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Synthetic polymers have been extensively used in biomedical fields for imaging and therapeutic purposes. These macromolecular nanomedicines have shown unique advantages, such as increased blood circulation time, decreased systemic toxicity, and improved therapeutic outcomes. However, the polydisperse molecular weight of conventional polymers greatly hampers the reproducibility and clinical translations of macromolecular nanomedicines. Unlike conventional polymers with molecular weight distributions, discrete polymers are characterized by precise molecular weight and no molecular weight distribution (Ɖ = 1.0). Although discrete polymers have similar chemical structures and compositions to those of their disperse counterparts, they can show drastically distinct physiochemical properties and thus different biomedical performance. In this review, we summarize the recent achievements of discrete polymers in optical imaging, magnetic resonance (MR) imaging, and therapeutic applications. In addition, the representative methods used to synthesize discrete polymers are briefly discussed. Although this field represents an emerging research direction, preliminary results are encouraging and promising in many aspects. Due to the structural similarity of discrete and disperse polymers, their precise structures and improved performance may greatly facilitate their clinical translation into precision nanomedicines. We hope that more effort will be devoted to the development of highly efficient synthetic strategies and the conduction of (pre)clinical studies of discrete polymers. An in-depth understanding of the structure-property relationships of discrete polymers can be advantageous to boost their practical applications. [Display omitted] • Discrete polymers show distinct physicochemical properties compared to their disperse counterpart. • Molecular weight distribution has a critical role in the biological performance of synthetic polymers. • It is possible to decode the chain sequence of discrete polymers and quantify their biodistribution in a label-free manner. • Discrete polymers provide an excellent platform for precise nanomedicines. [ABSTRACT FROM AUTHOR]

Abstract: This paper reports on a high-repetition-rate dual signal-wave (DSW) optical parametric oscillator (OPO) operating at the 1.5μm band with tunable wavelength intervals from 2.5nm to 69.1nm. Two periodically poled crystals, a periodically poled lithium niobate (PPLN) with multiple gratings and a single grating MgO-doped PPLN (PPMgOLN), are cascaded in the same OPO cavity to generate dual signal-waves by using quasi-phase-matched (QPM) technique. The pump source was a Q-switched diode-pumped Nd:YVO4 laser operating at 50kHz. At an incident pump power of 3W, an average output power of 169.6mW at 1489.2nm and 1558.3nm has been achieved. [Copyright &y& Elsevier]

Data assimilation (DA) techniques such as the Ensemble Kalman filter (EnKF) and its extensions allow for real-time corrections of state-space models and model parameters based on an assumption of Gaussian error. The hydrological DA literature primarily documents applications of the EnKF to solve sequential state estimation problems. Recent advances in the DA literature demonstrate the potential of applying EnKF-based methods as efficient, derivative-free algorithms to solve various general Bayesian inverse problems, such as parameter estimation, while simultaneously providing Uncertainty Quantification (UQ). In this paper, the authors employ the Ensemble Kalman Inversion (EKI) algorithm to infer the distribution of a set of routing parameters. Through this correction, we improve streamflow at locations upstream of the gauged site in a virtual catchment setting. The algorithm enables learning spatially distributed routing parameters with observations available only at the outlet. The study reveals that this method sufficiently improves model performance throughout the basin. The performance of this method is demonstrated in a virtual catchment for three different model/data configurations. Favorable results, even with model misspecification, indicate that this method holds promise for operational application and more general hydrologic parameter estimation problems. • Ensemble Kalman Inversion (EKI) to infer spatially distributed routing parameters. • Learned parameters well approximate the reference parameters for virtual catchments. • Improves streamflow upstream of gauged site for several metrics via indirect updates. [ABSTRACT FROM AUTHOR]