Study of the effects of oligosaccharides in liquid cultures of penicillium chrysogenum

Oligosaccharides and polysaccharides have different effects on the morphology and production of secondary metabolites by Penicillium chrysogenum P2 (ATCC 48271). Addition Of oligosaccharides, derived from sodium alginate and locust bean gum, to submerged cultures of P. chrysogenum P2, at milligram per litre concentration (150 mgL-1), increased secondary metabolite levels and spore production, caused changes in morphology and gerRiination of spores, and affected the production of Reactive Oxygen Species. The source of the oligosaccharides controlled their effects on the cultures. Oligosaccharides when added to submerged cultures of P. chrysogenum P2 increased both penicillin G and extracellular levels of 6-aminopenicillanic acid concentrations. The oligosaccharides had no significant effects on biomass levels. Locust bean gum-derived oligosaccharides (mannan oligosaccharides, DP 5-8), showed the highest levels of enhancement in both penicillin G and 6-aminopenicillanic acid concentrations. Sodium alginate-derived oligosaccharides, (oligoguluronate, DP 7 and oligomannuronate, DP 7), also induced elicitation of penicillin G and 6-aminopenicillanic acid. Oligomannuronate was shown to be more effective than oligoguluronate. In P. chrysogenum P2 cultures mannan, oligomannuronate and oligoguluronate oligosaccharides enhanced yields of penicillin G by 101%, 78% and 59%, respectively. Addition of mannan, oligomannuronate and oligoguluronate oligosaccharides enhanced the levels of 6- aminopenicillanic acid by 39%, 26% and 19%, respectively. The addition of oligosaccharides and polysaccharides to spores of P. chrysogenum P2 in liquid medium had varying (inhibitory or stimulatory) effects on germination, germ-tube and clump development. The addition of oligosaccharides to submerged cultures of P. chrysogenum P2 showed effects on clump size and hyphal tip numbers. Mannan oligosaccharides had the greatest effect on morphology followed by oligomannuronate and oligoguluronate oligosaccharides. Oligosaccharides also speeded-up the sporulation and increased the concentration of spores of P. chrysogenum P2 in liquid cultures. Mannan oligosaccharides had the greatest effect followed by oligomannuronate and oligoguluronate oligosaccharides. 8-aminonaphthalene-1,3,6-trisulphonate-tagged oligosaccharide studies showed that the oligosaccharides pass through the cell wall of P. chrysogenum P2 suggesting a possible mechanism through modulation of gene function. The elicitation pattern was shown to be similar to untagged oligosaccharides. Oligosaccharides and polysaccharides were shown to inhibit production of Reactive Oxygen Species in P. chrysogenum P2. The highest level of inhibition was elicited by mannan followed by oligornannuronate and oligoguluronate oligosaccharides, and then locust bean gum and alginate. The results of the study showed the potential of oligosaccharides as elicitors of secondary metabolites in P. chrysogenum P2 as a filamentous fungus model. Understanding the elicitation mechanism could provide routes for ftirther exploitation of the potential of filamentous fungi in production of commercial products

Modeling and Analysis of Power Processing Systems (MAPPS). Volume 2: Appendices

The computer programs and derivations generated in support of the modeling and design optimization program are presented. Programs for the buck regulator, boost regulator, and buck-boost regulator are described. The computer program for the design optimization calculations is presented. Constraints for the boost and buck-boost converter were derived. Derivations of state-space equations and transfer functions are presented. Computer lists for the converters are presented, and the input parameters justified

Acute promyelocytic leukemia after whole brain irradiation of primary brain lymphomainan HIV-infected patient

The occurrence of acute promyelocytic leukemia (APL) in HIV-infected patients has been reported in only five cases. Due to a very small number of reported HIV/APL patients who have been treated with different therapies with the variable outcome, the prognosis of APL in the setting of the HIV-infection is unclear. Here, we report a case of an HIV-patient who developed APL and upon treatment entered a complete remission. A 25-years old male patient was diagnosed with HIV-infection in 1996, but remained untreated. In 2004, the patient was diagnosed with primary central nervous system lymphoma. We treated the patient with antiretroviral therapy and whole-brain irradiation, resulting in complete remission of the lymphoma. In 2006, prompted by a sudden neutropenia, we carried out a set of diagnostic procedures, revealing APL. Induction therapy consisted of standard treatment with all-trans-retinoic-acid (ATRA) and idarubicin. Subsequent cytological and molecular analysis of bone marrow demonstrated complete hematological and molecular remission. Due to the poor general condition, consolidation treatment with ATRA was given in March and April 2007. The last follow-up 14 months later, showed sustained molecular APL remission. In conclusion, we demonstrated that a complete molecular APL remission in an HIV-patient was achieved by using reduced-intensity treatment

