Performance assessment of wheat genotypes based on the superiority index using additive main and multiplicative interaction effects and BLUP analysis

The simultaneous use of additive main and multiplicative interaction effects (AMMI) and best linear unbiased predictors (BLUP) has been reflected in the multi-location evaluation of trials for number of crops. The additional advantages of both these approaches would be combined in superiority index (SI) to have an edge over the commonly used approaches. The promising wheat genotypes had been considered under multi location trails in Peninsular zone of India during the cropping seasons of 2018-2019 and 2019-2020. The highly significant environmental effects contributed 44.1% & 35.3% of total sum of squares in the AMMI analysis, 20.6% & 26.2% were augmented by G × E interaction, while 10.8% & 7.5% were contributed by the genotypes.Wheat genotypes of UAS3001, MACS6222, GW322, and DDW48 expressed their superiority in BLUP values. Superiority indexes and adaptability measures had identified WHD964 and DDW48 genotypes for the second year of study. More than 75% variations among the considered measures were due to the first two interaction principal components (IPCA’s) under Biplot analysis. Number of superiority index measures were clustered with adaptability measures in the same quadrant. Superiority index, the weighted measure of yield and consistent performance of genotypes would be more appropriate for stability and adaptabilities studies

Wheat genotypes as assessed by Additive main & multiplicative interactions (AMMI) and Best linear unbiased prediction (BLUP) for stability analysis under rainfed timely sown trials in Northern Hills Zone of India

Stability analysis of wheat genotypes under rainfed timely sown trials in Northern Hills Zone of India by Additive main & multiplicative interactions (AMMI) analysis observed highly significant effects of the environment, GxE interaction and genotypes during 2018-19 and 2019-20. The ranking of genotypes had altered with utilization of more number of IPCA’s in AMMI and WAASB measures. Environments contributed about 53%, GxE interaction accounted for 30.5% and Genotypes explained only 5.4% of the total sum of squares due to treatments in the first year. Wheat genotypes HS668,  VL2035, VL2036 , HS562 had been selected by Analytic measures of adaptability and Superiority indexes. Different quadrants comprised of a cluster of arithmetic, geometric, harmonic means along with corresponding adaptability measures. Superiority Indexes considering averages grouped separately. This group maintained the right angles with a group of MASV & MASV1 measures. Clustering of Adaptability measures as per arithmetic, geometric and harmonic means placed in a quadrant. Second-year reflected VL2041,  HS675, HS676 & HS562, HPW471 genotypes selected by adaptability and superiority indexes. About 68% of the total variation with 38.4% and 30.2% contributions by PC1 & PC2. Adaptability measures maintained the right angle with other stability measures, with the exception of  Superiority indexes.  There is an additional advantage with these measures to assign variable weights to the yield and stability as per the goal of breeding trials. These indexes have the potential to provide reliable estimates of genotypes in future studies as they are considered more number of significant IPCA’s in biplots

Simultaneous application of AMMI measures and yield for stability analysis of wheat genotypes evaluated under irrigated late sown conditions of Central Zone of India

Reports on biased interpretation for the stability of the genotypes under AMMI analysis, considering only the first two interaction principal components, has been observed in recent past. Simultaneous use of yield and stability of genotypes in a single measure had been advocated for identification of highly productive and broadly adapted genotypes.  The performance of superiority index, allowed variable weighting mechanism for yield and stability, has been compared with AMMI based measures. For the first year (2018-19) Superiority index, weighting 0.65 and 0.35 for yield and stability, found UAS3002, MP3336 and HI1633 as of stable performance with high yield. Recent analytic measures the relative proportion of genotypic value (PRVG) and Harmonic mean of the relative proportion of genotypic value (MHPRVG) selected CG1029, HI1634 and HD2932 wheat genotypes.  Indirect relations were expressed by Superiority Index (SI) with other stability measures.  Superiority index saw stable performance along with high yield of HD2864  and HI1634 for the second year 2019-20. PRVG as well as MHPRVG measures observed suitability of  CG1029 and  HD2864 while MP3336  as unstable wheat genotypes. Values of SI measure had expressed only indirect relations of high degree with stability measures except with yield, PRVG and MHPRVG values.  Stability measures by the simultaneous use of AMMI and yield would be more meaning full and useful as compared to measures consider either the AMMI or yield of genotypes only

