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Russia's invasion of Ukraine in 2022 is not an accident but an inevitable consequence of how the West, especially the US, has utilised NATO in the post-Cold War period. This encompasses the activities which the US has supported NATO to pursue in the last 30 years: transformation, expansion, and participation in \"out of area\" actions. However, the US has never found it easy to help NATO remain the leading security organisation in Europe, not only because of threats posed by outside actors like Russia but also the mounting internal challenges, especially the long-standing issue of a \"two-tiered\" alliance. Washington has already shown reluctance to utilise NATO in Afghanistan, Libya, Syria, and now Ukraine to varying degrees. This paper seeks to understand why the US has remained committed to NATO and how the future US-NATO relationship will likely fare, including whether the Ukraine crisis can inject new life into the Alliance. It is believed that in the current context of uncertainty posed by NATO's \"Smart Defence\" and the US \"Pivot to Asia\", it seems highly likely that unless something changes, NATO will end up just doing less with less.

The formation of pre-metastatic niche is a key step in the metastatic burden. The pluripotent factor Lin28B is frequently expressed in breast tumors and is particularly upregulated in the triple negative breast cancer subtype. Here, we demonstrate that Lin28B promotes lung metastasis of breast cancer by building an immune-suppressive pre-metastatic niche. Lin28B enables neutrophil recruitment and N2 conversion. The N2 neutrophils are then essential for immune suppression in pre-metastatic lung by PD-L2 up-regulation and a dysregulated cytokine milieu. We also identify that breast cancer-released exosomes with low let-7s are a prerequisite for Lin28B-induced immune suppression. Moreover, Lin28B-induced breast cancer stem cells are the main sources of low-let-7s exosomes. Clinical data further verify that high Lin28B and low let-7s in tumors are both indicators for poor prognosis and lung metastasis in breast cancer patients. Together, these data reveal a mechanism by which Lin28B directs the formation of an immune-suppressive pre-metastatic niche. The establishment of a pre-metastatic niche is a key step preceding metastasis formation. Here the authors show that tumor-intrinsic Lin28B, a RNA-binding protein, has an essential role in the formation of an immune-suppressive pre-metastatic niche, promoting lung metastasis of breast cancer.

Establish a new classification system of distal radius fracture based on computed tomographic (CT), and evaluate its reliability and reproducibility preliminarily, and provide a new theoretical reference for clinicians to use the clinical classification system. The imaging data and clinical data of 204 inpatients with distal radius fracture during 6 years from January 1, 2014 to January 1, 2019 in orthopaedic department were analyzed retrospectively and classified based on CT. Three observers evaluated the image data of 48 randomly selected cases based on CT at different time nodes of T1 and T2. Cohen's kappa was used to calculate the consistency. At the last follow-up, patients' Disabilities of the Arm, Shoulder and Hand (DASH), Patient Rated Wrist Evaluation (PRWE), and VAS scores were collected. Among 204 cases, there were 12 cases of type 1, including 6 cases of type 1-D, 4 cases of type 1-V and 2 cases of type 1-R. There were 6 cases of type 2, including 2 cases of type 2-DV, 2 cases of type 2-DR and 2 cases of type 2-VR. There were 186 cases of type 3, including 32 cases of type 3–0, 127 cases of type 3–1 and 27 cases of type 3–2. There was no significant difference in DASH, PRWE and VAS scores among all types ( P  > 0.05). The results of interobserver reproducibility were kappa = 0.985, ICC = 0.984 in the first evaluation, kappa = 0.986, ICC = 0.986 in the second evaluation. The results of intraobserver reproducibility were O1 = 0.991, O2 = 0.991, O3 = 0.989 respectively. The new classification system of distal radius fracture based on CT has theoretical and practical significance for incision selection, fracture reduction and internal fixation. 123 classification system is clear, comprehensive, easy to understand and remember. Moreover, it has higher interobserver reliability and intraobserver reproducibility than other systems reported at present.

Hedyotis diffusa Willd (HDW) is one of the most well-known herbs used in the treatment of prostate cancer. However, the potential mechanisms of its anti-tumor effects have not been fully explored. Here, we applied a network pharmacology approach to explore the potential mechanisms of HDW against prostate cancer (PCa). We obtained 14 active compounds from HDW and 295 potential PCa related targets in total to construct a network, which indicated that quercetin and ursolic acid served as the main ingredients in HDW. Mitogen-activated Protein Kinase 8 (MAPK8), Interleukin 6 (IL6), Vascular Endothelial Growth Factor A (VEGFA), Signal Transducer and Activator of Transcription 3 (STAT3), Jun Proto-Oncogene (JUN), C-X-C Motif Chemokine Ligand 8 (CXCL8), Interleukin-1 Beta (IL1B), Matrix Metalloproteinase-9 (MMP9), C-C Motif Chemokine Ligand 2 (CCL2), RELA Proto-Oncogene (RELA), and CAMP Responsive Element Binding Protein 1 (CREB1) were identified as key targets of HDW in the treatment of PCa. The protein–protein interaction (PPI) cluster demonstrated that CREB1 was the seed in this cluster, indicating that CREB1 plays an important role in connecting other nodes in the PPI network. This enrichment demonstrated that HDW was highly related to translesion synthesis, unfolded protein binding, regulation of mitotic recombination, phosphatidylinositol and its kinase-mediated signaling, nucleotide excision repair, regulation of DNA recombination, and DNA topological change. The enrichment results also showed that the underlying mechanism of HDW against PCa may be due to its coordinated regulation of several cancer-related pathways, such as angiogenesis, cell differentiation, migration, apoptosis, invasion, and proliferation.

