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To effectively solve the problem that the measurement uncertainty evaluation result is not accurate and the calculation is complicated when the humidity measuring instrument is calibrated. The "Monte Carlo simulation method" (MCM) was proposed to evaluate the measurement uncertainty of humidity sensor calibration results. In this process, firstly, by analyzing the calibration process of humidity sensor, the measurement model that can accurately and completely reflect the actual measurement situation is constructed; then, design a performance testing method for the humidity generator to obtain parameter data that can truly reflect the performance of the current humidity generator; finally, taking the 55%RH calibration point as an example, by using the above measurement model and related parameters, single MCM method and adaptive MCM method were used to evaluate the measurement uncertainty of the humidity sensor calibration results. The evaluation results obtained are the same as: the best estimated value of humidity sensor measurement error ΔH = 0.01%RH, the standard uncertainty u(ΔH) = 0.14%RH, and the shortest coverage interval [ΔHlow, ΔHhigh] = [− 0.24%RH, 0.26%RH] when the coverage probability is 95%. Through this application experiment on the MCM method, it was found that compared to the GUM method, the MCM method can effectively improve the credibility of the measurement uncertainty results of the humidity sensor. Moreover, when the adaptive MCM method is applied to evaluate the measurement uncertainty of the humidity sensor, compared to the single MCM method, it can effectively reduce simulation times, reduce storage space resources, and improve evaluation efficiency. Prioritizing the adaptive MCM method in practical operation is recommended. [ABSTRACT FROM AUTHOR]

The Bayer red mud (RM) and phosphogypsum (PG) accumulation have caused significant environmental contamination. However, practical and effective resource utilization technologies are still lacking currently. This work aims to develop ferric sulfoaluminate cement (FSAC) employing low-cost materials including Bayer red mud, phosphogypsum, and other materials. This method effectively improves the utilization rate of Bayer red mud and phosphogypsum. Under the premise of ensuring the performance of FSAC, the utilization rate of solid waste can reach up to 48.56%. The effects of different red mud dosages on cement mineral formation, workability, and mechanical properties are investigated. Then, untreated phosphogypsum is adopted as a retarder for FSAC, and the hydration process, working properties, mechanical properties, types of hydration products, and morphology of FSAC are explored. The results suggest that the crystal transformation of Ye'elemite C 4 A 3 S ¯ is promoted with the increase of Bayer red mud content. Cubic crystal system Ye'elemite C 4 A 3 S ¯ - c with higher hydration activity is generated, which increases the early strength of cement but greatly reduces the setting time, hindering the later strength growth. Untreated phosphogypsum can effectively delay the early hydration process of FSAC, prolong the setting time of cement, and increase the strength of FSAC in the later stage. When the dosage of Bayer red mud and phosphogypsum is 17.64% and 9.21%, respectively, with phosphogypsum dosage of 20%, the prepared FSAC has satisfactory mechanical properties, and the 3-day and 90-day compressive strengths are 34.6 MPa and 57.1 MPa, respectively. In addition, the study of heavy metal leaching indicates that the FSAC prepared by Bayer red mud, phosphogypsum, and other raw materials will generate no environment pollution, and the solidification of heavy metal elements in the cement slurry is superior. [ABSTRACT FROM AUTHOR]

Objective: To determine the optimal scan duration for ultrafast DCE-MRI in effectively differentiating benign from malignant breast lesions. Methods: The study prospectively recruited participants who underwent breast ultrafast DCE-MRI from September 2021 to March 2023. A 30-phase breast ultrafast DCE-MRI on a 3.0-T MRI system was conducted with a 4.5-s temporal resolution. Scan durations ranged from 40.5 s to 135.0 s, during which the analysis is performed at three-phase intervals, forming eight dynamic sets (scan duration [SD]40.5s: 40.5 s, SD54s: 54.0 s, SD67.5s: 67.5 s, SD81s: 81.0 s, SD94.5s: 94.5 s, SD108s: 108.0 s, SD121.5s: 121.5 s, and SD135s: 135.0 s). Two ultrafast DCE-MRI parameters, maximum slope (MS) and initial area under the curve in 60 s (iAUC), were calculated for each dynamic set and compared between benign and malignant lesions. Areas under the receiver operating characteristic curve (AUCs) were used to assess their diagnostic performance. Results: A total of 140 women (mean age, 47 ± 11 years) with 151 lesions were included. MS and iAUC from eight dynamic sets exhibited significant differences between benign and malignant lesions (all p < 0.05), except iAUC at SD40.5s. The AUC of MS (AUC = 0.804) and iAUC (AUC = 0.659) at SD67.5s were significantly higher than their values at SD40.5s (AUC = 0.606 and 0.516; corrected p < 0.05). No significant differences in AUCs for MS and iAUC were observed from SD67.5s to SD135s (all corrected p > 0.05). Conclusions: Ultrafast DCE-MRI with a 67.5-s scan duration appears optimal for effectively differentiating malignant from benign breast lesions. Critical relevance statement: By evaluating scan durations (40.5–135 s) and analyzing two ultrafast DCE-MRI parameters, we found a scan duration of 67.5 s optimal for discriminating between these lesions and offering a balance between acquisition time and diagnostic efficacy. Key Points: Ultrafast DCE-MRI can effectively differentiate malignant from benign breast lesions. A minimum of 67.5-sec ultrafast DCE-MRI scan duration is required to differentiate benign and malignant lesions. Extending the scan duration beyond 67.5 s did not significantly improve diagnostic accuracy. [ABSTRACT FROM AUTHOR]

