Sub-cellular trafficking of phytochemicals explored using auto-fluorescent compounds in maize cells

BACKGROUND: Little is known regarding the trafficking mechanisms of small molecules within plant cells. It remains to be established whether phytochemicals are transported by pathways similar to those used by proteins, or whether the expansion of metabolic pathways in plants was associated with the evolution of novel trafficking pathways. In this paper, we exploited the induction of green and yellow auto-fluorescent compounds in maize cultured cells by the P1 transcription factor to investigate their targeting to the cell wall and vacuole, respectively. RESULTS: We investigated the accumulation and sub-cellular localization of the green and yellow auto-fluorescent compounds in maize BMS cells expressing the P1 transcription factor from an estradiol inducible promoter. We established that the yellow fluorescent compounds accumulate inside the vacuole in YFBs that resemble AVIs. The green fluorescent compounds accumulate initially in the cytoplasm in large spherical GFBs. Cells accumulating GFBs also contain electron-dense structures that accumulate initially in the ER and which later appear to fuse with the plasma membrane. Structures resembling the GFBs were also observed in the periplasmic space of plasmolized cells. Ultimately, the green fluorescence accumulates in the cell wall, in a process that is insensitive to the Golgi-disturbing agents BFA and monensin. CONCLUSIONS: Our results suggest the presence of at least two distinct trafficking pathways, one to the cell wall and the other to the vacuole, for different auto-fluorescent compounds induced by the same transcription factor in maize BMS cells. These compartments represent two of the major sites of accumulation of phenolic compounds characteristic of maize cells. The secretion of the green auto-fluorescent compounds occurs by a pathway that does not involve the TGN, suggesting that it is different from the secretion of most proteins, polysaccharides or epicuticular waxes. The yellow auto-fluorescent compounds accumulate in a vacuolar compartment, in structures that resemble the AVIs present in many cells accumulating anthocyanins. Together, our studies suggest that the accumulation of auto-fluorescent compounds can provide a powerful tool to dissect the trafficking of phytochemicals, knowledge necessary for the efficient engineering of plant metabolism

LABEL: Pediatric brain extraction using learning-based meta-algorithm

Magnetic resonance imaging of pediatric brain provides valuable information for early brain development studies. Automated brain extraction is challenging due to the small brain size and dynamic change of tissue contrast in the developing brains. In this paper, we propose a novel Learning Algorithm for Brain Extraction and Labeling (LABEL) specially for the pediatric MR brain images. The idea is to perform multiple complementary brain extractions on a given testing image by using a meta-algorithm, including BET and BSE, where the parameters of each run of the meta-algorithm are effectively learned from the training data. Also, the representative subjects are selected as exemplars and used to guide brain extraction of new subjects in different age groups. We further develop a level-set based fusion method to combine multiple brain extractions together with a closed smooth surface for obtaining the final extraction. The proposed method has been extensively evaluated in subjects of three representative age groups, such as neonate (less than 2 months), infant (1–2 years), and child (5–18 years). Experimental results show that, with 45 subjects for training (15 neonates, 15 infant, and 15 children), the proposed method can produce more accurate brain extraction results on 246 testing subjects (75 neonates, 126 infants, and 45 children), i.e., at average Jaccard Index of 0.953, compared to those by BET (0.918), BSE (0.902), ROBEX (0.901), GCUT (0.856), and other fusion methods such as Majority Voting (0.919) and STAPLE (0.941). Along with the largely-improved computational efficiency, the proposed method demonstrates its ability of automated brain extraction for pediatric MR images in a large age range

Automated detection of MRI-negative temporal lobe epilepsy with ROI-based morphometric features and machine learning

Objective: Temporal lobe epilepsy (TLE) predominantly originates from the anteromedial basal region of the temporal lobe, and its prognosis is generally favorable following surgical intervention. However, TLE often appears negative in magnetic resonance imaging (MRI), making it difficult to quantitatively diagnose the condition solely based on clinical symptoms. There is a pressing need for a quantitative, automated method for detecting TLE.Methods: This study employed MRI scans and clinical data from 51 retrospective epilepsy cases, dividing them into two groups: 34 patients in TLE group and 17 patients in non-TLE group. The criteria for defining the TLE group were successful surgical removal of the epileptogenic zone in the temporal lobe and a favorable postoperative prognosis. A standard procedure was used for normalization, brain extraction, tissue segmentation, regional brain partitioning, and cortical reconstruction of T1 structural MRI images. Morphometric features such as gray matter volume, cortical thickness, and surface area were extracted from a total of 20 temporal lobe regions in both hemispheres. Support vector machine (SVM), extreme learning machine (ELM), and cmcRVFL+ classifiers were employed for model training and validated using 10-fold cross-validation.Results: The results demonstrated that employing ELM classifiers in conjunction with specific temporal lobe gray matter volume features led to a better identification of TLE. The classification accuracy was 92.79%, with an area under the curve (AUC) value of 0.8019.Conclusion: The method proposed in this study can significantly assist in the preoperative identification of TLE patients. By employing this method, TLE can be included in surgical criteria, which could alleviate patient symptoms and improve prognosis, thereby bearing substantial clinical significance

eConnectome: A MATLAB toolbox for mapping and imaging of brain functional connectivity

We have developed a MATLAB-based toolbox, eConnectome (electrophysiological connectome), for mapping and imaging functional connectivity at both the scalp and cortical levels from the electroencephalogram (EEG), as well as from the electrocorticogram (ECoG). Graphical user interfaces were designed for interactive and intuitive use of the toolbox. Major functions of eConnectome include EEG/ECoG preprocessing, scalp spatial mapping, cortical source estimation, connectivity analysis, and visualization. Granger causality measures such as directed transfer function and adaptive directed transfer function were implemented to estimate the directional interactions of brain functional networks, over the scalp and cortical sensor spaces. Cortical current density inverse imaging was implemented using a generic realistic geometry brain-head model from scalp EEGs. Granger causality could be further estimated over the cortical source domain from the inversely reconstructed cortical source signals as derived from the scalp EEG. Users may implement other connectivity estimators in the framework of eConnectome for various applications. The toolbox package is open-source and freely available at http://econnectome.umn.edu under the GNU general public license for noncommercial and academic uses. (C) 2010 Elsevier B.V. All rights reserved