Biogas Upgradation by CO2 Sequestration and Simultaneous Production of Acetic Acid by Novel Isolated Bacteria

Anaerobic digestion produces biogas, which is a proven bioprocess for generating energy, recovering nutrients, and reusing waste materials. Generally, the biogas generated contains methane (CH4) and carbon dioxide (CO2) in a 3:2 ratio, which limits the usage of the biogas to only cooking gas. To further enhance the application of biogas to vehicular fuel and natural gas grids, CO2 must be removed for an enhanced calorific value. This study seeks to lower greenhouse gas emissions by sequestering carbon dioxide from biogas. CO2 sequestration by microorganisms to upgrade the biogas and simultaneously convert the CO2 into acetic acid is a less explored area of research. Therefore, this research focuses mainly on the analysis of CO2 consumption % and acetic acid yield by novel isolated bacteria from fruit waste and mixed consortia obtained from cow dung and digested samples. The research finding states that there was a 32% increase in methane yield shown by isolated strain A1, i.e., CH4% was increased from 60% to 90%, whereas only an 11% increase was shown by consortia, which was an increase from 60% to 80%. The highest biogas upgradation was shown by the A1 strain at 30 degrees C incubation temperature and pH 8. The A1 strain demonstrated the highest recorded yield of acetic acid, reaching a concentration of 2215 mg/L at pH 8. A pH range of 7-8 was found to be the best-suited pH, and a mesophilic temperature was optimum for CO2 consumption and acetic acid production. The major objective is to create an effective method for improving biogas so that it is acceptable for different energy applications by lowering the carbon dioxide content and raising the methane content. This development signifies a significant advancement in the enhancement of biogas upgradation, as well as the concurrent generation of value-added goods, thereby establishing a sustainable platform technology

Genome-Wide Association Analysis of Freezing Tolerance and Winter Hardiness in Winter Wheat of Nordic Origin

Climate change and global food security efforts are driving the need for adaptable crops in higher latitude temperate regions. To achieve this, traits linked with winter hardiness must be introduced in winter-type crops. Here, we evaluated the freezing tolerance (FT) of a panel of 160 winter wheat genotypes of Nordic origin under controlled conditions and compared the data with the winter hardiness of 74 of these genotypes from a total of five field trials at two locations in Norway. Germplasm with high FT was identified, and significant differences in FT were detected based on country of origin, release years, and culton type. FT measurements under controlled conditions significantly correlated with overwintering survival scores in the field (r <= 0.61) and were shown to be a reliable complementary high-throughput method for FT evaluation. Genome-wide association studies (GWAS) revealed five single nucleotide polymorphism (SNP) markers associated with FT under controlled conditions mapped to chromosomes 2A, 2B, 5A, 5B, and 7A. Field trials yielded 11 significant SNP markers located within or near genes, mapped to chromosomes 2B, 3B, 4A, 5B, 6B, and 7D. Candidate genes identified in this study can be introduced into the breeding programs of winter wheat in the Nordic region

Frontiers in the Solicitation of Machine Learning Approaches in Vegetable Science Research

Along with essential nutrients and trace elements, vegetables provide raw materials for the food processing industry. Despite this, plant diseases and unfavorable weather patterns continue to threaten the delicate balance between vegetable production and consumption. It is critical to utilize machine learning (ML) in this setting because it provides context for decision-making related to breeding goals. Cutting-edge technologies for crop genome sequencing and phenotyping, combined with advances in computer science, are currently fueling a revolution in vegetable science and technology. Additionally, various ML techniques such as prediction, classification, and clustering are frequently used to forecast vegetable crop production in the field. In the vegetable seed industry, machine learning algorithms are used to assess seed quality before germination and have the potential to improve vegetable production with desired features significantly; whereas, in plant disease detection and management, the ML approaches can improve decision-support systems that assist in converting massive amounts of data into valuable recommendations. On similar lines, in vegetable breeding, ML approaches are helpful in predicting treatment results, such as what will happen if a gene is silenced. Furthermore, ML approaches can be a saviour to insufficient coverage and noisy data generated using various omics platforms. This article examines ML models in the field of vegetable sciences, which encompasses breeding, biotechnology, and genome sequencing

Is label-free LC-MS/MS ready for biomarker discovery?

