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In this paper, the solubility of levodropropizine, an important cough suppressant drug, in 13 pure solvents (including 8 alcohol solvents and 5 acetate solvents) was studied with the aid of a laser dynamic solubility-monitor device at a temperature from 283.15 to 323.15 K and atmosphere pressure. In the measured temperature range, the solubility of LVDP increases with increasing temperature and follows the following order: iso-butanol > iso-pentanol > n-hexanol ≈ n-heptanol > n-pentanol > n-propanol > n-butanol > iso-propanol > ethyl acetate > n-propyl acetate > n-butyl acetate > n-pentyl acetate > iso-propyl acetate. Three semiempirical models (modified Apelblat, three-parameter van’t-Hoff, and λh) and two activity coefficient models (Wilson and NRTL) were used to correlate solubility of LVDP as a function of temperature and the mixing thermodynamic properties of the 13 solutions were evaluated based on measured solubility and the parameter values of Wilson’s model.

Temperature-sensitive genic male sterility (TGMS) lines are widely used in the breeding of hybrid crops1,2, but by what means temperature as a general environmental factor reverses the fertility of different TGMS lines remains unknown. Here, we identified an ArabidopsisTGMS line named reversible male sterile(rvms) that is fertile at low temperature (17 °C) and encodes a GDSL lipase. Cytological observations and statistical analysis showed that low temperature slows pollen development. Further screening of restorers of rvms, as well as crossing with a slow-growth line at normal temperature (24 °C), demonstrate that slowing of development overcomes the defects of rvmsmicrospores and allows them to develop into functional pollen. Several other ArabidopsisTGMS lines were identified, and their fertility was also restored by slowing of development. Given that male reproductive development is conserved3, we propose that slowing of development is a general mechanism applicable to the sterility–fertility conversion of TGMS lines from different plant species.

The microstructure and mechanical properties of the compact strip production (CSP) processed quenching and partitioning (Q&P) steels were investigated through experimental methods to address the challenge of designing high-performance Q&P steels. Compared with the conventional process (CP) produced samples, with slightly reduced strength, the total elongation of the CSP produced samples was increased by nearly 7%. Microstructural analysis revealed that variations in austenite stability were not the primary cause for the differences in mechanical properties between the CSP and the CP. The CSP processed Q&P steel exhibited milder center segregation behavior in contrast to the CP processed Q&P steel. Consequently, in the CSP processed Q&P steel, a higher proportion of austenite and a lower proportion of martensite were observed at the center position, delaying the crack initiation in the central region and contributing to the enhanced ductility. The investigation into the CSP process reveals its effect on alleviation of segregation and enhancement of mechanical properties of the Q&P steel.

Ductal carcinoma in situ (DCIS) is a severe breast disease. It generates little symptom and may be neglected in prodromal stage. In this study, we developed a novel DCIS detection method based on breast thermography, which can provide earlier alert than other exams. We created a 40 breast-thermogram dataset. We used six statistical measures, and we used fractal dimension to describe the texture measure. The extreme learning machine was used as the classifier. Our developed system yielded a sensitivity of 93.0 ± 2.6%, a specificity of 92.5 ± 2.6%, and an accuracy of 92.8 ± 1.8%. The extreme learning machine was better than support vector machine, artificial neural network, decision tree, and weighted k-nearest neighbors. Besides, our developed system was superior to six state-of-the-art approaches.

The realization of an ideal combination of mechanical and fatigue properties is prerequisites for practical application of titanium (Ti) microalloyed steel in automotive field. The fatigue behavior of four Ti microalloyed high-strength beam steels with different Ti contents was systematically studied. The results show that the content of microalloying element Ti has a significant effect on the fatigue properties, especially in the steel with a high Ti content. For the experimental Ti microalloyed steel, inclusion-induced crack initiation is the main fatigue failure mode. Different from general fatigue fracture mechanism in Ti-contained steel, no TiN, which is the most detrimental to fatigue behavior, was found in fatigue crack initiation area. However, the large-sized TiN and oxide complex inclusion with a core–shell structure is the dominant cause of fatigue fracture. Because of the intense-localized deformation at the interface between complex inclusion and matrix, the angular TiN in the outer shell has a serious deteriorating effect on the fatigue properties, which is consistent with the result of the Kernel average misorientation map. Besides, the modification effect of a small amount of MnS on large-sized inclusion is not obvious and has little effect on the fatigue behavior. For more practical guidance, the critical inclusion sizes of the experimental steels were also investigated by experimental extrapolation method. With the increasing tensile strength, the inclusion sensitivity of the experimental steels increases, leading to the small critical inclusion size.

Tuberculosis is a very deadly disease, with more than half of all tuberculosis cases dead in countries and regions with relatively poor health care resources. Fortunately, the disease is curable, and early diagnosis and medication can go a long way toward curing TB patients. Unfortunately, traditional methods of TB diagnosis rely on specialist doctors, which is lacking in areas with high TB mortality rates. Diagnostic methods based on artificial intelligence technology are one of the solutions to this problem. We propose a Deep Transferred EfficientNet with SVM (DTE-SVM), which replaces the pre-trained EfficientNet classification layer with an SVM classifier and achieves auspicious performance on a small dataset. After ten runs of 10-fold Cross-Validation, the DTE-SVM has a sensitivity of 93.89±1.96, a specificity of 95.35±1.31, a precision of 95.30±1.24, an accuracy of 94.62±1.00, and an F1-score of 94.62±1.00. In addition, our study conducted ablation studies on the effect of the SVM classifier on model performance and briefly discussed the results.

