Background: Mustard aphid is a major pest of Brassica oilseeds. No source for aphid resistance is presently available in Brassica juncea . A wild crucifer, Brassica fruticulosa is known to be resistant to mustard aphid. An artificially synthesized amphiploid, AD-4 (B. fruticulosa × B. rapa var. brown sarson) was developed for use as a bridge species to transfer fruticulosa resistance to B. juncea. Using the selfed backcross we could select a large number of lines with resistance to mustard aphid. This paper reports cytogenetic stability of introgression lines, molecular evidence for alien introgression and their reaction to mustard aphid infestation. Results: Majority of introgression lines had expected euploid chromosome number(2n= 36), showed normal meiosis and high pollen grain fertility. Well-distributed and transferable simple-sequence repeats (SSR) markers for all the 18 B. juncea chromosomes helped to characterize introgression events. Average proportions of recipient and donor genome in the substitution lines were 49.72 and 35.06%, respectively. Minimum alien parent genome presence (27.29%) was observed in the introgression line, Ad3K-280 . Introgressed genotypes also varied for their resistance responses to mustard aphid infestations under artificial release conditions for two continuous seasons. Some of the test genotypes showed consistent resistant reaction. Conclusions: B.juncea-fruticulosa introgression set may prove to be a very powerful breeding tool for aphid resistance related QTL/gene discovery and fine mapping of the desired genes/QTLs to facilitate marker assisted transfer of identified gene(s) for mustard aphid resistance in the background of commercial mustard genotypes.

Endometrial cancer is one of the most prevalent tumours in females and holds an 83% survival rate within 5 years of diagnosis. Hypoestrogenism is a major risk factor for the development of endometrial carcinoma (EC) therefore two major types are derived, type 1 being oestrogen-dependent and type 2 being oestrogen independent. Surgery, chemotherapeutic drugs, and radiation therapy are only a few of the treatment options for EC. Treatment of gynaecologic malignancies greatly depends on diagnosis or prognostic prediction. Diagnostic imaging data and clinical course prediction are the two core pillars of artificial intelligence (AI) applications. One of the most popular imaging techniques for spotting preoperative endometrial cancer is MRI, although this technique can only produce qualitative data. When used to classify patients, AI improves the effectiveness of visual feature extraction. In general, AI has the potential to enhance the precision and effectiveness of endometrial cancer diagnosis and therapy. This review aims to highlight the current status of applications of AI in endometrial cancer and provide a comprehensive understanding of how recent advancements in AI have assisted clinicians in making better diagnosis and improving prognosis of endometrial cancer. Still, additional study is required to comprehend its strengths and limits fully.

Quantification of Avoidable Yield Losses in OilseedBrassicaCaused by Insect Pests: A six year field study was conducted from 2001-2002 to 2006-2007 at Punjab Agricultural University, Ludhiana, India to study the losses in seed yield of differentBrassicaspecies (B. juncea, B. napus, B. carinata, B. rapaandEruca sativa) by the infestation of insect pests. The experiment was conducted in two different sets viz. protected/sprayed and unprotected, in a randomized block design, with three replications. Data on the infestation of insect pests, and seed yield were recorded at weekly intervals and at harvest, respectively. The loss in seed yield, due to mustard aphid and cabbage caterpillar, varied from 6.5 to 26.4 per cent.E. sativasuffered the least loss in seed yield and harboured the minimum population of mustard aphid (2.1 aphids/plant) and cabbage caterpillar (2.4 larvae/plant). On the other hand,B. carinatawas highly susceptible to the cabbage caterpillar (26.2 larvae/plant) and suffered the maximum yield loss (26.4%).