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Review question / Objective: To study on the efficacy and safety of Kunxian Capsule in treating patients with lupus nephritis( LN). Condition being studied: The study showed that Kunxian capsule significantly improved immune status and renal function and suppressed IL-10 and TNF in serum of lupus mice- α Of secretion, works better in combination with and reduces hormone dosage. At present, there are few studies in which Kunxian capsules are used in the clinic, and the conclusions of the studies are inconsistent, so this study was conducted in order to comprehensively evaluate the efficacy and safety of Kunxian capsules for the treatment of lupus nephritis.

Poverty, food insecurity and climate change are global issues facing humanity, threatening social, economic and environmental sustainability. Greenhouse cultivation provides a potential solution to these challenges. However, some greenhouses operate inefficiently and need to be optimized for more economical and cleaner crop production. In this paper, an economic model predictive control (EMPC) method for a greenhouse is proposed. The goal is to manage the energy-water‑carbon-food nexus for cleaner production and sustainable development. First, an optimization model that minimizes the greenhouse's operating costs, including costs associated with greenhouse heating/cooling, ventilation, irrigation, carbon dioxide (CO

In this paper, we study the influence of uncertainty in economic policy on the business operations distribution using data from China. In doing so, we rely on the China Economic Policy Uncertainty ...

Deep neural networks have proven to excel classical medical image reconstruction techniques. Some networks are based on fully connected (FC) layers to achieve domain transformation such as from the data acquisition domain to the image domain. However, FC layers result in huge numbers of parameters which take a lot of GPU memory. Hence, they do not scale well, and the overall performance is limited. For ultrasound computed tomography (USCT) application, we propose a memory-efficient convolutional network that reconstructs images from the frequency domain to image domain with much less parameters compared with multilayer perceptron, by using data-driven learning. Extensive experiments demonstrate that our method achieves high reconstruction quality. It improves the structural similarity measure (SSIM) from 0.73 to 0.99 when compared with state-of-the-art reconstruction methods in this field while reduces 2/3 parameters when compared with deep neural network with FC layers to reconstruct images from frequency domain to image domain.

Ultrasound transmission tomography promises a high potential and novel imaging method for early breast cancer diagnosis; it can quantitatively characterize tissues or materials by the attenuation and speed of sound (SoS). Reconstruction of ultrasound transmission tomography is an inverse problem that can be solved iteratively based on a paraxial approximation of the Helmholtz equation as forward model, which is highly non-linear and time-consuming. In order to address these problems and reconstruct desired images, we design a dual domain network architecture for ultrasound transmission tomography reconstruction. It can enhance the information of measurement domain and directly reconstruct from pressure field measurements without using any initialization of reconstruction and fully connected layer. We train the network on simulated ImageNet data and transfer it for ultrasound transmission tomography images to avoid overfitting when the amount of ultrasound transmission tomography images is limited. Our experimental results demonstrate that a dual domain network produces significant improvements over state-of-the-art methods. It improves the measured structural similarity measure (SSIM) from 0.54 to 0.90 and normalized root mean squared error (nRMSE) from 0.49 to 0.01 on average concerning the SoS reconstruction, and from 0.46 to 0.98 for SSIM, from 353 to 0.03 for nRMSE on average concerning the attenuation reconstruction.

Network security has become an important issue in our work and life. Hackers' attack mode has been upgraded from normal attack to APT( Advanced Persistent Threat, APT) attack. The key of APT attack chain is the penetration and intrusion of active directory, which can not be completely detected via the traditional IDS and antivirus software. Further more, lack of security protection of existing solutions for domain control aggravates this problem. Although researchers have proposed methods for domain attack detection, many of them have not yet been converted into effective market-oriented products. In this paper, we analyzes the common domain intrusion methods, various domain related attack behavior characteristics were extracted from ATT&CK matrix (Advanced tactics, techniques, and common knowledge) for analysis and simulation test. Based on analyzing the log file generated by the attack, the domain attack detection rules are established and input into the analysis engine. Finally, the available domain intrusion detection system is designed and implemented. Experimental results show that the network attack detection method based on the analysis of domain attack behavior can analyze the log file in real time and effectively detect the malicious intrusion behavior of hackers , which could facilitate managers find and eliminate network security threats immediately.

