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Oil pollution is a common type of soil organic pollution that is harmful to the ecosystem. Bioremediation, particularly microbe-assisted phytoremediation of oil-contaminated soil, has become a research hotspot in recent years. In order to explore more appropriate bioremediation strategies for soil oil contamination and the mechanism of remediation, we compared the remediation effects of three plants when applied in combination with a microbial agent and biochar. The combined remediation approach of Tagetes erecta, microbial agent, and biochar exhibited the best plant growth and the highest total petroleum hydrocarbons degradation efficiency (76.60%). In addition, all of the remediation methods provided varying degrees of restoration of carbon and nitrogen contents of soils. High-throughput sequencing found that microbial community diversity and richness were enhanced in most restored soils. Some soil microorganisms associated with oil degradation and plant growth promotion such as Cavicella, C1_B045, Sphingomonas, MND1, Bacillus and Ramlibacter were identified in this study, among which Bacillus was the major component in the microbial agent. Bacillus was positively correlated with all soil remediation indicators tested and was substantially enriched in the rhizosphere of T. erecta. Functional gene prediction of the soil bacterial community based on the KEGG database revealed that pathways of carbohydrate metabolism and amino acid metabolism were up-regulated during remediation of oil-contaminated soils. This study provides a potential method for efficient remediation of oil-contaminated soils and thoroughly examines the biochar–bacteria–plant combined remediation mechanisms of oil-contaminated soil, as well as the combined effects from the perspective of soil bacterial communities.

Phenylalanine ammonia-lyase (PAL, EC 4.3.1.5) catalyzes the deamination of phenylalanine, which is the initial step in the biosynthesis of phenylpropanoids. It serves as a crucial enzyme that facilitates the transfer of carbon from primary to secondary metabolism in plants. Duckweed is regarded as a promising chassis plant in synthetic biology research and application, due to its being rich in secondary metabolites and other advantages. The genes encoding PAL in Spirodela polyrhiza (L.) Schleid, the giant duckweed, were investigated in this study. Three SpPAL genes (SpPAL1–SpPAL3) were identified and cloned. All of them were successfully expressed in E. coli, and their recombinant proteins all showed PAL activity. In addition, SpPAL1 and SpPAL2 proteins could also utilize tyrosine as substrate, although the activity was low. A qRT-PCR analysis demonstrated that the expression of SpPAL3 was most pronounced in young fronds. It was found that the expression of SpPAL1 and SpPAL3 was significantly induced by MeJA treatment. Overexpression of SpPAL3 in Lemna turionifera inhibited the growth of fronds and adventitious roots in the transgenic plants, indicating the importance of SpPAL3 in duckweed besides its involvement in the secondary metabolism.

In this study, the physiological indicators, reactive oxygen species (ROS) levels, and activities and expressions of key enzymes related to ROS metabolism were monitored to explore the mechanism of ozone treatment on the shelf life of postharvest Korla fragrant pears. The results show that postharvest fragrant pears treated with ozone had a higher firmness and lower weight loss rate and decay rate during their shelf life, especially in the late stage. Ozone treatment could also delay the occurrence of the respiratory peak and reduce the peak value. The generation rate of superoxide anion (O2−), the hydrogen peroxide (H2O2) content, and the malondialdehyde (MDA) level were reduced in the ozone-treated group, while the activities of key enzymes, superoxide dismutase (SOD), catalase (CAT), and peroxidase (POD), related to ROS metabolism in postharvest fragrant pears were stimulated by ozone treatment, especially in the middle and late stages of shelf life. Analysis of the proteomics results indicates that the POD family enzymes were the main target protein in postharvest fragrant pears treated by ozone during the middle and late stages of shelf life. The activity and expression of antioxidant-related enzymes in postharvest fragrant pears were stimulated by ozone to accelerate the metabolism of ROS and maintain high quality, especially in the middle and late shelf lives.

Cold plasma (CP) is a green and efficient preservation technology, but its effect on preventing the postharvest softening of fruits and vegetables is still unclear, and the effects of different CP intensities on the postharvest softening of winter jujubes (Ziziphus jujuba Mill. cv. Dongzao) have been little studied. In this study, we investigated the effects of different CP intensities (0 kV, 40 kV, and 80 kV) on the postharvest storage quality of winter jujubes and the activities of key enzymes related to softening during storage. The results showed that compared with the control group, the contents of firmness, total soluble solids (TSS), titratable acidity (TA), and vitamin C (VC) in the CP treatment group were higher and the respiratory intensity and weight loss rate were lower. In addition, CP treatment can also inhibit the degradation of protopectin and cellulose and the accumulation of soluble pectin. Moreover, CP treatment inhibited the activities of polygalacturonase (PG), pectin methyl esterase (PME), 3α-L-arabinoside (α-L-Af), 4β-galactosidase (β-gal), cellulase (Cx), and β-glucosidase (β-Glu). This shows that CP treatment has a positive effect on the preservation of winter jujubes, and the preservation effect of the 80 kV treatment was better than that of the 40 kV treatment. This provides a certain basis for cold plasma treatment in the preservation of winter jujubes.

