GVVST: Image-Driven Style Extraction From Graph Visualizations for Visual Style Transfer.

Incorporating automatic style extraction and transfer from existing well-designed graph visualizations can significantly alleviate the designer's workload. There are many types of graph visualizations. In this paper, our work focuses on node-link diagrams. We present a novel approach to streamline the design process of graph visualizations by automatically extracting visual styles from well-designed examples and applying them to other graphs. Our formative study identifies the key styles that designers consider when crafting visualizations, categorizing them into global and local styles. Leveraging deep learning techniques such as saliency detection models and multi-label classification models, we develop end-to-end pipelines for extracting both global and local styles. Global styles focus on aspects such as color scheme and layout, while local styles are concerned with the finer details of node and edge representations. Through a user study and evaluation experiment, we demonstrate the efficacy and time-saving benefits of our method, highlighting its potential to enhance the graph visualization design process.

Multi-Cover Persistence (MCP)-based machine learning for polymer property prediction.

Accurate and efficient prediction of polymers properties is crucial for polymer design. Recently, data-driven artificial intelligence (AI) models have demonstrated great promise in polymers property analysis. Even with the great progresses, a pivotal challenge in all the AI-driven models remains to be the effective representation of molecules. Here we introduce Multi-Cover Persistence (MCP)-based molecular representation and featurization for the first time. Our MCP-based polymer descriptors are combined with machine learning models, in particular, Gradient Boosting Tree (GBT) models, for polymers property prediction. Different from all previous molecular representation, polymer molecular structure and interactions are represented as MCP, which utilizes Delaunay slices at different dimensions and Rhomboid tiling to characterize the complicated geometric and topological information within the data. Statistic features from the generated persistent barcodes are used as polymer descriptors, and further combined with GBT model. Our model has been extensively validated on polymer benchmark datasets. It has been found that our models can outperform traditional fingerprint-based models and has similar accuracy with geometric deep learning models. In particular, our model tends to be more effective on large-sized monomer structures, demonstrating the great potential of MCP in characterizing more complicated polymer data. This work underscores the potential of MCP in polymer informatics, presenting a novel perspective on molecular representation and its application in polymer science.

Efficient genome replication in influenza A virus requires NS2 and sequence beyond the canonical promoter.

Influenza A virus encodes promoters in both the sense and antisense orientations. These support the generation of new genomes, antigenomes, and mRNA transcripts. Using minimal replication assays-transfections with viral polymerase, nucleoprotein, and a genomic template-the influenza promoter sequences were identified as 13nt at the 5' end of the viral genomic RNA (U13) and 12nt at the 3' end (U12). Other than the fourth 3' nucleotide, the U12 and U13 sequences are identical between all eight RNA molecules that comprise the segmented influenza genome. Despite possessing identical promoters, individual segments can exhibit different transcriptional dynamics during infection. However flu promoter sequences were defined in experiments without influenza NS2, a protein which modulates transcription and replication differentially between genomic segments. This suggests that the identity of the "complete" promoter may depend on NS2. Here we assess how internal sequences of two genomic segments, HA and PB1, may contribute to NS2-dependent replication as well as map such interactions down to individual nucleotides in PB1. We find that the expression of NS2 significantly alters sequence requirements for efficient replication beyond the identical U12 and U13 sequence, providing a mechanism for the divergent replication and transcription dynamics across the influenza A virus genome.

High-coverage characterization and discovery of molecular markers for quality control of natural fragrant plant extracts using UPLC-HRMS-based untargeted metabolomics.

The chemical components of natural fragrant plant extracts are of high complexity, and the strategies for quality control (QC) and further discovery of fragrance mechanisms still need to be further investigated. This study integrated the strategies and methods of untargeted metabolomics and chemometrics and statistical modeling to attain the goal. The techniques of reversed-phase and HILIC analysis of ultra-performance liquid chromatography-high-resolution mass spectrometry (UPLC-HRMS) were simultaneously used to collect data in both positive and negative ion modes. The pattern analysis of fingerprints and discovery of characteristic molecular markers for QC analysis were comprehensively employed to reach in-depth analysis of the quality variation and discovery of differential molecules among natural fragrant plant extracts. The former uses fingerprint technique to analyze their overall similarities and differences, and the latter comprehensively discovers molecular substances characterizing the chemical characteristics of fragrant extracts with the help of metabolomics and univariate and multivariate methods. The findings are expected to be used as the molecular markers in product manufacturing, sales, and consumption to achieve accurate quality control and recognition of targeted molecules for potential quality monitoring using spectroscopy techniques. In this work, 27 natural fragrant extracts were applied as examples, and their chemical components were comprehensively analyzed with discovery of markers for quality control. After data integration, 1178 molecules were annotated, and 267 differential metabolite molecules with the values of variable importance in the projection (VIP) larger than 1.0 were found. The results show that the method proposed in this work is of great significance for high-coverage analysis, QC marker discovery, and aroma mechanism elucidation, which has potential applications in the areas of food, cosmetics, pharmaceuticals, tobacco, and others.

