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From April 2020 to June 2022, a Ca+ lidar with dual-wavelength and narrow-band emitting lasers utilizing all-solid-state technology was employed to conduct observations in Beijing (40.41°N, 116.01°E) for a total duration of 1797.79 hours over 280 nights. A total of 286 sporadic Ca+ layers were observed, with heights ranging from 88 to 120 km and densities varying from 5.33 to 2200 cm−3. We simultaneously observed the ionosonde, located 28 km from the lidar, and found a correlation of 61.89% between the sporadic Ca+ layer and the sporadic E layer. When the sporadic Ca+ layer has a downward phase trend, there is a correlation of 76.84%. By excluding the influence of ionosonde sensitivity, we focus only on calcium ions that have a density exceeding 140 cm−3, which exhibit a correlation of 82.25%. Above 100 km, the correlation significantly increases, reaching approximately 90%. Furthermore, the correlation between the sporadic Ca+ layer and the sporadic E layer is particularly noticeable during the summer season, reaching around 90%. This phenomenon can be attributed to the variations in metal ions that occur during different seasons.

To reduce the corrosion rate of biodegradable magnesium alloys, n-MgO/Mg–Zn–Ca composites (SR-3ZXM) were prepared using nano-MgO (n-MgO) reinforcement and screw rolling (SR). n-MgO addition and SR refined the grain size and improved the passivation ability and corrosion resistance of SR-3ZXM. After SR, adding n-MgO reduced the corrosion current density of SR-3ZXM to 20.99 μA/cm2. The corrosion rate calculated based on the material weight loss decreased to 0.88 mm/y, which is attributed to the grain size refinement and variation of secondary phase fraction. Thus, n-MgO addition and SR change the corrosion mechanism and improve the corrosion resistance of SR-3ZXM.

BackgroundCytokines play a pivotal role in the progression of heart failure (HF) by modulating inflammatory responses, promoting vasoconstriction, and facilitating endothelial injury. However, it is now difficult to distinguish the causal relationship between HF and cytokines in observational studies. Mendelian randomization (MR) analyses of cytokines probably could enhance our comprehension to the underlying biological processes of HF.MethodsThis study was to explore the correlation between 41 cytokines with HF at the genetic level by MR analysis. We selected a HF dataset from the Heart Failure Molecular Epidemiology for Therapeutic Targets (HERMES) 2018 and a cytokine dataset from a meta-analysis of cytokine levels in Finns. Two-sample, bidirectional MR analyses were performed using Inverse Variance Weighted (IVW), Weighted Median and MR- egger, and the results were tested for heterogeneity and pleiotropy, followed by sensitivity analysis.ResultsGenetic prediction of high levels of circulating Macrophage inflammatory pro-tein-1β(MIP-1β) (P = 0.0389), Interferon gamma induced protein 10(IP-10) (P = 0.0029), and Regu-lated on activation, normal T cell expressed and secreted(RANTES) (P = 0.0120) expression was associated with an elevated risk of HF. HF was associated with the increased levels of circulating Interleukin-2 receptor, alpha subunit(IL-2ra) (P = 0.0296), Beta nerve growth fac-tor(β-NGF) (P = 0.0446), Interleukin-17(IL-17) (P = 0.0360), Basic fibroblast growth factor(FGF-basic) (P = 0.0220), Platelet derived growth factor BB(PDGF-BB) (P = 0.0466), and Interferon-gamma(IFN-γ) (P = 0.0222); and with decreased levels of Eotaxin (P = 0.0133). The heterogeneity and pleiotropy of the cytokines were acceptable, except for minor heterogeneity of FGF-basic and IL-17.ConclusionThese findings provide compelling evidence for a genetically predictive relationship between cytokines and HF, emphasizing a great potential of targeted modulation of cytokines in slowing the progression of HF. This study draws further conclusions at the genetic level, providing a basis for future large-scale clinical trials.

Abstract Introduction Gestational trophoblastic neoplasia (GTN) with intracardiac metastasis is rare, and here we reported a patient with intracardiac metastasis of high-risk and refractory gestational choriocarcinoma and reviewed relevant literatures. Case presentation A 37-year-old woman presented with vaginal bleeding and high level of β-human chorionic gonadotropin (β-hCG) at 199,060 (mIU/mL). It was clinically diagnosed with gestational choriocarcinoma. The patient initially received eight cycles of chemotherapy but unsatisfactory response was observed, and the level of β-hCG still ranged between 5000 and 10,000. Then there was found intracardiac masses in the right atrium (2.6*1.7 cm), anterior chordae tendineae of the tricuspid valve (1.4*0.7 cm) and the right ventricle (4.1*2.9 cm) by ultrasonic cardiogram (UCG). PET/CT highly suspected the intracardiac metastasis of choriocarcinoma (SUVmax = 9.3) and no disease was found in the lung and pelvis. The patient undertook complete intracardiac masses resection. The pathology confirmed the intracardiac metastasis of disease. After a week of operation, the UCG found a 5.4*4.2 cm mass in the right atrium again. Considering the poor prognosis, the patient received palliative care and eventually died of disease progression. Conclusion Intracardiac metastasis of GTN is an aggressive sign of disease. Patients can benefit from chemotherapy and surgery. Future investigation of PD-1 immunotherapy combines with chemotherapy are expected to improve the prognosis in this group of patients.

The Phase II of Chinese Meridian Project atmosphere wind-temperature-metal-constituents LiDAR at Mohe (122°E, 53°N) station uses a resonance fluorescence scattering mechanism to detect the metal layer composition in the upper atmosphere at 80-120 km , and subsequently uses these metal components as tracers to study various complex chemical and kinetic processes in the middle and upper atmosphere. The time resolution of this LiDAR is 1.1 min and the spatial resolution is 30 m. With high spatial and temporal resolution, we obtained a high signal to noise ratio for Ca number density. Further, we analyzed the evolution of Ca number density with time and height in January 2023. We observed that the peak value of background and sporadic Ca number densities reached approximately 33.55 cm−3 and 53.64 cm−3, respectively. In comparison with that in Yanqing (116.0°E, 40.5°N) station, the Ca number density in Mohe station was higher. Moreover, while examining the Ca observation studies conducted at foreign stations, we found that the Ca number density in Mohe was close to that of Kuhlungsborn station (54°N, 12°E; Germany) and was higher than that of Observatoire de Haute Provence station (44°N, 6E; France). Additionally, during January 12–14, 2023, we observed calcium meteor trails, consistently appearing near the peak of the metal layer spectrum.

ObjectiveCardiac regenerative medicine has gained significant attention in recent years, and integrins are known to play a critical role in mediating cardiac development and repair, especially after an injury from the myocardial infarction (MI). Given the extensive research history and interdisciplinary nature of this field, a quantitative retrospective analysis and visualization of related topics is necessary.Materials and methodsWe performed a scientometric analysis of published papers on cardiac integrin adhesion complexes (IACs), including analysis of annual publications, disciplinary evolution, keyword co-occurrence, and literature co-citation.ResultsA total of 2,664 publications were finally included in the past 20 years. The United States is the largest contributor to the study and is leading this area of research globally. The journal Circulation Research attracts the largest number of high-quality publications. The study of IACs in cardiac repair/regenerative therapies involves multiple disciplines, particularly in materials science and developmental biology. Keywords of research frontiers were represented by Tenasin-C (2019–2023) and inflammation (2020–2023).ConclusionIntegrins are topics with ongoing enthusiasm in biological development and tissue regeneration. The rapidly emerging role of matricellular proteins and non-protein components of the extracellular matrix (ECM) in regulating matrix structure and function may be a further breakthrough point in the future; the emerging role of IACs and their downstream molecular signaling in cardiac repair are also of great interest, such as induction of cardiac proliferation, differentiation, maturation, and metabolism, fibroblast activation, and inflammatory modulation.

