A strong constitutive ethylene-response phenotype conferred on Arabidopsis plants containing null mutations in the ethylene receptors ETR1 and ERS1

Background: The ethylene receptor family of Arabidopsis consists of five members, falling into two subfamilies. Subfamily 1 is composed of ETR1 and ERS1, and subfamily 2 is composed of ETR2, ERS2, and EIN4. Although mutations have been isolated in the genes encoding all five family members, the only previous insertion allele of ERS1 (ers1-2) is a partial loss-of-function mutation based on our analysis. The purpose of this study was to determine the extent of signaling mediated by subfamily-1 ethylene receptors through isolation and characterization of null mutations. Results: We isolated new T-DNA insertion alleles of subfamily 1 members ERS1 and ETR1 (ers1-3 and etr1-9, respectively), both of which are null mutations based on molecular, biochemical, and genetic analyses. Single mutants show an ethylene response similar to wild type, although both mutants are slightly hypersensitive to ethylene. Double mutants of ers1-3 with etr1-9, as well as with the previously isolated etr1-7, display a constitutive ethylene-response phenotype more pronounced than that observed with any previously characterized combination of ethylene receptor mutations. Dark-grown etr1-9;ers1-3 and etr1-7;ers1-3 seedlings display a constitutive triple-response phenotype. Light-grown etr1-9;ers1-3 and etr1-7;ers1-3 plants are dwarfed, largely sterile, exhibit premature leaf senescence, and develop novel filamentous structures at the base of the flower. A reduced level of ethylene response was still uncovered in the double mutants, indicating that subfamily 2 receptors can independently contribute to signaling, with evidence suggesting that this is due to their interaction with the Raf-like kinase CTR1. Conclusion: Our results are consistent with the ethylene receptors acting as redundant negative regulators of ethylene signaling, but with subfamily 1 receptors playing the predominant role. Loss of a single member of subfamily 1 is largely compensated for by the activity of the other member, but loss of both subfamily members results in a strong constitutive ethylene-response phenotype. The role of subfamily 1 members is greater than previously suspected and analysis of the double mutant null for both ETR1 and ERS1 uncovers novel roles for the receptors not previously characterized

Tuning the Morphology and Surface Property of Mineral Particles by Grinding Media

Grinding of minerals for particle size reduction and liberation is a prerequisite for successful mineral flotation separation and powder modification. Different grinding media produce mineral particles with different physical morphology and surface chemistry properties. Different mill particles expose different proportions of cleavage surfaces which lead to different shape indexes and different surface reactivities to organics, such as collector. Rod mill produces scheelite particles with a higher exposure of more reactive {101} surface that are beneficial for a stronger interaction with collector. More exposure of {101} surface also causes the rod mill particles to possess such values as larger elongation and flatness that are essential for particles attachment to air bubbles by shortening the induction time. The rod mill particles have a lower critical surface tension, greater hydrophobicity and a better flotation recovery when treated with the collector. In addition, the rod mill particles with a narrow particle size distribution have a smaller specific surface area, so the full monolayer adsorption of the collector on their surfaces can be achieved at a relatively lower concentration. These findings will help establish the relation between the particle surface physicochemistry and wettability, hence providing valuable guidance for the optimization of flotation separation and powder modification technology

Matrix Neural Networks

Traditional neural networks assume vectorial inputs as the network is arranged as layers of single line of computing units called neurons. This special structure requires the non-vectorial inputs such as matrices to be converted into vectors. This process can be problematic. Firstly, the spatial information among elements of the data may be lost during vectorisation. Secondly, the solution space becomes very large which demands very special treatments to the network parameters and high computational cost. To address these issues, we propose matrix neural networks (MatNet), which takes matrices directly as inputs. Each neuron senses summarised information through bilinear mapping from lower layer units in exactly the same way as the classic feed forward neural networks. Under this structure, back prorogation and gradient descent combination can be utilised to obtain network parameters e ciently. Furthermore, it can be conveniently extended for multimodal inputs. We apply MatNet to MNIST handwritten digits classi cation and image super resolution tasks to show its e ectiveness. Without too much tweaking MatNet achieves comparable performance as the state-of-the-art methods in both tasks with considerably reduced complexity

Electrocoagulation: A promising method to treat and reuse mineral processing wastewater with high COD

