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Abstract The widespread adoption of fifth-generation mobile networks has spurred the rapid advancement of mobile edge computing (MEC). By decentralizing computing and storage resources to the network edge, MEC significantly enhances real-time data access services and enables efficient processing of large-scale dynamic data on resource-limited devices. However, MEC faces considerable security challenges, particularly in cross-domain service environments, where every device poses a potential security threat. To address this issue, this paper proposes a secure cross-domain authentication scheme based on a threshold signature tailored to MEC’s multi-subdomain nature. The proposed scheme employs a (t,n) threshold mechanism to bolster system resilience and security, catering to large-scale, dynamic, and decentralized MEC scenarios. Additionally, the proposed scheme features an efficient authorization update function that facilitates the revocation of malicious nodes. Security analysis confirmed that the proposed scheme satisfies unforgeability, collusion resistance, non-repudiation and forward security. Theoretical evaluation and experimental simulation verify the effectiveness and feasibility of the proposed scheme. Compared with existing schemes, the proposed scheme has higher computational performance while implementing secure authorization updates.

Background: Cervical cancer is the fourth most common cancer among females globally, with a high incidence and high mortality among females in developing countries. This retrospective case–control study aimed to investigate the association between oral contraceptives and cervical cancer, on which insufficient evidence still exists.Material and Methods: To examine the association between oral contraceptives and cervical cancer based on 7,496 females aged over 20 years from the National Health and Nutrition Examination Survey, multivariable logistic regression conducted from 1999 to 2016 was used.Results: Contraceptive use was positively associated with cervical cancer risk. In model 1 (unadjusted), a 195% increased risk of cervical cancer was observed among those who used oral contraceptives (odds ratio [OR] = 2.27, 95% confidence interval [CI] = 1.39–3.98, p = 0.002) compared to those who did not. In addition, the ORs for the exposed population were 1.74 (95% CI = 1.05–3.08, p = 0.041) and 1.93 (95% CI = 1.16–3.44, p = 0.017) in model 2 (adjusted for age, race, and body mass index [BMI]) and model 3 (adjusted for education level, ratio of family income to poverty, drinking status, smoking status, number of pregnancies, age at first sex, number of sexual partners, and whether to receive the human papillomavirus (HPV) vaccine in addition to model 2), respectively. Furthermore, subgroup analyses stratified by age, smoking status, BMI, age at first sex, number of sexual partners, and whether to receive the HPV vaccine also revealed that oral contraceptives were significantly associated with cervical cancer.Conclusion: This study demonstrated that oral contraceptive use increased the risk of cervical cancer. In addition, the higher risk, including individuals older than 45 years, having a high BMI (≥30 kg/m2), being current smokers, and having more than five sexual partners, may contribute to the development of cervical cancer.

Using data on Chinese A-share listed companies from 2010 to 2021, we employ the difference-in-differences (DID) estimation method as an exogenous impact of a quasi-natural experiment and analyze the effects of carbon risk on corporate financialization. We observe that increased carbon risk decreases with corporate financialization, which is more pronounced for financially constrained and state-owned enterprises. Furthermore, we find that carbon risk increases green innovation, which restrains corporate financialization. Our research not only enriches the relevant influence on carbon risk and corporate financialization but also provides Chinese evidence for the Porter hypothesis.

BackgroundPublic health emergencies have a lasting impact on a country's economic and social development. However, commercial insurance can disperse these negative consequences and reduce risk losses.MethodBased on the Chinese Household Tracking Survey and Peking University Digital Inclusive Finance Index, this study employed a difference-in-differences model to test the impact of the COVID-19 outbreak on commercial insurance participation and the impact mechanism.ResultsThe analysis showed that the outbreak of COVID-19 improved residents' risk perception, risk preference and digital finance and promoted their participation in commercial insurance, commercial endowment insurance, and commercial medical insurance.ConclusionMajor public health emergencies can increase commercial insurance participation, but the promotional effect of commercial insurance on rural and low-income individuals is relatively limited. To tap into potential customers, financial institutions should focus on vulnerable societal groups. This study supplements the relevant literature on the impact of major public health emergencies on commercial insurance participation.

This paper presents a systematic investigation and demonstration of a K-band circularly polarized liquid-crystal-based phased array (LCPA), including the design, over-the-air (OTA) in-array calibration, and experimental validation. The LCPA contains 16 phase-shifting radiating channels, each consisting of a circularly polarized stacked patch antenna and a liquid-crystal-based phase shifter (LCPS) based on a loaded differential line structure. Thanks to its slow-wave properties, the LCPS exhibits a maximum phase-shifting range of more than 360° with a figure of merit of 78.3(° )·dB−1 based on a liquid crystal layer with a thickness of only 5 μm. Furthermore, an automatic OTA calibration based on a state ergodic method is proposed, which enables the extraction of the phase–voltage curve of every individual LCPA channel. The proposed LCPA is manufactured and characterized with a total profile of only 1.76 mm, experimentally demonstrating a scanned circularly polarized beam from −40° to +40° with a measured peak gain of 12.5 dBic and a scanning loss of less than 2.5 dB. The bandwidth of the LCPA, which satisfies the requirements of port reflection (|S11|) < −15 dB, an axial ratio (AR) < 3 dB, beam squinting < 3°, and a gain variation < 2.2 dB, spans from 25.5 to 26.0 GHz. The total efficiency is about 34%, which represents a new state of the art. The use of the demonstrated low-profile LCPA to support circularly polarized scanning beams, along with the systematic design and calibration methodology, holds potential promise for a variety of millimeter-wave applications.

Abstract Optoelectronic logic gates have emerged as one of the key candidates for the creation of next generation logic devices. However, current optoelectronic logic gates can provide only one or two logic gates, severely limiting their applications. Here we report a self‐powered and mechanically flexible device based on a BaTiO3 ferroelectric film to produce multi‐modal logic gates. By exploiting the photo‐induced photovoltaic and pyroelectric effects of a Schottky junction which is created between BaTiO3 and LaNiO3, the device is able to provide five different optoelectronic logic gates, which can be operated using input lasers of different wavelength (405 or 785 nm). The mode of operation of the logic gate can be switched by controlling the wavelength and intensity of the input laser, where the switching process is both lossless and reversible. A logic gate array was designed to conduct the five logic operations, with 100% accuracy, thereby providing application potential for the Internet of Things, big data, and secure solutions for data processing and transmission.

