pwc-archive/papers-with-abstracts huggingface dataset:
The interplay of extinction and synchrony in the dynamics of metapopulation formation
Authors: John Vandermeer, Zachary Hajian-Forooshani
Date: 2021-04-04 00:00:00

The idea of a metapopulation has become canonical in ecology. Its original mean field form provides the important intuition that migration and extinction interact to determine the dynamics of a population composed of subpopulations. From its conception, it has been evident that the very essence of the metapopulation paradigm centers on the process of local extinction. We note that there are two qualitatively distinct types of extinction, gradual and catastrophic, and explore their impact on the dynamics of metapopulation formation using discrete iterative maps. First, by modifying the classic logistic map with the addition of the Allee effect, we show that catastrophic local extinctions in subpopulations are a pre-requisite of metapopulation formation. When subpopulations experience gradual extinction, increased migration rates force synchrony and drive the metapopulation below the Allee point resulting in migration induced destabilization of the system across parameter space. Second, a sawtooth map (an extension of the Bernoulli bit shift map) is employed to simultaneously explore the increasing and decreasing modes of population behavior. We conclude with four generalizations. 1. At low migration rates, a metapopulation may go extinct faster than completely unconnected subpopulations. 2. There exists a gradient between stable metapopulation formation and population synchrony, with critical transitions from no metapopulation to metapopulation to synchronization, the latter frequently inducing metapopulation extinction. 3. Synchronization patterns emerge through time, resulting in synchrony groups and chimeric populations existing simultaneously. 4. There are two distinct mechanisms of synchronization: i. extinction and rescue and, ii.) stretch reversals in a modification of the classic chaotic stretching and folding.
Utilizing a Mathematical Model to Estimate Abortion Decline Scenario
Authors: R. Sutiawan
Date: 2021-04-05 00:00:00

Abortion is one of the biggest causes of maternal deaths, accounting for 15% of maternal deaths in Southeast Asia. The increase in and effectiveness of using contraception are still considered to be the effective method to reduce abortion rate. Data pertaining to abortion incidence and effective efforts to reduce abortion rate in Indonesia is limited and difficult to access. Meanwhile such supporting information is necessary to enable the planning and evaluation of abortion control programs. This paper exemplifies the use of a mathematical model to explain an abortion decline scenario. The model employs determinants proposed by Bongaarts, which include average reproductive period, contraceptive prevalence and effectiveness, total fertility rate (TFR), and intended total fertility rate (ITFR), as well as birth and abortion intervals. The data used is from the 1991-2007 Indonesian Demography and Health Survey (Survei Demografi dan Kesehatan Indonesia/SDKI), and the unit of analysis is women who had been married and aged 15-49 years old. Based on the current contraceptive prevalence level in Indonesia at 59-61%, the estimated total abortion rate is 1.9-2.2. Based on the plot of this total abortion rate, an abortion decline scenario can be estimated. At the current TFR level of 2.6, the required contraceptive prevalence is 69% (9% increase) for a decrease of one abortion case per woman. With a delay of one year in the age of the first marriage and a birth interval of three years, it is estimated that the abortion rate will decline from 3.05 to 0.69 case per woman throughout her reproductive period. Based on the assumption of contraceptive prevalence growth at 1-1.4%, it can be estimated that abortion rate will reach nearly 0 between 2018 and 2022.
Mesoporous silica nanoparticles containing silver as novel antimycobacterial agents against Mycobacterium tuberculosis
Authors: Sandra Montalvo-Quiros, Sergio Gomez-Grana, Maria Vallet-Regi, Rafael C. Prados-Rosales, Blanca Gonzalez, Jose L. Luque-Garcia
Date: 2021-04-04 00:00:00

