bluuebunny/crossref_metadata_2025_split huggingface dataset:
A gastric cancer recognition algorithm on gastric pathological sections based on multistage attention‐DenseNet
Authors: Liu Bo , Zhao Yelong , Yang Bin , Zhao Shuangtao , Gu Rentao , Gahegan Mark 
Published: March 2021

SummaryAs an important method to diagnose gastric cancer, gastric pathological sections images (GPSI) are hard and time‐consuming to be recognized even by an experienced doctor. An efficient method was designed to detect gastric cancer in magnified (20×) GPSI using deep learning technology. A novel DenseNet architecture was applied, modified with a multistage attention module (MSA‐DenseNet). To develop this model focusing on gastric features, a two‐stage‐input attention module was adopted to select more semantic information of cancer. Moreover, the pretraining process was divided into two steps to improve the effect of the attention mechanism. After training, our method achieved a state‐of‐the‐art performance yielding 0.9947 F1 score and 0.9976 ROC AUC on a test dataset. In line with our expectation in clinical practice, a high recall (0.9929) was produced with high sensitivity to the positive samples. These results indicate that this new model performs better than current artificial detection approaches and its effectiveness is therefore validated in cancer pathological diagnoses.
A 140 GHz high efficiency frequency doubler based on a physical‐based SDD model of SBD
Authors: Yan Bo , Ren Tianhao , Wu Chenkai , Wei Haomiao , Wang Li , Li Yu , Xu Ruimin , Zhang Yong 
Published: March 2021

SummaryIn this paper, a physical‐based SDD (symbolically defined devices) model of SBD (Schottky barrier diode) is proposed, in which conventional C‐V characteristics of diode SPICE model are modified by terahertz C‐V characteristics obtained by physical‐based simulation. Detail modeling method and design process are introduced in detail. To verify the accuracy of the SDD model, a 140 GHz high efficiency doubler is designed, fabricated, and measured. The measured maximum efficiency is 34.3% under 90 mW input power, and the 3 dB bandwidth is about 7 GHz, which agrees well with the simulated results. The proposed physical‐based SDD model is demonstrated to be more accurate than traditional diode SPICE model.
Highly robust X‐band quasi circulator‐integrated low‐noise amplifier for high survivability of radio frequency front‐end systems
Authors: R Vignesh , Gorre Pradeep , Song Hanjung , Kumar Sandeep 
Published: March 2021

SummaryIn this brief, an X‐band quasi circulator (QC)‐integrated low‐noise amplifier (LNA) implemented in 65‐nm Complementary Metal Oxide Semiconductor (CMOS) technology is presented. This work is the first QC‐LNA for the X‐band to the author's best knowledge, which achieves 30‐dB flat gain in 8–12 GHz with only 0.5‐dB variation across the band. This QC‐LNA uses two‐stage current reused techniques with variable impedance load. QC provides the minimum insertion loss of 0.9 dB with good return and isolation losses. Statistical analysis is presented for QC‐LNA to predict the percentage error tolerance. Quasi‐Newton (QN) control algorithm is used to optimize the parameter of the whole design. The design of experiment (DoE) is performed to claim the contribution towards gain, return loss, and noise figure. The proposed LNA measurement provides a minimum NF of 1 dB at 9.5 GHz, which remains less than 1.4 dB across 8–12 GHz. The fabricated LNA works with a supply voltage of 1.2 V and is unconditionally stable across the frequency. The calculated chip area is 0.84 × 0.52 mm2. This QC‐LNA exhibits an input and output 1‐dB compression point (IP1dB and OP1dB) of −15 and +13.8 dBm, respectively. It also exhibits third‐order input and output intercept point (IIP3 and OIP3) of +10 dBm and of +40 dBm, respectively. The proposed QC‐LNA draws only 8.7 mA from 1.2 V.
Determinants of habitat suitability models transferability across geographically disjunct populations: Insights from Vipera ursinii ursinii
Authors: Cerasoli Francesco , Besnard Aurélien , Marchand Marc‐Antoine , D'Alessandro Paola , Iannella Mattia , Biondi Maurizio 
Published: March 2021

