Drought Susceptibility Index; a Preferred Criterion in Screening for Tolerance in Soybean

Soybean (Glycine max L.) yield and yield related traits are constrained by drought. Adaptation of soybean to changing environment could be improved by exploitation and introgression of diverse germplasm in breeding program. In present study, the response to drought conditions, especially at flowering stage, was evaluated to determine the potential soybean germplasm for future soybean breeding programs in Pakistan. Field experiment was conducted under two water regimes i.e. well-water and water-limited, to assess the effect of drought in seed yield and yield related traits. Although, drought led to overall reduction of ~15 % in thousand seed weight but still some soybean genotypes performed relatively better under water-limited conditions. These genotypes were also tolerant to drought, with a drought susceptibility index of \u3c 0.5. PCA also explained the pattern of variation existing in soybean germplasm grown under given water regimes i.e. well-water and water-limited conditions. The identified soybean genotypes could be a favorable resource to introduce high yielding soybean in local environment

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

Background Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide.Methods A multimethods analysis was performed as part of the GlobalSurg 3 study-a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital.Findings Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3.85 [95% CI 2.58-5.75]; p<0.0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63.0% vs 82.7%; OR 0.35 [0.23-0.53]; p<0.0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer.Interpretation Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised

COMBINING ABILITY ANALYSIS FOR ACHENE YIELD AND RELATED TRAITS IN SUNFLOWER ( Helianthus annuus L.)

Ten sunflower ( Helianthus annuus L.) lines, five testers, and 50 crosses developed in line × tester fashion were evaluated for general combining ability (GCA) and specific combining ability (SCA) effects in a triplicate randomized complete block design, in Faisalabad, during 2009-2010. Genetic variability among genotypes was assessed for days to flowering, days to maturity, plant height, internodal length, leaf area, number of leaves per plant, head diameter, stem girth, percentage of filled achenes, 100 achene weight, achene yield per plant. A-1, A-7, A-27 and A-39 had significant general combining ability effects for days to flowering, days to maturity, internodal length, leaf area, and achene yield per plant. Among testers, A-26 and HBRS-1 were good general combiners for days to flowering, days to maturity, plant height, leaf area, head diameter, stem girth, percentage of filled achenes, 100-achene weight, and achene yield per plant. Crosses A-165 × A-26, A-41 × A-35, A-1 × G-12, and A-41 × HBPS-1 had significant and positive SCA effects for percentage of filled achenes, 100 achene weight, and achene yield per plant. Four best SCA crosses are recommended to be the best hybrids for cultivation. Non-additive type of gene action was found for all of the plant traits, which is desirable for heterosis breeding and may be exploited in hybrid seed production

GENETIC VARIABILITY IN SUNFLOWER (HELIANTHUS ANNUUS L.) FOR ACHENE YIELD AND MORPHOLOGICAL CHARACTERS

ABSTRACT The research was conducted in the Department of Plant Breeding and Genetics, University of Agriculture, Faisalabad during year 2011. Ten accessions of sunflower were evaluated for genetic variability and association of morphological traits among themselves and with achene yield. The data were recorded on quantitative i.e. days to 50% flowering, days to 50% maturity, plant height, number of leaves per plant, leaf area, head diameter, % filled achene, achene weight per head and 100 achene weight and qualitative traits i.e. lead habit, leaf shape, head shape, head angle at maturity, achene size, achene stripes and achene colour and subjected to analysis of variance, correlation and path coefficient analysis. Differences among the accessions were significant for all the traits under study except % filled achenes. The accession A-79 showed better performance for number of leaves per plant, leaf area, head diameter, % filled achenes, 100 achene weight and achene weight per head.HBRS-1, G-33 and G-8 also had appreciable performance for many traits. Genotypic correlations of achene weight were positive and significant with leaf area, number of leaves per plant, head diameter and 100 achene weights. Phenotypic correlations of all the traits were non-significant with achene weight per head. The trait 100 achene weight had the highest direct effect on achene weight per head followed by leaf area and days to 50%maturity. Days to 50% maturity had the highest positive indirect effect on achene weight per head via head diameter followed by head diameter and leaf area through 100 achene weight. It is suggested that 100 achene weight, leaf area and head diameter may be used in breeding program for selection of high yielding sunflower types

Genome-Wide Characterization and Sequence Polymorphism Analyses of <i>Glycine max</i> Fibrillin (<i>FBN</i>) Revealed Its Role in Response to Drought Condition

The fibrillin (FBN) gene family is widely distributed in all photosynthetic organisms. Members of this gene family are involved in plant growth and development and their response to various biotic and abiotic stress factors. In this study, 16 members of FBN were identified in Glycine max and characterized by using different bioinformatics tools. Phylogenetic analysis classified FBN genes into seven groups. The presence of stress-related cis-elements in the upstream region of GmFBN highlighted their role in tolerance against abiotic stresses. To further decipher the function, physiochemical properties, conserved motifs, chromosomal localization, subcellular localization, and cis-acting regulatory elements were also analyzed. Gene expression analysis based on FPKM values revealed that GmFBNs greatly enhanced soybean drought tolerance and controlled the expression of several genes involved in drought response, except for GmFBN-4, GmFBN-5, GmFBN-6, GmFBN-7 and GmFBN-9. For high throughput genotyping, an SNP-based CAPS marker was also developed for the GmFBN-15 gene. The CAPS marker differentiated soybean genotypes based on the presence of either the GmFBN-15-G or GmFBN-15-A alleles in the CDS region. Association analysis showed that G. max accessions containing the GmFBN-15-A allele at the respective locus showed higher thousand seed weight compared to accessions containing the GmFBN-15-G allele. This research has provided the basic information to further decipher the function of FBN in soybean