Tuberculosis remains today a major public health issue with a total of 9 million new cases and 2 million deaths annually. The lack of an effective vaccine and the increasing emergence of new strains of Mycobacterium tuberculosis (Mtb) highly resistant to antibiotics, anticipate a complicated scenario in the near future. The use of nanoparticles features as an alternative to antibiotics in tackling this problem due to their potential effectiveness in resistant bacterial strains. In this context, silver nanoparticles have demonstrated high bactericidal efficacy, although their use is limited by their relatively high toxicity, which calls for the design of nanocarriers that allow silver based nanoparticles to be safely delivered to the target cells or tissues. In this work mesoporous silica nanoparticles are used as carriers of silver based nanoparticles as antimycobacterial agent against Mtb. Two different synthetic approaches have been used to afford, on the one hand, a 2D hexagonal mesoporous silica nanosystem which contains silver bromide nanoparticles distributed all through the silica network and, on the other hand, a core@shell nanosystem with metallic silver nanoparticles as core and mesoporous silica shell in a radial mesoporous rearrangement. Both materials have demonstrated good antimycobacterial capacity in in vitro test using Mtb, being lower the minimum inhibitory concentration for the nanosystem which contains silver bromide. Therefore, the interaction of this material with the mycobacterial cell has been studied by cryo-electron microscopy, establishing a direct connection between the antimycobactericidal effect observed and the damage induced in the cell envelope.
Finding Nano-Ötzi: Semi-Supervised Volume Visualization for Cryo-Electron Tomography
Authors: Ngan Nguyen, Ciril Bohak, Dominik Engel, Peter Mindek, Ondřej Strnad, Peter Wonka, Sai Li, Timo Ropinski, Ivan Viola
Date: 2021-04-04 00:00:00
Tasks: Electron Tomography, Segmentation

Cryo-Electron Tomography (cryo-ET) is a new 3D imaging technique with unprecedented potential for resolving submicron structural detail. Existing volume visualization methods, however, cannot cope with its very low signal-to-noise ratio. In order to design more powerful transfer functions, we propose to leverage soft segmentation as an explicit component of visualization for noisy volumes. Our technical realization is based on semi-supervised learning where we combine the advantages of two segmentation algorithms. A first weak segmentation algorithm provides good results for propagating sparse user provided labels to other voxels in the same volume. This weak segmentation algorithm is used to generate dense pseudo labels. A second powerful deep-learning based segmentation algorithm can learn from these pseudo labels to generalize the segmentation to other unseen volumes, a task that the weak segmentation algorithm fails at completely. The proposed volume visualization uses the deep-learning based segmentation as a component for segmentation-aware transfer function design. Appropriate ramp parameters can be suggested automatically through histogram analysis. Finally, our visualization uses gradient-free ambient occlusion shading to further suppress visual presence of noise, and to give structural detail desired prominence. The cryo-ET data studied throughout our technical experiments is based on the highest-quality tilted series of intact SARS-CoV-2 virions. Our technique shows the high impact in target sciences for visual data analysis of very noisy volumes that cannot be visualized with existing techniques.
Neural Representations of Cryo-EM Maps and a Graph-Based Interpretation
Authors: Nathan Ranno, Dong Si
Date: 2021-04-03 00:00:00
Tasks: Cryogenic Electron Microscopy (cryo-EM), Specificity

Advances in imagery at atomic and near-atomic resolution, such as cryogenic electron microscopy (cryo-EM), have led to an influx of high resolution images of proteins and other macromolecular structures to data banks worldwide. Producing a protein structure from the discrete voxel grid data of cryo-EM maps involves interpolation into the continuous spatial domain. We present a novel data format called the neural cryo-EM map, which is formed from a set of neural networks that accurately parameterize cryo-EM maps and provide native, spatially continuous data for density and gradient. As a case study of this data format, we create graph-based interpretations of high resolution experimental cryo-EM maps. Normalized cryo-EM map values interpolated using the non-linear neural cryo-EM format are more accurate, consistently scoring less than 0.01 mean absolute error, than a conventional tri-linear interpolation, which scores up to 0.12 mean absolute error. Our graph-based interpretations of 115 experimental cryo-EM maps from 1.15 to 4.0 Angstrom resolution provide high coverage of the underlying amino acid residue locations, while accuracy of nodes is correlated with resolution. The nodes of graphs created from atomic resolution maps (higher than 1.6 Angstroms) provide greater than 99% residue coverage as well as 85% full atomic coverage with a mean of than 0.19 Angstrom root mean squared deviation (RMSD). Other graphs have a mean 84% residue coverage with less specificity of the nodes due to experimental noise and differences of density context at lower resolutions. This work may be generalized for transforming any 3D grid-based data format into non-linear, continuous, and differentiable format for the downstream geometric deep learning applications.
Multiscale porosity in mesoporous bioglass 3D-printed scaffolds for bone regeneration
Authors: M. Natividad Gomez-Cerezo, Juan Pena, Saso Ivanovski, Daniel Arcos, Maria Vallet-Regi, Cedryck Vaquette
Date: 2021-04-03 00:00:00

