Spatial-Temporal Graph Enhanced DETR Towards Multi-Frame 3D Object Detection.

The Detection Transformer (DETR) has revolutionized the design of CNN-based object detection systems, showcasing impressive performance. However, its potential in the domain of multi-frame 3D object detection remains largely unexplored. In this paper, we present STEMD, a novel end-to-end framework that enhances the DETR-like paradigm for multi-frame 3D object detection by addressing three key aspects specifically tailored for this task. First, to model the inter-object spatial interaction and complex temporal dependencies, we introduce the spatial-temporal graph attention network, which represents queries as nodes in a graph and enables effective modeling of object interactions within a social context. To solve the problem of missing hard cases in the proposed output of the encoder in the current frame, we incorporate the output of the previous frame to initialize the query input of the decoder. Finally, it poses a challenge for the network to distinguish between the positive query and other highly similar queries that are not the best match. And similar queries are insufficiently suppressed and turn into redundant prediction boxes. To address this issue, our proposed IoU regularization term encourages similar queries to be distinct during the refinement. Through extensive experiments, we demonstrate the effectiveness of our approach in handling challenging scenarios, while incurring only a minor additional computational overhead. The code is publicly available at https://github.com/Eaphan/STEMD.

Propensity score matched comparison of transperitoneal laparoscopic ureterolithotomy and percutaneous nephrolithotomy for management of large impacted proximal ureteral stones with long-term follow-up.

Impacted proximal ureteral stones (IPUS) present challenging clinical scenarios due to their persistent nature and associated complications. While ureterorenoscopy (URS) lithotripsy is recommended as the primary treatment, controversies exist regarding the optimal management of such stones. In this retrospective analysis, we compared the operative outcomes and long-term results of transperitoneal laparoscopic ureterolithotomy (LU) and percutaneous nephrolithotomy (PCNL) for IPUS larger than 15 mm. Propensity score matching (PSM) was employed to mitigate potential selection biases. Following PSM, 83 patients in each cohort exhibited comparable baseline characteristics. LU demonstrated a superior surgical success rate (100% vs. 96.4%, p = 0.244) and significantly lower perioperative hemoglobin decline (0.6 ± 0.4 g/dL vs. 1.5 ± 0.7 g/dL, p = 0.036) compared to PCNL. Additionally, LU exhibited a higher stone-free rate after 2 months (100% vs. 91.6%, p = 0.043), but a longer duration of catheterization (7.4 ± 1.2 days vs. 3.5 ± 2.2 days vs., p = 0.011). Conversely, PCNL was associated with a higher incidence of total complications (21.7% vs. 9.6%, p = 0.033) and stone recurrence during a mean period of 40-month follow-up (20.5% vs. 8.4%, p = 0.027). Transperitoneal LU and PCNL represent effective interventions for managing IPUS exceeding 15 mm. Notably, LU emerges as a preferable option over PCNL, offering superior stone clearance rates, reduced perioperative complications, and lower recurrence rates.

Research progress on biosynthesis of erythritol and multi-dimensional optimization of production strategies.

Erythritol, as a new type of natural sweetener, has been widely used in food, medical, cosmetics, pharmaceutical and other fields due to its unique physical and chemical properties and physiological functions. In recent years, with the continuous development of strategies such as synthetic biology, metabolic engineering, omics-based systems biology and high-throughput screening technology, people's understanding of the erythritol biosynthesis pathway has gradually deepened, and microbial cell factories with independent modification capabilities have been successfully constructed. In this review, the cheap feedstocks for erythritol synthesis are introduced in detail, the environmental factors affecting the synthesis of erythritol and its regulatory mechanism are described, and the tools and strategies of metabolic engineering involved in erythritol synthesis are summarized. In addition, the study of erythritol derivatives is helpful in expanding its application field. Finally, the challenges that hinder the effective production of erythritol are discussed, which lay a foundation for the green, efficient and sustainable production of erythritol in the future and breaking through the bottleneck of production.

Molybdenum iron carbide-copper hybrid as efficient electrooxidation catalyst for oxygen evolution reaction and synthesis of cinnamaldehyde/benzalacetone.