【Objective】The soil environment of tobacco planting can be impacted by plastic film mulching, which affects the structure of the soil microbial community and the growth and development of flue-cured tobacco. The purpose of this study is to investigate the effect of a full growth period mulching on the physicochemical properties and bacterial community structure of tobacco planting soil.【Method】16S rDNA sequencing was applied to compare the differences in the structure and diversity of bacterial communities in different treatments by various analytical methods. The connection between soil physicochemical properties and bacterial communities was discovered through redundancy analysis (RDA) based on soil physicochemical properties.【Result】The output value of flue-cured tobacco was significantly increased by film mulching during the growth period. The yield was increased by 13.33%, 18.24%, and 21.36% by the double color film mulching (SY), black film mulching (HY), and white film mulching (BY) treatments, respectively, compared to the control (CK); Output value added was 20.26%, 15.87%, and 13.28%. At the same time, film mulching will also increase tobacco-planting soil temperature, total potassium, total phosphorus, total iron and effective copper contents, and decrease soil pH value, alkaline dissolved nitrogen, effective iron, total copper and total zinc contents. The contents of total phosphorus and available copper increased the most significantly, reaching 3.11%-27.33% and 4.76%-19.05%, respectively. The most significant decrease in alkali hydrolyzed nitrogen, available iron, and total zinc occurred at 10.45%-14.50%, 3.36%-13.43%, and 7.69%-10.01%, respectively. Plastic covering during the whole growth period has a certain negative impact on the abundance of bacterial communities. The abundance index Chao 1 of the control was significantly higher than the three mulching treatments and was 14.09%, 24.20%, and 24.81% higher than that of the white film mulching, double color film mulching, and black film mulching treatments, respectively. Redundancy analysis shows that the main soil physicochemical factors affecting the horizontal community structure of the bacterial phylum are soil temperature, alkaline nitrogen, available iron, total iron, available copper, and total copper content. Three shared genera with significantly different abundance from the control were present in the white film mulching and double color film mulching treatments, which belonged to three families in the phylum Proteobacteria: Sphingomonadaceae, Burkholderiaceae, and Xanthomonadaceae.【Conclusion】The full-life stage mulching cultivation will significantly affect the physicochemical properties and bacterial community structure of tobacco-planting soil, and improve the amount of output value of roasted tobacco, suitable for matching the color of the film is black and white bicolor film.

During the braking process of electric vehicles, both the regenerative braking system (RBS) and anti-lock braking system (ABS) modulate the hydraulic braking force, leading to control conflict that impacts the effectiveness and real-time capability of coordinated control. Aiming to enhance the coordinated control effectiveness of RBS and ABS within the electro-hydraulic composite braking system, this paper proposes a coordinated control strategy based on explicit model predictive control (eMPC-CCS). Initially, a comprehensive braking control framework is established, combining offline adaptive control law generation, online optimized control law application, and state compensation to effectively coordinate braking force through the electro-hydraulic system. During offline processing, eMPC generates a real-time-oriented state feedback control law based on real-world micro trip segments, improving the adaptiveness of the braking strategy across different driving conditions. In the online implementation, the developed three-dimensional eMPC control laws, corresponding to current driving conditions, are invoked, thereby enhancing the potential for real-time braking strategy implementation. Moreover, the state error compensator is integrated into eMPC-CCS, yielding a state gain matrix that optimizes the vehicle braking status and ensures robustness across diverse braking conditions. Lastly, simulation evaluation and hardware-in-the-loop (HIL) testing manifest that the proposed eMPC-CCS effectively coordinates the regenerative and hydraulic braking systems, outperforming other CCSs in terms of braking energy recovery and real-time capability.

This paper presents an enhanced ground vehicle localization method designed to address the challenges associated with state estimation for autonomous vehicles operating in diverse environments. The focus is specifically on the precise localization of position and orientation in both local and global coordinate systems. The proposed approach integrates local estimates generated by existing visual–inertial odometry (VIO) methods into global position information obtained from the Global Navigation Satellite System (GNSS). This integration is achieved through optimizing fusion in a pose graph, ensuring precise local estimation and drift-free global position estimation. Considering the inherent complexities in autonomous driving scenarios, such as the potential failures of a visual–inertial navigation system (VINS) and restrictions on GNSS signals in urban canyons, leading to disruptions in localization outcomes, we introduce an adaptive fusion mechanism. This mechanism allows seamless switching between three modes: utilizing only VINS, using only GNSS, and normal fusion. The effectiveness of the proposed algorithm is demonstrated through rigorous testing in the Carla simulation environment and challenging UrbanNav scenarios. The evaluation includes both qualitative and quantitative analyses, revealing that the method exhibits robustness and accuracy.

Over the past decade, the concept of a circular economy has increasingly gained attention as a framework for guiding businesses and policymakers. Given its significant environmental impact, the building industry plays a pivotal role in the transition toward a circular economy. To address this, our review proposes a bio-based building material, specifically straw bale, which elaborates on the circularity of bio-based buildings based on the 3R principles of a circular economy: reduce, reuse, and recycle. In terms of the “reduce” principle, straw-bale buildings can reduce construction waste, the environmental impact, energy requirements, and carbon emissions. Regarding the “reuse” principle, straw-bale buildings utilize agricultural waste resources and are easily disassembled due to their prefabrication. As for the “recycle” principle, straw-bale buildings can undergo physical, biological, and biochemical conversion processes (thermochemical conversion), yielding both wooden composite boards and potential biogas and biomass fuels for electricity and heating. This study evaluates the contribution of straw packaging construction and the use of straw as a raw material, using the 3R principles to determine future research opportunities for the construction industry to achieve a circular economy. The results of this study offer circular economy solutions and interdisciplinary research insights for researchers and practitioners interested in the building environment.

To improve the quality of natural seawater to meet the needs of aquaculture production, a large-scale subsurface-flow constructed wetland (HSFCW) was constructed and operated stably for 2 years to understand the reasons for its purification effect on natural seawater. The results of the study showed that the system could maintain a high purification effect on natural seawater during aquaculture; the average removal rates of COD, TSS, TN, NH4+-N, and DON (organic nitrogen) were 22.29%, 49.33%, 36.94%, 10.88%, and 44.08%, respectively. Additionally, the HSFCW could effectively remove harmful algae such as Cyanobacteria; the removal rate of dominant algae species in Cyanobacteria was 90.33–97.93%. The pyrosequencing of 16S ribosomal DNA revealed that Proteobacteria, Nitrospirae, and Chloroflexi were the main and key bacterial phyla in the system. Members of these key gates are regarded as playing important roles in resisting water purification. The study results suggest that the subsurface-flow wetland system can effectively improve seawater quality and reduce the density of harmful algae cells.

In this paper, a control method for trajectory planning and tracking of an intelligent vehicle is proposed. In terms of trajectory planning, a trajectory planning method for curved lane changes is designed based on conventional lane change trajectory planning and considering the adaptive correction of road curvature. In addition, curve trajectory tracking control strategy based on model predictive control is designed. Model predictive control is suitable for multi-input and multi-output nonlinear models, and it has the advantage of considering model constraints. This type of control makes the model output more in line with vehicle dynamics characteristics and improves the trajectory tracking accuracy. Finally, the simulation shows that the method proposed in this paper can generate a reasonable curved lane-changing trajectory, and under the consideration of the vehicle dynamics constraints, the MPC algorithm is used to effectively follow the expected trajectory, so that the vehicle can change lanes smoothly.

Free lane change (FLC) is an important research direction of intelligent driving vehicles. In this paper, a lane change decision model based on deep learning is established with ego and adjacent lane risk and properties of environmental vehicles. A long short-term memory neural network that can be associated with time series characteristics is used to model the lane change process based on analysis of factors affecting the lane change decision. The result of decision-making model training shows that the recognition accuracy of lane change and lane keeping decision reaches more than 92%. Then, a human-like FLC trajectory is planned based on polynomial curve, a quintic polynomial trajectory cluster based on preview distance is generated according to driving conditions, and the optimal FLC trajectory is selected by optimizing an objective function. The model predictive control method is used to dynamically control the vehicle to follow the trajectory. Finally, the lane change decision, trajectory planning, and tracking control model are built in simulation environment, and the control of vehicle dynamics model verifies the integrity and feasibility of free lane change function.

