Potential Applications Research Articles

Identification of non-canonical antagonistic bacteria via interspecies contact-dependent killing.

Canonical biocontrol bacteria were considered to inhibit pathogenic bacteria mainly by secreting antibiotic metabolites or enzymes. Recent studies revealed that some biocontrol bacteria can inhibit pathogenic bacteria through contact-dependent killing (CDK) mediated by contact-dependent secretion systems. The CDK was independent of antibiotic metabolites and often ignored in normal biocontrol activity assay. In this study, we aimed to use a pathogen enrichment strategy to isolate non-canonical bacteria with CDK ability. Rhizosphere soil samples from Chinese cabbage showing soft rot symptom were collected and Pectobacterium carotovorum subsp. carotovorum (Pcc), the pathogen of cabbage soft rot, were added into these samples to enrich bacteria which attached on Pcc cells. By co-culture with Pcc, four bacteria strains (named as PcE1, PcE8, PcE12 and PcE13) showing antibacterial activity were isolated from Chinese cabbage rhizosphere. These four bacteria strains showed CDK abilities to different pathogenic bacteria of horticultural plants. Among them, PcE1 was identified as Chryseobacterium cucumeris. Genome sequencing showed that PcE1 genome encoded a type VI secretion system (T6SS) gene cluster. By heterologous expression, four predicted T6SS effectors of PcE1 showed antibacterial activity to Escherichia coli. Overall, this study isolated four bacteria strains with CDK activity to various horticultural plant pathogens, and revealed possible involvement of T6SS of Chryseobacterium cucumeris in antibacterial activity. These results provide valuable insight for potential application of CDK activity in biocontrol bacteria. © 2024 Society of Chemical Industry.

Data Information integrated Neural Network (DINN) algorithm for modelling and interpretation performance analysis for energy systems

Developing a well-predictive machine learning model that also offers improved interpretability is a key challenge to widen the application of artificial intelligence in various application domains. In this work, we present a Data Information integrated Neural Network (DINN) algorithm that incorporates the correlation information present in the dataset for the model development. The predictive performance of DINN is also compared with a standard artificial neural network (ANN) model. The DINN algorithm is applied on two case studies of energy systems namely energy efficiency cooling (ENC) & energy efficiency heating (ENH) of the buildings, and power generation from a 365 MW capacity industrial gas turbine. For ENC, DINN presents lower mean RMSE for testing datasets (RMSE_test = 1.23 %) in comparison with the ANN model (RMSE_test = 1.41 %). Similarly, DINN models have presented better predictive performance to model the output variables of the two case studies. The input perturbation analysis following the Gaussian distribution for noise generation reveals the order of significance of the variables, as made by DINN, can be better explained by the domain knowledge of the power generation operation of the gas turbine. This research work demonstrates the potential advantage to integrate the information present in the data for the well-predictive model development complemented with improved interpretation performance thereby opening avenues for industry-wide inclusion and other potential applications of machine learning.

DIFUCOSIN: DIclofenac sodium salt loaded FUCOidan-SericIN nanoparticles for the management of chronic inflammatory diseases

The current investigation emphasizes the use of fucoidan and sericin as dual-role biomaterials for obtaining novel nanohybrid systems for the delivery of diclofenac sodium (DS) and the potential treatment of chronic inflammatory diseases. The innovative formulations containing 4 mg/ml of fucoidan and 3 mg/ml of sericin showed an average diameter of about 200 nm, a low polydispersity index (0.17) and a negative surface charge. The hybrid nanosystems demonstrated high stability at various pHs and temperatures, as well as in both saline and glucose solutions. The Rose Bengal assay evidenced that fucoidan is the primary modulator of relative surface hydrophobicity with a two-fold increase of this parameter when compared to sericin nanoparticles. The interaction between the drug and the nanohybrids was confirmed through FT-IR analysis. Moreover, the release profile of DS from the colloidal systems showed a prolonged and constant drug leakage over time both at pH 5 and 7. The DS-loaded nanohybrids (DIFUCOSIN) induced a significant decrease of IL-6 and IL-1β with respect to the active compound in human chondrocytes evidencing a synergistic action of the individual components of nanosystems and the drug and demonstrating the potential application of the proposed nanomedicine for the treatment of inflammation.

