bluuebunny/crossref_metadata_2025_split huggingface dataset:
Can Environmental Manipulation Help Suppress Cancer? Non‐Linear Competition Among Tumor Cells in Periodically Changing Conditions
Authors: Babajanyan S. G. , Koonin Eugene V. , Cheong Kang Hao 
Published: July 2020

AbstractIt has been shown that the tumor population growth dynamics in a periodically varying environment can drastically differ from the one in a fixed environment. Thus, the environment of a tumor can potentially be manipulated to suppress cancer progression. Diverse evolutionary processes play vital roles in cancer progression and accordingly, understanding the interplay between these processes is essential in optimizing the treatment strategy. Somatic evolution and genetic instability result in intra‐tumor cell heterogeneity. Various models have been developed to analyze the interactions between different types of tumor cells. Here, models of density‐dependent interaction between different types of tumor cells under fast periodical environmental changes are examined. It is illustrated that tumor population densities, which vary on a slow time scale, are affected by fast environmental variations. Finally, the intriguing density‐dependent interactions in metastatic castration‐resistant prostate cancer (mCRPC) in which the different types of tumor cells are defined with respect to the production of and dependence on testosterone are discussed.
Machine Learning Magnetic Parameters from Spin Configurations
Authors: Wang Dingchen , Wei Songrui , Yuan Anran , Tian Fanghua , Cao Kaiyan , Zhao Qizhong , Zhang Yin , Zhou Chao , Song Xiaoping , Xue Dezhen , Yang Sen 
Published: July 2020

AbstractHamiltonian parameters estimation is crucial in condensed matter physics, but is time‐ and cost‐consuming. High‐resolution images provide detailed information of underlying physics, but extracting Hamiltonian parameters from them is difficult due to the huge Hilbert space. Here, a protocol for Hamiltonian parameters estimation from images based on a machine learning (ML) architecture is provided. It consists in learning a mapping between spin configurations and Hamiltonian parameters from a small amount of simulated images, applying the trained ML model to a single unexplored experimental image to estimate its key parameters, and predicting the corresponding materials properties by a physical model. The efficiency of the approach is demonstrated by reproducing the same spin configuration as the experimental one and predicting the coercive field, the saturation field, and even the volume of the experiment specimen accurately. The proposed approach paves a way to achieve a stable and efficient parameters estimation.
Turfgrass performance of perennial wheatgrass species when grown as monocultures and mixtures
Authors: Robins Joseph G. , Bushman B. Shaun 
Published: July 2020

AbstractPerennial wheatgrasses are cool‐season Triticeae species native to arid and semi‐arid regions of the world. Due to their inherent drought tolerance and summer dormancy, they have potential in low‐ to no‐maintenance turf situations. However, there is an underlying need to understand and improve their turf quality and density in order to broaden their acceptance. This study evaluated the turfgrass performance of populations of crested wheatgrass [Agropyron cristatum (L.) Gaertn.], intermediate wheatgrass [Thinopyrum intermedium (Host) Barkworth & D.R. Dewey], thickspike wheatgrass [Elymus lanceolatus (Scribn. & J.G. Sm.) Gould], and western wheatgrass [Pascopyrum smithii (Rydb.) Á. Löve] in monoculture and in two‐ and three‐way mixtures with Kentucky bluegrass (Poa pratensis L.) and hard fescue (Festuca brevipila Tracey). Plots were seeded at a Millville, UT, field site in 2016 and evaluated under two irrigation levels (no irrigation and 50% evapotranspiration replacement) and two mowing heights (51 and 76 cm) during 2017 and 2018. In each year eight evaluations (approximately biweekly) of coverage, dark green color index, density, uniformity, and quality were conducted using digital imagery and then averaged across years. Other than turfgrass quality under mowing height, there were significant differences (P < .0001) between irrigation levels and mowing heights and among turfgrass entries for each trait. All perennial wheatgrass monocultures possessed low density and coverage but showed relatively higher dark green color index compared with the Kentucky bluegrass and hard fescue checks. Mixtures, particularly with Kentucky bluegrass, resulted in substantial improvement in coverage and density.
Genetic analysis of safflower populations under water stress and nonstress conditions
Authors: Alizadeh Yeloojeh Khadijeh , Saeidi Ghodratollah 
Published: July 2020

