Protocolos para assepsia de meristemas e cultura de tecidos para o gênero Brachiaria (Trin.) Griseb e melhor tratamento para duplicação de número cromossômico.

O objetivo neste trabalho é divulgar os protocolos utilizados na Embrapa Gado de Corte para a assepsia e micropropagação in vitro de explantes de braquiária. Estes foram coletados in vivo para a cultura de tecidos, com a finalidade de otimizar a micropropagação e a poliploidização de genótipos diplóides. A metodologia utilizada resultou na duplicação somática efetiva de algumas plantas do gênero. O domínio dessas técnicas contribui extraordinariamente para a economia de tempo e espaço em obter genótipos valiosos ao programa de melhoramento da referida gramínea. Plantas duplicadas permitirão o melhoramento intra-específico nesse gênero, caracterizado por plantas apomíticas tetraplóides de grande importância agronômica.bitstream/CNPGC-2009-09/12404/1/DOC169.pd

Geometrical Calibration for the Panrover: a Stereo Omnidirectional System for Planetary Rover

Abstract. A novel panoramic stereo imaging system is proposed in this paper. The system is able to carry out a 360° stereoscopic vision, useful for rover autonomous-driving, and capture simultaneously a high-resolution stereo scene. The core of the concept is a novel "bifocal panoramic lens" (BPL) based on hyper hemispheric model (Pernechele et al. 2016). This BPL is able to record a panoramic field of view (FoV) and, simultaneously, an area (belonging to the panoramic FoV) with a given degree of magnification by using a unique image sensor. This strategy makes possible to avoid rotational mechanisms. Using two BPLs settled in a vertical baseline (system called PANROVER) allows the monitoring of the surrounding environment in stereoscopic (3D) mode and, simultaneously, capturing an high-resolution stereoscopic images to analyse scientific cases, making it a new paradigm in the planetary rovers framework.Differently from the majority of the Mars systems which are based on rotational mechanisms for the acquisition of the panoramic images (mosaicked on ground), the PANROVER does not contain any moving components and can rescue a hi-rate stereo images of the context panorama.Scope of this work is the geometric calibration of the panoramic acquisition system by the omnidirectional calibration methods (Scaramuzza et al. 2006) based on Zhang calibration grid. The procedures are applied in order to obtain well rectified synchronized stereo images to be available for 3D reconstruction. We applied a Zhang chess boards based approach even during STC/SIMBIO-SYS stereo camera calibration (Simioni et al. 2014, 2017). In this case the target of the calibration will be the stereo heads (the BPLs) of the PANROVER with the scope of extracting the intrinsic parameters of the optical systems. Differently by previous pipelines, using the same data bench the estimate of the extrinsic parameters is performed

Aging Reduces The Primary Humoral Response And The In Vitro Cytokine Production In Mice.

Aging is accompanied by a decrease in several physiological functions that make older individuals less responsive to environmental challenges. In the present study, we analyzed the immune response of female BALB/c mice (N = 6) of different ages (from 2 to 96 weeks) and identified significant age-related alterations. Immunization with hapten-protein (trinitrophenyl-bovine serum albumin) conjugates resulted in lower antibody levels in the primary and secondary responses of old mice (72 weeks old). Moreover, young mice (2, 16, and 32 weeks old) maintained specific antibodies in their sera for longer periods after primary immunization than did old mice. However, a secondary challenge efficiently induced memory in old mice, as shown by the increased antibody levels in their sera. The number of CD4+ and CD8+ T cells in the spleen increased until 8 weeks of age but there was no change in the CD4+/CD8+ ratio with aging. Splenic T cells from old mice that had or had not been immunized were less responsive to concanavalin-A and showed reduced cytokine production compared to young mice (IL-2: 57-127 vs 367-1104 pg/mL, IFN-gamma: 2344-12,836 vs 752-23,106 pg/mL and IL-10: 393-2172 vs 105-2869 pg/mL in old and young mice, respectively). These data suggest that there are significant changes in the organization of the immune system throughout life. However, the relevance of these alterations for the functioning of the immune system is unknown.401111-2

