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The excessive usage of nanoplastics (NPs) has posed a serious threat to the ecological environment and human health, which can enter the brain and then result in neurotoxicity. However, research on the neurotoxic effects of NPs based on different exposure routes and modifications of functional groups is lacking. In this study, the neurotoxicity induced by NPs was studied using polystyrene nanoplastics (PS-NPs) of different modifications (PS, PS-COOH, and PS-NH2). It was found that PS-NH2 through intranasal administration (INA) exposure route exhibited the greatest accumulation in the mice brain after exposure for 7 days. After the mice were exposed to PS-NH2 by INA means for 28 days, the exploratory ability and spatial learning ability were obviously damaged in a dose-dependent manner. Further analysis indicated that these damages induced by PS-NH2 were closely related to the decreased ability of glymphatic system to clear β-amyloid (Aβ) and phosphorylated Tau (P-Tau) proteins, which was ascribed to the loss of aquaporin-4 (AQP4) polarization in the astrocytic endfeet. Moreover, the loss of AQP4 polarization might be regulated by the NF-κB pathway. Our current study establishes the connection between the neurotoxicity induced by PS-NPs and the glymphatic system dysfunction for the first time, which will contribute to future research on the neurotoxicity of NPs.

Abstract Dyes and heavy metals pollute the environment. Biochar-based hydrogel is an excellent adsorbent, but the competitive adsorption mechanism associated with the removal of pollutants using biochar is yet to be understood in detail. Biochar was prepared following the process of high-temperature lysis of marine green tide (Enteromorpha prolifera). The prepared biochar was cross-linked with water-soluble chitosan and compounded with nano-Fe3O4 to synthesize magnetically-modified Enteromorpha prolifera-based biochar hydrogel (MM-EBC-HD). The competitive removal performance of the hydrogel was studied, and the pollutant removal mechanism was analyzed against a binary system consisting of common environmental pollutants (methyl orange [MO] and hexavalent chromium [Cr (VI)]. The physical and chemical properties of the composites were studied before and after contaminant removal, and the associated pollutant removal mechanisms were analyzed by SEM, EDS, FTIR, XRD, and XPS techniques. The effects of pH, temperature and initial pollutant concentration on the adsorption performance of the materials were examined. The maximum adsorption of MO on MM-EBC-HD was 71.18 mg g−1, and adsorption equilibrium was attained at approximately 60 min. Electrostatic forces, hydrophobic bonds, and hydrogen bonds were exploited for MO adsorption. And the maximum adsorption amount of Cr (VI) was recorded to be 115.41 mg g−1, and equilibrium was attained in approximately 10 min. Electrostatic and ion exchange effects were exploited to adsorb Cr (VI) efficiently. The MO and Cr (VI) adsorption processes could be explained by the second-order kinetic model and Langmuir adsorption isotherms, respectively. The adsorption performance recorded for the binary adsorption system was poorer than that recorded for the single adsorption system for both pollutants. MO and Cr (VI) adsorption decreased from 74.88% to 47.65% and from 62.33% to 42.4%, respectively. Competition between MO and Cr (VI) in the dual system can be attributed to the presence of amino and hydroxyl groups. The MO–Cr complex, which was more compact in structure than a single contaminant, was formed, allowing few reactive groups to be exposed to the surface of the hydrogel. Additionally, MM-EBC-HD nanocomposites presented a recovery rate of 87% after 5 cycles and thus could be used to avoid adsorbents-caused environmental hazards. Graphical Abstract

Abstract In the process of contemporary agricultural production, the application of intelligent connection and automatic navigation technology of modern agricultural machinery is increasingly common. The application of visual navigation technology in the field of intelligent connection of agricultural machines can provide accurate and reliable positioning signals for agricultural machines so as to carry out intelligent and automatic agricultural machinery operations. In order to realise precision agricultural operation and intelligent navigation, a network of combining decision‐making by using visual navigation recognition and image processing is proposed. The system design mainly includes two parts: visual navigation system and control network. Agricultural machinery uses monocular vision image acquisition to collect farmland image. The computer analyzes the image, extracts the navigation centreline, obtains the navigation path information, and completes the identification and positioning of the navigation path. The simulation process verifies that the designed image navigation precision agricultural intelligent connection network can provide efficient, precise, and intelligent control of agricultural machinery.

