Miracle-dz/UniFAR-FTUs huggingface dataset:
Paradoxical Leader Behaviors and Followers Overall Justice and Citizenship Behaviors: The Role of Renqing Perception and Trait Agreeableness

Purpose Drawing on the uncertainty management theory, this study explores the relationships between paradoxical leader behaviors (PLB) and followers perceptions of overall justice and organizational citizenship behaviors (OCB), as well as the mediating role of two components of followers perceptions of renqing (affective interaction and discretionary consideration) and the moderating role of trait agreeableness. Methods This study employed two-wave surveys with the aim of reducing the potential risk of common method bias. Participants were 325 employees from seven enterprises located in Northern and Southwest China. A moderated-mediation path analysis based on Hayes’ Process Model was performed in AMOS to examine the hypotheses. Results Results from two-wave surveys of 325 Chinese employees indicated that PLB is positively related to followers perceptions of overall justice. Two components of followers perceptions of renqing significantly mediate the relationship between PLB and overall justice. Moreover, two components of perceptions of renqing and overall justice exert a serial mediation effect in the relationship of PLB and OCB. More importantly, followers trait agreeableness strengthens the effects of affective interaction on overall justice. Conclusion This study advances the current understandings of the influencing mechanisms between PLB and followers overall justice perceptions and OCB. It is suggested that leaders PLB will facilitate two components of followers perceptions of renqing first, then boost their perceptions of overall justice, which in turn leading to more OCB, especially for those followers who endorse more agreeableness.
Nomogram for accurate and quantitative prediction of the risk of psoriatic arthritis in Chinese adult patients with moderate and severe plaque psoriasis

Psoriatic arthritis (PsA) is an inflammatory form of arthritis that appears approximately 7–10 years after psoriasis and remains undiagnosed in most of patients. Currently, only a few quantitative and succinct PsA-risk prediction models are available. The aim of this study was to establish and validate a prediction model for quantitatively assessing the risk of PsA in moderate and severe plaque psoriasis patients. A non-interventional and cross-sectional study was conducted. Demographic, clinical, and laboratory records were collected and blindly reviewed. Logistic regression was used to develop this prediction model. With C-index and calibration curve, internal validation was performed. Five-fold cross validation, external validation and decision curve analysis (DCA) were also applied to assess this model. Among 405 patients, 111 patients had PsA. Arthralgia (OR = 39.346; 95% CI: 20.139–82.579), C-reactive protein (OR = 2.008; 95% CI: 1.051–3.838), lymphocyte level (OR = 0.341; 95% CI: 0.177–0.621), hypertension (OR = 0.235; 95% CI: 0.077–0.660) and disease duration (OR = 1.033; 95% CI: 0.998–1.071) were identified as potential predictors affecting the risk of transition from moderate and severe PsO to PsA. C-index for the prediction nomogram was 0.911 (95% CI: 0.879–0.943), and was confirmed to be 0.905 through 1000-time bootstrapping internal validation. Cross validation and external validation were preformed and proved the accuracy and generalizability of this prediction model. This study establishes a quantitative predictive nomogram with good predictive power for assessing the risk of PsA in patients with moderate and severe PsO.
Slow Escape from a Helical Misfolded State of the Pore-Forming Toxin Cytolysin A

The pore-forming toxin cytolysin A (ClyA) is expressed as a large α-helical monomer that, upon interaction with membranes, undergoes a major conformational rearrangement into the protomer conformation, which then assembles into a cytolytic pore. Here, we investigate the folding kinetics of the ClyA monomer with single-molecule Förster resonance energy transfer spectroscopy in combination with microfluidic mixing, stopped-flow circular dichroism experiments, and molecular simulations. The complex folding process occurs over a broad range of time scales, from hundreds of nanoseconds to minutes. The very slow formation of the native state occurs from a rapidly formed and highly collapsed intermediate with large helical content and nonnative topology. Molecular dynamics simulations suggest pronounced non-native interactions as the origin of the slow escape from this deep trap in the free-energy surface, and a variational enhanced path-sampling approach enables a glimpse of the folding process that is supported by the experimental data.
k-Distinct Branchings Admits a Polynomial Kernel