Preserved decision making ability in early multiple sclerosis

Background : The purpose of this study was to assess decision making in patients with multiple sclerosis (MS) at the earliest clinically detectable time point of the disease. Methods : Patients with definite MS (n = 109) or with clinically isolated syndrome (CIS, n = 56), a disease duration of 3 months to 5 years, and no or only minor neurological impairment (Expanded Disability Status Scale [EDSS] score 0-2.5) were compared to 50 healthy controls using the Iowa Gambling Task (IGT). Results : The performance of definite MS, CIS patients, and controls was comparable for the two main outcomes of the IGT (learning index: p = 0.7; total score: p = 0.6). The IGT learning index was influenced by the educational level and the co-occurrence of minor depression. CIS and MS patients developing a relapse during an observation period of 15 months dated from IGT testing demonstrated a lower learning index in the IGT than patients who had no exacerbation (p = 0.02). When controlling for age, gender and education, the difference between relapsing and non-relapsing patients was at the limit of significance (p = 0.06). Conclusion : Decision making in a task mimicking real life decisions is generally preserved in early MS patients as compared to controls. A possible consequence of MS relapsing activity in the impairment of decision making ability is also suspected in the early phase of M

Small inner marsh area delimitation using remote sensing spectral indexes and decision tree method in southern Brazil

