Computational spectrometer based on local feature-weighted spectral reconstruction.

The computational spectrometer enables the reconstruction of spectra from precalibrated information encoded. In the last decade, it has emerged as an integrated and low-cost paradigm with vast potential for applications, especially in portable or handheld spectral analysis devices. The conventional methods utilize a local-weighted strategy in feature spaces. These methods overlook the fact that the coefficients of important features could be too large to reflect differences in more detailed feature spaces during calculations. In this work, we report a local feature-weighted spectral reconstruction (LFWSR) method, and construct a high-accuracy computational spectrometer. Different from existing methods, the reported method learns a spectral dictionary via L 4-norm maximization for representing spectral curve features, and considers the statistical ranking of features. According to the ranking, weight features and update coefficients then calculate the similarity. What's more, the inverse distance weighted is utilized to pick samples and weight a local training set. Finally, the final spectrum is reconstructed utilizing the local training set and measurements. Experiments indicate that the reported method's two weighting processes produce state-of-the-art high accuracy.

Tuning-free and self-supervised image enhancement against ill exposure.

Complex lighting conditions and the limited dynamic range of imaging devices result in captured images with ill exposure and information loss. Existing image enhancement methods based on histogram equalization, Retinex-inspired decomposition model, and deep learning suffer from manual tuning or poor generalization. In this work, we report an image enhancement method against ill exposure with self-supervised learning, enabling tuning-free correction. First, a dual illumination estimation network is constructed to estimate the illumination for under- and over-exposed areas. Thus, we get the corresponding intermediate corrected images. Second, given the intermediate corrected images with different best-exposed areas, Mertens' multi-exposure fusion strategy is utilized to fuse the intermediate corrected images to acquire a well-exposed image. The correction-fusion manner allows adaptive dealing with various types of ill-exposed images. Finally, the self-supervised learning strategy is studied which learns global histogram adjustment for better generalization. Compared to training on paired datasets, we only need ill-exposed images. This is crucial in cases where paired data is inaccessible or less than perfect. Experiments show that our method can reveal more details with better visual perception than other state-of-the-art methods. Furthermore, the weighted average scores of image naturalness matrics NIQE and BRISQUE, and contrast matrics CEIQ and NSS on five real-world image datasets are boosted by 7%, 15%, 4%, and 2%, respectively, over the recent exposure correction method.

Contribution of Constituents in Rosemary-Magnolia Compound Essential Oil to Antibacterial, Antioxidant and Cytotoxicity Bioactivities Revealed by Grey Correlation Analysis.

The compound of essential oils (EOs) is a key approach to achieving the superimposed efficacy of plant EOs. In this article, grey correlation analysis was applied for the first time to explore the compound ratios and contribution between constituents and the bioactivity of the compound EOs. There were 12 active constituents shared in rosemary and magnolia EOs prepared by negative pressure distillation. With different proportions, these two EOs were blended and analyzed for the antioxidant, bacteriostatic and antitumor effects. According to the results of the inhibition circle, minimum bactericidal and inhibitory concentration, the most obvious inhibition effect of the compound EOs on different strains of bacteria was shown in Staphylococcus aureus. The results of antioxidant test showed that single EO from rosemary had the best antioxidant effect, and its EO content was directly proportional to the antioxidant effect. The cytotoxicity results showed that, there was a significant difference in the lethality of the compound EOs between tumor cells Mcf-7 (human breast cancer cells) and SGC-7901 cells (human gastric cancer cells). Furthermore, single EO from magnolia had an obvious inhibitory effect on the growth of Mcf-7 cells and SGC-7901 cells, and the cell lethality rate was as high as 95.19 % and 97.96 %, respectively. As the results of grey correlation analysis, the constituents with the maximal correlation of inhibitory effects on bacteria were as follows: S. aureus - Terpinolene (0.893), E. coli - Eucalyptol (0.901), B. subtilis - α-Pinene (0.823), B. cereus - Terpinolene (0.913) and Salmonella - α-Phellandrene (0.855). For the ABTS and DPPH scavenging effects, the constituents with the maximal correlation were (-)-Camphor (0.860) and ß-Pinene (0.780), respectively. In terms of the effects of the active constituents of compound EOs on the inhibitory activities of tumor cells Mcf-7 and SGC-7901, the three active constituents of γ-Terpinene, (R)-(+)-ß-Citronellol and (-)-Camphor were in the top three, and their correlation were Mcf-7 (0.833, 0.820, 0.795) and SGC-7901 (0.797, 0.766, 0.740). Our study determined the contribution degree of active constituents in the antibacterial, antioxidant, and antitumor bioactivities of rosemary-magnolia compound EOs, and also provided new insights for the research of EOs combination formulations.

