Robust IgM responses following intravenous vaccination with Bacille Calmette-Guérin associate with prevention of Mycobacterium tuberculosis infection in macaques.

Development of an effective tuberculosis (TB) vaccine has suffered from an incomplete understanding of the correlates of protection against Mycobacterium tuberculosis (Mtb). Intravenous (i.v.) vaccination with Bacille Calmette-Guérin (BCG) provides nearly complete protection against TB in rhesus macaques, but the antibody response it elicits remains incompletely defined. Here we show that i.v. BCG drives superior antibody responses in the plasma and the lungs of rhesus macaques compared to traditional intradermal BCG administration. While i.v. BCG broadly expands antibody titers and functions, IgM titers in the plasma and lungs of immunized macaques are among the strongest markers of reduced bacterial burden. IgM was also enriched in macaques that received protective vaccination with an attenuated strain of Mtb. Finally, an Mtb-specific IgM monoclonal antibody reduced Mtb survival in vitro. Collectively, these data highlight the potential importance of IgM responses as a marker and mediator of protection against TB.

Machine Learning-Enabled Image Classification for Automated Electron Microscopy.

Traditionally, materials discovery has been driven more by evidence and intuition than by systematic design. However, the advent of "big data" and an exponential increase in computational power have reshaped the landscape. Today, we use simulations, artificial intelligence (AI), and machine learning (ML) to predict materials characteristics, which dramatically accelerates the discovery of novel materials. For instance, combinatorial megalibraries, where millions of distinct nanoparticles are created on a single chip, have spurred the need for automated characterization tools. This paper presents an ML model specifically developed to perform real-time binary classification of grayscale high-angle annular dark-field images of nanoparticles sourced from these megalibraries. Given the high costs associated with downstream processing errors, a primary requirement for our model was to minimize false positives while maintaining efficacy on unseen images. We elaborate on the computational challenges and our solutions, including managing memory constraints, optimizing training time, and utilizing Neural Architecture Search tools. The final model outperformed our expectations, achieving over 95% precision and a weighted F-score of more than 90% on our test data set. This paper discusses the development, challenges, and successful outcomes of this significant advancement in the application of AI and ML to materials discovery.

MPpredictor: An Artificial Intelligence-Driven Web Tool for Composition-Based Material Property Prediction.

The applications of artificial intelligence, machine learning, and deep learning techniques in the field of materials science are becoming increasingly common due to their promising abilities to extract and utilize data-driven information from available data and accelerate materials discovery and design for future applications. In an attempt to assist with this process, we deploy predictive models for multiple material properties, given the composition of the material. The deep learning models described here are built using a cross-property deep transfer learning technique, which leverages source models trained on large data sets to build target models on small data sets with different properties. We deploy these models in an online software tool that takes a number of material compositions as input, performs preprocessing to generate composition-based attributes for each material, and feeds them into the predictive models to obtain up to 41 different material property values. The material property predictor is available online at http://ai.eecs.northwestern.edu/MPpredictor.

Phosphatidylserine-Targeting Monoclonal Antibodies Exhibit Distinct Biochemical and Cellular Effects on Anti-CD3/CD28-Stimulated T Cell IFN-γ and TNF-α Production.

