A Critical Review of Neural Networks for the Use with Spectroscopic Data

In recent years, neural networks have found increased use in the analysis of crystallographic characterization data, such as X-ray diffraction (XRD) patterns. Previous work has shown that neural networks can successfully identify crystalline phases from XRD patterns and classify their symmetry, even in multiphase mixtures. When compared with classical machine learning methods, such as Support Vector Machines or Decision Trees, CNNs show improved performance in the classification of XRD patterns and can even handle experimental artifacts such as peak shifts caused by strain, whereas the classification models would fail. Such an approach is readily extended to other spectroscopic techniques, including NMR, Raman or NIR. Those network models usually employ a convolutional neural network (CNN) architecture which has been developed for the use with images. Despite these promising results, our work reveals several key limitations of the CNN architecture with respect to spectroscopic analysis, and we show that these limitations can lead to failed classifications on relatively simple patterns. Convolutional layers are demonstrated to have very little benefit for classification, and their only important contribution comes from the pooling operations that shrink the size of the input while keeping relevant information regarding peak intensities. Those pooling operations compensate for peak shifts, and therefore classical models applied to the shrunken input perform equally well as the presented neural networks. Nonetheless, we show how to adapt various parameters in the neural networks, such as the choice of activation function, to train more robust models with improved accuracy on classification tasks. Based on our findings, we believe that neural networks have their place for use with spectroscopic data but require careful design of their architecture to handle peculiarities inherent to spectral data

A probabilistic deep learning approach to automate the interpretation of multi-phase diffraction spectra

Autonomous synthesis and characterization of inorganic materials requires the automatic and accurate analysis of X-ray diffraction spectra. For this task, we designed a probabilistic deep learning algorithm to identify complex multi-phase mixtures. At the core of this algorithm lies an ensemble convolutional neural network trained on simulated diffraction spectra, which are systematically augmented with physics-informed perturbations to account for artifacts that can arise during experimental sample preparation and synthesis. Larger perturbations associated with off-stoichiometry are also captured by supplementing the training set with hypothetical solid solutions. Spectra containing mixtures of materials are analyzed with a newly developed branching algorithm that utilizes the probabilistic nature of the neural network to explore suspected mixtures and identify the set of phases that maximize confidence in the prediction. Our model is benchmarked on simulated and experimentally measured diffraction spectra, showing exceptional performance with accuracies exceeding those given by previously reported methods based on profile matching and deep learning. We envision that the algorithm presented here may be integrated in experimental workflows to facilitate the high-throughput and autonomous discovery of inorganic materials

Autonomous decision making for solid-state synthesis of inorganic materials

To aid in the automation of inorganic materials synthesis, we introduce an algorithm (ARROWS3) that guides the selection of precursors used in solid-state reactions. Given a target phase, ARROWS3 iteratively proposes experiments and learns from their outcomes to identify an optimal set of precursors that leads to maximal yield of that target. Initial experiments are selected based on thermochemical data collected from first principles calculations, which enable the identification of precursors exhibiting large thermodynamic force to form the desired target. Should the initial experiments fail, their associated reaction paths are determined by sampling a range of synthesis temperatures and identifying their products. ARROWS3 then uses this information to pinpoint which intermediate reactions consume most of the available free energy associated with the starting materials. In subsequent experimental iterations, precursors are selected to avoid such unfavorable reactions and therefore maintain a strong driving force to form the target. We validate this approach on three experimental datasets containing results from more than 200 distinct synthesis procedures. When compared to several black-box optimization algorithms, ARROWS3 identifies the most effective set of precursors for each target while requiring substantially fewer experimental iterations. These findings highlight the importance of using domain knowledge in the design of optimization algorithms for materials synthesis, which are critical for the development of fully autonomous research platforms

Intrinsic tumor resistance to CAR T cells is a dynamic transcriptional state that is exploitable with low-dose radiation