Quantitative test of the barrier nucleosome model for statistical positioning of nucleosomes up- and downstream of transcription start sites

The positions of nucleosomes in eukaryotic genomes determine which parts of the DNA sequence are readily accessible for regulatory proteins and which are not. Genome-wide maps of nucleosome positions have revealed a salient pattern around transcription start sites, involving a nucleosome-free region (NFR) flanked by a pronounced periodic pattern in the average nucleosome density. While the periodic pattern clearly reflects well-positioned nucleosomes, the positioning mechanism is less clear. A recent experimental study by Mavrich et al. argued that the pattern observed in S. cerevisiae is qualitatively consistent with a `barrier nucleosome model', in which the oscillatory pattern is created by the statistical positioning mechanism of Kornberg and Stryer. On the other hand, there is clear evidence for intrinsic sequence preferences of nucleosomes, and it is unclear to what extent these sequence preferences affect the observed pattern. To test the barrier nucleosome model, we quantitatively analyze yeast nucleosome positioning data both up- and downstream from NFRs. Our analysis is based on the Tonks model of statistical physics which quantifies the interplay between the excluded-volume interaction of nucleosomes and their positional entropy. We find that although the typical patterns on the two sides of the NFR are different, they are both quantitatively described by the same physical model, with the same parameters, but different boundary conditions. The inferred boundary conditions suggest that the first nucleosome downstream from the NFR (the +1 nucleosome) is typically directly positioned while the first nucleosome upstream is statistically positioned via a nucleosome-repelling DNA region. These boundary conditions, which can be locally encoded into the genome sequence, significantly shape the statistical distribution of nucleosomes over a range of up to ~1000 bp to each side.Comment: includes supporting materia

A hybrid method for accurate iris segmentation on at-a-distance visible-wavelength images