The huge increase in the communication network rate has made the application fields and scenarios for vehicular ad hoc networks more abundant and diversified and proposed more requirements for the efficiency and quality of data transmission. To improve the limited communication distance and poor communication quality of the Internet of Vehicles (IoV), an optimal intelligent routing algorithm is proposed in this paper. Combined multi-weight decision algorithm with the greedy perimeter stateless routing protocol, designed and evaluated standardized function for link stability. Linear additive weighting is used to optimize link stability and distance to improve the packet delivery rate of the IoV. The blockchain system is used as the storage structure for relay data, and the smart contract incentive algorithm based on machine learning is used to encourage relay vehicles to provide more communication bandwidth for data packet transmission. The proposed scheme is simulated and analyzed under different scenarios and different parameters. The experimental results demonstrate that the proposed scheme can effectively reduce the packet loss rate and improve system performance.

Abstract Background Thyroid carcinoma (THCA) is the most common endocrine-related malignant tumor. Despite the good prognosis, some THCA patients may deteriorate into more aggressive diseases, leading to poor survival. This may be alleviated by developing a novel model to predict the risk of THCA, including recurrence and survival. Ferroptosis is an iron-dependent, oxidative, non-apoptotic form of cell death initially described in mammalian cells, and plays an important role in various cancers. To explore the potential prognostic value of ferroptosis in THCA, ferroptosis-related long non-coding RNAs (FRLs) were used to construct model for risk prediction of THCA. Methods RNA-sequencing data of THCA patients and ferroptosis-related genes were downloaded from The Cancer Genome Atlas (TCGA) and FerrDb, respectively. A total of 502 patients with complete data were randomly separated into a training cohort and a validation cohort at the ratio of 2:1. The Pearson correlation coefficients were calculated to determine the correlation between ferroptosis-related genes (FRGs) and the corresponding lncRNAs, and those meeting the screening conditions were defined as FRLs. Gene Expression Omnibus (GEO) database and qRT-PCR were used to verify the expression level of FRLs in THCA tissues. Univariate and multivariate cox regression analysis were performed to construct a FRLs signature based on lowest Akaike information criterion (AIC) value in the training cohort, then further tested in the validation cohort and the entire cohort. Gene set enrichment analysis (GSEA) and functional enrichment analysis were used to analyze the biological functions and signal pathways related to differentially expressed genes between the high-risk and low-risk groups. Finally, the relative abundance of different tumor-infiltrating immune cells were calculated by CIBERSORT algorithm. Results The patients were divided into high-risk group and low-risk group based on a 5-FRLs signature (AC055720.2, DPP4-DT, AC012038.2, LINC02454 and LINC00900) in training cohort, validation cohort and entire cohort. Through Kaplan–Meier analysis and area under ROC curve (AUC) value, patients in the high-risk group exhibited worse prognosis than patients in the low-risk group. GEO database and qRT-PCR confirmed that LINC02454 and LINC00900 were up-regulated in THCA. Univariate and multivariate cox regression analyses showed that the risk score was an independent prognostic indicator. GSEA and functional enrichment analysis confirmed that immune-related pathways against cancer were significantly activated in the low-risk THCA patients. Further analysis showed that the immune cells such as plasma cells, T cells CD8 and macrophages M1, and the expression of immune checkpoint molecules, including PD-1, PD-L1, CTLA4, and LAG3, were remarkably higher in the low-risk group. Conclusion Our study used the TCGA THCA dataset to construct a novel FRLs prognostic model which could precisely predict the prognosis of THCA patients. These FRLs potentially mediate anti-tumor immunity and serve as therapeutic targets for THCA, which provided the novel insight into treatment of THCA.

