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Esophageal squamous cell carcinoma (ESCC), a malignancy of the digestive system, is highly prevalent and the primary cause of cancer-related deaths worldwide due to the lack of early diagnostic biomarkers and effective therapeutic targets. Dysregulated ribonucleotide reductase (RNR) expression has been confirmed to be causally linked to tumorigenesis. This study demonstrated that ribonucleotide reductase small subunit M2 (RRM2) is significantly upregulated in ESCC tissue and that its expression is negatively correlated with clinical outcomes. Mechanistically, HuR promotes RRM2 mRNA stabilization by binding to the adenine/uridine (AU)-rich elements (AREs) within the 3′UTR, resulting in persistent overexpression of RRM2. Furthermore, bifonazole is identified as an inhibitor of HuR via computational screening and molecular docking analysis. Bifonazole disrupts HuR-ARE interactions by competitively binding to HuR at F65, R97, I103, and R153 residues, resulting in reduced RRM2 expression. Furthermore, bifonazole exhibited antitumor effects on ESCC patient-derived xenograft (PDX) models by decreasing RRM2 expression and the dNTP pool. In summary, this study reveals the interaction network among HuR, RRM2, and bifonazole and demonstrated that bifonazole is a potential therapeutic compound for ESCC through inhibition of the HuR/RRM2 axis.

Abstract Precast Concrete Sandwich Panel (PCSP) is composed of concrete load-bearing panels, thermal insulation panels, and decorative panels, which are assembled through connectors, integrating load-bearing, thermal insulation, and decorative functions. The connector bears the main shear force between the wall panels, and the shear resistance and insulation performance of the connector largely determine the mechanical stability and insulation effect of the wall panels, which is a key component in PCSPs. The current common practice is to cross assemble stainless steel insulation (SSI) connectors and Glass-Fiber-Reinforced Plastic (GFRP) connectors into PCSPs, which can reduce building energy consumption and save resources while meeting strength and insulation requirements. A large-scale pull-out test on a PCSP with intersecting SSI-GFRP connectors was conducted in this paper. The damage process and damage pattern of PCSP were observed and the shear performance of SSI-GFRP connectors was analyzed. Secondly, a numerical analysis model of the test PCSP was built using ABAQUS finite element software and its validity was verified through the test data. In addition, parameters such as connector diameter, connector number ratio and concrete strength were analyzed for their effect on the shear performance of SSI-GFRP connectors and it was found that connector diameter and connector number ratio had a significant effect. Finally, it is found that there are some differences between the classical theory for calculating the shear performance of SSI-GFRP connectors and the actual results. A theoretical correction factor (ζ) is given to improve the accuracy of the calculation of the classical theory, and its influencing factors and changing rules are investigated.

BackgroundA definite diagnosis goes undiscovered for a percentage of children with undiagnosed disorders, with significant medical, psychological, and social effects. Other than specialized clinical centers, exceptional molecular studies, common procedures, and devoted activities at the national and international levels, children with complex undiagnosed disorders require innovative approaches.MethodsIn March 2016, Children’s hospital of Fudan university represented the Children’s Undiagnosed Diseases Program (UDP). The purpose of this study is to describe the project findings and underline the critical significance of multidisciplinary teamwork in China’s undiagnosed rare illnesses program. We investigated the 758 cases in our UDP system retrospectively. Demographic information, laboratory test results, and genetic information were gathered.ResultsBetween January 2017 and December 2021, 758 cases were examined. Males made up 436 (57.5%) of the total. Over half of the patients were children under the age of five. The average patient course time preceding admission to UDP was 6.0 months (95% CI 10.512.6). These patients visited an average of 1.8 clinics during their diagnostic journey. Except for 69 individuals (90.9%), all had more than one presenting symptom in various organs: 460 (60.7%) had neurology difficulties, 151 (19.9%) had endocrine problems, and 141 (18.6%) had immunology problems. UDP has a diagnosis rate of 61.3%. Genetic testing was performed on 469 of the 758 patients, for a genetic diagnosis rate of 15.8%. The UDP method has a sensitivity of 94.5%, a specificity of 86.4%, a positive predictive value of 92.8%, and an negative predictive value of 89.5%.ConclusionOur UDP targets an unmet need, namely the diagnosis of patients with complicated, multisystem illnesses. Using a multidisciplinary team model approach, this UDP pilot study achieved a reasonable diagnosis success rate, increasing the possibility of more diagnoses and new scientific discoveries of difficult and rare diseases.