AbstractTransferability of habitat suitability models (HSMs), essential to accurately predict outside calibration conditions, has been seldom investigated at intraspecific level. We targeted Vipera ursinii ursinii, a meadow viper from southeastern France and central Italy, to assess determinants of transferability among geographically disjunct populations. We fitted HSMs upon occurrences of the Italian and French populations separately, as well as on the combined occurrence dataset. Internal transferability of HSMs, on spatially independent test data drawn from the calibration region, and their external transferability on the geographically disjunct populations were evaluated according to (a) use of full or spatially rarefied presence datasets; (b) ecology‐driven or statistics‐driven filtering of predictors; (c) modeling algorithm, testing generalized additive models and gradient boosting models; and (d) multivariate environmental novelty within test data. Niche overlap between French and Italian populations was also tested. Niche overlap was low, but niche divergence between the two populations’ clusters was not corroborated. Nonetheless, wider niche breadth and heterogeneity of background environmental conditions characterizing the French populations led to low intercluster transferability. Although models fitted on the combined datasets did not attain consistently higher internal transferability than those separately fitted for the French and Italian populations, ensemble projection from the HSMs fitted on the joint occurrences produced more consistent suitability predictions across the full range of V. u. ursinii. Spatial thinning of occurrences ameliorated internal transferability but did not affect external transferability. The two approaches to predictors filtering did not differ in transferability of the respective HSMs but led to discrepant estimated environment–occurrence relationships and spatial predictions, while the two algorithms attained different relative rankings depending on the considered prediction task. Multivariate novelty of projection sites was negatively correlated to both internal transferability and external transferability. Our findings clarify issues researchers should keep in mind when using HSMs to get predictions across geographically disjunct populations.
Using fractal self‐similarity to increase precision of shrub biomass estimates
Authors: Dial Roman J. , Schulz Bethany , Lewis‐Clark Eric , Martin Kaili , Andersen Hans‐Erik 
Published: March 2021

AbstractWe show that aerial tips are self‐similar fractals of whole shrubs and present a field method that applies this fact to improves accuracy and precision of biomass estimates of tall‐shrubs, defined here as those with diameter at root collar (DRC) ≥ 2.5 cm. Power function allometry of biomass to stem diameter generates a disproportionate prediction error that increases rapidly with diameter. Thus, biomass should be modeled as a single measure of stem diameter only if stem diameter is less than a threshold Dmax. When stem diameter exceeds Dmax, then the stem internode should be treated as a conic frustrum requiring two additional measures: a second, node‐adjacent diameter and a length. If the second diameter is less than Dmax, then the power function allometry can be applied to the aerial tip; otherwise an additional internode is measured. This “two‐component” allometry—internodes as frustra and aerial tips as shrubs—can reduce estimated biomass error propagated to the plot‐level by as much as 50% or more where very large shrubs are present Dmax is any diameter such that the ratio of single‐component to two‐component uncertainty exceeds the ratio of two‐component to single‐component measurement time. Guidelines for estimating Dmax based on pilot field data are provided. Tall shrubs are increasing in abundance and distribution across Arctic, alpine, boreal, and dryland ecosystems. Estimating their biomass is important for both ecological studies and carbon accounting. Reducing field‐sample prediction error increases precision in multi‐stage modeling because additional measures efficiently improve plot‐level biomass precision, reducing uncertainty for shrub biomass estimates.
Differences in dietary composition and preference maintained despite gene flow across a woodrat hybrid zone
Authors: Nielsen Danny P. , Matocq Marjorie D. 
Published: March 2021

Abstract

Ecotones, characterized by adjacent yet distinct biotic communities, provide natural laboratories in which to investigate how environmental selection influences the ecology and evolution of organisms. For wild herbivores, differential plant availability across sharp ecotones may be an important source of dietary‐based selection.
We studied small herbivore diet composition across a sharp ecotone where two species of woodrat, Neotoma bryanti and N. lepida, come into secondary contact with one another and hybridize. We quantified woodrat dietary preference through trnL metabarcoding of field‐collected fecal pellets and experimental choice trials. Despite gene flow, parental N. bryanti and N. lepida maintain distinct diets across this fine spatial scale, and across temporal scales that span both wet and dry conditions.

