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Background: Laser-assisted hatching (LAH) stands as the predominant technique for removing the zona pellucida (ZP) in embryos, primarily consisting of two methods: drilling laser-assisted hatching (D-LAH) and thinning laser-assisted hatching (T-LAH). Presently, both methods have limitations, and their comparative efficacy for embryo implantation and clinical pregnancy remains uncertain. Aim: Evaluate the impact of D-LAH and T-LAH on clinical pregnancy rates within assisted reproductive technology (ART). Methods: We systematically searched electronic databases including PubMed, Web of Science, and Cochrane Library until July 20, 2022. This study encompassed observational studies and randomized controlled trials (RCTs). A 95% confidence interval (CI) was utilized for assessing the risk ratio (RR) of pregnancy outcomes. The level of heterogeneity was measured using I2 statistics, considering a value exceeding 50% as indicative of substantial heterogeneity. Results: The meta-analysis scrutinized 9 studies involving 2405 clinical pregnancies from D-LAH and 2239 from T-LAH. Findings suggested no considerable variation in the clinical pregnancy rates between the two techniques (RR = 0.93, 95% CI: 0.79–1.10, I2 = 71%, P = 0.41). Subgroup analyses also revealed no substantial differences. However, D-LAH exhibited a notably higher occurrence of singleton pregnancies compared to T-LAH (RR = 2.28, 95% CI: 1.08–4.82, I2 = 89%, P = 0.03). There were no noteworthy distinctions observed in other secondary outcomes encompassing implantation rate, multiple pregnancies, ongoing pregnancy, miscarriage, premature birth, and live birth. Conclusion: Both the primary findings and subgroup analyses showed no marked variance in clinical pregnancy rates between D-LAH and T-LAH. Therefore, patients with varying conditions should select their preferred LAH technique after assessing their individual situation. However, due to the restricted number of studies involved, accurately gauging the influence of these laser techniques on clinical outcomes is challenging, necessitating further RCTs and high-quality studies to enhance the success rate of ART. Trial registration: PROSPERO: CRD42022347066. [ABSTRACT FROM AUTHOR]

The main purpose in this study was to understand the interactions of coal particles with three amphiphilic humic acid-based polymers, which were designed and synthesized through the copolymerization of humic acid (HA) with maleic anhydride-polyethylene glycol 400 monoester, methacrylatoethyl trimethyl ammonium chloride or both of them, respectively, abbreviated as HAM, HAD and HMD. The adsorption quantities of HMD and HAD increased monotonically with the equilibrium concentration of dispersant, which were well described by Freundlich adsorption equation, but the Langmuir adsorption equation was more suitable for HAD dispersant. Besides, the wettability of HMD solutions on the coal surface was better than others. Moreover, the HMD dispersant exhibited better viscosity-reducing and stabilizing capability for coal-water slurry (CWS) than that of HAD and HAM, which were due to the strong adsorption force that was good for improving the hydrophily and enhancing the steric hindrance and electrostatic repulsion among coal particles. Unlabelled Image • Three humic acid-based dispersants were synthesized and used as dispersants for CWS. • The adsorption isotherms of HMD and HAD were well described by Freundlich equation. • The wettability of HMD solutions on the coal surface was better than others. • The HMD showed the better viscosity-reducing and stabilizing ability in CWS than others. • The excellent performances of HMD were due to strong adsorption and steric hindrance. [ABSTRACT FROM AUTHOR]

Car-following (CF) and lane-changing (LC) behavior models have been widely studied separately as the core models of traffic simulation. However, in practice, CF and LC are inseparable and thus integrated driving (ID) models containing CF and LC behaviors emerge. Here, we proposed a new work to introduce the social force (SF) model to the operational ID behavioral model on the highway. First, a data-driven-based operational ID behavioral model is proposed in the hierarchical social force behavioral model framework. Then, the inputs/output of the SF-ID behavioral model is determined. SF-ID model is built by the feed-forward neural networks (FNN), and the network structure and other parameters are calibrated and verified by field data. Results of the test on CF and LC situations show that our proposed FNN SF-ID model has a good capability in reproducing/predicting the operational ID behaviors on the highway. In addition, we also analyzed the structural features of the FNN SF-ID models, and refine the original models by removing insignificant inputs. The comparison results showed that the refined model—FNN SF-ID (R)—performed better than the original models. [ABSTRACT FROM AUTHOR]

