Distinct neural pathway and its information flow for blind individual's Braille reading.

Natural Braille reading presents significant challenges to the brain networks of late blind individuals, yet its underlying neural mechanisms remain largely unexplored. Using natural Braille texts in behavioral assessments and functional MRI, we sought to pinpoint the neural pathway and information flow crucial for Braille reading performance in late blind individuals. In the resting state, we discovered a unique neural connection between the higher-order 'visual' cortex, the lateral occipital cortex (LOC), and the inferior frontal cortex (IFC) in late blind individuals, but not in sighted controls. The left-lateralized LOC-IFC connectivity was correlated with individual Braille reading proficiency. Prolonged Braille reading practice led to increased strength of this connectivity. During a natural Braille reading task, bidirectional information flow between the LOC and the IFC was positively modulated, with a predominantly stronger top-down modulation from the IFC to the LOC. This stronger top-down modulation contributed to higher Braille reading proficiency. We thus proposed a two-predictor multiple regression model to predict individual Braille reading proficiency, incorporating both static connectivity and dynamic top-down communication between the LOC-IFC link. This work highlights the dual contributions of the occipito-frontal neural pathway and top-down cognitive strategy to superior natural Braille reading performance, offering guidance for training late blind individuals.

Investigating the molecular mechanism of vitexin targeting CDK1 to inhibit colon cancer cell proliferation via GEO chip data mining, computer simulation, and biological activity verification.

The objective of this study is to explore the antiproliferative activity of the traditional Chinese medicine monomer vitexin on colon cancer HCT-116 cells and its underlying mechanism. The in vitro antiproliferative activity of vitexin on colon cancer HCT-116 cells was evaluated using the CCK-8 assay. Potential drug targets for colon cancer were identified through GEO chip data mining, and molecular docking using Schrödinger software was conducted. Molecular dynamics simulations were employed to deeply analyze the interaction between candidate compounds and target proteins. Flow cytometry was employed to examine the cell cycle. The impact of vitexin on the expression of CDK1/cyclinB proteins in HCT-116 cells was assessed through Western blot analysis, immunofluorescence, and CDK inhibition assay. Vitexin exhibited inhibitory effects on colon cancer HCT-116 cells, with a half inhibitory concentration (IC50) value of 203.27 ± 9.85 µmol/L. The analysis of differential gene expression in GEO and TCGA datasets, along with the GENECARD dataset of related disease genes, identified 91 disease targets, including "CDK1." Vitexin induced cell cycle arrest in the G2/M phase of HCT-116 cells. Molecular docking revealed a strong interaction between Vitexin and CDK1 (Docking score - 9.497), with molecular dynamics simulations confirming the stability of the Vitexin-CDK1 complex and comparable inhibitory effects to Flavopiridol. Vitexin can inhibit the expression of CDK1/cyclin B proteins in HCT-116 cells, with an IC50 of 58.06 ± 3.07 µmol/L. Vitexin may inhibit colon cancer HCT-116 cell proliferation by suppressing CDK1/cyclin B expression, leading to cell cycle arrest in the G2/M phase.

Robust and Adhesive Laminar Solid Electrolyte with Homogenous and Fast Li-Ion Conduction for High-Performance All-Solid-State Lithium Metal Battery.