PurposeWe aimed at establishing a nomogram to accurately predict the overall survival (OS) of non-metastatic invasive micropapillary breast carcinoma (IMPC).MethodsIn the training cohort, data from 429 patients with non-metastatic IMPC were obtained through the Surveillance, Epidemiology, and End Results (SEER) database. Other 102 patients were enrolled at the Xijing Hospital as validation cohort. Independent risk factors affecting OS were ascertained using univariate and multivariate Cox regression. A nomogram was established to predict OS at 3, 5 and 8 years. The concordance index (C-index), the area under a receiver operating characteristic (ROC) curve and calibration curves were utilized to assess calibration, discrimination and predictive accuracy. Finally, the nomogram was utilized to stratify the risk. The OS between groups was compared through Kaplan-Meier survival curves.ResultsThe multivariate analyses revealed that race (p = 0.047), surgery (p = 0.003), positive lymph nodes (p = 0.027), T stage (p = 0.045) and estrogen receptors (p = 0.019) were independent prognostic risk factors. The C-index was 0.766 (95% CI, 0.682-0.850) in the training cohort and 0.694 (95% CI, 0.527-0.861) in the validation cohort. Furthermore, the predicted OS was consistent with actual observation. The AUCs for OS at 3, 5 and 8 years were 0.786 (95% CI: 0.656-0.916), 0.791 (95% CI: 0.669-0.912), and 0.774 (95% CI: 0.688-0.860) in the training cohort, respectively. The area under the curves (AUCs) for OS at 3, 5 and 8 years were 0.653 (95% CI: 0.498-0.808), 0.683 (95% CI: 0.546-0.820), and 0.716 (95% CI: 0.595-0.836) in the validation cohort, respectively. The Kaplan-Meier survival curves revealed a significant different OS between groups in both cohorts (p

The integration of a microfluidic chip into terahertz time-domain attenuated total reflection (THz TD-ATR) spectroscopy is highly demanded for the accurate measurement of aqueous samples. Hitherto, however little work has been reported on this regard. Here, we demonstrate a strategy of fabricating a polydimethylsiloxane microfluidic chip (M-chip) suitable for the measurement of aqueous samples, and investigate the effects of its configuration, particularly the cavity depth of the M-chip on THz spectra. By measuring pure water, we find that the Fresnel formulae of two-interface model should be applied to analyze the THz spectral data when the depth is smaller than 210 μm, but the Fresnel formula of one-interface model can be applied when the depth is no less than 210 μm. We further validate this by measuring physiological solution and protein solution. This work can help promote the application of THz TD-ATR spectroscopy in the study of aqueous biological samples.

In order to solve the problem of insufficient number of dermoscopic image and the imbalance of image data among various diseases, a classification method of dermoscopic image based on class weighted cross entropy loss function and hierarchical convolution neural network was proposed.Firstly, the dermoscopic image was processed by color constancy to eliminate the ambient light noise.Then, the hierarchical convolution neural network based on ResNet 50 was constructed, and the two classification and multi classification convolution neural network models were constructed respectively, and the class weighted cross entropy loss function was set according to the quantitative characteristics of the dermatoscopic image.The experimental results show that the method achieves good classification effect, and the classification accuracy reaches 85.94%.Compared with the improved classification model ResNet 50, the test accuracy is improved by 5.752%.

Based on the configuration of the Stewart parallel mechanism (SPM), after analyzing the kinetostatic characteristic of a planar four-bar linkage with attached springs which works around the kinematic limb-singularity, a process of constructing a novel multi-degree of freedom (DOF) parallel passive compliant constant-force mechanism (CCFM) is presented. The kinematic limb-singularity of a rigid four-bar linkage with attached springs is obtained and then is applied to construct a constant-force compliant mechanism (CM) based on the pseudo-rigid-body-model by designing appropriate structure parameters to obtain appropriate equivalent spring stiffness of flexures. The CCFM is further used as the branches of the SPM and then a passive multi-DOF parallel mechanism (PM) is constructed. Here the method of replacing the original rigid branch of the PM with the CCFM is called the “Expanded Rigid-body Replacement Synthesis” (ERBRS). The multi-DOF parallel passive CCFM can generate a constant-force zone when makes a small pose transformation around a certain position, where each compliant branch is located at the constant-force zone. The proposed CCFM can be applied in practice such as the polishing machine which needs the constant contact-force between the polishing head and the workpiece. The proposed method of constructing a CCFM with one- or multi-degree of freedom mechanism has some important reference value on designing other types of CCFMs.