In order to increase the bone forming ability of MBG-PCL composite scaffold, microporosity was created in the struts of 3D-printed MBG-PCL scaffolds for the manufacturing of a construct with a multiscale porosity consisting of meso-, micro- and macro-pores. 3D-printing imparted macroporosity while the microporosity was created by porogen removal from the struts, and the MBG particles were responsible for the mesoporosity. The scaffolds were 3D-printed using a mixture of PCL, MBG and phosphate buffered saline (PBS) particles, subsequently leached out. Microporous-PCL (pPCL) as a negative control, microporous MBG-PCL (pMBG-PCL) and non-microporous-MBG-PCL (MBG-PCL) were investigated. Scanning electron microscopy, mercury intrusion porosimetry and micro-computed tomography demonstrated that the PBS removal resulted in the formation of micropores inside the struts with porosity of around 30% for both pPCL and pMBG-PCL, with both constructs displaying an overall porosity of 80-90%. In contrast, the MBG-PCL group had a microporosity of 6% and an overall porosity of 70%. Early mineralisation was found in the pMBG-PCL post-leaching out and this resulted in the formation a more homogeneous calcium phosphate layer when using a biomimetic mineralisation assay. Mechanical properties ranged from 5 to 25 MPa for microporous and non-microporous specimens, hence microporosity was the determining factor affecting compressive properties. MC3T3-E1 metabolic activity was increased in the pMBG-PCL along with an increased production of RUNX2. Therefore, the microporosity within a 3D-printed bioceramic composite construct may result in additional physical and biological benefits.
Effects of mesoporous SiO2-CaO nanospheres on the murine peritoneal macrophages -- Candida albicans interface
Authors: R. Diez-Orejas, L. Casarrubios, M. J. Feito, J. M. Rojo, M. Vallet-Regi, D. Arcos, M. T. Portoles
Date: 2021-04-03 00:00:00

The use of nanoparticles for intracellular drug delivery could reduce the toxicity and side effects of the drug but, the uptake of these nanocarriers could induce adverse effects on cells and tissues after their incorporation. Macrophages play a central role in host defense and are responsible for in vivo nanoparticle trafficking. Assessment of their defense capacity against pathogenic micro-organisms after nanoparticle uptake, is necessary to prevent infections associated with nanoparticle therapies. In this study, the effects of hollow mesoporous SiO2-CaO nanospheres labeled with fluorescein isothiocyanate (FITC-NanoMBGs) on the function of peritoneal macrophages was assessed by measuring their ability to phagocytize Candida albicans expressing a red fluorescent protein. Two macrophage-fungus ratios (MOI 1 and MOI 5) were used and two experimental strategies were carried out: a) pretreatment of macrophages with FITC-NanoMBGs and subsequent fungal infection; b) competition assays after simultaneous addition of fungus and nanospheres. Macrophage pro-inflammatory phenotype markers (CD80 expression and interleukin 6 secretion) were also evaluated. Significant decreases of CD80+ macrophage percentage and interleukin 6 secretion were observed after 30 min, indicating that the simultaneous incorporation of NanoMBG and fungus favors the macrophage noninflammatory phenotype. The present study evidences that the uptake of these nanospheres in all the studied conditions does not alter the macrophage function. Moreover, intracellular FITCNanoMBGs induce a transitory increase of the fungal phagocytosis by macrophages at MOI 1 and after a short time of interaction. In the competition assays, as the intracellular fungus quantity increased, the intracellular FITC-NanoMBG content decreased in a MOI- and timedependent manner...
JDOI Variance Reduction Method and the Pricing of American-Style Options
Authors: Johan Auster, Ludovic Mathys, Fabio Maeder
Date: 2021-04-03 00:00:00