Constructing composite solid electrolytes (CSEs) integrating the merits of inorganic and organic components is a promising approach to developing high-performance all-solid-state lithium metal batteries (ASSLMBs). CSEs are now capable of achieving homogeneous and fast Li-ion flux, but how to escape the trade-off between mechanical modulus and adhesion is still a challenge. Herein, a strategy to address this issue is proposed, that is, intercalating highly conductive, homogeneous, and viscous-fluid ionic conductors into robust coordination laminar framework to construct laminar solid electrolyte with homogeneous and fast Li-ion conduction (LSE-HFC). A 9 µm-thick LSH-HFC, in which poly(ethylene oxide)/succinonitrile is adsorbed by coordination laminar framework with metal-organic framework nanosheets as building blocks, is used here as an example to determine the validity. The Li-ion transfer mechanism is verified and works across the entire LSE-HFC, which facilitates homogeneous Li-ion flux and low migration energy barriers, endowing LSE-HFC with high ionic conductivity of 5.62 × 10-4 S cm-1 and Li-ion transference number of 0.78 at 25 °C. Combining the outstanding mechanical strength against punctures and the enhanced adhesion force with electrodes, LSE-HFC harvests uniform Li plating/stripping behavior. These enable the realization of high-energy-density ASSLMBs with excellent cycling stability when being assembled as LiFePO4/Li and LiNi0.6Mn0.2Co0.2O2/Li cells.

Functional connectivity development along the sensorimotor-association axis enhances the cortical hierarchy.

Human cortical maturation has been posited to be organized along the sensorimotor-association axis, a hierarchical axis of brain organization that spans from unimodal sensorimotor cortices to transmodal association cortices. Here, we investigate the hypothesis that the development of functional connectivity during childhood through adolescence conforms to the cortical hierarchy defined by the sensorimotor-association axis. We tested this pre-registered hypothesis in four large-scale, independent datasets (total n = 3355; ages 5-23 years): the Philadelphia Neurodevelopmental Cohort (n = 1207), Nathan Kline Institute-Rockland Sample (n = 397), Human Connectome Project: Development (n = 625), and Healthy Brain Network (n = 1126). Across datasets, the development of functional connectivity systematically varied along the sensorimotor-association axis. Connectivity in sensorimotor regions increased, whereas connectivity in association cortices declined, refining and reinforcing the cortical hierarchy. These consistent and generalizable results establish that the sensorimotor-association axis of cortical organization encodes the dominant pattern of functional connectivity development.

A practical evaluation of measures derived from compressed sensing diffusion spectrum imaging.

Diffusion Spectrum Imaging (DSI) using dense Cartesian sampling of q-space has been shown to provide important advantages for modeling complex white matter architecture. However, its adoption has been limited by the lengthy acquisition time required. Sparser sampling of q-space combined with compressed sensing (CS) reconstruction techniques has been proposed as a way to reduce the scan time of DSI acquisitions. However prior studies have mainly evaluated CS-DSI in post-mortem or non-human data. At present, the capacity for CS-DSI to provide accurate and reliable measures of white matter anatomy and microstructure in the living human brain remains unclear. We evaluated the accuracy and inter-scan reliability of 6 different CS-DSI schemes that provided up to 80% reductions in scan time compared to a full DSI scheme. We capitalized on a dataset of 26 participants who were scanned over eight independent sessions using a full DSI scheme. From this full DSI scheme, we subsampled images to create a range of CS-DSI images. This allowed us to compare the accuracy and inter-scan reliability of derived measures of white matter structure (bundle segmentation, voxel-wise scalar maps) produced by the CS-DSI and the full DSI schemes. We found that CS-DSI estimates of both bundle segmentations and voxel-wise scalars were nearly as accurate and reliable as those generated by the full DSI scheme. Moreover, we found that the accuracy and reliability of CS-DSI was higher in white matter bundles that were more reliably segmented by the full DSI scheme. As a final step, we replicated the accuracy of CS-DSI in a prospectively acquired dataset (n = 20, scanned once). Together, these results illustrate the utility of CS-DSI for reliably delineating in vivo white matter architecture in a fraction of the scan time, underscoring its promise for both clinical and research applications.

Development of white matter fiber covariance networks supports executive function in youth.

