Aerosol-into-liquid capture and detection of atmospheric soluble metals across the gas-liquid interface using Janus-membrane electrodes.

The soluble fraction of atmospheric transition metals is particularly associated with health effects such as reactive oxygen species compared to total metals. However, direct measurements of the soluble fraction are restricted to sampling and detection units in sequence burdened with a compromise between time resolution and system bulkiness. Here, we propose the concept of aerosol-into-liquid capture and detection, which allowed one-step particle capture and detection via the Janus-membrane electrode at the gas-liquid interface, enabling active enrichment and enhanced mass transport of metal ions. The integrated aerodynamic/electrochemical system was capable of capturing airborne particles with a cutoff size down to 50 nm and detecting Pb(II) with a limit of detection of 95.7 ng. The proposed concept can pave the way for cost-effective and miniaturized systems, for the capture and detection of airborne soluble metals in air quality monitoring, especially for abrupt air pollution events with high airborne metal concentrations (e.g., wildfires and fireworks).

Ultrasensitive Electrochemiluminescence Biosensor to Detect Ampicillin Resistance Gene (ARGAMP) Based on a Novel Near-Infrared Ruthenium Carbene Complex/TPrA/PEI Ternary ECL System.

The establishment of rapid target identification and analysis methods for antibiotic resistance genes (ARGs) is urgently needed. In this study, we unprecedently designed a target-catalyzed hairpin assembly (CHA) electrochemiluminescent (ECL) biosensor for the ultrasensitive detection of ampicillin resistance genes (ARGAMP) based on a novel, efficient near-infrared ruthenium carbene complex/TPrA/PEI ternary ECL system with low oxidation potential. The ternary NIR-ECL system illustrated in this work displayed double ECL intensity in comparison with their corresponding traditional binary ECL system. The as-prepared ECL biosensor illustrated in this work demonstrates highly selective and sensitive determination of ARGAMP from 1 fM to 1 nM and a low detection limit of 0.23 fM. Importantly, it also exhibits good accuracy and stabilities to identify ARGAMP in plasmid and bacterial genome DNA, which demonstrates its excellent reliability and great potential in detecting ARGAMP in real environmental samples.

Bis-tridentate Iridium(III) Complex with the N-Heterocyclic Carbene Ligand as a Novel Efficient Electrochemiluminescence Emitter for the Sandwich Immunoassay of the HHV-6A Virus.

Human herpesvirus type 6A (HHV-6A) can cause a series of immune and neurological diseases, and the establishment of a sensitive biosensor for the rapid detection of HHV-6A is of great significance for public health and safety. Herein, a bis-tridentate iridium complex (BisLT-Ir-NHC) comprising the N-heterocyclic carbene (NHC) ligand as a novel kind of efficient ECL luminophore has been unprecedently reported. Based on its excellent ECL properties, a new sensitive ECL-based sandwich immunosensor to detect the HHV-6A virus was successfully constructed by encapsulating BisLT-Ir-NHC into silica nanoparticles and embellishing ECL sensing interface with MXene@Au-CS. Notably, the immunosensor illustrated in this work not only had a wide linear range of 102 to 107 cps/µL but also showed outstanding recoveries (98.33-105.11%) in real human serum with an RSD of 0.85-3.56%. Undoubtedly, these results demonstrated the significant potential of the bis-tridentate iridium(III) complex containing an NHC ligand in developing ECL-based sensitive analytical methods for virus detection and exploring novel kinds of efficient iridium-based ECL luminophores in the future.

Accelerated 3D metabolite T1 mapping of the brain using variable-flip-angle SPICE.