Phosphatidylserine (PS)-targeting monoclonal Abs (mAbs) that directly target PS and target PS via ß2-gp1 (ß2GP1) have been in preclinical and clinical development for over 10 y for the treatment of infectious diseases and cancer. Although the intended targets of PS-binding mAbs have traditionally included pathogens as well as stressed tumor cells and its associated vasculature in oncology, the effects of PS-targeting mAbs on activated immune cells, notably T cells, which externalize PS upon Ag stimulation, is not well understood. Using human T cells from healthy donor PBMCs activated with an anti-CD3 + anti-CD28 Ab mixture (anti-CD3/CD28) as a model for TCR-mediated PS externalization and T cell stimulation, we investigated effects of two different PS-targeting mAbs, 11.31 and bavituximab (Bavi), on TCR activation and TCR-mediated cytokine production in an ex vivo paradigm. Although 11.31 and Bavi bind selectivity to anti-CD3/28 activated T cells in a PS-dependent manner, surprisingly, they display distinct functional activities in their effect on IFN-γ and TNF-ɑ production, whereby 11.31, but not Bavi, suppressed cytokine production. This inhibitory effect on anti-CD3/28 activated T cells was observed on both CD4+ and CD8+ cells and independently of monocytes, suggesting the effects of 11.31 were directly mediated by binding to externalized PS on activated T cells. Imaging showed 11.31 and Bavi bind at distinct focal depots on the cell membrane. Collectively, our findings indicate that PS-targeting mAb 11.31 suppresses cytokine production by anti-CD3/28 activated T cells.

Characterization of the Antigenic Heterogeneity of Lipoarabinomannan, the Major Surface Glycolipid of Mycobacterium tuberculosis, and Complexity of Antibody Specificities toward This Antigen.

Lipoarabinomannan (LAM), the major antigenic glycolipid of Mycobacterium tuberculosis, is an important immunodiagnostic target for detecting tuberculosis (TB) infection in HIV-1-coinfected patients, and is believed to mediate a number of functions that promote infection and disease development. To probe the human humoral response against LAM during TB infection, several novel LAM-specific human mAbs were molecularly cloned from memory B cells isolated from infected patients and grown in vitro. The fine epitope specificities of these Abs, along with those of a panel of previously described murine and phage-derived LAM-specific mAbs, were mapped using binding assays against LAM Ags from several mycobacterial species and a panel of synthetic glycans and glycoconjugates that represented diverse carbohydrate structures present in LAM. Multiple reactivity patterns were seen that differed in their specificity for LAM from different species, as well as in their dependence on arabinofuranoside branching and nature of capping at the nonreducing termini. Competition studies with mAbs and soluble glycans further defined these epitope specificities and guided the design of highly sensitive immunodetection assays capable of detecting LAM in urine of TB patients, even in the absence of HIV-1 coinfection. These results highlighted the complexity of the antigenic structure of LAM and the diversity of the natural Ab response against this target. The information and novel reagents described in this study will allow further optimization of diagnostic assays for LAM and may facilitate the development of potential immunotherapeutic approaches to inhibit the functional activities of specific structural motifs in LAM.

An Integrated Approach for Modeling Sustainability Risks in Freight Transportation Systems.

Integrating sustainability into freight transportation systems (FTSs) is a complex and challenging task due to the sheer number of inherent sustainability risks. Sustainability risks disrupt the economic, social and environmental objectives of freight operations and act as impediments in the development of sustainable freight transportation systems. The area of sustainability risk management is still unexplored and immature in the operational research domain. This study addresses these research gaps and contributes in a threefold manner. First, a total of 36 potential sustainability risks related to FTSs are identified and uniquely classified into seven categories using a rigourous approach. Second, the research proposes two prominent perspectives, namely, ontological and epistemological perspectives to understand risks and develops a novel framework for managing sustainability risks in FTSs. Third, a novel approach by integrating fuzzy evidential reasoning algorithm (FERA) with expected utility theory is developed to quantitatively model and profile sustainability risk for different risk preferences, namely, risk-averse, risk-neutral, and risk-taking scenarios. The proposed FERA is a flexible and robust approach, which transforms the experts' inputs into belief structures and aggregates them using the evidence combination rule proposed in Dempster-Shafer theory to overcome the problem of imprecise results caused by average scoring in existing models. A sensitivity analysis is conducted to demonstrate the robustness of the proposed model. Unlike conventional perception, our study suggests that most of the high priority sustainability risks are behaviorally and socially induced rather than financially driven. The results provide significant managerial implications including a focus on skills development, and on social and behavioral dimensions while managing risks in FTSs.