The present article revisits the Diffusion Operator Integral (DOI) variance reduction technique originally proposed in Heath and Platen (2002) and extends its theoretical concept to the pricing of American-style options under (time-homogeneous) L\'evy stochastic differential equations. The resulting Jump Diffusion Operator Integral (JDOI) method can be combined with numerous Monte Carlo based stopping-time algorithms, including the ubiquitous least-squares Monte Carlo (LSMC) algorithm of Longstaff and Schwartz (cf. Carriere (1996), Longstaff and Schwartz (2001)). We exemplify the usefulness of our theoretical derivations under a concrete, though very general jump-diffusion stochastic volatility dynamics and test the resulting LSMC based version of the JDOI method. The results provide evidence of a strong variance reduction when compared with a simple application of the LSMC algorithm and proves that applying our technique on top of Monte Carlo based pricing schemes provides a powerful way to speed-up these methods.
A Big Data Analysis of the Ethereum Network: from Blockchain to Google Trends
Authors: Dorsa Mohammadi Arezooji
Date: 2021-04-05 00:00:00

First, a big data analysis of the transactions and smart contracts made on the Ethereum blockchain is performed, revealing interesting trends in motion. Next, these trends are compared with the public's interest in Ether and Bitcoin, measured by the volume of online searches. An analysis of the crypto prices and search trends suggests the existence of big players (and not the regular users), manipulating the market after a drop in prices. Lastly, a cross-correlation study of crypto prices and search trends reveals the pairs providing more accurate and timely predictions of Ether prices.
Simulations analysis with comparative study of a PMSG performances for small WT application by FEM
Authors: H. Mellah, K. E. Hemsas
Date: 2013-03-29 00:00:00

Permanent magnet synchronous generators (PMSGs) have a bright prospect in the small wind turbine (WT) applications; PMSGs compared to the conventional electrically excitated generators have many advantages, that’s why they have attracted many and a strong interest of research. In this paper, a comparative PMSG performance study's is presented, these performances is studied as a function of physical material like the type of permanent magnet (high, poor, average and linear), as a function of the environmental conditions as rotor speed, finally, as a function of the design and geometrical parameters (rotor length, number of poles, number of stator slots). These results are obtained by finite element method (FEM); this approach is a powerful and useful tool to study and design PMSGs, as represented in this pape
Bioinformatics Analysis of Metabolomics Data Unveils Association of Metabolic Signatures with Methylation in Breast Cancer
Authors: Fadhl M. Alakwaa, Lana X Garmire, Masha G. Savelieff
Date: 2019-12-29 00:00:00

Breast cancer (BC) contributes the highest global cancer mortality in women. BC tumors are highly heterogeneous, so subtyping by cell-surface markers is inadequate. Omics-driven tumor stratification is urgently needed to better understand BC and tailor therapies for personalized medicine. We used unsupervised k-means and partition around medoids (pam) to cluster metabolomics data from two data sets. The first comprised 271 BC tumors (data set 1) that were estrogen receptor (ER) positive (ER+, n = 204) or negative (ER–, n = 67) with 162 identified and validated metabolites. The second data set contained 67 BC samples (data set 2; ER+, n = 33; ER–, n = 34) and 352 known metabolites. Significance Analysis of Microarrays (SAM) was used to identify the most significant metabolites among these clusters, which were then reassigned into new clusters using prediction analysis of microarrays (PAM). Generally, metabolome-defined BC subtypes identified from either data set 1 or data set 2 were different from the well-known receptor- or transcriptome-defined subtypes. Metabolomics-directed clustering of data set 2 identified distinctive BC tumors characterized by metabolome profiles that associated with DNA methylation (p-value = 0.000 048, χ2 test). Pathway analysis of cluster metabolites revealed that nitrogen metabolism and aminoacyl-tRNA biosynthesis were highly related to BC subtyping. The pipeline may be run from GitHub: https://github.com/FADHLyemen/Metabolomics_signature. Our proposed bioinformatics pipeline analyzed metabolomics data from BC tumors, revealing clusters characterized by unique metabolic signatures that may potentially stratify BC patients and tailor precision treatment.
Lilikoi: an R package for personalized pathway-based classification modeling using metabolomics data
Authors: Fadhl M AlAkwaa, Breck Yunits, Sijia Huang, Hassam Alhajaji, Lana X Garmire
Date: 2018-07-18 00:00:00
Tasks: Classification, feature selection, General Classification