Unlike the problem of deciding whether a digraph D = ( V, A ) has ℓ in-branchings (or ℓ out-branchings) is polynomial time solvable, the problem of deciding whether a digraph D = ( V, A ) has an in-branching B − and an out-branching B + which are arc-disjoint is NP -complete. Motivated by this, a natural optimization question that has been studied in the realm of Parameterized Complexity is called Rooted k -Distinct Branchings . In this problem, a digraph D = ( V, A ) with two prescribed vertices s, t are given as input and the question is whether D has an in-branching rooted at t and an out-branching rooted at s such that they differ on at least k arcs. Bang-Jensen et al. [ Algorithmica, 2016 ] showed that the problem is fixed parameter tractable ( FPT ) on strongly connected digraphs. Gutin et al. [ ICALP, 2017; JCSS, 2018 ] completely resolved this problem by designing an algorithm with running time 2 O ( k 2 log 2 k ) n O (1) . Here, n denotes the number of vertices of the input digraph. In this paper, answering an open question of Gutin et al., we design a polynomial kernel for Rooted k -Distinct Branchings . In particular, we obtain the following:
基于词信息嵌入的汉语构词结构识别研究(Chinese Word-Formation Prediction based on Representations of Word-Related Features)

“作为一种意合型语言,汉语中的构词结构刻画了构词成分之间的组合关系,是认知、理解词义的关键。 在中文信息处理领域,此前的构词结构识别工作大多沿用句法层面的粗粒度标签,且主要基于上下文等词间信息建模,忽略了语素义、词义等词内信息对构词结构识别的作用。 本文采用语言学视域下的构词结构标签体系,构建汉语构词结构及相关信息数据集,提出了一种基于Bi-LSTM和Self-attention的模型,以此来探究词内、词间等多方面信息对构词结构识别的潜在影响和能达到的性能。 实验取得了良好的预测效果,准确率77. 87%,F1值78. 36%;同时,对比测试揭示,词内的语素义信息对构词结构识别具有显著的贡献,而词间的上下文信息贡献较弱且带有较强的不稳定性。 该预测方法与数据集,将为中文信息处理的多种任务,如语素和词结构分析、词义识别与生成、语言文字研究与词典编纂等提供新的观点和方案。 ”
A sink host allows a specialist herbivore to persist in a seasonal source

In seasonal environments, sinks that are more persistent than sources may serve as temporal stepping stones for specialists. However, this possibility has to our knowledge, not been demonstrated to date, as such environments are thought to select for generalists, and the role of sinks, both in the field and in the laboratory, is difficult to document. Here, we used laboratory experiments to show that herbivorous arthropods associated with seasonally absent main (source) habitats can endure on a suboptimal (sink) host for several generations, albeit with a negative growth rate. Additionally, they dispersed towards this host less often than towards the main host and accepted it less often than the main host. Finally, repeated experimental evolution attempts revealed no adaptation to the suboptimal host. Nevertheless, field observations showed that arthropods are found in suboptimal habitats when the main habitat is unavailable. Together, these results show that evolutionary rescue in the suboptimal habitat is not possible. Instead, the sink habitat functions as a temporal stepping stone, allowing for the persistence of a specialist when the source habitat is gone.
Pioneering polyploids: the impact of whole-genome duplication on biome shifting in New Zealand Coprosma (Rubiaceae) and Veronica (Plantaginaceae)

The role of whole-genome duplication (WGD) in facilitating shifts into novel biomes remains unknown. Focusing on two diverse woody plant groups in New Zealand, Coprosma (Rubiaceae) and Veronica (Plantaginaceae), we investigate how biome occupancy varies with ploidy level, and test the hypothesis that WGD increases the rate of biome shifting. Ploidy levels and biome occupancy (forest, open and alpine) were determined for indigenous species in both clades. The distribution of low-ploidy (Coprosma: 2x, Veronica: 6x) versus high-ploidy (Coprosma: 4–10x, Veronica: 12–18x) species across biomes was tested statistically. Estimation of the phylogenetic history of biome occupancy and WGD was performed using time-calibrated phylogenies and the R package BioGeoBEARS. Trait-dependent dispersal models were implemented to determine support for an increased rate of biome shifting among high-ploidy lineages. We find support for a greater than random portion of high-ploidy species occupying multiple biomes. We also find strong support for high-ploidy lineages showing a three- to eightfold increase in the rate of biome shifts. These results suggest that WGD promotes ecological expansion into new biomes.
Red Light and Nitrogen Depletion Stimulate the Synthesis of Lipids and N-Alkadienes Susceptible to be Used as Biofuels in Botryococcus braunii UTEX 2441 (Race A)

