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Abstract Background Interleukin 24 (IL-24) has been implicated in the nociceptive signaling. However, direct evidence and the precise molecular mechanism underlying IL-24’s role in peripheral nociception remain unclear. Methods Using patch clamp recording, molecular biological analysis, immunofluorescence labeling, siRNA-mediated knockdown approach and behavior tests, we elucidated the effects of IL-24 on sensory neuronal excitability and peripheral pain sensitivity mediated by T-type Ca2+ channels (T-type channels). Results IL-24 enhances T-type channel currents (T-currents) in trigeminal ganglion (TG) neurons in a reversible and dose-dependent manner, primarily by activating the interleukin-22 receptor 1 (IL-22R1). Furthermore, we found that the IL-24-induced T-type channel response is mediated through tyrosine-protein kinase Lyn, but not its common downstream target JAK1. IL-24 application significantly activated protein kinase A; this effect was independent of cAMP and prevented by Lyn antagonism. Inhibition of PKA prevented the IL-24-induced T-current response, whereas inhibition of protein kinase C or MAPK kinases had no effect. Functionally, IL-24 increased TG neuronal excitability and enhanced pain sensitivity to mechanical stimuli in mice, both of which were suppressed by blocking T-type channels. In a trigeminal neuropathic pain model induced by chronic constriction injury of the infraorbital nerve, inhibiting IL-22R1 signaling alleviated mechanical allodynia, which was reversed by blocking T-type channels or knocking down Cav3.2. Conclusion Our findings reveal that IL-24 enhances T-currents by stimulating IL-22R1 coupled to Lyn-dependent PKA signaling, leading to TG neuronal hyperexcitability and pain hypersensitivity. Understanding the mechanism of IL-24/IL-22R1 signaling in sensory neurons may pave the way for innovative therapeutic strategies in pain management.

Abstract Background Whole-genome methylation sequencing of cfDNA is not cost-effective for tumor detection. Here, we introduce reduced representative methylome profiling (RRMP), which employs restriction enzyme for depletion of AT-rich sequence to achieve enrichment and deep sequencing of CG-rich sequences. Methods We first verified the ability of RRMP to enrich CG-rich sequences using tumor cell genomic DNA and analyzed differential methylation regions between tumor cells and normal whole blood cells. We then analyzed cfDNA from 29 breast cancer patients and 27 non-breast cancer individuals to detect breast cancer by building machine learning models. Results RRMP captured 81.9% CpG islands and 75.2% gene promoters when sequenced to 10 billion base pairs, with an enrichment efficiency being comparable to RRBS. RRMP allowed us to assess DNA methylation changes between tumor cells and whole blood cells. Applying our approach to cfDNA from 29 breast cancer patients and 27 non-breast cancer individuals, we developed machine learning models that could discriminate between breast cancer and non-breast cancer controls (AUC = 0.85), suggesting possibilities for truly non-invasive cancer detection. Conclusions We developed a new method to achieve reduced representative methylome profiling of cell-free DNA for tumor detection.

Geostrophic adaptation is another frontogenesis mechanism that forms a stationary front, which differs from the balanced front, but often leads to local catastrophic weather in the mountainous area of southern Anhui. Conducting analysis and research on the frontogenesis conditions of geostrophic adaptation is of great significance for how mountainous terrain causes catastrophic weather. Taking the formation of a stationary front on 22 February 2022, which resulted in a local blizzard as an example, the quasi-geostrophic frontogenesis function of topographic frontogenesis is calculated and analyzed based on the following dataset, including the high-resolution hourly reanalysis data from the European meteorological center ERA and conventional data from ground stations, the new generation Doppler (CINRAD/SA) weather radar data, the 3-D wind field data obtained with a 3-D wind field inversion technique and the interpolation method with the accuracy of 0.02°×0.02°. The results are as follows. (1) During this frontogenesis process, the northeast wind flow on the ground intensified, and the gradient of the flow function increased, resulting in discontinuous fluid density at the foot of the leeward slope (30.5°—31.0°N), which leads to geostrophic adjustment to frontogenesis in a narrow area at its front. Thus, a local snowstorm with a small scale and short duration occurred. The geostrophic adjustment to frontogenesis occurs in a stable atmospheric structure and an environment of wet symmetry instability. (2) By analyzing the four terms of the surface frontogenesis function and their related factors, it is shown that the contribution of vertical motion tilt term to frontogenesis is dominant, while the horizontal deformation and convergence have minor contributions. It indicates that the geostrophic adjustment to frontogenesis is mainly the result of the upward transportation of potential temperature, rather than the deformation of the wind field. (3) By analyzing the four terms of the high-altitude frontogenesis function and the mesoscale wind field retrieved by radar, during the adjusting of the wind field from a non-equilibrium state to an equilibrium state, a strong secondary circulation appears in the middle and lower layers of the atmosphere at an altitude of 1.5-3.0 km above the front zone, which leads to the cooling due to the rise of cold air over the cold zone. While above the warm zone, non-adiabatic heating released by snowfall condensation latent heat causes warming, resulting in an increase in the temperature gradient at the front zone and frontogenesis.