Ultrasound Computed Tomography (USCT) is a new high resolution imaging method with high potential for breast cancer detection. USCT image reconstruction requires the iterative solution of a wave equation and its adjoint, being very time-consuming. To address this time consumption problem, a convolutional neural network with frequency pressure field as input is presented, outperforming classical iterative optimization strategies to solve the inverse problem. The Optical and Acoustic Breast Phantom Database (OA-Breast) is used to generate simulated USCT data using paraxial approximation forward model both for training and testing. Extensive experiments demonstrate that our neural network outperforms the classical strategies both in reconstruction time and quality.

Determining a systematic whole-building retrofit plan for envelope components and indoor appliances to achieve targets such as cost savings and policy compliance is a challenging task. To be specific, the systematic whole-building retrofit problem, when solved by an optimization approach, is highly complicated. It is sometimes even impossible to find a solution with given computation resources and algorithms. In addition, a costly comprehensive bottom-up audit is required to establish the parameters of the problem. This study presents two models to reduce the complexity of the systematic whole-building retrofit optimization problem. Firstly, the proposed models use the grouping concept to optimize the retrofit of subsystems in a building instead of individual components/appliances, which reduces the dimension of the problem effectively. Secondly, the models use so-called ‘notch test’ data, which are sampled and verified savings of an intervention, to eliminate the need for bottom-up energy audits. This further simplifies the retrofit optimization problem and reduces the retrofit cost. The models are based on our previous work and aim at energy savings maximization and payback period minimization, considering the green building policy and tax incentive initiatives. A case study shows that about 2530 MWh energy savings and an A rating from the energy performance certificate standard can be obtained with a payback period of 59 months, which verifies the feasibility and effectiveness of the models proposed.

To promote sustainable development and expedite the progress on moving to a green building sector, the government of South Africa has developed an energy performance certificate (EPC) standard for buildings. A building is required to obtain a certain rating from the EPC in order to comply with the country's green building policy. Therefore, finding optimal retrofit plans for existing buildings are essential given the high investments involved in the retrofit of buildings that do not currently comply with the policy. This paper presents an optimization model to help decision makers to identify the best combination of retrofit options for buildings to ensure policy compliance in the most cost-effective way. The model determines optimal retrofit plans for a whole building in a systematic manner, taking into account both the envelope components and the indoor facilities. A roof top PV system is utilized to reduce the usage of electricity produced from fossil fuels. The model breaks down the long-term investment into yearly short-term investments that are more attractive to investors. Tax incentive program available in the country is taken into account to offset the long payback period of the investment. Economic analysis is also built into the model to help decision makers to make informed decisions. The retrofit of an existing office building is taken as a case study. The results show that 761.6 MWh energy savings and an A rating from the EPC can be obtained with a payback period of 70 months, which demonstrates the effectiveness of the model developed.

Retrofitting existing buildings with energy-efficient facilities is an effective method to improve their energy efficiency, especially for old buildings. A multi-objective optimization model for building envelope retrofitting is presented. Envelope components including windows, external walls and roofs are considered to be retrofitted. Installation of a rooftop solar panel system is also taken into consideration in this study. Rooftop solar panels are modeled with their degradation and a maintenance scheme is studied for sustainability of energy and its long-term effect on the retrofitting plan. The purpose is to make the best use of financial investment to maximize energy savings and economic benefits. In particular, net present value, the payback period and energy savings are taken as the main performance indicators of the retrofitting plan. The multi-objective optimization problem is formulated as a non-linear integer programming problem and solved by a weighted sum method. Results of applying the designed retrofitting plan to a 50-year-old building consisting of 66 apartments demonstrated the effectiveness of the proposed model.

