Adaptation of the human visual system to the statistics of letters and line configurations.

By adulthood, literate humans have been exposed to millions of visual scenes and pages of text. Does the human visual system become attuned to the statistics of its inputs? Using functional magnetic resonance imaging, we examined whether the brain responses to line configurations are proportional to their natural-scene frequency. To further distinguish prior cortical competence from adaptation induced by learning to read, we manipulated whether the selected configurations formed letters and whether they were presented on the horizontal meridian, the familiar location where words usually appear, or on the vertical meridian. While no natural-scene frequency effect was observed, we observed letter-status and letter frequency effects on bilateral occipital activation, mainly for horizontal stimuli. The findings suggest a reorganization of the visual pathway resulting from reading acquisition under genetic and connectional constraints. Even early retinotopic areas showed a stronger response to letters than to rotated versions of the same shapes, suggesting an early visual tuning to large visual features such as letters.

Quantum adiabatic brachistochrone.

We formulate a time-optimal approach to adiabatic quantum computation (AQC). A corresponding natural Riemannian metric is also derived, through which AQC can be understood as the problem of finding a geodesic on the manifold of control parameters. This geometrization of AQC is demonstrated through two examples, where we show that it leads to improved performance of AQC, and sheds light on the roles of entanglement and curvature of the control manifold in algorithmic performance.

Neural correlates of antisaccade deficits in schizophrenia, an fMRI study.

Schizophrenia patients were known to have oculomotor abnormalities for decades and several studies had found antisaccade impairment to be a biological marker of schizophrenia. In this study, we used functional magnetic resonance imaging (fMRI) to investigate the neural circuits responsible for antisaccade deficits in schizophrenia. Ten normal controls and 10 DSM-IV schizophrenia patients performed antisaccade tasks and control tasks during fMRI. Data were analyzed and task-specific activations were identified using Statistical Parametric Mapping (SPM-2). In normal subjects, antisaccade tasks activated bilateral frontal eye fields, supplementary eye fields, inferior frontal gyrus, superior parietal lobules, inferior parietal lobules, occipital visual cortex, cerebellum, thalamus, and lentiform nuclei (P<0.001). By contrast, schizophrenia patients failed to show activation in bilateral lentiform nucleus, bilateral thalamus, and left inferior frontal gyrus during antisaccade performance. Our findings suggest that schizophrenic antisaccade deficits are associated with dysfunction of fronto-striatal-thalamo-cortical circuits previously demonstrated to be responsible for suppression of the reflexive saccade. Left inferior frontal gyrus, which was known to be responsible for response inhibition on "go/no-go" testing, also plays an important role in schizophrenic antisaccade deficit.

A left-lateralized network for reading Chinese words: a 3 T fMRI study.

fMRI was used to investigate brain organization for reading in Chinese. Subjects were shown two-character Chinese words. A control task was used to eliminate the non-linguistic visual and motor confounds. Results show that naming of Chinese logographs is characterized by left-lateralized neuronal networks for the processing of orthographic, phonological, and semantic attributes. The orchestration of the middle frontal cortex, superior temporal cortex, superior parietal cortex, basal temporal area and extrastriate cortices of the left hemisphere may manifest the particularity of the central representation of simple word naming in Chinese.

Data mining with decision trees for diagnosis of breast tumor in medical ultrasonic images.

To increase the ability of ultrasonographic (US) technology for the differential diagnosis of solid breast tumors, we describe a novel computer-aided diagnosis (CADx) system using data mining with decision tree for classification of breast tumor to increase the levels of diagnostic confidence and to provide the immediate second opinion for physicians. Cooperating with the texture information extracted from the region of interest (ROI) image, a decision tree model generated from the training data in a top-down, general-to-specific direction with 24 co-variance texture features is used to classify the tumors as benign or malignant. In the experiments, accuracy rates for a experienced physician and the proposed CADx are 86.67% (78/90) and 95.50% (86/90), respectively.

Computer-aided diagnosis for surgical office-based breast ultrasound.