Ultrahigh-security single-pixel semantic encryption.

Single-pixel encryption is a recently developed encryption technique enabling the ciphertext amount to be decreased. It adopts modulation patterns as secret keys and uses reconstruction algorithms for image recovery in the decryption process, which are time-consuming and can easily be illegally deciphered if the patterns are exposed. We report an image-free single-pixel semantic encryption technique that significantly enhances security. The technique extracts semantic information directly from the ciphertext without image reconstruction, which significantly reduces computing resources for end-to-end real-time decoding. Moreover, we introduce a stochastic mismatch between keys and ciphertext, with random measurement shift and dropout, which effectively enhances the difficulty of illegal deciphering. Experiments on the MNIST dataset validate that 78 coupling measurements (0.1 sampling rate) with stochastic shift and random dropout achieved 97.43% semantic decryption accuracy. In the worst situation, when all the keys are illegally obtained by unauthorized attackers, only 10.80% accuracy can be achieved (39.47% in an ergodic manner).

Constitutional morphological features and risk of hip osteoarthritis: a case-control study using standard radiographs.

OBJECTIVES: To evaluate the risk of association with hip osteoarthritis (OA) of 14 morphological features measured on standard antero-posterior pelvis radiographs. METHODS: A case-control study of 566 symptomatic unilateral hip OA cases and 1108 controls without hip OA, using the Genetics of OA and Lifestyle database. Unaffected hips of cases were assumed to reflect pre-OA morphology of the contralateral affected hip. ORs with 95% CI adjusted for confounding factors were calculated using logistic regression. Hierarchical clustering on principal component method was used to identify clusters of morphological features. Proportional risk contribution (PRC) of these morphological features in the context of other risk factors of hip OA was estimated using receiver operating characteristic analysis. RESULTS: All morphological features showed right-left symmetry in controls. Each feature was associated with hip OA after adjusting for age, gender and body mass index. Increased sourcil angle had the strongest association (OR: 6.93, 95% CI 5.16 to 9.32). Three clusters were identified. The PRC varied between individual features, as well as between clusters. It was 35% (95% CI 31% to 40%) for all 14 morphological features, compared to 21% (95% CI 19% to 24%) for all other well-established risk factors. CONCLUSIONS: Constitutional morphological variation strongly associates with hip OA development and may explain much of its heritability. Relevant morphological measures can be assessed readily on standard radiographs to help predict risk of hip OA. Prospective studies are required to provide further support for causality.

Compression regulates gene expression of chondrocytes through HDAC4 nuclear relocation via PP2A-dependent HDAC4 dephosphorylation.

Biomechanics plays a critical role in the modulation of chondrocyte function. The mechanisms by which mechanical loading is transduced into intracellular signals that regulate chondrocyte gene expression remain largely unknown. Histone deacetylase 4 (HDAC4) is specifically expressed in chondrocytes. Mice lacking HDAC4 display chondrocyte hypertrophy, ectopic and premature ossification, and die early during the perinatal period. HDAC4 has a remarkable ability to translocate between the cell's cytoplasm and nucleus. It has been established that subcellular relocation of HDAC4 plays a critical role in chondrocyte differentiation and proliferation. However, it remains unclear whether subcellular relocation of HDAC4 in chondrocytes can be induced by mechanical loading. In this study, we first report that compressive loading induces HDAC4 relocation from the cytoplasm to the nucleus of chondrocytes via stimulation of Ser/Thr-phosphoprotein phosphatases 2A (PP2A) activity, which results in dephosphorylation of HDAC4. Dephosphorylated HDAC4 relocates to the nucleus to achieve transcriptional repression of Runx2 and regulates chondrocyte gene expression in response to compression. Our results elucidate the mechanism by which mechanical compression regulates chondrocyte gene expression through HDAC4 relocation from the cell's cytoplasm to the nucleus via PP2A-dependent HDAC4 dephosphorylation.

