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Superpixel is the over-segmentation region of an image, whose basic units "pixels" have similar properties. Although many popular seeds-based algorithms have been proposed to improve the segmentation quality of superpixels, they still suffer from the seeds initialization problem and the pixel assignment problem. In this paper, we propose Vine Spread for Superpixel Segmentation (VSSS) to form superpixel with high quality. First, we extract image color and gradient features to define the soil model that establishes a "soil" environment for vine, and then we define the vine state model by simulating the vine "physiological" state. Thereafter, to catch more image details and twigs of the object, we propose a new seeds initialization strategy that perceives image gradients at the pixel-level and without randomness. Next, to balance the boundary adherence and the regularity of the superpixel, we define a three-stage "parallel spreading" vine spread process as a novel pixel assignment scheme, in which the proposed nonlinear velocity for vines helps to form the superpixel with regular shape and homogeneity, the crazy spreading mode for vines and the soil averaging strategy help to enhance the boundary adherence of superpixel. Finally, a series of experimental results demonstrate that our VSSS offers competitive performance in the seed-based methods, especially in catching object details and twigs, balancing boundary adherence and obtaining regular shape superpixels.

In this study, a novel method-atom surface fragment contribution (ASFC)-was proposed for assessing the properties of compounds. We developed a predictive model using the ASFC method based on the sigma surface areas (S σ-surface ) of fragments/groups for estimating the toxicity of ILs. A toxicity dataset of 140 ILs towards leukemia rat cell line (ICP-81) was gathered and employed to train and validate models. The S σ-surface values of atoms in each group were firstly calculated from the COSMO profiles of cations and anions for ILs. Then the S σ-surface values of 26 groups were obtained and used as input descriptors for modelling. The R 2 and MSE of the built ASFC model were 0.924 and 0.071, respectively. Results indicate that the ASFC model developed by the new approach possesses great accuracy and reliability. In total, the ASFC method has extensive potential for the application of estimating diverse properties of ILs and other compounds due to its remarkable advantages., (Copyright © 2021 Elsevier B.V. All rights reserved.)

The Normalized Cut (NCut) model is a popular graph-based model for image segmentation. But it suffers from the excessive normalization problem and weakens the small object and twig segmentation. In this paper, we propose an Explored Normalized Cut (ENCut) model that establishes a balance graph model by adopting a meaningful-loop and a k-step random walk, which reduces the energy of small salient region, so as to enhance the small object segmentation. To improve the twig segmentation, our ENCut model is further enhanced by a new Random Walk Refining Term (RWRT) that adds local attention to our model with the help of an un-supervising random walk. Finally, a move-making based strategy is developed to efficiently solve the ENCut model with RWRT. Experiments on three standard datasets indicate that our model can achieve state-of-the-art results among the NCut-based segmentation models.

How the acetate and propionate accumulation impact anaerobic syntrophy during methane formation is not well understood. To investigate such effect, continuous acetate (35 g/L), propionate (11.25 g/L) and bicarbonate (30 g/L) supplementation were used during mesophilic anaerobic digestion. The high throughput sequencing (16S rRNA and mcrA), Real-Time quantitative PCR, and stable carbon isotope fingerprinting were applied to investigate the structure and activity of microbial community members. The results demonstrated that the abundance of syntrophic acetate oxidizing bacteria exhibited a gradual decrease coupled with heavier stable carbon isotopic signature of methane (δ 13 CH 4 ) in the three reagents impacted reactors. The increased acetate and propionate concentrations exerted negative influence on biogas production but the relatively stable hydrogenotrophic methanogens together with syntrophic acetate/propionate oxidizing bacteria kept the stable methane formation facing acetate and propionate accumulation. The functional genes copy number of the hydrogenotrophic Methanocellaceae and Methanomicrobiaceae correlated significantly with δ 13 CH 4 (R 2  > 0.74), but only the abundance of Methanocellaceae fitted well with δ 13 CH 4 (p < 0.05). The δ 13 CH 4 signatures can predict methanogenesis, as it directly reflects the main methanogenic pathway; yet, further investigation of isotope fractionation in acetate/propionate coupled with δ 13 CH 4 is needed. Collectively, these results provide deep insight into anaerobic syntrophy and reveal changes of synergistic relationships, both of which may contribute to the stability of biogas reactors., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Ionic liquids (ILs) as green solvents have been studied in the application of gas sweetening. However, it is a huge challenge to obtain all the experimental values because of the high costs and generated chemical wastes. This study pioneered a quantitative structure-property relationship (QSPR) model for estimating Henry's law constant (HLC) of H 2 S in ILs. A dataset consisting of the HLC data of H 2 S for 22 ILs within a wide range of temperature (298.15-363.15 K) were collected from published reports. The electrostatic potential surface area (S EP ) and molecular volume of these ILs were calculated and used as input descriptors together with temperature. The extreme learning machine (ELM) algorithm was employed for nonlinear modelling. Results showed that the determination coefficient (R 2 ) of the training set, test set and total set were 0.9996, 0.9989,0.9994, respectively, while the average absolute relative deviation (AARD%) of them were 1.3383, 2,4820 and 1.5820, respectively. The statistical parameters for the measurement of the model exhibited the great reliability, stability, and predictive power of the ELM model. The Applicability Domain (AD) of the ELM model is also investigated. It proves that the majority of ILs in the training and test sets are located in the model's AD and verifies the reliability of the model. The proposed model is potentially applicable to guide the application of ILs for gas sweetening., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Ionic liquids (ILs) have attracted increasing attention both in the scientific community and the industry in the past two decades. Their risk of being inevitable released to ecosystem lights up the urgent research on their toxicity to the environment. To reduce the time and capital consumption on testing tremendous ILs ecotoxicity experimentally, it is essential to construct predictive models for estimating their toxicity. The objective of this study is to provide a new approach for evaluating the ecotoxicity of ILs. A comprehensive ecotoxicity dataset for Vibrio fischeri involving 142 ILs, was collected and investigated. The electrostatic potential surface areas (S EP ) of separate cations and anions of ILs were firstly applied to develop predictive models for ecotoxicity on Vibrio fischeri. In addition, an intelligent algorithm named extreme learning machine (ELM) was employed to establish the predictive model. The squared correlation coefficients (R 2 ), the average absolute error (AAE%) and the root-mean-square error (RMSE) of the developed model are 0.9272, 0.2101 and 0.3262 for the entire set, respectively. The proposed approach based on the high R 2 and low deviation has remarkable potential for predicting ILs ecotoxicity on Vibrio fischeri., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

