Empagliflozin alleviates neuroinflammation by inhibiting astrocyte activation in the brain and regulating gut microbiota of high-fat diet mice.

A high-fat diet can modify the composition of gut microbiota, resulting in dysbiosis. Changes in gut microbiota composition can lead to increased permeability of the gut barrier, allowing bacterial products like lipopolysaccharides (LPS) to enter circulation. This process can initiate systemic inflammation and contribute to neuroinflammation. Empagliflozin (EF), an SGLT2 inhibitor-type hypoglycemic drug, has been reported to treat neuroinflammation. However, there is a lack of evidence showing that EF regulates the gut microbiota axis to control neuroinflammation in HFD models. In this study, we explored whether EF could improve neuroinflammation caused by an HFD via regulation of the gut microbiota and the mechanism underlying this phenomenon. Our data revealed that EF alleviates pathological brain injury, reduces the reactive proliferation of astrocytes, and increases the expression of synaptophysin. In addition, the levels of inflammatory factors in hippocampal tissue were significantly decreased after EF intervention. Subsequently, the results of 16S rRNA gene sequencing showed that EF could change the microbial community structure of mice, indicating that the abundance of Lactococcus, Ligilactobacillus and other microbial populations decreased dramatically. Therefore, EF alleviates neuroinflammation by inhibiting gut microbiota-mediated astrocyte activation in the brains of high-fat diet-fed mice. Our study focused on the gut-brain axis, and broader research on neuroinflammation can provide a more holistic understanding of the mechanisms driving neurodegenerative diseases and inform the development of effective strategies to mitigate their impact on brain health. The results provide strong evidence supporting the larger clinical application of EF.

Neck dissection of cN0 maxillary oral squamous cell carcinoma: A study based on SEER database.

OBJECTIVE: For patients with clinical nodal-negative (cN0) maxillary oral squamous cell carcinoma (MOSCC), neck dissection (ND) and clinical observation are the main two management strategies for the neck. However, the indications corresponding to these two options remain controversial. This study aimed to elucidate the clinical factors affecting ND treatment and to identify clinical characteristics of the population that may benefit from ND based on a retrospective analysis of cN0 MOSCC patient data from the Surveillance, Epidemiology, and End Results (SEER) database. METHODS: 8846 MOSCC patients were identified in the SEER database from 2000 to 2020. The Kaplan-Meier method was utilized to examine overall survival (OS) and disease-specific survival (DSS), while the hazard ratio (HR) was estimated using the stepwise multivariate Cox regression model. Furthermore, multi-subgroup analyses of DSS and OS were performed to compare ND and No ND. RESULTS: We included 2,512 cN0 MOSCC patients. Basic survival analysis and Cox regression modeling showed that ND was an independent prognostic factor that promoted DSS and OS. Additional subgroup analyses revealed that the primary site and T-stage might influence the efficacy of ND modality. Moreover, patients with T3/T4 stage of upper gingival squamous cell carcinoma (UGSCC) (DSS p = 0.009, OS p = 0.004), hard palate squamous cell carcinoma (HPSCC) (DSS p = 0.001, OS p < 0.001), and soft palate squamous cell carcinoma (SPSCC) (p = 0.029) showed a better survival benefit with ND in OS and DSS. Nonetheless, no differences were observed in OS and DSS between ND and No ND at the T1/T2 stage of the abovementioned primary tumor sites. Additionally, the DSS outcomes for T1/T2 stage upper lip squamous cell carcinoma (ULSCC) patients were significantly worse in the ND group than in the No ND group (p = 0.018). However, no significant differences were noted in OS (p = 0.140) as well as OS (p = 0.248) and DSS (p = 0.627) for T1/T2 and T3/T4 patients, respectively. CONCLUSION: Active surveillance might be a feasible strategy for managing all T-staged ULSCC as well as early-stage (T1/T2) UGSCC, SPSCC, and HPSCC, provided regular and meticulous follow-up is performed. Hence, concurrent ND is recommended for patients with intermediate to advanced (T3/T4) stage UGSCC, SPSCC, and HPSCC.

