Effect of organic and inorganic nutrients on rice (Oryza sativa var. CO 51) productivity and soil fertility in the Western zone of Tamil Nadu, India

In sustainable agriculture, to ensure high-quality food production, a combination of organic and inorganic nutrient sources are required. During the winter season of 2020, a field experiment was undertaken in the western zone of Tamil Nadu to assess the effects of organics and inorganics on the growth, yield, and soil properties of rice, Oryza sativa var. CO 51. The experiment was framed in Random Block Design (RBD) comprising of 8 treatments viz., Recommended dose of fertilizer Soil Test Crop Response (STCR) approach (T1), RDF 75 % + Farm yard manure @ 12.5 t ha-1 (T2), T2 + Seed treatment with Azospirillum and Phosphobacteria + Soil application of AM fungi (T3), RDF 75 % + Vermicompost @ 5 t ha-1 (T4), T4 + Seed treatment with Azospirillum and Phosphobacteria + Soil application of AM fungi (T5), FYM @ 12.5 t ha-1 + Seed treatment with Azospirillum and Phosphobacteria + Soil application of AM fungi (T6), Vermicompost @ 5 t ha-1+ Seed treatment with Azospirillum and Phosphobacteria + Soil application of AM fungi (T7) and absolute control (T8) , replicated thrice. Among the integrated nutrient management practices, T5 proved its superiority over other treatments with respect to growth and physiological parameters followed by T3. This would have been because of the solubilization of phosphorus in the soil by AM organisms which is made accessible for crop growth. Utilization of biofertilizer enhanced the N availability and solubilized the inaccessible phosphorus, which thus recorded higher N accessibility and better phosphorus uptake when applied along with a recommended dose of fertilizer for rice.

Hyper Vision Net: Kidney Tumor Segmentation Using Coordinate Convolutional Layer and Attention Unit

KiTs19 challenge paves the way to haste the improvement of solid kidney tumor semantic segmentation methodologies. Accurate segmentation of kidney tumor in computer tomography (CT) images is a challenging task due to the non-uniform motion, similar appearance and various shape. Inspired by this fact, in this manuscript, we present a novel kidney tumor segmentation method using deep learning network termed as Hyper vision Net model. All the existing U-net models are using a modified version of U-net to segment the kidney tumor region. In the proposed architecture, we introduced supervision layers in the decoder part, and it refines even minimal regions in the output. A dataset consists of real arterial phase abdominal CT scans of 300 patients, including 45964 images has been provided from KiTs19 for training and validation of the proposed model. Compared with the state-of-the-art segmentation methods, the results demonstrate the superiority of our approach on training dice value score of 0.9552 and 0.9633 in tumor region and kidney region, respectively

Raspberry PI Based Artificial Vision Assisting System for Blind Persons

The main aim of this paper is to implement a system that will help blind person. This system is used by a RASPBERRY PI circuit to provide for the identification of the objects, the first level localization. It also incorporates additional components to provide more refined location and orientation information. The input process is to capture every object around 10m and it is convert into the output processing in voice command which is adopted in Bluetooth headset which is used by blind people using RASPBERRY PI component

An Algorithm to Find All Identical Motifs in Multiple Biological Sequences

Sequence motifs are of greater biological importance in nucleotide and protein sequences. The conserved occurrence of identical motifs represents the functional significance and helps to classify the biological sequences. In this paper, a new algorithm is proposed to find all identical motifs in multiple nucleotide or protein sequences. The proposed algorithm uses the concept of dynamic programming. The application of this algorithm includes the identification of (a) conserved identical sequence motifs and (b) identical or direct repeat sequence motifs across multiple biological sequences (nucleotide or protein sequences). Further, the proposed algorithm facilitates the analysis of comparative internal sequence repeats for the evolutionary studies which helps to derive the phylogenetic relationships from the distribution of repeats. © 2010 Springer-Verlag

The role of molecular chaperonins in warm ischemia and reperfusion injury in the steatotic liver: A proteomic study

