bluuebunny/crossref_metadata_2025_split huggingface dataset:
The latitudinal gradient in plant community assembly processes: A meta‐analysis
Authors: Nishizawa Keita , Shinohara Naoto , Cadotte Marc W. , Mori Akira S. 
Published: May 2022

AbstractBeta(β)‐diversity, or site‐to‐site variation in species composition, generally decreases with increasing latitude, and the underlying processes driving this pattern have been challenging to elucidate because the signals of community assembly processes are scale‐dependent. In this meta‐analysis, by synthesising the results of 103 studies that were distributed globally and conducted at various spatial scales, we revealed a latitudinal gradient in the detectable assembly processes of vascular plant communities. Variations in plant community composition at low and high latitudes were mainly explained by geographic variables, suggesting that distance decay and dispersal limitations causing spatial aggregation are influential in these regions. In contrast, variation in species composition correlated most strongly with environmental variables at mid‐latitudes (20–30°), reflecting the importance of environmental filtering, although this unimodal pattern was not statistically significant. Importantly, our analysis revealed the effects of different spatial scales, such that the correlation with spatial variables was stronger at smaller sampling extents, and environmental variables were more influential at larger sampling extents. We concluded that plant communities are driven by different community assembly processes in distinct biogeographical regions, suggesting that the latitudinal gradient of biodiversity is created by a combination of multiple processes that vary with environmental and species size differences.
Prospective longitudinal study on prognostic factors of visual recovery and structural change after a first episode of optic neuritis
Authors: Deschamps Romain , Shor Natalia , Vignal Catherine , Guillaume Jessica , Boudot de la Motte Marine , Salviat Flore , Lecler Augustin , Marignier Romain , Hage Rabih , Coulette Sarah , Bidot Samuel , Gueguen Antoine , Mauget‐Faysse Martine , Bensa Caroline , Vasseur Vivien , Gout Olivier , Lamirel Cedric 
Published: May 2022

AbstractBackground and purposeThis study was undertaken to determine the role of optical coherence tomography (OCT) in predicting the final visual and structural outcome, and to evaluate the correlation between functional eye outcome and retinal changes, in patients with a first episode of optic neuritis (ON).MethodsIn this prospective study, consecutive adult patients with acute ON underwent ophthalmological evaluation at baseline and at 1 and 12 months, including OCT measurements of peripapillary retinal nerve fiber layer (pRNFL), macular ganglion cell and inner plexiform layer, and inner nuclear layer thicknesses; high‐ and low‐contrast visual acuity; visual field assessment; and baseline brain magnetic resonance imaging. Univariate and multivariate linear regressions were used to assess predictive factors of outcome. Correlations between 12‐month visual function and retinal structure were estimated by Spearman coefficients. Two groups of patients were analyzed, with or without multiple sclerosis (MS).ResultsAmong 116 patients, 79 (68.1%) had MS, and 37 (31.9%) had ON not related to MS (including 19 idiopathic [i.e., isolated] ON, and 13 and five with myelin oligodendrocyte glycoprotein and aquaporin‐4 antibodies, respectively). We found no independent predictive factor of visual and retinal outcome. Analysis of the relationship between the visual field test (mean deviation) and pRNFL thickness demonstrated a threshold of 75.4 μm and 66.4 μm, below which the mean deviation was worse, for patients with MS (p = 0.007) and without MS (p < 0.001), respectively.ConclusionsWe found that inner retinal layer measurements during the first month are not predictive of final outcome. The critical threshold of axonal integrity, below which visual function is damaged, is different between patients with and without MS.
Developer load balancing bug triage: Developed load balance
Authors: Yadav Asmita , Baljon Mohammed , Mishra Shailendra , Singh Sandeep Kumar , Saxena Sharad , Sharma Sunil Kumar 
Published: May 2022

