Fingerprint authentication based on deep convolutional descent inversion tomography.

Fingerprint authentication is widely used in various areas. While existing methods effectively extract and match fingerprint features, they encounter difficulties in detecting wet fingers and identifying false minutiae. In this paper, a fast fingerprint inversion and authentication method based on Lamb waves is developed by integrating deep learning and multi-scale fusion. This method speeds up the inversion performance through deep fast inversion tomography (DeepFIT) and uses Mask R-CNN to improve authentication accuracy. DeepFIT utilizes fully connected and convolutional operations to approach the descent gradient, enhancing the efficiency of ultrasonic array reconstruction. This suppresses artifacts and accelerates sub-millimeter-level fingerprint minutia inversion. By identifying the overall morphological relationships of various minutia in fingerprints, meaningful minutia representing individual identities are extracted by the Mask R-CNN method. It segments and matches multi-scale fingerprint features, improving the reliability of authentication results. Results indicate that the proposed method has high accuracy, robustness, and speed, optimizing the entire fingerprint authentication process.

NIR-Driven Self-Healing Phase-Change Solid Slippery Surface with Stability and Promising Antifouling and Anticorrosion Properties.

Slippery liquid-infused porous surfaces (SLIPSs) have great potential to replace traditional antifouling coatings due to their efficient, green, and broad-spectrum antifouling performance. However, the lubricant dissipation problem of SLIPS severely restricts its further development and application, and the robust SLIPS continues to be extremely challenging. Here, a composite phase-change lubricant layer consisting of paraffin, silicone oil, and MXene is designed to readily construct a stable and NIR-responsive self-healing phase-change solid slippery surface (PCSSS). Collective results showed that PCSSS could rapidly achieve phase-change transformation and complete self-healing under NIR irradiation and keep stable after high-speed water flushing, centrifugation, and ultrasonic treatment. The antifouling performance of PCSSS evaluated by protein, bacteria, and algae antiadhesion tests demonstrated the adhesion inhibition rate was as high as 99.99%. Moreover, the EIS and potentiodynamic polarization experiments indicated that PCSSS had stable and exceptional corrosion resistance (|Z|0.01Hz = 3.87 × 108 Ω·cm2) and could effectively inhibit microbiologically influenced corrosion. The 90 day actual marine test reveals that PCSSS has remarkable antifouling performance. Therefore, PCSSS presents a novel, facile, and effective strategy to construct a slippery surface with the prospect of facilitating its application in marine antifouling and corrosion protection.

Deep Learning With Physics-Embedded Neural Network for Full Waveform Ultrasonic Brain Imaging.

The convenience, safety, and affordability of ultrasound imaging make it a vital non-invasive diagnostic technique for examining soft tissues. However, significant differences in acoustic impedance between the skull and soft tissues hinder the successful application of traditional ultrasound for brain imaging. In this study, we propose a physics-embedded neural network with deep learning based full waveform inversion (PEN-FWI), which can achieve reliable quantitative imaging of brain tissues. The network consists of two fundamental components: forward convolutional neural network (FCNN) and inversion sub-neural network (ISNN). The FCNN explores the nonlinear mapping relationship between the brain model and the wavefield, replacing the tedious wavefield calculation process based on the finite difference method. The ISNN implements the mapping from the wavefield to the model. PEN-FWI includes three iterative steps, each embedding the F CNN into the ISNN, ultimately achieving tomography from wavefield to brain models. Simulation and laboratory tests indicate that PEN-FWI can produce high-quality imaging of the skull and soft tissues, even starting from a homogeneous water model. PEN-FWI can achieve excellent imaging of clot models with constant uniform distribution of velocity, randomly Gaussian distribution of velocity, and irregularly shaped randomly distributed velocity. Robust differentiation can also be achieved for brain slices of various tissues and skulls, resulting in high-quality imaging. The imaging time for a horizontal cross-sectional imag e of the brain is only 1.13 seconds. This algorithm can effectively promote ultrasound-based brain tomography and provide feasible solutions in other fields.

Improving water resistance and mechanical properties of starch-based films by incorporating microcrystalline cellulose in a dynamic network structure.

