Analysis of Clinical Features, Pathological Features and Misdiagnosis of Pulmonary Lymphoma

Primary pulmonary lymphoma is a relatively rare extranodal lymphoma, and the incidence rate has increased in recent years. In the past, the disease mainly relied on surgery to obtain the pathological basis, so the clinical misdiagnosis rate was high. How to improve its early diagnosis and treatment has attracted much attention. By exploring the imaging manifestations of primary pulmonary lymphoma, we can further understand and improve the imaging diagnosis level of primary pulmonary lymphoma. This paper discusses the classification, imaging manifestations, diagnosis and identification of pulmonary lymphoma. The clinical data and imaging findings of primary pulmonary lymphoma diagnosed in a hospital were retrospectively summarized, and their imaging features were analyzed. We observe the clinicopathological characteristics and immunohistochemical phenotypes of multiple masses with cavitation type primary lung lymphoma, and analyze the virus and imaging characteristics of hybridization. The results of the study show that the CT (Computed Tomography) manifestations of primary lung lymphoma are diverse. The characteristics of cross-leaf distribution are more characteristic, and enhanced scanning lesions are usually mild to moderate. In the case of simultaneous masses and pneumonialike consolidation in the lungs, this disease needs to be considered.

Numerical simulation of the effects of canopy properties on airflow and pollutant dispersion in street canyons

The air flow and pollutant concentration fields in a street canyon affected by trees could affect the comfort and health of residents. At present, the description of the non-uniform/discontinuous distribution of leaves is difficult. In this study, the leaf distribution in the canopy was characterized by establishing non-continuous (uniform/random) algorithm based on a numerical simulation method, and the effects of canopy properties including, height, porosity and uniform/random leaf distribution, on the airflow and pollutant concentration fields in urban street canyons were investigated. The position of the tree canopy was found to directly affect the airflow field form and the air velocity distribution in the street canyon at low inflows. The average air velocity in the street canyon could be reduced significantly when the top of the tree canopy is near the top of the street canyon. The air velocity and pollutant concentration in the street canyon would vary only slightly due to the canopy porosity. Due to the increasing canopy porosity, the air velocity would increase, and the pollutant concentration would be reduced. The leaves are non-continuous and uniformly distributed at constant porosity, which does not significantly change the velocity distribution and pollutant concentration in the street canyon.

A new empirical equation to describe the vertical leaf distribution profile of maize

The characteristic traits of maize (Zea mays L.) leaves affect light interception and photosynthesis. Measurement or estimation of individual leaf area has been described using discontinuous equations or bell-shaped functions. However, new maize hybrids show different canopy architecture, such as leaf angle in modern maize which is more upright and ear leaf and adjacent leaves which are longer than older hybrids. The original equations and their parameters, which have been used for older maize hybrids and grown at low plant densities, will not accurately represent modern hybrids. Therefore, the aim of this paper was to develop a new empirical equation that captures vertical leaf distribution. To characterize the vertical leaf profile, we conducted a field experiment in Jilin province, Northeast China from 2015 to 2018. Our new equation for the vertical distribution of leaf profile describes leaf length, width or leaf area as a function of leaf rank, using parameters for the maximum value for leaf length, width or area, the leaf rank at which the maximum value is obtained, and the width of the curve. It thus involves one parameter less than the previously used equations. By analysing the characteristics of this new equation, we identified four key leaf ranks (4, 8, 14 and 20) for which leaf parameter values need to be quantified in order to have a good estimation of leaf length, width and area. Together, the method of leaf area estimation proposed here adds versatility for use in modern maize hybrids and simplifies the field measurements by using the four key leaf ranks to estimate vertical leaf distribution in maize canopy instead of all leaf ranks.

Nanoparticle surface charge influences translocation and leaf distribution in vascular plants with contrasting anatomy

Root uptake, translocation, and distribution of engineered nanoparticles by plants are dependent on both plant species and nanoparticle surface charge.

DENSE MATCHING COMPARISON BETWEEN CENSUS AND A CONVOLUTIONAL NEURAL NETWORK ALGORITHM FOR PLANT RECONSTRUCTION

3D reconstruction of plants is hard to implement, as the complex leaf distribution highly increases the difficulty level in dense matching. Semi-Global Matching has been successfully applied to recover the depth information of a scene, but may perform variably when different matching cost algorithms are used. In this paper two matching cost computation algorithms, Census transform and an algorithm using a convolutional neural network, are tested for plant reconstruction based on Semi-Global Matching. High resolution close-range photogrammetric images from a handheld camera are used for the experiment. The disparity maps generated based on the two selected matching cost methods are comparable with acceptable quality, which shows the good performance of Census and the potential of neural networks to improve the dense matching.

On the Shape of the Leaves

In this paper, we study the shape of the leaves. More specifically, we show that leaf shapes are affected by genes and external environment in different species; and affected by leaf vein and leaf distribution In the same species. Based on shape features, color features and vein features, aProbabilistic Neural Network (PNN) Modelis established by using Polar Fourier Transform. Finally, our experimental test shows that the total classification accuracy is 85.7%.