Abstract Background Impaired pattern separation occurs in the early stage of Alzheimer’s disease (AD), and hippocampal dentate gyrus (DG) neurogenesis participates in pattern separation. Here, we investigated whether spatial memory discrimination impairment can be improved by promoting the hippocampal DG granule cell neogenesis-mediated pattern separation in the early stage of AD by electroacupuncture (EA). Methods Five familial AD mutations (5 × FAD) mice received EA treatment at Baihui and Shenting points for 4 weeks. During EA, mice were intraperitoneally injected with BrdU (50 mg/kg) twice a day. rAAV containing Wnt5a shRNA was injected into the bilateral DG region, and the viral efficiency was evaluated by detecting Wnt5a mRNA levels. Cognitive behavior tests were conducted to assess the impact of EA treatment on cognitive function. The hippocampal DG area Aβ deposition level was detected by immunohistochemistry after the intervention; The number of BrdU+/CaR+ cells and the gene expression level of calretinin (CaR) and prospero homeobox 1(Prox1) in the DG area of the hippocampus was detected to assess neurogenesis by immunofluorescence and western blotting after the intervention; The gene expression levels of FZD2, Wnt5a, DVL2, p-DVL2, CaMKII, and p-CaMKII in the Wnt signaling pathway were detected by Western blotting after the intervention. Results Cognitive behavioral tests showed that 5 × FAD mice had impaired pattern separation (P

BackgroundThe number of people with dementia is soaring. Cognitive reserve has been thought to be associated with dementia risk. It is not clear at which period in the life course and which cognitive reserve proxies contribute to the reduced risk of dementia.MethodsBy scanning four databases (PubMed, Embase, Web of Science, and MEDLINE) up to Jun 3, 2023, longitudinal studies of life-course cognitive reserve and risk of dementia were found. The HRs and 95% CIs for each study were summarized using random effects models. Subgroup analyses and sensitivity analyses were conducted. Utilizing funnel plots, Begg and Egger tests, publication bias was investigated.ResultsA total of 27 studies were included, containing 10 in early-life, 10 in middle-life, and 13 in late-life. All studies used validated questionnaires to measure cognitive reserve, and dementia diagnosis followed recognized worldwide guidelines. All included studies were of medium or low risk. Cognitive reserve in early-life (Hazard ratio (HR): 0.82; 95% confidence interval (CI): 0.79–0.86), middle-life (HR: 0.91; 95% CI: 0.84–0.98) and late-life (HR: 0.81; 95% CI: 0.75–0.88) all have protective effects on dementia risk. Multiple sensitivity analyses showed consistent results.ConclusionDementia risk is reduced by the buildup of cognitive reserves during life-course. Accumulation of proxies for cognitive reserve in early and late life had the greatest effect on dementia risk reduction. Social connection may be an effective approach to lower dementia risk.

In this paper, we propose an improved Nested Error Regression model in which the random effects for each area are given a prior distribution using the Poisson–Dirichlet Process. Based on this model, we mainly investigate the construction of the parameter estimation using the Empirical Bayesian(EB) estimation method, and we adopt various methods such as the Maximum Likelihood Estimation(MLE) method and the Markov chain Monte Carlo algorithm to solve the model parameter estimation jointly. The viability of the model is verified using numerical simulation, and the proposed model is applied to an actual small area estimation problem. Compared to the conventional normal random effects linear model, the proposed model is more accurate for the estimation of complex real-world application data, which makes it suitable for a broader range of application contexts.