During adolescence, the brain undergoes extensive changes in white matter structure that support cognition. Data-driven approaches applied to cortical surface properties have led the field to understand brain development as a spatially and temporally coordinated mechanism that follows hierarchically organized gradients of change. Although white matter development also appears asynchronous, previous studies have relied largely on anatomical tract-based atlases, precluding a direct assessment of how white matter structure is spatially and temporally coordinated. Harnessing advances in diffusion modeling and machine learning, we identified 14 data-driven patterns of covarying white matter structure in a large sample of youth. Fiber covariance networks aligned with known major tracts, while also capturing distinct patterns of spatial covariance across distributed white matter locations. Most networks showed age-related increases in fiber network properties, which were also related to developmental changes in executive function. This study delineates data-driven patterns of white matter development that support cognition.

A reproducible and generalizable software workflow for analysis of large-scale neuroimaging data collections using BIDS Apps.

Neuroimaging research faces a crisis of reproducibility. With massive sample sizes and greater data complexity, this problem becomes more acute. Software that operates on imaging data defined using the Brain Imaging Data Structure (BIDS) - BIDS Apps - have provided a substantial advance. However, even using BIDS Apps, a full audit trail of data processing is a necessary prerequisite for fully reproducible research. Obtaining a faithful record of the audit trail is challenging - especially for large datasets. Recently, the FAIRly big framework was introduced as a way to facilitate reproducible processing of large-scale data by leveraging DataLad - a version control system for data management. However, the current implementation of this framework was more of a proof of concept, and could not be immediately reused by other investigators for different use cases. Here we introduce the BIDS App Bootstrap (BABS), a user-friendly and generalizable Python package for reproducible image processing at scale. BABS facilitates the reproducible application of BIDS Apps to large-scale datasets. Leveraging DataLad and the FAIRly big framework, BABS tracks the full audit trail of data processing in a scalable way by automatically preparing all scripts necessary for data processing and version tracking on high performance computing (HPC) systems. Currently, BABS supports jobs submissions and audits on Sun Grid Engine (SGE) and Slurm HPCs with a parsimonious set of programs. To demonstrate its scalability, we applied BABS to data from the Healthy Brain Network (HBN; n=2,565). Taken together, BABS allows reproducible and scalable image processing and is broadly extensible via an open-source development model.

Establishing the Validity of Compressed Sensing Diffusion Spectrum Imaging.

Diffusion Spectrum Imaging (DSI) using dense Cartesian sampling of q-space has been shown to provide important advantages for modeling complex white matter architecture. However, its adoption has been limited by the lengthy acquisition time required. Sparser sampling of q-space combined with compressed sensing (CS) reconstruction techniques has been proposed as a way to reduce the scan time of DSI acquisitions. However prior studies have mainly evaluated CS-DSI in post-mortem or non-human data. At present, the capacity for CS-DSI to provide accurate and reliable measures of white matter anatomy and microstructure in the living human brain remains unclear. We evaluated the accuracy and inter-scan reliability of 6 different CS-DSI schemes that provided up to 80% reductions in scan time compared to a full DSI scheme. We capitalized on a dataset of twenty-six participants who were scanned over eight independent sessions using a full DSI scheme. From this full DSI scheme, we subsampled images to create a range of CS-DSI images. This allowed us to compare the accuracy and inter-scan reliability of derived measures of white matter structure (bundle segmentation, voxel-wise scalar maps) produced by the CS-DSI and the full DSI schemes. We found that CS-DSI estimates of both bundle segmentations and voxel-wise scalars were nearly as accurate and reliable as those generated by the full DSI scheme. Moreover, we found that the accuracy and reliability of CS-DSI was higher in white matter bundles that were more reliably segmented by the full DSI scheme. As a final step, we replicated the accuracy of CS-DSI in a prospectively acquired dataset (n=20, scanned once). Together, these results illustrate the utility of CS-DSI for reliably delineating in vivo white matter architecture in a fraction of the scan time, underscoring its promise for both clinical and research applications.

ModelArray: An R package for statistical analysis of fixel-wise data.

