Identification of cholinoceptive glycinergic neurons in the mammalian retina

The light-evoked release of acetylcholine (ACh) affects the responses of many retinal ganglion cells, in part via nicotinic acetylcholine receptors (nAChRs). nAChRs that contain beta2alpha3 neuronal nicotinic acetylcholine receptors have been identified and localized in the rabbit retina; these nAChRs are recognized by the monoclonal antibody mAb210. We have examined the expression of beta2alpha3 nAChRs by glycinergic amacrine cells in the rabbit retina and have identified different subpopulations of nicotinic cholinoceptive glycinergic cells using double and triple immunohistochemistry with quantitative analysis. Here we demonstrate that about 70% of the cholinoceptive amacrine cells in rabbit retina are glycinergic cells. At least three nonoverlapping subpopulations of mAb210 glycine-immunoreactive cells can be distinguished with antibodies against calretinin, calbindin, and gamma-aminobutyric acid (GABA)(A) receptors. The cholinergic cells in rabbit retina are thought to synapse only on other cholinergic cells and ganglion cells. Thus, the expression of beta2alpha3 nAChRs on diverse populations of glycinergic cells is puzzling. To explore this finding, the subcellular localization of beta2alpha3 was studied at the electron microscopic level. mAb210 immunoreactivity was localized on the dendrites of amacrines and ganglion cells throughout the inner plexiform layer, and much of the labeling was not associated with recognizable synapses. Thus, our findings indicate that ACh in the mammalian retina may modulate glycinergic circuits via extrasynaptic beta2alpha3 nAChRs. (C) 2002 Wiley-Liss, Inc.

Identification and characterization of pptA: a gene involved in the phase-variable expression of phosphorylcholine on pili of Neisseria meningitidis

Pili of pathogenic Neisseria are major virulence factors associated with adhesion, cytotoxicity, twitching motility, autoaggregation, and DNA transformation. Pili are modified posttranslationally by the addition of phosphorylcholine. However, no genes involved in either the biosynthesis or the transfer of phosphorylcholine in Neisseria meningitidis have been identified. In this study, we identified five candidate open reading frames (ORFs) potentially involved in the biosynthesis or transfer of phosphorylcholine to pilin in N. meningitidis. Insertional mutants were constructed for each ORF in N. meningitidis strain C311#3 to determine their effect on phosphorylcholine expression. The effect of the mutant ORFs on the modification by phosphorylcholine was analyzed by Western analysis with phosphorylcholine-specific monoclonal antibody TEPC-15. Analysis of the mutants showed that ORF NMB0415, now defined as pptA (pilin phosphorylcholine transferase A), is involved in the addition of phosphorylcholine to pilin in N. meningitidis. Additionally, the phase variation (high frequency on-off switching of expression) of phosphorylcholine on pilin is due to changes in a homopolymeric guanosine tract in pptA.

Automated traffic route identification through the shared nearest neighbour algorithm

Many organisations need to extract useful information from huge amounts of movement data. One example is found in maritime transportation, where the automated identification of a diverse range of traffic routes is a key management issue for improving the maintenance of ports and ocean routes, and accelerating ship traffic. This paper addresses, in a first stage, the research challenge of developing an approach for the automated identification of traffic routes based on clustering motion vectors rather than reconstructed trajectories. The immediate benefit of the proposed approach is to avoid the reconstruction of trajectories in terms of their geometric shape of the path, their position in space, their life span, and changes of speed, direction and other attributes over time. For clustering the moving objects, an adapted version of the Shared Nearest Neighbour algorithm is used. The motion vectors, with a position and a direction, are analysed in order to identify clusters of vectors that are moving towards the same direction. These clusters represent traffic routes and the preliminary results have shown to be promising for the automated identification of traffic routes with different shapes and densities, as well as for handling noise data.