Cartilage oligomeric matrix protein and hyaluronic acid are sensitive serum biomarkers for early cartilage lesions in the knee joint.

The purpose of this study was to evaluate the relationship between five previously established serum osteoarthritis biomarkers and the severity of cartilage lesions in the knee. Cartilage damage (classified according to the Outerbridge scoring system) and serum concentrations of cartilage oligomeric matrix protein (COMP), collagen type II C-telopeptide (CTX-II), matrix metalloproteinase-3 (MMP-3), collagen type III N-propeptide, (PIIINP), and hyaluronic acid (HA) were determined in 79 patients who underwent knee arthroscopy or total knee replacement. HA and COMP concentrations were significantly higher in the Outerbridge score 1 and 2 groups, respectively. These results suggest that serum COMP and HA concentrations can be used to predict early cartilage lesions in the knee.

[The role of infrapatellar fat pad in knee osteoarthritis and total knee arthroplasty].

Knee osteoarthritis (KOA) is the most common knee joint disease. Although KOA belongs to a disease of degeneration of knee joint cartilage, its pathology involves cartilage, subchondral bone, meniscus, synovial membrane, articular capsule and other joint tissue. The infrapatellar fat pad (IPFP), an intracapsular but extrasynovial structure, has some special characteristics of endocrine metabolism, then it has double roles in the development of KOA, but its protective effect is much more than harmful effect. Considering protective roles of IPFP in KOA and some serious complications after IPFP resection, the surgeon shall protect IPFP as far as possible if total knee arthroplasty surgical field is good. If it is necessary to improve the surgical field, its fibrotic tissue even all part can be removed.

The Role of miRNAs in Cartilage Homeostasis.

Osteoarthritis (OA) is an age-related disease with poorly understood pathogenesis. Recent studies have demonstrated that miRNA might play a key role in OA initiation and development. We reviewed recent publications and elucidated the connection between miRNA and OA cartilage anabolic and catabolic signals, including four signaling pathways: TGF-ß/Smads and BMPs signaling, associated with cartilage anabolism; and MAPK and NF-KB signaling, associated with cartilage catabolism. We also explored the relationships with MMP, ADAMTS and NOS (NitricOxide Synthases) families, as well as with the catabolic cytokines IL-1 and TNF-α. The potential role of miRNAs in biological processes such as cartilage degeneration, chondrocyte proliferation, and differentiation is discussed. Collective evidence indicates that miRNAs play a critical role in cartilage degeneration. These findings will aid in understanding the molecular network that governs articular cartilage homeostasis and in to elucidate the role of miRNA in the pathogenesis of OA.

Indian hedgehog in synovial fluid is a novel marker for early cartilage lesions in human knee joint.

To determine whether there is a correlation between the concentration of Indian hedgehog (Ihh) in synovial fluid (SF) and the severity of cartilage damage in the human knee joints, the knee cartilages from patients were classified using the Outer-bridge scoring system and graded using the Modified Mankin score. Expression of Ihh in cartilage and SF samples were analyzed with immunohistochemistry (IHC), western blot, and enzyme-linked immunosorbent assay (ELISA). Furthermore, we detected and compared Ihh protein levels in rat and mice cartilages between normal control and surgery-induced osteoarthritis (OA) group by IHC and fluorescence molecular tomography in vivo respectively. Ihh expression was increased 5.2-fold in OA cartilage, 3.1-fold in relative normal OA cartilage, and 1.71-fold in OA SF compared to normal control samples. The concentrations of Ihh in cartilage and SF samples was significantly increased in early-stage OA samples when compared to normal samples (r = 0.556; p < 0.001); however, there were no significant differences between normal samples and late-stage OA samples. Up-regulation of Ihh protein was also an early event in the surgery-induced OA models. Increased Ihh is associated with the severity of OA cartilage damage. Elevated Ihh content in human knee joint synovial fluid correlates with early cartilage lesions.