Diffusion MRI is the dominant non-invasive imaging method used to characterize white matter organization in health and disease. Increasingly, fiber-specific properties within a voxel are analyzed using fixels. While tools for conducting statistical analyses of fixel-wise data exist, currently available tools support only a limited number of statistical models. Here we introduce ModelArray, an R package for mass-univariate statistical analysis of fixel-wise data. At present, ModelArray supports linear models as well as generalized additive models (GAMs), which are particularly useful for studying nonlinear effects in lifespan data. In addition, ModelArray also aims for scalable analysis. With only several lines of code, even large fixel-wise datasets can be analyzed using a standard personal computer. Detailed memory profiling revealed that ModelArray required only limited memory even for large datasets. As an example, we applied ModelArray to fixel-wise data derived from diffusion images acquired as part of the Philadelphia Neurodevelopmental Cohort (n = 938). ModelArray revealed anticipated nonlinear developmental effects in white matter. Moving forward, ModelArray is supported by an open-source software development model that can incorporate additional statistical models and other imaging data types. Taken together, ModelArray provides a flexible and efficient platform for statistical analysis of fixel-wise data.

Development of White Matter Fiber Covariance Networks Supports Executive Function in Youth.

The white matter architecture of the human brain undergoes substantial development throughout childhood and adolescence, allowing for more efficient signaling between brain regions that support executive function. Increasingly, the field understands grey matter development as a spatially and temporally coordinated mechanism that follows hierarchically organized gradients of change. While white matter development also appears asynchronous, previous studies have largely relied on anatomical atlases to characterize white matter tracts, precluding a direct assessment of how white matter structure is spatially and temporally coordinated. Here, we leveraged advances in diffusion modeling and unsupervised machine learning to delineate white matter fiber covariance networks comprised of structurally similar areas of white matter in a cross-sectional sample of 939 youth aged 8-22 years. We then evaluated associations between fiber covariance network structural properties with both age and executive function using generalized additive models. The identified fiber covariance networks aligned with the known architecture of white matter while simultaneously capturing novel spatial patterns of coordinated maturation. Fiber covariance networks showed heterochronous increases in fiber density and cross section that generally followed hierarchically organized temporal patterns of cortical development, with the greatest increases in unimodal sensorimotor networks and the most prolonged increases in superior and anterior transmodal networks. Notably, we found that executive function was associated with structural features of limbic and association networks. Taken together, this study delineates data-driven patterns of white matter network development that support cognition and align with major axes of brain maturation.

Curation of BIDS (CuBIDS): A workflow and software package for streamlining reproducible curation of large BIDS datasets.

The Brain Imaging Data Structure (BIDS) is a specification accompanied by a software ecosystem that was designed to create reproducible and automated workflows for processing neuroimaging data. BIDS Apps flexibly build workflows based on the metadata detected in a dataset. However, even BIDS valid metadata can include incorrect values or omissions that result in inconsistent processing across sessions. Additionally, in large-scale, heterogeneous neuroimaging datasets, hidden variability in metadata is difficult to detect and classify. To address these challenges, we created a Python-based software package titled "Curation of BIDS" (CuBIDS), which provides an intuitive workflow that helps users validate and manage the curation of their neuroimaging datasets. CuBIDS includes a robust implementation of BIDS validation that scales to large samples and incorporates DataLad--a version control software package for data--as an optional dependency to ensure reproducibility and provenance tracking throughout the entire curation process. CuBIDS provides tools to help users perform quality control on their images' metadata and identify unique combinations of imaging parameters. Users can then execute BIDS Apps on a subset of participants that represent the full range of acquisition parameters that are present, accelerating pipeline testing on large datasets.

Linear Block Copolymer Synthesis.