The widespread use of starch-based films is hindered by inadequate tensile strength and high water sensitivity. To address these limitations, a novel starch film with a dynamic network structure was produced via the dehydration-condensation reaction of N, N'-methylene diacrylamide (MBA) and microcrystalline cellulose (MCC). The improvement in mechanical properties was enhanced by the incorporation of MCC, which was achieved through intermolecular hydrogen bonding and chemical crosslinking. To verify the interactions among MCC, MBA, and starch, x-ray photoelectron spectroscopy (XPS), fourier transform infrared spectroscopy (FTIR), and x-ray diffraction (XRD) were conducted. The results established the predicted interactions. The dynamic network structure of the film reduced the water absorption capacity (WAC) of starch and MCC hydroxyl groups, as confirmed by differential scanning calorimeter (DSC) and dynamic mechanical thermal analysis (DMTA). These analyses showed a restriction in the mobility of starch chains, resulting in a higher glass transition temperature (Tg) of 69.26 °C. The modified starch films exhibited excellent potential for packaging applications, demonstrating a higher contact angle (CA) of 89.63°, the lowest WAC of 4.73 g/g, and the lowest water vapor transmission rate (WVTR) of 13.13 g/m2/d, along with improved mechanical properties and identical light transmittance compared to pure starch films.

Variation characteristics of acid rain in Zhuzhou, Central China over the period 2011-2020.

Zhuzhou was one of the most polluted cities in China with the serious acid rain. Due to the implementation of air pollution control measures from 2016 to 2018, the acid rain pollution in this city has reduced. In order to understand the recent situation, a comprehensive study on the acid rain was carried out from January 2011 to December 2020. The pH values during the study period varied from 3.3 to 7.5, with a volume-weighted mean value of 4.7. The predominant acidic components of the precipitation were SO42- and NO3-, accounting for 89.3% of the total anions. The ratio of non-sea-salt SO42- to NO3- showed a decreasing trend, revealing that the pollution type of acid rain changed from sulfuric acid type to sulfuric acid and nitric acid compound type. The correlation analysis (p < 0.05) showed that SO42- was positively correlated with NH4+, Ca2+, and Mg2+; hence, it predominated in precipitation as (NH4)2SO4, NH4HSO4, CaSO4, and MgSO4. Significant positive correlation of Ca2+ with Mg2+ shows that they may originated mainly from crust. Significant positive correlation between SO42- and F- and Cl- indicate that their source may be related to the non-ferrous metal smelting industry in Zhuzhou. Further correlation analysis shows that emissions from the non-ferrous metal smelting industry in the area have a large significant on SO42- and F- in precipitation, while Cl- may still be emitted from other anthropogenic sources.

3D Ultrasonic Brain Imaging with Deep Learning Based on Fully Convolutional Networks.

Compared to magnetic resonance imaging (MRI) and X-ray computed tomography (CT), ultrasound imaging is safer, faster, and more widely applicable. However, the use of conventional ultrasound in transcranial brain imaging for adults is predominantly hindered by the high acoustic impedance contrast between the skull and soft tissue. This study introduces a 3D AI algorithm, Brain Imaging Full Convolution Network (BIFCN), combining waveform modeling and deep learning for precise brain ultrasound reconstruction. We constructed a network comprising one input layer, four convolution layers, and one pooling layer to train our algorithm. In the simulation experiment, the Pearson correlation coefficient between the reconstructed and true images was exceptionally high. In the laboratory, the results showed a slightly lower but still impressive coincidence degree for 3D reconstruction, with pure water serving as the initial model and no prior information required. The 3D network can be trained in 8 h, and 10 samples can be reconstructed in just 12.67 s. The proposed 3D BIFCN algorithm provides a highly accurate and efficient solution for mapping wavefield frequency domain data to 3D brain models, enabling fast and precise brain tissue imaging. Moreover, the frequency shift phenomenon of blood may become a hallmark of BIFCN learning, offering valuable quantitative information for whole-brain blood imaging.

Structural basis of the human negative elongation factor NELF-B/C/E ternary complex.

Negative elongation factor (NELF) is a four-subunit transcription elongation factor that mainly functions in maintaining the paused state of RNA polymerase II in eukaryotes. Upon binding to Pol II, NELF works synergistically with DRB sensitivity-inducing factor (DSIF) and inhibits transcription elongation of Pol II, which subsequently retains a stably paused state 20-60 base pairs downstream of the promoter. The promoter-proximal pausing of Pol II caused by NELF is a general mechanism of transcriptional regulation for most signal-responsive genes. To date, structural studies have significantly advanced our understanding of the molecular mechanisms of NELF. However, a high quality structural model clarifying the interaction details of this complex is still lacking. In this study, we solved the high resolution crystal structure of the NELF-B/C/E ternary complex. We observed detailed interactions between subunits and identified residues important for the association between NELF-B and NELF-E. Our work presents a precise model of the NELF complex, which will facilitate our understanding of its in vivo function.

