Study on Optimization Method for InSAR Baseline Considering Changes in Vegetation Coverage.

Time-series Interferometric Synthetic Aperture Radar (InSAR) technology, renowned for its high-precision, wide coverage, and all-weather capabilities, has become an essential tool for Earth observation. However, the quality of the interferometric baseline network significantly influences the monitoring accuracy of InSAR technology. Therefore, optimizing the interferometric baseline is crucial for enhancing InSAR's monitoring accuracy. Surface vegetation changes can disrupt the coherence between SAR images, introducing incoherent noise into interferograms and reducing InSAR's monitoring accuracy. To address this issue, we propose and validate an optimization method for the InSAR baseline that considers changes in vegetation coverage (OM-InSAR-BCCVC) in the Yuanmou dry-hot valley. Initially, based on the imaging times of SAR image pairs, we categorize all interferometric image pairs into those captured during months of high vegetation coverage and those from months of low vegetation coverage. We then remove the image pairs with coherence coefficients below the category average. Using the Small Baseline Subset InSAR (SBAS-InSAR) technique, we retrieve surface deformation information in the Yuanmou dry-hot valley. Landslide identification is subsequently verified using optical remote sensing images. The results show that significant seasonal changes in vegetation coverage in the Yuanmou dry-hot valley lead to noticeable seasonal variations in InSAR coherence, with the lowest coherence in July, August, and September, and the highest in January, February, and December. The average coherence threshold method is limited in this context, resulting in discontinuities in the interferometric baseline network. Compared with methods without baseline optimization, the interferometric map ratio improved by 17.5% overall after applying the OM-InSAR-BCCVC method, and the overall inversion error RMSE decreased by 0.5 rad. From January 2021 to May 2023, the radar line of sight (LOS) surface deformation rate in the Yuanmou dry-hot valley, obtained after atmospheric correction by GACOS, baseline optimization, and geometric distortion region masking, ranged from -73.87 mm/year to 127.35 mm/year. We identified fifteen landslides and potential landslide sites, primarily located in the northern part of the Yuanmou dry-hot valley, with maximum subsidence exceeding 100 mm at two notable points. The OM-InSAR-BCCVC method effectively reduces incoherent noise caused by vegetation coverage changes, thereby improving the monitoring accuracy of InSAR.

Coleps shanghaiensis n. sp. challenges the validity of the genus Levicoleps (Ciliophora, Prostomatida).

Coleps is a common genus of pelagic ciliates in freshwater and brackish water habitats. Classification and phylogeny of Coleps species are, however, still full of confusion. In this study, we investigated Coleps shanghaiensis n. sp., collected from a river in Shanghai, China, by living observation, protargol staining, and molecular methods. Coleps shanghaiensis is about 70-90 µm × 35-55 µm in size, has a barrel-shaped body with three posterior spines, and possesses 21-24 ciliary rows, each composed of two perioral dikinetids and 19-22 monokinetids, and six caudal cilia. In SSU rRNA gene phylogenies, C. shanghaiensis fell within the clade of subspecies of Levicoleps biwae, which questions the validity of the genus Levicoleps. Furthermore, the biogeography of the genus Coleps is discussed.

The role and future prospects of artificial intelligence algorithms in peptide drug development.

Peptide medications have been more well-known in recent years due to their many benefits, including low side effects, high biological activity, specificity, effectiveness, and so on. Over 100 peptide medications have been introduced to the market to treat a variety of illnesses. Most of these peptide medications are developed on the basis of endogenous peptides or natural peptides, which frequently required expensive, time-consuming, and extensive tests to confirm. As artificial intelligence advances quickly, it is now possible to build machine learning or deep learning models that screen a large number of candidate sequences for therapeutic peptides. Therapeutic peptides, such as those with antibacterial or anticancer properties, have been developed by the application of artificial intelligence algorithms.The process of finding and developing peptide drugs is outlined in this review, along with a few related cases that were helped by AI and conventional methods. These resources will open up new avenues for peptide drug development and discovery, helping to meet the pressing needs of clinical patients for disease treatment. Although peptide drugs are a new class of biopharmaceuticals that distinguish them from chemical and small molecule drugs, their clinical purpose and value cannot be ignored. However, the traditional peptide drug research and development has a long development cycle and high investment, and the creation of peptide medications will be substantially hastened by the AI-assisted (AI+) mode, offering a new boost for combating diseases.