Rapid quantification of grapeseed oil multiple adulterations using near-infrared spectroscopy coupled with a novel double ensemble modeling method.

As a high-quality edible oil, grapeseed oil is often adulterated with low-price/quality vegetable oils. A novel ensemble modeling method is proposed for quantitative analysis of grapeseed oil adulterations combined with near-infrared (NIR) spectroscopy. The method combines Monte Carlo (MC) sampling and whale optimization algorithm (WOA) to build numerous partial least squares (PLS) sub-models, named MC-WOA-PLS. A total of 80 adulterated grapeseed oil samples were prepared by mixing grapeseed oil with soybean oil, palm oil, cottonseed oil, and corn oil with the designed mass percentages. NIR spectra of the 80 samples were measured in a transmittance mode in the range of 12,000-4000 cm-1. Parameters in MC-WOA-PLS including the number of latent variables (LVs) in PLS, iteration number of WOA, whale number, number of PLS sub-models, and percentage of training subsets were optimized. To validate the prediction performance of the model, root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), root mean squared error of prediction (RMSEP), correlation coefficient (R), residual predictive deviation (RPD), and standard deviation (S.D.) were used. Compared with PLS, standard normal variate-PLS (SNV-PLS), uninformative variable elimination-PLS (UVE-PLS), Monte Carlo uninformative variable elimination-PLS (MCUVE-PLS), randomization test-PLS (RT-PLS), variable importance in projection-PLS (VIP-PLS), and WOA-PLS, MC-WOA-PLS achieves the best prediction accuracy and stability for quantification of the five pure oils in adulterated grapeseed oil samples.

Discretized butterfly optimization algorithm for variable selection in the rapid determination of cholesterol by near-infrared spectroscopy.

The blood cholesterol level is strongly associated with cardiovascular disease. It is necessary to develop a rapid method to determine the cholesterol concentration of blood. In this study, a discretized butterfly optimization algorithm-partial least squares (BOA-PLS) method combined with near-infrared (NIR) spectroscopy is firstly proposed for rapid determination of the cholesterol concentration in blood. In discretized BOA, the butterfly vector is described by 1 or 0, which represents whether the variable is selected or not, respectively. In the optimization process, four transfer functions, i.e., arctangent, V-shaped, improved arctangent (I-atan) and improved V-shaped (I-V), are introduced and compared for discretization of the butterfly position. The partial least squares (PLS) model is established between the selected NIR variables and cholesterol concentrations. The iteration number, transfer functions and the performance of butterflies are investigated. The proposed method is compared with full-spectrum PLS, multiplicative scatter correction-PLS (MSC-PLS), max-min scaling-PLS (MMS-PLS), MSC-MMS-PLS, uninformative variable elimination-PLS (UVE-PLS), Monte Carlo uninformative variable elimination-PLS (MCUVE-PLS) and randomization test-PLS (RT-PLS). Results show that the I-V function is the best transfer function for discretization. Both preprocessing and variable selection can improve the prediction performance of PLS. Variable selection methods based on BOA are better than those based on statistics. Furthermore, I-V-BOA-PLS has the highest predictive accuracy among the seven variable selection methods. MSC-MMS can further improve the prediction ability of I-V-BOA-PLS. Therefore, BOA-PLS combined with NIR spectroscopy is promising for the rapid determination of cholesterol concentration in blood.

Variational Mode Decomposition for Raman Spectral Denoising.

