No abstract

Estrogen receptors (ERs) are normally expressed in breast tissues and mediate hormonal functions during development and in female reproductive physiology. In the majority of breast cancers, ERs are involved in regulating tumor cell proliferation and serve as prognostic markers and therapeutic targets in the management of hormonedependent tumors. At the molecular level, ERs function as ligand-dependent transcription factors and activate targetgene expression following hormone stimulation. Recent transcriptomic and whole-genome-binding studies suggest, however, that ligand-activated ERs can also repress the expression of a significant subset of target genes. To characterize the molecular mechanisms of transcriptional repression by ERs, we examined recruitment of nuclear receptor coregulators, histone modifications and RNA polymerase II docking at ER-binding sites and cisregulatory regions adjacent to repressed target genes. Moreover, we utilized gene expression data from patient samples to determine potential roles of repressed target genes in breast cancer biology. Results from these studies indicate that nuclear receptor corepressor recruitment is a key feature of ligand-dependent transcriptional repression by ERs, and some repressed target genes are associated with disease progression and response to endocrine therapy. These findings provide preliminary insights into a novel aspect of the molecular mechanisms of ER functions and their potential roles in hormonal carcinogenesis and breast cancer biology.

This paper describes a method for the estimation of the 3D ground reaction force (GRF) during human walking using novel nanocomposite piezo-responsive foam (NCPF) sensors. Nine subjects (5 male, 4 female) walked on a force-instrumented treadmill at 1.34 m/s for 120 s each while wearing a shoe that was instrumented with four NCPF sensors. GRF data, measured via the treadmill, and sensor data, measured via the NCPF inserts, were used in a tenfold cross validation process to calibrate a separate model for each individual. The calibration model estimated average anterior-posterior, mediolateral and vertical GRF with mean average errors (MAE) of 6.52 N (2.14%), 4.79 N (6.34%), and 15.4 N (2.15%), respectively. Two additional models were created using the sensor data from all subjects and subject demographics. A tenfold cross validation process for this combined data set resulted in models that estimated average anterior-posterior, mediolateral and vertical GRF with less than 8.16 N (2.41%), 6.63 N (7.37%), and 19.4 N (2.31%) errors, respectively. Intra-subject estimates based on the model had a higher accuracy than inter-subject estimates, likely due to the relatively small subject cohort used in creating the model. The novel NCPF sensors demonstrate the ability to accurately estimate 3D GRF during human movement outside of the traditional biomechanics laboratory setting.

American football has both the highest rate of concussion incidences as well as the highest number of concussions of all contact sports due to both the number of athletes and nature of the sport. Recent research has linked concussions with long term health complications such as chronic traumatic encephalopathy and early onset Alzheimer's. Understanding the mechanical characteristics of concussive impacts is critical to help protect athletes from these debilitating diseases and is now possible using helmet-based sensor systems. To date, real time on-field measurement of head impacts has been almost exclusively measured by devices that rely on accelerometers or gyroscopes attached to the player's helmet, or embedded in a mouth guard. These systems monitor motion of the head or helmet, but do not directly measure impact energy. This paper evaluates the accuracy of a novel, multifunctional foam-based sensor that replaces a portion of the helmet foam to measure impact. All modified helmets were tested using a National Operating Committee Standards for Athletic Equipment-style drop tower with a total of 24 drop tests (4 locations with 6 impact energies). The impacts were evaluated using a headform, instrumented with a tri-axial accelerometer, mounted to a Hybrid III neck assembly. The resultant accelerations were evaluated for both the peak acceleration and the severity indices. These data were then compared to the voltage response from multiple Nano Composite Foam sensors located throughout the helmet. The foam sensor system proved to be accurate in measuring both the HIC and Gadd severity index, as well as peak acceleration while also providing additional details that were previously difficult to obtain, such as impact energy.

This study develops highly flexible, adaptable, portable gauges that give real-time measurements in many applications where compression is of interest. They can be embedded into elastomeric foams, and preserve the desirable physical properties of the foams in dispersing impact energy. We anticipate that these novel and inexpensive sensors will enable real-time measurement of human impacts and athletic performance based on data collected in the field, rather than the current standard of trying to replicate these experiences in the lab. In previous work, we have validated the performance of tensile strain sensors based on a similar technology embedded in thin sheets of silicone. These sensors are capable of measuring up to 50% strains in real time with minimal interference in tissue motion. With the addition of the sensors described in the present work, it is possible to measure both tensile and compressive strains.