[Corrigendum] Induction of microRNA­let­7a inhibits lung adeno-carcinoma cell growth by regulating cyclin D1.

After the publication of the article, an interested reader drew to the authors' attention that, in the western blots shown in Fig. 5C and D, a pair of data panels were inadvertently duplicated comparing between panels (C) and (D); in addition, the cell migration data shown in Fig. 7F on p. 1852 were selected incorrectly. The authors have examined their original data, and realize that these errors arose inadvertently as a consequence of their mishandling of their data. The revised versions of Figs. 5 and 7, featuring the corrected data for the caspase-8 experiment in Fig. 5C and alternative data for the cell migration assay experiments in Fig. 7F, are shown on the next two pages. The revised data shown for these Figures do not affect the overall conclusions reported in the paper. All the authors agree to the publication of this corrigendum, and are grateful to the Editor of Oncology Reports for allowing them the opportunity to publish this. Furthermore, the authors apologize to the readership for any inconvenience caused. [Oncology Reports 40: 1843-1854, 2018; DOI: 10.3892/or.2018.6593].

A new contribution to the raptorial ciliate genus Lacrymaria (Protista: Ciliophora): a brief review and comprehensive descriptions of two new species from Changjiang Estuary.

Ciliates serve as excellent indicators for water quality monitoring. However, their utilization is hindered by various taxonomic confusions. The ciliate genus Lacrymaria Bory de Saint-Vincent, 1824 is commonly found in different aquatic habitats, but its taxonomy has been sparsely investigated using state-of-the-art methods. This study investigated two new Lacrymaria species from Nanhui Wetland, Shanghai, China, using living observation, protargol staining, and molecular phylogeny methods. Lacrymaria songi sp. nov. is 180-340 × 20-25 µm in size and possesses 12-16 somatic kineties, 1 terminal contractile vacuole, 2 macronuclear nodules, and 2 types of rod-shaped extrusomes. Lacrymaria dragescoi sp. nov. is distinguished from its congeners by its cell size of 210-400 × 25-35 µm, 14-17 somatic kineties, 1 terminal contractile vacuole, 1 macronucleus, and 2 types of rod-shaped extrusomes. Phylogenetic analyses based on SSU rRNA gene sequences indicate that Lacrymariidae is monophyletic but Lacrymaria is not. Additionally, a brief review of the genus Lacrymaria is provided in this study. We suggest that L. bulbosa Alekperov, 1984, L. lanceolata Kahl, 1930, and L. ovata Burkovsky, 1970 be removed from the genus and propose Phialina lanceolata nov. comb. and Phialina ovata nov. comb. for the latter two. ZooBank registration: Present work: urn:lsid:zoobank.org:pub:CDFB1EBD-80BD-4533-B391-CEE89F62EDC4 Lacrymaria songi sp. nov.: urn:lsid:zoobank.org:act:417E7C2D-DAEC-4711-90BB-64AB3CD2F7D5 Lacrymaria dragescoi sp. nov.: urn:lsid:zoobank.org:act:8778D6B0-1F2E-473C-BE19-3F685391A40D.

Genome-wide DNA methylation analysis reveals layer-specific methylation patterns in deer antler tissue.

