Recognition of Prior Learning (RPL) and Skill Deficit: The Role of Open Distance Learning (ODL)

Skills acquisition is vital for any economy to compete and grow, particularly in an era of economic and technological change. Skill needs are widespread in most developing countries , including India . Vocational Education and Training (VET) is a direct means of providing workers with skills more relevant to the evolving needs and equitable but must be linked directly to industry needs and requirements. Skilling India may be the biggest challenge facing the country today. Training half a billion people by 2022 is the most ambitious goal ever set by any country in the field of education and training. On the other hand in India there are millions of people who have considerable level of skill in a particular area but they do not have any form of certification to testify their existing skills, as a result they are unable to use this to progress further for training or improved employment. Hence, there is need for a credit and qualifications framework against which individuals' skills could be mapped. Recognition of Prior Learning ( RPL) is a new concept for India. Presently no system is designed for assessment and certification of RPL.The Indian Government vide its executive orders notified the National Qualifications  Education Framework,  ( NVEQF)  and assigned the task of assessment and certification of  RPL for skills at the lower level of occupations mostly engaged in the unorganized  sector to Open Schooling and along with the Industry through Sector Skills councils( SSC) .  Recognition of Prior Learning is a crucial area in open and distance learning system. Given the magnitude of the skill development challenge, Recognition of prior learning enables effective and maximum utilization of human resources. Hence can be considered as a ‘tool’.This paper will portray the framework developed and discuss the issues related to the implementation of this RPL Framework in the diverse country like India. Key words ; Recognition of Prior Learning, skill deficit ,Sector Skills Council

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Not AvailableScirpophaga incertulas Walker the yellow stem borer (YSB) is a monophagous rice pest and attacks rice crop both at vegetative stage and reproductive stages and also found infesting rice crop in all of the diverse rice ecosystems viz., deep-water rice, irrigated and rainfed (Deka and Barthakur, 2010). Symbiotic relationship with microbiota provides several advantages to insect’s and to develop innovative pest management strategies molecular level analysis of gut symbionts and insect commensal microbiota will help a lot (Weibing et al., 2010). To our knowledge till now no extensive work was traceable on the gut bacterial diversity of YSB larva. Larval associated gut bacterial microbiota of YSB from different locations was explored and found to be diverse. Bacteria belonging to phylum Firmicutes (53%) is major followed by Proteobacteria (40%) and then Actinobacteria (7%). Bacillus (53%) is the predominant genus being found associated with YSB larva and also noticed to be present in all the locations studied. All the isolated bacteria are morphologically and biochemically characterised. Functional significance of the gut harbouring bacteria from two locations i.e., NRRI where insecticide usage is common and Phulbani where insecticide use is negligible/nil are explored and found to be distinct and exhibited differential growth in minimal media inoculated with chlorpyriphos insecticide where bacteria from NRRI YSB population grown even in 75% more than the recommended dose (RD) of Chlorpyriphos whereas bacteria from Phulbani collected larvae not shown growth in 75% more than RD. The gut bacteria from diapause and non-diapause YSB larvae were also explored and found to be distinct. The bacteria isolated from non-diapause YSB larvae grown even in 75% more than the recommended dose (RD) of Chlorpyriphos whereas bacteria from diapause YSB larvae not shown growth in more than the RD of insecticide.Not Availabl

Mapping of PARK2 and PACRG Overlapping Regulatory Region Reveals LD Structure and Functional Variants in Association with Leprosy in Unrelated Indian Population Groups

<div><p>Leprosy is a chronic infectious disease caused by <i>Mycobacterium Leprae</i>, where the host genetic background plays an important role toward the disease pathogenesis. Various studies have identified a number of human genes in association with leprosy or its clinical forms. However, non-replication of results has hinted at the heterogeneity among associations between different population groups, which could be due to differently evolved LD structures and differential frequencies of SNPs within the studied regions of the genome. A need for systematic and saturated mapping of the associated regions with the disease is warranted to unravel the observed heterogeneity in different populations. Mapping of the PARK2 and PACRG gene regulatory region with 96 SNPs, with a resolution of 1 SNP per 1 Kb for PARK2 gene regulatory region in a North Indian population, showed an involvement of 11 SNPs in determining the susceptibility towards leprosy. The association was replicated in a geographically distinct and unrelated population from Orissa in eastern India. <i>In vitro</i> reporter assays revealed that the two significantly associated SNPs, located 63.8 kb upstream of PARK2 gene and represented in a single BIN of 8 SNPs, influenced the gene expression. A comparison of BINs between Indian and Vietnamese populations revealed differences in the BIN structures, explaining the heterogeneity and also the reason for non-replication of the associated genomic region in different populations.</p></div

A schematic lay-out of the BIN structure (r²≥0.80) in the regulatory region of the PARK2 and PACRG genes in North Indian and East Indian-Orissa and Vietnamese population for 41 SNPs spanning 148 Kb region of Chromosome 6q26, where 36 SNPs are common to both Vietnamese and Indian population and 5 significant SNPs (No. 20, 22, 23, 26, 32) are exclusively studied in the Indian population.

