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Finding the optimal treatment strategy to accelerate wound healing is of utmost importance, but it presents a formidable challenge owing to the intrinsic nonlinear nature of the process. We propose an adaptive closed-loop control framework that incorporates deep learning, optimal control and reinforcement learning to accelerate wound healing. By adaptively learning a linear representation of nonlinear wound healing dynamics using deep learning and interactively training a deep reinforcement learning agent for tracking the optimal signal derived from this representation without the need for intricate mathematical modelling, our approach has not only successfully reduced the wound healing time by 45.56% compared to the one without any treatment, but also demonstrates the advantages of offering a safer and more economical treatment strategy. The proposed methodology showcases a significant potential for expediting wound healing by effectively integrating perception, predictive modelling and optimal adaptive control, eliminating the need for intricate mathematical models.

Abstract Microcarrier is a promising drug delivery system demonstrating significant value in treating cancers. One of the main goals is to devise microcarriers with ingenious structures and functions to achieve better therapeutic efficacy in tumors. Here, inspired by the nucleus-cytoplasm structure of cells and the material exchange reaction between them, we develop a type of biorthogonal compartmental microparticles (BCMs) from microfluidics that can separately load and sequentially release cyclooctene-modified doxorubicin prodrug (TCO-DOX) and tetrazine-modified indocyanine green (Tz-ICG) for tumor therapy. The Tz-ICG works not only as an activator for TCO-DOX but also as a photothermal agent, allowing for the combination of bioorthogonal chemotherapy and photothermal therapy (PTT). Besides, the modification of DOX with cyclooctene significantly decreases the systemic toxicity of DOX. As a result, the developed BCMs demonstrate efficient in vitro tumor cell eradication and exhibit notable tumor growth inhibition with favorable safety. These findings illustrate that the formulated BCMs establish a platform for bioorthogonal prodrug activation and localized delivery, holding significant potential for cancer therapy and related applications. Graphical Abstract

The anticancer potential of quassinoids has attracted a great deal of attention for decades, and scientific data revealing their possible applications in cancer management are continuously increasing in the literature. Aside from the potent cytotoxic and antitumor properties of these degraded triterpenes, several quassinoids have exhibited synergistic effects with anticancer drugs. This article provides an overview of the potential anticancer properties of quassinoids, including their cytotoxic and antitumor activities, mechanisms of action, safety evaluation, and potential benefits in combination with anticancer drugs.

Highlights Derived from silica photonic crystals, inverse opal microspheres have a regularly connected porous structure and inherit structural color properties. Combined with the stable scaffold and the photothermal phase-transition of the secondary filling material, the inverse opal composite microspheres are endowed with self-healing properties and the ability for controllable drug release. Inverse opal microspheres were significantly treated for diabetic wound, via promoting tissue regeneration, collagen deposition and angiogenesis. Meanwhile, the release of drugs could be monitored by the structural color characteristic.

Abstract A series of 20 novel gefitinib derivatives incorporating the 1,2,3-triazole moiety were designed and synthesized. The synthesized compounds were evaluated for their potential anticancer activity against EGFR wild-type human non-small cell lung cancer cells (NCI-H1299, A549) and human lung adenocarcinoma cells (NCI-H1437) as non-small cell lung cancer. In comparison to gefitinib, Initial biological assessments revealed that several compounds exhibited potent anti-proliferative activity against these cancer cell lines. Notably, compounds 7a and 7j demonstrated the most pronounced effects, with an IC50 value of 3.94 ± 0.17 µmol L−1 (NCI-H1299), 3.16 ± 0.11 µmol L−1 (A549), and 1.83 ± 0.13 µmol L−1 (NCI-H1437) for 7a, and an IC50 value of 3.84 ± 0.22 µmol L−1 (NCI-H1299), 3.86 ± 0.38 µmol L−1 (A549), and 1.69 ± 0.25 µmol L−1 (NCI-H1437) for 7j. These two compounds could inhibit the colony formation and migration ability of H1299 cells, and induce apoptosis in H1299 cells. Acute toxicity experiments on mice demonstrated that compound 7a exhibited low toxicity in mice. Based on these results, it is proposed that 7a and 7j could potentially be developed as novel drugs for the treatment of lung cancer.

