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Development of efficient bioconversion processes is limited by the ability to predictably improve metabolic flux. Here we deployed Bayesian Metabolic Control Analysis as a platform to integrate multi-omics data with metabolic modeling and evaluated its ability to predict genetic interventions that improve metabolic flux. Global Metabolomics and proteomics data was collected from 17 Aspergillus niger strains engineered to produce the platform biochemical 3-hydroxypropionic acid from which seven actional genetic interventions were predicted from significant flux control coefficients. Of the suggested genetic interventions, two were present within the intuitively designed strains used for training (malonic semialdehyde dehydrogenase and pyruvate carboxylase) while five predicted targets were present within non-intuitive areas of the metabolic network including 5-formyltetrahydrofolate deformylase and four mitochondrial enzymes, alcohol dehydrogenase, succinyl-CoA ligase, aspartate aminotransferase, and malate dehydrogenase. Six of the targets were validated in the highest performing 3-HP strain used for multi-omics data generation which contained a prior disruption of the highest scoring target malonic semialdehyde dehydrogenase. Predicted directional perturbation of five of the six tested targets significantly improved titer and rate of 3-HP production and two significantly improved yield. The greatest improvements were observed following disruption of the non-intuitive target succinyl-CoA ligase which increased titer by 39% and yield by 29% (to 20.4 g/L 3-HP and 0.31 g 3-HP/g glucose) over the strains used for training. This study demonstrates the utility of Bayesian Metabolic Control Analysis and highlights the ability to predict meaningful genetic targets in unexpected areas of metabolism to improve engineered strains for bioconversion.

Ground Truth program was designed to evaluate social science modeling approaches using simulation test beds with ground truth intentionally and systematically embedded to understand and model complex Human Domain systems and their dynamics Lazer et al. (Science 369:1060–1062, 2020). Our multidisciplinary team of data scientists, statisticians, experts in Artificial Intelligence (AI) and visual analytics had a unique role on the program to investigate accuracy, reproducibility, generalizability, and robustness of the state-of-the-art (SOTA) causal structure learning approaches applied to fully observed and sampled simulated data across virtual worlds. In addition, we analyzed the feasibility of using machine learning models to predict future social behavior with and without causal knowledge explicitly embedded. In this paper, we first present our causal modeling approach to discover the causal structure of four virtual worlds produced by the simulation teams—Urban Life, Financial Governance, Disaster and Geopolitical Conflict. Our approach adapts the state-of-the-art causal discovery (including ensemble models), machine learning, data analytics, and visualization techniques to allow a human-machine team to reverse-engineer the true causal relations from sampled and fully observed data. We next present our reproducibility analysis of two research methods team’s performance using a range of causal discovery models applied to both sampled and fully observed data, and analyze their effectiveness and limitations. We further investigate the generalizability and robustness to sampling of the SOTA causal discovery approaches on additional simulated datasets with known ground truth. Our results reveal the limitations of existing causal modeling approaches when applied to large-scale, noisy, high-dimensional data with unobserved variables and unknown relationships between them. We show that the SOTA causal models explored in our experiments are not designed to take advantage from vasts amounts of data and have difficulty recovering ground truth when latent confounders are present; they do not generalize well across simulation scenarios and are not robust to sampling; they are vulnerable to data and modeling assumptions, and therefore, the results are hard to reproduce. Finally, when we outline lessons learned and provide recommendations to improve models for causal discovery and prediction of human social behavior from observational data, we highlight the importance of learning data to knowledge representations or transformations to improve causal discovery and describe the benefit of causal feature selection for predictive and prescriptive modeling.

Abstract A major challenge in synthetic biology is properly balancing evolved and engineered functions without compromising microbial fitness. Many microbial proteins are not required for growth in regular laboratory conditions, but it is unclear what fraction of the proteome can be eliminated to increase bioproduction and maintain fitness. Here, we investigated the effects of massive genome reduction in E. coli on the expression level and evolutionary stability of a model biosynthetic pathway to produce the pigment protodeoxyviolacein (PDV). We identified an amino acid metabolism imbalance and compromised growth that were correlated with elimination of genes associated with significant proteome fraction. Proteomic profiling suggested that increased amino acid pools are responsible for an alleviation of fitness defects associated with PDV expression. In addition, all strains with genome reductions that significantly affected the proteome exhibited decreased stability of PDV production compared to the wild-type strain under persistent PDV expression conditions despite the alleviation of fitness defects. These findings exhibit the importance of balancing evolved functions with engineered ones to achieve an optimal balance of fitness and bioproduction. Competing Interest Statement The authors have declared no competing interest.