Deep neural networks have achieved significant success in the artificial intelligence community and various downstream tasks. They encode images or texts into dense feature representations and are supervised by a large amount of labeled data. Due to the expensiveness of high-quality labeled data, a huge number of easy-to-access instances are collected to conduct supervised learning. However, they have not been annotated by experts and thus can contain numerous noisy instances, which will degrade the performance. To learn robust feature representations despite misleading noisy labels, we employ supervised contrastive learning to directly perform supervision in the hidden space, rather than in the prediction space like the prevalent cross-entropy loss function. However, cutting-edge noisy label learning methods with supervised contrastive learning always discard the data considered to be noisy, and thus cannot tolerate high-ratio noisy datasets. Therefore, we propose a novel training strategy named Supervised Contrastive Learning with Corrected Labels (Scl 2 ) to defend against the attack of noisy labels. Scl 2 corrects the noisy labels with an empirical small-loss assumption and conducts supervised contrastive learning using these corrected data. Specifically, we employ the generated soft labels as supervisory information to facilitate our implementation of supervised contrastive learning. This expansion of contrastive learning ensures the integrity of the supervisory information while effectively enhancing the learning process. In addition, samples sharing the same soft labels are treated as positive sample pairs, while those with different soft labels are considered to be negative sample pairs. With this strategy, the representations from neural networks keep the local discrimination in one mini-batch. Besides, we also employ a prototype contrastive learning technique to ensure global discrimination. Our Scl 2 has demonstrated excellent performance on numerous benchmark datasets, showcasing its effectiveness in various standardized evaluation scenarios. Additionally, our model has proven to be highly valuable when applied to real-world noisy datasets. [ABSTRACT FROM AUTHOR]

Traffic sign poles are crucial components of the highway system, and their maintenance mainly relies on the subjective judgment of staff, which is low-efficiency and will lead to mistaken inspections. This paper proposes a convenient and effective method to monitor the health of sign poles by using a mobile phone. It is unknown whether a mobile phone can be used as a conventional acceleration sensor. Therefore, the performance of the mobile phone was initially tested to ensure its suitability for acquiring the acceleration data of the traffic sign pole. The results show that the acceleration sensor of mobile phones is high-performance and can be used as a traditional acceleration sensor under the similar sampling frequency. The mobile phone can measure the 1st, 2nd and 5th natural frequencies of the traffic sign pole. Although the 1st and 5th natural frequencies have a large error with simulation, the 2nd natural frequency is accurate and stable. The looseness of the base connection greatly impacts on the 2nd natural frequency, which can be used as a measure of the loose base connection. The 2nd natural frequency was measured for 21 times and found to conform to a normal distribution. The relationship between the 2nd natural frequency and base connection loosening was investigated, which fitted to the equation. The fitting result is good and can be used to predict the degree of sign pole base connection loosening. Therefore, the mobile phone based convenient health monitoring method for traffic sign poles is feasible. [ABSTRACT FROM AUTHOR]

Loess environmental parameters reflect regional environmental evolution and provide basis for regional environmental change studies. Loess samples from different soil layers in Zhengzhou were collected, and their hyperspectral data measured. The characteristics of the original spectra, first-order differential spectra, second-order differential spectra, de-enveloping line spectra, and reciprocal logarithm spectra were analysed and correlated with environmental parameters, including mean particle size, standard deviation, Fe2O3, CaO, MgO, and total organic carbon content, based on partial least squares regression. Inversion models were constructed. After transformation, the correlations between the various spectra and the environmental parameters were enhanced. Among them, the first-order and second-order differential spectra showed the greatest correlation with average particle size (correlation coefficient: 0.74). The correlation coefficients of the order differential spectrum with standard deviation, Fe2O3, and CaO were − 0.69, 0.67, and 0.75, respectively; the accuracy grade of the original spectrum inversion model was D. After spectral transformation, it was mostly improved; the best inversion model accuracy grade for standard deviation was C, that of the other parameters was B. Partial transformation is the optimal spectral transformation to establish an inversion model for these environmental parameters. This provides a new soil parameter inversion and monitoring method using hyperspectral remote sensing technology. [ABSTRACT FROM AUTHOR]

