bluuebunny/crossref_metadata_2025_split huggingface dataset:
Using very high-resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapes
Authors: Duporge Isla , Isupova Olga , Reece Steven , Macdonald David W. , Wang Tiejun 
Published: September 2020

AbstractSatellites allow large-scale surveys to be conducted in short time periods with repeat surveys possible <24hrs. Very high-resolution satellite imagery has been successfully used to detect and count a number of wildlife species in open, homogeneous landscapes and seascapes where target animals have a strong contrast with their environment. However, no research to date has detected animals in complex heterogeneous environments or detected elephants from space using very high-resolution satellite imagery and deep learning.In this study we apply a Convolution Neural Network (CNN) model to automatically detect and count African elephants in a woodland savanna ecosystem in South Africa. We use WorldView-3 and 4 satellite data – the highest resolution satellite imagery commercially available. We train and test the model on eleven images from 2014-2019. We compare the performance accuracy of the CNN against human accuracy. Additionally, we apply the model on a coarser resolution satellite image (GeoEye-1) captured in Kenya to test if the algorithm can generalise to an elephant population outside of the training area.Our results show the CNN performs with high accuracy, comparable to human detection capabilities. The detection accuracy (i.e., F2 score) of the CNN models was 0.78 in heterogeneous areas and 0.73 in homogenous areas. This compares with the detection accuracy of the human labels with an averaged F2 score 0.77 in heterogeneous areas and 0.80 in homogenous areas. The CNN model can generalise to detect elephants in a different geographical location and from a lower resolution satellite.Our study demonstrates the feasibility of applying state-of-the-art satellite remote sensing and deep learning technologies for detecting and counting African elephants in heterogeneous landscapes. The study showcases the feasibility of using high resolution satellite imagery as a promising new wildlife surveying technique. Through creation of a customised training dataset and application of a Convolutional Neural Network, we have automated the detection of elephants in satellite imagery with as high accuracy as human detection capabilities. The success of the model to detect elephants outside of the training data site demonstrates the generalisability of the technique.
Nicotine dosimetry and stability in Cambridge Filter PADs (CFPs) following different smoking regimen protocols and condition storage
Authors: Zuccarello Pietro , Rust Sonja , Caruso Massimo , Emma Rosalia , Pulvirenti Roberta , Favara Claudia , Polosa Riccardo , Volti Giovanni Li , Ferrante Margherita 
Published: September 2020

ABSTRACTDespite the growing numbers of studies with cigarettes and other electronic nicotine delivery products (ENDs), there is no standard covering nicotine dosimetry and its stability in various matrix. The aim of the present study was to provide a protocol to normalize nicotine concentration adsorbed in Cambridge Filter PADs (CFPs) and their storage method. Smoke/vapor generated by a reference tobacco cigarette (1R6F) and ENDs with different exposure regimes (ISO, HCI and CRM81) was collected in CFPs. For each exposure, some CFPs were analyzed at time zero, whereas the others were stored under different conditions for nicotine assessment after 30 days. Principal Component Analysis (PCA) was also performed to establish the best parameter for nicotine normalization. PCA showed the best correlation between nicotine in CFPs and TPM. Our results showed differences between products and puffing regimes, but storage of CFPs at −80°C was always effective in maintaining the nicotine content. In conclusion, this study highlights that different exposure regimens and products can affect the preservation of nicotine titer in CFPs and samples storage at −80°C may prevent the loss of nicotine. These conditions are recommended and should be adopted for Inter-laboratory comparison studies on ENDs to ensure harmonization between participating laboratories.
Substantial gene flow caused by long-term translocation between wild populations of the Peruvian scallop (Argopecten purpuratus) is supported by RAD-Seq analyses
Authors: Velez-Zuazo Ximena , Barahona Sergio P. , Melo Omar G. , Hanschke Eric , Hanschke Ian , Santa-Maria Monica C. 
Published: September 2020