Deep Auto-Encoders (DAE) have been widely used in dimensionality reduction and feature extraction of brain functional networks. However, the features in aggregation matrices of functional networks obtained by DAE dimensionality reduction might lose part of time-varying information, so that DAE cannot learn the distribution of original features well. To solve these problems, we extracted and analyzed brain functional statuses for early Mild Cognitive Impairment (eMCI) based on Variational Auto-Encoder (VAE). The high dimensions of features in aggregation matrices of dynamic functional networks were reduced by VAE to obtain the corresponding hidden variable matrices. Gaussian Mixture Model (GMM) was used to cluster the features in the hidden variable matrices to form several Common Functional Networks (CFNs) representing different functional statuses. We analyzed the similarities and differences of functional statuses between eMCI subjects and normal subjects in different sub-segments, as well as the switching of functional statuses in the entire time series. The experimental results show that there are similarities between the most frequent functional statuses of the two types of subjects and differences between the least frequent functional statuses. The proposed method can more significantly reveal the similarity and difference of functional statuses between eMCI subjects and normal subjects than the comparable methods, and the switching rule of functional statuses can help better understand the dynamic characteristics of brain functional networks for eMCI patients.

Cerebral microbleed (CMB) is a serious public health concern. It is associated with dementia, which can be detected with brain magnetic resonance image (MRI). CMBs often appear as tiny round dots on MRIs, and they can be spotted anywhere over brain. Therefore, manual inspection is tedious and lengthy, and the results are often short in reproducible. In this paper, a novel automatic CMB diagnosis method was proposed based on deep learning and optimization algorithms, which used the brain MRI as the input and output the diagnosis results as CMB and non-CMB. Firstly, sliding window processing was employed to generate the dataset from brain MRIs. Then, a pre-trained VGG was employed to obtain the image features from the dataset. Finally, an ELM was trained by Gaussian-map bat algorithm (GBA) for identification. Results showed that the proposed method VGG-ELM-GBA provided better generalization performance than several state-of-the-art approaches.

Diffusion tensor imaging (DTI) is a non-invasive magnetic resonance imaging technique and a special type of magnetic resonance imaging, which has been widely used to study the diffusion process in the brain. The signal-to-noise ratio of DTI data is relatively low, the shape and direction of the noisy tensor data are destroyed. This limits the development of DTI in clinical applications. In order to remove the Rician noise and preserve the diffusion tensor geometry of DTI, we propose a DTI denoising algorithm based on Riemann nonlocal similarity. Firstly, DTI tensor is mapped to the Riemannian manifold to preserve the structural properties of the tensor. The Riemann similarity measure is used to search for non-local similar blocks to form similar patch groups. Then the Gaussian mixture model is used to learn the prior distribution of patch groups. Finally, the noisy patch group is denoised by Bayesian inference and the denoised patch group is reconstructed to obtain the final denoised image. The denoising experiments of real and simulated DTI data are carried out to verify the feasibility and effectiveness of the proposed algorithm. The experimental results show that our algorithm not only effectively removes the Rician noise in the DTI image, but also preserves the nonlinear structure of the DTI image. Comparing to the existing denoising algorithms, our algorithm has better improvement of the principal diffusion direction, lower absolute error of fractional anisotropy and higher peak signal-to-noise ratio.

Module can not only affect the integration of network functions, but also contribute to understand the characteristics of local connections in the network. However, the connection between nodes in the network changes with the passage of time, and the module structure changes accordingly. To overcome this drawback, we propose a method of applying weighted clustering ensemble to partition multilayer brain functional networks into modules. Firstly, k-means clustering is adopted to carry out base clustering for a certain layer of functional network for several times, and each clustering is corresponding to a subordinate matrix and a similarity matrix. Then clustering validity index is used to assess each partitioning and the assessed values are taken as the weights of similarity matrix. Finally, the weighted similarity matrix is partitioned by means of fuzzy C-means clustering, and the results are evaluated by modularity function to obtain the optimal partitioned modules. Experimental results show that the effect of module partitioning resulting from weighted clustering ensemble is better than that of other comparable methods. The proposed framework could be promising to analyze the differences between corresponding modules of patients with Alzheimer’s disease and normal people, so as to better understanding some dynamical pathological changes in brain connectome of patients.

Unmanned aerial vehicles (UAVs) have been widely applied for their high mobility and low cost, and the visible light communication (VLC) aided UAV can combine the flexibility of UAV and the high transmission rate of VLC. However, the VLC links of a UAV are inevitably affected by the jittering effects, which were rarely characterized. In this letter, the channel between a light emitting diode (LED) and a hovering UAV is modeled considering the random orientation error of the UAV. Closed-form expressions are derived for the probability density function (PDF) and the cumulative distribution function (CDF) of the orientation error, which also leads to closed-form asymptotic PDF and CDF expressions of the UAV’s VLC channel gain. Simulation and experimental results are demonstrated to verify the theoretical derivations. The work lays a theoretical foundation for VLC-aided UAVs, providing insightful suggestions for the system design.