Expectation propagation (EP) is a widely successful way to approximate the posteriors of complex Bayesian models. However, it suffers from expensive memory and time overheads, since it involves local approximations with locally specific messages. A recent art, namely averaged EP (AEP), upgrades EP by leveraging the average message effect on the posterior distribution, instead of the locally specific ones, so as to simultaneously reduce memory and time costs. In this paper, we extend AEP to a novel black-box expectation propagation (abbr. BBEP) algorithm, which can be directly applied to many Bayesian models without model-specific derivations. We leverage three ideas of black-box learning, leading to three versions of BBEP, referred to as BBEP m , BBEP g and BBEP o with Monte Carlo moment matching, Monte Carlo gradients and objective of AEP, respectively. For variance reduction, the importance sampling is used, and the proposal distribution selection as well as high dimensionality setting is discussed. Furthermore, we develop online versions of BBEP for optimization speedup given large-scale data sets. We empirically compare BBEP against the state-of-the-art black-box baseline algorithms on both synthetic and real-world data sets. Experimental results demonstrate that BBEP outperforms the baseline algorithms and it is even on a par with analytical solutions in some settings. [ABSTRACT FROM AUTHOR]

In this study, numerical simulations were carried out to analyze the influence of caves in different positions and shapes, in combination with structural planes, on the stability of the slope and the failure characteristics of a rock slope in a deep foundation pit with high inclination structural planes and cave development. The schemes for substituting a single karst cave for karst caves were constructed. Based on the penetration failure characteristics of karst caves between parallel structural planes, calculation methods for the safety factor of the rock foundation pits and the upper bound of the lateral pressure of supporting structures under the combined influence of the caves and structural planes have been developed, which can be used to assess the safety factor of a rock mass and to calculate the lateral pressure under complex geological conditions. The example of Xuzhou Metro Line 3 Sanhuan South Road Station is taken as an example to verify. The displacement calculation results show that the proposed rock and soil pressure calculation method is close to the actual measured value. [ABSTRACT FROM AUTHOR]

Introduction: Breast cancer metastasis is the main cause of cancer-related death in women worldwide. Current therapies have remarkably improved the prognosis of breast cancer patients but still fail to manage metastatic breast cancer. Here, the present study was set to explore the role of microRNA (miR)-660 from tumor-associated macrophages (TAMs) in breast cancer, particularly in metastasis. Materials and methods: We collected breast cancer tissues and isolated their polarized macrophages as well as extracellular vesicles (EVs), in which we measured the expression of miR-660, Kelch-like Protein 21 (KLHL21), and nuclear factor-κB (NF-κB) p65. Breast cancer cells were transfected with miR-660 mimic, miR-660 inhibitor, and sh-KLHL21 and then the cells were co-cultured with EVs or TAMs followed by detection of invasion and migration. Finally, mouse model of breast cancer was established to detect the effect of miR-660 or KLHL21 on metastasis by measuring the lymph node metastasis (LNM) foci in femur and lung. Results: KLHL21 was poorly expressed, whereas miR-660 was highly expressed in breast cancer tissues and cells. Of note, low KLHL21 expression or high miR-660 expression was related to poor overall survival. EVs-contained miR-660 was identified to bind to KLHL21, reducing the binding between KLHL21 and inhibitor kappa B kinase β (IKKβ) to activate the NF-κB p65 signaling pathway. Interestingly, EV-loaded miR-660 from TAMs could be internalized by breast cancer cells. Moreover, silencing of KLHL21 increased the number of lung LNM foci in vivo, while EVs-contained miR-660 promoted cancerous cell invasion and migration. Discussion: Taken altogether, our work shows that TAMs-EVs-shuttled miR-660 promotes breast cancer progression through KLHL21-mediated IKKβ/NF-κB p65 axis. [ABSTRACT FROM AUTHOR]

Crop growth monitoring and yield estimate information can be obtained via appropriate metrics such as the leaf area index (LAI) and biomass. Such information is crucial for guiding agricultural production, ensuring food security, and maintaining sustainable agricultural development. Traditional methods of field measurement and monitoring typically have low efficiency and can only give limited untimely information. Alternatively, methods based on remote sensing technologies are fast, objective, and nondestructive. Indeed, remote sensing data assimilation and crop growth modeling represent an important trend in crop growth monitoring and yield estimation. In this study, we assimilate the leaf area index retrieved from Sentinel-2 remote sensing data for crop growth model of the simple algorithm for yield estimation (SAFY) in wheat. The SP-UCI optimization algorithm is used for fine-tuning for several SAFY parameters, namely the emergence date (D0), the effective light energy utilization rate (ELUE), and the senescence temperature threshold (STT) which is indicative of biological aging. These three sensitive parameters are set in order to attain the global minimum of an error function between the SAFY model predicted values and the LAI inversion values. This assimilation of remote sensing data into the crop growth model facilitates the LAI, biomass, and yield estimation. The estimation results were validated using data collected from 48 experimental plots during 2014 and 2015. For the 2014 data, the results showed coefficients of determination (R2) of the LAI, biomass and yield of 0.73, 0.83 and 0.49, respectively, with corresponding root-mean-squared error (RMSE) values of 0.72, 1.13 t/ha and 1.14 t/ha, respectively. For the 2015 data, the estimated R2 values of the LAI, biomass, and yield were 0.700, 0.85, and 0.61, respectively, with respective RMSE values of 0.83, 1.22 t/ha, and 1.39 t/ha, respectively. The estimated values were found to be in good agreement with the measured ones. This shows high applicability of the proposed data assimilation scheme in crop monitoring and yield estimation. As well, this scheme provides a reference for the assimilation of remote sensing data into crop growth models for regional crop monitoring and yield estimation. [ABSTRACT FROM AUTHOR]

