Ta-Te Lin

Cryopreservation of late embryos and early larvae in the oyster and hard clam.

Cryopreservation of shellfish embryos and larvae may facilitate aquaculture management and stock enhancement programs. Late embryos and early larvae of oysters (Crassostrea gigas) and hard clams (Meretrix lusoria) were selected to establish the Cryopreservation protocols. Survival rates ranging from 62.3 to 75.1% were obtained in oysters using a stepwise freezing protocol. Late embryos or early larvae of oysters (4 h at 28 °C after artificial fertilization) were equilibrated in 2 M dimethyl sulfoxide (DMSO) + 0.06 M trehalose plus sea water for 10 min at 27 °C and were then cooled at −1 °C/min from 0 °C to −12 °C. Straws containing more than 1000 embryos were held at −12 °C for 10–15 min allowing equilibration after seeding. Embryos/larvae were then slowly cooled at −2 °C/min to −35 °C and allowed 10–20 min for equilibration before quenching in liquid nitrogen. After rapid thawing in a water bath at 28 °C, they were placed in sea water to remove DMSO. Besides an increase in survival rate, embryos/larvae that survived exhibited rotary motion immediately following thawing. For hard clam embryos/larvae with the cryoprotectants 2 M DMSO + 0.06 M glucose, survival rates ranging from 73.3 to 84.2% were achieved using a similar freezing protocol. In a further simplified protocol without seeding, embryos/larvae were brought rapidly from room temperature to 0 °C and then to −7 °C. After holding at −7 °C for 3 minutes, a slow freezing rate of −0.3 °C/min was chosen until −35 °C was reached. Five minutes later, they were quenched in liquid nitrogen. Vitrification freezing of 8 h oyster larvae along a modified Drosophila protocol resulted in an average survival rate of 14.7%.

LEAF SHAPE MODELING AND ANALYSIS USING GEOMETRIC DESCRIPTORS DERIVED FROM BEZIER CURVES

Algorithms were developed to extract the leaf boundary of selected vegetable seedlings. The leaf boundary was fitted with Bezier curves, and geometric descriptors of the leaf shape were then derived. In the boundary extraction phase, a color image of a seedling leaf was first obtained and segmented into binary images. The centroid of each leaf was found, and the boundary signature was determined as a function of radial angle. Points at the leaf apex and leaf base were then located from the curvature of boundary points. These points were used as the initial control points of the Bezier curves to fit the leaf boundary. Given a mathematical representation of the leaf boundary (i.e., the Bezier function) and the coordinates of the normalized control points, the shape of a leaf may be quantified and reconstructed. Leaf features, including apex angle, base angle, control line ratios, and fitting error, were subsequently derived from the fitted Bezier curves. These features are independent of size and orientation. The efficacy of using Bezier curves to model leaf boundary and derivation of leaf features was examined by comparing the actual leaf area and the modeled leaf area. Experiments were also performed to demonstrate the use of derived leaf features for plant identification. A classification rate of 95.1% was achieved in classifying four varieties of vegetable seedlings using a back–propagation neural network. The descriptors derived from Bezier curves provide a means for leaf shape description that may be useful for plant identification and growth measurement. Leaf shape modeled by simple Bezier curves also contributes a significant data reduction, compared with that using discrete boundary points, while preserving reasonable accuracy.

Osmometric behavior of Drosophila melanogaster embryos

The osmometric behavior of Drosophila melanogaster embryos in permeabilized eggs was studied in a microscope diffusion chamber designed to impose a rapid change in osmotic environment at various temperatures. A numerical model of NaCl diffusion in the chamber predicted that radial variations in concentration arising from the presence of a thin film of solution at the top of the chamber were negligible. On the basis of transient electrical conductance measurements in the chamber, characteristic time constants for the change in concentration averaged over the chamber depth occupied by the eggs were 0.99, 0.77, and 0.60 min at 0, 10, and 20 degrees C, respectively. The chamber response was sufficiently rapid that the characteristic response of the embryo was not masked. Equilibrium volumetric behavior of the embryos indicated that they behaved as nearly ideal osmometers over the range of 0.256 to 2.000 osm, and followed the relation FVeq = 0.123C-1 + 0.541, where FVeq is equilibrium fractional volume and C is osmolality. Nonlinear regression of volumetric data during osmotic contraction yielded an average Lp of 0.722 micron/(min.atm) at 20 degrees C and an apparent activation energy delta E of 8.11 kcal/mol. The coefficients of variation in the Lp estimates among individual embryos were 38, 18, and 47% at 0, 10, and 20 degrees C, respectively. With the use of probability rules and a model for volumetric behavior during freezing, it was determined that the observed variability in Lp (assuming delta E is fixed) considerably broadens the transition range of cooling rates over which the predicted probability of intracellular ice formation goes from 0 to 1. However, experimental observations (21) show the actual transition range is even wider, indicating that there exist other important sources of variability which determine the event of ice formation in D. melanogaster embryos.