In this paper, the solubility of methanesulfonamide (MSAM) in selected 13 pure solvents including 9 alcohols (methanol, ethanol, n-propanol, iso-propanol, n-butanol, sec-butanol, iso-butanol, n-pentanol, and iso-pentanol) and 4 esters (ethyl acetate, propyl acetate, iso-propyl acetate, and methyl propionate) at atmospheric pressure and temperatures from 283.15 to 323.15 K was determined. The solubility as a function of temperature was regressed using modified Apelblat, Van’t Hoff, λh, Wilson, and nonrandom two-liquid (NRTL) models. Based on the experimental and the simulation results, the thermodynamic mixing properties of the solutions, mixing Gibbs energy, enthalpy, and entropy were evaluated.

White blood cells (WBCs) are a portion of the immune system which fights against germs. Leukemia is the most common blood cancer which may lead to death. It occurs due to the production of a large number of immature WBCs in the bone marrow that destroy healthy cells. To overcome the severity of this disease, it is necessary to diagnose the shapes of immature cells at an early stage that ultimately reduces the modality rate of the patients. Recently different types of segmentation and classification methods are presented based upon deep-learning (DL) models but still have some limitations. This research aims to propose a modified DL approach for the accurate segmentation of leukocytes and their classification. The proposed technique includes two core steps: preprocessing-based classification and segmentation. In preprocessing, synthetic images are generated using a generative adversarial network (GAN) and normalized by color transformation. The optimal deep features are extracted from each blood smear image using pretrained deep models i.e., DarkNet-53 and ShuffleNet. More informative features are selected by principal component analysis (PCA) and fused serially for classification. The morphological operations based on color thresholding with the deep semantic method are utilized for leukemia segmentation of classified cells. The classification accuracy achieved with ALL-IDB and LISC dataset is 100% and 99.70% for the classification of leukocytes i.e., blast, no blast, basophils, neutrophils, eosinophils, lymphocytes, and monocytes, respectively. Whereas semantic segmentation achieved 99.10% and 98.60% for average and global accuracy, respectively. The proposed method achieved outstanding outcomes as compared to the latest existing research works.

Supplemental Digital Content is available in the text.Breast cancer is an aggressive malignancy with high morbidity in females worldwide. Extensive studies reveal that long noncoding RNAs (lncRNAs) are abnormally expressed and act as key regulators in various cancers, including breast cancer. In this work, we investigated the role and regulatory mechanism of lncRNA prostate cancer-associated transcript 6 (PCAT6) in breast cancer progression. Our findings revealed that PCAT6 was overexpressed in breast cancer tissues and cell lines. Furthermore, elevation of PCAT6 reflected an adverse prognosis of patients. Functional experiments indicated that PCAT6 knockdown hampered cell proliferation, facilitated apoptosis and cell cycle arrest in vitro, and inhibited tumor growth in vivo. We also found that the transcription factor SP1 could bind to the PCAT6 promoter and promoted its expression. Subsequently, it was verified that PCAT6 was a molecular sponge for microRNA-326 (miR-326), and the leucine-rich repeat containing the eight family member E (LRRC8E) was a direct target of miR-326. Rescue assays revealed that LRRC8E overexpression attenuated the suppressive effect of PCAT6 knockdown on cellular progression of breast cancer. In summary, this study demonstrated that SP1-activated PCAT6 promoted the malignant behaviors of breast cancer cells by regulating the miR-326/LRRC8E axis.

Trust relationship plays an important role in online shopping, recommendation systems, internet of things, etc. The problem of trust evaluation among users in online social network (OSN) has attracted much attention, and has become a hot issue in the domain of social computing. However, the way of trust propagation and aggregation in OSN is still not clear, as well as the accuracy of trust calculation. In order to calculate the indirect trust, an ELM-NeuralWalk algorithm to implement trust propagation and aggregation is proposed. ELM-WalkNet firstly learns two-hop trust calculation rules, then calculates two-hop trust among users in the OSN. After that, ELM-NeuralWalk updates the OSN with the calculated trust value, so as to realise the calculation of multi-hop trust among users through iterative calling ELM-WalkNet. Unlike traditional solutions that use inference methods, ELM-WalkNet can learn trust calculation rules in an inductive way and accurately calculate indirect trust between users. Experiments on two real OSN datasets showed that ELM-NeuralWalk outperforms existing solutions.

Solid-state phase transformation is usually associated with excellent mechanical properties in steel materials. A deep understanding of the formation and evolution of phase structure is essential to tailor their service performance. As a powerful tool for capturing the evolution of complex microstructures, phase-field simulation quantitatively calculates the phase structures evolution without explicit assumptions about transient microstructures. With the development of advanced numerical technology and computing ability, phase-field methods have been successfully applied to solid-state phase transformation in steels and greatly support the research and development of advanced steel materials. The phase-field simulations of solid-phase transformation in steels were summarized, and the future development was proposed.