Semantics Analysis of Agricultural Experts’ Opinions for Crop Productivity through Machine Learning

Semantic analysis is a particular technique, which is an interesting area of research that associates with Natural Language Processing (NLP), artificial intelligence, opinion mining, text clustering, and classification. Numerous text processing techniques are being used to find out sentiments from the comments, such as social media tweets, hoax, fiction, nonfiction, novels, books, movies, health care, and stock exchange. Agrarian experts’ opinions play a vital role in the agriculture sector that yields good crop productivity. This paper presents a descriptive analysis of agriculture experts’ opinions through machine learning methods based on textual data collection. The data has been collected by surveying various academia, research institute, and industry of Punjab, Pakistan. The impact of various agricultural inputs such as seed quality, soil quality, soil-intensive tillage, climate changes, water shortage, synthetic fertilizer, and precision technologies on crop productivity have been collected through questionnaires. This research provides a descriptive analysis of collected agrarians experts opinions to increase the crop yield by providing awareness regarding current agriculture inputs to farmers by using machine learning. The current research provides a cohesive expert guideline for improving crop productivity, useful for agricultural policymaking, and conveys adequate farmers’ knowledge. Consequently, the proposed method is an innovative way of discovering recommendations of agrarians through sentiment analysis in survey data using machine learning methods. Furthermore, to the best of our knowledge, agrarians experts opinions on enhancing crop productivity have been considered for the first time in Pakistan

Data_Sheet_1_Genome-wide characterization and sequence polymorphism analyses of cysteine-rich poly comb-like protein in Glycine max.xlsx

Cysteine-rich poly comb-like protein (CPP) is a member of cysteine-rich transcription factors that regulates plant growth and development. In the present work, we characterized twelve CPP transcription factors encoding genes in soybean (Glycine max). Phylogenetic analyses classified CPP genes into six clades. Sequence logos analyses between G. max and G. soja amino acid residues exhibited high conservation. The presence of growth and stress-related cis-acting elements in the upstream regions of GmCPPs highlight their role in plant development and tolerance against abiotic stress. Ka/Ks levels showed that GmCPPs experienced limited selection pressure with limited functional divergence arising from segmental or whole genome duplication events. By using the PAN-genome of soybean, a single nucleotide polymorphism was identified in GmCPP-6. To perform high throughput genotyping, a kompetitive allele-specific PCR (KASP) marker was developed. Association analyses indicated that GmCPP-6-T allele of GmCPP-6 (in exon region) was associated with higher thousand seed weight under both water regimes (well-water and water-limited). Taken together, these results provide vital information to further decipher the biological functions of CPP genes in soybean molecular breeding.</p

Image_1_Genome-wide characterization and sequence polymorphism analyses of cysteine-rich poly comb-like protein in Glycine max.JPEG

Cysteine-rich poly comb-like protein (CPP) is a member of cysteine-rich transcription factors that regulates plant growth and development. In the present work, we characterized twelve CPP transcription factors encoding genes in soybean (Glycine max). Phylogenetic analyses classified CPP genes into six clades. Sequence logos analyses between G. max and G. soja amino acid residues exhibited high conservation. The presence of growth and stress-related cis-acting elements in the upstream regions of GmCPPs highlight their role in plant development and tolerance against abiotic stress. Ka/Ks levels showed that GmCPPs experienced limited selection pressure with limited functional divergence arising from segmental or whole genome duplication events. By using the PAN-genome of soybean, a single nucleotide polymorphism was identified in GmCPP-6. To perform high throughput genotyping, a kompetitive allele-specific PCR (KASP) marker was developed. Association analyses indicated that GmCPP-6-T allele of GmCPP-6 (in exon region) was associated with higher thousand seed weight under both water regimes (well-water and water-limited). Taken together, these results provide vital information to further decipher the biological functions of CPP genes in soybean molecular breeding.</p

Optimizing the phosphorus use in cotton by using CSM-CROPGRO-cotton model for semi-arid climate of Vehari-Punjab, Pakistan

Crop nutrient management is an essential component of any cropping system. With increasing concerns over environmental protection, improvement in fertilizer use efficiencies has become a prime goal in global agriculture system. Phosphorus (P) is one of the most important nutrients, and strategies are required to optimize its use in important arable crops like cotton (Gossypium hirsutum L.) that has great significance. Sustainable P use in crop production could significantly avoid environmental hazards resulting from over-P fertilization. Crop growth modeling has emerged as an effective tool to assess and predict the optimal nutrient requirements for different crops. In present study, Decision Support System for Agro-technology Transfer (DSSAT) sub-model CSM-CROPGRO-Cotton-P was evaluated to estimate the observed and simulated P use in two cotton cultivars grown at three P application rates under the semi-arid climate of southern Punjab, Pakistan. The results revealed that both the cultivars performed best at medium rate of P application (57\ua0kg\ua0ha-1) in terms of days to anthesis, days to maturity, seed cotton yield, total dry matter production, and harvest index during 2013 and 2014. Cultivar FH-142 performed better than MNH-886 in terms of different yield components. There was a good agreement between observed and simulated days to anthesis (0 to 1\ua0day), days to maturity (0 to 2\ua0days), seed cotton yield, total dry matter, and harvest index with an error of -4.4 to 15%, 12-7.5%, and 13-9.5% in MNH-886 and for FH-142, 4-16%, 19-11%, and 16-8.3% for growing years 2013 and 2014, respectively. CROPGRO-Cotton-P would be a useful tool to forecast cotton yield under different levels of P in cotton production system of the semi-arid climate of Southern Punjab