An Assessment of Accessibility and Connectivity of Some Important Places in Kolhapur City of Maharashtra: A Road Network Analysis

There are various methods to find out the accessibility and connectivity of road networks. The method that used to determine the accessibility and connectivity is quantitative or qualitative; sometime it includes both quantitative and qualitative aspects of transportation network. The degree to which any place is served by transportation network (on which it stands) defines the accessibility of that place. And the connectivity is the ratio between total number of arcs and total number of nodes in transportation network. The connectivity is directly proportional to the total number of arcs and inversely proportional to the total number of nodes. If the degree of connectivity within a transportation network is higher; then it means that transportation system will be more efficient. Therefore, the present paper attempts to access the accessibility and connectivity of road networks in Kolhapur city. The accessibility of study area is determined by the shortest path matrix. The connectivity is determined by the beta (?) index given by K.J. Kansky. After analyzing the accessibility and connectivity of study area, easily accessible and shortest paths are also suggested in order to reach the important centers very easily

Mapping and characterization of structural variation in 17,795 human genomes

A key goal of whole-genome sequencing for studies of human genetics is to interrogate all forms of variation, including single-nucleotide variants, small insertion or deletion (indel) variants and structural variants. However, tools and resources for the study of structural variants have lagged behind those for smaller variants. Here we used a scalable pipeline1 to map and characterize structural variants in 17,795 deeply sequenced human genomes. We publicly release site-frequency data to create the largest, to our knowledge, whole-genome-sequencing-based structural variant resource so far. On average, individuals carry 2.9 rare structural variants that alter coding regions; these variants affect the dosage or structure of 4.2 genes and account for 4.0–11.2% of rare high-impact coding alleles. Using a computational model, we estimate that structural variants account for 17.2% of rare alleles genome-wide, with predicted deleterious effects that are equivalent to loss-of-function coding alleles; approximately 90% of such structural variants are noncoding deletions (mean 19.1 per genome). We report 158,991 ultra-rare structural variants and show that 2% of individuals carry ultra-rare megabase-scale structural variants, nearly half of which are balanced or complex rearrangements. Finally, we infer the dosage sensitivity of genes and noncoding elements, and reveal trends that relate to element class and conservation. This work will help to guide the analysis and interpretation of structural variants in the era of whole-genome sequencing

An Inside to Outside approach: Focussing on parametric pattern generation for spatial organization, satisfying experiential demands

Identification and application of suitable computational methods to effectively handle complexity issues in architectural design process. The associated research questions are: - How can perceptual demands for an exhibition be satisfied using parametric - pattern based approach? - How can such experential demands be addressed together with other functional related concerns optimally? The application to verify the validity of the study concerns the design of an art center in an urban context.Computational design and fabrication technologies in ArchitectureArchitectureArchitectur

The genome of Chenopodium quinoa

Chenopodium quinoa (quinoa) is a highly nutritious grain identified as an important crop to improve world food security. Unfortunately, few resources are available to facilitate its genetic improvement. Here we report the assembly of a high-quality, chromosome-scale reference genome sequence for quinoa, which was produced using single-molecule real-time sequencing in combination with optical, chromosome-contact and genetic maps. We also report the sequencing of two diploids from the ancestral gene pools of quinoa, which enables the identification of sub-genomes in quinoa, and reduced-coverage genome sequences for 22 other samples of the allotetraploid goosefoot complex. The genome sequence facilitated the identification of the transcription factor likely to control the production of anti-nutritional triterpenoid saponins found in quinoa seeds, including a mutation that appears to cause alternative splicing and a premature stop codon in sweet quinoa strains. These genomic resources are an important first step towards the genetic improvement of quinoa

Erratum: Corrigendum: The genome of Chenopodium quinoa

The Acknowledgements section of this Article should have included the following sentence: “Metabolite imaging was conducted at Metabolomics Australia (School of BioSciences, The University of Melbourne, Australia), a NCRIS initiative under Bioplatforms Australia Pty Ltd.”. The original Article has been corrected online

Recurrent noncoding U1 snRNA mutations drive cryptic splicing in SHH medulloblastoma

In cancer, recurrent somatic single-nucleotide variants—which are rare in most paediatric cancers—are confined largely to protein-coding genes1–3. Here we report highly recurrent hotspot mutations (r.3A>G) of U1 spliceosomal small nuclear RNAs (snRNAs) in about 50% of Sonic hedgehog (SHH) medulloblastomas. These mutations were not present across other subgroups of medulloblastoma, and we identified these hotspot mutations in U1 snRNA in only <0.1% of 2,442 cancers, across 36 other tumour types. The mutations occur in 97% of adults (subtype SHHδ) and 25% of adolescents (subtype SHHα) with SHH medulloblastoma, but are largely absent from SHH medulloblastoma in infants. The U1 snRNA mutations occur in the 5′ splice-site binding region, and snRNA-mutant tumours have significantly disrupted RNA splicing and an excess of 5′ cryptic splicing events. Alternative splicing mediated by mutant U1 snRNA inactivates tumour-suppressor genes (PTCH1) and activates oncogenes (GLI2 and CCND2), and represents a target for therapy. These U1 snRNA mutations provide an example of highly recurrent and tissue-specific mutations of a non-protein-coding gene in cancer