Phun With Phages: Discovering Novel Bacteriophages in the Soil

We used three bacterial hosts: Mycobacterium smegmatis, Microbacterium foliorum, and Gordonia terrae, to isolate novel bacteriophages from soil samples. We named these phages, created high titer lysates, and purified their DNA genomes. We have archived the high titer lysates at Northwestern College and the University of Pittsburgh. The genomes of three of our phages were sequences at the University of Pittsburgh and we will be sequencing the remaining genomes this summer. Additionally, we are planning to image our phages with transmission electron microscopy at the University of Iowa or Nebraska yet this semester

Potently neutralizing and protective human antibodies against SARS-CoV-2

The COVID-19 pandemic is a major threat to global health1 for which there are limited medical countermeasures2,3. Moreover, we currently lack a thorough understanding of mechanisms of humoral immunity4. From a larger panel of human monoclonal antibodies (mAbs) targeting the spike (S) glycoprotein5, we identified several that exhibited potent neutralizing activity and fully blocked the receptor-binding domain of S (SRBD) from interacting with human ACE2 (hACE2). Competition-binding, structural, and functional studies allowed clustering of the mAbs into classes recognizing distinct epitopes on the SRBD as well as distinct conformational states of the S trimer. Potent neutralizing mAbs recognizing non-overlapping sites, COV2-2196 and COV2-2130, bound simultaneously to S and synergistically neutralized authentic SARS-CoV-2 virus. In two mouse models of SARS-CoV-2 infection, passive transfer of either COV2-2196 or COV2-2130 alone or a combination of both mAbs protected mice from weight loss and reduced viral burden and inflammation in the lung. In addition, passive transfer of each of two of the most potently ACE2 blocking mAbs (COV2-2196 or COV2-2381) as monotherapy protected rhesus macaques from SARS-CoV-2 infection. These results identify protective epitopes on SRBD and provide a structure-based framework for rational vaccine design and the selection of robust immunotherapeutics

Energy methods for analyzing drag and inertia in cycling kinematics

Thesis: S.B., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2016.Cataloged from PDF version of thesis.Includes bibliographical references (pages 53-54).A model was developed for measuring the drag and effects of inertia for a cyclist during a race. Professional cyclist data from the Tour de France was acquired for several athletes. The data contained elevation, distance, velocity, and power as a function of time. Rolling resistance, drag, inertial energy, and potential energy were then evaluated. An integral energy equation relating these terms to input power was developed. This is much more stable numerically than differential equations in the power and force equations. This formula gave excellent agreement with the theoretical assumption that inertial effects are negligible. Additionally, the measured drag agreed with wind tunnel results. This work is the first to extract drag data from a cyclist during actual race conditions. In the future, this evaluation of drag variation coupled with energy equations could lead to optimizing cycling strategy.by Emma Marie Steinhardt.S.B

A State-Wide Analysis of Pre-Hospital Injuries and Fatalities Resulting from Motorcycle Road Accidents in Queensland

The present study aimed to examine the prevalence and characteristics of pre-hospital injuries and fatalities involving motorcycle riders in Queensland. A retrospective analysis was undertaken of all motorcycle road traffic crashes that were attended by the Queensland Ambulance Service (QAS) over a five year period (1999-2003). A total of 6145 motorcycle-related incidents were recorded by the QAS during the study period, of which 113 incidents involved a deceased rider. Importantly, there was a 34% increase in the total number of motorcycle injury-related cases over the 5 year period, as well as an increase in the number of pre-hospital deaths. A further comparative analysis revealed some level of disparity between QAS data and corresponding official motorcycle injury records that are predominantly utilised to inform motorcycle policy. The reasons for this discrepancy and the implications of the findings will be further examined in the paper

AutoConnect: computational design of 3D-printable connectors

Copyright is held by the owner/author(s). We present AutoConnect, an automatic method that creates customized, 3D-printable connectors attaching two physical objects together. Users simply position and orient virtual models of the two objects that they want to connect and indicate some auxiliary information such as weight and dimensions. Then, AutoConnect creates several alternative designs that users can choose from for 3D printing. The design of the connector is created by combining two holders, one for each object. We categorize the holders into two types. The first type holds standard objects such as pipes and planes. We utilize a database of parameterized mechanical holders and optimize the holder shape based on the grip strength and material consumption. The second type holds free-form objects. These are procedurally generated shell-gripper designs created based on geometric analysis of the object. We illustrate the use of our method by demonstrating many examples of connectors and practical use cases

