Association of periodontitis with all-cause and cause-specific mortality among individuals with depression: a population-based study.

To detect the association between periodontitis and all-cause as well as cause-specific mortality rates among adults diagnosed with depression. Participants diagnosed with depression were selected from NHANES across three periods (1988-1994; 1999-2004; 2009-2014). Cox proportional hazards and Weibull accelerated failure time (AFT) models were utilized to calculate hazard ratios (HRs), time ratios (TRs), and their 95% confidence intervals (CIs) to evaluate the association between moderate-to-severe periodontitis and all-cause as well as cause-specific mortality among participants with depression. white blood counts and C-reactive protein were used to assess the mediating role of systemic inflammation. Among the 1,189 participants with a median follow-up of 9.25 years, 133 deaths were recorded. After adjusting for multiple variables, moderate-to-severe periodontitis was obvious associated with an increased risk of cancer-related mortality in individuals with depression (Cox: HR 3.22, 95% CI 1.51-6.83, P = 0.002; AFT: TR 0.70, 95% CI 0.52-0.94, P = 0.017). Neither WBC nor CRP significantly mediate the association between periodontitis and cancer-related mortality. The risk of cancer-related mortality rose with the severity of periodontitis (P for trend = 0.021). However, no association was observed between moderate-to-severe periodontitis and other kinds of mortality. Moderate-to-severe periodontitis is linked to an elevated risk of cancer-related mortality among adults diagnosed with depression, with the mortality risk increasing alongside the severity of periodontitis. No significant mediating effect of systemic inflammation was found in this association. These findings highlight the importance of addressing periodontal health in individuals with depression. By uncovering the association between periodontitis and mortality in this population, our study underscores the potential benefits of preventive dental care and periodontal treatment in reducing the risk of cancer-related mortality in individuals with depression.

Using Entropy as the Convergence Criteria of Ant Colony Optimization and the Application at Gene Chip Data Analysis.

INTRODUCTION: When Ant Colony Optimization algorithm (ACO) is adept at identifying the shortest path, the temporary solution is uncertain during the iterative process. All temporary solutions form a solution set. METHOD: Where each solution is random. That is, the solution set has entropy. When the solution tends to be stable, the entropy also converges to a fixed value. Therefore, it was proposed in this paper that apply entropy as a convergence criterion of ACO. The advantage of the proposed criterion is that it approximates the optimal convergence time of the algorithm. RESULTS: In order to prove the superiority of the entropy convergence criterion, it was used to cluster gene chip data, which were sampled from patients of Alzheimer's Disease (AD). The clustering algorithm is compared with six typical clustering algorithms. The comparison shows that the ACO using entropy as a convergence criterion is of good quality. CONCLUSION: At the same time, applying the presented algorithm, we analyzed the clustering characteristics of genes related to energy metabolism and found that as AD occurs, the entropy of the energy metabolism system decreases; that is, the system disorder decreases significantly.

Associations of tooth loss with risk of all-cause and cause-specific mortality among US adults with diabetes mellitus.

OBJECTIVES: To determine whether tooth loss affects all-cause and cause-specific mortality in a nationally representative sample of adults with diabetes mellitus (DM) in the United States. METHODS: This prospective cohort study involved 8207 participants aged 30 years or older at baseline, all diagnosed with diabetes mellitus and enrolled in the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018. Tooth loss was stratified into 28 teeth (complete), 20-27 teeth (tooth loss), 9-19 teeth (lacking functional), 1-8 teeth (severe tooth loss) and edentulism. To estimate hazard ratios (HRs) and 95 % confidence intervals (CIs) for all-cause and specific-cause mortality in diabetes mellitus participants according to tooth loss, multivariate cox proportional hazards regression models were used. Relationships between mortality and quartiles of mean tooth loss levels were analyzed, with the lowest quartile as the baseline for comparisons. RESULTS: During a median of 6.92 years of follow-up, 2317 deaths were documented. After multivariate adjustments, higher tooth loss levels were significantly and non-linearly associated with higher risks of all-cause, CVD-related and DM-related mortality among participants with DM. When compared with the reference group of mean tooth loss levels, the highest quartile showed significantly increased risks: all-cause mortality (HR, 2.11; 95 % CI, 1.53-2.91, P-trend < 0.001), CVD-related mortality (HR, 3.24, 95 % CI, 1.54-6.85, P-trend < 0.001) and DM-related mortality (HR, 2.78, 95 % CI, 1.15-6.68, P-trend < 0.001). CONCLUSIONS: Tooth loss is associated with an increased risk of all-cause, CVD-related and diabetes mellitus mortality among adults with diabetes mellitus in the US. CLINICAL SIGNIFICANCE: This study presents evidence for physicians and dentists that higher tooth loss was significantly associated with increased risk of all-cause, CVD-related and diabetes mellitus mortality in a dose-response manner among adults with diabetes mellitus. Therefore, assessment of survival in individuals with diabetes mellitus could pay attention to the tooth loss.

