Search

IntroductionEndometriosis (EMs) and adenomyosis (AD) are common gynecological diseases that impact women's health, and they share symptoms such as dysmenorrhea, chronic pain, and infertility, which adversely affect women's quality of life. Current diagnostic approaches for EMs and AD involve invasive surgical procedures, and thus, methods of noninvasive differentiation between EMs and AD are needed. This retrospective cohort study introduces a novel, noninvasive classification methodology employing a stacked ensemble machine learning (ML) model that utilizes peripheral blood and coagulation markers to distinguish between EMs and AD.MethodsThe study included a total of 558 patients (329 with EMs and 229 with AD), in whom key hematological and coagulation markers were analyzed to identify distinctive profiles. Feature selection was conducted through ML (logistic regression, support vector machine, and K-nearest neighbors) to determine significant hematological markers.ResultsRed cell distribution width, mean corpuscular hemoglobin concentration, activated partial thromboplastin time, international normalized ratio, and antithrombin III were proved to be the key distinguishing indexes for disease differentiation. Among all the ML classification models developed, the stacked ensemble model demonstrated superior performance (area under the curve = 0.803, 95% credibility interval = 0.701–0.904). Our findings demonstrate the effectiveness of the stacked ensemble ML model for classifying EMs and AD.DiscussionIntegrating biomarkers into this multi-algorithm framework offers a novel approach to noninvasive diagnosis. These results advocate for the application of stacked ensemble ML utilizing cost-effective and readily available peripheral blood and coagulation indicators for the early, rapid, and noninvasive differential diagnosis of EMs and AD, offering a potentially transformative approach for clinical decision-making and personalized treatment strategies.

Abstract Banana Fusarium wilt, which is known as Panama disease and caused by Fusarium oxysporum f. sp. cubense (Foc), is a destructive disease that can lead to plant death and complete loss of banana plantations. For obtaining resistant varieties against Fusarium wilt, research is required to elucidate the mechanisms of genetic resistance to Foc. In this research, the banana plants infected with banana bunchy top virus (BBTV) showed resistance to Foc. RNA-seq results showed that the infection with Foc upregulated many resistance-related genes in BBTV-positive plants, especially genes related to ROS production. It was also found that more H2O2 in BBTV-positive plants was induced during Foc infection. The movement protein (MP) of BBTV could increase H2O2 levels by promoting the accumulation of MaSGT1a (suppressor of the G-two allele of SKP1) via direct interaction. Thus, the resistance to Foc induced by BBTV may attribute to MP increasing MaSGT1a protein levels to induce ROS production. MaSGT1a could promote the accumulation of R proteins MaRPM1 and MaRPP8, and increase H2O2 levels in Nicotiana benthamiana. The expression of R proteins in Nicotiana benthamiana caused an increase in H2O2 levels and promoted resistance to Foc. Thus, the MP of BBTV could induce plant defense through interaction with MaSGT1a. We speculate that MaSGT1a is likely involved in R gene-mediated resistance and thereby promotes the resistance of banana against Foc.

Abstract The development of hydroelectric projects has adversely affected the reproductive activities of downstream fish species. To facilitate the natural reproduction of fish and restore spawning grounds post‐dam construction, it is imperative to explore the ecological factors crucial for their reproduction. Currently, various research methods with different advantages and limitations are employed for this purpose. Using identified spawning locations and periods as clues, we quantitatively investigate the flow velocity, water depth, water temperature, and riverbed substrate required for spawning. The results are validated using habitat simulation methods, aiming to establish a more scientific approach to explore ecological factors affecting fish reproduction. This study provides a more scientific, systematic, and detailed report on the ecological factors required for the spawning of Gymnocypris eckloni: flow velocity ranging from 0.19 to 0.97 m/s, water depth from 0.28 to 1.12 m, water temperature between 11.4 and 15.2°C, and predominantly gravel substrate. The reliability of the results was verified in another spawning ground, with good verification results. This research provides crucial data for the bio‐mimetic reproductive technology of Gymnocypris eckloni and the restoration of spawning grounds for natural fish reproduction post‐dam construction. It addresses the lack of suitable ecological factor data for protective fish species in the upper reaches of the Yellow River. The method exhibits strong scientific, accurate, and implementable characteristics.