This review aims to explore the current application of Cranial Ultrasound Screening (CUS) in the diagnosis and treatment of brain diseases in extremely preterm infants. It also discusses the potential role of emerging ultrasound-derived technologies such as Super Microvascular Structure Imaging (SMI), Shear Wave Elastography (SWE), Ultrafast Doppler Ultrasound (UfD), and 3D ventricular volume assessment and automated segmentation techniques in clinical practice. A systematic search of medical databases was conducted using the keywords ''(preterm OR extremely preterm OR extremely low birth weight) AND (ultrasound OR ultrasound imaging) AND (neurodevelopment OR brain development OR brain diseases OR brain injury OR neuro*)'' to identify relevant literature. The titles, abstracts, and full texts of the identified articles were carefully reviewed to determine their relevance to the research topic. CUS offers unique advantages in early screening and monitoring of brain diseases in extremely preterm infants, as it can be performed at the bedside without the need for anesthesia or special monitoring. This technique facilitates early detection and intervention of conditions such as intraventricular hemorrhage, white matter injury, hydrocephalus, and hypoxic-ischemic injury in critically ill preterm infants. Continuous refinement of the screening and follow-up processes provides reliable clinical decision-making support for healthcare professionals and parents. Emerging ultrasound technologies, such as SWE, SMI, and UfD, are being explored to provide more accurate and in-depth understanding of brain diseases in extremely preterm infants. SWE has demonstrated its effectiveness in assessing the elasticity of neonatal brain tissue, aiding in the localization and quantification of potential brain injuries. SMI can successfully identify microvascular structures in the brain, offering a new perspective on neurologic diseases. UfD provides a high-sensitivity and quantitative imaging method for the prevention and treatment of neonatal brain diseases by detecting subtle changes in red blood cell movement and accurately assessing the status and progression of brain diseases. CUS and its emerging technologies have significant applications in the diagnosis and treatment of brain diseases in extremely preterm infants. Future research aims to address current technical challenges, optimize and enhance the clinical decision-making capabilities related to brain development, and improve the prevention and treatment outcomes of brain diseases in extremely preterm infants.

As an emerging method in engineering construction, concrete 3D printing has experienced rapid development in recent years due to its advantages in terms of automation, digitization, and intelligence. In order to comprehensively understand the research progress of concrete 3D printing technology, the literature on concrete 3D printing technology published in the Web of Science core database from 2014 to 2023 is used as a data source, and the visualization tool CiteSpace is utilized to research and analyze these results from the following aspects: number of publications, collaboration networks, research hotspots, and evolutionary trends. The results show that the number of publications in the field of concrete 3D printing technology has been increasing from year to year, with a gradually accelerating growth rate. The top three journals in terms of publication volume are Construction and Building Materials, Cement and Concrete Composites, and Materials. Research in the field of concrete 3D printing technology is mainly conducted through collaborations among research groups or institutions, while there are relatively fewer interactions among researchers from different institutions, regions, and countries. The current research direction mainly focuses on 3D printing materials, process parameters, and technology. The research content has progressed from initial explorations of concrete 3D printing technology to detailed studies of 3D printing materials and their performance. Overall, the research in this field continues to add further details to our knowledge of concrete 3D printing technology, and its application in engineering is gradually increasing. The automated, digitized, and intelligent construction methods of concrete 3D printing technology serve as a powerful driving force for the transformation and upgrading of the field of architecture.