ZIF-8-based Nanoparticles for Inflammation Treatment and Oxidative Stress Reduction in Periodontitis.

Periodontitis, an inflammatory bone resorption disease associated with dental plaque, poses significant challenges for effective treatment. In this study, we developed Mino@ZIF-8 nanoparticles inspired by the periodontal microenvironment and the unique properties of zeolitic imidazolate framework 8, aiming to address the complex pathogenesis of periodontitis. Transcriptome analysis revealed the active engagement of Mino@ZIF-8 nanoparticles in innate and adaptive inflammatory host defense and cellular metabolic remodeling. Through sustained release of the anti-inflammatory and antibacterial agent minocycline hydrochloride (Mino) and the generation of Zn2+ with pro-antioxidant effects during degradation, Mino@ZIF-8 nanoparticles synergistically alleviate inflammation and oxidative damage. Notably, our study focuses on the pivotal role of zinc ions in mitochondrial oxidation protection. Under lipopolysaccharide (LPS) stimulation, periodontal ligament cells undergo a metabolic shift from oxidative phosphorylation (OXPHOS) to glycolysis, leading to reduced ATP production and increased reactive oxygen species levels. However, Zn2+ effectively rebalances the glycolysis-OXPHOS imbalance, restoring cellular bioenergetics, mitigating oxidative damage, rescuing impaired mitochondria, and suppressing inflammatory cytokine production through modulation of the AKT/GSK3ß/NRF2 pathway. This research not only presents a promising approach for periodontitis treatment but also offers novel therapeutic opportunities for zinc-containing materials, providing valuable insights into the design of biomaterials targeting cellular energy metabolism regulation.

Theoretical study of electronic structures, magnetic properties, and ultrafast spin manipulation in transition metal adsorbed polycyclic-aromatic-hydrocarbon molecules.

We present a first-principles study of the structural, electronic, and magnetic properties of TM(PAH)0/+ (TM = Fe, Co, Ni; PAH = C10H8, C16H10, C24H12, C32H14) complexes and explore the laser-induced spin dynamics as well as their stability with respect to various laser parameters. For each complex, the most stable configuration shows that the TM atom prefers to adsorb at the hollow site of the carbon ring with a slight deviation from the center. The electronic structure and spin localization of the complexes are found to be largely affected by the TM type. Driven by various laser pulses, spin-crossover scenarios are achieved in all structures, while spin-transfer between TM and PAH is achieved in Ni(C10H8), Ni(C16H10), and Ni(C24H12). The influence of the laser energy and chirp on the dynamics is also investigated, providing important information regarding the stability and sensitivity of the dynamical process. All results are believed to reveal the physics nature of the TM-PAH systems, to guide the experimental realization of their ultrafast spin dynamics and thus to promote their applications in future spintronics.

PLGA micro/nanoparticle vaccination elicits non-tumor antigen specific resident memory CD8+ T cell protection from hepatocellular carcinoma.

