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BACKGROUND:Embryonic aneuploidy may result in miscarriage, implantation failure, or birth defects. Thus, it is clinically necessary to avoid the selection of aneuploid embryos during in vitro fertilization treatment.AIM:The aim of this study was to identify the morphokinetic differences by analyzing the development of euploid and aneuploid embryos using a time-lapse technology. We also checked the accuracy of a previously described model for selection of euploid embryos based on morphokinetics in our study population.MATERIALS AND METHODS:It is a retrospective study of 29 cycles undergoing preimplantation genetic screening from October 2013 to April 2015 at our center. Of 253 embryos, 167 suitable for biopsy embryos were analyzed for their chromosomal status using array-comparative genome hybridization (CGH). The morphokinetic behavior of these embryos was further analyzed in embryoscope using time-lapse technology.RESULTS:Among the analyzed embryos, 41 had normal and 126 had abnormal chromosome content. No significant difference in morphokinetics was found between euploid and aneuploid embryos. The percentage of embryos with blastulation was similar in the euploid (65.85%, 27/41) and aneuploid (60.31%, 76/126) embryos (P = 0.76). Although hard to define, majority of the chromosomal defects might be due to meiotic errors. On applying embryo selection model from Basile et al., embryos falling within optimal ranges for time to division to 5 cells (t5), time period of the third cell cycle (CC3), and time from 2 cell division to 5 cell division (t5-t2) exhibited greater proportion of normal embryos than those falling outside the optimal ranges (28.6%, 25.9%, and 26.7% vs. 17.5%, 20.8%, and 14.3%).CONCLUSION:Keeping a track of time interval between two stages can help us recognize aneuploid embryos at an earlier stage and prevent their selection of transfer. However, it cannot be used as a substitute for array CGH to select euploid embryos for transfer.

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Agriculture provides food for human existence, where insects damage the crops. The identification of the insect is a difficult process and subjected to expert opinion. In recent years, researches using deep learning in fields of object detection have been widespread and show accuracy as a result. This study show the comparison of three widely used deep learning meta-architectures (Faster R-CNN, SSD Inception and SSD Mobilenet) as object detection for selected flying insects namely Phyllophaga spp., Helicoverpa armigera and Spodoptera litura. The proposed study is focused on accuracy performance of selected meta-architectures using small dataset of insects. The meta-architecture was tested with same environment for all three architectures and Faster RCNN meta-architecture performs outstanding with an accuracy of 95.33%

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Congenital triangular alopecia also known as temporal triangular alopecia or Brauer nevus may be present at birth or acquired during the first decade of life. It can present as triangular, oval, or lancet-shaped patch of alopecia. It may be misdiagnosed as alopecia areata, traction alopecia, trichotillomania, tinea capitis, and aplasia cutis congenita. Histopathological features and dermoscopic features help in its diagnosis. There is no effective treatment for it and, in most cases, there is no need for therapeutic intervention. Therapeutic modalities include topical minoxidil, surgical excision, and hair transplantation.

A study was carried out under field conditions on 50 acyclic/ anestrus buffaloes to evaluate the efficacy of four standard estrus synchronization protocols, viz., Doublesynch, Estradoublesynch, Ovsynch, and Ovsynch Plus (10 buffaloes in each protocol, and in untreated control group) in terms of estrus induction response, conception rates at induced estrus with FTAI and monitoring plasma progesterone, protein and cholesterol profile at different time intervals during treatment and day 12 post-AI. All the animals received pre-synchronization treatment, i.e., Inj. 100 mg ivermectin s/c, Inj. tono-vita 20 ml, and multi-minerals 1 bolus daily for 7 days. The conception rates obtained at induced estrus (FTAI) were 50.0, 40.0, 30.0 and 50.0 % with Doublesynch, Estradoublesynch, Ovsynch and Ovsynch Plus protocol, respectively. The rests were taken as non-conceived ones. The plasma progesterone concentrations monitored on day 0 (start of treatment), 7/9 (PGF2 α injection), 10/12 (FTAI) and on day 12 post-AI revealed significant (p Lass Than 0.01) effect of sampling days in all four protocols with higher values on day of PGF2 α injection and on day 12 post-AI compared to other days. Moreover, the plasma progesterone concentrations were significantly (p Lass Than 0.05) higher in conceiving than the non-conceiving buffaloes on day 12 post-AI in all 4 protocols. The mean plasma protein and cholesterol profile did not differ significantly between days in any of the protocols. The animals under Doublesynch protocol however had significantly higher protein values as compared to Ovsynch and Ovsynch Plus protocols. Moreover, the non-conceiving buffaloes under Ovsynch Plus protocol had significantly (p Lass Than 0.05) lower mean plasma protein (5.73±0.15 vs. 6.49±0.13 g/dl) and cholesterol (57.42±1.19 vs. 76.68±1.85 mg/dl) concentrations compared to their counterparts. It was concluded that all four hormonal protocols improved plasma progesterone profile and conception rates in acyclic buffaloes without altering plasma protein and cholesterol profile. The maximal benefit was with Doublesynch and Ovsynch plus protocols, hence these can be practiced under field conditions to manage acyclic buffaloes.

In agriculture, Pests are decreasing agricultural productivity. Identifying a pest is a challenging process and subject to expert opinion. Nowadays, lots of work carried out for automatic pest detection. It becomes possible because of emerging Deep Learning’s object detection architectures. This paper shows the multi-class pest detection using Faster R-CNN architecture and compared the performance results of image augmentation with focused on the accuracy performance along with small dataset. We have used Horizontal Flip and 90 Degree Rotation augmentation parameters for solving class imbalance problem. We found that trained pest detection model with augmentation options can perform better with an accuracy of 91.02% using Faster R-CNN architecture.