A proposal for the morphological classification and nomenclature of neurons

The morphological and functional characteristics of neurons are quite varied and complex. There is a need for a comprehensive approach for distinguishing and classifying neurons. Similar to the biological species classification system, this study proposes a morphological classification system for neurons based on principal component analysis. Based on four principal components of neuronal morphology derived from principal component analysis, a nomenclature system for neurons was obtained. This system can accurately distinguish between the same type of neuron from different species.

Genetic analysis of morphological index and its related taxonomic traits for classification of indica

The Cheng index distinguishes indica andjaponica rice based on six taxonomic traits.This index has been widely used for classifi- cation of indica and japonica varieties in China.In this study,a double haploid(DH)popula-tion derived from anther culture of ZYQ8/JX17 F,a typical inter-subspecies hybrid,was used to investigate the six taxonomictraits,i.e.leaf hairiness(LH),color of hullwhen heading(CHH),hairiness of hull(HH),length of the first and second panicle internode(LPI),length/width of grain(L/W),andphenol reaction(PH).The morphological in- dex(MI)was also calculated.Based on themolecular linkage map constructed from this

Classification of dendrocola nematode-trapping fungi

Pine wilt disease,caused by Bursaphelenchus xylophilus and Bursaphelenchus mucnatus,is a serious quarantined disease.Arboreal nematode-trapping fungi of P inus spp.are effective predators on nematodes and have strong host adaptability.The development of these fungal resources may be an effective way to control pine wood nematodes.We collected 515 samples of pine wilt disease from the areas of Ninghai City(Zhejiang province),Shuangbai County(Yunnan province),and Daxing'anling(Heilongjiang province),China.Through isolation,culture and identification,6 species of nematode-trapping fungi(A rthrobotrys cladodesr,A.oligospora,A.musiformis,A.dendroides,Dactylellina ellipsospora,Monacrosporium thaumasium)were identified for predation against B.xylophilus,and 9 species(Arthrobotrys dactyloides,A.cladodes r A.oligospora A.dendroides,Dactylellina ellipsospora,Dactylella asthenopaga,D.leptospora,Arthrobotrys superba,Monacrosporium drechseri)were identified for predation against B.mucnatus.This study provides information in the classification of arboreal predator nematodes and provides an important basis for future biological control of pine wood nematodes.

Soybean Leaf Morphology Classification Based on FPN-SSD and Knowledge Distillation

Soybean leaf morphology is one of the most important morphological and biological characteristics of soybean.The germplasm gene differences of soybeans can lead to different phenotypic traits,among which soybean leaf morphology is an important parameter that directly reflects the difference in soybean germplasm.To realize the morphological classification of soybean leaves,a method was proposed based on deep learning to automatically detect soybean leaves and classify leaf morphology.The morphology of soybean leaves included lanceolate,oval,ellipse and round.First,an image collection platform was designed to collect images of soybean leaves.Then,the feature pyramid networks–single shot multibox detector(FPN-SSD)model was proposed to detect the top leaflets of soybean leaves on the collected images.Finally,a classification model based on knowledge distillation was proposed to classify different morphologies of soybean leaves.The obtained results indicated an overall classification accuracy of 0.956 over a private dataset of 3200 soybean leaf images,and the accuracy of classification for each morphology was 1.00,0.97,0.93 and 0.94.The results showed that this method could effectively classify soybean leaf morphology and had great application potential in analyzing other phenotypic traits of soybean.

Study of Substrate and Physico-Chemical Base Classification of the Rivers of Nepal

The rivers in Nepal are classified in terms of geographical regions but a more scientific classification such as on the ba-sis of morphology is clearly lacking. This study was done in 9 rivers namely Jhikhukhola of the Koshi system, Aandhikhola, Arungkhola, East Rapti, Karrakhola, Seti and main channel Narayani of the Gandaki system, and two independent systems within Nepal, Bagmati and Tinau. Among the morphologies, river bed or the substratum was taken as the main variable for the analysis which was categorized into 7 types as rocks, boulders, cobbles, pebbles, gravels, sand and silt. There were 23 sampling sites each with 2 stretches of around 100m in those rivers. The data were taken as a percentage, and to avoid biases it was observed visually by the same person for a complete year in every season. With 23 sites each with 2 stretches and 4 replicates corresponding to 4 seasons, there are altogether 184 observations, each termed as a case, that constitute this work. Canonical Discrimination Analysis (CDA) which is most suitable when the data pool is huge was applied to see if the rivers studied distinguish themselves in terms of its morphology. The result was remarkably successful and was close to the established regional classification of the rivers. This kind of river classification has great application in the utilization, conservation and restoration of the most important natural re-source of the country.

Comparison of an automated digital cell morphology analysis system with manual counting

Background:Bionovation's CSFA800 is a new automated digital cell imaging analyzer.We evaluated the performance of the CSFA800 by comparing it with artificial peripheral blood white blood cell counting.Methods:According to inclusion and exclusion criteria,131 randomly selected samples(77 abnormal samples and 54 normal samples)were compared.Correlations between automated and manual counting results were analyzed.Manual counting was carried out according to the guidelines of the Association of Clinical and Laboratory Standards.Results:Counts of neutrophils,lymphocytes,monocytes,eosinophils,baso-phils,and immature granulocytes obtained from CSFA800 and artificial methods were linearly and positively correlated,with R values of 0.73,0.65,0.24,0.2,0.4,and 0.63,respectively,all p<0.05.Therefore,correlations be-tween CSFA800 and manual counting are acceptable.Compared with the DI‐60 Automated Digital Cell Morphology System(DI‐60;Sysmex),CSFA800 is more efficient and can analyze 20,000 cells in 1 min.However,the overall accuracy of CSFA800 is not as good as DI‐60,although its counting perfor-mance is better for basophils.Conclusions:The performance of CSFA800 for WBC counts is acceptable,and it displayed good performance for neutrophils,lymphocytes,and imma-ture granulocytes.Compared to DI‐60,CSFA800 is more efficient but has slightly lower overall accuracy.To some extent,CSFA800 is helpful to opti-mize the clinical laboratory workflow and improve the working efficiency of inspectors.

Morphology and features of Cambrian oncoids and responses to palaeogeography of the North China Platform

The Cambrian strata in the North China Platform are fully exposed. A wide variety of carbonate oncoids with different shapes occur in the Xuzhuang and Zhangxia formations(Miaolingian Series) from six Cambrian sections in the study area. A comprehensive study involving outcrop description, microscopic observation, scanning electron microscopy(SEM), energy dispersive X-ray spectroscopy(EDX), X-ray diffraction(XRD), and carbon and oxygen isotope analysis is conducted to determine the facies, morphology, internal structure, and geochemical properties of the oncoids. The oncoids are divided into six types based on their morphology and internal structure.Microscopic and ultrastructural observations reveal typical microbial fossils(Girvanella) and microbially-related sediments(framboidal pyrite), indicating the biogenicity of the oncoids. Additionally, the XRD and carbon and oxygen isotope analysis results suggest that the formational environments of these oncoids are different due to terrestrial influences. Statistical data on the oncoids from the six sections show that there are obvious differences in the types of oncoids and the proportions of different varieties in each section. The spatial differences in the oncoid morphologies are associated with different palaeogeographic settings. The rough oncoid growth patterns developed in nearshore environments were influenced by terrigenous debris and steep terrain, whereas the delicate oncoid growth patterns developed in offshore environments were less affected by terrestrial factors and were featured by more stable depositional processes related to microbial mats.