With the increasing demand for electricity, more and more fossil fuels are used to generate electricity which leads to energy shortage and environmental pollution. Therefore, using Renewable Energy Sources (RESs) and developing sustainable smart grid have become a common global priority: Since RESs, like solar and wind energy, are inherently unstable, hydrogen energy, as a completely clean new energy, has received widespread attention: I-Energy, which combines information and energy, is a new research direction in smart grid. Furthermore, the household electricity usage accounts for 41% of the total power consumption. Therefore, Household Intelligent Electricity System (HIES), combining hydrogen energy and i-Energy, becomes smart solutions. In this paper, we review the scientific literature for hydrogen energy and i-Energy on HIES, including recognition of electricity appliances, establishment of power consumption model, human activity analysis, smart interactive terminal, home energy management system, distributed power supply and district coordinated power utilization. Finally, we summarize the challenges and give the solutions concerning HIES, and this work can give a useful reference to new energy used model and environment protection. • A new application of Hydrogen Energy on Household Intelligent Electricity System (HIES). • A comprehensive overview of Hydrogen Energy and I-energy on HIES. • Combining internet with traditional electricity to analyses I-energy on HIES. • Summarizing the challenges and corresponding solutions concerning HIES. [ABSTRACT FROM AUTHOR]

The smart cities development requires reducing energy consumption and using as much renewable energy as possible, so the widespread use of new energy vehicles is a very important measure. In this work, for the energy system configuration and energy efficiency balance of new energy vehicles, we propose an energy matching method to study its energy efficiency from the view point for energy life cycle. Nowadays, new energy vehicles mainly include battery electric vehicles (BEV) and hydrogen fuel cell vehicles (HFCEV). Firstly, we proposed the Source to Range (STR) model. Then, based on STR model, we used energy efficiency analysis chart to visually represent the conversion, delivery and consumption of the vehicle energy life cycle. Furthermore, we proposed a Source Energy Consumption Rate (SECR), which is used to evaluate the vehicles energy efficiency. Finally, based on STR model, we obtained the dividing line of the same SECR for new energy vehicles and equivalent fuel vehicles, which provides constraints on the vehicle energy system design. The results show that STR model can provide an effective tool for energy matching and energy efficiency analysis of new energy vehicles, and has a reference for product development of new energy vehicles. • An energy matching method for battery electric vehicle and hydrogen fuel cell vehicle. • Source Energy Consumption Rate model to evaluate vehicle's energy efficiency. • The whole life cycle of vehicle energy is from raw material stage to wheel stage. • Energy information system matching method based on Source Energy Consumption Rate. [ABSTRACT FROM AUTHOR]

Motor vehicle exhaust emission has become an important source of global atmospheric emissions, and the urgency of protection and management of this issues has become increasingly prominent. The cooperative control strategy of road vehicles can comprehensively manage the individual vehicles from the whole perspective, which is a powerful means to achieve the goal of dual carbon goal. In the preliminary stage of the IoV, limited communication bandwidth increases the difficulty of designing the above cooperative cruise control scheme. To solve this problem, we designed a new novel periodic intermittent cruise controller to track and assess the real-time operating status and relevant environmental indicators of road vehicles. Compared with the previous continuous controller, the proposed controller subdivides the time domain into each sub-period, and the controller is activated merely during the working time interval of each period, which can effectively reduce the control cost. Besides, when the leading vehicle state changes, the following vehicle can quickly respond to the above adjustment and track the leading vehicles with the new desired speed and headway, thereby reduce the duration time and amplitude of road vehicle state oscillation. Based on the Lyapunov-like function method, the sufficient condition for the existence of designed periodically intermittent cruise controller is given, which guarantees that the closed-loop error dynamic system is satisfies exponential stable, and the controller gain coefficients in proposed control protocol can be obtained via solving a set of linear matrix inequalities with some commercial software built-in toolbox. At last, based on the VT-Microenvironment experiment evaluation model, we have carried out some numerical simulation experiments, and the results show that the periodic intermittent cruise control protocol is feasible in improving vehicle safety tracking performance, and remain valid in reducing energy consumption and exhaust emissions such as CO, HC, NOx, and approximately decreased by 2.52%, 6.65%, 34.75%, and 15.70% respectively from the rural value. This research result provides a technical reference to road traffic safety and environmental assessment. [ABSTRACT FROM AUTHOR]