Click preparation of triazole-bridged teicoplanin-bound chiral stationary phases for efficient separating amino acid enantiomers

Enantioseparation of amino acids is considered as a challenging task due to the extreme structural similarity of their enantiomers. Herein, teicoplanin was modified with different chemical equivalents of azide groups and attached to silica particles by employing Click Chemistry for resolution of chiral amino acids for the first time. Interestingly, teicoplanin modified with 5-fold the chemical equivalent of azide groups (TK-2 CSP) exhibited superior amino acid separation ability compared to two other columns: one modified with only 1-fold the chemical equivalent of azide groups (TK-1 CSP), and the other modified with excess azide groups (TK-3 CSP). Additionally, the TK-2 CSP exhibited superior enantioselectivity when separating amino acids containing hydrophobic alkyl side chains in comparison to other teicoplanin-based CSPs. The TK-2 CSP column allows the baseline separation of 7 native amino acids. Molecular docking demonstrates that effective enantioseparation arises from distinct patterns of interaction between the host and guest molecules. Moreover, (p-methyl) phenylcarbaminoylated-teicoplanin CSP (TK-4, TK-5 CSP) were prepared by post-modification from TK-1 CSP and TK-2 CSP to isolate Fmoc-modified amino acids. This work explores the impact of various modification methods on the enantioseparation effects of host molecules and paves the way for expanding the potential applications of teicoplanin and macrocyclic glycopeptide molecules.

The IPCC reports and HE Geography: opportunities lost and found

ABSTRACT Climate change education within UK HE Geography has not produced a common curriculum, despite having an obvious set of learning resources, in the form of the Intergovernmental Panel on Climate Change (IPCC) report series, which has in many ways structured knowledge and understanding on climate change, its impacts and mitigation globally for several decades. Use of the IPCC reports is devolved to module or course-unit level, and is very varied as a consequence. Examples of alternative approaches to supporting climate change learning with IPCC reports are briefly discussed, before attention is drawn to the relatively new, online Interactive Atlas that was released as part of the IPCC Sixth Assessment Report. This makes available key datasets from the Working Group 1 (The Physical Science Basis) section of the report. As a highly accessible climate GIS, the Atlas facilitates flexible spatial and temporal analyses of trends and changes in regional impacts that can greatly enrich learning and teaching in the geographical aspects of the intensifying climate crisis. Being entirely free to the end user, and not requiring any specialist IT equipment or training, the Atlas has major benefits, and some potential learning applications are suggested.

Node‐Reinforced Design and Selective Remanufacturing of Auxetic Lattice Structures

Auxetic structures have many potential applications due to their counterintuitive deformation behavior and desirable mechanical properties. However, auxetic structures have some drawbacks, such as bending or rotational deformation of ligaments, leading to relatively low energy absorption (EA) performance and stability. In this article, designing nodal reinforcement of the sinusoidal lattice is proposed, and silicone rubber is selectively 3D‐printed inside the structure. As a result, under quasi‐static loading, the nodes of the structure improve the EA performance by 214%; compared to resin reinforcement, silicone‐rubber gradient reinforcement not only continues to provide load‐bearing capacity to the structure but also improves EA by a significant 112%. The assignment of silicone‐rubber nodes is found to effectively prevent the lateral slip of the structure after ligament rupture, resulting in a more stable and controllable deformation of the sinusoidally auxetic structure. Under low‐velocity dynamic loading, silicone rubber's designed nodes improve the structure's deformation stability more significantly and enhance the EA performance, which increases with the strain rate. The design and fabrication techniques have the potential for the integrated fabrication of multi‐material lightweight structures with complex shapes, superior mechanical properties, and multifunctional attributes.

Predicting quantum emitter fluctuations with time-series forecasting models

2D materials have important fundamental properties allowing for their use in many potential applications, including quantum computing. Various Van der Waals materials, including Tungsten disulfide (WS2), have been employed to showcase attractive device applications such as light emitting diodes, lasers and optical modulators. To maximize the utility and value of integrated quantum photonics, the wavelength, polarization and intensity of the photons from a quantum emission (QE) must be stable. However, random variation of emission energy, caused by the inhomogeneity in the local environment, is a major challenge for all solid-state single photon emitters. In this work, we assess the random nature of the quantum fluctuations, and we present time series forecasting deep learning models to analyse and predict QE fluctuations for the first time. Our trained models can roughly follow the actual trend of the data and, under certain data processing conditions, can predict peaks and dips of the fluctuations. The ability to anticipate these fluctuations will allow physicists to harness quantum fluctuation characteristics to develop novel scientific advances in quantum computing that will greatly benefit quantum technologies.