Oxygen evolution reaction (OER) is the efficiency limiting half-reaction in water electrolysis for green hydrogen production due to the 4-electron multistep process with sluggish kinetics. The electrooxidation of thermodynamically more favorable organics accompanied by CC coupling is a promising way to synthesize value-added chemicals instead of OER. Efficient catalyst is of paramount importance to fulfill such a goal. Herein, a molybdenum iron carbide-copper hybrid (Mo2C-FeCu) was designed as anodic catalyst, which demonstrated decent OER catalytic capability with low overpotential of 238 mV at response current density of 10 mA cm-2 and fine stability. More importantly, the Mo2C-FeCu enabled electrooxidation assisted aldol condensation of phenylcarbinol with α-H containing alcohol/ketone in weak alkali electrolyte to selective synthesize cinnamaldehyde/benzalacetone at reduced potential. The hydroxyl and superoxide intermediate radicals generated at high potential are deemed to be responsible for the electrooxidation of phenylcarbinol and aldol condensation reactions to afford cinnamaldehyde/benzalacetone. The current work showcases an electrochemical-chemical combined CC coupling reaction to prepare organic chemicals, we believe more widespread organics can be synthesized by tailored electrochemical reactions.

A spatiotemporal graph transformer approach for Alzheimer's disease diagnosis with rs-fMRI.

Alzheimer's disease (AD) is a neurodegenerative disease accompanied by cognitive impairment. Early diagnosis is crucial for the timely treatment and intervention of AD. Resting-state functional magnetic resonance imaging (rs-fMRI) records the temporal dynamics and spatial dependency in the brain, which have been utilized for automatically diagnosis of AD in the community. Existing approaches of AD diagnosis using rs-fMRI only assess functional connectivity, ignoring the spatiotemporal dependency mining of rs-fMRI. In addition, it is difficult to increase diagnosis accuracy due to the shortage of rs-fMRI sample and the poor anti-noise ability of model. To deal with these problems, this paper proposes a novel approach for the automatic diagnosis of AD, namely spatiotemporal graph transformer network (STGTN). The proposed STGTN can effectively extract spatiotemporal features of rs-fMRI. Furthermore, to solve the sample-limited problem and to improve the anti-noise ability of the proposed model, an adversarial training strategy is adopted for the proposed STGTN to generate adversarial examples (AEs) and augment training samples with AEs. Experimental results indicate that the proposed model achieves the classification accuracy of 92.58%, and 85.27% with the adversarial training strategy for AD vs. normal control (NC), early mild cognitive impairment (eMCI) vs. late mild cognitive impairment (lMCI) respectively, outperforming the state-of-the-art methods. Besides, the spatial attention coefficients reflected from the designed model reveal the importance of brain connections under different classification tasks.

A new perspective: deciphering the aberrance and clinical implication of disulfidptosis signatures in clear cell renal cell carcinoma.

Recent research has discovered disulfidptosis as a form of programmed cell death characterized by disulfide stress. However, its significance in clear cell renal cell carcinoma (ccRCC) remains unclear. To investigate this, data from The Cancer Genome Atlas were collected and used to identify ccRCC subgroups. Unsupervised clustering was employed to determine ccRCC heterogeneity. The mutation landscape and immune microenvironment of the subgroups were analyzed. The Disulfidptosis-Related Score was calculated using the LASSO-penalized Cox regression algorithm. The E-MATB-1980 cohort was used to validate the signature. The role of SLC7A11 in ccRCC metastasis was explored using western blotting and Transwell assays. Disulfidptosis-related genes are commonly downregulated in cancers and are linked to hypermethylation and copy number variation. The study revealed that ccRCC is divided into two sub-clusters: the disulfidptosis-desert sub-cluster, which is associated with a poor prognosis, a higher mutation frequency, and an immunosuppressive microenvironment. A 14-gene prognostic model was developed using differentially expressed genes and was validated in the E-MATB-1980 cohort. The low-risk group demonstrated longer overall and disease-free survival and responded better to targeted immunotherapy. Results from in vitro experiments identified SLC7A11 as a key participant in ccRCC metastasis.

Low flow-resistance solid phase extraction of fluoroquinolones in water and food samples by high-pressure wet spinning porous polyimide microfibers.