As a fast and non-destructive spectroscopic analysis technique, Raman spectroscopy has been widely applied in chemistry. However, noise is usually unavoidable in Raman spectra. Hence, denoising is an important step before Raman spectral analysis. A novel spectral denoising method based on variational mode decomposition (VMD) was introduced to solve the above problem. The spectrum is decomposed into a series of modes (uk) by VMD. Then, the high-frequency noise modes are removed and the remaining modes are reconstructed to obtain the denoised spectrum. The proposed method was verified by two artificial noised signals and two Raman spectra of inorganic materials, i.e., MnCo ISAs/CN and Fe-NCNT. For comparison, empirical mode decomposition (EMD), Savitzky-Golay (SG) smoothing, and discrete wavelet transformation (DWT) are also investigated. At the same time, signal-to-noise ratio (SNR) was introduced as evaluation indicators to verify the performance of the proposed method. The results show that compared with EMD, VMD can significantly improve mode mixing and the endpoint effect. Moreover, the Raman spectrum by VMD denoising is more excellent than that of EMD, SG smoothing and DWT in terms of visualization and SNR. For the small sharp peaks, some information is lost after denoising by EMD, SG smoothing, DWT and VMD while VMD loses fewest information. Therefore, VMD may be an alternative method for Raman spectral denoising.

Near infrared spectroscopic variable selection by a novel swarm intelligence algorithm for rapid quantification of high order edible blend oil.

The quantification of single oil in high order edible blend oil is a challenging task. In this research, a novel swarm intelligence algorithm, discretized whale optimization algorithm (WOA), was first developed for reducing irrelevant variables and improving prediction accuracy of hexanary edible blend oil samples. The WOA is inspired by hunting strategy of humpback whales, which mainly includes three behaviors, i.e., encircling prey, bubble-net attacking and searching for prey. In discretized WOA, positions of whales were updated and then discretized by arctangent function. The whale population performance, iteration number and whale number of WOA were investigated. To validate the performance of selected variables, partial least squares (PLS) was used to build model and predict single oil contents in hexanary blend oil. Results show that WOA-PLS can provide the best prediction accuracy compared with full-spectrum PLS, continuous wavelet transform-PLS (CWT-PLS), uninformative variable elimination-PLS (UVE-PLS), Monte Carlo uninformative variable elimination-PLS (MCUVE-PLS) and randomization test-PLS (RT-PLS). Furthermore, CWT-WOA-PLS can further produce better results with fewer variables compared with WOA-PLS.

Linking trait network to growth performance of submerged macrophytes in response to ammonium pulse.

Extreme precipitation events caused by climate change leads to large variation of nitrogen input to aquatic ecosystems. Our previous study demonstrated the significant effect of different ammonium pulse patterns (differing in magnitude and frequency) on submersed macrophyte growth based on six plant morphological traits. However, how connectivity among plant traits responds to nitrogen pulse changes, which in turn affects plant performance, has not yet been fully elucidated. The response of three common submersed macrophytes (Myriophyllum spicatum, Vallisneria natans and Potamogeton maackianus) to three ammonium pulse patterns was tested using plant trait network (PTN) analysis based on 18 measured physiological and morphological traits. We found that ammonium pulses enhanced trait connectivity in PTN, which may enable plants to assimilate ammonium and/or mitigate ammonium toxicity. Large input pulses with low frequency had stronger effects on PTNs compared to low input pulses with high frequency. Due to the cumulative and time-lagged effect of the plant response to the ammonium pulse, there was a profound and prolonged effect on plant performance after the release of the pulse. The highly connected traits in PTN were those related to biomass allocation (e.g., plant biomass, stem ratio, leaf ratio and ramet number) rather than physiological traits, while phenotype-related traits (e.g., plant height, root length and AB ratio) and energy storage-related traits (e.g., stem starch) were least connected. V. natans showed clear functional divergence among traits, making it more flexible to cope with unfavorable habitats (i.e., high input pulses with low frequencies). M. spicatum with high RGR revealed strong correlations among traits and thus supported nitrogen accumulation from favourable environments (i.e., low input pulses with high frequencies). Our study highlights the responses of PTN for submerged macrophytes to ammonium pulses depends on their intrinsic metabolic rates, the magnitude, frequency and duration of the pulses, and our results contribute to the understanding of the impact of resource pulses on the population dynamics of submersed macrophytes within the context of global climate change.

High-flux nanofiltration membrane with modified highly dispersed MOF particles as nano filler.