Exploring Multi-modal Spatial-Temporal Contexts for High-performance RGB-T Tracking.

In RGB-T tracking, there exist rich spatial relationships between the target and backgrounds within multi-modal data as well as sound consistencies of spatial relationships among successive frames, which are crucial for boosting the tracking performance. However, most existing RGB-T trackers overlook such multi-modal spatial relationships and temporal consistencies within RGB-T videos, hindering them from robust tracking and practical applications in complex scenarios. In this paper, we propose a novel Multi-modal Spatial-Temporal Context (MMSTC) network for RGB-T tracking, which employs a Transformer architecture for the construction of reliable multi-modal spatial context information and the effective propagation of temporal context information. Specifically, a Multi-modal Transformer Encoder (MMTE) is designed to achieve the encoding of reliable multi-modal spatial contexts as well as the fusion of multi-modal features. Furthermore, a Quality-aware Transformer Decoder (QATD) is proposed to effectively propagate the tracking cues from historical frames to the current frame, which facilitates the object searching process. Moreover, the proposed MMSTC network can be easily extended to various tracking frameworks. New state-of-the-art results on five prevalent RGB-T tracking benchmarks demonstrate the superiorities of our proposed trackers over existing ones.

Globulin and Albumin/Globulin Ratios as Potential Biomarkers for the Diagnosis of Acute and Chronic Peri-Prosthetic Joint Infections: A Retrospective Study.

Background: Peri-prosthetic joint infection (PJI) is the most serious complication after prosthetic joint replacement. However, the diagnosis of PJI remains challenging for clinicians because of the lack of a gold standard. The purpose of this study was to investigate the diagnostic significance of serum globulin, albumin/globulin, and other biomarkers in acute and chronic periprosthetic infections. Patients and Methods: A retrospective study of 162 patients with PJI and aseptic loosening between January 2016 and March 2021 at our institution was performed in three groups. There were 20 patients with acute infection in group A, 36 patients with chronic infection in group B, and 106 patients with aseptic loosening in group C. Globulin, albumin/globulin, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), platelet count (PLT), mean platelet volume (MPV), d-dimer, and platelet count/mean platelet volume ratio (PMR) levels were recorded. The area under the curve (AUC) was used to measure the diagnostic value of globulin and albumin/globulin with other biomarkers for PJI. Results: Compared with the aseptic loosening group, the acute and chronic PJI group had higher levels of CRP, ESR, d-dimer, globulin, PLT, and PMR (p < 0.01) and lower levels of albumin/globulin and MPV (p < 0.01). The optimal cutoff, AUC, sensitivity, and specificity of CRP, albumin/globulin, ESR, and globulin were: CRP, 8.3 mg/L, 0.903, 78.57%, and 88.68%; albumin/globulin, 1.31, 0.899, 91.07%, and 73.58%; ESR, 32 mm/h, 0.888, 75.%, and 85.85%; globulin, 29.5 g/L, 0.880, 91.07%, and 72.64%. Conclusions: Globulin and albumin/globulin have excellent diagnostic value for acute and chronic PJI and are promising potential biomarkers for the diagnosis of PJI. The diagnostic performance of albumin/globulin is superior to that of ESR and similar to that of CRP.

[Correlation between preoperative MRI measurement of cross-sectional area of hamstring tendon and graft in anterior cruciate ligament reconstruction].

