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Methods based on convolutional neural networks have improved the performance of biomedical image segmentation. However, most of these methods cannot efficiently segment objects of variable sizes and train on small and biased datasets, which are common for biomedical use cases. While methods exist that incorporate multi-scale fusion approaches to address the challenges arising with variable sizes, they usually use complex models that are more suitable for general semantic segmentation problems. In this paper, we propose a novel architecture called Multi-Scale Residual Fusion Network (MSRF-Net), which is specially designed for medical image segmentation. The proposed MSRF-Net is able to exchange multi-scale features of varying receptive fields using a Dual-Scale Dense Fusion (DSDF) block. Our DSDF block can exchange information rigorously across two different resolution scales, and our MSRF sub-network uses multiple DSDF blocks in sequence to perform multi-scale fusion. This allows the preservation of resolution, improved information flow and propagation of both high-and low-level features to obtain accurate segmentation maps. The proposed MSRF-Net allows to capture object variabilities and provides improved results on different biomedical datasets. Extensive experiments on MSRF-Net demonstrate that the proposed method outperforms the cutting-edge medical image segmentation methods on four publicly available datasets. We achieve the Dice Coefficient (DSC) of 0.9217, 0.9420, and 0.9224, 0.8824 on Kvasir-SEG, CVC-ClinicDB, 2018 Data Science Bowl dataset, and ISIC-2018 skin lesion segmentation challenge dataset respectively. We further conducted generalizability tests and achieved DSC of 0.7921 and 0.7575 on CVC-ClinicDB and Kvasir-SEG, respectively.

Pesticides belonging to pyrethroid group are widely used in agricultural fields to check pest infestation in different crops for enhanced food production. In spite of beneficial effects, non-judicious use of pesticides imposes harmful effect on human health as their residues reach different food materials and ground water via leaching, percolation and bioaccumulation. Looking into the potential of microbial degradation of toxic compounds under natural environment, a cypermethrin-degrading Bacillus sp. was isolated from pesticide-contaminated soil of a rice field of Distt. Udham Singh Nagar, Uttarakhand, India. The bacteria degraded the compound up to 81.6 % within 15 days under standard growth conditions (temperature 32 °C pH 7 and shaking at 116 rpm) in minimal medium. Analysis of intermediate compounds of biodegraded cypermethrin revealed that the bacteria opted a new pathway for cypermethrin degradation. GC–MS analysis of biodegraded cypermethrin showed the presence of 4-propylbenzoate, 4-propylbenzaldehyde, phenol M-tert-butyl and 1-dodecanol, etc. which was not reported earlier in cypermethrin metabolism; hence a novel biodegradation pathway of cypermethrin with Bacillus sp. strain SG2 is proposed in this study.Electronic supplementary materialThe online version of this article (doi:10.1007/s13205-016-0372-3) contains supplementary material, which is available to authorized users.

The dissipation of the insecticide from soil, cabbage and cauliflower appeared to occur in a single phase and conformed to first-order kinetics. The half-lives of spinosad residues in cabbage, cauliflower and soil were calculated as 1.5, 2.8 and 2.8 days respectively for the 17.5 g ha(-1) treatment, and as 2.6, 2.0 and 2.0 days for the 35 g ha(-1) treatment.

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