Carbon: Nitrogen (C:N) ratio level variation influences microbial community of the system and growth as well as immunity of shrimp (Litopenaeus vannamei) in biofloc based culture system.

Biofloc technology (BFT) is a novel modern aquaculture farming technique used to reduce toxic nitrogen concentration, act as in situ food source and eradicate pollutants using carbon and therefore to control C:N ratio in an aquaculture system. In this study, effect of different C:N ratios of a biofloc based system on water quality such as the level of Total ammonia nitrogen (TAN) nitrite-nitrogen (NO2--N) and nitrate nitrogen (NO3--N) were explored. Further, the growth and immunity status of shrimp L. vannamei under the influence of different C:N ratios were evaluated. Two of the C:N ratios (15 and 20) could significantly (P < 0.05) reduce TAN, NO2-N and NO3-N levels (0.456 ±â€¯0.01, 0.145 ±â€¯0.09, and 0.102 ±â€¯0.02 ppm) compared to control (1.45 ±â€¯0.1, 0.749 ±â€¯0.14 and 0.675 ±â€¯0.16 ppm). Large variations in the frequency distribution of operational taxonomic units (OTUs) for the bacterial community in water with different C:N ration (BFT) and control were observed. Vibrios often considered as opportunistic pathogens, where the most dominant bacterial flora of water in control (79%) and C:N5 (37%) group. In C:N10, Thauera (62%) was most represented genus. Similarly, Attheyaceae (56%), followed by Peridiniaceae (30%) were the most dominant groups in C:N15 treatment. The diversity of bacterial flora was more spread in C:N20 treatments with Psychrobacter (26%), Proteobacteria (25%) and Peridiniaceae (20%) as the major groups. The trend of Vibrio dominance decreased with the increase in C:N ratios and thus confirming the dominance of heterotrophic bacteria in high C:N ratio groups. Upon challenge with pathogens, shrimps from C:N10, C:N15 and C:N20 groups showed significantly higher survival (P < 0.05) compared to the C:N5 and control group. Similarly, better growth rate was also observed in BFT tanks compared to control both during the culture and at harvest. Comparatively higher expression of four immune-related genes (ras-related nuclear gene (RAN), serine proteinase gene (SP), prophenoloxidase activating enzyme (PPAE), and crustin were observed in different C:N ratio ponds than control and these were in increasing trend with the C:N ratio. Gene expression analysis showed that the transcripts of those immune genes were significantly increased among all C:N treatments than that of control. Overall, these findings demonstrated that with optimum C:N ratio, BFT can be used to optimize the bacterial community composition for both optimal water quality and optimal shrimp health. This study thus indicates the possibility of obtaining better performance of L. vannamei culture with proper adjustment of C:N ratio in a biofloc based system.

Effective FCM noise clustering algorithms in medical images.

The main motivation of this paper is to introduce a class of robust non-Euclidean distance measures for the original data space to derive new objective function and thus clustering the non-Euclidean structures in data to enhance the robustness of the original clustering algorithms to reduce noise and outliers. The new objective functions of proposed algorithms are realized by incorporating the noise clustering concept into the entropy based fuzzy C-means algorithm with suitable noise distance which is employed to take the information about noisy data in the clustering process. This paper presents initial cluster prototypes using prototype initialization method, so that this work tries to obtain the final result with less number of iterations. To evaluate the performance of the proposed methods in reducing the noise level, experimental work has been carried out with a synthetic image which is corrupted by Gaussian noise. The superiority of the proposed methods has been examined through the experimental study on medical images. The experimental results show that the proposed algorithms perform significantly better than the standard existing algorithms. The accurate classification percentage of the proposed fuzzy C-means segmentation method is obtained using silhouette validity index.

Improved fuzzy clustering algorithms in segmentation of DC-enhanced breast MRI.

Segmentation of medical images is a difficult and challenging problem due to poor image contrast and artifacts that result in missing or diffuse organ/tissue boundaries. Many researchers have applied various techniques however fuzzy c-means (FCM) based algorithms is more effective compared to other methods. The objective of this work is to develop some robust fuzzy clustering segmentation systems for effective segmentation of DCE - breast MRI. This paper obtains the robust fuzzy clustering algorithms by incorporating kernel methods, penalty terms, tolerance of the neighborhood attraction, additional entropy term and fuzzy parameters. The initial centers are obtained using initialization algorithm to reduce the computation complexity and running time of proposed algorithms. Experimental works on breast images show that the proposed algorithms are effective to improve the similarity measurement, to handle large amount of noise, to have better results in dealing the data corrupted by noise, and other artifacts. The clustering results of proposed methods are validated using Silhouette Method.

Effective fuzzy c-means based kernel function in segmenting medical images.

The objective of this paper is to develop an effective robust fuzzy c-means for a segmentation of breast and brain magnetic resonance images. The widely used conventional fuzzy c-means for medical image segmentations has limitations because of its squared-norm distance measure to measure the similarity between centers and data objects of medical images which are corrupted by heavy noise, outliers, and other imaging artifacts. To overcome the limitations this paper develops a novel objective function based standard objective function of fuzzy c-means that incorporates the robust kernel-induced distance for clustering the corrupted dataset of breast and brain medical images. By minimizing the novel objective function this paper obtains effective equation for optimal cluster centers and equation to achieve optimal membership grades for partitioning the given dataset. In order to solve the problems of clustering performance affected by initial centers of clusters, this paper introduces a specialized center initialization method for executing the proposed algorithm in segmenting medical images. Experiments are performed with synthetic, real breast and brain images to assess the performance of the proposed method. Further the validity of clustering results is obtained using silhouette method and this paper compares the results with the results of other recent reported fuzzy c-means methods. The experimental results show the superiority of the proposed clustering results.

Congenital hypomyelinating neuropathy.

An eleven-month-old baby born out of non-consanguineous parentage presented with history of delayed motor milestones. The weakness was predominantly distal; there was intercostal muscle weakness, generalized hypotonia and areflexia. The nerve conduction velocities were unobtainable in all the four limbs. Sural nerve biopsy was consistent with the diagnosis of congenital hypomyelinating neuropathy, a rare form of hereditary motosensory neuropathy.

Infundibular metastasis and panhypopituitarism.

A patient with no known history of primary neoplasia presented with panhypopituitarism secondary to metastasis. Complete anterior pituitary failure was documented by pituitary reserve testing. However, at autopsy there was extensive carcinomatous invasion of the hypothalamus and infundibulum but no adenohypophyseal involvement. The possibility of metastatic disease should be considered in patients presenting with panhypopituitarism. The literature covering this topic is reviewed.