Identification of Novel Structural Determinants in MW965 Env That Regulate the Neutralization Phenotype and Conformational Masking Potential of Primary HIV-1 Isolates.

The subtype C HIV-1 isolate MW965.26 is a highly neutralization-sensitive tier 1a primary isolate that is widely used in vaccine studies, but the basis for the sensitive neutralization phenotype of this isolate is not known. Substituting the MW965.26 V1/V2 domain into a neutralization-sensitive SF162 Env clone resulted in high resistance to standard anti-V3 monoclonal antibodies, demonstrating that this region possesses strong masking activity in a standard Env backbone and indicating that determinants elsewhere in MW965.26 Env are responsible for its unusual neutralization sensitivity. Key determinants for this phenotype were mapped by generating chimeric Envs between MW965.26 Env and a typical resistant Env clone, the consensus C (ConC) clone, and localized to two residues, Cys384 in the C3 domain and Asn502 in the C5 domain. Substituting the sensitizing mutations Y384C and K502N at these positions into several resistant primary Envs resulted in conversion to neutralization-sensitive phenotypes, demonstrating the generalizability of this effect. In contrast to the sensitizing effects of these substitutions on normally masked epitopes, these mutations reduced the sensitivity of VRC01-like epitopes overlapping the CD4-binding domain, while they had no effect on several other classes of broadly neutralizing epitopes, including members of several lineages of V2-dependent quaternary epitopes and representatives of N332 glycan-dependent epitopes (PGT121) and quaternary, cleavage-dependent epitopes centered at the gp41-gp120 interface on intact HIV-1 Env trimers (PGT151). These results identify novel substitutions in gp120 that regulate the expression of alternative conformations of Env and differentially affect the exposure of different classes of epitopes, thereby influencing the neutralization phenotype of primary HIV-1 isolates.IMPORTANCE A better understanding of the mechanisms that determine the wide range of neutralization sensitivity of circulating primary HIV-1 isolates would provide important information about the natural structural and conformational diversity of HIV-1 Env and how this affects the neutralization phenotype. A useful way of studying this is to determine the molecular basis for the unusually high neutralization sensitivities of the limited number of available tier 1a viruses. This study localized the neutralization sensitivity of MW965.26, an extremely sensitive subtype C-derived primary isolate, to two rare substitutions in the C3 and C5 domains and demonstrated that the sequences at these positions differentially affect the presentation of epitopes recognized by different classes of standard and conformation-dependent broadly neutralizing antibodies. These results provide novel insight into how these regions regulate the neutralization phenotype and provide tools for controlling the Env conformation that could have applications both for structural studies and in vaccine design.

Prediction of seebeck coefficient for compounds without restriction to fixed stoichiometry: A machine learning approach.

The regression model-based tool is developed for predicting the Seebeck coefficient of crystalline materials in the temperature range from 300 K to 1000 K. The tool accounts for the single crystal versus polycrystalline nature of the compound, the production method, and properties of the constituent elements in the chemical formula. We introduce new descriptive features of crystalline materials relevant for the prediction the Seebeck coefficient. To address off-stoichiometry in materials, the predictive tool is trained on a mix of stoichiometric and nonstoichiometric materials. The tool is implemented into a web application (http://info.eecs.northwestern.edu/SeebeckCoefficientPredictor) to assist field scientists in the discovery of novel thermoelectric materials. © 2017 Wiley Periodicals, Inc.

A Novel Sensitive Immunoassay Targeting the 5-Methylthio-d-Xylofuranose-Lipoarabinomannan Epitope Meets the WHO's Performance Target for Tuberculosis Diagnosis.