Together, tumor and virus-specific tissue-resident CD8+ memory T cells (TRMs) of hepatocellular carcinoma (HCC) patients with Hepatitis B virus (HBV) infection can provide rapid frontline immune surveillance. The quantity and activity of CD8+ TRMs were correlated with the relapse-free survival of patients with improved health. However, HBV-specific CD8+ TRMs have a more exhausted phenotype and respond more actively under anti-PDL1 or PD1 treatment of HBV+HCC patients. Vaccination strategies that induce a strong and sustained CD8+ TRMs response are quite promising. Herein, a biodegradable poly(D,L-lactide-co-glycolide) microsphere and nanosphere particle (PLGA N.M.P) delivery system co-assembled by anti-PD1 antibodies (aPD1) and loaded with ovalbumin (OVA-aPD1 N.M.P) was fabricated and characterized for size (200 nm and 1 µm diameter), charge (-15 mV), and loading efficiencies of OVA (238 µg mg-1 particles) and aPD1 (40 µg mg-1 particles). OVA-aPD1 N.M.P could stimulate the maturation of BMDCs and enhance the antigen uptake and presentation by 2-fold compared to free OVA. The nanoparticles also induced the activation of macrophages (RAW 264.7) to produce a high level of cytokines, including TNF-α, IL-6 and IL-10. In vivo stimulation of mice using OVA-aPD1 N.M.P robustly enhanced IFN-γ-producing-CD8+ T cell infiltration in tumor tissues and the secretion of IgG and IgG2a/IgG1 antibodies. OVA-aPD1 N.M.P delivered OVA to increase the activation and proliferation of OVA-specific CD8+ TRMs, and its combination with anti-PD1 antibodies promoted complete tumor rejection by the reversal of tumor-infiltrating CD8+ T cell exhaustion. Thus, PLGA N.M.P could induce a strong CD8+ TRMs response, further highlighting its therapeutic potential in enhancing an antitumor immune response.

Biomechanical investigation of a customized interspinous spacer system in the treatment of degenerative disc diseases: A finite element analysis.

BACKGROUND: A novel interspinous fixation system based on anatomical parameters and incorporating transfacetopedicular screws, was developed to treat degenerative disc diseases. The biomechanical characteristics of the novel system were evaluated using finite element analysis in comparison to other classical interspinous spacers. METHODS: The L1-S1 lumbar spine finite element models were surgically implanted with the novel system, Coflex and DIAM devices at the L4/L5 segment to assess the range of motion, the pression distribution of intervertebral disc, the peak stresses on the spinous process and implant during various motions. FINDINGS: Range of motions of the L4/L5 surgical segment were reduced by 29.13%, 61.27%, 77.35%, 33.33%, and the peak stresses of intervertebral disc were decreased by 36.82%, 67.31%, 73.00%, 69.57% for the novel system in flexion, extension, lateral bending, and axial rotation when compared with the Coflex, and they were declined by 34.53%, 57.86%, 75.81%, 25.21%; 36.22%, 67.31%, 75.01%, 71.40% compared with DIAM. The maximum stresses of the spinous process were 29.93 MPa, 24.66 MPa, 14.45 MPa, 24.37 MPa in the novel system, and those of Coflex and DIAM were 165.3 MPa, 109 MPa, 84.79 MPa, 47.66 MPa and 52.59 MPa, 48.78 MPa, 50.27 MPa, 44.16 MPa during the same condition. INTERPRETATION: Compared to other interspinous spacer devices, the novel interspinous fixation system demonstrated excellent stability, effectively distributing load on the intervertebral disc, and reducing the risk of spinous process fractures. The personalized design of the novel interspinous fixation system could be a viable option for treating degenerative disc diseases.

The role of periodontitis in the development of atherosclerotic cardiovascular disease in participants with the components of metabolic syndrome: a systematic review and meta-analysis.