To investigate the pore structure distribution and the coupled heat and moisture transfer during the drying process of the grains, this study focuses on fixed-bed corn drying with varying levels of broken kernel rate. A model of internal flow and conjugate heat and mass transfer was established for the drying process. Random packing models of whole and half corn kernels with different proportions were generated using rigid body dynamics (RBD), and the porosity, airflow distribution, and coupling of temperature and moisture transfer in fixed beds with different levels of broken kernel rate were analyzed. A fixed-bed corn drying device was developed, and the effects of broken particle contents of 0%, 10%, 20%, and 30% on drying characteristics were studied. The research findings reveal that the radial porosity in the fixed bed exhibits an oscillating distribution, with the localized porosity decreasing as the broken kernel rate increases. Increasing the broken kernel rate intensifies the curvature of the airflow paths within the fixed bed, increasing the pressure drop in the bed. The broken kernels fill the gaps between the whole kernels, improving the uniformity of the velocity distribution within the fixed bed. Under various packing models, the average discrepancy between pressure drop obtained from Particle-resolved Computational Fluid Dynamics (PRCFD) simulations with experimental remains below 15%. The increase in broken kernel rate within the fixed bed enlarges the heat transfer area, resulting in an elevation of the transient heat transfer characteristic parameters during drying. Simultaneously, the broken kernel rate increases the surface area of mass transfer, thereby enhancing the moisture transfer rate within the fixed bed. Compared to the fixed bed without broken kernels (0%), which requires 560 min to dry the corn pile to a safe moisture of 14% (d.b.), the drying time is reduced by 60 min, 100 min, and 130 min for the respective broken kernel contents of 10%, 20%, and 30%, respectively. The PRCFD method successfully simulates the processes of convective heat and mass transfer in the fluid phase and thermal and mass diffusion in the solid phase, exhibiting a strong correlation with experimental data.

Background: The incidence and mortality of influenza in children had risen, but data are limited on children with severe influenza virus infection in China. Methods: We conducted a retrospective case–control study and collected the patients' clinical data. Clinical data including demography, clinical presentation, laboratory findings, radiologic findings, treatment and outcomes were collected. Children were clinically confirmed to have virus infection in Shanghai in three hospitals from June 2014 to June 2019. Results: During the study, 36,047 children were enrolled. Among them, 118 met the criteria for severe flu. Clinical symptoms such as fever, cough, gastrointestinal symptoms, coma and epilepsy were higher in the severe group. Complications such as pneumorrhagia, heart failure, septic shock, acute renal failure and influenza-associated encephalitis were higher in the severe influenza group than the death group. The laboratory findings including decreased hemoglobin, high alanine aminotransferase, high urea nitrogen and high lactate levels were risk factors for death in children with influenza. Conclusion: Influenza-associated encephalopathy (IAE), acute respiratory distress syndrome (ARDS) were the common clinical manifestations and complications for the severe influenza, and delayed use of oseltamivir was found to be associated with fatality.

Although different types of drugs are available for postmenopausal osteoporosis, the limitations of the current therapies including drug resistances and adverse effects require identification of novel anti-osteoporosis agents. Here, we defined that norlichexanthone (NOR), a natural product, is a ligand of estrogen receptor-alpha (ERα) and revealed its therapeutic potential for postmenopausal osteoporosis. We used mammalian-one hybrid assay to screen for ERα modulators from crude extracts of several plant endophytes. As a result, NOR purified from the extract of endophyte ARL-13 was identified as a selective ERα modulator. NOR directly bound to ERα with an affinity in nanomolar range, revealing that it is a natural ligand of ERα. NOR induced osteoblast formation in MC3T3-E1 precursor cells. Conversely, NOR inhibited receptor activator of nuclear factor-kappa B ligand (RANKL)-induced osteoclast formation in both RAW264.7 macrophages and mouse primary monocytes. Mechanistically, NOR inhibited RANKL-induced association of ERα and TRAF6 to prevent ERα-mediated TRAF6 activation via Lys63-linked ubiquitination. Importantly, NOR exhibited potent anti-osteoporosis efficacy in an ovariectomized mouse model. Comparing to estrogen, NOR was of much less capability in stimulating endometrial hyperplasia and promoting mammalian cancer cell proliferation. Taken together, our study identified NOR as a natural and high affinity ligand of ERα with substantial anti-osteoporosis but less estrogenic activity.

