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Increasing studies have indicated that long non-coding RNAs (lncRNAs) play important roles in many physiological and pathological pathways. Identifying lncRNA-disease associations not only contributes to the understanding of biological processes, but also provides new strategies for the diagnosis and prevention of diseases. In this article, an end to end computational model based on graph attention network (GANLDA) is proposed to predict associations between lncRNAs and diseases. In our method, it combines heterogeneous data of lncRNA and disease as original features. Then, the principal component analysis (PCA) is used to reduce the noise of the original features. Further, the graph attention network is utilized to extract the useful information from features of lncRNA and disease. Finally, the multi-layer perceptron is employed to infer lncRNA-disease associations. The experimental results show GANLDA outperforms than other four state-of-the-art methods in 10-fold cross validation and devono test. The case studies also demonstrate that GANLDA is an effective method for lncRNA-disease associations identification.

Community detection is a significant methodology in network science. Traditional methods show limitations in dealing with multi-scale and high-dimensional complex data. As one of the most popular unsupervised algorithms, affinity propagation algorithm (AP) has been widely applied in community detection. However, its negative Euclidean similarity and inflexible parameter may lead to high time or memory consumption and excessive detection. Thus, this article presents a novel affinity propagation algorithm in t-distribution (APT), integrated with manifold learning, for detecting community structure. In APT algorithm, the data is compressed by dimensionality reduction, and joint probability is applied to construct the similarity matrix. Further, based on optimized modularity, parameters are adjusted to improve the accuracy. Experiments show that APT has better adaptability and universality than AP algorithm. In contrast to other mainstream algorithms, our algorithm can extract more meaningful communities from multi-scale and high-dimensional networks.

Anaerobic oxidation of methane (AOM) in wetland soils is widely recognized as a key sink for the greenhouse gas methane (CH

Mountain rivers not only play an important role in flood control, but also perform other functions such as conserving water sources, regulating microclimate, and maintaining water ecology and biodiversity. Because the conventional approach to managing mountain rivers overlooks the intrinsic requirements of river water, alters the natural landscape of river channels, and destroys habitats, it leads to water quality deterioration and ecological imbalance. The Yang River is characteristic of northern mountain rivers, and so the ecological management project for this river – at the site of the forthcoming Beijing 2022 Olympic Winter Games – was selected as an example of comprehensive ecological management. This comprises an ecological, permeable dam–pond–wetland system. Results showed that: (Zhu et al., 2006): river water quality improved substantially; improvements in the water quality indices exhibited the trend suspended solids (SS) > ammonium-nitrogen (NH3-N) > chemical oxygen demand (COD) (Mikus et al., 2019). The Shannon–Wiener diversity index, Margalef species richness index, and Pielou evenness index all indicated increased phytoplankton by an average of 52.1%, 2.4%, and 98.9%, respectively. These three diversity indices showed increased zooplankton, by an average of 1.2%, 0.5%, and 3.3%, respectively. Water quality in the Yang River was found to have changed from moderate to light pollution (Kuemmerlen et al., 2019). Each subsystem increased removal of pollutants from the Yang River. Removal efficiencies of COD, total phosphorous (TP), total nitrogen (TN), NH3-N, and SS by the dam system were 28.57%, 28.3%, 20.5%, 24.0%, and 95%, respectively. Corresponding pollutant removal efficiencies by the pond system were 16.7%, 29.58%, 21.63%, 20.0%, and 99.8%, respectively. The wetland system achieved pollutant removal efficiencies of 44.0%, 17.1%, 6.7%, 13.9%, and 80.0%, respectively. This approach can mitigate flood control pressure, restore river ecology, and improve water quality in northern mountain rivers, and allow such rivers to achieve “retention–storage–restoration” effects. The ecological treatment approach provides an important theoretical foundation and technical reference for global mountain river management, and is especially applicable to mountain rivers in cold and arid regions in the northern hemisphere.