HYPOTHESIS: The computer-aided diagnostic system is an intelligent system with great potential for categorizing solid breast nodules. It can be used conveniently for surgical office-based digital ultrasonography (US) of the breast. DESIGN: Retrospective, nonrandomized study. SETTING: University teaching hospital. PATIENTS: We retrospectively reviewed 243 medical records of digital US images of the breast of pathologically proved, benign breast tumors from 161 patients (ie, 136 fibroadenomas and 25 fibrocystic nodules), and carcinomas from 82 patients (ie, 73 invasive duct carcinomas, 5 invasive lobular carcinomas, and 4 intraductal carcinomas). The digital US images were consecutively recorded from January 1, 1997, to December 31, 1998. INTERVENTION: The physician selected the region of interest on the digital US image. Then a learning vector quantization model with 24 autocorrelation texture features is used to classify the tumor as benign or malignant. In the experiment, 153 cases were arbitrarily selected to be the training set of the learning vector quantization model and 90 cases were selected to evaluate the performance. One experienced radiologist who was completely blind to these cases was asked to classify these tumors in the test set. MAIN OUTCOME MEASURE: Contribution of breast US to diagnosis. RESULTS: The performance comparison results illustrated the following: accuracy, 90%; sensitivity, 96.67%; specificity, 86.67%; positive predictive value, 78.38%; and negative predictive value, 98.11% for the computer-aided diagnostic (CAD) system and accuracy, 86.67%; sensitivity, 86.67%; specificity, 86.67%; positive predictive value, 76.47%; and negative predictive value, 92.86% for the radiologist. CONCLUSION: The proposed CAD system provides an immediate second opinion. An accurate preoperative diagnosis can be routinely established for surgical office-based digital US of the breast. The diagnostic rate was even better than the results of an experienced radiologist. The high negative predictive rate by the CAD system can avert benign biopsies. It can be easily implemented on existing commercial diagnostic digital US machines. For most available diagnostic digital US machines, all that would be required for the CAD system is only a personal computer loaded with CAD software.

Adaptive predictive multiplicative autoregressive model for medical image compression.

In this paper, an adaptive predictive multiplicative autoregressive (APMAR) method is proposed for lossless medical image coding. The adaptive predictor is used for improving the prediction accuracy of encoded image blocks in our proposed method. Each block is first adaptively predicted by one of the seven predictors of the JPEG lossless mode and a local mean predictor. It is clear that the prediction accuracy of an adaptive predictor is better than that of a fixed predictor. Then the residual values are processed by the MAR model with Huffman coding. Comparisons with other methods [MAR, SMAR, adaptive JPEG (AJPEG)] on a series of test images show that our method is suitable for reversible medical image compression.

INP51, a yeast inositol polyphosphate 5-phosphatase required for phosphatidylinositol 4,5-bisphosphate homeostasis and whose absence confers a cold-resistant phenotype.

Sequence analysis of Saccharomyces cerevisiae chromosome IX identified a 946 amino acid open reading frame (YIL002C), designated here as INP51, that has carboxyl- and amino-terminal regions similar to mammalian inositol polyphosphate 5-phosphatases and to yeast SAC1. This two-domain primary structure resembles the mammalian 5-phosphatase, synaptojanin. We report that Inp51p is associated with a particulate fraction and that recombinant Inp51p exhibits intrinsic phosphatidylinositol 4,5-bisphosphate 5-phosphatase activity. Deletion of INP51 (inp51) results in a "cold-tolerant" phenotype, enabling significantly faster growth at temperatures below 15 degreesC as compared with a parental strain. Complementation analysis of an inp51 mutant strain demonstrates that the cold tolerance is strictly due to loss of 5-phosphatase catalytic activity. Furthermore, deletion of PLC1 in an inp51 mutant does not abrogate cold tolerance, indicating that Plc1p-mediated production of soluble inositol phosphates is not required. Cells lacking INP51 have a 2-4-fold increase in levels of phosphatidylinositol 4,5-bisphosphate and inositol 1,4, 5-trisphosphate, whereas cells overexpressing Inp51p exhibit a 35% decrease in levels of phosphatidylinositol 4,5-bisphosphate. We conclude that INP51 function is critical for proper phosphatidylinositol 4,5-bisphosphate homeostasis. In addition, we define a novel role for a 5-phosphatase loss of function mutant that improves the growth of cells at colder temperatures without alteration of growth at normal temperatures, which may have useful commercial applications.