The Chagan Depression is an important oil producing area in the Yingen-Ejinaqi Basin, northern China. The organic matter of the Early Cretaceous Bayingebi 2 member is mainly type II kerogen, and is the main source rock in this depression. However, the study of the hydrocarbon generation history of the Bayingebi 2 member is limited due to the lack of the hydrocarbon generation kinetic parameters. A source rock sample with type II kerogen of the Bayingebi 2 member was selected to for a gold tube-autoclave pyrolysis experiment, and the hydrocarbon generation kinetic parameters were obtained. Then, the hydrocarbon generation history of a typical well was reconstructed using the BasinMod 1D based on the thermal history of the Chagan Depression. The results show that the activation energy of generated gas is between 49 and 66 kcal/mol, with a frequency factor of 9.43 × 1011 s−1. The activation energy of oil is between 44 and 53 kcal/mol, with a frequency factor of 5.17 × 1012 s−1. The hydrocarbon generation potential of type II kerogen in the Chagan Depression is 273.7 mg/g TOC. The source rocks of the typical well have gone through two stages of hydrocarbon generation during the Suhongtu 1 member and Yingen Formation deposition. The maximum mass of generated oil and gas are 312 and 105 kg/m2 rock, respectively. This work provides important parameters for re-evaluating the hydrocarbon resources of the Chagan Depression. [ABSTRACT FROM AUTHOR]

Skeletal muscle growth plays a critical role during porcine muscle development stages. Genome-wide transcriptome analysis reveals that long intergenic non-coding RNAs (lincRNAs) are implicated as crucial regulator involving in epigenetic regulation. However, comprehensive analysis of lincRNAs in embryonic muscle development stages remain still elusive. Here, we investigated the transcriptome profiles of Duroc embryonic muscle tissues from days 33, 65, and 90 of gestation using RNA-seq, and 228 putative lincRNAs were identified. Moreover, these lincRNAs exhibit the characteristics of shorter transcripts length, longer exons, less exon numbers and lower expression level compared with protein-coding transcripts. Expression profile analysis showed that a total of 120 lincRNAs and 2638 mRNAs were differentially expressed. In addition, we also performed quantitative trait locus (QTL) mapping analysis for differentially expressed lincRNAs (DE lincRNAs), 113 of 120 DE lincRNAs were localized on 2200 QTLs, we observed many QTLs involved in growth and meat quality traits. Furthermore, we predicted potential target genes of DE lincRNAs in cis or trans regulation. Gene ontology and pathway analysis reveals that potential targets of DE lincRNAs mostly were enriched in the processes and pathways related to tissue development, MAPK signaling pathway, Wnt signaling pathway, TGF-beta signaling pathway and insulin signaling pathway, which involved in skeletal muscle physiological functions. Based on cluster analysis, co-expression network analysis of DE lincRNAs and their potential target genes indicated that DE lincRNAs highly regulated protein-coding genes associated with skeletal muscle development. In this study, many of the DE lincRNAs may play essential roles in pig muscle growth and muscle mass. Our study provides crucial information for further exploring the molecular mechanisms of lincRNAs during skeletal muscle development. [ABSTRACT FROM AUTHOR]

Accurate soybean yield estimates are important for establishing effective agricultural and soybean trade policies aimed at ensuring food security in China. Unmanned aerial vehicles (UAV) have been used to collect hyperspectral remote sensing data for soybean yield inversion owing to the flexibility, efficiency, and low cost of the technology. Traditional yield inversion algorithms are based on physiological and ecological parameters. However, meteorological factors also influence crop growth and yield and can even eliminate differences caused by physiological and ecological parameters at certain growth stages. The paper proposed a new yield estimation parameter index of effective accumulated temperature, incorporating the influence of meteorological factors into yield inversion models, which can improve the accuracy and universality of crop yield estimation. In the current study, soybean yield inversion models were developed for leaf area index (LAI), fresh biomass, and in-season estimated yield (INSEY) at different growth stages. The accuracies of the various models were evaluated by comparing yield estimates with ground-based sampling data. The analyses revealed that plant parameters, including plant height, LAI, dry biomass, and fresh biomass, correlated significantly with soybean yields, and that LAI and fresh biomass demonstrated the highest correlations with soybean yields. The results of soybean yield estimation at the single growth stage showed that the R2 value of the model and validation increased by 50.00, 26.60, and 34.40%, and 47.20, 12.50, and 31.90%, respectively, at the flowering stage, pod stage, and seed filling stage, indicating the highest accuracy of yield estimation at the beginning seed stage. The analysis of yield estimation results of soybean at multiple growth stages showed that R2 values of model and validation of soybean yield estimation models at three different growth stages increased by 4.50, 7.20, and 15.80%, and 4.00, 5.30, and 8.90%, respectively, compared with the beginning seed stage with the highest yield estimation accuracy. Compared with the yield estimation model of the single growth stage, the yield estimation accuracy of multiple growth stages is higher. [ABSTRACT FROM AUTHOR]