In an era of increasing globalization, the imperative for understanding multilingual texts elevated the role of translation to an everyday necessity. The efficacy of contemporary Neural Machine Translation (NMT) systems was heavily dependent on the availability of substantial training data. As such, the creation of an expansive parallel corpus became a strategic focal point, providing a bedrock for the evolution of high-caliber NMT systems. However, building a parallel corpus often posed challenges due to its complexity and cost, especially for language pairs that lacked sufficient parallel data. To tackle this challenge, this study proposed a novel data augmentation method for the corpus. Bilingual news texts sourced from the KEKE English website were utilized, and SentenceBERT was employed to ensure accurate sentence alignment. Subsequently, the parallel partial sentences within the corpus were filtered and used to augment the dataset. Finally, the effectiveness of the method was assessed by calculating BLEU, chrF and METEOR scores based on both the original corpus and the data-augmented corpus using the base translation model. The experimental results indicated that compared to the baseline method, the optimal method showed an improvement of 0.4-2.1 points in BLEU scores, an improvement of 0.5-2.7 points in chrF scores, and an increase of 0.5-1.6 points in METEOR scores.

Abstract Operating experience has shown that characteristic gases can be found in oil‐immersed power transformers and high‐voltage reactors under strong vibration without discharge or thermal faults occurring. This will cause misdiagnosis of Dissolved Gases Analysis (DGA). In this study, generation characteristics and mechanism of characteristic gases in transformer oil dependent on vibration are investigated. The results show that there is a vibration pressure threshold that makes characteristic gases such as hydrogen (H2), methane (CH4), ethylene (C2H4), ethane (C2H6), and acetylene (C2H2) appear in transformer oil. The concentration of each characteristic gas increases with the increase of vibration pressure. Furthermore, misdiagnosis of insulation status which may be caused by vibration is illustrated based on Duval triangles method. Besides, vibration bubbling phenomenon in the experiment is observed. The generation mechanism of characteristic gases is analyzed with bubble dynamics under vibration. Higher vibration pressure induces cavitation processes and creates local thermal effects, which is the main reason for the generation of characteristic gases. A method based on combination of Duval triangle 1 and assistant triangle is proposed to distinguish characteristic gases generated by vibration from those generated by discharge and thermal faults. The results may provide theoretical support for fault identification and insulation state assessment of oil‐immersed power transformers and high‐voltage reactors.

Introduction: In the wave of urbanization, the increase of public lighting equipment in buildings has brought about more prominent problems of energy saving and consumption reduction.Methods: In order to solve the above problems, this paper designs a set of intelligent lighting solutions for digital buildings by combining the smart grid and non-dominant sorting genetic algorithms. Firstly, an intelligent lighting monitoring solution is constructed through ZigBee ad hoc network and sensor technology to monitor the relevant environment and lighting control of the laboratory building. Secondly, this paper uses the DIALux software network to build a public lighting light distributiona public lighting light distribution model in the building, and deeply studies the dimming control strategy of the system under the principle of making full use of sunlight and natural light.Results: The purpose ofself-adaptive intelligent control of desktop illuminance, finally using this scheme to achieve the optimal balance of desktop lighting.Discussion: The simulation experiment counts the power data of the intelligent lighting system under different weather conditions. The experimental results verify that the intelligent lighting control scheme can effectively reduce the output luminous flux of the lamps, thereby reducing power consumption.

The complexity inherent in navigating intricate traffic environments poses substantial hurdles for intelligent driving technology. The continual progress in mapping and sensor technologies has equipped vehicles with the capability to intricately perceive their exact position and the intricate interplay among surrounding traffic elements. Building upon this foundation, this paper introduces a deep reinforcement learning method to solve the decision-making and trajectory planning problem of intelligent vehicles. The method employs a deep learning framework for feature extraction, utilizing a grid map generated from a blend of static environmental markers such as road centerlines and lane demarcations, in addition to dynamic environmental cues including vehicle positions across varied lanes, all harmonized within the Frenet coordinate system. The grid map serves as the input for the state space, and the input for the action space comprises a vector encompassing lane change timing, velocity, and vertical displacement at the lane change endpoint. To optimize the action strategy, a reinforcement learning approach is employed. The feasibility, stability, and efficiency of the proposed method are substantiated via experiments conducted in the CARLA simulator across diverse driving scenarios, and the proposed method can increase the average success rate of lane change by 6.8% and 13.1% compared with the traditional planning control algorithm and the simple reinforcement learning method.

In a dynamic environment, autonomous driving vehicles require accurate decision-making and trajectory planning. To achieve this, autonomous vehicles need to understand their surrounding environment and predict the behavior and future trajectories of other traffic participants. In recent years, vectorization methods have dominated the field of motion prediction due to their ability to capture complex interactions in traffic scenes. However, existing research using vectorization methods for scene encoding often overlooks important physical information about vehicles, such as speed and heading angle, relying solely on displacement to represent the physical attributes of agents. This approach is insufficient for accurate trajectory prediction models. Additionally, agents’ future trajectories can be diverse, such as proceeding straight or making left or right turns at intersections. Therefore, the output of trajectory prediction models should be multimodal to account for these variations. Existing research has used multiple regression heads to output future trajectories and confidence, but the results have been suboptimal. To address these issues, we propose QINET, a method for accurate multimodal trajectory prediction for all agents in a scene. In the scene encoding part, we enhance the feature attributes of agent vehicles to better represent the physical information of agents in the scene. Our scene representation also possesses rotational and spatial invariance. In the decoder part, we use cross-attention and induce the generation of multimodal future trajectories by employing a self-learned query matrix. Experimental results demonstrate that QINET achieves state-of-the-art performance on the Argoverse motion prediction benchmark and is capable of fast multimodal trajectory prediction for multiple agents.

Abstract Background Breastfeeding might prevent childhood cancer by stimulating the immune system. Methods The following databases, including PubMed, Embase, and Cochrane Library, were searched from inception to January 10, 2021. Results In dose-dependent manner, there was a statistically significant inverse association between any breastfeeding and the incidence of childhood cancer. There was no evidence that breastfeeding was inversely related to childhood cancer of the skeletal, reproductive, or sensory systems. However, breastfeeding was inversely associated with the incidence of hematological malignancies and cancers of the nervous and urinary systems. Among hematological malignancies, the relationship was significant for acute lymphocytic leukemia (ALL) and acute myeloid leukemia (AML), but not for acute non-lymphocytic leukemia (ANLL), Hodgkin’s lymphoma (HL), or non-HL. Conclusions The evidences demonstrated that breastfeeding have a potential protective role in preventing selective childhood cancer growth, especially for ALL, AML, cancer of nervous and urinary systems. This study recommended that breastfeeding be extended for as long as possible or maintained for at least 6 months to prevent selective childhood cancer growth.

