A prefrontal-habenular circuitry regulates social fear behaviour.

The medial prefrontal cortex (mPFC) has been implicated in the pathophysiology of social impairments including social fear. However, the precise subcortical partners that mediate mPFC dysfunction on social fear behaviour have not been identified. Employing a social fear conditioning paradigm, we induced robust social fear in mice and found that the lateral habenula (LHb) neurons and LHb-projecting mPFC neurons are synchronously activated during social fear expression. Moreover, optogenetic inhibition of the mPFC-LHb projection significantly reduced social fear responses. Importantly, consistent with animal studies, we observed an elevated prefrontal-habenular functional connectivity in subclinical individuals with higher social anxiety characterized by heightened social fear. These results unravel a crucial role of the prefrontal-habenular circuitry in social fear regulation and suggest that this pathway could serve as a potential target for the treatment of social fear symptom often observed in many psychiatric disorders.

Deep random forest with ferroelectric analog content addressable memory.

Deep random forest (DRF), which combines deep learning and random forest, exhibits comparable accuracy, interpretability, low memory and computational overhead to deep neural networks (DNNs) in edge intelligence tasks. However, efficient DRF accelerator is lagging behind its DNN counterparts. The key to DRF acceleration lies in realizing the branch-split operation at decision nodes. In this work, we propose implementing DRF through associative searches realized with ferroelectric analog content addressable memory (ACAM). Utilizing only two ferroelectric field effect transistors (FeFETs), the ultra-compact ACAM cell performs energy-efficient branch-split operations by storing decision boundaries as analog polarization states in FeFETs. The DRF accelerator architecture and its model mapping to ACAM arrays are presented. The functionality, characteristics, and scalability of the FeFET ACAM DRF and its robustness against FeFET device non-idealities are validated in experiments and simulations. Evaluations show that the FeFET ACAM DRF accelerator achieves ∼106×/10× and ∼106×/2.5× improvements in energy and latency, respectively, compared to other DRF hardware implementations on state-of-the-art CPU/ReRAM.

A Novel Fault Diagnosis Strategy for Diaphragm Pumps Based on Signal Demodulation and PCA-ResNet.

The efficient and accurate identification of diaphragm pump faults is crucial for ensuring smooth system operation and reducing energy consumption. The structure of diaphragm pumps is complex and using traditional fault diagnosis strategies to extract typical fault characteristics is difficult, facing the risk of model overfitting and high diagnostic costs. In response to the shortcomings of traditional methods, this study innovatively combines signal demodulation methods with residual networks (ResNet) to propose an efficient fault diagnosis strategy for diaphragm pumps. By using a demodulation method based on principal component analysis (PCA), the vibration signal demodulation spectrum of the fault condition is obtained, the typical fault characteristics of the diaphragm pump are accurately extracted, and the sample features are enhanced, reducing the cost of fault diagnosis. Afterward, the PCA-ResNet model is applied to the fault diagnosis of diaphragm pumps. A reasonable model structure and advanced residual block design can effectively reduce the risk of model overfitting and improve the accuracy of fault diagnosis. Compared with the visual geometry group (VGG) 16, VGG19, ResNet50, and autoencoder models, the proposed model has improved accuracy by 35.89%, 80.27%, 2.72%, and 6.12%. Simultaneously, it has higher operational efficiency and lower loss rate, solving the problem of diagnostic lag in practical engineering. Finally, a model optimization strategy is proposed through model evaluation metrics and testing. The reasonable parameter range of the model is obtained, providing a reference and guarantee for further optimization of the model.

Ferroelectric compute-in-memory annealer for combinatorial optimization problems.

Computationally hard combinatorial optimization problems (COPs) are ubiquitous in many applications. Various digital annealers, dynamical Ising machines, and quantum/photonic systems have been developed for solving COPs, but they still suffer from the memory access issue, scalability, restricted applicability to certain types of COPs, and VLSI-incompatibility, respectively. Here we report a ferroelectric field effect transistor (FeFET) based compute-in-memory (CiM) annealer for solving larger-scale COPs efficiently. Our CiM annealer converts COPs into quadratic unconstrained binary optimization (QUBO) formulations, and uniquely accelerates in-situ the core vector-matrix-vector (VMV) multiplication operations of QUBO formulations in a single step. Specifically, the three-terminal FeFET structure allows for lossless compression of the stored QUBO matrix, achieving a remarkably 75% chip size saving when solving Max-Cut problems. A multi-epoch simulated annealing (MESA) algorithm is proposed for efficient annealing, achieving up to 27% better solution and ~ 2X speedup than conventional simulated annealing. Experimental validation is performed using the first integrated FeFET chip on 28nm HKMG CMOS technology, indicating great promise of FeFET CiM array in solving general COPs.

