bluuebunny/crossref_metadata_2025_split huggingface dataset:
Quasi-Entropy Closure: A Fast and Reliable Approach to Close the Moment Equations of the Chemical Master Equation
Authors: Wagner Vincent , Castellaz Benjamin , Oesting Marco , Radde Nicole 
Published: December 2021

MotivationThe Chemical Master Equation is the most comprehensive stochastic approach to describe the evolution of a (bio-)chemical reaction system. Its solution is a time-dependent probability distribution on all possible configurations of the system. As the number of possible configurations is typically very large, the Master Equation is often practically unsolvable. The Method of Moments reduces the system to the evolution of a few moments of this distribution, which are described by a system of ordinary differential equations. Those equations are not closed, since lower order moments generally depend on higher order moments. Various closure schemes have been suggested to solve this problem, with different advantages and limitations. Two major problems with these approaches are first that they are open loop systems, which can diverge from the true solution, and second, some of them are computationally expensive.ResultsHere we introduce Quasi-Entropy Closure, a moment closure scheme for the Method of Moments which estimates higher order moments by reconstructing the distribution that minimizes the distance to a uniform distribution subject to lower order moment constraints. Quasi-Entropy closure is similar to Zero-Information closure, which maximizes the information entropy. Results show that both approaches outperform truncation schemes. Moreover, Quasi-Entropy Closure is computationally much faster than Zero-Information Closure. Finally, our scheme includes a plausibility check for the existence of a distribution satisfying a given set of moments on the feasible set of configurations. Results are evaluated on different benchmark problems.Abstract Figure
Sentinel2GlobalLULC: A deep-learning-ready Sentinel-2 RGB image dataset for global land use/cover mapping
Authors: Benhammou Yassir , Alcaraz-Segura Domingo , Guirado Emilio , Khaldi Rohaifa , Achchab Boujemâa , Herrera Francisco , Tabik Siham 
Published: December 2021

ABSTRACTLand-Use and Land-Cover (LULC) mapping is relevant for many applications, from Earth system and climate modelling to territorial and urban planning. Global LULC products are continuously developing as remote sensing data and methods grow. However, there is still low consistency among LULC products due to low accuracy for some regions and LULC types. Here, we introduce Sentinel2GlobalLULC, a Sentinel-2 RGB image dataset, built from the consensus of 15 global LULC maps available in Google Earth Engine. Sentinel2GlobalLULC v1.1 contains 195572 RGB images organized into 29 global LULC mapping classes. Each image is a tile that has 224 × 224 pixels at 10 × 10 m spatial resolution and was built as a cloud-free composite from all Sentinel-2 images acquired between June 2015 and October 2020. Metadata includes a unique LULC type annotation per image, together with level of consensus, reverse geo-referencing, and global human modification index. Sentinel2GlobalLULC is optimized for the state-of-the-art Deep Learning models to provide a new gate towards building precise and robust global or regional LULC maps.
A mammalian herbivore prefers locally adapted populations in a common garden: implications for climate change mitigation
Authors: Lauger Kayla K. , Mahoney Sean M. , Rothwell Elizabeth M. , Corbin Jaclyn P. M. , Whitham Thomas G. 
Published: December 2021

AbstractClimate change is expected to alter habitat more rapidly than the pace of evolution, leading to tree populations that are maladapted to new local conditions. Assisted migration is a mitigation strategy that proposes preemptively identifying and planting genotypes that are robust to the expected climate change-induced alterations of an area. Assisted migration however, may impact the broader community, including herbivores which often coevolved with local plant genotypes and their defenses. Although this question has been examined in arthropod herbivores, few studies have assessed this question in mammalian herbivores, and fewer still have leveraged experimental design to disentangle the genetic contribution to herbivore preference.We examined the hypothesis that North American porcupine (Erethizon dorsatum) browsing on Fremont cottonwood (Populus fremontii) is under genetic control in a common garden, which allowed us to uncouple genetic and environmental contributions to browse preference.Generally, porcupines selected local trees and trees from climatically similar areas, where trees from local and cooler climate populations suffered over 2x more extensive herbivory than trees from warmer areas. Plant genotype was a significant factor for selection, with the most heavily browsed genotype having on average >10x more herbivory than the least heavily browsed. Because genotypes within and among populations were replicated, we calculated broad-sense heritability in which tree palatability by porcupines was H2B = 0.28 (95% CI: 0.13-0.48) among genotypes.Synthesis and applications. Our results indicate a genetic component to tree defenses against porcupine herbivory that can be predicted by the climate of the source population. This result has important implications for mammalian herbivores if climate change renders local tree genotypes maladaptive to new conditions. We recommend assisted migration efforts consider this implication and plant stock from both warmer and climatically similar areas to maintain genetic diversity in a changing environment, productivity and forage for mammalian herbivores.
Mathematical characterization of population dynamics in breast cancer cells treated with doxorubicin
Authors: Yang Emily Y. , Howard Grant R. , Brock Amy , Yankeelov Thomas E. , Lorenzo Guillermo 
Published: December 2021

