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Background Adaptive evolution is one of the crucial mechanisms for organisms to survive and thrive in new environments. Recent studies suggest that adaptive evolution could rapidly occur in species to respond to novel environments or environmental challenges during range expansion. However, for environmental adaptation, many studies successfully detected phenotypic features associated with local environments, but did not provide ample genetic evidence on microevolutionary dynamics. It is therefore crucial to thoroughly investigate the genetic basis of rapid microevolution in response to environmental changes, in particular on what genes and associated variation are responsible for environmental challenges. Here, we genotyped genome-wide gene-associated microsatellites to detect genetic signatures of rapid microevolution of a marine tunicate invader, Ciona robusta , during recent range expansion to the harsh environment in the Red Sea. Results The Red Sea population was significantly differentiated from the other global populations. The genome-wide scan, as well as multiple analytical methods, successfully identified a set of adaptive genes. Interestingly, the allele frequency largely varied at several adaptive loci in the Red Sea population, and we found significant correlations between allele frequency and local environmental factors at these adaptive loci. Furthermore, a set of genes were annotated to get involved in local temperature and salinity adaptation, and the identified adaptive genes may largely contribute to the invasion success to harsh environments. Conclusions All the evidence obtained in this study clearly showed that environment-driven selection had left detectable signatures in the genome of Ciona robusta within a few generations. Such a rapid microevolutionary process is largely responsible for the harsh environmental adaptation and therefore contributes to invasion success in different aquatic ecosystems with largely varied environmental factors.

Shear velocity is an important parameter in pre-stack seismic reservoir description. However, in the real study, the high cost of array acoustic logging leads to lacking a shear velocity curve. Thus, it is crucial to use conventional well-logging data to predict shear velocity. The shear velocity prediction methods mainly include empirical formulas and theoretical rock physics models. When using the empirical formula method, calibration should be performed to fit the local data, and its accuracy is low. When using rock physics modeling, many parameters about the pure mineral must be optimized simultaneously. We present a deep learning method to predict shear velocity from several conventional logging curves in tight sandstone of the Sichuan Basin. The XGBoost algorithm has been used to automatically select the feature curves as the model’s input after quality control and cleaning of the input data. Then, we construct a deep-feed neuro network model (DFNN) and decompose the whole model training process into detailed steps. During the training process, parallel training and testing methods were used to control the reliability of the trained model. It was found that the prediction accuracy is higher than the empirical formula and the rock physics modeling method by well validation.

In spatial crowdsourcing services, the trajectories of the workers are sent to a central server to provide more personalized services. However, for the honest-but-curious servers, it also poses a challenge in terms of potential privacy leakage of the workers. Local differential privacy (LDP) is currently the latest technique to protect data privacy. However, most of LDP-based schemes have limitations in providing good utility due to extensive noise in perturbing trajectories. In this work, to balance the privacy and utility, we propose a novel pattern-aware privacy protection method called trajectory-aware privacy-preserving with local differential privacy (TALDP). The key idea is that, rather than applying the same degree of perturbation to all location points, we employ adaptive privacy budget allocation, assigning varied privacy budgets to individual location points, thereby mitigating the perturbation’s impact and enhancing overall utility. Meanwhile, to ensure the privacy, we give the different perturbing points to different privacy budgets according to their important degree for the patterns of the trajectories. In particular, we use Karman filter method to select the important location points and decide their privacy budgets. We conduct extensive experiments on three real datasets. The results show that our approach improves the utility over many other current methods while still provide good the privacy protection.

The labeling of point clouds is the fundamental task in airborne laser scanning (ALS) point clouds processing. Many supervised methods have been proposed for the point clouds classification work. Training samples play an important role in the supervised classification. Most of the training samples are generated by manual labeling, which is time-consuming. To reduce the cost of manual annotating for ALS data, we propose a framework that automatically generates training samples using a two-dimensional (2D) topographic map and an unsupervised segmentation step. In this approach, input point clouds, at first, are separated into the ground part and the non-ground part by a DEM filter. Then, a point-in-polygon operation using polygon maps derived from a 2D topographic map is used to generate initial training samples. The unsupervised segmentation method is applied to reduce the noise and improve the accuracy of the point-in-polygon training samples. Finally, the super point graph is used for the training and testing procedure. A comparison with the point-based deep neural network Pointnet++ (average F1 score 59.4%) shows that the segmentation based strategy improves the performance of our initial training samples (average F1 score 65.6%). After adding the intensity value in unsupervised segmentation, our automatically generated training samples have competitive results with an average F1 score of 74.8% for ALS data classification while using the ground truth training samples the average F1 score is 75.1%. The result shows that our framework is feasible to automatically generate and improve the training samples with low time and labour costs.

