Molecular Cloning of Vacuolar H+-Pyrophosphatase and Its Developmental Expression in Growing Hypocotyl of Mung Bean1

Vacuolar proton-translocating inorganic pyrophosphatase and H+-ATPase acidify the vacuoles and power the vacuolar secondary active transport systems in plants. Developmental changes in the transcription of the pyrophosphatase in growing hypocotyls of mung bean (Vigna radiata) were investigated. The cDNA clone for the mung bean enzyme contains an uninterrupted open reading frame of 2298 bp, coding for a polypeptide of 766 amino acids. Hypocotyls were divided into elongating and mature regions. RNA analysis revealed that the transcript level of the pyrophosphatase was high in the elongating region of the 3-d-old hypocotyl but was extremely low in the mature region of the 5-d-old hypocotyl. The level of transcript of the 68-kD subunit of H+-ATPase also decreased after cell maturation. In the elongating region, the proton-pumping activity of pyrophosphatase on the basis of membrane protein was 3 times higher than that of H+-ATPase. After cell maturation, the pyrophosphatase activity decreased to 30% of that in the elongating region. The decline in the pyrophosphatase activity was in parallel with a decrease in the enzyme protein content. These findings indicate that the level of the pyrophosphatase, a main vacuolar proton pump in growing cells, is negatively regulated after cell maturation at the transcriptional level.

Circadian gating of cell division in cyanobacteria growing with average doubling times of less than 24 hours.

To ascertain whether the circadian oscillator in the prokaryotic cyanobacterium Synechococcus PCC 7942 regulates the timing of cell division in rapidly growing cultures, we measured the rate of cell division, DNA content, cell size, and gene expression (monitored by luminescence of the PpsbAI::luxAB reporter) in cultures that were continuously diluted to maintain an approximately equal cell density. We found that populations dividing at rates as rapid as once per 10 h manifest circadian gating of cell division, since phases in which cell division slows or stops recur with a circadian periodicity. The data clearly show that Synechococcus cells growing with doubling times that are considerably faster than once per 24 h nonetheless express robust circadian rhythms of cell division and gene expression. Apparently Synechococcus cells are able to simultaneously sustain two timing circuits that express significantly different periods.

Location of light-repressible, small GTP-binding protein of the YPT/rab family in the growing zone of etiolated pea stems.

YPT/rab proteins are ras-like small GTP-binding proteins that serve as key regulators of vesicular transport. The mRNA levels of two YPT/rab genes in pea plants are repressed by light, with the process mediated by phytochrome. Here, we examined the mRNA expression and the location of the two proteins, pra2- and pra3-encoded proteins, using monoclonal antibodies. The pra2 and pra3 mRNA levels were highest in the stems of dark-grown seedlings. The corresponding proteins were found in the cytosol and the membranes of the stems. Most of the pra2 protein was in the growing internodes, especially in the growing region, but the pra3 protein was widespread. These results suggest that the pra2 protein is important for vesicular transport in stems, possibly contributing to stem growth in the dark, and that the pra3 protein is important for general vesicular transport. The amounts of pra2 and pra3 proteins decreased with illumination. The decrease in these proteins may be related to the phytochrome-dependent inhibition of stem growth that occurs in etiolated pea seedlings.

Fisiologia e formação de partículas lipídicas durante o crescimento da levedura Yarrowia lipolytica IMUFRJ 50682.