AbstractThe development of chemoresistance remains a significant cause of treatment failure in breast cancer. We posit that a mathematical understanding of chemoresistance could assist in developing successful treatment strategies. Towards that end, we have developed a model that describes the effects of the standard chemotherapeutic drug doxorubicin on the MCF-7 breast cancer cell line. We assume that the tumor is composed of two subpopulations: drug-resistant cells, which continue proliferating after treatment, and drug-sensitive cells, which gradually transition from proliferating to treatment-induced death. The model is fit to experimental data including variations in drug concentration, inter-treatment interval, and number of doses. Our model recapitulates tumor growth dynamics in all these scenarios (as quantified by the concordance correlation coefficient, CCC > 0.95). In particular, superior tumor control is observed with higher doxorubicin concentrations, shorter inter-treatment intervals, and a higher number of doses (p < 0.05). Longer inter-treatment intervals require adapting the model parameterization after each doxorubicin dose, suggesting the promotion of chemoresistance. Additionally, we propose promising empirical formulas to describe the variation of model parameters as functions of doxorubicin concentration (CCC > 0.78). Thus, we conclude that our mathematical model could deepen our understanding of the effects of doxorubicin and could be used to explore practical drug regimens achieving optimal tumor control.
Comparing management strategies for conserving communities of climate-threatened species with a stochastic metacommunity model
Authors: Backus Gregory A. , Huang Yansong , Baskett Marissa L. 
Published: December 2021

AbstractMany species are shifting their ranges to keep pace with climate change, but habitat fragmentation and limited dispersal could impede these range shifts. In the case of climate-vulnerable foundation species such as tropical reef corals and temperate forest trees, such limitations might put entire communities at risk of extinction. Restoring connectivity through corridors, stepping-stones, or enhanced quality of existing patches could prevent the extinction of several species, but dispersal-limited species might not benefit if other species block their dispersal. Alternatively, managers might relocate vulnerable species between habitats through assisted migration, but this is generally a species-by-species approach. To evaluate the relative efficacy of these strategies, we simulated the climate-tracking of species in randomized competitive metacommunities with alternative management interventions. We found that corridors and assisted migration were the most effective strategies at reducing extinction. Assisted migration was especially effective at reducing the extinction likelihood for short-dispersing species, but it often required moving several species repeatedly. Assisted migration was more effective at reducing extinction in environments with higher stochasticity, and corridors were more effective at reducing extinction in environments with lower stochasticity. We discuss the application of these approaches to an array of systems ranging from tropical corals to temperate forests.
Multivariate autoregressive model estimation for high dimensional intracranial electrophysiological data
Authors: Endemann Christopher , Krause Bryan M. , Nourski Kirill V. , Banks Matthew I. , Van Veen Barry 
Published: December 2021

AbstractFundamental to elucidating the functional organization of the brain is the assessment of causal interactions between different brain regions. Multivariate autoregressive (MVAR) modeling techniques applied to multisite electrophysiological recordings are a promising avenue for identifying such causal links. They estimate the degree to which past activity in one or more brain regions is predictive of another region’s present activity, while simultaneously accounting for the mediating effects of other regions. Including in the model as many mediating variables as possible has the benefit of drastically reducing the odds of detecting spurious causal connectivity. However, effective bounds on the number of MVAR model coefficients that can be estimated reliably from limited data make exploiting the potential of MVAR models challenging. Here, we utilize well-established dimensionality-reduction techniques to fit MVAR models to human intracranial data from ∽100 – 200 recording sites spanning dozens of anatomically and functionally distinct cortical regions. First, we show that high dimensional MVAR models can be successfully estimated from long segments of data and yield plausible connectivity profiles. Next, we use these models to generate synthetic data with known ground-truth connectivity to explore the utility of applying principal component analysis and group least absolute shrinkage and selection operator (LASSO) to reduce the number of parameters (connections) during model fitting to shorter data segments. We show that group LASSO is highly effective for recovering ground truth connectivity in the limited data regime, capturing important features of connectivity for high-dimensional models with as little as 10 s of data. The methods presented here have broad applicability to the analysis of high-dimensional time series data in neuroscience, facilitating the elucidation of the neural basis of sensation, cognition, and arousal.
Development of Web Application for the Comparison of Segment Variability with Sequence Evolution and Immunogenic Properties for Highly Variable Proteins: An Application to Viruses
Authors: Bala Sumit , Ghosh Ambarnil , Pradhan Subhra 
Published: December 2021