The synthesis of optimized thin film nanocomposite (TFN) membrane with no or few defects is an efficacious method which can improve nanofiltration performance. However, poor dispersion of fillers in the organic phase and wrong compatibility between fillers and polymerizate are still a serious problem. In this study, the particle size of metal organic framework (MOF), aluminum-based metal-organic frameworks (CAU-1) was modulated and for the first time, dodecyl aldehyde was used to modify the surface hydrophobicity of CAU-1, which improved the dispersibility and inhibited the aggregation in the trimesoyl chloride (TMC)/n-hexane solution; later CAU-1 and modified CAU-1 were incorporated into the polyamide (PA) selective layer to synthesize TFN membrane by interfacial polymerization (IP). The particle size modulation and modification of the CAU-1 were demonstrated by X-ray diffraction (XRD), scanning electron microscopy (SEM) and Fourier transform infrared (FTIR) characterization. The characterization showed that PA selective layer was synthesized on the top layer of polysulfone (PSF) substrate. The pure water flux of the TFN membrane was increased to 79.89 ± 1.24 L·m-2·h-1·bar-1 compared to the original thin film composite (TFC) membrane, which was due to the polymerization of 100 nm modified CAU-1 on the PA layer to form a new water molecular channel, thus increasing the water flux by about 70%.

Structural Manipulation of 3D Graphene-Based Macrostructures for Water Purification.

The rapid development of graphene-based nanotechnologies in recent years has drawn extensive attention in environmental applications, especially for water treatment. Three-dimensional graphene-based macrostructures (GBMs) have been considered to be promising materials for practical water purification due to their well-defined porous structure and integrated morphology, and displayed outstanding performance in pollutant abatement with easy recyclability. Three-dimensional GBMs could not only retain the intrinsic priorities of 2D graphene, but also emerge with extraordinary properties by structural manipulation, so rational design and construction of 3D GBMs with desirable microstructures are important to exploit their potential for water treatment. In this review, some important advances in surface modification (chemical doping, wettability, surface charge) and geometrical control (porous structure, oriented arrangement, shape and density) with respect to 3D GBMs have been described, while their applications in water purification including adsorption (organic pollutants, heavy metal ions), catalysis (photocatalysis, Fenton-like advanced oxidation) and capacitive desalination (CDI) are detailly discussed. Finally, future challenges and prospective for 3D GBMs in water purification are proposed.

Spectral denoising based on Hilbert-Huang transform combined with F-test.

Due to the influence of uncontrollable factors such as the environment and instruments, noise is unavoidable in a spectral signal, which may affect the spectral resolution and analysis result. In the present work, a novel spectral denoising method is developed based on the Hilbert-Huang transform (HHT) and F-test. In this approach, the original spectral signal is first decomposed by empirical mode decomposition (EMD). A series of intrinsic mode functions (IMFs) and a residual (r) are obtained. Then, the Hilbert transform (HT) is performed on each IMF and r to calculate their instantaneous frequencies. The mean and standard deviation of instantaneous frequencies are calculated to further illustrate the IMF frequency information. Third, the F-test is used to determine the cut-off point between noise frequency components and non-noise ones. Finally, the denoising signal is reconstructed by adding the IMF components after the cut-off point. Artificially chemical noised signal, X-ray diffraction (XRD) spectrum, and X-ray photoelectron spectrum (XPS) are used to validate the performance of the method in terms of the signal-to-noise ratio (SNR). The results show that the method provides superior denoising capabilities compared with Savitzky-Golay (SG) smoothing.

Enhanced photocatalytic organic pollutant degradation and H2 evolution reaction over carbon nitride nanosheets: N defects abundant materials.

