Advancements in Structural Basis of Covalent Inhibitors Targeting SARS-CoV-2 Essential Proteins.

Covalent inhibitors play a pivotal role in the development of pharmaceutical therapies, as they form stable, irreversible bonds with target biomolecules, leading to prolonged therapeutic effects and enhanced efficacy. Since covalent inhibitors first appeared in the late 1800s, the field has become innovative rapidly, and covalent inhibitors now account for around 30% of all marketed therapeutics. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes the pandemic of Coronavirus Disease 2019 (COVID-19).

Impact of Acacia-derived biochar to mitigate salinity stress in Zea mays L. by morpho-physiological and biochemical indices.

Climate change has caused many challenges to soil ecosystems, including soil salinity. Consequently, many strategies are advised to mitigate this issue. In this context, biochar is acknowledged as a useful addition that can alleviate the detrimental impacts of salt stress on plants.

Combination of Biogas Residues and Interactions Stimulates Crop Production and Salinity Tolerance in .

Stress tolerance in cereal crops like Sorghum is important to address food security and land development for saline agriculture. Salinity is considered one of the most devastating abiotic stresses affecting plant growth and yield, specifically in water-scared areas of the world. Biogas residue is a good source of plant nutrients with enriched fertilizer for crop yield and productivity.

Multidimensional role of selenium nanoparticles to promote growth and resilience dynamics of against sodium fluoride stress.

High fluoride (F) concentrations negatively affect the seed germination, plant growth, development, and yield of crops. L. is an F-sensitive crop frequently grown on marginal lands affected by F salts.

Best-subset instrumental variable selection method using mixed integer optimization with applications to health-related quality of life and education-wage analyses.

The classical best-subset selection method has been demonstrated to be nondeterministic polynomial-time hard and thus presents computational challenges. This problem can now be solved via advanced mixed integer optimization (MIO) algorithms for linear regression. We extend this methodology to linear instrumental variable (IV) regression and propose the best-subset instrumental variable (BSIV) method incorporating the MIO procedure.