Grain storage pressure is an important factor affecting grain pile temperature, and its influencing mechanism needs to be studied further. The distribution and variation of the temperature field of a bulk grain pile under different vertical pressures and a temperature difference of 25 °C are studied by a model test and numerical simulation. Initially, the temperature change and heat transfer law at different points in the bulk grain pile space are studied under different vertical pressures using a self-made test device. Thereafter, a multi-field coupling software platform COMSOL is used to simulate and study the distribution law of the temperature field in the bulk grain pile under different vertical pressures. The influence mechanism of vertical pressure on the temperature field of the grain pile is discussed based on the micro-airflow velocity field obtained by numerical simulation. The results show that the numerical simulation and experimental results are in good agreement. With the increase in vertical pressure, the heat transfer rate of the grain pile increases gradually, the convective heat transfer in the grain pile is hindered, and the temperature distribution gradually attains uniformity. When the vertical pressure increases from 50 kPa to 200 kPa, the temperature of the grain pile decreases by approximately 0.6–2.7 °C, and the rate of change of the temperature gradient reaches 7.4%. Under different vertical pressures, the proportion of the high-temperature area decreases linearly with the storage duration. The micro-airflow velocity field affects the temperature transfer in the bulk grain pile, resulting in the temperature at the top of the storage structure being higher than that at the bottom. The research methods and conclusions in this study can provide theoretical support and reference for the multi-field coupling research on bulk grain pile storage.

The vertical pressure lead to increased airflow resistance through the grain bulk, which affected the efficiency of ventilation and drying. The effects of vertical pressures at 50, 150, and 250 kPa on the pressure drop characteristics of soybeans were studied using experiment and numerical simulation. The random packing and different compression states for soybean packed beds were generated by the Discrete Element Method (DEM). The Discrete Element Method (DEM) and Computational Fluid Dynamics (CFD) were coupled to investigate the radial velocity and pressure drop of soybean bulk. The simulation results showed that the radial porosity had an oscillating distribution, and the radial average dimensionless velocity was consistent with the distribution trend of porosity. The increase in vertical pressure causes a decrease in porosity and an increase in local velocity. The PathFinder code was used as a supplementary method to calculate the pore path and pore characterization parameters, and the resistance coefficient term in the Forchheimer equation was determined. The compression of soybeans measured by the experiment mainly occurred within two hours after loading. The pressure drop of soybeans increased with the vertical pressure, with the average pressure drop at vertical pressures of 50, 150, and 250 kPa being 36%, 57%, and 92% higher than the uncompressed state (0 kPa). The pressure drop of soybeans calculated by the DEM-CFD method and the Forchheimer equation under different vertical pressures were in close agreement with the experimental results, and an average relative difference was found to be less than 10%. These results provide guidance for estimating the pressure drop of soybeans at different grain depths.

ObjectivesCentral nervous system (CNS) infection has a high incidence and mortality in neonates, but conventional tests are time-consuming and have a low sensitivity. Some rare genetic diseases may have some similar clinical manifestations as CNS infection. Therefore, we aimed to evaluate the performance of metagenomic next-generation sequencing (mNGS) in diagnosing neonatal CNS infection and to explore the etiology of neonatal suspected CNS infection by combining mNGS with whole exome sequencing (WES).MethodsWe prospectively enrolled neonates with a suspected CNS infection who were admitted to the neonatal intensive care unit(NICU) from September 1, 2019, to May 31, 2020. Cerebrospinal fluid (CSF) samples collected from all patients were tested by using conventional methods and mNGS. For patients with a confirmed CNS infection and patients with an unclear clinical diagnosis, WES was performed on blood samples.ResultsEighty-eight neonatal patients were enrolled, and 101 CSF samples were collected. Fourty-three blood samples were collected for WES. mNGS showed a sample diagnostic yield of 19.8% (20/101) compared to 4.95% (5/101) for the conventional methods. In the empirical treatment group, the detection rate of mNGS was significantly higher than that of conventional methods [27% vs. 6.3%, p=0.002]. Among the 88 patients, 15 patients were etiologically diagnosed by mNGS alone, five patients were etiologically identified by WES alone, and one patient was diagnosed by both mNGS and WES. Twelve of 13 diagnoses based solely on mNGS had a likely clinical effect. Six patients diagnosed by WES also experienced clinical effect.ConclusionsFor patients with a suspected CNS infections, mNGS combined with WES might significantly improve the diagnostic rate of the etiology and effectively guide clinical strategies.

