HSP70-3 Interacts with Phospholipase Dδ and Participates in Heat Stress Defense.

Heat shock proteins (HSPs) function as molecular chaperones and are key components responsible for protein folding, assembly, translocation, and degradation under stress conditions. However, little is known about how HSPs stabilize proteins and membranes in response to different hormonal or environmental cues in plants. Here, we combined molecular, biochemical, and genetic approaches to elucidate the involvement of cytosolic HSP70-3 in plant stress responses and the interplay between HSP70-3 and plasma membrane (PM)-localized phospholipase Dδ (PLDδ) in Arabidopsis (Arabidopsis thaliana). Analysis using pull-down, coimmunoprecipitation, and bimolecular fluorescence complementation revealed that HSP70-3 specifically interacted with PLDδ. HSP70-3 bound to microtubules, such that it stabilized cortical microtubules upon heat stress. We also showed that heat shock induced recruitment of HSP70-3 to the PM, where HSP70-3 inhibited PLDδ activity to mediate microtubule reorganization, phospholipid metabolism, and plant thermotolerance, and this process depended on the HSP70-3-PLDδ interaction. Our results suggest a model whereby the interplay between HSP70-3 and PLDδ facilitates the re-establishment of cellular homeostasis during plant responses to external stresses and reveal a regulatory mechanism in regulating membrane lipid metabolism.

An open auscultation dataset for machine learning-based respiratory diagnosis studies.

Machine learning enabled auscultating diagnosis can provide promising solutions especially for prescreening purposes. The bottleneck for its potential success is that high-quality datasets for training are still scarce. An open auscultation dataset that consists of samples and annotations from patients and healthy individuals is established in this work for the respiratory diagnosis studies with machine learning, which is of both scientific importance and practical potential. A machine learning approach is examined to showcase the use of this new dataset for lung sound classifications with different diseases. The open dataset is available to the public online.

Efficiency and driving force assessment of an integrated urban water use and wastewater treatment system: Evidence from spatial panel data of the urban agglomeration on the middle reaches of the Yangtze River.

With the rapid development of urban agglomerations, urban water use and wastewater environments have gradually constrained sustainable development and caused increasing concern. In this paper, we selected the urban agglomeration on the middle reaches of the Yangtze River (UAMRYZ) as a typical area. We proposed an integrated urban water use and wastewater treatment (UWUWT) system and its urban water use (UWU) subsystem and urban wastewater treatment (UWT) subsystem. Moreover, an integrated two-stage slacks-based measure (SBM) data envelopment analysis (DEA) model was used to evaluate the efficiency of the UWUWT system and its subsystem during 2010 to 2017. Additionally, the multiscale geographically weighted regression (MGWR) model was adopted to analyze the influence mechanism of each factor on the defined system. The results indicated that the tendency of UWU efficiency and UWUWT efficiency were similar and mainly showed the same trend with an 'N' shape in a time-dependent manner for the UAMRYZ and provinces, respectively. Furthermore, the UWU efficiency and UWUWT efficiency of each city showed strong spatiotemporal heterogeneity. The UWT efficiency of the UAMRYZ and its representative cities was stable in the region and always had a higher value. With continuous economic development and increased interregional foreign trade, the UWU subsystem efficiency and the UWUWT system efficiency had a significant increase for cities along the entire river in the Yangtze Basin. The regional differences of the overall UWU efficiency, UWT efficiency and UWUWT efficiency gradually decreased and the efficiency has gradually improved from 2010 to 2017. Attribution analysis showed that the secondary industry was the main constraining factor, while the water resource was the most acceleration factor for the UWUWT system in most areas and the UWT subsystem for all cities. Our study evaluated the specific insufficiencies of the defined system and supported regulatory policies.

Towards more efficient ophthalmic disease classification and lesion location via convolution transformer.