Neotoma bryanti maintained a more diverse diet, with Frangula californica (California coffeeberry) making up a large portion of its diet. Neotoma lepida maintains a less diverse diet, with Prunus fasciculata (desert almond) comprising more than half of its diet. Both F. californica and P. fasciculata are known to produce potentially toxic plant secondary compounds (PSCs), which should deter herbivory, yet these plants have relatively high nutritional value as measured by crude protein content.

Neotoma bryanti and N. lepida consumed F. californica and P. fasciculata, respectively, in greater abundance than these plants are available on the landscape—indicating dietary selection. Finally, experimental preference trials revealed that N. bryanti exhibited a preference for F. californica, while N. lepida exhibited a relatively stronger preference for P. fasciculata. We find that N. bryanti exhibit a generalist herbivore strategy relative to N. lepida, which exhibit a more specialized feeding strategy in this study system.
Our results suggest that woodrats respond to fine‐scale environmental differences in plant availability that may require different metabolic strategies in order to balance nutrient acquisition while minimizing exposure to potentially toxic PSCs.
Evidence for a synergistic effect of post‐translational modifications and genomic composition of eEF‐1α on the adaptation of Phytophthora infestans
Authors: Wang Yan‐Ping , Wu E‐Jiao , Lurwanu Yahuza , Ding Ji‐Peng , He Dun‐Chun , Waheed Abdul , Nkurikiyimfura Oswald , Liu Shi‐Ting , Li Wen‐Yang , Wang Zong‐Hua , Yang Lina , Zhan Jiasui 
Published: March 2021

AbstractGenetic variation plays a fundamental role in pathogen's adaptation to environmental stresses. Pathogens with low genetic variation tend to survive and proliferate more poorly due to their lack of genotypic/phenotypic polymorphisms in responding to fluctuating environments. Evolutionary theory hypothesizes that the adaptive disadvantage of genes with low genomic variation can be compensated for structural diversity of proteins through post‐translation modification (PTM) but this theory is rarely tested experimentally and its implication to sustainable disease management is hardly discussed. In this study, we analyzed nucleotide characteristics of eukaryotic translation elongation factor‐1α (eEF‐lα) gene from 165 Phytophthora infestans isolates and the physical and chemical properties of its derived proteins. We found a low sequence variation of eEF‐lα protein, possibly attributable to purifying selection and a lack of intra‐genic recombination rather than reduced mutation. In the only two isoforms detected by the study, the major one accounted for >95% of the pathogen collection and displayed a significantly higher fitness than the minor one. High lysine representation enhances the opportunity of the eEF‐1α protein to be methylated and the absence of disulfide bonds is consistent with the structural prediction showing that many disordered regions are existed in the protein. Methylation, structural disordering, and possibly other PTMs ensure the ability of the protein to modify its functions during biological, cellular and biochemical processes, and compensate for its adaptive disadvantage caused by sequence conservation. Our results indicate that PTMs may function synergistically with nucleotide codes to regulate the adaptive landscape of eEF‐1α, possibly as well as other housekeeping genes, in P. infestans. Compensatory evolution between pre‐ and post‐translational phase in eEF‐1α could enable pathogens quickly adapting to disease management strategies while efficiently maintaining critical roles of the protein playing in biological, cellular, and biochemical activities. Implications of these results to sustainable plant disease management are discussed.
The restricted mean survival time as a replacement for the hazard ratio and the number needed to treat in long‐term studies
Authors: Messori Andrea , Bartoli Laura , Trippoli Sabrina 
Published: March 2021