Direct strip casting (DSC) is one of the cutting-edge technologies for the steel industry in the twenty-first century. Under the background of carbon peak and carbon neutrality, DSC technology has a bright future of applications as it requires less production time and space with reduced energy consumption. Owing to its sub-rapid cooling rate during solidification and low reduction during hot rolling, DSC process exhibits a series of unique physical metallurgy characteristics. The process characteristics of DSC process and the microstructural evolution during the thermomechanical processing of low-carbon microalloyed steel are reviewed. The effects of hot rolling, cooling, coiling temperatures and microalloying elements on the microstructure and mechanical properties are then discussed. Finally, the future development orientations of DSC technology are suggested to fully utilize its unique features for the enhancement of its competitiveness and for the promotion of carbon neutrality of the steel industry.

How and when Earth’s volatile content was established is controversial with several mechanisms postulated, including planetesimal evaporation, core formation and the late delivery of undifferentiated chondrite-like materials. The isotopes of volatile elements such as sulfur can be fractionated during planetary accretion and differentiation and thus are potential tracers of these processes. Using first-principles calculations, we examine sulfur isotope fractionation during core formation and planetesimal evaporation. We find no measurable sulfur isotope fractionation between silicate and metallic melts at core-forming conditions, indicating that the observed light sulfur isotope composition of the bulk silicate Earth relative to chondrites cannot be explained by metal–silicate fractionation. Our thermodynamic calculations show that sulfur evaporates mostly as H2S during planetesimal evaporation when nebular H2is present. The observed bulk Earth sulfur isotope signature and abundance can be reproduced by evaporative loss of about 90% sulfur mainly as H2S from molten planetesimals before nebular H2is dissipated. The heavy sulfur isotope composition of the Moon relative to the Earth is consistent with evaporative sulfur loss under 94–98% saturation condition during the Moon-forming giant impact. In summary, volatile evaporation from molten planetesimals before Earth’s formation probably played a key role in establishing Earth’s volatile element content.

Using the test particle simulation method, we investigate the stochastic motion of electrons with energy of 300 keV in a monochromatic magnetosonic (MS) wave field. This study is motivated by the violation of the quasi‐linear theory assumption, when strong MS waves (amplitude up to ~1 nT) are present in the Earth’s magnetosphere. First, electron motion can become stochastic when the wave amplitude exceeds a certain threshold. If an electron initially resonates with the MS wave via bounce resonance, as the bounce resonance order increases, the amplitude threshold of electron stochastic motion increases until it reaches the peak at about the 11th order in our study, then the amplitude threshold slowly declines. Further, we find that the coexistence of bounce and Landau resonances between electrons and MS waves will significantly reduce the amplitude threshold. In some cases, the electron motion can become stochastic in the field of an MS wave with amplitudes below 1 nT. Regardless, if neither the bounce nor Landau resonance condition is satisfied initially, then the amplitude threshold of stochastic motion shows an increasing trend for lower frequencies and a decreasing trend for higher frequencies, even though the amplitude threshold is always very large (> 5 nT). Our study suggests that electron stochastic motion should also be considered when modeling electron dynamics regulated by intense MS waves in the Earth’s magnetosphere. Test particle simulations show that electron motion can become stochastic in the MS wave field when the wave amplitude exceeds a certain threshold.We have thoroughly studied the effects of bounce resonance order, pitch angle, and wave frequency on the amplitude threshold.The coexistence of bounce and Landau resonances induced by MS wave can significantly reduce the amplitude threshold. Test particle simulations show that electron motion can become stochastic in the MS wave field when the wave amplitude exceeds a certain threshold. We have thoroughly studied the effects of bounce resonance order, pitch angle, and wave frequency on the amplitude threshold. The coexistence of bounce and Landau resonances induced by MS wave can significantly reduce the amplitude threshold.

Immune checkpoint blockade has shown low response rates for advanced breast cancer, and combination strategies are needed. Microwave ablation (MWA) may be a trigger of antitumor immunity. This window-of-opportunity trial (ClinicalTrials.gov: NCT04805736) was conducted to determine the safety and feasibility of preoperative camrelizumab (an anti-PD-1 antibody) combined with MWA in the treatment of early-stage breast cancer.

Improving the photodetection performance of thin-film transistor (TFT)-based UV photodetectors (UVPDs), using thick channel layers to promote photocurrent (Iph) or using thin channel layers to suppress dark current (Idark) is typically a trade-off. In this work, UVPDs based on oxygen-doped Si-Zn-Sn-O (SZTO) TFT with a stack of Pt/NiO capping layers (CLs) to release the trade-off between Idarkand Iphare demonstrated. The Pt CL creates a wide depletion region in the channel layer to allow the use of thick channels, but still maintains low Idark, while the NiO CL forms a pn heterojunction to provide additional photogenerated carriers and enhance Iphunder UV irradiation. Experimental results show that the proposed 95 nm-thick oxygen-doped SZTO TFT with a stack of Pt/NiO dual CLs exhibits an excellent photoresponsivity of 2026 A W−1and photosensitivity of 9.3 × 107A A−1, which are about 76× and 82.5× higher than a conventional 45 nm-thick SZTO TFT under 275 nm UV irradiation.