Curating knowledge from multiple siloed sources that contain both structured and unstructured data is a major challenge in many real-world applications. Pattern matching and querying represent fundamental tasks in modern data analytics that leverage this curated knowledge. The development of such applications necessitates overcoming several research challenges, including data extraction, named entity recognition, data modeling, and designing query interfaces. Moreover, the explainability of these functionalities is critical for their broader adoption. The emergence of Large Language Models (LLMs) has accelerated the development lifecycle of new capabilities. Nonetheless, there is an ongoing need for domain-specific tools tailored to user activities. The creation of digital assistants has gained considerable traction in recent years, with LLMs offering a promising avenue to develop such assistants utilizing domain-specific knowledge and assumptions. In this context, we introduce an advanced query and reasoning system, GraphAide, which constructs a knowledge graph (KG) from diverse sources and allows to query and reason over the resulting KG. GraphAide harnesses both the KG and LLMs to rapidly develop domain-specific digital assistants. It integrates design patterns from retrieval augmented generation (RAG) and the semantic web to create an agentic LLM application. GraphAide underscores the potential for streamlined and efficient development of specialized digital assistants, thereby enhancing their applicability across various domains.

Information visualization tools are proliferating, with language and library-based visualization tools serving scientific, business and artistic endeavors. While partial formalizations of these tools exist, many details are unspecified. This leaves the tools as ad hoc implementations of ephemeral ideas. This thesis presents Stencil, a domain-specific language for specifying visualizations that has a well-founded basis. Stencil has a formal semantics that establish testable conditions for resource-bounded and deterministic execution. The semantics enable treatment of dynamic data in a principled fashion. Internal consistency of a visualization id defined in terms of these semantics and the implementation is shown to maintain that consistency. Stencil includes a task-parallel execution model that dramatically improves runtimes when working with dynamic data. The improvements are realized by application of the semantics to reason about the runtime, and employing persistent data structures to provide noninterference. As a domain-specific language, Stencil enables compact description of visualization programs. The key to providing this compactness is the Stencil compiler's ability to infer contextual information. Contextual inference avoids restatement of program constructs and allows guarantees about the inferred information. This contextual inference is used to create axes, legends and other guide structures in a fashion that is guaranteed to reflect both the corresponding analysis and the input data. The lessons learned developing the Stencil system are applicable to other visualization frameworks. For example, other libraries can employ the data structures implemented for Stencil. More importantly, the formalisms presented provide a basis to define and test properties of a visualization framework implementation. This thesis demonstrates the feasibility of treating visualization programs in a principled fashion and some of the benefits (including greater performance and reduced programming effort) of doing so.

We present the \\(Y_0\\) Python package, which implements causal identification algorithms that apply interventional, counterfactual, and transportability queries to data from (randomized) controlled trials, observational studies, or mixtures thereof. \\(Y_0\\) focuses on the qualitative investigation of causation, helping researchers determine whether a causal relationship can be estimated from available data before attempting to estimate how strong that relationship is. Furthermore, \\(Y_0\\) provides guidance on how to transform the causal query into a symbolic estimand that can be non-parametrically estimated from the available data. \\(Y_0\\) provides a domain-specific language for representing causal queries and estimands as symbolic probabilistic expressions, tools for representing causal graphical models with unobserved confounders, such as acyclic directed mixed graphs (ADMGs), and implementations of numerous identification algorithms from the recent causal inference literature. The \\(Y_0\\) source code can be found under the MIT License at https://github.com/y0-causal-inference/y0 and it can be installed with pip install y0.

Photonic devices operating in the ultraviolet UV-C range (100–280 nm) have diverse applications from super-resolution microscopy to optical communications, and their advances promise to unlock new opportunities across science and technology. However, generating and detecting ultrafast light signals in this spectral range remains a major challenge. Here, we report an integrated UV-C source-sensor platform that combines phase-matched second-order processes in nonlinear optical crystals for the efficient generation of femtosecond UV-C laser pulses with a new class of room temperature photodetectors based on two-dimensional (2D) semiconductors. Unexpectedly, these 2D sensors exhibit a linear to super-linear photocurrent response to pulse energy, a highly desirable property, laying the foundation for UV-C-based photonics operating on femtosecond timescales over a wide range of pulse energies and repetition rates. As proof of concept, we demonstrate a free-space communication system. A UV-C source-sensor communication system combines phase-matched second-order processes in nonlinear optical crystals for generation of femtosecond UV-C laser pulses with fast-sensors based on two-dimensional semiconductors.