Nucleic-acid-base photofunctional cocrystal for information security and antimicrobial applications

Cocrystal engineering is an efficient and simple strategy to construct functional materials, especially for the exploitation of novel and multifunctional materials. Herein, we report two kinds of nucleic-acid-base cocrystal systems that imitate the strong hydrogen bond interactions constructed in the form of complementary base pairing. The two cocrystals studied exhibit different colors of phosphorescence from their monomeric counterparts and show the feature of rare high-temperature phosphorescence. Mechanistic studies reveal that the strong hydrogen bond network stabilizes the triplet state and suppresses non-radiative transitions, resulting in phosphorescence even at 425 K. Moreover, the isolation effects of the hydrogen bond network regulate the interactions between the phosphor groups, realizing the manipulation from aggregation to single-molecule phosphorescence. Benefiting from the long-lived triplet state with a high quantum yield, the generation of reactive oxygen species by energy transfer is also available to utilize for some applications such as in photodynamic therapy and broad-spectrum microbicidal effects. In vitro experiments show that the cocrystals efficiently kill bacteria on a tooth surface and significantly help prevent dental caries. This work not only provides deep insight into the relationship of the structure-properties of cocrystal systems, but also facilitates the design of multifunctional cocrystal materials and enriches their potential applications.

Characterization and safety assessment of bamboo shoot shell cellulose nanofiber: Prepared by acidolysis combined with dynamic high-pressure microfluidization

The preparation of cellulose nanofiber (CNF) using traditional methods is currently facing challenges due to concerns regarding environmental pollution and safety. Herein, a novel CNF was obtained from bamboo shoot shell (BSS) by low-concentration acid and dynamic high-pressure microfluidization (DHPM) treatment. The resulting CNF was then characterized, followed by in vitro and in vivo safety assessments. Compared to insoluble dietary fiber (IDF), the diameters of HIDF (IDF after low-concentration acid hydrolysis) and CNF were significantly decreased to 167.13 nm and 70.97 nm, respectively. Meanwhile, HIDF and CNF showed a higher crystallinity index (71.32 % and 74.35 %). Structural analysis results indicated the successful removal of lignin and hemicellulose of HIDF and CNF, with CNF demonstrating improved thermostability. In vitro, a high dose of CNF (1500 μg/mL) did not show any signs of cytotoxicity on Caco-2 cells. In vivo, no death was observed in the experimental mice, and there was no significant difference between CNF (1000 mg/kg·bw) and control group in hematological index and histopathological analysis. Overall, this study presents an environmentally friendly method for preparing CNF from BSS while providing evidence regarding its safety through in vitro and in vivo assessments, laying the foundation for its potential application in food.

Copper and Copper Complexes in Tumor Therapy.

Copper (Cu), a crucial trace element in physiological processes, has garnered significant interest for its involvement in cancer progression and potential therapeutic applications. The regulation of cellular copper levels is essential for maintaining copper homeostasis, as imbalances can lead to toxicity and cell death. The development of drugs that target copper homeostasis has emerged as a promising strategy for anticancer treatment, with a particular focus on copper chelators, copper ionophores, and novel copper complexes. Recent research has also investigated the potential of copper complexes in cancer therapy.