PURPOSE: To develop a practical method to enable 3D T1 mapping of brain metabolites. THEORY AND METHODS: Due to the high dimensionality of the imaging problem underlying metabolite T1 mapping, measurement of metabolite T1 values has been currently limited to a single voxel or slice. This work achieved 3D metabolite T1 mapping by leveraging a recent ultrafast MRSI technique called SPICE (spectroscopic imaging by exploiting spatiospectral correlation). The Ernst-angle FID MRSI data acquisition used in SPICE was extended to variable flip angles, with variable-density sparse sampling for efficient encoding of metabolite T1 information. In data processing, a novel generalized series model was used to remove water and subcutaneous lipid signals; a low-rank tensor model with prelearned subspaces was used to reconstruct the variable-flip-angle metabolite signals jointly from the noisy data. RESULTS: The proposed method was evaluated using both phantom and healthy subject data. Phantom experimental results demonstrated that high-quality 3D metabolite T1 maps could be obtained and used for correction of T1 saturation effects. In vivo experimental results showed metabolite T1 maps with a large spatial coverage of 240 × 240 × 72 mm3 and good reproducibility coefficients (< 11%) in a 14.5-min scan. The metabolite T1 times obtained ranged from 0.99 to 1.44 s in gray matter and from 1.00 to 1.35 s in white matter. CONCLUSION: We successfully demonstrated the feasibility of 3D metabolite T1 mapping within a clinically acceptable scan time. The proposed method may prove useful for both T1 mapping of brain metabolites and correcting the T1-weighting effects in quantitative metabolic imaging.

Simultaneous high-resolution whole-brain MR spectroscopy and [18F]FDG PET for temporal lobe epilepsy.

PURPOSE: Precise lateralizing the epileptogenic zone in patients with drug-resistant mesial temporal lobe epilepsy (mTLE) remains challenging, particularly when routine MRI scans are inconclusive (MRI-negative). This study aimed to investigate the synergy of fast, high-resolution, whole-brain MRSI in conjunction with simultaneous [18F]FDG PET for the lateralization of mTLE. METHODS: Forty-eight drug-resistant mTLE patients (M/F 31/17, age 12-58) underwent MRSI and [18F]FDG PET on a hybrid PET/MR scanner. Lateralization of mTLE was evaluated by visual inspection and statistical classifiers of metabolic mappings against routine MRI. Additionally, this study explored how disease status influences the associations between altered N-acetyl aspartate (NAA) and FDG uptake using hierarchical moderated multiple regression. RESULTS: The high-resolution whole-brain MRSI data offers metabolite maps at comparable resolution to [18F]FDG PET. Visual examinations of combined MRSI and [18F]FDG PET showed an mTLE lateralization accuracy rate of 91.7% in a 48-patient cohort, surpassing routine MRI (52.1%). Notably, out of 23 MRI-negative mTLE, combined MRSI and [18F]FDG PET helped detect 19 cases. Logistical regression models combining hippocampal NAA level and FDG uptake improved lateralization performance (AUC=0.856), while further incorporating extrahippocampal regions such as amygdala, thalamus, and superior temporal gyrus increased the AUC to 0.939. Concurrent MRSI/PET revealed a moderating influence of disease duration and hippocampal atrophy on the association between hippocampal NAA and glucose uptake, providing significant new insights into the disease's trajectory. CONCLUSION: This paper reports the first metabolic imaging study using simultaneous high-resolution MRSI and [18F]FDG PET, which help visualize MRI-unidentifiable lesions and may thus advance diagnostic tools and management strategies for drug-resistant mTLE.

Tissue specific LRRK2 interactomes reveal a distinct striatal functional unit.

Mutations in LRRK2 are the most common genetic cause of Parkinson's disease. Despite substantial research efforts, the physiological and pathological role of this multidomain protein remains poorly defined. In this study, we used a systematic approach to construct the general protein-protein interactome around LRRK2, which was then evaluated taking into consideration the differential expression patterns and the co-expression behaviours of the LRRK2 interactors in 15 different healthy tissue types. The LRRK2 interactors exhibited distinct expression features in the brain as compared to the peripheral tissues analysed. Moreover, a high degree of similarity was found for the LRRK2 interactors in putamen, caudate and nucleus accumbens, thus defining a potential LRRK2 functional cluster within the striatum. The general LRRK2 interactome paired with the expression profiles of its members constitutes a powerful tool to generate tissue-specific LRRK2 interactomes. We exemplified the generation of the tissue-specific LRRK2 interactomes and explored the functions highlighted by the "core LRRK2 interactors" in the striatum in comparison with the cerebellum. Finally, we illustrated how the LRRK2 general interactome reported in this manuscript paired with the expression profiles can be used to trace the relationship between LRRK2 and specific interactors of interest, here focusing on the LRRK2 interactors belonging to the Rab protein family.