AbstractDuring the software development process, numerous bugs are reported daily in the software bug repositories. Bug triage assigned these bugs to the most relevant and expert developer for resolution. Moreover, assigning bugs to an incompetent or an over‐engaged developer causes repeated reassignment to other developers until it is resolved. This problem can be solved by devising a triage process that assigns bugs to not only expert developers but also to those who are either under‐engaged or reasonably engaged but are not over‐engaged in work. This paper has designed and implemented work engagement sensitive bug triage that resolves the issue of assigning bugs to developers considering their due work engagement, expertise as well as the current state of activity. For this purpose, a developer profile is built by using metrics to generate three types of scores: technical skill, work engagement and work experience. Metadata features like developer‐name, email, developer‐work‐experience in bug resolution, last and present‐work‐activity, timestamp, component and priority are used for it. A multi‐criteria‐based Henry–Garret technique is used to generate a single ranked list of developers from three ranked lists. The performance of the proposed approach is evaluated on four large OOS projects: Mozilla, Eclipse, Netbeans, and Open Office covering 895,439 bug reports accumulated for 33 years of development. The overall system accuracy of the proposed triage using all four datasets is 91.96 ± 0.05% which is 5.87% better than previously published work. The proposed method is achieved up to 90.30 ± 0.05 and 97.1 ± 0.05 MRR and accuracy of reassignment respectively that indicates how significantly it re‐assigns the bug to the relevant developers. The results demonstrate improvement in the accuracy of software‐bugs triaging as well as a reduction in the probability of bug‐tossing.
A genetic algorithm‐based on‐orbit self‐repair implementation for SRAM FPGAs
Authors: Zhang Fan , Guo Chenguang , Zhang Shifeng , Zeng Qinqin , Nguyen Tri Gia 
Published: May 2022

AbstractThe reconfigurable capability of static random‐access memory (SRAM) field programmable gate array (FPGA) can be used for its fault self‐repair method. As a machine learning method, the genetic algorithm (GA) is an FPGA fault repair method that can be automatically executed on‐orbit without any ground support. However, the GA‐based fault repair method has disadvantages, such as the dependency on processors, the knowledge requirement for user designs in FPGAs, and the small size of repaired circuits. To address these issues, this paper presents a comprehensive analysis of the FPGA bitstream in the aerospace industry. An accurate on‐orbit fault location can be identified by bitstream copying and exhaustive test and the executed area of the GA can be reduced to one tile. In addition, the probability function of the algorithm is optimized, which converts floating‐point operations into integer arithmetic operations that are easily implemented in FPGAs without processors. The method is outstanding compared with existing ones, considering: (1) The size of repaired circuits is hundreds of times larger than those from other methods. (2) Its implementations are totally up to FPGAs' own logic, with no requirement for processors. (3) There is no knowledge requirement for user design. (4) It reaches the leading level with a success rate of 81%–93%. The method has been verified by various applications in XC7VX330T, which demonstrates its engineering practicability.
A novel multi‐view ordinal classification approach for software bug prediction
Authors: Yildirim Taser Pelin 
Published: May 2022

AbstractSoftware bug prediction aims to enhance software quality and testing efficiency by constructing predictive classification models using code properties. This enables the prompt detection of fault‐prone modules. There are several machine learning‐based software bug prediction studies, which mainly focus on single view data by disregarding the natural ordering relation among the class labels in the literature. Thus, these studies cause losing each view's own intrinsic structure and the inherent order of the labels that positively affect the prediction performance. To overcome this drawback, this study focuses on integrating ordering information and a multi‐view learning strategy. This paper proposes a novel approach multi‐view ordinal classification (MVOC), which learns from different views (complexity, coupling, cohesion, inheritance and scale) of the software dataset separately and predicts software bugs taking the inherent order of class labels (non‐buggy, less buggy and more buggy) into consideration. To demonstrate its prediction performance, the MVOC approach was executed on the 40 different real‐world software datasets using six different classification algorithms as base learners. In the experiments, the MVOC approach was compared with traditional classifiers and their multi‐view implementations in terms of precision, recall, f‐measure and accuracy rate metrics. The results indicate that the MVOC approach presents better prediction performance on average than the multi‐view‐based and traditional classifiers. It is also observed from the results that the MVOC.RF model achieved the highest classification performance with an average accuracy rate of 85.65%.
RFFS: Recursive random forest feature selection based ensemble algorithm for chronic kidney disease prediction
Authors: Theerthagiri Prasannavenkatesan , Ruby A. Usha 
Published: May 2022