The whole temperature distribution of a turbine blade is an important information for heat transfer design for turbine, but it is very difficult and expensive to measure the whole temperature filed, especially for the internal points of a blade. It is necessary to reconstruct the whole temperature distribution by calculating reversely unknown parameters based on sparse temperature values on the blade surface. This paper proposes a new stacking model method for solving inverse heat transfer problems of boundary conditions and it can be applied to cope with multi-input and multi-output regression problems. In order to improve the prediction performance of the stacking model, the Random Forest Regression, the XGBoost Regression and the K-Nearest Neighbors Regression are selected as the base estimators, and the Linear Regression is selected as the meta estimator. An application example for an aero-engine turbine blade is modeled and solved, in which the boundary condition parameters of the exhaust flows are reversely calculated and temperature distributions of a turbine blade surface can be obtained. Numerical experiments have demonstrated that the stacking model outperforms individual base models. This research is of considerable significance to heat transfer design, experimental research and decision support for real-time remote monitoring of aero-engines.

Abstract Selenocompounds protect against damage to healthy cells and induce the death of tumor cells by apoptosis; for this reason, they are attractive compounds for cancer research. In the present study, two series of novel phenoxy-((phenylethynyl) selanyl) propan-2-ol derivatives were synthesized, and their anti-proliferation activities were evaluated. Of the 23 compounds synthesized, most showed potent anti-proliferative activity against human cancer cell lines. Specifically, compounds 3h, 3g, and 3h-2, which had a 2- or 4-position halogen substituent on 1-((phenylethynyl)selanyl)-3-phenoxypropan-2-ol, exhibited the best anti-proliferative activity against tumor cells. Flow cytometry demonstrated that 3h, 3g, and 3h-2 induced G2/M phase arrest and apoptosis in A549 cells. Cellular studies demonstrated that the induction of apoptosis by 3h correlated with changes in the expression of cell cycle-related proteins and apoptosis-related proteins. Xenograft tumor experiments in nude mice revealed that compound 3h has antitumor effects in vivo and no evident toxic effects in nude mice. In addition, compound 3h alleviated cisplatin-induced liver and kidney damage. These findings uncover the applicability of compound 3h as a novel lead compound for cancer treatment.

In order to fully understand the research progress of human factors and traffic safety in curve driving, from the perspective of driver-vehicle-road-environment dynamic traffic system, this paper explored the current research status and development trend of human factors of curve driving, and displayed the development process and structural relationship of human factors research of curve driving by using scientific knowledge map. Through the core collection database of Web of Science, 1408 English literatures related to human factors research of curve driving published from 2012 to 2022 (as of October 1, 2022) were obtained, and the literatures in this field were sorted and analyzed based on the VOSviewer visualization software. The results show that China, Tongji University and Accident Analysis and Prevention are the country, institution and journal with the largest contribution rate in the field of human factors research on curve driving. Co-citation analysis shows that the research contents in this field are divided into 5 clusters: driver's visual characteristics, risk of collision, vehicle dynamics characteristics, the influence of traffic engineering facilities on driving behavior, selection of driving speed. The co-occurrence analysis of keywords shows that the topics of curve geometry design and vehicle dynamics, driving behavior and risk, driving speed and safety, behavior prediction and intervention measures are the current research hotspots in the research field. It is found that the development trend of traffic safety improvement in curves is to construct a continuous, consistent, multi-level visual reference frame conforming to driving expectation through visual guiding technology, and summarizes the technical concept of linear visual guidance. This study can provide a reference for the study of human factors of curve driving.

The appearance of dried fruit clearly influences the consumer’s perception of the quality of the product but is a subtle and nuanced characteristic that is difficult to quantitatively measure, especially online. This paper describes a method that combines several simple strategies to assess a suitable surrogate for the elusive quality using imaging, combined with multivariate statistics and machine learning. With such a convenient tool, this study also shows how one can vary the pretreatments and drying conditions to optimize the resultant product quality. Specifically, an image batch processing method was developed to extract color (hue, saturation, and value) and morphological (area, perimeter, and compactness) features. The accuracy of this method was verified using data from a case study experiment on the pretreatment of hot-air-dried kiwifruit slices. Based on the extracted image features, partial least squares and random forest models were developed to satisfactorily predict the moisture ratio (MR) during drying process. The MR of kiwifruit slices during drying could be accurately predicted from changes in appearance without using any weighing device. This study also explored determining the optimal drying strategy based on appearance quality using principal component analysis. Optimal drying was achieved at 60 °C with 4 mm thick slices under ultrasonic pretreatment. For the 70 °C, 6 mm sample groups, citric acid showed decent performance.

Abstract Background Garlic (Allium sativum), the underground bulb of the Allium genus, has been consumed on Earth for thousands of years. Many clinical trials of garlic supplementation on components of metabolic syndrome (MetS) have emerged in recent years, but there is no consensus on the effect. This meta-analysis aimed at systematically evaluating the effect of garlic supplementation on components of MetS. Methods In this meta-analysis, we searched Pubmed, Embase, Cochrane, Medline, Web of Science databases, and clinical trials online sites from inception to November 1, 2022, with language restrictions to English. We engaged participants > 18 years and eligible for the clinical diagnosis of MetS or those with metabolic disorders and garlic was the only intervention. Outcomes included waist circumference, and body mass index, triglycerides, total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, blood pressure, and fasting blood glucose. Meta-regression and subgroup analyses were conducted based on six covariates (total sample size, the mean age, the mean dose, the duration of intervention, the oral form of garlic, and the dietary intervention). Results Results from 19 RCTs were included engaging 999 participants. Compared to placebo, garlic significantly reduced TG [SMD (95%CI) = -0.66 (-1.23, -0.09)], TC [SMD (95%CI) = -0.43 (-0.86, -0.01)], LDL [SMD (95%CI) = -0.44(-0.88, -0.01)], DBP [SMD (95%CI) = -1.33 (-2.14, -0.53)], BMI [SMD (95%CI) = -1.10(-1.90, -0.20)], and WC [SMD (95%CI) = -0.78(-1.09, -0.47)]. Meta-regression showed age and sample size are potential effect modifiers. Conclusion According to the results of meta-analysis, the modulatory effect of garlic on some MetS components is evident. More high-quality, large-scale RCTs are needed to confirm iat based on the high heterogeneity and potential publication bias of the current data. Trial registration https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=373228 , ID: CRD42022373228.

ObjectiveAs a pseudokinase, Tribbles Pseudokinase 3 (TRIB3) is implicated in a wide array of biological processes, including cell signal transduction, metabolic regulation, stress responses, and immune regulation. While its significant role in the immune regulation of certain cancers is well-established, the specific functions and impact of TRIB3 in head and neck squamous cell carcinoma (HNSC) remain unclear.MethodsThe data of RNA-sequence was acquired from the TCGA database to analyze the expression patterns of TRIB3 and elucidate its prognostic value in HNSC patients. Furthermore, the correlation between TRIB3 and tumor mutation burden, clinical data, immune checkpoint genes, and immune cell infiltration was explored. Moreover, the TRIB3 location in tumor tissues and subcellular structures was identified via Tisch in the HPA database, and the potential protein interaction molecules for TRIB3 were elucidated in the STRING database. The potential TRIB3 gene function was assessed using gene set enrichment analysis (GSEA), whereas the TRIB3 expression levels in clinical HNSC samples were verified by RT-qPCR and immunohistochemistry. the role of TRIB3 in enhancing the malignant behavior of HNSC cells was validated in vitro through a series of methods including RT-qPCR, CCK8 assay, wound healing assay, and transwell assay.ResultsIt was revealed that TRIB3 was significantly overexpressed in the nucleus and cytoplasm of HNSC. Furthermore, this overexpression markedly enhanced the migration ability of tumor cells. As an independent prognostic factor, TRIB3 was associated with advanced tumor T stage and was significantly involved with tumor mutation burden and immune cell infiltration in HNSC. Moreover, it was observed that TRIB3 was not a predicted factor for PD1/PDL1 and ATL4 inhibitor treatment; however, it was substantially correlated with various immune evasion-related genes in HNSC.ConclusionTRIB3 could serve as a potential prognostic marker for HNSC and might be a key gene mediating HNSC immune evasion.

