Shigella infections are an important cause of diarrhea in young children and can result in severe complications. Disparities in Shigella infections are well documented among US adults. Our objective was to characterize disparities in incidence and severity of Shigella infections among US children. We analyzed laboratory-diagnosed Shigella infections reported to FoodNet, an active, population-based surveillance system in 10 US sites, among children during 2009-2018. We calculated the incidence rate stratified by sex, age, race/ethnicity, Shigella species, and disease severity. Criteria for severe classification were hospitalization, bacteremia, or death. The odds of severe infection were calculated using logistic regression. During 2009-2018, 10 537 Shigella infections were reported in children and 1472 (14.0%) were severe. The incidence rate was 9.5 infections per 100 000 child-years and the incidence rate of severe infections was 1.3 per 100 000 child-years. Incidence was highest among children aged 1-4 years (19.5) and lowest among children aged 13-17 years (2.3); however, children aged 13-17 years had the greatest proportion of severe infections (21.2%). Incidence was highest among Black (16.2 total; 2.3 severe), Hispanic (13.1 total; 2.3 severe), and American Indian/Alaska Native (15.2 total; 2.5 severe) children. Infections caused by non-sonnei species had higher odds of severity than infections caused by Shigella sonnei (adjusted odds ratio 2.58; 95% confidence interval 2.12-3.14). The incidence and severity of Shigella infections among US children vary by age, race/ethnicity, and Shigella species, warranting investigation of unique risk factors among pediatric subpopulations.The incidence and severity of Shigella infections among US children vary by age, race/ethnicity, and Shigella species, warranting investigation of unique risk factors among pediatric subpopulations.In June of 2013 an application of dinotefuran on an ornamental planting of European linden trees (Tilia cordata Mill. [Malvales Malvalceae]) in a shopping mall parking lot in Wilsonville, Oregon provoked the largest documented pesticide kill of bumble bees in North America. Based on geographic information systems and population genetic analysis, we estimate that between 45,830 and 107,470 bumble bees originating from between 289 and 596 colonies were killed during this event. Dinotefuran is a neonicotinoid that is highly effective in exterminating and/or harming target pest insects and non-target beneficial insects. Analysis to detect the concentration of pesticides in flowers that received foliar application revealed that the minimum reported dinotefuran concentration of a sampled T. cordata flower was 7.4 ppm, or in excess of 737% above the LC50 of the beneficial pollinator, the honey bee (Apis mellifera Linnaeus, 1758 [Hymenoptera Apidae]). Furthermore, sampled Vosnesensky bumble bees (Bombus vosnesenskii Radoskowski, 1862 [Hymenoptera Apidae]) were found to have an average dinotefuran concentration of 0.92 ppm at the time of death, which exceeds the maximum LC50 of A. mellifera (0.884 ppm). Our study underscores the lethal impact of the neonicotinoid pesticide dinotefuran on pollinating insect populations in a suburban environment. To our knowledge, the documentation and impact of pesticide kills on wild populations of beneficial insects has not been widely reported in the scientific literature. It is likely that the vast majority of mass pesticide kills of beneficial insects across other environments go unnoticed and unreported.Autoantibodies are a hallmark of numerous neurologic disorders, including multiple sclerosis (MS), autoimmune encephalitides and neuromyelitis optica (NMO). While well understood in peripheral myeloid cells, the pathophysiological significance of autoantibody-induced Fc receptor (FcR) signaling in microglia remains unknown, in part due to the lack of a robust in vivo model. Moreover, application of therapeutic antibodies for neurodegenerative disease also highlights the importance of understanding FcR signaling in microglia. TMZ chemical manufacturer Here, we describe a novel in vivo experimental paradigm that allows for selective engagement of Fc receptors within the CNS by peripherally injecting anti-myelin oligodendrocyte glycoprotein (MOG) monoclonal antibodies (mAbs) in normal wild-type mice. MOG antigen-bound immunoglobulins were detected throughout the CNS and triggered a rapid and tightly regulated proliferative response in both brain and spinal cord microglia. This microglial response was abrogated when anti-MOG antibodies weific FcR and BTK-driven responses to both pathogenic and therapeutic antibodies in CNS homeostasis and disease. Ion mobility spectrometry (IMS) separations are increasingly used in conjunction with mass spectrometry (MS) for separation and characterization of ionized molecular species. Information obtained from IMS measurements includes the ion's collision cross section (CCS), which reflects its size and structure and constitutes a descriptor for distinguishing similar species in mixtures that cannot be separated using conventional approaches. Incorporating CCS into MS-based workflows can improve the specificity and confidence of molecular identification. At present, there is no automated, open-source pipeline for determining CCS of analyte ions in both targeted and untargeted fashion, and intensive user-assisted processing with vendor software and manual evaluation is often required. We present AutoCCS, an open-source software to rapidly determine CCS values from IMS-MS measurements. We conducted various IMS experiments in different formats to demonstrate the flexibility of AutoCCS for automated CCS calculation 1) stepped-field methods for drift tube-based IMS (DTIMS), 2) single-field methods for DTIMS (supporting two calibration methods a standard and a new enhanced method) and 3) non-linear calibration methods for traveling wave based-IMS (TWIMS) in Waters Synapt and Structures for Lossless Ion Manipulations (SLIM). We demonstrated that AutoCCS offers an accurate and reproducible determination of CCS for both standard and unknown analyte ions in various IMS-MS platforms, IMS-field methods, ionization modes, and collision gases, without requiring manual processing. https//github.com/PNNL-Comp-Mass-Spec/AutoCCS. Supplementary data are available at Bioinformatics online.Supplementary data are available at Bioinformatics online.