AbstractThe Peruvian scallop (Argopecten purpuratus, Lamarck 1989) is a marine bivalve of high commercial value in the aquaculture industry, with wild populations distributing from northern Peru to Chile. Its growing demand in the world aquaculture markets and limited availability of hatchery-based seeds, caused long-term seed translocations among wild populations to recover depleted local populations and for production needs. We investigated long-term translocations effects on the genetic diversity and structure of wild populations using next-generation RAD sequencing. We sampled individuals from Sechura, Lobos de Tierra, Samanco and Bahia Independencia in Peru, and La Rinconada in Northern Chile. We identified 6275 polymorphic RAD tags and 8345 SNPs for the five populations. We estimated high observed heterozygosity for all populations and high SNP frequency compared to similar studies on marine bivalves. We detected no spatial divergence among populations in Peru (pairwise FST ranged from 0 to 0.003), but strong differentiation with the population in Chile. Migration rate estimates suggested asymmetric directionality of seed translocation. Overall, our results support a remnant effect of an intense historic translocation and on-going gene flow among wild populations in Peru, challenging the identification of outlier loci and certification of sustainable origin of cultured scallops using genetic markers.
Chromosome-level Genome Assembly of a Regenerable Maize Inbred Line A188
Authors: Lin Guifang , He Cheng , Zheng Jun , Koo Dal-Hoe , Le Ha , Zheng Huakun , Tamang Tej Man , Lin Jinguang , Liu Yan , Zhao Mingxia , Hao Yangfan , McFraland Frank , Wang Bo , Qin Yang , Tang Haibao , McCarty Donald R , Wei Hairong , Cho Myeong-Je , Park Sunghun , Kaeppler Heidi , Kaeppler Shawn M , Liu Yunjun , Springer Nathan , Schnable Patrick S , Wang Guoying , White Frank F , Liu Sanzhen 
Published: September 2020

ABSTRACTThe highly embryogenic and transformable maize inbred line A188 is an attractive model for analyzing maize gene function. Here we constructed a chromosome-level genome assembly of A188 using long reads and optical maps. Genome comparison of A188 with the reference line B73 identified pervasive structural variation, including a 1.8 Mb duplication on theGametophyte factor1locus for unilateral cross-incompatibility and six inversions of 0.7 Mb or greater. Increased copy number of the gene,carotenoid cleavage dioxygenase 1(ccd1) in A188 is associated with elevated expression during seed development. Highccd1expression together with low expression ofyellow endosperm 1(y1) condition reduced carotenoid accumulation, which accounts for the white seed phenotype of A188 that contrasts with the yellow seed of B73 that has high expression ofy1and low expression of the single-copyccd1. Further, transcriptome and epigenome analyses with the A188 reference genome revealed enhanced expression of defense pathways and altered DNA methylation patterns of embryonic callus.
Identifiability of Tissue Material Parameters from Uniaxial Tests using Multi-start Optimization
Authors: Safa Babak N. , Santare Michael H. , Ethier C. Ross , Elliott Dawn M. 
Published: September 2020

AbstractDetermining tissue biomechanical material properties from mechanical test data is frequently required in a variety of applications, e.g. tissue engineering. However, the validity of the resulting constitutive model parameters is the subject of debate in the field. Common methods to perform fitting, such as nonlinear least-squares, are known to be subject to several limitations, most notably the uniqueness of the fitting results. Parameter optimization in tissue mechanics often comes down to the “identifiability” or “uniqueness” of constitutive model parameters; however, despite advances in formulating complex constitutive relations and many classic and creative curve-fitting approaches, there is no accessible framework to study the identifiability of tissue material parameters. Our objective was to assess the identifiability of material parameters for established constitutive models of fiber-reinforced soft tissues, biomaterials, and tissue-engineered constructs. To do so, we generated synthetic experimental data by simulating uniaxial tension and compression tests, commonly used in biomechanics. We considered tendon and sclera as example tissues, using constitutive models that describe these fiber-reinforced tissues. We demonstrated that not all of the model parameters of these constitutive models were identifiable from uniaxial mechanical tests, despite achieving virtually identical fits to the stress-stretch response. We further show that when the lateral strain was considered as an additional fitting criterion, more parameters are identifiable, but some remain unidentified. This work provides a practical approach for addressing parameter identifiability in tissue mechanics.Statement of SignificanceData fitting is a powerful technique commonly used to extract tissue material parameters from experimental data, and which thus has applications in tissue biomechanics and engineering. However, the problem of “uniqueness” or “identifiability” of the fit parameters is a significant issue, limiting the fit results’ validity. Here we provide a novel method to evaluate data fitting and assess the uniqueness of results in the tissue mechanics constitutive models. Our results indicate that the uniaxial stress-stretch experimental data are not adequate to identify all the tissue material parameters. This study is of potential interest to a wide range of readers because of its application for the characterization of other engineering materials, while addressing the problem of uniqueness of the fitted results.
A smartphone app for individual anesthetic calculation decreased anesthesia-related mortality in mice
Authors: Jara Carlos Poblete , Carraro Rodrigo S , Zanesco Ariane , Andrade Beatriz , Moreira Karina , Nogueira Guilherme , Souza Bruno L. , Prado Thais Paulino , Póvoa Valeria , Velloso Licio A. , Araújo Eliana P. 
Published: September 2020

