A Parameter Based Modified Fuzzy Possibilistic C-Means Clustering Algorithm for Lung Image Segmentation

Image processing is a technique necessary for modifying an image. The important part of image processing is Image segmentation. The identical medical images can be segmented manually. However the accurateness of image segmentation using the segmentation algorithms is more when compared with the manual segmentation. Medical image segmentation is an indispensable pace for the majority of subsequent image analysis tasks. In this paper, FCM and different extension of FCM Algorithm is discussed. The unique FCM algorithm yields better results for segmenting noise free images, but it fails to segment images degraded by noise, outliers and other imaging artifact. This paper presents an image segmentation approach using Modified Fuzzy Possibilistic C-Means algorithm (MFPCM). This approach is a generalized adaptation of standard Fuzzy C-Means Clustering (FCM) algorithm and Fuzzy Possibilistic C-Means algorithm. The drawback of the conventional FCM technique is eliminated in modifying the standard technique. The Modified FCM algorithm is formulated by modifying the distance measurement of the standard FCM algorithm to permit the labeling of a pixel to be influenced by other pixels and to restrain the noise effect during segmentation. Instead of having one term in the objective function, a second term is included, forcing the membership to be as high as possible without a maximum frontier restraint of one. Experiments are carried out on real images to examine the performance of the proposed modified Fuzzy Possibilistic FCM technique in segmenting the medical images. Standard FCM, Modified FCM, Possibilistic C-Means algorithm (PCM), Fuzzy Possibilistic C-Means algorithm (FPCM) and Modified FPCM are compared to explore the accuracy of our proposed approach

An integrative approach to discovering cryptic species within the Bemisia tabaci whitefly species complex

Bemisia tabaci is a cryptic whitefly-species complex that includes some of the most damaging pests and plant-virus vectors of a diverse range of food and fibre crops worldwide. We combine experimental evidence of: (i) differences in reproductive compatibility, (ii) hybrid verification using a specific nuclear DNA marker and hybrid fertility confirmation and (iii) high-throughput sequencing-derived mitogenomes, to show that the “Mediterranean” (MED) B. tabaci comprises at least two distinct biological species; the globally invasive MED from the Mediterranean Basin and the “African silver-leafing” (ASL) from sub-Saharan Africa, which has no associated invasion records. We demonstrate that, contrary to its common name, the “ASL” does not induce squash silver-leafing symptoms and show that species delimitation based on the widely applied 3.5% partial mtCOI gene sequence divergence threshold produces discordant results, depending on the mtCOI region selected. Of the 292 published mtCOI sequences from MED/ASL groups, 158 (54%) are low quality and/or potential pseudogenes. We demonstrate fundamental deficiencies in delimiting cryptic B. tabaci species, based solely on partial sequences of a mitochondrial barcoding gene. We advocate an integrative approach to reveal the true species richness within cryptic species complexes, which is integral to the deployment of effective pest and disease management strategies

Neuromuscular disease genetics in under-represented populations: increasing data diversity

\ua9 The Author(s) 2023. Published by Oxford University Press on behalf of the Guarantors of Brain. Neuromuscular diseases (NMDs) affect ∼15 million people globally. In high income settings DNA-based diagnosis has transformed care pathways and led to gene-specific therapies. However, most affected families are in low-to-middle income countries (LMICs) with limited access to DNA-based diagnosis. Most (86%) published genetic data is derived from European ancestry. This marked genetic data inequality hampers understanding of genetic diversity and hinders accurate genetic diagnosis in all income settings. We developed a cloud-based transcontinental partnership to build diverse, deeply-phenotyped and genetically characterized cohorts to improve genetic architecture knowledge, and potentially advance diagnosis and clinical management. We connected 18 centres in Brazil, India, South Africa, Turkey, Zambia, Netherlands and the UK. We co-developed a cloud-based data solution and trained 17 international neurology fellows in clinical genomic data interpretation. Single gene and whole exome data were analysed via a bespoke bioinformatics pipeline and reviewed alongside clinical and phenotypic data in global webinars to inform genetic outcome decisions. We recruited 6001 participants in the first 43 months. Initial genetic analyses \u27solved\u27 or \u27possibly solved\u27 ∼56% probands overall. In-depth genetic data review of the four commonest clinical categories (limb girdle muscular dystrophy, inherited peripheral neuropathies, congenital myopathy/muscular dystrophies and Duchenne/Becker muscular dystrophy) delivered a ∼59% \u27solved\u27 and ∼13% \u27possibly solved\u27 outcome. Almost 29% of disease causing variants were novel, increasing diverse pathogenic variant knowledge. Unsolved participants represent a new discovery cohort. The dataset provides a large resource from under-represented populations for genetic and translational research. In conclusion, we established a remote transcontinental partnership to assess genetic architecture of NMDs across diverse populations. It supported DNA-based diagnosis, potentially enabling genetic counselling, care pathways and eligibility for gene-specific trials. Similar virtual partnerships could be adopted by other areas of global genomic neurological practice to reduce genetic data inequality and benefit patients globally