In this work, porous polyimide microfibers (PI-µF) were prepared by high-pressure wet spinning method, and successfully applied as adsorbents for solid phase extraction (SPE) of fluoroquinolones (FQs) in water and food samples. The PI-µFs of ∼10, 25, 50, 100 µm in diameter could be controlled by the inner diameter of quartz capillary nozzles. The flow resistance of SPE cartridges packed with 10 µm PI microfiber (10-PI-µF) and 25-PI-µF was comparable to or even lower than that of commercial SPE cartridges, while the flow resistance of 50-PI-µF and 100-PI-µF SPE cartridges was increased obviously due to tiny broken pieces. The 10-PI-µF and 25-PI-µF have a specific surface area of 102 m2 g-1 and 76 m2 g-1, mesopores of 22-32 nm, and large breakthrough volume of 110 mL/5 mg and 85 mL/5 mg for FQs, while the 50-PI-µF and 100-PI-µF had much lower specific surface area and hardly had retention for FQs. FQs from tap water, egg and milk samples were then extracted by PI-µF SPE, and analyzed by high performance liquid chromatography-fluorescence detector (HPLC-FLD). SPE parameters as type of elution solvent, elution solvent volume, pH value of sample solution, flow rate of sample solution, and breakthrough volume were first optimized in detail. Under the optimal conditions, the PI-µF SPE/HPLC-FLD method showed high recoveries (96.8%-107%), wide linearity (0.05-50 µg L-1, or 0.01-10 µg L-1), high determination coefficients (R2 ≥0.9992), and low limits of detection (LODs, 0.005-0.014 µg L-1). For the real tap water, egg and milk samples, the recoveries and RSDs were 81-119% and 0.8-9.8%, respectively. The results show that porous microfiber up to 25 µm in diameter is a promising solid-phase extraction adsorbent with the lowest flow resistance that can be used for trace organic pollutants in water and food samples.

An integrated machine learning framework for developing and validating a diagnostic model of major depressive disorder based on interstitial cystitis-related genes.

BACKGROUND: Major depressive disorder (MDD) and interstitial cystitis (IC) are two highly debilitating conditions that often coexist with reciprocal effect, significantly exacerbating patients' suffering. However, the molecular underpinnings linking these disorders remain poorly understood. METHODS: Transcriptomic data from GEO datasets including those of MDD and IC patients was systematically analyzed to develop and validate our model. Following removal of batch effect, differentially expressed genes (DEGs) between respective disease and control groups were identified. Shared DEGs of the conditions then underwent functional enrichment analyses. Additionally, immune infiltration analysis was quantified through ssGSEA. A diagnostic model for MDD was constructed by exploring 113 combinations of 12 machine learning algorithms with 10-fold cross-validation on the training sets following by external validation on test sets. Finally, the "Enrichr" platform was utilized to identify potential drugs for MDD. RESULTS: Totally, 21 key genes closely associated with both MDD and IC were identified, predominantly involved in immune processes based on enrichment analyses. Immune infiltration analysis revealed distinct profiles of immune cell infiltration in MDD and IC compared to healthy controls. From these genes, a robust 11-gene (ABCD2, ATP8B4, TNNT1, AKR1C3, SLC26A8, S100A12, PTX3, FAM3B, ITGA2B, OLFM4, BCL7A) diagnostic signature was constructed, which exhibited superior performance over existing MDD diagnostic models both in training and testing cohorts. Additionally, epigallocatechin gallate and 10 other drugs emerged as potential targets for MDD. CONCLUSION: Our work developed a diagnostic model for MDD employing a combination of bioinformatic techniques and machine learning methods, focusing on shared genes between MDD and IC.

Discovery of a Novel CSF-1R Inhibitor with Highly Improved Pharmacokinetic Profiles and Superior Efficacy in Colorectal Cancer Immunotherapy.

Blocking CSF-1/CSF-1R pathway has emerged as a promising strategy to remodel tumor immune microenvironment (TME) by reprogramming tumor-associated macrophages (TAMs). In this work, a novel CSF-1R inhibitor C19 with a highly improved pharmacokinetic profile and in vivo anticolorectal cancer (CRC) efficiency was successfully discovered. C19 could effectively reprogram M2-like TAMs to M1 phenotype and reshape the TME by inducing the recruitment of CD8+ T cells into tumors and reducing the infiltration of immunosuppressive Tregs/MDSCs. Deeper mechanistic studies revealed that C19 facilitated the infiltration of CD8+ T cells by enhancing the secretion of chemokine CXCL9, thus significantly potentiating the anti-CRC efficiency of PD-1 blockade. More importantly, C19 combined with PD-1 mAb could induce durable antitumor immune memory, effectively overcoming the recurrence of CRC. Taken together, our findings suggest that C19 is a promising therapeutic option for sensitizing CRC to anti-PD-1 therapy.