[Effect of Clean Heating on Carbonaceous Aerosols in PM2.5 During the Heating Period in Baoding].

In order to study the effects of clean heating measures on the concentration and source of carbonaceous aerosols in PM2.5 in Baoding, we collected PM2.5 samples in Baoding during the winter heating periods of 2014 and 2019. The concentrations of OC and EC in the samples were determined by using a DRI Model 2001A thermo-optical carbon analyzer.The results showed that the average values of ρ(OC) and ρ(EC) in the heating period in 2014 were 60.92 µg·m-3 and 18.15 µg·m-3, and the average values of ρ(OC) and ρ(EC) in the heating period in 2019 were 36.63 µg·m-3 and 6.07 µg·m-3. Compared with those in 2014, the concentrations of OC and EC decreased by 39.87% and 66.56%, respectively, in 2019; the decrease in EC was larger than that in OC, and the meteorological conditions in 2019 were more severe than those in 2014, which was not conducive to the spread of pollutants.The correlation analysis and SOC estimation of OC and EC indicated that the correlation R2 of OC and EC in Baoding in 2014 and 2019 were 0.874 and 0.811, respectively, indicating that OC and EC in Baoding had relatively consistent sources. The average values of ρ(SOC) in 2014 and 2019 were 16.59 µg·m-3 and 11.31 µg·m-3, respectively, and the contribution rates to OC were 27.23% and 30.87%, respectively. This showed that in 2019, compared with that in 2014, the primary pollution decreased, but the secondary pollution increased, and the atmospheric oxidation increased.The analysis of the pollution sources of carbonaceous aerosols revealed that in 2014 and 2019 before and after the implementation of clean heating, the carbonaceous aerosols in the atmosphere were mainly from biomass combustion, coal combustion, and vehicle exhaust emissions. However, the contribution from biomass burning and coal burning decreased in 2019 compared to that in 2014. The decrease in OC and EC concentrations was attributed to the control of coal-fired and biomass-fired sources by clean heating. At the same time, the implementation of clean heating measures reduced the contribution of primary emissions to carbonaceous aerosols in PM2.5 in Baoding City.

Significant association between HIV infection and increased risk of COVID-19 mortality: a meta-analysis based on adjusted effect estimates.

To investigate the relationship between human immunodeficiency virus (HIV) infection and the risk of mortality among coronavirus disease 2019 (COVID-19) patients based on adjusted effect estimate by a quantitative meta-analysis. A random-effects model was used to estimate the pooled effect size (ES) with corresponding 95% confidence interval (CI). I2 statistic, sensitivity analysis, Begg's test, meta-regression and subgroup analyses were also conducted. This meta-analysis presented that HIV infection was associated with a significantly higher risk of COVID-19 mortality based on 40 studies reporting risk factors-adjusted effects with 131,907,981 cases (pooled ES 1.43, 95% CI 1.25-1.63). Subgroup analyses by male proportion and setting yielded consistent results on the significant association between HIV infection and the increased risk of COVID-19 mortality. Allowing for the existence of heterogeneity, further meta-regression and subgroup analyses were conducted to seek the possible source of heterogeneity. None of factors might be possible reasons for heterogeneity in the further analyses. Sensitivity analysis indicated the robustness of this meta-analysis. The Begg's test manifested that there was no publication bias (P = 0.2734). Our findings demonstrated that HIV infection was independently associated with a significantly increased risk of mortality in COVID-19 patients. Further well-designed studies based on prospective study estimates are warranted to confirm our findings.

Peripheral artery disease independently associated with significantly higher risk for COVID-19 mortality: Evidence based on adjusted effect estimates.