<p>[It may be noted that a similar BIN structure was observed in the North-Indian and East-Indian-Orissa populations]. Physical location of the studied chromosomal region is given in Mb on top. Vietnamese population information of Mira et al, 2004 and common SNPs between Indian and Vietnamese population (Alter et al, 2012) was shared by Prof. Schurr. Rest of the SNPs & BIN structure information was retrieved from Alter et al (2012). SNPs in star shape indicate the significant association (in two respective populations-Indian and Vietnamese). 11 significantly associated SNPs in studied Indian populations are distributed in two BINs (BIN 1 with 8 and BIN 2 with 3 SNPs). Distribution of significant SNPs in Vietnamese population is shown in BIN 1, BIN 2 and BIN3. SNPs, rs10945859 (No. 1) and rs9347684 (No. 9), although shared significance in both the Indian and Vietnamese population, but these showed no significant difference in expression in <i>in vitro</i> reporter assay for the alternative alleles. Each SNP is designated by a No. ranging from 1 to 41 according to increasing order of the chromosomal position. Filled Black Star - Significant SNPs in Indian (North and East Indian-Orissa) population, Unfilled Star - Significant SNPs in Vietnamese population, Filled Black Dot - Non-Significant SNPs in North and East Indian-Orissa population, Unfilled Dot - Non-Significant SNPs in Vietnamese population, Black Circled Dot and Black Circled Star SNPs (No. 5, 7, 8, 24, 35) studied by us earlier <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003578#pgen.1003578-Malhotra1" target="_blank">[26]</a>.</p

Haplotype structure, haplotype frequencies, significant <i>p</i> values and odds ratio between patients versus healthy controls of 3 SNPs representing BIN-2 of Indian population.

<p>Column: Hap-Score shows haplotype score statistic; Base, part of the baseline; Frequencies and disease association of haplotype of SNP alleles was tested using <i>haplo.cc</i> extended application of Haplo.stasts software (v1.4.4) which combines the results of <i>haplo.score</i>, <i>haplo.group</i> and <i>haplo.glm</i>. Haplotype frequency was computed by maximum likelihood estimates of haplotype probabilities with progressive insertion algorithm and <i>haplo.cc</i> computed score statistic to test association between haplotype and traits with adjustment for non-genetic covariates (sex).</p><p>p^a Indicates the haplotype comparison statistics for patients vs controls.</p

Luciferase expression assay of upstream SNPs of PARK2 gene (rs9365492 (T/C) and rs9355403 (G/A)): C & A respectively represent risk allele for the SNP.

<p>Bar with standard error shows the mean expression values in three different cell lines (HepG2, MCF7 and HeLa) for different Clones in PGL3 promoter vector: Clone1, with protective allele combination - rs9365492(T)-rs9355403(G); Clone2, with risk and protective allele combination - rs9365492(C)-rs9355403(G); Clone3, with protective and risk allele combination - rs9365492(T)-rs9355403(A) and Clone4, with risk allele combination - rs9365492(C)-rs9355403(A). <i>P</i>-Values for comparison of mean (one way ANOVA) expression between clones with different allele combination of 2 SNPs is also shown.</p

Allele and genotype frequencies for 11 significant SNPs within PARK2 and PACRG gene regulatory region in two different cohorts of patients with Leprosy.

<p><b>p^a</b> and <b>p^b</b> value for 2×2 chi test for overall allelic frequencies comparison of samples from North Indian and samples from Orissa. <b>p^c</b> value for 2×2 chi test for overall allelic frequencies comparison of combined samples, PB and MB samples. <b>p^d</b> and <b>p^e</b> values for genotypic model by logistic regression for combines samples, PB and MB samples before and after adjustment for sex as a covariate. Bonferroni correction of 32 SNPs was applied for multiples testing. Out of total 96 SNPs tested 64 were in 14 bin set (r²>0.8) [data not shown].</p

Haplotype structure, haplotype frequencies, significant <i>p</i> values and odds ratio between patients versus healthy controls of 11 significantly associated SNPs.

<p>Column: Hap-Score shows haplotype score statistic; Base, part of the baseline; Frequencies and disease association of haplotype of SNP alleles was tested using <i>haplo.cc</i> extended application of Haplo.stasts software (v1.4.4) which combines the results of <i>haplo.score</i>, <i>haplo.group</i> and <i>haplo.glm</i>. Haplotype frequency was computed by maximum likelihood estimates of haplotype probabilities with progressive insertion algorithm and <i>haplo.cc</i> computed score statistic to test association between haplotype and traits with adjustment for non-genetic covariates (sex).</p><p>p^a Indicates the haplotype comparison statistics for patients vs controls.</p

The association statistics of the 11 significant SNPs in the regulatory region of the PARK2 and PACRG genes; presented as negative logarithm of the <i>P</i>-Value and their linkage disequilibrium (LD) plot based on pairwise LD for r² cut off value ≥0.80.

<p>The association statistics of the 11 significant SNPs in the regulatory region of the PARK2 and PACRG genes; presented as negative logarithm of the <i>P</i>-Value and their linkage disequilibrium (LD) plot based on pairwise LD for r² cut off value ≥0.80.</p

Haplotype structure, haplotype frequencies, significant <i>p</i> values and odds ratio between patients versus healthy controls of 8 SNPs representing BIN-1 of Indian population.

<p>Column: Hap-Score shows haplotype score statistic; Base, part of the baseline; Frequencies and disease association of haplotype of SNP alleles was tested using <i>haplo.cc</i> extended application of Haplo.stasts software (v1.4.4) which combines the results of <i>haplo.score</i>, <i>haplo.group</i> and <i>haplo.glm</i>. Haplotype frequency was computed by maximum likelihood estimates of haplotype probabilities with progressive insertion algorithm and <i>haplo.cc</i> computed score statistic to test association between haplotype and traits with adjustment for non-genetic covariates (sex).</p><p>p^a Indicates the haplotype comparison statistics for patients vs controls.</p