The difference between the skeletal muscle growth rates of Western and domestic breeds is remarkable, but the potential regulatory mechanism involved is still unclear. Numerous studies have pointed out that long intergenic noncoding RNA (lincRNA) plays a key role in skeletal muscle development. This study used published Yorkshire (LW) and Tibetan pig (TP) transcriptome data to explore the possible role of lincRNA in the difference in skeletal muscle development between the two breeds. 138 differentially expressed lincRNAs (DELs) were obtained between the two breeds, and their potential target genes (PTGs) were predicted. The results of GO and KEGG analysis revealed that PTGs are involved in multiple biological processes and pathways related to muscle development. The quantitative trait loci (QTLs) of DELs were predicted, and the results showed that most QTLs are related to muscle development. Finally, we constructed a co-expression network between muscle development related PTGs (MDRPTGs) and their corresponding DELs on the basis of their expression levels. The expression of DELs was significantly correlated with the corresponding MDRPTGs. Also, multiple MDRPTGs are involved in the key regulatory pathway of muscle fiber hypertrophy, which is the IGF-1-AKT-mTOR pathway. In summary, multiple lincRNAs that may cause differences in skeletal muscle development between the two breeds were identified, and their possible regulatory roles were explored. The findings of this study may provide a valuable reference for further research on the role of lincRNA in skeletal muscle development. [ABSTRACT FROM AUTHOR]

Transgenic pigs play an important role in biomedicine and agriculture. The "safe harbor" locus maintains consistent foreign gene expression in cells and is important for transgenic pig generation. However, as only several safe harbor loci(Rosa26, pH11 and Pifs501) have been identified in pigs, meeting the needs of the insertion of various foreign genes is difficult. In this study, we develop a novel strategy for the efficient knock-in of gene-of-interest fragments into endogenous beta-actin(ACTB) gene via CRISPR/Cas9 mediated homologous recombination with normal expression of ACTB. Thus, we provide an alternative strategy to integrate exogenous genes into the pig genome that can be applied to agricultural breeding and biomedical models. [ABSTRACT FROM AUTHOR]

Heart failure is the leading cause of mortality in patients with end-stage renal disease, and progressive cardiac remodeling is the key pathological basis of heart failure. However, the mechanism by which uremia-induced cardiac remodeling occurs is not well understood. Here, we showed that platelets were significantly activated in 5/6 nephrectomy-operated mice, and cardiac remodeling in the uremic mice was significantly improved when platelets were effectively depleted. A cardiac fibrosis-related gene expression profile revealed that Mmp7, encoding matrix metalloproteinase-7 (MMP-7), exhibited the greatest degree of downregulation in the hearts of uremic mice with platelets depleted. Using fluorescence-activated cell sorting, we discovered that MMP-7 was mainly expressed in M1 and M4 cardiac macrophages, although it was also extensively expressed in heart tissues. For the upstream therapeutic target, neutralization of platelet factor 4 (PF4) with monoclonal antibody not only significantly suppressed M4 macrophages in vivo, but also notably prevented collagen destruction in heart tissues. For the downstream therapeutic target, the pharmacological inhibition of MMP-7 with selective inhibitor failed to notably affect the platelet status, but significantly reduced heart collagen destruction in mice, a further indication that MMP-7 is a crucial downstream molecular target of platelet activation. In vitro, platelets interacted with macrophages and drove them to upregulate MMP-7 expression via free molecules, especially PF4. Taken together, the data suggest that MMP-7 is a key downstream target of platelet activation during uremia. Thus, MMP-7 is a likely and novel therapeutic target for intervention of cardiac remodeling during uremia. [ABSTRACT FROM AUTHOR]

"The Guide to the Expression of Uncertainty in Measurement" (GUM) method has many disadvantages when evaluating the uncertainty of wind speed measured by pitot tube, and the "Monte Carlo method" (MCM) is put forward to evaluate the uncertainty of wind speed. The measured quantity value of wind speed by the S-shaped pitot tube is the research object, and the uncertainty is evaluated by GUM and MCM, respectively, and the simulation test is carried out. GUM has errors due to its adoption of approximate linear model. MCM adopts real simulation strategy and has higher credibility than the GUM method, so the GUM method can be validated by the MCM method. When the partial input quantity is changed into triangular distribution, uniform distribution and arcsine distribution, the GUM method is found to be inapplicable. In the evaluation of MCM method, the complex calculation of sensitivity coefficient is avoided, and some uncertainty with small influencing quantity is not needed to be discarded. The evaluation result is more complete. Through comparing the uncertainty evaluation results of MCM before and after the water vapor correction term is discarded, the influence of the water vapor correction term is quantified. Therefore, compared with the GUM method, the MCM method has more advantages when wind speed is measured by pitot tube. [ABSTRACT FROM AUTHOR]

