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A ‘confined space’ provides a unique environment to regulate the crystallization thermodynamics and kinetics by confining the reactants in the restricted space dimensions. Solid-state crystal-to-crystal transitions in confined space are controlled by the preassembly of molecules in crystal lattice and occur inside the lattice. Herein we report the first case of construction of crystalline porous polyacetylene frameworks (PPFs) through solid-state cross-linking of acetylenic groups-bridged 2D crystalline covalent organic frameworks (COFs) in spatially limited systems. Specifically, this transformation is thermally induced yielding PPFs with superlative properties like outstanding enhancement in crystallinity, specific surface area and stability. We further demonstrate the PPFs as high conductivity polymers after iodine doping. This work underscores the opportunity in using lattice-constrained solid-state cross-linking to develop more versatile and feature-rich polyacetylene frameworks.

Predicting brain aging can help in the early detection and prognosis of neurodegenerative diseases. Longitudinal cohorts of healthy subjects scanned through magnetic resonance imaging (MRI) have been essential to understand the structural brain changes due to aging. However, these cohorts suffer from missing data due to logistic issues in the recruitment of subjects. This paper proposes a methodology for filling up missing data in longitudinal cohorts with anatomically plausible images that capture the subject-specific aging process. The proposed methodology is developed within the framework of diffeomorphic registration. First, two novel modules are introduced within Synthmorph, a fast, state-of-the-art deep learning-based diffeomorphic registration method, to simulate the aging process between the first and last available MRI scan for each subject in three-dimensional (3D). The use of image registration also makes the generated images plausible by construction. Second, we used six image similarity measurements to rearrange the generated images to the specific age range. Finally, we estimated the age of every generated image by using the assumption of linear brain decay in healthy subjects. The methodology was evaluated on 2662 T1-weighted MRI scans from 796 healthy participants from 3 different longitudinal cohorts: Alzheimer's Disease Neuroimaging Initiative, Open Access Series of Imaging Studies-3, and Group of Neuropsychological Studies of the Canary Islands (GENIC). In total, we generated 7548 images to simulate the access of a scan per subject every 6 months in these cohorts. We evaluated the quality of the synthetic images using six quantitative measurements and a qualitative assessment by an experienced neuroradiologist with state-of-the-art results. The assumption of linear brain decay was accurate in these cohorts (R

The lack of effective methods of diagnosis and treatment presents a major barrier to combat against tumor. The biomimetic concept is an emerging field that expresses great application potential in tumor fighting. Strategy for combining nano-systems with biomimetic technology has gained increasing attention that is proved bioinspired, environmentally benign, and promising. Herein, we provide an up-to-date review of biomimetic nano-systems as well as their applications in tumor therapy. In addition, the challenges and future directions of biomimetic nano-systems to achieve clinical translation are also pointed out.

In recent years, the porous sorbent-assisted atmospheric water harvesting (AWH) method has emerged as an effective approach for solving water crises without geographical restrictions. However, there is a limited array of porous adsorbent materials that can be used for AWH, which are inadequate to meet the needs under different climatic conditions. In light of this, herein, we synthesize a new crystalline porous organic salt (CPOS; denoted as CPOS-6) possessing a dual hydrogen bond system and verify its applicability toward AWH for the first time. Unlike other reported CPOSs, CPOS-6 displays an S-shaped water sorption isotherm owing to the presence of the dual hydrogen bond system. Under simulated drought conditions in Xinjiang Uygur Autonomous Region, CPOS-6 exhibits long-term water adsorption-desorption cycling stability, low water desorption temperature, and ultrarapid adsorption-desorption kinetics. The results confirm that CPOS-6 is an effective sorbent material for AWH.

Deuterium with high purity can be widely used in non-radioactive isotope tracking, neutron scattering and nuclear fusion. Current approaches for deuterium separation with Pd membrane and quantum sieving-based nanoporous materials are performed at high temperature (673 K) and ultralow temperature (30 K), respectively, both requiring high energy consumption but possessing low separation efficiency. However, efficient separation of deuterium under ambient conditions is still a remaining challenge. Here, we efficiently extract high-purity deuterium from an isotopic mixture under ambient conditions via spatial confinement between Pd nanoparticles and benzene skeleton, and reducing activation energy of the reaction. In-situ FT-IR results confirm that Pd nanoparticles (ca. 1.1 nm) in crystalline porous organic framework (CPOF-1) can chemically adsorb deuterium at room temperature. Density functional theory (DFT) calculations indicated that the spatial confinement effect reduces the reaction activation energy, and leads to Pd-deuterium reaction at room temperature. This study provides a new concept for deuterium separation with low energy consumption, low cost and high efficiency, and we envision that it could be of great significance in practical applications.

Unmethylated cytosine-phosphate-guanosine (CpG) oligodeoxynucleotides (ODNs) as an adjuvant can activate innate and adaptive immune responses and simultaneously produce a large number of proinflammatory cytokines and up-regulate costimulatory molecules to treat cancer, allergy, asthma and infectious diseases. Polycationic biomaterials as delivery system can bind to CpG ODNs through electrostatic mechanism and overcome challenging requirements of CpG delivery including low cellular uptake efficiency, short half-life and quick release time and ease to be degraded by DNA enzyme (Dnase). This review provides new developments for the immunizing research of CpG ODNs and also focuses on cationic polymers for enhancing CpG-mediated cancer immunotherapy, together with brief discussions and perspectives for the future research direction.

