Yves Ghislain , Allen Emily J. , Wu Yihan , Kay Kendrick , Naselaris Thomas 
Published: January 2022

AbstractDeep neural networks (DNNs) trained to perform visual tasks learn representations that align with the hierarchy of visual areas in the primate brain. This finding has been taken to imply that the primate visual system forms representations by passing them through a hierarchical sequence of brain areas, just as DNNs form representations by passing them through a hierarchical sequence of layers. To test the validity of this assumption, we optimized DNNs not to perform visual tasks but to directly predict brain activity in human visual areas V1–V4. Using a massive sampling of human brain activity, we constructed brain-optimized networks that predict brain activity even more accurately than task-optimized networks. We show that brain-optimized networks can learn representations that diverge from those formed in a strict hierarchy. Brain-optimized networks do not need to align representations in V1–V4 with layer depth; moreover, they are able to accurately model anterior brain areas (e.g., V4) without computing intermediary representations associated with posterior brain areas (e.g., V1). Our results challenge the view that human visual areas V1–V4 act—like the early layers of a DNN—as a serial pre-processing sequence for higher areas, and suggest they may subserve their own independent functions.
Evaluating the Phytotoxicity of Methanolic Extracts of Parthenium hysterophorus L. on Selected Crops and Weeds
Authors: Khairul Bashar H.M. , Juraimi Abdul Shukor , Ahmad-Hamdani Muhammad Saiful , Uddin Md Kamal , Asib Norhayu , Anwar Md. Parvez , Rahaman Ferdoushi , Haque Mohammad Amdadul , Hossain Akbar 
Published: January 2022

AbstractHerbicides made from natural molecules may be a good environmentally friendly alternative to synthetic chemical herbicides for weed control. As a result, this investigation was carried out to ascertain the phytotoxicity of Parthenium hysterophorus L. as well as to identify its phenolic components. Germination of seeds and development of seedlings of Vigna subterranea (L.) Verdc, Raphanus sativus (L.) Domin, Cucurbita maxima Duchesne., Cucumis sativus L., Solanum lycopersicum L., Capsicum frutescens L., Zea mays L., Abelmoschus esculentus (L.) Moench, Daucus carota L., Digitaria sanguinalis (L.) Scop and Eleusine indica (L.) Gaertn were investigated using P. hysterophorus leaf, stem, and flower methanol extracts. Six concentrations (25, 50, 75, 100, and 150 g L−1) were comparison to the control (distilled water). The concentration of extracts increased, the rate of the seed sprouting and seedling growth decreased. EC50 values showed that the extraction of leaf of P. hysterophorus (811) was phytotoxic in comparison to the stem (1554) and flower (1109) extract. According to PCA analysis, Raphanus sativus, Solanum lycopersicum, Capsicum frutescens, Abelmoschus esculentus, Daucus carota, Digitaria sanguinalis, and Eleusine indica were all very susceptible to allelochemicals. A LC-MS analysis revealed that the P. hysterophorus leaf extract contained 7 phenolic compounds that were responsible for inhibition. These studies also revealed that the leaf of P. hysterophorus is a major source of phytotoxicity, which could be valuable in the future for developing a natural herbicide.
Cardiovascular Risk Models in Chronic Kidney Disease: A Systematic Review and External Validation of Models
Authors: Major Rupert W , Grant Robert , Shepherd David , Medcalf James F , Jesus-Silva Jorge , Gray Laura J , Brunskill Nigel J 
Published: January 2022

AbstractBackground and ObjectivesRisk factors for cardiovascular disease in chronic kidney disease differ to the general population due to the increased role of inflammation, calcification and arteriosclerosis. Over 350 cardiovascular risk models (“models”) exist in the general population but most have not undergone testing (“external validation”) in other populations, such as cohorts with chronic kidney disease. We aimed to update a previous systematic review of models in chronic kidney disease and then perform external validation of these and general population models in a chronic kidney disease cohort.Design, Setting, Participants and MeasurementsWe searched Medline up to 26th August 2020 for models in chronic kidney disease. We performed external validation of models using a primary care chronic kidney disease cohort of 17,248 individuals with 2,072 (12.0%) cardiovascular events, 5,108 (29.6%) deaths and a median follow-up of 5.0 years. Model discrimination and calibration was assessed and where appropriate models were re-calibrated. Multiple imputation was used to account for missing data.ResultsSeven chronic kidney disease specific models were identified. These models and three general population models underwent external validation. All models had worse discrimination of events than in their original cohorts, particularly in the chronic kidney disease specific models. General population models were miscalibrated and overpredicted risk. The major contributor to this was the high competing risk of death from non-cardiovascular causes.ConclusionsExisting chronic kidney disease cardiovascular risk models performed poorly in external validation. General population cardiovascular disease risk models should be interpreted with caution in individuals with chronic kidney disease as they may overestimate risk due to the competing risk of death. Further development of models to include simultaneous risk prediction for cardiovascular and non-cardiovascular disease risk is required.