Analysis and Visualization of Single-Cell Sequencing Data with Scanpy and MetaCell: A Tutorial.

The emergence and development of single-cell RNA sequencing (scRNA-seq) techniques enable researchers to perform large-scale analysis of the transcriptomic profiling at cell-specific resolution. Unsupervised clustering of scRNA-seq data is central for most studies, which is essential to identify novel cell types and their gene expression logics. Although an increasing number of algorithms and tools are available for scRNA-seq analysis, a practical guide for users to navigate the landscape remains underrepresented. This chapter presents an overview of the scRNA-seq data analysis pipeline, quality control, batch effect correction, data standardization, cell clustering and visualization, cluster correlation analysis, and marker gene identification. Taking the two broadly used analysis packages, i.e., Scanpy and MetaCell, as examples, we provide a hands-on guideline and comparison regarding the best practices for the above essential analysis steps and data visualization. Additionally, we compare both packages and algorithms using a scRNA-seq dataset of the ctenophore Mnemiopsis leidyi, which is representative of one of the earliest animal lineages, critical to understanding the origin and evolution of animal novelties. This pipeline can also be helpful for analyses of other taxa, especially prebilaterian animals, where these tools are under development (e.g., placozoan and Porifera).

Bridging Visual and Textual Semantics: Towards Consistency for Unbiased Scene Graph Generation.

Scene Graph Generation (SGG) aims to detect visual relationships in an image. However, due to long-tailed bias, SGG is far from practical. Most methods depend heavily on the assistance of statistics co-occurrence to generate a balanced dataset, so they are dataset-specific and easily affected by noises. The fundamental cause is that SGG is simplified as a classification task instead of a reasoning task, thus the ability capturing the fine-grained details is limited and the difficulty in handling ambiguity is increased. By imitating the way of dual process in cognitive psychology, a Visual-Textual Semantics Consistency Network (VTSCN) is proposed to model the SGG task as a reasoning process, and relieve the long-tailed bias significantly. In VTSCN, as the rapid autonomous process (Type1 process), we design a Hybrid Union Representation (HUR) module, which is divided into two steps for spatial awareness and working memories modeling. In addition, as the higher order reasoning process (Type2 process), a Global Textual Semantics Modeling (GTS) module is designed to individually model the textual contexts with the word embeddings of pairwise objects. As the final associative process of cognition, a Heterogeneous Semantics Consistency (HSC) module is designed to balance the type1 process and the type2 process. Lastly, our VTSCN raises a new way for SGG model design by fully considering human cognitive process. Experiments on Visual Genome, GQA and PSG datasets show our method is superior to state-of-the-art methods, and ablation studies validate the effectiveness of our VTSCN. The source codes are released on GitHub: https://github.com/Nora-Zhang98/VTSCN.

Wnt signaling: Modulating tumor-associated macrophages and related immunotherapeutic insights.

Wnt signaling pathways are highly conserved cascades that mediate multiple biological processes through canonical or noncanonical pathways, from embryonic development to tissue maintenance, but they also contribute to the pathogenesis of numerous cancers. Recent studies have revealed that Wnt signaling pathways critically control the interplay between cancer cells and tumor-associated macrophages (TAMs) in the tumor microenvironment (TME) and potentially impact the efficacy of cancer immunotherapy. In this review, we summarize the evidence that Wnt signaling pathways boost the maturation and infiltration of macrophages for immune surveillance in the steady state but also polarize TAMs toward immunosuppressive M2-like phenotypes for immune escape in the TME. Both cancer cells and TAMs utilize Wnt signaling to transmit signals, and this interaction is crucial for the carcinogenesis and progression of common solid cancers, such as colorectal, gastric, hepatocellular, breast, thyroid, prostate, kidney, and lung cancers; osteosarcoma; and glioma. Specifically, compared with those in solid cancers, Wnt signaling pathways play a distinct role in the pathogenesis of leukemia. Efforts to develop Wnt-based drugs for cancer treatment are still ongoing, and some indeed enhance the anticancer immune response. We believe that the combination of Wnt signaling-based therapy with conventional or immune therapies is a promising therapeutic approach and can facilitate personalized treatment for most cancers.

