Recycled paper mill effluent (RPME) consists of various organic and inorganic compounds. In this study, modified anaerobic hybrid baffled (MAHB) bioreactor has been successfully used to anaerobically digest RPME. The anaerobic digestion was investigated in relation to methane production rate, lignin removal, and chemical oxygen demand (COD) removal, with respect to organic loading rate (OLR) and hydraulic retention time (HRT). The analysis using kinetic study was carried out under mesophilic conditions (37 ± 2 °C) and influent COD concentrations (1000-4000 mg L-1), to prove its practicability towards RPME treatment. First-order kinetic model was used to clarify the behavior of RPME anaerobic digestion under different OLRs (0.14-4.00 g COD L-1 d-1) and HRT (1-7 d). The result shows that the highest COD removal efficiency and methane production rate were recorded to be 98.07% and 2.2223 L CH4 d-1, respectively. This result was further validated by evaluating the biokinetic coefficients (reaction rate constant (k) and maximum biogas production (ym)), which gave values of k = 0.57 d-1 and ym = 0.331 L d-1. This kinetic data concludes that MAHB presented satisfactory performance towards COD removal with relatively high methane production, which can be further utilized as on-site energy supply.
Rapid technological advancement in high-throughput genomics, microarray, and deep sequencing technologies has accelerated the possibility of more complex precision medicine research using large amounts of heterogeneous health-related data from patients, including genomic variants. Genomic variants can be identified and annotated based on the reference human genome either within the sequence as a whole or in a putative functional genomic element. The American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) mutually created standards and guidelines for the appraisal of proof to expand consistency and straightforwardness in clinical variation interpretations. Various efforts toward precision medicine have been facilitated by many national and international public databases that classify and annotate genomic variation. In the present study, several resources are highlighted with recognition and data spreading of clinically important genetic variations.