Tetrahydrofurfuryl alcohol, a cost-effective biomass derivative, offers a sustainable path for synthesizing 1,5-pentanediol through hydrogenolysis. To develop the efficient production of 1,5-pentanediol from this alcohol, we have prepared a series of MgAl2O4-modified Pt/WOx/γ-Al2O3 catalysts with varying compositions via impregnation–calcination methods. The physicochemical properties of these catalysts were subsequently characterized using diverse techniques. Characterization revealed that magnesia–alumina spinel modification enhanced Pt particle dispersion, CO adsorption on Pt/WOx/γ-Al2O3, reduced Pt particle reduction temperature, diminished the acid content in the catalysts, and increased the surface oxygen vacancy concentration. These alterations appear to influence the catalyst performance, though other factors cannot be ruled out. Catalytic activity tests demonstrated that magnesia–alumina spinel modification improved tetrahydrofurfuryl alcohol hydrogenolysis activity and the 1,5-pentanediol selectivity of Pt/WOx/γ-Al2O3. Optimal performance was achieved at 12% magnesia–alumina spinel loading, with a tetrahydrofurfuryl alcohol conversion of 47.3% and 1,5-pentanediol selectivity of 88.4%.

Porous asphalt pavements are widely used in rainy and wet areas for their skid resistance, noise reduction, runoff minimization and environmental sustainability. Long-term moisture vapor erosion and the destabilization of large pore structures can easily result in pavement problems such as fragmentation, spalling, cracking, and excessive permanent deformation. To this end, four different preventive maintenance materials, including the rejuvenation (RJ), cohesion reinforcement (CEM), polymerization reaction, and emulsified asphalt (EA) types, were selected in this paper to improve the high-viscosity porous asphalt pavement. The effects of the different preventive maintenance materials on the temperature sensitivity, rheological properties and fatigue performance of high-viscosity modified asphalt were evaluated through temperature sweep, frequency sweep, multi-stress creep recovery (MSCR), linear amplitude sweep (LAS), and bending beam rheometer (BBR) tests. The results showed that the four preventive maintenance materials exhibit different enhancement mechanisms and effects. RJ improves the fatigue properties, deformation resistance and low-temperature cracking resistance of aged asphalt by adding elastomeric components; CEM materials are more conducive to increasing the low-temperature crack resistance of aged asphalt; while GL1 and EA improve the viscoelastic behavior of aged asphalt, but the effect of the dosing ratio needs to be considered.

The generation and prediction of daily human mobility patterns have raised significant interest in many scientific disciplines. Using various data sources, previous studies have examined several deep learning frameworks, such as the RNN and GAN, to synthesize human movements. Transformer models have been used frequently for image analysis and language processing, while the applications of these models on human mobility are limited. In this study, we construct a transformer model, including a self-attention-based embedding component and a Generative Pre-trained Transformer component, to learn daily movements. The embedding component takes regional attributes as input and learns regional relationships to output vector representations for locations, enabling the second component to generate different mobility patterns for various scenarios. The proposed model shows satisfactory performance for generating and predicting human mobilities, superior to a Long Short-Term Memory model in terms of several aggregated statistics and sequential characteristics. Further examination indicates that the proposed model learned the spatial structure and the temporal relationship of human mobility, which generally agrees with our empirical analysis. This observation suggests that the transformer framework can be a promising model for learning and understanding human movements.