Rewritable Surface-Grafted Polymer Brushes with Dynamic Covalent Linkages.

Surface grafting of polymer brushes drastically modifies surface properties, including wettability, compatibility, responsiveness, etc. A broad variety of functionalities can be introduced to the surface via different types of polymers. Bringing together properties of two or more types of polymer brushes to one surface opens up even more possibilities in brush-modified materials. However, while it is generally feasible to introduce several chemical compositions along the brushes via copolymerization, it is challenging to vary the types of polymer brushes along a surface. Although previous studies have demonstrated binary brushes via orthogonal polymerization techniques or partial deactivation/regrafting, they commonly limit the number of polymer types to two. Here, we propose a strategy to introduce dynamic covalent diketoenamine linkages at the root of polymer brushes. The grafting density could be precisely tuned during surface functionalization. The surface-anchored polymer brushes were cleaved by the addition of small molecule amines. New polymer brushes can be regrafted from the surface following refunctionalization of exposed sites. The maneuverability allows tuning of the types and densities of the polymer brushes, pointing the way to the preparation of a new generation of well-defined brush-modified materials with mixed grafts, with potential applications in the design of smart materials and surfaces.

Structural characterization and hypoglycemic effect of polysaccharides of Polygonatum sibiricum.

Polygonatum sibiricum polysaccharide (PSP) was extracted and purified from raw material obtained from P. sibiricum. The structural features of PSP were investigated by Congo red, circular dichroism spectrum, high-performance gel permeation chromatography, scanning electron microscope, atomic force microscope, ultraviolet spectroscopy, and Fourier transform infrared spectroscopy analysis. In vitro simulations were conducted to investigate the kinetics of PSP enzyme inhibition. Moreover, a type II diabetes mouse model (T2DM) with streptozotocin-induced insulin resistance was established, and the indexes of lipid quadruple, insulin resistance index, oral glucose tolerance (OGTT), organ index, and pancreatic morphology of model mice were measured. The results showed that PSP mainly consists of monosaccharides, such as mannose, glucose, galactose, xylose, and arabinose. It also has a ß-glycosidic bond of a pyranose ring and an irregular reticulated aggregated structure with a triple helix. In vitro enzyme inhibition assays revealed that PSP acts as a reversible competitive inhibitor of α-glucosidase and α-amylase. Furthermore, PSP was found to reduce insulin resistance index, increase OGTT and serum insulin levels, decrease free fatty acid content to improve lipid metabolism, and lower glycated serum protein content to enhance glucose metabolism in T2DM mice, thereby leading to a reduction in blood glucose concentration. Additionally, PSP exhibited reparative effects on the damaged liver tissue cells and pancreatic tissue in T2DM mice. The experiment results provide a preliminary basis for the therapeutic mechanism of PSP about type II diabetes and a theoretical reference for application in food and pharmaceutical development.

Discovering Neurophysiological Characteristics of Pathological High-Frequency Oscillations in Epilepsy with an Explainable Deep Generative Model.

Interictal high-frequency oscillation (HFO) is a promising biomarker of the epileptogenic zone (EZ). However, objective definitions to distinguish between pathological and physiological HFOs have remained elusive, impeding HFOs' clinical applications. We employed self-supervised deep generative variational autoencoders to learn such discriminative HFO features directly from their morphologies in a data-driven manner. We studied a large retrospective cohort of 185 patients who underwent intracranial monitoring and analyzed 686,410 candidate HFO events collected from 18,265 brain contacts across diverse brain regions. The model automatically clustered HFOs into distinct morphological groups in the latent space. One cluster consisted of putative morphologically defined pathological HFOs (mpHFOs): HFOs in that cluster were observed to be associated with spikes and exhibited high signal intensity both in the HFO band (>80 Hz) at detection and in the sub-HFO band (10-80 Hz) surrounding the detection and were primarily localized in the seizure onset zone (SOZ). Moreover, resection of brain regions based on a higher prevalence of interictal mpHFOs better predicted postoperative seizure outcomes than current clinical standards based on SOZ removal. Our self-supervised, explainable, deep generative model distills pathological HFOs and thus potentially helps delineate the EZ purely from interictal intracranial EEG data.

