Quantitative analysis of the spatial diversity of Moraceae in China

Changes in distribution patterns of economically essential forest species under global change are urgently needed in the scientific forecast, and large-scale spatial modeling is a crucial tool. Using diversity pattern indicators and other data obtained through geographic information systems (GIS) and spatial data on Moraceae species obtained from published data, we quantitatively studied the spatial diversity patterns of genera in the Moraceae in China. The results revealed that the patch richness, diversity index, and total shape index of the genera with multiple species were significantly higher than those of the monotypic genera. Monotypic genera had no spatial diversity and no distribution in patterns of spatial diversity. Maclura had the most concentrated spatial distribution and the lowest distribution area among the Moraceae in China. The number of patches and the total area were the smallest, while the most significant patch index was the highest. Maclura had no spatial diversity. Streblus had the highest patch abundance compared to other genera with fewer species. Streblus had the smallest number of patches and total area of distribution, the lowest spatial distribution, and a small total shape index, indicating its concentrated distribution. The values of the Shannon’s Diversity Index (SHDI) and Simpson’s Diversity Index (SIDI) were the highest, and the spatial distribution was the most diverse among the genera with fewer species. The patch type of Streblus had a more considerable value than other genera, but the number of patches was small, and the total shape index was low. Streblus was primarily distributed in the south of Yunnan, western Guangxi, the west and central parts of Hainan, and southern Guangdong. Most of these areas were mountainous. The temperature decreased with elevation, providing diverse environmental conditions for the narrow-stem genus. Among the Moraceae in China, the spatial distribution of Ficus was the most diverse, with the highest number of patches, patch types, total shape index, SHDI, and SIDI values. The spatial diversity of Ficus could be used as a protected area for Moraceae in China

TTSVD: an efficient sparse decision making model with two-way trust recommendation in the AI enabled IoT systems

The convergence of AI and IoT enables data to be quickly explored and turned into vital decisions, and however, there are still some challenging issues to be further addressed. For example, lacking of enough data in AI-based decision making (so called Sparse Decision Making, SDM) will decrease the efficiency dramatically, or even disable the intelligent IoT networks. Taking the intelligent IoT networks as the network infrastructure, the recommendation systems have been facing such SDM problems. A naive solution is to introduce so-called trust information. However, trust information also maybe face the difficulty of sparse trust evidence (a.k.a sparse trust problem). In our work, an accurate sparse decision making model with two-way trust recommendation in the AI enabled IoT systems is proposed by us, named TT-SVD. Our model incorporates both trust information and rating information more completely, which can efficiently alleviate the above mentioned sparse trust problem and therefore be able to solve the cold start and data sparsity problems. Specifically, we first consider the two-fold trust influences from both trustees and trustors, which can be represented by a factor called trust propensity. To this end, we propose a dual model, including the trustor model (TrustorSVD) and a trustee model (TrusteeSVD) based on an existing rating-only recommendation model called SVD++, which are integrated by the weighted average and yield the final model, TT-SVD. The experimental results show that our model outperforms the state of the art including SVD and TrustSVD in both the ”all users” and ”cold start users” cases, and the accuracy improvement can reach a maximum of 29%. Complexity analysis shows that our model is equally suitable for the case of large sparse datasets. In a word, our model can effectively solve the sparse decision problem by introducing the two-way trust recommendation, and hence improve the efficiency of the intelligent recommendation systems

Review of Recently Progress on Neural Electronics and Memcomputing Applications in Intrinsic SiOx-Based Resistive Switching Memory

In this chapter, we focus on the recent process on memcomputing (memristor + computing) in intrinsic SiOx-based resistive switching memory (ReRAM or called memristor). In the first section of the chapter, we investigate neuromorphic computing by mimicking the synaptic behaviors in integrating one-diode and one-resistive switching element (1D-1R) architecture. The power consumption can be minimized further in synaptic functions because sneak-path current has been suppressed and the capability for spike-induced synaptic behaviors has been demonstrated, representing critical milestones and achievements for the application of conventional SiOx-based materials in future advanced neuromorphic computing. In the next section of chapter, we will discuss an implementation technique of implication operations for logic-in-memory computation by using a SiOx-based memristor. The implication function and its truth table have been implemented with the unipolar or nonpolar operation scheme. Furthermore, a circuit with 1D-1R architecture with a 4 × 4 crossbar array has been demonstrated, which realizes the functionality of a one-bit full adder as same as CMOS logic circuits with lower design area requirement. This chapter suggests that a simple, robust approach to realize memcomputing chips is quite compatible with large-scale CMOS manufacturing technology by using an intrinsic SiOx-based memristor

Global trends and performances in diabetic retinopathy studies: A bibliometric analysis