Post thermal treatment of bulk graphitic carbon nitride (g-C3N4) by ammonia gas acts as a significant structure regulation approach, while pure ammonia-assisted g-C3N4 synthesis from precursors like melamine is rarely investigated. Here we prove the synthesis of N-defects abundant carbon nitride nanosheets (ACN) through a one-pot thermal polymerization of melamine in pure ammonia gas, for photocatalytic organic pollutant removal in water and H2 evolution applications. Compared to bulk g-C3N4 (BCN), ACN-550 (ACN prepared at 550 °C) exhibited thin-layered porous morphology with higher surface area and abundant N defects, resulting in wider distribution of active sites. Moreover, the abundant N defects in the heptazine heterocycle structure could change the electronic structure of g-C3N4, leading to more efficient transport of photogenerated charge carriers and enhanced photoreduction potential, which gives rise to notable improvement activities in photocatalytic reaction. With superoxide ion radical and photoinduced holes as the predominant reactive species, ACN-550 realized efficient photocatalytic bisphenol A (BPA) degradation, which is 1.6- and 4.7-fold high over commercial TiO2 (P25) and BCN, respectively. ACN-550 exhibited excellent reusability and stability in five consecutive photocatalytic BPA degradation tests. In photo-reductive H2 production system by ACN-550, 761.8 ± 4.3 µmol/h/g H2 was produced, which was 11.6-fold as high as that by BCN.

Variational Mode Decomposition Weighted Multiscale Support Vector Regression for Spectral Determination of Rapeseed Oil and Rhizoma Alpiniae Offcinarum Adulterants.

The accurate prediction of the model is essential for food and herb analysis. In order to exploit the abundance of information embedded in the frequency and time domains, a weighted multiscale support vector regression (SVR) method based on variational mode decomposition (VMD), namely VMD-WMSVR, was proposed for the ultraviolet-visible (UV-Vis) spectral determination of rapeseed oil adulterants and near-infrared (NIR) spectral quantification of rhizoma alpiniae offcinarum adulterants. In this method, each spectrum is decomposed into K discrete mode components by VMD first. The mode matrix Uk is recombined from the decomposed components, and then, the SVR is used to build sub-models between each Uk and target value. The final prediction is obtained by integrating the predictions of the sub-models by weighted average. The performance of the proposed method was tested with two spectral datasets of adulterated vegetable oils and herbs. Compared with the results from partial least squares (PLS) and SVR, VMD-WMSVR shows potential in model accuracy.

A Review of Advanced Methods for the Quantitative Analysis of Single Component Oil in Edible Oil Blends.

Edible oil blends are composed of two or more edible oils in varying proportions, which can ensure nutritional balance compared to oils comprising a single component oil. In view of their economical and nutritional benefits, quantitative analysis of the component oils in edible oil blends is necessary to ensure the rights and interests of consumers and maintain fairness in the edible oil market. Chemometrics combined with modern analytical instruments has become a main analytical technology for the quantitative analysis of edible oil blends. This review summarizes the different oil blend design methods, instrumental techniques and chemometric methods for conducting single component oil quantification in edible oil blends. The aim is to classify and compare the existing analytical techniques to highlight suitable and promising determination methods in this field.

Peroxymonosulfate oxidation via paralleled nonradical pathways over iron and nitrogen doped porous carbons.

Hierarchically porous iron/nitrogen-doped carbons (Fe-N-PC) were developed for the oxidation of ibuprofen (IBP) with peroxymonosulfate (PMS). The incorporation of trace-level iron and nitrogen dopants promoted the catalytic performance remarkably, leading to 4.8, 16.4 and 22.9-fold enhancement over N-doped carbon (N-PC), porous carbon (PC), and Fe-doped carbon (Fe-PC), respectively. Fe(III) was anchored in nitrogen-coordinated pots (Fe-Nx) in the sp2-hybridized carbon network, and graphitic-N could synergistically boost the catalysis. Notably, methyl phenyl sulfoxide (PMSO) transformation, quenching tests, in situ electrochemical analysis and Raman spectroscopy verified high-valent iron-oxo species and direct electron transfer pathway accounted for pollutant oxidation. The relationship between the kinetic constants (lnkobs) and the oxidation peak potential (Eop) of pollutants was established with good correlation, manifesting particular selectivity toward oxidizing electron-rich pollutants and great immunity to background inorganic ions and natural organic matters (NOMs) for real wastewater treatment. The deactivation mechanisms of Fe-N-PC were revealed via surface oxidation and dopant refabrication. This work delicates to deepen the understanding of the nonradical mechanisms and structure-oriented PMS activation by engineered carbonaceous materials.

