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Background & Aims: Continuing recalcitrance to therapy cements pancreatic cancer (PC) as the most lethal malignancy, which is set to become the second leading cause of cancer death in our society. The study aim was to investigate the association between DNA damage response (DDR), replication stress, and novel therapeutic response in PC to develop a biomarker-driven therapeutic strategy targeting DDR and replication stress in PC. Methods: We interrogated the transcriptome, genome, proteome, and functional characteristics of 61 novel PC patient–derived cell lines to define novel therapeutic strategies targeting DDR and replication stress. Validation was done in patient-derived xenografts and human PC organoids. Results: Patient-derived cell lines faithfully recapitulate the epithelial component of pancreatic tumors, including previously described molecular subtypes. Biomarkers of DDR deficiency, including a novel signature of homologous recombination deficiency, cosegregates with response to platinum (P <.001) and PARP inhibitor therapy (P <.001) in vitro and in vivo. We generated a novel signature of replication stress that predicts response to ATR (P <.018) and WEE1 inhibitor (P <.029) treatment in both cell lines and human PC organoids. Replication stress was enriched in the squamous subtype of PC (P <.001) but was not associated with DDR deficiency. Conclusions: Replication stress and DDR deficiency are independent of each other, creating opportunities for therapy in DDR-proficient PC and after platinum therapy.

One of the fundamental challenges for a hyperspectral imaging surveillance system is the detection of sub-pixel objects in background clutter. The background surrounding the object, which acts as interference, provides the major obstacle to successful detection. Two additional limiting factors are the spectral variabilities of the background and the object to be detected. In this paper, we evaluate the performance of detection algorithms for sub-pixel objects using a replacement signal model, where the spectral variability is modeled by multivariate normal distributions. The detection algorithms considered are the classical matched filter, the matched filter with false alarm mitigation, the mixture tuned matched filter and the finite target matched filter. These algorithms are compared using simulated and actual hyperspectral imaging data., United States. Dept. of Defense (Air Force Contract FA8721-05-C-0002)

One of the fundamental challenges for a hyperspectral imaging surveillance system is the detection of sub-pixel objects in background clutter. The background surrounding the object, which acts as interference, provides the major obstacle to successful detection. Two additional limiting factors are the spectral variabilities of the background and the object to be detected. In this paper, we evaluate the performance of detection algorithms for sub-pixel objects using a replacement signal model, where the spectral variability is modeled by multivariate normal distributions. The detection algorithms considered are the classical matched filter, the matched filter with false alarm mitigation, the mixture tuned matched filter and the finite target matched filter. These algorithms are compared using simulated and actual hyperspectral imaging data., United States. Dept. of Defense (Air Force Contract FA8721-05-C-0002)

Nonvolatile resistive memory consisting of gold nanoparticles embedded in the conducting polymer poly(4-n-hexylphenyldiphenylamine) examined using admittance spectroscopy. The frequency dependence of the devices indicates space-charge-limited transport in the high-conductivity "on" state, as well as evidence for similar transport in the lower-conductivity "off" state. Furthermore, the larger dc capacitance of the on state indicates that a greater amount of filling of the midgap nanoparticle trap levels increases the overall device conductivity, leading to the memory effect.

Nonvolatile resistive memory consisting of gold nanoparticles embedded in the conducting polymer poly(4-n-hexylphenyldiphenylamine) examined using admittance spectroscopy. The frequency dependence of the devices indicates space-charge-limited transport in the high-conductivity "on" state, as well as evidence for similar transport in the lower-conductivity "off" state. Furthermore, the larger dc capacitance of the on state indicates that a greater amount of filling of the midgap nanoparticle trap levels increases the overall device conductivity, leading to the memory effect.

Nonvolatile resistive memory consisting of gold nanoparticles embedded in the conducting polymer poly(4-n-hexylphenyldiphenylamine) examined using admittance spectroscopy. The frequency dependence of the devices indicates space-charge-limited transport in the high-conductivity "on" state, as well as evidence for similar transport in the lower-conductivity "off" state. Furthermore, the larger dc capacitance of the on state indicates that a greater amount of filling of the midgap nanoparticle trap levels increases the overall device conductivity, leading to the memory effect.

Nonvolatile resistive memory consisting of gold nanoparticles embedded in the conducting polymer poly(4-n-hexylphenyldiphenylamine) examined using admittance spectroscopy. The frequency dependence of the devices indicates space-charge-limited transport in the high-conductivity "on" state, as well as evidence for similar transport in the lower-conductivity "off" state. Furthermore, the larger dc capacitance of the on state indicates that a greater amount of filling of the midgap nanoparticle trap levels increases the overall device conductivity, leading to the memory effect.

Nonvolatile resistive memory consisting of gold nanoparticles embedded in the conducting polymer poly(4-n-hexylphenyldiphenylamine) examined using admittance spectroscopy. The frequency dependence of the devices indicates space-charge-limited transport in the high-conductivity "on" state, as well as evidence for similar transport in the lower-conductivity "off" state. Furthermore, the larger dc capacitance of the on state indicates that a greater amount of filling of the midgap nanoparticle trap levels increases the overall device conductivity, leading to the memory effect.