副溶血性弧菌温度-盐度双因素预测模型的建立

本文以副溶血性弧菌VP BJ1.1997为研究对象, 采用均匀设计试验方法, 建立并验证了温度范围为7°C~43°C, 盐度范围为0.5%~9.5%NaCl的生长动力学模型。结果表明, 所选一级模型的拟合效果优劣依次为Logistic方程>Gompertz方程>Linear方程, 以Logistic方程为一级模型计算生长参数; 二级模型采用平方根模型进行拟合, 得到模型相关系数r为0.9863, 最低生长温度T min为9.0506°C, 最高生长盐度为5.93%NaCl(对应最低生长水分活度Aw min     

Dosimetric and radiobiological comparison in head-and-neck radiotherapy using JO-IMRT and 3D-CRT

IntroductionDosimetric and radiobiological evaluations for the Jaws-only Intensity-modulated radiotherapy (JO-IMRT) technique for head and neck jaws-only intensity-modulated radiation therapy (JO-IMRT) and 3D conformal radiation therapy (3D-CRT). To compare the head-and-neck therapeutic approaches utilizing JO-IMRT and 3D-CRT techniques, different radiation dose indices were calculated, including: conformity index (CI), homogeneity index (HI), and radiobiological variables like Niemierko's equivalent uniform dose based tumor control probability (TCP) of planning target volume (PTV), normal tissue complication probability (NTCP) of organs at risk (OAR) (brainstem, spinal cord, and parotid grand).Materials and methodsTwenty-five nasopharynx patients were studied using the Prowess Panther Treatment Planning System (Prowess Inc). The results were compared with the dose distribution obtained using 3D-CRT.ResultsRegarding tumor coverage and CI, JO-IMRT showed better results than 3D-CRT. The average doses received by the PTVs were quite similar: 72.1 ± 0.8 Gy by 3D-CRT and 72.5 ± 0.6 Gy by JO-IMRT plans (p > 0.05). The mean doses received by the parotid gland were 56.7 ± 0.7 Gy by 3D-CRT and 26.8 ± 0.3 Gy by JO-IMRT (p > 0.05). The HI and CI were 0.13 ± 0.01 and 0.14 ± 0.05 and (p > 0.05) by 3D-CRT and 0.83 ± 0.05 and 0.73 ± 0.10 by JO-IMRT (p < 0.05). The average TCP of PTV was 0.82 ± 0.08 by 3D-CRT and 0.92 ± 0.02 by JO-IMRT. Moreover, the NTCP of the parotid glands, brain stem, and spinal cord were lower using the JO-IMRT than 3D-CRT plans. In comparison to the 3D-CRT approach, the JO-IMRT technique was able to boost dose coverage to the PTV, improve the target's CI and HI, and spare the parotid glands. This suggests the power of the JO-IMRT over 3D-CRT in head-and-neck radiotherapy.     

天山云杉天然林群落空间结构异质性分析

为了解天山云杉(Picea schrenkiana var. tianschanica)天然林群落空间结构的变异规律,沿天山山脉由西至东选择5个地点(昭苏、新源、呼图壁、天池、奇台),在每一地点沿海拔布设样带进行天山云杉群落调查,样带海拔区间为1700~2900 m。利用角尺度、大小比数分析其群落空间结构特征。结果表明: (1) 5个样带天山云杉角尺度平均值为0.545,群落呈团状分布格局;大小比数平均值为0.490,群落内林木的生长状态介于亚优势与优势之间。(2) 不同海拔高度天山云杉角尺度的变化显示随海拔高度的增加该群落呈现聚集分布-随机分布-聚集分布-随机分布的交替变化规律;大小比数随海拔高度的变化显示群落中天山云杉林木生长分化特征随海拔的增加呈现出亚优势-中庸-亚优势-劣势的规律。(3) 5个地点天山云杉角尺度平均值随着经度的变化较小,各地点均呈明显的整体团状分布;而林木大小分化由西至东表现为中庸木-亚优势木-中庸木的转变特征。     