The only currently commercialized point-of-care assay for tuberculosis (TB) that measures lipoarabinomannan (LAM) in urine (Alere LF-LAM) has insufficient sensitivity. We evaluated the potential of 100 novel monoclonal antibody pairs targeting a variety of LAM epitopes on a sensitive electrochemiluminescence platform to improve the diagnostic accuracy. In the screening, many antibody pairs showed high reactivity to purified LAM but performed poorly at detecting urinary LAM in clinical samples, suggesting differences in antigen structure and immunoreactivity of the different LAM sources. The 12 best antibody pairs from the screening were tested in a retrospective case-control study with urine samples from 75 adults with presumptive TB. The best antibody pair reached femtomolar analytical sensitivity for LAM detection and an overall clinical sensitivity of 93% (confidence interval [CI], 80% to 97%) and specificity of 97% (CI, 85% to 100%). Importantly, in HIV-negative subjects positive for TB by sputum smear microscopy, the test achieved a sensitivity of 80% (CI, 55% to 93%). This compares to an overall sensitivity of 33% (CI, 20% to 48%) of the Alere LF-LAM and a sensitivity of 13% (CI, 4% to 38%) in HIV-negative subjects in the same sample set. The capture antibody targets a unique 5-methylthio-d-xylofuranose (MTX)-dependent epitope in LAM that is specific to the Mycobacterium tuberculosis complex and shows no cross-reactivity with fast-growing mycobacteria or other bacteria. The present study provides evidence that improved assay methods and reagents lead to increased diagnostic accuracy. The results of this work have informed the development of a sensitive and specific novel LAM point-of-care assay with the aim to meet the WHO's performance target for TB diagnosis.

Extracting Grain Orientations from EBSD Patterns of Polycrystalline Materials Using Convolutional Neural Networks.

We present a deep learning approach to the indexing of electron backscatter diffraction (EBSD) patterns. We design and implement a deep convolutional neural network architecture to predict crystal orientation from the EBSD patterns. We design a differentiable approximation to the disorientation function between the predicted crystal orientation and the ground truth; the deep learning model optimizes for the mean disorientation error between the predicted crystal orientation and the ground truth using stochastic gradient descent. The deep learning model is trained using 374,852 EBSD patterns of polycrystalline nickel from simulation and evaluated using 1,000 experimental EBSD patterns of polycrystalline nickel. The deep learning model results in a mean disorientation error of 0.548° compared to 0.652° using dictionary based indexing.

Alkaptonuric Ochronosis.

Alkaptonuria is an autosomal recessive metabolic disorder characterized by joints and spine involvement, ochronosis and presence of homogentisic acid in urine and its deposition in cartilage, intervertebral disc and other connective tissues, leading to disabling arthritis in elderly individual.

Simultaneously improving accuracy and computational cost under parametric constraints in materials property prediction tasks.

Modern data mining techniques using machine learning (ML) and deep learning (DL) algorithms have been shown to excel in the regression-based task of materials property prediction using various materials representations. In an attempt to improve the predictive performance of the deep neural network model, researchers have tried to add more layers as well as develop new architectural components to create sophisticated and deep neural network models that can aid in the training process and improve the predictive ability of the final model. However, usually, these modifications require a lot of computational resources, thereby further increasing the already large model training time, which is often not feasible, thereby limiting usage for most researchers. In this paper, we study and propose a deep neural network framework for regression-based problems comprising of fully connected layers that can work with any numerical vector-based materials representations as model input. We present a novel deep regression neural network, iBRNet, with branched skip connections and multiple schedulers, which can reduce the number of parameters used to construct the model, improve the accuracy, and decrease the training time of the predictive model. We perform the model training using composition-based numerical vectors representing the elemental fractions of the respective materials and compare their performance against other traditional ML and several known DL architectures. Using multiple datasets with varying data sizes for training and testing, We show that the proposed iBRNet models outperform the state-of-the-art ML and DL models for all data sizes. We also show that the branched structure and usage of multiple schedulers lead to fewer parameters and faster model training time with better convergence than other neural networks. Scientific contribution: The combination of multiple callback functions in deep neural networks minimizes training time and maximizes accuracy in a controlled computational environment with parametric constraints for the task of materials property prediction.

Monitored COVID-19 vaccine humoral response in immunocompromised solid organ transplant recipients.