The complementary operation of hydropower and photovoltaic power, aimed at meeting real-time demand, has led to frequent adjustments in power generation, causing significant fluctuations in hydrological systems and adversely affecting fish reproduction. The traditional hydrological regime alteration assessment index is based on index of hydrologic alternation (IHA) and mostly focuses on annual and daily runoff alterations. This study proposes a new set of indicators considering the characteristics of subdaily hydrological regime alterations, including magnitude, rate of change, duration, frequency, and timing. Using the hourly outflow from Longyangxia, an analysis of indicator redundancy was conducted. The alteration of the indicators before and after hydropower and photovoltaic operation was then analyzed using the cumulative probability distribution curve. Additionally, a concentration index was introduced to analyze the variations in hydrological impacts during different months. The results show that the hydro–photovoltaic complementary operation changed the subdaily natural flow regime, significantly increasing the rate of flow increase or decrease and the duration, with most indexes increasing by more than 100% compared with the natural flow regime. Furthermore, the concentration values of the indexes for the hydro–photovoltaic complementary operation were less than 10, indicating a more significant impact on the subdaily flow regime throughout the year. This research provides crucial data for mitigating ecological impacts under multi-source complementary scheduling.

Abstract Background Falls are the leading cause of injury-related death among older Americans. While some research has found that insomnia heightens falls, health care resource utilization (HCRU) and costs, the impact of insomnia treatments on fall risk, mortality, HCRU and costs in the elderly population, which could be of substantial interest to payers, has not been fully elucidated. This study evaluated the risk of falls and related consequences among adults ≥ 65 years of age treated with common prescription medications for insomnia compared with non-sleep disordered controls. Methods This was a retrospective cohort analysis of deidentified Medicare claims from January 2011 through December 2017. Medicare beneficiaries treated for insomnia receiving zolpidem extended-release, zolpidem immediate-release, trazodone, or benzodiazepines were matched with non-sleep disordered controls. The main outcomes were falls, mortality, healthcare resource utilization (HCRU), and medical costs during the 12 months following the earliest fill date for the insomnia medication of interest. Generalized linear models controlled for several key covariates, including age, race, sex, geographic region and Charlson Comorbidity Index score. Results The study included 1,699,913 Medicare beneficiaries (59.9% female, mean age 75 years). Relative to controls, adjusted analyses showed that beneficiaries receiving insomnia medication experienced over twice as many falls (odds ratio [OR] = 2.34, 95% CI: 2.31–2.36). In adjusted analyses, patients receiving benzodiazepines or trazodone had the greatest risk. Crude all-cause mortality rates were 15-times as high for the insomnia-treated as controls. Compared with controls, beneficiaries receiving insomnia treatment demonstrated higher estimated adjusted mean number of inpatient, outpatient, and emergency department visits and longer length of inpatient stay. All-cause total adjusted mean costs were higher among insomnia treated patients ($967 vs $454). Conclusions Individuals receiving insomnia treatment had an increased risk of falls and mortality and higher HCRU and costs compared with matched beneficiaries without sleep disorders. Trazodone and benzodiazepines were associated with the greatest risk of falls. This analysis suggests that significant risks are associated with common, older generation insomnia medication treatments in the elderly. Nonetheless, these results should be interpreted with caution as the use of these medications may be indicative of underlying morbidity with potential for residual confounding.

Background and objective Polycystic ovary syndrome is one of the most common types of endocrine and metabolic diseases in women of reproductive age that needs to be screened early and assessed non-invasively. The objective of the current study was to develop prediction models for polycystic ovary syndrome based on data of tongue and pulse using machine learning techniques. Methods A dataset of 285 polycystic ovary syndrome patients and 201 healthy women were investigated to identify the significant tongue and pulse parameters for predicting polycystic ovary syndrome. In this study, feature selection was performed using least absolute shrinkage and selection operator regression. Several machine learning algorithms (multilayer perceptron classifier, eXtreme gradient boosting classifier, and support vector machine) were used to construct the classification models to predict the presence of polycystic ovary syndrome. Results TB-L, TB-a, TB-b, TC-L, TC-a, h 3 , and h 4 /h 1 in tongue and pulse parameters were statistically associated with polycystic ovary syndrome presence. Among the several machine learning techniques, the support vector machine model was optimal for the comprehensive evaluation of this dataset and deduced the area under the receiver operating characteristic curve, DeLong test, calibration curve, and decision curve analysis. Conclusion The machine learning model with tongue and pulse factors can predict the existence of polycystic ovary syndrome precisely.