With the explosive developments of deep learning, learning–based computer–generated holography (CGH) has become an effective way to achieve real–time and high–quality holographic displays. Plentiful learning–based methods with various deep neural networks (DNNs) have been proposed. In this paper, we focus on the rapid progress of learning–based CGH in recent years. The generation principles and algorithms of CGH are introduced. The DNN structures frequently used in CGH are compared, including U–Net, ResNet, and GAN. We review the developments and discuss the outlook of the learning–based CGH.

Background and ObjectivesTo establish a prognostic stratification nomogram for T1–2 breast cancer with 1–3 positive lymph nodes to determine which patients can benefit from postmastectomy radiotherapy (PMRT).MethodsA population-based study was conducted utilizing data collected from the Surveillance, Epidemiology, and End Results database. Chi-square test or Fisher exact test was used to compare the distribution of characteristics. Cox analysis identified significant prognostic factors for survival. A prognostic stratification model was constructed by R software. Propensity score matching was applied to balance characteristics between PMRT cohort and control cohort. Kaplan-Meier method was performed to evaluate the performance of stratification and the benefits of PMRT in the total population and three risk groups.ResultsThe overall performance of the nomogram was good (3-year, 5-year, 10-year AUC were 0.75, 0.72 and 0.67, respectively). The nomogram was performed to excellently distinguish low-risk, moderate-risk, and high-risk groups with 10-year overall survival (OS) of 86.9%, 73.7%, and 62.7%, respectively (P

In order to address the problems of low screening efficiency, easy blocking of screen holes, and short service life of key parts commonly used in vibrating screen equipment, the TRIZ (Theory of the Solution of Inventive Problems) was applied in the present work to design a four-degree-of-freedom (4-DOF; three translational and one rotational movements) chaotic vibrating screen with a chaotic vibration exciter as the main power source and a 3-DOF (three translational movements) parallel kinetic chain as the kinematic constraint mechanism of the outer screen frame. Based on the topological structure theory, a hybrid mechanism with structure [4SOC−Ci1∥Ri2∥Ri3−+R,i=1,2,3,4] was constructed as the kinematic constraint mechanism of the inner screen box of the chaotic vibrating screen to solve the freedom of motion and POC (position and orientation characteristic) equations of parallel kinematic constraint mechanism of the outer screen frame and hybrid constraint mechanism of the inner screen box. The dynamic simulation of a virtual prototype of the chaotic vibrating screen was carried out in ADAMS software, and MATLAB was used to chaos recognition of the simulation results. It was found that the chaotic exciter moved aperiodically in X-, Y-, and Z-directions when the chaotic exciter motor rotated at uniform speed, and the amplitude, velocity, and acceleration of the outer screen frame of the vibrating screen had characteristics of reciprocating aperiodic and irregular changes. Through the phase trajectories of the eccentric block and inner screen box of the exciter in all directions, it was observed that the motion output of the vibrating screen was a chaotic vibration. Therefore, the present paper can provide an important reference for the design and application of chaotic vibrating screens.