Abstract Background Drug-target interaction (DTI) prediction plays an important role in drug discovery and repositioning. However, most of the computational methods used for identifying relevant DTIs do not consider the invariance of the nearest neighbour relationships between drugs or targets. In other words, they do not take into account the invariance of the topological relationships between nodes during representation learning. It may limit the performance of the DTI prediction methods. Results Here, we propose a novel graph convolutional autoencoder-based model, named SDGAE, to predict DTIs. As the graph convolutional network cannot handle isolated nodes in a network, a pre-processing step was applied to reduce the number of isolated nodes in the heterogeneous network and facilitate effective exploitation of the graph convolutional network. By maintaining the graph structure during representation learning, the nearest neighbour relationships between nodes in the embedding space remained as close as possible to the original space. Conclusions Overall, we demonstrated that SDGAE can automatically learn more informative and robust feature vectors of drugs and targets, thus exhibiting significantly improved predictive accuracy for DTIs.

Abstract Objective To describe myeloid sarcoma (MS) that mimic gynecological tumors and provide guidelines for improving the diagnosis and treatment of patients. Methods This case series study retrospectively analyzed the clinicopathological characteristics and oncological outcomes of female patients who were histologically diagnosed with MS after initially presenting with reproductive-system tumors at the Peking Union Medical College Hospital between January 2000 and March 2022. Results There were eight cases in which MS mimicked cervical cancer, ovarian cancer, or hysteromyoma. Six patients had isolated MS, and the other two had acute myeloid leukemia (AML)-M2. The average age was 39.00 ± 14.26. They each sought advice from a gynecological oncologist at the initial visit, complaining of irregular bleeding (3/8), low abdominal pain (3/8), dysmenorrhea (1/8), or an accidentally found mass (1/8). CT/MRI exams revealed that the average tumor size reached 5.65 ± 2.35 cm, with 50% of the tumors being larger than 8 cm. The final diagnoses were confirmed by biopsy (2/8) or postoperative pathology (6/8); the most frequent positive immunohistochemical markers were Ki-67 (60–90%), MPO (100%), LCA (62.5%), CD43 (62.5%), CD117 (62.5%), CD99 (50%), vimentin (37.5%), and lysozyme (25%). MLL/AF9 gene fusions and CEBPA, JAK2, NRAS, and FLT3-TKD mutations were found in the patients. Six (75%) of the patients showed a complete response after upfront treatment using chemotherapy + surgery and experienced no recurrence during follow-up. The overall survival (OS) rate was 72.9%, and the 5-year OS rate was 72.9% (95%CI: 0.4056–1.000). The median OS was 26 months (range: 3–82). Conclusion For patients with isolated MS, treatment by chemotherapy and surgery are radical procedure, and initial treatment using chemotherapy alone should be considered for MS with synchronous intramedullary AML. Poor response to chemotherapy, short interval to leukemia occurrence, and heavy tumor burden (> 10 cm) could indicate a poor prognosis for patients with MS.

Abstract Pancreatic ductal adenocarcinoma (PDAC) is one of the lethal malignancies, with limited biomarkers identified to predict its prognosis and treatment response of immune checkpoint blockade (ICB). This study aimed to explore the predictive ability of T cell marker genes score (TMGS) to predict their overall survival (OS) and treatment response to ICB by integrating single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data. Multi-omics data of PDAC were applied in this study. The uniform manifold approximation and projection (UMAP) was utilized for dimensionality reduction and cluster identification. The non-negative matrix factorization (NMF) algorithm was applied to molecular subtypes clustering. The Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression was adopted for TMGS construction. The prognosis, biological characteristics, mutation profile, and immune function status between different groups were compared. Two molecular subtypes were identified via NMF: proliferative PDAC (C1) and immune PDAC (C2). Distinct prognoses and biological characteristics were observed between them. TMGS was developed based on 10 T cell marker genes (TMGs) through LASSO-Cox regression. TMGS is an independent prognostic factor of OS in PDAC. Enrichment analysis indicated that cell cycle and cell proliferation-related pathways are significantly enriched in the high-TMGS group. Besides, high-TMGS is related to more frequent KRAS, TP53, and CDKN2A germline mutations than the low-TMGS group. Furthermore, high-TMGS is significantly associated with attenuated antitumor immunity and reduced immune cell infiltration compared to the low-TMGS group. However, high TMGS is correlated to higher tumor mutation burden (TMB), a low expression level of inhibitory immune checkpoint molecules, and a low immune dysfunction score, thus having a higher ICB response rate. On the contrary, low TMGS is related to a favorable response rate to chemotherapeutic agents and targeted therapy. By combining scRNA-seq and bulk RNA-seq data, we identified a novel biomarker, TMGS, which has remarkable performance in predicting the prognosis and guiding the treatment pattern for patients with PDAC.

This paper leverages turbulence theory from physics to examine the similarities and differences between financial market volatility and turbulent phenomena on a statistical physics level. By drawing analogies between the dynamics of financial markets and fluid turbulence, an innovative analytical framework has been developed to enhance our understanding of the complexity inherent in financial markets. The research methodology involves a comparative analysis of several national stock market indices and simulated turbulent velocity time series, with a particular focus on key statistical properties such as probability distributions, correlation structures, and power spectral densities. Furthermore, a financial market capital flow model has been established, and corresponding solutions have been proposed. Through computational simulations and data analysis, it was discovered that financial market volatility shares some statistical characteristics with turbulence, yet there are significant differences in the shape of probability distributions and the timescales of correlations. This indicates that although financial markets exhibit patterns similar to turbulence, as a multivariate-driven complex system, their behavioral patterns do not completely correspond to natural turbulence phenomena, highlighting the limitations of directly applying turbulence theory to financial market analysis. Additionally, the study explores the use of Bézier curves to simulate market volatility and, based on these analyses, formulates trading strategies that demonstrate practical applications in risk management. This research provides fresh perspectives for the fields of financial market theory and econophysics, offering new insights into the complexity of financial markets and the prevention and management of financial risks.

In situ combustion (ISC), an efficient and economical method for enhancing heavy oil recovery in high-pressure, high-viscosity, and thermally challenged reservoirs, relies on the kinetics of crude oil oxidation. Despite an increased focus on kinetic models, there is a gap in understanding how oxidation kinetic parameters impact ISC effectiveness in heavy oil reservoirs. This study addresses this by selecting heavy oil samples from the G Block in the Liaohe oilfield and the M Block in the Huabei oilfield and conducting ramped temperature oxidation (RTO), pressure differential scanning calorimetry (PDSC), and thermogravimetric analysis (TGA) experiments. RTO detailed the thermal conversion process, categorizing oxidation into low-temperature oxidation (LTO), fuel deposition (FD), and high-temperature oxidation (HTO) stages. PDSC and TGA provided thermal characteristics and kinetic parameters. The feasibility of fire flooding was evaluated. Using CMG-STARS, an ISC model was established to analyze the impact of kinetic parameter changes. Activation energy significantly affected coke combustion, while the pre-exponential factor had a notable impact on cracking reactions. The recommended values for activation energy and the pre-exponential factor are provided. This study not only guides fire flooding experiments but also supports field engineering practices, particularly for in situ combustion in heavy oil reservoirs.

