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Physical (PHY) layer abstraction is an effective method to reduce the runtimes compared with link simulations but still accurately characterize the link performance. As a result, PHY layer abstraction for IEEE 802.11 WLAN and 3GPP LTE/5G has been widely configured in the network simulators such as ns-3, which achieve faster system-level simulations quantifying the network performance. Since the first publicly accessible 5G NR Sidelink (SL) link simulator has been recently developed, it provides a possibility of implementing the first PHY layer abstraction on 5G NR SL. This work deploys an efficient PHY layer abstraction method (i.e., EESM-log-SGN) for 5G NR SL based on the offline NR SL link simulation. The obtained layer abstraction which is further stored in ns-3 for use aims at the common 5G NR SL scenario of OFDM unicast single layer mapping in the context of Independent and Identically Distributed (i.i.d.) frequency-selective channels. We provide details about implementation, performance, and validation. 

Abstract Type 1 polyketides are a major class of natural products used as antiviral, antibiotic, antifungal, antiparasitic, immunosuppressive, and antitumor drugs. Analysis of public microbial genomes leads to the discovery of over sixty thousand type 1 polyketide gene clusters. However, the molecular products of only about a hundred of these clusters are characterized, leaving most metabolites unknown. Characterizing polyketides relies on bioactivity-guided purification, which is expensive and time-consuming. To address this, we present Seq2PKS, a machine learning algorithm that predicts chemical structures derived from Type 1 polyketide synthases. Seq2PKS predicts numerous putative structures for each gene cluster to enhance accuracy. The correct structure is identified using a variable mass spectral database search. Benchmarks show that Seq2PKS outperforms existing methods. Applying Seq2PKS to Actinobacteria datasets, we discover biosynthetic gene clusters for monazomycin, oasomycin A, and 2-aminobenzamide-actiphenol.