Local land-use plans help guide future development, but it is often difficult to compare content across jurisdictions, making regional coordination and plan evaluation challenging. This research reviews federal, state, and local data infrastructure guidance for land-use plans and compares such guidance to compliance with a California use-case. Findings indicate a number of obstacles to fostering data sharing and comparative analysis of plans: there is currently no central repository of land-use plans; plans are not uniform in format and are often out of date; many plans are not machine-readable thereby inhibiting text extraction, and planning language varies so greatly that there are numerous synonyms for terms of interest. Nonetheless, we demonstrate that the creation of digital platforms for archiving and searching across plans is currently feasible and enables large-scale quantitative analysis. Based on currently available metadata in existing land-use plans, we designed and piloted a structured database to enable users to search for terms and phrases across over 500 land-use plans. To center issues of social equity, the open access platform was developed in collaboration with state agencies and community organizations focused on environmental justice. Based on the pilot, we conclude with a framework for both developing plan data infrastructure given current constraints in standardized plan metadata and availability as well as guidance for plan formatting using FAIR standards (Findable, Accessible, Interoperable, and Reusable).

Elevated expression of the complement component 4A (C4A) protein has been linked to an increased risk of schizophrenia (SCZ). However, there are few human models available to study the mechanisms by which C4A contributes to the development of SCZ. In this study, we established a C4A overexpressing neuroimmune cortical organoid (NICO) model, which includes mature neuronal cells, astrocytes, and functional microglia. The C4A NICO model recapitulated several neuroimmune endophenotypes observed in SCZ patients, including modulation of inflammatory genes and increased cytokine secretion. C4A expression also increased microglia-mediated synaptic uptake in the NICO model, supporting the hypothesis that synapse and brain volume loss in SCZ patients may be due to excessive microglial pruning. Our results highlight the role of C4A in the immunogenetic risk factors for SCZ and provide a human model for phenotypic discovery and validation of immunomodulating therapies.

An emerging application of Raman spectroscopy is monitoring the state of chemical reactors during biologic drug production. Raman shift intensities scale linearly with the concentrations of chemical species and thus can be used to analytically determine real-time concentrations using non-destructive light irradiation in a label-free manner. Chemometric algorithms are used to interpret Raman spectra produced from complex mixtures of bioreactor contents as a reaction evolves. Finding the optimal algorithm for a specific bioreactor environment is challenging due to the lack of freely available Raman mixture datasets. The RaMix Python package addresses this challenge by enabling the generation of synthetic Raman mixture datasets with controllable noise levels to assess the utility of different chemometric algorithm types for real-time monitoring applications. To demonstrate the capabilities of this package and compare the performance of different chemometric algorithms, 48 datasets of simulated spectra were generated using the RaMix Python package. The four tested algorithms include partial least squares regression (PLS), a simple neural network, a simple convolutional neural network (simple CNN), and a 1D convolutional neural network with a ResNet architecture (ResNet). The performance of the PLS and simple CNN model was found to be comparable, with the PLS algorithm slightly outperforming the other models on 83\% of the data sets. The simple CNN model outperforms the other models on large, high noise datasets, demonstrating the superior capability of convolutional neural networks compared to PLS in analyzing noisy spectra. These results demonstrate the promise of CNNs to automatically extract concentration information from unprocessed, noisy spectra, allowing for better process control of industrial drug production. Code for this project is available at github.com/DexterAntonio/RaMix.

Surface-enhanced Raman scattering (SERS) is a powerful technique for sensitive label-free analysis of chemical and biological samples. While much recent work has established sophisticated automation routines using machine learning and related artificial intelligence methods, these efforts have largely focused on downstream processing (e.g., classification tasks) of previously collected data. While fully automated analysis pipelines are desirable, current progress is limited by cumbersome and manually intensive sample preparation and data collection steps. Specifically, a typical lab-scale SERS experiment requires the user to evaluate the quality and reliability of the measurement (i.e., the spectra) as the data are being collected. This need for expert user-intuition is a major bottleneck that limits applicability of SERS-based diagnostics for point-of-care clinical applications, where trained spectroscopists are likely unavailable. While application-agnostic numerical approaches (e.g., signal-to-noise thresholding) are useful, there is an urgent need to develop algorithms that leverage expert user intuition and domain knowledge to simplify and accelerate data collection steps. To address this challenge, in this work, we introduce a machine learning-assisted method at the acquisition stage. We tested six common algorithms to measure best performance in the context of spectral quality judgment. For adoption into future automation platforms, we developed an open-source python package tailored for rapid expert user annotation to train machine learning algorithms. We expect that this new approach to use machine learning to assist in data acquisition can serve as a useful building block for point-of-care SERS diagnostic platforms.

High electron affinity (EA) molecules p-type dope low ionization energy (IE) polymers, resulting in an equilibrium doping level based on the energetic driving force (IE-EA), reorganization energy, and dopant concentration. Anion exchange doping (AED) is a process whereby the dopant anion is exchanged with a stable ion from an electrolyte. We show that the AED level can be predicted using an isotherm equilibrium model. The exchange of the dopant anion (FeCl3-) for a bis(trifluoromethanesulfonamide) (TFSI-) anion in the polymers poly(3-hexylthiophene-2,5-diyl) (P3HT) and poly[3-(2,2-bithien-5-yl)-2,5-bis(2-hexyldecyl)-2,5-dihydropyrrolo[3,4-c]pyrrole-1,4-dione-6,5-diyl] (PDPP-2T) highlights two cases in which the process is nonspontaneous and spontaneous, respectively. For P3HT, FeCl3 provides a high doping level but an unstable counterion, so exchange results in an air stable counterion with a marginal increase in doping. For PDPP-2T, FeCl3 is a weak dopant, but the exchange of FeCl3- for TFSI- is spontaneous, so the doping level increases by >10× with AED.

Zeolites, an important class of 3-dimensional nanoporous materials, have been widely explored for a variety of applications including gas storage, separations, and catalysis. As the properties of these aluminosilicate materials depend on a number of factors (e.g., framework topology, Si/Al ratio, extra-framework cations etc.), detailed experiments (e.g., catalytic properties, adsorption capacities etc.) are often limited to only a handful of materials. Computational methods have played an important role in (1) providing molecular level insights to rationalize experimental observations, and (2) screening large libraries of zeolites to identify promising candidates for experimental synthesis and validation. Different levels of theory and computational chemistry codes are necessary to describe the range of relevant phenomena such as adsorption (e.g., grand canonical Monte Carlo), diffusion (e.g., molecular dynamics), and chemical reactions (e.g., density functional theory). Manipulation of atomic structures, handling of input files, and developing robust workflows becomes quite cumbersome. To mitigate these challenges, we describe the development of the Multiscale Atomic Zeolite Simulation Environment (MAZE) – a Python package that simplifies zeolite-specific calculation workflows by providing a user-friendly interface for systematically manipulating zeolite structures.

In this study, we use a modeling approach to evaluate the potential impact of microbial metabolism on the organic composition of cloud droplets and atmospheric aerosols. Microbial consumption rates for small organic molecules typically found in cloud and aerosol water were incorporated into a 0-D multiphase photochemical atmospheric chemistry model. We then use the model to simulate the evolution of the organic content of individual cloud and aerosol particles, along with the atmospheric gas phase. We find that metabolically active microorganisms may significantly impact organic acid concentrations in the individual aerosols and cloud droplets in which they reside. However, as a result of the low density of metabolically active cells in the atmosphere, the impact of these processes on the chemical composition of the overall population of cloud droplets or aerosols, or on gas-phase chemistry, is likely negligible.