AbstractHigh rate of mutation and structural flexibilities in viral proteins quickly make them resistant to the host immune system and existing antiviral strategies. For most of the pathogenic viruses, the key survival strategies lie in their ability to evolve rapidly through mutations that affects the protein structure and function. Along with the experimental research related to antiviral development, computational data mining also plays an important role in deciphering the molecular and genomic signatures of the viral adaptability. Uncovering conserved regions in viral proteins with diverse chemical and biological properties is an important area of research for developing antiviral therapeutics, though assigning those regions is not a trivial work. Advancement in protein structural information databases and repositories, made by experimental research accelerated the in-silico mining of the data to generate more integrative information. Despite of the huge effort on correlating the protein structural information with its sequence, it is still a challenge to defeat the high mutability and adaptability of the viral genomics structure. In this current study, the authors have developed a user-friendly web application interface that will allow users to study and visualize protein segment variabilities in viral proteins and may help to find antiviral strategies. The present work of web application development allows thorough mining of the surface properties and variabilities of viral proteins which in combination with immunogenicity and evolutionary properties make the visualization robust. In combination with previous research on 20-Dimensional Euclidian Geometry based sequence variability characterization algorithm, four other parameters has been considered for this platform: [1] predicted solvent accessibility information, [2] B-Cell epitopic potential, [3] T-Cell epitopic potential and [4] coevolving region of the viral protein. Uniqueness of this study lies in the fact that a protein sequence stretch is being characterized rather than single residue-based information, which helps to compare properties of protein segments with variability. In current work, as an example, beside presenting the web application platform, five proteins of SARS-CoV2 was presented with keeping focus on protein-S. Current web-application database contains 29 proteins from 7 viruses including a GitHub repository of the raw data used in this study. The web application is up and running in the following address: http://www.protsegvar.com.
ScRNA-Seq study of neutrophils reveals vast heterogeneity and breadth of inflammatory responses in severe COVID-19 patients
Authors: Xu Jintao , He Bing , Carver Kyle , Vanheyningen Debora , Parkin Brian , Garmire Lana X. , Olszewski Michal A. , Deng Jane C. 
Published: December 2021

AbstractSevere cases of COVID-19 are characterized by dysregulated immune responses, but specific mechanisms contributing to the most severe outcomes remain unclear. Neutrophils are the most abundant leukocyte population in human hosts and reach markedly high numbers during severe COVID-19. However, a detailed examination of their responses has been largely overlooked in the COVID-19 literature to date. Here, we report for the first time a dedicated study of neutrophil responses using single-cell RNA sequencing (scRNA-Seq) of fresh leukocytes from 11 hospitalized adult patients with mild and severe COVID-19 disease and 5 healthy controls. We observed that neutrophils display a pronounced inflammatory profile, with dramatic disruption of predicted cell-cell interactions as the severity of the disease increases. We also identified unique mature and immature neutrophil subpopulations based on transcriptomic profiling, including an antiviral phenotype, and changes in the proportion of each population linked to the severity of the disease. Finally, pathway analysis revealed increased markers of oxidative phosphorylation and ribosomal genes, along with downregulation of many antiviral and host defense pathway genes during severe disease compared to mild infections. Collectively, our findings indicate that neutrophils are capable of mounting effective antiviral defenses but adopt a form of immune dysregulation characterized by excess cellular stress, thereby contributing to the pathogenesis of severe COVID-19.
Microenvironmental factors in cell segregation and heterogeneity in breast cancer development
Authors: Romero-Arias J. Roberto , González-Castro Carlos A. , Ramírez-Santiago Guillermo 
Published: December 2021

ABSTRACTWe analyzed a quantitative model that describes the epigenetic dynamics during the growth and evolution of an avascular tumor. A gene regulatory network (GRN) formed by a set of ten genes that are believed to play an important role in breast cancer development was kinetically coupled to the microenvironmental agents: glucose, estrogens and oxygen. The dynamics of spontaneous mutations was described by a Yule-Furry master equation whose solution represents the probability that a given cell in the tissue undergoes a certain number of mutations at a given time. We assumed that mutations rate is modified by nutrients spatial gradients. The tumor mass was grown by means of a cellular automata supplemented with a set of reaction diffusion equations that described the transport of the microenvironmental agents. By analyzing the epigenetic states space described by the GRN dynamics, we found three attractors that were identified with the cellular epigenetic states: normal, precancer and cancer. For two-dimensional (2D) and three-dimensional (3D) tumors we calculated the spatial distributions of the following quantities: (i) number of mutations, (ii) mutations of each gene and, (iii) phenotypes. Using estrogens as the principal microenvironmental agent that regulates cells proliferation process, we obtained the tumor shapes for different values of the estrogen consumption and supply rates. It was found that he majority of mutations occurred in cells that were located close to the 2D tumor perimeter or close to the 3D tumor surface. Also It was found that the occurrence of different phenotypes in the tumor are controlled by the levels of estrogen concentration since they can change the individual cell threshold and gene expression levels. All the results were consistently observed for 2D and 3D tumors.
The Evolutionary Dynamics of Hyperparasites
Authors: Northrup Graham R , Parratt Steven R , Rozins Carly , Laine Anna-Liisa , Boots Mike 
Published: December 2021