Abstract 5400: CD160 regulates angiogenesis in activated human endothelial cells

Abstract Angiogenesis, the process of forming new blood vessels from pre-existing ones, plays a pivotal role in various physiological and pathological conditions. CD160, a cell surface receptor known for its modulatory function in the immune response, has recently garnered attention as a surface marker for activated endothelial cells. In this study, we investigate the anti-angiogenic effects of a novel anti-CD160 antibody. Using the tube formation assay, we evaluated the novel anti-CD160 antibody's ability to inhibit neovascularization potential in activated Human umbilical vein endothelial cells (HUVEC) in vitro. Our results revealed a significant reduction in total tube length, the number of branches, and closed networks, indicating the antibody's efficacy in impeding neovascularization. In addition, we explored the molecular mechanisms underlying CD160's anti-angiogenic properties, showing its ability to selectively decrease fibroblast growth factor-2-induced angiogenesis. Furthermore, we assessed the safety profile of our new anti-CD160 antibody by examining its impact on endothelial cell apoptosis. Encouragingly, our findings demonstrated no induction of apoptosis, suggesting the antibody's selective targeting of angiogenic processes without compromising endothelial cell viability, thus supporting CD160's vascular normalization properties. In conclusion, our research provides compelling evidence for the anti-angiogenic effects of the CD160 antibody, as evidenced by its inhibition of tube formation without inducing apoptosis in activated endothelial cells. These findings hold promising therapeutic implications for targeting angiogenesis in various pathological conditions, warranting further exploration for potential clinical applications. Keywords: CD160, Angiogenesis, Endothelial cells. HUVECs. Citation Format: Abdel-ilah AZIZ. CD160 regulates angiogenesis in activated human endothelial cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5400.

Abstract 1334: Evaluation of NK cell combination therapies in mouse models of cancer

Abstract Adoptive immunotherapy with natural killer (NK) cells has emerged as a promising innovative approach against hematological malignancies and solid tumors. To enhance the functional aspects of NK cells and to achieve stronger, sustained anti-tumor responses, combination strategies are under development. They are focused on optimization of NK cell potency, persistence and tumor targeting using monoclonal antibodies, multi-specific NK cell engagers and immune modulators like cytokines. SAR445419 is a universal off-the-shelf allogeneic human NK cell (hNK) product made using Sanofi’s proprietary platform with potential broad application across hematologic and solid tumor indications. We evaluated the therapeutic potential of these NK cells in combination with internal assets and standard of care treatments in preclinical mouse models. The studies were performed in immunodeficient NOG mouse strains transgenic for hIL-15, supporting long-term engraftment of functional hNK cells. We demonstrated the therapeutic advantage of NK cell combination with an anti-CD20 monoclonal antibody (mAb) in a disseminated lymphoma model. We also reported prolonged lifespan of leukemia-bearing animals treated with NK cells and pegenzileukin, a PEGylated non-alpha rhIL-2 variant engineered to preferentially bind to the IL-2 βγ receptors and promote NK cell function. The anti-tumor response correlated with pegenzileukin-induced increase in NK cell numbers in the blood. A combination of CD38KO NK cells with an anti-CD38 mAb was also investigated in a model of multiple myeloma (MM). FcγR-KO immunodeficient mice were used in these studies to limit the confounding contribution of residual murine immune cells engaging with the Fc-competent therapeutic antibody and to specifically detect hNK-cell mediated activity. The comparison of CD38KO versus WT NK cell numbers in the blood of animals treated with an anti-CD38 mAb showed a significant decrease in CD38-expressing NK cell count that was mitigated by the genetic deletion of CD38. Obtained results suggest that CD38KO NK cells are resistant to anti-CD38 mAb-mediated fratricide and provide a rationale for the combination of CD38KO NK cells with anti-an CD38 mAb against MM. Overall, these preclinical in vivo studies have a pivotal role in the evaluation of NK cell combination therapies against cancer and reveal their therapeutic potential. Citation Format: Katarzyna Franciszkiewicz, Pauline Rettman, Beatrice Gauthier, Laurence Noblet, Celine Nicolazzi, Andre Kunert, Rob Igarashi, Loreley Calvet, Valeria Fantin, Sukhvinder Sidhu. Evaluation of NK cell combination therapies in mouse models of cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1334.

Abstract 6805: CD81 and CD82 in tumor infiltrating lymphocytes within the NSCLC tumor microenvironment play a crucial role in T cell activation and cytokine production