Among the gynecological cancers, ovarian cancer is the most lethal. Its therapeutic options include a combination of chemotherapy with platinum-based compounds and cytoreductive surgery. Most ovarian cancer patients exhibit an initial response to platinum-based therapy, however, platinum resistance has led to up to 80% of this responsive cohort becoming refractory. Ovarian cancer recurrence and drug resistance to current chemotherapeutic options is a global challenge. Chemo-resistance is a complex phenomenon that involves multiple genes and signal transduction pathways. Therefore, it is important to elucidate on the underlying molecular mechanisms involved in chemo-resistance. This inform decisions regarding therapeutic management and help in the identification of novel and effective drug targets. Studies have documented the individual biomarkers of platinum-resistance in ovarian cancer that are potential therapeutic targets. This review summarizes the molecular mechanisms of platinum resistance in ovarian cancer, novel drug targets, and clinical outcomes.

Abstract Background Glioblastoma multiforme (GBM) is the most common aggressive malignant brain tumor. However, the molecular mechanism of glioblastoma formation is still poorly understood. To identify candidate genes that may be connected to glioma growth and development, weighted gene co-expression network analysis (WGCNA) was performed to construct a gene co-expression network between gene sets and clinical characteristics. We also explored the function of the key candidate gene. Methods Two GBM datasets were selected from GEO Datasets. The R language was used to identify differentially expressed genes. WGCNA was performed to construct a gene co-expression network in the GEO glioblastoma samples. A custom Venn diagram website was used to find the intersecting genes. The GEPIA website was applied for survival analysis to determine the significant gene, FUBP3. OS, DSS, and PFI analyses, based on the UCSC Cancer Genomics Browser, were performed to verify the significance of FUBP3. Immunohistochemistry was performed to evaluate the expression of FUBP3 in glioblastoma and adjacent normal tissue. KEGG and GO enrichment analyses were used to reveal possible functions of FUBP3. Microenvironment analysis was used to explore the relationship between FUBP3 and immune infiltration. Immunohistochemistry was performed to verify the results of the microenvironment analysis. Results GSE70231 and GSE108474 were selected from GEO Datasets, then 715 and 694 differentially expressed genes (DEGs) from GSE70231 and GSE108474, respectively, were identified. We then performed weighted gene co-expression network analysis (WGCNA) and identified the most downregulated gene modules of GSE70231 and GSE108474, and 659 and 3915 module genes from GSE70231 and GSE108474, respectively, were selected. Five intersection genes (FUBP3, DAD1, CLIC1, ABR, and DNM1) were calculated by Venn diagram. FUBP3 was then identified as the only significant gene by survival analysis using the GEPIA website. OS, DSS, and PFI analyses verified the significance of FUBP3. Immunohistochemical analysis revealed FUBP3 expression in GBM and adjacent normal tissue. KEGG and GO analyses uncovered the possible function of FUBP3 in GBM. Tumor microenvironment analysis showed that FUBP3 may be connected to immune infiltration, and immunohistochemistry identified a positive correlation between immune cells (CD4 + T cells, CD8 + T cells, and macrophages) and FUBP3. Conclusion FUBP3 is associated with immune surveillance in GBM, indicating that it has a great impact on GBM development and progression. Therefore, interventions involving FUBP3 and its regulatory pathway may be a new approach for GBM treatment.

The frequency of human suffering from cancer is increasing annually across the globe. This has fueled numerous investigations aimed at the prevention and cure of various cancers. Long non-coding RNA (lncRNA) are known to play a crucial role in cancer. For instance, cancer susceptibility candidate 11 (CASC11), as one of the long non-coding RNAs, has been reported to be overexpressed in various tumors. This review elucidates the mechanism by which lncRNA CASC11 regulates tumors' biological processes and affirms its value as a therapeutic target for tumors. Through systematic analysis and review of relevant articles in PubMed, we revealed the pathophysiological mechanism of CASC11 on the tumor by regulating the biological processes of tumor such as proliferation, autophagy, apoptosis, thereby promoting tumor metastasis. We also revealed the regulatory pathways of CASC11 in different tumors, for instance by acting on a variety of microRNAs, oncogenic proteins, carcinogens, and transcription factors. Consequently, CASC11 regulates cancer proliferation, apoptosis, and invasion by altering the WNT/β-catenin signaling pathway and epithelial–mesenchymal transition (EMT). Furthermore, CASC11 expression has a high pertinence with clinical prognosis, suggesting that it is a potential marker for malignant tumors or a clinical adjuvant therapy in the future.

The machine learning paradigms driven by the sixth-generation network (6G) facilitate an ultra-fast and low-latency communication environment. However, specific research and practical applications have revealed that there are still various issues regarding their applicability. A system named Incentivizing Secure Federated Learning Systems (ISFL-Sys) is proposed, consisting of a blockchain module and a federated learning module. A data-security-oriented trustworthy federated learning mechanism called Efficient Trustworthy Federated Learning (ETFL) is introduced in the system. Utilizing a directed acyclic graph as the ledger for edge nodes, an incentive mechanism has been devised through the use of smart contracts to encourage the involvement of edge nodes in federated learning. Experimental simulations have demonstrated the efficient security of the proposed federated learning mechanism. Furthermore, compared to benchmark algorithms, the mechanism showcases improved convergence and accuracy.

Under the trend of digitization and global carbon reductions, clay construction has increasingly attracted attention due to advantages such as an excellent thermal performance, low carbon emissions, and high cost-effectiveness. In this article, an in-depth investigation is conducted into the potential, challenges, and future in the development of digital manufacturing technologies for clay architecture, especially 3D printing, robotic construction, and prefabrication systems. It is revealed through a review that digital clay construction is under rapid development and likely to provide a viable solution to achieving global carbon neutrality, which is conducive to addressing various regional issues. The key findings from this review are as follows. Firstly, as a flexible, precise, and low-carbon industrial solution, 3D printing lays a foundation for the extensive research on materials, equipment, and algorithm optimization. Secondly, the use of machine construction methods such as modern rammed earth technology and drone spraying is effective in improving efficiency and lowering costs. Lastly, the prefabricated building system shows its potential in renewing and developing the rammed earth architecture culture. These findings indicate a massive potential of digital clay construction to support sustainability efforts in the future.

Background: Pyroptosis, a programmed cell death (PCD) with highly inflammatory form, has been recently found to be associated with the origin of hematopoietic malignancies. Long noncoding RNA (lncRNA) had emerged as an essential mediator to regulate gene expression and been involved in oncogenesis. However, the roles of pyroptosis-related lncRNA (PRlncRNA) in acute myeloid leukemia (AML) have not yet been completely clarified.Methods: We collected AML datasets from public databases to obtain PRlncRNA associated with survival and constructed a PRlncRNA signature using Lasso-Cox regression analysis. Subsequently, we employed RT-PCR to confirm its expression difference and internal training to further verify its reliability. Next, AML patients were classified into two subgroups by the median risk score. Finally, the differences between two groups in immune infiltration, enrichment analysis and drug sensitivity were further explored.Results: A PRlncRNA signature and an effective nomogram combined with clinicopathological variables to predict the prognosis of AML were constructed. The internal validations showed that the PRlncRNA risk score model was an accurate and productive indicator to predict the outcome of AML. Furthermore, this study indicated that higher inflammatory cell and immunosuppressive cells, and less sensitive to conventional chemotherapy drugs were highlighted in the high-risk group.Conclusion: Through comprehensive analysis of PRlncRNA model, our study may offer a valuable basis for future researches in targeting pyroptosis and tumor microenvironment (TME) and provide new measures for prevention and treatment in AML.