OBJECTIVE: To investigate the influence of peripheral artery disease (PAD) on the risk of mortality among coronavirus disease 2019 (COVID-19) patients based on adjusted effect estimates. METHODS: Systematic searches were performed through electronic databases. A random-effect model was applied to calculate the pooled effect and corresponding 95% confidence interval (CI). Inconsistency index (I2) was used to evaluate the heterogeneity across studies. Sensitivity analysis, subgroup analysis, and Begg's test were all implemented. RESULTS: On the basis of 16 eligible studies with 142,832 COVID-19 patients, the meta-analysis showed that PAD significantly increased the risk for mortality among COVID-19 patients (pooled effect = 1.29, 95% CI: 1.10-1.51). The significant association was also observed in the subgroup analysis stratified by hospitalized patients, mean age ≥ 60 years, Europe and North America. Sensitivity analysis verified the robustness of our findings. Begg's test (P = 0.15) showed there was no potential publication bias. CONCLUSIONS: COVID-19 patients with PAD may have a greater risk of mortality. Clinicians and nursing staff are supposed to identify and monitor these high-risk patients in a timely manner and provide appropriate clinical treatment for them.

Passivating Defects at the Bottom Interface of Perovskite by Ethylammonium to Improve the Performance of Perovskite Solar Cells.

The interface of perovskite solar cells (PSCs) plays a significant role in influencing their performance, yet there is still scarce research focusing on their difficult-to-expose bottom interfaces. Herein, ethylammonium bromide (EABr) is introduced into the bottom interface and its passivation effects are studied directly. First, EABr can improve substrate wettability, which is beneficial for the perovskite-film deposition. By lifting off the perovskite film spontaneously from the substrate, it is found that EABr can significantly reduce the amount of unreacted PbI2 at the bottom interface. These PbI2 crystals have been recently identified as a major defect source and degradation site for perovskite film. Meanwhile, EABr also lifts the valence band maximum at the bottom side of perovskite from -5.38 to -5.09 eV, facilitating better hole transfer. Such a improvement is also verified by the study of charge carrier dynamics. Through introducing EABr, all photovoltaic parameters of the inverted PSCs are improved, and their power conversion efficiency (PCE) increases from 20.41% to 21.06%. The study highlights the importance of direct characterization of the bottom interface for a better passivation effect.

Transcranial Ultrasound Imaging With Decomposition Descent Learning-Based Full Waveform Inversion.

Noninvasive brain diagnosis is extremely important because of its efficiency, low cost, and painless nature in the prediction of stroke, cerebral hemorrhage, and other brain research. At present, achieving full 3-D quantitative ultrasonic imaging of the human brain is a cutting-edge challenge due to the complex structures of the human brain and the strong scattering caused by the skulls. In this article, we achieved quantitative ultrasonic imaging of inside-brain anomalies with our proposed method, the decomposition descent learning-based full waveform inversion (DDL-FWI). The proposed method adopts a linear residual decomposing technique to greatly alleviate the computation burden in fast inversion tomography (FIT) with enhanced convergence guaranteed by residual functions. Testing results in both simulation and laboratory experiments demonstrated that our method can achieve high-quality quantitative imaging of brain soft tissues and skulls even starting from homogeneous water background in 2-D, and this method is capable of reconstructing both complex brain tissues and clots in 2-D and 3-D cases using either clean or noisy signals, with a robust 3-D clot resolution as small as 18 mm and 2-D reconstruction speed in 11.20 s. Combined with advanced ultrasonic hardware, DDL-FWI can be easily trained and used for brain imaging efficiently that frees patients from harmful influences from traditional imaging techniques, e.g., ionization radiations from X-ray computed tomography (CT).

Sargassum fusiforme Polysaccharide-Based Hydrogel Microspheres Enhance Crystal Violet Dye Adsorption Properties.

In this study, a polysaccharide-based hydrogel microsphere (SFP/SA) was prepared using S. fusiforme polysaccharide (SFP) and sodium alginate (SA). Fourier transform infrared spectroscopy (FT-IR) demonstrated that SFP was effectively loaded onto the hydrogel microsphere. Texture profile analysis (TPA) and differential scanning calorimetry (DSC) showed that, with the increase of SFP concentration, the hardness of SFP/SA decreased, while the springiness and cohesiveness of SFP/SA increased, and the thermal stability of SFP/SA improved. The equilibrium adsorption capacity of SFP/SA increased from 8.20 mg/g (without SFP) to 67.95 mg/g (SFP accounted 80%) without swelling, and from 35.05 mg/g (without SFP) to 81.98 mg/g (SFP accounted 80%) after 24 h swelling. The adsorption of crystal violet (CV) dye by SFP/SA followed pseudo-first order and pseudo-second order kinetics (both with R2 > 0.99). The diffusion of intraparticle in CV dye was not the only influencing factor. Moreover, the adsorption of CV dye for SFP/SA (SFP accounted 60%) fit the Langmuir and Temkin isotherm models. SFP/SA exhibited good regenerative adsorption capacity. Its adsorption rate remained at > 97% at the 10th consecutive cycle while SFP accounted for 80%. The results showed that the addition of Sargassum fusiforme polysaccharide could increase the springiness, cohesiveness and thermal stability of the hydrogel microsphere, as well as improve the adsorption capacity of crystal violet dye.