OBJECTIVE: To study the corretation between the cross-sectional area of hamstring tenden measured by MRI and gragt in anterior cruciate ligament rexonstruction. METHODS: MRI data of 50 patients who planned to undergo anterior cruciate ligament reconstruction from November 2021 to March 2022 were collected, including 32 males and 18 females, aged from 19 to 48 years old with an average of(31.1±8.7) years. Before the operation, the semitendinosus and gracilis tendons were measured and recorded by MRI, and then the anterior cruciate ligament was reconstructed under arthroscope. During the operation, gracilis and semitendinosus tendons were taken to prepare the final tendon to be transplanted, and the diameter of the prepared final graft was measured during the operation. Finally, the data were analyzed by statistical software. RESULTS: The cross sectional areas of semitendinosus tendon, gracilis tendon, semitendinosus tendon and gracilis tendon measured by MRI were significantly and positively correlated with the diameter of grafts required in anterior cruciate ligament surgery, the r values were 0.858, 0.728, 0.842(P<0.001), respectively. The area under curre (AUC), sensitivity, and specificity of the sum of the cross sectional areas of semitendinosus tendon and gracilis tendon were 0.925, 90.48%, and 85.71%, respectively. CONCLUSION: In patients undergoing anterior cruciate ligament reconstruction, preoperative MRI measurement has a strong statistical correlation with the diameter of hamstring muscle transplantation during operation. The sum of the cross sectional areas of semitendinosus tendon and gracilis tendon has a high predictive value for the diameter of grafts during anterior cruciate ligament reconstruction, and can predict the size of grafts during operation.

Mitigating Modality Discrepancies for RGB-T Semantic Segmentation.

Semantic segmentation models gain robustness against adverse illumination conditions by taking advantage of complementary information from visible and thermal infrared (RGB-T) images. Despite its importance, most existing RGB-T semantic segmentation models directly adopt primitive fusion strategies, such as elementwise summation, to integrate multimodal features. Such strategies, unfortunately, overlook the modality discrepancies caused by inconsistent unimodal features obtained by two independent feature extractors, thus hindering the exploitation of cross-modal complementary information within the multimodal data. For that, we propose a novel network for RGB-T semantic segmentation, i.e. MDRNet + , which is an improved version of our previous work ABMDRNet. The core of MDRNet + is a brand new idea, termed the strategy of bridging-then-fusing, which mitigates modality discrepancies before cross-modal feature fusion. Concretely, an improved Modality Discrepancy Reduction (MDR + ) subnetwork is designed, which first extracts unimodal features and reduces their modality discrepancies. Afterward, discriminative multimodal features for RGB-T semantic segmentation are adaptively selected and integrated via several channel-weighted fusion (CWF) modules. Furthermore, a multiscale spatial context (MSC) module and a multiscale channel context (MCC) module are presented to effectively capture the contextual information. Finally, we elaborately assemble a challenging RGB-T semantic segmentation dataset, i.e., RTSS, for urban scene understanding to mitigate the lack of well-annotated training data. Comprehensive experiments demonstrate that our proposed model surpasses other state-of-the-art models on the MFNet, PST900, and RTSS datasets remarkably.

Therapeutic drug combinations against COVID-19 obtained by employing a collaborative filtering method.

The outbreak of coronavirus disease 2019 (COVID-19) has severely harmed human society and health. Because there is currently no specific drug for the treatment and prevention of COVID-19, we used a collaborative filtering algorithm to predict which traditional Chinese medicines (TCMs) would be effective in combination for the prevention and treatment of COVID-19. First, we performed drug screening based on the receptor structure prediction method, molecular docking using q-vina to measure the binding ability of TCMs, TCM formulas, and neo-coronavirus proteins, and then performed synergistic filtering based on Laplace matrix calculations to predict potentially effective TCM formulas. Combining the results of molecular docking and synergistic filtering, the new recommended formulas were analyzed by reviewing data platforms or tools such as PubMed, Herbnet, the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, the Guide to the Dispensing of Medicines for Clinical Evidence, and the Dictionary of Chinese Medicine Formulas, as well as medical experts' treatment consensus in terms of herbal efficacy, modern pharmacological studies, and clinical identification and typing of COVID-19 pneumonia, to determine the recommended solutions. We found that the therapeutic effect of a combination of six TCM formulas on the COVID-19 virus is the result of the overall effect of the formula rather than that of specific components of the formula. Based on this, we recommend a formula similar to that of Jinhua Qinggan Granules for the treatment of COVID-19 pneumonia. This study may provide new ideas and new methods for future clinical research. Classification: Biological Science.