The authors show that graded molecular heterogeneity in metabotropic pathways underlies a continuum of temporal responses in cerebellar unipolar brush cells (UBCs). This allows the UBC population to serve as a cell-autonomous basis for temporal integration and learning over multiple time scales.

Abstract In this paper, a novel energy management strategy with the improved adaptability to various conditions for plug‐in hybrid electric vehicle (PHEV) is proposed. The control parameters, derived from the benchmark test, are optimized offline for different driving conditions. The optimized parameters are implemented according to different driving behaviours identified online. The offline and online cooperation improves performance of energy management strategy in different driving conditions. Three main efforts have been made: Firstly, the valuable features that describe different driving conditions are extracted by random forest (RF) and the features are used for determining driving condition categories, utilized for online driving condition identification by support vector machine (SVM). Secondly, the control thresholds in the developed control strategy are optimized by whale optimization algorithm (WOA) under different driving conditions. The optimal control thresholds for different driving conditions will be called online after certain traffic condition is categorized. At last, simulation‐based evaluation is performed, validating the enhanced performance of the proposed methods in energy‐saving in different driving conditions.

BackgroundEctopic lipid deposition plays a promoting role in many chronic metabolic diseases. Abnormal adipogenic differentiation of mesenchymal stem cells (MSCs) is an important cause of lipid deposition in organs. Studies have shown that serum angiopoietin-like protein 8 (ANGPTL8) levels are increased in patients with many chronic metabolic diseases (such as type 2 diabetes, obesity, and hepatic steatosis), while the role of ANGPTL8 in ectopic lipid accumulation has not been reported.MethodsWe used the Gene Expression Omnibus (GEO) database to analyze the expression of ANGPTL8 in subcutaneous adipose tissue of obese patients and qPCR to analyze the expression of ANGPTL8 in the liver of high-fat diet (HFD)-induced obese mice. To explore the potential roles of ANGPTL8 in the progression of ectopic lipid deposition, ANGPTL8 knockout (KO) mice were constructed, and obesity models were induced by diet and ovariectomy (OVX). We analyzed lipid deposition (TG) in the liver, kidney, and heart tissues of different groups of mice by Oil Red O, Sudan black B staining, and the single reagent GPO-PAP method. We isolated and characterized MSCs to analyze the regulatory effect of ANGPTL8 on Wnt/β-Catenin, a key pathway in adipogenic differentiation. Finally, we used the pathway activator LiCl and a GSK3β inhibitor (i.e., CHIR99021) to analyze the regulatory mechanism of this pathway by ANGPTL8.ResultsANGPTL8 is highly expressed in the subcutaneous adipose tissue of obese patients and the liver of HFD-induced obese mice. Both normal chow diet (NCD)- and HFD-treated ANGPTL8 KO male mice gained significantly less weight than wild-type (WT) male mice and reduced ectopic lipid deposition in organs. However, the female mice of ANGPTL8 KO, especially the HFD group, did not show differences in body weight or ectopic lipid deposition because HFD could induce estrogen overexpression and then downregulate ANGPTL8 expression, thereby counteracting the reduction in HFD-induced ectopic lipid deposition by ANGPTL8 deletion, and this result was also further proven by the OVX model. Mechanistic studies demonstrated that ANGPTL8 could promote the differentiation of MSCs into adipocytes by inhibiting the Wnt/β-Catenin pathway and upregulating PPARγ and c/EBPα mRNA expression.ConclusionsANGPTL8 promotes the differentiation of MSCs into adipocytes, suggesting that ANGPTL8 may be a new target for the prevention and treatment of ectopic lipid deposition in males.

Abstract In order to solve the problem between low power of Internet of Things devices and the high cost of cryptography, lightweight cryptography is required. The improvement of the scalar multiplication can effectively reduce the complexity of elliptic curve cryptography (ECC). In this paper, we propose a fast formula for point septupling on elliptic curves over binary fields using division polynomial and multiplexing of intermediate values to accelerate the computation by more than 14%. We also propose a scalar multiplication algorithm based on the step multi-base representation using point halving and the septuple formula we proposed, which significantly reduces the computational cost. The experimental results show that our method is more efficient over binary fields and contributes to reducing the complexity of ECC.

This paper introduces an energy management strategy design method for a plug-in hybrid electric vehicle based on the data-driven approach and online signal analysis. It includes two parts, mode division strategy design, and power distribution strategy design. Using the random forest in data mining technology to analyze optimization results of dynamic programming can quickly extract key information and establish optimal and understandable mode division strategy with high precision and stability directly. Besides, integrating the classic “Engine optimal operating curve control” strategy with wavelet transform and Markov prediction, which not only enhances the adaptability of the strategy to different driving conditions but also improves the fuel economy by reducing the impact of transient power on the engine operation. At the same time, to improve the prediction accuracy of the algorithm without increasing the computational complexity, this paper adds a prediction result correction function to the first-order Markov prediction model to reduce the impact of slow update of the probability matrix on prediction accuracy. The simulation results show the average prediction error of the improved Markov prediction model is reduced by 5.3% and the new energy management strategy designed reduces fuel consumption by 8.28% at the cost of a small increase in electricity consumption.

Ever since the deployment of the first-generation of mobile telecommunications, wireless communication technology has evolved at a dramatically fast pace over the past four decades. The upcoming fifth-generation (5G) holds a great promise in providing an ultra-fast data rate, a very low latency, and a significantly improved spectral efficiency by exploiting the millimeter-wave spectrum for the first time in mobile communication infrastructures. In the years beyond 2030, newly emerged data-hungry applications and the greatly expanded wireless network will call for the sixth-generation (6G) communication that represents a significant upgrade from the 5G network - covering almost the entire surface of the earth and the near outer space. In both the 5G and future 6G networks, millimeter-wave technologies will play an important role in accomplishing the envisioned network performance and communication tasks. In this paper, the relevant millimeter-wave enabling technologies are reviewed: they include the recent developments on the system architectures of active beamforming arrays, beamforming integrated circuits, antennas for base stations and user terminals, system measurement and calibration, and channel characterization. The requirements of each part for future 6G communications are also briefly discussed.

Abstract Artificial intelligence (AI) based on deep learning has shown excellent diagnostic performance in detecting various diseases with good-quality clinical images. Recently, AI diagnostic systems developed from ultra-widefield fundus (UWF) images have become popular standard-of-care tools in screening for ocular fundus diseases. However, in real-world settings, these systems must base their diagnoses on images with uncontrolled quality (“passive feeding”), leading to uncertainty about their performance. Here, using 40,562 UWF images, we develop a deep learning–based image filtering system (DLIFS) for detecting and filtering out poor-quality images in an automated fashion such that only good-quality images are transferred to the subsequent AI diagnostic system (“selective eating”). In three independent datasets from different clinical institutions, the DLIFS performed well with sensitivities of 96.9%, 95.6% and 96.6%, and specificities of 96.6%, 97.9% and 98.8%, respectively. Furthermore, we show that the application of our DLIFS significantly improves the performance of established AI diagnostic systems in real-world settings. Our work demonstrates that “selective eating” of real-world data is necessary and needs to be considered in the development of image-based AI systems.