This paper explored using of deer antlers as a model for studying rapid growth and cartilage formation in mammals. The genes and regulatory mechanisms involved in antler chondrogenesis are poorly understood, however, previous research has suggested that DNA methylation played a key role in antler regeneration. By using fluorescence-labeled methylation-sensitive amplified polymorphism (F-MSAP), this study measured DNA methylation levels in cartilage (CA) and reserve mesenchyme (RM) cells and tissues. Results showed that RM cells (RMCs) DNA methylation levels were significantly lower than those of CA, suggesting that DNA demethylation may be involved in antler fast cartilage differentiation. The study also identified 20 methylated fragments specific to RMCs or CA using the methylation-sensitive amplified polymorphism (MSAP) technique and confirmed these findings using southern blot analysis. The data provide the first experimental evidence of a link between epigenetic regulation and rapid cartilage differentiation in antlers.

Autocollimation angle-measurement method with a large range based on spot deformation.

A novel hollow cylindrical cube-corner reflector (HCCCR) for the autocollimator (AC) is proposed. The angle measuring range of AC will be effectively increased by using the parallel propagation characteristics of the reflected light and the incident light in local area of this reflector. And the yaw and pitch angles of HCCCR will be measured through the morphological changes of the reflected beam. The experimental results show that the measuring range of the autocollimation angle measurement method is extended from ±30' to ±30°, and the dynamic measurement distance is 0.2∼5m, the measurement accuracy of pitch angle and yaw angle is better than 69" and 51", respectively.

A Teenager Physical Fitness Evaluation Model Based on 1D-CNN with LSTM and Wearable Running PPG Recordings.

People attach greater importance to the physical health of teenagers because adolescence is a critical period for the healthy development of the human body. With the progress of biosensing technologies and artificial intelligence, it is feasible to apply wearable devices to continuously record teenagers' physiological signals and make analyses based on modern advanced methods. To solve the challenge that traditional methods of monitoring teenagers' physical fitness lack accurate computational models and in-depth data analyses, we propose a novel evaluation model for predicting the physical fitness of teenagers. First, we collected 1024 teenagers' PPGs under the guidance of the proposed three-stage running paradigm. Next, we applied the median filter and wavelet transform to denoise the original signals and obtain HR and SpO2. Then, we used the Pearson correlation coefficient method to finalize the feature set, based on the extracted nine physical features. Finally, we built a 1D-CNN with LSTM model to classify teenagers' physical fitness condition into four levels: excellent, good, medium, and poor, with an accuracy of 98.27% for boys' physical fitness prediction, and 99.26% for girls' physical fitness prediction. The experimental results provide evidence supporting the feasibility of predicting teenagers' physical fitness levels by their running PPG recordings.

Roll angle autocollimator measurement method based on a cylindrical cube-corner reflector with a high resolution and large range.

A novel high-resolution and large-range autocollimator measurement system for roll angle is proposed. The system retains the basic internal structure of the traditional autocollimator (AC), which only uses a novel non-standard cylindrical cube-corner reflector (CCCR) instead of the planar reflector. In the article, the mathematical relationship between the structure of this special reflector and the spatial coordinate vector change of the reflected beam is deduced, and the measurement formula of the roll angle autocollimator (RAC) measurement system is established based on this mathematical relationship. The effectiveness of the measurement system and method is verified by experiments. Experimental results show that this method can effectively enhance the range to ±20°, and the whole measurement accuracy is 6.1", the measuring resolution is 1".

Reformulating the Hydrolytic Enzyme Cocktail of Trichoderma reesei by Combining XYR1 Overexpression and Elimination of Four Major Cellulases to Improve Saccharification of Corn Fiber.