ObjectiveAiming to investigate diabetic retinopathy (DR) risk factors and predictive models by machine learning using a large sample dataset.DesignRetrospective study based on a large sample and a high dimensional database.SettingA Chinese central tertiary hospital in Beijing.ParticipantsInformation on 32 452 inpatients with type-2 diabetes mellitus (T2DM) were retrieved from the electronic medical record system from 1 January 2013 to 31 December 2017.MethodsSixty variables (including demography information, physical and laboratory measurements, system diseases and insulin treatments) were retained for baseline analysis. The optimal 17 variables were selected by recursive feature elimination. The prediction model was built based on XGBoost algorithm, and it was compared with three other popular machine learning techniques: logistic regression, random forest and support vector machine. In order to explain the results of XGBoost model more visually, the Shapley Additive exPlanation (SHAP) method was used.ResultsDR occurred in 2038 (6.28%) T2DM patients. The XGBoost model was identified as the best prediction model with the highest AUC (area under the curve value, 0.90) and showed that an HbA1c value greater than 8%, nephropathy, a serum creatinine value greater than 100 µmol/L, insulin treatment and diabetic lower extremity arterial disease were associated with an increased risk of DR. A patient’s age over 65 was associated with a decreased risk of DR.ConclusionsWith better comprehensive performance, XGBoost model had high reliability to assess risk indicators of DR. The most critical risk factors of DR and the cut-off of risk factors can be found by SHAP method to render the output of the XGBoost model clinically interpretable.

Deep learning is robust to the perturbation of a point cloud, which is an important data form in the Internet of Things. However, it cannot effectively capture the local information of the point cloud and recognize the fine-grained features of an object. Different levels of features in the deep learning network are integrated to obtain local information, but this strategy increases network complexity. This paper proposes an effective point cloud encoding method that facilitates the deep learning network to utilize the local information. An axis-aligned cube is used to search for a local region that represents the local information. All of the points in the local region are available to construct the feature representation of each point. These feature representations are then input to a deep learning network. Two well-known datasets, ModelNet40 shape classification benchmark and Stanford 3D Indoor Semantics Dataset, are used to test the performance of the proposed method. Compared with other methods with complicated structures, the proposed method with only a simple deep learning network, can achieve a higher accuracy in 3D object classification and semantic segmentation.

Background Dof transcription factors (TFs) containing C 2 -C 2 zinc finger domains are plant-specific regulatory proteins, playing crucial roles in a variety of biological processes. However, little is known about Dof in Camelina sativa, an important oil crop worldwide, with high stress tolerance. In this study, a genome-wide characterization of Dof proteins is performed to examine their basic structural characteristics, phylogenetics, expression patterns, and functions to identify the regulatory mechanism underlying lipid/oil accumulation and the candidate Dofs mediating stress resistance regulation in C. sativa . Results Total of 103 CsDof genes unevenly distributed on 20 chromosomes were identified from the C. sativa genome, and they were classified into four groups (A, B, C and D) based on the classification of Arabidopsis Dof gene family. All of the CsDof proteins contained the highly-conserved typic CX 2 C-X 21 -CX 2 C structure. Segmental duplication and purifying selection were detected for CsDof genes. 61 CsDof genes were expressed in multiple tissues, and 20 of them showed tissue-specific expression patterns, suggesting that CsDof genes functioned differentially in different tissues of C. sativa. Remarkably, a set of CsDof members were detected to be possible involved in regulation of oil/lipid biosynthesis in C. sativa . Six CsDof genes exhibited significant expression changes in seedlings under salt stress treatment. Conclusions The present data reveals that segmental duplication is the key force responsible for the expansion of CsDof gene family, and a strong purifying pressure plays a crucial role in CsDofs’ evolution. Several CsDof TFs may mediate lipid metabolism and stress responses in C. sativa. Several CsDof TFs may mediate lipid metabolism and stress responses in C. sativa. Collectively, our findings provide a foundation for deep understanding the roles of CsDofs and genetic improvements of oil yield and salt stress tolerance in this species and the related crops.