Effects of four light types, blue, green, white and red, as well as the incubation time were evaluated on culture growth and intracellular lipid and hydrocarbon production in Botryococcus braunii UTEX 2441. It was found that the microalga uses more efficiently the low energy photons to stimulate its cellular growth, and during the period of nitrogen deficiency, red light strongly influenced the microalga to synthesize high amounts of lipids and hydrocarbons. Compared with white light (control), red light significantly increased the specific growth rate, biomass productivity, photosynthetic efficiency, nitrogen consumption rate and lipids and hydrocarbons accumulation. At the end of the experiments, the lipid and hydrocarbon concentrations were 27% and 25%, respectively, by dried weight; 96% of the hydrocarbons produced were a mixture of the n-alkadienes C29H56 and C31H60. Results provide information of the combined effect of wavelength and nitrogen depletion over the intracellular lipid and hydrocarbon content, and the amounts in which they are produced, and suggests that the wavelength and incubation time are variables that can be used to manage the metabolism of biotechnological products in this alga.
Unravelling the mystery of female meiotic drive: where we are

Female meiotic drive is the phenomenon where a selfish genetic element alters chromosome segregation during female meiosis to segregate to the egg and transmit to the next generation more frequently than Mendelian expectation. While several examples of female meiotic drive have been known for many decades, a molecular understanding of the underlying mechanisms has been elusive. Recent advances in this area in several model species prompts a comparative re-examination of these drive systems. In this review, we compare female meiotic drive of several animal and plant species, highlighting pertinent similarities.
ΔGunfold leaderless, a package for high-throughput analysis of translation initiation regions (TIRs) at the transcriptome scale and for leaderless mRNA optimization

Background Translation initiation is an essential step for fidelity of gene expression, in which the ribosome must bind to the translation initiation region (TIR) and position the initiator tRNA in the P-site (1). For this to occur correctly, the TIR encompassing the ribosome binding site (RBS) needs to be highly accessible (2-5). ΔGunfold is a metric for computing accessibility of the TIR, but there is no automated way to compute it manually with existing software/tools limiting throughput. Results ΔGunfold leaderless allows users to automate the ΔGunfold calculation to perform high-throughput analysis. Importantly, ΔGunfold leaderless allows calculation of TIRs of both leadered mRNAs and leaderless mRNAs which lack a 5’ UTR and which are abundant in bacterial, archaeal, and mitochondrial transcriptomes (4, 6, 7). The ability to analyze leaderless mRNAs also allows one additional feature where users can computationally optimize leaderless mRNA TIRs to maximize their gene expression (8, 9). Conclusions The ΔGunfold leaderless package facilitates high-throughput calculations of TIR accessibility, is designed to calculate TIR accessibility for leadered and leaderless mRNA TIRs which are abundant in bacterial/archaeal/organellar transcriptomes and allows optimization of leaderless mRNA TIRs for biotechnology.
Efficient Algorithms for Least Square Piecewise Polynomial Regression

