Indoor rigid sphere recognition based on 3D point cloud data

This paper presents a method for recognising spherical shapes in 3D point cloud XYZ coordinate data obtained by scanning an indoor environment using a LIDAR scanner. Firstly, bilateral smoothing is performed to smooth the surfaces consisting of points. Then, the surface curvature and surface roughness of each point in the scan are extracted by analysing the point cloud data. Finally, a three layer multilayer perceptron neural network trained by the Levenberg-Marquardt algorithm is used to automatically distinguish points belonging to spheres from all the other points making use of extracted features. A novel feedback technique is applied in which the neural network is used several times on the recognised data. This method can be applied to automate 3D scan alignment

Error Metric for Indoor 3D Point Cloud Registration

An increase in commercial availability of 3D scanning technology has led to an increase of 3D perception for a variety of applications. High quality scanners require to be stationary and so multiple scans are required and subsequently need to be registered. A new error metric for registration based on the deviation of registered planar surfaces is introduced here and compared with a commonly used metric: mean square point-to-point distance. Four different sets of features are used to register six scans, the point-to-point errors are compared to the new error metric, planar surface deviation, and a disparity is observed for certain sets of features. The two metrics agree as to which sets of features gave the best registration but disagree as to which set produced the worst registration. It is concluded that further analysis and evaluation is required to determine which metric is more meaningful as a representative measure of registration accuracy and to also investigate other error metrics

Prediction of academic dropout in university students using data mining: Engineering case

Student dropout is considered an important indicator for measuring social mobility and reflecting the social contribution that universities offer. In economic terms, there is evidence that students attribute their decision to defect from their academic programs because of their economic situation. Dropout causes significant waging gaps among people who complete their tertiary studies compared to those who do not, leading to a lack of skilled human capital that pays greater productivity to economic development of a country. Given the above, the objective of this study is to present a tree-based classification of decisions (CBAD) with optimized parameters to predict the dropout of students at Colombian universities. The study analyses 10,486 cases of students from three private universities with similar characteristics. The result of the application of this technique with optimized parameters achieved a precision ratio of 88.14%

NSC23925, Identified in a High-Throughput Cell-Based Screen, Reverses Multidrug Resistance

Multidrug resistance (MDR) is a major factor which contributes to the failure of cancer chemotherapy, and numerous efforts have been attempted to overcome MDR. To date, none of these attempts have yielded a tolerable and effective therapy to reverse MDR; thus, identification of new agents would be useful both clinically and scientifically.To identify small molecule compounds that can reverse chemoresistance, we developed a 96-well plate high-throughput cell-based screening assay in a paclitaxel resistant ovarian cancer cell line. Coincubating cells with a sublethal concentration of paclitaxel in combination with each of 2,000 small molecule compounds from the National Cancer Institute Diversity Set Library, we identified a previously uncharacterized molecule, NSC23925, that inhibits Pgp1 and reverses MDR1 (Pgp1) but does not inhibit MRP or BCRP-mediated MDR. The cytotoxic activity of NSC23925 was further evaluated using a panel of cancer cell lines expressing Pgp1, MRP, and BCRP. We found that at a concentration of >10 microM NSC23925 moderately inhibits the proliferation of both sensitive and resistant cell lines with almost equal activity, but its inhibitory effect was not altered by co-incubation with the Pgp1 inhibitor, verapamil, suggesting that NSC23925 itself is not a substrate of Pgp1. Additionally, NSC23925 increases the intracellular accumulation of Pgp1 substrates: calcein AM, Rhodamine-123, paclitaxel, mitoxantrone, and doxorubicin. Interestingly, we further observed that, although NSC23925 directly inhibits the function of Pgp1 in a dose-dependent manner without altering the total expression level of Pgp1, NSC23925 actually stimulates ATPase activity of Pgp, a phenomenon seen in other Pgp inhibitors.The ability of NSC23925 to restore sensitivity to the cytotoxic effects of chemotherapy or to prevent resistance could significantly benefit cancer patients

Reforzando medidas higienicas en la producción de alimentos especializados

This publication is prepared to assist processors of specialty food, distributors, vendors, and food regulators of backgrounds and experience in finding specific information on producing safe specialty food products. This publication discusses the unique characteristics of specialty foods, identifies food safety concerns in specialty foods production, and provides examples of specific control measures for ensuring food safety of specialty food products.Published January 2013. Facts and recommendations in this publication may no longer be valid. Please look for up-to-date information in the OSU Extension Catalog: http://extension.oregonstate.edu/catalo