The large amounts of ammonia emissions generated from industrial production have caused serious environmental pollution problems, such as soil acidification, eutrophication, the formation of fine particles and changes in the global greenhouse balance, and also greatly endanger human health. At present, effectively reducing ammonia emissions or recovering ammonia is still a huge challenge. Ionic liquids (ILs) as a new class of green solvent have been introduced for ammonia absorption with great potential, but a huge number on combination systems of ILs lead to the difficulty of measuring the ammonia solubility in all ILs by experiments (e.g., danger and cost). Hereby, this study proposed a novel approach for estimating the ammonia solubility in different ILs. A predictive model was developed based on the novel Algorithm - extreme learning machine (ELM) and the molecular descriptors of electrostatic potential surface areas (S EP ) as input parameters. Besides, 502 data points of ammonia solubility in 17 ILs were gathered with a wide range of pressure and temperature. For the total set, the determination coefficient (R 2 ) and the average absolute relative deviation (AARD) of the developed model were 0.9937 and 2.95%, respectively. The regression plots revealed good consistency between predictive and experimental data points. Results show the good performance and reliability of the developed model, indicating that the proposed approach can be potentially applied for screening reasonable ILs to absorb ammonia from chemical industry processes., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Peripheral nerve injury repair has been considered a difficult problem in the field of trauma for a long time. Conventional surgical methods are not applicable in some special types of nerve injury, prompting scholars to seek to develop more effective nerve translocation repair technologies. The purpose of this study was to explore the functional state of neurons in injured lower limbs after translocation repair, with a view to preliminarily clarify the molecular mechanisms underlying this process. Eighteen Sprague-Dawley rats were divided into the normal, tibial nerve in situ repair, and common peroneal nerve transposition repair tibial nerve groups. Nerve function assessment and immunohistochemical staining of neurofilament 200 (NF-200), protein kinase B (Akt), mammalian target of rapamycin (mTOR), and ribosomal protein S6 kinase (p70S6K) in the dorsal root ganglia were performed at 12 weeks after surgery. Tibial nerve function and neuroelectrophysiological analysis, osmic acid staining, muscle strength testing, and muscle fiber staining showed that the nerve translocation repair could restore the function of the recipient nerve to a certain extent; however, the repair was not as efficient as the in situ repair. Immunohistochemical staining showed that the translocation repair resulted in changes in the microstructure of neuronal cell bodies, and the expressions of Akt, mTOR, and p70S6K in the three dorsal root ganglia groups were significantly different ( p < 0.05). This study demonstrates that the nerve translocation repair technology sets up a new reflex loop, with the corresponding neuroskeletal adjustments, in which, donor neurons dominate the recipient nerves. This indicates that nerve translocation repair technology can lead to neuronal remodeling and is important as a supplementary treatment for a peripheral nerve injury. Furthermore, the Akt/mTOR/p70S6K signaling pathway may be involved in the formation of the new neural reflex loop created as a result of the translocation repair., (Copyright © 2020 Yuan, Li, Yu, Kang, Xu and Zhang.)

In this paper, we propose a novel random walk model, called Dynamic Random Walk (DRW), which adds a new type of dynamic node to the original RW model and reduces redundant calculation by limiting the walk range. To solve the seed-lacking problem of the proposed DRW, we redefine the energy function of the original RW and use the first arrival probability among each node pair to avoid the interference for each partition. Relaxation of our DRW is performed with the help of a greedy strategy and the Weighted Random Walk Entropy(WRWE) that uses the gradient feature to approximate the stationary distribution. The proposed DRW not only can enhance the boundary adherence but also can run with linear time complexity. To extend our DRW for superpixel segmentation, a seed initialization strategy is proposed. It can evenly distribute seeds in both 2D and 3D space and generate superpixels in only one iteration. The experimental results demonstrate that our DRW is faster than existing RW models and better than the state-of-the-art superpixel segmentation algorithms with respect to both efficiency and segmentation effects.

Limited information on the potential toxicity of ionic liquids (ILs) becomes the bottleneck that creates a barrier in their large-scale application. In this work, two quantitative structure-activity relationships (QSAR) models were used to evaluate the toxicity of ILs toward the acetylcholinesterase enzyme using multiple linear regression (MLR) and extreme learning machine (ELM) algorithms. The structures of 57 cations and 21 anions were optimized using quantum chemistry calculations. The electrostatic potential surface area ( S EP ) and the screening charge density distribution area ( S σ ) descriptors were calculated and used for prediction of IL toxicity. Performance and predictive aptitude between MLR and ELM models were analyzed. Highest squared correlation coefficient ( R 2 ), and also lowest average absolute relative deviation (AARD%) and root-mean-square error (RMSE) were observed for training set, test set, and total set for the ELM model. These findings validated the superior performance of ELM over the MLR toxicity prediction model.