Like single-label learning, multi-label learning also suffers from the curse of dimensionality. Due to the existence of high-dimensional data, feature selection as a preprocessing tool always plays a key position in the multi-label learning. Although a variety of multi-label feature selection methods have been developed, they neglect to consider the redundancy of features, thereby degrading learning performance. To address this problem, we present a novel multi-label feature selection approach with uncorrelated regression and adaptive spectral graph. Specifically, we first construct a manifold framework with uncorrelated regression model to hunt for uncorrelated yet discriminative features, which also utilize the low-dimensional representation based on feature space to fit the label distribution. Then, a spectral graph term based on information entropy is incorporated into the manifold framework, so as to ensure the local geometric structure of data in subsequent learning process. Following the above principles, we design an objective function to achieve multi-label feature selection, and propose a detailed optimization method. Comprehensive experiments are conducted on multiple public multi-label data sets, the results show that our method is superior to other compared methods.

Electrical dispersion of a water-in-oil emulsion under an electric field has an inhibitory effect on the eletrocoalescence. To clarify the mechanism of droplet breakup at the microscopic level, molecular dynamics simulation was performed to study the deformation and breakup characteristics of a moving droplet at different field strengths. The results showed that interparticle spacings increased with the growth of the field strength, leading to significantly higher values of the electrostatic potential and van der Waals potential, and hence an increase of the deformation degree. There was a substantial rise in the non-bonded potential energy with a sharp fall in the overall velocity of the droplet when the electric field was higher than 2.1 V nm−1, causing the development of droplet breakup. Through hydrogen bond and hydration analysis, it was found that the hydrogen bond lifetime and ion distribution were responsible for the formation of different breakup patterns. These results demonstrated that droplet break-up is highly dependent on weak intermolecular interactions such as strong attraction.

The building sector is a large consumer of the world's energy and is responsible for 40% of the European Union's (EU) total final energy consumption. The most effective and efficient method to reduce energy consumption of buildings is to retrofit existing buildings with more energy efficient facilities. This paper presents a multi-objective optimization model for building envelope retrofit planning which aims at maximizing energy savings and economic benefits with given investment budget. It also takes thermal comfort and life-cycle impact into consideration. Results from application to the retrofit planning of a 70 years old family house show that energy consumption can be reduced effectively by the presented model.

Raw data of point cloud is often noisy and with\ud topological defects (such as holes), which cause problems including faulty connection and inaccurate structure. As\ud a result, the surface reconstruction of point cloud data is\ud a highly challenging problem. This work proposes a novel\ud method, which improves the surface quality compared with\ud existing methods. Our method combines both the local detailed\ud features and the global topological information during the\ud reconstruction process. To facilitate the feature refinement,\ud we first pre-process the point cloud data by relocating each\ud point, upsampling the point data, and optimizing normals to\ud enhance the features and geometric details. We then identify\ud the topological information by segmenting the geometry and\ud constructing curve skeletons for each part and guide the surface reconstruction with the skeletons by minimal user interaction. We demonstrate the effectiveness of our methods with various examples, where our reconstruction can fill out missing data and preserve sharp features.

Reducing energy consumption of buildings has become an effective way of mitigating carbon footprint because of the high energy intensity of the building sector. Retrofitting existing buildings to be more energy efficient is an integral part of an energy efficient building sector given the slow construction rate of new buildings. In this study, an optimization model for building envelope retrofit is presented to help decision makers to obtain an optimal retrofit plan with a given budget. The envelope components considered for retrofit are windows, walls and roof. Installation of a PV power supply system is also built into the optimization model in view of the rich solar resource in South Africa. An additional objective of this study is to obtain at least a grade D rating from the established energy performance certificate (EPC) program in South Africa to ensure the continued suitability of the building with respect of the EPC standard and green building policy. A case study of a public office building in South Africa is carried out to illustrate the effectiveness and benefits of the model presented.