Abstract A challenge of chronic diseases that remains to be solved is how to liberate patients and medical resources from the burdens of long-term monitoring and periodic visits. Precise management based on artificial intelligence (AI) holds great promise; however, a clinical application that fully integrates prediction and telehealth computing has not been achieved, and further efforts are required to validate its real-world benefits. Taking congenital cataract as a representative, we used Bayesian and deep-learning algorithms to create CC-Guardian, an AI agent that incorporates individualized prediction and scheduling, and intelligent telehealth follow-up computing. Our agent exhibits high sensitivity and specificity in both internal and multi-resource validation. We integrate our agent with a web-based smartphone app and prototype a prediction-telehealth cloud platform to support our intelligent follow-up system. We then conduct a retrospective self-controlled test validating that our system not only accurately detects and addresses complications at earlier stages, but also reduces the socioeconomic burdens compared to conventional methods. This study represents a pioneering step in applying AI to achieve real medical benefits and demonstrates a novel strategy for the effective management of chronic diseases.

The polarization-independence optical metasurface has attracted great interests due to breaking the polarization limitations. In general, polarization-independence characteristics of metasurface almost always originates from the polarization-independence characteristics of the used resonant antenna itself. In this paper, a novel approach is proposed to achieve polarization-independence metasurface based on the irrotational nanobricks. Firstly, the intrinsic characteristics of a half wave plate has been investigated, which can realize co-polarization transmission of X-linear-polarization (XLP) and Y-linear-polarization (YLP) lights, and cross-polarization transmission of circular polarization (CP) light, simultaneously. Eight high-efficiency silicon nanobricks with the phase shifts over 2π are designed as the basic unit cell, which have the same transmitted amplitude and phase for the CP and XLP light, and the phase difference for XLP and YLP light. We have also designed the beam deflector, metalens and vortex beam generator, which possess identical function for cross-polarized CP and co-polarized XLP and YLP light, respectively. These results can promote the deeper understanding of metasurface, and give a new method to design polarization-independent optical devices.

In this paper, a 37-42.5GHz (N260 Band) 64 channel full-digital multi-beam array for 5G massive multiple input multiple output (MIMO) applications is developed. The multi-beam array is built on a time division duplexing (TDD) architecture in which the transmitter and receivers are independent and working separately to enable more flexible channel configuration and hardware distribution. The 64 antenna elements of the array are aligned in a way that 16 elements lie in the horizontal and 4 in the vertical direction to deliver higher beam resolution in the horizontal plane. By using cables of unequal lengths, the 16 × 4 antenna elements are connected to a cluster of 8 × 8 transmitters/receivers. The hardware design of the transmitters, receivers, antennas and other circuits is demonstrated, with excellent measured RF performance achieved. Furthermore, the calibration of the massive MIMO array is presented, and the influence of inequality of array feedlines on calibration accuracy in a wide bandwidth is analyzed and experimentally verified. Finally, experimental studies on single beam scanning, multiple concurrent beam generation and over-the-air (OTA) performance of the transmitting and receiving array have been done. Simulation and measurement results suggest that the array has an excellent beam-forming capability that supports beam steering and generation of multiple concurrent beams. The OTA test reveals that the array can provide high modulation and demodulation accuracy for communication.

The Cooperative Adaptive Cruise Control (CACC) is considered to be an effective method to improve traffic flow. However, the comfortability and economy need to be paid more attention to besides stability and safety. In this paper, a CACC strategy is proposed for an electric vehicle platoon to improve the economy, following performance, safety, and comfortability characteristics, based on the model predictive control (MPC) with simulated annealing-particle swarm optimization (SA-PSO) algorithm. Firstly, the braking force distribution strategy of electric vehicles is designed to improve the efficiency of regenerative braking. Secondly, based on the variable vehicle spacing with fixed time headway, a vehicle platoon following strategy is established to meet the following performance and safety. Thirdly, an MPC controller is used to control the states of the vehicles in the platoon to satisfy the performance. A multi-objective function of the MPC controller is established, including the economy, following performance, comfortability, and safety of the vehicles in the platoon. The SA-PSO algorithm effectively solves the problem of the discrete variables in the objective function. Simulations are conducted to validate the sufficient conditions of the economy, following performance, comfortability, and safety. Simulation results demonstrate that the economic efficiency of the CACC strategy with the economic index is 16.5% higher than that of the existing ACC strategy. Meanwhile, the other characteristics can also meet the control requirement.

PurposeTo develop deep learning classifiers and evaluate their diagnostic performance in detecting the static gonioscopic angle closure and peripheral anterior synechia (PAS) based on swept source optical coherence tomography (SS-OCT) images.Materials and MethodsSubjects were recruited from the Glaucoma Service at Zhongshan Ophthalmic Center of Sun Yat-sun University, Guangzhou, China. Each subject underwent a complete ocular examination, such as gonioscopy and SS-OCT imaging. Two deep learning classifiers, using convolutional neural networks (CNNs), were developed to diagnose the static gonioscopic angle closure and to differentiate appositional from synechial angle closure based on SS-OCT images. Area under the receiver operating characteristic (ROC) curve (AUC) was used as outcome measure to evaluate the diagnostic performance of two deep learning systems.ResultsA total of 439 eyes of 278 Chinese patients, which contained 175 eyes of positive PAS, were recruited to develop diagnostic models. For the diagnosis of static gonioscopic angle closure, the first deep learning classifier achieved an AUC of 0.963 (95% CI, 0.954–0.972) with a sensitivity of 0.929 and a specificity of 0.877. The AUC of the second deep learning classifier distinguishing appositional from synechial angle closure was 0.873 (95% CI, 0.864–0.882) with a sensitivity of 0.846 and a specificity of 0.764.ConclusionDeep learning systems based on SS-OCT images showed good diagnostic performance for gonioscopic angle closure and moderate performance in the detection of PAS.

Subretinal fluid (SRF) can lead to irreversible visual loss in patients with central serous chorioretinopathy (CSC) if not absorbed in time. Early detection and intervention of SRF can help improve visual prognosis and reduce irreversible damage to the retina. As fundus image is the most commonly used and easily obtained examination for patients with CSC, the purpose of our research is to investigate whether and to what extent SRF depicted on fundus images can be assessed using deep learning technology. In this study, we developed a cascaded deep learning system based on fundus image for automated SRF detection and macula-on/off serous retinal detachment discerning. The performance of our system is reliable, and its accuracy of SRF detection is higher than that of experienced retinal specialists. In addition, the system can automatically indicate whether the SRF progression involves the macula to provide guidance of urgency for patients. The implementation of our deep learning system could effectively reduce the extent of vision impairment resulting from SRF in patients with CSC by providing timely identification and referral.