Influence of academic involution atmosphere on college students' stress response: the chain mediating effect of relative deprivation and academic involution.

BACKGROUND: In recent years, the phenomenon of academic involution atmosphere among college students has gradually attracted the focus of education and social circles. Thus, this study targets college students as the research object and constructs a hypothetical model to explore the relationship between academic involution atmosphere and college students' stress response, as well as the mediating role of relative deprivation and academic involution. METHODS: A survey was conducted on 1090 college students using the Academic Involution Atmosphere Scale, Relative Deprivation Scale, Personal Academic Involution Scale, and Stress Response Scale. RESULTS: The results show that: (1) Academic involution atmosphere, relative deprivation, and academic involution are significantly and positively correlated with stress response; (2) Academic involution atmosphere not only directly predicts college students' stress response, but also indirectly predicts them through relative deprivation and academic involution, respectively; (3) Relative deprivation and academic involution have a chain mediating effect between academic involution atmosphere and stress response. CONCLUSIONS: The findings of this study reveal the influence of academic involution atmosphere on college students' stress response and the mechanism, providing beneficial insights for reducing college students' stress response and maintaining their psychological well-being.

Evaluating annual soil carbon emissions under biochar-added farmland subjecting from freeze-thaw cycle.

Straw biochar is a commonly recognized agricultural amendment that can improve soil quality and reduce carbon emissions while sequestering soil carbon. However, the mechanisms underlying biochar's effects on annual soil carbon emissions in seasonally frozen soil areas and intrinsic drivers have not been clarified. Here, a 2-y field experiment was conducted to investigate the effects of different biochar dosages (0, 15, and 30, t ha-1; B0 (CK), B15, and B30, respectively) on carbon emissions (CO2 and CH4) microbial colony count, and soil-environment factors. The study period was the full annual cycle, including the freeze-thaw period (FTP) and the crop growth period (CP). Structural equation modeling (SEM) was developed to reveal the key drivers and potential mechanisms of biochar on carbon emissions. Biochar application reduced soil carbon emissions, with the reduction rate positively related to the biochar application rate (B30 best). During FTP, the reduction rate was 11.5% for CO2 and 48.2% for CH4. During CP, the reduction rate was 17.9% for CO2 and 34.5% for CH4. Overall, compared with CK, B30 treatment had a significant effect on reducing total soil carbon emissions (P < 0.05), with an average decrease of 16.7% during the two-year test period. The study also showed that for soils with continuous annual cycles (FTP and CP), carbon emissions were best observed from 10:00-13:00. After two years of freeze-thaw cycling, biochar continued to improve soil physical and chemical properties, thereby increasing soil microbial colony count. Compared with B0, the B30 treatment significantly increased the total colony count by 74.3% and 263.8% during FTP and CP (P < 0.05). Structural equation modeling (SEM) indicated that, with or without biochar application, the soil physicochemical properties directly or indirectly affected soil CO2 and CH4 emission fluxes through microbial colony count. The total effects of biochar application on CO2 emission fluxes were 0.50 (P < 0.05) and 0.64 (P < 0.01), respectively, but there was no significant effect on CH4 emission fluxes (P > 0.05). Among them, soil water content (SWC), soil temperature (ST) and soil organic carbon (SOC) were the main environmental determinants of CO2 emission fluxes during the FTP and CP. The total effects were 0.57, 0.65, and 0.53, respectively. For CH4, SWC, soil salinity (SS) and actinomycete colony count were the main environmental factors affecting its emission. The total effects were 0.50, 0.45, 0.44, respectively. For freeze-thaw alternating soils, the application of biochar is a feasible option for addressing climate change through soil carbon sequestration and greenhouse gas emissions mitigation. Soil water-heat-salt-fertilization and microbial communities are important for soil carbon emissions as the reaction matrix and main participants of soil carbon and nitrogen biochemical transformation.