Coherent spinor dynamics in a spin-1 Bose condensate

Collisions in a thermal gas are perceived as random or incoherent as a consequence of the large numbers of initial and final quantum states accessible to the system. In a quantum gas, e.g. a Bose-Einstein condensate or a degenerate Fermi gas, the phase space accessible to low energy collisions is so restricted that collisions be-come coherent and reversible. Here, we report the observation of coherent spin-changing collisions in a gas of spin-1 bosons. Starting with condensates occupying two spin states, a condensate in the third spin state is coherently and reversibly created by atomic collisions. The observed dynamics are analogous to Josephson oscillations in weakly connected superconductors and represent a type of matter-wave four-wave mixing. The spin-dependent scattering length is determined from these oscillations to be -1.45(18) Bohr. Finally, we demonstrate coherent control of the evolution of the system by applying differential phase shifts to the spin states using magnetic fields.Comment: 19 pages, 3 figure

Potential effects of oilseed rape expressing oryzacystatin-1 (OC-1) and of purified insecticidal proteins on larvae of the solitary bee Osmia bicornis

Despite their importance as pollinators in crops and wild plants, solitary bees have not previously been included in non-target testing of insect-resistant transgenic crop plants. Larvae of many solitary bees feed almost exclusively on pollen and thus could be highly exposed to transgene products expressed in the pollen. The potential effects of pollen from oilseed rape expressing the cysteine protease inhibitor oryzacystatin-1 (OC-1) were investigated on larvae of the solitary bee Osmia bicornis (= O. rufa). Furthermore, recombinant OC-1 (rOC-1), the Bt toxin Cry1Ab and the snowdrop lectin Galanthus nivalis agglutinin (GNA) were evaluated for effects on the life history parameters of this important pollinator. Pollen provisions from transgenic OC-1 oilseed rape did not affect overall development. Similarly, high doses of rOC-1 and Cry1Ab as well as a low dose of GNA failed to cause any significant effects. However, a high dose of GNA (0.1%) in the larval diet resulted in significantly increased development time and reduced efficiency in conversion of pollen food into larval body weight. Our results suggest that OC-1 and Cry1Ab expressing transgenic crops would pose a negligible risk for O. bicornis larvae, whereas GNA expressing plants could cause detrimental effects, but only if bees were exposed to high levels of the protein. The described bioassay with bee brood is not only suitable for early tier non-target tests of transgenic plants, but also has broader applicability to other crop protection products

Circuit dissection of the role of somatostatin in itch and pain

Stimuli that elicit itch are detected by sensory neurons that innervate the skin. This information is processed by the spinal cord; however, the way in which this occurs is still poorly understood. Here we investigated the neuronal pathways for itch neurotransmission, particularly the contribution of the neuropeptide somatostatin. We find that in the periphery, somatostatin is exclusively expressed in Nppb+ neurons, and we demonstrate that Nppb+somatostatin+ cells function as pruriceptors. Employing chemogenetics, pharmacology and cell-specific ablation methods, we demonstrate that somatostatin potentiates itch by inhibiting inhibitory dynorphin neurons, which results in disinhibition of GRPR+ neurons. Furthermore, elimination of somatostatin from primary afferents and/or from spinal interneurons demonstrates differential involvement of the peptide released from these sources in itch and pain. Our results define the neural circuit underlying somatostatin-induced itch and characterize a contrasting antinociceptive role for the peptide

Dropout-permanence analysis of university students using data mining

Dropout is a rejection method present in every educational system, related to the various selection processes, academic performance, and the efficiency of the system in general, that is, the result of the combination and effect of different variables. In this sense, the dropout of university students related to their academic performance is a matter of concern since several years ago. Academic information is analyzed in order to identify factors that influence students´ dropout at the University of Mumbai, India, by using a data mining technique. The data source contains information provided to the entrance (personal and educational background) and that is generated during the study period. The data selection and cleansing are made using different criteria of representation and implementation of classification algorithms such as decision trees, Bayesian networks, and rules. the following factors are identified as influential variables in the desertion: approved courses, quantity and results of attended courses, origin and age of entry of the student. Through this process, it was possible to identify the attributes that characterize the dropout cases and their relationship with the academic performance, especially in the first year of the career

LipocalinPred: a SVM-based method for prediction of lipocalins

<p>Abstract</p> <p>Background</p> <p>Functional annotation of rapidly amassing nucleotide and protein sequences presents a challenging task for modern bioinformatics. This is particularly true for protein families sharing extremely low sequence identity, as for lipocalins, a family of proteins with varied functions and great diversity at the sequence level, yet conserved structures.</p> <p>Results</p> <p>In the present study we propose a SVM based method for identification of lipocalin protein sequences. The SVM models were trained with the input features generated using amino acid, dipeptide and secondary structure compositions as well as PSSM profiles. The model derived using both PSSM and secondary structure emerged as the best model in the study. Apart from achieving a high prediction accuracy (>90% in leave-one-out), lipocalinpred correctly differentiates closely related fatty acid-binding proteins and triabins as non-lipocalins.</p> <p>Conclusion</p> <p>The method offers a promising approach as a lipocalin prediction tool, complementing PROSITE, Pfam and homology modelling methods.</p