The association between stroke and COVID-19-related mortality: a systematic review and meta-analysis based on adjusted effect estimates.

OBJECTIVE: To investigate the association between stroke and the risk for mortality among coronavirus disease 2019 (COVID-19) patients. METHODS: We performed systematic searches through electronic databases including PubMed, Embase, Scopus, and Web of Science to identify potential articles reporting adjusted effect estimates on the association of stroke with COVID-19-related mortality. To estimate pooled effects, the random-effects model was applied. Subgroup analyses and meta-regression were performed to explore the possible sources of heterogeneity. The stability of the results was assessed by sensitivity analysis. Publication bias was evaluated by Begg's test and Egger's test. RESULTS: This meta-analysis included 47 studies involving 7,267,055 patients. The stroke was associated with higher COVID-19 mortality (pooled effect = 1.30, 95% confidence interval (CI): 1.16-1.44; I2 = 89%, P < 0.01; random-effects model). Subgroup analyses yielded consistent results among area, age, proportion of males, setting, cases, effect type, and proportion of severe COVID-19 cases. Statistical heterogeneity might result from the different effect type according to the meta-regression (P = 0.0105). Sensitivity analysis suggested that our results were stable and robust. Both Begg's test and Egger's test indicated that potential publication bias did not exist. CONCLUSION: Stroke was independently associated with a significantly increased risk for mortality in COVID-19 patients.

Deep learning inversion with supervision: A rapid and cascaded imaging technique.

Machine learning has been demonstrated to be extremely promising in solving inverse problems, but deep learning algorithms require enormous training samples to obtain reliable results. In this article, we propose a new solution, the deep learning inversion with supervision (DLIS) and applied it for corrosion mapping in guided wave tomography. The inversion results show that when dealing with multiple defects of complex shape on a plate-like structure, DLIS methods can reduce the scale of training set effectively compared with other deep learning algorithms in experiment because a good starting model is provided and the nonlinearity between the global minimum and observed wave field is greatly reduced. In terms of reconstruction accuracy using experimental data, the thickness maps produced by DLIS are reliable with high accuracy. With few modifications, this method can be conveniently extended to 3D cases. These results imply that DLIS is one of the promising methods to be applied in fields with similar physics like non-destructive evaluation (NDE), biomedical imaging and geophysical prospecting.

Comorbid Asthma Increased the Risk for COVID-19 Mortality in Asia: A Meta-Analysis.

We aimed to explore the influence of comorbid asthma on the risk for mortality among patients with coronavirus disease 2019 (COVID-19) in Asia by using a meta-analysis. Electronic databases were systematically searched for eligible studies. The pooled odds ratio (OR) with 95% confidence interval (CI) was estimated by using a random-effect model. An inconsistency index (I2) was utilized to assess the statistical heterogeneity. A total of 103 eligible studies with 198,078 COVID-19 patients were enrolled in the meta-analysis; our results demonstrated that comorbid asthma was significantly related to an increased risk for COVID-19 mortality in Asia (pooled OR = 1.42, 95% CI: 1.20−1.68; I2 = 70%, p < 0.01). Subgroup analyses by the proportion of males, setting, and sample sizes generated consistent findings. Meta-regression indicated that male proportion might be the possible sources of heterogeneity. A sensitivity analysis exhibited the reliability and stability of the overall results. Both Begg's analysis (p = 0.835) and Egger's analysis (p = 0.847) revealed that publication bias might not exist. In conclusion, COVID-19 patients with comorbid asthma might bear a higher risk for mortality in Asia, at least among non-elderly individuals.

Spinal cord stimulation reduces cardiac pain through microglial deactivation in rats with chronic myocardial ischemia.