In population-oriented ergonomics product design and musculoskeletal kinetics analysis, digital spine models of different shape, pose and material property are in great demand. The purpose of this study was to construct a parameterized finite element spine model with adjustable spine shape and material property. We used statistical shape model approach to learn inter-subject shape distribution from 65 CT images of training subjects. Second order polynomial regression was used to model the age-dependent changes in vertebral material property derived from spatially aligned CT images. Finally, a parametric spine generator was developed to create finite element instances of different shapes and material properties. For quantitative analysis, the generalization ability to emulate spine shapes of different people was evaluated by fitting into 17 test CT images. The median fitting accuracy was 0.8 for Dice coefficient and 0.43 mm for average surface distance. The age-dependent bone density regression curve was also proved to well agree with large population statistics data. Finite element simulation was performed to compare how shape parameters influenced the biomechanics distribution of spine. The proposed parametric finite element whole spine model will assist the design process of new devices and biomechanical simulation towards a wide range of population.

Abstract To obtained fungal resources with excellent tolerance and accumulation capacity to rare earth yttrium ions (Y3+), rare earth ore samples were collected and used for microbial screening. A fungus hyper-resistant to Y3+ was obtained and the effects of the fungus in three physiological states (growth process, mycelial pellets with physiological activity and the fungus powder after being ground) on the Y3+ accumulation were investigated. The Y3+ resistant fungus was identified as Penicillium sp. ZD28, and its mycelium pellets (about 1 mm in diameter) showed poor ability to accumulate Y3+ with an adsorption capacity of less than 81 μmol/g. However, the fungus was able to remove 99% of Y3+ during the growth process, at an initial concentration of less than 600 μM. Bioaccumulation of Y was observed on the cell surface of the ZD28 strain by elemental mapping using scanning electron microscopy-energy dispersive X-ray spectroscopy. The adsorbent (the dry fungal powder) had a remarkable adsorption property for Y3+ that was greater than 455 μmol/g in conditions of 465 μM

BackgroundRoutine laboratory investigations are not rapidly available to assist clinicians in the diagnosis of pediatric acute infections. Our objective was to evaluate some common blood parameters and use them for the differential diagnosis of childhood infections.MethodsThis retrospective study was conducted between October 2019 and September 2020 at Guangzhou Women and Children's Medical Center, China. We performed blood tests in patients infected with DNA viruses (n = 402), RNA viruses (n = 602), gram-positive organisms (G+; n = 421), gram-negative organisms (G-; n = 613), or Mycoplasma pneumoniae (n = 387), as well as in children without infection (n = 277). The diagnostic utility of blood parameters to diagnose various infections was evaluated by logistic regression analysis.ResultsThe most common G+ organism, G- organism, and virus were Streptococcus pneumoniae (39.7%), Salmonella typhimurium (18.9%), and influenza A virus (40.2%), respectively. The value of logit (P) = 0.003 × C-reactive protein (CRP) - 0.011 × hemoglobin (HGB) + 0.001 × platelets (PLT) was significantly different between the control, RNA virus, DNA virus, M. pneumoniae, G- organism, and G+ organism groups (2.46 [95% CI, 2.41-2.52], 2.60 [2.58-2.62], 2.70 [2.67-2.72], 2.78 [2.76-2.81], 2.88 [2.85-2.91], and 2.97 [2.93-3.00], respectively; p = 0.00 for all). The logistic regression-based model showed significantly greater accuracy than the best single discriminatory marker for each group (logit [Pinfection] vs. CRP, 0.90 vs. 0.84, respectively; logit [PRNA] vs. lymphocytes, 0.83 vs. 0.77, respectively; p = 0.00). The area under curve values were 0.72 (0.70-0.74) for HGB and 0.81 (0.79-0.82) for logit (Pvirus/bacteria) to diagnose bacterial infections, whereas they were 0.72 (0.68-0.74) for eosinophils and 0.80 (0.78-0.82) for logit (Pvirus/bacteria) to diagnose viral infections. Logit (Pvirus/bacteria) < -0.45 discriminated bacterial from viral infection with 78.9% specificity and 70.7% sensitivity.ConclusionsThe combination of CRP, HGB, PLT, eosinophil, monocyte, and lymphocyte counts can distinguish between the infectious pathogens in children.