A levedura Yarrowia lipolytica tem sido muito investigada, especialmente por ser um microrganismo oleaginoso, ou seja, capaz de acumular grandes quantidades de lipídios, o que ocorre majoritariamente em organelas denominadas partículas lipídicas. Estes lipídios apresentam várias potenciais aplicações biotecnológicas, como por exemplo na produção de óleo microbiano (single cell oil) e na produção de biodiesel. Durante este projeto de mestrado, objetivou-se estudar a fisiologia de duas linhagens da levedura Y. lipolytica, sendo uma tradicionalmente estudada pela comunidade científica internacional (linhagem w29) e outra isolada da Baía da Guanabara, no Rio de Janeiro (linhagem IMUFRJ 50682). Foram realizados cultivos em frascos agitados tipo Erlenmeyer com defletores tampados com algodão (volume total 500 mL, volume de meio 100 mL, 28 oC e 200 rotações por minuto), durante os quais foi possível: 1) escolher um meio de cultivo de composição totalmente definida, com tiamina como único fator de crescimento, adequado a estudos de fisiologia quantitativa com esta levedura; 2) verificar que Y. lipolytica não é capaz de crescer com sacarose ou xilose como única fonte de carbono; 3) verificar que Y. lipolytica cresce com velocidade específica de crescimento máxima (Máx) de 0,49 h-1 num meio complexo contendo glicose, extrato de levedura e peptona (meio YPD), 0,31 h-1 em meio definido com glicose como única fonte de carbono e 0,35 h-1 no mesmo meio, mas com glicerol como única fonte de carbono, sem excreção de metabólitos para o meio de cultivo; 4) verificar que ocorreu limitação por oxigênio nos cultivos em frasco agitado, sendo este o motivo pelo qual as células deixaram de crescer exponencialmente; 5) verificar que o uso de ureia, em vez de sulfato de amônio, como fonte de nitrogênio, contribui para uma variação menor do pH durante os cultivos, sem prejuízo ao crescimento da levedura; 6) observar que, ao se restringir a oferta de nitrogênio à levedura (aumento da relação C/N inicial no meio de 12,6 para 126), as células têm sua morfologia alterada e apresentam maior quantidade de partículas lipídicas; 7) determinar uma composição elementar para a biomassa de Y. lipolytica (CH1,98O0,58N0,13), em que os átomos de carbono encontram-se em média mais reduzidos do que na biomassa de leveduras como Saccharomyces cerevisiae e Dekkera bruxellensis. Foram também realizados cultivos em biorreator em batelada (1 L de volume útil, 28 oC, aerobiose plena e pH controlado em 5,0), durante os quais foi possível: a) estabelecer um protocolo de cultivo para Y. lipolytica em biorreator (que envolvem agitação mecânica, aeração e uso de anti-espumante, entre outras diferenças em relação aos cultivos em frasco); b) confirmar os valores dos principais parâmetros fisiológicos apresentados por esta levedura, anteriormente obtidos a partir de cultivos em frasco; c) confirmar que o fator de conversão de substrato a células (Yx/s) é maior para cultivos realizados com glicerol como fonte única de carbono (0,53 g/g para a linhagem IMUFRJ 50682), do que com glicose (0,48 g/g para a mesma linhagem). Finalmente, cultivos realizados em quimiostato com glicerol como fonte de carbono e energia, limitados por amônio (fonte de nitrogênio, relação C/N 126), às vazões específicas de 0,25 h-1 e 0,15 h-1, permitiram observar que o número de partículas lipídicas por célula de Y. lipolytica permaneceu em torno de 2 em ambas as situações e houve uma diminuição no teor de nitrogênio nas células quando a velocidade específica de crescimento diminuiu de 0,25 para 0,15 h-1.

GPGPU implementation of growing neural gas: application to 3D scene reconstruction

Self-organising neural models have the ability to provide a good representation of the input space. In particular the Growing Neural Gas (GNG) is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation. However, this type of learning is time-consuming, especially for high-dimensional input data. Since real applications often work under time constraints, it is necessary to adapt the learning process in order to complete it in a predefined time. This paper proposes a Graphics Processing Unit (GPU) parallel implementation of the GNG with Compute Unified Device Architecture (CUDA). In contrast to existing algorithms, the proposed GPU implementation allows the acceleration of the learning process keeping a good quality of representation. Comparative experiments using iterative, parallel and hybrid implementations are carried out to demonstrate the effectiveness of CUDA implementation. The results show that GNG learning with the proposed implementation achieves a speed-up of 6× compared with the single-threaded CPU implementation. GPU implementation has also been applied to a real application with time constraints: acceleration of 3D scene reconstruction for egomotion, in order to validate the proposal.

Growing Neural Gas approach for obtaining homogeneous maps by restricting the insertion of new nodes

The Growing Neural Gas model is used widely in artificial neural networks. However, its application is limited in some contexts by the proliferation of nodes in dense areas of the input space. In this study, we introduce some modifications to address this problem by imposing three restrictions on the insertion of new nodes. Each restriction aims to maintain the homogeneous values of selected criteria. One criterion is related to the square error of classification and an alternative approach is proposed for avoiding additional computational costs. Three parameters are added that allow the regulation of the restriction criteria. The resulting algorithm allows models to be obtained that suit specific needs by specifying meaningful parameters.