OBJECTIVES: Prevention of atherosclerotic cardiovascular disease (ASCVD) is important in individuals with metabolic syndrome components (MetS), and periodontitis may play an important role in this process. This study aims to evaluate the association between periodontitis and ASCVD in participants with the components of MetS, including obesity, dysglycemia, hypertension, and dyslipidemia. MATERIALS AND METHODS: This study conducted followed the MOOSE reporting guidelines and the PRISMA 2020 guidelines. EMBASE, MEDLINE, Web of Science, Cochrane Library, PubMed and OpenGrey were searched for observational studies about the linkage of periodontitis to ASCVD in people with MetS components up to April 9, 2023. Cohort, case-control and cross-sectional studies were included after study selection. Quality evaluation was carried out using the original and modified Newcastle-Ottawa Scale as appropriate. Random-effects model was employed for meta-analysis. RESULTS: Nineteen studies were finally included in the quality analysis, and all of them were assessed as moderate to high quality. Meta-analyses among fifteen studies revealed that the participants with periodontitis were more likely to develop ASCVD in those who have dysglycemia (RR = 1.25, 95% CI = 1.13-1.37; p < 0.05), obesity (RR = 1.13, 95% CI = 1.02-1.24; p < 0.05), dyslipidemia (RR = 1.36, 95% CI = 1.13-1.65; p < 0.05), or hypertension (1.20, 95% CI = 1.05-1.36; p < 0.05). CONCLUSIONS: Periodontitis promotes the development of ASCVD in participants with one MetS component (obesity, dysglycemia, hypertension or dyslipidemia). CLINICAL RELEVANCE: In people with MetS components, periodontitis may contribute to the ASCVD incidence.

Retrofit and application of pulverized coal burners with LNG and oxygen ignition in utility boiler under ultra-low load operation.

An oxygen-rich and low NOx burner integrated with liquefied natural gas (LNG) was proposed to address unstable combustion and high NOx emissions from a 330 MW subcritical boiler under ultra-low load operation in China. To assess the effectiveness of the retrofit, Chemkin and Fluent softwares were utilized to construct a new NOx model and calculate NOx generation, based on the combustion of pulverized coal gas and LNG. Further, an eddy dissipation concept (EDC) model, which can reflect detailed chemical reactions, was applied to calculate gas-phase reactions in the furnace. The results showed that when performing the deep peak shaving after the retrofit, the combustion in the furnace was stable under 50% or more load, and NOx emission level at the furnace outlet was lower than 350 mg/m3 (6% O2 content, dry basis). Under 25% load, the oxygen-rich burner integrated with LNG was applied, and the pulverized coal flow entered the furnace in a state of high-intensity combustion, which effectively promoted the stability of combustion in the furnace. The reductive combustion state with reductive free radicals generated by LNG decomposition inhibited NOx formation. Consequently, NOx emissions from the furnace outlet decreased from 380 mg/m3 to 316 mg/m3.

A Self-Assembled 3D Model Demonstrates How Stiffness Educates Tumor Cell Phenotypes and Therapy Resistance in Pancreatic Cancer.

Pancreatic ductal adenocarcinoma (PDAC) is characterized by a dense and stiff extracellular matrix (ECM) associated with tumor progression and therapy resistance. To further the understanding of how stiffening of the tumor microenvironment (TME) contributes to aggressiveness, a three-dimensional (3D) self-assembling hydrogel disease model is developed based on peptide amphiphiles (PAs, PA-E3Y) designed to tailor stiffness. The model displays nanofibrous architectures reminiscent of native TME and enables the study of the invasive behavior of PDAC cells. Enhanced tuneability of stiffness is demonstrated by interacting thermally annealed aqueous solutions of PA-E3Y (PA-E3Yh) with divalent cations to create hydrogels with mechanical properties and ultrastructure similar to native tumor ECM. It is shown that stiffening of PA-E3Yh hydrogels to levels found in PDAC induces ECM deposition, promotes epithelial-to-mesenchymal transition (EMT), enriches CD133+/CXCR4+ cancer stem cells (CSCs), and subsequently enhances drug resistance. The findings reveal how a stiff 3D environment renders PDAC cells more aggressive and therefore more faithfully recapitulates in vivo tumors.

Frequency and time dependent viscoelastic characterization of pediatric porcine brain tissue in compression.

