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Place-based short-term crime prediction models leverage the spatio-temporal patterns of historical crimes to predict aggregate volumes of crime incidents at specific locations over time. Under the umbrella of thecrime opportunity theory, that suggests that human mobility can play a role in crime generation, increasing attention has been paid to the predictive power of human mobility in place-based short-term crime models. Researchers have used call detail records (CDR), data from location-based services such as Foursquare or from social media to characterize human mobility; and have shown that mobility metrics, together with historical crime data, can improve short-term crime prediction accuracy. In this paper, we propose to use a publicly available fine-grained human mobility dataset from a location intelligence company to explore the effects of human mobility features on short-term crime prediction. For that purpose, we conduct a comprehensive evaluation across multiple cities with diverse demographic characteristics, different types of crimes and various deep learning models; and we show that adding human mobility flow features to historical crimes can improve the F1 scores for a variety of neural crime prediction models across cities and types of crimes, with improvements ranging from 2% to 7%. Our analysis also shows that some neural architectures can slightly improve the crime prediction performance when compared to non-neural regression models by at most 2%.

 

Ratiometric indicators with long emission wavelengths are highly preferred in modern bioimaging and life sciences. Herein, we elucidated the working mechanism of a standalone red fluorescent protein (FP)-based Ca2+ biosensor, REX-GECO1, using a series of spectroscopic and computational methods. Upon 480 nm photoexcitation, the Ca2+-free biosensor chromophore becomes trapped in an excited dark state. Binding with Ca2+ switches the route to ultrafast excited-state proton transfer through a short hydrogen bond to an adjacent Glu80 residue, which is key for the biosensor’s functionality. Inspired by the 2D-fluorescence map, REX-GECO1 for Ca2+ imaging in the ionomycin-treated human HeLa cells was achieved for the first time with a red/green emission ratio change (ΔR/R0) of ~300%, outperforming many FRET- and single FP-based indicators. These spectroscopy-driven discoveries enable targeted design for the next-generation biosensors with larger dynamic range and longer emission wavelengths. 

The development of smart materials and surfaces with multiple antibacterial actions is of great importance for both fundamental research and practical applications, but this has proved to be extremely challenging. In this work, we proposed to integrate salt-responsive polyDVBAPS (poly(3-(dimethyl(4-vinylbenzyl) ammonio)propyl sulfonate)), antifouling polyHEAA (poly( N -hydroxyethyl acrylamide)), and bactericidal TCS (triclosan) into single surfaces by polymerizing and grafting polyDVBAPS and polyHEAA onto the substrate in a different way to form two types of polyDVBAPS/poly(HEAA- g -TCS) and poly(DVBAPS- b -HEAA- g -TCS) brushes with different hierarchical structures, as confirmed by X-ray photoelectron spectroscopy (XPS), atom force microscopy (AFM), and ellipsometry. Both types of polymer brushes demonstrated their tri-functional antibacterial activity to resist bacterial attachment by polyHEAA, to release ∼90% of dead bacteria from the surface by polyDVBAPS, and to kill ∼90% of bacteria on the surface by TCS. Comparative studies also showed that removal of any component from polyDVBAPS/poly(HEAA- g -TCS) and poly(DVBAPS- b -HEAA- g -TCS) compromised the overall antibacterial performance, further supporting a synergistic effect of the three compatible components. More importantly, the presence of salt-responsive polyDVBAPS allowed both brushes to regenerate with almost unaffected antibacterial capacity for reuse in multiple kill-and-release cycles. The tri-functional antibacterial surfaces present a promising design strategy for further developing next-generation antibacterial materials and coatings for antibacterial applications.