BACKGROUND This systematic review aims to evaluate the efficacy of Gegen Qinlian Decoction (GQD) for ulcerative colitis (UC). METHODS PubMed, EMBASE, Springer LINK, Cochrane Library, the China National Knowledge Infrastructure, Chongqing Weipu Database for Chinese Technical Periodicals, Wan-fang Database, and Chinese Biomedicine Database were searched from their inception to December 2018 for randomized controlled trials comparing the use of GQD alone or in combination with western medicine (WM) with that of WM therapies for UC. Outcomes on the therapy's effectiveness rate, ulcerative colitis endoscopic index of severity (UCEIS), recurrence rate, and adverse events were extracted and analyzed by Review Manager 5.3 software. Meta-analysis was combined with fixed or random-effects model, and risk ratios (RR) and 95% confidence intervals (CI) were calculated for all outcomes. Two researchers independently reviewed each trial to determine its inclusion. The Cochrane risk of bias assessment tool was used for quality assessment. RESULTS We included 22 trials involving 2028 patients with UC. When compared with WM therapy, GQD significantly improved the clinical effectiveness (n = 591, RR = 1.21, 95% CI: 1.12-1.31, P

Phosphorylation is an important part of post-translational modifications of proteins, and is essential for many biological activities. Phosphorylation and dephosphorylation can regulate signal transduction, gene expression, and cell cycle regulation in many cellular processes. Phosphorylation is extremely important for both basic research and drug discovery to rapidly and correctly identify the attributes of a new protein phosphorylation sites. Moreover, abnormal phosphorylation can be used as a key medical feature related to a disease in some cases. The using of computational methods could improve the accuracy of detection of phosphorylation sites, which can provide predictive guidance for the prevention of the occurrence and/or the best course of treatment for certain diseases. Furthermore, this approach can effectively reduce the costs of biological experiments. In this study, a flexible neural tree (FNT), particle swarm optimization, and support vector machine algorithms were used to classify data with secondary encoding according to the physical and chemical properties of amino acids for feature extraction. Comparison of the classification results obtained from the three classifiers showed that the classification of the FNT was the best. The three classifiers were then integrated in the form of a minority subordinate to the majority vote to obtain the results. The performance of the integrated model showed improvement in sensitivity (87.41%), specificity (87.60%), and accuracy (87.50%).

As a mechanism compensating for obstructive coronary artery disease, coronary collateral circulation (CCC) has attracted cardiologists for a long time to explore its potential impact. In the present study, Chinese patients suffering from ≥95% coronary stenosis, as diagnosed by angiography, have been investigated for the correlation between CCC and lipoprotein(a) [Lp(a)] levels. A cohort of 654 patients was divided into four categories according to Rentrop grades 0, 1, 2, and 3. Lp(a) levels were divided into model 1, discretized with critical values of 33 and 66%, and model 2, discretized with a cutoff value of 30.0 mg/dL. Furthermore, we evaluated the correlation between CCC and serum Lp(a) levels. The four groups had significantly different Lp(a) levels (25.80±24.72, 18.99±17.83, 15.39±15.80, and 8.40±7.75 mg/dL; P30.0 group (OR=6.77, 95%CI=4.44-10.4) was greater than that of Lp(a)

To successfully prepare drug delivery system, nanoparticles (NPs) with simple and effective method avoiding use of potentially toxic solvent and components. Baicalin was chosen as a model low molecular weight chemotherapeutic drug and baicalin-loaded NPs were prepared with choosing baicalin as the parcel drugs, chitosan to pack and TPP to solidify. The resultant baicalin NPs had a spherical structure with a diameter about 316 nm. The encapsulation efficiency of baicalin NPs is 91.01 ± 2.13%. The loading capacity is 13.22 ± 1.28%.This study suggested that the NP will be used as a potential carrier of low molecular weight chemotherapeutic drug and has many advantages.

A failure analysis was performed on a power plant’s boiler superheater tubes that had burst. The macromorphology, chemical composition, microstructure, and mechanical properties were analyzed by experimental testing. Results showed that the superheater tube bursts can be classified as creep ductile cracks because the two tubes operated in overtemperature and low heat transfer efficiency conditions for a long time, and one of the tubes did not meet the material requirements. Finally, preventative measures were put forward.