AbstractAn important target in safflower (Carthamus tinctorius L.) breeding programs is its genetic improvement aimed at enhancing drought tolerance. The present experiment was carried out to study the combining ability and the nature of gene action for agronomic and physiological traits in safflower populations under water stress and non‐stress conditions. In this study, 28 entries (21 F2 populations derived from a half‐diallel cross along with their seven parental lines) were evaluated under the above conditions. The interactions of general and specific combining abilities (GCA and SCA) with irrigation regimes were significant for the measured traits. A higher ratio of GCA/SCA effects (>0.70) for all the traits under both conditions indicated the greater importance of additive gene effects for their genetic control. The parental lines C129 and K115 were identified as superior general combiners for high seed yield (367.4 and 228.4 under nonstress; 166.1 and 175.2 under stress), and the parental line Saffire showed a good GCA for early maturity (–3.36 and –2.69, respectively, under nonstress and stress conditions). It may, therefore, be claimed that these parents can be beneficially exploited as the genetic materials in safflower breeding programs to improve seed yield, early maturity, and developing drought‐tolerant cultivars.
A Surface‐Oxide‐Rich Activation Layer (SOAL) on Ni2Mo3N for a Rapid and Durable Oxygen Evolution Reaction
Authors: Yuan Yao , Adimi Samira , Guo Xuyun , Thomas Tiju , Zhu Ye , Guo Haichuan , Priyanga G. Sudha , Yoo Pilsun , Wang Jiacheng , Chen Jian , Liao Peilin , Attfield J. Paul , Yang Minghui 
Published: July 2020

AbstractThe oxygen evolution reaction (OER) is key to renewable energy technologies such as water electrolysis and metal–air batteries. However, the multiple steps associated with proton‐coupled electron transfer result in sluggish OER kinetics and catalysts are required. Here we demonstrate that a novel nitride, Ni2Mo3N, is a highly active OER catalyst that outperforms the benchmark material RuO2. Ni2Mo3N exhibits a current density of 10 mA cm−2 at a nominal overpotential of 270 mV in 0.1 m KOH with outstanding catalytic cyclability and durability. Structural characterization and computational studies reveal that the excellent activity stems from the formation of a surface‐oxide‐rich activation layer (SOAL). Secondary Mo atoms on the surface act as electron pumps that stabilize oxygen‐containing species and facilitate the continuity of the reactions. This discovery will stimulate the further development of ternary nitrides with oxide surface layers as efficient OER catalysts for electrochemical energy devices.
σ‐Aromaticity‐Induced Stabilization of Heterometallic Supertetrahedral Clusters [Zn6Ge16]4− and [Cd6Ge16]4−
Authors: Xu Hong‐Lei , Popov Ivan A. , Tkachenko Nikolay V. , Wang Zi‐Chuan , Muñoz‐Castro Alvaro , Boldyrev Alexander I. , Sun Zhong‐Ming 
Published: July 2020

AbstractIn this work, the largest heterometallic supertetrahedral clusters, [Zn6Ge16]4− and [Cd6Ge16]4−, were directly self‐assembled through highly‐charged [Ge4]4− units and transition metal cations, in which 3‐center–2‐electron σ bonding in Ge2Zn or Ge2Cd triangles plays a vital role in the stabilization of the whole structure. The cluster structures have an open framework with a large central cavity of diameter 4.6 Å for Zn and 5.0 Å for Cd, respectively. Time‐dependent HRESI‐MS spectra show that the larger clusters grow from smaller components with a single [Ge4]4− and ZnMes2 units. Calculations performed at the DFT level indicate a very large HOMO–LUMO energy gap in [M6Ge16]4− (2.22 eV), suggesting high kinetic stability that may offer opportunities in materials science. These observations offer a new strategy for the assembly of heterometallic clusters with high symmetry.
Cs[Cl3F10]: A Propeller‐Shaped [Cl3F10]− Anion in a Peculiar A[5]B[5] Structure Type
Authors: Scheibe Benjamin , Karttunen Antti J. , Müller Ulrich , Kraus Florian 
Published: July 2020

