Epigenomic mechanism regulating the quality and ripeness of apple fruit with differing harvest maturity.

Harvest maturity significantly affects the quality of apple fruit in post-harvest storage process. Although the regulatory mechanisms underlying fruit ripening have been studied, the associated epigenetic modifications remain unclear. Thus, we compared the DNA methylation changes and the transcriptional responses of mature fruit (MF) and immature fruit (NF). There were significant correlations between DNA methylation and gene expression. Moreover, the sugar contents (sucrose, glucose, and fructose) were higher in MF than in NF, whereas the opposite pattern was detected for the starch content. The expression-level differences were due to DNA methylations and ultimately resulted in diverse fruit textures and ripeness. Furthermore, the higher ethylene, auxin, and abscisic acid levels in MF than in NF, which influenced the fruit texture and ripening, were associated with multiple differentially expressed genes in hormone synthesis, signaling, and response pathways (ACS, ACO, ZEP, NCED, and ABA2) that were regulated by DNA methylations. Multiple transcription factor genes involved in regulating fruit ripening and quality via changes in DNA methylation were identified, including MIKCC-type MADS-box genes and fruit ripening-related genes (NAP, SPL, WRKY, and NAC genes). These findings reflect the diversity in the epigenetic regulation of gene expression and may be relevant for elucidating the epigenetic regulatory mechanism underlying the ripening and quality of apple fruit with differing harvest maturity.

Exploring Mechanical Responses of Cells to Geometric Information Using Micropatterned DNA-Based Molecular Tension Probes.

The geometric shape of a cell is strongly influenced by the cytoskeleton, which, in turn, is regulated by integrin-mediated cell-extracellular matrix (ECM) interactions. To investigate the mechanical role of integrin in the geometrical interplay between cells and the ECM, we proposed a single-cell micropatterning technique combined with molecular tension fluorescence microscopy (MTFM), which allows us to characterize the mechanical properties of cells with prescribed geometries. Our results show that the curvature is a key geometric cue for cells to differentiate shapes in a membrane-tension- and actomyosin-dependent manner. Specifically, curvatures affect the size of focal adhesions (FAs) and induce a curvature-dependent density and spatial distribution of strong integrins. In addition, we found that the integrin subunit ß1 plays a critical role in the detection of geometric information. Overall, the integration of MTFM and single-cell micropatterning offers a robust approach for investigating the nexus between mechanical cues and cellular responses, holding potential for advancing our understanding of mechanobiology.

Adaptive-weight water quality assessment and human health risk analysis for river water in Hong Kong.

The water quality of Hong Kong's four water control zones (Tolo Harbour and Channel, Port Shelter, Victoria Harbour, and Junk Bay) is of vital importance and has attracted much attention. This study aims to more objectively and comprehensively assess the water quality and its health impact based on the four-year monitoring data of 21 parameters collected from four zones. First, physicochemical characteristics of the water system were investigated based on multivariate statistical approaches, including Kruskal-Wallis test, hierarchical cluster analysis, and Mann-Kendall test. Then, water quality levels over space and time and the element sources were analyzed using adaptive-weight water quality index (AWQI) method, and factor analysis, respectively. Finally, the potential harm of trace elements for humankind was identified based on the health risk assessment model. The results revealed that (1) the values of more than half of the water quality parameters exhibited significant interannual changes, and the values of all parameters distinctly varied over space; (2) The water quality status in four water control zones showed a steady and long-term improvement trend from 2016 to 2019; (3) The sources of pollution elements impacting water quality status were related to the comprehensive influence of human activities and natural processes; (4) The carcinogenic risks of all trace elements were negligible or acceptable, while Mn and As may cause noncarcinogenic harm to humankind.

Analysis of spatio-temporal variations of river water quality and construction of a novel cost-effective assessment model: a case study in Hong Kong.

Assessment of river water quality has been attracting a great deal of attention because of its important implications for the living environment of human beings and aquatic organisms. River water quality is commonly assessed using dozens of different water quality parameters. However, different parameters may contain redundant information, which could lead to the waste of monitoring efforts. Thus, this study constructed a novel cost-effective assessment model of river water quality using the 1-year monitoring data collected from 23 sampling stations in the water control zone of Tolo Harbour and Channel in Hong Kong. First, the spatio-temporal variations of water quality parameters and the overall status of river water quality were analyzed based on all 19 parameters using Kruskal-Wallis test, hierarchical cluster analysis, and the water quality index (WQI). The results indicated that most water quality parameters and overall water quality status varied significantly over space, but did not exhibit obvious seasonal differences; and 99.27% of water samples were identified to be in good or excellent status of overall WQI. Then, using principal component analysis (PCA)/factor analysis (FA) and Pearson's correlation analysis, eight parameters, including 5-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), ammonia-nitrogen (NH3-N), nitrate-nitrogen (NO3-N), chlorophyll-a (Chl-a), fluoride (F-), total suspended solids (TSS), and arsenic (As), were verified to be responsible for the greatest contributions to water quality, the assessment of overall water quality status. These eight crucial parameters were further employed to establish six cost-effective water quality assessment models. Using the overall WQI as the benchmark, the results of linear regression analysis demonstrated that the cost-effective model constructed based on BOD5, COD, NH3-N, NO3-N, F-, TSS, and As were the optimal water quality assessment model, which can achieve the most reliable results with reduced parameters.