Background & Aims Liver inflammation is key in the progression of chronic viral hepatitis to cirrhosis and hepatocellular carcinoma. The magnitude of viral replication and the specific anti-viral immune responses should govern the degree of inflammation, but a direct correlation is not consistently found in chronic viral hepatitis patients. We aim to better define the mechanisms that contribute to chronic liver inflammation. Methods Intrahepatic CD14 + myeloid cells from healthy donors (n=19) and patients with viral-related liver cirrhosis (HBV, HBV/HDV or HCV; n=15) were subjected to detailed phenotypic, molecular and functional characterisation. Results Unsupervised analysis of multi-parametric data showed that liver disease was associated with the intrahepatic expansion of activated myeloid cells mainly composed of pro-inflammatory CD14 + HLA-DR hi CD206 + cells, which spontaneously produced TNFα and GM-CSF. These cells only showed heightened pro-inflammatory responses to bacterial TLR agonists and were more refractory to endotoxin-induced tolerance. A liver-specific enrichment of CD14 + HLA-DR hi CD206 + cells was also detected in a humanised mouse model of liver inflammation. This accumulation was abrogated following oral antibiotic treatment, suggesting a direct involvement of translocated gut-derived microbial products in liver injury. Conclusions Viral-related chronic liver inflammation is driven by the interplay between non-endotoxin-tolerant pro-inflammatory CD14 + HLA-DR hi CD206 + myeloid cells and translocated bacterial products. Deciphering this mechanism paves the way for the development of therapeutic strategies specifically targeting CD206 + myeloid cells in viral-related liver disease patients. Lay summary: Viral-related chronic liver disease is driven by intrahepatic pro-inflammatory myeloid cells accumulating in a gut-derived bacterial product-dependent manner. Our findings support the use of oral antibiotics to ameliorate liver inflammation in these patients.

Automatically recognizing the visual phrase of an image is a challenging issue in computer vision. In this paper, we propose a method to discover and identify the visual phrase by automatically analyzing 3D spatial geometric structure of an image. It includes two steps: (1) learning 3D spatial geometric model; and (2) recognizing visual phrase. To achieve the first goal, we propose 3D geometric models (3DSG) that jointly capture both the features of objects and 3D spatial layout among objects in a visual phrase. In the second step, we transform the visual phrase recognition into verification by measuring the similarity of spatial configuration between the given visual pattern and the 3DSG model. The nature of our method makes itself precisely determine whether the given visual pattern belongs to a specific 3DSG model or not by maximizing the joint probability of the given visual pattern and a 3DSG model. Experiments conducted on several datasets show that our model outperforms the state-of-the-art models in modeling 3D spatial geometric structure as well as recognizing visual phrase. The results also demonstrate that modeling 3D spatial configuration between objects can significantly improve the deeper image understanding.

Microorganisms play an important role in the biogeochemical cycle and ecological function regulation of wetlands, have a major impact on global climate change and are critical for maintaining the health of the global ecosystem. In order to investigate the relationships among plants, environmental variables, and microbial communities in coastal wetlands in the Yellow River Delta, we selected soils growing plants such as Suaeda salsa, Tamarix chinensis, Phragmites australis, and cotton etc. The results show that there were differences in microbial diversity among areas with different vegetation cover and the microbial abundance in Phragmites australis and Tamarix chinensis areas was higher than that in mudflat, Suaeda glauca and cotton field, plants increased the diversity of soil microorganisms. The structure and diversity of soil microorganisms were significantly higher than that of endophytes. The Shannon index of soil bacteria was about 4-5.5, while that of endophytes was about 0-4. The soil bacteria were mainly Firmicutes, Proteobacteria, Bacteroidetes and Actinobacteria, accounting for more than 90.0% in all samples. The Mn4+, Fe3+ and hydrolytic nitrogen contents in the soil of vegetation covered areas was lower than that of the bare beach, the content of hydrolytic nitrogen in Phragmites australis area was generally higher, and the content of SO42- and NO2- in the area was lowest near oil fields. Redundancy analysis shows that the explanatory rates of environmental factors at the phylum and genus levels were 89.70% and 86.80%, respectively, and K (23.40%), NO2- (11.80%), Mn4+ (9.80%) and Na (8.00%) were the main factors explaining the structural changes and composition of microbial flora at the phylum level. This study provides an ecological perspective for understanding the influence mechanism between wetland microbial diversity and wetland ecosystem function. It is helpful for us to understand the interactions among plants, environmental variables, and microbial communities in the coastal wetland of the Yellow River Delta, and has important guiding significance for the scientific research of soil environmental remediation in the degraded coastal wetland of the Yellow River Delta.