We present approximation and exact algorithms for piecewise regression of univariate and bivariate data using fixed-degree polynomials. Specifically, given a set S of n data points ( x 1 , y 1 ) , . , ( x n , y n ) ∈ R d × R where d ∈ { 1 , 2 } , the goal is to segment x i ’s into some (arbitrary) number of disjoint pieces P 1 , . , P k , where each piece P j is associated with a fixed-degree polynomial f j : R d → R , to minimize the total loss function λk + P n i =1 ( y i − f ( x i )) 2 , where λ ≥ 0 is a regularization term that penalizes model complexity (number of pieces) and f : F k j =1 P j → R is the piecewise polynomial function defined as f | P j = f j . The pieces P 1 , . , P k are disjoint intervals of R in the case of univariate data and disjoint axis-aligned rectangles in the case of bivariate data. Our error approximation allows use of any fixed-degree polynomial, not just linear functions. Our main results are the following. For univariate data, we present a (1 + ε )-approximation algorithm with time complexity O ( nε log 1 ε ), assuming that data is presented in sorted order of x i ’s. For bivariate data, we present three results: a sub-exponential exact algorithm with running time n O ( √ n ) ; a polynomial-time constant-approximation algorithm; and a quasi-polynomial time approximation scheme (QPTAS). The bivariate case is believed to be NP-hard in the folklore but we could not find a published record in the literature, so in this paper we also present a hardness proof for completeness.
Spatial ecology of conflicts: unravelling patterns of wildlife damage at multiple scales

Human encroachment into natural habitats is typically followed by conflicts derived from wildlife damage to agriculture and livestock. Spatial risk modelling is a useful tool to gain the understanding of wildlife damage and mitigate conflicts. Although resource selection is a hierarchical process operating at multiple scales, risk models usually fail to address more than one scale, which can result in the misidentification of the underlying processes. Here, we addressed the multi-scale nature of wildlife damage occurrence by considering ecological and management correlates interacting from household to landscape scales. We studied brown bear (Ursus arctos) damage to apiaries in the North-eastern Carpathians as our model system. Using generalized additive models, we found that brown bear tendency to avoid humans and the habitat preferences of bears and beekeepers determine the risk of bear damage at multiple scales. Damage risk at fine scales increased when the broad landscape context also favoured damage. Furthermore, integrated-scale risk maps resulted in more accurate predictions than single-scale models. Our results suggest that principles of resource selection by animals can be used to understand the occurrence of damage and help mitigate conflicts in a proactive and preventive manner.
Using Auto-Encoder Neural Networks for Memory Fault Tolerance in Gesture Recognition System

Data faults and retention characteristics in memories induce inaccuracy and failure in conventional electronic systems. While intelligent hardware which using AI algorithms can tolerate these faults with the advantage of neural network. This paper proposes using auto-encoder (AE) neural networks for memory fault tolerance in gesture recognition system based on RF sensors. This paper models the data faults of memories with defects or operating in ultra-low power state by a binary-type noise distribution. Then the model is used to test the effect of AE neural network in gesture recognition algorithm. Experimental results show AE neural network compress and extract useful features from noisy RF images, and higher gesture recognition accuracy is achieved based on these features. The algorithm achieves a recognition accuracy of 93% considering 20% bit level faults in RF images. The purpose of this method is to reduce the power consumption and improve the yield of the embedded RF based gesture recognition chip.
Excluding Loci With Substitution Saturation Improves Inferences From Phylogenomic Data

The historical signal in nucleotide sequences becomes eroded over time by substitutions occurring repeatedly at the same sites. This phenomenon, known as substitution saturation, is recognized as one of the primary obstacles to deep-time phylogenetic inference using genome-scale data sets. We present a new test of substitution saturation and demonstrate its performance in simulated and empirical data. For some of the 36 empirical phylogenomic data sets that we examined, we detect substitution saturation in around 50% of loci. We found that saturation tends to be flagged as problematic in loci with highly discordant phylogenetic signals across sites. Within each data set, the loci with smaller numbers of informative sites are more likely to be flagged as containing problematic levels of saturation. The entropy saturation test proposed here is sensitive to high evolutionary rates relative to the evolutionary timeframe, while also being sensitive to several factors known to mislead phylogenetic inference, including short internal branches relative to external branches, short nucleotide sequences, and tree imbalance. Our study demonstrates that excluding loci with substitution saturation can be an effective means of mitigating the negative impact of multiple substitutions on phylogenetic inferences.
A novel regulatory gene promotes novel cell fate by suppressing ancestral fate in the sea anemone Nematostella vectensis