AbstractEvolutionary theory has typically focused on pairwise interactions, such as those between hosts and parasites, with relatively little work on more complex interactions including hyperparasites: parasites of parasites. Hyperparasites are common in nature, with the chestnut blight fungus virus CHV-1 a well-known natural example, but also notably include the phages of important human bacterial diseases. Theory on hyperparasitism has mostly focused on their impact on the evolution of virulence of their parasite host and relatively little is known about evolutionary trajectories of hyperparasites themselves. Our general modeling framework highlights the central role the that ability of a hyperparasite to be transmitted with its parasite plays in their evolution. Hyperparasites which transmit with their parasite hosts (hitchhike) will be selected for lower virulence, trending towards hypermutualism or hypercommensalism and select against causing a reduction in parasite virulence (hypovirulence). We examine the impact on the evolution of hyperparasite systems a of a wide range of host and parasite traits showing, for example, that high parasite virulence selects for higher hyperparasite virulence feeding back into selection for hypovirulence in the parasite. Our results have implications for hyperparasite research, both as biocontrol agents and for understanding of how hyperparasites shape community ecology and evolution.
Cationic inhalable particles for enhanced drug delivery to M. tuberculosis infected macrophages
Authors: Sharma Pallavi Raj , Dravid Ameya Atul , Kalapala Yeswanth Chakravarthy , Gupta Vishal K , Jeyasankar Sharumathi , Goswami Avijit , Agarwal Rachit 
Published: December 2021

AbstractInhalable microparticle-based drug delivery platforms are being investigated extensively for Tuberculosis (TB) treatment as they offer efficient deposition in lungs and improved pharmacokinetics of the encapsulated cargo. However, the effect of physical parameters of microcarriers on interaction with Mycobacterium tuberculosis (Mtb) infected mammalian cells is underexplored. In this study, we report that Mtb-infected macrophages are highly phagocytic and microparticle surface charge plays a major role in particle internalization by infected cells. Microparticles of different sizes (0.5 - 2 μm) were internalized in large numbers by Mtb-infected THP-1 macrophages and murine primary Bone Marrow Derived Macrophages in vitro. Drastic improvement in particle uptake was observed with cationic particles in vitro and in mice lungs. Rapid uptake of rifampicin-loaded cationic microparticles allowed high intracellular accumulation of the drug and lead to enhanced anti-bacterial function when compared to non-modified rifampicin-loaded microparticles. Cytocompatibility assay and histological analysis in vivo confirmed that the formulations were safe and did not elicit any adverse reaction. Additionally, pulmonary delivery of cationic particles in mice resulted in two-fold higher uptake in resident alveolar macrophages compared to non-modified particles. This study provides a framework for future design of drug carriers to improve delivery of anti-TB drugs inside Mtb-infected cells.
DeepLPI: a novel deep learning-based model for protein-ligand interaction prediction for drug repurposing
Authors: Wei Bomin , Zhang Yue , Gong Xiang 
Published: December 2021

AbstractThe substantial cost of new drug research and development has consistently posed a huge burden for both pharmaceutical companies and patients. In order to lower the expenditure and development failure rate, repurposing existing and approved drugs by identifying interactions between drug molecules and target proteins based on computational methods have gained growing attention. Here, we propose the DeepLPI, a novel deep learning-based model that mainly consists of ResNet-based 1-dimensional convolutional neural network (1D CNN) and bi-directional long short term memory network (biLSTM), to establish an end-to-end framework for protein-ligand interaction prediction. We first encode the raw drug molecular sequences and target protein sequences into dense vector representations, which go through two ResNet-based 1D CNN modules to derive features, respectively. The extracted feature vectors are concatenated and further fed into the biLSTM network, followed by the MLP module to finally predict protein-ligand interaction. We downloaded the well-known BindingDB and Davis dataset for training and testing our DeepLPI model. We also applied DeepLPI on a COVID-19 dataset for externally evaluating the prediction ability of DeepLPI. To benchmark our model, we compared our DeepLPI with the state-of-the-art methods of DeepCDA and DeepDTA, and observed that our DeepLPI outperformed these methods, suggesting the high accuracy of the DeepLPI towards protein-ligand interaction prediction. The high prediction performance of DeepLPI on the different datasets displayed its high capability of protein-ligand interaction in generalization, demonstrating that the DeepLPI has the potential to pinpoint new drug-target interactions and to find better destinations for proven drugs.