Adaptive Resource Allocation for Workflow Containerization on Kubernetes

In a cloud-native era, the Kubernetes-based workflow engine enables workflow containerized execution through the inherent abilities of Kubernetes. However, when encountering continuous workflow requests and unexpected resource request spikes, the engine is limited to the current workflow load information for resource allocation, which lacks the agility and predictability of resource allocation, resulting in over and under-provisioning resources. This mechanism seriously hinders workflow execution efficiency and leads to high resource waste. To overcome these drawbacks, we propose an adaptive resource allocation scheme named ARAS for the Kubernetes-based workflow engines. Considering potential future workflow task requests within the current task pod's lifecycle, the ARAS uses a resource scaling strategy to allocate resources in response to high-concurrency workflow scenarios. The ARAS offers resource discovery, resource evaluation, and allocation functionalities and serves as a key component for our tailored workflow engine (KubeAdaptor). By integrating the ARAS into KubeAdaptor for workflow containerized execution, we demonstrate the practical abilities of KubeAdaptor and the advantages of our ARAS. Compared with the baseline algorithm, experimental evaluation under three distinct workflow arrival patterns shows that ARAS gains time-saving of 9.8% to 40.92% in the average total duration of all workflows, time-saving of 26.4% to 79.86% in the average duration of individual workflow, and an increase of 1% to 16% in CPU and memory resource usage rate

Influence of heat stress on leaf morphology and nitrogen–carbohydrate metabolisms in two wucai (Brassica campestris L.) genotypes

Heat stress is a major environmental stress that limits plant growth and yield worldwide. The present study was carried out to explore the physiological mechanism of heat tolerant to provide the theoretical basis for heat-tolerant breeding. The changes of leaf morphology, anatomy, nitrogen assimilation, and carbohydrate metabolism in two wucai genotypes (WS-1, heat tolerant; WS-6, heat sensitive) grown under heat stress (40°C/30°C) for 7 days were investigated. Our results showed that heat stress hampered the plant growth and biomass accumulation in certain extent in WS-1 and WS-6. However, the inhibition extent of WS-1 was significantly smaller than WS-6. Thickness of leaf lamina, upper epidermis, and palisade mesophyll were increased by heat in WS-1, which might be contributed to the higher assimilation of photosynthates. During nitrogen assimilation, WS-1 possessed the higher nitrogen-related metabolic enzyme activities, including nitrate reductase (NR), glutamine synthetase (GS), glutamate synthase (GOGAT), and glutamate dehydrogenase (GDH), which were reflected by higher photosynthetic nitrogen-use efficiency (PNUE) with respect to WS-6. The total amino acids level had no influence in WS-1, whereas it was reduced in WS-6 by heat. And the proline contents of both wucai genotypes were all increased to respond the heat stress. Additionally, among all treatments, the total soluble sugar content of WS-1 by heat got the highest level, including higher contents of sucrose, fructose, and starch than those of WS-6. Moreover, the metabolism efficiency of sucrose to starch in WS-1 was greater than WS-6 under heat stress, proved by higher activities of sucrose phosphate synthase (SPS), sucrose synthase (SuSy), acid invertase (AI), and amylase. These results demonstrated that leaf anatomical alterations resulted in higher nitrogen and carbon assimilation in heat-tolerant genotype WS-1, which exhibited a greater performance to resist heat stress

Corrigendum to: The TianQin project: current progress on science and technology

In the originally published version, this manuscript included an error related to indicating the corresponding author within the author list. This has now been corrected online to reflect the fact that author Jun Luo is the corresponding author of the article

New perspective of jasmonate function in leaf senescence

Jasmonates (JAs) induce leaf senescence in many plant species. The Arabidopsis F-box protein coronatine insensitive 1 (COI1) is required for various JA-regulated plant responses including plant fertility, defense responses and leaf senescence. However, the molecular basis for COI1-dependent JA-induced leaf senescence remains unknown. In our Plant Physiology paper, we identified a COI1-dependent JA-repressed protein, Rubisco activase (RCA) in Arabidopsis. Further genetic and physiological analyses showed that the COI1-dependent JA repression of RCA correlated with JA-induced leaf senescence, and that loss of RCA led to typical senescence-associated features. Therefore, we suggested that the COI1-dependent JA repression of RCA played an important role in JA-induced leaf senescence. In this addendum, we made a relatively deep discussion on RCA function in JA-induced leaf senescence and JA-mediated defense responses. We also discussed the possible role of JA in plant natural senescence