To predict visual acuity (VA) and post-therapeutic optical coherence tomography (OCT) images 1, 3, and 6 months after laser treatment in patients with central serous chorioretinopathy (CSC) by artificial intelligence (AI). Real-world clinical and imaging data were collected at Zhongshan Ophthalmic Center (ZOC) and Xiamen Eye Center (XEC). The data obtained from ZOC (416 eyes of 401 patients) were used as the training set; the data obtained from XEC (64 eyes of 60 patients) were used as the test set. Six different machine learning algorithms and a blending algorithm were used to predict VA, and a pix2pixHD method was adopted to predict post-therapeutic OCT images in patients after laser treatment. The data for VA predictions included clinical features obtained from electronic medical records (20 features) and measured features obtained from fundus fluorescein angiography, indocyanine green angiography, and OCT (145 features). The data for OCT predictions included 480 pairs of pre- and post-therapeutic OCT images. The VA and OCT images predicted by AI were compared with the ground truth. In the VA predictions of XEC dataset, the mean absolute errors (MAEs) were 0.074–0.098 logMAR (within four to five letters), and the root mean square errors were 0.096–0.127 logMAR (within five to seven letters) for the 1-, 3-, and 6-month predictions, respectively; in the post-therapeutic OCT predictions, only about 5.15% (5 of 97) of synthetic OCT images could be accurately identified as synthetic images. The MAEs of central macular thickness of synthetic OCT images were 30.15 ± 13.28 μm and 22.46 ± 9.71 μm for the 1- and 3-month predictions, respectively. This is the first study to apply AI to predict VA and post-therapeutic OCT of patients with CSC. This work establishes a reliable method of predicting prognosis 6 months in advance; the application of AI has the potential to help reduce patient anxiety and serve as a reference for ophthalmologists when choosing optimal laser treatments.

Purpose: To predict central serous chorioretinopathy (CSC) recurrence 3 and 6 months after laser treatment by using machine learning.Methods: Clinical and imaging features of 461 patients (480 eyes) with CSC were collected at Zhongshan Ophthalmic Center (ZOC) and Xiamen Eye Center (XEC). The ZOC data (416 eyes of 401 patients) were used as the training dataset and the internal test dataset, while the XEC data (64 eyes of 60 patients) were used as the external test dataset. Six different machine learning algorithms and an ensemble model were trained to predict recurrence in patients with CSC. After completing the initial detailed investigation, we designed a simplified model using only clinical data and OCT features.Results: The ensemble model exhibited the best performance among the six algorithms, with accuracies of 0.941 (internal test dataset) and 0.970 (external test dataset) at 3 months and 0.903 (internal test dataset) and 1.000 (external test dataset) at 6 months. The simplified model showed a comparable level of predictive power.Conclusion: Machine learning achieves high accuracies in predicting the recurrence of CSC patients. The application of an intelligent recurrence prediction model for patients with CSC can potentially facilitate recurrence factor identification and precise individualized interventions.

Circuitry of the cerebellar cortex is regionally and functionally specialized. Unipolar brush cells (UBCs), and Purkinje cell (PC) synapses made by axon collaterals in the granular layer, are both enriched in areas that control balance and eye movement. Here, we find a link between these specializations in mice: PCs preferentially inhibit metabotropic glutamate receptor type 1 (mGluR1)-expressing UBCs that respond to mossy fiber (MF) inputs with long lasting increases in firing, but PCs do not inhibit mGluR1-lacking UBCs. PCs inhibit about 29% of mGluR1-expressing UBCs by activating GABAA receptors (GABAARs) and inhibit almost all mGluR1-expressing UBCs by activating GABAB receptors (GABABRs). PC to UBC synapses allow PC output to regulate the input layer of the cerebellar cortex in diverse ways. Based on optogenetic studies and a small number of paired recordings, GABAAR-mediated feedback is fast and unreliable. GABABR-mediated inhibition is slower and is sufficiently large to strongly influence the input-output transformations of mGluR1-expressing UBCs.

In the field of new energy vehicles, 4WD PHEVs show strong energy-saving potential. A single energy management strategy, nevertheless, has difficulty achieving the energy-saving potential due to the complex, nonlinear energy system of the 4WD PHEV. To cope with it, a hierarchical energy management strategy (H-EMS) for 4WD PHEVs is proposed in this paper to achieve energy management optimization. Firstly, the future speed information is predicted by the speed prediction method, and the upper energy management strategy adopts the model predictive control (MPC) based on the future speed information to carry out the power source distribution between the engine and the battery. Secondly, the lower energy management strategy performs the power component distribution of the front motor and the rear motor based on an equivalent consumption minimization strategy (ECMS). Finally, the simulation based on MATLAB/Simulink is performed, validating that the proposed method has more energy-saving capabilities, and the economy is improved by 11.87% compared with the rule-based (RB) energy management strategies.

An accurate theoretical model to predict the extent and mechanical behavior of the excavation damage zone (EDZ) in the surrounding rock of deep-buried tunnel is of great importance for the practical engineering applications. Using the elastic-plastic theory and combined with the analysis on the stress characteristics of the tunnel surrounding rock, this paper present a predict model for the EDZ formation and evolution. A three-zone composite mechanical model was established for the tunnel surround rock and the corresponding stress state and displacement field of three zones were derived. The effects of the strain softening and dilatancy during rock deformation was taken into account. The correctness of the proposed model was validated by the existing theoretical models. A sensitivity analysis for different influencing factors in this model was also performed. The results can benefit for the future numerical and experimental studies.

Purpose: In order to compensate for the early intrauterine growth restriction, small-for-gestational age (SGA) infants have “catch-up growth” after birth. Increased caloric intake has been suggested for SGA infants conventionally. It is important to determine if the early growth rate of body mass index (BMI) is associated with risk of persistent obesity later in life. In this longitudinal cohort study, we assessed the BMI of a large cohort of children who were SGA at birth to determine their risk of persistent obesity at school age (6–7 years) due to excessive weight gain in the first 3 years of life.Methods: We collected the height and weight data of 23,871 SGA babies. A polynomial function was used to fit the BMI-for-age z-score (BAZ) values of 0–6 years old SGA children and interpolate their growth trajectory. In addition, we screened out 6,959 children from 23,871 children to further evaluate the dynamic changes of early childhood BMI. We divided the school-age children into groups as non-obese (BAZ < 2) and obese (BAZ > 2), and determined the association between changes in BMI and school-age obesity.Results: From the perspective of BMI distribution, the interpolated growth trajectory indicated that SGA children reaching overweight status or developing obesity by 3 years of age, continued to have obesity until school age (R2, 0.65; R2, 0.21). The retrospective analysis showed that children who were overweight and had obesity during school age had a high BMI from early age. By analyzing the changes in early BMI, we found that the fastest growth of SGA children occurred in the early infancy before 6 months and they continued to grow rapidly for a period of time. Interestingly, former SGA children who maintained a near overweight (1 < BAZ < 2) status before the age of 2 maintained an appropriate growth rate and usually did not develop obesity.Conclusions: A rapid increase in BMI during early infancy in former SGA newborns leads to a persistent risk of obesity. The energy intake of SGA infants should appropriately meet the infants' growth needs and early BMI changes should be closely monitored for an optimal integrated management.