[EN] This work describes a new hybrid method for accurate iris segmentation from full-face images independently of the ethnicity of the subject. It is based on a combination of three methods: facial key-point detection, integro-differential operator (IDO) and mathematical morphology. First, facial landmarks are extracted by means of the Chehra algorithm in order to obtain the eye location. Then, the IDO is applied to the extracted sub-image containing only the eye in order to locate the iris. Once the iris is located, a series of mathematical morphological operations is performed in order to accurately segment it. Results are obtained and compared among four different ethnicities (Asian, Black, Latino and White) as well as with two other iris segmentation algorithms. In addition, robustness against rotation, blurring and noise is also assessed. Our method obtains state-of-the-art performance and shows itself robust with small amounts of blur, noise and/or rotation. Furthermore, it is fast, accurate, and its code is publicly available.Fuentes-Hurtado, FJ.; Naranjo Ornedo, V.; Diego-Mas, JA.; Alcañiz Raya, ML. (2019). A hybrid method for accurate iris segmentation on at-a-distance visible-wavelength images. EURASIP Journal on Image and Video Processing (Online). 2019(1):1-14. https://doi.org/10.1186/s13640-019-0473-0S11420191A. Radman, K. Jumari, N. Zainal, Fast and reliable iris segmentation algorithm. IET Image Process.7(1), 42–49 (2013).M. Erbilek, M. Fairhurst, M. C. D. C Abreu, in 5th International Conference on Imaging for Crime Detection and Prevention (ICDP 2013). Age prediction from iris biometrics (London, 2013), pp. 1–5. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6913712&isnumber=6867223 .A. Abbasi, M. Khan, Iris-pupil thickness based method for determining age group of a person. Int. Arab J. Inf. Technol. (IAJIT). 13(6) (2016).G. Mabuza-Hocquet, F. Nelwamondo, T. Marwala, in Intelligent Information and Database Systems. ed. by N. Nguyen, S. Tojo, L. Nguyen, B. Trawiński. Ethnicity Distinctiveness Through Iris Texture Features Using Gabor Filters. ACIIDS 2017. Lecture Notes in Computer Science, vol. 10192 (Springer, Cham, 2017).S. Lagree, K. W. Bowyer, in 2011 IEEE International Conference on Technologies for Homeland Security (HST). Predicting ethnicity and gender from iris texture (IEEEWaltham, 2011). p. 440–445. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6107909&isnumber=6107829 .J. G. Daugman, High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Anal. Mach. Intell.15(11), 1148–1161 (1993).N. Kourkoumelis, M. Tzaphlidou. Medical Safety Issues Concerning the Use of Incoherent Infrared Light in Biometrics, eds. A. Kumar, D. Zhang. Ethics and Policy of Biometrics. ICEB 2010. Lecture Notes in Computer Science, vol 6005 (Springer, Berlin, Heidelberg, 2010).R. P. Wildes, Iris recognition: an emerging biometric technology. Proc. IEEE. 85(9), 1348–1363 (1997).M. Kass, A. Witkin, D. Terzopoulos, Snakes: Active contour models. Int. J. Comput. Vision. 1(4), 321–331 (1988).S. J. Pundlik, D. L. Woodard, S. T. Birchfield, in 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Non-ideal iris segmentation using graph cuts (IEEEAnchorage, 2008). p. 1–6. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4563108&isnumber=4562948 .H. Proença, Iris recognition: On the segmentation of degraded images acquired in the visible wavelength. IEEE Trans. Pattern Anal. Mach. Intell.32(8), 1502–1516 (2010). http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5156505&isnumber=5487331 .T. Tan, Z. He, Z. Sun, Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition. Image Vision Comput.28(2), 223–230 (2010).C. -W. Tan, A. Kumar, in CVPR 2011 WORKSHOPS. Automated segmentation of iris images using visible wavelength face images (Colorado Springs, 2011). p. 9–14. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5981682&isnumber=5981671 .Y. -H. Li, M. Savvides, An automatic iris occlusion estimation method based on high-dimensional density estimation. IEEE Trans. Pattern Anal. Mach. Intell.35(4), 784–796 (2013).M. Yahiaoui, E. Monfrini, B. Dorizzi, Markov chains for unsupervised segmentation of degraded nir iris images for person recognition. Pattern Recogn. Lett.82:, 116–123 (2016).A. Radman, N. Zainal, S. A. Suandi, Automated segmentation of iris images acquired in an unconstrained environment using hog-svm and growcut. Digit. Signal Proc.64:, 60–70 (2017).N. Liu, H. Li, M. Zhang, J. Liu, Z. Sun, T. Tan, in 2016 International Conference on Biometrics (ICB). Accurate iris segmentation in non-cooperative environments using fully convolutional networks (Halmstad, 2016). p. 1–8. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7550055&isnumber=7550036 .Z. Zhao, A. Kumar, in 2017 IEEE International Conference on Computer Vision (ICCV). Towards more accurate iris recognition using deeply learned spatially corresponding features (Venice, 2017). p. 3829–3838. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8237673&isnumber=8237262 .P. Li, X. Liu, L. Xiao, Q. Song, Robust and accurate iris segmentation in very noisy iris images. Image Vision Comput.28(2), 246–253 (2010).D. S. Jeong, J. W. Hwang, B. J. Kang, K. R. Park, C. S. Won, D. -K. Park, J. Kim, A new iris segmentation method for non-ideal iris images. Image Vision Comput.28(2), 254–260 (2010).Y. Chen, M. Adjouadi, C. Han, J. Wang, A. Barreto, N. Rishe, J. Andrian, A highly accurate and computationally efficient approach for unconstrained iris segmentation. Image Vision Comput. 