均匀设计在候选内参基因RT—qPCR实验条件优化中的应用

目的探讨均匀设计在候选内参基因RT—qPCR实验条件优化中的应用,有助于后期实验筛选不同状态下THP～1细胞的稳定内参基因。方法以THP-1细胞cDNA为模板,eDNA模板量、引物浓度和退火温度3个因素为影响因素,应用均匀设计3因素8水平,探索影响候选内参基因RT-qPCR的扩增条件,检测不同实验条件下的扩增效果,在最优扩增条件下建立候选内参基因定量扩增的相对标准曲线,并检测扩增效率。结果通过均匀设计,完成对eDNA模板量、引物浓度和退火温度3因素8水平的实验条件优化,建立候选内参基因RT—qPCR检测的最佳组合为eDNA模板量0．5big、引物浓度200μmol／L、退火温度55℃。结论本实验选用均匀设计优化RT—qPCR实验条件,筛选出THP-1细胞候选内参基因RT—qPCR的最优实验条件。均匀设计适合于本实验研究多因素多水平试验条件的配比。     

样本区间长度的渐近分布

设(X1,X2,…,Xn)为服从I=[0,1]上的均匀分布的简单随机样本,它们将 [0,1]分成(n 1)个样本区间,以Y0,Y1,…,Yn分别表示这些样本区间的长度．本文利用顺序统计量的性质讨论了Yi(0≤i≤n)的渐近分布以及Yi,Yj(i≠j)的渐近独立性．     

Use of uniform shots for robust planning of mask-based treatment in Gamma Knife Icon

PurposeTo verify whether Icon automatic correction is robust in preserving plan quality.Materials/methodsAn end-to-end phantom was used to verify Icon’s correction accuracy qualitatively. For quantitative assessment, two plans, a composite- and a uniform-shot-only, were created for an elliptical- (E) and a sausage-shaped (S) lesion inside a PseudoPatient head phantom with a film insert. The phantom was irradiated in the planned and three other positions under each plan: 14° pitch (B); 14° rotation + 8° pitch (C); 95° rotation + 4-cm shift (D).ResultsIcon accurately corrects the locations of the shots. For the uniform-shot plans: all gamma index passing rates were >97%, and the differences between the planned and the delivery doses (minimum, maximum, and mean) were all ≤0.1 Gy. For the composite-shot plans, however, the dose differences increased as the phantom was shifted through positions B-D, with a gamma index passing rate of 61% for lesion-E in position D, and 92%, 79%, and 45% for lesion-S in positions B, C, and D, respectively.ConclusionsPlans using only uniform shots are more robust to deviations in treatment position. The tolerance for such deviations may be lower for plans using composite shots.     

popsicleR: A R Package for Pre-processing and Quality Control Analysis of Single Cell RNA-seq Data

The advent of single-cell sequencing is providing unprecedented opportunities to disentangle tissue complexity and investigate cell identities and functions. However, the analysis of single cell data is a challenging, multi-step process that requires both advanced computational skills and biological sensibility. When dealing with single cell RNA-seq (scRNA-seq) data, the presence of technical artifacts, noise, and biological biases imposes to first identify, and eventually remove, unreliable signals from low-quality cells and unwanted sources of variation that might affect the efficacy of subsequent downstream modules. Pre-processing and quality control (QC) of scRNA-seq data is a laborious process consisting in the manual combination of different computational strategies to quantify QC-metrics and define optimal sets of pre-processing parameters.Here we present popsicleR, a R package to interactively guide skilled and unskilled command line-users in the pre-processing and QC analysis of scRNA-seq data. The package integrates, into several main wrapper functions, methods derived from widely used pipelines for the estimation of quality-control metrics, filtering of low-quality cells, data normalization, removal of technical and biological biases, and for cell clustering and annotation. popsicleR starts from either the output files of the Cell Ranger pipeline from 10X Genomics or from a feature-barcode matrix of raw counts generated from any scRNA-seq technology. Open-source code, installation instructions, and a case study tutorial are freely available at https://github.com/bicciatolab/popsicleR.     

More Parsimonious Linar-By-Linear Associatin Model in the Analysis of Cross-Classifications Having Ordered Categories

For the analysis of cross-classifications having ordered categories, this paper proposes a model which is more parsimonious than the linear-by-linear association (LL) model (that is, which can be described in terms of fewer parameters than the LL model). In a special case, this model is more parsimonious than the uniform association (U) model. Under this model, the expected frequency on a log scale is a linear function of row and column variables for fixed column and row variables, respectively. For the well-known operation and dumping severity data, the parsimonious U model proposed here fits well, and new interpretations are added.