BINOL-Based Chiral Macrocycles and Cages.

Chirality, a fundamental principle in chemistry, biology, and medicine, is prevalent in nature and in organisms. Chiral molecules, such as DNA, RNA, and proteins, are crucial in biomolecular synthesis, as well as in the development of functional materials. Among these, 1,1'-binaphthyl-2,2'-diol (BINOL) stands out for its stable chiral configuration, versatile functionality, and commercial availability. BINOL is widely employed in asymmetric catalysis and chiral materials. This review mainly focuses on recent research over the past five years concerning the use of BINOL derivatives for constructing chiral macrocycles and cages. Their contributions to chiral luminescence, enantiomeric separation, transmembrane transport, and asymmetric catalysis were examined.

Novel Electrochemiluminescent Biosensor to Ultrasensitively Detect U94 Gene in Human Herpesvirus 6 Using Metal-Organic Framework-Based Nanoemitters Comprising Iridium(III) Complexes via One-Pot Coordination Reaction Strategy.

The detection of the U94 gene in human herpesvirus 6 is crucial for early diagnosis of HHV-6 infections, which could induce acute febrile illness in infants. In this work, the first ultrasensitive electrochemiluminescence (ECL) biosensor for detecting U94 gene in Human Herpesvirus 6 was successfully designed by utilizing efficient novel metal-organic framework (MOF)-based ECL nanoemitters comprising iridium(III) complexes (Ir-ZIF-8-NH2) synthesized via one-pot coordination reaction strategy as an ECL indicator and a target-catalyzed hairpin assembly (CHA) signal amplification strategy. The as-prepared ECL indicator Ir-ZIF-8-NH2 exhibited an approximately 2.7-fold ECL intensity compared with its small molecular analogue of emissive iridium(III) complex named IrppymIM formed by in situ coordination reaction between iridium(III) solvent complex and imidazole ligands. In addition, a target-catalyzed hairpin assembly (CHA) strategy was employed to further improve the sensitivity of the proposed ECL biosensor, which demonstrated a wide linear range from 1 fM to 1 µM and the limit of detection as low as 0.113 fM (S/N = 3). Significantly, this biosensor was successfully applied to detect U94 gene in plasmids and real virus samples. The recoveries were in the range of 97.0-109.0% for plasmids and 95.7-107.5% for real virus samples with a relative standard deviation (RSD) of 1.87-2.53%. These satisfactory experimental results from the proposed ECL biosensor in this work would inevitably promote the development of new time/cost-effective and sensitive methods to detect HHV-6 with a major global health threat and substantial burden on healthcare in the future.

JSENSE-Pro: Joint sensitivity estimation and image reconstruction in parallel imaging using pre-learned subspaces of coil sensitivity functions.