28(2), 261–269 (2010).Z. Zhao, A. Kumar, in 2015 IEEE International Conference on Computer Vision (ICCV). An accurate iris segmentation framework under relaxed imaging constraints using total variation model (Santiago, 2015). p. 3828–3836. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7410793&isnumber=7410356 .Y. Hu, K. Sirlantzis, G. Howells, Improving colour iris segmentation using a model selection technique. Pattern Recogn. Lett.57:, 24–32 (2015).E. Ouabida, A. Essadique, A. Bouzid, Vander lugt correlator based active contours for iris segmentation and tracking. Expert Systems Appl.71:, 383–395 (2017).C. -W. Tan, A. Kumar, Unified framework for automated iris segmentation using distantly acquired face images. IEEE Trans. Image Proc.21(9), 4068–4079 (2012).C. -W. Tan, A. Kumar, in Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012). Human identification from at-a-distance images by simultaneously exploiting iris and periocular features (Tsukuba, 2012). p. 553–556. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6460194&isnumber=6460043 .C. -W. Tan, A. Kumar, Towards online iris and periocular recognition under relaxed imaging constraints. IEEE Trans. Image Proc.22(10), 3751–3765 (2013).K. Y. Shin, Y. G. Kim, K. R. Park, Enhanced iris recognition method based on multi-unit iris images. Opt. Eng.52(4), 047201–047201 (2013).CASIA iris databases. http://biometrics.idealtest.org/ . Accessed 06 Sept 2017.WVU iris databases. hhttp://biic.wvu.edu/data-sets/synthetic-iris-dataset . Accessed 06 Sept 2017.UBIRIS iris database. http://iris.di.ubi.pt . Accessed 06 Sept 2017.MICHE iris database. http://biplab.unisa.it/MICHE/ . Accessed 06 Sept 2017.P. J. Phillips, et al, in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), 1. Overview of the face recognition grand challenge (San Diego, 2005). p. 947–954. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1467368&isnumber=31472 .D. S. Ma, J. Correll, B. Wittenbrink, The chicago face database: A free stimulus set of faces and norming data. Behav. Res. Methods. 47(4), 1122–1135 (2015).P. Soille, Morphological Image Analysis: Principles and Applications (Springer, 2013).A. K. Jain, Fundamentals of Digital Image Processing (Prentice-Hall, Inc., Englewood Cliffs, 1989).J. Daugman, How iris recognition works. IEEE Trans. Circ. Syst. Video Technol.14(1), 21–30 (2004).A. Asthana, S. Zafeiriou, S. Cheng, M. Pantic, in 2014 IEEE Conference on Computer Vision and Pattern Recognition. Incremental face alignment in the wild (Columbus, 2014). p. 1859–1866. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6909636&isnumber=6909393 .T. Baltrusaitis, P. Robinson, L. -P. Morency, in 2013 IEEE International Conference on Computer Vision Workshops. Constrained local neural fields for robust facial landmark detection in the wild (Sydney, 2013). p. 354–361. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6755919&isnumber=6755862 .X. Zhu, D. Ramanan, in Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference On. Face detection, pose estimation, and landmark localization in the wild (IEEEBerlin Heidelberg, 2012), pp. 2879–2886.G. Tzimiropoulos, in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Project-out cascaded regression with an application to face alignment (Boston, 2015). p. 3659–3667. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7298989&isnumber=7298593 .H. Hofbauer, F. Alonso-Fernandez, P. Wild, J. Bigun, A. Uhl, in 2014 22nd International Conference on Pattern Recognition. A ground truth for iris segmentation (Stockholm, 2014). p. 527–532. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6976811&isnumber=6976709 .H. Proença, L. A. Alexandre, in 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems. The NICE.I: Noisy Iris Challenge Evaluation - Part I (Crystal City, 2007). p. 1–4. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4401910&isnumber=4401902 .J. Daugman, in European Convention on Security and Detection. High confidence recognition of persons by rapid video analysis of iris texture, (1995). p. 244–251. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=491729&isnumber=10615 .Code of Matlab implementation of Daugman’s integro-differential operator (IDO). https://es.mathworks.com/matlabcentral/fileexchange/15652-iris-segmentation-using-daugman-s-integrodifferential-operator/ . Accessed 06 Sept 2017.Code of Matlab implementation of Zhao and Kumar’s iris segmentation framework under relaxed imaging constraints using total variation model. http://www4.comp.polyu.edu.hk/~csajaykr/tvmiris.htm/ . Accessed 06 Sept 2017.Code of Matlab implementation of presented work. https://gitlab.com/ffuentes/hybrid_iris_segmentation/ . Accessed 06 Sept 2017.Face and eye detection with OpenCV. https://docs.opencv.org/trunk/d7/d8b/tutorial_py_face_detection.html . Accessed 07 Sept 2018.A. K. Boyat, B. K. Joshi, 6. A review paper:noise models in digital image processing signal & image processing. An International Journal (SIPIJ), (2015), pp. 63–75. https://doi.org/10.5121/sipij.2015.6206 .A. Buades, Y. Lou, J. M. Morel, Z. Tang, Multi image noise estimation and denoising (2010). Available: https://hal.archives-ouvertes.fr/hal-00510866/