ObjectiveThe objective of this study is to conduct a comprehensive bibliometric analysis to identify and evaluate global trends in diabetic retinopathy (DR) research and visualize the focus and frontiers of this field.MethodsDiabetic retinopathy-related publications from the establishment of the Web of Science (WOS) through 1 November 2022 were retrieved for qualitative and quantitative analyses. This study analyzed annual publication counts, prolific countries, institutions, journals, and the top 10 most cited literature. The findings were presented through descriptive statistics. VOSviewer 1.6.17 was used to exhibit keywords with high frequency and national cooperation networks, while CiteSpace 5.5.R2 displayed the timeline and burst keywords for each term.ResultsA total of 10,709 references were analyzed, and the number of publications continuously increased over the investigated period. America had the highest h-index and citation frequency, contributing to the most influence. China was the most prolific country, producing 3,168 articles. The University of London had the highest productivity. The top three productive journals were from America, and Investigative Ophthalmology Visual Science had the highest number of publications. The article from Gulshan et al. (2016; co-citation counts, 2,897) served as the representative and symbolic reference. The main research topics in this area were incidence, pathogenesis, treatment, and artificial intelligence (AI). Deep learning, models, biomarkers, and optical coherence tomography angiography (OCTA) of DR were frontier hotspots.ConclusionBibliometric analysis in this study provided valuable insights into global trends in DR research frontiers. Four key study directions and three research frontiers were extracted from the extensive DR-related literature. As the incidence of DR continues to increase, DR prevention and treatment have become a pressing public health concern and a significant area of research interest. In addition, the development of AI technologies and telemedicine has emerged as promising research frontiers for balancing the number of doctors and patients

A cohort study of the efficacy and safety of immune checkpoint inhibitors plus anlotinib versus immune checkpoint inhibitors alone as the treatment of advanced non-small cell lung cancer in the real world

BACKGROUND: Anlotinib is a new multi-target tyrosine kinase inhibitor (TKI) and has been shown to have antitumor effects and synergistic antitumor effects with immunotherapy only in animal studies and in the 2nd-line treatment in small clinical trials. A real-world study with large sample to compare the efficacy and safety of anlotinib plus immune checkpoint inhibitors (ICIs) with ICIs alone in the multiline treatment of advanced non-small cell lung cancer (NSCLC) was urgently needed. METHODS: The data of 535 advanced NSCLC patients were collected from January 1, 2018, to December 31, 2021. The patients were divided into 2 groups: (I) ICI monotherapy (230 patients); (II) ICI + anlotinib (305 patients). After propensity-score matching (PSM) to reduce the effects of biases and confounding variables, the progression-free survival time (PFS), occurrence of adverse events, disease control rate (DCR), and objective response rate (ORR) of the 2 groups were compared. The effects of clinical factors, including age, gender, gene mutations, tumor proportion score, metastases, and combined radiotherapy, were also analyzed. RESULTS: After PSM, the baseline clinical characteristics were well balanced and the 2 group had a good comparability. Patients in the ICI + anlotinib group had significantly longer median PFS in both the 2nd-line treatment (7.73 vs. 4.70 months; P=0.003) and 3rd-line treatment (5.90 vs. 3.37 months; P=0.020), but the difference lacked statistical significance in the 1st-line treatment (8.40 vs. 5.20 months; P=0.229). The overall median PFS of patients in the ICI + anlotinib group was also much longer than that of patients in the ICI monotherapy group (6.37 vs. 3.90 months; P<0.001). The ICI + anlotinib group also tended to have a higher DCR, a higher ORR, and a higher probability of severe adverse drug reactions during the treatment than the ICI monotherapy group, but the differences were not statistically significant. Combining ICI + anlotinib could improve the outcomes of patients with bone metastasis. CONCLUSIONS: Anlotinib + ICI therapy could have greater efficacy in the treatment of advanced NSCLC patients than ICI monotherapy. The probability of adverse events might increase in the combined treatment, but could be controlled

Association between adiposity and facial aging: results from a Mendelian randomization study