The Effect of Roughness Geometries on Heat Transfer Enhancement in Solar Air Heater - A Review

Artificial roughness applied on the absorber plate in the solar air heater is the most acclaimed method to improve thermal performance. Moreover it is required to understand how flow field is affected by particular roughness geometry with artificial roughness. This roughness creates turbulence in flowing air by disturbing laminar sub-layer as turbulence increases there in increament of heat transfer rate.Some distinguished roughness geometries have been compared on the basis of heat transfer enhancements, Nusselt number and friction factor correlations as function of system and operating parameters for predicting performance of the system having investigated type of roughness geometry.Arti?cial roughness in the form of ribs is a convenient method for enhancing thermal performance of solar air heaters.W shape rectangular ribs in discrete form with double passwill show the significant increase in heat transfer rate and friction loss over the smooth channel in the range of parameter of Renolds no 10000 to 12000, relative roughness height 0.043 to0.044, relative roughness pitch 10 to 12, angle of attack 60° to 70°

Assessment of awareness towards pharmacovigilance programme of india and reporting of adverse drug reactions among nurses in a tertiary care hospital

Background: The success of PvPI depends upon spontaneous reporting of ADRs by health care professionals especially nurses as they are usually first contact persons for patients in case of ADRs after use of medicines. Underreporting of ADRs due to inadequate reporting culture among health care professionals is the main hindrance in the path of this programme. So, to assess the awareness, attitude and practices of nurses regarding PvPI and ADR reporting this study was undertaken.Methods: It was a cross-sectional, questionnaire-based study in which 130 nurses responded. The 12-items questionnaire feedback form provided by Indian Pharmacopoeia Commission (IPC) was used to assess the awareness of nurses towards pharmacovigilance programme and Adverse Drug Reaction (ADR) reporting practices.Results: After analysing the questionnaire, it was observed that, despite satisfactory level of awareness and interest of the nurses to participate in this programme, still there is meagre ADR reporting practices among the nurses.Conclusions: Lack of reporting culture and improper communication is the root of problem which should be overcome in future by proper training for patient safety

Non parametric measures to estimate GxE interaction of dual purpose barley genotypes for grain yield under multi-location trials

GxE interaction of seventeen dual purpose barley genotypes evaluated at ten major barley locations of the country by non parametric methods. Non parametric measures had been well established and expressed ad-vantages over their counter parts i.e. parametric measures. Simple descriptive measures based on the ranks of gen-otypes i.e. Mean of ranks (MR) pointed towards RD2925 and BH1008 and standard deviation of ranks (SD) for KB1401 and UPB1054 whereas Coefficient of variation (CV) for JB322 and RD2925 as stable genotypes. Nonpara-metric measures based on original values (Si1, Si2, Si3, Si4, Si5, Si6, Si7) indicated the stable performance of NDB1650, JB322 and UPB1054 while UPB1053, RD2715, RD2927 and RD2035 were observed of unstable nature. CSi1, CSi2, CSi3, CSi4, CSi5, CSi6 and CSi7 measures based on the ranks of corrected grain yield identified JB322, RD2552, RD2925 and NDB1650 as stable genotypes. Spearman’s rank correlation established highly significant positive correlation of yield with SD (0.67), Si1(0.65), Si2(0.59), Si5(0.68), Si7(0.67) whereas negative association observed for CMR (Mean of corrected ranks) (-0.62), CMed (Median of corrected ranks) (-0.60). NPi(2) expressed negative correlation with CV(-0.32), Si6 (-0.30), CMR(-0.34) and CMed(-0.48). More over NPi(3) maintained negative correlation with most of the measures though the magnitude was of low magnitude