AbstractReaction of CsF with ClF3 leads to Cs[Cl3F10]. It contains a molecular, propeller‐shaped [Cl3F10]− anion with a central μ3‐F atom and three T‐shaped ClF3 molecules coordinated to it. This anion represents the first example of a heteropolyhalide anion of higher ClF3 content than [ClF4]− and is the first Cl‐containing interhalogen species with a μ‐bridging F atom. The chemical bonds to the central μ3‐F atom are highly ionic and quite weak as the bond lengths within the coordinating XF3 units (X = Cl, and also calculated for Br, I) are almost unchanged in comparison to free XF3 molecules. Cs[Cl3F10] crystallizes in a very rarely observed A[5]B[5] structure type, where cations and anions are each pseudohexagonally close packed, and reside, each with coordination number five, in the trigonal bipyramidal voids of the other.
Girls' access to adolescent friendly sexual and reproductive health services in Kaski, Nepal
Authors: Shrestha Sunita , Wærdahl Randi 
Published: July 2020

AbstractThe purpose of this study is to explore the limitations to girls' access to adolescent friendly sexual and reproductive health services in public health institutions in Kaski, Nepal. Seven interviews with health workers and eight group interviews with adolescent girls are analysed using the conceptual framework of access to health care by Levesque, Harris, and Russell and an understanding of gender as embedded in all matters and choices concerning sexual and reproductive health. We find that girls demonstrate poor literacy about sexual and reproductive health, and that most challenges and barriers occur before girls reach health institutions. The strict gender norms, stigmatisation and lack of autonomy are the highest barriers for adolescent girls in seeking care. We find that health institutions are not adolescent friendly and have a passive outreach strategy.
Generating segmentation masks of herbarium specimens and a data set for training segmentation models using deep learning
Authors: White Alexander E. , Dikow Rebecca B. , Baugh Makinnon , Jenkins Abigail , Frandsen Paul B. 
Published: July 2020

PremiseDigitized images of herbarium specimens are highly diverse with many potential sources of visual noise and bias. The systematic removal of noise and minimization of bias must be achieved in order to generate biological insights based on the plants rather than the digitization and mounting practices involved. Here, we develop a workflow and data set of high‐resolution image masks to segment plant tissues in herbarium specimen images and remove background pixels using deep learning.Methods and ResultsWe generated 400 curated, high‐resolution masks of ferns using a combination of automatic and manual tools for image manipulation. We used those images to train a U‐Net‐style deep learning model for image segmentation, achieving a final Sørensen–Dice coefficient of 0.96. The resulting model can automatically, efficiently, and accurately segment massive data sets of digitized herbarium specimens, particularly for ferns.ConclusionsThe application of deep learning in herbarium sciences requires transparent and systematic protocols for generating training data so that these labor‐intensive resources can be generalized to other deep learning applications. Segmentation ground‐truth masks are hard‐won data, and we share these data and the model openly in the hopes of furthering model training and transfer learning opportunities for broader herbarium applications.
A new fine‐grained method for automated visual analysis of herbarium specimens: A case study for phenological data extraction
Authors: Goëau Hervé , Mora‐Fallas Adán , Champ Julien , Love Natalie L. Rossington , Mazer Susan J. , Mata‐Montero Erick , Joly Alexis , Bonnet Pierre 
Published: July 2020

PremiseHerbarium specimens represent an outstanding source of material with which to study plant phenological changes in response to climate change. The fine‐scale phenological annotation of such specimens is nevertheless highly time consuming and requires substantial human investment and expertise, which are difficult to rapidly mobilize.MethodsWe trained and evaluated new deep learning models to automate the detection, segmentation, and classification of four reproductive structures of Streptanthus tortuosus (flower buds, flowers, immature fruits, and mature fruits). We used a training data set of 21 digitized herbarium sheets for which the position and outlines of 1036 reproductive structures were annotated manually. We adjusted the hyperparameters of a mask R‐CNN (regional convolutional neural network) to this specific task and evaluated the resulting trained models for their ability to count reproductive structures and estimate their size.ResultsThe main outcome of our study is that the performance of detection and segmentation can vary significantly with: (i) the type of annotations used for training, (ii) the type of reproductive structures, and (iii) the size of the reproductive structures. In the case of Streptanthus tortuosus, the method can provide quite accurate estimates (77.9% of cases) of the number of reproductive structures, which is better estimated for flowers than for immature fruits and buds. The size estimation results are also encouraging, showing a difference of only a few millimeters between the predicted and actual sizes of buds and flowers.DiscussionThis method has great potential for automating the analysis of reproductive structures in high‐resolution images of herbarium sheets. Deeper investigations regarding the taxonomic scalability of this approach and its potential improvement will be conducted in future work.
Using computer vision on herbarium specimen images to discriminate among closely related horsetails (Equisetum)
Authors: Pryer Kathleen M. , Tomasi Carlo , Wang Xiaohan , Meineke Emily K. , Windham Michael D. 
Published: July 2020