Medical imaging is an essential medical diagnosis system subsequently integrated with artificial intelligence for assistance in clinical diagnosis. The actual medical images acquired during the image capturing procedures generate poor quality images as a result of numerous physical restrictions of the imaging equipment and time constraints. Recently, medical image super-resolution (SR) has emerged as an indispensable research subject in the community of image processing to address such limitations. SR is a classical computer vision operation that attempts to restore a visually sharp high-resolution images from the degraded low-resolution images. In this study, an effective medical super-resolution approach based on weighted least squares optimisation via multiscale convolutional neural networks (CNNs) has been proposed for lesion localisation. The weighted least squares optimisation strategy that particularly is well-suited for progressively coarsening the original images and simultaneously extract multiscale information has been executed. Subsequently, a SR model by training CNNs based on wavelet analysis has been designed by carrying out wavelet decomposition of optimized images for multiscale representations. Then multiple CNNs have been trained separately to approximate the wavelet multiscale representations. The trained multiple convolutional neural networks characterize medical images in many directions and multiscale frequency bands, and thus facilitate image restoration subject to increased number of variations depicted in different dimensions and orientations. Finally, the trained CNNs regress wavelet multiscale representations from a LR medical images, followed by wavelet synthesis that forms a reconstructed HR medical image. The experimental performance indicates that the proposed model SR restoration approach achieve superior SR efficiency over existing comparative methods

ABSTRACTOsteoporosis (OP) characterizes a decrease in bone density and bone mass which leads to brittle fractures and serious damages to individuals. In recent years, various researches have proved that miRNAs act pivotally in the onset of bone-related diseases. In our research, we probed into the impact of miR-181a-5P on viability, differentiation, as well as apoptosis of human bone marrow mesenchymal stem cells (hBMSCs). Our study reported that overexpressing miR-181a-5p considerably reduced the cell growth, whereas the miR-181a-5p inhibition showed opposite results. Furthermore, the hBMSCs apoptosis percentage was visually elevated or minimized after overexpressing or silencing miR-181a-5p, respectively. Our data also indicated that miR-181a-5p overexpression significantly inhibited ALP activity, and level of OPN, Runx2 and OCN at mRNA and protein level, whereas miR-181a-5p inhibition presented opposite results. In addition, based on luciferase reporter assay, sirtuin 1 (Sirt1) was confirmed as the target of miR-181a-5p in hBMSCs. Finally, Sirt1 overexpression significantly inhibited the impact of miR-181a-5p mimic on apoptosis and inhibited differentiation, while silencing Sirt1 eliminated the inhibitory effects of miR-181a-5p on apoptosis and promoted differentiation via PI3K/AKT pathway. In conclusion, this work revealed that miR-181a-5p could regulate hBMSCs apoptosis as well as differentiation via regulating Sirt1/PI3K/AKT signaling pathway.Graphi Cal Abstract

In this paper, whether the coal fines can be induced by shear failure during drainage process has been discussed in detail. By coupling with the percolation theory, the elasticity mechanics were used to construe the extra stresses in the formation surrounding with the hydraulic fracture. The safe window of the bottom hole pressure was also calculated from the failure envelope. The research shows that the formation pressure on the fracture surface of the coal seam is negatively related with the bottom hole pressure, and the induced stress is positively related with the bottom hole pressure during the drainage process of fractured CBM wells. The pore pressure around the fracture changed due to pore-elastic effects, which also caused a significant change of the in situ stresses. In order to avoid the breakout of the coal seam around hydraulic fracture during drainage process, the model of the reasonable bottom hole pressure is also built.

The concept of the Solar Ring mission was gradually formed from L5/L4 mission concept, and the proposal of its pre-phase study was funded by the National Natural Science Foundation of China in November 2018 and then by the Strategic Priority Program of Chinese Academy of Sciences in space sciences in May 2019. Solar Ring mission will be the first attempt to routinely monitor and study the Sun and inner heliosphere from a full 360-degree perspective in the ecliptic plane. The current preliminary design of the Solar Ring mission is to deploy six spacecraft, grouped in three pairs, on a sub-AU orbit around the Sun. The two spacecraft in each group are separated by about 30° and every two groups by about 120°. This configuration with necessary science payloads will allow us to establish three unprecedented capabilities: (1) determine the photospheric vector magnetic field with unambiguity, (2) provide 360-degree maps of the Sun and the inner heliosphere routinely, and (3) resolve the solar wind structures at multiple scales and multiple longitudes. With these capabilities, the Solar Ring mission aims to address the origin of solar cycle, the origin of solar eruptions, the origin of solar wind structures and the origin of severe space weather events. The successful accomplishment of the mission will advance our understanding of the star and the space environment that hold our life and enhance our capability of expanding the next new territory of human.

In this paper, the solubility of chlorpropamide (CPA) in 14 solvents including 8 alcohols (ethanol, n-propanol, isopropanol, n-butanol, 2-butanol, iso-butanol, n-pentanol, and isopentanol) and 6 acetate esters (ethyl acetate, propyl acetate, isopropyl acetate, butyl acetate, amyl acetate, and methyl propionate) at temperatures of 283.15 to 323.15 K and atmospheric pressure was determined using a laser method. The solubility as a function of temperature was regressed using modified Apelblat, Van’t Hoff, λh, Wilson, and nonrandom two-liquid (NRTL) models. On the basis of the experimental and the simulation results, the mixing properties of the solutions, that is, mixing Gibbs energy, enthalpy, and entropy, were estimated.