Lidar observations of metal layers play a significant role in research on the chemistry and dynamics of the mesosphere and lower thermosphere. This work reports on Fe lidar observations conducted in Beijing and Mohe. Utilizing the same laser emission system, a 1064 nm seed laser was injected into an Nd: YAG laser to generate a single longitudinal-mode pulse 532 nm laser, which pumped a dye laser to produce a 572 nm laser. The 572 nm laser and the remaining 1064 nm fundamental frequency laser passed through a sum–frequency module to generate a 372 nm laser to detect the Fe layer. According to a total of 52.6 h of observations for 10 nights in Beijing, the Fe layer has an average column density of 1.24 × 1010 cm−2, an RMS width of 4.4 km and a centroid altitude of 89.4 km. In Mohe, observed for 16 nights and a total of 91.5 h, the Fe layer has an average column density of 1.08 × 1010 cm−2, an RMS width of 4.6 km and a centroid altitude of 89.5 km. The probability of the occurrence of sporadic Fe layers was 42.4% in Beijing and 29.4% in Mohe. Compared to simultaneously observed Na layers, the occurrence probabilities of sporadic Fe layers were higher than those of sporadic Na layers in both stations. Based on the two cases observed in Beijing, it is conjectured that the formation mechanism of sporadic metal layers above approximately 100 km has a more significant influence on sporadic Fe layers than on sporadic Na layers. The lower thermospheric Fe layers with densities significantly larger than those of the main layer were observed during two nights in Mohe. This work contributes to the refinement of the global distribution of Fe layers and provides abundant observational data for the modeling and study of the metal layers.

Alzheimer's disease (AD) is a progressive neurodegenerative disease that has still not been effectively treated. Paeoniflorin is a traditional Chinese medicine with potential neuroprotective effects against brain injury; however, the beneficial effects and mechanisms of action in AD have not been clarified. We aimed to explore the mechanisms of action of paeoniflorin in AD using network pharmacology and experimental validation. Network pharmacology analysis revealed 30 candidate targets through the intersection of the targets of paeoniflorin and related genes in AD, which were mainly enriched in oxidative stress and inflammation. Moreover, key targets of paeoniflorin against AD, namely Nrf2 (encoded by NFE2L2) and TLR4, were screened and found to be closely related to oxidative stress and inflammation. Subsequent in vivo experiments revealed that paeoniflorin treatment improved the cognition of APP/PS1 mice by ameliorating oxidative stress and neuroinflammation, which were associated with the upregulation of Nrf2 and HO1, and the downregulation of TLR4. Collectively, the present study demonstrates that paeoniflorin alleviates cognitive impairment in AD by regulating oxidative stress and neuroinflammation, and that Nrf2, HO1, and TLR4 could be key targets.

Abstract In the new century, cardiac amyloidosis has received more attention from many countries and institutions, leading to innovations in the essence of the pathology, biological markers, noninvasive tests, and staging diagnoses and treatments for this disease. However, few reviews have summarized the research trends and hotspots in cardiac amyloidosis. Bibliometrics analysis is a statistically based approach to research that visualizes the contributions of academic institutions and changes in research hotspots. Therefore, in this paper, we used Citespace and VOSviewer software to conduct co-occurrence analysis and collaborative network analysis on the countries, institutions, and authors in the articles related to cardiac amyloidosis since the new century. And further find out burst keywords and references to obtain the research history, disciplinary development, and new hotspots and topics.

People’s economic activities constitute a payment network for money transfers. The existing studies on the transfer law of money in payment networks are relatively few and need to explain the money transfer mechanism in detail. To quantitatively describe the transfer law of funds in payment networks, this paper constructs a payment network energy model for in-depth study. To quantitatively describe the transfer distance, probability, and intensity of funds in the payment network nodes, this paper first constructs a model describing the transfer of funds in the payment network based on quantum financial algorithms and a transfer matrix describing the evolution of funds in it is constructed in the model. Then we compare the transfer of funds in the payment network and the quantum wandering of microscopic particles. The intensity of payment in the network and the quantum wandering on the microscopic network have similar properties. Finally, an empirical strategy is constructed with real data for empirical evidence, which supports the theory in the model of this paper. In terms of empirical evidence, we selected exchange rate data for 49 national foreign exchange markets from January 1, 2017, to August 1, 2022, considering that the exchange rate corresponds to a complex payment network behind the exchange rate. We construct a descriptive strategy for statistical funds leaping between nodes according to the model in this paper and empirically validate the data by a clustering algorithm. It is found that the leap distance and probability of funds in the payment network generally show a cardinal distribution with d.f. = 1, in which the intensity of funds transfer is a smooth asymptotic process from strong to weak, and some aggregation and non-uniform distribution phenomena are also found in it. This paper fully reveals the transfer mechanism of funds on the payment network, which is of some significance to understanding the payment law of economic activities.

Abstract Background Personalized therapy has been at the forefront of cancer care, making cancer treatment more effective. Since cancer patients respond individually to drug therapy, predicting the sensitivity of each patient to specific drugs is very helpful to apply therapeutic agents. Traditional methods focus on node (molecular) information but ignore relevant interactions among different nodes, which has very limited application in complex situations, such as cancer drug responses in real clinical practice. Results Treatment evaluation with Quantified Network (TreeQNet) is a webserver which could predict sensitivity to drugs for patients through the innovative use of proteomic and phosphoproteomic network from tumor tissues. Conclusion TreeQNet service: http://bioinfo.ustc.edu.cn/ . TreeQNet source code: https://github.com/Really00/treeqnet-web-front/ .

In March 2020, an all-solid-state dual-wavelength narrow-band lidar system was deployed. A total of 226 nights spanning from March 2020 to July 2022 were employed in order to investigate the seasonal variations of calcium atoms and ions in the mesosphere over Beijing (40.41°N, 116.01°E). The Ca+ layer shows general annual variation, while a semiannual variation is observed on the Ca layer. The calcium atomic column densities ranged from 2.0 × 106 to 1.1 × 108 cm−2, and the calcium ion column densities ranged from 1.6 × 106 to 4.2 × 108 cm−2. The mean centroid heights of Ca+ and Ca are 98.6 km and 93.0 km, respectively, and the centroid heights of Ca+ and Ca are mostly influenced by annual variations. The seasonal variation in the Ca+ and Ca layers in Beijing exhibits similarities to that of Kühlungsborn (54°N). While the peak density of Ca+ in Beijing are similar to those observed in Kühlungsborn, the peak density of the Ca layer in Beijing is about half of that reported in the Ca layer at 54°N. We provide an explanation for the disparities in the column abundance and centroid altitude of the Ca layer between Yanqing and Kühlungsborn, discussing variations in neutralization among different metal ions.

