Composting is one of the most economical and environmentally safe methods of recycling organic waste. Soil microorganisms play a significant role in decomposition and the availability of plant nutrients. This study was designed to prepare a suitable microbial inoculum and its evaluation on composting heap to decrease the time of waste degradation. The bacteria were isolated and molecular characterized from the soil near composting area by using 16S ribotyping technique. The identified strains of bacillus cereus used as an inoculum gives better results to expedite the degradation of organic waste. The prepared bacterial inoculum with molasses was also compared with commercial inoculum by optimizing physical and chemical parameters (temperature, oxygen, C: N, pH, and moisture content) of composting heap. Monthly reading of these parameters was taken from experimental and control treatments. The highest decomposition rate of organic waste was recorded in treatment A (experimental heap) where molasses and bacterial inoculum were added and less decomposition was observed in treatment D (experimental heap) where no inoculum and molasses were added. It was concluded from the studies that the prepared bacterial inoculum with two strains of Bacillus was effective and prepared mature compost in 2.5 months by increasing decomposition efficiency of organic waste. Furthermore, the prepared compost was also sustainable in its physical and chemical characteristic.

Lung cancer is the most common cause of cancer deaths worldwide, and lung adenocarcinoma (LUAD) is the most common histological subtype. However, the prognostic and predictive outcomes differ because of the heterogeneity of programmed cell death. The purpose of this work is to investigate and develop a cuproptosis-associated lncRNA-based LUAD prediction marker. We firstly performed bioinformatic analysis of the Cuprotosis database and The Cancer Genome Atlas (TCGA) database to obtain 19 cuprotosis-related gene datasets and transcriptional data for LUAD. Univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis were utilized to construct cuproptosis-associated lncRNA modes. LUAD patients were thus classified into high-risk and low-risk categories based on prognostic risk values, with a median of It acted as a boundary. Risk models were evaluated and validated using Kaplan-Meier analysis, principal component analysis (PCA), gene set enrichment analysis (GSEA) and nomograms. Utilizing the TCGA-LUAD dataset, we identified seven predicted cuproptosis-associated lncRNAs in tumor microenvironment to create the risk model. 95.54% (214/224) of high-risk category tumor samples included cuproptosis-associated gene alterations, compared to 85.65% (203/237) of low-risk category tumor samples, with TP53 accounting for the bulk of occurrences. According to these findings, risk value was superior to other clinical variables and tumor mutation burden as a predictor of 1-, 3-, and 5-year overall survival (OS). The predictive validity of the cuproptosis-associated lncRNA-based risk model for LUAD is high, and this may have implications for how lung cancer patients are treated individually.