Block copolymers form the basis of the most ubiquitous materials such as thermoplastic elastomers, bridge interphases in polymer blends, and are fundamental for the development of high-performance materials. The driving force to further advance these materials is the accessibility of block copolymers, which have a wide variety in composition, functional group content, and precision of their structure. To advance and broaden the application of block copolymers will depend on the nature of combined segmented blocks, guided through the combination of polymerization techniques to reach a high versatility in block copolymer architecture and function. This review provides the most comprehensive overview of techniques to prepare linear block copolymers and is intended to serve as a guideline on how polymerization techniques can work together to result in desired block combinations. As the review will give an account of the relevant procedures and access areas, the sections will include orthogonal approaches or sequentially combined polymerization techniques, which increases the synthetic options for these materials.

Deficits in Seizure Threshold and Other Behaviors in Adult Mice without Gross Neuroanatomic Injury after Late Gestation Transient Prenatal Hypoxia.

Intrauterine hypoxia is a common cause of brain injury in children resulting in a broad spectrum of long-term neurodevelopmental sequela, including life-long disabilities that can occur even in the absence of severe neuroanatomic damage. Postnatal hypoxia-ischemia rodent models are commonly used to understand the effects of ischemia and transient hypoxia on the developing brain. Postnatal models, however, have some limitations. First, they do not test the impact of placental pathologies on outcomes from hypoxia. Second, they primarily recapitulate severe injury because they provoke substantial cell death, which is not seen in children with mild hypoxic injury. Lastly, they do not model preterm hypoxic injury. Prenatal models of hypoxia in mice may allow us to address some of these limitations to expand our understanding of developmental brain injury. The published rodent models of prenatal hypoxia employ multiple days of hypoxic exposure or complicated surgical procedures, making these models challenging to perform consistently in mice. Furthermore, large animal models suggest that transient prenatal hypoxia without ischemia is sufficient to lead to significant functional impairment to the developing brain. However, these large animal studies are resource-intensive and not readily amenable to mechanistic molecular studies. Therefore, here we characterized the effect of late gestation (embryonic day 17.5) transient prenatal hypoxia (5% inspired oxygen) on long-term anatomical and neurodevelopmental outcomes in mice. Late gestation transient prenatal hypoxia increased hypoxia-inducible factor 1 alpha protein levels (a marker of hypoxic exposure) in the fetal brain. Hypoxia exposure predisposed animals to decreased weight at postnatal day 2, which normalized by day 8. However, hypoxia did not affect gestational age at birth, litter size at birth, or pup survival. No differences in fetal brain cell death or long-term gray or white matter changes resulted from hypoxia. Animals exposed to prenatal hypoxia did have several long-term functional consequences, including sex-dichotomous changes. Hypoxia exposure was associated with a decreased seizure threshold and abnormalities in hindlimb strength and repetitive behaviors in males and females. Males exposed to hypoxia had increased anxiety-related deficits, whereas females had deficits in social interaction. Neither sex developed any motor or visual learning deficits. This study demonstrates that late gestation transient prenatal hypoxia in mice is a simple, clinically relevant paradigm for studying putative environmental and genetic modulators of the long-term effects of hypoxia on the developing brain.

Learning Deformable Image Registration From Optimization: Perspective, Modules, Bilevel Training and Beyond.

Conventional deformable registration methods aim at solving an optimization model carefully designed on image pairs and their computational costs are exceptionally high. In contrast, recent deep learning-based approaches can provide fast deformation estimation. These heuristic network architectures are fully data-driven and thus lack explicit geometric constraints which are indispensable to generate plausible deformations, e.g., topology-preserving. Moreover, these learning-based approaches typically pose hyper-parameter learning as a black-box problem and require considerable computational and human effort to perform many training runs. To tackle the aforementioned problems, we propose a new learning-based framework to optimize a diffeomorphic model via multi-scale propagation. Specifically, we introduce a generic optimization model to formulate diffeomorphic registration and develop a series of learnable architectures to obtain propagative updating in the coarse-to-fine feature space. Further, we propose a new bilevel self-tuned training strategy, allowing efficient search of task-specific hyper-parameters. This training strategy increases the flexibility to various types of data while reduces computational and human burdens. We conduct two groups of image registration experiments on 3D volume datasets including image-to-atlas registration on brain MRI data and image-to-image registration on liver CT data. Extensive results demonstrate the state-of-the-art performance of the proposed method with diffeomorphic guarantee and extreme efficiency. We also apply our framework to challenging multi-modal image registration, and investigate how our registration to support the down-streaming tasks for medical image analysis including multi-modal fusion and image segmentation.

