Spatial Control of Rabies on Heterogeneous Landscapes

Rabies control in terrestrial wildlife reservoirs relies heavily on an oral rabies vaccine (ORV). In addition to direct ORV delivery to protect wildlife in natural habitats, vaccine corridors have been constructed to control the spread; these corridors are often developed around natural barriers, such as rivers, to enhance the effectiveness of vaccine deployment. However, the question of how to optimally deploy ORV around a river (or other natural barrier) to best exploit the barrier for rabies control has not been addressed using mathematical models. Given an advancing epidemic wave, should the vaccine be distributed on both sides of barrier, behind the barrier, or in front of it? Here, we introduce a new mathematical model for the dynamics of raccoon rabies on a spatially heterogeneous landscape that is both simple and realistic. We demonstrate that the vaccine should always be deployed behind a barrier to minimize the recurrence of subsequent epidemics. Although the oral rabies vaccine is sufficient to induce herd immunity inside the vaccinated area, it simultaneously creates a demographic refuge. When that refuge is in front of a natural barrier, seasonal dispersal from the vaccine corridor into an endemic region sustains epidemic oscillations of raccoon rabies. When the vaccine barrier creates a refuge behind the river, the low permeability of the barrier to host movement limits dispersal of the host population from the protected populations into the rabies endemic area and limits subsequent rabies epidemics

Availability of coal resources for mining in Illinois : Newton and Princeville quadrangles, Jasper, Peoria, and Stark Counties

U.S. Geological Survey under assistance award No. 1434-94-A-1266Ope

Trends in the Diversity of Pediatric Faculty: 2000 to 2020

OBJECTIVE: Academic medicine diversity is important for addressing health disparities and training the next generation. A recent study highlighted the lack of diversity among pediatric trainees over time. However, trends in US pediatric faculty diversity have not been clearly illuminated. The aim of this study is to evaluate pediatric faculty diversity trends and compare racial/ethnic representation between pediatric faculty and the US population. METHODS: Repeat cross-sectional study of the Association of American Medical Colleges Faculty Roster of pediatric faculty from 2000 to 2020. Trends in sex, race, ethnicity, and rank were compared with the Cochran-Armitage test. Data on faculty race/ethnicity were compared with the general and child population by using US Census Bureau data. RESULTS: Trends in underrepresented in medicine (URiM) faculty representation significantly increased at all ranks: professor (+3.5%, P \u3c .0001), associate professor (+3.0%, P = .0001), and assistant professor (+2.5%, P = .0001). URiM male representation remained unchanged (P = .14), whereas significantly increased trends occurred in URiM female representation (+3.4%, P \u3c .0001). African American/Black males significantly decreased representation at associate (-0.4%, P = .04) and assistant professor levels (-0.6%, P \u3c .0001), and American Indian/Alaska Native males significantly decreased representation at the assistant professor rank (-0.1%, P \u3c .0001). The percentage of URiM pediatric faculty representation was considerably lower compared with 2020 US overall and longitudinal child population representation. CONCLUSION: The stagnation of URiM male representation and lack of faculty diversity reflective of the US population may have a critical impact on the ability to recruit/retain a diverse pediatric workforce and promote equitable care

The impact of targeting all elderly persons in England and Wales for yearly influenza vaccination: excess mortality due to pneumonia or influenza and time trend study.

OBJECTIVE: To investigate the impact on mortality due to pneumonia or influenza of the change from risk-based to age group-based targeting of the elderly for yearly influenza vaccination in England and Wales. DESIGN: Excess mortality estimated using time series of deaths registered to pneumonia or influenza, accounting for seasonality, trend and artefacts. Non-excess mortality plotted as proxy for long-term trend in mortality. SETTING: England and Wales. PARTICIPANTS: Persons aged 65-74 and 75+ years whose deaths were registered to underlying pneumonia or influenza between 1975/1976 and 2004/2005. OUTCOME MEASURES: Multiplicative effect on average excess pneumonia and influenza deaths each winter in the 4-6 winters since age group-based targeting of vaccination was introduced (in persons aged 75+ years from 1998/1999; in persons aged 65+ years from 2000/2001), estimated using multivariable regression adjusted for temperature, antigenic drift and vaccine mismatch, and stratified by dominant circulating influenza subtype. Trend in baseline weekly pneumonia and influenza death rates. RESULTS: There is a suggestion of lower average excess mortality in the six winters after age group-based targeting began compared to before, but the CI for the 65-74 years age group includes no difference. Trend in baseline pneumonia and influenza mortality shows an apparent downward turning point around 2000 for the 65-74 years age group and from the mid-1990s in the 75+ years age group. CONCLUSIONS: There is weakly supportive evidence that the marked increases in vaccine coverage accompanying the switch from risk-based to age group-based targeting of the elderly for yearly influenza vaccination in England and Wales were associated with lower levels of pneumonia and influenza mortality in older people in the first 6 years after age group-based targeting began. The possible impact of these policy changes is observed as weak evidence for lower average excess mortality as well as a turning point in baseline mortality coincident with the changes

AMAnD: an automated metagenome anomaly detection methodology utilizing DeepSVDD neural networks

The composition of metagenomic communities within the human body often reflects localized medical conditions such as upper respiratory diseases and gastrointestinal diseases. Fast and accurate computational tools to flag anomalous metagenomic samples from typical samples are desirable to understand different phenotypes, especially in contexts where repeated, long-duration temporal sampling is done. Here, we present Automated Metagenome Anomaly Detection (AMAnD), which utilizes two types of Deep Support Vector Data Description (DeepSVDD) models; one trained on taxonomic feature space output by the Pan-Genomics for Infectious Agents (PanGIA) taxonomy classifier and one trained on kmer frequency counts. AMAnD's semi-supervised one-class approach makes no assumptions about what an anomaly may look like, allowing the flagging of potentially novel anomaly types. Three diverse datasets are profiled. The first dataset is hosted on the National Center for Biotechnology Information's (NCBI) Sequence Read Archive (SRA) and contains nasopharyngeal swabs from healthy and COVID-19-positive patients. The second dataset is also hosted on SRA and contains gut microbiome samples from normal controls and from patients with slow transit constipation (STC). AMAnD can learn a typical healthy nasopharyngeal or gut microbiome profile and reliably flag the anomalous COVID+ or STC samples in both feature spaces. The final dataset is a synthetic metagenome created by the Critical Assessment of Metagenome Annotation Simulator (CAMISIM). A control dataset of 50 well-characterized organisms was submitted to CAMISIM to generate 100 synthetic control class samples. The experimental conditions included 12 different spiked-in contaminants that are taxonomically similar to organisms present in the laboratory blank sample ranging from one strain tree branch taxonomic distance away to one family tree branch taxonomic distance away. This experiment was repeated in triplicate at three different coverage levels to probe the dependence on sample coverage. AMAnD was again able to flag the contaminant inserts as anomalous. AMAnD's assumption-free flagging of metagenomic anomalies, the real-time model training update potential of the deep learning approach, and the strong performance even with lightweight models of low sample cardinality would make AMAnD well-suited to a wide array of applied metagenomics biosurveillance use-cases, from environmental to clinical utility

1,1′-diacetyl-2,2′-biimidazole

A crystallographic twofold rotation axis passes through the C-C bond joining the imidazole rings of the title compound, C10H10N4O2. the molecule crystallizes in a cis disposition. the planar acetyl group is twisted by 5.0 (3)° with respect to the imidazole ring and the two imidazole rings are tilted by 60.53 (5)° in relation to one another