DELTA: A Distal Enhancer Locating Tool Based on AdaBoost Algorithm and Shape Features of Chromatin Modifications

<div><p>Accurate identification of DNA regulatory elements becomes an urgent need in the post-genomic era. Recent genome-wide chromatin states mapping efforts revealed that DNA elements are associated with characteristic chromatin modification signatures, based on which several approaches have been developed to predict transcriptional enhancers. However, their practical application is limited by incomplete extraction of chromatin features and model inconsistency for predicting enhancers across different cell types. To address these issues, we define a set of non-redundant shape features of histone modifications, which shows high consistency across cell types and can greatly reduce the dimensionality of feature vectors. Integrating shape features with a machine-learning algorithm AdaBoost, we developed an enhancer predicting method, DELTA (Distal Enhancer Locating Tool based on AdaBoost). We show that DELTA significantly outperforms current enhancer prediction methods in prediction accuracy on different datasets and can predict enhancers in one cell type using models trained in other cell types without loss of accuracy. Overall, our study presents a novel framework for accurately identifying enhancers from epigenetic data across multiple cell types.</p></div

Prediction accuracy of AdaBoost models with different feature sets.

<p>(A) Validation rates, (B) Misclassification rates and (C) F-scores of enhancer predictions using AdaBoost models with three histone modification sets (all modifications, strong predictor modifications and H3K4me1/2/3) in 10% genome in CD4⁺ T and H1 (D-F). (G) Validation rates, (H) Misclassification rates and (I) F-scores of predictions using AdaBoost models with three feature extraction methods (binned-vector, intensity only and shape and intensity features) in 10% genome in CD4⁺ T and H1 (J-L). In CD4⁺ T, validation rates were measured as overlap with either p300 binding sites, DNase-I hypersensitive sites (DHS) or TF binding sites including CBP, ETS1, FOXP3, RUNX1 and STAT5, and misclassification rates were measured as overlap with UCSC TSSs, versus total number of enhancers determined by taking different probability cutoffs. In H1, validation rates were measured as overlap with either p300 binding sites, DHSs or sequence-specific TF binding sites from FactorBook, and misclassification rates were measured as overlap with UCSC TSSs, versus total number of enhancers determined by taking different probability cutoffs.</p

Quantification of intensity and shape features of ChIP-seq signals.

<p>A) Graphical representation of four parameters <i>Max</i>, <i>Kurtosis</i>, <i>Skewness</i> and <i>Bimodality</i>. The dashed curves indicate standard normal distribution, and the light blue area under curve indicates a probability distribution with the corresponding parameter changed. The boxplots of the four parameters and significances of the difference between enhancers and promoters for B) H3K4me1, C) H3K4me2 and D) H3K4me3 in CD4⁺ T cells. Significance (<i>P</i>-value) of the difference between two means was calculated by Wilcoxon rank-sum difference test.</p

Enhancer predictions in five ENCODE cell types using DELTA.

<p>(A) Validation rates, (B) Misclassification rates and (C) F-scores of enhancer predictions using AdaBoost models trained in the same cell type (solid lines) and other four cell types (dashed lines) in five cell types. Validation rates were measured as overlap with either p300 binding sites, DNase-I hypersensitive sites (DHS) or sequence-specific TF binding sites from FactorBook, and misclassification rates were measured as overlap with UCSC TSSs, versus total number of enhancers determined by taking different probability cutoffs. (D) False Discovery Rate (FDR) for each cell type plotted as a function of prediction probability. (E) Percentages of validated and misclassified enhancer predictions in five cell types at a FDR of 5% with number of enhancer predictions shown at the left of the bar.</p

Determination of optimal window size and number of boosting iteration for DELTA.

<p>5-fold cross-validation ROC curves at different window sizes (1, 2, 4 and 6 kb) are shown in (A) CD4⁺ T and (B) H1 cells, and a window size of 2 kb shows the largest AUC both in CD4⁺ T and H1. Train and test error curves at different numbers of iteration of AdaBoost are shown in (C) CD4⁺ T and (D) H1 cells. The train error continuously drops as number of iteration increases, but the test error becomes stable beyond 50-th iteration in CD4⁺ T and 150-th in H1. An iteration number of 150 is chosen for training AdaBoost as it appears to be optimal for both cases.</p