Boosting Random Forests to Reduce Bias; One-Step Boosted Forest and Its Variance Estimate

Adaptive cluster sampling (ACS) has been widely used for data collection of environment and natural resources. However, the randomness of its final sample size often impedes the use of this method. To control the final sample sizes, in this study, a [Formula: see text]-step ACS based on Horvitz–Thompson (HT) estimator was developed and an unbiased estimator was derived. The [Formula: see text]-step ACS-HT was assessed first using a simulated example and then using a real survey for numbers of plants for three species that were characterized by clustered and patchily spatial distributions. The effectiveness of this sampling design method was assessed in comparison with ACS Hansen–Hurwitz (ACS-HH) and ACS-HT estimators, and [Formula: see text]-step ACS-HT estimator. The effectiveness of using different [Formula: see text]-step sizes was also compared. The results showed that [Formula: see text]-step ACS-HT estimator was most effective and ACS-HH was the least. Moreover, stable sample mean and variance estimates could be obtained after a certain number of steps, but depending on plant species. [Formula: see text]-step ACS without replacement was slightly more effective than that with replacement. In [Formula: see text]-step ACS, the variance estimate of one-step ACS is much larger than other [Formula: see text]-step ACS ([Formula: see text]), but it is smaller than ACS. This implies that [Formula: see text]-step ACS is more effective than traditional ACS, besides, the final sample size can be controlled easily in population with big clusters.

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An Interactive Minicomputer Program for the Indexing and Simulation of Electron Diffraction Patterns

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100092670 ◽

1976 ◽

Vol 34 ◽

pp. 542-543

Author(s):

R.P. Goehner ◽

W.T. Hatfield ◽

Prakash Rao

Keyword(s):

Electron Diffraction ◽

Real Time ◽

Pattern Analysis ◽

Flow Diagram ◽

Hard Copy ◽

Time Analysis ◽

Real Time Analysis ◽

Transmission Electron ◽

One Step ◽

Diffraction Patterns

Computer programs are now available in various laboratories for the indexing and simulation of transmission electron diffraction patterns. Although these programs address themselves to the solution of various aspects of the indexing and simulation process, the ultimate goal is to perform real time diffraction pattern analysis directly off of the imaging screen of the transmission electron microscope. The program to be described in this paper represents one step prior to real time analysis. It involves the combination of two programs, described in an earlier paper(l), into a single program for use on an interactive basis with a minicomputer. In our case, the minicomputer is an INTERDATA 70 equipped with a Tektronix 4010-1 graphical display terminal and hard copy unit.A simplified flow diagram of the combined program, written in Fortran IV, is shown in Figure 1. It consists of two programs INDEX and TEDP which index and simulate electron diffraction patterns respectively. The user has the option of choosing either the indexing or simulating aspects of the combined program.

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Nutrient-regulated gene expression in eukaryotes

Biochemical Society Symposium ◽

10.1042/bss0730085 ◽

2006 ◽

Vol 73 ◽

pp. 85-96 ◽

Cited By ~ 8

Author(s):

Richard J. Reece ◽

Laila Beynon ◽

Stacey Holden ◽

Amanda D. Hughes ◽

Karine Rébora ◽

...

Keyword(s):

Gene Expression ◽

Rna Polymerase ◽

Rna Polymerase Ii ◽

Transcriptional Control ◽

Direct Interaction ◽

Signalling Pathways ◽

A Cell ◽

One Step ◽

Higher Eukaryotes ◽

Regulated Gene Expression

The recognition of changes in environmental conditions, and the ability to adapt to these changes, is essential for the viability of cells. There are numerous well characterized systems by which the presence or absence of an individual metabolite may be recognized by a cell. However, the recognition of a metabolite is just one step in a process that often results in changes in the expression of whole sets of genes required to respond to that metabolite. In higher eukaryotes, the signalling pathway between metabolite recognition and transcriptional control can be complex. Recent evidence from the relatively simple eukaryote yeast suggests that complex signalling pathways may be circumvented through the direct interaction between individual metabolites and regulators of RNA polymerase II-mediated transcription. Biochemical and structural analyses are beginning to unravel these elegant genetic control elements.

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