AbstractChronic kidney disease is a global health issue that affects millions of people worldwide and causes significant social, economic, and medical issues. Several automated detection systems can diagnose chronic kidney disease. This paper proposes the recursive random forest feature selection (RFFS) based ensemble learning algorithm to diagnose chronic kidney diseases (CKD). In the decision point, decision tree‐based classifiers are used. The accuracy and kappa scores are used to determine the classification results. According to the results of the proposed algorithm's performance analyses, the ensemble learning classifiers outperform other classifiers for classifying CKD. The proposed RFFS algorithm achieves 6%–40% improved prediction accuracy using the feature selection algorithm. Further, it attains 15%–39% of reduced mean square error. The performance metrics, precision, sensitivity, specificity, f1 score, and Jaccard scores, have been analysed and show greater results for the RFFS algorithm. Thus, the proposed RFE‐GB algorithm results prove it as a prominent model for CKD estimation and treatment.
Examining mechanisms linking economic insecurity to interparental conflict among couples with low income
Authors: Lee Joyce Y. , Lee Shawna J. , Volling Brenda L. , Grogan‐Kaylor Andrew C. 
Published: May 2022

AbstractObjectiveThe current study used the family stress model to test the mechanisms by which economic insecurity contributes to mothers' and fathers' mental health and couples' relationship functioning.BackgroundAlthough low household income has been a focus of poverty research, material hardship—defined as everyday challenges related to making ends meet including difficulties paying for housing, utilities, food, or medical care—is common among American families.MethodsParticipants were from the Building Strong Families project. Couples were racially diverse (43.52% Black; 28.88% Latinx; 17.29% White; 10.31% Other) and living with low income (N = 2794). Economic insecurity included income poverty and material hardship. Bayesian mediation analysis was employed, taking advantage of the prior evidence base of the family stress model.ResultsMaterial hardship, but not income poverty, predicted higher levels of both maternal and paternal depressive symptoms. Only paternal depressive symptoms were linked with higher levels of destructive interparental conflict (i.e., moderate verbal aggression couples use that could be harmful to the partner relationship). Mediation analysis confirmed that material hardship operated primarily through paternal depressive symptoms in its association with destructive interparental conflict.ConclusionThe economic stress of meeting the daily material needs of the family sets the stage for parental mental health problems that carry over to destructive interparental conflict, especially through paternal depressive symptoms.ImplicationsFamily‐strengthening programs may want to consider interventions to address material hardship (e.g., comprehensive needs assessments, connections to community‐based resources, parents' employment training) as part of their efforts to address parental mental health and couples' destructive conflict behaviors.
Modeling of crack path in layered architectures composed of dissimilar materials
Authors: Lu Songsong , Reed Philippa , Cook Richard , Zhang Yi 
Published: May 2022

AbstractIn order to make full use of the potential fatigue crack growth resistance provided by layered architectures, a validated crack path simulation algorithm for crack propagation through different elements of the layered architectures was established. The crack path approaching a material interface was predicted by using the maximum tangential strain (MTSN) criterion and the crack behavior at the interface was simulated by a developed two‐step method (a modified stress‐and‐energy‐based cohesive zone method considering the change in direction of an interface penetrating crack). The crack path simulation by using this algorithm in layered example architectures indicates (1) there are two criteria zones for the transition between crack deflection and penetration in terms of the relationship between interfacial strength and toughness; (2) the likelihood of a crack deflecting out of the interface will increase with the propagation of an interfacial crack; and (3) the architecture difference which affects shielding or anti‐shielding behavior has a significant effect on crack deflection or penetration events.
Bioclimatic context of species' populations determines community stability
Authors: Evans Luke Christopher , Melero Yolanda , Schmucki Reto , Boersch‐Supan Philipp H. , Brotons Lluís , Fontaine Colin , Jiguet Frédéric , Kuussaari Mikko , Massimino Dario , Robinson Robert A. , Roy David B. , Schweiger Oliver , Settele Josef , Stefanescu Constanti , van Turnhout Chris A. M. , Oliver Tom Henry 
Published: May 2022