This study explores the quantum leapfrog mechanism within the context of quantum finance and presents a new interpretation of established financial models through a quantum perspective. In quantum physics, the well-documented phenomenon of particles tunneling through energy barriers has a parallel in finance. We propose a quantum financial leapfrog model in which asset prices make quantum leaps, penetrating market “energy barriers” in non-sequential advances. By leveraging the Hamiltonian operator and the Schrödinger equation, our approach simulates the dynamics of asset prices in a manner akin to the trajectories of particles in quantum mechanics. We draw an analogy between financial markets and gravitational fields, and from this we derive energy equations for pricing orbits. Using path integration techniques, we map out potential price transitions between these orbits, which are guided by the calculation of minimal energy barriers. Furthermore, we introduce a market “propagator” that aligns with the uncertainty principle, identifying the optimal price pathways. Our findings provide new insights and methodologies for navigating the complexities of financial markets, underscoring the significant potential of quantum approaches in the field of finance. These findings have theoretical implications for a variety of market stakeholders, offering strategic guidance and a reference point. We expect that the advancement of the quantum financial leapfrog theory will refine analytical methods and enhance investment strategies in practical financial applications.

VLF magneto-electric (ME) antennas have gained attention for their compact size and high radiation efficiency in lossy conductive environments. However, the need for a large DC magnetic field bias presents challenges for miniaturization, limiting portability. This study introduces a self-biased ME antenna with an asymmetric design using two magneto materials, inducing a magnetization grading effect that reduces the resonant frequency during bending. Operating principles are explored, and performance parameters, including the radiation mechanism, intensity and driving power, are experimentally assessed. Leveraging its excellent direct and converse magneto-electric effect, the antenna proves adept at serving as both a transmitter and a receiver. The results indicate that, at 2.09 mW and a frequency of 24.47 kHz, the antenna has the potential to achieve a 2.44 pT magnetic flux density at a 3 m distance. A custom modulation–demodulation circuit is employed, applying 2ASK and 2PSK to validate communication capability at baseband signals of 10 Hz and 100 Hz. This approach offers a practical strategy for the lightweight and compact design of VLF communication systems.

Abstract Objective Spastic paraplegia‐12 (SPG12) is a subtype of hereditary spastic paraplegia caused by Reticulon‐2 (RTN2) mutations. We described the clinical and genetic features of three SPG12 patients, functionally explored the potential pathogenic mechanism of RTN2 mutations, and reviewed RTN2‐related cases worldwide. Methods The three patients were 31, 36, and 50 years old, respectively, with chronic progressive lower limb spasticity and walking difficulty. Physical examination showed elevated muscle tone, hyperreflexia and Babinski signs in the lower limbs. Patients 1 and 3 additionally had visual, urinary, and/or coordination dysfunctions. Patient 2 also had epileptic seizures. RTN2 mutations were identified by whole‐exome sequencing, followed by Sanger sequencing, segregation analysis, and phenotypic reevaluation. Functional examination of identified mutations was further explored. Results Three variants in RTN2 were identified in Patient 1 (c.103C>T, p.R35X), Patient 2 (c.230G>A, p.G77D), and Patient 3 (c.337C>A, p.P113T) with SPG, respectively. Western blotting revealed the p.R35X with smaller molecular weight than WT and other two missense mutants. Immunostaining showed the wild type colocalized with endoplasmic reticulum (ER) in vitro. p.R35X mutant diffusely distributes in the cytoplasm, losing colocalization with ER. p.G77D and p.P113T co‐localized with ER, which was abnormally aggregated in clumps. Interpretation In this study, we identified three cases with complicated SPG12 due to three novel RTN2 mutations, respectively, presenting various phenotypes: classic SPG symptoms with (1) visual abnormalities and sphincter disturbances or (2) seizures. The phenotypic heterogeneity might arise from the abnormal subcellular localization of mutant Reticulon‐2 and improper ER morphogenesis, revealing the RTN2‐related spectrum is still expanding.

Based on the resonance fluorescence scattering mechanism, a narrowband sodium (Na) lidar can measure temperature and wind in the mesosphere and lower thermosphere (MLT) region. By using a narrowband spectral filter, background light noise during the day can be suppressed, allowing for continuous observations. To obtain full-diurnal-cycle temperature and wind measurement results, a complex and precise retrieval process is required, along with necessary corrections to minimize measurement errors. This paper introduces the design of a data acquisition unit for three frequencies in three directions of the Na lidar system in the Chinese Meridian Project (Phase II) and investigates the calibration and retrieval methods for obtaining diurnal temperature and horizontal wind in the MLT region, using a Na Doppler lidar with Faraday anomalous dispersion optical filter (FADOF). Furthermore, these methods are applied to observations conducted by a Na lidar in Beijing, China. The wind and temperature results over full diurnal cycles obtained from the all-solid-state Na Doppler lidar are reported for the first time and compared with temperature measurements from satellite, as well as wind observations from a meteor radar. The comparison demonstrates a reasonable agreement between the results, indicating the rationality of the lidar-retrieved results and the feasibility and effectiveness of the data correction and retrieval method.

We report an all-solid-state narrowband lidar system for the simultaneous detection of Ca and Ca+ layers over Yanqing (40.41°N, 116.01°E). The uniqueness of this lidar lies in its transmitter, which is based on optical parametric oscillation (OPO) and optical parametric amplification (OPA) techniques. The injection seeded OPO and the OPA are pumped by the second harmonic of an injection-seeded Nd:YAG laser, which can generate coherent light at the wavelength of 786 nm or 846 nm lasers, whose second harmonics in turn generate the 393 nm or 423 nm pulses, respectively, for the detection of thermospheric and ionospheric Ca+ and Ca layers. Compared to the conventional dye-based system, this lidar transmitter is a narrowband system (bandwidth < 200 MHz), which has produced a factor of two more output power with higher stability and reliability. The lidar system in Yingqing demonstrated Ca+ detection sensitivity of 0.1 atoms-cm−3 for long-term observation and reached a height of ~300 km. Potential applications and further improvements in this lidar technique are also discussed in this paper.

Abstract Background Attributed to the immunosuppression caused by malignancy itself and its treatments, cancer patients are vulnerable to developing nosocomial infections. This study aimed to develop a nomogram to predict the in-hospital death risk of these patients. Methods This retrospective study was conducted at a medical center in Northwestern China. The univariate and multivariate logistic regression analyses were adopted to identify predictive factors for in-hospital mortality of nosocomial infections in cancer patients. A nomogram was developed to predict the in-hospital mortality of each patient, with receiver operating characteristic curves and calibration curves being generated to assess its predictive ability. Furthermore, decision curve analysis (DCA) was also performed to estimate the clinical utility of the nomogram. Results A total of 1,008 nosocomial infection episodes were recognized from 14,695 cancer patients. Extended-spectrum β-lactamase (ESBL)-producing Escherichia coli (15.5%) was the most predominant causative pathogen. Besides, multidrug-resistant strains were discovered in 25.5% of cases. The multivariate analysis indicated that Eastern Cooperative Oncology Group Performance Status 3–4, mechanical ventilation, septic shock, hypoproteinemia, and length of antimicrobial treatment

Abstract Background Adipose-derived stem cells (ADSCs) and their extracellular vesicles (EVs) have therapeutic potential in ischemic brain injury, but the underlying mechanism is poorly understood. The current study aimed to explore the contribution of miRNAs in ADSC-EVs to the treatment of cerebral ischemia. Methods After the intravenous injection of ADSC-EVs, therapeutic efficacy was evaluated by neurobehavioral tests and brain atrophy volume. The polarization of microglia was assessed by immunostaining and qPCR. We further performed miRNA sequencing of ADSC-EVs and analyzed the relationship between the upregulated miRNAs in ADSC-EVs and microglial polarization-related proteins using Ingenuity Pathway Analysis (IPA). Results The results showed that ADSC-EVs reduced brain atrophy volume, improved neuromotor and cognitive functions after mouse ischemic stroke. The loss of oligodendrocytes was attenuated after ADSC-EVs injection. The number of blood vessels, as well as newly proliferated endothelial cells in the peri-ischemia area were higher in the ADSC-EVs treated group than that in the PBS group. In addition, ADSC-EVs regulated the polarization of microglia, resulting in increased repair-promoting M2 phenotype and decreased pro-inflammatory M1 phenotype. Finally, STAT1 and PTEN were highlighted as two downstream targets of up-regulated miRNAs in ADSC-EVs among 85 microglia/macrophage polarization related proteins by IPA. The inhibition of STAT1 and PTEN by ADSC-EVs were confirmed in cultured microglia. Conclusions In summary, ADSC-EVs reduced ischemic brain injury, which was associated with the regulation of microglial polarization. miRNAs in ADSC-EVs partly contributed to their function in regulating microglial polarization by targeting PTEN and STAT1.

