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Abstract Training machine learning models for tasks such asde novosequencing or spectral clustering requires large collections of confidently identified spectra. Here we describe a dataset of 2.8 million high-confidence peptide-spectrum matches derived from nine different species. The dataset is based on a previously described benchmark but has been re-processed to ensure consistent data quality and enforce separation of training and test peptides. 

ABSTRACT Protein tandem mass spectrometry data are most often interpreted by matching observed mass spectra to a protein database derived from the reference genome of the sample being analyzed. In many application domains, however, a relevant protein database is unavailable or incomplete, and in such settings de novo sequencing is required. Since the introduction of the DeepNovo algorithm in 2017, the field of de novo sequencing has been dominated by deep learning methods, which use large amounts of labeled mass spectrometry data to train multi‐layer neural networks to translate from observed mass spectra to corresponding peptide sequences. Here, we describe these deep learning methods, outline procedures for evaluating their performance, and discuss the challenges in the field, both in terms of methods development and evaluation protocols. 

Paraphlomis jinggangshanensis (Lamiaceae), a new species from Jiangxi Province, China, is described and illustrated. The new species is morphologically similar to P. intermedia , but can be easily distinguished from the latter by its cordate leaf base ( vs. cuneate, decurrent), stem and calyx tube with glandular hairs ( vs. short pubescent), and glabrous anthers ( vs. ciliate anthers). A phylogenetic analysis, based on ITS regions, suggests that P. jinggangshanensis represents a separate branch in Paraphlomis and is closely related to Clade II. It is currently known only from Jinggangshan National Natural Reserve. Because of its limited distribution and small population size, the species was assessed as Near Threatened (NT) according to the IUCN Red List Categories and Criteria. 

Rich, diverse cybersecurity data are critical for efforts by the intelligence and security informatics (ISI) community. Although open-access data repositories (OADRs) provide tremendous benefits for ISI researchers and practitioners, determinants of their adoption remain understudied. Drawing on affordance theory and extant ISI literature, this study proposes a factor model to explain how the essential and unique affordances of an OADR (i.e., relevance, accessibility, and integration) affect individual professionals' intentions to use and collaborate with AZSecure, a major OADR. A survey study designed to test the model and hypotheses reveals that the effects of affordances on ISI professionals' intentions to use and collaborate are mediated by perceived usefulness and ease of use, which then jointly determine their perceived value. This study advances ISI research by specifying three important affordances of OADRs; it also contributes to extant technology adoption literature by scrutinizing and affirming the interplay of essential user acceptance and value perceptions to explain ISI professionals' adoptions of OADRs.