Application of dexmedetomidine for lung injury in elderly patients undergoing one-lung ventilation.

Introduction: This study aimed to evaluate the effects of dexmedetomidine (DEX) on lung injury, the oxygenation index and perioperative pulmonary complications in elderly patients who underwent thoracotomy with one-lung ventilation (OLV). Material and methods: A total of 120 elderly patients with lung cancer were included in the present study. According to the random number table method, these patients were randomly divided into two groups: group D and group C. Patients in group D were intravenously pumped with 0.5 µg/kg/h of DEX before anesthesia. The infusion was completed within 15 min, and anesthesia was induced by venous injection. Patients in group C were pumped with equal volumes of normal saline. Results: At T2 and T3, compared with group C, group D had a significant decrease in cardiac index, mean arterial pressure and central venous pressure (p < 0.05). At T2, T3 and T4, compared with group C, group D had a significant increase in pH and PaO2 (p < 0.05). At T2, T3 and T4, compared with group C, group D had a significant decrease in Qs/Qt (p < 0.05). At T6, compared with group C, group D had a significant decrease in the supernatant of bronchoalveolar lavage fluid of tumor necrosis factor-α and interleukin 6 (p < 0.05). At T5, compared with group C, group D had a significant decrease in Visual Analogue Scale score (p < 0.05), and a significant increase in Ramsay Sedation Scale score (p < 0.05), and the number of respiratory and cardiovascular events also decreased (p < 0.05). Conclusions: In elderly patients, dexmedetomidine can reduce Qs/Qt and increase PaO2 during OLV in surgery. It can reduce lung injury. Moreover, DEX reduced respiratory and cardiovascular complications in the perioperative period.

A general urban spreading pattern of COVID-19 and its underlying mechanism.

Currently, the global situation of COVID-19 is aggravating, pressingly calling for efficient control and prevention measures. Understanding the spreading pattern of COVID-19 has been widely recognized as a vital step for implementing non-pharmaceutical measures. Previous studies explained the differences in contagion rates due to the urban socio-political measures, while fine-grained geographic urban spreading pattern still remains an open issue. Here, we fill this gap by leveraging the trajectory data of 197,808 smartphone users (including 17,808 anonymous confirmed cases) in nine cities in China. We find a general spreading pattern in all cities: the spatial distribution of confirmed cases follows a power-law-like model and the spreading centroid human mobility is time-invariant. Moreover, we reveal that long average traveling distance results in a high growth rate of spreading radius and wide spatial diffusion of confirmed cases in the fine-grained geographic model. With such insight, we adopt the Kendall model to simulate the urban spreading of COVID-19 which can well fit the real spreading process. Our results unveil the underlying mechanism behind the spatial-temporal urban evolution of COVID-19, and can be used to evaluate the performance of mobility restriction policies implemented by many governments and to estimate the evolving spreading situation of COVID-19.

The Intrinsic Similarity of Topological Structure in Biological Neural Networks.

Most previous studies mainly have focused on the analysis of structural properties of individual neuronal networks from C. elegans. In recent years, an increasing number of synapse-level neural maps, also known as biological neural networks, have been reconstructed. However, it is not clear whether there are intrinsic similarities of structural properties of biological neural networks from different brain compartments or species. To explore this issue, we collected nine connectomes at synaptic resolution including C. elegans, and analyzed their structural properties. We found that these biological neural networks possess small-world properties and modules. Excluding the Drosophila larval visual system, these networks have rich clubs. The distributions of synaptic connection strength for these networks can be fitted by the truncated pow-law distributions. Additionally, compared with the power-law model, a log-normal distribution is a better model to fit the complementary cumulative distribution function (CCDF) of degree for these neuronal networks. Moreover, we also observed that these neural networks belong to the same superfamily based on the significance profile (SP) of small subgraphs in the network. Taken together, these findings suggest that biological neural networks share intrinsic similarities in their topological structure, revealing some principles underlying the formation of biological neural networks within and across species.

Use of temporal contact graphs to understand the evolution of COVID-19 through contact tracing data.

