Fetal programming of schizophrenia: select mechanisms

Mounting evidence indicates that schizophrenia is associated with adverse intrauterine experiences. An adverse or suboptimal fetal environment can cause irreversible changes in brain that can subsequently exert long-lasting effects through resetting a diverse array of biological systems including endocrine, immune and nervous. It is evident from animal and imaging studies that subtle variations in the intrauterine environment can cause recognizable differences in brain structure and cognitive functions in the offspring. A wide variety of environmental factors may play a role in precipitating the emergent developmental dysregulation and the consequent evolution of psychiatric traits in early adulthood by inducing inflammatory, oxidative and nitrosative stress (IO&NS) pathways, mitochondrial dysfunction, apoptosis, and epigenetic dysregulation. However, the precise mechanisms behind such relationships and the specificity of the risk factors for schizophrenia remain exploratory. Considering the paucity of knowledge on fetal programming of schizophrenia, it is timely to consolidate the recent advances in the field and put forward an integrated overview of the mechanisms associated with fetal origin of schizophrenia.

Graphene oxide nanoparticles as a nonbleaching optical probe for two-photon luminescence imaging and cell therapy

Lasting glow: Under femtosecond laser irradiation, graphene oxide nanoparticles (GONs) give strong two-photon luminescence (TPL; see picture). The presence of GONs also induces microbubbling, which causes cell death at an order of magnitude lower laser power than when cells are not labeled. The results show that GONs can be used for TPL-based imaging and photothermal cancer therapy.

Intelligent animal fiber classification with artificial neural networks

Artificial neural networks (ANN) are increasingly used to solve many problems related to pattern recognition and object classification. In this paper, we report on a study using artificial neural networks to classify two kinds of animal fibers: merino and mohair. We have developed two different models, one extracting nine scale parameters with image processing, and the other using an unsupervised artificial neural network to extract features automatically, which are determined in accordance with the complexity of the scale structure and the accuracy of the model. Although the first model can achieve higher accuracy, it requires more effort for image processing and more prior knowledge, since the accuracy of the ANN largely depends on the parameters selected. The second model is more robust than the first, since only raw images are used. Because only ordinary optical images taken with a microscope are employed, we can use the approach for many textile applications without expensive equipment such as scanning electron microscopy.

Refractive index tomography of turbid media by bifocal optical coherence refractometry

We demonstrate tomographic imaging of the refractive index of turbid media using bifocal optical coherence refractometry (BOCR). The technique, which is a variant of optical coherence tomography, is based on the measurement of the optical pathlength difference between two foci simultaneously present in a medium of interest. We describe a new method to axially shift the bifocal optical pathlength that avoids the need to physically relocate the objective lens or the sample during an axial scan, and present an experimental realization based on an adaptive liquid-crystal lens. We present experimental results, including video clips, which demonstrate refractive index tomography of a range of turbid liquid phantoms, as well as of human skin in vivo.

Feature extraction for animal fiber identification

Fiber identification has been a very important task in many industries such as wool growing, textile processing, archaeology, histochernical engineering, and zoology. Over the years, animal fibers have been identified using physical and chemical approaches. Recently, objective identification of animal fibers has been developed based on the cuticular information of fibers. Effective and accurate extraction of representative features is essential to animal fiber identification and classification. In the current work, two different strategies are developed for this purpose. In the first method, explicit features are extracted using image processing. However, only implicit features are used in the second method with an unsupervised artificial neural network. It is found that the use of explicit features increases the accuracy of fiber identification but requires more effort on processing images and solid knowledge of what features are representative ones.

Identification and classification of animal fibres using artificial neural networks

It has been an important and challenging task to classify and evaluate the contents in wool blends. Quantitative characterisation of animal fibre scale patterns has attracted considerable attention, since it is the major evidence for identification and subsequent classification purpose. Although techniques such as imaging processing and linear demarcation functions have been used to identify unknown fibre type with some success, a more comprehensive approach is required to perform this task. In this paper, a new approach is presented, which employs non-linear demarcation functions by using an artificial neural network (ANN). Based on scale pattern features extracted by using image processing techniques the artificial neural network (ANN) model is to classify mohair and merino fibres. It is observed that the techniques developed in this work are very effective and have the potential to be applied to other animal fibres.