Controllable Supramolecular Aggregation of Hydroazaheptacene Tetraimides and Derivatives in Nonpolar and Polar Solvents.

Rational control of the supramolecular aggregation of π-conjugated molecules plays an important role in determining their optoelectronic properties and applications. Here, we report a systematic study of the factors, including solvent polarity, concentration, and surfactants, that affect the aggregation behavior of a brominated hydroazaheptacene tetraimide (HATI) and its thiophene-substituted derivative, Th-HATI, as near-infrared fluorophores, in both nonpolar and polar solvents. The thermal stability of the aggregates is also studied by monitoring their optical absorption against temperature change. Our results indicate that the aggregation of HATI is highly sensitive to the solvent polarity. Moreover, the average aggregation number of HATI inside the colloidal nanoparticles formed in aqueous media can be controlled by surfactants. The substitution of the bromo groups in HATI by thiophene units induces a slight blue shift of the optical absorption, enhanced crystallinity, distinct aggregation behavior in both nonpolar and polar solvents, and improved thermal stability. The multifacet understanding of the supramolecular aggregation of these systems may offer insight for other π-conjugated molecular chromophores with various optoelectronic properties and applications.

Injectable ultrasonic sensor for wireless monitoring of intracranial signals.

Direct and precise monitoring of intracranial physiology holds immense importance in delineating injuries, prognostication and averting disease1. Wired clinical instruments that use percutaneous leads are accurate but are susceptible to infection, patient mobility constraints and potential surgical complications during removal2. Wireless implantable devices provide greater operational freedom but include issues such as limited detection range, poor degradation and difficulty in size reduction in the human body3. Here we present an injectable, bioresorbable and wireless metastructured hydrogel (metagel) sensor for ultrasonic monitoring of intracranial signals. The metagel sensors are cubes 2 × 2 × 2 mm3 in size that encompass both biodegradable and stimulus-responsive hydrogels and periodically aligned air columns with a specific acoustic reflection spectrum. Implanted into intracranial space with a puncture needle, the metagel deforms in response to physiological environmental changes, causing peak frequency shifts of reflected ultrasound waves that can be wirelessly measured by an external ultrasound probe. The metagel sensor can independently detect intracranial pressure, temperature, pH and flow rate, realize a detection depth of 10 cm and almost fully degrade within 18 weeks. Animal experiments on rats and pigs indicate promising multiparametric sensing performances on a par with conventional non-resorbable wired clinical benchmarks.

The Consistency of Anti-Toxocara IgG Between the Aqueous Humor and Vitreous of Patients With Clinically Suspected Ocular Toxocariasis.

PURPOSE: To assess the consistencies of anti-Toxocara IgG (T-IgG) and Goldmann-Witmer coefficient (GWC) between paired aqueous humor (AH) and vitreous samples from patients with clinically suspected ocular toxocariasis (OT). DESIGN: Inter-test reliability assessment. METHODS: A total of 47 patients with clinically suspected OT who underwent vitrectomy were included. AH, vitreous, and serum samples from each patient were collected, and levels of specific T-IgG in them were detected. The association and agreement of T-IgG and GWC between AH and vitreous were evaluated. The area under the receiver operating characteristic curve was generated to assess the diagnostic performance of AH. RESULTS: The T-IgG levels and GWC values in vitreous were higher than those in AH (P = .023 and P = .029, respectively), but similar positivity rates in the T-IgG (P = 1.000) and GWC >3 (P = 1.000) were apparent between vitreous and AH. In addition, there was a positive correlation between the AH and vitreous T-IgG levels (rs = 0.944, P < .001) and the GWC values (rs = 0.455, P = .022). Moreover, the consistencies between AH and vitreous samples in their T-IgG and GWC positivity rates were almost perfect (both, κ = 0.915, 95% CI = 0.799-1.000) in both. The area under the receiver operating characteristic curve reached 0.991, with a 95% confidence interval of 0.971 to 1.000. The best cut-off value for accurate OT diagnosis was found at 1.434, yielding 96% sensitivity and 100% specificity. CONCLUSIONS: Our findings demonstrate that AH and vitreous samples had significant correlations and perfect agreements for both T-IgG and GWC, suggesting that the AH may serve as a proxy for vitreous to provide a safer, earlier, and more convenient screening of OT.