With the rapid popularization of robots, the risks brought by robot communication have also attracted the attention of researchers. Because current traffic classification methods based on plaintext cannot classify encrypted traffic, other methods based on statistical analysis require manual extraction of features. This paper proposes (i) a traffic classification framework based on a capsule neural network. This method has a multilayer neural network that can automatically learn the characteristics of the data stream. It uses capsule vectors instead of a single scalar input to effectively classify encrypted network traffic. (ii) For different network structures, a classification network structure combining convolution neural network and long short-term memory network is proposed. This structure has the characteristics of learning network traffic time and space characteristics. Experimental results show that the network model can classify encrypted traffic and does not require manual feature extraction. And on the basis of the previous tool, the recognition accuracy rate has increased by 8%

The smart grid is one of the core technologies that enable sustainable economic and social developments. In recent years, various cyber attacks have targeted smart grid systems, which have led to severe, harmful consequences. It would be challenging to build a real smart grid system for cybersecurity experimentation and validation purposes. Hence, analytical techniques, with simulations, can be considered as a practical solution to make smart grid cybersecurity experimentation possible. This paper first provides a literature review on the current state-of-the-art in smart grid attack analysis. We then apply graphical security modeling techniques to design and implement a Cyber Attack Analysis Framework for Smart Grids, named GridAttackAnalyzer. A case study with various attack scenarios involving Internet of Things (IoT) devices is conducted to validate the proposed framework and demonstrate its use. The functionality and user evaluations of GridAttackAnalyzer are also carried out, and the evaluation results show that users have a satisfying experience with the usability of GridAttackAnalyzer. Our modular and extensible framework can serve multiple purposes for research, cybersecurity training, and security evaluation in smart grids.

In this article, an anti-jamming technique based on homologous localisation test in multi-static radar system is proposed to counter multi-range-false-target deception jamming. At first, pre-process is implemented in each receiver to group the targets that locate in the same direction and improve the accuracy of angle measurement. Then, the localisation of bistatic radar system is performed. Finally, homologous localisation test is implemented in fusion centre by using the localisation results from all the receivers. Simulation results demonstrate that the proposed suppression method can discriminate false targets effectively and realise the localisation of the true target.

Pore structure is an important factor affecting the airflow distribution in a grain pile, and hence the variation in its structural parameters under different vertical pressures should be studied further. In this study, grain slice imaging and image processing technologies were comprehensively used to study the changes in the number, size, and shape of the pores in soybean grain piles under vertical pressures of 0, 50, 100, 150, 200, 250, and 300 kPa, and to explore the effects of vertical pressure on the porosity, pore volume, pore diameter, and pore fractal dimension of the grain piles. The results show that the porosity of the grain pile decreased with an increase in the vertical pressure, and the change rate of grain pile porosity was 28% when the vertical pressure was 0-300 kPa. When the vertical pressure increased from 50 to 300 kPa, the grain pile porosity variation increased from 6.49 to 24.33% as compared with the natural state (0 kPa). The pore area and diameter of the grain pile gradually decreased with the increase in vertical pressure. The distribution of the grain pile pore area along the horizontal direction from the side wall to centre showed great differences under different vertical pressures. With an increase in vertical pressure, the proportion of the pore diameter of < 2.5 mm increased, whereas the proportion of the pore diameter of > 2.5 mm decreased. An increase in the vertical pressure will lead to an increase in the grain pile pore fractal dimension. When the vertical pressure was 0-300 kPa, the change rate of the grain pile pore fractal dimension was 10%. The relevant research methods and conclusions drawn in this study may be used to provide theoretical support and a reference source for the airflow distribution and resistance changes in the bulk grain piles. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet, the types of webpages are more abundant than in previous decades. However, it becomes severe that people are facing more and more significant network security risks and enormous losses caused by phishing webpages, which imitate the interface of real webpages and deceive the victims. To better identify and distinguish phishing webpages, a visual feature extraction method and a visual similarity algorithm are proposed. First, the visual feature extraction method improves the Vision-based Page Segmentation (VIPS) algorithm to extract the visual block and calculate its signature by perceptual hash technology. Second, the visual similarity algorithm presents a one-to-one correspondence based on the visual blocks' coordinates and thresholds. Then the weights are assigned according to the tree structure, and the similarity of the visual blocks is calculated on the basis of the measurement of the visual features' Hamming distance. Further, the visual similarity of webpages is generated by integrating the similarity and weight of different visual blocks. Finally, multiple pairs of phishing webpages and legitimate webpages are evaluated to verify the feasibility of the algorithm. The experimental results achieve excellent performance and demonstrate that our method can achieve 94% accuracy. [ABSTRACT FROM AUTHOR]

