Investigating the Bromoform Membrane Interactions Using Atomistic Simulations and Machine Learning: Implications for Climate Change Mitigation.

Methane emissions from livestock contribute to global warming. Seaweeds used as food additive offer a promising emission mitigation strategy because seaweeds are enriched in bromoform─a methanogenesis inhibitor. Therefore, understanding bromoform storage and production in seaweeds and particularly in a cell-like environment is crucial.

ML-enhanced peroxisome capacity enables compartmentalization of multienzyme pathway.

Repurposing an organelle for specialized metabolism provides an avenue for fermentable, unicellular organisms such as Saccharomyces cerevisiae to mimic compartmentalization of metabolic pathways within different plant tissues. Peroxisomes are attractive organelles for repurposing as they are not required for yeast viability when grown on glucose and can efficiently compartmentalize heterologous enzymes to enable physical separation of cytosolic native metabolism and peroxisomal engineered metabolism. However, when not required, peroxisomes are repressed, leading to low functional capacities for heterologous proteins.

Development and evaluation of statistical and artificial intelligence approaches with microbial shotgun metagenomics data as an untargeted screening tool for use in food production.

Unlabelled: The increasing knowledge of microbial ecology in food products relating to quality and safety and the established usefulness of machine learning algorithms for anomaly detection in multiple scenarios suggests that the application of microbiome data in food production systems for anomaly detection could be a valuable approach to be used in food systems. These methods could be used to identify ingredients that deviate from their typical microbial composition, which could indicate food fraud or safety issues. The objective of this study was to assess the feasibility of using shotgun sequencing data as input into anomaly detection algorithms using fluid milk as a model system.

A High-Throughput Method for Quantifying Fecundity.

The fruit fly, , is an experimentally tractable model system that has recently emerged as a powerful "new approach methodology" (NAM) for chemical safety testing. As oogenesis is well conserved at the molecular and cellular level, measurements of fecundity can be useful for identifying chemicals that affect reproductive health across species. However, standard fecundity assays have been difficult to perform in a high-throughput manner because experimental factors such as the physiological state of the flies and environmental cues must be carefully controlled to achieve consistent results.

Foundations of reasoning with uncertainty via real-valued logics.

Interest in logics with some notion of real-valued truths has existed since at least Boole and has been increasing in AI due to the emergence of neuro-symbolic approaches, though often their logical inference capabilities are characterized only qualitatively. We provide foundations for establishing the correctness and power of such systems. We introduce a rich class of multidimensional sentences, with a sound and complete axiomatization that can be parameterized to cover many real-valued logics, including all the common fuzzy logics, and extend these to weighted versions, and to the case where the truth values are probabilities.

A high-throughput method for quantifying fecundity.

Measurements of Drosophila fecundity are used in a wide variety of studies, such as investigations of stem cell biology, nutrition, behavior, and toxicology. In addition, because fecundity assays are performed on live flies, they are suitable for longitudinal studies such as investigations of aging or prolonged chemical exposure. However, standard Drosophila fecundity assays have been difficult to perform in a high-throughput manner because experimental factors such as the physiological state of the flies and environmental cues must be carefully controlled to achieve consistent results.

AI in cellular engineering and reprogramming.

During the last decade, artificial intelligence (AI) has increasingly been applied in biophysics and related fields, including cellular engineering and reprogramming, offering novel approaches to understand, manipulate, and control cellular function. The potential of AI lies in its ability to analyze complex datasets and generate predictive models. AI algorithms can process large amounts of data from single-cell genomics and multiomic technologies, allowing researchers to gain mechanistic insights into the control of cell identity and function.

Unsupervised ensemble-based phenotyping enhances discoverability of genes related to left-ventricular morphology.

Recent genome-wide association studies have successfully identified associations between genetic variants and simple cardiac morphological parameters derived from cardiac magnetic resonance images. However, the emergence of large databases, including genetic data linked to cardiac magnetic resonance facilitates the investigation of more nuanced patterns of cardiac shape variability than those studied so far. Here we propose a framework for gene discovery coined unsupervised phenotype ensembles.