Lilikoi (the Hawaiian word for passion fruit) is a new and comprehensive R package for personalized pathway-based classification modeling using metabolomics data. Four basic modules are presented as the backbone of the package: feature mapping module, which standardizes the metabolite names provided by users and maps them to pathways; dimension transformation module, which transforms the metabolomic profiles to personalized pathway-based profiles using pathway deregulation scores; feature selection module, which helps to select the significant pathway features related to the disease phenotypes; and classification and prediction module, which offers various machine learning classification algorithms. The package is freely available under the GPLv3 license through the github repository at: https://github.com/lanagarmire/lilikoi and CRAN: https://cran.r-project.org/web/packages/lilikoi/index.html.
In-Line Image Transformations for Imbalanced, Multiclass Computer Vision Classification of Lung Chest X-Rays
Authors: Alexandrea K. Ramnarine
Date: 2021-04-06 00:00:00

Artificial intelligence (AI) is disrupting the medical field as advances in modern technology allow common household computers to learn anatomical and pathological features that distinguish between healthy and disease with the accuracy of highly specialized, trained physicians. Computer vision AI applications use medical imaging, such as lung chest X-Rays (LCXRs), to facilitate diagnoses by providing second-opinions in addition to a physician's or radiologist's interpretation. Considering the advent of the current Coronavirus disease (COVID-19) pandemic, LCXRs may provide rapid insights to indirectly aid in infection containment, however generating a reliably labeled image dataset for a novel disease is not an easy feat, nor is it of highest priority when combating a global pandemic. Deep learning techniques such as convolutional neural networks (CNNs) are able to select features that distinguish between healthy and disease states for other lung pathologies; this study aims to leverage that body of literature in order to apply image transformations that would serve to balance the lack of COVID-19 LCXR data. Furthermore, this study utilizes a simple CNN architecture for high-performance multiclass LCXR classification at 94 percent accuracy.
Text-guided Legal Knowledge Graph Reasoning
Authors: Luoqiu Li, Zhen Bi, Hongbin Ye, Shumin Deng, Hui Chen, Huaixiao Tou
Date: 2021-04-06 00:00:00
Tasks: Knowledge Graph Completion

Recent years have witnessed the prosperity of legal artificial intelligence with the development of technologies. In this paper, we propose a novel legal application of legal provision prediction (LPP), which aims to predict the related legal provisions of affairs. We formulate this task as a challenging knowledge graph completion problem, which requires not only text understanding but also graph reasoning. To this end, we propose a novel text-guided graph reasoning approach. We collect amounts of real-world legal provision data from the Guangdong government service website and construct a legal dataset called LegalLPP. Extensive experimental results on the dataset show that our approach achieves better performance compared with baselines. The code and dataset are available in \url{https://github.com/zxlzr/LegalPP} for reproducibility.
Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression
Authors: Zigang Geng, Ke Sun, Bin Xiao, Zhaoxiang Zhang, Jingdong Wang
Date: 2021-04-06 00:00:00
Proceeding: CVPR 2021 1
Tasks: Keypoint Detection, Pose Estimation

In this paper, we are interested in the bottom-up paradigm of estimating human poses from an image. We study the dense keypoint regression framework that is previously inferior to the keypoint detection and grouping framework. Our motivation is that regressing keypoint positions accurately needs to learn representations that focus on the keypoint regions. We present a simple yet effective approach, named disentangled keypoint regression (DEKR). We adopt adaptive convolutions through pixel-wise spatial transformer to activate the pixels in the keypoint regions and accordingly learn representations from them. We use a multi-branch structure for separate regression: each branch learns a representation with dedicated adaptive convolutions and regresses one keypoint. The resulting disentangled representations are able to attend to the keypoint regions, respectively, and thus the keypoint regression is spatially more accurate. We empirically show that the proposed direct regression method outperforms keypoint detection and grouping methods and achieves superior bottom-up pose estimation results on two benchmark datasets, COCO and CrowdPose. The code and models are available at https://github.com/HRNet/DEKR.