PremiseEquisetum is a distinctive vascular plant genus with 15 extant species worldwide. Species identification is complicated by morphological plasticity and frequent hybridization events, leading to a disproportionately high number of misidentified specimens. These may be correctly identified by applying appropriate computer vision tools.MethodsWe hypothesize that aerial stem nodes can provide enough information to distinguish among Equisetum hyemale, E. laevigatum, and E. ×ferrissii, the latter being a hybrid between the other two. An object detector was trained to find nodes on a given image and to distinguish E. hyemale nodes from those of E. laevigatum. A classifier then took statistics from the detection results and classified the given image into one of the three taxa. Both detector and classifier were trained and tested on expert manually annotated images.ResultsIn our exploratory test set of 30 images, our detector/classifier combination identified all 10 E. laevigatum images correctly, as well as nine out of 10 E. hyemale images, and eight out of 10 E. ×ferrissii images, for a 90% classification accuracy.DiscussionOur results support the notion that computer vision may help with the identification of herbarium specimens once enough manual annotations become available.
Taurine Metabolism Aggravates the Progression of Lupus by Promoting the Function of Plasmacytoid Dendritic Cells
Authors: Li Jun , Ding Huihua , Meng Yao , Li Guanhua , Fu Qiong , Guo Qiang , Yin Zhihua , Ye Zhizhong , Zhou Haibo , Shen Nan 
Published: July 2020

ObjectiveType I interferons (IFNs) are critical in the development of systemic lupus erythematosus (SLE). Metabolic abnormalities cause dysregulation of multiple immune cells, but the metabolic regulation of type I IFN production is not well clarified in SLE. We undertook this study to define amino acid metabolism features in SLE and to explore the function of disease‐relevant metabolites in the control of plasmacytoid dendritic cell (pDC)–mediated type I IFN production and the progression of SLE.MethodsMetabolomic profiling of the serum from SLE patients and healthy controls was performed by mass spectrometry. The effects of SLE‐related metabolites on type I IFN production were explored in human and mouse pDCs. The reactive oxygen species (ROS) levels of pDCs from wild‐type and Ncf1−/− mice were measured by flow cytometry. Mechanisms were investigated by RNA sequencing and immunoblotting. In vivo effects of SLE‐relevant metabolites were systemically analyzed in B6.Cg‐Sle1NZM2410/Aeg Yaa/DcrJ mice.ResultsTaurine was higher in the serum from SLE patients compared to healthy controls (P < 0.001) and rheumatoid arthritis patients (P < 0.001). Taurine content was positively correlated with disease activity and the expression of IFN signature genes. The addition of taurine facilitated IFN regulatory factor 7 phosphorylation and enhanced type I IFN production by reducing the ROS levels in pDCs in a neutrophil cytosolic factor 1–dependent manner. Taurine supplementation promoted expression of type I IFN–induced genes, activated lymphocytes, and increased autoantibodies and proteinuria, leading to more serious nephritis.ConclusionTaurine metabolism is involved in the development of SLE by enhancing pDC‐mediated type I IFN production. Targeted inhibition of taurine or implementation of a taurine‐restricted diet has therapeutic potential in SLE.
Modellierung des Verankerungsverhaltens getränkter textiler Bewehrungen
Authors: Preinstorfer Philipp , Pinzek Andreas , Kollegger Johann 
Published: July 2020