Abstract Metagenomic binning is an essential technique for genome-resolved characterization of uncultured microorganisms in various ecosystems but hampered by the low efficiency of binning tools in adequately recovering metagenome-assembled genomes (MAGs). Here, we introduce BASALT (Binning Across a Series of Assemblies Toolkit) for binning and refinement of short- and long-read sequencing data. BASALT employs multiple binners with multiple thresholds to produce initial bins, then utilizes neural networks to identify core sequences to remove redundant bins and refine non-redundant bins. Using the same assemblies generated from Critical Assessment of Metagenome Interpretation (CAMI) datasets, BASALT produces up to twice as many MAGs as VAMB, DASTool, or metaWRAP. Processing assemblies from a lake sediment dataset, BASALT produces ~30% more MAGs than metaWRAP, including 21 unique class-level prokaryotic lineages. Functional annotations reveal that BASALT can retrieve 47.6% more non-redundant opening-reading frames than metaWRAP. These results highlight the robust handling of metagenomic sequencing data of BASALT.

Abstract Dipterocarpoideae species form the emergent layer of Asian rainforests. They are the indicator species for Asian rainforest distribution, but they are severely threatened. Here, to understand their adaptation and population decline, we assemble high-quality genomes of seven Dipterocarpoideae species including two autotetraploid species. We estimate the divergence time between Dipterocarpoideae and Malvaceae and within Dipterocarpoideae to be 108.2 (97.8‒118.2) and 88.4 (77.7‒102.9) million years ago, and we identify a whole genome duplication event preceding dipterocarp lineage diversification. We find several genes that showed a signature of selection, likely associated with the adaptation to Asian rainforests. By resequencing of two endangered species, we detect an expansion of effective population size after the last glacial period and a recent sharp decline coinciding with the history of local human activities. Our findings contribute to understanding the diversification and adaptation of dipterocarps and highlight anthropogenic disturbances as a major factor in their endangered status.

Abstract Background The COVID-19 pandemic is a persistent global threat to public health. As for the emerging variants of SARS-CoV-2, it is necessary to develop vaccines that can induce broader immune responses, particularly vaccines with weak cellular immunity. Methods In this study, we generated a double-layered N-S1 protein nanoparticle (N-S1 PNp) that was formed by desolvating N protein into a protein nanoparticle as the core and crosslinking S1 protein onto the core surface against SARS-CoV-2. Results Vaccination with N-S1 PNp elicited robust humoral and vigorous cellular immune responses specific to SARS-CoV-2 in mice. Compared to soluble protein groups, the N-S1 PNp induced a higher level of humoral response, as evidenced by the ability of S1-specific antibodies to block hACE2 receptor binding and neutralize pseudovirus. Critically, N-S1 PNp induced Th1-biased, long-lasting, and cross-neutralizing antibodies, which neutralized the variants of SARS-CoV-2 with minimal loss of activity. N-S1 PNp induced strong responses of CD4+ and CD8+ T cells, mDCs, Tfh cells, and GCs B cells in spleens. Conclusions These results demonstrate that N-S1 PNp vaccination is a practical approach for promoting protection, which has the potential to counteract the waning immune responses against SARS-CoV-2 variants and confer broad efficacy against future new variants. This study provides a new idea for the design of next-generation SARS-CoV-2 vaccines based on the B and T cells response coordination. Graphical Abstract Steps involved in the preparation of double-layered N-S1 protein nanoparticle vaccines and experimental design performed in combating virus infection. After intramuscular immunization of mice, the double-layered N-S1 protein nanovaccine could effectively promote the maturation of antigen-presenting and mature dendritic cells, robust broad-spectrum neutralizing antibody production, cytokines secretion, robust mDC, Tfh cell, and GCs B cell responses induction, T-cell memory formation and durable antibody responses, and unique global transcriptome characteristics, thus achieving a robust cellular immunity and broad antibody responses against SARS-CoV-2 based on the B and T cells response coordination

Abstract The management of infected wounds is always of great significance and urgency in clinical and biomedical fields. Recent efforts in this area are focusing on the development of functional wound patches with effective antibacterial, drug delivery, and sensor properties. Here, we present novel hyaluronic acid (HA) microneedle patches with these features by encapsulating aminobenzeneboronic acid‐modified gold nanoclusters (A‐GNCs) for infected wound management. The A‐GNCs loaded microneedle patches were derived from negative‐mold replication and showed high mechanical strength to penetrate the skin. The release of the A‐GNCs was realized by the degradation of HA, and the self‐monitor of the released actives was based on the dynamic bright orange fluorescence emitted from A‐GNCs under ultraviolet radiation. As the A‐GNCs could destroy bacteria membranes, the microneedle patches were with excellent in vitro antibiosis ability. Based on these features, we have demonstrated the bacteria inhibition, residual drug self‐monitoring, and wound healing promotion abilities of the microneedle patches in Escherichia coli‐ or Staphylococcus aureus‐infected wound management. These results indicated the great potential of such A‐GNCs loaded microneedle patches for clinical applications.