PURPOSE: To improve calibrationless parallel imaging using pre-learned subspaces of coil sensitivity functions. THEORY AND METHODS: A subspace-based joint sensitivity estimation and image reconstruction method was developed for improved parallel imaging with no calibration data. Specifically, we proposed to use a probabilistic subspace model to capture prior information of the coil sensitivity functions from previous scans acquired using the same receiver system. Both the subspace basis and coefficient distributions were learned from a small set of training data. The learned subspace model was then incorporated into the regularized reconstruction formalism that includes a sparsity prior. The nonlinear optimization problem was solved using alternating minimization algorithm. Public fastMRI brain dataset was retrospectively undersampled by different schemes for performance evaluation of the proposed method. RESULTS: With no calibration data, the proposed method consistently produced the most accurate coil sensitivity estimation and highest quality image reconstructions at all acceleration factors tested in comparison with state-of-the-art methods including JSENSE, DeepSENSE, P-LORAKS, and Sparse BLIP. Our results are comparable to or even better than those from SparseSENSE, which used calibration data for sensitivity estimation. The work also demonstrated that the probabilistic subspace model learned from T2 w data can be generalized to aiding the reconstruction of FLAIR data acquired from the same receiver system. CONCLUSION: A subspace-based method named JSENSE-Pro has been proposed for accelerated parallel imaging without the acquisition of companion calibration data. The method is expected to further enhance the practical utility of parallel imaging, especially in applications where calibration data acquisition is not desirable or limited.

B1 mapping using pre-learned subspaces for quantitative brain imaging.

PURPOSE: To develop a machine learning-based method for estimation of both transmitter and receiver B1 fields desired for correction of the B1 inhomogeneity effects in quantitative brain imaging. THEORY AND METHODS: A subspace model-based machine learning method was proposed for estimation of B1t and B1r fields. Probabilistic subspace models were used to capture scan-dependent variations in the B1 fields; the subspace basis and coefficient distributions were learned from pre-scanned training data. Estimation of the B1 fields for new experimental data was achieved by solving a linear optimization problem with prior distribution constraints. We evaluated the performance of the proposed method for B1 inhomogeneity correction in quantitative brain imaging scenarios, including T1 and proton density (PD) mapping from variable-flip-angle spoiled gradient-echo (SPGR) data as well as neurometabolic mapping from MRSI data, using phantom, healthy subject and brain tumor patient data. RESULTS: In both phantom and healthy subject data, the proposed method produced high-quality B1 maps. B1 correction on SPGR data using the estimated B1 maps produced significantly improved T1 and PD maps. In brain tumor patients, the proposed method produced more accurate and robust B1 estimation and correction results than conventional methods. The B1 maps were also applied to MRSI data from tumor patients and produced improved neurometabolite maps, with better separation between pathological and normal tissues. CONCLUSION: This work presents a novel method to estimate B1 variations using probabilistic subspace models and machine learning. The proposed method may make correction of B1 inhomogeneity effects more robust in practical applications.

Robust frame synchronization for continuous-variable quantum key distribution with coherent states.

In a practical continuous variable quantum key distribution (CVQKD) system, frame synchronization is crucial for its operation, especially in conditions of low signal-to-noise ratio (SNR) and phase drift. This paper introduces a robust frame synchronization scheme for CVQKD systems that only utilizes quantum signals. The proposed scheme effectively employs randomly selected segments of quantum signals to achieve frame synchronization, eliminating the need for additional modulation. The performance of this scheme applied in a local local oscillator scenario is thoroughly analyzed through numerical simulations. The results demonstrate that the proposed scheme is capable of withstanding low SNR and arbitrary slow phase drift, as well as fast phase drift originates from two independent lasers, while also slightly improving the secret key rate compared to the scheme using inserted synchronization frames. These findings validate the feasibility of implementing the proposed scheme for long-distance CVQKD in practical scenarios.

Predicting the Onset of Ischemic Stroke With Fast High-Resolution 3D MR Spectroscopic Imaging.