Developing Photoaffinity Probes for Dopamine Receptor D2 to Determine Targets of Parkinson’s Disease Drugs

Dopaminergic pathways control highly consequential aspects of physiology and behavior. One of the most therapeutically important and best-studied receptors in these pathways is dopamine receptor D2 (DRD2). Unfortunately, DRD2 is challenging to study with traditional molecular biological techniques, and most drugs designed to target DRD2 are ligands for many other receptors. Here, we developed probes able to both covalently bind to DRD2 using photoaffinity labeling as well as provide a chemical handle for detection or affinity purification. These probes behaved like good DRD2 agonists in traditional biochemical assays and were able to perform in chemical biological assays of cell and receptor labeling. Rat whole brain labeling and affinity enrichment using the probe permitted proteomic analysis of the probes’ interacting proteins. Bioinformatic study of the hits revealed that the probes bound non-canonically targeted proteins in the Parkinson’s disease network as well as the retrograde endocannabinoid signaling, neuronal nitric oxide synthase, muscarinic acetylcholine receptor M1, GABA receptor, and dopamine receptor D1 (DRD1) signaling networks. Follow-up analysis may yield insights into how this pathway relates specifically to Parkinson’s disease symptoms or provide new targets for treatments. This work reinforces the notion that the combination of chemical biology and omics-based approaches provide a broad picture of a molecule’s “interactome,” and may also give insight into the pleiotropy of effects observed for a drug, or perhaps indicate new applications

School-Based Serosurveys to Assess the Validity of Using Routine Health Facility Data to Target Malaria Interventions in the Central Highlands of Madagascar

Presented in part: 64th Annual Meeting of the American Society of Tropical Medicine and Hygiene, 25–29 October 2015, Philadelphia, PA.International audienceBackgroundIn low-malaria–transmission areas of Madagascar, annual parasite incidence (API) from routine data has been used to target indoor residual spraying at subdistrict commune level. To assess validity of this approach, we conducted school-based serological surveys and health facility (HF) data quality assessments in 7 districts to compare API to gold-standard commune-level serological measures.MethodsAt 2 primary schools in each of 93 communes, 60 students were randomly selected with parents and teachers. Capillary blood was drawn for rapid diagnostic tests (RDTs) and serology. Multiplex bead-based immunoassays to detect antibodies to 5 Plasmodium falciparum antigens were conducted, and finite mixture models used to characterize seronegative and seropositive populations. Reversible catalytic models generated commune-level annual seroconversion rates (SCRs). HF register data were abstracted to assess completeness and accuracy.ResultsRDT positivity from 12 770 samples was 0.5%. Seroprevalence to tested antigens ranged from 17.9% (MSP-1) to 59.7% (PF13). Median commune-level SCR was 0.0108 (range, 0.001–0.075). Compared to SCRs, API identified 71% (95% confidence interval, 51%–87%) of the 30% highest-transmission communes; sensitivity declined at lower levels. Routine data accuracy did not substantially affect API performance.ConclusionsAPI performs reasonably well at identifying higher-transmission communes but sensitivity declined at lower transmission levels

STRUCTURAL ANALYSIS BY X-RAY INTENSITY ANGULAR CROSS CORRELATIONS

This chapter reviews the topic of angular intensity correlations in X-ray diffraction. A basic theoretical description of quantities related to angular correlations in a simple X-ray scattering description in the far-field, or Fraunhofer, limit of diffraction in the kinematic approximation is discussed. The chapter reviews the applications of the cross-correlation functions (CCFs) in X-ray studies of materials divided into two major groups. The first part of applications is related to the problem of a single-particle structure recovery in the fluctuation X-ray scattering (FXS) experiments. The second part is related to the studies of structural properties of disordered and partially ordered systems, such as colloids, metallic glasses, liquid crystals, and polymers. The chapter demonstrates that X-ray beams focused to small sizes in combination with cross-correlation analysis can indeed provide valuable information about the structure of partially ordered materials, complementary to the results of conventional small-angle X-ray scattering (SAXS) or grazing-incidence X-ray diffraction (GIXD) analysis