Reducing methane emissions in the oil and gas industry is a top priority for the current international community in addressing climate change. Methane emissions from the energy sector exhibit strong temporal variability and ground monitoring networks can provide time-continuous measurements of methane concentrations, enabling the rapid detection of sudden methane leaks in the oil and gas industry. Therefore, identifying specific locations within oil fields to establish a cost-effective and reliable methane monitoring ground network is an urgent and significant task. In response to this challenge, this study proposes a technical workflow that, utilizing emission inventories, atmospheric transport models, and intelligent computing techniques, automatically determines the optimal locations for monitoring stations based on the input quantity of monitoring sites. This methodology can automatically and quantitatively assess the observational effectiveness of the monitoring network. The effectiveness of the proposed technical workflow is demonstrated using the Shengli Oilfield, the second-largest oil and gas extraction base in China, as a case study. We found that the Genetic Algorithm can help find the optimum locations effectively. Besides, the overall observation effectiveness grew from 1.7 to 5.6 when the number of site increased from 1 to 9. However, the growth decreased with the increasing site number. Such a technology can assist the oil and gas industry in better monitoring methane emissions resulting from oil and gas extraction.

IntroductionNitrososphaeria, formerly known as Thaumarchaeota, constitute a diverse and widespread group of ammonia-oxidizing archaea (AOA) inhabiting ubiquitously in marine and terrestrial environments, playing a pivotal role in global nitrogen cycling. Despite their importance in Earth’s ecosystems, the cellular organization of AOA remains largely unexplored, leading to a significant unanswered question of how the machinery of these organisms underpins metabolic functions.MethodsIn this study, we combined spherical-chromatic-aberration-corrected cryo-electron tomography (cryo-ET), scanning transmission electron microscopy (STEM), and energy dispersive X-ray spectroscopy (EDS) to unveil the cellular organization and elemental composition of Nitrosopumilus maritimus SCM1, a representative member of marine Nitrososphaeria.Results and DiscussionOur tomograms show the native ultrastructural morphology of SCM1 and one to several dense storage granules in the cytoplasm. STEM-EDS analysis identifies two types of storage granules: one type is possibly composed of polyphosphate and the other polyhydroxyalkanoate. With precise measurements using cryo-ET, we observed low quantity and density of ribosomes in SCM1 cells, which are in alignment with the documented slow growth of AOA in laboratory cultures. Collectively, these findings provide visual evidence supporting the resilience of AOA in the vast oligotrophic marine environment.

Current research has increasingly focused on the preventive role of individual legal socialization in crime. The socialization of legal emotions is an important part of legal socialization. Building upon existing literature, this study, conducted through two sub-studies, investigated the influencing factors of legal emotions in N mainland Chinese university students and the mechanisms through which legal emotions impact aggressive behavior. In study 1, the results indicated that mother-child attachment, innovation spirit, and positive emotional expression positively predicted positive legal emotion, while mother-child attachment, dependency dimension in adult attachment, and positive emotional expression negatively predicted negative legal emotions. The anxiety dimension in adult attachment and negative emotional expression positively predicted negative legal emotions. In study 2, Positive legal emotion among university students could directly negatively predict aggressive behavior or exert influence through social alienation. Negative legal emotions could not only directly positively predict aggressive behavior but also partly affect it through social alienation. In summary, our study not only identified factors that influence legal emotions, but also found that legal emotions have an impact on aggressive behavior directly or indirectly through social alienation. Our research findings have significant implications for cultivating positive legal emotion in university students and curbing aggressive behavior. This can be achieved by promoting the legal socialization of university students and ultimately contributing to crime prevention.

Coronavirus disease 2019 (COVID-19), a severely spreading pandemic, has dramatically brought physiological and economical burdens to people. Although the injectable vaccines have some achievements for coronavirus defense, they still generate accompanied pain, untoward reaction and cannot take part in mucosal immunity. Inhalable vaccines, as a safe, facile and efficient strategy, have been presented to protect body from virus by inducing robust mucosal immunity. Here, we give a perspective of an inhalable COVID-19 vaccine composed of lung-derived exosomes (a type of virus-like particle) conjugated with viral receptor-binding domain. The lung-derived exosomes act as carriers, such inhalable particles successfully reach at lung and reveal wider distribution and longer retention on respiratory mucosa. In addition, such vaccines induce the high production of specific antibodies and T cells in lung, significantly protecting host against coronavirus invasion. It is conceived that inhalable virus-like particles with long-term stability wound open a new avenue for vaccines delivery and further achieve vaccine popularization to against with COVID-19 pandemic.