Background WRKY transcription factors are a superfamily of regulators involved in diverse biological processes and stress responses in plants. However, there is limited knowledge about the WRKY family in camelina ( Camelina sativa ), an important Brassicaceae oil crop with strong tolerance for various stresses. Here, a genome-wide characterization of WRKY proteins is performed to examine their gene structures, phylogenetics, expression, conserved motif organizations, and functional annotation to identify candidate WRKYs that mediate stress resistance regulation in camelinas. Results A total of 242 CsWRKY proteins encoded by 224 gene loci distributed unevenly over the chromosomes were identified, and they were classified into three groups by phylogenetic analysis according to their WRKY domains and zinc finger motifs. The 15 CsWRKY gene loci generated 33 spliced variants. Orthologous WRKY gene pairs were identified, with 173 pairs in the C. sativa and Arabidopsis genomes as well as 282 pairs in the C. sativa and B. napus genomes, respectively. A total of 137 segmental duplication events were observed, but there was no tandem duplication in the camelina genome. Ten major conserved motifs were examined, with WRKYGQK being the most conserved, and several variants were present in many CsWRKYs. Expression analysis revealed that 50% more CsWRKY genes were expressed constitutively, and a set of them displayed tissue-specific expression. Notably, 11 CsWRKY genes exhibited significant expression changes in seedlings under cold, salt, and drought stresses, showing a preferentially inducible expression pattern in response to the stress. Conclusions The present article describes a detailed analysis of the CsWRKY gene family and its expression profiles in 12 tissues and under several stress conditions. Segmental duplication is the major force underlying the broad expansion of this gene family, and a strong purifying pressure occurred for CsWRKY proteins during their evolution. CsWRKY proteins play important roles in plant development, with differential functions in different tissues. Exceptionally, eleven CsWRKYs, particularly five alternative spliced isoforms, were found to be the possible key players in mediating plant responses to various stresses. Overall, our results provide a foundation for understanding the roles of CsWRKYs and the precise mechanism through which CsWRKYs regulate high stress resistance as well as the development of stress tolerance cultivars among Cruciferae crops.

IFN regulatory factor 3 (IRF3) is the transcription factor crucial for the production of type I IFN in viral defence and inflammatory responses. The activity of IRF3 is strictly modulated by post-translational modifications (PTMs) to effectively protect the host from infection while avoiding excessive immunopathology. Here, we report that zebrafish myosin-regulated light chain interacting protein b ( mylipb ) inhibits virus-induced type I IFN production via two synergistic mechanisms: induction of autophagic degradation of irf3 and reduction of irf3 phosphorylation. In vivo , mylipb -null zebrafish exhibit reduced lethality and viral mRNA levels compared to controls. At the cellular level, overexpression of mylipb significantly reduces cellular antiviral capacity, and promotes viral proliferation. Mechanistically, mylipb associates with irf3 and targets Lys 352 to increase K6-linked polyubiquitination, dependent on its E3 ubiquitin ligase activity, leading to autophagic degradation of irf3. Meanwhile, mylipb acts as a decoy substrate for the phosphokinase tbk1 to attenuate irf3 phosphorylation and cellular antiviral responses independent of its enzymatic activity. These findings support a critical role for zebrafish mylipb in the limitation of antiviral innate immunity through two synergistic mechanisms targeting irf3.

In this article, we conducted the evaluation of artificial intelligence research from 1990–2014 by using bibliometric analysis. We introduced spatial analysis and social network analysis as geographic information retrieval methods for spatially-explicit bibliometric analysis. This study is based on the analysis of data obtained from database of the Science Citation Index Expanded (SCI-Expanded) and Conference Proceedings Citation Index-Science (CPCI-S). Our results revealed scientific outputs, subject categories and main journals, author productivity and geographic distribution, international productivity and collaboration, and hot issues and research trends. The growth of article outputs in artificial intelligence research has exploded since the 1990s, along with increasing collaboration, reference, and citations. Computer science and engineering were the most frequently-used subject categories in artificial intelligence studies. The top twenty productive authors are distributed in countries with a high investment of research and development. The United States has the highest number of top research institutions in artificial intelligence, producing most single-country and collaborative articles. Although there is more and more collaboration among institutions, cooperation, especially international ones, are not highly prevalent in artificial intelligence research as expected. The keyword analysis revealed interesting research preferences, confirmed that methods, models, and application are in the central position of artificial intelligence. Further, we found interesting related keywords with high co-occurrence frequencies, which have helped identify new models and application areas in recent years. Bibliometric analysis results from our study will greatly facilitate the understanding of the progress and trends in artificial intelligence, in particular, for those researchers interested in domain-specific AI-driven problem-solving. This will be of great assistance for the applications of AI in alternative fields in general and geographic information science, in particular.