Existing image segmentation algorithms used for the computed tomography (CT) images of asphalt concrete mostly ignore the similarity of aggregate phase geometry between adjacent CT slices, thus increasing the variability in the aggregate phase pixel values between adjacent slices and leading to a large number of model defects, e.g., interconnected aggregates, flaky aggregates, and incomplete aggregates. The developed mesostructural models with these defects pose a challenge to following simulation operations. To address this issue, an improved procedure for the 3D reconstruction of asphalt concrete mesostructures considering the similarity of aggregate phase geometry between adjacent slices was developed, which includes two adjacent-slice pixel-value-correction algorithms, a multi-directional multiple-correction method, and an image pixel interpolation process. First, the bilinear interpolation algorithm was employed to improve the pixel density of 2D CT images and the average filtering algorithm was used to reduce the noise of the CT images. Subsequently, the OTSU method was employed to separate the asphalt mortar matrix phase from the aggregate phase, and the marker-based watershed segmentation method was used to separate the interconnected aggregates. Finally, the adjacent-slice pixel-value-correction algorithm was used to recover the similarity of aggregate phase geometry between adjacent CT slices, and the multi-directional multiple-correction method was used to further enhance the geometric similarity. The results show that the developed 3D reconstruction procedure removes most of the model defects in the 3D mesostructural model of asphalt concrete, thus realistically maintaining the 3D spatial distribution features and contour characteristics.

In order to research investigations on the shear behavior of asphalt mixture, a new shear testing device is developed which can apply torque to a prismatic specimen. This test configuration incorporates a loading application and instrumentation systems to measure and record the response of these mixtures. The loading application can be subjected to individual or combined axial and torsional loads; in particular, the axial load can be dynamically controlled to remain constant. The paper first uses the mechanical theory to analyze the stress state of a prismatic specimen under a torsional load in unconfined compression and confined compression, respectively, and illustrates the influence factor, the shear strength parameter, and the failure criterion for the torsional shear test of the asphalt mixture. Then, the size and the preparation procedure of specimen are explained, and the experimental plan is described. Finally, the torsional shear test apparatus is used to conduct two types of shear tests of asphalt mixtures. The type I test in unconfined compression consists of two conditions: under a constant loading speed (2.4 rad/min) at four temperatures (30 ∘C, 40 ∘C, 50 ∘C, and 60 ∘C), and under a constant temperature (40 ∘C) at three loading speeds (2.4 rad/min, 4.0 rad/min, and 8.5 rad/min). The type II test in confined compression is performed under a loading speed of 2.4 rad/min and a temperature of 40 ∘C, at 0.125 MPa, 0.200 MPa, 0.355 MPa, 0.465 MPa, and 0.570 MPa normal stress levels, respectively. The results prove that (1) temperatures, loading speeds, and normal stress levels are the issues to be considered on torsional shear testing; (2) the pure shear model can be realized by the prismatic specimen, therefore, the cohesion average value obtained is 0.519 MPa; (3) the compression-shear model can be achieved by the prismatic specimen similarly, so the cohesion and the friction angle are simulated based on the Mohr–Coulomb failure criterion, which are 0.546 MPa and 44.15°, respectively; and (4) at the high temperature and low normal stress level, the Mohr–Coulomb failure criterion does not agree well with measured data, so the nonlinear failure envelope should not be ignored.

