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Heat shock protein 27 (HSP27) is a multifunctional protein that undergoes significant changes in its expression and phosphorylation in response to shear stress stimuli, suggesting that it may be involved in mechanotransduction. However, the mechanism of HSP27 affecting tumor cell migration under shear stress is still not clear. In this study, HSP27-enhanced cyan fluorescent protein (ECFP) and HSP27-Ypet plasmids are constructed to visualize the self-polymerization of HSP27 in living cells based on fluorescence resonance energy transfer technology. The results show that shear stress induces polar distribution of HSP27 to regulate the dynamic structure at the cell leading edge. Shear stress also promotes HSP27 depolymerization to small molecules and then regulates polar actin accumulation and focal adhesion kinase (FAK) polar activation, which further promotes tumor cell migration. This study suggests that HSP27 plays an important role in the regulation of shear stress-induced HeLa cell migration, and it also provides a theoretical basis for HSP27 as a potential drug target for metastasis.

Network systems with clustering have been given much attention due to their wide occurrence in the real world. One focus of these studies has been on robustness of single clustered networks and interdependent clustered networks under random attack (RA) or hub-targeted attack. However, infrastructure networks could suffer from a damage that is localized, i.e. a group of neighboring nodes attacked or fail, a topic that was not studied earlier on clustered networks. In this paper, we analytically and via simulations study the robustness under localized attack (LA) of single Erdős–Rényi clustered network and interdependent clustered network. For generating networks with clustering we use two models: (i) double Poisson distribution (DPD) and (ii) fixed degree distribution (FDD). For the LA case, the DPD model shows a second order phase transition behavior for a single clustered network, while for dependent networks, the system undergoes a change of percolation phase transition from a first order (abrupt transition) to a second order (continuous) transition when the coupling strength q decreases below a critical value q _c . Our results imply that single networks become significantly more vulnerable with increasing clustering coefficient c with respect to LA. This is in contrast to RA where the robustness is almost independent of c . We obtain similar results when testing different real networks. For LA on dependent networks, we also observe that the system becomes more vulnerable as c increases. This is again in contrast to RA, where for, q

An integrated design and optimization approach was developed for radial inflow turbines, which consists of two modules, an automated preliminary design module, and a flexible three-dimensional multidisciplinary optimization module. In this paper, the first module about the automated preliminary design approach was presented in detail and validated by the experimental data. The approach employs a genetic algorithm to explore the design space defined by the loading coefficient, flow coefficient, and rotational speed. The aim is to obtain the best design scheme with high aerodynamic performance under specified constraints and to reduce the dependency on human experiences when designing a radial inflow turbine. The validation results show that the present approach is able to get the optimal design and alleviate the dependence on the designer’s expertise under specified constraints at the preliminary design stage. Furthermore, the optimization results indicate that using the present optimization approach the total-to-static efficiency of the optimized T-100 radial inflow turbine can be increased by 1.0% under design condition and the rotor weight can be decreased by 0.35 kg (26.7%) as compared with that of the original case.

This paper proposes an integrated design and optimization approach for radial inflow turbines consisting of an automated preliminary design module and a flexible three-dimensional multidisciplinary optimization module. The latter was constructed by an evolution algorithm, a genetic algorithm-assisted self-learning artificial neural network and a dynamic sampling database. The 3-D multidisciplinary optimization approach was validated by the original T-100 turbine and the T-100re turbine obtained from the automated preliminary design approach, for maximizing the total-to-static efficiency and minimizing the rotor weight while keeping the mass flow rate constant and stress limitation satisfied. The validation results indicate that the total-to-static efficiency is 89.6%, increased by 1.3%, and the rotor weight is reduced by 0.14 kg (14.6%) based on the T-100re turbine, while the efficiency is 88.2%, increased by 2.2% and the weight is reduced by 0.49 kg (37.4%) based on the original T-100 turbine. Moreover, the T-100re turbine shows better performance at the preliminary design stage and conserves this advantage to the end, though both the aerodynamic performance of the T-100 and the T-100re turbine are improved after 3-D optimization. At the same time, it is implied that the preliminary design plays an essential role in the radial inflow turbine design process, and it is hard for only 3-D optimization to get a further performance improvement.

We study the overall topological structure properties of global oil trade network, such as degree, strength, cumulative distribution, information entropy and weight clustering. The structural evolution of the network is investigated as well. We find the global oil import and export networks do not show typical scale-free distribution, but display disassortative property. Furthermore, based on the monthly data of oil import values during 2005.01-2014.12, by applying random matrix theory, we investigate the complex spatiotemporal dynamic from the country level and fitness evolution of the global oil market from a demand-side analysis. Abundant information about global oil market can be obtained from deviating eigenvalues. The result shows that the oil market has experienced five different periods, which is consistent with the evolution of country clusters. Moreover, we find the changing trend of fitness function agrees with that of gross domestic product (GDP), and suggest that the fitness evolution of oil market can be predicted by forecasting GDP values. To conclude, some suggestions are provided according to the results.