Digital twins for health: a scoping review.

The use of digital twins (DTs) has proliferated across various fields and industries, with a recent surge in the healthcare sector. The concept of digital twin for health (DT4H) holds great promise to revolutionize the entire healthcare system, including management and delivery, disease treatment and prevention, and health well-being maintenance, ultimately improving human life. The rapid growth of big data and continuous advancement in data science (DS) and artificial intelligence (AI) have the potential to significantly expedite DT research and development by providing scientific expertise, essential data, and robust cybertechnology infrastructure.

Readout-Induced Suppression and Enhancement of Superconducting Qubit Lifetimes.

It has long been known that the lifetimes of superconducting qubits suffer during readout, increasing readout errors. We show that this degradation is due to the anti-Zeno effect, as readout-induced dephasing broadens the qubit so that it overlaps "hot spots" of strong dissipation, likely due to two-level systems in the qubit's bath. Using a flux-tunable qubit to probe the qubit's frequency-dependent loss, we accurately predict the change in lifetime during readout with a new self-consistent master equation that incorporates the modification to qubit relaxation due to measurement-induced dephasing.

Ring-Opening Polymerization of Cyclic Esters and Carbonates with (Thio)urea/Cyclopropenimine Organocatalytic Systems.

Organocatalyzed ring-opening polymerization is a powerful tool for the synthesis of a variety of functional, readily degradable polyesters and polycarbonates. We report the use of (thio)ureas in combination with cyclopropenimine bases as a unique catalyst for the polymerization of cyclic esters and carbonates with a large span of reactivities. Methodologies of exceptionally effective and selective cocatalyst combinations were devised to produce polyesters and polycarbonates with narrow dispersities ( = 1.

Multimodal Pediatric Lymphoma Detection using PET and MRI.

Lymphoma is one of the most common types of cancer for children (ages 0 to 19). Due to the reduced radiation exposure, PET/MR systems that allow simultaneous PET and MR imaging have become the standard of care for diagnosing cancers and monitoring tumor response to therapy in the pediatric population. In this work, we developed a multimodal deep learning algorithm for automatic pediatric lymphoma detection using PET and MRI.

Methods for improving colorectal cancer annotation efficiency for artificial intelligence-observer training.

Background: Missing occult cancer lesions accounts for the most diagnostic errors in retrospective radiology reviews as early cancer can be small or subtle, making the lesions difficult to detect. Second-observer is the most effective technique for reducing these events and can be economically implemented with the advent of artificial intelligence (AI).

Aim: To achieve appropriate AI model training, a large annotated dataset is necessary to train the AI models.

A tradeoff between enterovirus A71 particle stability and cell entry.

A central role of viral capsids is to protect the viral genome from the harsh extracellular environment while facilitating initiation of infection when the virus encounters a target cell. Viruses are thought to have evolved an optimal equilibrium between particle stability and efficiency of cell entry. In this study, we genetically perturb this equilibrium in a non-enveloped virus, enterovirus A71 to determine its structural basis.

Formulation Graphs for Mapping Structure-Composition of Battery Electrolytes to Device Performance.

Advanced computational methods are being actively sought to address the challenges associated with the discovery and development of new combinatorial materials, such as formulations. A widely adopted approach involves domain-informed high-throughput screening of individual components that can be combined together to form a formulation. This manages to accelerate the discovery of new compounds for a target application but still leaves the process of identifying the right "formulation" from the shortlisted chemical space largely a laboratory experiment-driven process.

pH-Responsive Polymeric Micelle Dynamic Complexes for Selective Killing of .