An infrared range camera-based approach for three-dimensional locomotion tracking and pose reconstruction in a rodent.

We herein introduce an automated three-dimensional (3D) locomotion tracking and pose reconstruction system for rodents with superior robustness, rapidity, reliability, resolution, simplicity, and cost. An off-the-shelf composite infrared (IR) range camera was adopted to grab high-resolution depth images (640×480×2048 pixels at 20Hz) in our system for automated behavior analysis. For the inherent 3D structure of the depth images, we developed a compact algorithm to reconstruct the locomotion and body behavior with superior temporal and solid spatial resolution. Since the range camera operates in the IR spectrum, interference from the visible light spectrum did not affect the tracking performance. The accuracy of our system was 98.1±3.2%. We also validated the system, which yielded strong correlation with automated and manual tracking. Meanwhile, the system replicates a detailed dynamic rat model in virtual space, which demonstrates the movements of the extremities of the body and locomotion in detail on varied terrain.

A novel STFT-ranking feature of multi-channel EMG for motion pattern recognition

STFT-ranking feature is efficient for multi-channel EMG signal analysis.STFT-ranking feature can characterize relationships between multi-channel signals.Recognition accuracy over 90% was achieved applying the STFT-ranking feature.The performance of STFT-ranking feature is superior to conventional features.STFT-ranking feature can be applied to other multi-channel signals applications. Electromyography (EMG) is widely applied for neural engineering. For motion pattern recognition, many features of multi-channel EMG signals were investigated, but the relationships between muscles were not considered. In this study, a novel STFT-ranking feature based on short-time Fourier transform (STFT) is proposed. The novelty of STFT-ranking features is considering and covering the relationship information between EMG signals and multiple muscles in a motion pattern. With an exoskeleton robot arm, two series of motion patterns corresponding to the shoulder and elbow in the sagittal plane were investigated. EMG signals from six muscles were acquired in arm motion patterns when participants worn the robot arm. Four types of feature combinations, including seven conventional features, were compared with the STFT-ranking feature. The principal component analysis (PCA) and support vector machine (SVM) were used to build the motion recognition model. With the STFT-ranking feature, the recognition performance (93.9%) is superior to the conventional features (33.3-90.8%). The recognition variation is smaller (SD=4.3%) than the other features tested (SD=5.9-13.8%). These achievements will contribute to the advancement of control method of exoskeleton robots or power orthoses based on multi-channel EMG signals in the future. Based on the principle of STFT-ranking feature, the method also has potential for other multi-channel signal applications, such as electroencephalography (EEG) signal processing, speech recognition, and acoustic analysis.

Robust ellipse detection based on hierarchical image pyramid and Hough transform.

In this research we propose a fast and robust ellipse detection algorithm based on a multipass Hough transform and an image pyramid data structure. The algorithm starts with an exhaustive search on a low-resolution image in the image pyramid using elliptical Hough transform. Then the image resolution is iteratively increased while the candidate ellipses with higher resolution are updated at each step until the original image resolution is reached. After removing the detected ellipses, the Hough transform is repeatedly applied in multiple passes to search for remaining ellipses, and terminates when no more ellipses are found. This approach significantly reduces the false positive error of ellipse detection as compared with the conventional randomized Hough transform method. The analysis shows that the computing complexity of this algorithm is Θ(n(5/2)), and thus the computation time and memory requirement are significantly reduced. The developed algorithm was tested with images containing various numbers of ellipses. The effects of noise-to-signal ratio combined with various ellipse sizes on the detection accuracy were analyzed and discussed. Experimental results revealed that the algorithm is robust to noise. The average detection accuracies were all above 90% for images with less than seven ellipses, and slightly decreased to about 80% for images with more ellipses. The average false positive error was less than 2%.

NON-DESTRUCTIVE GROWTH MEASUREMENT OF SELECTED VEGETABLE SEEDLINGS USING ORTHOGONAL IMAGES

A non-destructive measurement method of plant features using image processing technique provides a means to analyze the continuous growth process of a plant. In a previous study, we developed a non-destructive measurement method by using elliptical Hough transform to search for seedling leaves from a top-view image. To improve the accuracy of the leaf number estimation and leaf area measurement, this study further incorporates two side-view images with a top-view image to extract and reconstruct the three-dimensional structure of selected vegetable seedlings, and thus to measure the features related to plant growth. By registering correspondences between the three orthogonal images, the position, orientation, and dimensions of leaves in a seedling can be more exactly derived than from a single top-view image alone. The leaf skeletons traced in the side-view images can be used to correct the projected leaf area in the top-view image for better estimation of leaf area. Experiments were performed to test the performance of the measurement system by comparing the non-destructively estimated leaf area and the actual leaf area of selected vegetable seedlings. The average relative errors of total leaf estimation for cabbage and broccoli seedlings were 14.5% and 13.1%, respectively, using a single top-view image. By incorporating orthogonal images, the relative errors were significantly reduced to 1.6% and 4.9%, respectively. Using this non-destructive measurement system, the continuous growth of vegetable seedlings can be effectively measured.