Prevalencia Dioctophyme renale en un área vulnerable de la provincia de Buenos Aires

Desde el año 2005 a la fecha, de manera ininterrumpida, el grupo de trabajo realiza tareas en un área vulnerable de la Prov. de Bs. As., barrio “El Molino” en la localidad Punta Lara, municipio de Ensenada. El excesivo desmonte y posterior antropización originaron un área suburbana inundable y de elevada vulnerabilidad social y sanitaria, ya que los vecinos vuelcan sus efluentes cloacales sin tratamiento previo. Se estudiaron 692 caninos y 257 personas de la zona, tomando muestras de orina para verificar la presencia de huevos de Dioctophyme renale.Facultad de Ciencias Veterinaria

Prevalencia Dioctophyme renale en un área vulnerable de la provincia de Buenos Aires

Desde el año 2005 a la fecha, de manera ininterrumpida, el grupo de trabajo realiza tareas en un área vulnerable de la Prov. de Bs. As., barrio “El Molino” en la localidad Punta Lara, municipio de Ensenada. El excesivo desmonte y posterior antropización originaron un área suburbana inundable y de elevada vulnerabilidad social y sanitaria, ya que los vecinos vuelcan sus efluentes cloacales sin tratamiento previo. Se estudiaron 692 caninos y 257 personas de la zona, tomando muestras de orina para verificar la presencia de huevos de Dioctophyme renale.Facultad de Ciencias Veterinaria

Prevalencia de enteroparasitosis humanas en un área vulnerable de la provincia de Buenos Aires

El barrio “El Molino”, ubicado en el Municipio de Ensenada, Provincia de Buenos Aires, República Argentina (34° 49′ S, 57° 58′ W), alberga una población precarizada con conductas higiénico-sanitarias inadecuadas para la salud. El equipo de trabajo, integrado por docentes y alumnos de 4 Facultades de la UNLP, desarrolla un proyecto de Extensión Universitaria destinado a contribuir en el diagnóstico y prevención de zoonosis parasitarias, desde el año 2005 ininterrumpidamente. El objetivo del presente estudio es diagnosticar las parasitosis intestinales zoonóticas (de carácter desatendido) en la población de un área de riesgo sanitario y analizar su relación con las costumbres de las familias.Facultad de Ciencias Veterinaria

Prevalencia de enteroparasitosis humanas en un área vulnerable de la provincia de Buenos Aires

El barrio “El Molino”, ubicado en el Municipio de Ensenada, Provincia de Buenos Aires, República Argentina (34° 49′ S, 57° 58′ W), alberga una población precarizada con conductas higiénico-sanitarias inadecuadas para la salud. El equipo de trabajo, integrado por docentes y alumnos de 4 Facultades de la UNLP, desarrolla un proyecto de Extensión Universitaria destinado a contribuir en el diagnóstico y prevención de zoonosis parasitarias, desde el año 2005 ininterrumpidamente. El objetivo del presente estudio es diagnosticar las parasitosis intestinales zoonóticas (de carácter desatendido) en la población de un área de riesgo sanitario y analizar su relación con las costumbres de las familias.Facultad de Ciencias Veterinaria

Prevalencia Dioctophyme renale en un área vulnerable de la provincia de Buenos Aires

Desde el año 2005 a la fecha, de manera ininterrumpida, el grupo de trabajo realiza tareas en un área vulnerable de la Prov. de Bs. As., barrio “El Molino” en la localidad Punta Lara, municipio de Ensenada. El excesivo desmonte y posterior antropización originaron un área suburbana inundable y de elevada vulnerabilidad social y sanitaria, ya que los vecinos vuelcan sus efluentes cloacales sin tratamiento previo. Se estudiaron 692 caninos y 257 personas de la zona, tomando muestras de orina para verificar la presencia de huevos de Dioctophyme renale.Facultad de Ciencias Veterinaria