Revista oficial de la Asociación Española de Teledetección[EN] Vast small inner marsh (SIM) areas have been lost in the past few decades through the conversion to agricultural, urban and industrial lands. The remaining marshes face several threats such as drainage for agriculture, construction of roads and port facilities, waste disposal, among others. This study integrates 17 remote sensing spectral indexes and decision tree (DT) method to map SIM areas using Sentinel 2A images from Summer and Winter seasons. Our results showed that remote sensing indexes, although not developed specifically for wetland delimitation, presented satisfactory results in order to classify these ecosystems. The indexes that showed to be more useful for marshes classification by DT techniques in the study area were NDTI, BI, NDPI and BI_2, with 25.9%, 17.7%, 11.1% and 0.8%, respectively. In general, the Proportion Correct (PC) found was 95.9% and 77.9% for the Summer and Winter images respectively. We hypothetize that this significant PC variation is related to the rice-planting period in the Summer and/or to the water level oscillation period in the Winter. For future studies, we recommend the use of active remote sensors (e.g., radar) and soil maps in addition to the remote sensing spectral indexes in order to obtain better results in the delimitation of small inner marsh areas.[ES] En las últimas décadas se han perdido grandes áreas de pequeñas marismas interiores (SIM) a través de la conversión a tierras agrícolas, urbanas e industriales. Las marismas restantes enfrentan varias amenazas, como el drenaje para la agricultura, la construcción de carreteras e instalaciones portuarias, la eliminación de residuos, entre otras. Este estudio integra 17 índices espectrales de teledetección y un método basado en árboles de decisión (DT) para cartografiar áreas de pequeñas marismas interiores utilizando imágenes del satélite Sentinel 2A de verano e invierno. Los resultados muestran que los índices de teledetección, aunque no han sido desarrollados específicamente para la delimitación de marismas, presentan resultados satisfactorios para clasificar estos ecosistemas. Los índices que demostraron ser más útiles para la clasificación de marismas mediante técnicas de DT en el área de estudio fueron el NDTI, BI, NDPI y BI_2, con 25.9%, 17.7%, 11.1% y 0.8%, respectivamente. En general, la proporción correcta encontrada fue de 95.9% y 77.9% para las imágenes de verano e invierno, respectivamente. Nuestra hipótesis es que esta variación significativa de la proporción correcta está relacionada con el período de siembra del arroz en verano y/o con el período de oscilación del nivel del agua en invierno. Para futuras investigaciones, recomendamos el uso de sensores remotos activos (por ejemplo, radar) y mapas de suelo además de los índices espectrales de teledetección para obtener mejores resultados en la delimitación de pequeñas áreas de marismas interiores.João Paulo Delapasse Simioni thanks the CAPES agency for providing a doctoral fellowship. The au-thors acknowledge the Center for Remote Sensing and Meteorology (CEPSRM) at the Federal University of Rio Grande do Sul (UFRGS) for the support provided for this research.Simioni, JPD.; Guasselli, LA.; Ruiz, LFC.; Nascimento, VF.; De Oliveira, G. (2018). Delimitación de pequeñas marismas interiores mediante índices espectrales y árboles de decisión en el sur de Brasil. Revista de Teledetección. (52):55-66. doi:10.4995/raet.2018.10366SWORD556652Artigas, F. J., Yang, J. 2006. Spectral discrimination of marsh vegetation types in the New Jersey Meadowlands, USA. 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XXIX ISPRS Congress Technical Commission VII: Interpretation of Photographic and Remote Sensing Data, 691-698.Congalton, R. G. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1), 35-46. https:// doi.org/10.1016/0034-4257(91)90048-BDeering, D. W. 1975. Measuring forage production of grazing units from Landsat MSS data. Proceedings of 10th International Symposium on Remote Sensing of Environment, 1975, 1169-1178.Delegido, J., Verrelst, J., Alonso, L., Moreno, J. 2011. Evaluation of sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content. Sensors, 11(7), 7063-7081. https://doi.org/10.3390/s110707063Di Vittorio, C. A., Georgakakos, A. P. 2018. Land cover classification and wetland inundation mapping using MODIS. Remote Sensing of Environment, 204, 1-17. https://doi.org/10.1016/j.rse.2017.11.001Dong, Z., Wang, Z., Liu, D., Song, K., Li, L., Jia, M., Ding, Z. 2014. 