Identification of micro satellite markers on chromosomes of bread wheat showing an association with karnal bunt resistance

A set of 104 wheat recombinant inbred lines developed from a cross between parents resistant (HD 29) and susceptible (WH 542) to karnal bunt (caused by Neovossia indica) were screened and used toidentify SSR markers linked with resistance to karnal bunt as these would allow indirect marker assisted selection of karnal bunt resistant genotypes. The two parents were analysed with 46 SSR primer pairs. Of these, 15 (32%) were found polymorphic between the two parental genotypes. Using these primer pairs, we carried out bulked segregate analysis on two bulked DNAs, one obtained by pooling DNA from 10 karnal bunt resistant recombinant inbred lines and the other similarly derived by pooling DNA from 10 karnal bunt susceptible recombinant inbred lines. Two molecular markers, Xgwm 337-1D and Xgwm 637-4A showed apparent linkage with resistance to karnal bunt. This was confirmed following selective genotyping of individual recombinant inbred lines included in the bulks. These markers may be useful in marker assisted selection for karnal bunt resistance in wheat

Enhanced quantum-based neural network learning and its application to signature verification

© 2017, Springer-Verlag GmbH Germany, part of Springer Nature. In this paper, an enhanced quantum-based neural network learning algorithm (EQNN-S) which constructs a neural network architecture using the quantum computing concept is proposed for signature verification. The quantum computing concept is used to decide the connection weights and threshold of neurons. A boundary threshold parameter is introduced to optimally determine the neuron threshold. This parameter uses min, max function to decide threshold, which assists efficient learning. A manually prepared signature dataset is used to test the performance of the proposed algorithm. To uniquely identify the signature, several novel features are selected such as the number of loops present in the signature, the boundary calculation, the number of vertical and horizontal dense patches, and the angle measurement. A total of 45 features are extracted from each signature. The performance of the proposed algorithm is evaluated by rigorous training and testing with these signatures using partitions of 60–40 and 70–30%, and a tenfold cross-validation. To compare the results derived from the proposed quantum neural network, the same dataset is tested on support vector machine, multilayer perceptron, back propagation neural network, and Naive Bayes. The performance of the proposed algorithm is found better when compared with the above methods, and the results verify the effectiveness of the proposed algorithm

Prognostic factors associated with small for gestational age babies in a tertiary care hospital of Western Nepal: a cross-sectional study.

Background Small for gestational age (SGA) is common among newborns in low‐income countries like Nepal and has higher immediate mortality and morbidities. Objectives To study the prevalence and prognostic factors of SGA babies in Western Nepal. Methods A cross‐sectional study (November 2016‐October 2017) was conducted in a tertiary care hospital in Western Nepal. Socio‐demographic, lifestyle factors including diet, and exposures including smoking and household air pollution in mothers who delivered newborns appropriate for gestational age (AGA), SGA and large for gestational age (LGA) were recorded. Logistic regression was carried out to find the odds ratio of prognostic factors after adjusting for potential confounders. Results Out of 4000 delivered babies, 77% (n = 3078) were AGA, 20.3% (n = 813) were SGA and 2.7% (n = 109) were LGA. The proportion of female‐SGA was greater in comparison to male‐SGA (n = 427, 52.5% vs n = 386, 47.5%). SGA babies were born to mothers who had term, preterm, and postterm delivery in the following proportions 70.1%, 19.3%, and 10.6%, respectively. The average weight gain (mean ± SD) by mothers in AGA pregnancies was 10.3 ± 2.4 kg, whereas in SGA were 9.3 ± 2.4 kg. In addition to low socioeconomic status (OR 1.9, 95% CI 1.1, 3.2), other prognostic factors associated with SGA were lifestyle factors such as low maternal sleep duration (OR 5.1, CI 3.6, 7.4) and monthly or less frequent meat intake (OR 5.0, CI 3.2, 7.8). Besides smoking (OR 8.8, CI 2.1, 36.3), the other major environmental factor associated with SGA was exposure to household air pollution (OR 5.4, 4.1, 6.9) during pregnancy. Similarly, some of the adverse health conditions associated with a significantly higher risk of SGA were anemia, oligohydramnios, and gestational diabetes. Conclusions SGA is common in Western Nepal and associated with several modifiable prognostic factors