Blades are key components in modern aero engines. Their leading and trailing edges are extremely difficult to machine using electrochemical machining (ECM) because of their small thickness and radius of curvature and their marginal location. Traditional ECM of blades requires gaps between the convex part cathode and concave part cathode at the leading and trailing edges to allow passage of electrolyte. The presence of these gaps may lead to large variations in electric field intensity at the end of the machining process as the leading and trailing edges are forming. This paper proposes a new design of cross-structural cathodes to decrease the dynamic variation of the electric field at marginal locations. A mathematical model of material removal is established, taking account principally of the electric field factor. The forming process of the leading edge is simulated, and changes in electric field intensity during the final stages of the process are analyzed. The simulation results show that the maximum fluctuation in the electric field intensity at the leading edge is 62.42% when a conventional cathode is used, but only 30.51% with cross-structural cathodes. Experiments are conducted to compare the performance of conventional and cross-structural cathodes. The results show that, in comparison with conventional cathodes, the use of cross-structural cathodes reduces variations in machining current in the forming stage of margin profiles and leads to more accurate repeatability of the process. [ABSTRACT FROM AUTHOR]

In electrochemical machining of aero structural components, the allowance distribution between the blank and the model is generally not uniform, which may influence the final precision of components. However, proper leveling can reduce the negative impacts of this nonuniformity and enhance machining accuracy. This paper proposes a mathematical model for pulse electrochemical machining (ECM) to simulate the corrosion process in order to improve the leveling. Several machining parameters, such as power voltage, pulse duty, and frequency, are considered to optimize the leveling ratio. The optimal parameters are obtained from the simulation. The simulation results show that the leveling ratio improves with the decrease of pulse duty and voltage and the increase of frequency. Furthermore, the pulse duty has a more significant effect on the leveling ability than the voltage and the frequency. In addition, verified experiments are carried out and the results are consistent with the simulation. It indicates that the proposed model is effective and can be used for complex structural components in ECM. [ABSTRACT FROM AUTHOR]

As reported by the United Nations in 2021, road accidents cause 1.3 million deaths and 50 million injuries worldwide each year. Detecting traffic anomalies timely and taking immediate emergency response and rescue measures are essential to reduce casualties, economic losses, and traffic congestion. This paper proposed a three-stage method for video-based traffic anomaly detection. In the first stage, the ViVit network is employed as a feature extractor to capture the spatiotemporal features from the input video. In the second stage, the class and patch tokens are fed separately to the segment-level and video-level traffic anomaly detectors. In the third stage, we finished the construction of the entire composite traffic anomaly detection framework by fusing outputs of two traffic anomaly detectors above with different granularity. Experimental evaluation demonstrates that the proposed method outperforms the SOTA method with 2.07% AUC on the TAD testing overall set and 1.43% AUC on the TAD testing anomaly subset. This work provides a new reference for traffic anomaly detection research. [ABSTRACT FROM AUTHOR]

[Display omitted] • Magnetic layered double hydroxide/metal–organic framework was prepared for simultaneous enrichment of triazole fungicides. • This is the first method for enantiomeric analysis of paclobutrazol, uniconazole and bromuconazole. • The molecular docking technology was used to interpret the adsorption mechanism between MIL-100(Fe) and triazole fungicides. • The developed enantioselective method was suitable for water and juice monitoring. For the first time, magnetic layered double hydroxide/metal–organic framework (Fe 3 O 4 @CuZnAl-LDH@MIL-100(Fe)) was successfully fabricated and applied for enrichment of triazole fungicides from water and juice samples prior to chiral HPLC-UV analysis. Several key parameters influencing the extraction efficiency including extraction time, desorption time, sample solution pH, the ionic strength of sample solution, adsorbent amount, desorption volume, desorption solvent and sample volume were investigated by Plackett-Burman design and Box-Behnken design. Under the optimum conditions, the limit of detections (LODs) and limit of quantifications (LOQs) were in the ranges of 0.35 to 1.20 μg L−1 and from 1.00 to 3.80 μg L−1, respectively. The average spiked recoveries and enrichment factors ranged from 86.28-98.22 % and 21.6–23.8, respectively. The linearity was in the range of (5–1000 μg L−1) with satisfactory correlation coefficients (R2) values (0.9987–0.9999) and a good precision (RSDs ≤ 5.74 %). Moreover, the computational simulation was first employed to analyze adsorption properties of MIL-100(Fe) against triazole fungicides. This work provides a promising method for trace chiral pesticide analysis in aqueous samples. [ABSTRACT FROM AUTHOR]