In this study, a multi-stage planning framework was constructed by using SWMM simulation modeling and NSGA-II and applied to optimize the layout of integrated grey–green infrastructure (IGGI) under land use change and climate change scenarios. The land use change scenarios were determined based on the master plan of the study area, with imperviousness of 50.7% and 62.0% for stage 1 and stage 2, respectively. Rainfall trends for stage 1 and stage 2 were determined using Earth-E3 from the CMIP6 model. The rainfall in stage 2 increased by 14.9% from stage 1. Based on these two change scenarios, the spatial configuration of IGGI layouts with different degrees of centralization of the layout (DCL) under the two phases was optimized, with the lowest life cycle cost (LCC) as the optimization objective. The results showed that the layout with DCL = 0 had better performance in terms of LCC. The LCC of the layout with DCL = 0 was only 66.9% of that of the layout with DCL = 90.9%. In terms of Tech-R, stage 2 had better performance than stage 1. Furthermore, the average technological resilience (Tech-R) index of stage 2 was 0.8–3.4% higher than that of stage 1. Based on the LCC and Tech-R indices of all of the layouts, TOPSIS was used to compare the performance of the layouts under the two stages, and it was determined that the layout with DCL = 0 had the best economic and performance benefits. The results of this study will be useful in exploring the spatial configuration of urban drainage systems under land use change and climate change for sustainable stormwater management.

The flow around two tandem circular cylinders in proximity to a wall is investigated using particle image velocimetry (PIV) for Re = 2 × 103. The spacing ratios L/D are 1, 2, and 5, and the gap ratios G/D are 0.3, 0.6, and 1. The proper orthogonal decomposition (POD) method and λci vortex identification method are used to investigate the evolution of flow structure, and the influences of L/D and G/D on flow physics are shown. At L/D = 2 and G/D = 0.3, a “pairing” process occurs between the wall shear layer and the upstream cylinder’s lower shear layer, resulting in a small separation bubble behind the upstream cylinder. At L/D = 1, the Strouhal number (St) increases with decreasing G/D. At three gap ratios, the St gradually decreases as L/D increases. At G/D = 0.3, there is nearly a 49.98% decrease from St = 0.3295 at L/D = 1 to St = 0.1648 at L/D = 5, which is larger than the reductions in cases of G/D = 0.6 and G/D = 1. The effects of L/D on the evolution of flow structure at G/D = 0.6 are revealed in detail. At L/D = 1, the vortex shedding resembles that of the single cylinder. As L/D increases to 2, a squarish flow structure is formed between two cylinders, and a small secondary vortex is formed due to induction of the lower shear layer of the upstream cylinder. At L/D = 5, there is a vortex merging process between the upper wake vortices of the upstream and downstream cylinders, and the lower wake vortex of the upstream cylinder directly impinges the downstream cylinder. In addition, the shear layers and wake vortices of the upstream cylinder interact with the wake of the downstream cylinder as L/D increases, resulting in reductions in velocity fluctuations, and the production and turbulent diffusion of turbulent kinetic energy are decreased behind the downstream cylinder.

Abstract Drug resistance occurs frequently in triple-negative breast cancer (TNBC) and leads to early relapse and short survival. Targeting the DNA damage response (DDR) has become an effective strategy for overcoming TNBC chemoresistance. CENPF (centromere protein) is a key regulator of cell cycle progression, but its role in TNBC chemotherapy resistance remains unclear. Here, we found that CENPF, which is highly expressed in TNBC, is associated with a poor prognosis in patients receiving chemotherapy. In addition, in vitro CENPF knockdown significantly increased adriamycin (ADR)-induced cytotoxicity in MDA-MB-231 cells and ADR-resistant cells (MDA-MB-231/ADR). Then, we demonstrated that CENPF targets Chk1-mediated G2/M phase arrest and binds to Rb to compete with E2F1 in TNBC. Considering the crucial role of E2F1 in the DNA damage response and DNA repair, a novel mechanism by which CENPF regulates the Rb-E2F1 axis will provide new horizons to overcome chemotherapy resistance in TNBC.