AbstractAnnually, millions of animals are used for experimental purposes. Despite the recommended anesthetic doses being well-known worldwide, the final amounts applied to mice could be different than those calculated. Here, we developed, tested, and validated a mobile app where researchers and operators were able to use personal devices to process body weight, calculate a master anesthetic cocktail, and then apply the individual volume to each mouse. Our objective was to refine anesthesia procedures using information technologies. Our data showed that the “Labinsane” mobile app decreased anesthetic-related deaths upon using weight-adjusted doses of ketamine and xylazine. Also, we validated that the Labinsane mobile app matched all calculations of anesthetic doses. To our knowledge, this is the first report with hundreds of anesthetized mice records and validation and implementation of a mobile app to solve an old but transversal challenge for researchers working with experimental mice.
Effects of Long-Term Meditation Practices on Sensorimotor Rhythm Based BCI Learning
Authors: Jiang Xiyuan , Lopez Emily , Stieger James , Greco Carol , He Bin 
Published: September 2020

AbstractSensorimotor rhythm (SMR) based brain-computer interfaces (BCIs) provide an alternative pathway for users to perform motor control using motor imagery (MI). Despite the non-invasiveness, ease of use and low cost, this kind of BCI has limitation due to long training times and BCI inefficiency— where a subpopulation cannot generate decodable EEG signals to perform the control task. Meditation is a mental training method to improve mindfulness and awareness, and is reported to have a positive effect on one’s mental state. Here we investigate the behavioral and electrophysiological differences between experienced meditators and meditation naïve subjects in 1-dimensional and 2-dimensional cursor control tasks. We found that within subjects who have room for improvement, meditators outperformed control subjects in both tasks, and there were fewer BCI insufficient subjects in the meditator group. Finally, we also explored the neurophysiological difference between the two groups, and showed that meditators had higher SMR predictor and were better able to generate decodable EEG signals to achieve SMR BCI control.
Robust Preclinical Evidence in Somatic Cell Genome Editing: A Key Driver of Responsible and Efficient Therapeutic Innovations
Authors: Bittlinger Merlin , Schwietering Johannes , Strech Daniel 
Published: September 2020

AbstractSomatic cell genome editing (SCGE) is highly promising for therapeutic innovation. Multifold financial and academic incentives exist for the quickest possible translation from preclinical to clinical studies. This study demonstrates that the majority of 46 preclinical SCGE studies discussed in expert reviews as particularly promising for clinical translation do not report on seven key elements for robust and confirmatory research practices: (1) randomization, (2) blinding, (3) sample size calculation, (4) data handling, (5) pre-registration, (6) multi-centric study design, and (7) independent confirmation. Against the background of the high incentives for clinical translation and recent concerns about the reproducibility of published preclinical evidence, we present the here examined reporting standards (1-4) and the new NIH funding criteria for SCGE research (6-7) as a viable solution to protect this promising field from backlashes. We argue that the implementation of the novel methodological standards, e.g. “confirmation” and “pre-registration”, is promising for preclinical SCGE research and provides an opportunity to become a lighthouse example for trust-worthy and useful translational research.
Incorporation of Heterogeneity in a Case-Control Study Through a Mixture Model
Authors: Paul Subrata , Santorico Stephanie A. 
Published: September 2020