In order to explore the influence of water-vapor correction term on wind speed uncertainty, the actual measured value of wind speed by pitot tube is the research object, Firstly, the uncertainty value of the wind speed value obtained by the simplified model (discarding the water-vapor correction term) is evaluated by GUM method. Then, the MCM evaluates the uncertainty of the wind speed value obtained from the original model (including the water-vapor correction term). Finally, two kinds of the evaluation results obtained by the method are compared in the form of probability distribution graphics, and the numerical analysis is carried out. After the water-vapor correction item is discarded, the fluctuation degree and dispersion degree of the measurement result will be reduced, and the degree of reduction is 1.0988% and 1.0929%, respectively. The degree of volatility is about 25.8513% of the degree of dispersion. The degree of influence on the volatility and dispersion of the measurement result is about 0.2305% and 0.8987% of the measured value of wind speed. In the application, for the occasion of pursuing the accuracy of measured value of wind speed, it is recommended to use the original model to measure the real-time wind speed. For the occasion of the stability of the wind speed measurement result, the GUM method can be used to evaluate the uncertainty of the simplified model. [ABSTRACT FROM AUTHOR]

Topic modeling for short texts faces a tough challenge, owing to the sparsity problem. An effective solution is to aggregate short texts into long pseudo-documents before training a standard topic model. The main concern of this solution is the way of aggregating short texts. A recent developed self-aggregation-based topic model (SATM) can adaptively aggregate short texts without using heuristic information. However, the model definition of SATM is a bit rigid, and more importantly, it tends to overfitting and time-consuming for large-scale corpora. To improve SATM, we propose a generalized topic model for short texts, namely latent topic model (LTM). In LTM, we assume that the observable short texts are snippets of normal long texts (namely original documents) generated by a given standard topic model, but their original document memberships are unknown. With Gibbs sampling, LTM drives an adaptive aggregation process of short texts, and simultaneously estimates other latent variables of interest. Additionally, we propose a mini-batch scheme for fast inference. Experimental results indicate that LTM is competitive with the state-of-the-art baseline models on short text topic modeling. [ABSTRACT FROM AUTHOR]

A series of coconut shell activated carbon catalysts, modified by metal oxides, were prepared by an ultrasound-assisted incipient wetness method for the removal of methyl mercaptan (CHSH). The catalysts were investigated using XRD, BET, XPS, TEM and TA.The results showed that the catalyst combined with 2 wt% Fe loading and iron (Fe): copper (Cu) (mole ratio) 10 : 3, and calcination at 300 °C had a superior removal efficiency. The high activity could be attributed to the generation of highly dispersed Fe-Cu nanocomposites. The results revealed that calcination temperature not only influenced the chemical states and nanocomposite size of iron and copper, but also affected the pore structures of the catalysts. Compared with Fe/AC, the interaction between the iron and copper oxides resulted in smaller nanoparticles and high dispersion for Fe-Cu/AC. Product analysis results suggested dimethyl disulfide, metal methanesulfonates and methyl thiolates were the oxidation products which adsorbed on the activated carbon. [ABSTRACT FROM AUTHOR]

The deposition of IgG4 of antibodies against phospholipase A2 receptor (anti-PLAR) is predominating in the kidneys of patients with idiopathic membranous nephropathy, while its predictive value has not been determined. It was a retrospective study, and 438 patients were included. Serum samples of two time points [before intervention (baseline) and after 1.5-year treatment (endpoint)] were detected for total and IgG4 anti-PLAR. IgG4 <0.26 RU/mL or total <20 RU/mL was considered as seronegativity. Bi-positivity/bi-negativity was defined when patients'antibodies were found positive or negative both at the baseline and endpoint. Completed remission (CR) was a major clinical outcome. A series of complement ingredients (MASP-1/2, MBL, C3a, C5a, Factor B, Ba, Bb and C5b-9) were measured in the patients of bi-positivity and bi-negativity: (1) meta-analysis based on six papers conducted seropositivity of anti-PLAR was a useful predictor for achieving CR, but there was a high heterogeneity; (2) there was significant correlation between the baseline and decrease in IgG4 subclass and the achievement of CR; (3) bi-negativity of IgG4 has a high accuracy of predicting CR compared with total antibodies; (4) in patients of bi-positivity, those achieving CR showed lower MASP-1/2, MBL, C3a, C5a, FB, Ba and Bb than patients failing to achieve CR; (5) the titers of endpoint and decrease in Ba and Bb were associated with improvement of 24 h-UP in those of bi-positivity; and (6) the decrease in Ba was a significant factor for achieving CR in those of bi-positivity. Continuous IgG4 negativity was a useful tool to predict the achievement of CR; however, in patients of continuous IgG4 positivity, those with lower activation of lectin and alternative pathways would still more probably achieve CR. [ABSTRACT FROM AUTHOR]