Abstract Background: The role of tetraspanins, particularly CD81 and CD82, in T cell activation, shedding light on their contribution to anti-tumor effects in the tumor microenvironment. The findings provide insights for potential applications of these tetraspanins in immunotherapy. Method: The study employed 10x Genomics for single-cell RNA sequencing and processed the data using Seurat. It focused on the tumor microenvironment, categorizing cell types, especially TIL subsets. T cells were sorted based on CD81 and CD82 expression and activated, and their activation markers and cytokine production were analyzed. Bulk RNA sequencing was performed on both sorted T cells and tumor samples, and the TCR repertoire was examined. Cytotoxicity of CD81 and CD82 transduced cells was measured using the xCELLigence RTCA HT system. Result: Tetraspanins have been linked to T cell co-stimulation and activation, with CD81 and CD82 playing a significant role in TCR signaling and activation. Single-cell and bulk RNA sequencing data from lung cancer patients reveal high expression of CD81 and CD82 in the tumor microenvironment, especially in T cells, correlating with T cell activation and cytokine production. In vitro experiments confirm the activation and effector cytokines, especially IFN-γ, TNF-α, and IL-2, producing potential of T cells expressing CD81 and CD82, suggesting their importance in anti-cancer immunity. Overexpression of CD81 and CD82 enhances T cell cytotoxicity against cancer cells, further supporting their role in T cell function. Despite some limitations, these findings shed light on the significant impact of tetraspanins in modulating immune responses, particularly in the context of cancer immunotherapy. Conclusion: We report that CD81 and CD82 serve as specific markers for activated T cells within the TME. CD81 and CD82 are correlated with the activation and regulation of T cells, and they may also mediate the regulation of relevant cytokines. Overall, our findings provide better insights into the molecular mechanisms regulating TILs present in the TME of non-small cell carcinoma and propose CD81 and CD82 as associated markers. These discoveries could serve as a foundation for the development of new immunotherapies targeting the TME. Citation Format: Kwangmin Na, Seul Lee, Dong Kwon Kim, Young Seob Kim, Joon Yeon Hwang, Seong-san Kang, Sujeong Baek, Chai Young Lee, Seung Min Yang, Yu Jin Han, Mi hyun Kim, Heekyung Han, Youngtaek Kim, Jae Hwan Kim, Seunghyun Jeon, Youngseon Byeon, Jii Bum Lee, Sun Min Lim, Min Hee Hong, Kyoung-Ho Pyo, Byoung Chul Cho. CD81 and CD82 in tumor infiltrating lymphocytes within the NSCLC tumor microenvironment play a crucial role in T cell activation and cytokine production [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6805.

Abstract 6177: Machine learning enables prediction of ADC targets from whole slide H&E images

Abstract Background: The efficacy of antibody-drug conjugates (ADCs) depends on the expression and specificity of the target antigen by tumor cells. Although H&E-stained slides are routinely collected during cancer care, specialized IHC staining is typically required to ascertain antigen expression. Such stains are not always available or readily deployed. The need to perform separate IHC tests for each candidate ADC may burden clinical labs and can hinder access to care in resource-limited settings. Here we develop an ensemble of machine learning models to accurately predict the expression of 166 distinct ADC targets directly from H&E images. Methods: For each ADC-targeted gene, patients with copy-number amplifications (CNAs) were identified from somatic whole exome sequencing. Genes differentially expressed in patients with CNAs were identified from bulk transcriptomics. For each gene, an expression signature was developed based on the expression levels of differentially upregulated genes. Next, whole-slide, H&E-stained histopathology images were embedded into a lower-dimensional representation via a transformer model trained with self-supervised learning. Neural networks were developed to predict a patient’s probability of having a CNA in an ADC-targeted gene, as indicated by an expression signature exceeding the p90. All evaluation metrics were ascertained by 5-fold cross-validation, with training and evaluation on independent patients. Results: For each of the 166 ADC-targeted genes, a median of 154 patients were found to harbor a CNA, and the expression signature included a median of 180 genes. In all cases, patients with CNAs had significantly higher expression signatures than those without. For predicting likely CNA status directly from H&E histopathology images, the mean AUROC was 0.888 (95% CI, 0.876-0.900) and the mean AUPRC was 0.571 (95% CI, 0.531-0.611). Among the 166 ADC-targeted genes, the AUROC exceeded 0.95 for 31.3%, 0.8 for 80.7%, and 0.725 for 100%. The best-predicted ADC target was SLC7A5 (AUROC: 0.995 [95% CI, 0.994-0.998]; AUPRC: 0.967 [95% CI, 0.963-0.976]). Conclusion: We have developed models that accurately predict the likely expression of ADC targets based solely on H&E images. The ability to accurately discern the presence of ADC antigens from H&E images has numerous potential applications, including cohort refinement, computer-aided diagnosis, and personalized treatment planning. Citation Format: Zachary Ryan McCaw, Anna Shcherbina, Yajas Shah, insitro Research Team, Philip Tagari, Daphne Koller, Christopher Probert. Machine learning enables prediction of ADC targets from whole slide H&E images [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6177.