As a fundamental element of the traffic system, traffic signs reduce the risk of accidents by providing essential information about the road condition to drivers, pedestrians, etc. With the rapid progress of computer vision and artificial intelligence, traffic-signs recognition systems have been applied for the advanced driver assistance system and auto driving system, to help drivers and self-driving vehicles capture the important road information precisely. However, in real applications, small traffic-signs recognition is still challenging. In this article, we propose an efficient method for small-size traffic-signs recognition, named traffic-signs recognition small-aware, with the inspiration of the state-of-the-art object detection framework YOLOv4 and YOLOv5. In general, there are four contributions in our work: (1) for the Backbone of the model, we introduce high-level features to construct a better detector head; (2) for the Neck of the model, receptive field block-cross is utilized for capturing the contextual information of feature map; (3) for the Head of the model, we refine the detector head grid to achieve more accurate detection of small traffic signs; (4) for the input, we propose a data augmentation method named Random Erasing-Attention, which can increase difficult samples and enhance the robustness of the model. Real experiments on the challenging dataset TT100K demonstrate that our method can achieve significant performance improvement compared with the state of the art. Moreover, it is a real-time method and shows huge potential applications in advanced driver assistance system and auto driving system. [ABSTRACT FROM AUTHOR]

Background and Objectives: To date, few studies have comprehensively explored the associations between milk and dairy product intake and dental caries. Therefore, this study aimed to simultaneously assess the associations between whole milk, low-fat milk, skim milk, yogurt, milk desserts, cheese, creams, and total fluid milk intake and the risk of dental caries in children and adolescents.Methods and Study Design: Data were from the National Health and Nutrition Examination Survey (NHANES) 2011-2016. Two 24-hour dietary recall interviews measured dietary milk and dairy product intake. Primary teeth caries was diagnosed by the dfs (decayed or filled primary tooth surfaces) index, and permanent teeth caries was diagnosed by the DMFS (decayed, missing, or filled permanent tooth surfaces) index. We used logistic regression to explore the associations between milk and dairy product intake and the risk of dental caries.Results: A total of 6885 individuals aged 2-17 years were included in this study. In the fully adjusted model, the odds ratios (95% confidence intervals) of dental caries were 0.66 (0.47-0.93) for intake ≥123 g/day of yogurt and 0.82 (0.69-0.98) for intake <22.6 g/day of cheese, as compared with non-consumers.Conclusions: Our study indicates that high yogurt and low cheese intake were associated with a decreased risk of dental caries among American children and adolescents. These findings may be applied to update and supplement the evidence that informs public health policies on milk and dairy products and the prevention of dental caries. [ABSTRACT FROM AUTHOR]

• A new safety lane change trajectory model for autonomous vehicles. • Minimum safety distances in different steering frequency domain. • Vehicle collision avoidance control strategies are proposed. • Typical lane change scenarios for intelligent vehicles are designed. Lane change maneuvers, are important contributors to road traffic accidents on highway. In this paper, we propose a new safe lane change trajectory model and collision avoidance control method for Society of Automotive Engineers (SAE)-level 2 automatic driving vehicles. First, a Gaussian distribution is used to describe the new trajectory model that uses pure steering and combined braking. According to regional and progressive states, a new safe lane change meaning is defined. Second, we design a new four-level automatic driving mode and an effective decision mechanism that considers safety and ergonomics. Moreover, a new trajectory tracking controller combined with a decision mechanism was designed using feedback linearization, verified using typical lane-change scenarios. Finally, based on a physical simulation platform, PreScan, the hardware-in-the-loop simulation result demonstrate the feasibility and effectiveness of our method. This paper provides a valuable reference on an expert and intelligent system methodology for automatic driving vehicles, which will be helpful for improving highway traffic safety and efficiency. [ABSTRACT FROM AUTHOR]