Chromosome-scale genome assembly of medicinal plant Tinospora sagittata (Oliv.) Gagnep. from the Menispermaceae family.

Tinospora sagittata (Oliv.) Gagnep. is an important medicinal tetraploid plant in the Menispermaceae family. Its tuber, Radix Tinosporae, used in traditional Chinese medicine, is rich in diterpenoids and benzylisoquinoline alkaloids (BIAs). To enhance our understanding of medicinal compounds' biosynthesis and Menispermaceae's evolution, we herein report assembling a high-quality chromosome-scale genome with both PacBio HiFi and Illumina sequencing technologies. PacBio Sequel II generated 2.5 million circular consensus sequencing (CCS) reads, and a hybrid assembly strategy with Illumina sequencing resulted in 4483 contigs. The assembled genome size was 2.33 Gb, consisting of 4070 scaffolds (N50 = 42.06 Mb), of which 92.05% were assigned to 26 pseudochromosomes. T. sagittata's chromosomal-scale genome assembly, the first species in Menispermaceae, aids Menispermaceae evolution and T. sagittata's secondary metabolites biosynthesis understanding.

Low remnant cholesterol and the subsequent risk of new-onset atrial fibrillation: A prospective cohort study.

BACKGROUND: Atrial fibrillation (AF) is a common arrhythmia with high morbidity and mortality implications. Several studies have described a paradoxical inverse relationship between serum cholesterol and the risk of AF, but it remains unknown whether remnant cholesterol (RC) is associated with AF incidence. OBJECTIVE: This study aims to prospectively investigate the association between RC and AF. METHODS: A total of 392,783 participants free of AF at baseline from the UK Biobank were included for the analysis. Cox proportional hazards model, subgroup analysis, and sensitivity analyses were used to evaluate the independent association between RC levels and the risk of new-onset AF. Furthermore, we performed a discordance analysis by using the median cutoff points of low-density lipoprotein cholesterol (LDL-C) and RC. RESULTS: After a median follow-up of 12.8 years (interquartile range 12.0-13.6 years), a total of 23,558 participants experienced incident AF. Compared with the highest RC level, the lower RC level was associated with an increased risk of AF incidence (quartile 1 vs quartile 4: hazard ratio 1.396; 95% confidence interval [CI] 1.343-1.452). The results remained robust across a series of sensitivity analyses. In the discordance analyses, a significantly higher risk of AF was observed in participants with discordant low RC/high LDL-C levels than in those with concordant high RC/LDL-C levels. In the low LDL-C group, RC reduction even contributed to an additional 15.8% increased rate of incident AF (low RC/low LDL-C: hazard ratio 1.303; 95% CI 1.260-1.348 vs high RC/low LDL-C: hazard ratio 1.125; 95% CI 1.079-1.172). CONCLUSION: Low RC levels were associated with an increased risk of incident AF independent of traditional cardiovascular risk factors.

PyHFO: lightweight deep learning-powered end-to-end high-frequency oscillations analysis application.

Objective. This study aims to develop and validate an end-to-end software platform, PyHFO, that streamlines the application of deep learning (DL) methodologies in detecting neurophysiological biomarkers for epileptogenic zones from EEG recordings.Approach. We introduced PyHFO, which enables time-efficient high-frequency oscillation (HFO) detection algorithms like short-term energy and Montreal Neurological Institute and Hospital detectors. It incorporates DL models for artifact and HFO with spike classification, designed to operate efficiently on standard computer hardware.Main results. The validation of PyHFO was conducted on three separate datasets: the first comprised solely of grid/strip electrodes, the second a combination of grid/strip and depth electrodes, and the third derived from rodent studies, which sampled the neocortex and hippocampus using depth electrodes. PyHFO demonstrated an ability to handle datasets efficiently, with optimization techniques enabling it to achieve speeds up to 50 times faster than traditional HFO detection applications. Users have the flexibility to employ our pre-trained DL model or use their EEG data for custom model training.Significance. PyHFO successfully bridges the computational challenge faced in applying DL techniques to EEG data analysis in epilepsy studies, presenting a feasible solution for both clinical and research settings. By offering a user-friendly and computationally efficient platform, PyHFO paves the way for broader adoption of advanced EEG data analysis tools in clinical practice and fosters potential for large-scale research collaborations.

An omics-based characterization of Wolfiporia cocos reveals three CYP450 members involved in the biosynthetic pathway of pachymic acid.

