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Leaf traits are essential for understanding many physiological and ecological processes. Partial least squares regression (PLSR) models with leaf spectroscopy are widely applied for trait estimation, but their transferability across space, time, and plant functional types (PFTs) remains unclear.We compiled a novel dataset of paired leaf traits and spectra, with 47 393 records for > 700 species and eight PFTs at 101 globally distributed locations across multiple seasons. Using this dataset, we conducted an unprecedented comprehensive analysis to assess the transferability of PLSR models in estimating leaf traits.While PLSR models demonstrate commendable performance in predicting chlorophyll content, carotenoid, leaf water, and leaf mass per area prediction within their training data space, their efficacy diminishes when extrapolating to new contexts. Specifically, extrapolating to locations, seasons, and PFTs beyond the training data leads to reducedR²(0.12–0.49, 0.15–0.42, and 0.25–0.56) and increased NRMSE (3.58–18.24%, 6.27–11.55%, and 7.0–33.12%) compared with nonspatial random cross‐validation. The results underscore the importance of incorporating greater spectral diversity in model training to boost its transferability.These findings highlight potential errors in estimating leaf traits across large spatial domains, diverse PFTs, and time due to biased validation schemes, and provide guidance for future field sampling strategies and remote sensing applications. 

Background Despite more than 60 years of research, the etiology of bacterial vaginosis (BV) remains controversial. In this pilot study, we used shotgun metagenomic sequencing to characterize vaginal microbial community changes before the development of incident BV (iBV). Methods A cohort of African American women with a baseline healthy vaginal microbiome (no Amsel criteria, Nugent score 0–3 with no Gardnerella vaginalis morphotypes) were followed for 90 days with daily self-collected vaginal specimens for iBV (≥2 consecutive days of a Nugent score of 7–10). Shotgun metagenomic sequencing was performed on select vaginal specimens from 4 women, every other day for 12 days before iBV diagnosis. Sequencing data were analyzed through Kraken2 and bioBakery 3 workflows, and specimens were classified into community state types. Quantitative polymerase chain reaction was performed to compare the correlation of read counts with bacterial abundance. Results Common BV-associated bacteria such as G. vaginalis , Prevotella bivia , and Fannyhessea vaginae were increasingly identified in the participants before iBV. Linear modeling indicated significant increases in G. vaginalis and F . vaginae relative abundance before iBV, whereas the relative abundance of Lactobacillus species declined over time. The Lactobacillus species decline correlated with the presence of Lactobacillus phages. We observed enrichment in bacterial adhesion factor genes on days before iBV. There were also significant correlations between bacterial read counts and abundances measured by quantitative polymerase chain reaction. Conclusions This pilot study characterizes vaginal community dynamics before iBV and identifies key bacterial taxa and mechanisms potentially involved in the pathogenesis of iBV. 

Illicit website owners frequently rely on traffic distribution systems (TDSs) operated by less-than-scrupulous advertising networks to acquire user traffic. While researchers have described a number of case studies on various TDSs or the businesses they serve, we still lack an understanding of how users are differentiated in these ecosystems, how different illicit activities frequently leverage the same advertisement networks and, subsequently, the same malicious advertisers. We design ODIN (Observatory of Dynamic Illicit ad Networks), the first system to study cloaking, user differentiation and business integration at the same time in four different types of traffic sources: typosquatting, copyright-infringing movie streaming, ad-based URL shortening, and illicit online pharmacy websites. ODIN performed 874,494 scrapes over two months (June 19, 2019–August 24, 2019), posing as six different types of users (e.g., mobile, desktop, and crawler) and accumulating over 2TB of data. We observed 81% more malicious pages compared to using only the best performing crawl profile by itself. Three of the traffic sources we study redirect users to the same traffic broker domain names up to 44% of the time and all of them often expose users to the same malicious advertisers. Our experiments show that novel cloaking techniques could decrease by half the number of malicious pages observed. Worryingly, popular blacklists do not just suffer from the lack of coverage and delayed detection, but miss the vast majority of malicious pages targeting mobile users. We use these findings to design a classifier, which can make precise predictions about the likelihood of a user being redirected to a malicious advertiser.