A facile method to synthesize 3D nanosheets of Fe/S dopedα-Ni(OH)2 as an electrocatalyst for improved oxygen evolution reaction.

S-doped Fe/Ni oxide and Fe/Ni hydride oxide catalysts exhibit good oxygen evolution reaction (OER) performance. Nevertheless, the over-doping of S and the agglomeration of active sites still hinder the improvement of the performance of these catalysts. The S/O ratio regulation can optimize the electronic structure effectively so as to improve the OER performance of the catalysts, but few studies have focused on this study. Here, we find a facile room-temperature method to synthesize the unique 3D ultra-thin FeNiOS nanosheets with an adjustable S/O ratio for OER. The FeNiOS-NS catalysts exhibit excellent OER performance with an overpotential of 235 mV at 10 mA cm-2and a small Tafel slope of 64.2 mV dec-1in 0.1 M KOH, which originated from the sufficient exposure of the active Fe-Ni component and the optimized electronic structure due to the tunable S/O ratio. This study demonstrates a novel strategy to optimize the OER performance of Ni-based catalysts.

Dehydrogenation Coupling of Methane Using Catalyst-Loaded Proton-Conducting Perovskite Hollow Fiber Membranes.

Catalytic dehydrogenation coupling of methane (DCM) represents an effective way to convert natural gas to more useful C2 products (C2H6, C2H4). In this work, BaCe0.85Tb0.05Co0.1O3-δ (BCTCo) perovskite hollow fiber membranes were fabricated by the combined phase inversion and sintering method. SrCe0.95Yb0.05O3-δ (SCYb) perovskite oxide was loaded as a catalyst onto the inner hollow fiber membrane surface, which promoted the CH4 conversion and the C2 hydrocarbon selectivity during the DCM reaction. The introduction of steam into the methane feed gas mixture elevated the C2 selectivity and yield due to the alleviation of coke deposition. Switching N2 to air as the sweep gas further increased the C2 selectivity and yield. However, the conversion of methane was limited by both the low permeability of the membrane and the insufficient catalytic activity of the catalyst, leading to low C2 yield.

Graphitic carbon nitride nanosheets via acid pretreatments for promoted photocatalysis toward degradation of organic pollutants.

Acid treatment serves as an effective engineering strategy to modify the structure of graphitic carbon nitride (g-C3N4) for enhanced metal-free photocatalysis, while their lacks a comprehensive understanding about the impacts of different acid species and acid treatment approaches on the intrinsic structure and properties of g-C3N4 and structure-activity relationships are ambiguous. Employing inorganic/organic acids including hydrochloric acid (HCl), nitric acid (HNO3), acetic acid (HAc), sulphuric acid (H2SO4), or oxalic acid (H2C2O4) as treatment acids, herein, we compare the impacts of different acid pretreatment approaches on the structure and properties of g-C3N4. Due to different acid-melamine interaction modes and the activation roles of various acids, the obtained g-C3N4 samples exhibit varied structures, physiochemical properties and photocatalytic activities. Compared with bulk graphitic carbon nitride (BCN), g-C3N4 prepared by acid pretreatment show enhanced photocatalytic performance on bisphenol A (BPA) degradation. The photocatalytic degradation rates of BPA by g-C3N4 prepared by HNO3, HAc, H2SO4, H2C2O4, or HCl pretreatment are about 2.2, 2.7, 2.8, 3.2 and 3.8 folds faster than that by BCN. HCl pretreatment proves to be the optimal approach, with the derived g-C3N4 (HTCN) showing more intact heptazine structural units, and increased specific surface area, which promote the exposure of more active sites, accelerate charge transfer, and give rise to a notable improvement in photocatalysis, eventually. Mechanistic investigations through quenching experiments and electron paramagnetic resonance (EPR) characterization unveil that superoxide ion radical (O2-) and photo-induced holes (h+) worked principally in the photodegradation reaction. This work provides new insights for the rational selection of acid types and treatment methods to synthesize metal-free carbon nitrides with improved activity for photocatalytic applications.