The industrial fungus Trichoderma reesei has an outstanding capability of secreting an enzyme cocktail comprising multiple plant biomass-degrading enzymes. Herein, the overexpression of XYR1, the master transactivator controlling (hemi)cellulase gene expression, was performed in T. reesei lacking four main cellulase-encoding genes. The resultant strain Δ4celOExyr1 was able to produce a dramatically different profile of secretory proteins on soluble glucose or lactose compared with that of the wild-type T. reesei. The Δ4celOExyr1 secretome included cellulases EGIII and BGLI as well as several hemicellulases and nonhydrolytic cellulose degradation-associated proteins that are not preferentially induced in the wild-type T. reesei strain. Δ4celOExyr1 produced a significant amount of α-arabinofuranosidase I on lactose, and the crude enzyme cocktail of Δ4celOExyr1 not only released a considerable quantity of glucose but also exhibited remarkable performance in the hydrolytic release of xylose, arabinose, and mannose from un-pretreated corn fiber. These results showed that the engineered T. reesei strain holds great potential for improving the saccharification efficiency of the hemicellulosic constituents within corn fiber.

Influences of genetically perturbing synthesis of the typical yellow pigment on conidiation, cell wall integrity, stress tolerance, and cellulase production in Trichoderma reesei.

The prominent protein producing workhorse Trichoderma reesei secretes a typical yellow pigment that is synthesized by a gene cluster including two polyketide synthase encoding genes sor1 and sor2. Two transcription factors (YPR1 and YPR2) that are encoded in the same cluster have been shown to regulate the expression of the sor genes. However, the physiological relevance of the yellow pigment synthesis in T. reesei is not completely clear. In this study, a yellow pigment hyper-producer OEypr1 and three yellow pigment non-producers, OEypr1-sor1, Δypr1, and OEypr2, were constructed. Their phenotypic features in mycelial growth, conidiation, cell wall integrity, stress tolerance, and cellulase production were determined. Whereas hyperproduction of the yellow pigment caused significant defects in all the physiological aspects tested, the non-producers showed similar colony growth, but improved conidiation, maintenance of cell wall integrity, and stress tolerance compared to the control strain. Moreover, in contrast to the severely compromised extracellular cellobiohydrolase production in the yellow pigment hyperproducer, loss of the yellow pigment hardly affected induced cellulase gene expression. Our results demonstrate that interfering with the yellow pigment synthesis constitutes an engineering strategy to endow T. reesei with preferred features for industrial application.

Improved production of germacrene A, a direct precursor of ß-elemene, in engineered Saccharomyces cerevisiae by expressing a cyanobacterial germacrene A synthase.

BACKGROUND: The sesquiterpene germacrene A is a direct precursor of ß-elemene that is a major component of the Chinese medicinal herb Curcuma wenyujin with prominent antitumor activity. The microbial platform for germacrene A production was previously established in Saccharomyces cerevisiae using the germacrene A synthase (LTC2) of Lactuca sativa. RESULTS: We evaluated the performance of LTC2 (LsGAS) as well as nine other identified or putative germacrene A synthases from different sources for the production of germacrene A. AvGAS, a synthase of Anabaena variabilis, was found to be the most efficient in germacrene A production in yeast. AvGAS expression alone in S. cerevisiae CEN.PK2-1D already resulted in a substantial production of germacrene A while LTC2 expression did not. Further metabolic engineering the yeast using known strategies including overexpression of tHMGR1 and repression of squalene synthesis pathway led to an 11-fold increase in germacrene A production. Site-directed mutagenesis of AvGAS revealed that while changes of several residues located within the active site cavity severely compromised germacrene A production, substitution of Phe23 located on the lateral surface with tryptophan or valine led to a 35.2% and 21.8% increase in germacrene A production, respectively. Finally, the highest production titer of germacrene A reached 309.8 mg/L in shake-flask batch culture. CONCLUSIONS: Our study highlights the potential of applying bacterial sesquiterpene synthases with improved performance by mutagenesis engineering in producing germacrene A.

An AI-Application-Oriented In-Class Teaching Evaluation Model by Using Statistical Modeling and Ensemble Learning.