OBJECTIVE: A retina optical coherence tomography (OCT) image differs from a traditional image due to its significant speckle noise, irregularity, and inconspicuous features. A conventional deep learning architecture cannot effectively improve the classification accuracy, sensitivity, and specificity of OCT images, and noisy images are not conducive to further diagnosis.  This paper proposes a novel lesion-localization convolution transformer (LLCT) method, which combines both convolution and self-attention to classify ophthalmic diseases more accurately and localize the lesions in retina OCT images. METHODS: A novel architecture design is accomplished through applying customized feature maps generated by convolutional neutral network (CNN) as the input sequence of self-attention network. This design takes advantages of CNN's extracting image features and transformer's consideration of global context and dynamic attention. Part of the model is backward propagated to calculate the gradient as a weight parameter, which is multiplied and summed with the global features generated by the forward propagation process to locate the lesion. RESULTS: Extensive experiments show that our proposed design achieves improvement of about 7.6% in overall accuracy, 10.9% in overall sensitivity, and 9.2% in overall specificity compared with previous methods. And the lesions can be localized without the labeling data of lesion location in OCT images. CONCLUSION: The results prove that our method significantly improves the performance and reduces the computation complexity in artificial intelligence assisted analysis of ophthalmic disease through OCT images. SIGNIFICANCE: Our method has a significance boost in ophthalmic disease classification and location via convolution transformer. This is applicable to assist ophthalmologists greatly.1.

River algal blooms are well predicted by antecedent environmental conditions.

River algal blooms have become a challenging environmental problem worldwide due to strong interference of human activities and megaprojects (e.g., big dams and large-scale water transfer projects). Previous studies on algal blooms were mainly focused on relatively static water bodies (i.e., lakes and reservoirs), but less on the large rivers. As the largest tributary of the Yangtze River of China and the main freshwater source of the South-to-North Water Diversion Project (SNWDP), the Han River has experienced frequent algal blooms in recent decades. Here we investigated the algal blooms during a decade (2003-2014) in the Han River by two gradient boosting machine (GBM) models with k-fold cross validation, which used explanatory variables from current 10-day (GBMc model) or previous 10-day period (GBMp model). Our results advocate the use of GBMp due to its higher accuracy (median Kappa = 0.9) and practical predictability (using antecedent observations) compared to GBMc. We also revealed that the algal blooms in the Han River were significantly modulated by antecedent water levels in the Han River and the Yangtze River and water level variation in the Han River, whereas the nutrient concentrations in the Han River were usually above thresholds and not limiting algal blooms. This machine-learning-based study potentially provides scientific guidance for preemptive warning and risk management of river algal blooms through comprehensive regulation of water levels during the dry season by making use of water conservancy measures in large rivers.

Occurrence of organophosphate esters and their diesters degradation products in industrial wastewater treatment plants in China: Implication for the usage and potential degradation during production processing.

Influent, effluent and sludge samples were collected from eight industrial wastewater treatment plants (WWTPs) in the Pearl River Delta, China. Samples were investigated for the occurrence of 10 organophosphate esters (OPEs) and 7 organophosphate diesters (Di-OPs). Most of the 10 OPEs were detected at total concentrations of OPEs (∑OPEs) ranging from 65.8 to 2842 ng L-1 in influent samples and exhibited different composition profiles. The results indicated that OPEs were widely used with different proportions in versatile OPE-containing products. Meanwhile, ∑OPEs in effluent were found in the range of 6.37-2710 ng L-1; these residuals were subsequently transported to municipal WWTPs for further treatment and posed extra pressure on the plants. Seven Di-OPs, possible degradation products of the OPEs, were also detected in influent, effluent and sludge. The results suggested that OPEs underwent degradation during rigorous industrial manufacturing procedures, such as thermal processes, highly alkaline pH, or processes that involved metal/metal oxide. In addition, high levels of dibutyl phosphate, di(2-ethylhexyl) phosphate and diphenyl phosphate were found in influent suggesting their possible industrial application.