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The southwestern region of China is a global biodiversity hotspot. Understanding the environmental mechanisms behind treeline formation in high-altitude areas is crucial for predicting ecosystem changes, such as the upward movement of the treeline due to climate warming and the disappearance of high-altitude rocky beach and shrub ecosystems. Globally, observations show that growing seasonal temperatures at treelines are typically 6–7 °C, but trees do not always reach the predicted elevations. Spatial heterogeneity exists in the deviation (Dtreeline) between actual treeline elevation and the thermal treeline; however, the main driving factors for Dtreeline in many areas remain unclear. This study uses Yulong Snow Mountain as an example, employing machine learning methods like Support Vector Machine (SVM) to precisely identify actual treeline elevation and Extreme Gradient Boosting Tree (XGBoost) to explore the main environmental factors driving the spatial heterogeneity of Dtreeline. Our research found that (1) more than half of the treelines deviated from the thermal treeline, with the average elevation of the thermal treeline (3924 ± 391 m) being about 56 m higher than the actual treeline (3863 ± 223 m); (2) Dtreeline has a complex relationship with environmental factors. In addition to being highly correlated with temperature, precipitation and wind speed also significantly influence the treeline in this region; and (3) the influence of individual variables such as precipitation and wind speed on the spatial variation of Dtreeline is limited, often nonlinear, and involves threshold effects. This knowledge is essential for developing comprehensive protection strategies for Yunnan's high-altitude ecological systems in response to climate warming. Furthermore, it plays a significant role in understanding the changes in biological communities and the response of high-altitude areas to climate change. [ABSTRACT FROM AUTHOR]

Accurate prediction is of great significance to the construction of a smart city. However, current models only focus on mining the relationship among sequences and ignore the influence of the predicted sequences on future predictions, so we propose a Dynamic Attention Neural Network (DANN) based on encoder-decoder, which combines encoder context vectors and newly generated decoder context vectors to jointly dynamically representation learning, then generates corresponding predicted values. DANN processes data via the Bi-directional Long Short-Term Memory (Bi-LSTM) network as the fundamental structure of the network between encoder and decoder. What's more, in order to produce a new feature representation with low redundancy, gate mechanism network module is used to adaptively learn the interdependence of multivariate feature data. The relevant experiments show that compared with baseline models, DANN has the most stable long-term prediction performance, which reduces the problem of error accumulation to a certain degree. [ABSTRACT FROM AUTHOR]

Nonsyndromic hearing loss (NSHL) is a genetically diverse, highly heterogeneous condition characterised by deafness, and Gasdermin E (GSDME) variants have been identified as directly inducing autosomal dominant NSHL. While many NSHL cases associated with GSDME involve the skipping of exon 8, there is another, less understood pathogenic insertion variant specifically found in Chinese pedigrees that causes deafness, known as autosomal dominant 5 (DFNA5) hearing loss. In this study, we recruited a large Chinese pedigree, conducted whole‐exome and Sanger sequencing to serve as a comprehensive clinical examination, and extracted genomic DNA samples for co‐segregation analysis of the members. Conservation and expression analyses for GSDME were also conducted. Our clinical examinations revealed an autosomal dominant phenotype of hearing loss in the family. Genetic analysis identified a novel insertion variant in GSDME exon 8 (GSDME: NM_004403.3: c.1113_1114insGGGGTGCAGCTTACAGGGTGGGTGT: p. P372fs*36). This variant is segregated with the deafness phenotype of this pedigree. The GSDME gene was highly conserved in the different species we analysed, and its mRNA expression was ubiquitously low in different human tissues. In conclusion, we have successfully identified a novel pathogenic insertion variant of GSDME in a Chinese pedigree that causes deafness, shedding light on the genetic basis of hearing loss within this specific family. Our findings expand the spectrum of known variants associated with GSDME‐related deafness and may further support both the underlying gain‐of‐function mechanism and functional associations of GSDME hearing loss variants. [ABSTRACT FROM AUTHOR]

The solvothermal synthesis technique was employed to successfully fabricate a series of rare earth doped SnO2-Sb electrodes on the TNTs array substrate, serving as anode material for electrocatalytic degradation of phenol. The electrode doped with rare earth elements demonstrated superior electrocatalytic activity and stability in comparison to the undoped electrode. The influence of adding rare earth elements (i.e., Gd and Nd) into the precursor solution on the structural and property of TNTs/SnO2-Sb electrodes was studied in detail. The results obtained from SEM and XRD indicated that, compared to TNTs/SnO2-Sb-Nd, TNTs/SnO2-Sb-Gd exhibited a finer grain size due to the smaller ionic radius of the Gd element. This facilitated its incorporation into the SnO2 lattice interior and inhibited grain growth, resulting in a significant decrease in particle size for exposing more active sites. The influence mechanism of rare earth doping on electrochemical activity was investigated through XPS, EPR, LSV, EIS and Hydroxyl radicals (•OH) generation tests. The results demonstrated that the enhanced electrocatalytic activity can be attributed to an increased generation of oxygen vacancies on the electrode surface, which act as active sites for enhancing the adsorption of oxygen species and promoting •OH generation. [ABSTRACT FROM AUTHOR]