Understanding the evolution of microorganisms and metabolites during wine fermentation is essential for controlling its production. The structural composition and functional capacity of the core microbiota determine the quality and quantity of fruit wine. Nanfeng tangerine wine fermentation involves a complex of various microorganisms and a wide variety of metabolites. However, the microbial succession and functional shift of the core microbiota in this product fermentation remain unclear. Therefore, high-throughput sequencing (HTS) and headspace-gas chromatography-mass spectrometry (HS/GC-MS) were employed to reveal the core functional microbiota for the production of volatile flavors during spontaneous fermentation (SF) and inoculated fermentation (IF) with Saccharomyces cerevisiae of Nanfeng tangerine wine. A total of 13 bacterial and 8 fungal genera were identified as the core microbiota; Lactobacillus and Acetobacter were the dominant bacteria in SF and IF, respectively. The main fungal genera in SF and IF were Hanseniaspora, Pichia, and Saccharomyces with a clear succession. In addition, the potential correlations analysis between microbiota succession and volatile flavor dynamics revealed that Lactobacillus, Acetobacter, Hanseniaspora, and Saccharomyces were the major contributors to the production of the volatile flavor of Nanfeng tangerine wine. The results of the present study provide insight into the effects of the core functional microbiota in Nanfeng tangerine wine and can be used to develop effective strategies for improving the quality of fruit wines.

Human movements have raised broad attention, and many models have been developed to reproduce them. However, most studies focus on reproducing the statistical properties of human mobility, such as the travel distance and the visiting frequency. In this paper, a two-step Markov Chain model is proposed to generate daily human movements, and spatial and spatiotemporal attributes of reproduced mobility are examined. In the first step, people’s statuses in the next time slot are conditioned on their previous travel patterns; and in the second step, individual location in such a slot is probabilistically determined based on his/her status. Our model successfully reproduces the spatial and spatiotemporal characteristics of human daily movements, and the result indicates that people’s future statuses can be inferred based on travel patterns they made, regardless of exactly where they have traveled, and when trips happen. We also revisit the energy concept, and show that the energy expenditure is stable over years. This idea is further used to predict the proportion of long-distance trips for each year, which gives insights into the probabilities of statuses in the next time slot. Finally, we interpret the constant energy expenditure as the constant ‘cost’ over years.

With the development of pavement recycling technology, the requirement of reclaimed asphalt pavement (RAP) is substantially increasing. Warm-mix recycled asphalt (WMRA) technology has made great progress, which can effectively decrease the working temperature and improve the RAP content. In this study, the rheological properties of recycled binders with incorporation of high-percentage artificial RAP binder (30–70%) were evaluated using two types of warm-mix asphalt (WMA) additives, i.e., polyethylene wax R and surfactant M. The dynamic shear rheometer (DSR) and beam bending rheometer (BBR) tests were conducted on the recycled binders. The results showed that the temperature and frequency played an important role in determining the complex shear modulus of the high-percentage WMRA binders. The dependency of phase angle on frequency increased after the long-term aging. The WMA additive R had a relatively huge impact on the rheological properties of asphalt, which mainly occurred before the PAV aging of recycled asphalt binder; the WMA additive M had no significant impact on the rheological properties of recycled asphalt binder. The WMA additive R enhanced the low-temperature rheology of recycled asphalt binder, while the WMA additive M enhanced the high-temperature rheology of recycled asphalt binder. Both of these types of WMA additives improved the antifatigue performance of recycled asphalt binder. The increased content of RAP binder improved the high-temperature performance and reduced the low-temperature performance of the recycled asphalt binder. However, it had no obvious impact on the fatigue performance. In addition, there was a good linear relation between the RAP binder content and the two indexes of the multiple stress creep recovery (MSCR) test.

Abstract We report the enhancement of the polarization and internal quantum efficiency (IQE) of deep-UV LEDs by evaporating Al nanoparticles on the device surface to induce localized surface plasmons (LSPs). The deep-UV LEDs polarization is improved due to part of TM emission turns into TE emission through LSPs coupling. The significantly enhanced IQE is attributed to LSPs coupling, which suppress the participation of delocalized and dissociated excitons to non-radiative recombination process.

