21794 research outputs found
Sort by
Antimicrobial-Resistant Enterococcus In Urban Communities: Insights Into Human Infection Risks
No full textThe complexity of community risk factors and transmission routes has been a major challenge to the containment of community-acquired antibiotic resistance. Possible factors range from environmental contamination, antibiotic misuse, and human exposure via animals, food, the environment, and human-to-human transmission. What has been missing from previous research is the integration of findings in animal and environmental sectors with bacteria implicated in community infections. To gain a better understanding of antibiotic resistance acquired in the community, a one health approach is clearly needed. Specifically, we aim to investigate the prevalence of antibiotic-resistant Enterococcus in wildlife, their gene profiles in comparison with bacteria from community-acquired infections. A total of 140 fecal samples were collected from birds and rodents across 10 areas in Metro-Detroit. Culture based isolation, PCR identification and antimicrobial susceptibility testing were done. Enterococcus was recovered from 102 (72.8%) samples which further resulted in 270 isolates and 268 were analyzed. Approximately 28% (74/268) of isolates were E. faecalis, 26% (71/268) were E. faecium, while 46% (123/268) belonged to other species. Resistance to at least one antibiotic was found in 97% of isolates, with the top three resistance phenotypes being lincomycin (89.55%), quinupristin/dalfopristin (Q/D) (32.84%), and nitrofurantoin (17.54 %). Multidrug resistant (MDR) was shown by 17% of the isolates. The cadmium resistance gene cadA was detected in 28% of isolates altogether but more prevalent in E. faecalis (90.5%), suggesting species variation in cadA acquisition and calling for further research on plasmid profiling as cadA is often associated with plasmids. The data demonstrates urban wildlife as a significant reservoir of antibiotic-resistant Enterococcus. To compare wildlife resistance patterns to human samples, resistance was profiled in human isolates by analyzing 81 vancomycin resistant Enterococcus (VRE) from outpatients who had no extensive antibiotic or hospital exposure at Henry Ford Health System from primarily urine (78%) and blood samples (12%). A total of 17 E. faecalis and 19 E. faecium were randomly selected based on areas where wildlife samples were collected and tested for antibiotic susceptibility resulting in 100% resistance to vancomycin and ciprofloxacin, and over 90% to erythromycin, kanamycin, lincomycin, and tylosin tartrate. Daptomycin and nitrofurantoin resistance was only found in E. faecium samples while Q/D resistance was only found in E. faecalis. cadA gene screening revealed a prevalence of 74% (60/81), including E. faecium (78%) and E. faecalis (68%), contrasting wildlife data. This can be explained by the different selective pressure bacteria encounter in the environment versus during treatment for chronic diseases. Further research is warranted to explore cadmium as an alternative selective pressure for antibiotic resistance in Enterococcus species. The higher resistances in human samples shown to multiple antibiotics in our study indicates that there are more selective pressures for transmission of antibiotics in hospitals than in wildlife posing a significant public health challenge. Finally, to explore genomic landscape of antibiotic resistance and virulence, whole genome sequencing (WGS), using in-silco multi-locus sequence typing (MLST), resistance gene profiling using Qiagen microbial insight - antimicrobial resistance (QMI-AR) database, and virulence gene analysis using virulence factor database (VFDB) was performed on 26 isolates (20 wildlife, 6 human). Fourteen sequence types (ST) were identified (8 from E. faecium and 6 from E. faecalis), with human isolates clustering tightly while wildlife isolates spread out. Thirty-two resistance genes across glycopeptides (human-dominant), aminoglycosides, and macrolides-lincosamides-streptogramines (MLS) (wildlife dominant), fluoroquinolones, tetracycline, and trimethoprim were identified. Two wildlife samples, 106a and 123b carried vanX-A, vanS-C, and vanXY-C genes while 3 tetracycline resistance genes, tet(L), tet(M), and tet(U) were found across 5 human and 2 wildlife isolates. efmA which confers resistance to fluoroquinolones was found in 9 samples. Similarly, ErmB which confers resistance against Q/D was also found in isolates 1 wildlife and 4 human isolates. Twenty-nine different virulence genes were identified in human and wildlife isolates across 4 different categories namely, adherence, biofilm formation, exoenzyme, and immune modulation with wildlife isolates like 13a and 19b mirroring human strain 106656 and 