Leukocytospermia and sperm preparation - a flow cytometric study

Reprod Biol Endocrinol. 2009 Nov 19;7:12

Regional Image Features Model for Automatic Classification between Normal and Glaucoma in Fundus and Scanning Laser Ophthalmoscopy (SLO) Images

Glaucoma is one of the leading causes of blindness. There is no cure for glaucoma but detection at its earliest stage and subsequent treatment can aid patients to prevent blindness. Currently, optic disc and retinal imaging facilitates glaucoma detection but this method still requires manual post-imaging modifications that are time-consuming and do not totally remove subjectivity in image assessment. Therefore, it is necessary to automate this process. In this work, we have first proposed a novel computer aided approach for automatic glaucoma detection based on Regional Image Features Model (RIFM) which can automatically perform classification between normal and glaucoma images on the basis of regional information. Different from all the existing methods, our approach can extract both geometric (e.g. morphometric properties) and non-geometric based properties (e.g. pixel appearance/intensity values, texture) from images and significantly increase the classification performance. Our proposed approach consists of three new major contributions including automatic localisation of optic disc, automatic segmentation of disc, and classification between normal and glaucoma based on geometric and non-geometric properties of different regions of an image. We have compared our method with existing approaches and tested it on both fundus and Scanning laser ophthalmoscopy (SLO) images. The experimental results show that our proposed approach outperforms the state-of-the-art approaches using either geometric or non-geometric properties. The overall glaucoma classification accuracy for fundus is 94.4% and accuracy of detection of suspicion of glaucoma in SLO images is 93.9%

Contribution of honeybees to soybean yield

Despite the economic importance of soybean [Glycine max (L.) Merr.], knowledge on the contribution of entomological pollination on seed yield is scarce. This study estimates the production of soybean resulting from pollination by honeybees (Apis mellifera L.) in two consecutive growing seasons in Paraná (Argentina). Experiments had two treatments: excluded flower-visiting insects (EV) and non-excluded flower-visiting insects (NEV). The abundance of honeybees was similar in both years, although soybean production differed significantly (P < 0.05) between years. The NEV treatment out-yielded (P < 0.001) the EV treatment by 18% (5224 vs. 4415 kg ha−1) in year 1, which was associated with an increase in the seeds per unit area but not with individual seed weight. In contrast, seed yield (on average 3830 kg ha−1) and seeds per unit area did not differ between treatments in year 2. Individual seed weight was 3–5% (P < 0.05) higher in EV than in NEV in both years. The mechanisms involved in the seed yield increase could be related with pollen sterility in relegated flowers in secondary racemes or in distal locations of primary racemes under favorable conditions, as recorded in year 1. Thus, the action of honeybees carrying pollen from fertile flowers to relegated flowers may have increased the pod and seed set in treatment NEV in year 1.EEA ParanáFil: Blettler, Diego César. Provincia de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Universidad Autónoma de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción; ArgentinaFil: Fagundez, Guillermina Andrea. Provincia de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Universidad Autónoma de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción; ArgentinaFil: Caviglia, Octavio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Paraná. Grupo Ecología Forestal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ciencias Agropecuarias; Argentin