Leymus chinensis is a dominant grass in the Songnen grassland of Northern China. The lower germination caused by the presence of lemmas has proved to be an obstacle for the use of the seeds of this plant by humans. However, it is still unknown if the lemmas have other ecological roles such as resisting drought and saline conditions. Three experiments were designed to investigate the ecological roles of the lemmas in Leymus chinensis seeds. The results showed that lemmas significantly improved the amount of water uptake and slowed down the dehydration rate of the seeds under dry conditions. Likewise, the lemmas induced seed dormancy, and removal of the lemmas improved the germination at all temperatures. Although germination percentage of the seeds without lemmas were higher than that of seeds with lemmas under salinity stress, the recovery and total percentage were significantly lower than the seeds with lemmas, especially at 400 mM stress. These results suggest that the lemmas play a vital function in water uptake, dehydration and salt tolerance during the germination stage of the seeds as a response to adverse environmental conditions. Although lemmas showed a dormancy effect, if we want to plant this species in salinity soil in Northeast China, the approach of removing the lemmas by artificial means and improving the seed germination percentage is not feasible.

Two new p-hydroxybenzoic acid glycosides, namely p-hydroxybenzoic acid-4-O-α-d-manopyranosyl-(1 → 3)-α-l-rhamnopyranoside (compound 1) and 4-O-α-l-rhamnopyran-osyl-(1 → 6)-α-d-manopyranosyl-(1 → 3)-α-l-rhamnopyranoside (compound 2), and seven known compounds, compound 3, 6, 7 (acid components), compound 8, 9 (flavonoids), compound 4 (a coumarin) and compound 5 (an alkaloid), were isolated from the 70% ethanol aqueous extract of the aerial parts of Melilotus officinalis (Linn.) Pall. The structures of all compounds were elucidated by use of extensive spectroscopic methods Infrared Spectroscopy (IR), High resolution electrospray ionization mass spectrometry (HR-ESI-MS), and 1H and 13C-NMR). Sugar residues obtained after acid hydrolysis were identified by high-performance liquid chromatography (HPLC). The antioxidant activity of all the compounds was evaluated by 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS+) and 1,1-diphenyl-2-picrylhydrazyl (DPPH). The anti-inflammatory effects of the compounds were also evaluated in lipopolysaccharide (LPS)-stimulated RAW 264.7 macrophages. All compounds were shown to inhibit LPS-induced nitric oxide (NO) and prostaglandin E 2 (PGE 2) production by suppressing the expression of inducible NO synthase (iNOS) and cyclooxygenase-2 (COX-2), respectively, in LPS-stimulated RAW 264.7 cells. The inhibitory effect of all the compounds on MCF-7 cells was determined by Cell Counting Kit-8 (CCK-8) method. The results showed that compounds 1, 2, 7, 8, 9 exhibited better antioxidant activity compared to the other compounds. compounds 1–9 had different inhibitory effects on the release of NO, TNF-α and IL-6 in LPS-stimulated RAW264.7 cells by LPS, of which compound 7 was the most effective against inflammatory factors. compounds 1 and 2 have better antitumor activity compared to other compounds. Further research to elucidate the chemical composition and pharmacological effects of Melilotus officinalis (Linn.) Pall is of major importance towards the development and foundation of clinical application of the species.

Photovoltaic (PV) power station faults in the natural environment mainly occur in the PV array, and the accurate fault diagnosis is of particular significance for the safe and efficient PV power plant operation. The PV array’s electrical behavior characteristics under fault conditions is analyzed in this paper, and a novel PV array fault diagnosis method is proposed based on fuzzy C-mean (FCM) and fuzzy membership algorithms. Firstly, clustering analysis of PV array fault samples is conducted using the FCM algorithm, indicating that there is a fixed relationship between the distribution characteristics of cluster centers and the different fault, then the fault samples are classified effectively. The membership degrees of all fault data and cluster centers are then determined by the fuzzy membership algorithm for the final fault diagnosis. Simulation analysis indicated that the diagnostic accuracy of the proposed method was 96%. Field experiments further verified the correctness and effectiveness of the proposed method. In this paper, various types of fault distribution features are effectively identified by the FCM algorithm, whether the PV array operation parameters belong to the fault category is determined by fuzzy membership algorithm, and the advantage of the proposed method is it can classify the fault data from normal operating data without foreknowledge.