, the world's most common chronic infection-causing pathogen, is responsible for causing gastric ulcers, the fourth-leading cause of cancer-related death globally in 2020. In recent years, the effectiveness of the current treatment regimen (two antibiotics and one proton pump inhibitor) has often been plagued with problems such as resistance and the undesired elimination of commensal bacteria. Herein, we report the synthesis of block and random copolycarbonates, functionalized with cationic guanidinium and anionic acetate functional groups, aimed at selectively killing in the acidic environment of the stomach, while remaining nontoxic to the commensal bacteria in the gut.

Anisotropic proximity-induced superconductivity and edge supercurrent in Kagome metal, KVSb.

Materials with Kagome nets are of particular importance for their potential combination of strong correlation, exotic magnetism, and electronic topology. KVSb was discovered to be a layered topological metal with a Kagome net of vanadium. Here, we fabricated Josephson Junctions of KVSb and induced superconductivity over long junction lengths.

Harnessing the power of artificial intelligence to advance cell therapy.

Cell therapies are powerful technologies in which human cells are reprogrammed for therapeutic applications such as killing cancer cells or replacing defective cells. The technologies underlying cell therapies are increasing in effectiveness and complexity, making rational engineering of cell therapies more difficult. Creating the next generation of cell therapies will require improved experimental approaches and predictive models.

Demonstrating multi-round subsystem quantum error correction using matching and maximum likelihood decoders.

Quantum error correction offers a promising path for performing high fidelity quantum computations. Although fully fault-tolerant executions of algorithms remain unrealized, recent improvements in control electronics and quantum hardware enable increasingly advanced demonstrations of the necessary operations for error correction. Here, we perform quantum error correction on superconducting qubits connected in a heavy-hexagon lattice.

Nanocomplexes of Biodegradable Anticancer Macromolecules: Prolonged Plasma Half-Life, Reduced Toxicity, and Increased Tumor Targeting.

Anticancer drug resistance is a large contributing factor to the global mortality rate of cancer patients. Anticancer macromolecules such as polymers have been recently reported to overcome this issue. Anticancer macromolecules have unselective toxicity because they are highly positively charged.

Linking Interfacial Bonding and Thermal Conductivity in Molecularly-Confined Polymer-Glass Nanocomposites with Ultra-High Interfacial Density.

Thermal transport in polymer nanocomposites becomes dependent on the interfacial thermal conductance due to the ultra-high density of the internal interfaces when the polymer and filler domains are intimately mixed at the nanoscale. However, there is a lack of experimental measurements that can link the thermal conductance across the interfaces to the chemistry and bonding between the polymer molecules and the glass surface. Characterizing the thermal properties of amorphous composites are a particular challenge as their low intrinsic thermal conductivity leads to poor measurement sensitivity of the interfacial thermal conductance.

Decoding CAR T cell phenotype using combinatorial signaling motif libraries and machine learning.

Chimeric antigen receptor (CAR) costimulatory domains derived from native immune receptors steer the phenotypic output of therapeutic T cells. We constructed a library of CARs containing ~2300 synthetic costimulatory domains, built from combinations of 13 signaling motifs. These CARs promoted diverse human T cell fates, which were sensitive to motif combinations and configurations.

Quantitation of ethanol in UTI assay for volatile organic compound detection by electronic nose using the validated headspace GC-MS method.

Disease detection through gas analysis has long been the topic of many studies because of its potential as a rapid diagnostic technique. In particular, the pathogens that cause urinary tract infection (UTI) have been shown to generate different profiles of volatile organic compounds, thus enabling the discrimination of causative agents using an electronic nose. While past studies have performed data collection on either agar culture or jellified urine culture, this study measures the headspace volume of liquid urine culture samples.

Predicting Epitope Candidates for SARS-CoV-2.

Epitopes are short amino acid sequences that define the antigen signature to which an antibody or T cell receptor binds. In light of the current pandemic, epitope analysis and prediction are paramount to improving serological testing and developing vaccines. In this paper, known epitope sequences from SARS-CoV, SARS-CoV-2, and other Coronaviridae were leveraged to identify additional antigen regions in 62K SARS-CoV-2 genomes.