Label-free LC-MS methods are attractive for high-throughput quantitative proteomics, as the sample processing is straightforward and can be scaled to a large number of samples. Label-free methods therefore facilitate biomarker discovery in studies involving dozens of clinical samples. However, despite the increased popularity of label-free workflows, there is a hesitance in the research community to use it in clinical proteomics studies. Therefore, we here discuss pros and cons of label free LC-MS/MS for biomarker discovery, and delineate the main prerequisites for its successful employment. Furthermore, we cite studies where label-free LC-MS/MS was successfully used to identify novel biomarkers, and foresee an increased acceptance of label-free techniques by the proteomics community in the near future. This article is protected by copyright. All rights reserved

The Combination of Low-Cost, Red–Green–Blue (RGB) Image Analysis and Machine Learning to Screen for Barley Plant Resistance to Net Blotch

Challenges of climate change and growth population are exacerbated by noticeable environmental changes, which can increase the range of plant diseases, for instance, net blotch (NB), a foliar disease which significantly decreases barley (Hordeum vulgare L.) grain yield and quality. A resistant germplasm is usually identified through visual observation and the scoring of disease symptoms; however, this is subjective and time-consuming. Thus, automated, non-destructive, and low-cost disease-scoring approaches are highly relevant to barley breeding. This study presents a novel screening method for evaluating NB severity in barley. The proposed method uses an automated RGB imaging system, together with machine learning, to evaluate different symptoms and the severity of NB. The study was performed on three barley cultivars with distinct levels of resistance to NB (resistant, moderately resistant, and susceptible). The tested approach showed mean precision of 99% for various categories of NB severity (chlorotic, necrotic, and fungal lesions, along with leaf tip necrosis). The results demonstrate that the proposed method could be effective in assessing NB from barley leaves and specifying the level of NB severity; this type of information could be pivotal to precise selection for NB resistance in barley breeding

Combating heavy metals in wheat grains under drought - is alien or ancient germplasm a solution to secure food and health?

Alien and ancient wheat germplasms have been utilized to combat diseases and improve yield performance under climate change. However, the potential risk of excessive heavy metal uptake with these germplasms has been less studied. In order to ensure food security, this study aimed to evaluate the levels of cadmium (Cd), lead (Pb) and mercury (Hg) in 30 wheat lines, including modern, old and wheat-rye introgression genotypes grown under three conditions i.e., control, early drought and late drought. The results of this study revealed a generally higher Cd grain accumulation in old and 1R genotypes than in the other genotype groups evaluated here, while old genotypes also showed an excess Pb grain concentration. The induced late drought resulted in an increased Cd uptake in wheat, leading to significantly elevated grain Cd concentration in modern, 1R, 1RS and 2R genotypes, while similar results were not obtained for the other heavy metals e.g. Pb or Hg. Specifically, an old genotype, 207, showed an extremely high Cd value across control and drought conditions. There was a greater genotypic variation in Pb concentration compared to Cd, while consistently high Hg concentrations were observed in several genotypes carrying 1R or 1RS. Some wheat-rye introgression genotypes, particularly those with the 3R chromosome, showed a low Cd accumulation across all treatments. The results from the present study pin-point the necessity of a rigorous assessment of heavy metal accumulation in wheat grain when utilizing ancient and alien genetic resources in breeding for disease resistance, and wheat resilience to environmental stress and climate change. Furthermore, the specific lines identified in this study with elevated heavy metal accumulation should be avoided in breeding programs. Additionally, mechanisms for the found differences in heavy metals accumulation among genotypes and treatments should be further revealed

Climate Change Impact on Wheat Performance-Effects on Vigour, Plant Traits and Yield from Early and Late Drought Stress in Diverse Lines

Global climate change is threatening wheat productivity; improved yield under drought conditions is urgent. Here, diverse spring-wheat lines (modern, old and wheat-rye introgressions) were examined in an image-based early-vigour assay and a controlled-conditions (Biotron) trial that evaluated 13 traits until maturity. Early root vigour was significantly higher in the old Swedish lines (root length 8.50 cm) and introgressed lines with 1R (11.78 cm) and 1RS (9.91 cm) than in the modern (4.20 cm) and 2R (4.67 cm) lines. No significant correlation was noted between early root and shoot vigour. A higher yield was obtained under early drought stress in the 3R genotypes than in the other genotype groups, while no clear patterns were noted under late drought. Evaluating the top 10% of genotypes in terms of the stress-tolerance index for yield showed that root biomass, grains and spikes per plant were accountable for tolerance to early drought, while 1000-grain weight and flag-leaf area were accountable for tolerance to late drought. Early root vigour was determined as an important focus trait of wheat breeding for tolerance to climate-change-induced drought. The responsible genes for the trait should be searched for in these diverse lines. Additional drought-tolerance traits determined here need further elaboration to identify the responsible genes

Unraveling the Genetic Basis of Key Agronomic Traits of Wrinkled Vining Pea (Pisum sativum L.) for Sustainable Production