Decametric (DAM) radio emissions are one of the main windows through which one can reveal and understand the Jovian magnetospheric dynamics and its interaction with the moons. DAMs are generated by energetic electrons through cyclotron‐maser instability. For Io (the most active moon) related DAMs, the energetic electrons are sourced from Io volcanic activities, and quickly trapped by neighboring Jovian magnetic field. To properly interpret the physical processes behind DAMs, it is important to precisely locate the source field lines from which DAMs are emitted. Following the work by Hess et al. (2008, 2010), we develop a method to locate the source region as well as the associated field lines for any given DAM emission recorded in a radio dynamic spectrum by, e.g., Wind/WAVES or STEREO/WAVES. The field lines are calculated by the state‐of‐art analytical model, called JRM09 (Connerney et al., 2018). By using this method, we may also derive the emission cone angle and the energy of associated electrons. If multiple radio instruments at different perspectives observe the same DAM event, the evolution of its source region and associated field lines is able to be revealed. We apply the method to an Io‐DAM event, and find that the method is valid and reliable. Some physical processes behind the DAM event are also discussed.

Corona virus disease 2019 (COVID-19) is an acute infectious pneumonia and its pathogen is novel and was not previously found in humans. As a diagnostic method for COVID-19, chest computed tomography (CT)is more sensitive than reverse transcription polymerase chain reaction. However, the interpretation of COVID-19 based on chest CT is mainly done manually by radiologists and takes about 5 to 15 minutes for one patient. To shorten the time of interpreting the CT image and improve the reliability of identification of COVID-19. In this paper, a novel chest CT-based method for the automatic detection of COVID-19 was proposed. Our algorithm is a hybrid method composed of (i) wavelet Renyi entropy, (ii) feedforward neural network, and (iii) a proposed three-segment biogeography-based optimization (3SBBO) algorithm. The wavelet Renyi entropy is used to extract the image features. The novel optimization method of 3SBBO can optimize weights, biases of the network, and Renyi entropy order. Finally, we used 296 chest CT images to evaluate the detection performance of our proposed method. In order to reduce randomness and get unbiased result, the 10 runs of 10-fold cross validation are introduced. Experimental outcomes show that our proposed method is superior to state-of-the-art approaches in terms of sensitivity, specificity, precision, accuracy, and F1.

Using measurements by the Magnetospheric Multiscale spacecraft in the magnetotail, we studied electron distribution functions across an electron diffusion region. The dependence of the nongyrotropic distribution on the energy and vertical distance from the electron diffusion region midplane was revealed for the first time. The nongyrotropic distribution was observed everywhere except for an extremely narrow layer right at the electron diffusion region midplane. The energy of the nongyrotropic distribution increased with growth of the vertical distance from the midplane. For the electrons within certain energy range, they exhibited the nongyrotropic distribution at the distance further away from the midplane than that expected from the meandering motion. The correlation between the crescent‐shaped distribution with multiple stripes and the large Hall electric field was established. It appears that the measured nongyrotropic distribution and the crescent‐shaped distribution were caused by the meandering motion and the Hall electric field together. The Magnetospheric Multiscale mission is designed to study electron physics of magnetic reconnection, a key process for many explosive phenomena in solar flares and magnetosphere. Understanding electron motion is highly important in the study of magnetic reconnection, and the electron velocity distribution is an intuitive and effective way to study the electron dynamics. In this paper, we present a complete electron diffusion region crossing by Magnetospheric Multiscale during a nearly symmetric magnetic reconnection in the magnetotail and show the evolution of the electron velocity distributions across the electron diffusion region. In addition, the relationship between Hall electric field and crescent‐shaped electron distribution is also shown in this paper. The electron nongyrotropic distribution depended on the energy and the normal distance from the midplane of the electron diffusion regionThe electron crescent distributions were caused by the meandering motion and the Hall electric field togetherThe fine substructures were observed in the narrow electron diffusion region

Drug resistance, including epirubicin-based therapeutic resistance, is one of the major reasons responsible for the unfavorable prognosis of patients diagnosed with breast cancer (BC). Acquired chemoresistance and metastatic properties have been identified to be closely associated with the process of epithelial-mesenchymal transition (EMT). Recently, dysregulation of long non-coding RNAs (lncRNAs) have been increasingly reported to perform promotive or suppressive functions in chemoresistance and EMT process in multiple cancers. However, relevant novel lncRNA participating in epirubicin resistance and EMT and its underlying molecular mechanisms remain unknown in BC. Herein, we established the epirubicin-resistant breast cancer cell subline (MCF-7/ADR), which presented mesenchymal phenotype and increased metastatic potential. A panel of differentially expressed lncRNAs, including 268 upregulated and 49 downregulated lncRNAs, were identified by high-flux microarray investigation in MCF-7 and MCF-7/ADR cells. The novel lncRNA NONHSAT101069 was significantly overexpressed in BC specimens, BC cell lines, and epirubicin-resistant cell sublines. The knockdown of NONHSAT101069 significantly repressed, whereas overexpression of NONHSAT101069 promoted the epirubicin resistance, migration, invasion and EMT process of BC cells both in vitro and in vivo. Further mechanism-related researches uncovered that NONHSAT101069 functioned as a ceRNA (competing endogenous RNA) via sponging miR-129-5p. Twist1 was a direct downstream protein of NONHSAT101069/miR-129-5p axis in BC cells. To conclude, NONHSAT101069 was upregulated in BC tissues and promoted epirubicin resistance, migration and invasion of BC cells via regulation of NONHSAT101069/miR-129-5p/Twist1 axis, highlighting its potential as an oncogene and a therapeutic biomarker for BC.