The SARS-CoV-2 pandemic has resulted in rapid research and vaccine development to help curtail unchecked transmission. However, these studies cannot be applied as easily among every population, such as immunocompromised individuals. In this study, we observed the humoral response of 70 total heart and renal transplant patients to mRNA SARS-CoV-2 vaccinations to help further understand the effectiveness of vaccination in post-transplant patients following second or booster vaccinations. Antibodies were measured by bead technology to detect IgG, as well as IgG/IgM Rapid Cassette tests for confirmation. Immunocompromised patients had a noticeably lower humoral response than non-immunocompromised populations, with an even lower response among Black patients. Our findings also show for the first time various antibody responses to different motifs of the virus, with the lowest being against the S2 motif. A potential link between the duration of immunosuppression and vaccine response was also observed, where patients on immunosuppressants for longer had a stronger response to vaccination compared to recent transplant patients in our study. In addition, younger transplant recipients had a better humoral response to vaccination, and vaccine effectiveness was disproportionate between races. This finding reinforces the continuation of the guidelines for accelerated vaccination schedules for immunocompromised patients.

Improving deep learning model performance under parametric constraints for materials informatics applications.

Modern machine learning (ML) and deep learning (DL) techniques using high-dimensional data representations have helped accelerate the materials discovery process by efficiently detecting hidden patterns in existing datasets and linking input representations to output properties for a better understanding of the scientific phenomenon. While a deep neural network comprised of fully connected layers has been widely used for materials property prediction, simply creating a deeper model with a large number of layers often faces with vanishing gradient problem, causing a degradation in the performance, thereby limiting usage. In this paper, we study and propose architectural principles to address the question of improving the performance of model training and inference under fixed parametric constraints. Here, we present a general deep-learning framework based on branched residual learning (BRNet) with fully connected layers that can work with any numerical vector-based representation as input to build accurate models to predict materials properties. We perform model training for materials properties using numerical vectors representing different composition-based attributes of the respective materials and compare the performance of the proposed models against traditional ML and existing DL architectures. We find that the proposed models are significantly more accurate than the ML/DL models for all data sizes by using different composition-based attributes as input. Further, branched learning requires fewer parameters and results in faster model training due to better convergence during the training phase than existing neural networks, thereby efficiently building accurate models for predicting materials properties.

Insights into next generation sequencing guided antibody selection strategies.

Therapeutic antibody discovery often relies on in-vitro display methods to identify lead candidates. Assessing selected output diversity traditionally involves random colony picking and Sanger sequencing, which has limitations. Next-generation sequencing (NGS) offers a cost-effective solution with increased read depth, allowing a comprehensive understanding of diversity. Our study establishes NGS guidelines for antibody drug discovery, demonstrating its advantages in expanding the number of unique HCDR3 clusters, broadening the number of high affinity antibodies, expanding the total number of antibodies recognizing different epitopes, and improving lead prioritization. Surprisingly, our investigation into the correlation between NGS-derived frequencies of CDRs and affinity revealed a lack of association, although this limitation could be moderately mitigated by leveraging NGS clustering, enrichment and/or relative abundance across different regions to enhance lead prioritization. This study highlights NGS benefits, offering insights, recommendations, and the most effective approach to leverage NGS in therapeutic antibody discovery.

Accelerating pairwise statistical significance estimation for local alignment by harvesting GPU's power.

BACKGROUND: Pairwise statistical significance has been recognized to be able to accurately identify related sequences, which is a very important cornerstone procedure in numerous bioinformatics applications. However, it is both computationally and data intensive, which poses a big challenge in terms of performance and scalability. RESULTS: We present a GPU implementation to accelerate pairwise statistical significance estimation of local sequence alignment using standard substitution matrices. By carefully studying the algorithm's data access characteristics, we developed a tile-based scheme that can produce a contiguous data access in the GPU global memory and sustain a large number of threads to achieve a high GPU occupancy. We further extend the parallelization technique to estimate pairwise statistical significance using position-specific substitution matrices, which has earlier demonstrated significantly better sequence comparison accuracy than using standard substitution matrices. The implementation is also extended to take advantage of dual-GPUs. We observe end-to-end speedups of nearly 250 (370) × using single-GPU Tesla C2050 GPU (dual-Tesla C2050) over the CPU implementation using Intel Corei7 CPU 920 processor. CONCLUSIONS: Harvesting the high performance of modern GPUs is a promising approach to accelerate pairwise statistical significance estimation for local sequence alignment.