Clinical application of the mixed reality technique to assist the location and anatomy of perforator vessels of the anterolateral femoral flap / 口腔疾病防治

Objective@#To explore the clinical application value of mixed reality technology in locating perforator vessels and assisting perforator vessel dissection to harvest anterolateral thigh flaps.@*Methods@#Six patients who needed anterolateral thigh flap repair after resection of oral and maxillofacial tumors were recruited from the Department of Oral and Maxillofacial Surgery of Nanchong Central Hospital from January 2020 to January 2021. Before surgery, the CT angiography data of the lower limbs of the patients carrying the calibration points were imported into the data workstation to perform 3D reconstruction of the perforator vessels and surrounding tissues of the thigh, and the reconstruction results were imported into Microsoft HoloLens 2 glasses. During the operation, calibration was performed at the calibration point of the operative area so that the preoperative reconstruction results were superimposed on the operative area through Microsoft HoloLens 2 glasses. The clinical application value of mixed reality technology assisted perforator vessel location and anatomy of anterolateral femoral perforator flap was discussed from six aspects: whether the perforator vessel was reconstructed preoperatively, intraoperative calibration time, whether the actual position of the perforating vessels passing through the fascia lata fulcrum deviated from the preoperative reconstruction result within 1 cm, time required to harvest the flap, and whether the actual route of the perforator vessel was consistent with the reconstruction result, and whether the postoperative flap survived.@*Results @# The position and course of perforating vessels were successfully reconstructed in 6 cases before the operation. The actual course of perforating vessels during the operation was consistent with the reconstruction results. The deviation between the actual position of the perforating points and the preoperative reconstruction results was within 1 cm, which met the requirements of the actual asisting of the anterolateral thigh flap. The average time of flap harvest was (70.50 ± 7.20) min. The average calibration time was (13.33 ± 5.50) min. All flaps survived.@* Conclusions @# Mixed reality technology projects the reconstruction results of anterolateral femoral perforator vessels directly into the operative area, which provides a new method for asisting localization and anatomy of anterolateral femoral flap perforator vessels and reduces the possibility of injury to perforator vessels.

Rapid determination of hemoglobin concentration by a novel ensemble extreme learning machine method combined with near-infrared spectroscopy.

A novel ensemble extreme learning machine (ELM) approach that combines Monte Carlo (MC) sampling and least absolute shrinkage and selection operator (LASSO), named as MC-LASSO-ELM, is proposed to determine hemoglobin concentration of blood. It employs MC sampling to randomly select samples from the training set and LASSO further to choose variables from selected samples to establish plenty of ELM sub-models. The final prediction is obtained by combining the predictions of these sub-models. Combined with near-infrared spectroscopy, MC-LASSO-ELM is used to determine the hemoglobin concentration of blood. Compared with ELM, MC-ELM and LASSO-ELM, MC-LASSO-ELM can obtain the best stability and highest accuracy.

A new ensemble modeling method for multivariate calibration of near infrared spectra.

Ensemble modeling has gained increasing attention for improving the performance of quantitative models in near infrared (NIR) spectral analysis. Based on Monte Carlo (MC) resampling, least absolute shrinkage and selection operator (LASSO) and partial least squares (PLS), a new ensemble strategy named MC-LASSO-PLS is proposed for NIR spectral multivariate calibration. In this method, the training subsets for building the sub-models are generated by sampling from both samples and variables to ensure the diversity of the models. In detail, a certain number of samples as sample subsets are randomly selected from training set. Then, LASSO is used to shrink the variables of the sample subset to form the training subset, which is used to build the PLS sub-model. This process is repeated N times and N sub-models are obtained. Finally, the predictions of these sub-models are used to produce the final prediction by simple average. The prediction ability of the proposed method was compared with those of LASSO-PLS, MC-PLS and PLS models on the NIR spectra of corn, blend oil and orange juice samples. The superiority of MC-LASSO-PLS in prediction ability is demonstrated.