Association between Quantitative MR Markers of Cortical Evolving Organization and Gene Expression during Human Prenatal Brain Development.

The relationship between structural changes of the cerebral cortex revealed by Magnetic Resonance Imaging (MRI) and gene expression in the human fetal brain has not been explored. In this study, we aimed to test the hypothesis that relative regional thickness (a measure of cortical evolving organization) of fetal cortical compartments (cortical plate [CP] and subplate [SP]) is associated with expression levels of genes with known cortical phenotype. Mean regional SP/CP thickness ratios across age measured on in utero MRI of 25 healthy fetuses (20-33 gestational weeks [GWs]) were correlated with publicly available regional gene expression levels (23-24 GW fetuses). Larger SP/CP thickness ratios (more pronounced cortical evolving organization) was found in perisylvian regions. Furthermore, we found a significant association between SP/CP thickness ratio and expression levels of the FLNA gene (mutated in periventricular heterotopia, congenital heart disease, and vascular malformations). Further work is needed to identify early MRI biomarkers of gene expression that lead to abnormal cortical development.

Inter-brain amplitude correlation differentiates cooperation from competition in a motion-sensing sports game.

Cooperation and competition are two basic modes of human interaction. Their underlying neural mechanisms, especially from an interpersonal perspective, have not been fully explored. Using the electroencephalograph-based hyperscanning technique, the present study investigated the neural correlates of both cooperation and competition within the same ecological paradigm using a classic motion-sensing tennis game. Both the inter-brain coupling (the inter-brain amplitude correlation and inter-brain phase-locking) and the intra-brain spectral power were analyzed. Only the inter-brain amplitude correlation showed a significant difference between cooperation and competition, with different spatial patterns at theta, alpha and beta frequency bands. Further inspection revealed distinct inter-brain coupling patterns for cooperation and competition; cooperation elicited positive inter-brain amplitude correlation at the delta and theta bands in extensive brain regions, while competition was associated with negative occipital inter-brain amplitude correlation at the alpha and beta bands. These findings add to our knowledge of the neural mechanisms of cooperation and competition and suggest the significance of adopting an inter-brain perspective in exploring the neural underpinnings of social interaction in ecological contexts.

A Novel Polymer Electrolyte Matrix Incorporating Ionic Liquid into Waterborne Polyurethane for Lithium-Ion Battery.

Ionic liquid has relatively high conductivity at room temperature and good electrochemical stability. Ionic liquid polymer electrolytes have some advantages of both ionic liquid and polymer. In this work, 1-alkyl-3-(2',3'-dihydroxypropyl)imidazolium chloride (IL-Cl) was incorporated into waterborne polyurethane chain to composite all-solid-state polymer electrolyte matrices. The structure, thermal stability, mechanical property and ionic conductivity of the matrices were investigated by Fourier transform infrared spectroscopy (FTIR), thermogravimetric Analysis (TGA), tensile measurement and electrochemical impedance spectroscopy (EIS). The results demonstrated that when the content of IL-Cl was 14 wt%, the mechanical property of film was optimized, with a maximum tensile strength of 36 MPa and elongation at break of 1030%. In addition, as for the film with IL-Cl content of 16 wt%, its oxygen index value increased to 25.2% and ionic conductivity reached a maximum of 1.2 × 10-5 S·cm-1 at room temperature, showing high flame retardancy and ionic conductivity.

Regularized-Ncut: Robust and homogeneous functional parcellation of neonate and adult brain networks.