BACKGROUND: Neurometabolite concentrations provide a direct index of infarction progression in stroke. However, their relationship with stroke onset time remains unclear. PURPOSE: To assess the temporal dynamics of N-acetylaspartate (NAA), creatine, choline, and lactate and estimate their value in predicting early (<6 hours) vs. late (6-24 hours) hyperacute stroke groups. STUDY TYPE: Cross-sectional cohort. POPULATION: A total of 73 ischemic stroke patients scanned at 1.8-302.5 hours after symptom onset, including 25 patients with follow-up scans. FIELD STRENGTH/SEQUENCE: A 3 T/magnetization-prepared rapid acquisition gradient echo sequence for anatomical imaging, diffusion-weighted imaging and fluid-attenuated inversion recovery imaging for lesion delineation, and 3D MR spectroscopic imaging (MRSI) for neurometabolic mapping. ASSESSMENT: Patients were divided into hyperacute (0-24 hours), acute (24 hours to 1 week), and subacute (1-2 weeks) groups, and into early (<6 hours) and late (6-24 hours) hyperacute groups. Bayesian logistic regression was used to compare classification performance between early and late hyperacute groups by using different combinations of neurometabolites as inputs. STATISTICAL TESTS: Linear mixed effects modeling was applied for group-wise comparisons between NAA, creatine, choline, and lactate. Pearson's correlation analysis was used for neurometabolites vs. time. P < 0.05 was considered statistically significant. RESULTS: Lesional NAA and creatine were significantly lower in subacute than in acute stroke. The main effects of time were shown on NAA (F = 14.321) and creatine (F = 12.261). NAA was significantly lower in late than early hyperacute patients, and was inversely related to time from symptom onset across both groups (r = -0.440). The decrease of NAA and increase of lactate were correlated with lesion volume (NAA: r = -0.472; lactate: r = 0.366) in hyperacute stroke. Discrimination was improved by combining NAA, creatine, and choline signals (area under the curve [AUC] = 0.90). DATA CONCLUSION: High-resolution 3D MRSI effectively assessed the neurometabolite changes and discriminated early and late hyperacute stroke lesions. EVIDENCE LEVEL: 1. TECHNICAL EFFICACY: Stage 2.

Development of anxiety in early Parkinson's disease: A clinical and biomarker study.

BACKGROUND: Anxiety affects approximately 40% of Parkinson's disease (PD) patients. However, little is known about its predictors and development over time. OBJECTIVE: To identify the clinical factors and biomarkers associated with development of anxiety in patients with newly diagnosed PD, and to test which risk factors predict increases in anxiety over time. METHODS: Data from the Parkinson's Progression Markers Initiative (PPMI) were utilized. The primary outcome was the State-Trait Anxiety Inventory (STAI). Covariates were demographics, motor and non-motor symptoms, cognitive functions, dopamine transporter imaging data, and cerebrospinal fluid (CSF) biomarkers. We examined the association of risk factors at baseline and over 4 years with changes in anxiety scores over time. RESULTS: A total of 252 patients met the inclusion criteria (mean age: 61.36 years, SD 9.53). At year 4, 42 patients had developed anxiety. Baseline predictors of increase in anxiety scores were greater autonomic dysfunction, dysexecutive function, CSF t-tau levels, excessive daytime sleepiness, and lower olfactory function scores but not motor scores. Over 4 years, change in anxiety scores correlated with deterioration in overall cognitive function, excessive daytime sleepiness, as well as depression and disability, and to a lesser degree worsening of Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) motor scores and caudate dopaminergic uptake changes. CONCLUSIONS: These findings suggest that development of anxiety in PD is not primarily based on a dopaminergic deficit in the basal ganglia but related to non-dopaminergic or extrastriatal pathology. Early dysexecutive function predicts development of anxiety but increase in anxiety levels correlates most strongly with more global cognitive decline.

Singlet Oxygen Generation via an Electrochemical Reduction Method Assisted by a Ruthenium(II) Complex.

In this work, singlet oxygen (1O2) is unprecedently recorded in the electrochemical reduction of tris(2,2'-bipyridine)ruthenium(II) [Ru(bpy)32+] in an acetonitrile solution with dissolved oxygen, which is well characterized by the specific probe of Singlet Oxygen Sensor Greens and the technique of electron-spin resonance in this work. Importantly, this new electrochemical method to produce 1O2 shows higher efficiency than the conventional photodriven method. Furthermore, taken together with the inherent advantages of electrochemical techniques compared with the photochemical/chemical-driven method, this electrochemical method would inevitably show great promise in reactive-oxygen-species-related studies in the future.