Robust gait recognition via deterministic learning and multiple views fusion.Multiple views fusion strategy is employed to form synthesized silhouette.Human gait can be characterized with four time-varying gait features.Gait variability are effectively modeled via deterministic learning.A rapid recognition scheme is chosen for encouraging recognition accuracy. Gait characteristics extracted from one single camera are limited and not comprehensive enough to develop a robust recognition system. This paper proposes a robust gait recognition method using multiple views fusion and deterministic learning. First, a multiple-views fusion strategy is introduced, in which gaits collected under different views are synthesized as a kind of synthesized silhouette images. Second, the synthesized silhouettes are characterized with four kinds of time-varying gait features, including three width features of the silhouette and one silhouette area feature. Third, gait variability underlying different individuals' time-varying gait features is effectively modeled by using deterministic learning algorithm. This kind of variability reflects the change of synthesized silhouettes while preserving temporal dynamics information of human walking. Gait patterns are represented as the gait variability underlying time-varying gait features and a rapid recognition scheme is presented in published gait databases. Experimental results show that encouraging recognition accuracy can be achieved.

The frequent outbreaks of cyanobacterial blooms which caused serious societal and economic loss have become a worldwide problem. Interactions between toxic cyanobacteria and heterotrophic bacteria competitors play a pivotal role in the formation of toxic cyanobacterial bloom, but the underlying mechanisms of interactions between them await further research. The antagonist activity of Pseudomonas grimontii (P.grimontii) was confirmed by reduction in chlorophyll a concentration of Microcystis aeruginosa (M. aeruginosa) in an infected culture for a 7d period. The initial concentration of P.grimontii affected the M. aeruginosa activity significantly. When the 10% (V/V) concentration of P.grimontii A01 and P.grimontii A14 cultures were infected, the reduction of M. aeruginosa reached to 91.81% and 78.25% after 7 days, respectively. While a 0.1% (v/v) concentration of P.grimontii A01 and P.grimontii A14 cultures were infected, the M. aeruginosa increased 31.13% and 16.67% occurred, respectively. The content of reactive oxygen species (ROS) and malondialdehyde (MDA) increased with increasing of P.grimontii fermentation liquid, indicating the M. aeruginosa underwent oxidative stress. Using matrix-assisted laser desorption ionization–time of flight (MALDI-TOF) imaging mass spectrometry (IMS) profiling of co-cultures of M. aeruginosa and its antagonist P.grimontii, we revealed novel interspecies allelopathic interactions and compete molecule. We showed the spatial secondary metabolites may mediate the interactions in which P.grimontii inhibits growth of M. aeruginosa. Additionally, we revealed how M. aeruginosa feedback to the P.grimontii, which stimulates secondary metabolites such as [D-Asp3]-microcystin-LR released by M. aeruginosa. IMS method highlights the significance of allelopathic interactions between a widely distributed toxic cyanobacteria and its bacteria competitors.

Recovering phosphorus from livestock wastewater could partly mitigate the global phosphorus resource crisis. Crystallization is a promising method for removing phosphorus from wastewater, but the costs of calcium- and magnesium-containing reagents are increasing. Cheap, available, efficient materials are required to replace conventional calcium and magnesium reagents. Here, we describe a new approach to removing and recovering phosphorus from livestock wastewater of a large pig farm, containing a high phosphorus concentration. The effects of the pH, stirring speed, stirring time, and extract dose (containing calcium and magnesium) on phosphorus removal from livestock wastewater were investigated. The product was characterized by X-ray diffractometry, Fourier-transform infrared spectroscopy, and scanning electron microscopy. Under optimized conditions (pH 9.0, stirring speed 200 r/m, stirring time 600 s, Ca 207.62 mg/L, Mg 122.86 mg/L), 92% of the phosphorus was removed from livestock wastewater. The product was mainly the hydroxyapatite (Ca5(PO4)3OH) precursor amorphous calcium phosphate but also contained 1.65% (by mass) magnesium ammonium phosphate (MgNH4PO4·6H2O) crystals. The cost of dolomite to treat 1 m3 of high-phosphorus wastewater was 0.20 yuan (45.9%, 25.9%, and 75.9% lower than for pure MgCl2, MgSO4, and CaCl2, respectively) in 2019. Using dolomite to provide calcium and magnesium effectively decreases the crystallization process cost and should encourage the use of crystallization to remove phosphorus from wastewater.