Understanding the viscoelastic behavior of pediatric brain tissue is critical to interpret how external mechanical forces affect head injury in children. However, knowledge of the viscoelastic properties of pediatric brain tissue is limited, and this reduces the biofidelity of developed numeric simulations of the pediatric head in analysis of brain injury. Thus, it is essential to characterize the viscoelastic behavior of pediatric brain tissue in various loading conditions and to identify constitutive models. In this study, the pediatric porcine brain tissue was investigated in compression with determine the viscoelasticity under small and large strain, respectively. A range of frequencies between 0.1 and 40 Hz was applied to determine frequency-dependent viscoelastic behavior via dynamic mechanical analysis, while brain samples were divided into three strain rate groups of 0.01/s, 1/s and 10/s for compression up to 0.3 strain level and stress relaxation to obtain time-dependent viscoelastic properties. At frequencies above 20 Hz, the storage modulus did not increase, while the loss modulus increased continuously. With strain rate increasing from 0.01/s to 10/s, the mean stress at 0.1, 0.2 and 0.3 strain increased to approximate 6.8, 5.6 and 4.4 times, respectively. The brain compressive response was sensitive to strain rate and frequency. The characterization of brain tissue will be valuable for development of head protection systems and prediction of brain injury.

Developing a machine learning model for accurate nucleoside hydrogels prediction based on descriptors.

Supramolecular hydrogels derived from nucleosides have been gaining significant attention in the biomedical field due to their unique properties and excellent biocompatibility. However, a major challenge in this field is that there is no model for predicting whether nucleoside derivative will form a hydrogel. Here, we successfully develop a machine learning model to predict the hydrogel-forming ability of nucleoside derivatives. The optimal model with a 71% (95% Confidence Interval, 0.69-0.73) accuracy is established based on a dataset of 71 reported nucleoside derivatives. 24 molecules are selected via the optimal model external application and the hydrogel-forming ability is experimentally verified. Among these, two rarely reported cation-independent nucleoside hydrogels are found. Based on their self-assemble mechanisms, the cation-independent hydrogel is found to have potential applications in rapid visual detection of Ag+ and cysteine. Here, we show the machine learning model may provide a tool to predict nucleoside derivatives with hydrogel-forming ability.

Identification of the toxin components of Rhizoctonia solani AG1-IA and its destructive effect on plant cell membrane structure.

Rice sheath blight is a fungal disease caused mainly by Rhizoctonia solani AG1-IA. Toxins are a major pathogenic factor of R. solani, and some studies have reported their toxin components; however, there is no unified conclusion. In this study, we reported the toxin components and their targets that play a role in R. solani AG1-IA. First, toxins produced by R. solani AG1-IA were examined. Several important phytotoxins, including benzoic acid (BZA), 5-hydroxymethyl-2-furanic aid (HFA), and catechol (CAT), were identified by comparative analysis of secondary metabolites from AG1-IA, AG1-IB, and healthy rice. Follow-up studies have shown that the toxin components of this fungus can rapidly disintegrate the biofilm structure while maintaining the content of host plant membrane components, thereby affecting the organelles, which may also explain the lack of varieties highly resistant to sheath blight.

Potential of lncRNAs to regulate cuproptosis in hepatocellular carcinoma: Establishment and validation of a novel risk model.

Cuproptosis, a distinct form of programmed cell death, is an emerging field in oncology with promising implications. This novel mode of cell death has the potential to become a regulatory target for tumor therapy, thus expanding the currently limited treatment options available for patients with cancer. Our research team focused on investigating the role of functional long non-coding RNA (lncRNAs) in hepatocellular carcinoma (HCC). We were particularly intrigued by the potential implications of HCC-lncRNAs on cuproptosis. Through a comprehensive analysis, we identified three cuproptosis-related lncRNAs (CRLs): AC018690.1, AL050341.2, and LINC02038. These lncRNAs were found to influence the sensitivity of HCC to cuproptosis. Based on our results, we constructed a risk model represented by the equation: risk score = 0.82 * AC018690.1 + 0.65 * AL050341.2 + 0.61 * LINC02038. Notably, significant disparities were observed in clinical features, such as the response rate to immunotherapy and targeted therapy, as well as in cellular characteristics, including the composition of the tumor immune microenvironment (TIME), when comparing the high- and low-risk groups. Most importantly, knockdown of these CRLs was confirmed to significantly weaken the resistance to cuproptosis in HCC. This effect resulted from the accelerated accumulation of lipoacylated-DLAT and lipoacylated-DLST. In summary, we identified three CRLs in HCC and established a novel risk model with potential clinical applications. Additionally, we proposed a potential therapeutic method consisting of sorafenib-copper ionophores-immunotherapy.