Digital contact tracing has been recently advocated by China and many countries as part of digital prevention measures on COVID-19. Controversies have been raised about their effectiveness in practice as it remains open how they can be fully utilized to control COVID-19. In this article, we show that an abundance of information can be extracted from digital contact tracing for COVID-19 prevention and control. Specifically, we construct a temporal contact graph that quantifies the daily contacts between infectious and susceptible individuals by exploiting a large volume of location-related data contributed by 10,527,737 smartphone users in Wuhan, China. The temporal contact graph reveals five time-varying indicators can accurately capture actual contact trends at population level, demonstrating that travel restrictions (e.g., city lockdown) in Wuhan played an important role in containing COVID-19. We reveal a strong correlation between the contacts level and the epidemic size, and estimate several significant epidemiological parameters (e.g., serial interval). We also show that user participation rate exerts higher influence on situation evaluation than user upload rate does, indicating a sub-sampled dataset would be as good at prediction. At individual level, however, the temporal contact graph plays a limited role, since the behavior distinction between the infected and uninfected individuals are not substantial. The revealed results can tell the effectiveness of digital contact tracing against COVID-19, providing guidelines for governments to implement interventions using information technology.

Wavelength and frequency optimization in spatial frequency domain imaging for two-layer tissue.

Spatial frequency domain imaging is a non-contact, wide-field, fast-diffusion optical imaging technique, which in principle uses steady-state spatially modulated light to irradiate biological tissue, reconstruct two-dimensional or three-dimensional tissue optical characteristic map through optical transmission model, and further quantify the spatial distribution of tissue physiological parameters by multispectral imaging technique. The selection of light source wavelength and light field spatial modulation frequency is directly related to the accuracy of tissue optical properties and tissue physiological parameters extraction. For improvement of the measurement accuracy of optical properties and physiological parameters in the two-layer tissue, a multispectral spatial frequency domain imaging system is built based on liquid crystal tunable filter, and a data mapping table of spatially resolved diffuse reflectance and optical properties of two-layer tissue is established based on scaling Monte Carlo method. Combined with the dispersion effect and window effect of light-tissue interaction, the study applies numerical simulation to optimize the wavelength in the 650-850 nm range with spectral resolution of 10 nm. In order to minimize the uncertainty of the optical properties, Cramér-Rao bound is used to optimize the optical field spatial modulation frequency by transmitting the uncertainty of optical properties. The results showed that in order to realize the detection of melanin, oxyhemoglobin, deoxyhemoglobin, water and other physiological parameters in two-layer tissue, the best wavelength combination was determined as 720, 730, 760 and 810 nm according to the condition number. The findings of the Cramér-Rao bound analysis reveal that the uncertainty of optical characteristics for the frequency combinations [0, 0.3] mm-1, [0, 0.2] mm-1, and [0, 0.1] mm-1 increases successively. Under the optimal combination of wavelength and frequency, the diffuse reflectance of the gradient gray-scale plate measured by the multi-spectral spatial frequency domain imaging system is linearly correlated with the calibration value. The error between the measured liquid phantom absorption coefficient and the collimation projection system based on colorimetric dish is less than 2%. The experimental results of human brachial artery occlusion indicate that under the optimal wavelength combination, the change of the second layer absorption coefficient captured by the three frequency combinations decreases in turn, so as the change of oxygen saturation.

Modeling the Evolution of Biological Neural Networks Based on Caenorhabditis elegans Connectomes across Development.

Knowledge of the structural properties of biological neural networks can help in understanding how particular responses and actions are generated. Recently, Witvliet et al. published the connectomes of eight isogenic Caenorhabditis elegans hermaphrodites at different postembryonic ages, from birth to adulthood. We analyzed the basic structural properties of these biological neural networks. From birth to adulthood, the asymmetry between in-degrees and out-degrees over the C. elegans neuronal network increased with age, in addition to an increase in the number of nodes and edges. The degree distributions were neither Poisson distributions nor pure power-law distributions. We have proposed a model of network evolution with different initial attractiveness for in-degrees and out-degrees of nodes and preferential attachment, which reproduces the asymmetry between in-degrees and out-degrees and similar degree distributions via the tuning of the initial attractiveness values. In this study, we present the well-preserved structural properties of C. elegans neuronal networks across development, and provide some insight into understanding the evolutionary processes of biological neural networks through a simple network model.