A novel role of DOT1L in kidney diseases.

BACKGROUND: We systematically summarized the structure and biological function of DOT1L in detail, and further discussed the role of DOT1L in kidney diseases through different mechanisms. METHODS AND RESULTS: We first described the role of DOT1L in various kidney diseases including AKI, CKD, DN and kidney tumor diseases. CONCLUSIONS: A better understanding of DOT1L as a histone methylase based on characteristics of regulating telomere silencing, transcriptional extension, DNA damage repair and cell cycle could lead to the development of new therapeutic targets for various kidney diseases, thereby improving the prognosis of kidney disease patients.

Clinical and Epidemiological Characteristics of Rhabdomyolysis: A Retrospective Study.

Background: Rhabdomyolysis (RM) refers to a clinical syndrome in which muscle cells are damaged by various causes and the clinical manifestations are mainly muscle pain, weakness, and dark urine. Acute kidney injury (AKI) is a serious complication of RM with complex mechanisms and high mortality. Therefore, understanding the pathogenesis and clinical manifestations, early diagnosis and treatment of RM are crucial to improve its prognosis. Method: Analysis of medical records of RM patients admitted to Tianjin Medical University General Hospital from October 2019 to October 2022. Statistical software SPSS 25.0 was used to analyze the data. The risk factors of RM-complicated AKI were analyzed by logistic regression. The receiver operating characteristic (ROC) curve was plotted, the area under the curve (AUC) was calculated, and the optimal cutoff value was determined by the Youden index. P < 0.05 indicates a statistically significant difference between the groups. Result: Among the 71 patients, the median age of the patients was 53.0 (30.0, 71.0) years and was 2.5 times higher in men than in women. Infection was the most common etiology. History of alcohol consumption, CK, and creatinine were independent influencing factors for AKI due to RM. Logistic regression analysis showed that CK combined with creatinine had a better predictive value than the single index. Conclusion: Our study revealed the clinical and laboratory characteristics of RM in the population attending the Tianjin Medical University General Hospital in the last three years, which is a reference for future multicenter, prospective studies.

Macrocycle with Equatorial Coordination Sites Provides New Opportunity for Structure-Diverse Metallacages.

Reported here is the synthesis of a macrocycle with equatorial coordination sites for the construction of self-assembled metallacages. The macrocycle is prepared via a post-modification on the equator of biphen[n]arene. Utilizing this macrocycle as a ligand, three prismatic cages and one octahedral cage were synthesized by regulating the geometric structures and coordination number of metal acceptors. The multi-cavity configuration of prismatic cage was revealed by single-crystal structure. We prove that a macrocycle with equatorial coordination sites can be an excellent building block for synthesizing structure-diverse metallacages. Our results provide a typical example and a general method for the design and synthesis of metallacages.

SCFusion: Infrared and Visible Fusion Based on Salient Compensation.

The aim of infrared and visible image fusion is to integrate the complementary information of the two modalities for high-quality fused images. However, many deep learning fusion algorithms have not considered the characteristics of infrared images in low-light scenes, leading to the problems of weak texture details, low contrast of infrared targets and poor visual perception in the existing methods. Therefore, in this paper, we propose a salient compensation-based fusion method that makes sufficient use of the characteristics of infrared and visible images to generate high-quality fused images under low-light conditions. First, we design a multi-scale edge gradient module (MEGB) in the texture mainstream to adequately extract the texture information of the dual input of infrared and visible images; on the other hand, the salient tributary is pre-trained by salient loss to obtain the saliency map based on the salient dense residual module (SRDB) to extract salient features, which is supplemented in the process of overall network training. We propose the spatial bias module (SBM) to fuse global information with local information. Finally, extensive comparison experiments with existing methods show that our method has significant advantages in describing target features and global scenes, the effectiveness of the proposed module is demonstrated by ablation experiments. In addition, we also verify the facilitation of this paper's method for high-level vision on a semantic segmentation task.