Abstract 232: Multi-dimensional patient-derived organoid analysis highly correlates with progression-free survival of pancreatic cancer patients in a retrospective clinical trial

Abstract Patient-derived tumor organoids (PDOs) are promising cancer models, since they preserve the clonal heterogeneity, mutational landscape, and histological architecture of the originating patient tumor1-2. Recent studies have even provided first evidence that PDOs responded similarly to the corresponding patient when treated with the same standard of care therapy, but these studies were only able to predict clinical responses in a subset of patients3,4. This restriction was largely due to the limitation of current analysis methods. The gold-standard analysis method for PDO-based research, CellTiter-Glo 3D,5,6 fails to account for the heterogeneity of PDOs by relying on a bulk lysis approach, and thus only extracts a fraction of the clinically-relevant information that PDOs could provide7. Therefore, we hypothesized that using higher-dimensional analysis methods, will further unlock the predictive performance of PDOs and facilitate translation of research. In this study, we developed an artificial intelligence-driven analysis platform for live-PDO imaging. By combining drug screening metrics with dynamic quantification of organoids on a single-organoid resolution, we unraveled patient-specific tumor heterogeneity and PDAC aggressiveness in response to therapies8. Using a fully characterized a PDAC organoid panel (n=8) we matched our PDO analysis with retrospective clinical patient response to standard of care therapies (gemcitabine-paclitaxel and FOLFIRINOX). Our results demonstrated that our PDO analysis method identified patient-specific sensitives to therapy that were in-line with clinical outcomes. Moreover, our PDO readouts highly correlated with progression-free survival of matched patients (R=0.97), which was a significant improvement to the current, conventional drug response readouts. Taken together, our approach increased the clinical translatability of PDOs by more accurately recapitulating and measuring the complexity of human tumors. This work highlights the potential applications (extendable to other tumor types) and clinical translatability of our approach in drug discovery and clinical care, in the emerging era of personalized medicine.

Abstract 6258: Inference of gene expression using fragmentation patterns from targeted high-depth sequencing of cell-free DNA

Abstract Cell-free DNA (cfDNA) circulating in blood plasma arises primarily from cellular chromatin fragmentation and release due to cell death. Studies have demonstrated that the assessment of fragmentomic features of cfDNA enables gene expression inference and tissue-of-origin classification with potential applications for noninvasive cancer detection. However, due to low depth of coverage of sites of interest, current whole genome sequencing (WGS) methods have not been able to infer expression of individual genes or limited gene sets. Therefore, we developed a targeted sequencing approach that enables individual gene expression inference from cfDNA fragmentation patterns based on transcriptional start-site gene activation probability (TSS-GAP). To assess the sensitivity of this method for cfDNA-inferred gene expression profiling, we applied TSS-GAP to blends of micrococcal nuclease (MNase)-digested DNA from a cancer cell line (LS180) and sorted peripheral blood mononuclear cells (PBMC), with LS180 DNA at varying concentrations (0.1-10%). This enabled the determination of the limit of detection (LoD), relative to healthy donor background levels, on the basis of the genes’ activation probabilities (TSS-GAP scores). We found 936 genes with high inferred expression in the LS180 cancer cell line and low inferred expression in non-cancer samples (i.e. CD4, CD14, and plasma cfDNA from 4 healthy donors) and used those for healthy donor background and LoD assessment. Using these gene-level background values as cutoffs, blends were assessed to determine the lowest blend level at which expression of the gene was able to be differentiated from background. Out of the 936 genes, 326 (34.8%) were detectable at a blend level of 0.1%, 59 (6.3%) at 0.3%, 31 (3.3%) at 1%, 19 (2.0%) at 3%, and 39 (4.2%) at 10%. In total, 416 genes could be detected by TSS-GAP at levels 1% of or lower above background, highlighting the potential of our method for detecting expression of certain genes even from a small fraction of cells contributing cfDNA to liquid biopsy samples. Further expansion of this work could consider the utility of such a method for circulating tumor DNA (ctDNA) detection, cfDNA tissue-of-origin classification, and deconvolution of cell types contributing to cfDNA found in blood. Citation Format: Emily Leff, Alexander Tseng, Victoria Cheung, Ehsan Tabari, Kimberly Walter, Alex Lovejoy, Cheng-Ho J. Lin. Inference of gene expression using fragmentation patterns from targeted high-depth sequencing of cell-free DNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6258.