AbstractAimsWe applied the restricted mean survival time (RMST) to analyse the survival data reported in the PARADIGM‐HT trial in which sacubitril + valsartan was studied in comparison with enalapril in patients with heart failure. The estimates of this parameter were compared with the published values of hazard ratio (HR).MethodsTwo endpoints were evaluated: a composite of death or hospitalization and cardiovascular death. Our analyses were performed by considering the original follow‐up of 41.4 months and on the basis of a lifetime perspective. All statistical calculations were carried out using specific packages developed under the R‐platform.ResultsAccording to our RMST analysis, the results for the composite endpoint in the comparison of sacubitril + valsartan vs. enalapril showed an improvement from 32.9 to 34.2 months (gain of 1.25 months). This result is based on a time horizon of 41.4 months. The results for the cardiovascular mortality endpoint showed a RMST of 37.2 months for sacubitril + valsartan vs. 36.2 for enalapril (gain of 0.96 months). In the two lifetime analyses, the improvements were much more relevant and yielded a gain of 25.8 months for the composite endpoint and 27.6 months for survival free from cardiovascular death.ConclusionsUsing the data of the PARADIGM‐HT trial, our analysis confirmed that the RMST has documented advantages over the HR, particularly when the clinical study is characterized by a long follow‐up. The number needed to treat (NNT) has a more specific methodological role and cannot be replaced by the RMST.
From casting to forging—The combined simulation for a steel component
Authors: Böhmichen Uwe , Schubert Nadine , Lehnert Tim , Sterzing Andreas , Mauermann Reinhard 
Published: March 2021

AbstractIn the aluminum sector as well as in the manufacture of semi‐finished products, there are numerous industrial examples for combinations of casting and forming processes. In the past, the processes usually were considered separately. However, with the development of digital technologies, it has recently become possible to jointly simulate casting and forging processes and set up continuous process simulations. In the manufacturing of steel components, such a combination is not known industrially. The present paper describes a forged steel component based on a cast preform. To predict the expected properties, the necessary simulations were linked, whereby the focus of the investigations is on the principle feasibility and methodology of such a combination. Based on the conditions assumed in the simulations, test components were manufactured. The simulation results, especially voids and porosity were compared with the real components. In addition, the microstructure of the components was investigated. Approaches for further investigations and the improvement of the combined simulation are presented.
Study on bi‐level multi‐objective collaborative optimization model for integrated energy system considering source‐load uncertainty
Authors: Wu Jing , De Gejirifu , Tan Zhongfu , Li Yan 
Published: March 2021

AbstractThe uncertainty of both the generation side and the user side brings certain impacts on the source‐load coordinated operation of integrated energy system (IES). At first, the source‐load characteristics of the IES with large‐scale distributed energy is analyzed. Secondly, considering lower operating cost and higher renewable energy consumption rate, a bilevel optimization model of the source‐load coordination based on the IES operation structure is established, which considers the uncertainty of both the generation side, such as wind and photovoltaic (PV) power output, and the load side. The robust optimization theory and stochastic chance‐constrained programming theory are introduced to establish the upper layer and the lower layer models of the IES. Besides, the firefly algorithm is used to design the solution process of the bilevel optimization model. The results show that the uncertainty of the wind power, PV power, and the load are positively correlated with the comprehensive operation cost. Also, based on the full consideration of the bilevel uncertainties, the system operation cost is significantly reduced. Meanwhile, the improvement of the confidence level of the objective function and constraint conditions will enhance the optimistic value of the comprehensive operation cost of the system, which means gaining higher reliability by sacrificing partial system benefits. Overall, the bilevel optimization model considering the source‐load uncertainty can both improve the renewable energy consumption rate and reduce the comprehensive operation cost of the IES system.
Has the COVID‐19 crisis changed our relationship to the future?
Authors: Jahel Camille , Bourgeois Robin , Pesche Denis , de Lattre‐Gasquet Marie , Delay Etienne 
Published: March 2021