Estimating the allelic variation and exploring the genetic basis of quantitatively inherited complex traits are the two foremost breeding scenarios for sustainable crop production. The current study utilized 188 wrinkled vining pea genotypes comprising historical varieties and breeding lines to evaluate the existing genetic diversity and to detect molecular markers associated with traits relevant to vining pea production, such as wrinkled vining pea yield (YTM100), plant height (PH), earliness (ERL), adult plant resistance to downy mildew (DM), pod length (PDL), numbers of pods per plant (PDP), number of peas per pod (PPD), and percent of small wrinkled vining peas (PSP). Marker-trait associations (MTAs) were conducted using 6902 quality single nucleotide polymorphism (SNP) markers generated from the diversity arrays technology sequencing (DArTseq) and Genotyping-by-sequencing (GBS) sequencing methods. The best linear unbiased prediction (BLUP) values were estimated from the two-decadeslong (1999–2020) unbalanced phenotypic data sets recorded from two private breeding programs, the Findus and the Birds eye, now owned by Nomad Foods. Analysis of variance revealed a highly significant variation between genotypes and genotype-byenvironment interactions for the ten traits. The genetic diversity and population structure analyses estimated an intermediate level of genetic variation with two optimal subgroups within the current panel. A total of 48 significant (P < 0.0001) MTAs were identified for eight different traits, including five for wrinkled vining pea yield on chr2LG1, chr4LG4, chr7LG7, and scaffolds (two), and six for adult plant resistance to downy mildew on chr1LG6, chr3LG5 (two), chr6LG2, and chr7LG7 (two). We reported several novel MTAs for different crucial traits with agronomic importance in wrinkled vining pea production for the first time, and these candidate markers could be easily validated and integrated into the active breeding programs for marker-assisted selection

Genotype and environment interaction study shows fungal diseases and heat stress are detrimental to spring wheat production in Sweden

Spring wheat is an economically important crop for Scandinavia and its cultivation is likely to be affected by climate change. The current study focused on wheat yield in recent years, during which climate change-related yield fluctuations have been more pronounced than previously observed. Here, effects of the environment, together with the genotype and fungicide treatment was evaluated. Spring wheat multi-location trials conducted at five locations between 2016 and 2020 were used to understand effects of the climate and fungicides on wheat yield. The results showed that the environment has a strong effect on grain yield, followed by the genotype effect. Moreover, temperature has a stronger (negative) impact than rainfall on grain yield and crop growing duration. Despite a low rainfall in the South compared to the North, the southern production region (PR) 2 had the highest yield performance, indicating the optimal environment for spring wheat production. The fungicide treatment effect was significant in 2016, 2017 and 2020. Overall, yield reduction due to fungal diseases ranged from 0.98 (2018) to 13.3% (2017) and this reduction was higher with a higher yield. Overall yield reduction due to fungal diseases was greater in the South (8.9%) than the North zone (5.3%). The genotypes with higher tolerance to diseases included G4 (KWS Alderon), G14 (WPB 09SW025-11), and G23 (SW 11360) in 2016; G24 (SW 11360), G25 (Millie), and G19 (SEC 526-07-2) in 2017; and G19 (WPB 13SW976-01), G12 (Levels), and G18 (SW 141011) in 2020. The combined best performing genotypes for disease tolerance and stable and higher yield in different locations were KWS Alderon, SEC 526-07-2, and WPB 13SW976-01 with fungicide treatment and WPB Avonmore, SEC 526-07-2, SW 131323 without fungicide treatment. We conclude that the best performing genotypes could be recommended for Scandinavian climatic conditions with or without fungicide application and that developing heat-tolerant varieties for Scandinavian countries should be prioritized

Transcriptome profiling by combined machine learning and statistical R analysis identifies TMEM236 as a potential novel diagnostic biomarker for colorectal cancer

Colorectal cancer (CRC) is a common cause of cancer-related deaths worldwide. The CRC mRNA gene expression dataset containing 644 CRC tumor and 51 normal samples from the cancer genome atlas (TCGA) was pre-processed to identify the significant differentially expressed genes (DEGs). Feature selection techniques Least absolute shrinkage and selection operator (LASSO) and Relief were used along with class balancing for obtaining features (genes) of high importance. The classification of the CRC dataset was done by ML algorithms namely, random forest (RF), K-nearest neighbour (KNN), and artificial neural networks (ANN). The significant DEGs were 2933, having 1832 upregulated and 1101 downregulated genes. The CRC gene expression dataset had 23,186 features. LASSO had performed better than Relief for classifying tumor and normal samples through ML algorithms namely RF, KNN, and ANN with an accuracy of 100%, while Relief had given 79.5%, 85.05%, and 100% respectively. Common features between LASSO and DEGs were 38, from them only 5 common genes namely, VSTM2A, NR5A2, TMEM236, GDLN, and ETFDH had shown statistically significant survival analysis. Functional review and analysis of the selected genes helped in downsizing the 5 genes to 2, which are VSTM2A and TMEM236. Differential expression of TMEM236 was statistically significant and was markedly reduced in the dataset which solicits appreciation for assessment as a novel biomarker for CRC diagnosis