Pear is a kind of common temperate fruit, whose metabolite composition that contributes to the difference in fruit quality is unclear. This study identified and quantified the metabolites using a widely targeted LC-MS/MS approach in three pear species, including Pyrus bretschneideri (PB), Pyrus usssuriensis (PU) and Pyrus pyrifolia (PP). A total of 493 metabolites were identified, consisting of 68 carbohydrates, 47 organic acids, 50 polyphenols, 21 amino acids, 20 vitamins, etc. The results of PCA and OPLS-DA demonstrated that the metabolite compositions differed distinctly with cultivar variability. Our results also involved some metabolic pathways that may link to the fruit quality based on KEGG pathway analysis, the pathway of phenylalanine metabolism revealed significant differences between PB and PP (p < 0.05). Furthermore, the study selected D-xylose, formononetin, procyanidin A1 and β-nicotinamide mononucleotide as the major differentially expressed metabolites in the three species. The present study can open new avenues for explaining the differences in fruit quality of the major commercial pear cultivars in China.

A Virtual energy assessment (VEA) refers to the assessment of the energy flow in a building without physical data collection. It has been occasionally conducted before the COVID-19 pandemic to residential and commercial buildings. However, there is no established framework method for conducting this type of energy assessment. The COVID-19 pandemic has catalysed the implementation of remote energy assessments and remote facility management. In this paper, a novel framework for VEA is developed and tested on case study buildings at the University of Melbourne. The proposed method is a hybrid of top-down and bottom-up approaches: gathering the general information of the building and the historical data, in addition to investigating and modelling the electrical consumption with artificial neural network (ANN) with a projection of the future consumption. Through sensitivity analysis, the outdoor temperature was found to be the most sensitive (influential) parameter to electrical consumption. The lockdown of the buildings provided invaluable opportunities to assess electrical baseload with zero occupancies and usage of the building. Furthermore, comparison of the baseload with the consumption projection through ANN modelling accurately quantifies the energy consumption attributed to occupation and operational use, referred to as ‘operational energy’ in this paper. Differentiation and quantification of the baseload and operational energy may aid in energy conservation measures that specifically target to minimise these two distinct energy consumptions.

Background As global climate change accelerates, ecologists and conservationists are increasingly investigating changes in biodiversity and predicting species distribution based on species observed at sites, but rarely consider those plant species that could potentially inhabit but are absent from these areas (i.e., the dark diversity and its distribution). Here, we estimated the dark diversity of vascular plants in China and picked up threatened dark species from the result, and applied maximum entropy (MaxEnt) model to project current and future distributions of those dark species in their potential regions (those regions that have these dark species). Methods We used the Beals probability index to estimate dark diversity in China based on available species distribution information and explored which environmental variables had significant impacts on dark diversity by incorporating bioclimatic data into the random forest (RF) model. We collected occurrence data of threatened dark species (Eucommia ulmoides, Liriodendron chinense, Phoebe bournei, Fagus longipetiolata, Amentotaxus argotaenia, and Cathaya argyrophylla) and related bioclimatic information that can be used to predict their distributions. In addition, we used MaxEnt modeling to project their distributions in suitable areas under future (2050 and 2070) climate change scenarios. Results We found that every study region’s dark diversity was lower than its observed species richness. In these areas, their numbers of dark species are ranging from 0 to 215, with a generally increasing trend from western regions to the east. RF results showed that temperature variables had a more significant effect on dark diversity than those associated with precipitation. The results of MaxEnt modeling showed that most threatened dark species were climatically suitable in their potential regions from current to 2070. Discussions The results of this study provide the first ever dark diversity patterns concentrated in China, even though it was estimated at the provincial scale. A combination of dark diversity and MaxEnt modeling is an effective way to shed light on the species that make up the dark diversity, such as projecting the distribution of specific dark species under global climate change. Besides, the combination of dark diversity and species distribution models (SDMs) may also be of value for ex situ conservation, ecological restoration, and species invasion prevention in the future.