Interaction between contrasting rice genotypes and soil physical conditions induced by hydraulic stresses typical of alternate wetting and drying irrigation of soil

Background and aims: Alternate wetting and drying (AWD) saves water in paddy rice production but could influence soil physical conditions and root growth. This study investigated the interaction between contrasting rice genotypes, soil structure and mechanical impedance influenced by hydraulic stresses typical of AWD. Methods: Contrasting rice genotypes, IR64 and deeper- rooting Black Gora were grown in various soil conditions for 2 weeks. For the AWD treatments the soil was either maintained in a puddled state, equilibrated to −5 kPa (WET), or dried to −50 kPa and then rewetted at thewater potential of −5 kPa (DRY-WET). There was an additional manipulated macropore structure treatment, i.e. the soil was broken into aggregates, packed into cores and equilibrated to −5 kPa (REPACKED). A flooded treatment (puddled soil remained flooded until harvest) was set as a control (FLOODED). Soil bulk density, penetration resistance and X-ray Computed Tomography (CT) derived macropore structure were measured. Total root length, root surface area, root volume, average diameter, and tip number were determined by WinRhizo. Results: AWD induced formation of macropores and slightly increased soil mechanical impedance. The total root length of the AWD and REPACKED treatments were 1.7–2.2 and 3.5–4.2 times greater than that of the FLOODED treatment. There was no significant difference between WET and DRY-WET treatments. The differences between genotypes were minimal. Conclusions: AWD influenced soil physical properties and some root characteristics of rice seedlings, but drying soil initially to −50 kPa versus −5 kPa had no impact. Macropores formed intentionally from repacking caused a large change in root characteristics

Population differentiation of Southern Indian male lineages correlates with agricultural expansions predating the caste system

Christina J. Adler, Alan Cooper, Clio S.I. Der Sarkissian and Wolfgang Haak are contributors to the Genographic ConsortiumPrevious studies that pooled Indian populations from a wide variety of geographical locations, have obtained contradictory conclusions about the processes of the establishment of the Varna caste system and its genetic impact on the origins and demographic histories of Indian populations. To further investigate these questions we took advantage that both Y chromosome and caste designation are paternally inherited, and genotyped 1,680 Y chromosomes representing 12 tribal and 19 non-tribal (caste) endogamous populations from the predominantly Dravidian-speaking Tamil Nadu state in the southernmost part of India. Tribes and castes were both characterized by an overwhelming proportion of putatively Indian autochthonous Y-chromosomal haplogroups (H-M69, F-M89, R1a1-M17, L1-M27, R2-M124, and C5-M356; 81% combined) with a shared genetic heritage dating back to the late Pleistocene (10–30 Kya), suggesting that more recent Holocene migrations from western Eurasia contributed, <20% of the male lineages. We found strong evidence for genetic structure, associated primarily with the current mode of subsistence. Coalescence analysis suggested that the social stratification was established 4–6 Kya and there was little admixture during the last 3 Kya, implying a minimal genetic impact of the Varna(caste) system from the historically-documented Brahmin migrations into the area. In contrast, the overall Y-chromosomal patterns, the time depth of population diversifications and the period of differentiation were best explained by the emergence of agricultural technology in South Asia. These results highlight the utility of detailed local genetic studies within India, without prior assumptions about the importance of Varna rank status for population grouping, to obtain new insights into the relative influences of past demographic events for the population structure of the whole of modern India.GaneshPrasad ArunKumar, David F. Soria-Hernanz, Valampuri John Kavitha, Varatharajan Santhakumari Arun, Adhikarla Syama, Kumaran Samy Ashokan, Kavandanpatti Thangaraj Gandhirajan, Koothapuli Vijayakumar, Muthuswamy Narayanan, Mariakuttikan Jayalakshmi, Janet S. Ziegle, Ajay K. Royyuru, Laxmi Parida, R. Spencer Wells, Colin Renfrew, Theodore G. Schurr, Chris Tyler Smith, Daniel E. Platt, Ramasamy Pitchappan, The Genographic Consortiu