In-class teaching evaluation, which is utilized to assess the process and effect of both teachers' teaching and students' learning in a classroom environment, plays an increasingly crucial role in supervising and promoting education quality. With the rapid development of artificial intelligence (AI) technology, the concept of smart education has been constantly improved and gradually penetrated into all aspects of education application. Considering the dominant position of classroom teaching in elementary and undergraduate education, the introduction of AI technology into in-class teaching evaluation has become a research hotspot. In this paper, we propose a statistical modeling and ensemble learning-based comprehensive model, which is oriented towards in-class teaching evaluation by using AI technologies such as computer vision (CV) and intelligent speech recognition (ISR). Firstly, we present an index system including a set of teaching evaluation indicators combining traditional assessment scales with new values derived from CV and ISR-based AI analysis. Next, we design a comprehensive in-class teaching evaluation model by using both the analytic hierarchy process-entropy weight (AHP-EW) and AdaBoost-based ensemble learning (AdaBoost-EL) methods. Experiments not only demonstrate that the two modules in the model are respectively applicable to the calculation of indicators with different characteristics, but also verify the performance of the proposed model for AI-based in-class teaching evaluation. In this comprehensive in-class evaluation model, for students' concentration and participation, ensemble learning module is chosen with less root mean square error (RMSE) of 8.318 and 9.375. In addition, teachers' media usage and teachers' type evaluated by statistical modeling module approach higher accuracy with 0.905 and 0.815. Instead, the ensemble learning approaches the accuracy of 0.73 in evaluating teachers' style, which performs better than the statistical modeling module with the accuracy of 0.69.

A Random Forest-Based Accuracy Prediction Model for Augmented Biofeedback in a Precision Shooting Training System.

In the military, police, security companies, and shooting sports, precision shooting training is of the outmost importance. In order to achieve high shooting accuracy, a lot of training is needed. As a result, trainees use a large number of cartridges and a considerable amount of time of professional trainers, which can cost a lot. Our motivation is to reduce costs and shorten training time by introducing an augmented biofeedback system based on machine learning techniques. We are designing a system that can detect and provide feedback on three types of errors that regularly occur during a precision shooting practice: excessive hand movement error, aiming error and triggering error. The system is designed to provide concurrent feedback on the hand movement error and terminal feedback on the other two errors. Machine learning techniques are used innovatively to identify hand movement errors; the other two errors are identified by the threshold approach. To correct the excessive hand movement error, a precision shot accuracy prediction model based on Random Forest has proven to be the most suitable. The experimental results show that: (1) the proposed Random Forest (RF) model achieves the prediction accuracy of 91.27%, higher than any of the other reference models, and (2) hand movement is strongly related to the accuracy of precision shooting. Appropriate use of the proposed augmented biofeedback system will result in a lower number of rounds used and shorten the precision shooting training process.

Cube-corner autocollimator with expanded measurement range.

A cube-corner is employed as a photoelectric autocollimator's reflector to extend the angle measurement range and the corresponding algorithms are deduced. Experimental investigations reveal that the measurement range is extended from 1.2° to 12° without enlarging the objective lens's aperture. The accuracy is better than 35" when the deflection is less than 8°.

Induction of microRNA­let­7a inhibits lung adenocarcinoma cell growth by regulating cyclin D1.

Lung cancer is the most common cause of cancer­associated mortality. MicroRNAs (miRNAs), as oncogenes or tumor suppressor genes, serve crucial roles not only in tumorigenesis, but also in tumor invasion and metastasis. Although miRNA­let­7a (let­7a) has been reported to suppress cell growth in multiple cancer types, the biological mechanisms of let­7a in lung adenocarcinoma are yet to be fully elucidated. In the present study, the molecular roles of let­7a in lung adenocarcinoma were investigated by detecting its expression in lung adenocarcinoma tissues and exploring its roles in the regulation of lung cancer cell proliferation. Let­7a expression was identified to be downregulated in lung adenocarcinoma tissues compared with normal tissues. Overexpression of let­7a effectively suppressed cancer cell proliferation, migration and invasion in H1299 and A549 cells. Let­7a also induced cell apoptosis and cell cycle arrest. Furthermore, let­7a significantly inhibited cell growth by directly regulating cyclin D1 signals. This novel regulatory mechanism of let­7a in lung adenocarcinoma provides possible avenues for future targeted therapies of lung cancer.