Predatory vision : 3D imaging and the transformation of screen-space

Despite Wheatstone’s academic interests in the device, the stereoscope languished somewhat as an optical toy. Yet the advent of 3D screen-spaces for home and mass entertainment suggests today’s consumers and practitioners of screen culture hold the view that screen culture will be ‘improved’ through 3D imaging technologies. Like cinema and photography, stereoscopic 3D imaging has the potential to transform visual culture. But what is transformed, as optics and electronic imaging techniques deliver Alice in Wonderland in 3D? This paper links the advent of 3D cinema and TV to the notion that vision is itself a ‘technology of the visual’. As such, our innate binocular stereoacuity is ripe for exploitation by developers of 3D imaging technologies. I argue that contemporary 3D imaging marks an epistemological visual-perceptual shift: toward screenspaces becoming spaces for potential action. Such a shift entails seeing as doing rather than seeing as thinking. 3D imaging exploits binocular vision’s spatial acuity (stereopsis), but is effective only for objects within near distal space. The 3D effect tapers off dramatically for objects only some metres away, because the two retinal images lack significant lateral disparity (difference) to trigger stereopsis: the imagery flattens out and becomes ‘monoscopic’. Information available from conventional 2D media entails a peculiarly unspecified spatiality. Perceptually, the contents of a conventional cinematic screen are like those of a painting: they are situated neither near nor far, and constitute a shared and ambiguous visual space. Our own eyes are like those of a cat: frontally placed for predatory action. The visuality of 3D screen-spaces assumes a perceptuality of the near-by and close at hand, since this is the structure of the visible information to which stereopsis is adapted to respond. Noting the binocular acuity of predatory animals, as well as some etymological links, this paper examines the implications of perceptually ‘capturing’ the sensation of visually solid objects in one’s immediate space. Stereopsis is about decisive action within an immediate environment: but it also presupposes the single viewpoint of an active observer toward which the 3D imagery is targeted.

Metrics of the normal cornea : anterior segment imaging with the Visante OCT

Purpose:  The purpose of the study was to obtain anterior segment biometry for 40 normal eyes and to measure variables that may be useful to design large diameter gas permeable contact lenses that sit outside the region normally viewed by corneal topographers. Also, the distribution of these variables in the normal eye and how well they correlated to each other were determined.

Methods:  This is a cross-sectional study, in which data were collected at a single study visit. Corneal topography and imaging of the anterior segment of the eye were performed using the Orbscan II and Visante OCT. The variables that were collected were horizontal K reading, central corneal/scleral sagittal depth at 15 mm chord, and nasal and temporal angles at the 15 mm chord using the built-in software measurement tools.

Results:  The central horizontal K readings for the 40 eyes were 43 ± 1.73 D (7.85 ± 0.31 mm), with ± 95% confidence interval (CI) of 38.7 (8.7 mm) and 46.6 D (7.24 mm). The mean corneal/scleral sagittal depth at the 15 mm chord was 3.74 ± 0.19 mm and the range was 3.14 to 4.04 mm. The average nasal angle (which was not different from the temporal angle) at the 15 mm chord was 39.32 ± 3.07 degrees and the ± 95%CI was 33.7 and 45.5 degrees. The correlation coefficient comparing the K reading and the corneal/scleral sagittal depth showed the best correlation (0.58, p < 0.001). The corneal/scleral sagittal depth at 15 mm correlated less with the nasal angle (0.44, p = 0.004) and the weakest correlation was for the nasal angle at 15 mm with the horizontal readings (0.32, p = 0.046).

Conclusion:  The Visante OCT is a valuable tool for imaging the anterior segment of the eye. The Visante OCT is especially effective in providing the biometry of the peripheral cornea and sclera and may help in fitting GP lenses with a higher percentage of initial lens success, when the corneal sag and lens sag are better matched.