In recent years, biomacromolecules have been widely used in anti-tumor delivery systems due to the biocompatibility and biodegradability. However, their applications are limited due to the lack of specific targeting. Hyaluronic acid (HA) is a natural polysaccharide and presents in extracellular matrix and synovial fluid which can specifically recognize receptors over-expressed by tumor cells. In addition, they can self-assemble into nanoparticles. HA nanoparticles provide new hierarchical targeting strategies: passively targeting tumor tissue by enhanced permeability and retention effect, actively targeting tumor cells by cluster determinant 44 (CD44) receptor, and then entering cells through receptor-mediated endocytosis. In this review, the synthesis of HA nanoparticles is described in detail from several aspects and applications are also discussed for improving the delivery of hydrophobic drugs, nucleic acids and photosensitizers into the tumor cells. In addition, the modification of HA for improving the targeting and drug releasing characteristics are also discussed.

Ultra-selective and fast transport of K

A prevailing problem in many machine learning tasks is that the training (i.e., source domain) and test data (i.e., target domain) have different distribution [i.e., non-independent identical distribution (i.i.d.)]. Unsupervised domain adaptation (UDA) was proposed to learn the unlabeled target data by leveraging the labeled source data. In this article, we propose a guide subspace learning (GSL) method for UDA, in which an invariant, discriminative, and domain-agnostic subspace is learned by three guidance terms through a two-stage progressive training strategy. First, the subspace-guided term reduces the discrepancy between the domains by moving the source closer to the target subspace. Second, the data-guided term uses the coupled projections to map both domains to a unified subspace, where each target sample can be represented by the source samples with a low-rank coefficient matrix that can preserve the global structure of data. In this way, the data from both domains can be well interlaced and the domain-invariant features can be obtained. Third, for improving the discrimination of the subspaces, the label-guided term is constructed for prediction based on source labels and pseudo-target labels. To further improve the model tolerance to label noise, a label relaxation matrix is introduced. For the solver, a two-stage learning strategy with teacher teaches and student feedbacks mode is proposed to obtain the discriminative domain-agnostic subspace. In addition, for handling nonlinear domain shift, a nonlinear GSL (NGSL) framework is formulated with kernel embedding, such that the unified subspace is imposed with nonlinearity. Experiments on various cross-domain visual benchmark databases show that our methods outperform many state-of-the-art UDA methods. The source code is available at https://github.com/Fjr9516/GSL .

Zero-shot Learning (ZSL) aims to transfer knowledge from seen image categories to unseen ones by leveraging semantic information. It is generally assumed that the seen and unseen classes share a common semantic space. A number of methods propose to design a common space to accomplish the projection between image and class embeddings by learning a compatibility function, which make up sample pairs to train the object function. However, considering the drawbacks of previous compatibility function, we design a new compatibility function in this paper. Different from previous compatibility pattern, our proposed compatibility function is more discriminative by employing label vectors, which can measure the similarity between the projected image features and all seen class prototypes. Extensive experiments on four benchmark datasets show the effectiveness of our proposed approach.

Remote loading technology is an outstanding achievement in liposome-based drug delivery systems. Compared with conventional passive loading, remote loading technology exhibits unique superiority in terms of high drug loading efficiency, low leakage rate and adequate drug accumulation. In the intra-liposome aqueous phase, the counterion of the trapping agent can control the state of aggregation/crystallization of the drug-counterion salt, and thereby contribute to control the efficiency of remote loading. Herein, irinotecan (CPT-11)-loaded liposomes were developed using three trapping agents: ammonium sulfate (AS), sulfobutylether-β-cyclodextrin (SBE-β-CD) and sucrose octasulfate (SOS). The corresponding formulations were named as AS liposomal CPT-11, TEA-SBE-β-CD liposomal CPT-11 and TEA-SOS liposomal CPT-11, respectively. Cryo-transmission electron micrographs showed that bundles of CPT-11 fibers were gathered inside TEA-SOS liposomal CPT-11. Furthermore, compared with AS liposomal CPT-11 and TEA-SBE-β-CD liposomal CPT-11, TEA-SOS liposomal CPT-11 demonstrated slower drug release, prolonged circulation time and significantly improved antitumor efficiency. To avoid the protection of ONIVYDE®-related patents, a number of other liposomal CPT-11 formulations are under preclinical investigation or even in clinical trials. Our study gives new insights into the impact of the trapping agent on remote loading, and provides valuable information to evaluate the development of CPT-11 loaded liposomes.

A prevailing problem in many machine learning tasks is that the training and test data have different distribution (non i.i.d). Previous methods to solve this problem are called Transfer Learning (TL) or Domain Adaptation (DA), which belong to one stage models. In this paper, we propose a new, simple but effective paradigm, Guided Learning (GL), for multi-stage progressive training. This new paradigm is motivated by the “tutor guides student” learning mode in human world. Further, under the framework of GL, a Guided Subspace Learning (GSL) method is proposed for domain disparity reduction, which aims to learn an optimal, invariant and discriminative subspace through the guided learning strategy. Extensive experiments on various databases show that our method outperforms many state-of-the-art TL/DA methods.

The search for new types of membrane materials has been of continuous interest in both academia and industry, given their importance in a plethora of applications, particularly for energy-efficient separation technology. In this contribution, we demonstrate for the first time that a metal–organic framework (MOF) can be grown on the covalent-organic framework (COF) membrane to fabricate COF-MOF composite membranes. The resultant COF-MOF composite membranes demonstrate higher separation selectivity of H2/CO2 gas mixtures than the individual COF and MOF membranes. A sound proof for the synergy between two porous materials is the fact that the COF-MOF composite membranes surpass the Robeson upper bound of polymer membranes for mixture separation of a H2/CO2 gas pair and are among the best gas separation MOF membranes reported thus far.