Foot-and-mouth disease (FMD) affects economically important livestock and is one of the most contagious viral diseases. The most commonly used FMD diagnostic assay is a sandwich ELISA. However, the main disadvantage of this ELISA is that it requires anti-FMD virus (FMDV) serotype-specific antibodies raised in small animals. This problem can be, in part, overcome by using anti-FMDV monoclonal antibodies (MAbs) as detecting reagents. However, the long-term use of MAbs may be problematic and they may need to be replaced. Here we have constructed chimeric antibodies (mouse/rabbit D9) and Fabs (fragment antigen-binding) (mouse/cattle D9) using the Fv (fragment variable) regions of a mouse MAb, D9 (MAb D9), which recognises type O FMDV. The mouse/rabbit D9 chimeric antibody retained the FMDV serotype-specificity of MAb D9 and performed well in a FMDV detection ELISA as well as in routine laboratory assays. Cryo-electron microscopy analysis confirmed engagement with antigenic site 1 and peptide competition studies identified the aspartic acid at residue VP1 147 as a novel component of the D9 epitope. This chimeric expression approach is a simple but effective way to preserve valuable FMDV antibodies, and has the potential for unlimited generation of antibodies and antibody fragments in recombinant systems with the concomitant positive impacts on the 3Rs (Replacement, Reduction and Refinement) principles.

Medical therapeutics for weight loss are changing the landscape of obesity but impacts on obesity-associated cancer remain unclear. We report that in pre-clinical models with significant retatrutide (RETA, LY3437943)-induced weight loss, pancreatic cancer engraftment was reduced, tumor onset was delayed, and progression was attenuated resulting in a 14-fold reduction in tumor volume compared to only 4-fold reduction in single agonist semaglutide-treated mice. Despite weight re-gain after RETA withdrawal, the anti-tumor benefits of RETA persisted. Remarkably, RETA-induced protection extends to a lung cancer model with 50% reduced tumor engraftment, significantly delayed tumor onset, and mitigated tumor progression, with a 17-fold reduction in tumor volume compared to controls. RETA induced immune reprogramming systemically and in the tumor microenvironment with durable anti-tumor immunity evidenced by elevated circulating IL-6, increased antigen presenting cells, reduced immunosuppressive cells, and activation of pro-inflammatory pathways. In sum, our findings suggest that patients with RETA-mediated weight loss may also benefit from reduced cancer risk and improved outcomes.

Tuberculosis (TB) and COVID-19 are the two leading causes of infectious disease mortality worldwide, and their overlap is likely frequent and inevitable. Previous research has shown increased mortality in TB/COVID-coinfected individuals, and emerging evidence suggests that COVID-19 may increase susceptibility to TB. However, the immunological mechanisms underlying these interactions remain unclear. In this study, we aimed to elucidate the impact of prior or concurrent COVID-19 infection on immune profiles of TB patients and those with other respiratory diseases (ORD). Serum and nasopharyngeal samples were collected from 161 Gambian adolescents and adults with either TB or an ORD. Concurrent COVID-19 infection was determined by PCR, while prior COVID-19 was defined by antibody seropositivity. Multiplex cytokine immunoassays were used to quantify 27 cytokines and chemokines in patient serum samples at baseline, and throughout treatment in TB patients. Strikingly, TB and ORD patients with prior COVID-19 infection were found to have significantly reduced expression of several cytokines, including IL-1β, TNF-α and IL-7, compared to those without (p<0.035). Moreover, at month-six of anti-TB treatment, seropositive patients had lower serum Basic FGF (p=0.0115), IL-1β (p=0.0326) and IL-8 (p=0.0021) than seronegative. TB patients with acute COVID-19 coinfection had lower levels of IL-8, IL-13, TNF-α and IP-10 than TB-only patients, though these trends did not reach significance (p>0.035). Our findings demonstrate that COVID-19 infection alters the subsequent response to TB and ORDs, potentially contributing to pathogenesis. Further work is necessary to determine whether COVID-19 infection accelerates TB disease progression, though our results experimentally support this hypothesis.