Mineral processing wastewater contains large amounts of reagents which can lead to severe environmental problems, such as high chemical oxygen demand (COD). Inspired by the wastewater treatment in such industries as those of textiles, food, and petrochemistry, in the present work, electrocoagulation (EC) is applied for the first time to explore its feasibility in the treatment of wastewater with an initial COD of 424.29 mg/L from a Pb/Zn sulfide mineral flotation plant and its effect on water reuse. Typical parameters, such as anode materials, current density, initial pH, and additives, were characterized to evaluate the performance of the EC method. The results showed that, under optimal conditions, i.e., iron anode, pH 7.1, electrolysis time 70 min, 19.23 mA/cm2 current density, and 4.1 g/L activated carbon, the initial COD can be reduced to 72.9 mg/L, corresponding to a removal rate of 82.8%. In addition, compared with the untreated wastewater, EC-treated wastewater was found to benefit the recovery of galena and sphalerite, with galena recovery increasing from 25.01% to 36.06% and sphalerite recovery increasing from 59.99% to 65.33%. This study confirmed that EC is a promising method for the treatment and reuse of high-COD-containing wastewater in the mining industry, and it possesses great potential for wide industrial applications

Continual Learning with Strong Experience Replay

Continual Learning (CL) aims at incrementally learning new tasks without forgetting the knowledge acquired from old ones. Experience Replay (ER) is a simple and effective rehearsal-based strategy, which optimizes the model with current training data and a subset of old samples stored in a memory buffer. To further reduce forgetting, recent approaches extend ER with various techniques, such as model regularization and memory sampling. However, the prediction consistency between the new model and the old one on current training data has been seldom explored, resulting in less knowledge preserved when few previous samples are available. To address this issue, we propose a CL method with Strong Experience Replay (SER), which additionally utilizes future experiences mimicked on the current training data, besides distilling past experience from the memory buffer. In our method, the updated model will produce approximate outputs as its original ones, which can effectively preserve the acquired knowledge. Experimental results on multiple image classification datasets show that our SER method surpasses the state-of-the-art methods by a noticeable margin

Efficient and Interpretable Compressive Text Summarisation with Unsupervised Dual-Agent Reinforcement Learning

Recently, compressive text summarisation offers a balance between the conciseness issue of extractive summarisation and the factual hallucination issue of abstractive summarisation. However, most existing compressive summarisation methods are supervised, relying on the expensive effort of creating a new training dataset with corresponding compressive summaries. In this paper, we propose an efficient and interpretable compressive summarisation method that utilises unsupervised dual-agent reinforcement learning to optimise a summary's semantic coverage and fluency by simulating human judgment on summarisation quality. Our model consists of an extractor agent and a compressor agent, and both agents have a multi-head attentional pointer-based structure. The extractor agent first chooses salient sentences from a document, and then the compressor agent compresses these extracted sentences by selecting salient words to form a summary without using reference summaries to compute the summary reward. To our best knowledge, this is the first work on unsupervised compressive summarisation. Experimental results on three widely used datasets (e.g., Newsroom, CNN/DM, and XSum) show that our model achieves promising performance and a significant improvement on Newsroom in terms of the ROUGE metric, as well as interpretability of semantic coverage of summarisation results.Comment: The 4th Workshop on Simple and Efficient Natural Language Processing (SustaiNLP 2023), co-located with ACL 202

Organophosphate Ester Flame Retardants and Plasticizers in ocean sediments from the North Pacific to the Arctic Ocean

The occurence of organophosphate ester (OPE) flame retardants and plasticizers in surface sediment from the North Pacific to Arctic Ocean was observed for the first time during the fourth National Arctic Research Expedition of China in the summer of 2010. The samples were analyzed for three halogenated OPEs [tris(2-chloroethyl) phosphate (TCEP), tris(1-chloro-2-propyl) phosphate (TCPP), and tris(dichloroisopropyl) phosphate], three alkylated OPEs [triisobutyl phosphate (TiBP), tri-n-butyl phosphate, and tripentyl phosphate], and triphenyl phosphate. Σ7OPEs (total concentration of the observed OPEs) was in the range of 159–4658 pg/g of dry weight. Halogenated OPEs were generally more abundant than the nonhalogenated OPEs; TCEP and TiBP dominated the overall concentrations. Except for that of the Bering Sea, Σ7OPEs values increased with increasing latitudes from Bering Strait to the Central Arctic Ocean, while the contributions of halogenated OPEs (typically TCEP and TCPP) to the total OPE profile also increased from the Bering Strait to the Central Arctic Ocean, indicating they are more likely to be transported to the remote Arctic. The median budget of 52 (range of 17–292) tons for Σ7OPEs in sediment from the Central Arctic Ocean represents only a very small amount of their total production volume, yet the amount of OPEs in Arctic Ocean sediment was significantly larger than the sum of polybrominated diphenyl ethers (PBDEs) in the sediment, indicating they are equally prone to long-range transport away from source regions. Given the increasing level of production and usage of OPEs as substitutes of PBDEs, OPEs will continue to accumulate in the remote Arctic