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Methodology is described for synthesis of C6 derivatives of raloxifene, a prescribed drug for the treatment and prevention of osteoporosis. Studies explore the incorporation of electron-withdrawing substituents at C6 of the benzothiophene core. Effi-cient processes are also examined to introduce hydrogen bond donor and acceptor functionality. Raloxifene derivatives are evaluated with in vitro testing to determine estrogen receptor (ER) binding affinity and gene expression in MC3T3 cells. 

Osteogenesis imperfecta (OI) is a hereditary bone disease in which gene mutations affect collagen formation, leading to a weak, brittle bone phenotype that can cause severe skeletal deformity and increased fracture risk. OI interventions typically repurpose osteoporosis medications to increase bone mass, but this approach does not address compromised tissue-level material properties. Raloxifene (RAL) is a mild anti-resorptive used to treat osteoporosis that has also been shown to increase bone strength by a-cellularly increasing bone bound water content, but RAL cannot be administered to children due to its hormonal activity. The goal of this study was to test a RAL analog with no estrogen receptor (ER) signaling but maintained ability to reduce fracture risk. The best performing analog from a previous analog characterization project, named RAL-ADM, was tested in an in vivo study. Female wildtype (WT) and Col1a2G610C/+ (G610C) mice were randomly assigned to treated or untreated groups, for a total of 4 groups (n=15). Starting at 10 weeks of age, all mice underwent compressive tibial loading 3×/week to induce an anabolic bone formation response in conjunction with RAL-ADM treatment (0.5 mg/kg; 5×/week) for 6 weeks. Tibiae were scanned via microcomputed tomography then tested to failure in four-point bending. RAL-ADM had reduced ER affinity, and increased post-yield properties, but did not improve bone strength in OI animals, suggesting some properties can be improved by RAL analogs but further development is needed to create an analog with decidedly positive impacts to OI bone. 

Many types of investigative work involve verifying the legitimacy of visual evidence by identifying the precise geographic location where a photo or video was taken. Professional geolocation is often a manual, time-consuming process that can involve searching large areas of satellite imagery for potential matches. In this paper, we explore how crowdsourcing can be used to support expert image geolocation. We adapt an expert diagramming technique to overcome spatial reasoning limitations of novice crowds so that they can support an expert's search. In an experiment (n=540), we found that diagrams work significantly better than ground-level photos and allow crowds to reduce a search area by half before any expert intervention. We also discuss hybrid approaches to complex image analysis combining crowds, experts, and computer vision. 

Geolocation, the process of identifying the precise location in the world where a photo or video was taken, is central to many types of investigative work, from debunking fake news posted on social media to locating terrorist training camps. Professional geolocation is often a manual, time-consuming process that involves searching large areas of satellite imagery for potential matches. In this paper, we explore how crowdsourcing can be used to support expert image geolocation. We adapt an expert diagramming technique to overcome spatial reasoning limitations of novice crowds, allowing them to support an expert’s search. In two experiments (n=1080), we found that diagrams work significantly better than ground-level photos and allow crowds to reduce a search area by half before any expert intervention. We also discuss hybrid approaches to complex image analysis combining crowds, experts, and computer vision.