The clinical implication and translational research of OSCC differentiation.

BACKGROUND: The clinical value and molecular characteristics of tumor differentiation in oral squamous cell carcinoma (OSCC) remain unclear. There is a lack of a related molecular classification prediction system based on pathological images for precision medicine. METHODS: Integration of epidemiology, genomics, experiments, and deep learning to clarify the clinical value and molecular characteristics, and develop a novel OSCC molecular classification prediction system. RESULTS: Large-scale epidemiology data (n = 118,817) demonstrated OSCC differentiation was a significant prognosis indicator (p < 0.001), and well-differentiated OSCC was more chemo-resistant than poorly differentiated OSCC. These results were confirmed in the TCGA database and in vitro. Furthermore, we found chemo-resistant related pathways and cell cycle-related pathways were up-regulated in well- and poorly differentiated OSCC, respectively. Based on the characteristics of OSCC differentiation, a molecular grade of OSCC was obtained and combined with pathological images to establish a novel prediction system through deep learning, named ShuffleNetV2-based Molecular Grade of OSCC (SMGO). Importantly, our independent multi-center cohort of OSCC (n = 340) confirmed the high accuracy of SMGO. CONCLUSIONS: OSCC differentiation was a significant indicator of prognosis and chemotherapy selection. Importantly, SMGO could be an indispensable reference for OSCC differentiation and assist the decision-making of chemotherapy.

Association between periodontitis and mortality of patients with cardiovascular diseases: A cohort study based on NHANES.

BACKGROUND: The association between periodontitis and cardiovascular disease (CVD) has been widely explored, but little is known about the effect of periodontitis on the mortality of CVD patients. This study aims to clarify the effect of periodontitis on all-cause and cause-specific mortality of CVD patients. METHODS: We included 2,135 individuals with CVD from the National Health and Nutrition Examination Survey. Mortality data were ascertained by linkage to National Death Index records through 31 December 2019. We used Cox proportional hazards models for all-cause mortality and competing risk models for CVD and cancer mortality to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Further covariate adjustments, stratification analyses, and a variety of sensitivity analyses were conducted to test the reliability and robustness of the results. RESULTS: The all-cause mortality in CVD patients with moderate/severe periodontitis was significantly higher than in those with no/mild periodontitis (HR: 1.25; 95% CI: 1.02-1.52; P = 0.03). The all-cause mortality in participants with severe clinical attachment loss was significantly higher (HR: 1.07; 95% CI: 1.01-1.14; P = 0.01). However, no discrepancy in CVD or cancer mortality was observed between CVD patients with different periodontal status. CONCLUSIONS: Our findings from a longitudinal study with a large sample indicated significant but slightly higher all-cause mortality in CVD patients with moderate/severe periodontitis than in those with no/mild periodontitis.

Periodontitis and the risk of all-cause and cause-specific mortality among US adults with diabetes: A population-based cohort study.

AIM: To evaluate the association between periodontitis, all-cause and cause-specific mortality, and its prognostic utility among adults with diabetes. MATERIALS AND METHODS: Periodontal health records were retrieved from the NHANES database for 4297 participants with diabetes aged >30 years at baseline during 1988-1994, 1999-2004 and 2009-2014. Multivariable Cox proportional hazards regression model was applied to calculate the hazard ratios (HRs) and 95% confidence intervals (CIs) for moderate/severe periodontitis with all-cause and cause-specific mortality in participants with diabetes. Area under the curve (AUC) was used to assess predictive value. RESULTS: During a median follow-up of 15.41 years, 1701 deaths occurred. After multivariate adjustments, moderate/severe periodontitis was significantly associated with increased risk of all-cause (HR: 1.27; 95% CI: 1.07-1.50; p = .005) and cardiovascular disease (CVD)-related (HR: 1.35, 95% CI: 1.03-1.76, p = .031) mortality in participants with diabetes. The absolute risk difference based on the cumulative incidence information was 0.022 (5-year, 95% CI: 0.021-0.023) and 0.044 (10-year, 95% CI: 0.041-0.048). Periodontitis improved the prediction of all-cause (AUC: 0.652; 95% CI: 0.627-0.676) and CVD-related (AUC: 0.649; 95% CI: 0.624-0.676) mortality over standard risk factors (all-cause: AUC: 0.631; 95% CI: 0.606-0.656; CVD-related: AUC: 0.629; 95% CI: 0.604-0.655). CONCLUSIONS: Moderate/severe periodontitis is associated with an increased risk of all-cause and CVD-related mortality in adults with diabetes. Periodontitis might represent a marker for residual risk.