AbstractMost common human diseases and complex traits are etiologically heterogeneous. Genome-wide Association Studies (GWAS) aim to discover common genetic variants that are associated with complex traits, typically without considering heterogeneity. Heterogeneity, as well as im-precise phenotyping, significantly reduces the power to find genetic variants associated with human diseases and complex traits. Disease subtyping through unsupervised clustering techniques such as latent class analysis can explain some of the heterogeneity; however, subtyping methods do not typically incorporate heterogeneity into the association framework. Here, we use a finite mixture model with logistic regression to incorporate heterogeneity into the association testing framework for a case-control study. In the proposed method, the disease outcome is modeled as a mixture of two binomial distributions. One of the component distributions refers to the subgroup of the population for which the genetic variant is not associated with the disease outcome and another component distribution corresponds to the subgroup for which the genetic variant is associated with the disease outcome. The mixing parameter corresponds to the proportion of the population for which the genetic variant is associated with the disease outcome. A simulation study of a trait with differing levels of prevalence, SNP minor allele frequency, and odds ratio was performed, and effect size estimates compared between the models with and without incorporating heterogeneity. The proposed mixture model yields lower bias of odds ratios while having comparable power compared to classical logistic regression.
Adaptability and evolution of the cell polarization machinery in budding yeast
Authors: Brauns Fridtjof , Iñigo de la Cruz Leila M. , Daalman Werner K.-G. , de Bruin Ilse , Halatek Jacob , Laan Liedewij , Frey Erwin 
Published: September 2020

SummaryHow can a self-organized cellular function evolve, adapt to perturbations, and acquire new sub-functions? To make progress in answering these basic questions of evolutionary cell biology, we analyze, as a concrete example, the cell polarity machinery ofSaccharomyces cerevisiae. This cellular module exhibits an intriguing resilience: it remains operational under genetic perturbations and recovers quickly and reproducibly from the deletion of one of its key components. Using a combination of modeling, conceptual theory, and experiments, we show that multiple, redundant self-organization mechanisms coexist within the protein network underlying cell polarization and are responsible for the module’s resilience and adaptability. Based on our mechanistic understanding of polarity establishment, we hypothesize how scaffold proteins, by introducing new connections in the existing network, can increase the redundancy of mechanisms and thus increase the evolvability of other network components. Moreover, our work suggests how a complex, redundant cellular module could have evolved from a more rudimental ancestral form.
Neuronal Odor Coding in the Larval Sensory Cone ofAnopheles coluzzii: Complex Responses from a Simple System
Authors: Sun Huahua , Liu Feng , Baker Adam , Zwiebel Laurence J. 
Published: September 2020

AbstractAnopheles mosquitoes are the sole vectors of malaria and other diseases that represent significant threats to global public health. While adult female mosquitoes are responsible for disease transmission, the pre-adult larval stages of the malaria vectorAnopheles coluzziiand other mosquitoes rely on a broad spectrum of sensory cues to navigate their aquatic habitats efficiently to avoid predators and search for food. Of these, mosquito larvae rely heavily on volatile chemical signals that directly activate their olfactory apparatus. Because most studies on mosquito olfaction focus on adults, a paucity of attention has been given to the larval olfactory system, in which the peripheral components are associated with the sensory cone of the larval antennae. To address this, we have investigated the electrophysiological response profile of the larval sensory cone in Anopheles mosquitoes. We found that the larval sensory cone is particularly tuned to alcohols, thiazoles and heterocyclics. Furthermore, these responses can be assigned to discrete groups of sensory cone neurons with distinctive, dose-dependent odorant-response profiles that also provide larvae with the ability to discriminate among compounds with similar chemical structures. A correlation analysis was conducted to determine the relationship between specific larval chemosensory receptors and the response profiles of sensory cone neuron groups. These studies reveal that the larval sensory cone is a highly sophisticated organ that is sensitive to a broad range of compounds and is capable of a remarkable degree of chemical discrimination. Taken together, this study presents critical insights into olfactory coding processes inAn. coluzziilarvae that further our understanding of larval chemical ecology and will contribute to the development of novel larval-based strategies and tools for mosquito control and the reduction of vector-borne disease transmission.
Quantification of Cas9 binding and cleavage across diverse guide sequences maps landscapes of target engagement
Authors: Boyle Evan A , Becker Winston R , Bai Hua B , Chen Janice S , Doudna Jennifer A , Greenleaf William J 
Published: September 2020