Homeobox A10 ( HOXA10), a well-known transcriptional factor that can be regulated by estrogen (E) and progesterone (P), is necessary not only for endometrial differentiation but also for establishing the conditions required for implantation. However, little research has focused on the regulation of the porcine HOXA10 gene. In this study, we aimed to (1) characterize the genetic structure of the porcine HOXA10 gene, (2) analyze the tissue expression pattern and differential expression levels in the endometrium of HOXA10 in different developmental stages of Meishan and commercial Yorkshire pigs, and (3) identify the E2/P4 response element in the promoter region of the porcine HOXA10 gene and verify that it induces HOXA10 in human endometrial adenocarcinoma (Ishikawa) cells. The results indicated that the cDNA of porcine HOXA10 was 2,628 bp in length with an open reading frame (ORF) of 1,236 bp encoding a peptide of 411-amino acid residues, which showed 90 and 95 % sequence similarity to that of human and mouse homologs, respectively. Semiquantitative RT-PCR confirmed that the porcine HOXA10 gene is highly expressed in the endometrium, bladder and kidney. Real-time PCR showed that the expression of HOXA10 was significantly higher on day 15 of gestation (gd 15) in comparison to gd 26 ( P < 0.01), gd 50 ( P < 0.01) and day 15 of the estrous cycle (ed 15) in the endometrium of Meishan pigs. In contrast, the highest expression in the endometrium of Yorkshire pigs was on gd 50. Moreover, the abundance of HOXA10 mRNA was the highest on gd 15 in Meishan pigs than in any other stage tested in the two breeds. Deletion analysis and reporter expression assays identified a promoter region (−1044 bp to +18 bp) which is responsible for E- and P-induced HOXA10 transcription. Moreover, this promoter region enhanced the E2/P4-induced HOXA10 expression in Ishikawa cells. In conclusion, we identified an E/P response element of the porcine HOXA10 gene for the first time. The data from the present study contribute to the mechanism by which the porcine HOXA10 gene regulates embryo implantation and prolificacy traits. [ABSTRACT FROM AUTHOR]

Vaccines can have adverse side-effects, and these are predominantly associated with the inclusion of chemical additives such as aluminum hydroxide adjuvant. The objective of this study was to establish an in vitro model system amenable to mechanistic investigations of cytotoxicity induced by hepatitis B vaccine, and to investigate the mechanisms of vaccine-induced cell death. The mouse liver hepatoma cell line Hepa1-6 was treated with two doses of adjuvanted (aluminium hydroxide) hepatitis B vaccine (0.5 and 1 μg protein per ml) and cell integrity was measured after 24, 48 and 72 h. Hepatitis B vaccine exposure increased cell apoptosis as detected by flow cytometry and TUNEL assay. Vaccine exposure was accompanied by significant increases in the levels of activated caspase 3, a key effector caspase in the apoptosis cascade. Early transcriptional events were detected by qRT-PCR. We report that hepatitis B vaccine exposure resulted in significant upregulation of the key genes encoding caspase 7, caspase 9, Inhibitor caspase-activated DNase (ICAD), Rho-associated coiled-coil containing protein kinase 1 (ROCK-1), and Apoptotic protease activating factor 1 (Apaf-1). Upregulation of cleaved caspase 3,7 were detected by western blot in addition to Apaf-1 and caspase 9 expressions argues that cell death takes place via the intrinsic apoptotic pathway in which release of cytochrome c from the mitochondria triggers the assembly of a caspase activation complex. We conclude that exposure of Hepa1-6 cells to a low dose of adjuvanted hepatitis B vaccine leads to loss of mitochondrial integrity, apoptosis induction, and cell death, apoptosis effect was observed also in C2C12 mouse myoblast cell line after treated with low dose of vaccine (0.3, 0.1, 0.05 μg/ml). In addition In vivo apoptotic effect of hepatitis B vaccine was observed in mouse liver. [ABSTRACT FROM AUTHOR]

MCF-7/ADR cells have been widely used as a multidrug-resistant breast cancer cell model in cancer research. The origin of MCF-7/ADR has been a matter of debate since MCF-7/ADR cells were re-designated NCI/ADR-RES in 1998. Many recent studies still describe MCF-7/ADR cells as originating from the breast cancer cell line MCF-7. Thus, the real origin of MCF-7/ADR cells remains more unclear. In this study, a new adriamycin (ADR)-resistant cell line MCF-7/ADR′ was reproduced using the same procedure employed during the initial establishment of MCF-7/ADR. Since the MCF-7/ADR′ cell line was definitely derived from parental MCF-7 cells, we were able to directly compare these cell lines together with MCF-7/ADR using immunocytochemical, morphological, and consecutive DNA fingerprinting analyses to determine the true origin of MCF-7/ADR. Both ADR-resistant cell lines displayed some similar phenotypic characteristics, such as high levels of P-glycoprotein (P-gp) expression, increased vacuolation, abundant filamentous material, and irregular pseudopodia. With increasing concentrations of ADR, the DNA fingerprints of MCF-7/ADR′ cells were always identical to the parental MCF-7 cells. However, the DNA fingerprints of MCF-7/ADR cells did not relate to MCF-7 or MCF-7/ADR′. MCF-7/ADR and the breast cancer cell line MCF-7 are not of the same origin. Long-time culture in the presence of ADR does not cause significant changes in DNA fingerprint patterns. [ABSTRACT FROM AUTHOR]