Combining visual features and Growing Neural Gas networks for robotic 3D SLAM

The use of 3D data in mobile robotics provides valuable information about the robot’s environment. Traditionally, stereo cameras have been used as a low-cost 3D sensor. However, the lack of precision and texture for some surfaces suggests that the use of other 3D sensors could be more suitable. In this work, we examine the use of two sensors: an infrared SR4000 and a Kinect camera. We use a combination of 3D data obtained by these cameras, along with features obtained from 2D images acquired from these cameras, using a Growing Neural Gas (GNG) network applied to the 3D data. The goal is to obtain a robust egomotion technique. The GNG network is used to reduce the camera error. To calculate the egomotion, we test two methods for 3D registration. One is based on an iterative closest points algorithm, and the other employs random sample consensus. Finally, a simultaneous localization and mapping method is applied to the complete sequence to reduce the global error. The error from each sensor and the mapping results from the proposed method are examined.

3D-based reconstruction using growing neural gas landmark: application to rapid prototyping in shoe last manufacturing

Customizing shoe manufacturing is one of the great challenges in the footwear industry. It is a production model change where design adopts not only the main role, but also the main bottleneck. It is therefore necessary to accelerate this process by improving the accuracy of current methods. Rapid prototyping techniques are based on the reuse of manufactured footwear lasts so that they can be modified with CAD systems leading rapidly to new shoe models. In this work, we present a shoe last fast reconstruction method that fits current design and manufacturing processes. The method is based on the scanning of shoe last obtaining sections and establishing a fixed number of landmarks onto those sections to reconstruct the shoe last 3D surface. Automated landmark extraction is accomplished through the use of the self-organizing network, the growing neural gas (GNG), which is able to topographically map the low dimensionality of the network to the high dimensionality of the contour manifold without requiring a priori knowledge of the input space structure. Moreover, our GNG landmark method is tolerant to noise and eliminates outliers. Our method accelerates up to 12 times the surface reconstruction and filtering processes used by the current shoe last design software. The proposed method offers higher accuracy compared with methods with similar efficiency as voxel grid.

Improving 3D Keypoint Detection from Noisy Data Using Growing Neural Gas

3D sensors provides valuable information for mobile robotic tasks like scene classification or object recognition, but these sensors often produce noisy data that makes impossible applying classical keypoint detection and feature extraction techniques. Therefore, noise removal and downsampling have become essential steps in 3D data processing. In this work, we propose the use of a 3D filtering and down-sampling technique based on a Growing Neural Gas (GNG) network. GNG method is able to deal with outliers presents in the input data. These features allows to represent 3D spaces, obtaining an induced Delaunay Triangulation of the input space. Experiments show how the state-of-the-art keypoint detectors improve their performance using GNG output representation as input data. Descriptors extracted on improved keypoints perform better matching in robotics applications as 3D scene registration.

3D reconstruction of medical images from slices automatically landmarked with growing neural models

In this study, we utilise a novel approach to segment out the ventricular system in a series of high resolution T1-weighted MR images. We present a brain ventricles fast reconstruction method. The method is based on the processing of brain sections and establishing a fixed number of landmarks onto those sections to reconstruct the ventricles 3D surface. Automated landmark extraction is accomplished through the use of the self-organising network, the growing neural gas (GNG), which is able to topographically map the low dimensionality of the network to the high dimensionality of the contour manifold without requiring a priori knowledge of the input space structure. Moreover, our GNG landmark method is tolerant to noise and eliminates outliers. Our method accelerates the classical surface reconstruction and filtering processes. The proposed method offers higher accuracy compared to methods with similar efficiency as Voxel Grid.

Pacific Mills, Lawrence, Mass., Dover, N.H., Columbia, S.C... are the largest manufacturers in the world of cotton goods

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Payment order for Josiah Cotton, 1722 September 10

Handwritten order to Penn Townsend to pay scholarship funds to student Josiah Cotton (Harvard AB 1722), signed by Benjamin Wadsworth, Thomas Foxcroft, Samuel Marshall, and Jonathan Williams.

Payment order for Rowland Cotton, 1726 October 17

Handwritten order to Penn Townsend to pay scholarship funds to Rowland Cotton on behalf of his son Ward Cotton (Harvard AB 1729), signed by Thomas Foxcroft, John Marion, Samuel Marshall, and Jonathan Williams.

[Admittatur of Cotton Tufts (Harvard AB 1749)], 1745

Notebook with a handwritten copy of the 1734 College laws in English prepared by Harvard undergraduate Cotton Tufts and signed by President Edward Holyoke, Tutor Belcher Hancock, Fellow Joseph Mayhew, and Tutor Thomas Marsh on November 8, 1745.