Resource constrained Internet-of-Things (IoT) devices are highly likely to be compromised by attackers, because strong security protections may not be suitable to be deployed. This requires an alternative approach to protect vulnerable components in IoT networks. In this article, we propose an integrated defense technique to achieve intrusion prevention by leveraging cyberdeception (i.e., a decoy system) and moving target defense (i.e., network topology shuffling). We evaluate the effectiveness and efficiency of our proposed technique analytically based on a graphical security model in a software-defined networking (SDN)-based IoT network. We develop four strategies (i.e., fixed/random and adaptive/hybrid) to address “when” to perform network topology shuffling and three strategies (i.e., genetic algorithm/decoy attack path-based optimization/random) to address “how” to perform network topology shuffling on a decoy-populated IoT network, and we analyze which strategy can best achieve a system goal, such as prolonging the system lifetime, maximizing deception effectiveness, maximizing service availability, or minimizing defense cost. We demonstrated that a softwaredefined IoT network running our intrusion prevention technique at the optimal parameter setting prolongs system lifetime, increases attack complexity of compromising critical nodes, and maintains superior service availability compared with a counterpart IoT network without running our intrusion prevention technique. Further, when given a single goal or a multi-objective goal (e.g., maximizing the system lifetime and service availability while minimizing the defense cost) as input, the best combination of “when” and “how” strategies is identified for executing our proposed technique under which the specified goal can be best achieved. [ABSTRACT FROM AUTHOR]

The rapid development of Internet of Things (IoT) technology has made previously unavailable data available, and applications can take advantage of device data for people to visualize, explore, and build complex analyses. As the size of the network and the number of network users continue to increase, network requests tend to aggregate on a small number of network resources, which results in uneven load on network requests. Real-time, highly reliable network file distribution technology is of great importance in the Internet of Things. This paper studies real-time and highly reliable file distribution technology for large-scale networks. In response to this topic, this paper studies the current file distribution technology, proposes a file distribution model, and proposes a corresponding load balancing method based on the file distribution model. Experiments show that the system has achieved real-time and high reliability of network transmission. [ABSTRACT FROM AUTHOR]

Energy storage is an important technical means to increase the consumption of renewable energy and reduce greenhouse gas emissions. Electrochemical energy storage, represented by lithium-ion batteries, has a promising developmental prospect. The performance of lithium-ion batteries continues to decline in the process of application, and the differences between batteries are increasing. Therefore, accurate estimation of the state of health (SOH) of batteries is the key to the safe and efficient operation of energy storage systems. In this paper, the electrochemical impedance spectroscopy (EIS) characteristics of Li-ion batteries under different states of charge and health were studied. Three groups of Li-ion battery impedance module values under different frequencies were selected as characteristic parameters, and the SOH estimation model of Li-ion batteries was built by using the support vector regression (SVR) algorithm. The results show that: the model with the second group of frequency-point combinations as characteristic parameters takes into account both accuracy and efficiency; the cumulative time of the characteristic frequency test and SOH evaluation of lithium-ion batteries is less than 10 s; and this technology has good engineering application value.