Alkaline phosphatase (AKP), from the succus entericus of silkworm, was purified using 10%–50% ammonium sulfate fractions, ion exchange chromatography of DEAE-Sepharose, and size exclusion chromatography of Sephacryl S-200. The purification fold was 464 times and specified activity was 3 936 U/mg. Optimum pH value of the phosphatase was 10.5, and was stable between pH 7.5 and 11. The optimum temperature of the phosphatase was 40°C and it was unstable over 50°C. K m value of the phosphatase was 1.25 mmol/L. In a given condition, the phosphatase was selectively modified by PCMB, NBS, PMSF, TNBS, SUAN, DTT, BrAc, and IAc, the results indicate that PMSF, SUA, BrAc, IAc, and TNBS could obviously inhibit the activity of the phosphatase, and the degree of inhibition depended on the concentration of these reagents. There was little effect on the activity of phosphatase after treatment by PMSF, DTT, and NBT. We primarily conclude that mercapto and imidazole are essential for AKP from silkworm. Also, Lys residue and disulfide bands are necessary to protect the catalysis of the AKP. [ABSTRACT FROM AUTHOR]

Canopy temperature is an important parameter for monitoring the physiological status of maize. The study of its change law can help to improve the growth environment and increase the yield and quality. Based on the RGB, thermal infrared, and multispectral images obtained by an Unmanned Aerial Vehicle (UAV), two peak threshold methods were used in this study to eliminate the soil background and obtain the maize canopy information in the study area. Then, by using the radiation transmission model and Planck’s law, the maize canopy temperature was extracted, and the correlation of canopy temperature change with the regional coverage and soil drought degree was analysed. Results indicated that the accuracy of canopy temperature extraction can be effectively enhanced by eliminating the influence of soil background. At jointing and filling stages, the Pearson correlation coefficients r of canopy temperature and coverage were 0.731 and 0.722, respectively, and the slope of the linear fitting model was less than 0, indicating that the coverage has a negative impact on canopy temperature, i.e., the higher the coverage, the lower the canopy temperature, and vice versa. Additionally, statistical analysis of the canopy temperature and temperature vegetation drought index (TVDI) clustering results at the jointing and filling stages demonstrated that the total overlapping rates of the categories were 72.5% and 41.0% respectively, suggesting that maize canopy temperature is related to regional soil drought degree, the change range of canopy temperature increases with the TVDI value, and the soil drought degree at the jointing stage has a large impact on maize canopy temperature change than that at the filling stage. This study provides technical support for extracting and analysing crop canopy temperature change by using UAV thermal infrared technology. [ABSTRACT FROM AUTHOR]

Against the background of global climate change, spatial-temporal variation in net primary productivity (NPP) has attracted much attention. To analyze NPP spatial-temporal variation within the context of changes in hydrothermal conditions, the Vegetation Photosynthesis Model (VPM) is used to elucidate the mathematical relationship between NPP and hydrothermal conditions. Based on this spatial-temporal pattern of NPP and hydrothermal conditions in the Lancang-Mekong River Basin, regression statistics, an empirical model of land evaporation, and the water and thermal product index (K) are used to evaluate correlations between NPP and hydrothermal conditions in terms of their distribution pattern and interaction. The results show the following. (1) From 2000 to 2014, NPP in the Lancang-Mekong River Basin was highest in the central region and gradually decreased toward the southwest and northwest, whereas the annual change rate in NPP showed no significant increasing trend. (2) In the Lancang Basin, the correlation between hydrothermal conditions and NPP was high with respect to their distribution patterns, though this correlation was low in the Mekong Basin. (3) Correlation between K and NPP is high in the region where the effects of water and thermal factors on vegetation growth are similar. (4) K is an effective complement to the correlation between a single hydrothermal factor (temperature or precipitation) and NPP, and the influence of hydrothermal conditions on NPP was positive in the Lancang River and negative in the Mekong River Basin. Our study quantitatively analyzes the spatial-temporal correlation between NPP and hydrothermal conditions. The findings can reflect the vegetation change tendency and provide scientific data for ecological environment development and protection in the study area. [ABSTRACT FROM AUTHOR]

Identification of genomic signatures that help reveal mechanisms underlying desirable traits in domesticated pigs is of significant biological, agricultural and medical importance. To identify the genomic footprints left by selection during domestication of the Enshi black pig, a typical native and meat-lard breed in China, we generated about 72-fold coverage of the pig genome using pools of genomic DNA representing three different populations of Enshi black pigs from three different locations. Combining this data with the available whole genomes of 13 Chinese wild boars, we identified 417 protein-coding genes embedded in the selected regions of Enshi black pigs. These genes are mainly involved in developmental and metabolic processes, response to stimulus, and other biological processes. Signatures of selection were detected in genes involved in body size and immunity (RPS10 and VASN), lipid metabolism (GSK3), male fertility (INSL6) and developmental processes (TBX19). These findings provide a window into the potential genetic mechanism underlying development of desirable phenotypes in Enshi black pigs during domestication and subsequent artificial selection. Thus, our results illustrate how domestication has shaped patterns of genetic variation in Enshi black pigs and provide valuable genetic resources that enable effective use of pigs in agricultural production. [ABSTRACT FROM AUTHOR]