Microbial source tracking of human and animal waste pollution of diverse watersheds and of urban drainage systems using molecular methods

The central hypothesis of microbial source tracking (MST) is that there exists a distinguishable relationship between microbes excreted in feces and their particular hosts. These associations could be due to adaptations of microbes to their host’s gut environment and may involve coevolution between microbes and their hosts. In this study, we performed phylogenetic analyses on 16S rRNA gene sequences associated with different host groups to determine if Bacteroides dorei and Enterococcus faecium show co-evolutionary characteristics, thus, host specificity. Bioinformatic analyses were also conducted on E. faecium surface protein gene (espfm) for host specificity. The results of our analyses provide good support for B. dorei human host specificity, and very little support for such specificity in E. faecium.Given these findings, a redesigned primer and probe set was developed for a probe based qPCR assay that specifically targets bacterial sequences from a human-specific B. dorei lineage (HF68-HR183rc) and another targeting the E. faecium espfm gene. The results from comparative assays of HF68-HR183rc against a HF183 protocol obtained from the literature showed similar sensitivity, but an improvement in the specificity of our newly designed protocol. However, assays of rabbit samples showed a high number of positive assays with both the redesigned (58%) and the HF183 (67%) protocol. The newly designed HF68-HR183rc assay could be of considerable use to screen a watershed for human fecal sources of pollution if it is known not to be impacted by rabbits. Assays of the redesigned espfm protocol were not as successful as a quantitative assay, requiring an enrichment process.The HF68-HR183rc qPCR MST protocol was used to help characterize the sources of pollution of sub-watersheds of the temperate environs of Philadelphia/Delaware watershed; watersheds of the tropical island of Puerto Rico; and different types of green infrastructures in Philadelphia and New York.Keywords: microbial source tracking; Bacteroides dorei; Enterococcus faecium; qPCR; molecular (genetic) markers; 16S rRNA gene; bioinformatics; phylogeneticPh.D., Civil Engineering -- Drexel University, 201

A point-of-care clinical trial comparing insulin administered using a sliding scale versus a weight-based regimen

Background Clinical trials are widely considered the gold standard in comparative effectiveness research (CER) but the high cost and complexity of traditional trials and concerns about generalizability to broad patient populations and general clinical practice limit their appeal. Unsuccessful implementation of CER results limits the value of even the highest quality trials. Planning for a trial comparing two standard strategies of insulin administration for hospitalized patients led us to develop a new method for a clinical trial designed to be embedded directly into the clinical care setting thereby lowering the cost, increasing the pragmatic nature of the overall trial, strengthening implementation, and creating an integrated environment of research-based care

COVID-19 symptoms at hospital admission vary with age and sex: results from the ISARIC prospective multinational observational study

Background: The ISARIC prospective multinational observational study is the largest cohort of hospitalized patients with COVID-19. We present relationships of age, sex, and nationality to presenting symptoms. Methods: International, prospective observational study of 60 109 hospitalized symptomatic patients with laboratory-confirmed COVID-19 recruited from 43 countries between 30 January and 3 August 2020. Logistic regression was performed to evaluate relationships of age and sex to published COVID-19 case definitions and the most commonly reported symptoms. Results: ‘Typical’ symptoms of fever (69%), cough (68%) and shortness of breath (66%) were the most commonly reported. 92% of patients experienced at least one of these. Prevalence of typical symptoms was greatest in 30- to 60-year-olds (respectively 80, 79, 69%; at least one 95%). They were reported less frequently in children (≤ 18 years: 69, 48, 23; 85%), older adults (≥ 70 years: 61, 62, 65; 90%), and women (66, 66, 64; 90%; vs. men 71, 70, 67; 93%, each P < 0.001). The most common atypical presentations under 60 years of age were nausea and vomiting and abdominal pain, and over 60 years was confusion. Regression models showed significant differences in symptoms with sex, age and country. Interpretation: This international collaboration has allowed us to report reliable symptom data from the largest cohort of patients admitted to hospital with COVID-19. Adults over 60 and children admitted to hospital with COVID-19 are less likely to present with typical symptoms. Nausea and vomiting are common atypical presentations under 30 years. Confusion is a frequent atypical presentation of COVID-19 in adults over 60 years. Women are less likely to experience typical symptoms than men

