Search

Luminescent solar concentrators (LSCs) are a promising technology to help integrate solar cells into the built environment, as they are colorful, semi-transparent, and can collect diffuse light. While LSCs have traditionally been cuboidal, in recent years a variety of unconventional geometries have arisen, for example circular, curved, polygonal, wedged, and leaf-shaped designs. These new designs can help reduce optical losses, facilitate incorporation into the built environment or unlock new applications. However, as fabrication of complex geometries can be time- and resource-intensive, the ability to simulate the expected LSC performance prior to production would be highly advantageous. While a variety of softwares exist to model LSCs, they either cannot be applied to unconventional geometries, are not open-source, or are not tractable for most users. Therefore, here we introduce a significant upgrade of the widely-used Monte Carlo ray-trace software pvTrace to include: (i) capability to characterize unconventional geometries and improved relevance to standard measurement configurations; (ii) increased computational efficiency; and (iii) a graphical user interface (GUI) for ease-of-use. We first test these upgrades using devices from the literature, as well as experimental results from in-house fabricated LSCs, with agreement within 1% obtained for the simulated versus measured external photon efficiency. We then demonstrate the broad applicability of pvTrace by simulating 20 different unconventional geometries, including a variety of different shapes and manufacturing techniques. We show that pvTrace can be used to predict meaningful physical phenomena, including enhanced optical efficiency using 3D printed devices. The more versatile and accessible computational workflow afforded by the upgraded pvTrace, coupled with 3D printed prototypes, will enable rapid screening of more intricate LSC architectures, while reducing experimental waste. Our goal is that this accelerates sustainability-driven design in the LSC field, leading to higher optical efficiency or increased utility.

Building accurate protein models into moderate resolution (3-5Å) cryo-electron microscopy (cryo-EM) maps is challenging and error-prone. While the majority of solved cryo-EM structures are at these resolutions, there are few model validation metrics that can precisely evaluate the local quality of atomic models built into these maps. We have developed MEDIC (Model Error Detection in Cryo-EM), a robust statistical model to identify residue-level errors in protein structures built into cryo-EM maps. Trained on a set of errors from obsoleted protein structures, our model draws off two major sources of information to predict errors: the local agreement of model and map compared to expected, and how “native-like” the neighborhood around a residue looks, as predicted by a deep learning model. MEDIC is validated on a set of 28 structures that were subsequently solved to higher-resolutions, where our model identifies the differences between low- and high-resolution structures with 68% precision and 60% recall. We additionally use this model to rebuild 12 deposited structures, fixing 2 sequence registration errors, 51 areas with improper secondary structure, 51 incorrect loops, and 16 incorrect carbonyls, showing the value of this approach to guide model building.

This study aimed to help determine the effect of dietary supplements on symptom course and quality of life in patients with mild-to-moderate COVID-19 infection.We modified the Wisconsin Upper Respiratory Symptom Survey (WURSS) to conduct a 3 arm, parallel, randomized, double-blind, placebo-controlled trial, enrolling patients with mild-to-moderate symptoms of COVID-19 infection. Patients took placebo (Ninety Eight (98 out of 104 recruited; mean age = 52 years) patients completed the study. Outcomes were calculated as differences from baseline scores on each of 2 WURSS-derived surveys and analyzed using a spline regression analysis. Regarding symptom progression, those patients taking placebo and vitamin C progressed at the same rate. When compared with those taking placebo (coefficient = -1.09 (95% confidence interval [CI] = -1.39 to -0.8) the group taking melatonin had a faster resolution of symptoms (coefficient = -0.63 [95% CI -1.02 to -0.21]Vitamin C 1000 mg once daily has no effect on disease progression. Melatonin 10 mg daily may have a statistically significant effect but it is unclear if this represents a clinically significant benefit to those with mild-to-moderate symptoms of COVID-19 infection. Further study is warranted.