Anticancer effect of exogenous hydrogen sulfide in cisplatin­resistant A549/DDP cells.

Despite huge advances in lung cancer treatment, resistance to cisplatin­based chemotherapy remains one of the major obstacles, and the elucidation of cisplatin resistance remains challenging. As an important biological and pharmacological mediator, hydrogen sulfide (H2S) performs a variety of homeostatic functions related to cancer formation and development. However, the effects of H2S on cisplatin­resistance lung cancer remain largely unknown. In the present study, we investigated the anticancer effects and relevant mechanisms of NaHS (an exogenous donor of H2S) on A549/DDP cells (cisplatin­resistant). The intracellular H2S was first evaluated using a fluorescence probe in A549 (cisplatin­sensitive) and A549/DDP cells. We found that H2S production was markedly decreased in A549/DDP cells compared with that in A549 cells, accomplished by the downregulation of cystathionine ß­synthase (CBS), an endogenous H2S­producing enzyme. In view of these findings, we then observed the effects of NaHS treatment on A549/DDP cells. The results showed that NaHS exposure exhibited an inhibitory effect on cell viability and the IC50 of cisplatin in A549/DDP cells decreased markedly during NaHS treatment (800 µmol/l). In addition, our data revealed that NaHS treatment of A549/DDP cells resulted in the induction of apoptosis, cell cycle arrest and inhibition of cell migration and invasion. Finally, we demonstrated that the marked changes in the A549/DDP cell response to NaHS may be triggered by the activation of p53, and overexpression of p21, caspase­3, Bax and MMP­2, as well as the downregulation of Bcl­xL. The findings of the present study provide novel evidence that NaHS administration may represent a new strategy for the treatment of cisplatin­resistant lung cancer.

Molecular basis of peripheral olfactory sensing during oviposition in the behavior of the parasitic wasp Anastatus japonicus.

Anastatus japonicus is a parasitic wasp and natural enemy of the litchi pest Tessaratoma papillosa, and for decades in China, A. japonicus has been mass-reared inside the eggs of Antheraea pernyi to control T. papillosa. A series of experiments was performed to explore the olfactory mechanism underlying the oviposition behavior of A. japonicus. First, a transcriptomic analysis was performed on the antennae of A. japonicus, and the resulting assemblies led to the generation of 70,473 unigenes. Subsequently, 21,368 unigenes were matched to known proteins, 48 odorant receptors (ORs) (including Orco) and 13 antennal ionotropic receptors (IRs) (including the co-receptors IR8a and IR25a) were identified and predicted to form complete open reading frames (ORFs). The FPKM (fragments per Kb per million reads) values and RT-PCR results showed that AjapOrco, AjapOR10, AjapOR27, AjapOR33 and AjapOR35 were either highly abundant or expressed specifically in the olfactory organs. Furthermore, AjapOrco silencing resulted in a significant decrease in both the parasitism rate and the host-seeking time of A. japonicus, whereas dsRNA injection showed that IR8a and IR25a did not produce significant behavioral changes, suggesting that the oviposition behavior of A. japonicus is more reliant on OR-based pathways than IR-based pathways. Our previous GC-MS data derived twenty-nine compounds which were abundent from these host plants and host insects. We performed electrophysiological and oviposition assays on A. japonicus, and eight odorants were found to elicit a significant electroantennogram (EAG) response. Among these odorants, ß-Caryophyllene, Undecane, (E)-α-Farnesene (+)-Aromadendrene and Cis-3-Hexen-ol had strong attractant effects on oviposition, whereas 2-Ethyl-1-Hexan-ol, Ethyl Acetate and α-Caryophyllene had a strong repellant effects. Thus, these chemicals might influence oviposition guidance/repulsion behavior in A. japonicus. To further explore the target ORs that are tuned to the functional odorants, the nine candidate ORs described above were silenced by RNA interference, and the results showed that a large decrease in the EAG response of all the tested functional odorants in the AjapOrco-silencing group. In addition, the AjapOR35-silencing group showed a significant decrease in the EAG response to ß-Caryophyllene and (E)-α-Farnesene, indicating that AjapOR35 is tuned to these two oviposition attractants ß-Caryophyllene and (E)-α-Farnesene. Further binary-choice oviposition assays showed that the oviposition attractant effect of ß-Caryophyllene and (E)-α-Farnesene vanished after AjapOR35 was silenced, indicating that the emission of these attractants from host plants can guide A. japonicus to locate eggs for ovipositioning and indicated that AjapOR35 is correlated with the olfactory detection oviposition behavior of this species. This study provides a better understanding of the molecular basis and functional chemicals underlying the oviposition behavior of A. japonicus, and the results may help improve biocontrol approaches.