Small Low-Contrast Target Detection: Data-Driven Spatiotemporal Feature Fusion and Implementation.

Detecting small low-contrast targets in the airspace is an essential and challenging task. This article proposes a simple and effective data-driven support vector machine (SVM)-based spatiotemporal feature fusion detection method for small low-contrast targets. We design a novel pixel-level feature, called a spatiotemporal profile, to depict the discontinuity of each pixel in the spatial and temporal domains The spatiotemporal profile is a local patch of the spatiotemporal feature maps concatenated by the spatial feature maps and temporal feature maps in channelwise, which are generated by the morphological black-hat filter and a ghost-free dark-focusing frame difference methods, respectively. Instead of the handcrafted feature fusion mechanisms in previous works, we use the labeled spatiotemporal profiles to train an SVM classifier to learn the spatiotemporal feature fusion mechanism automatically. To speed up detection for high-resolution videos, the serial SVM classification process on central processing units (CPUs) is reformed as parallel convolution operations on graphics processing unit (GPUs), which exhibits over 1000+ times speedup in our real experiments. Finally, blob analysis is applied to generate final detection results. Elaborate experiments are conducted, and experimental results demonstrate that the proposed method performs better than 12 baseline methods for the small low-contrast target detection. The field tests manifest that the parallel implementation of the proposed method can realize real-time detection at 15.3 FPS for videos at a resolution of 2048×1536 and the maximum detection distance can reach 1 km for drones in sunny weather.

PlaneFusion: Real-Time Indoor Scene Reconstruction With Planar Prior.

Real-time dense SLAM techniques aim to reconstruct the dense three-dimensional geometry of a scene in real time with an RGB or RGB-D sensor. An indoor scene is an important type of working environment for these techniques. The planar prior can be used in this scenario to improve the reconstruction quality, especially for large low-texture regions that commonly occur in an indoor scene. This article fully explores the planar prior in a dense SLAM pipeline. First, we propose a novel plane detection and segmentation method that runs at 200 Hz on a modern graphics processing unit. Our algorithm for constructing global plane constraints is very efficient; hence, we use it in the process of each input frame for the camera pose estimation while maintaining the real-time performance. Second, we propose herein a plane-based map representation that greatly reduces the memory footprint of plane regions while keeping the geometric details on planes. The experiments reveal that our system yields superior reconstruction results with planar information running at more than 30 fps. Aside from speed and storage improvements, our technique also handles the low-texture problem in plane regions.

Clinical therapeutic effects of eradication of Helicobacter pylori in treating patients with type 2 diabetes mellitus: A protocol for systematic review and meta-analysis.

BACKGROUND: Previous studies have demonstrated that Helicobacter pylori is a critical factor in the development of gastrointestinal diseases. However, only limited studies have reported results on the relationship between H pylori infection and patients with type 2 diabetes mellitus (T2DM). Moreover, the conclusions from these past studies are variable. Because there are contradictory results on this issue, the present study aims to examine the clinical therapeutic impacts of H pylori eradication to treat patients experiencing T2DM. METHODS: The present protocol is drafted according to the provisions of the Preferred Reporting Items for Systematic Review and Meta-analyses Protocols guidelines. PubMed, Cochrane Central Register of Controlled Trials databases, EMBASE, Web of Science, China National Knowledge Infrastructure, and Chinese BioMedical Literature Database will be searched up to May 2021 to obtain randomized controlled trials evaluating the clinical therapeutic effects of H pylori eradication to treat patients experiencing T2DM. We will use 2 investigators independently to carry out study selection, data extraction, and employ the Cochrane Collaboration criteria to evaluate their risks of bias. Furthermore, we will apply Stata 16.0 software to perform data analysis. RESULTS: We intend to evaluate the clinical therapeutic impacts of H pylori eradication to treat patients suffering from T2DM. CONCLUSIONS: Our findings may support existing evidence on the clinical therapeutic impacts of H pylori eradication to treat patients with T2DM. ETHICS AND DISSEMINATION: Since all data will be extracted from the published literature, the study does not require an ethical approval. OSF REGISTRATION NUMBER: May 31, 2021.osf.io/qtexu. (https://osf.io/qtexu/).

Chiral stationary phase based on cellulose derivative coated polymer microspheres and its separation performance.

