Biotoxic effects of the herbicides on growth, seed yield, and grain protein of greengram

We studied the effects of atrazine, isoproturon, metribuzin and sulfosulfuron on plant vigour, nodulation, chlorophyll content, seed yield and protein content in seeds, in greengram inoculated with Bradyrhizobium sp. (vigna). The pre-emergence application of the four herbicides at 400 µg kg-1 of soil adversely affected the measured parameters. The average maximum increase of 10 % in seed yield occurred at 200 µg kg-1 of sulfosulfuron, while atrazine at 200 and 400 µg kg-1 of soil decreased the seed yield by 25 % and 40%, respectively. The average maximum chlorophyll content of 1.2 mg g-1 was obtained at 200 µg kg-1 of sulfosulfuron which declined consistently for all herbicides and increasing dose rates. Sulfosulfuron at 200 µg kg-1 increased the number of nodules found per plant by 7 % at 45 days after seeding the greengram. In contrast, the tested dose rates of atrazine, isoproturon and metribuzin significantly reduced the nodulation (nodule number and dry mass). The average maximum grain protein of 182 mg g-1 was obtained for sulfosulfuron at 400 µg kg-1, while minimum grain protein was obtained at 400 µg kg-1- of isoproturon (124 mg g-1) and atrazine (125 mg g-1) application. Among the herbicides tested, atrazine and metribuzin showed a large degree of phytotoxicity to the crop, inhibiting its vegetative growth and was thus incompatible with greengram. Journal of Applied Sciences and Environmental Management Vol. 10(3) 2006: 141-14

Complete loss of TP53 and RB1 is associated with complex genome and low immune infiltrate in pleomorphic rhabdomyosarcoma

Rhabdomyosarcoma accounts for roughly 1% of adult sarcomas, with pleomorphic rhabdomyosarcoma (PRMS) as the most common subtype. Survival outcomes remain poor for patients with PRMS, and little is known about the molecular drivers of this disease. To better characterize PRMS, we performed a broad array of genomic and immunostaining analyses on 25 patient samples. In terms of gene expression and methylation, PRMS clustered more closely with other complex karyotype sarcomas than with pediatric alveolar and embryonal rhabdomyosarcoma. Immune infiltrate levels in PRMS were among the highest observed in multiple sarcoma types and contrasted with low levels in other rhabdomyosarcoma subtypes. Lower immune infiltrate was associated with complete loss of both TP53 and RB1. This comprehensive characterization of the genetic, epigenetic, and immune landscape of PRMS provides a roadmap for improved prognostications and therapeutic exploration

A high-performance matrix-matrix multiplication methodology for CPU and GPU architectures

Current compilers cannot generate code that can compete with hand-tuned code in efficiency, even for a simple kernel like matrix–matrix multiplication (MMM). A key step in program optimization is the estimation of optimal values for parameters such as tile sizes and number of levels of tiling. The scheduling parameter values selection is a very difficult and time-consuming task, since parameter values depend on each other; this is why they are found by using searching methods and empirical techniques. To overcome this problem, the scheduling sub-problems must be optimized together, as one problem and not separately. In this paper, an MMM methodology is presented where the optimum scheduling parameters are found by decreasing the search space theoretically, while the major scheduling sub-problems are addressed together as one problem and not separately according to the hardware architecture parameters and input size; for different hardware architecture parameters and/or input sizes, a different implementation is produced. This is achieved by fully exploiting the software characteristics (e.g., data reuse) and hardware architecture parameters (e.g., data caches sizes and associativities), giving high-quality solutions and a smaller search space. This methodology refers to a wide range of CPU and GPU architectures

Refining transcriptional programs in kidney development by integration of deep RNA-sequencing and array-based spatial profiling

<p>Abstract</p> <p>Background</p> <p>The developing mouse kidney is currently the best-characterized model of organogenesis at a transcriptional level. Detailed spatial maps have been generated for gene expression profiling combined with systematic <it>in situ </it>screening. These studies, however, fall short of capturing the transcriptional complexity arising from each locus due to the limited scope of microarray-based technology, which is largely based on "gene-centric" models.</p> <p>Results</p> <p>To address this, the polyadenylated RNA and microRNA transcriptomes of the 15.5 dpc mouse kidney were profiled using strand-specific RNA-sequencing (RNA-Seq) to a depth sufficient to complement spatial maps from pre-existing microarray datasets. The transcriptional complexity of RNAs arising from mouse RefSeq loci was catalogued; including 3568 alternatively spliced transcripts and 532 uncharacterized alternate 3' UTRs. Antisense expressions for 60% of RefSeq genes was also detected including uncharacterized non-coding transcripts overlapping kidney progenitor markers, Six2 and Sall1, and were validated by section <it>in situ </it>hybridization. Analysis of genes known to be involved in kidney development, particularly during mesenchymal-to-epithelial transition, showed an enrichment of non-coding antisense transcripts extended along protein-coding RNAs.</p> <p>Conclusion</p> <p>The resulting resource further refines the transcriptomic cartography of kidney organogenesis by integrating deep RNA sequencing data with locus-based information from previously published expression atlases. The added resolution of RNA-Seq has provided the basis for a transition from classical gene-centric models of kidney development towards more accurate and detailed "transcript-centric" representations, which highlights the extent of transcriptional complexity of genes that direct complex development events.</p