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images

Microscopy is a central method in life sciences. Many popular methods, such as antibody labeling, are used to add physical fluorescent labels to specific cellular constituents. However, these approaches have significant drawbacks, including inconsistency; limitations in the number of simultaneous labels because of spectral overlap; and necessary perturbations of the experiment, such as fixing the cells, to generate the measurement. Here, we show that a computational machine-learning approach, which we call "in silico labeling" (ISL), reliably predicts some fluorescent labels from transmitted-light images of unlabeled fixed or live biological samples. ISL predicts a range of labels, such as those for nuclei, cell type (e.g., neural), and cell state (e.g., cell death). Because prediction happens in silico, the method is consistent, is not limited by spectral overlap, and does not disturb the experiment. ISL generates biological measurements that would otherwise be problematic or impossible to acquire

Theoretical and Behavioral Mediators of a Weight Loss Intervention for Men

OBJECTIVE: Men are currently underrepresented in weight loss trials despite similar obesity rates, which limits our understanding about the most effective elements of treatment for men. The purpose of this study was to test the theoretical (autonomous motivation, self-efficacy, outcome expectancies, and self-regulation) and behavioral (calorie intake, physical activity, self-weighing) mediators of a men-only, Internet-delivered weight loss intervention focused on innovative and tailored treatment elements specifically for men. METHOD: Data come from a six-month randomized trial (N = 107) testing the intervention compared to a waitlist control group. Changes in the theoretical mediators between baseline and three months were tested as mediators of the intervention effect on weight change at six months in both single and multiple mediator models. Changes in behaviors between baseline and six months were tested in the same manner. RESULTS: The intervention produced greater weight losses compared to the control group (−5.57 kg ± 6.6 vs. −0.65 kg ± 3.3, p < 0.001) and significant changes (p’s < 0.05) in most of the theoretical and behavior mediators. In multiple mediator models, changes in diet-related autonomous motivation, self-efficacy, and self-regulation all significantly mediated the relationship between the intervention and weight loss. The intervention effect was also mediated by changes in dietary intake and self-weighing frequency. CONCLUSIONS: By testing the theoretical mediators of this intervention in a multiple mediator context, this study contributes to current knowledge related to the development of weight loss interventions for men and suggests that interventions should target diet-focused constructs

Rethinking ‘responsibility’ in precision agriculture innovation: lessons from an interdisciplinary research team

We examine the interactions, decisions, and evaluations of an interdisciplinary team of researchers tasked with developing an artificial intelligence-based agricultural decision support system that can provide farmers site-specific information about managing nutrients on their land. We answer the following research questions: (1) How does a relational perspective help an interdisciplinary team conceptualize ‘responsibility’ in a project that develops precision agriculture (PA)? and (2) What are some lessons for a research team embarking on a similar interdisciplinary technology development project? We show that how RI is materialized in practice within an interdisciplinary research team can produce different understandings of responsibility, notions of measurement of ‘matter,’ and metrics of success. Future interdisciplinary projects should (1) create mechanisms for project members to see how power and privilege are exercised in the design of new technology and (2) harness social sciences as a bridge between natural sciences and engineering for organic and equitable collaborations

Rethinking ‘responsibility’ in precision agriculture innovation: lessons from an interdisciplinary research team

ABSTRACTWe examine the interactions, decisions, and evaluations of an interdisciplinary team of researchers tasked with developing an artificial intelligence-based agricultural decision support system that can provide farmers site-specific information about managing nutrients on their land. We answer the following research questions: (1) How does a relational perspective help an interdisciplinary team conceptualize ‘responsibility' in a project that develops precision agriculture (PA)? and (2) What are some lessons for a research team embarking on a similar interdisciplinary technology development project? We show that how RI is materialized in practice within an interdisciplinary research team can produce different understandings of responsibility, notions of measurement of ‘matter,’ and metrics of success. Future interdisciplinary projects should (1) create mechanisms for project members to see how power and privilege are exercised in the design of new technology and (2) harness social sciences as a bridge between natural sciences and engineering for organic and equitable collaborations