AbstractThe first wave of the COVID‐19 pandemic crisis introduced a sudden discontinuity into the functioning of human societies worldwide by affecting individual habits as well as economic and social life. This paper is a first attempt to investigate whether that discontinuity also altered our relationship to the future, in particular through the production of scenarios of a “world after” significantly different from the world before. We analyzed the representations of the future produced at the height of the first wave of the crisis, between March and June 2020, through a selective review of these productions. From the 60 sources found, we selected 23 texts yielding 83 scenarios. We used a classic four‐category typology (Continued Growth, Discipline, Collapse and Transformation) that allowed us to identify scenarios of continuity and discontinuity. The results show a paradoxical predominance of continuity scenarios, contradicting our hypothesis that the crisis would have fostered creativity regarding the “world after.” The discussion focuses on potential explanatory elements. These relate essentially to the way the scenarios were produced, notably in terms of time horizon, explanation of the methods and selection of the variables structuring the scenarios. These elements seem to indicate that these scenarios were rather generated from a reactive posture, showing a reluctance to rethink the present as a moment of discontinuity opening up the horizon of possibilities. This initial work paves the way for a more systematic exploration of the practice of anticipation and the capacity to produce creative/imaginative futures in times of crisis.
Combining machine learning and domain decomposition methods for the solution of partial differential equations—A review
Authors: Heinlein Alexander , Klawonn Axel , Lanser Martin , Weber Janine 
Published: March 2021

AbstractScientific machine learning (SciML), an area of research where techniques from machine learning and scientific computing are combined, has become of increasing importance and receives growing attention. Here, our focus is on a very specific area within SciML given by the combination of domain decomposition methods (DDMs) with machine learning techniques for the solution of partial differential equations. The aim of the present work is to make an attempt of providing a review of existing and also new approaches within this field as well as to present some known results in a unified framework; no claim of completeness is made. As a concrete example of machine learning enhanced DDMs, an approach is presented which uses neural networks to reduce the computational effort in adaptive DDMs while retaining their robustness. More precisely, deep neural networks are used to predict the geometric location of constraints which are needed to define a robust coarse space. Additionally, two recently published deep domain decomposition approaches are presented in a unified framework. Both approaches use physics‐constrained neural networks to replace the discretization and solution of the subdomain problems of a given decomposition of the computational domain. Finally, a brief overview is given of several further approaches which combine machine learning with ideas from DDMs to either increase the performance of already existing algorithms or to create completely new methods.
Classification and image processing with a semi‐discrete scheme for fidelity forced Allen–Cahn on graphs
Authors: Budd Jeremy , van Gennip Yves , Latz Jonas 
Published: March 2021

AbstractThis paper introduces a semi‐discrete implicit Euler (SDIE) scheme for the Allen‐Cahn equation (ACE) with fidelity forcing on graphs. The continuous‐in‐time version of this differential equation was pioneered by Bertozzi and Flenner in 2012 as a method for graph classification problems, such as semi‐supervised learning and image segmentation. In 2013, Merkurjev et. al. used a Merriman‐Bence‐Osher (MBO) scheme with fidelity forcing instead, as heuristically it was expected to give similar results to the ACE. The current paper rigorously establishes the graph MBO scheme with fidelity forcing as a special case of an SDIE scheme for the graph ACE with fidelity forcing. This connection requires the use of the double‐obstacle potential in the ACE, as was already demonstrated by Budd and Van Gennip in 2020 in the context of ACE without a fidelity forcing term. We also prove that solutions of the SDIE scheme converge to solutions of the graph ACE with fidelity forcing as the discrete time step converges to zero. In the second part of the paper we develop the SDIE scheme as a classification algorithm. We also introduce some innovations into the algorithms for the SDIE and MBO schemes. For large graphs, we use a QR decomposition method to compute an eigendecomposition from a Nyström extension, which outperforms the method used by, for example, Bertozzi and Flenner in 2012, in accuracy, stability, and speed. Moreover, we replace the Euler discretization for the scheme's diffusion step by a computation based on the Strang formula for matrix exponentials. We apply this algorithm to a number of image segmentation problems, and compare the performance with that of the graph MBO scheme with fidelity forcing. We find that while the general SDIE scheme does not perform better than the MBO special case at this task, our other innovations lead to a significantly better segmentation than that from previous literature. We also empirically quantify the uncertainty that this segmentation inherits from the randomness in the Nyström extension.