In millimeter-wave (mmWave) massive multi-input multi-output (MIMO) systems, beam selection can enhance channel capacity and reduce error rate. However, existing beam selection methods for MIMO systems rely on traditional optimization techniques, which may not be feasible for real-time data transmission. Hence, this paper proposes a novel beam prediction approach via multi-modal fusion and spatio-temporal-enabled features. Specifically, the proposed method can improve MIMO system performance by integrating information from multiple perspectives, such as temporal, spatial, and frequency domains. Moreover, the proposed approach is based on a deep learning framework utilizing 3-dimensional convolutional neural networks (3DCNNs) and transformer module to capture spatio-temporal data correlations. Extensive simulation results show that the proposed mechanism outperforms massive benchmarks in terms of beam prediction accuracy from 2.63% to 11%. [ABSTRACT FROM AUTHOR]

• Proposed a knowledge transfer method to promote the utilization of emergency experience and get it reused. • Adaptively combined policy distillation with actor-critic structured multi-agent reinforcement learning algorithm. • Developed a fast emergency decision making method for freeway accident optimal onsite disposal. • Established an MDP model to simulate task-level accident disposal, modelled commonality among different freeway accidents. A large number of freeway accident disposals are well-recorded by accident reports and surveillance videos, but it is not easy to get the emergency experience reused from past recorded accidents. To reuse emergency experience for better emergency decision-making, this paper proposed a knowledge-based experience transfer method to transfer task-level freeway accident disposal experience via multi-agent reinforcement learning algorithm with policy distillation. First, the Markov decision process is used to simulate the emergency decision-making process of multi-type freeway accident scene at the task level. Then, an adaptive knowledge transfer method named policy distilled multi-agent deep deterministic policy gradient (PD-MADDPG) algorithm is proposed to reuse experience from past freeway accident records to current accidents for fast decision-making and optimal onsite disposal. The performance of the proposed algorithm is evaluated on instantiated cases of freeway accidents that occurred on the freeway in Shaanxi Province, China. Aside from achieving better emergency decisions performance than various typical decision-making methods, the result shows decision maker with transferred knowledge owns 65.22%, 11.37%, 9.23%, 7.76% and 1.71% higher average reward than those without in the five studied cases, respectively. Indicating that the emergency experience transferred from past accidents contributes to fast emergency decision-making and optimal accident onsite disposal. [ABSTRACT FROM AUTHOR]

The effects of pore throat structure on gas permeability in tight sand reservoir were investigated using helium-measured porosity, pulse decay permeability, casting thin sections, scanning electron microscopy (SEM), nuclear magnetic resonance (NMR), and constant-rate mercury injection for five sandstone samples from the Upper Triassic Yanchang Formation in the Western Ordos Basin, China. Results show that permeability values are in the range of 0.474–2.290 mD. The strong variation may be due to the effect of mineral composition and pore-throat structure. Generally, sandstone samples of higher content of quartz, chlorite film, and slit-shaped throat tend to have higher permeability. The movable fluid porosity numerically equals to the product of porosity and the movable fluid saturation based on the film bound water, which can comprehensively reflect the influence of fluid flow channels on porosity while neglecting the influence of bound fluids in the fine pores of clay minerals in the tight reservoirs. Larger movable fluid porosity values mean larger pore spaces for the movable fluids, which contribute to larger permeability. It is more reasonable to analyze the movable fluid pore-throat radius as a range. The homogeneous pore throat structure lead to a narrow lower limit range of the movable fluid pore-throat radius, which is favorable for the fluid flow. Meanwhile, if the lower bound of the lower limit range of the movable fluid pore-throat radius is small, pores and throats are less plugged, and thus make the conditions favorable for fluid flow. The contribution of throats to the pore volume is critical in influencing reservoir permeability as it can directly control the lower limit of the movable fluid pore-throat size. The pore-throat radius where throats account for the smallest amount in the pore space increases with the permeability, which indicates that larger proportions of large throats result in larger throat radius and higher pore connectivity, leading to higher permeability. [ABSTRACT FROM AUTHOR]

• The mechanical strength exhibits a rapid growth in the initial stage of drying. • The drying process promotes the further hydration of the residual calcium aluminate phases. • The conversion of metastable hydration products takes place in the early stage of drying. • The crystallinity improvement of the hydration products contributes to the growth of strength. Calcium aluminate cement (CAC) is extensively applied as castable binder, and the drying process is a crucial step before castables come into service. Limited researches have focused on the hydration and phase transformation of CAC in the castables during the evaporation stage of drying process, in which the free water is gradually discharged. This work involves the investigation of the strength development of CAC-bonded alumina castables and phase evolution of the hydrates during drying at 110 °C. After curing for 24 h at 30 °C, the CAC-bonded alumina castables and matrices were dried at 110 °C for different periods of time to explore the effect of drying time on the mechanical property of CAC-bonded castables and on the phase assemblages and microstructure of the castable matrices. The results demonstrate that the initial drying period prompts the rapid conversion from metastable C 2 AH 8 to stable C 3 AH 6 as well as the continued hydration of the residual CA and CA 2. The drying strength of the castables is significantly enhanced in comparison with demolding strength. [ABSTRACT FROM AUTHOR]