The accuracy of D-dimer in the diagnosis of periprosthetic infections: a systematic review and meta-analysis.

BACKGROUND: Periprosthetic joint infection (PJI) is a devastating complication after total hip arthroplasty (THA) or total knee arthroplasty (TKA). It is scarce and contradicting evidence supporting the use of serum D-dimer to diagnose PJI in revision THA and TKA. This systematic review and meta-analysis aimed to investigate the accuracy of D-dimer in the diagnosis of periprosthetic infections. METHODS: The PubMed, Embase, Web of Science were systematically searched from the inception dates to August 15, 2020. We included all diagnostic studies of D-dimer in the diagnosis of periprosthetic infections. The literature's quality was evaluated by the QUADAS-2 tool, and Stata16 and Revman5.3 software carried out the meta-analysis. RESULTS: Of 115 citations identified by the search strategy, 10 studies (comprising 1756 participants) met the inclusion criteria. The literature quality assessment results show that most of the literature is low-risk bias literature. The combined sensitivity of D-dimer in diagnosing periprosthetic infections was 0.81 (95% confidence interval [CI] 0.71-0.88), combined specificity was 0.74 (95% CI 0.61-0.84), combined positive likelihood ratio was 3.1 (95% CI 2.0-5.0), combined negative likelihood ratio 0.26 (95% CI 0.16-0.41), combined diagnosis odds ratio 12 (95% CI 5-27), area under the Summary Receiver Operator Characteristic Curve (SROC) is 0.85 (95% CI 0.81-0.88). The data are statistically significant. CONCLUSION: D-dimer has a high diagnostic value in diagnosing PJI and has clinical significance in diagnosing periprosthetic infection. In addition, there are relatively few studies on the threshold of D-dimer, different sampling types, different laboratory detection methods, and different races, so more prospective trials with large samples, multi-centers, and scientific design should be carried out in the future.

Middle-Level Feature Fusion for Lightweight RGB-D Salient Object Detection.

Most existing RGB-D salient object detection (SOD) models adopt a two-stream structure to extract the information from the input RGB and depth images. Since they use two subnetworks for unimodal feature extraction and multiple multi-modal feature fusion modules for extracting cross-modal complementary information, these models require a huge number of parameters, thus hindering their real-life applications. To remedy this situation, we propose a novel middle-level feature fusion structure that allows to design a lightweight RGB-D SOD model. Specifically, the proposed structure first employs two shallow subnetworks to extract low- and middle-level unimodal RGB and depth features, respectively. Afterward, instead of integrating middle-level unimodal features multiple times at different layers, we just fuse them once via a specially designed fusion module. On top of that, high-level multi-modal semantic features are further extracted for final salient object detection via an additional subnetwork. This will greatly reduce the network's parameters. Moreover, to compensate for the performance loss due to parameter deduction, a relation-aware multi-modal feature fusion module is specially designed to effectively capture the cross-modal complementary information during the fusion of middle-level multi-modal features. By enabling the feature-level and decision-level information to interact, we maximize the usage of the fused cross-modal middle-level features and the extracted cross-modal high-level features for saliency prediction. Experimental results on several benchmark datasets verify the effectiveness and superiority of the proposed method over some state-of-the-art methods. Remarkably, our proposed model has only 3.9M parameters and runs at 33 FPS.

Immunomodulatory mechanisms of abatacept: A therapeutic strategy for COVID-19.