Reliability risks for two different types of through-silicon-vias (TSVs) are discussed in this paper. The first is a partially-filled copper TSV, if which the copper layer covers the side walls and bottom. A polymer is used to fill the rest of the cavity. Stresses in risk sites are studied and ranked for this TSV structure by FEA modeling. Parametric studies for material properties (modulus and thermal expansion) of TSV polymer are performed. The second type is a high aspect ratio TSV filled by polycrystalline silicon (poly Si). Potential risks of the voids in the poly Si due to filling defects are studied. Fracture mechanics methods are utilized to evaluate the risk for two different assembly conditions: package assembled to printed circuit board (PCB) and package assembled to flexible substrate. The effect of board/substrate/die thickness and the size and location of the void are discussed.

A realistic semiclassical dynamics simulation study is reported for the photoinduced ring-opening reaction of spiropyran. The main simulation results show that one pathway involves hydrogen out-of-plane (HOOP) torsion of phenyl ring nearby N atom in 254 fs on the excited state and the isomerization from cis- to trans-SP that is complete in about 10 ps on the ground state after the electron transition πσ*; the other dominate pathway corresponds to the ring-opening reaction of trans-SP to form the most stable merocyanine (MC) product. Unlike the previous theoretical finding, one C−C bond cleavage on the real molecule rather than the C−N dissociation of the model one is more probable than the ring-opening reaction after the photoexcitation of SP. The simulation findings provide more important complementarity for interpreting experimental observations, confirming the previously theoretical studies of photochromic ring-opening process and even supplying other possible reaction mechanisms.

In the title one-dimensional coordination polymer, [Ag(NO3)(C12H6N2O2)]n, the AgI atom is pentacoordinated by two N atoms from a 1,10-phenanthroline-5,6-dione (phen-dione) ligand, one O atom from the nitrate anion and two O atoms from another phen-dione ligand. The coordination environment around silver is slightly distorted square-pyramidal. Interestingly, the Ag—O distances to the phen-dione ligand are different [Ag—O = 2.612 (6) and 2.470 (5) Å]. The one-dimensional chains run parallel to [101] and are further interconnected by weak hydrogen bonds (C—H...O) and π–π stacking interactions [centroid–centroid distances 3.950 (4) and 3.792 (4) Å], forming a three-dimensional supramolecular network.

In the title compound, C14H12ClNO, the central acetamide plane forms dihedral angles of 76.0 (2) and 64.0 (2)° with the phenyl rings and the phenyl rings form a dihedral angle of 71.8 (2)° with each other.

In the title compound, C18H10ClNO2, the naphthalimide ring system is almost planar, the rings forming dihedral angles of 2.05 (3), 2.26 (3) and 0.80 (3)°. The attached benzene ring of the 4-chlorophenyl substituent is inclined to the mean plane of the naphthalimide ring system by 75.77 (11)°. In the crystal structure, symmetry-related molecules are linked by C—H...O interactions. There are also weak π–π contacts between the naphthalimide rings [centroid–centroid distance = 3.732 (3) Å].

BackgroundA major issue with the current management of psoriasis is our inability to predict treatment response.ObjectiveOur aim was to evaluate the ability to use baseline molecular expression profiling to assess treatment outcome for patients with psoriasis.MethodsWe conducted a longitudinal study of 46 patients with chronic plaque psoriasis treated with anti-TNF agent etanercept, and molecular profiles were assessed in more than 200 RNA-seq samples.ResultsWe demonstrated correlation between clinical response and molecular changes during the course of the treatment, particularly for genes responding to IL-17A/TNF in keratinocytes. Intriguingly, baseline gene expressions in nonlesional, but not lesional, skin were the best marker of treatment response at week 12. We identified USP18, a known regulator of IFN responses, as positively correlated with Psoriasis Area and Severity Index (PASI) improvement (P = 9.8 × 10-4) and demonstrate its role in regulating IFN/TNF responses in keratinocytes. Consistently, cytokine gene signatures enriched in baseline nonlesional skin expression profiles had strong correlations with PASI improvement. Using this information, we developed a statistical model for predicting PASI75 (ie, 75% of PASI improvement) at week 12, achieving area under the receiver-operating characteristic curve value of 0.75 and up to 80% accurate PASI75 prediction among the top predicted responders.ConclusionsOur results illustrate feasibility of assessing drug response in psoriasis using nonlesional skin and implicate involvement of IFN regulators in anti-TNF responses.

The robustness of complex networks against node failure and malicious attack has been of interest for decades, while most of the research has focused on random attack or hub-targeted attack. In many real-world scenarios, however, attacks are neither random nor hub-targeted, but localized, where a group of neighboring nodes in a network are attacked and fail. In this paper we develop a percolation framework to analytically and numerically study the robustness of complex networks against such localized attack. In particular, we investigate this robustness in Erdős–Rényi networks, random-regular networks, and scale-free networks. Our results provide insight into how to better protect networks, enhance cybersecurity, and facilitate the design of more robust infrastructures.