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This article describes the development of polymer brush-based heterogeneous photocatalysts for PET-RAFT polymerization in aqueous environments.

 

Abstract We present new Spitzer Infrared Array Camera (IRAC) 3.6 and 4.5 μ m mosaics of three fields, E-COSMOS, DEEP2-F3, and ELAIS-N1. Our mosaics include both new IRAC observations as well as reprocessed archival data in these fields. These fields are part of the HSC-Deep grizy survey and have a wealth of additional ancillary data. The addition of these new IRAC mosaics is critical in allowing for improved photometric redshifts and stellar population parameters at cosmic noon and earlier epochs. The total area mapped by this work is ∼17 deg 2 with a mean integration time of ≈1200s, providing a median 5 σ depth of 23.7(23.3) at 3.6(4.5) μ m in AB. We perform SExtractor photometry both on the combined mosaics as well as the single-epoch mosaics taken ≈6 months apart. The resultant IRAC number counts show good agreement with previous studies. In combination with the wealth of existing and upcoming spectrophotometric data in these fields, our IRAC mosaics will enable a wide range of galactic evolution and AGN studies. With that goal in mind, we make the combined IRAC mosaics and coverage maps of these three fields publicly available. 

Abstract Optimizing the impact on the economy of control strategies aiming at containing the spread of COVID-19 is a critical challenge. We use daily new case counts of COVID-19 patients reported by local health administrations from different Metropolitan Statistical Areas (MSAs) within the US to parametrize a model that well describes the propagation of the disease in each area. We then introduce a time-varying control input that represents the level of social distancing imposed on the population of a given area and solve an optimal control problem with the goal of minimizing the impact of social distancing on the economy in the presence of relevant constraints, such as a desired level of suppression for the epidemics at a terminal time. We find that with the exception of the initial time and of the final time, the optimal control input is well approximated by a constant, specific to each area, which contrasts with the implemented system of reopening ‘in phases’. For all the areas considered, this optimal level corresponds to stricter social distancing than the level estimated from data. Proper selection of the time period for application of the control action optimally is important: depending on the particular MSA this period should be either short or long or intermediate. We also consider the case that the transmissibility increases in time (due e.g. to increasingly colder weather), for which we find that the optimal control solution yields progressively stricter measures of social distancing. We finally compute the optimal control solution for a model modified to incorporate the effects of vaccinations on the population and we see that depending on a number of factors, social distancing measures could be optimally reduced during the period over which vaccines are administered to the population. 

Vibrational spectroscopy has been widely used to investigate various structural aspects of the glass network, and there are a plethora of papers reporting subtle but consistent changes in infrared and Raman spectral features of glass upon alterations of glass compositions, thermal histories, mechanical stresses, or surface treatments. However, interpretations of such spectral features are still obscured due to the lack of well‐established physical principles accurately describing vibrational modes of the non‐crystalline glass network. Due to the non‐equilibrium nature of the glass network, three‐dimensionally connected without any long‐range orders, vibrational spectral features of glass cannot be interpreted using the analogy with those of isolated molecular moieties or crystalline counterparts. This feature article explains why such comparisons are outdated and describes the recent advances made from theoretical calculations of vibrational spectral features of amorphous networks or comparisons of computational results with experimental data. For the interpretation of vibrational spectral features of silica and silicate glasses, the following empirical relationships are suggested: (i) the intensity‐weighted peak position of the Si‐O‐Si stretch mode negatively correlates with the weighted average of the Si‐O bond length distribution, and (ii) the broad band of the Si‐O‐Si bending mode negatively correlates with the Si‐O‐Si bond angle distribution. Selected examples of vibrational spectroscopic imaging of surface defects are discussed to deliberate the implication of these findings in the structure‐property relationship of silica and silicate glass materials. Unanswered questions and continuing research challenges are identified.

 

The effects of molecularly targeted drug perturbations on cellular activities and fates are difficult to predict using intuition alone because of the complex behaviors of cellular regulatory networks. An approach to overcoming this problem is to develop mathematical models for predicting drug effects. Such an approach beckons for co-development of computational methods for extracting insights useful for guiding therapy selection and optimizing drug scheduling. Here, we present and evaluate a generalizable strategy for identifying drug dosing schedules that minimize the amount of drug needed to achieve sustained suppression or elevation of an important cellular activity/process, the recycling of cytoplasmic contents through (macro)autophagy. Therapeutic targeting of autophagy is currently being evaluated in diverse clinical trials but without the benefit of a control engineering perspective. Using a nonlinear ordinary differential equation (ODE) model that accounts for activating and inhibiting influences among protein and lipid kinases that regulate autophagy (MTORC1, ULK1, AMPK and VPS34) and methods guaranteed to find locally optimal control strategies, we find optimal drug dosing schedules (open-loop controllers) for each of six classes of drugs and drug pairs. Our approach is generalizable to designing monotherapy and multi therapy drug schedules that affect different cell signaling networks of interest.