Automatic prebent customized prosthesis for pectus excavatum minimally invasive surgery correction

Pectus excavatum is the most common deformity of the thorax. A minimally invasive surgical correction is commonly carried out to remodel the anterior chest wall by using an intrathoracic convex prosthesis in the substernal position. The process of prosthesis modeling and bending still remains an area of improvement. The authors developed a new system, i3DExcavatum, which can automatically model and bend the bar preoperatively based on a thoracic CT scan. This article presents a comparison between automatic and manual bending. The i3DExcavatum was used to personalize prostheses for 41 patients who underwent pectus excavatum surgical correction between 2007 and 2012. Regarding the anatomical variations, the soft-tissue thicknesses external to the ribs show that both symmetric and asymmetric patients always have asymmetric variations, by comparing the patients’ sides. It highlighted that the prosthesis bar should be modeled according to each patient’s rib positions and dimensions. The average differences between the skin and costal line curvature lengths were 84 ± 4 mm and 96 ± 11 mm, for male and female patients, respectively. On the other hand, the i3DExcavatum ensured a smooth curvature of the surgical prosthesis and was capable of predicting and simulating a virtual shape and size of the bar for asymmetric and symmetric patients. In conclusion, the i3DExcavatum allows preoperative personalization according to the thoracic morphology of each patient. It reduces surgery time and minimizes the margin error introduced by the manually bent bar, which only uses a template that copies the chest wall curvature.

Component identification through program slicing

This paper reports on the development of specific slicing techniques for functional programs and their use for the identification of possible coherent components from monolithic code. An associated tool is also introduced. This piece of research is part of a broader project on program understanding and re-engineering of legacy code supported by formal methods

Automatic segmentation and 3D feature extraction of protein aggregates in Caenorhabditis Elegans

In the last years, it has become increasingly clear that neurodegenerative diseases involve protein aggregation, a process often used as disease progression readout and to develop therapeutic strategies. This work presents an image processing tool to automatic segment, classify and quantify these aggregates and the whole 3D body of the nematode Caenorhabditis Elegans. A total of 150 data set images, containing different slices, were captured with a confocal microscope from animals of distinct genetic conditions. Because of the animals’ transparency, most of the slices pixels appeared dark, hampering their body volume direct reconstruction. Therefore, for each data set, all slices were stacked in one single 2D image in order to determine a volume approximation. The gradient of this image was input to an anisotropic diffusion algorithm that uses the Tukey’s biweight as edge-stopping function. The image histogram median of this outcome was used to dynamically determine a thresholding level, which allows the determination of a smoothed exterior contour of the worm and the medial axis of the worm body from thinning its skeleton. Based on this exterior contour diameter and the medial animal axis, random 3D points were then calculated to produce a volume mesh approximation. The protein aggregations were subsequently segmented based on an iso-value and blended with the resulting volume mesh. The results obtained were consistent with qualitative observations in literature, allowing non-biased, reliable and high throughput protein aggregates quantification. This may lead to a significant improvement on neurodegenerative diseases treatment planning and interventions prevention

Automatic modeling of an orthotic bracing for nonoperative correction of PectusCarinatum

Pectus Carinatum is a deformity of the chest wall, characterized by an anterior protrusion of the sternum, often corrected surgically due to cosmetic motivation. This work presents an alternative approach to the current open surgery option, proposing a novel technique based on a personalized orthosis. Two different processes for the orthosis’ personalization are presented. One based on a 3D laser scan of the patient chest, followed by the reconstruction of the thoracic wall mesh using a radial basis function, and a second one, based on a computer tomography scan followed by a neighbouring cells algorithm. The axial position where the orthosis is to be located is automatically calculated using a Ray-Triangle intersection method, whose outcome is input to a pseudo Kochenek interpolating spline method to define the orthosis curvature. Results show that no significant differences exist between the patient chest physiognomy and the curvature angle and size of the orthosis, allowing a better cosmetic outcome and less initial discomfort

Automatic prebent customized prosthesis for pectus excavatum minimally invasive surgery correction

Pectus excavatum is the most common deformity of the thorax. A minimally invasive surgical correction is commonly carried out to remodel the anterior chest wall by using an intrathoracic convex prosthesis in the substernal position. The process of prosthesis modeling and bending still remains an area of improvement. The authors developed a new system, i3DExcavatum, which can automatically model and bend the bar preoperatively based on a thoracic CT scan. This article presents a comparison between automatic and manual bending. The i3DExcavatum was used to personalize prostheses for 41 patients who underwent pectus excavatum surgical correction between 2007 and 2012. Regarding the anatomical variations, the soft-tissue thicknesses external to the ribs show that both symmetric and asymmetric patients always have asymmetric variations, by comparing the patients’ sides. It highlighted that the prosthesis bar should be modeled according to each patient’s rib positions and dimensions. The average differences between the skin and costal line curvature lengths were 84 ± 4 mm and 96 ± 11 mm, for male and female patients, respectively. On the other hand, the i3DExcavatum ensured a smooth curvature of the surgical prosthesis and was capable of predicting and simulating a virtual shape and size of the bar for asymmetric and symmetric patients. In conclusion, the i3DExcavatum allows preoperative personalization according to the thoracic morphology of each patient. It reduces surgery time and minimizes the margin error introduced by the manually bent bar, which only uses a template that copies the chest wall curvature.