Anatomy of a hash-based long read sequence mapping algorithm for next generation DNA sequencing.

MOTIVATION: Recently, a number of programs have been proposed for mapping short reads to a reference genome. Many of them are heavily optimized for short-read mapping and hence are very efficient for shorter queries, but that makes them inefficient or not applicable for reads longer than 200 bp. However, many sequencers are already generating longer reads and more are expected to follow. For long read sequence mapping, there are limited options; BLAT, SSAHA2, FANGS and BWA-SW are among the popular ones. However, resequencing and personalized medicine need much faster software to map these long sequencing reads to a reference genome to identify SNPs or rare transcripts. RESULTS: We present AGILE (AliGnIng Long rEads), a hash table based high-throughput sequence mapping algorithm for longer 454 reads that uses diagonal multiple seed-match criteria, customized q-gram filtering and a dynamic incremental search approach among other heuristics to optimize every step of the mapping process. In our experiments, we observe that AGILE is more accurate than BLAT, and comparable to BWA-SW and SSAHA2. For practical error rates (< 5%) and read lengths (200-1000 bp), AGILE is significantly faster than BLAT, SSAHA2 and BWA-SW. Even for the other cases, AGILE is comparable to BWA-SW and several times faster than BLAT and SSAHA2. AVAILABILITY: http://www.ece.northwestern.edu/~smi539/agile.html.

Moving closer to experimental level materials property prediction using AI.

While experiments and DFT-computations have been the primary means for understanding the chemical and physical properties of crystalline materials, experiments are expensive and DFT-computations are time-consuming and have significant discrepancies against experiments. Currently, predictive modeling based on DFT-computations have provided a rapid screening method for materials candidates for further DFT-computations and experiments; however, such models inherit the large discrepancies from the DFT-based training data. Here, we demonstrate how AI can be leveraged together with DFT to compute materials properties more accurately than DFT itself by focusing on the critical materials science task of predicting "formation energy of a material given its structure and composition". On an experimental hold-out test set containing 137 entries, AI can predict formation energy from materials structure and composition with a mean absolute error (MAE) of 0.064 eV/atom; comparing this against DFT-computations, we find that AI can significantly outperform DFT computations for the same task (discrepancies of [Formula: see text] eV/atom) for the first time.

Generative Adversarial Networks and Mixture Density Networks-Based Inverse Modeling for Microstructural Materials Design.

There are two broad modeling paradigms in scientific applications: forward and inverse. While forward modeling estimates the observations based on known causes, inverse modeling attempts to infer the causes given the observations. Inverse problems are usually more critical as well as difficult in scientific applications as they seek to explore the causes that cannot be directly observed. Inverse problems are used extensively in various scientific fields, such as geophysics, health care and materials science. Exploring the relationships from properties to microstructures is one of the inverse problems in material science. It is challenging to solve the microstructure discovery inverse problem, because it usually needs to learn a one-to-many nonlinear mapping. Given a target property, there are multiple different microstructures that exhibit the target property, and their discovery also requires significant computing time. Further, microstructure discovery becomes even more difficult because the dimension of properties (input) is much lower than that of microstructures (output). In this work, we propose a framework consisting of generative adversarial networks and mixture density networks for inverse modeling of structure-property linkages in materials, i.e., microstructure discovery for a given property. The results demonstrate that compared to baseline methods, the proposed framework can overcome the above-mentioned challenges and discover multiple promising solutions in an efficient manner.