Coronavirus disease 2019 (COVID-19) caused by coronavirus-2 (SARS-CoV-2) infection has rapidly spread throughout the world and become a major threat to human beings. Cytokine storm is a major cause of death in severe patients. Abatacept can suppress cytokines used as antirheumatic drugs in clinical applications. This study analyzed the molecular mechanisms of abatacept treatment for COVID-19. Differentially expressed genes (DEGs) were identified by analyzing expression profiling of abatacept treatment for rheumatoid arthritis (RA) patients and SARS-CoV-2 infection patients. We found that 59 DEGs were upregulated in COVID-19 patients and downregulated following abatacept treatment. Gene set enrichment analysis (GSEA) and Gene Ontology (GO) analysis showed that immune and inflammatory responses were potential regulatory mechanisms. Moreover, we verified 8 targeting genes and identified 15 potential drug candidates for the treatment of COVID-19. Our study illustrated that abatacept could be a promising property for preventing severe COVID-19, and we predicted alternative potential drugs for the treatment of SARS-CoV-2 infection.

Sensitive electrochemical aptasensor for determination of sulfaquinoxaline based on AuPd NPs@UiO-66-NH2/CoSe2 and RecJf exonuclease-assisted signal amplification.

The authors designed a sensitive label-free electrochemical aptasensor for the detection of sulfaquinoxaline (SQX), including the AuPd NPs@UiO-66-NH2/CoSe2 nanocomposites and RecJf exonuclease-assisted target recycle signal amplification strategy. AuPd NPs@UiO-66-NH2/CoSe2 nanocomposite with excellent conductivity and numerous active sites was successfully synthesized to provide a favorable sensing platform and load more double-strand DNA (dsDNA) on the electrode surface. The negatively charged phosphate group of the oligonucleotide and [Fe (CN)6] 3-/4- repel each other electrostatically, resulting in very low electrical signals. In the presence of SQX, its corresponding aptamer will be released from the double-stranded structure and then digested by RecJf exonuclease, which resulted in the SQX being released to initiate the next recycling to help amplify the DPV signal. Under the optimal conditions, the peak current has a linear relationship with the logarithmic of SQX concentration in the range of 1 pg/mL∼100 ng/mL and the obtained detection limit was 0.547 pg/mL. Furthermore, the contrasted aptasensor possess reliable specificity, reproducibility and stability toward SQX, and has been applied to detect SQX in pork samples with a satisfied recovery varied from 94.40% to 95.98%.

Engineering oxygen vacancies and surface chemical reconstruction of MOF-derived hierarchical CoO/Ni2P-Co2P nanosheet arrays for advanced aqueous zinc-ion batteries.

Aqueous zinc-ion batteries (ZIBs) are emerging as promising alternatives among various energy storage devices. However, the lack of research on cathode materials with both high capacity and electrochemical stability restricts widespread applications of ZIBs. Herein, surface chemical reconstruction and partial phosphorization strategies are employed to synthesize MOF-derived hierarchical CoO/Ni2P-Co2P nanosheet arrays on Ni foam substrates as cathodes for ZIBs. The unique hierarchical nanostructure and multiple components with exposed surfaces and rich oxygen vacancies accelerate charge transfer and ion diffusion, expose more active sites, and promote the accessibility between the active materials and electrolyte. The oxide/phosphide composites obtained by novel partial phosphorization achieve a common improvement of performance and stability. As expected, the CoO/Ni2P-Co2P electrode delivers a high specific capacity (370.4 mA h g-1 at 3 A g-1) and excellent rate performance (63.3% retention after a six-fold increase in the current density). Moreover, when employed as the cathode of the CoO/Ni2P-Co2P-30//Zn battery, the assembled battery exhibits a superior specific capacity (322.8 mA h g-1 at 2 A g-1), a long cycle life (104.9% retention after 6000 cycles), a favorable energy density (547.5 W h kg-1) and power density (9.7 kW kg-1). Therefore, this study provides a suitable candidate which meets the requirements of high-performance cathode materials for ZIBs.