Abstract We report a world record of lidar profiling of metallic Ca+ ions up to 300 km in the midlatitude nighttime ionosphere during geomagnetic quiet time. Ca+ measurements (∼80–300 km) were made over Beijing (40.42°N, 116.02°E) with an Optical‐Parametric‐Oscillator‐based lidar from March 2020 through June 2021. Main Ca+ layers (80–100 km) persist through all nights, and high‐density sporadic Ca+ layers (∼100–120 km) frequently occur in summer. Thermosphere‐ionosphere Ca+ (TICa+) layers (∼110–300 km) are likely formed via Ca+ uplifting from these sporadic layers. The lidar observations capture the complete evolution of TICa+ layers from onset to ending, revealing intriguing features. Concurrent ionosonde measurements show strong sporadic E layers developed before TICa+ and spread F onset. Neutral winds can partially account for observed vertical transport but enhanced electric fields are required to explain the results. Such lidar observations promise new insights into E‐ and F‐region coupling and plasma inhomogeneities.

The involvement of refugee families in early childhood education and care (ECEC) is a complex issue in many countries. In this paper, we explore how early childhood (EC) educators construct refugee families’ sense of belonging in two metropolitan and one regional EC setting in Victoria, Australia. We undertook a multiple case study, analyzing interview data. We implemented Bourdieu’s notion of cultural capital and identity capital, drawing on the Provision Articles from the Convention on the Rights of the Child (1989) and the Australian Early Years Learning Framework 2.0. Our study revealed the tensions between creating a sense of belonging and recognizing the identity and cultural capital of refugee children and their families as they access ECEC programs. We conclude with suggestions for ways forward to navigate this space, including the need for greater awareness of research on the importance of a sense of belonging for refugee families in ECEC settings.

Undesirable chatter is one of the key problems that restrict the improvement of robot milling quality and efficiency. The prediction of chatter stability, which is used to guide the selection of process parameters, is an effective method to avoid chatter in robot milling. Due to the weak stiffness of the robot, deformation caused by milling forces becomes an unavoidable problem, which will change the tool–workpiece contact area and affect the stability prediction. However, it is often simplified and neglected. In this paper, a multipoint contact dynamic model of robot milling is established, which considers the influence of force-induced deformation on the regenerative effect and process damping. The tool–workpiece contact area is discretized into a finite number of nodes along the axial direction so that the force and deformation at each node can be calculated separately. The different contact forms of the tool–workpiece under different process parameters are discussed in different cases, and the interaction process between cutting force and force-induced deformation is analyzed in detail. An iterative strategy is used to calculate the deformation of each node and the result of the tool–workpiece contact boundary. Finally, chatter stability of robot milling is predicted by a fully discrete method. Robot milling experiments were carried out to verify the predicted results. The results show that force-induced deformation is an important factor improving the stability prediction accuracy of robot milling, and a more accurate prediction result can be obtained by simultaneously considering force-induced deformation and process damping.

The interval changes in yellow light signals significantly impact driving behaviors that affect traffic safety. This study aims to compare the applicability of different interval changes in yellow light signals from the perspectives of drivers. Data acquisition was conducted in a naturalistic driving experiment with equipment including V-BOX, Ergo LAB, and I-View HED4 to acquire vehicle running index data, driver physiological and psychological index data, and eye movement index data, respectively. Twenty experienced drivers were recruited as subjects and were categorized as safe, average, or dangerous drivers. Based on the data obtained, driving behaviors for different interval changes in yellow light signals were studied using an Atlas. Observations of the driving behavior Atlas reveal that there are notable differences in driving behaviors for different interval changes in yellow light signals. Also, it was found that dangerous drivers are more likely to be affected by the interval changes in yellow light signals. It is necessary to choose reasonable signal transition modes to improve the safety of intersections based on actual road conditions and types of drivers. The results can guide traffic management departments when choosing a reasonable transition method for yellow light signals and help formulate customized training programs for different types of drivers. In particular, the results help in providing training on driving speed and deceleration control for dangerous drivers, which will help improve traffic safety.

Cardiovascular disease (CVD) has become a huge challenge for the global public health system due to its high morbidity, mortality and severe economic burden. In recent years, angiotensin receptor neprilysin inhibitor (ARNI), a new class of drugs, has shown good therapeutic effects on CVD patients in several clinical studies, reducing the morbidity and mortality of CVD patients. In this study, we retrieved publications on ARNI research in the cardiovascular field from the Web of Science core collection and analyzed the annual output, spatial and temporal distribution, institutions and authors, core journals, keywords and co-cited literature based on CiteSpace. As a result, 604 publications were retrieved, and the number of annual publications generally increased year by year, with the largest number of articles. The analysis of the co-occurrence of output countries and authors showed that a few developed countries such as the United States, Canada, and United Kingdom are the most active in this field, forming academic groups represented by John Joseph Valentine McMurray and Scott D. Solomon, and New England Journal of Medicine, Cirulation, and Journal of the American College of Cardiology are the most popular journals in the field, with research hotspots focused on ARNI in the treatment of total ejection fraction heart failure, hypertension and its target organ damage, with the potential for future benefit throughout the cardiovascular event chain as research progresses. This study reveals the prospective application of ARNI in the cardiovascular field and the research hotspots, providing broader and deeper guidance for its use in the clinic, which is beneficial to improve the treatment and prognosis of CVD patients.

Hepatocellular carcinoma (HCC), leading cancer worldwide, has a high degree of genetic heterogeneity; next-generation sequencing (NGS) technology has contributed significantly to the discovery of driving genes as well as high-frequency mutations in HCC. The detection of gene alterations may allow us to predict prognosis and adverse drug reactions for individuals, paving the way for personalized medicine in HCC patients. In this review, we summarized the common systemic therapy regimens for HCC and the predictive efficacy of genetic biomarkers on the prognosis of patients under these treatments. Finally, we put forward a future perspective on the potential of NGS technology for the guidance of targeted therapy and immunotherapy in HCC.