AbstractAimIt is important to understand the factors affecting community stability because ecosystem function is increasingly at risk from biodiversity loss. Here, we evaluate how a key factor, the position of local environmental conditions within the thermal range of the species, influences the stability of butterfly communities at a continental scale.LocationSpain, UK and Finland.Time period1999–2017.Major taxa studiedButterflies.MethodsWe tested the following hypotheses about how species responses to temperature anomalies aggregate to influence stability: Hypothesis 1, species have contrasting responses to local temperature anomalies at opposing edges of their thermal range; hypothesis 2, communities with central thermal range positions have higher community stability; and the impacts of thermal range position on community stability are driven by hypothesis 3, population asynchrony, or hypothesis 4, additive population stability. Data were analysed at 876 sites for 157 species.ResultsWe found some support for hypothesis 1, because there were interactions between thermal range and response to temperature anomalies such that species at different range edges could provide weak compensatory dynamics. However, responses were nonlinear, suggesting strong declines with extreme anomalies, particularly at the hot range edge. Hypothesis 2 was supported in part, because community stability increased with central thermal range positions and declined at the edges, after accounting for species richness and community abundance. Thermal range position was weakly correlated with asynchrony (hypothesis 3) and population stability (hypothesis 4), although species richness and population abundance had larger impacts.Main conclusionsFuture extreme heat events will be likely to impact species negatively across their thermal range, but might be particularly impactful on populations at the hottest end of the thermal range. Thermal range position influenced community stability because range edge communities were stable. However, the prediction of community stability from thermal range position is challenging because of nonlinear responses to temperature, with small temperature anomalies producing weak compensatory dynamics, but large extreme events synchronizing dynamics.
Infants preferentially learn from surprising teachers
Authors: Stahl Aimee E. , Woods Larissa 
Published: May 2022

AbstractInfants have sophisticated knowledge about the physical world, and show enhanced learning about objects that violate physical principles. However, it is unknown whether infants also preferentially learn from the individual who produces an outcome that violates expectations. We investigated whether 15‐month‐old infants (N = 48) selectively imitate individuals who produce surprising outcomes. In Experiment 1, infants watched an experimenter hide a ball and produce an expected outcome in which the ball was revealed where it was hidden, or a surprising outcome in which the ball was revealed in a different location. The experimenter then demonstrated a novel action: using her head to activate a light while her hands were free. Infants imitated that novel action more if the experimenter had previously produced a surprising than an expected outcome. In Experiment 2, infants witnessed the experimenter produce the surprising outcome, then use her head to activate the light while her hands were occupied. Infants did not differentially imitate the head‐touch action relative to either condition in Experiment 1, perhaps indicating a tension between surprise‐induced learning and rational imitation. These experiments show that surprising events are pedagogical opportunities: infants selectively learn from surprising individuals, but also may account for rationality in their surprise‐induced social learning.
Carbon‐containing droplet combustion–carbothermal synthesis of well‐distributed AlN nanometer powders
Authors: Chu Aimin , Zhao Yuping , Ud‐din Rafi , Hu Hairong , Zhi Qian , Wang Zerui 
Published: May 2022

AbstractA novel three‐step technique was employed to synthesize the well‐distributed AlN nanopowders. First, the hollow spherical precursor particles with an average diameter of 2–5 μm, consisting of an amorphous structure mixture of Al2O3 and C, was prepared by carbon‐containing droplet combustion method by using glucose, urea, and aluminum nitrate as starting materials. The carbothermal reduction and nitridation (CRN) was carried out at 1500°C under N2 flow for 2 h and subsequently the CRN product was calcined at 700°C in air for 1 h to remove residual carbon and transform the CRN product to high‐purity AlN powders consisting of nanostructured hollow spheres. The formation mechanism of precursor and AlN hollow spheres was discussed in detail. The AlN powder exhibited well‐distributed spherical particles with a size of 30–50 nm and good sinterability. After additive‐free and pressureless sintering at 1800°C for 2 h, the relative density of the sintered AlN sample was measured to be 99.02%.
Climate refugia for three Afromontane forest‐dependent bird species in south‐eastern South Africa
Authors: Mulvaney Jake M. , Cherry Michael I. , Matthee Conrad A. 
Published: May 2022

