A novel approach for estimation of above-ground biomass of sugar beet based on wavelength selection and optimized support vector machine

Timely diagnosis of sugar beet above-ground biomass (AGB) is critical for the prediction of yield and optimal precision crop management. This study established an optimal quantitative prediction model of AGB of sugar beet by using hyperspectral data. Three experiment campaigns in 2014, 2015 and 2018 were conducted to collect ground-based hyperspectral data at three different growth stages, across different sites, for different cultivars and nitrogen (N) application rates. A competitive adaptive reweighted sampling (CARS) algorithm was applied to select the most sensitive wavelengths to AGB. This was followed by developing a novel modified differential evolution grey wolf optimization algorithm (MDE-GWO) by introducing differential evolution algorithm (DE) and dynamic non-linear convergence factor to grey wolf optimization algorithm (GWO) to optimize the parameters c and gamma of a support vector machine (SVM) model for the prediction of AGB. The prediction performance of SVM models under the three GWO, DE-GWO and MDE-GWO optimization methods for CARS selected wavelengths and whole spectral data was examined. Results showed that CARS resulted in a huge wavelength reduction of 97.4% for the rapid growth stage of leaf cluster, 97.2% for the sugar growth stage and 97.4% for the sugar accumulation stage. Models resulted after CARS wavelength selection were found to be more accurate than models developed using the entire spectral data. The best prediction accuracy was achieved after the MDE-GWO optimization of SVM model parameters for the prediction of AGB in sugar beet, independent of growing stage, years, sites and cultivars. The best coefficient of determination (R-2), root mean square error (RMSE) and residual prediction deviation (RPD) ranged, respectively, from 0.74 to 0.80, 46.17 to 65.68 g/m(2) and 1.42 to 1.97 for the rapid growth stage of leaf cluster, 0.78 to 0.80, 30.16 to 37.03 g/m(2) and 1.69 to 2.03 for the sugar growth stage, and 0.69 to 0.74, 40.17 to 104.08 g/m(2) and 1.61 to 1.95 for the sugar accumulation stage. It can be concluded that the methodology proposed can be implemented for the prediction of AGB of sugar beet using proximal hyperspectral sensors under a wide range of environmental conditions

Assessing Heavy Metal Pollution of the Largest Nature Reserve in Tianjin City, China

Embargo until June 10, 2023Beidagang Wetland (BW) Nature Reserve is centrally situated in Tianjin City, experiencing an extreme industrial development. This study uses index characteristic analysis systems for assessing the individual and combined heavy metal pollution loading in the water during the spring and autumn seasons. By combining the pollution level of single pollutant, a more comprehensive evaluation of water quality in BW was achieved. Water quality was worst during autumn due to high level of Cd and Pb, which indicate the type of anthropogenic activities have a serious effect on heavy metal pollution in BW. In addition, high exchangeable amounts of Cd (> 40%) were found in the sediments of BW, indicating Cd pollution has emerged. There is a need for appropriate abatement actions curbing heavy metal loading and improving water quality of the BW Nature Reserve, thereby ensuring a sustainable management of its ecosystem services.acceptedVersio

The R Protein of SARS-CoV: Analyses of Structure and Function Based on Four Complete Genome Sequences of Isolates BJ01-BJ04

The R (replicase) protein is the uniquely defined non-structural protein (NSP) responsible for RNA replication, mutation rate or fidelity, regulation of transcription in coronaviruses and many other ssRNA viruses. Based on our complete genome sequences of four isolates (BJ01-BJ04) of SARS-CoV from Beijing, China, we analyzed the structure and predicted functions of the R protein in comparison with 13 other isolates of SARS-CoV and 6 other coronaviruses. The entire ORF (open-reading frame) encodes for two major enzyme activities, RNA-dependent RNA polymerase (RdRp) and proteinase activities. The R polyprotein undergoes a complex proteolytic process to produce 15 function-related peptides. A hydrophobic domain (HOD) and a hydrophilic domain (HID) are newly identified within NSP1. The substitution rate of the R protein is close to the average of the SARS-CoV genome. The functional domains in all NSPs of the R protein give different phylogenetic results that suggest their different mutation rate under selective pressure. Eleven highly conserved regions in RdRp and twelve cleavage sites by 3CLP (chymotrypsin-like protein) have been identified as potential drug targets. Findings suggest that it is possible to obtain information about the phylogeny of SARS-CoV, as well as potential tools for drug design, genotyping and diagnostics of SARS