This paper presents a prescient energy management strategy based on the model predictive control (MPC) for the parallel plug-in hybrid electric vehicles (PHEVs). In this hierarchical strategy, dynamic programming (DP), with its improved calculation speed, is chosen as the solution algorithm to calculate the optimal power distribution combinations in the predicted receding horizon and under the given terminal battery state-of-charge (SOC) terminal constraint. A synthesized velocity profile prediction (SVPP) method is adopted. The macroscopically and microcosmically predicted velocities obtained by the participatory sensing data (PSD)-based method and the Markov chain (MC), respectively, are synthesized by the linear regression method, obtaining the final velocity profile. In the linear regression step, a particle filter (PF) is implemented for the parameter estimation. According to the characteristics of the driving conditions and components, the terminal battery SOC in each control horizon is constrained by a novel method. Finally, we demonstrate the capability of the proposed scheme in terms of fuel economy improvement by comparing the value of this metric with those of other strategies through simulation.

Juglans mandshurica is a monoecious heterodichogamous species with protogynous and protandrous mating strategies that occur at a 1:1 ratio and are randomly distributed in the population. The inconsistent male and female flowering periods of the same mating type result in an imbalance of the ratio of male and female flowers, contributing to the low yield of this species. However, little more is known about its floral development. Following three consecutive years of observations, histological analysis, and scanning electron microscopy, we found that the morphological and anatomical development of the male and female flowers were synchronous. The male floral morphological development of J. mandshurica was divided into seven phases, while that of the female flower was nine. Four stages were shared between the male and female flower’s anatomical development. Our findings indicate that there was minimal overlap between sexual functions within the same mating type, guaranteeing synchronization, mutual non-interference, outcrossing, and avoidance of self-fertilization. These results provide a theoretical basis for the improvement of fruit yield and quality through the reasonable allocation of protogynous and protandrous individuals in a population, and for artificial pollination control. Further, these findings lay a foundation for further research on the genetic mechanisms and environmental effects on flower development of heterodichogamous J. mandshurica.

Summary: Granule cells (GCs) of the cerebellar input layer express high-affinity δ GABAA subunit-containing GABAA receptors (δGABAARs) that respond to ambient GABA levels and context-dependent neuromodulators like steroids. We find that GC-specific deletion of δGABAA (cerebellar [cb] δ knockout [KO]) decreases tonic inhibition, makes GCs hyperexcitable, and in turn, leads to differential activation of cb output regions as well as many cortical and subcortical brain areas involved in cognition, anxiety-like behaviors, and the stress response. Cb δ KO mice display deficits in many behaviors, but motor function is normal. Strikingly, δGABAA deletion alters maternal behavior as well as spontaneous, stress-related, and social behaviors specifically in females. Our findings establish that δGABAARs enable the cerebellum to control diverse behaviors not previously associated with the cerebellum in a sex-dependent manner. These insights may contribute to a better understanding of the mechanisms that underlie behavioral abnormalities in psychiatric and neurodevelopmental disorders that display a gender bias.

In this article, we demonstrate several optical vortex metalens (OVMs) based on the Pancharatnam–Berry (PB) geometric phase concept utilizing silicon nanobricks. The nanostructured OVMs are designed via coalescing the phase profiles of metalens and vortex phase plate, which can produce and focus the vortex beam simultaneously. Through analyzing four single-focal OVMs suitable for left circularly polarized (LCP) or right circularly polarized (RCP) incident light respectively, we find that the topological sign of vortex light is strongly associated with polarization state of incident light. Through multiplexing two OVMs only suitable for one kind of circularly polarized light, we design one polarization multiplexing OVM, which can focus different circularly polarized (CP) vortex beam to the same position. We also design one longitudinal bifocal OVM, which can focus LCP vortex beam to the different positions. Thus, these OVMs have the promising applications in integrated optics, classical and quantum optics, and vortex optics.

Background: Urinary incontinence (UI) is a common and refractory complication for patients with neurogenic detrusor overactivity (NDO) or idiopathic overactive bladder (IOAB).Objectives: To evaluate the effect of Botulinum toxin A (BTX-A) based on different dosages strategy for UI.Method: The MEDLINE, Ovid EMbase, The Cochrane Central Register of Controlled Trials (CENTRAL), China National Knowledge Internet (CNKI), and WanFang database were searched for relevant published randomized controlled trials (RCTs) between 1969 to September 31, 2018. All database were searched to identify relevant randomized controlled trials (RCTs) that investigated the clinical benefit of BTX-A for management of UI in patients with NDO and IOAB.Results: This meta-analysis involved 19 original studies. The BTX-A was superior to placebo in reducing episodes of UI for NDO patients in all subgroups of different dosages for different durations, and also reduced maximum detrusor pressure in all kinds of 200U and 300U at 6 weeks. However, it increased post void residual in different dosages of 200U at 2 weeks. For IOAB patients, compared to placebo, BTX-A increased detrusor compliance for different dosages of 200U and 300U at 12 and 36 weeks, but it increased risk of urinary tract infections at other dosages.Conclusions: This meta-analysis indicated that BTX-A 200U and 300U are more effective than placebo in the treatment of NDO, with minimal, local, and manageable adverse events. Furthermore, BTX-A 300U and 200U could also improve detrusor compliance of IOAB. However, more RCTs would still be necessary to explore the effect of BTX-A on management of UI in NDO and IOAB patients.

Background: Approximately 1 in 33 newborns is affected by congenital anomalies worldwide. We aimed to develop a practical model for identifying infants with a high risk of congenital cataracts (CCs), which is the leading cause of avoidable childhood blindness. Methods: This case-control study was performed in the Zhongshan Ophthalmic Center and involved 2005 subjects, including 1274 children with CCs and 731 healthy controls. The CC identification models were established based on birth conditions, family medical history, and family environmental factors using the random forest (RF) and adaptive boosting methods (trained by 1129 CC cases and 609 healthy controls), which were tested by internal 4-fold cross-validation and external validation (145 CC cases and 122 healthy controls). The models were also tested using 4 datasets with gradually reduced proportions of CC patients (bilateral cases) to validate their performance in an approximate simulation of a clinical environment with a relatively low disease prevalence. Findings: The CC identification models showed high discrimination in both the 4-fold cross validation (area under the curve (AUC)=0.91 [95% confidence interval: 0.88–0.94] in bilateral cases; 0.82 [0.77–0.89] in unilateral cases) and external validation (AUC=0.93±0.05 in bilateral cases; 0.86±0.01 in unilateral cases), and achieved stable performance in the clinical tests (AUC=0.94–0.96 in the four subgroups by RF). Furthermore, family history of CC, low parental education level, and comorbidity were identified as the top three most relevant factors to both bilateral and unilateral CC diagnosis. Interpretation: Our CC identification models can accurately discriminate CC patients from healthy children and have the potential to serve as a complementary screening procedure, especially in undeveloped and remote areas. Keywords: Congenital anomaly, Congenital cataract, Identification model, Machine learning