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R., Bertness, M. D. 2017. Biogeography of salt marsh plant zonation on the Pacific coast of South America. Journal of Biogeography, 12, 238-247. https://doi.org/10.1111/ jbi.13109Fluet-Chouinard, E., Lehner, B., Rebelo, L. M., Papa, F., Hamilton, S. K. 2015. Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data. Remote Sensing of Environment, 158, 348-361. https://doi.org/10.1016/j.rse.2014.10.015Friedl, M.A. M. A., Brodley, C. E. C. E. 1997. Decision tree classification of land cover from remotely sensed data. Remote Sensing of Environment, 61(3), 399- 409. https://doi.org/10.1016/S0034-4257(97)00049-7Gao, B. C. 1996. NDWI - A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257- 266. https://doi.org/10.1016/S0034-4257(96)00067-3Gedan, K. B., Crain, C. M., Bertness, M. D. 2009. Smallmammal herbivore control of secondary succession in New-England tidal marshes. Ecology, 90(2), 430- 440. https://doi.org/10.1890/08-0417.1Gitelson, A. A., Kaufman, Y. J., Merzlyak, M. N. 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58(3), 289-298. https://doi.org/10.1016/S0034-4257(96)00072-7Huete, A. R. 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295- 309. https://doi.org/10.1016/0034-4257(88)90106-XJensen, J. R. 2007. Remote sensing of the environment : an earth resource perspective. Pearson Prentice Hall.Judd, C., Steinberg, S., Shaughnessy, F., Crawford, G. 2007. Mapping salt marsh vegetation using aerial hyperspectral imagery and linear unmixing in Humboldt Bay, California. Wetlands, 27(4), 1144-1152. https://doi.org/10.1672/0277- 5212(2007)27[1144:msmvua]2.0.co;2Junk. 2013. Definição e Classificação das Áreas Úmidas (AUs) Brasileiras : Base Científica para uma Nova Política de Proteção e Manejo Sustentável Prefácio : Lista dos autores e suas instituições : Centro de Pesquisa Do Pantanal, BrazilJunk, W. J., Bayley, P. B., Sparks, R. E. 1989. The Flood Pulse Concept in River-Floodplain Systems. International Large River Symposium.Junk, W. J., Piedade, M. F. 2015. Áreas Úmidas (AUs) Brasileiras: Avanços e Conquistas Recentes. Boletim Ablimno, 41(2), 20-24.Junk, W. J., Piedade, M. T. F., Lourival, R., Wittmann, F., Kandus, P., Lacerda, L. D., Agostinho, A. A. 2014. Brazilian wetlands: Their definition, delineation, and classification for research, sustainable management, and protection. Aquatic Conservation: Marine and Freshwater Ecosystems, 24(1), 5-22. https://doi. org/10.1002/aqc.2386Kandus, P., Minotti, P., Malvárez, A. I. 2008. Distribution of wetlands in Argentina estimated from soil charts. Acta Scientiarum - Biological Sciences, 30(4), 403-409. https://doi.org/10.4025/actascibiolsci.v30i4.5870Kaplan, G., Avdan, U. 2017. Mapping and Monitoring Wetlands Using SENTINEL 2 Satellite Imagery. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, IV, 271-277. https:// doi.org/10.5194/isprs-annals-IV-4-W4-271-2017Kaplan, G., Avdan, U. 2017. Wetland Mapping Using Sentinel 1 SAR Data. In Suha Ozden, R. Cengiz Akbulak, Cuneyt Erenoglu, Oznur Karaca, Faize Saris, & Mustafa Avcioglu (Eds.), International Symposium on GIS Applications in Geography & Geosciences.Kaufman, Y., Tanre, D. 1992. 1992. Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Transactions on Geoscience and Remote Sensing, 30(2). https://doi.org/10.1109/36.134076Kulawardhana, R. W., Thenkabail, P. S., Vithanage, J., Biradar, C., Islam, M. A. a, Gunasinghe, S., Alankara, R. 2007. Evaluation of the wetland mapping methods using Landsat ETM+ and SRTM data. Journal of Spatial Hydrology, 7(2), 62-96. https://doi. org/10.1017/CBO9780511806049Lacaux, J. P., Tourre, Y. M., Vignolles, C., Ndione, J. A., Lafaye, M. 2007. Classification of ponds from highspatial resolution remote sensing: Application to Rift Valley Fever epidemics in Senegal. Remote Sensing of Environment, 106(1), 66-74. https://doi.org/10.1016/j. rse.2006.07.012Leite, M. G., Guasselli, L. A. 2013. Spatio-temporal dynamics of aquatic macrophytes in Banhado Grande, Gravataí River basin,. Para Onde!?, 7(1), 17-24.Liu, L., Liu, Y. H., Liu, C. X., Wang, Z., Dong, J., Zhu, G. F., Huang, X. 2013. Potential effect and accumulation of veterinary antibiotics in Phragmites australis under hydroponic conditions. Ecological Engineering, 53, 138-143. https://doi.org/10.1016/j. ecoleng.2012.12.033Mahdavi, S., Salehi, B., Amani, M., Granger, J. E., Brisco, B., Huang, W., Hanson, A. 2017. ObjectBased Classification of Wetlands in Newfoundland and Labrador Using Multi-Temporal PolSAR Data. Canadian Journal of Remote Sensing, 43(5), 432-450. https://doi.org/10.1080/07038992.2017.1342206Maltchik, L., Rolon, A. S., Guadagnin, D. L., Stenert, C. 2004. Wetlands of Rio Grande do Sul, Brazil: a classification with emphasis on plant communities. Acta Limnol. Bras, 16(2), 137-151.Mao, R., Ye, S.