Chiral stationary phases (CSPs) have always been research hotspot in enantiomer separation. Currently, most of the CSPs are based on silica platform. In this research, monodisperse, porous glycidyl methacrylate-divinylbenzene copolymer particles (poly(GMA-DVB)) were designed and prepared. Then the GMA was further reacted with ethylenediamine to introduce amino groups onto the polymer, which provide anchoring sites for cellulose derivatives. Herein, Cellulose-tris (3,5-dimethylphenylcarbamate) (CDMPC) was successfully coated onto the polymer microspheres, achieving a stable and successful CSP. The porous structure and the surface moieties of the CSPs were studied in detail. The chromatographic separation was optimized. Hexaconazole,methyl DL-mandelate,benzoin and tebuconazole have been successfully separated on the CSP column, with column efficiency as high as 10,200 plates/m, which is comparable with some silica-based CSPs. The research has indicated that the poly(GMA-DVB) is a promising candidate for constructing CSPs for chiral separation.

Multiple Attacks Detection in Cyber-Physical Systems Using Random Finite Set Theory.

To invade a cyber-physical system (CPS) successfully, hackers are prone to simultaneously launching multiple cyber attacks on different sensors in a CPS. However, little attention has been paid to the problem of detecting multiple cyber attacks up to now. Therefore, in this paper, we deal with the problem on how to efficiently detect multiple cyber attacks aiming at different sensors in CPSs. To achieve the goal of simultaneously detecting both the number of attacks and the attacked sensors, we formulate this problem via a random finite set (RFS) theory, and then apply an iterative RFS-based Bayesian filter and its approximation to solve the problem. Four numerical experiments with different attacks are provided, and the results have demonstrated the effectiveness of the RFS-based approach for the problem of multiple attacks detection in CPSs.

Fast separation of water-soluble vitamins by hydrophilic interaction liquid chromatography based on submicrometer flow-through silica microspheres.

In the present study, submicrometer flow-through silica microspheres (Sub-FTSiO2) was for the first time obtained via a suspension polymerization method coupled with sol-gel transition and phase separation. The Sub-FTSiO2 was characteristic of rich mesopores, penetrable macropores and small particle size, which would be beneficial to fast mass transfer, low column backpressure and high column efficiency. It was directly used as the hydrophilic interaction liquid chromatographic (HILIC) stationary phase, and the fast separation of seven water-soluble vitamins in 2.2 min was realized. The proposed method was successfully applied to the determination of water-soluble vitamins in two functional beverages on the market. The prepared Sub-FTSiO2 was well demonstrated for fast HILIC, and would be potential as the stationary phase matrix for fast liquid chromatography in diverse separation modes.

Low-cost multi-core inertial microfluidic centrifuge for high-throughput cell concentration.

We developed a low-cost multi-core inertial microfluidic centrifuge (IM-centrifuge) to achieve a continuous-flow cell/particle concentration at a throughput of up to 20 mL/min. To lower the cost of our IM-centrifuge, we clamped a disposable multilayer film-based inertial microfluidic (MFIM) chip with two reusable plastic housings. The key MFIM chip was fabricated in low-cost materials by stacking different polymer-film channel layers and double-sided tape. To increase processing throughput, multiplexing spiral inertial microfluidic channels were integrated within an all-in-one MFIM chip, and a novel sample distribution strategy was employed to equally distribute the sample into each channel layer. Then, we characterized the focusing performance in the MFIM chip over a wide flow-rate range. The experimental results showed that our IM-centrifuge was able to focus various-sized particles/cells to achieve volume reduction. The sample distribution strategy also effectively ensured identical focusing and concentration performances in different cores. Finally, our IM-centrifuge was successfully applied to concentrate microalgae cells with irregular shapes and highly polydisperse sizes. Thus, our IM-centrifuge holds the potential to be employed as a low-cost, high-throughput centrifuge for disposable use in low-resource settings.

Application of alveolar recruitment strategy and positive end-expiratory pressure combined with autoflow in the one-lung ventilation during thoracic surgery in obese patients.