Brain network parcellation based on resting-state functional MRI (rs-fMRI) is affected by noise, resulting in spurious small patches and decreased functional homogeneity within each network. Obtaining robust and homogeneous parcellation of neonate brain is more difficult, because neonate rs-fMRI is associated with relatively higher level of noise and no prior knowledge from a functional neonate atlas is available as spatial constraints. To meet these challenges, we developed a novel data-driven Regularized Normalized-cut (RNcut) method. RNcut is formulated by adding two regularization terms, a smoothing term using Markov random fields and a small-patch removal term, to conventional normalized-cut (Ncut) method. The RNcut and competing methods were tested with simulated datasets with known ground truth and then applied to both adult and neonate rs-fMRI datasets. Based on the parcellated networks generated by RNcut, intra-network connectivity was quantified. The test results from simulated datasets demonstrated that the RNcut method is more robust (p < 0.01) to noise and can delineate parcellated functional networks with significantly better (p < 0.01) spatial contiguity and significantly higher (p < 0.01) functional homogeneity than competing methods. Application of RNcut to neonate and adult rs-fMRI dataset revealed distinctive functional brain organization of neonate brains from that of adult brains. Collectively, we developed a novel data-driven RNcut method by integrating conventional Ncut with two regularization terms, generating robust and homogeneous functional parcellation without imposing spatial constraints. A broad range of brain network applications and analyses, especially neonate and infant brain parcellation with noisy and large sample of datasets, can potentially benefit from this RNcut method.

Triple Redox Responsive Poly(Ethylene Glycol)-Polycaprolactone Polymeric Nanocarriers for Fine-Controlled Drug Release.

Stimuli-responsive nanocarriers with the ability to respond to tumorous heterogeneity have been extensively developed for drug delivery. However, the premature release during blood circulation and insufficient intracellular drug release are still a significant issue. Herein, three disulfide bonds are introduced into the amphiphilic poly(ethylene glycol)-polycaprolactone copolymer blocks to form triple-sensitive cleavable polymeric nanocarrier (tri-PESC NPs) to improve its sensitivity to narrow glutathione (GSH) concentration. The tri-PESC NPs keep intact during blood circulation due to the limited cleaving of triple-disulfide bonds, whereas the loaded drug is efficiently released at tumor cells with the increased concentration of GSH. In vitro studies of doxorubicin-loaded tri-PESC NPs show that the nanocarriers achieve sufficient drug release in cancerous cells and inhibit the tumor cells growth, though they only bring minimum damage to normal cells. Therefore, the tri-PESC NPs with triple-sensitive cleavable bonds hold great promise to improve the therapeutic index in cancer therapy.

Biogenic silica composition and δ13C abundance in the Changjiang (Yangtze) and Huanghe (Yellow) Rivers with implications for the silicon cycle.

The study was carried out to address a method for separation of terrestrial and marine biogenic silica (BSi) in estuaries based on BSi compositions and δ13C values in BSi associated organic matter (δ13CBSi). We used two world-class major rivers - the Changjiang (Yangtze) and Huanghe (Yellow) Rivers as examples to illustrate our approach. Our results for these rivers indicate that riverine BSi is comprised mainly of phytoliths and diatoms. River BSi concentrations vary with terrestrial inputs and in-stream primary production. Although the fluvial BSi sources are complex, the terrestrial δ13CBSi signals are quite unique (-24.7±0.8), significantly lower than the marine δ13CBSi values (-21.3±0.07, central Yellow Sea) (p<0.01). Thus, the variation of δ13C within BSi organic matter can provide terrestrial source information on the biogeochemistry of silicon in estuaries and the adjacent shelf. The δ13CBSi combination could potentially act as an efficient tool to study environmental change in coastal areas on decadal time-scales since this index may respond to variable terrestrial fluxes from land, as well as to changed phytoplankton assemblages in the coastal ocean.