Co3X8 (X = Cl and Br): multiple phases and magnetic properties of the Kagome lattice.

The unique magnetic properties of two-dimensional (2D) materials have demonstrated huge potential for applications in nanodevices and spintronics. In this work, we propose a new Kagome lattice, Co3X8 (X = Cl and Br), based on density functional theory (DFT) calculation. We find that Co/X in Co3X8 has spontaneous movement in the lattice, resulting in 156- and 12-phases of Co3X8 and diverse magnetic and electronic properties. We show that the magnetic and electronic properties of Co3X8 can be engineered by strain, and the magnetic properties of Co3X8 are highly related to the spontaneous movement of X. Moreover, the transmission property of 12-Co3X8 shows clear angle-dependent features due to the symmetry breaking as caused by the spontaneous movement of X. Our findings may provide not only a possible Kagome lattice with unique properties, but also a strategy for designing nanodevices and for spintronics.

Zn-induced formation of polymetallic carbonate hydroxide cathodes with high mass loading for high performance aqueous alkaline Zn-based batteries.

Developing high mass loading cathodes with high capacity and durable life cycles is greatly worthwhile and challenging for alkaline aqueous rechargeable Zn-based batteries (AAZBs). Herein, we demonstrate an efficient zinc-induced strategy to rationally develop Zn-Ni-Co carbonate hydroxides/hydroxides heterostructure nanosheet array with an extremely high mass loading of 9.2 mg cm-2 on Ni foam (ZNC/NF) as such a superior cathode for AAZBs. It is discovered that Ni-Co hydroxide nanowires can be transformed into Zn-Ni-Co carbonate hydroxides/hydroxides heterostructure nanosheet with rich defect structures after the introduction of Zn during the synthetic process. The formed heterostructures and rich defect structures can enhance ion and electron transfer efficiency, thus ensuring the excellent electrochemical performance under high loading condition. Consequently, the ZNC/NF//Zn battery shows an outstanding areal capacity of 2.1 mAh cm-2 at 5 mA cm-2, with an ultrahigh energy density of 3.6 mWh cm-2. Moreover, the battery can still retain a high capacity of 0.42 mAh cm-2 after 5000 cycles at 50 mA cm-2, suggesting strong long-term cycling stability. This research enables pave the way for the rational design and manufacture of advanced electrode materials with large mass loadings.

Automatic Chinese Food recognition based on a stacking fusion model.

With commercialization of deep learning models, daily precision dietary record based on images from smartphones becomes possible. This study took advantage of Deep-learning techniques on visual recognition tasks and proposed a big-data-driven Deep-learning model regressing from food images. We established the largest data set of Chinese dishes to date, named CNFOOD-241. It contained more than 190,000 images with 241 categories, covering Staple food, meat, vegetarian diet, mixed meat and vegetables, soups, dessert category. This study also compares the prediction results of three popular deep learning models on this dataset, ResNeXt101_32x32d achieving up to 82.05% for top-1 accuracy and 97.13% for top-5 accuracy. Besides, this paper uses a multi-model fusion method based on stacking in the field of food recognition for the first time. We built a meta-learner after the base model to integrate the three base models of different architectures to improve robustness. The accuracy achieves 82.88% for top-1 accuracy.Clinical Relevance-This study proves that the application of artificial intelligence technology in the identification of Chinese dishes is feasible, which can play a positive role in people who need to control their diet, such as diabetes and obesity.