PEP06 is a novel endostatin-Arg-Gly-Asp-Arg-Gly-Asp (RGDRGD) 30-amino-acid polypeptide featuring a terminally fused RGDRGD hexapeptide at the N terminus. The active endostatin fragment of PEP06 directly targets tumor cells and exerts an antitumoral effect. However, little is known about the kinetics and degradation products of PEP06 in vitro or in vivo. In this study, we investigated the in vitro metabolic stability of PEP06 after it was incubated with living cells obtained from animals of different species; we further identified the degradation characteristics of its cleavage products. PEP06 underwent rapid enzymatic degradation in multiple types of living cells, and the liver, kidney, and blood play important roles in the metabolism and clearance of the peptides resulting from the molecular degradation of PEP06. We identified metabolites of PEP06 using full-scan mass spectrometry (MS) and tandem MS (MS2), wherein 43 metabolites were characterized and identified as the degradation metabolites from the parent peptide, formed by successive losses of amino acids. The metabolites were C and N terminal truncated products of PEP06. The structures of 11 metabolites (M6, M7, M16, M17, M21, M25, M33, M34, M39, M40, and M42) were further confirmed by comparing the retention times of similar full MS spectrum and MS2 spectrum information with reference standards for the synthesized metabolites. We have demonstrated the metabolic stability of PEP06 in vitro and identified a series of potentially bioactive downstream metabolites of PEP06, which can support further drug research.

Abstract Objectives To investigate lymphoid enhancer factor 1 (LEF-1) protein expression in medulloblastomas (MBs) and its correlation with molecular grouping of MBs. Methods Expressions of LEF-1 and β-catenin were detected by immunohistochemistry, and molecular grouping was performed based on the NanoString and sequencing techniques for 30 MBs. Results By genetic defining, 3 MBs were WNT-activated, 11 were SHH-activated, 3 were in Group 3 and 13 in Group 4 respectively. Nuclear LEF-1 staining was found in 8 MBs using immunohistochemical method. Three out of 8 showed diffuse and strong nuclear LEF-1 staining which were proved to be WNT-activated genetically, while the other 5 MBs with focal staining were SHH-activated genetically. The expression of LEF-1 protein was significantly correlated with genetically defined WNT-activated MBs (P

BackgroundHearing loss affects over 1.5 billion individuals worldwide. Their disability and limited access to the coronavirus (COVID-19) pandemic information make them suffer a greater degree than ordinary people. However, the quantitative studies on the implementation of behavior compliance with preventive health measures for vulnerable groups such as people with hearing disability were limited. The purpose of this study was to explore the compliance with pandemic-related protective health measures among people with hearing disability.DesignA cross-sectional survey, population-based cohort study of students aged 12–26 years with and without hearing disability was conducted. Behavioral compliance with preventive health measures was collected from the general education institutions and special education schools using an online questionnaire. Logistic regression and structural equation model were used to determine the associations among the demographic variables, different degrees of mental health status and psychological impacts, and preventive health behaviors.ResultsAmong 1,589 participants, 485 (30.5%) students are with hearing disability (SHD), and 1,104 (69.5%) students with normal hearing (SNH). The SHD has a significantly lower degree of behavioral compliance with the preventive health measures than SNH has. Hearing disability and anxiety [odds ratio (OR) = 1.54–1.76, p < 0.05] are risk factors for avoiding sharing of utensils during mealtime. Hearing disability, male sex, father's education level, mother's profession, bedtime after 11:00 p.m., anxiety, and depression (OR = 1.45–2.95, p < 0.05) are risk factors for hand hygiene. Male sex (OR = 2.13, p < 0.001) is risk factor and being aged below 18 years old (OR = 0.62, p = 0.03) is protective factor for wearing masks. Exercise (OR = 0.32–0.70, p < 0.01) is the most protective factor for preventive health behaviors. Mediating effect of mental health status and psychological impacts between hearing level and the compliance with the preventive health measures was −0.044 (95% CI: −0.068 to −0.027).ConclusionsTo reduce the risk of contraction, update pandemic information, essential communication services, extra assistance, and support should be provided to these disabled persons who are more susceptible to a public health emergency.