In this letter, we have investigated the correlation between lower band chorus waves and electron beam‐like distribution with long‐term THEMIS data. Cold, hot, and beam electrons are assumed in the plasma. For quasi‐parallel chorus waves, they are usually observed along with a relatively weak electron beam. Moreover, the density ratio between beam and hot electrons nb/nhis positively correlated with the wave parallel electric field and inversely correlated with the hot electron density, in agreement with a formation of the beam by Landau acceleration during wave propagation, while, along with oblique waves, there typically exists a strong electron beam. Notably, the nb/nhis found to be independent of both the parallel electric field and hot electron density, indicating that the electron beam‐like distribution should be a precondition for exciting oblique waves rather than caused by waves. Our study provides some new insights into understanding the generation and evolution of chorus waves. The interaction between whistler‐mode chorus waves and electrons has long been a hot topic, which is important for understanding the evolution and generation of chorus waves. Both theoretical and simulation studies have indicated that quasi‐parallel chorus waves are initially excited near the magnetic equator and then propagate toward high‐latitude regions with increasing wave normal angles and enhanced parallel electric fields. During the propagation, the Landau resonance between chorus waves and electrons begins to take effect, causing the damping of waves and the formation of local electron beam. Within the equatorial region, there also exist a number of oblique chorus waves with large wave normal angles, which should be also locally excited. To generate oblique waves, a beam‐like electron distribution is needed to suppress the Landau damping of oblique chorus waves, which should already exist before those waves. Although the two physical pictures are well known in this community, actually, there is still a lack of observational support. In this paper, we have made some new progress in understanding these two different scenarios by studying the correlation between chorus waves and electron beam‐like distribution with long‐term THEMIS data. We have investigated the correlation between lower‐band chorus waves and electron beam‐like distribution using THEMIS data.Along with quasi‐parallel chorus waves, a weak elelctron beam can be observed, which is caused by Landau resonance with waves.But for oblique chorus waves, the electron beam‐like distribution should be a precondition for exciting waves rather than caused by waves.

In this work, the solubility of bezafibrate in 16 pure solvents including 9 alcohols (methanol, ethanol, n-propanol, iso-propanol, n-butanol, iso-butanol, sec-butanol, 1-pentanol, and iso-pentanol), 4 alkyl acetate (ethyl acetate, n-propyl acetate, n-butyl acetate, and methyl propionate), 2 ketones (methyl ethyl ketone and cyclohexanone) and one nitrile (acetonitrile) and in binary mixed solvents (methanol + acetonitrile) at temperature of 283.15–323.15 K was determined. The solubility of bezafibrate in pure solvents was simulated using modified Apelblat, Van’t Hoff, Wilson, and NRTL model. That in binary mixed solvents was simulated using the modified Apelblat and Apelblat–Jouyban–Acree model. The thermodynamic mixing properties (ΔmixG, ΔmixH, ΔmixS, γ1∞, and H1E,∞) of the bezafibrate-pure solvent systems were evaluated based on the solubility data and Wilson model.

Based on the Magnetospheric Multiscale observations in the magnetotail, we present a complete crossing of the current sheet with ongoing magnetic reconnection. The field‐aligned inflowing electrons were observed in both separatrix regions (SRs) and their energy extended up to several times of the thermal energy. Along the SR, a net parallel electrostatic potential was estimated and could be the reason for the inflowing electron streaming. In the northern SR, the electron frozen‐in condition was violated and nonideal electric field was inferred to be caused by the gradient of the electron pressure tensor. The nongyrotropic electron distribution and significant energy dissipation were observed at the same region. The observations indicate that the inner electron diffusion region can extend along the separatrices or some electron‐scale instability can be destabilized in the SR. The microphysics in the separatrix region (SR) plays an important role for the energy conversion in reconnection. In this letter, we present nonideal electric field in the SR and the electron acceleration therein. These observations indicate that a significant part of energy conversion takes place in the SR during reconnection. Nonideal electric field was observed in the separatrix region and inferred to be caused by gradient of the electron pressure tensorThe nongyrotropic electron distribution and substantial energy dissipation were observed in the separatrix regionAsymmetry between two sides of the current sheet was observed in the symmetric reconnection

In this work, the solubility of dimethyl terephthalate (DMT) in alcohols (methanol, ethanol, n-propanol, i-propanol, n-butanol, and i-butanol), esters (ethyl acetate, i-propyl acetate, n-propyl acetate, n-butyl acetate, n-amyl acetate, and methyl propionate), ketones (acetone, methyl ethyl ketone, and cyclohexanone), acetonitrile, and chloroform was experimentally determined. The solubility of DMT in these solvents follows the order: chloroform > esters and ketones > acetonitrile > alcohols and increases with increasing temperature. The solubility of DMT as a function of temperature has been regressed in terms of three semiempirical models (modified Apelblat, λh, and three-parameter Van’t Hoff) and two activity coefficient models (Wilson and nonrandom two-liquid), which gave a maximum relative average deviation of 2.32% and a maximum root-mean-square deviation of 31.63 × 10–5. Better fittings were obtained with the activity coefficient models. In addition, the mixing properties (Gibbs energy, enthalpy, entropy, as well as activity coefficients and reduced excess enthalpy at infinitesimal concentration) were evaluated using the Wilson model.