Cnidocytes (“stinging cells”) are an unequivocally novel cell type used by cnidarians (corals, jellyfish, and their kin) to immobilize prey. Although they are known to share a common evolutionary origin with neurons, the developmental program that promoted the emergence of cnidocyte fate is not known. Using functional genomics in the sea anemone, Nematostella vectensis, we show that cnidocytes evolved by suppression of neural fate in a subset of neurons expressing RFamide. We further show that a single regulatory gene, a C2H2-type zinc finger transcription factor (ZNF845), coordinates both the gain of novel (cnidocyte-specific) traits and the inhibition of ancestral (neural) traits during cnidocyte development and that this gene arose by domain shuffling in the stem cnidarian. Thus, we uncover a mechanism by which a truly novel regulatory gene (ZNF845) promoted the origin of a truly novel cell type (cnidocyte) through duplication of an ancestral cell lineage (neuron) and inhibition of its ancestral identity (RFamide). Significance In this study, we demonstrate how new cell types can arise in animals through duplication of an ancestral (old) cell type followed by functional divergence of the new daughter cell. Specifically, we show that stinging cells in cnidarians (jellyfish and corals) evolved by duplication of an ancestral neuron followed by inhibition of the RFamide neuropeptide it once secreted. This is the first evidence that stinging cells evolved from a specific subtype of neurons and suggests some neurons may be easier to co-opt for novel functions than others.
The rise of grasslands is linked to atmospheric CO2 decline in the late Palaeogene

Grasslands are predicted to experience a major biodiversity change by the year 2100. A better understanding of how grasslands have responded to past environmental changes will help predict the outcome of current and future environmental changes. Here, we explore the relationship between past atmospheric CO 2 and temperature fluctuations and the shifts in diversification rate of Poaceae (grasses) and Asteraceae (daisies), two exceptionally species-rich grassland families (~11,000 and ~23,000 species, respectively). To this end, we develop a Bayesian approach that simultaneously estimates diversification rates through time from time-calibrated phylogenies and correlations between environmental variables and diversification rates. Additionally, we present a statistical approach that incorporates the information of the distribution of missing species in the phylogeny. We find strong evidence supporting a simultaneous increase in diversification rates for grasses and daisies after the most significant reduction of atmospheric CO 2 in the Cenozoic (~34 Mya). The fluctuations of paleo-temperatures, however, appear not to have had a significant relationship with the diversification of these grassland families. Overall, our results shed new light on our understanding of the origin of grasslands in the context of past environmental changes. A better understanding of how grasslands have responded to past environmental changes will help predict the outcomes of future changes. This study explores past climatic fluctuations and shifts in the diversification rate of grasses and daisies, finding strong evidence for a simultaneous increase in their diversification rates following a reduction of atmospheric CO 2 in the Cenozoic.
Intervene or wait? Modelling the timing of intervention in conservation conflicts adaptive management under uncertainty

The timing of biodiversity managers’ interventions can be critical to the success of conservation, especially in situations of conflict between conservation objectives and human livelihood, i.e., conservation conflicts. Given the uncertainty associated with complex social-ecological systems and the potentially irreversible consequences of delayed action for biodiversity and livelihoods, managers tend to simply intervene as soon as possible by precaution. However, refraining from intervening when the situation allows it can be beneficial, notably by saving critical management resources. Here, we introduce a strategy for managers to decide, based on monitoring, whether intervention is required or if waiting is possible. This study evaluates the performance of this waiting strategy compared to a strategy of unconditional intervention at every opportunity. We built an individual-based model of conservation conflict between a manager aiming to conserve an animal population and farmers aiming to maximize yield by protecting their crop from wildlife damage. We then simulated a budget-constrained adaptive management over time applying each strategy, while accounting for uncertainty around population dynamics and around decision-making of managers and farmers. Our results showed that when the decision for the manager to intervene was based on a prediction of population size trajectory, the waiting strategy performed at least as well as unconditional intervention while also allowing managers to save resources by avoiding unnecessary interventions. Under difficult budgetary constraints on managers, this waiting strategy ensured as high yields as unconditional intervention while significantly improving conservation outcomes by compensating managers’ lack of resources with the benefits accrued over waiting periods. This suggests that waiting strategies are worth considering in conservation conflicts, as they can facilitate equitable management with a more efficient use of management resources, which are often limiting in biodiversity conservation.