Methane prediction equations including genera of rumen bacteria as predictor variables improve prediction accuracy.

Methane (CH) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories. Existing models rely solely on dietary and host animal-related data, ignoring the predicting power of rumen microbiota, the source of CH. To address this limitation, we developed novel CH prediction models incorporating rumen microbes as predictors, alongside animal- and feed-related predictors using four statistical/machine learning (ML) methods.

Advancing secondary structure characterization of monoclonal antibodies using Microfluidic Modulation Spectroscopy.

Infrared (IR) spectroscopy is rapidly gaining traction for monitoring biotherapeutic critical quality attributes. Microfluidic Modulation Spectroscopy (MMS), a novel automated IR technology, has been shown to be an effective technique for generating high quality, reproducible secondary structure data for protein therapeutics including monoclonal antibodies. In this study, monoclonal antibodies (mAbs) at concentrations ranging from 0.

Determining Spectroscopic Quantitation Limits for Misfolded Structures.

Protein secondary structures are frequently assessed using infrared and circular dichroism spectroscopies during drug development (e.g., during product comparability and biosimilarity studies, reference standard characterization, etc.

Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database.

Enteric methane (CH ) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH is complex, expensive, and impractical at large scales; therefore, models are commonly used to predict CH production. However, building robust prediction models requires extensive data from animals under different management systems worldwide.