Newer insights of H1N1: Swine Flu Virus

Swine flu, caused by the H1N1 influenza virus, is a subtype of influenza A that affects both the upper and lower respiratory tracts. It is primarily found in pigs and can be transmitted to humans through genetic variations in the virus. The 1918 Spanish flu pandemic resulted in the deaths of 50 to 100 million individuals. In 2009, the pandemic affected 178 countries, resulting in an estimated 43 to 89 million cases and 1799 deaths. The pathophysiology of H1N1 involves inflammation of the respiratory tract, with an incubation period of 1 to 4 days and a contagious period lasting 5 to 7 days. The signs and symptoms of swine flu include cough, sore throat, fever, myalgia, congestion, headache, rhinorrhoea, dizziness, sneezing, loss of appetite, fatigue, abdominal pain, shortness of breath, and in rare cases, vomiting and diarrhoea. The most common cause of death is respiratory failure, and neurological symptoms can occur due to high fever. To diagnose swine flu, various tests such as haematological, biochemical, and microbiological tests are conducted, including the collection of nasal or oral swabs for reverse transcriptase polymerase chain reaction (RT-PCR). Prevention and control measures include managing swine flu in pigs through herd management, hygiene practices, and vaccination. Treatment options vary based on the severity of the case. Mild to moderate cases can be managed with rest, antipyretics, NSAIDs, antihistamines, and oral rehydration therapy. Severe cases may require intravenous hydration, antibiotics for bacterial infections, antiviral therapy, and respiratory support

Parametric vis-a-vis non parametric measures describing G x E interactions for salt salinity tolerant barley genotypes in multi-environment trials

GxE interaction to know adaptability of 19 salt salinity tolerant barley genotypes was studied by parametric and non-parametric measures. Genotypes KB1516, RD2907 and RD2794 showed minimum environmental variance over different environments. Superiority index identified genotypes RD2907 and NDB1445 with lowest value accompanied with higher. Wricke’s measure exhibited lower values of DWRB168,  DWRB165 and NDB1445. Higher values of GAI showed consistent performance of RD2907, NDB1445 and RD2552. Non-parametric measures Si(1), Si(3) and Si(6) the considered DWRB165 and DWRB168  as desirable genotypes. Thennarasu’s first measure NPi(1) found DWRB168 and NDB1445 as desirable adaptable and KB1546, RD2907 and NDB1173 were unstable genotypes. Wricke’s parameter was positively correlated with NPi(1), NPi(3) and Kang. GAI had significant positive with Pi and Kang while negative with Si(6), NPi(2) & NPi(4). Worth to mention the negative association of  Pi with Si(6), NPi(2), NPi(4). Non parametric measures Si (3) Si (6) NPi (2) & NPi (4) clubbed together while Kang, Wi 2, s2i ,Si (1),Si (2) ,NPi (1) & NPi (3)  joined in another cluster.  Left over parametric measures were grouped in two separate clusters i.e. (bi, S2xi ,CVi),(Yield, GAI Pi) respectively.  Biplot analysis based on first two principal components showed three groups among the measures

G x E evaluation for feed barley genotypes evaluated in country by AMMI analysis

AMMI analysis of feed barley genotypes exhibited highly significant effects of environments, genotypes and interactions for both the years. The major portion of the total variance was described by the environmental effects up to 45.6% and 42.3% in respective years. The genotypes effects contributed marginally as of only 8.6% and 6.9% of total variation. The significant interaction effects were partitioned into IPCA1, IPCA2 , IPCA3 and IPCA4; which explained upto 42.4, 18.3, 9.7 and 8.1% of the first year and 32.2, 20.3, 15.6 and 10.5% for second year. The cumulative effect of first two interaction principal components comes out to 60.7% and 52.3% respectively. Maximum genotype yield during study period varied from 49.8 to 48 whereas the lowest yield ranged from 37 to 36.4 q/ha. AMMI stability index identified genotypes G9(BH 972), G15(JB 274) for former and G23(DWRB 109) & G2(KB 1205) for latter year. AMMI distance marked G15(JB 274) & G7(NDB 1561) for first and genotypes G26(UPB 1034) & G23(DWRB 109) for the second year. Desirable genotypes for selection would be G11(PL 871), G27(PL 872) and G23(DWRB 109), G20(BH 946) for respective years a per the GSI score. Genotypes with IPCA-1 scores close to zero identified G1(PL 751), G9(BH 972) and G27(PL 872 ) for first year and G5(RD 2786), G4(NDB 1554) and G24 (UPB 1036) for second year would have wider adaptation to the tested environments as per AMMI graphical plots