MOTIVATION: Native top-down proteomics (nTDP) integrates native mass spectrometry (nMS) with top-down proteomics (TDP) to provide comprehensive analysis of protein complexes together with proteoform identification and characterization. Despite significant advances in nMS and TDP software developments, a unified and user-friendly software package for analysis of nTDP data remains lacking. RESULTS: We have developed MASH Native to provide a unified solution for nTDP to process complex datasets with database searching capabilities in a user-friendly interface. MASH Native supports various data formats and incorporates multiple options for deconvolution, database searching, and spectral summing to provide a "one-stop shop" for characterizing both native protein complexes and proteoforms. AVAILABILITY AND IMPLEMENTATION: The MASH Native app, video tutorials, written tutorials, and additional documentation are freely available for download at https://labs.wisc.edu/gelab/MASH_Explorer/MASHSoftware.php. All data files shown in user tutorials are included with the MASH Native software in the download .zip file.

With the increasing use of the internet, cyber threats and malicious activities are becoming ubiquitous. To avoid unsuspecting attacks, gathering enough information about different threats is crucial. According to the Pyramid of Pain, Indicators of Compromise (IOCs) are the simplest artifacts to observe, which help cyber security professionals to design the corresponding precautions. Cyber Threat Intelligence (CTI) is data that presents current threat events, threat actors’ targets, and attack behaviors; hence, collecting and analyzing CTI in advance can be beneficial to defend against cyberattacks. In this paper, we construct a named entity recognition dataset using our annotation method by collecting 1,854 threat intelligence reports. Additionally, we fine-tuned four pre-trained language models and compared the efficiency of each model. Among the four models, we realized that the fine-tuned ELECTRA model could extract new IOCs correctly, and the FeedRef2022 dataset could train NER models for detecting IOCs.