AbstractDas Verbundverhalten und darauf aufbauend das Verankerungsverhalten textiler Bewehrungen lässt sich aufgrund unterschiedlicher geometrischer und mechanischer Eigenschaften der Bewehrung nicht direkt aus den bekannten Regeln des Stahlbetonbaus ableiten. Im Textilbetonbau stellt sich zudem für Bewehrungen, deren maßgebender Verbundmechanismus der Formverbund ist, in vielen Fällen ein Versagen zufolge Sprengrissbildung ein. Die Beurteilung, ob ein Bauteil sprengrissgefährdet ist, erfolgte in der Vergangenheit primär experimentell durch direkte Berücksichtigung in den Versuchen. Im folgenden Beitrag wird hingegen ein Weg aufgezeigt, wie die Sprengrissgefährdung dieser Textilkonfigurationen rechnerisch erfasst werden kann. Dazu wird in einem ersten Schritt das Verbundverhalten textiler Bewehrungen anhand eines Versuchsaufbaus, bei dem eine Sprengrissbildung ausgeschlossen wird, ermittelt. Das Versagen tritt in Form eines Abscherens der Faserstrangrippen (periodische Aufweitung innerhalb einer Maschenweite) auf, was auf hohe örtliche Beanspruchungen schließen lässt. Aufbauend auf den Erkenntnissen wird ein numerisches Modell unter der Diskretisierung der Verbundkraftübertragung in einzelnen Verbundpunkten (die diese hohe örtliche Beanspruchung repräsentieren) entwickelt, das eine Untersuchung des Verankerungsverhaltens und des Normalkraftverlaufs im Faserstrang für Verankerungslängen als Vielfaches der Maschenweite erlaubt. Durch Gegenüberstellung der Verankerungskräfte in den einzelnen Rippen mit einem Spaltwiderstand kann dann überprüft werden, ob das Bauteil sprengrissgefährdet ist.
Competitive and Coordinative Interactions between Body Parts Produce Adaptive Developmental Outcomes
Authors: Gawne Richard , McKenna Kenneth Z. , Levin Michael 
Published: July 2020

AbstractLarge‐scale patterns of correlated growth in development are partially driven by competition for metabolic and informational resources. It is argued that competition between organs for limited resources is an important mesoscale morphogenetic mechanism that produces fitness‐enhancing correlated growth. At the genetic level, the growth of individual characters appears independent, or “modular,” because patterns of expression and transcription are often highly localized, mutations have trait‐specific effects, and gene complexes can be co‐opted as a unit to produce novel traits. However, body parts are known to interact over the course of ontogeny, and these reciprocal exchanges can be an important determinant of developmental outcomes. Genetic mechanisms underlie cell and tissue behaviors that allow organs to communicate with one another, but they also create evolutionarily adaptive competitive dynamics that are driven by physiological and biophysical processes. Advances in the understanding of competitive and closely related coordinative interactions across scales will complement existing research programs that emphasize the role of cellular mechanisms in morphogenesis. Study of the large‐scale order produced by competitive dynamics promises to facilitate advances in basic evolutionary and developmental biology, as well as applied research in fields such as bioengineering and regenerative medicine that aim to regulate patterning outcomes.
Effects of mirror neuron system‐based training on rehabilitation of stroke patients
Authors: Mao Huiwen , Li Yan , Tang Li , Chen Ye , Ni Jiawei , Liu Liang , Shan Chunlei 
Published: July 2020

AbstractObjectiveTo investigate the clinical effects of the mirror neuron system (MNS)‐based training on upper extremity motor function and cognitive function in stroke patients.MethodsSixty stroke patients (time from stroke onset 3–9 months) with upper extremity paresis (Brunnstrom stage II–IV) and cognitive impairment (MoCA score ≥ 15) were enrolled in this study. Patients were randomly allocated into MNS treatment group (N = 30) and control group (N = 30). Both groups underwent regular training for upper extremity motor function and cognitive function, and the MNS group was trained with a therapeutic apparatus named mirror neuron system training (MNST) including different levels of action observation training (AOT). Training lasted 20 min/day, 5 days/week for 8 weeks. MoCA, reaction time, and Wisconsin Card Sorting Test (WCST) were assessed at baseline and 8 weeks after training. Furthermore, Fugl‐Meyer assessment (FMA) and Modified Barthel index (MBI) were adopted to evaluated upper extremity motor function and daily life ability.ResultsAfter 8 consecutive weeks’ training, both groups showed significant improvements on the upper extremity motor function, cognitive function, and daily life ability score after training (p < .05). The MNS group showed significantly improved upper extremity motor function and cognitive function (p < .05) compared with control group.ConclusionsCombining MNS‐based and conventional training can improve upper extremity motor function and cognitive function in stroke patients.