Electrocatalytic reduction of nitrate to ammonia by Pd/In modified Nickel foam electrode in aqueous solution.

Nitrate pollution in surface water and ground water has drawn wide attention, which has brought challenges to human health and natural ecology. Electroreduction of nitrate to NH3 in waste water was a way to turn waste into wealth, which has attracted interest of many researchers. Using Nickel foam as substrate, we prepared Pd/In bimetallic electrode (NF-Pd/In) according to a two-step electrodeposition method. There are many irregularly shaped particles in the size range of 10 nm-100 nm accumulated on the surface of prepared NF-Pd/In electrode, which could supply high specific area and more active sites for nitrate electroreduction. FESEM-EDS, XRD and XPS analysis confirmed the uniform distribution of Pd and In on the surface of prepared NF-Pd/In electrode, with a mass ratio of 4.5/1. Above 96% of 100 mg/L NO3--N was removed and 95% of NH3 selectivity was reached after 5 h of reaction under -1.6 V vs. Ag/AgCl sat. KCl when using 0.05 mol/L of Na2SO4 as electrolyte. High concentration of NaCl (0.05 mol/L) in the test solution dramatically decreased the NH3 selectivity because the produced NH3 could be further oxidized to N2 by the formed HClO from Cl-. EIS tests indicated that the prepared NF-Pd/In electrode showed much lower electrode resistance than NF due to the adsorptive property and electrocatalytic ability for nitrate removal. Density functional theory (DFT) calculations indicated that the presence of In could promote the conversion of NO3- to *NO3 during the process of nitrate electroreduction to NH3. Circulating tests demonstrated the stability of prepared NF-Pd/In electrode.

An Integrated Machine Learning Framework Identifies Prognostic Gene Pair Biomarkers Associated with Programmed Cell Death Modalities in Clear Cell Renal Cell Carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is a common and lethal urological malignancy for which there are no effective personalized therapeutic strategies. Programmed cell death (PCD) patterns have emerged as critical determinants of clinical prognosis and immunotherapy responses. However, the actual clinical relevance of PCD processes in ccRCC is still poorly understood. METHODS: We screened for PCD-related gene pairs through single-sample gene set enrichment analysis (ssGSEA), consensus cluster analysis, and univariate Cox regression analysis. A novel machine learning framework incorporating 12 algorithms and 113 unique combinations were used to develop the cell death-related gene pair score (CDRGPS). Additionally, a radiomic score (Rad_Score) derived from computed tomography (CT) image features was used to classify the CDRGPS status as high or low. Finally, we conclusively verified the function of PRSS23 in ccRCC. RESULTS: The CDRGPS was developed through an integrated machine learning approach that leveraged 113 algorithm combinations. CDRGPS represents an independent prognostic biomarker for overall survival and demonstrated consistent performance between training and external validation cohorts. Moreover, CDRGPS showed better prognostic accuracy compared to seven previously published cell death-related signatures. In addition, patients classified as high-risk by CDRGPS exhibited increased responsiveness to tyrosine kinase inhibitors (TKIs), mammalian Target of Rapamycin (mTOR) inhibitors, and immunotherapy. The Rad_Score demonstrated excellent discrimination for predicting high versus low CDRGPS status, with an area under the curve (AUC) value of 0.813 in the Cancer Imaging Archive (TCIA) database. PRSS23 was identified as a significant factor in the metastasis and immune response of ccRCC, thereby validating experimental in vitro results. CONCLUSIONS: CDRGPS is a robust and non-invasive tool that has the potential to improve clinical outcomes and enable personalized medicine in ccRCC patients.

Magnetic polyimide nanocomposite for analysis of parabens in cooking wine by magnetic solid-phase extraction coupled with gas chromatography - Mass spectrometry.