The shield tunneling above an existing line has been a commonly encountered problem in urban construction in recent years. It is obvious that the over-crossing tunneling will adversely affect and even damage the existing tunnels if the induced deformation exceeds the design limit of tunnel structures. In order to obtain a better understanding of the effects of the above-crossing tunneling on the existing tunnels and provide a quick and effective tool for evaluating the behaviors of underlying tunnels prior to construction, in this study a simplified analytical method is proposed. In this model, the tunnel is represented by a series of short beams resting on a Winkler foundation connected by tensile springs, compressional springs and shear springs, so that the rotational effect and shearing effect of the joints between lining rings can be captured. Our new model is compared with some existing models, such as Euler–Bernoulli beam and a series of short beams connected only by shear springs, by comparing their predictions with the field-measured longitudinal deformation of two Shanghai subway tunnels. Results of these case studies show that the predictions of our model are much closer to field monitored data than those existing models. In addition, a parametric analysis is also conducted to investigate the influences of the different factors, including advancing distance, clearance distance and stiffness of joints, on the behaviors of the existing tunnels. [ABSTRACT FROM AUTHOR]

With the development of information communication and artificial intelligence, the ICV (intelligent connected vehicle) will inevitably play an important part in future urban transport system. In this paper, we study the car following behaviour under the heterogeneous ICV environment. The time to receive information varies from vehicle to vehicle, since the manual vehicles and autonomous vehicles co-exist on the road. By introducing time-varying lags function, a new car following model is proposed, and the cooperative control strategy of this model is studied. Based on Lyapunov function theory and linear matrix inequality (LMI) approach, the sufficient condition that the existence of the feedback controller is given, which makes the closed-loop system asymptotically stable under mixed traffic flow environment. That is to say, traffic congestion phenomenon under heterogeneous traffic flow can be effectively suppressed, and the feedback controller gain matrix can be obtained via solving linear matrix inequality. Finally, by simulation the method is verified effective in alleviating traffic congestions and reducing fuel consumption and exhaust emissions. It could be a useful reference to Cooperative Vehicle Infrastructure System and Smart City. • An extended car following model considering the manual vehicles effect and automatic vehicles co-exist is proposed. • The switch variables is introduced and we designs the collaborative controller for the mix traffic flow. • The sufficient conditions to our designed controller existence is obtained by applying the Lyapunov function theory. • The validity of our designed controller to traffic jams suppression and exhaust emissions reduction is confirmed. [ABSTRACT FROM AUTHOR]