The relationship between the formation of characteristic Maotai-flavor substances (MTFS) and the dominant bacteria in Maotai Daqu (MTDQ) has long been a topic of research interest in the field of liquor brewing in China. To investigate the connection between MTFS and the Bacillus subtilis (one of dominant bacteria in MTDQ) cultured on solid plates of wheat extract medium at, temperatures of 37, 46, and up to 55°C (Group A), and at a constant 37°C (Group B), the transcriptomes of the bacteria grown in the two groups were studied. About 10 out of 84 differentially expressed genes (DEGs) were related to promoting sporulation. Furthermore, observations made with transmission electron microscopy (TEM) showed that a thicker spore cortex appeared in Group A. The content of 2, 6-pyridinedicarboxylic acid (DPA), an important component of the spore, was 49.77 (±2.50) and 38.23 (±3.96) μg/mg of dried spores from the bacteria cultured in Groups A and B, respectively. Combined with the production process of Maotai liquor, more DPA accumulates in the high-temperature fermentation stage and is then released by spore germination during the subsequent temperature-drop stage. We suggest that DPA (or its derivatives) can then be transformed into MTFS by the Maillard reaction after many rounds of microbial fermentation. The viewpoint that there is a potential correlation between bacterial sporulation and the production of MTFS is proposed.

A high refractive index (RI) sensor based on an in-line Mach–Zehnder mode interferometer (MZI) is proposed. The sensor was realized by splicing a 2-cm length of cladding-etched thin core fiber (TCF) between two single mode fibers (SMFs). The TCF-structured MZI exhibited good fringe visibility as high as 15 dB in air and the high RI sensitivity attained a value of 1143.89 nm/RIU at a RI of 1.447. The experimental data revealed that the MZI has high RI sensitivity after HF etching realizing 2599.66 nm/RIU. Studies were performed on the temperature characteristics of the device. It is anticipated that this high RI sensor will be deployed in new and diverse applications in the chemical and biological fields.

RNA virus invasion induces a cytosolic RIG-I-like receptor (RLR) signaling pathway by promoting assembly of the Mitochondrial antiviral-signaling protein (MAVS) signalosome and triggers the rapid production of type I interferons (IFNs) and proinflammatory cytokines. During this process, the pivotal kinase TANK binding kinase 1 (TBK1) is recruited to the MAVS signalosome to transduce a robust innate antiviral immune response by phosphorylating transcription factors interferon regulatory factor 3 (IRF3) and nuclear factor (NF)-κB and promoting their nuclear translocation. However, the molecular mechanisms underlying the negative regulation of TBK1 are largely unknown. In the present study, we found that THO complex subunit 7 homolog (THOC7) negatively regulated the cellular antiviral response by promoting the proteasomal degradation of TBK1. THOC7 overexpression potently inhibited Sendai virus- or polyI:C-induced IRF3 dimerization and phosphorylation and IFN-β production. In contrast, THOC7 knockdown had the opposite effects. Moreover, we simulated a node-activated pathway to show that THOC7 regulated the RIG-I-like receptors (RLR)-/MAVS-dependent signaling cascade at the TBK1 level. Furthermore, THOC7 was involved in the MAVS signalosome and promoted TBK1 degradation by increasing its K48 ubiquitin-associated polyubiquitination. Together, these findings suggest that THOC7 negatively regulates type I IFN production by promoting TBK1 proteasomal degradation, thus improving our understanding of innate antiviral immune responses.