Abstract As a “third code”, translational language attracts considerable attention in linguistics research due to its distinctive features. Adopting the quantitative linguistic approach, the current study examines its features by investigating the mean dependency distance (MDD), as well as the probability distribution of the individual dependency distances (DDs) and distribution of a high-frequency dependency type in translational language. The MDD and the distributions were tested in a self-built corpus which contains parallel and comparable language materials in both Chinese-English and English-Chinese translations. The results show that: (1) compared with source texts and native texts, translated texts in both translation directions yield an MDD in between; (2) both the distribution of DDs and that of the dependency type nsubj follow the Zipf-Alekseev distribution in translated texts, as in source texts and native texts; (3) the in-between feature is further confirmed by parameters a and b in Chinese-English translation materials when fitting the distribution of DDs to Zipf-Alekseev distribution; (4) translational texts in both directions show higher a and lower b than their source and native texts when fitting the DD Distribution of dependency type nsubj to Zipf-Alekseev distribution. These findings suggest that, on the one hand, dependency distance minimization (DDM) occurs in translational language, which is consistent with native language and reflects a general tendency of natural languages to reduce cognitive load; on the other hand, translational language presents distinctive feature in nsubj type, but in most cases, it is subject to the gravitational pull of both source and target language systems, exhibiting a “compromise” feature in between. The current study highlights the contribution of syntactic quantitative methods to deeper understanding of the complexity of translational language and its cognitive underpinnings.

Objective: To systematically evaluate the efficacy and safety of Gong Liu Ning as a complimentary therapy in treatment for uterine fibroids. Methods: Randomized controlled trials on Gong Liu Ning for uterine fibroids from China National Knowledge Infrastructure, Wanfang, China Science and Technology Journal Database, PubMed, Cochrane Library, Embase, Web of Science, Chinese Clinical Trial Registry, and ClinicalTrials.gov were retrieved from inception to March 20, 2023. The risk of bias tool was used to evaluate the quality of the selected studies after they met inclusion and exclusion criteria. The GRADE tool was used to evaluate the quality of evidence, and the RevMan5.3 software was used for data analysis. Results: Twenty studies with 1907 patients were analyzed. The meta-analysis results of 15 studies showed that Gong Liu Ning combined with mifepristone was more efficacious than those observed in controls taking only Western medicine (risk ratio [RR] = 1.22, 95% confidence interval [CI] = [1.11, 1.33], P

Range-resolved CH4 concentration measurement is important prior data for atmospheric physical and chemical models. Ground-based differential absorption lidar (DIAL) can measure the vertical distribution of CH4 concentration in the atmosphere. The traditional method uses lidar observational data and the lidar equation to calculate profiles, but the inversion accuracy is greatly affected by noise. Although some denoising methods can improve accuracy at low altitudes, the low signal-to-noise ratio caused by the effect of aerosol Mie scattering and lower aerosol concentrations at high altitudes cannot be solved. Here, an improved cubic smoothing spline fitting CH4 concentration profile inversion method is proposed to address this challenge. By adding a penalty term of the second derivative of the conventional cubic spline function to the objective function, this penalty term acts to smooth the fitting, allowing the fitting function to avoid necessarily passing through those noisy sampling points. This avoids the large fluctuations caused by noisy sampling points, effectively suppresses noise, captures signals with lower noise levels, and thereby enhances the inversion accuracy of the profiles. Simulations and case studies demonstrated the superiority of the proposed method. Compared with the traditional method, cubic smoothing spline fitting can reduce the mean error of the whole CH4 profile by 85.54%. The standard deviation of CH4 concentration retrieved is 3.59 ppb–90.29 ppb and 0.01 ppb–6.75 ppb smaller than the traditional method and Chebyshev fitting, respectively. Three real cases also indicate that the CH4 concentration retrieved by cubic smoothing spline fitting is more consistent with in-situ measurements. In addition, long-term DIAL observations have also revealed notable diurnal and seasonal trends in CH4 concentration at observation sites.