Automatic modeling of pectus excavatum corrective prosthesis using artificial neural networks

Pectus excavatum is the most common deformity of the thorax. Pre-operative diagnosis usually includes Computed Tomography (CT) to successfully employ a thoracic prosthesis for anterior chest wall remodeling. Aiming at the elimination of radiation exposure, this paper presents a novel methodology for the replacement of CT by a 3D laser scanner (radiation-free) for prosthesis modeling. The complete elimination of CT is based on an accurate determination of ribs position and prosthesis placement region through skin surface points. The developed solution resorts to a normalized and combined outcome of an artificial neural network (ANN) set. Each ANN model was trained with data vectors from 165 male patients and using soft tissue thicknesses (STT) comprising information from the skin and rib cage (automatically determined by image processing algorithms). Tests revealed that ribs position for prosthesis placement and modeling can be estimated with an average error of 5.0 ± 3.6 mm. One also showed that the ANN performance can be improved by introducing a manually determined initial STT value in the ANN normalization procedure (average error of 2.82 ± 0.76 mm). Such error range is well below current prosthesis manual modeling (approximately 11 mm), which can provide a valuable and radiation-free procedure for prosthesis personalization.

Cytogenetic and identification of the nucleolus organizer region in Heliconia bihai (L.) L.

The genus Heliconia is not much studied and the number of existing species in this genus is still uncertain. It is known that this number relies between 150 to 250 species. In Brazil, about 40 species are native and known by many different names. The objective of this paper was to characterize morphometrically and to identify the NOR (active nucleolus organizer regions) by Ag-NOR banding of chromosomes of Heliconia bihai (L) L. Root meristems were submitted to blocking treatment in an amiprofos-methyl (APM) solution, fixed in methanol-acetic acid solution for 24 hours, at least. The meristems were washed in distilled water and submitted to enzymatic digestion with pectinase enzyme. The slides were prepared by dissociation of the root meristem, dried in the air and also on hot plate at 50°C. Subsequently, some slides were submitted to 5% Giemsa stain for karyotype construction and to a solution of silver nitrate (AgNO3) 50% for Ag-NOR banding. The species H. bihai has 2n = 22 chromosomes, 4 pairs of submetacentric chromosomes and 7 pairs of metacentric chromosomes, and graded medium to short (3.96 to 0.67 μM), with the presence of active NOR in pairs 1 and 2 and interphase cells with 2 nucleoli. These are the features of a diploid species.

Identification of 'Ubá' mango tree zygotic and nucellar seedlings using ISSR markers

Polyembryonic seeds are characterized by the development of over one embryo in the same seed, which can be zygotic and nucellar. The objective of this work was to identify the genetic origin, whether zygotic or nucellar, of seedlings of polyembryonic seeds of 'Ubá' mango tree using ISSR markers, and relating them with the vigor of the seedlings. Thus, mangos were harvested in Visconde do Rio Branco (accession 102) and Ubá (accessions 112, 138, 152 and 159), whose seeds were germinated in plastic trays filled with washed sand. Fifty days after sowing, seedlings from five seeds of each one of the accessions 102, 112, 138, 159 and from 10 seeds of the accession 152, were analyzed. These sseedlings were characterized and evaluated for plant height, stem circumference and mass of fresh aerial part and the most vigorous seedling was the one displaying at least two of these traits higher than the other seedlings from seed. Leaves were collected for genomic DNA extraction, which was amplified using seven ISSR primers previously selected based on the amplification profile and considering the number and resolution of fragments. Zygotic seedlings were found in 18 seeds, which were the most vigorous in six seeds. The results evidenced the existence of genetic variability in orchards using seedlings grown from seeds, because the farmer usually uses the most vigorous ones, assuming that this is of nucellar origin. These results also indicate that the most vigorous seedling are not always nucellar, inasmuch as of 20% of the total seeds evaluated, the zygotic seedling was the most vigorous.

