The identification of bots is an important and complicated task. The bot classifier "Botometer" was successfully introduced as a way to estimate the number of bots in a given list of accounts and, as a consequence, has been frequently used in academic publications. Given its relevance for academic research and our understanding of the presence of automated accounts in any given Twitter discourse, we are interested in Botometer's diagnostic ability over time. To do so, we collected the Botometer scores for five datasets (three verified as bots, two verified as human; n = 4,134) in two languages (English/German) over three months. We show that the Botometer scores are imprecise when it comes to estimating bots; especially in a different language. We further show in an analysis of Botometer scores over time that Botometer's thresholds, even when used very conservatively, are prone to variance, which, in turn, will lead to false negatives (i.e., bots being classified as humans) and false positives (i.e., humans being classified as bots). This has immediate consequences for academic research as most studies in social science using the tool will unknowingly count a high number of human users as bots and vice versa. We conclude our study with a discussion about how computational social scientists should evaluate machine learning systems that are developed for identifying bots.

Background: Per- and polyfluoroalkyl substances (PFAS) are a large class of synthetic (man-made) chemicals widely used in consumer products and industrial processes. Thousands of distinct PFAS exist in commerce. The 2019 U.S. Environmental Protection Agency (U.S. EPA) Per- and Polyfluoroalkyl Substances (PFAS) Action Plan outlines a multiprogram national research plan to address the challenge of PFAS. One component of this strategy involves the use of systematic evidence map (SEM) approaches to characterize the evidence base for hundreds of PFAS. Objective: SEM methods were used to summarize available epidemiological and animal bioassay evidence for a set of PFAS that were prioritized in 2019 by the U.S. EPA’s Center for Computational Toxicology and Exposure (CCTE) for in vitro toxicity and toxicokinetic assay testing. Methods: Systematic review methods were used to identify and screen literature using manual review and machine-learning software. The Populations, Exposures, Comparators, and Outcomes (PECO) criteria were kept broad to identify mammalian animal bioassay and epidemiological studies that could inform human hazard identification. A variety of supplemental content was also tracked, including information on in vitro model systems; exposure measurement–only studies in humans; and absorption, distribution, metabolism, and excretion (ADME). Animal bioassay and epidemiology studies meeting PECO criteria were summarized with respect to study design, and health system(s) were assessed. Because animal bioassay studies with exposure duration (or reproductive/developmental study design) were most useful to CCTE analyses, these studies underwent study evaluation and detailed data extraction. All data extraction is publicly available online as interactive visuals with downloadable metadata. Results: More than 40,000 studies were identified from scientific databases. Screening processes identified 44 animal and 148 epidemiology studies from the peer-reviewed literature and 95 animal and 50 epidemiology studies from gray literature that met PECO criteria. Epidemiological evidence (available for 15 PFAS) mostly assessed the reproductive, endocrine, developmental, metabolic, cardiovascular, and immune systems. Animal evidence (available for 40 PFAS) commonly assessed effects in the reproductive, developmental, urinary, immunological, and hepatic systems. Overall, 45 PFAS had evidence across animal and epidemiology data streams. Discussion: Many of the PFAS were data poor. Epidemiological and animal evidence were lacking for most of the PFAS included in our search. By disseminating this information, we hope to facilitate additional assessment work by providing the initial scoping literature survey and identifying key research needs. Future research on data-poor PFAS will help support a mo...