The growing need for automation in industries has foreseen drastic advancements in the development of soft robotics in the areas of navigation systems, textiles, healthcare, and biometric monitoring. Electroadhesion (EA)‐based robotic applications benefit from ultralow power consumption, low maintenance, choices in versatile surfaces, sustainable life cycles, applicability in harsh and vacuum environments, and exteroceptive/proprioceptive abilities. It is of scientific and technological importance to comprehend the sequence of developments in working mechanisms, modeling, and materials to explore its full potential for future intelligent robotics systems. This review provides an all‐inclusive roadmap of EA technology from its inception to unprecedented developments and complex relationships across multidisciplines, viz., robotics, solid and fluid mechanics, electrostatics, haptics, space technologies, nanotechnology, and IoT.

The flow characteristics and vortex evolution behind a circular cylinder near a wavy wall are studied using a large-eddy simulation framework. The Reynolds number based on the cylinder diameter D is ReD = 1500, the gap ratio G/D is fixed to 1, and the slope S = λ/2k varies from 2 to 8 (where G is the distance between the lower surface of the cylinder and the crest, λ is the wavelength, and k is the wave amplitude). The flow field characteristics, hydrodynamic coefficients, pressure distributions, vortex evolution, and three-dimensional flow features are presented and discussed. The phase of the flow field is determined by proper orthogonal decomposition, and the vortex identification method is applied to capture vortex structures. Compared with a smooth wall, the wavy wall changes the pressure distribution of the flow field and decreases (increases) the mean drag (lift) coefficient. As the wall amplitude increases, the Strouhal number increases slightly, attaining values of 0.25 for S = 8, 0.25 for S = 4, and 0.26 for S = 2. The wall pressure coefficient has an approximately periodic distribution, while the wall friction coefficient varies according to the evolution of secondary vortices on the wall. Two small secondary vortices appear either side of the crest before merging into a new secondary vortex that moves downstream with the upper and lower wake vortices. The energy of the first two modes obtained by proper orthogonal decomposition decreases compared with the smooth-wall case, whereas that of the third and fourth modes increases, reflecting the greater large-scale vortex structures between the wavy walls. This is confirmed by examining the instantaneous three-dimensional vortex structures using the Q-criterion.

Free-space optical communication is restricted by random media-stimulated beam degradation. However, partially coherent structured beams modulated by the coherence structure can potentially mitigate this negative effect. By employing the extended Huygens–Fresnel integral, we provide an examination of the second-order statistical features of a common type of partly coherent structured beams, self-splitting structured beams, in a turbulent ocean. The implications of turbulence parameters relating to the ocean and beginning beam parameters corresponding to the progression of such beam propagation attributes are fully investigated. Our numerical outcomes show that, for turbulence with a low-dissipation kinetic energy rate per unit mass of fluid, small Kolmogorov inner scale, large relative strength of temperature to salinity undulations, and large dissipation rate of mean-square temperature has a greater negative effect on the structured beams. In addition, we suggest an effective approach, enhancing the order of the beam and reducing the coherence length of the beams, to lower the oceanic turbulence-induced negative effects, and thus have future extensive possibilities in free-space optical communication.

Turbulent channel flows around a wall-mounted hemisphere numerically are investigated by large eddy simulation, and the Reynolds number based on the hemisphere’s diameter is 3 × 104. The statistical characteristics and turbulent structure evolution are revealed in the Eulerian frameworks and Lagrangian frameworks. The vortex identification and Dynamic Mode Decomposition (DMD) are used to study the evolution of turbulent structure in the Eulerian frameworks, and the finite-time Lyapunov exponents are applied to identify Lagrangian coherent structures (LCS) in the Lagrangian framework. It is found that the developing angle of the hairpin vortex is ∼7° at two frameworks. What is more, there are some hairpin vortices formed behind the hemisphere and some turbulent structures formed near the wall by DMD method. The correlation analysis is applied to investigate the angle variation and scale variation of turbulent structures, and it is observed that the angle of turbulent structures is negative at Y/d ≥ 1.2 and the spanwise length scales of turbulent structures increase as it moves downstream. By studying the LCS behind a wall-mounted hemisphere, there is formation of “kink” caused by viscous interaction between some hairpin vortex legs, which is the characteristic of hairpin vortex deformation. The comparisons of statistical characteristics between Eulerian frameworks and Lagrangian frameworks are conducted by the correlation analysis, the spectrum analysis, and the structure functions.