Subfreezing volumetric behavior and stochastic modeling of intracellular ice formation in Drosophila melanogaster embryos

Cryomicroscopic observations were made of the volumetric behavior and kinetics of intracellular ice formation (IIF) in Drosophila melanogaster embryos in a modified cell culture medium (BD.20) or BD.20 + 2 M ethylene glycol. After rapid cooling to a given temperature, transient volumetric contraction of the embryos during the isothermal period was quantified by computerized video image analysis. Fitting these data to the numerical solution of the volume flux equation yielded estimates of the hydraulic permeability coefficient (Lp) for individual embryos at various subfreezing temperatures. Lp approximately followed an Arrhenius relation between -2 and -9 degrees C, with a value of 0.168 microns/(min-atm) extrapolated to 0 degrees C and an apparent activation energy delta E of 38.9 kcal/mol. IIF during an isothermal period occurred at random times whose characteristic temperature range and kinetics were affected by the presence of ethylene glycol. A stochastic process model developed to fit these data indicated the influence of both time-dependent and instantaneous components of IIF, presumed to be the result of seeding and heterogeneous nucleation, respectively. The presence of 2 M ethylene glycol depressed the characteristic temperature of instantaneous IIF by about 12 degrees C and reduced the rate constant for time-dependent IIF. Comparison with observed incidences of IIF yielded an estimate of the supercooling tolerance of 3 to 5 degrees C.

LEAF AREA MEASUREMENT OF SELECTED VEGETABLE SEEDLINGS USING ELLIPTICAL HOUGH TRANSFORM

An image–processing algorithm using the elliptical Hough transform was developed to determine position, orientation, and leaf area of seedling leaves from top–view images. The algorithm significantly reduced the computational effort and memory requirement and was capable of identifying partly occluded leaves. Four varieties of vegetable seedlings, namely cabbage, Chinese mustard, edible amaranth (A. mangostanus Linn.), and edible amaranth (A. inamoenus Willd.), at various growth stages were used to test the efficacy of the measurement algorithm. For measurements of individual leaf area, the relative estimation errors were 8.2 µ9.3%, 14.4 µ11.7%, 27.5 µ14.4%, and 16.0 µ11.0%, respectively. For measurements of total leaf area, the relative estimation errors were 16.0 µ6.4%, 17.0 µ9.8%, 24.9 µ8.1%, and 18.4 µ9.4% in corresponding order. The sources of error were mainly due to tilting leaves and unsuccessful identification of small or severely occluded leaves of the seedling.

Strawberry foliar anthracnose assessment by hyperspectral imaging

Hyperspectral imaging has proven to be an effective non-destructive method for assessing strawberry foliar anthracnose.The incubation stage, in which symptoms are not yet visible, can be distinguished.Several hyperspectral imaging analysis methods were investigated using 3 duplicate sets of experiments.Significant wavelengths for strawberry foliar anthracnose are 551, 706, 750 and 914nm. Hyperspectral imaging provides comprehensive spectral and spatial information about observed objects. This technology has been applied to many fields, such as geology, mining, surveillance and agriculture. Strawberry qualities have been examined using hyperspectral imaging in several studies. However, none of the previous literature presented a non-destructive method for diagnosing the infection stages of anthracnose, a devastating disease for strawberries. This study examined strawberry foliar anthracnose using three different hyperspectral imaging analyzing methods: spectral angle mapper (SAM), stepwise discriminant analysis (SDA) and self-developed correlation measure (CM). Three different infection stages, including healthy, incubation and symptomatic stages, were investigated using these methods. The incubation stage is a stage at which the symptoms are still not yet visible. The three infection stage classification results were promising, with a classification accuracy of approximately 80%. For two infection stage classification (healthy and symptomatic stages), an average accuracy of high 80% was attained. In fact, an average accuracy of 93% was achieved by SDA for two-stage classification. This study not only proves the feasibility of hyperspectral imaging for diagnosing strawberry foliar anthracnose infection, but also identifies a smaller set of significant wavelengths at which similar classification performance was accomplished. For significant wavelength selection, partial least squares (PLS) regression is an standard wavelength selection method and it was applied to be compared with SDA and CM. Wavelengths of 551, 706, 750 and 914nm formed the multispectral imaging observing bands that showed an accuracy of 80% when classifying the three infection stages. Therefore, using either hyperspectral or multispectral imaging to detect anthracnose infected foliar areas is more practical and efficient than classic destructive methods. In particular, early detection (the incubation stage), something that cannot be seen via naked eyes, reaches 80% classification accuracy with both SDA and CM. Strawberry farmers could profit greatly from this technology.