Abstract Background Traumatic brain injury (TBI) is a complex condition and remains a prominent public and medical health issue in individuals of all ages. A rapid increase in extracellular glutamate occurs after TBI, leading to glutamate-induced excitotoxicity, which causes neuronal damage and further functional impairments. Although inhibition of glutamate carboxypeptidase II (GCP II) is considered a potential approach for reducing glutamate-induced excitotoxicity after TBI, further detailed evidence regarding its efficacy is required. Therefore, in this study, we examined the differences in the metabolite status between wild-type (WT) and GCP II gene-knockout (KO) mice after TBI using proton magnetic resonance spectroscopy (1H-MRS) and T2-weighted magnetic resonance (MR) imaging with a 7-tesla imaging system, and brain water-content analysis. Results Evaluation of glutamate and N-acetylaspartate concentrations revealed a decrease in both levels in the ipsilateral hippocampus at 24 h post-TBI; however, the reduction in glutamate and N-acetylaspartate levels was less marked in GCP II-KO mice than in WT mice (p

Three-term conjugate gradient methods have attracted much attention for large-scale unconstrained problems in recent years, since they have attractive practical factors such as simple computation, low memory requirement, better descent property and strong global convergence property. In this paper, a hybrid three-term conjugate gradient algorithm is proposed and it owns a sufficient descent property, independent of any line search technique. Under some mild conditions, the proposed method is globally convergent for uniformly convex objective functions. Meanwhile, by using the modified secant equation, the proposed method is also global convergence without convexity assumption on the objective function. Numerical results also indicate that the proposed algorithm is more efficient and reliable than the other methods for the testing problems.

Abstract Background Astaxanthin (ATX) is a carotenoid pigment with pleiotropic pharmacological properties that is seen as a possible drug for treating cerebral ischemic injury and subarachnoid hemorrhage. Na+–K+–2Cl− co-transporter-1 (NKCC1), an intrinsic membrane protein expressed by many cell types, is activated by various insults, leading to the formation of cell swelling and brain edema. We previously established that ATX attenuated brain edema and improved neurological outcomes by modulating NKCC1 expression after traumatic brain injury in mice. This paper explored the molecular mechanism of ATX-mediated inhibition of NKCC1 utilizing an in vitro astrocyte stretch injury model. Results Stretch injury in cultured astrocytes lowered cell viability time-dependently, which was substantially reducing by pretreating with ATX (50 μmol/L). Stretch injury increased Bax level and cleaved caspase-3 activity, and decreased Bcl-2 level and pro-caspase 3 activity, resulting in the apoptosis of astrocytes. Additionally, stretch injury substantially raised the gene and protein expressions of interleukin (IL)-1β, IL-6, and tumor necrosis factor (TNF)-α and prompted the expression and nuclear translocation of NF-κB. Pretreatment with ATX remarkably prevented the trauma-induced initiation of NF-κB, expressions of pro-inflammatory cytokines, and cell apoptosis. Moreover, stretch injury markedly elevated the gene and protein expression of NKCC1, which was partly blocked by co-treatment with ATX (50 µmol/L) or an NF-κB inhibitor (PDTC, 10 µmol/L). Cleaved caspase-3 activity was partially reduced by PDTC (10 µmol/L) or an NKCC1 inhibitor (bumetanide, 50 µmol/L). Conclusions ATX attenuates apoptosis after stretch injury in cultured astrocytes by inhibiting NKCC1 expression, and it acts by reducing the expression of NF-κB-mediated pro-inflammatory factors.

In this paper, an adaptive proximal bundle method is proposed for a class of nonconvex and nonsmooth composite problems with inexact information. The composite problems are the sum of a finite convex function with inexact information and a nonconvex function. For the nonconvex function, we design the convexification technique and ensure the linearization errors of its augment function to be nonnegative. Then, the sum of the convex function and the augment function is regarded as an approximate function to the primal problem. For the approximate function, we adopt a disaggregate strategy and regard the sum of cutting plane models of the convex function and the augment function as a cutting plane model for the approximate function. Then, we give the adaptive nonconvex proximal bundle method. Meanwhile, for the convex function with inexact information, we utilize the noise management strategy and update the proximal parameter to reduce the influence of inexact information. The method can obtain an approximate solution. Two polynomial functions and six DC problems are referred to in the numerical experiment. The preliminary numerical results show that our algorithm is effective and reliable.