In order to alleviate the congestion and overload in high-voltage distribution network (HVDN), a load-transfer optimization method has been developed based on Link-path model. The topology characteristics of HVDN have been analyzed and the basic topological unit (BTU) is adopted. All the typical connection modes in 110kV substations can be easily described with BTU. Link-path model (LPM) is a modelling method used in the field of communication and routing technologies in which there are similarities between LPM and the connection relationships of HVDN. Thus, based on ideas of LPF, the `domain', `set of link', `set of path' and so on are defined to depict HVDN topology. The Link-path based load-transfer optimization model is much more straightforward and easily obtained. The model only needs to search from each unit based on the network topology and form relative data sets. It avoids the high dimension problem which appears in other methods like incidence matrix and tree model. Thus this model could reduce the nonlinear degree and solving difficulty in load-transfer optimization problem. The method is suitable for system dispatching analysis and strategy-making for HVDN system. A load-transfer optimization model for HVPN has been developed and the results with one real test system proved the feasibility and validity of the proposed LPM method.

The spatial resolution of magnetic resonance images (MRI) is limited by the hardware capacity, sampling time, signal-to-noise ratio (SNR), and patient comfort. Recently, deep convolutional neural networks (CNN) have achieved impressive success in MRI super-resolution (SR) reconstruction. Increasing network depth or width can enlarge the receptive field to improve SR accuracy, however, it is impractical for MRI reconstruction in clinical applications because of high computational loads. To address this issue, we propose a novel dilated convolutional encoder-decoder (DCED) network to improve the resolution of MRI. We exploit three-dimensional (3D) dilated convolutions as encoders to extract high-frequency features. The dilated encoders capture wider contextual information by exponentially enlarging the receptive field, without introducing additional parameters or layers. Then we decode the features using deconvolution operations to alleviate gridding artifacts and restore fine details. To improve information flow, the encoders and decoders are aggregated into symmetrically connected blocks. The output of each block is passed to the final convolution layer, which facilitates to extract hierarchical features. In addition, we also exploit a geometric self-ensemble 3D wavelet fusion method to improve the potential performance of MRI SR. Experimental results on four public available brain datasets show that our proposed method outperforms NLM (non-local means), LRTV (low-rank and total variation) and current CNN-based SR methods, which demonstrates that our method achieves a new state-of-the-art performance in MRI SR task.

Outsourcing computing is an application of cloud computing. It is an important method to solve the problem of client resource limitation. However, outsourcing computing brings security and efficiency challenges. Matrix multiplication computation (MMC) outsourcing has attracted many researchers to carry out research and has achieved many admirable results. Unfortunately, there is still a problem with zero elements of information protection for MMC outsourcing. In this paper, we are motivated to design a protocol, which can protect the private information of the zero elements, for outsourcing MMC to the cloud server. Our proposed protocol not only solves the problem of protecting zero elements information but also has no large client-overhead. The main idea is to add a special matrix to the original matrix for hiding zero elements before transforming the computation task. Then, the cloud server computes the received encryption task and returns the result to the client. Finally, the client decrypts and verifies the result. The experiment verifies the efficiency and practicability of our protocol. The comparison with previous schemes shows that the protocol improves security without much client overhead.

Studies have shown that electroacupuncture (EA) can effectively improve vascular cognitive impairment (VCI), but its mechanisms have not been clearly elucidated. This study is aimed at investigating the mechanisms underlying the effects of EA treatment on hippocampal synaptic transmission efficiency and plasticity in rats with VCI. Methods. Sprague–Dawley rats were subjected to VCI with bilateral common carotid occlusion (2VO). EA stimulation was applied to Baihui (GV20) and Shenting (GV24) acupoints for 30 min once a day, five times a week, for four weeks. Our study also included nonacupoint groups to confirm the specificity of EA therapy. The Morris water maze (MWM) was used to assess cognitive function. Electrophysiological techniques were used to detect the field characteristics of the hippocampal CA3–CA1 circuit in each group of rats, including input-output (I/O), paired-pulse facilitation ratios (PPR), field excitatory postsynaptic potential (fEPSP), and excitatory postsynaptic current (EPSC). The expression of synapse- and calcium-mediated signal transduction associated proteins was detected through western blotting. Results. The MWM behavioural results showed that EA significantly improved cognitive function in VCI model rats. EA increased the I/O curve of VCI model rats from 20 to 90 μA. No significant differences were observed in hippocampal PPR. The fEPSP of the hippocampal CA3–CA1 circuit was significantly increased after EA treatment compared with that after nonacupuncture treatment. We found that EA led to an increase in the EPSC amplitude and frequency, especially in the decay and rise times. In addition, the protein expression and phosphorylation levels of N-methyl-D-aspartate receptor 2B, α-amino-3-hydroxy-5-methyl-4-isoxazole propionate receptor 1, and Ca2+-calmodulin-dependent protein kinase II increased to varying degrees in the hippocampus of VCI model rats. Conclusion. EA at GV20 and GV24 acupoints increased the basic synaptic transmission efficiency and synaptic plasticity of the hippocampal CA3–CA1 circuit, thereby improving learning and memory ability in rats with VCI.