BACKGROUND: The present study aims to evaluate the influence of alveolar recruitment strategy (ARS) and positive end-expiratory pressure (PEEP) combined with autoflow on respiratory mechanics, the oxygen index (OI), pulmonary shut [Qs/Qt(%)], and the concentrations of IL-6 and TNF-α in venous blood after surgery in obese patients who experienced thoracic surgery with one-lung ventilation (OLV). METHODS: A total of 36 obese patients with ASAII-III degree, who experienced selective pulmonary lobectomy, were within 36-74 years old, and had a BMI of 30-40 kg/m2, were randomly divided into two groups: control group (C group) and protective ventilation group (P group). In the P group, ARS was given once when OLV began. Then, ventilation at 7 mmHg of PEEP and autoflow were given. The Ppeak before OLV (T1), at 30 minutes after OLV (T2), and at the 5 minutes after two-lung ventilation (TLV) (T3), and the changes of Pplat and Cdyn were recorded. Then, arteriovenous blood was drawn at T1, T2, T3 and T4 (6 hours after the operation), blood-gas indicators, including SPO2, PaCO2 and PaO2, were measured, and the value of Qs/Qt(%) was calculated. Afterwards, venous blood was collected at T1 and T5 (18 hours after surgery), and the concentrations of IL-6 and TNF-α were detected. The clinical pulmonary infection score (CPIS) was determined at the first day and seventh day after the operation. RESULTS: In both groups, Cdyn and OI decreased, while Pplat, Ppeak and Qs/Qt(%) increased (P<0.05) at T2, when compared with those at T1. At T2 and T3, Pplat and Ppeak decreased (P<0.05) in the P group, when compared with the C group. At T2, T3 and T4, OI increased (P<0.05) in the P group, when compared with the C group. At T2, T3 and T4, PaCO2 and Qs/Qt(%) decreased in the P group, when compared with the C group. The concentrations of IL-6 and TNF-α decreased in the P group, when compared with the C group. CONCLUSIONS: The ventilation model of ARS and PEEP combined with autoflow can better reduce airway pressure and the production of injurious inflammatory cytokines in blood in obese patients. Furthermore, it can reduce Qs/Qt during and at 6 hours after thoracotomy, improve OI and maintain the acid-base balance of the internal environment, which may be applied in clinical work. This brings new enlightenment and needs to be clarified through further studies.

Flow stabilizer on a syringe tip for hand-powered microfluidic sample injection.

Precise, portable, low-cost sample injection is strongly demanded for use in point-of-care testing devices in resource-poor settings; however, current microfluidic sample injection techniques are often expensive, bulky and electricity-powered. To address this issue, we propose a novel syringe flow-stabilizer for hand-powered, precise, continuous-flow microfluidic sample injection. Our syringe flow-stabilizer applies the principle of passive flow-resistance compensation to stabilize the unstable sample flow and has the special advantages of easy-to-use, simple structure, low cost and high stability. The flow stabilizing performance of the stabilizer is characterized via a series of experiments and the results show that our stabilizer is capable of outputting a constant flow rate up to several milliliters per minute under a low threshold pressure. Finally, the fabricated syringe flow-stabilizer is integrated with an inertial microfluidic cell concentrator for high-throughput continuous concentration of trace blood cells from large-volume biofluids. The use of our stabilizer makes the concentration performance totally independent of operation. We envision wide applications of our syringe flow-stabilizer as a hand-powered sample injection unit in various point-of-care testing devices in resource-poor settings.

Waxberry-like hierarchically porous ethyl-bridged hybrid silica microsphere: A substrate for enzyme catalysis and high-performance liquid chromatography.

In this study, the ethyl-bridged hybrid silica microsphere with hierarchically meso-macroporous structure was initially synthesized through a method combining dispersion polymerization with sol-gel transition and phase separation. The flow-through macropores rendered the microsphere a rough surface like a waxberry, and thus the material was named as waxberry-like ethyl-bridged hybrid silica sphere (WEHS). WEHS was characteristic of appropriate alkali-stability, which was highly difficult for the pure silica. Additionally, WEHS possessed hierarchical meso- and macropores, which added additional value for faster mass transfer than the conventional fully porous silica materials. Taking the advantages of WEHS, it was successfully applied as the substrate to immobilize lipase; the prepared immobilized lipase exhibited high catalytic activity and favorable reusability under alkaline conditions, which was significant in pitch control of neutral-alkaline papermaking industry. Moreover, as the high-performance liquid chromatographic stationary phase matrix, WEHS made the separation under alkaline mobile phase into a reality for the silica-based materials. Besides, an ultra-fast and efficient separation in minutes was achieved with lower consumption of solvents and saving analytical time, which is highly desired in modern analysis. In general, WEHS was a novel and promising candidate in the myriads of silica-based materials.