Background. The incidence of diabetic gastroparesis (DGP) is mainly blamed to abnormity of interstitial cells of Cajal (ICCs). Autophagy could degrade damaged proteins and organelles to keep intracellular homeostasis, and it could directly influence structure and number of cells. In this study, we aimed to figure out the relationship between DGP and autophagy of ICCs. Methods. Sixty Sprague-Dawley (SD) rats were randomly divided into normal control group (NC, 10) and modeling group (50). Rats in the modeling group were injected 2% streptozotocin (STZ) and fed with high-glucose and high-fat diet for 8 weeks in order to establish DGP rat model. After modeling, 30 successfully modeled rats were randomly selected and separated into diabetic gastroparesis group (DGP, 10), GDP rats with electroacupuncture group (EA, 10), and GDP rats with metoclopramide group (MP, 10). When the intervention was completed, blood glucose was measured by ONE TOUCH glucometer and gastrointestinal propulsive rate was detected through measuring optical density. Autophagosomes were observed under transmission electron microscope (TEM). The expression of LC3 protein and P62 protein was measured by Western blot. When ICCs were transfected with GFP-RFP-LC3 plasmid, autophagy flux was observed by laser scanning confocal microscope. Results. (1) After intervention, compared with blood glucose of rats in the NC group, all of the DGP, EA, and MP groups were remarkably increased (P<0.01); compared with the DGP group, the blood glucose of the EA and MP groups was decreased greatly (P<0.01). (2) Compared with gastrointestinal propulsive rate of rats in the NC group, no matter gastric emptying rate or intestinal propulsive rate, the EA and MP groups were significantly reduced (P<0.01); compared with the NC group, gastric emptying rate and intestinal propulsive rate in the EA group were obviously decreased (P<0.05, P<0.01); compared with the DGP group, the EA and MP groups were increased significantly (P<0.01). (3) Compared with the NC group, intensity of RFP and GFP in the DGP group was obviously increased (P<0.05, P<0.01), in other words, the DGP group accompanying suppression of autophagy; compared with the DGP group, intensity of RFP and GFP in the EA group was decreased significantly (P<0.05, P<0.01). (4) There was no autophagosome in the NC group, and an autophagosome existed in the DGP group. Both EA and MP groups found autophagy. (5) When coming to LC3 II/LC3 I, compared with the NC group, the ratio was enhanced in the DGP and EA groups (P<0.01, P<0.05); compared with the DGP group, LC3 II/LC3 I was dramatically decreased in the MP and EA groups (P<0.01). (6) As the substrate of degradation, the expression of P62 in the other three groups was significantly increased (P<0.01) compared with the NC group; compared with the DGP group, the amount of P62 in the EA and MP groups was reduced greatly (P<0.01). Conclusion. The impaired autophagy flux in ICCs is the pathological basis of diabetic gastroparesis, blaming to fusion dysfunction of autophagosome and lysosome and electroacupuncture (EA) could ease the suppression of autophagy to improve gastric motility.