In this paper, physical experiments and numerical simulations were applied to systematically investigate the non-Newtonian flow characteristics of heavy oil in porous media. Rheological experiments were carried out to determine the rheology of heavy oil. Threshold pressure gradient (TPG) measurement experiments performed by a new micro-flow method and flow experiments were conducted to study the effect of viscosity, permeability and mobility on the flow characteristics of heavy oil. An in-house developed novel simulator considering the non-Newtonian flow was designed based on the experimental investigations. The results from the physical experiments indicated that heavy oil was a Bingham fluid with non-Newtonian flow characteristics, and its viscosity-temperature relationship conformed to the Arrhenius equation. Its viscosity decreased with an increase in temperature and a decrease in asphaltene content. The TPG measurement experiments was impacted by the flow rate, and its critical flow rate was 0.003 mL/min. The TPG decreased as the viscosity decreased or the permeability increased and had a power-law relationship with mobility. In addition, the critical viscosity had a range of 42–54 mPa∙s, above which the TPG existed for a given permeability. The validation of the designed simulator was positive and acceptable when compared to the simulation results run in ECLIPSE V2013.1 and Computer Modelling Group (CMG) V2012 software as well as when compared to the results obtained during physical experiments. The difference between 0.0005 and 0.0750 MPa/m in the TPG showed a decrease of 11.55% in the oil recovery based on the simulation results, which demonstrated the largely adverse impact the TPG had on heavy oil production.

Gas turbines are considered as one kind of the most important devices in power engineering and have been widely used in power generation, airplanes, and naval ships and also in oil drilling platforms. However, they are monitored without man on duty in the most cases. It is highly desirable to develop techniques and systems to remotely monitor their conditions and analyze their faults. In this work, we introduce a remote system for online condition monitoring and fault diagnosis of gas turbine on offshore oil well drilling platforms based on a kernelized information entropy model. Shannon information entropy is generalized for measuring the uniformity of exhaust temperatures, which reflect the overall states of the gas paths of gas turbine. In addition, we also extend the entropy to compute the information quantity of features in kernel spaces, which help to select the informative features for a certain recognition task. Finally, we introduce the information entropy based decision tree algorithm to extract rules from fault samples. The experiments on some real-world data show the effectiveness of the proposed algorithms.

In order to study the effects of maneuvering overload on the main shaft bearing (abbreviation: bearing) reaction force in an aero-engine, a bending-torsion coupling dynamic model of the dual rotor system under maneuvering overload was established. Combined with the output parameters of the system model, the bearing reaction force analysis model was established. The maneuvering overload parameters are input into system model. Newmark-ß and Newton-raphson methods are used to obtain the bearing reaction force response, and the change laws of bearing reaction force under the different maneuvering overloads are analyzed. The results show that gravity will make the transient response of bearing reaction force offset, and maneuvering overload will make the offset shift. Maneuvering overload will cause bearing to produce a circumferential asymmetric reaction force. For bearing 1, the additional gyroscopic torque in maneuvering overload has a significant effect on the dynamic eccentricity, resulting in a larger offset of reaction force of ʸ direction. For bearings 3 and 4, reaction force of ˣ direction has a larger offset. Under the influence of bent-torsional coupling, the combined frequencies of base frequencies of rotational speed appear in the reaction force spectrum diagram. [ABSTRACT FROM AUTHOR]

Maneuvering flight can generate the asymmetry of the stiffness or damping to an engine rotor system, as so to influent the whirl direction and characteristics of rotor system. In this article, the identification method for orbit direction is provided for a rotor system under maneuvering flight condition. By a simulation example, the variable movement shapes about rotor under maneuvering flight are illustrated through cases as the mixed and the pending whirls. The results show that the original low-order critical speed in the backward or forward whirl mode changes to the mixed whirl while considering the impact of maneuvering flight, where some backward and forward critical speeds in the same order are almost overlapped as one critical speed which in the mixed whirl mode. Maneuvering flight results in dynamic eccentricity of the rotor shaft, and the orbits of nodes in the mixed whirl are a series of ellipses with variable inclination angles. The case with pending whirl direction is finally determined as a mixed whirl by the orbit direction graph. The whirl directions of orbits on bearings or the contact angle of bearings maybe different under maneuvering flight can be explained from the rotor dynamical view. [ABSTRACT FROM AUTHOR]