-Y., Zhang, X.-H. 2018. SoilAggregate-Associated Organic Carbon Along Vegetation Zones in Tidal Salt Marshes in the Liaohe Delta. CLEAN - Soil, Air, Water, 1-7. https://doi.org/10.1002/clen.201800049McFeeters, S. K. 1996. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425-1432. https://doi.org/10.1080/01431169608948714Mcowen, C. J., Weatherdon, L. V, Bochove, J.-W. Van, Sullivan, E., Blyth, S., Zockler, C., Fletcher, S. 2017. A global map of saltmarshes. Biodiversity Data Journal, 5(5), e11764. https://doi.org/10.3897/BDJ.5.e11764Miranda, C. de S., Paranho Filho, A. C., Pott, A. 2018. Changes in vegetation cover of the Pantanal wetland detected by vegetation index: a strategy for conservation. Biota Neotropica, 18(1), 1-6. https://doi.org/10.1590/1676-0611-bn-2016-0297Mondal, I., Bandyopadhyay, J. 2014. Coastal Wetland Modeling Using Geoinformatics Technology of Namkhana Island, South 24 Parganas, WB, India. Open Access Library Journal, 975, 1-17. https://doi.org/10.4236/oalib.1100975Nielsen, S. 1994. Geomorfologia da bacia do rio GravataíRS. In Bacia do rio Gravataí-RS: informações básicas para a gestão territorial (pp. 1-18). Porto Alegre: Proteger.Nunes da Cunha, C., Piedade, M. T. F., Junk, W. J. 2015. Classificação e Delineamento das Áreas Úmidas Brasileiras e de seus Macrohabitats. EdUFMT (Vol. 1). Cuiaba. https://doi.org/10.1017/CBO9781107415324.004Pearson, R. L., Miller, L. D. 1972. Remote Mapping of Standing Crop Biomass for Estimation of the Productivity of the Shortgrass Prairie. Remote Sensing of Environment, 8, 1355-1365.Pontius, R. G., Millones, M. 2011. Death to Kappa: Birth of quantity disagreement and allocation disagreement for accuracy assessment. International Journal of Remote Sensing, 32(15), 4407-4429. https://doi.org/10.1080/01431161.2011.552923Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H., Sorooshian, S. 1994. A modified soil adjusted vegetation index. Remote Sensing of Environment, 48(2), 119-126. https://doi.org/10.1016/0034-4257(94)90134-1Ramos, R. A., Pasqualetto, A. I., Balbueno, R. A., Quadros, E. L. L. de, Neves, D. D. das. 2014. Mapeamento e diagnóstico de áreas úmidas no Rio Grande do Sul, com o uso de ferramentas de geoprocessamento. In Anais do Simposio de Áreas Protegidas (pp. 17-21). Viçosa.Ramsar. 2002. A Framework for Wetland Inventory. 8th Meeting of the Conference of the Contracting Parties to the Convention on Wetlands. Valencia. Retrieved from http://archive.ramsar.org/pdf/inventoryframework-2002.pdfRichardson, A. J., Wiegand, C. L. 1977. Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing, 43(12), 1541-1552.Rossato, M. S. 2011. Os climas do Rio Grande do Sul: variabilidade, tendências e tipologia. Universidade Federal do Rio Grande do Sul.Rouse, J. W., Hass, R. H., Schell, J. A., Deering, D. W. 1973. Monitoring vegetation systems in the great plains with ERTS. Third Earth Resources Technology Satellite (ERTS) Symposium, 1, 309-317. https://doi.org/citeulike-article-id:12009708Ruiz, L. F. C., Caten, A. ten, Dalmolin, R. S. D. 2014. Árvore de decisão e a densidade mínima de amostras no mapeamento da cobertura da terra. Ciência Rural, 44(6), 1001-1007. https://doi.org/10.1590/S0103-84782014000600008Sakané, N., Alvarez, M., Becker, M., Böhme, B., Handa, C., Kamiri, H. W., Langensiepen, M., Menz, G., Misana, S., Mogha, N. G., Möseler, B. M., Mwita, E. J., Oyieke, H. A., Van Wijk, M. T. 2011. Classification, characterisation, and use of small wetlands in East Africa. Wetlands, 31, 1103. https://doi.org/10.1007/s13157-011-0221-4Sharma, A., Panigrahy, S., Singh, T. S., Patel, J. G., Tanwar, H. 2014. Wetland Information System Using Remote Sensing and GIS in Himachal Pradesh , India. Asian Journal of Geoinformatics, 14(4), 13-22.Sharpe, P. J., Kneipp, G., Forget, A. 2016. Comparison of Alternative Approaches for Wetlands Mapping: A Case Study from three U.S. National Parks. Wetlands, 36(3), 547-556. https://doi.org/10.1007/s13157-016-0764-5Silva, R. C. da. 2016. Estudo da dinâmica da fragilidade ambiental na Bacia Hidrográfica do Rio Gravataí, RS. Universidade Federal da Bahia.Simioni, J. P. D., Guasselli, L. A., Etchelar, C. B. 2017. Connectivity among Wetlands of EPA of Banhado Grande, RS Conetividade entre Áreas Úmidas, APA do Banhado Grande, RS. Brazilian Journal of Water Resources, 22(15). https://doi.org/10.1590/2318-0331.011716096Stefano, L. de. 2003. WWF ' s Water and Wetland Index Summary of Water Framework Directive results. 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Análise de viabilidade econômica de um sistema de produção modal de eucalipto para lenha na região de Itapeva, SP.