This paper proposes a novel feature fusion method for the protein subcellular multiple-site localization prediction. Several types of features are employed in this novel protein coding method. The first one is the composition of amino acids. The second is pseudo amino acid composition, which mainly extract the location information of each amino acid residues in protein sequence. Lastly, the information for local sequence of amino acids is taken into consideration in this research. Generally, k nearest neighbor, supporting vector machine and other methods, has been used in the field of protein subcellular localization prediction. In our research, the multi-label k nearest neighbor algorithm has been employed in the classification model. The overall accuracy rate may reach 66.7304% in Gnos-mploc dataset.

To improve the bioavailability of nitrendipine microspheres, a sustained-release microspheres having solid dispersion structure were prepared in one step. Two types of polymer, i.e. solid dispersing and sustained-release polymers, were employed to prepare the microspheres by the spherical crystallization technique, i.e. quasi-emulsion solvent diffusion method. The factors of effect on micromeritic properties and release profiles of the resultant microspheres were investigated. And the bioavailability of nitrendipine microspheres was evaluated in six healthy dogs. The results showed that the particle size of microspheres was determined mainly by the agitation speed. The dissolution rate of nitrendipine from microspheres was enhanced significantly with increasing the amount of dispersing agents, and sustained by adding retarding agents. The release rate of microspheres could be controlled as desired by adjusting the combination ratio of dispersing agents to retarding agents. The results of X-ray diffraction and differential scanning calorimetry analysis indicated that the crystalline form of nitrendipine was disordered, suggesting that nitrendipine was highly dispersed in microspheres, so as amorphous state. The release profiles and content of the microspheres stored at a temperature of 40°C and a relative humidity of 75% were unchanged during 3 months of accelerating condition of storage. And the relative bioavailability of the sustained-release microspheres compared with the Baypress™ tablets and the conventional tablets was 107.78% and 309.82%. In conclusion, the sustained-release microspheres with solid dispersion structure improved the bioavailability of the water insoluble drug and prolonged the T max value.

Fe2+ and Zn2+ levels in perilymph of guinea pigs injected with gentamicin (GM) were examined and compared with the corresponding concentrations in CSF, serum and hairs. We observed a preventive and therapeutic action of sea buckthorn oil and injectio gastrodini to hearing loss. The results showed that: (1) the Fe2+ content in GM-injected guinea pigs was increased in perilymph and hairs; it was decreased after prevention and treatment, but there was no obvious change in CSF and serum; (2) the Zn2+ content in perilymph and CSF was increased in GM-injected guinea pigs. It rose further after prevention, but in serum and hairs it decreased. The results indicate that the ototoxic reaction to GM is related to the rise of the Fe2+ content in perilymph. The elevation of Zn2+ is a compensatory reaction. The changes in the chemical composition of perilymph are more important than those in CSF, serum and hairs for pathological changes of the cochlea. Sea buckthorn oil can prevent GM ototoxicity.

Human leukocyte elastase (HLE) is a serine protease implicated in several inflammatory diseases, and represents a major target for anti-inflammatory drug development. In the present study, nordivaricatic acid (1), divarinyl divarate (2), and trivaric acid (3), three depsides isolated from the culture of a soil derived fungal strain were identified as inhibitors of HLE. Two didepsides 1 and 2 showed low inhibitory activity. In contrast, trivaric acid, a para-tridepside, exhibited highly potent inhibitory activity with an IC(50) value of 1.8 µM and a K(i) of 0.6 µM. Kinetic investigations with trivaric acid showed that this inhibition is reversible, competitive pattern. Further studies on the selectivity of three depsides toward serine proteases showed that they did not inhibit chymotrypsin, trypsin and thrombin even at 150 µM.