Wolfiporia cocos is a medicinal mushroom used in China. It biosynthesizes pachymic acid (PA), a main therapeutic triterpene associated with therapies. Nowadays, the unknown PA biosynthesis leads to difficulties in increasing its content in W. cocos. Herein, we report sequencing, assembling, and characterization of the genome and several transcriptomes of W. cocos. Sequence mining determined candidate genes that encode lanosterol synthase, sterol O-acyltransferase, and sterol C-24 methyltransferase likely involved in the steps from lanosterol to PA. Gene cluster analysis identified four CYP450 cDNAs likely involved in the biosynthesis of PA, namely WcCYP64-1, WcCYP64-2, WcCYP52, and WcCYP_FUM15, which were subjected to both overexpression and silencing in mycelia. The overexpression of each of WcCYP64-1, WcCYP52 and WcCYP_FUM15 increased the content of PA, 16α-hydroxytrametenolic acid, eburicoic acid, and tumulosic acid, while the silencing of each gene either significantly or slightly decreased the contents of these four compounds, indicating their involvement in the PA biosynthesis. In addition, different temperatures affected the expression of these genes and the formation of PA. By contrast, the overexpression and silencing of WcCYP64-2 did not alter the formation of these compounds. Taken together, these findings determine more potential steps in the biosynthetic pathway of PA for metabolic engineering.

Targeting PCSK9 to upregulate MHC-II on the surface of tumor cells in tumor immunotherapy.

BACKGROUND: Proprotein convertase subtilisin/kexin type 9 (PCSK9), the last member of the proprotein convertase family, functions as a classic regulator of low-density lipoprotein (LDL) by interacting with low-density lipoprotein receptor (LDLR). Recent studies have shown that PCSK9 can affect the occurrence and development of tumors and can be used as a novel therapeutic target. However, a comprehensive pan-cancer analysis of PCSK9 has yet to be conducted. METHODS: The potential oncogenic effects of PCSK9 in 33 types of tumors were explored based on the datasets of The Cancer Genome Atlas (TCGA) dataset. In addition, the immune regulatory role of PCSK9 inhibition was evaluated via in vitro cell coculture and the tumor-bearing mouse model. Finally, the antitumor efficacy of targeted PCSK9 combined with OVA-II vaccines was verified. RESULTS: Our results indicated that PCSK9 was highly expressed in most tumor types and was significantly correlated with late disease stage and poor prognosis. Additionally, PCSK9 may regulate the tumor immune matrix score, immune cell infiltration, immune checkpoint expression, and major histocompatibility complex expression. Notably, we first found that dendritic cell (DC) infiltration and major histocompatibility complex-II (MHC-II) expression could be upregulated by PCSK9 inhibition and improve CD8+ T cell activation in the tumor immune microenvironment, thereby achieving potent tumor control. Combining PCSK9 inhibitors could enhance the efficacies of OVA-II tumor vaccine monotherapy. CONCLUSIONS: Conclusively, our pan-cancer analysis provided a more comprehensive understanding of the oncogenic and immunoregulatory roles of PCSK9 and demonstrated that targeting PCSK9 could increase the efficacy of long peptide vaccines by upregulating DC infiltration and MHC-II expression on the surface of tumor cells. This study reveals the critical oncogenic and immunoregulatory roles of PCSK9 in various tumors and shows the promise of PCSK9 as a potent immunotherapy target.

Bacteria-mediated resistance of neutrophil extracellular traps to enzymatic degradation drives the formation of dental calculi.

Dental calculi can cause gingival bleeding and periodontitis, yet the mechanism underlying the formation of such mineral build-ups, and in particular the role of the local microenvironment, are unclear. Here we show that the formation of dental calculi involves bacteria in local mature biofilms converting the DNA in neutrophil extracellular traps (NETs) from being degradable by the enzyme DNase I to being degradation resistant, promoting the nucleation and growth of apatite. DNase I inhibited NET-induced mineralization in vitro and ex vivo, yet plasma DNases were ineffective at inhibiting ectopic mineralization in the oral cavity in rodents. The topical application of the DNA-intercalating agent chloroquine in rodents fed with a dental calculogenic diet reverted NET DNA to its degradable form, inhibiting the formation of calculi. Our findings may motivate therapeutic strategies for the reduction of the prevalence of the deposition of bacteria-driven calculi in the oral cavity.

The association between tinnitus and risk of cardiovascular events and all-cause mortality: insight from the UK Biobank.