Plasminogen activator inhibitor-1 (PAI-1) is an acute phase protein that has been shown to play a role in experimental fibrosis caused by bile duct ligation (BDL) in mice. However, its role in more severe models of hepatic fibrosis (e.g., carbon tetrachloride; CCl(4)) has not been determined and is important for extrapolation to human disease. Wild-type or PAI-1 knockout mice were administered CCl(4) (1 ml/kg body wt ip) 2x/wk for 4 wk. Plasma (e.g., transaminase activity) and histological (e.g., Sirius red staining) indexes of liver damage and fibrosis were evaluated. Proliferation and apoptosis were assessed by PCNA and TdT-mediated dUTP nick-end labeling (TUNEL) staining, respectively, as well as by indexes of cell cycle (e.g., p53, cyclin D1). In contrast to previous studies with BDL, hepatic fibrosis was enhanced in PAI-1(-/-) mice after chronic CCl(4) administration. Indeed, all indexes of liver damage were elevated in PAI-1(-/-) mice compared with wild-type mice. This enhanced liver damage correlated with impaired hepatocyte proliferation. A similar effect on proliferation was observed after one bolus dose of CCl(4), without concomitant increases in liver damage. Under these conditions, a decrease in phospho-p38, coupled with elevated p53 protein, was observed; these results suggest impaired proliferation and a potential G(1)/S cell cycle arrest in PAI-1(-/-) mice. These data suggest that PAI-1 may play multiple roles in chronic liver diseases, both protective and damaging, the latter mediated by its influence on inflammation and fibrosis and the former via helping maintain hepatocyte division after an injury.

Studies in rodents suggest that the adipocytokine resistin causes insulin resistance via impairing normal insulin signaling. However, in humans, resistin may play a more important role in inflammation than in insulin resistance. Whether resistin contributes to inflammation in rodents is unclear. Therefore, the purpose of the present study was to determine the effect of resistin exposure on the basal and stimulated [lipopolysaccharide (LPS)] inflammatory response in mouse liver in vivo. Resistin alone had no major effects on hepatic expression of insulin-responsive genes, either in the presence or absence of LPS. Although it had no effect alone, resistin significantly enhanced hepatic inflammation and necrosis caused by LPS. Resistin increased expression of proinflammatory genes, e.g., plasminogen activator inhibitor (PAI)-1, and activity of mitogen-activated protein (MAP) kinase, extracellular signal-regulated kinase 1/2, caused by LPS, but had little effect on anti-inflammatory gene expression. Resistin also enhanced fibrin deposition (an index of hemostasis) caused by LPS. The increase in PAI-1 expression, fibrin deposition, and liver damage caused by LPS ϩ resistin was almost completely prevented either by inhibiting the coagulation cascade, hirudin, or by blocking MAP kinase signaling, U0126 [1,4-diamino-2,3-dicyano-1,4-bis(2-aminophenylthio) butadiene], indicating that these pathways play a causal role in observed enhanced liver damage caused by resistin. Taken together, the augmentation of LPS-induced liver damage caused by resistin seems to involve, at least in part, up-regulation of hepatic inflammation via mechanisms most likely involving the coagulation cascade and fibrin accumulation. These data also suggest that resistin may have proinflammatory roles in mouse liver independent of its effects on insulin signaling, analogous to previous work in humans.Hepatic disease (e.g., nonalcoholic steatohepatitis) is a common complication of insulin resistance and type II diabetes and is mediated, at least in part, by enhanced systemic and local inflammation (Hotamisligil et al., 1993). Inflammation during insulin resistance has often correlated with overproduction of local proinflammatory cytokines, such as TNF␣ (Lehrke et al., 2004). Furthermore, recent work has indicated that some adipokines, whose release from adipose tissue is altered during insulin resistance (i.e., leptin and adiponectin), also directly coordinate the local inflammatory response in liver (Tsochatzis et al., 2006). However, mechanisms by which adipokines modulate hepatic inflammation and the possible role of other adipokines in this process are unclear.Resistin, also known as FIZZ3 and adipocyte-derived secretory factor (Holcomb et al., 2000;Steppan et al., 2001b;Rajala et al., 2002), is a 12.5-kDa polypeptide synthesized and secreted by adipocytes. In rodents, serum levels of resistin were elevated in models of obesity, and high-dose resistin altered glucose metabolism through impairment of insulin action, particularly in the live...