AbstractAimPleistocene climate shifts were influential in shaping biodiversity patterns for forest‐dependent species. Within southern Africa, palaeoclimatic shifts possibly homogenised subtropical Afromontane forest biodiversity, yet these forests continue to harbour unique diversity. For the three songbird species with different natural histories, we investigated the refugial role of subtropical Afromontane and scarp forests and explored specifically how palaeoclimatic events impacted genetic connectivity among forest patches.LocationMaputaland‐Pondoland‐Albany Biodiversity Hotspot, south‐eastern South Africa.TaxonBatis capensis, Phylloscopus ruficapilla and Pogonocichla stellata.MethodsMitochondrial control region sequences and microsatellite data were used to assess genetic diversity and population structure among 406 birds. Demographic change was inferred using Bayesian skyline plots (BSPs), and approximate Bayesian computations (ABCs) were used to identify gene flow trends among putative refugia. Environmental niche models (ENMs) were used to infer past occurrence probabilities.ResultsSpecies BSPs supported regional presence predating the Last Glacial Maximum (21 kya) and indicated post‐glacial population expansions. ABC modelling revealed that present‐day gene flow trends emerged largely during the current interglacial (<12 kya), suggesting that thermal maxima promote regional forest expansion. The north‐eastern source of gene flow in all the three species suggested a post‐glacial influx from refugia further north, while southern scarp forests sustained secondary source populations for B. capensis and P. ruficapilla. High gene flow signatures from south‐western forests in B. capensis and especially P. stellata alluded to hidden source populations in the under‐surveyed southern Afrotemperate forests—the southernmost Afromontane forest bloc. ENMs corroborated both scarp and southern Afrotemperate forests as glacial refugia and demonstrated persistent regional population presence over the past 120 kya.Main ConclusionsThe population genetics and palaeodistribution of the three bird species indicate their regional persistence throughout the late Pleistocene, suggesting that Afromontane and scarp forests of south‐eastern South Africa served as refugia for subtropical African avian forest biota.
A novel DNA damage and repair‐related gene signature to improve predictive capacity of overall survival for patients with gliomas
Authors: Li Xiaodong , Wang Yichang , Wu Wei , Xiang Jianyang , Wang Maode , Yu Hai 
Published: May 2022

AbstractGliomas, as the most lethal and malignant brain tumours in adults, remain a major challenge worldwide. DNA damage and repair‐related genes (DDRRGs) appear to play a significant role in gliomas, but the studies of DDRRGs are still insufficient. Herein, we systematically explored and analysed 1547 DDRRGs in 938 glioma samples from TCGA and CGGA datasets. Using least absolute shrinkage and selection operator (LASSO) Cox regression analysis, we identified a 16‐DDRRG signature, characterized by high‐risk and low‐risk patterns. This risk model harbours robust predictive capability for overall survival of glioma patients. We found the high‐risk score is strongly associated with well‐known malignant features of gliomas, such as the mesenchymal subtype, IDH‐wildtype, 1p/19q non‐codeletion and MGMT promoter unmethylated status. In addition, we found that the high‐risk score is also linked with multiple oncogenic pathways and therapeutic resistance. Significantly, we found the high‐risk group has higher enrichment of immunosuppressive cells (M2‐type macrophages, Tregs and MDSCs) and immune inhibition biomarkers (PD‐1, PD‐L1 and CTLA‐4). Lastly, we proved that SMC4, which has the highest positive regression coefficient in our risk model, is strongly linked with malignant progression and TMZ resistance of gliomas in a E2F1‐dependent manner.