A magnetic polyimide (PI) nanocomposite has been synthesized by phase inversion of PI and simultaneous encapsulation of Fe3O4 nanoparticles. The Fe3O4/PI nanocomposite was characterized by a variety of characterization techniques, including infrared spectroscopy, scanning electron microscopy, transmission electron microscopy, nitrogen adsorption-desorption isotherms, and vibrating sample magnetometry. The results showed that the prepared nanocomposite had a homogeneous structure, adequate specific surface area (76.1 m2/g) and high saturation magnetization (42.9 emu/g). Using parabens as model analytes, the performance of the Fe3O4/PI nanocomposite as an adsorbent for magnetic solid-phase extraction (MSPE) was evaluated. The extracted parabens were desorbed and determined by gas chromatography-mass spectrometry (GC-MS). The parameters affecting the extraction and desorption efficiency of parabens were optimized. Under the optimal conditions, the developed MSPE/GC-MS method was successfully applied to the determination of parabens in cooking wine. The MSPE/GC-MS method exhibited broad linearity (0.2-100 µg/L), low detection limits (0.04-0.05 µg/L), and satisfactory extraction recoveries (79.2 %-113.3 %) with relative standard deviations (RSDs) ranging from 0.7 % to 10.4 %. For real cooking wine samples, the spiked recoveries ranged from 91.7 % to 118.7 % with RSDs of 1.0 %-11.2 %. The results demonstrated that the Fe3O4/PI nanocomposite was an effective adsorbent, and this work provides a novel reference for the easy preparation of magnetic adsorbent materials.

Molybdenum, tungsten doped cobalt phosphides as efficient catalysts for coproduction of hydrogen and formate by glycerol electrolysis.

H2 and formate are important energy carriers in fuel-cells and feedstocks in chemical industry. The hydrogen evolution reaction (HER) coupling with electro-oxidative cleavage of thermodynamically favorable polyols is a promising way to coproduce H2 and formate via electrochemical means, highly active catalysts for HER and electrooxidative cleavage of polycols are the key to achieve such a goal. Herein, molybdenum (Mo), tungsten (W) doped cobalt phosphides (Co2P) deposited onto nickel foam (NF) substrate, denoted as Mo-Co2P/NF and W-Co2P/NF, respectively, were investigated as catalytic electrodes for HER and electrochemical glycerol oxidation reaction (GOR) to yield H2 and formate. The W-Co2P/NF electrode exhibited low overpotential (η) of 113 mV to attain a current density (J) of -100 mA cm-2 for HER, while the Mo-Co2P/NF electrode demonstrated high GOR efficiency for selective production of formate. In situ Raman and infrared spectroscopic characterizations revealed that the evolved CoO2 from Co2P is the genuine catalytic sites for GOR. The asymmetric electrolyzer based on W-Co2P/NF cathode and Mo-Co2P/NF anode delivered a J = 100 mA cm-2 at 1.8 V voltage for glycerol electrolysis, which led to 18.2 % reduced electricity consumption relative to water electrolysis. This work highlights the potential of heteroelement doped phosphide in catalytic performances for HER and GOR, and opens up new avenue to coproduce more widespread commodity chemicals via gentle and sustainable electrocatalytic means.

CSF1R inhibition reprograms tumor-associated macrophages to potentiate anti-PD-1 therapy efficacy against colorectal cancer.

PD-1 blockade therapy has made great breakthroughs in treatment of multiple solid tumors. However, patients with microsatellite-stable (MSS) colorectal cancer (CRC) respond poorly to anti-PD-1 immunotherapy. Although CRC patients with microstatellite instability (MSI) or microsatellite instability-high (MSI-H) can benefit from PD-1 blockade therapy, there are still some problems such as tumor recurrence. Tumor-associated macrophages (TAMs), most abundant immune components in tumor microenvironment (TME), largely limit the therapeutic efficacy of anti-PD-1 against CRC. The CSF1/CSF1R pathway plays a key role in regulating macrophage polarization, and blocking CSF1R signaling transduction may be a potential strategy to effectively reprogram macrophages and remodel TME. Here, we found that increasing expression of CSF1R in macrophages predicted poor prognosis in CRC cohort. Furthermore, we discovered a novel potent CSF1R inhibitor, PXB17, which significantly reprogramed M2 macrophages to M1 phenotype. Mechanically, PXB17 significantly blocked activation of PI3K/AKT/mTORC1 signaling, resulting in inhibition of cholesterol biosynthesis. Results from 3D co-culture system suggested that PXB17-repolarized macrophages could induce infiltration of CD8+ T lymphocytes in tumors and improve the immunosuppressive microenvironment. In vivo, PXB17 significantly halted CRC growth, with a stronger effect than PLX3397. In particular, PXB17 potently enhanced therapeutic activity of PD-1 mAb in CT-26 (MSS) model and prevented tumor recurrence in MC-38 (MSI-H) model by promoting formation of long-term memory immunity. Our study opens a new avenue for CSF1R in tumor innate and adaptive anti-tumor immunomodulatory activity and suggests that PXB17 is a promising immunotherapy molecule for enhancing the efficacy of PD-1 mAb or reducing tumor recurrence of CRC.