bitstream/item/141820/1/Comunicado-Tecnico-365-2015.pd

Increased Cardiovascular Risk Associated with Chemical Sensitivity to Perfluoro-Octanoic Acid: Role of Impaired Platelet Aggregation

Perfluoro\u2013alkyl substances (PFAS), particularly perfluoro\u2013octanoic acid (PFOA), are persisting environmental chemicals showing bioaccumulation in human tissues. Recently, exposure to PFAS has been associated with increased prevalence of cardiovascular diseases (CVDs). However, a causal role of PFAS in atherosclerosis pathogenesis is under-investigated. Here, we investigated the effect of PFOA exposure on platelets\u2019 function, a key player in atherosclerosis process. PFOA accumulation in platelets was evaluated by liquid chromatography-mass spectrometry. Changes in platelets\u2019 membrane fluidity and activation after dose-dependent exposure to PFOA were evaluated by merocyanine 540 (MC540) and anti P-Selectin immune staining at flow cytometry, respectively. Intracellular calcium trafficking was analyzed with Fluo4M probe, time-lapse live imaging. Platelets\u2019 aggregation state was also evaluated with Multiplate\uae aggregometry analyzer in 48 male subjects living in a specific area of the Veneto region with high PFAS environmental pollution, and compared with 30 low-exposure control subjects. Platelets\u2019 membrane was the major target of PFOA, whose dose-dependent accumulation was associated in turn with increased membrane fluidity, as expected by a computational model; increased activation at resting condition; and both calcium uptake and aggregation upon activation. Finally, exposed subjects had higher serum and platelets levels of PFOA, together with increased aggregation parameters at Multiplate\uae, compared with controls. These data help to explain the emerging association between PFAS exposure and CVD

Pharmacological and partial biochemical characterization of Bmaj-9 isolated from Bothrops marajoensis snake venom

Bmaj-9, a basic PLA2 (13679.33 Da), was isolated from Bothrops marajoensis snake venom through only one chromatographic step in reversed phase HPLC on ¼-Bondapak C-18 column. The amino acid composition showed that Bmaj-9 had a high content of Lys, His, and Arg, typical of a basic PLA2. The sequence of Bmaj-9 contains 124 amino acid residues with a pI value of 8.55, such as DLWQWGQMIL KETGKLPFSY YTAYGCYCGW GGRGGKPKAD TDRCCFVHDC, revealing a high homology with Asp49 PLA2 from other snake venoms. It also exhibited a pronounced phospholipase A2 activity when compared with crude venom. In chick biventer cervicis preparations, the time for 50% and 100% neuromuscular paralysis was respectively (in minutes): 110 ± 10 (1 µg/mL); 40 ± 6 and 90 ± 2 (5 µg/mL); 30 ± 3 and 70 ± 5 (10 µg/mL); 42 ± 1 and 60 ± 2 (20 µg/mL), with no effect on the contractures elicited by either exogenous ACh (110 µM) or KCl (20 mM). Bmaj-9 (10 µg/mL) neither interfered with the muscular response to direct electrical stimulation in curarized preparations nor significantly altered the release of CK at 0, 15, 30 and 60 minutes incubations (27.4 ± 5, 74.2 ± 8, 161.0 ± 21 and 353.0 ± 47, respectively). The histological analysis showed that, even causing blockade at the maximum dosage (5 µg/mL), the toxin does not induce significant morphological alterations such as necrosis or infiltration of inflammatory cells. These results identified Bmaj-9 as a new member of the basic Asp49 PLA2 family able to interact with the motor nerve terminal membrane, thereby inducing a presynaptic neuromuscular blockade181627