BACKGROUND: The molecular basis of the increased susceptibility of steatotic livers to warm ischemia/reperfusion (I/R) injury during transplantation remains undefined. Animal model for warm I/R injury was induced in obese Zucker rats. Lean Zucker rats provided controls. Two dimensional differential gel electrophoresis was performed with liver protein extracts. Protein features with significant abundance ratios (p < 0.01) between the two cohorts were selected and analyzed with HPLC/MS. Proteins were identified by Uniprot database. Interactive protein networks were generated using Ingenuity Pathway Analysis and GRANITE software. RESULTS: The relative abundance of 105 proteins was observed in warm I/R injury. Functional grouping revealed four categories of importance: molecular chaperones/endoplasmic reticulum (ER) stress, oxidative stress, metabolism, and cell structure. Hypoxia up-regulated 1, calcium binding protein 1, calreticulin, heat shock protein (HSP) 60, HSP-90, and protein disulfide isomerase 3 were chaperonins significantly (p < 0.01) down-regulated and only one chaperonin, HSP-1was significantly upregulated in steatotic liver following I/R. CONCLUSION: Down-regulation of the chaperones identified in this analysis may contribute to the increased ER stress and, consequently, apoptosis and necrosis. This study provides an initial platform for future investigation of the role of chaperones and therapeutic targets for increasing the viability of steatotic liver allografts

The microaerophilic microbiota of de-novo paediatric inflammatory bowel disease: the BISCUIT study

&lt;p&gt;Introduction: Children presenting for the first time with inflammatory bowel disease (IBD) offer a unique opportunity to study aetiological agents before the confounders of treatment. Microaerophilic bacteria can exploit the ecological niche of the intestinal epithelium; Helicobacter and Campylobacter are previously implicated in IBD pathogenesis. We set out to study these and other microaerophilic bacteria in de-novo paediatric IBD.&lt;/p&gt; &lt;p&gt;Patients and Methods: 100 children undergoing colonoscopy were recruited including 44 treatment naïve de-novo IBD patients and 42 with normal colons. Colonic biopsies were subjected to microaerophilic culture with Gram-negative isolates then identified by sequencing. Biopsies were also PCR screened for the specific microaerophilic bacterial groups: Helicobacteraceae, Campylobacteraceae and Sutterella wadsworthensis.&lt;/p&gt; &lt;p&gt;Results: 129 Gram-negative microaerophilic bacterial isolates were identified from 10 genera. The most frequently cultured was S. wadsworthensis (32 distinct isolates). Unusual Campylobacter were isolated from 8 subjects (including 3 C. concisus, 1 C. curvus, 1 C. lari, 1 C. rectus, 3 C. showae). No Helicobacter were cultured. When comparing IBD vs. normal colon control by PCR the prevalence figures were not significantly different (Helicobacter 11% vs. 12%, p = 1.00; Campylobacter 75% vs. 76%, p = 1.00; S. wadsworthensis 82% vs. 71%, p = 0.312).&lt;/p&gt; &lt;p&gt;Conclusions: This study offers a comprehensive overview of the microaerophilic microbiota of the paediatric colon including at IBD onset. Campylobacter appear to be surprisingly common, are not more strongly associated with IBD and can be isolated from around 8% of paediatric colonic biopsies. S. wadsworthensis appears to be a common commensal. Helicobacter species are relatively rare in the paediatric colon.&lt;/p&gt

Functional analysis of structural variants in single cells using Strand-seq

Somatic structural variants (SVs) are widespread in cancer, but their impact on disease evolution is understudied due to a lack of methods to directly characterize their functional consequences. We present a computational method, scNOVA, which uses Strand-seq to perform haplotype-aware integration of SV discovery and molecular phenotyping in single cells by using nucleosome occupancy to infer gene expression as a readout. Application to leukemias and cell lines identifies local effects of copy-balanced rearrangements on gene deregulation, and consequences of SVs on aberrant signaling pathways in subclones. We discovered distinct SV subclones with dysregulated Wnt signaling in a chronic lymphocytic leukemia patient. We further uncovered the consequences of subclonal chromothripsis in T cell acute lymphoblastic leukemia, which revealed c-Myb activation, enrichment of a primitive cell state and informed successful targeting of the subclone in cell culture, using a Notch inhibitor. By directly linking SVs to their functional effects, scNOVA enables systematic single-cell multiomic studies of structural variation in heterogeneous cell populations

Combined burden and functional impact tests for cancer driver discovery using DriverPower

The discovery of driver mutations is one of the key motivations for cancer genome sequencing. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumour types, we describe DriverPower, a software package that uses mutational burden and functional impact evidence to identify driver mutations in coding and non-coding sites within cancer whole genomes. Using a total of 1373 genomic features derived from public sources, DriverPower’s background mutation model explains up to 93% of the regional variance in the mutation rate across multiple tumour types. By incorporating functional impact scores, we are able to further increase the accuracy of driver discovery. Testing across a collection of 2583 cancer genomes from the PCAWG project, DriverPower identifies 217 coding and 95 non-coding driver candidates. Comparing to six published methods used by the PCAWG Drivers and Functional Interpretation Working Group, DriverPower has the highest F1 score for both coding and non-coding driver discovery. This demonstrates that DriverPower is an effective framework for computational driver discovery