Amorphous-crystalline heterostructures enable energy-level matching of cobalt sulfide/nickel iron layered double hydroxide for efficient oxygen evolution reaction.

Interface engineering of heterostructures has emerged as a promising approach to enhance the catalytic activity of nonprecious electrocatalysts. Herein, a novel amorphous cobalt sulfide-crystalline nickel iron layered double hydroxide (a-CoS@NiFe-LDH) hybrid material is presented for application as an electrocatalyst for oxygen evolution reaction (OER). Benefitting from the well-matched energy level structures, the a-CoS@NiFe-LDH catalyst delivers a low overpotential of 221 ± 14 mV at an OER current density of 20 mA cm-2 and a small Tafel slope of 83.1 mV dec-1, showing good OER properties. First-principle computations reveal that the electronic interaction between amorphous cobalt sulfide (a-CoS) and crystalline nickel iron layered double hydroxide (NiFe-LDH) components within a-CoS@NiFe-LDH promotes the adsorbate evolution mechanism and reduces the adsorption energies for oxygen intermediates, thereby enhancing the activity and stability for OER. This work opens up a new avenue to enhance the OER catalytic efficiency via the construction of amorphous-crystalline heterostructures.

"Floating Catalytic Foam" with prominent heat-induced convection for the effective photocatalytic removal of antibiotics.

Immobilized photocatalysts represent a promising candidate for the wastewater treatments due to their good reusability, high stability and low eco-risk. Mass transfer within the immobilized catalytic bed is a crucial process that determines the contacting, adsorption, and degradation kinetics in the photodegradation. In this study, a floating catalytic foam (FCF) with a prominent pumping effect was designed to promote mass transfer. The polyurethane foam immobilized with rGO/TiO2/ultrathin-g-C3N4 photocatalyst (PRTCN) was prepared by a simple dip-coating and Uv-light aging process. It was found that the hydrophilic-hydrophobic interfaces could not only contribute to the floating of the catalyst but also establish a temperature gradient across the floating immobilized catalyst. In addition, the temperature gradient induced convection could serve as a built-in pump to effectively promote the diffusion and adsorption of target antibiotic molecules during the photocatalytic process. Therefore, the PRTCN demonstrated a high photodegradation and mineralization efficiency with excellent reusability and anti-interference capability. Moreover, the photodegradation mechanism and the intermediates' toxicity of norfloxacin were detailly investigated by ultra-high resolution electrospray time-of-flight mass spectrometry, density functional theory simulation and ECOSAR estimation. This work proposed a facile and sustainable strategy to enhance the mass transfer problem on immobilized photocatalysts, which could promote the application of the immobilized photocatalysts in the real water-treatment scenarios.

Stepwise Synthesis of Cl-Decorated Trinuclear-Cu Cluster-Based Frameworks for C2H2/C2H4 and C2H2/CO2 Separation.

A novel Cl-decorated trinuclear-Cu cluster-based MOF (NbU-7-Cl, NbU denotes Ningbo University) was synthesized by a stepwise synthesis strategy. Compared to one-step reactions, the strategy of combining cationic templates with single-crystal-to-single-crystal transformation provides more possibilities for the design and postsynthetic modification of multifunctional materials. Note that the chloride ions are attached to the copper ions of the planar trinuclear cluster nodes in a fully symmetric or partially asymmetric manner. The insertion of the chloride ion can alter the overall symmetry and adsorption energy in addition to occupying the appropriate asymmetric orbit and reducing the effective active sites of metal. The activated NbU-7-Cl displays improved C2H2 uptake capacity and C2H2/C2H4 and C2H2/CO2 separation performance, which is proved by breakthrough experiments.