Modification of the effect of maturity variation on nondestructive detection of apple quality based on the compensation model.

In this study, the effect of maturity variation on the prediction of the soluble solids content (SSC) and firmness of apples was determined using visible and near-infrared spectroscopy. In 2018, 520 apples from six ripening stages were collected. The single maturity model and multi-maturity model of SSC and firmness were established using partial least-squares regression. Apples at the same and different maturity stages were used to verify the developed model. Whereas the single maturity model was affected by maturity variation, the multi-maturity model could accurately predict the SSC and firmness of apples at different maturity stages. The multi-maturity model developed based on six maturity calibration sets had the best predictive performance. The root mean square error of prediction (RMSEP) of SSC and firmness was 0.614-0.802 °Brix and 0.402-0.650 kg/cm2, respectively. The long-term performance of the optimal multi-maturity model was evaluated using validation sets. The predictive performance was decreased and the RMSEP increased when the model was used to predict the SSC and firmness of apples in different seasons. The predictive performance of the model was improved after slope/bias (S/B) correction, and the RMSEP of SSC and firmness decreased to 0.405-0.587°Brix and 0.518-0.628 kg/cm2 respectively. Overall, the multi-maturity model eliminated the effect of maturity variation, and the multi-maturity model coupled with S/B correction permitted the rapid and accurate detection of the SSC and firmness of apples at different maturity stages and in different seasons.

Numerical Analysis on the Thermal Performance in an Excavating Roadway with Auxiliary Ventilation System.

A steady and proper thermal environment in deep underground is imperative to ensure worker health and production safety. Understanding the thermal performance in the roadway is the premise of temperature prediction; ventilation design; and improvement in cooling efficiency. A full coupled model incorporated with a moving mesh method was adopted; reflecting the dynamic condition of roadway construction. This study revealed the characteristics of the thermal performance and its evolution law in an excavating roadway. Several scenarios were performed to examine the designs of the auxiliary ventilation system on thermal performance in the roadway. The results show that there is a limitation in the cooling effect by continuously increasing the ventilation volume. Reducing the diameter of the air duct or distances between the duct outlet and the working face will aggravate the heat hazard in the roadway. The heat release from the roadway wall increases with the increase of the advance rate of the working face or roadway section size. Furthermore; an orthogonal experiment was conducted to investigate the effect of major factors on the average air temperature and local heat accumulation in the roadway.

Transcriptomic and Metabolic Analyses Provide New Insights into the Apple Fruit Quality Decline during Long-Term Cold Storage.

Long-term low-temperature conditioning (LT-LTC) decreases apple fruit quality, but the underlying physiological and molecular basis is relatively uncharacterized. We identified 12 clusters of differentially expressed genes (DEGs) involved in multiple biological processes (i.e., sugar, malic acid, fatty acid, lipid, complex phytohormone, and stress-response pathways). The expression levels of genes in sugar pathways were correlated with decreasing starch levels during LT-LTC. Specifically, starch-synthesis-related genes (e.g., BE, SBE, and GBSS genes) exhibited downregulated expression, whereas sucrose-metabolism-related gene expression levels were up- or downregulated. The expression levels of genes in the malic acid pathway (ALMT9, AATP1, and AHA2) were upregulated, as well as the content of malic acid in apple fruit during LT-LTC. A total of 151 metabolites, mainly related to amino acids and their isoforms, amines, organic acids, fatty acids, sugars, and polyols, were identified during LT-LTC. Additionally, 35 organic-acid-related metabolites grouped into three clusters, I (3), II (22), and III (10), increased in abundance during LT-LTC. Multiple phytohormones regulated the apple fruit chilling injury response. The ethylene (ET) and abscisic acid (ABA) levels increased at CS2 and CS3, and jasmonate (JA) levels also increased during LT-LTC. Furthermore, the expression levels of genes involved in ET, ABA, and JA synthesis and response pathways were upregulated. Finally, some key transcription factor genes (MYB, bHLH, ERF, NAC, and bZIP genes) related to the apple fruit cold acclimation response were differentially expressed. Our results suggest that the multilayered mechanism underlying apple fruit deterioration during LT-LTC is a complex, transcriptionally regulated process involving cell structures, sugars, lipids, hormones, and transcription factors.

Mining Approximate Order Preserving Clusters in the Presence of Noise.

Subspace clustering has attracted great attention due to its capability of finding salient patterns in high dimensional data. Order preserving subspace clusters have been proven to be important in high throughput gene expression analysis, since functionally related genes are often co-expressed under a set of experimental conditions. Such co-expression patterns can be represented by consistent orderings of attributes. Existing order preserving cluster models require all objects in a cluster have identical attribute order without deviation. However, real data are noisy due to measurement technology limitation and experimental variability which prohibits these strict models from revealing true clusters corrupted by noise. In this paper, we study the problem of revealing the order preserving clusters in the presence of noise. We propose a noise-tolerant model called approximate order preserving cluster (AOPC). Instead of requiring all objects in a cluster have identical attribute order, we require that (1) at least a certain fraction of the objects have identical attribute order; (2) other objects in the cluster may deviate from the consensus order by up to a certain fraction of attributes. We also propose an algorithm to mine AOPC. Experiments on gene expression data demonstrate the efficiency and effectiveness of our algorithm.