AbstractThe RNA-guided nuclease Cas9 has unlocked powerful methods for perturbing both the genome through targeted DNA cleavage and the regulome through targeted DNA binding, but limited biochemical data has hampered efforts to quantitatively model sequence perturbation of target binding and cleavage across diverse guide sequences. We present scalable, sequencing-based platforms for high-throughput filter binding and cleavage, then perform 62,444 quantitative binding and cleavage assays on 35,047 on- and off-target DNA sequences across 90 Cas9 ribonucleoproteins (RNPs) loaded with distinct guide RNAs. We observe that binding and cleavage efficacy, as well as specificity, vary substantially across RNPs; canonically studied guides often have atypically high specificity; sequence context surrounding the target significantly influences Cas9 on-rate; and Cas9 RNPs may sequester targets in nonproductive states that contribute to “proofreading” capability. Finally, we distill our findings into an interpretable biophysical model that predicts changes in binding and cleavage for diverse target sequence perturbations.
Phylotype-Level Characterization of Complex Lactobacilli Communities Using a High-Throughput, High-Resolution Phenylalanyl-tRNA Synthetase(pheS)Gene Amplicon Sequencing Approach
Authors: Silvaraju Shaktheeshwari , Menon Nandita , Fan Huan , Lim Kevin , Kittelmann Sandra 
Published: September 2020

ABSTRACTThe ‘lactobacilli’ to date encompass more than 270 closely related species that were recently re-classified into 26 genera. Because of their relevance to industry, there is a need to distinguish between closely related, yet metabolically and regulatory distinct species, e.g., during monitoring of biotechnological processes or screening of samples of unknown composition. Current available methods, such as shotgun metagenomics or rRNA-based amplicon sequencing have significant limitations (high cost, low resolution, etc.). Here, we generated a lactobacilli phylogeny based on phenylalanyl-tRNA synthetase (pheS) genes and, from it, developed a high-resolution taxonomic framework which allows for comprehensive and confident characterization of lactobacilli community diversity and structure at the species-level. This framework is based on a total of 445pheSgene sequences, including sequences of 277 validly described species and subspecies (out of a total of 283, coverage of 98%). It allows differentiation between 263 lactobacilli species-level clades out of a total of 273 validly described species (including the proposed speciesL. timonensis) and a further two subspecies. The methodology was validated through next-generation sequencing of mock communities. At a sequencing depth of ∼30,000 sequences, the minimum level of detection was approximately 0.02 pg per μl DNA (equalling approximately 10 genome copies per µl template DNA). ThepheSapproach along with parallel sequencing of partial 16S rRNA genes revealed a considerable lactobacilli diversity and distinct community structures across a broad range of samples from different environmental niches. This novel complementary approach may be applicable to industry and academia alike.IMPORTANCESpecies within the former generaLactobacillusandPediococcushave been studied extensively at the genomic level. To accommodate for their exceptional functional diversity, the over 270 species were recently re-classified into 26 distinct genera. Despite their relevance to both academia and industry, methods that allow detailed exploration of their ecology are still limited by low resolution, high cost or copy number variations. The approach described here makes use of a single copy marker gene which outperforms other markers with regards to species-level resolution and availability of reference sequences (98% coverage). The tool was validated against a mock community and used to address lactobacilli diversity and community structure in various environmental matrices. Such analyses can now be performed at broader scale to assess and monitor lactobacilli community assembly, structure and function at the species (in some cases even at sub-species) level across a wide range of academic and commercial applications.