Automatic modeling of pectus excavatum corrective prosthesis using artificial neural networks

Pectus excavatum is the most common deformity of the thorax. Pre-operative diagnosis usually includes Computed Tomography (CT) to successfully employ a thoracic prosthesis for anterior chest wall remodeling. Aiming at the elimination of radiation exposure, this paper presents a novel methodology for the replacement of CT by a 3D laser scanner (radiation-free) for prosthesis modeling. The complete elimination of CT is based on an accurate determination of ribs position and prosthesis placement region through skin surface points. The developed solution resorts to a normalized and combined outcome of an artificial neural network (ANN) set. Each ANN model was trained with data vectors from 165 male patients and using soft tissue thicknesses (STT) comprising information from the skin and rib cage (automatically determined by image processing algorithms). Tests revealed that ribs position for prosthesis placement and modeling can be estimated with an average error of 5.0 ± 3.6 mm. One also showed that the ANN performance can be improved by introducing a manually determined initial STT value in the ANN normalization procedure (average error of 2.82 ± 0.76 mm). Such error range is well below current prosthesis manual modeling (approximately 11 mm), which can provide a valuable and radiation-free procedure for prosthesis personalization.

Automatic prebent customized prosthesis for pectus excavatum minimally invasive surgery correction

Pectus excavatum is the most common deformity of the thorax. A minimally invasive surgical correction is commonly carried out to remodel the anterior chest wall by using an intrathoracic convex prosthesis in the substernal position. The process of prosthesis modeling and bending still remains an area of improvement. The authors developed a new system, i3DExcavatum, which can automatically model and bend the bar preoperatively based on a thoracic CT scan. This article presents a comparison between automatic and manual bending. The i3DExcavatum was used to personalize prostheses for 41 patients who underwent pectus excavatum surgical correction between 2007 and 2012. Regarding the anatomical variations, the soft-tissue thicknesses external to the ribs show that both symmetric and asymmetric patients always have asymmetric variations, by comparing the patients’ sides. It highlighted that the prosthesis bar should be modeled according to each patient’s rib positions and dimensions. The average differences between the skin and costal line curvature lengths were 84 ± 4 mm and 96 ± 11 mm, for male and female patients, respectively. On the other hand, the i3DExcavatum ensured a smooth curvature of the surgical prosthesis and was capable of predicting and simulating a virtual shape and size of the bar for asymmetric and symmetric patients. In conclusion, the i3DExcavatum allows preoperative personalization according to the thoracic morphology of each patient. It reduces surgery time and minimizes the margin error introduced by the manually bent bar, which only uses a template that copies the chest wall curvature.

Automatic modeling of an orthotic bracing for nonoperative correction of pectus carinatum

Pectus Carinatum is a deformity of the chest wall, characterized by an anterior protrusion of the sternum, often corrected surgically due to cosmetic motivation. This work presents an alternative approach to the current open surgery option, proposing a novel technique based on a personalized orthosis. Two different processes for the orthosis’ personalization are presented. One based on a 3D laser scan of the patient chest, followed by the reconstruction of the thoracic wall mesh using a radial basis function, and a second one, based on a computer tomography scan followed by a neighbouring cells algorithm. The axial position where the orthosis is to be located is automatically calculated using a Ray-Triangle intersection method, whose outcome is input to a pseudo Kochenek interpolating spline method to define the orthosis curvature. Results show that no significant differences exist between the patient chest physiognomy and the curvature angle and size of the orthosis, allowing a better cosmetic outcome and less initial discomfort.

Artificial neural networks for automatic modelling of the pectus excavatum corrective prosthesis

Pectus excavatum is the most common deformity of the thorax and usually comprises Computed Tomography (CT) examination for pre-operative diagnosis. Aiming at the elimination of the high amounts of CT radiation exposure, this work presents a new methodology for the replacement of CT by a laser scanner (radiation-free) in the treatment of pectus excavatum using personally modeled prosthesis. The complete elimination of CT involves the determination of ribs external outline, at the maximum sternum depression point for prosthesis placement, based on chest wall skin surface information, acquired by a laser scanner. The developed solution resorts to artificial neural networks trained with data vectors from 165 patients. Scaled Conjugate Gradient, Levenberg-Marquardt, Resilient Back propagation and One Step Secant gradient learning algorithms were used. The training procedure was performed using the soft tissue thicknesses, determined using image processing techniques that automatically segment the skin and rib cage. The developed solution was then used to determine the ribs outline in data from 20 patient scanners. Tests revealed that ribs position can be estimated with an average error of about 6.82±5.7 mm for the left and right side of the patient. Such an error range is well below current prosthesis manual modeling (11.7±4.01 mm) even without CT imagiology, indicating a considerable step forward towards CT replacement by a 3D scanner for prosthesis personalization.