Hereditary spastic paraplegia (HSP) represents a group of rare inherited neurodegenerative conditions and is characterized by progressive lower limb spasticity. Ubiquitin-associated protein 1 (UBAP1)-related HSP is classified as spastic paraplegia-80 (SPG80), which is an autosomal-dominant (AD) juvenile-onset neurologic disorder and mainly affects the lower limbs. We described the clinical and genetic features of two patients in the same family caused by heterozygous mutation of the UBAP1 gene. The proband was a 34-year-old woman with progressive spasticity and hyperreflexia in the lower limbs for 26 years. Her mother also had similar symptoms since the age of 6. The proband and her mother only had motor dysfunctions, such as unsteady gait, hypertonia, and hyperreflexia of lower limbs. Other system functions (sensory, urinary, visual, and cognitive impairments) were not involved. WES disclosed a frameshift mutation (c.371dupT) in the UBAP1 gene, which was predicted to be “likely pathogenic” and was co-segregated in the pedigree. c.371dupT, encoding the truncated UBAP1 protein with 72.6% missing of the normal amino acid sequence, is responsible for the spastic paraplegia (SPG) in this family. In combination with clinical characteristics, genetic testing results, and co-segregation analysis, the diagnosis is considered to be pure spastic paraplegia-80 (SPG80), which is an AD disease. By retrospectively analyzing the documented cases, we comprehensively review the phenotypic features and summarize the genotype spectrum of SPG80 to enhance earlier recognition and therapeutic strategies.

Abstract Background Biomedical event extraction is a fundamental and in-demand technology that has attracted substantial interest from many researchers. Previous works have heavily relied on manual designed features and external NLP packages in which the feature engineering is large and complex. Additionally, most of the existing works use the pipeline process that breaks down a task into simple sub-tasks but ignores the interaction between them. To overcome these limitations, we propose a novel event combination strategy based on hybrid deep neural networks to settle the task in a joint end-to-end manner. Results We adapted our method to several annotated corpora of biomedical event extraction tasks. Our method achieved state-of-the-art performance with noticeable overall F1 score improvement compared to that of existing methods for all of these corpora. Conclusions The experimental results demonstrated that our method is effective for biomedical event extraction. The combination strategy can reconstruct complex events from the output of deep neural networks, while the deep neural networks effectively capture the feature representation from the raw text. The biomedical event extraction implementation is available online at http://www.predictor.xin/event_extraction .

Background: Lung adenocarcinoma (LUAD) remains a lethal disease worldwide, with numerous studies exploring its potential prognostic markers using traditional RNA sequencing (RNA-seq) data. However, it cannot detect the exact cellular and molecular changes in tumor cells. This study aimed to construct a prognostic model for LUAD using single-cell RNA-seq (scRNA-seq) and traditional RNA-seq data.Methods: Bulk RNA-seq data were downloaded from The Cancer Genome Atlas (TCGA) database. LUAD scRNA-seq data were acquired from Gene Expression Omnibus (GEO) database. The uniform manifold approximation and projection (UMAP) was used for dimensionality reduction and cluster identification. Weighted Gene Correlation Network Analysis (WGCNA) was utilized to identify key modules and differentially expressed genes (DEGs). The non-negative Matrix Factorization (NMF) algorithm was used to identify different subtypes based on DEGs. The Cox regression analysis was used to develop the prognostic model. The characteristics of mutation landscape, immune status, and immune checkpoint inhibitors (ICIs) related genes between different risk groups were also investigated.Results: scRNA-seq data of four samples were integrated to identify 13 clusters and 9cell types. After applying differential analysis, NK cells, bladder epithelial cells, and bronchial epithelial cells were identified as significant cell types. Overall, 329 DEGs were selected for prognostic model construction through differential analysis and WGCNA. Besides, NMF identified two clusters based on DEGs in the TCGA cohort, with distinct prognosis and immune characteristics being observed. We developed a prognostic model based on the expression levels of six DEGs. A higher risk score was significantly correlated with poor survival outcomes but was associated with a more frequent TP53 mutation rate, higher tumor mutation burden (TMB), and up-regulation of PD-L1. Two independent external validation cohorts were also adopted to verify our results, with consistent results observed in them.Conclusion: This study constructed and validated a prognostic model for LUAD by integrating 10× scRNA-seq and bulk RNA-seq data. Besides, we observed two distinct subtypes in this population, with different prognosis and immune characteristics.

This is a project report to introduce the Comprehensive Sexuality Education Technical Guideline—Adaptation of Global Standards for Potential Use in China (First Edition) (hereafter referred to as the Guideline) as the first adaptation of the International Technical Guidance on Sexuality Education (ITGSE) in China. The project was supported by the United Nations Population Fund (UNFPA) and the United Nations Educational, Scientific and Cultural Organization (UNESCO) from 2018 to 2022. The development process included rounds of participatory consultation, validation, and revisions led by the project team and a group of technical advisers from diverse backgrounds. The Guideline is developed to respond to the increasing demands of a technical tool integrating international standards and local context that can be used by all comprehensive sexuality education (CSE) stakeholders in China. While keeping the structure of the ITGSE, the Guideline made modifications and additions in light of the up-to-date Chinese policies, laws, and relevant national programs, as well as Chinese culture and social norms. It is expected that the Guideline could be widely acknowledged, distributed, and used to inform and support the future development of CSE in China.

Objectives: Assessing implant stability is integral to dental implant therapy. This study aimed to construct a multi-task cascade convolution neural network to evaluate implant stability using cone-beam computed tomography (CBCT). Methods: A dataset of 779 implant coronal section images was obtained from CBCT scans, and matching clinical information was used for the training and test datasets. We developed a multi-task cascade network based on CBCT to assess implant stability. We used the MobilenetV2-DeeplabV3+ semantic segmentation network, combined with an image processing algorithm in conjunction with prior knowledge, to generate the volume of interest (VOI) that was eventually used for the ResNet-50 classification of implant stability. The performance of the multitask cascade network was evaluated in a test set by comparing the implant stability quotient (ISQ), measured using an Osstell device. Results: The cascade network established in this study showed good prediction performance for implant stability classification. The binary, ternary, and quaternary ISQ classification test set accuracies were 96.13%, 95.33%, and 92.90%, with mean precisions of 96.20%, 95.33%, and 93.71%, respectively. In addition, this cascade network evaluated each implant’s stability in only 3.76 s, indicating high efficiency. Conclusions: To our knowledge, this is the first study to present a CBCT-based deep learning approach CBCT to assess implant stability. The multi-task cascade network accomplishes a series of tasks related to implant denture segmentation, VOI extraction, and implant stability classification, and has good concordance with the ISQ.

This paper presents generic structural cryptanalysis against type-I generalized Feistel networks (GFN), in which all the inner transformations are unknown. The target of our attack is to retrieve all the unknown round functions. We provide an improved yoyo game distinguisher, in which one rejects a large group of start guesses by a single wrong guess, hence is quite advantageous for reducing the complexity. Next, we exploit this distinguisher to develop a recovery attack of such structure and find the look-up tables of the first, eighth, and ninth round functions. Then by the encryption and decryption similarity, we recover the LUTs of the second, third, and tenth round functions from the decrypt direction. Finally, we retrieve the rest rounds by using the analytic relationships between the plaintexts and their four-round encryption results. Our complete recovery requires time complexity O(23.36n) and memory O(2n), where n is the branch size. For 64-bit block cipher, our result will approximate a real-life attack. This paper is the first recovery attack against ten-round type-I GFN.

The inflammatory response is one of the key events in cerebral ischemia, causing secondary brain injury and neuronal death. Studies have shown that the NLRP3 inflammasome is a key factor in initiating the inflammatory response and that Dl-3-n-butylphthalide (NBP) can attenuate the inflammatory response and improve neuronal repair during ischemic stroke. However, whether NBP attenuates the inflammatory response via inhibition of NLRP3 remains unclear. A 90 min middle cerebral artery occlusion was induced in 62 2-month-old adult male ICR mice, and NBP was administered by gavage zero, one, or two days after ischemia. Brain infarct volume, neurological deficits, NLRP3, microglia, and neuronal death were examined in sacrificed mice to explore the correction between NBP effects and NLRP3 expression. NBP significantly reduced infarct volume and attenuated neurological deficits after ischemic stroke compared to controls (p < 0.05). Moreover, it inhibited ASC+ microglia activation and NLRP3 and CASP1 expression in ischemic mice. In addition, neuronal apoptosis was reduced in NBP-treated microglia cultures (p < 0.05). Our results indicate that NBP attenuates the inflammatory response in ischemic mouse brains, suggesting that NBP protects against microglia activation via the NLRP3 inflammasome.