Uncovering the immune microenvironment and molecular subtypes of hepatitis B-related liver cirrhosis and developing stable a diagnostic differential model by machine learning and artificial neural networks

Background: Hepatitis B-related liver cirrhosis (HBV-LC) is a common clinical disease that evolves from chronic hepatitis B (CHB). The development of cirrhosis can be suppressed by pharmacological treatment. When CHB progresses to HBV-LC, the patient’s quality of life decreases dramatically and drug therapy is ineffective. Liver transplantation is the most effective treatment, but the lack of donor required for transplantation, the high cost of the procedure and post-transplant rejection make this method unsuitable for most patients.Methods: The aim of this study was to find potential diagnostic biomarkers associated with HBV-LC by bioinformatics analysis and to classify HBV-LC into specific subtypes by consensus clustering. This will provide a new perspective for early diagnosis, clinical treatment and prevention of HCC in HBV-LC patients. Two study-relevant datasets, GSE114783 and GSE84044, were retrieved from the GEO database. We screened HBV-LC for feature genes using differential analysis, weighted gene co-expression network analysis (WGCNA), and three machine learning algorithms including least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) for a total of five methods. After that, we constructed an artificial neural network (ANN) model. A cohort consisting of GSE123932, GSE121248 and GSE119322 was used for external validation. To better predict the risk of HBV-LC development, we also built a nomogram model. And multiple enrichment analyses of genes and samples were performed to understand the biological processes in which they were significantly enriched. And the different subtypes of HBV-LC were analyzed using the Immune infiltration approach.Results: Using the data downloaded from GEO, we developed an ANN model and nomogram based on six feature genes. And consensus clustering of HBV-LC classified them into two subtypes, C1 and C2, and it was hypothesized that patients with subtype C2 might have milder clinical symptoms by immune infiltration analysis.Conclusion: The ANN model and column line graphs constructed with six feature genes showed excellent predictive power, providing a new perspective for early diagnosis and possible treatment of HBV-LC. The delineation of HBV-LC subtypes will facilitate the development of future clinical treatment of HBV-LC

Latitudinal differences in early growth of largehead hairtail (Trichiurus japonicus) in relation to environmental variables

Largehead hairtail (Trichiurus japonicus) in the China Seas shows an increasing catch trend, despite continued overexploitation, which could be attributed to improved recruitment as a result of strengthened early growth. To understand the early growth variability of largehead hairtail, we examined the linkages between early growth, as revealed by otolith microstructure, and the associated environmental variables over both spatial and temporal scales. Young‐of‐the‐Year largehead hairtail were collected from three regions in the Bohai, Yellow and East China Seas between 29° and 39° N. Daily increment widths of sagittal otoliths were measured and used as a proxy for somatic growth. We found two spawning cohorts, Spring‐ and Summer‐spawned cohorts, that showed latitudinal differences in both mean growth and growth pattern. The transition time from larval to juvenile stage was identified at around 40 days. Daily increment widths of two cohorts showed similar growth pattern in the first 40 days, while location had a marked effect on daily growth over 41–110 days. This suggests physiologically constrained growth pattern in larval stage, but more plastic growth subject to habitat‐specific influences in juvenile stage. The gradient forest analysis identified sea bottom temperature, vertical temperature gradient, and sea surface salinity, as the most important variables in determining early growth. Latitudinal differences in early growth pattern and their response to environmental variables suggest adaptive plasticity of early growth, which has notable implication for the management and sustainable utilization of this important but heavily exploited resource in the China Seas.acceptedVersio

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Ecosemiotics and biosemiotics: a comparative study

Ecological semiotics belongs to the field of culture, and biological semiotics refers to biology. There are both similarities and differences between ecological semiotics (ecosemiotics) and biological semiotics (biosemiotics). “Co-existence and co-prosperity” are the highest true meaning of human beings and nature. Faced with the increasingly serious ecological crisis, human beings, as the only semiotic animal that can reflect on sign activities, are ultimately responsible for other species and the entire ecological community