BACKGROUND: The potential influence of tinnitus on cardiovascular disease (CVD) and all-cause mortality has yet to be explored. We aim to examine the correlations between tinnitus and the risk of cardiovascular events and all-cause mortality. METHODS: We conducted a prospective cohort study utilising data from the UK Biobank. The presence of tinnitus was evaluated through a questionnaire. The primary outcome was defined as a composition of cardiovascular events, including myocardial infarction (MI), stroke, and mortality from CVD, as well as all-cause mortality. Cox proportional hazard models were employed to examine the associations between tinnitus and both the primary outcome and its individual components. Sensitivity analyses were conducted to evaluate the robustness of the primary analysis. RESULTS: A total of 140,146 participants were included in the study. The presence of tinnitus was found to be associated with a higher incident rate of the primary outcome (HR = 1.057, 95%CI: 1.017-1.099, p = 0.005), MI (HR = 1.139, 95%CI: 1.061-1.222, p < 0.001) and all-cause mortality (HR = 1.053, 95%CI: 1.003-1.105, p = 0.038) after adjusting for confounders. However, there was no significant association between tinnitus and stroke or mortality from CVD. Subgroup analysis revealed that the association between tinnitus and the primary outcome was significant in females, participants with abnormal BMI, and those without hearing difficulty, depression or anxiety. Sensitivity analyses yielded consistent results. CONCLUSION: The findings from this study contribute to the existing body of evidence suggesting an association between tinnitus and an increased risk of cardiovascular events and all-cause mortality.

Association between D-dimer levels and clinicopathological characteristics of pancreatic cancer and its role in prognosis: A systematic review and meta-analysis.

According to previous studies, D-dimer levels are associated with the prognosis of patients with pancreatic cancer (PC). However, the results of current studies are limited and controversial. Therefore, we performed this meta-analysis to assess the relationship between D-dimer levels and prognostic and pathological characteristics of PC patients. We first searched the databases of PubMed, Embase, The Cochrane Library, Web Of Science, CBM, VIP, CNKI and Wanfang to identify available studies. The relationship between pretreatment d-dimer levels and prognosis in PC patients was assessed using the combined hazard ratio (HR) and 95% confidence interval (CI). The combined odds ratio (OR) and 95% confidence interval (CI) were used in assessing the relationship between pathological characteristics and d-dimer levels in PC patients. Stata 12.0 software was used for all statistical analyses. In total, we included 13 studies involving 2777 patients. The results showed that elevated pre-treatment d -dimer levels were significantly associated with OS deterioration (HR = 1.46 95% CI: 1.34-1.59; p < 0.001). We also performed subgroup analyses based on sample size, d -dimer threshold, follow-up time, and HR source to further validate the prognostic value of pretreatment d -dimer levels in PC. In addition, according to the analysis, high pretreatment d -dimer levels in PC patients were associated with late tumor stage (OR = 4.78, 95% CI 1.73-13.20, p < 0. 005), larger tumor size (OR = 1.72, 95% CI 1.25-2.35, p < 0.005), and distant metastasis of tumor (OR = 5.06, 95% CI 2.45-10.43, p < 0.005) were significantly associated. In contrast, other clinicopathological factors, including age, gender and lymph node metastasis, were not associated with d-dimer levels. In conclusion, we found that high pre-treatment d-dimer levels were associated with a poor prognosis in PC patients, in relation to later tumor stage, larger tumor size and the development of distant metastases. Plasma d-dimer levels can be used as a biomarker of prognosis in PC patients.

Molecular and Supramolecular Materials: From Light-Harvesting to Quantum Information Science and Technology.

The past two decades have witnessed immense advances in quantum information technology (QIT), benefited by advances in physics, chemistry, biology, and materials science and engineering. It is intriguing to consider whether these diverse molecular and supramolecular structures and materials, partially inspired by quantum effects as observed in sophisticated biological systems such as light-harvesting complexes in photosynthesis and the magnetic compass of migratory birds, might play a role in future QIT. If so, how? Herein, we review materials and specify the relationship between structures and quantum properties, and we identify the challenges and limitations that have restricted the intersection of QIT and chemical materials. Examples are broken down into two categories: materials for quantum sensing where nonclassical function is observed on the molecular scale and systems where nonclassical phenomena are present due to intermolecular interactions. We discuss challenges for materials chemistry and make comparisons to related systems found in nature. We conclude that if chemical materials become relevant for QIT, they will enable quite new kinds of properties and functions.