Polyhalogenated Carbohydrates: Synthesis and Applications of Sugar Halides from Fluorine to Iodine

AbstractPolyfluorinated carbohydrates have attracted attention in the last decades. This is due to the interesting properties of such organofluorines. What about the other halogens? Since the discovery of sucralose (a trichlorinated disaccharide) more than 50 years ago, only a handful of chlorinated, brominated, or iodinated carbohydrates have been reported. This review focuses on the synthesis of polyhalogenated carbohydrates, including sugar inter‐halides, but excluding polyfluorinated sugars. An overview of the current state of the art of their potential application is also described.

Mechanically Adaptive Polymers Constructed from Dynamic Coordination Equilibria.

Designing materials capable of adapting their mechanical properties in response to external stimuli is the key to preventing failure and extending their service life. However, existing mechanically adaptive polymers are hindered by limitations such as inadequate load-bearing capacity, difficulty in achieving reversible changes, high cost, and a lack of multiple responsiveness. Herein, we address these challenges using dynamic coordination bonds. A new type of mechanically adaptive material with both rate- and temperature-responsiveness was developed. Owing to the stimuli-responsiveness of the coordination equilibria, the prepared polymers, PBMBD-Fe and PBMBD-Co, exhibit mechanically adaptive properties, including temperature-sensitive strength modulation and rate-dependent impact hardening. Benefitting from the dynamic nature of the coordination bonds, the polymers exhibited impressive energy dissipation, damping capacity (loss factors of 1.15 and 2.09 at 1.0 Hz), self-healing, and 3D printing abilities, offering durable and customizable impact resistance and protective performance. The development of impact-resistant materials with comprehensive properties has potential applications in the sustainable and intelligent protection fields.

Mechanically Adaptive Polymers Constructed from Dynamic Coordination Equilibria

AbstractDesigning materials capable of adapting their mechanical properties in response to external stimuli is the key to preventing failure and extending their service life. However, existing mechanically adaptive polymers are hindered by limitations such as inadequate load‐bearing capacity, difficulty in achieving reversible changes, high cost, and a lack of multiple responsiveness. Herein, we address these challenges using dynamic coordination bonds. A new type of mechanically adaptive material with both rate‐ and temperature‐responsiveness was developed. Owing to the stimuli‐responsiveness of the coordination equilibria, the prepared polymers, PBMBD‐Fe and PBMBD‐Co, exhibit mechanically adaptive properties, including temperature‐sensitive strength modulation and rate‐dependent impact hardening. Benefitting from the dynamic nature of the coordination bonds, the polymers exhibited impressive energy dissipation, damping capacity (loss factors of 1.15 and 2.09 at 1.0 Hz), self‐healing, and 3D printing abilities, offering durable and customizable impact resistance and protective performance. The development of impact‐resistant materials with comprehensive properties has potential applications in the sustainable and intelligent protection fields.

Electrochemical Removal of HF from Carbonate-based LiPF6-containing Li-ion Battery Electrolytes

Due to the hydrolytic instability of LiPF6 in carbonate-based solvents, HF is a typical impurity in Li-ion battery electrolytes. HF significantly influences the performance of Li-ion batteries, for example by impacting the formation of the solid electrolyte interphase at the anode and by affecting transition metal dissolution at the cathode. Additionally, HF complicates studying fundamental interfacial electrochemistry of Li-ion battery electrolytes, such as direct anion reduction, because it is electrocatalytically relatively unstable, resulting in a LiF passivation layer. Methods to selectively remove ppm levels of HF from LiPF6-containing carbonate-based electrolytes are limited. We introduce and benchmark a simple yet efficient electrochemical method to selectively remove ppm amounts of HF from LiPF6-containing carbonate-based electrolytes. The basic idea is the application of a suitable potential to a high surface-area metallic electrode upon which only HF reacts (electrocatalytically) while all other electrolyte components are unaffected under the respective conditions.