ArfGAP with coiled-coil, ankyrin repeat and PH domains 3 (ACAP3) level has been confirmed to be downregulated in papillary thyroid carcinoma (PTC). Histone deacetylase inhibitors (HDACIs) have therapeutic effects on PTC. Accordingly, this study probed into the potential relation of histone deacetylase 2 (HDAC2) and ACAP3 in PTC. Expressions of ACAP3 and HDAC2 in PTC were investigated by quantitative real-time polymerase chain reaction (qRT-PCR). The relationship between HDAC2 and ACAP3 was predicted by Pearson analysis. Cell functional assays (cell counting kit-8, transwell, wound healing and flow cytometry assays) and rescue assay were carried out to determine the effects of HDAC2/ACAP3 axis on biological behaviors of PTC cells. Expressions of apoptosis-, epithelial-mesenchymal transition-, Protein Kinase B (AKT)-, and P53-related proteins were measured by Western blot. ACAP3 level was downregulated in PTC tissues and cells. ACAP3 overexpression (oe-ACAP3) suppressed viability, proliferation, migration and invasion of PTC cells, facilitated apoptosis, downregulated the expressions of Protein Kinase B (Bcl-2) and N-cadherin, upregulated the expressions of Bcl-2 associated protein X (Bax) and E-cadherin, diminished the p-AKT/AKT ratio and elevated the p-p53/p53 ratio; however, ACAP3 silencing or HDAC2 overexpression (oe-HDAC2) did the opposite. HDAC2 negatively correlated with ACAP3. The tumor-suppressing effect of oe-ACAP3 in PTC was reversed by oe-HDAC2. Collectively, ACAP3 negatively regulated by HDAC2 suppresses the proliferation and metastasis while facilitating apoptosis of PTC cells., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Deep-learning-based approaches have achieved remarkable progress for complex real scenario denoising, yet their accuracy-efficiency tradeoff is still understudied, particularly critical for mobile devices. As real noise is unevenly distributed relative to underlay signals in different frequency bands, we introduce a frequency-aware divide-and-conquer strategy to develop a frequency-aware denoising network (FADN). FADN is materialized by stacking frequency-aware denoising blocks (FADBs), in which a denoised image is progressively predicted by a series of frequency-aware noise dividing and conquering operations. For noise dividing, FADBs decompose the noisy and clean image pairs into low-and high-frequency representations via a wavelet transform (WT) followed by an invertible network and recover the final denoised image by integrating the denoised information from different frequency bands. For noise conquering, the separated low-frequency representation of the noisy image is kept as clean as possible by the supervision of the clean counterpart, while the high-frequency representation combining the estimated residual from the successive FADB is purified under the corresponding accompanied supervision for residual compensation. Since our FADN progressively and pertinently denoises from frequency bands, the accuracy-efficiency tradeoff can be controlled as a requirement by the number of FADBs. Experimental results on the SIDD, DND, and NAM datasets show that our FADN outperforms the state-of-the-art methods by improving the peak signal-to-noise ratio (PSNR) and decreasing the model parameters. The code is released at https://github.com/NekoDaiSiki/FADN.

The Internet of Vehicles(IoV) employs vehicle-to-everything (V2X) technology to establish intricate interconnections among the Internet, the IoT network, and the Vehicle Networks (IVNs), forming a complex vehicle communication network. However, the vehicle communication network is very vulnerable to attacks. The implementation of an intrusion detection system (IDS) emerges as an essential requisite to ensure the security of in-vehicle/inter-vehicle communication in IoV. Within this context, the imbalanced nature of network traffic data and the diversity of network attacks stand as pivotal factors in IDS performance. On the one hand, network traffic data often heavily suffer from data imbalance, which impairs the detection performance. To address this issue, this paper employs a hybrid approach combining the Synthetic Minority Over-sampling Technique (SMOTE) and RandomUnderSampler to achieve a balanced class distribution. On the other hand, the diversity of network attacks constitutes another significant factor contributing to poor intrusion detection model performance. Most current machine learning-based IDSs mainly perform binary classification, while poorly dealing with multiclass classification. This paper proposes an adaptive tree-based ensemble network as the intrusion detection engine for the IDS in IoV. This engine employs a deep-layer structure, wherein diverse ML models are stacked as layers and are interconnected in a cascading manner, which enables accurate and efficient multiclass classification, facilitating the precise identification of diverse network attacks. Moreover, a machine learning-based approach is used for feature selection to reduce feature dimensionality, substantially alleviating the computational overhead. Finally, we evaluate the proposed IDS performance on various cyber-attacks from the in-vehicle and external networks in IoV by using the network intrusion detection dataset CICIDS2017 and the vehicle security dataset Car-Hacking. The experimental results demonstrate remarkable performance, with an F1-score of 0.965 on the CICIDS2017 dataset and an F1-score of 0.9999 on the Car-Hacking dataset. These scores demonstrate that our IDS can achieve efficient and precise multiclass classification. This research provides a valuable reference for ensuring the cybersecurity of IoV.