This paper focuses on using a TI's DSP to design an AC electricity acquisition module, which can accomplish the function of distributed synchronous AC electrical parameters sampling. The hardware diagram of the design used a DSP and other auxiliary devices to set up an AC electricity acquisition module with the ability of high frequency distributed simultaneous electrical parameters sampling. Specifically, a GPS was mounted on the module to synchronize sampling between different modules and a SD card was employed to store the collected data. Regarding the software design, a PC control/monitor program was developed. The electricity acquisition terminal is capable of 200KHz simultaneous sampling when it works without SD card while it is verified that the highest sample frequency is 10KHz when it works with a SD card.

The advances in the modeling and control methodologies of heavy haul trains are reviewed from energy efficiency perspective. The developments in railway systems are also reviewed as background of the train modeling and control problem. Until present, there exist two types of models: physical principle model and data-driving model. As far as the control methods are concerned, two different classifications are presented in this paper. One is to classify them into transient control and cruise control according to the focused process. The other is to classify them into switching control and action control according to the framework of the control methods.

Three novel members of angucycline family named N05WA963A (1), B (2) and D (4), together with a new anthracycline named N05WA963C (3) were isolated from the culture broth of Streptomyces sp. N05WA963. The structures were elucidated on the basis of comprehensive spectral data analysis. All four compounds have shown antiproliferative effects on a panel of cancer cell lines such as SW620, K-562, MDA-MB-231, YES-4, T-98 and U251SP.

The shape of dendrimer amphiphiles has an unexpected effect on their self-assembly. A series of diaminobutane poly(propylenimine) generation 3 dendrimer (DAB-dendr-(NH(2))(16)) amphiphiles has been synthesized, bearing an average of five (PD5), three (PD3) and one (PD1) palmitoyl group(s) per dendrimer molecule. Additionally DAB-dendr-(NH(2))(16) was derivatized with a layer of poly(ethylene glycol) (PEG, degree of polymerization = 12) groups and conjugated to an average of 1 palmitoyl group at the PEG end (PPD1). A final amphiphile resulted from the conjugation of DAB-dendr-(NH(2))(16) with 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-succinimidylpropionate (DSPE-PEG(3400)-SPA), i.e.: DPD5 (with 4 DSPE-PEG arms). The critical micellar concentration in aqueous media followed the trend: DPD5

Dihydroorotate Dehydrogenase (DHODH) catalyzes the rate-limiting step in pyrimidine biosynthesis, and its inhibitors have been developed as drugs for treatment of immune diseases. We studied new DHODH inhibitors from microbial metabolites.We established a rapid and effective high throughput screening method for screening DHODH inhibitors from microbial metabolites. The active compounds were isolated from the candidate strain by column chromatography and preparative HPLC.We picked out F01WB-1315 strain as candidate from 4560 fungal strains. We isolated two active compounds F01WB-1315A and B, with IC50 of 0.07 microg/mL and 0.51 microg/mL, respectively. F01WB-1315B could completely inhibit the spleen lymphocytes proliferation stimulated by ConA in vitro, but F01WB-1315A only had 31.62% inhibitory activity. F01WB-1315A, B were identified to be Ascofuranone and Ascochlorin by their physicochemical properties, MS, 13C-NMR and 1H-NMR analysis.F01WB-1315A and B are two strong specific DHODH inhibitors and show moderate inhibitory activity against spleen lymphocytes proliferation.

In order to screen the active constituents of Vaccinium vitis-idaea L., the compounds absorbed into the rat blood after oral administration of ethanol extract of the stems and leaves of V. vitis-idaea (EEV) have been analyzed. Two compounds were detected in the plasma and identified as arbutin and fraxin, which are originally existed in the crude drug. Furthermore, the pharmacological effects of the two compounds involving EEV for curing acute and chronic respiratory tract infection were tested. The results showed that both arbutin and fraxin have the anti-inflammatory, anti-coughing and phlegm-removing effects. Therefore, it was ascertained that the arbutin and fraxin would be the main active constituents of V. vitis-idaea L.