Angina pectoris is cardiac pain that is a common clinical symptom often resulting from myocardial ischemia. Spinal cord stimulation (SCS) is effective in treating refractory angina pectoris, but its underlying mechanisms have not been fully elucidated. The spinal dorsal horn is the first region of the central nervous system that receives nociceptive information; it is also the target of SCS. In the spinal cord, glial (astrocytes and microglia) activation is involved in the initiation and persistence of chronic pain. Thus, the present study investigated the possible cardiac pain­relieving effects of SCS on spinal dorsal horn glia in chronic myocardial ischemia (CMI). CMI was established by left anterior descending artery ligation surgery, which induced significant spontaneous/ongoing cardiac pain behaviors, as measured using the open field test in rats. SCS effectively improved such behaviors as shown by open field and conditioned place preference tests in CMI model rats. SCS suppressed CMI­induced spinal dorsal horn microglial activation, with downregulation of ionized calcium­binding adaptor protein­1 expression. Moreover, SCS inhibited CMI­induced spinal expression of phosphorylated­p38 MAPK, which was specifically colocalized with the spinal dorsal horn microglia rather than astrocytes and neurons. Furthermore, SCS could depress spinal neuroinflammation by suppressing CMI­induced IL­1ß and TNF­α release. Intrathecal administration of minocycline, a microglial inhibitor, alleviated the cardiac pain behaviors in CMI model rats. In addition, the injection of fractalkine (microglia­activating factor) partially reversed the SCS­produced analgesic effects on CMI­induced cardiac pain. These results indicated that the therapeutic mechanism of SCS on CMI may occur partially through the inhibition of spinal microglial p38 MAPK pathway activation. The present study identified a novel mechanism underlying the SCS­produced analgesic effects on chronic cardiac pain.

High precision implicit function learning for forecasting supercapacitor state of health based on Gaussian process regression.

State of health (SOH) prediction of supercapacitors aims to provide reliable lifetime control and avoid system failure. Gaussian process regression (GPR) has emerged for SOH prediction because of its capability of capturing nonlinear relationships between features, and tracking SOH attenuations effectively. However, traditional GPR methods based on explicit functions require multiple screenings of optimal mean and covariance functions, which results in data scarcity and increased time consumption. In this study, we propose a GPR-implicit function learning, which is a prior knowledge algorithm for calculating mean and covariance functions from a preliminary data set instead of screening. After introducing the implicit function, the average root mean square error (Average RMSE) is 0.0056 F and the average mean absolute percent error (Average MAPE) is 0.6%, where only the first 5% of the data are trained to predict the remaining 95% of the cycles, thereby decreasing the error by more than three times than previous studies. Furthermore, less cycles (i.e., 1%) are trained while still obtaining low prediction errors (i.e., Average RMSE is 0.0094 F and Average MAPE is 1.01%). This work highlights the strength of GPR-implicit function model for SOH prediction of energy storage devices with high precision and limited property data.

Projections from the lateral parabrachial nucleus to the lateral and ventral lateral periaqueductal gray subregions mediate the itching sensation.

ABSTRACT: Lateral and ventral lateral subregions of the periaqueductal gray (l/vlPAG) have been proved to be pivotal components in descending circuitry of itch processing, and their effects are related to the subclassification of neurons that were meditated. In this study, lateral parabrachial nucleus (LPB), one of the most crucial relay stations in the ascending pathway, was taken as the input nucleus to examine the modulatory effect of l/vlPAG neurons that received LPB projections. Anatomical tracing, chemogenetic, optogenetic, and local pharmacological approaches were used to investigate the participation of the LPB-l/vlPAG pathway in itch and pain sensation in mice. First, morphological evidence for projections from vesicular glutamate transporter-2-containing neurons in the LPB to l/vlPAG involved in itch transmission has been provided. Furthermore, chemogenetic and optogenetic activation of the LPB-l/vlPAG pathway resulted in both antipruritic effect and analgesic effect, whereas pharmacogenetic inhibition strengthened nociceptive perception without affecting spontaneous scratching behavior. Finally, in vivo pharmacology was combined with optogenetics which revealed that AMPA receptor-expressing neurons in l/vlPAG might play a more essential role in pathway modulation. These findings provide a novel insight about the connections between 2 prominent transmit nuclei, LPB and l/vlPAG, in both pruriceptive and nociceptive sensations and deepen the understanding of l/vlPAG modulatory roles in itch sensation by chosen LPB as source of ascending efferent projections.