DNA oligonucleotides with a 5-base mutation at the 3'-terminus were investigated by terahertz (THz) spectroscopy in a marker-free manner. The four single-stranded oligonucleotides with 17nt have been detected with specificity on a microfluidic chip, and corroborated by spectral measurements with split-ring resonators. The number of hydrogen bonds formed between the oligonucleotide and its surrounding water molecules, deemed a key contribution to the THz absorption of biological solutions, was explored by molecular dynamics simulations to explain the experimental findings. Our work underlies the feasibility of THz spectroscopy combined with microstructures for marker-free detection of DNA, which may form the basis of a prospective diagnostic tool for studying genic mutation.

To reduce the intense terahertz (THz) wave absorption of water and increase the signal-to-noise ratio, the THz spectroscopy detection of biomolecules usually operates using the nanofluidic channel technologies in practice. The effects of confinement due to the existence of nanofluidic channels on the conformation and dynamics of biomolecules are well known. However, studies of confinement effects on the THz spectra of biomolecules are still not clear. In this work, extensive all-atom molecular dynamics simulations are performed to investigate the THz spectra of the glycine oligopeptide solutions in free and confined environments. THz spectra of the oligopeptide solutions confined in carbon nanotubes (CNTs) with different radii are calculated and compared. Results indicate that with the increase of the degree of confinement (the reverse of the radius of CNT), the THz absorption coefficient decreases monotonically. By analyzing the diffusion coefficient and dielectric relaxation dynamics, the hydrogen bond life, and the vibration density of the state of the water molecules in free solution and in CNTs, we conclude that the confinement effects on the THz spectra of biomolecule solutions are mainly to slow down the dynamics of water molecules and hence to reduce the THz absorption of the whole solution in confined environments.

Terahertz signature detection of biological samples in aqueous solution remains a great challenge due to the strong terahertz absorption of water. Here we propose a new preparation process for fabricating a microfluidic chip and use it as an effective sensor to probe the terahertz absorption signatures of microcystin aptamer (a linear single-stranded DNA with 60 nucleotides) dissolved in TE buffer with different concentrations. The microfluidic chip made of silicon includes thousands of 2.4 μm × 2.4 μm square-cross-section channels. One repeatable terahertz absorption signature is detected and recognized around 830 GHz, fitted to a Lorentz oscillator. This signature is theorized to originate from the bending of hydrogen bonds formed between adjacent hydrated DNA bases surrounded by water molecules. Furthermore, the low-lying vibrational modes are also investigated by molecular dynamics simulations which suggest that strong resonant oscillations are highly probable in the 815⁻830 GHz frequency band.

The improvement of the quality and efficiency of vehicle wireless network data transmission is always a key concern in the Internet of Vehicles (IoV). Routing transmission solved the limitation of transmission distance to a certain extent. Traditional routing algorithm cannot adapt to complex traffic environment, resulting in low transmission efficiency. In order to improve the transmission success rate and quality of vehicle network routing transmission, make the routing algorithm more suitable for complex traffic environment, and reduce transmission power consumption to improve energy efficiency, a comprehensive optimized routing transmission algorithm is proposed. Based on the routing transmission algorithm, an optimization algorithm based on road condition, vehicle status and network performance is proposed to improve the success rate of routing transmission in the IoV. Relative distance difference and density are used as decision-making indicators to measure Road Side Unit (RSU) assisted transmission. And the Ambient backscatter communication (AmBC) technology and energy collection are used to reduce the energy consumption of routing relay transmission. An energy collection optimization algorithm is proposed to optimize the energy efficiency of AmBC and improve the energy efficiency of transmission. Simulation results show that the proposed routing optimization algorithm can effectively improve the success rate of packet transmission in vehicular ad hoc networks (VANETs), and the AmBC optimization algorithm an effectively reduce energy consumption in the transmission process. The proposed optimization algorithm achieves comprehensive optimization of routing transmission performance and energy efficiency. [ABSTRACT FROM AUTHOR]