123b carrying van resistance genes and multiple virulence gene categories making them highly virulent. This overlap suggests a common reservoir between wildlife and human and the transmission potential between the two. The presence of isolates carrying a range of antibiotic resistance genes and virulence factors indicate potential public health concern. To summarize, urban wildlife is a reservoir of antibiotic-resistant Enterococcus carrying a diversity of antibiotic resistance genes and virulence genes. Our findings highlight the need for extensive monitoring of environmental contamination and their public health implication. Continued research is also needed to explore the role of metal contamination in the urban environment as well as the transmission potential of resistant strains from wildlife
Aboriginal Australian Writing and Student Resistance to Learning About Race: A “Take It Slant” Approach
No full textThis article argues that Australian literature offers a particular benefit in teaching students in the United States about race through the “slant” approach of a different racial context, one about which they know very little, thus limiting preconceptions. This approach provides a productive response not only to the current antagonistic environment being created by the federal government, but also to students who genuinely feel both uncomfortable and curious about racial issues. While white students do not want to feel they are the “bad guys,” students of color do not want to feel either like victims or like expert witnesses. Distance from difficult topics can make them easier to discuss and learn about; therefore, incorporating the historic and ongoing treatment of Aboriginal Australians encourages American students to engage in these critical discussions while not feeling personally implicated, putting all students on equal footing. By carefully selecting texts written by Aboriginal peoples and connecting those readings to bigger theoretical ideas about the experiences of racism and global colonization, educators can engender productive discussions about race in the American classroom
Gender Diversity, Substance Cognitions, and Alcohol, Nicotine/Tobacco, and Cannabis Use among Youth
No full textPurpose: We aimed to classify youth using a longitudinal, multidimensional construct of gender, and examine associations of gender subgroups with substance cognitions and substance use.
Methods: We used data from the Adolescent Brain Cognitive Development Study (N=11,868 youth ages 9-10 years at baseline [2016-2018] through the year 4 follow-up [ages 13-14 years, 2020-2022]) to conduct latent class models using measures of gender identity, felt gender, gender expression, and gender non-contentedness. We used multivariable logistic regression to assess associations of gender classes with curiosity to use, intention to use, and use of alcohol, nicotine/tobacco, and cannabis, respectively, adjusting for sociodemographic factors.
Results: A four-class model was selected based on model fit: transgender (2.5%), questioning (9.0%), naïve (36.3%), and cisgender (52.1%). Youth in the questioning and transgender classes were more likely to report curiosity to use (adjusted odds ratio [aOR] range 1.68-2.45, p\u3c 0.001) and intention to use (aOR range 1.69-3.14, p\u3c 0.01) but not actual use of alcohol, nicotine/tobacco, and cannabis, whereas members of the naïve class were less likely to report curiosity to use, intention to use, and use of alcohol, nicotine/tobacco, and cannabis (aOR range 0.48-0.81, p\u3c 0.001), relative to cisgender youth.
Conclusion: These findings suggest that a more nuanced understanding of gender among preadolescent youth and their heterogeneous risk for substance use is critical for the development of early prevention services. The timing of prevention efforts may be ideal during this developmental period
Greenwash And Greenhush: Applications Of Signaling To Corporate Sustainability Communication
No full textCorporate environmental communication has become increasingly important as stakeholders demand greater transparency and accountability from firms regarding their sustainability practices. However, this has also given rise to the phenomena of greenwashing, where firms engage in misleading or deceptive environmental claims, and greenhushing, where firms deliberately underreport or fail to disclose their positive environmental actions. This dissertation provides a comprehensive review and of the literature on greenwash and greenhush, highlighting the drivers, consequences, and theoretical underpinnings. It introduces a novel framework that characterizes these practices as forms of communication decoupling, drawing on signaling theory to analyze the strategic interactions between firms and their stakeholders.