Nitrite and sulfite play important roles in human health and environmental science, so it is desired to develop a facile and efficient method to evaluate NO2- and SO32- concentrations. In this article, the use of green alternatives with the potential of multi-functionality has been synthesized to detect nitrite and sulfite based on fluorescent probe. The carbon dots (CDs) with starch as only raw materials show fluorescence turn “on-off-on” response towards NO2- and SO32- with the limits of detection of 0.425 and 0.243 μМ, respectively. Once nitrite was present in the solution, the fluorescence of CDs was quenched rapidly due to the charge transfer. When sulfite was introduced, the quenching fluorescence of CDs was effectively recovered because of the redox reaction between NO2- and SO32-, and thus providing a new way for NO2- and SO32- detection. Owing to their excellent analytical characteristics and low cytotoxicity, the “on-off-on” sensor was successfully employed for intracellular bioimaging of NO2- and SO32-.

BackgroundThe concurrence of anti-contactin 1 (CNTN1) antibody-associated chronic inflammatory demyelinating polyneuropathy (CIDP) and membranous nephropathy (MN) has previously been reported in the literature. CIDP with autoantibodies against paranodal proteins are defined as autoimmune nodopathies (AN) in the latest research. In view of the unclear relationship between CIDP and MN, we performed a case study and literature review to investigate the clinical characteristics of anti-CNTN antibody-associated AN with MN.MethodsWe detected antibodies against NF155, NF186, CNTN1, CNTN2, CASPR1 and PLA2R in blood samples of a patient with clinically manifested MN and concomitant peripheral neuropathy via double immunofluorescence staining and conducted a quantitative measurement of anti-PLA2R IgG antibodies via enzyme-linked immunosorbent assay (ELISA). Case reports of anti-CNTN1 antibody-associated AN, anti-CNTN1 antibody-associated AN with MN, and CIDP with MN were retrieved through a literature search for a comparative analysis of clinical characteristics. The cases were grouped according to the chronological order of CIDP and MN onset for the comparison of clinical characteristics.ResultsA 57-year-old man with anti-PLA2R positive MN was admitted to the hospital due to limb numbness, weakness, and proprioceptive sensory disorder. He was diagnosed with anti-CNTN1 antibody-associated AN and recovered well after immunotherapy. Our literature search returned 22 cases of CIDP with MN that occurred before, after, or concurrently with CIDP. Good responses were achieved with early single-agent or combination immunotherapy, but eight out of the 22 patients with CIDP and concomitant MN ultimately developed different motor sequelae. Five patients had anti-CNTN1 antibody-associated AN with MN. Among these patients, males accounted for the majority of cases (male:female=4:1), the mean age at onset was late (60.2 ± 15.7 years, range 43–78 years), and 40% had acute to subacute onset. Clinical manifestations included sensory-motor neuropathy, sensory ataxia caused by proprioceptive impairment, and elevated cerebrospinal fluid protein levels.ConclusionThe age at onset of CIDP with MN was earlier than that of anti-CNTN1 antibody-associated AN. MN may occur before, after or concurrently with CIDP. The early detection and isotyping of anti-CNTN1 and anti-PLA2R antibodies and the monitoring of isotype switching may be essential for suspected CIDP patients.

