Product Development from Veneer-Mill Residues: An Application of the Taguchi's Method

The raw material used for decorative (face) veneer manufacturing consists mainly of hardwood logs, the highest in quality harvested for industrial purposes. Besides the common sawmill residuals, the clipping operation in the process produces quite long, strand-type vestiges, and large end-clipping cutoffs. During the course of the research project presented in this article, structural composite materials were designed and formulated using these clipping residues as principal furnish materials. A robust statistical product development technique, the Taguchi's method, helped to identify the effect of component factors on the expected mechanical properties of these novel products.Results of three-factor/three-level analyses indicated that there is a linear positive correlation between target density and performance attributes (MOE and MOR). Increasing the content of end-clippings up to 25% resulted in decline of strength and stiffness. However, when the ratio was over 1 to 4, this trend proved to be negligible. Resin solid content within the selected range had no significant control over the examined panel properties

The Effect of Spectral Resolution Upon the Accuracy of Brain Tumor Segmentation from Multi-Spectral MRI Data

Ensemble learning methods are frequently employed for brain tumor segmentation from multi-spectral MRI data. These techniques often require involving several hundreds of computed features for the characterization of the voxels, causing a rise in the necessary storage space by two order of magnitude. Processing such amounts of data also represents a serious computational burden. Under such circumstances it is useful to optimize the feature generation process. This paper proposes to establish the optimal spectral resolution of multispectral MRI data based feature values that allows for the best achievable brain tumor segmentation accuracy without causing unnecessary computational load and storage space waste. Experiments revealed that an 8-bit spectral resolution of the MRI-based feature data is sufficient to obtain the best possible accuracy of ensemble learning methods, while it allows for 50% reduction of the storage space required by the segmentation procedure, compared to the usually deployed featured encoding techniques

Brain Tumor Segmentation from MRI Data Using Ensemble Learning and Multi-Atlas

Atlases are frequently employed to assist medical image segmentation with prior information. This paper introduces a multi-atlas architecture that is trained to locally characterize the appearance (average intensity and standard deviation) of normal tissues in various observed and computed data channels of brain MRI records. The multiple atlas is then deployed to enhance the accuracy of an ensemble learning based brain tumor segmentation procedure that uses binary decision trees. The proposed method is validated using the low-grade tumor volumes of the BraTS 2016 train data set. The use of atlases improve the segmentation quality, causing a rise of up to 1.5% in average Dices scores

Brain Tumor Segmentation from Multi-Spectral MRI Data Using Cascaded Ensemble Learning

Ensemble learning methods are frequently employed in medical decision support. In image segmentation problems the ensemble based decisions require a postprocessing, because the ensemble cannot adequately handle the strong correlation of neighbor voxels. This paper proposes a brain tumor segmentation procedure based on an ensemble cascade. The first ensemble consisting of binary decision trees is trained to separate focal lesions from normal tissues based on four observed and 100 computed features. Starting from the intermediary labels provided by the first ensemble, six local features are computed for each voxel that serve as input for the second ensemble. The second ensemble is a classical random forest that enforces the correlation between neighbor pixels, regularizes the shape of the lesions. The segmentation accuracy is characterized by 85.5% overall Dice Score, 0.5% above previous solutions

Brain Tumor Segmentation from Multi-Spectral Magnetic Resonance Image Data Using an Ensemble Learning Approach

The automatic segmentation of medical images represents a research domain of high interest. This paper proposes an automatic procedure for the detection and segmentation of gliomas from multi-spectral MRI data. The procedure is based on a machine learning approach: it uses ensembles of binary decision trees trained to distinguish pixels belonging to gliomas to those that represent normal tissues. The classification employs 100 computed features beside the four observed ones, including morphological, gradients and Gabor wavelet features. The output of the decision ensemble is fed to morphological and structural post-processing, which regularize the shape of the detected tumors and improve the segmentation quality. The proposed procedure was evaluated using the BraTS 2015 train data, both the high-grade (HG) and the low-grade (LG) glioma records. The highest overall Dice scores achieved were 86.5% for HG and 84.6% for LG glioma volumes

Tissue specific requirement of Drosophila Rcd4 for centriole duplication and ciliogenesis.

Rcd4 is a poorly characterized Drosophila centriole component whose mammalian counterpart, PPP1R35, is suggested to function in centriole elongation and conversion to centrosomes. Here, we show that rcd4 mutants exhibit fewer centrioles, aberrant mitoses, and reduced basal bodies in sensory organs. Rcd4 interacts with the C-terminal part of Ana3, which loads onto the procentriole during interphase, ahead of Rcd4 and before mitosis. Accordingly, depletion of Ana3 prevents Rcd4 recruitment but not vice versa. We find that neither Ana3 nor Rcd4 participates directly in the mitotic conversion of centrioles to centrosomes, but both are required to load Ana1, which is essential for such conversion. Whereas ana3 mutants are male sterile, reflecting a requirement for Ana3 for centriole development in the male germ line, rcd4 mutants are fertile and have male germ line centrioles of normal length. Thus, Rcd4 is essential in somatic cells but is not absolutely required in spermatogenesis, indicating tissue-specific roles in centriole and basal body formation

Tissue specific requirement of Drosophila Rcd4 for centriole duplication and ciliogenesis

Rcd4 is a poorly characterized Drosophila centriole component whose mammalian counterpart, PPP1R35, is suggested to function in centriole elongation and conversion to centrosomes. Here, we show that rcd4 mutants exhibit fewer centrioles, aberrant mitoses, and reduced basal bodies in sensory organs. Rcd4 interacts with the C-terminal part of Ana3, which loads onto the procentriole during interphase, ahead of Rcd4 and before mitosis. Accordingly, depletion of Ana3 prevents Rcd4 recruitment but not vice versa. We find that neither Ana3 nor Rcd4 participates directly in the mitotic conversion of centrioles to centrosomes, but both are required to load Ana1, which is essential for such conversion. Whereas ana3 mutants are male sterile, reflecting a requirement for Ana3 for centriole development in the male germ line, rcd4 mutants are fertile and have male germ line centrioles of normal length. Thus, Rcd4 is essential in somatic cells but is not absolutely required in spermatogenesis, indicating tissue-specific roles in centriole and basal body formation