To date, little is known about the pleiotropic genetic variants among depression, cognition, and memory. The current research aimed to identify the potential pleiotropic single nucleotide polymorphisms (SNPs), genes, and pathways of the three phenotypes by conducting a multivariate genome-wide association study and an additional pleiotropy analysis among Chinese individuals and further validate the top variants in the UK Biobank (UKB). In the discovery phase, the participants were 139 pairs of dizygotic twins from the Qingdao Twins Registry. The genome-wide efficient mixed-model analysis identified 164 SNPs reaching suggestive significance (P < 1 × 10 -5 ). Among them, rs3967317 (P = 1.21 × 10 -8 ) exceeded the genome-wide significance level (P < 5 × 10 -8 ) and was also demonstrated to be associated with depression and memory in pleiotropy analysis, followed by rs9863698, rs3967316, and rs9261381 (P = 7.80 × 10 -8 -5.68 × 10 -7 ), which were associated with all three phenotypes. After imputation, a total of 457 SNPs reached suggestive significance. The top SNP chr6:24597173 was located in the KIAA0319 gene, which had biased expression in brain tissues. Genes and pathways related to metabolism, immunity, and neuronal systems demonstrated nominal significance (P < 0.05) in gene-based and pathway enrichment analyses. In the validation phase, 12 of the abovementioned SNPs reached the nominal significance level (P < 0.05) in the UKB. Among them, three SNPs were located in the KIAA0319 gene, and four SNPs were identified as significant expression quantitative trait loci in brain tissues. These findings may provide evidence for pleiotropic variants among depression, cognition, and memory and clues for further exploring the shared genetic pathogenesis of depression with Alzheimer's disease., (© 2022. The Author(s).)

Background and Objectives: To date, few studies have comprehensively explored the associations between milk and dairy product intake and dental caries. Therefore, this study aimed to simultaneously assess the associations between whole milk, low-fat milk, skim milk, yogurt, milk desserts, cheese, creams, and total fluid milk intake and the risk of dental caries in children and adolescents., Methods and Study Design: Data were from the National Health and Nutrition Examination Survey (NHANES) 2011-2016. Two 24-hour dietary recall interviews measured dietary milk and dairy product intake. Primary teeth caries was diagnosed by the dfs (decayed or filled primary tooth surfaces) index, and permanent teeth caries was diagnosed by the DMFS (decayed, missing, or filled permanent tooth surfaces) index. We used logistic regression to explore the associations between milk and dairy product intake and the risk of dental caries., Results: A total of 6885 individuals aged 2-17 years were included in this study. In the fully adjusted model, the odds ratios (95% confidence intervals) of dental caries were 0.66 (0.47-0.93) for intake ≥123 g/day of yogurt and 0.82 (0.69-0.98) for intake <22.6 g/day of cheese, as compared with non-consumers., Conclusions: Our study indicates that high yogurt and low cheese intake were associated with a decreased risk of dental caries among American children and adolescents. These findings may be applied to update and supplement the evidence that informs public health policies on milk and dairy products and the prevention of dental caries.

Ribonucleotide reductase (RR) has been reported to be associated with several types of cancer while the expression and role of RR in thyroid carcinoma (TC) has not been investigated. Here, we first examined the expression level of three RR subunit proteins (RRM1, RRM2, and RRM2B) in papillary thyroid carcinoma (PTC) and undifferentiated thyroid carcinoma (UTC) patient samples by immunohistochemistry. The results showed that RRM1 was higher expressed in 95.2 % cancer tissues compared with their adjacent normal tissues in 146 PTC samples. The expression level of RRM1 was positively correlated with T stage, lymph node metastasis (LNM), extrathyroidal invasion (ETI), and TNM stage in PTC patients. However, in 12 UTC samples, RRM1 expression was negatively expressed in six cases. To further determine the biological role of RRM1 in TC, ectopic expression or siRNA-mediated knockdown of RRM1 were carried out in the high-differentiated thyroid carcinoma cell line TPC-1 and the poor-differentiated thyroid carcinoma cell line SW579, respectively. In TPC-1 and SW579 cells, overexpression and siRNA knockdown of RRM1 demonstrated that RRM1 promoted DNA synthesis and proliferation in both cell lines as shown by EdU incorporation and cell viability assays. However, RRM1 enhanced cell migration and invasion in TPC-1 cells but inhibited that in SW579 cells as shown by wound healing and transwell assays. Moreover, we also found that RRM1 promoted PTEN expression and reduced Akt phosphorylation in a RR-activity-independent manner in the low-differentiated TC cells but not in the high-differentiated TC cells. In contrast, RRM2 expression was higher expressed in both PTC and UTC patient samples, consisting with its oncogenic role in other cancers. Therefore, we suggest that RRM1 promotes thyroid carcinoma proliferation as a component of RR but may play a different role in the invasion and metastasis of differently differentiated thyroid carcinomas through a non-RR pathway, which could be meaningful to precision treatment of thyroid carcinoma with RR inhibitors.