With 64‐month magnetic data from Van Allen Probes, we have studied not only the global distribution, wave normal angle (θ), and ellipticity (ε) of electromagnetic ion cyclotron (EMIC) waves, but also the dependence of their occurrence rates and magnetic amplitudes on the AE*index (the mean value of AE index over previous 1 hr). Our results show that H+band waves are preferentially detected at 5 ≤ L≤ 6.5, in the noon sector. They typically have small θ(<30°) and weakly left‐hand polarization but become more oblique and linearly polarized at larger magnetic latitudes or L‐shells. With the increase of AE*index, their occurrence rate significantly increases in the noon sector, and their source region extends to dusk sector. He+band waves usually occur in the predawn and morning sectors at 3 ≤ L≤ 4.5. They generally have moderate θ(30 °  − 40°) and left‐hand polarization and also become more oblique and linearly polarized at larger latitudes or L‐shells. There is a clear enhancement of occurrence rate and amplitude during active geomagnetic periods, especially in the dusk and evening sectors. O+band waves mainly occur at 3 ≤ L≤ 4in the predawn sector. They have either very small θ(<20°) or very large θ(>50°), and typically linear or weakly right‐hand polarization. During active periods, they mostly occur at the midnight sector and L< 3.5. As a valuable supplement to previous statistical studies, our result provides not only a more compresentive EMIC wave model for evaluating their effects on the radiation belt, but also detailed observational constraints on generation mechanisms of EMIC waves. A statistical study of EMIC waves is performed using long‐term Van Allen Probes data in the inner magnetosphereWe have presented the distributions of occurrence rate, wave normal angle, and ellipticity for H+, He+, and O+band wavesThe dependences of the occurrence rate and magnetic amplitude on the AE*index are also studied

Most N6-methyladenosine (m6A) associated regulatory proteins (i.e., m6A writer, eraser, and reader proteins) are involved in the pathogenesis of various cancers, mostly in m6A-dependent manners. As a component in the m6A ‘writers’, KIAA1429 is reported to be an RNA-binding protein and involved in the m6A modification, mRNA splicing and processing. Till now, the functions of KIAA1429 in tumorigenesis and related mechanism have not been reported. In the present study, we found KIAA1429 was highly expressed in breast cancer tissues, but frequently down-regulated in non-cancerous breast tissues. The overall survival of breast cancer patients with high-expression KIAA1429 was significantly shorter than those with low-expression KIAA1429. Then, we demonstrated that KIAA1429 was associated with breast cancer proliferation and metastasis in vivo and in vitro. The potential targeting genes of KIAA1429 in breast cancer were identified by RNA immunoprecipitation sequencing. One of these genes is cyclin-dependent kinase 1 (CDK1), which plays an oncogenic role in cancers. Furthermore, we confirmed that KIAA1429 played its oncogenic role in breast cancer by regulating CDK1 by an m6A-independent manner. 5′-fluorouracil was found to be very effective in reducing the expression of KIAA1429 and CDK1 in breast cancer. These findings indicated that KIAA1429 could promote breast cancer progression and was correlated with pathogenesis. It may represent a promising therapeutic strategy on breast cancer, especially in combination with CDK1 treatment.

AbstractToxoplasma gondiiis an important zoonotic parasite infecting humans and various animals with a worldwide distribution. However, limited information is available on T. gondiiinfection in wild rats. The present study aimed to examine the prevalence and characterize the genotypes of T. gondiiin wild rats in two regions of China. Brain tissues were collected from 111 Edward's long-tailed rats (Leopoldamys edwardsi) and 117 Bower's white-toothed rats (Berylmys bowersi) between November 2017 and January 2018. Genomic DNA was extracted and amplified by PCR targeting the T. gondiiB1 gene. B1 gene-positive samples were genotyped at 10 genetic markers (SAG1, SAG2 [5′, 3′] and [alternative], SAG3, BTUB, GRA6, c22-8, c29-2, L358, PK1, and Apico) using multilocus nested polymerase chain reaction/restriction fragment length polymorphism. Six (5.41%, 6/111) Edward's long-tailed rats from Chongqing Municipality were positive for T. gondiiB1 gene, whereas no T. gondiiinfection was detected in Bower's white-toothed rats (n= 117) from Guangdong province. T. gondiiprevalence in female and male rats was 1.77% (2/113) and 3.48 (4/115), respectively. Four of the six positive DNA samples were completely genotyped at 10 genetic loci and were identified as ToxoDB#20. The present study revealed the occurrence of T. gondiiinfection in Edward's long-tailed rats. These findings raised public health concerning about T. gondiiinfection in wild rats. These results provide reference data for understanding the distribution of T. gondiigenotypes in wild rats in China.