Lipopolysaccharide Downregulates Kruppel-Like Factor 2 (KLF2) via Inducing DNMT1-Mediated Hypermethylation in Endothelial Cells.

KLF2 plays a protective role in antiinflammation and endothelial function, and can be regulated by promoter methylation alteration. Lipopolysaccharide (LPS) is a mediator of inflammatory responses, which causes epigenetic change of certain genes in host cells. We thus aimed to determine whether LPS could control the KLF2 expression by inducing methylation in promoter region. DNA methylation of 16 CpG sites within KLF2 promoter region was detected by bisulfite sequencing PCR. Results showed that methylation at 12 CpG sites were significantly increased in HUVECs after exposure to LPS among the total 16 sites, and the average level was increased by 57%. The KLF2 expressions assessed by reverse transcription quantitative real-time PCR and Western blot were significantly downregulated compared that without LPS simulation. Moreover, both messenger RNA and protein levels of KLF2 in HUVEC co-treated with LPS and DNA methyltransferase (DNMT) 1 small interfering RNA were dramatically higher than that treated with LPS only. Similar result was obtained when the cells were incubated in combination with LPS and 5-aza-2'-deoxycytidine (AZA), suggesting that the reduction of KLF2 expression induced by LPS can be reversed by DNMT1 inhibition. Finally, the presence of AZA changed the expression of genes that depends on KLF2 in LPS-stimulated HUVECs, which downregulated the E-selectin and VCAM and increased the eNOS and thrombomodulin expression. Our data demonstrated that LPS exposure resulted in hypermethylation in KLF2 promoter in HUVECs, which subsequently led to downregulation of the KLF2 expression. The study suggested that epigenetic alteration is involved in LPS-induced inflammatory response and provided a new insight into atherogenesis.

Context Sensing System Analysis for Privacy Preservation Based on Game Theory.

In a context sensing system in which a sensor-equipped mobile phone runs an unreliable context-aware application, the application can infer the user's contexts, based on which it provides personalized services. However, the application may sell the user's contexts to some malicious adversaries to earn extra profits, which will hinder its widespread use. In the real world, the actions of the user, the application and the adversary in the context sensing system affect each other, so that their payoffs are constrained mutually. To figure out under which conditions they behave well (the user releases, the application does not leak and the adversary does not retrieve the context), we take advantage of game theory to analyze the context sensing system. We use the extensive form game and the repeated game, respectively, to analyze two typical scenarios, single interaction and multiple interaction among three players, from which Nash equilibriums and cooperation conditions are obtained. Our results show that the reputation mechanism for the context-sensing system in the former scenario is crucial to privacy preservation, so is the extent to which the participants are concerned about future payoffs in the latter one.