Chimeric antigen receptor (CAR) T-cell therapy represents a major advancement for hematologic malignancies, with some patients achieving long-term remission. However, the majority of treated patients still die of their disease. A consistent predictor of response is tumor quantity, wherein a higher disease burden before CAR T-cell therapy portends a worse prognosis. Focal radiation to bulky sites of the disease can decrease tumor quantity before CAR T-cell therapy, but whether this strategy improves survival is unknown. We find that substantially reducing systemic tumor quantity using high-dose radiation to areas of bulky disease, which is commonly done clinically, is less impactful on overall survival in mice achieved by CAR T cells than targeting all sites of disease with low-dose total tumor irradiation (TTI) before CAR T-cell therapy. This finding highlights another predictor of response, tumor quality, the intrinsic resistance of an individual patient\u27s tumor cells to CAR T-cell killing. Little is known about whether or how an individual tumor\u27s intrinsic resistance may change under different circumstances. We find a transcriptional death receptor score that reflects a tumor\u27s intrinsic sensitivity to CAR T cells can be temporarily increased by low-dose TTI, and the timing of this transcriptional change correlates with improved in vivo leukemia control by an otherwise limited number of CAR T cells. This suggests an actionable method for potentially improving outcomes in patients predicted to respond poorly to this promising therapy and highlights that intrinsic tumor attributes may be equally or more important predictors of CAR T-cell response as tumor burden

Comparative assessment of phototherapy protocols for reduction of oxidative stress in partially transected spinal cord slices undergoing secondary degeneration

Background: Red/near-infrared light therapy (R/NIR-LT) has been developed as a treatment for a range of conditions, including injury to the central nervous system (CNS). However, clinical trials have reported variable or sub-optimal outcomes, possibly because there are few optimized treatment protocols for the different target tissues. Moreover, the low absolute, and wavelength dependent, transmission of light by tissues overlying the target site make accurate dosing problematic. Results: In order to optimize light therapy treatment parameters, we adapted a mouse spinal cord organotypic culture model to the rat, and characterized myelination and oxidative stress following a partial transection injury. The ex vivo model allows a more accurate assessment of the relative effect of different illumination wavelengths (adjusted for equal quantal intensity) on the target tissue. Using this model, we assessed oxidative stress following treatment with four different wavelengths of light: 450 nm (blue); 510 nm (green); 660 nm (red) or 860 nm (infrared) at three different intensities: 1.93 × 10¹⁶ (low); 3.85 × 10¹⁶ (intermediate) and 7.70 × 10¹⁶ (high) photons/cm²/s. We demonstrate that the most effective of the tested wavelengths to reduce immunoreactivity of the oxidative stress indicator 3-nitrotyrosine (3NT) was 660 nm. 860 nm also provided beneficial effects at all tested intensities, significantly reducing oxidative stress levels relative to control (p ≤ 0.05). Conclusions: Our results indicate that R/NIR-LT is an effective antioxidant therapy, and indicate that effective wavelengths and ranges of intensities of treatment can be adapted for a variety of CNS injuries and conditions, depending upon the transmission properties of the tissue to be treated.12 page(s

Faithful chaperones

This review describes the properties of some rare eukaryotic chaperones that each assist in the folding of only one target protein. In particular, we describe (1) the tubulin cofactors, (2) p47, which assists in the folding of collagen, (3) α-hemoglobin stabilizing protein (AHSP), (4) the adenovirus L4-100 K protein, which is a chaperone of the major structural viral protein, hexon, and (5) HYPK, the huntingtin-interacting protein. These various-sized proteins (102–1,190 amino acids long) are all involved in the folding of oligomeric polypeptides but are otherwise functionally unique, as they each assist only one particular client. This raises a question regarding the biosynthetic cost of the high-level production of such chaperones. As the clients of faithful chaperones are all abundant proteins that are essential cellular or viral components, it is conceivable that this necessary metabolic expenditure withstood evolutionary pressure to minimize biosynthetic costs. Nevertheless, the complexity of the folding pathways in which these chaperones are involved results in error-prone processes. Several human disorders associated with these chaperones are discussed

Proceedings of the Thirteenth International Society of Sports Nutrition (ISSN) Conference and Expo

Meeting Abstracts: Proceedings of the Thirteenth International Society of Sports Nutrition (ISSN) Conference and Expo Clearwater Beach, FL, USA. 9-11 June 201