It is necessary to establish effective communication between two horizontal wells during the preheating period of steam-assisted gravity-drainage (SAGD). However, the preheating time is usually very long, which results in high steam consumption and CO2 emissions. There is little research on the effects of different wellbore fluids during the preheating period. The heat transfer and heating characteristics of different wellbore fluids–water, heat-conduction oil, and air–were explored by using physical experiments and numerical simulations. In this study, the results indicated that the heat-transfer performance of heat-conduction oil is the best. The numerical simulation’s results indicated that compared with the wellbore saturated with water, the heat-conduction oil reduced the viscosity of crude oil, and energy consumption was not obvious during the preheating stage. The super-heavy oil flowed into the wellbore due to the solubility of the heat-conduction oil and its own gravity. As a result, the super-heavy oil content in the wellbore gradually accumulated, increasing the risk of coking. Those experiments showed that the use of electrical heating provides good potential to improve SAGD efficiency during the preheating period, and water is the best injection fluid for wellbores during the electrical heating process.

Usually, the quasi-normal fluctuations in practical applications are described via symmetric uncertainty variables, which is a common phenomenon in the manufacturing industry. However, it is relatively scarce in the literature to discuss two-fold uncertainty due to the its complexity. To deal with roughness and ambiguity to accommodate inherent uncertainties, fuzzy rough programming approaches are put forward. In this paper, we pay attention to exploring two kinds of programming problems, namely fuzzy rough single-objective programming and fuzzy rough multi-objective programming, in which objective functions and/or constraints involve fuzzy rough variables (FRV). In accordance with the related existing research of FRVs, such as the chance measure and the expected value (EV) operator, this paper further discusses the EV model, convexity theory, and the crisp equivalent model of fuzzy rough programming. After that, combined with the latest published NIA-S fuzzy simulation technique, a new fuzzy rough simulation algorithm is developed to calculate the EVs of complicated functions for handling the presented fuzzy rough programming problems. In the end, the two types of numerical examples are provided for demonstration.

Parkinson’s disease (PD) is the second most common neurodegenerative disorder, and the mechanism of its occurrence is still not fully elucidated. Accumulating evidence has suggested that the gut acts as a potential origin of PD pathogenesis. Recent studies have identified that inflammatory bowel disease acts as a risk factor for Parkinson’s disease, although the underlying mechanisms remain elusive. The aim of this study was to further explore the molecular mechanism between PD and Crohn’s disease (CD). The gene expression profiles of PD (GSE6613) and CD (GSE119600) were downloaded from the Gene Expression Omnibus (GEO) database and were identified as the common differentially expressed genes (DEGs) between the two diseases. Next, analyses were performed, including functional enrichment analysis, a protein–protein interaction network, core genes identification, and clinical correlation analysis. As a result, 178 common DEGs (113 upregulated genes and 65 downregulated genes) were found between PD and CD. The functional analysis found that they were enriched in regulated exocytosis, immune response, and lipid binding. Twelve essential hub genes including BUB1B, BUB3, DLGAP5, AURKC, CBL, PCNA, RAF1, LYN, RPL39L, MRPL13, RSL24D1, and MRPS11 were identified from the PPI network by using cytoHubba. In addition, inflammatory and metabolic pathways were jointly involved in these two diseases. After verifying expression levels in an independent dataset (GSE99039), a correlation analysis with clinical features showed that LYN and RAF1 genes were associated with the severity of PD. In conclusion, our study revealed the common pathogenesis of PD and CD. These common pathways and hub genes may provide novel insights for mechanism research.

Offshore sites show greater potential for wind energy utilization than most onshore sites. When planning an offshore wind power farm, the speed of offshore wind is used to estimate various operation parameters, such as the power output, extreme wind load, and fatigue load. Accurate speed prediction is crucial to the running of wind power farms and the security of smart grids. Unlike onshore wind, offshore wind has the characteristics of random, intermittent, and chaotic, which will cause the time series of wind speeds to have strong nonlinearity. It will bring greater difficulties to offshore wind speed predictions, which traditional recurrent neural networks cannot deal with for lacking in long-term dependency. An offshore wind speed prediction method is proposed by using a clockwork recurrent network (CWRNN). In a CWRNN model, the hidden layer is subdivided into several parts and each part is allocated a different clock speed. Under the mechanism, the long-term dependency of the recurrent neural network can be easily addressed, which can furthermore effectively solve the problem of strong nonlinearity in offshore speed winds. The experiments are performed by using the actual data of two different offshore sites located in the Caribbean Sea and one onshore site located in the interior of the United States, to verify the performance of the model. The results show that the prediction model achieves significant accuracy improvement.

Abstract: Based on the systematic summary of current research on oil bank, the definition of oil bank in the process of fire flooding and its quantitative indices were proposed; and a new one-dimensional positive dry-fire flooding model considering temperature gradient was established based on the steady flow theory of gas and liquid phases. Single factor analysis and orthogonal experiments were adopted to verify the reliability and reveal the formation mechanisms and the controlling factors of the oil bank. Then the optimal conditions for the oil bank to form were discussed. The study results show the formation of the oil bank is controlled by 3 factors: (1) Oil bank would come into being within a certain temperature interval and above a critical value of temperature gradient (absolute value), with temperature too high or too low and temperature gradient absolute value lower than the critical value, the oil bank couldn't form. (2) For fire flooding process in heavy oil reservoirs, the viscosity of oil influences the width of oil bank and the speed at which oil bank forms; the lower the oil viscosity is, the wider the oil bank is and the faster the oil bank forms. (3) Oil saturation could affect the developing temperature and speed of oil bank. The favorable temperature at which oil bank develops gets lower and the accumulating speed of oil gets faster when the oil saturation is higher. By orthogonal experiments with the model, the optimal combinations of reservoir conditions for forming oil bank during fire flooding in heavy oil reservoirs can be worked out. Key words: fire flooding heavy oil, oil bank, flow model, single factor analysis, controlling factor, orthogonal experiment

Aiming to investigate the role and mechanism of nano MgO on the hot compressive deformation behavior of Mg alloys, the Mg-3Zn-0.2Ca alloy (MZC, in wt%) and the 0.2MgO/Mg-3Zn-0.2Ca alloy (MZCM, in wt%) were investigated systematically in the temperature range of 523–673 K and the strain rate range of 0.001–1 s−1. MZCM shows finer grains and second phase because of the refinement effects of added MgO. Flow behavior analysis shows that the addition of nano MgO promotes the dynamic recrystallization (DRX) of MZC. The flow stress of MZCM is lower than that of MZC during deformation at 523–623 K but exhibits a reverse trend at 673 K and 0.1–1 s−1. The constitutive analysis indicates that dislocation climb is the dominant deformation mechanism for MZC and MZCM. The addition of nano MgO particles decreases the stress sensitivity and deformation resistance for thermal deformation and improves the plasticity of the MZC. Besides, according to the processing map constructed at strains of 0.7 and corresponding microstructure evolution, MZCM exhibits higher power dissipation efficiency and smaller instability regions than MZC, and the optimum hot working condition for MZCM was determined to be at 623–653 K and 0.01–0.001 s−1.