Abstract Background Skin, as a sociologically meaningful interface, has psychological implications different from other organs, particularly in the context of the global population aging. Growing evidence suggests that facial aging is associated with an increased risk of adiposity. Existing research, however, were observational, and while they may find some correlations, it is difficult to simply disentangle non-causal or reverse-causal links because these associations may be confounded or fail to accurately reflect true causative linkages. Objectives We conducted a 2-sample Mendelian randomization (MR) study to examine the potential effect of facial aging on the risk of broad obesity and its three major adiposity indicators, including body mass index (BMI), body fat percentage (BF%) and waist circumference (WC). Methods Genetic instruments from IEU OpenGWAS project, one of the largest available genome-wide association studies (GWAS) for facial aging (423,999 samples) were used to investigate the relation to broad obesity (32,858 cases, 65,839 controls). Using the inverse-variance weighted (IVW) technique, single nucleotide polymorphisms (SNPs) associated with adiposity indicators (BMI (461,460 samples), BF% (454,633 samples), and WC (462,166 samples)) were investigated in relationship to facial aging. Further sensitivity analyses were performed, including Mendelian randomization-Egger (MR-Egger), weighted median estimates, and leave-one-out analysis, to evaluate the consistency of the results and related potential issues in MR studies. Results We identified strong and significant correlations between adiposity and facial aging in the 17 broad obesity-associated SNPs (IVW estimate of odds ratio OR = 1.020, 95% CI 1.010–1.029, P = 7.303e − 05), 458 BMI-associated SNPs (IVW estimate of odds ratio OR = 1.047, 95% CI 1.0357–1.058, P = 1.154e − 16),for the 395 BF%-associated SNPs (OR = 1.056, 95%CI 1.040–1.072,P = 7.617e − 12), or for the 374 WC-associated SNPs (OR = 1.072, 95% CI 1057–1.087,P = 1.229e − 23). A range of complementary methodologies have been employed to evaluate horizontal pleiotropy and related potential caveats occurring in MR research. Conclusions Using Mendelian randomization as an alternative approach to investigate causality, we found a causal relationship between adiposity and facial aging, which was statistically strong and significant

Lexicographic Orders of Intuitionistic Fuzzy Values and Their Relationships

Intuitionistic fuzzy multiple attribute decision making deals with the issue of ranking alternatives based on the decision information quantified in terms of intuitionistic fuzzy values. Lexicographic orders can serve as efficient and indispensable tools for comparing intuitionistic fuzzy values. This paper introduces a number of lexicographic orders by means of several measures such as the membership, non-membership, score, accuracy and expectation score functions. Some equivalent characterizations and illustrative examples are provided, from which the relationships among these lexicographic orders are ascertained. We also propose three different compatible properties of preorders with respect to the algebraic sum and scalar product operations of intuitionistic fuzzy values, and apply them to the investigation of compatible properties of various lexicographic orders. In addition, a benchmark problem regarding risk investment is further explored to give a comparative analysis of different lexicographic orders and highlight the practical value of the obtained results for solving real-world decision-making problems

Identification of Key Genes Related to the Prognosis of Esophageal Squamous Cell Carcinoma Based on Chip Re-Annotation

Esophageal cancer (EC) is one of the deadliest cancers worldwide. However, reliable biomarkers for early diagnosis, or those for the prognosis of therapy, remain unfulfilled goals for its subtype esophageal squamous cell carcinoma (ESCC). The purpose of this study was to identify reliable biomarkers for the diagnosis and prognosis of ESCC by gene chip re-annotation technique and downstream bioinformatics analysis. In our research, the GSE53624 dataset was downloaded from the GEO database. Then, we reannotated the gene expression probe and obtained the gene expression matrix. Differential expressed genes (DEGs) were found by R packages and they were subjected to Gene Ontology enrichment analysis and protein–protein interaction (PPI) network construction. As a result, a total of 28,885 mRNA probes were reannotated, among which 210 down-regulated and 80 up-regulated DEGs were screened out. By combining these genes set in clinical prognosis information and Western blot analysis, we found four genes with diagnostic and prognostic significance, including MMP13, SPP1, MMP10, and COL1A1. Furthermore, markers of infiltrating immune cells exhibited different DEG-related immune infiltration patterns

Relationship between Polymorphism of Nicotinic Acetylcholine Receptor Gene CHRNA3 and Susceptibility of Lung Cancer

Objective: To investigate the relationship between polymorphism of nicotinic acetylcholine receptor gene CHRNA3 and susceptibility of lung cancer. Methods: Sixty hundred patients with lung cancer and 600 healthy people were respectively selected. TaqMan-MGB probe technique was applied to detect rs3743073 (T &gt; G) genotypes at SNPs site on CHRNA3. The difference of genotype distribution among groups was compared, and its relationship with lung cancer was also investigated. Results: There was statistical significance regarding the distributions of CHRNA3 rs3743073 (T&gt; G) genotype and allele frequencies in patients with lung cancer and healthy people (P &lt; 0.05). By comparison to the patients with TT genotype, the risk of developing lung cancer in patients with TG and GG genotypes was increased 1.68 times (95%CI ＝ 1.30 － 2.19, P &lt; 0.05) and 1.30 times (95%CI ＝ 1.05 － 1.61, P &lt; 0.05), respectively, while the risk of developing lung cancer in patients with rs3743073G (TG and GG) was increased 0.65 times (95%CI ＝ 0.50 － 0.84, P &lt; 0.05) . Further analysis demonstrated that of the patients more than 60 years old, male and smoking, the risk of developing lung cancer in those with rs3743073G mutant genotypes was elevated significantly (P &lt; 0.05). Conclusion: The risk of developing lung cancer in patients with rs3743073G mutant genotypes of CHRNA3 gene is increased markedly, especially in those more than 60 years old, males and smoking ones