Interpersonal communication and interaction are important parts of college life. However, college students with high social anxiety fear others’ negative evaluation, feel distress, and avoid social situations, which adversely affects their academic achievement and mental health. Therefore, it is crucial to explore the factors that influence social anxiety. This research aimed to examine the relationship between rejection sensitivity and social anxiety among Chinese undergraduate students, and further investigated the multiple mediating roles of loneliness and self-esteem. A total of 618 Chinese undergraduate students (64.7% female) aged 17 to 25 years completed an online survey. Participants were recruited through online methods from several colleges and universities in the Zhejiang province of China. Significant correlations were found between social anxiety, rejection sensitivity, loneliness, and self-esteem. Higher rejection sensitivity was related to higher social anxiety, and loneliness and self-esteem respectively not only played a partial mediating role but also served as chain mediators between rejection sensitivity and social anxiety. These findings deepen our understanding of the psychological processes underlying the link between rejection sensitivity and social anxiety, which in turn provides a new perspective for college administrators, psychology teachers, college counselors, and parents to help promote undergraduates’ academic achievement and mental health.

Invasive species represent promising models to study species’ responses to rapidly changing environments. Although local adaptation frequently occurs during contemporary range expansion, the associated genetic signatures at both population and genomic levels remain largely unknown. Here, we use genome-wide gene-associated microsatellites to investigate genetic signatures of natural selection in a model invasive ascidian, Ciona robusta . Population genetic analyses of 150 individuals sampled in Korea, New Zealand, South Africa and Spain showed significant genetic differentiation among populations. Based on outlier tests, we found high incidence of signatures of directional selection at 19 loci. Hitchhiking mapping analyses identified 12 directional selective sweep regions, and all selective sweep windows on chromosomes were narrow (~8.9 kb). Further analyses indentified 132 candidate genes under selection. When we compared our genetic data and six crucial environmental variables, 16 putatively selected loci showed significant correlation with these environmental variables. This suggests that the local environmental conditions have left significant signatures of selection at both population and genomic levels. Finally, we identified “plastic” genomic regions and genes that are promising regions to investigate evolutionary responses to rapid environmental change in C. robusta .

The water-flooded zone in a sandstone reservoir with a complex porosity–permeability relationship is difficult to interpret quantitatively. Taking the P Formation of Kalamkas Oilfield in Kazakhstan as an example, this paper proposed a reservoir classification method that introduces the J-function into the crossplot of resistivity and oil column height to realize the classification of sandstone reservoirs with a complex porosity–permeability relationship. Based on the classification results, the initial resistivity calculation models of classified reservoirs were established. The oil–water seepage experiment was performed for classified reservoirs to measure the lithoelectric parameters and establish the relationship between water production rate and resistivity for these reservoirs, and then water production was quantitatively calculated according to the difference between the inverted initial resistivity and the measured resistivity. The results show that the reservoirs with an unclear porosity–permeability relationship can be classified by applying the J-function corresponding to grouped capillary pressure curves to the crossplot of oil column height and resistivity, according to the group average principle of capillary pressure curves. This method can solve the problem that difficult reservoir classification caused by a weak porosity–permeability correlation. Moreover, based on the results of reservoir classification, the water production rate and resistivity model of classified reservoirs is established. In this way, the accuracy of quantitative interpretation of the water-flooded zone in the reservoir can be greatly improved.

Pu’er raw tea, a representative tea with a positive correlation between quality and storage time, has a unique aging process. It can not only reduce the heavy astringent taste of newly produced tea, but also has a complex and fragrant aging aroma. However, the extremely slow natural aging process often takes years or even decades for quality transformation, along with the risk of termite infestation, odor absorption, etc. This study found that UV-C irradiation could significantly accelerate the aging process of tea. It enhanced the richness of the aroma, while reducing astringency and creating a smoother and mellower taste. The results of substances analysis revealed an increase in key aroma compounds such as alcohols, aldehydes and terpenes, and a decrease in catechin and caffeine with UV-C irradiation, which is consistent with the natural aging trend. An efficient and safe rapid aging technology of Pu’er raw tea has been successfully established.

Significance Two-pore channels (TPCs) are a recently discovered family of endolysosomal ion channels, but their regulation is controversial. By defining the TPC interactome, we provide a community resource that illuminates TPC complex regulation and resolves associations with novel partners and processes. Physical interactions with endolysosomal trafficking regulators predominate, and Rab GTPases impart isoform-specific roles for TPCs in organelle proliferation and cellular pigmentation. These data imply a fundamental role for TPCs in trafficking that augurs significance for disease states exhibiting lysosomal proliferation where TPC dysregulation may drive pathogenesis.