The dissertation proceeds in several chapters. First, it provides a systematic review of the greenhush literature, and proposes an integrated framework to understand the phenomenon through the lens of stakeholder theory. Second, it develops a novel model of greenwash in the presence of social network effects. This chapter explores how the social value of green consumption affects the eco-labeling strategies of firms. Third, it develops a model of eco-labeling which explains greenhush and greenwash as the result of conflicting stakeholder demands
Cotton Town Blues: An Investigation of Social Inequality, Diet, and Health in a 19th Century Industrializing Population—St Peter’s Churchyard, Blackburn, Lancashire
No full textHistorical accounts document that the shift from rural settlement to urban settings and from employment in cottage industries to factory working at the turn of the 19th century had a negative impact on the lower classes.
In 2015, the relocation of the cemetery of St Peter’s Churchyard in Blackburn, Lancashire, provided an opportunity to study the effects of industrialization on the diets and health of an early to mid-19th-century population through osteological and biomolecular analyses. Those interred at St Peter’s represent a socioeconomic cross-section of the church’s parishioners. In this paper, we present an evaluation of the lifetime dietary biographies of the St Peter’s population modelled from bone collagen and incremental dentine stable isotope ratios (δ13C, δ15N and δ34S). These data are combined with the results of aDNA and osteological analyses to explore the relationship between diet, health and socioeconomic status.
Dietary models determined from stable isotope data suggest that contrary to historical accounts there are no substantial differences between the diets of the lower, and middle/upper classes. Nor are there differences between the diets of most men and women, although there are a small number of women who did consume a greater proportion of sugar or maize than the rest of the population. Weaning typically commenced before one year of age and was completed by two years. Notably, there were no isotopically discernible differences in the weaning practices evident between those of lower and higher socioeconomic status. Additionally, there was little evidence for physiological stress during the weaning process
Improving The Robustness Of Compressed Deep Learning Models Against Class Imbalance
No full textDeep Learning (DL) models are deployed ubiquitously, as they power a wide range of critical applications, including image classification, fraud detection, autonomous vehicles, robots, and NLP. However, their massive size and huge memory footprint (overparameterization) represent a serious challenge to the efficient deployment of such models, especially in resource-scarce environments such as wearable devices, smartphones, edge devices, and embedded systems. Therefore, model compression techniques are typically used to shrink the model size to the currently available computational and memory budget and to accelerate training and inference without sacrificing model accuracy and performance. Therefore, the DL research community considers model compression an increasingly important field.
DL model compression techniques, such as pruning, regularization, quantization, and knowledge distillation, have recently undergone significant advances. However, the robustness of the compressed DL models is yet to be fully understood and comprehensively addressed. Adversarial robustness and out-of-distribution robustness of compressed DL models have received more attention than other aspects of DL model robustness, such as class imbalance. Class imbalance, a well-known DL research problem, refers to the unbalanced sample distribution in the training dataset is not balanced, as some classes have more samples (majority classes) than other classes (minority classes).
This PhD dissertation analyzes and quantifies the effect of class imbalance in training datasets on the robustness of compressed DL models. We first define empirical robustness and use it as a metric to measure the robustness of compressed DL models against class imbalance. We find that compressed DL models are not robust against class imbalance. We also show how different compression techniques, namely pruning, quantization, and knowledge distillation, have different impacts on the class imbalance robustness of DL models. We also demonstrate the impact of different class imbalance ratios and class imbalance types on the class imbalance robustness of compressed DL models.
We propose and implement a robustness-aware Bayesian compressive sensing-based pruning framework to address the problem discussed above. We estimate the criticality of a subset of the model’s parameters to the performance of each class (per-class F-1 score). We leverage Bayesian learning to stochastically select such parameters (measurements). We then utilize compressive sensing to obtain the criticality of the remainder of the model’s parameters. We train ResNet-20 on imbalanced CIFAR-10 and use our proposed framework to prune the model. Our results demonstrate that this approach preserves model robustness against varying degrees of class imbalance in the training dataset under mild, moderate, and severe pruning ratios