Perceptually Motivated Generative Model for Magnetic Resonance Image Denoising.

Image denoising is an important preprocessing step in low-level vision problems involving biomedical images. Noise removal techniques can greatly benefit raw corrupted magnetic resonance images (MRI). It has been discovered that the MR data is corrupted by a mixture of Gaussian-impulse noise caused by detector flaws and transmission errors. This paper proposes a deep generative model (GenMRIDenoiser) for dealing with this mixed noise scenario. This work makes four contributions. To begin, Wasserstein generative adversarial network (WGAN) is used in model training to mitigate the problem of vanishing gradient, mode collapse, and convergence issues encountered while training a vanilla GAN. Second, a perceptually motivated loss function is used to guide the training process in order to preserve the low-level details in the form of high-frequency components in the image. Third, batch renormalization is used between the convolutional and activation layers to prevent performance degradation under the assumption of non-independent and identically distributed (non-iid) data. Fourth, global feature attention module (GFAM) is appended at the beginning and end of the parallel ensemble blocks to capture the long-range dependencies that are often lost due to the small receptive field of convolutional filters. The experimental results over synthetic data and MRI stack obtained from real MR scanners indicate the potential utility of the proposed technique across a wide range of degradation scenarios.

Image denoising in fluorescence microscopy using feature based gradient reconstruction.

Purpose: The utility of fluorescence microscopy imaging comes with the challenge of low resolution acquisitions, which severely limits information extraction and quantitative analysis. Image denoising is a technique that aims to remove noise from microscopy acquisitions by taking into account prior statistics of the corrupting noise. In this work, we propose an image denoising technique for fluorescence microscopy imaging. Approach: The proposed technique is based on the principle of multifractal feature extraction from a noisy sample followed by a reconstruction technique from these features. It is observed that by following a proper hierarchical classification procedure, meaningful features can be extracted from a noisy image. A denoised image is then estimated from this sparse feature set through proper formulation of an optimization problem. Results: Experiments are performed on both synthetic image databases as well as on real fluorescence microscopy data. Superior denoising results, in comparison to multiple comparing techniques, validate the potential of the proposed approach. Conclusion: The proposed method gives superior denoising results for low resolution fluorescence microscopy image acquisitions and can be used for post processing of data by biologists.

Geo-environmental factors and the effectiveness of mulberry leaf extract in managing malaria.

Malaria prevalence has become medically important and a socioeconomic impediment for the endemic regions, including Purulia, West Bengal. Geo-environmental variables, humidity, altitude, and land use patterns are responsible for malaria. For surveillance of the endemic nature of Purulia's blocks, statistical and spatiotemporal factors analysis have been done here. Also, a novel approach for the Pf malaria treatment using methanolic leaf extract of Morus alba S1 has significantly reduced the parasite load. The EC50 value (1.852) of the methanolic extract of M. alba S1 with P. falciparum 3D7 strain is close to the EC50 value (0.998) of the standard drug chloroquine with the same chloroquine-sensitive strain. Further studies with an in-silico model have shown successful interaction between DHFR and the phytochemicals. Both 1-octadecyne and oxirane interacted favourably, which was depicted through GC-MS analysis. The predicted binary logistic regression model will help the policy makers for epidemiological surveillance in malaria-prone areas worldwide when substantial climate variables create a circumstance favourable for malaria. From the in vitro and in silico studies, it can be concluded that the methanolic extract of M. alba S1 leaves were proven to have promising antiplasmodial activity. Thus, there is a scope for policy-driven approach for discovering and developing these lead compounds and undermining the rising resistance to the frontline anti-malarial drugs in the world.

Study of epidemiological behaviour of malaria and its control in the Purulia district of West Bengal, India (2016-2020).

Purulia is a malaria-prone district in West Bengal, India, with approximately half of the blocks defined as malaria endemic. We analyzed the malaria case in each block of the Purulia district from January 1, 2016, to December 31, 2020. As per the API, 20 blocks of Purulia were assigned to four different categories (0-3) and mapped using ArcGIS software. An exponential decay model was fitted to forecast the trend of malaria cases for each block of Purulia (2021-2025). There was a sharp decrease in total malaria cases and API from 2016 to 2020 due to the mass distribution of LLINs. The majority of cases (72.63%) were found in ≥ 15-year age group. Males were more prone to malaria (60.09%). Malaria was highly prevalent among Scheduled Tribes (48.44%). Six blocks were reported in Category 3 (high risk) and none in Category 0 (no risk) in 2016, while no blocks were determined to be in Category 3, and three blocks were in Category 0 in 2020. The exponential decay model prediction is oriented towards gaining malaria-free status in thirteen blocks of Purulia by 2025. This study will incite the government to uphold and strengthen the current efforts to meet the malaria elimination goals.

A Feature based Reconstruction Model for Fluorescence Microscopy Image Denoising.

The advent of Fluorescence Microscopy over the last few years have dramatically improved the problem of visualization and tracking of specific cellular objects for biological inference. But like any other imaging system, fluorescence microscopy has its own limitations. The resultant images suffer from the effect of noise due to both signal dependent and signal independent factors, thereby limiting the possibility of biological inferencing. Denoising is a class of image processing algorithms that aim to remove noise from acquired images and has gained wide attention in the field of fluorescence microscopy image restoration. In this paper, we propose an image denoising algorithm based on the concept of feature extraction through multifractal decomposition and then estimate a noise free image from the gradients restricted to these features. Experimental results over simulated and real fluorescence microscopy data prove the merit of the proposed approach, both visually and quantitatively.

Micro-organisms Associated with Febrile Neutropenia in Patients with Haematological Malignancies in a Tertiary Care Hospital in Eastern India.

There is paucity of information from eastern India with regard to observed dominant micro-organisms causing febrile neutropenia (FN) in patients with haematological malignancies. To identify the prevalence of pathogenic microorganisms associated with FN. A total number of 268 episodes of FN were analysed from September'2010 to October'2013. The blood samples were inoculated into brain heart infusion broth, glucose broth, Hicombi dual performance media (Himedia, LQ-12) at 37° C for 168 h and Bactec method was also performed for these samples. Blood agar, chocolate agar, MacConkey's agar and cystine lactose electrolyte deficient agar were used for isolation of the microorganisms. A total number of 78 (29.10 %) episodes revealed positive growths. Gram negative bacilli and Gram positive cocci were isolated in 61.53 and 34.61 % cases respectively. The eight commonest isolates were Pseudomonas aeruginosa (14.10 %), methicillin resistant Staphylococcus aureus (MRSA-12.82 %), Acinetobacter sps (11.53 %), coagulase negative Staphylococcus (10.25 %), Klebsiella pneumoniae (8.97 %), Escherichia coli (8.97 %), ESBL E. coli (6.41 %), methicillin sensitive S. aureus (MSSA-6.41 %). Amongst other less common isolates were Citrobacter kosseri (3.84 %), Citrobacter freundii (2.56 %), Ralstonia paucula (2.56 %), Cedecia neteri (1.28 %), methicillin resistant coagulase negative Staphylococcus (2.56 %). Candida spp. including two cases of Candida non-albicans was isolated in 3.84 % of cases. P. aeruginosa was the commonest pathogenic isolates in FN patients associated with haematological malignancies in this study. Gram negative bacteria were the commonest isolates in FN including significant numbers of rare opportunistic micro-organisms.