Accurate wind speed prediction is a premise that guarantees the reliable operation of the power grid. This study presents a combined prediction model that integrates data preprocessing, cascade optimization, and deep learning prediction to improve prediction performance. In data preprocessing, the wavelet soft threshold denoising (WSTD) is employed to filter the blurring noise of the original data. Then, the robust empirical mode decomposition (REMD) and adaptive variational mode decomposition (AVMD) are adopted to carry out a two-stage adaptive decomposition. Spearman correlation is used to quantify the mode that need to be decomposed for the second time. In the cascade optimization, the hybrid grey wolf algorithm (HGWO) is employed to optimize the parameters of the VMD and the gated recurrent unit (GRU), which overcomes the problem of empirical parameter adjustment. The HGWO is also adopted in the prediction strategy to optimize the GRU model to predict the grouped intrinsic mode functions (IMFs). Lastly, the final wind speed prediction result is obtained by superimposing the values of all the predicted models. The proposed model was validated with the measured wind speed data of the four quarters in the Bay area of China and was compared with 20 models of the classic method to further evaluate the effectiveness of the model. The results show that the whole process of the proposed model is adaptive, the final multi-step prediction performance is good, and high prediction accuracy can be attained.

For cancer diagnosis, many DNA methylation markers have been identified. However, few studies have tried to identify DNA methylation markers to diagnose diverse cancer types simultaneously, i.e., pan-cancers. In this study, we tried to identify DNA methylation markers to differentiate cancer samples from the respective normal samples in pan-cancers. We collected whole genome methylation data of 27 cancer types containing 10,140 cancer samples and 3386 normal samples, and divided all samples into five data sets, including one training data set, one validation data set and three test data sets. We applied machine learning to identify DNA methylation markers, and specifically, we constructed diagnostic prediction models by deep learning. We identified two categories of markers: 12 CpG markers and 13 promoter markers. Three of 12 CpG markers and four of 13 promoter markers locate at cancer-related genes. With the CpG markers, our model achieved an average sensitivity and specificity on test data sets as 92.8% and 90.1%, respectively. For promoter markers, the average sensitivity and specificity on test data sets were 89.8% and 81.1%, respectively. Furthermore, in cell-free DNA methylation data of 163 prostate cancer samples, the CpG markers achieved the sensitivity as 100%, and the promoter markers achieved 92%. For both marker types, the specificity of normal whole blood was 100%. To conclude, we identified methylation markers to diagnose pan-cancers, which might be applied to liquid biopsy of cancers.

Whole-genome bisulfite sequencing generates a comprehensive profiling of the gene methylation levels, but is limited by a high cost. Recent studies have partitioned the genes into landmark genes and target genes and suggested that the landmark gene expression levels capture adequate information to reconstruct the target gene expression levels. This inspired us to propose that the methylation level of the promoters in landmark genes might be adequate to reconstruct the promoter methylation level of target genes, which would eventually reduce the cost of promoter methylation profiling. Here, we propose a deep learning model called Deep-Gene Promoter Methylation (D-GPM) to predict the whole-genome promoter methylation level based on the promoter methylation profile of the landmark genes from The Cancer Genome Atlas (TCGA). D-GPM-15%-7000 × 5, the optimal architecture of D-GPM, acquires the least overall mean absolute error (MAE) and the highest overall Pearson correlation coefficient (PCC), with values of 0.0329 and 0.8186, respectively, when testing data. Additionally, the D-GPM outperforms the regression tree (RT), linear regression (LR), and the support vector machine (SVM) in 95.66%, 92.65%, and 85.49% of the target genes by virtue of its relatively lower MAE and in 98.25%, 91.00%, and 81.56% of the target genes based on its relatively higher PCC, respectively. More importantly, the D-GPM predominates in predicting 79.86% and 78.34% of the target genes according to the model distribution of the least MAE and the highest PCC, respectively.

Graphitic carbon nitride (g-C3N4) is a promising semiconductor material which has been widely studied in nanoscience. However, the effect of modifying the performance of g-C3N4 is still under debate. In this communication, we show the size and functional group effects on the g-C3N4 using density functional theory (DFT) calculations. It was found that a molecule with six repeated g-C3N4 units (g-C3N4-6) could be the smallest unit that converges to the limit of its HOMO–LUMO gap. Calculations of g-C3N4-6 with varying numbers of substituted C≡N, C=O, and O−H functional groups show that C≡N and C=O could narrow down the HOMO–LUMO gap, while O−H could slightly raise the gap. This study shows that the change of substituents could tune the band gap of g-C3N4, suggesting that rationally modifying the substituent at the edge of g-C3N4-based materials could help to significantly increase the photocatalytic properties of a metal-free g-C3N4.