Accumulating studies have indicated that long non-coding RNAs (lncRNAs) play crucial roles in large amount of biological processes. Predicting lncRNA-disease associations can help biologist to understand the molecular mechanism of human disease and benefit for disease diagnosis, treatment and prevention. In this paper, we introduce a computational framework based on graph autoencoder matrix completion (GAMCLDA) to identify lncRNA-disease associations. In our method, the graph convolutional network is utilized to encode local graph structure and features of nodes for learning latent factor vectors of lncRNA and disease. Further, the inner product of lncRNA factor vector and disease factor vector is used as decoder to reconstruct the lncRNA-disease association matrix. In addition, the cost-sensitive neural network is utilized to deal with the imbalance between positive and negative samples. The experimental results show GAMLDA outperforms other state-of-the-art methods in prediction performance which is evaluated by AUC value, AUPR value, PPV and F1-score. Moreover, the case study shows our method is the effectively tool for potential lncRNA-disease prediction.

Little is known about the effects of straw incorporation on saline soil aggregate formation or salt distribution in coastal zones. In this study, a 4-year coastal wheat/maize rotation field experiment was employed. In each growing season, maize/wheat straw was applied at the rates of 5.0 × 10³ kg ha⁻¹ (S) and 1.0 × 10⁴ kg ha⁻¹ (2S), and inorganic N was applied at the rates of 75 kg ha⁻¹ (N1/2), 150 kg ha⁻¹ (N), and 300 kg ha⁻¹ (N2). Treatment without straw addition and applied with 150 kg ha⁻¹ inorganic N was used as the control (CK). Dry- and wet-sieving techniques were used to fractionate the soils into large macroaggregates (> 2 mm, LM), small macroaggregates (0.25–2.0 mm, SM), microaggregates (0.053–0.25 mm, MI), and silt-plus-clay particles ( 0.25 mm, LA + SA) ranged from 74.2 %–88.3 % and 28.4 %–37.6 % in straw applied treatments, which significantly increased by 14.7 %–19.0 % and 21.1 %–32.4 % compared to CK, respectively (p < 0.05). The mean weight diameter (MWD) and aggregate stability rate (AR) enhanced as straw and N application rate increased. After straw application, soil organic carbon (SOC) preferentially accumulated in MI fractions, which was significantly higher than other aggregate fractions, except SN1/2 and CK (p < 0.05). MWD and AR were significantly negatively correlated with soil salinity (p < 0.05), and soil salinity decreased by 20.5 %–26.9 % in straw treatments compared to the initial soil salinity. In the CK and SN1/2 treatments, soil salinity of LA + SA fractions was significantly higher than < 0.25 mm fractions (MI + CS) (p < 0.05). The same difference in soluble Ca²⁺ was also observed in CK, SN1/2, or SN. In conclusion, saline soil aggregate formation and stability improved after straw and N application, which caused the salinity to decrease and affected salt distribution in aggregates. The findings of this study suggest that adequate N should be applied in order to maximize saline soil reclamation efficiency with straw.

Protein kinases have been implicated in a number of diseases, where kinases participate many aspects that control cell growth, movement and death. The deregulated kinase activities and the knowledge of these disorders are of great clinical interest of drug discovery. The most critical issue is the development of safe and efficient disease diagnosis and treatment for less cost and in less time. It is critical to develop innovative approaches that aim at the root cause of a disease, not just its symptoms. Bioinformatics including genetic, genomic, mathematics and computational technologies, has become the most promising option for effective drug discovery, and has showed its potential in early stage of drug-target identification and target validation. It is essential that these aspects are understood and integrated into new methods used in drug discovery for diseases arisen from deregulated kinase activity. This article reviews bioinformatics techniques for protein kinase data management and analysis, kinase pathways and drug targets and describes their potential application in pharma ceutical industry.

SummaryPrimary human cells can be transformed into tumor cells by a defined set of genetic alterations including telomerase, oncogenic RasV12, and the tumor suppressors p53 and pRb. SV40 small T (ST) is required for anchorage-independent growth in vitro and in vivo. Here, we identify the Hippo tumor suppressor pathway as a critical target of ST in cellular transformation. We report that ST uncouples YAP from the inhibitory activity of the Hippo pathway through PAK1-mediated inactivation of NF2. Membrane-tethered activated PAK is sufficient to bypass the requirement for ST in anchorage-independent growth. PAK acts via YAP to mediate the transforming effects of ST. Activation of endogenous YAP is required for ST-mediated transformation and is sufficient to bypass ST in anchorage-independent growth and xenograft tumor formation. Our findings uncover the Hippo tumor suppressor pathway as a final gatekeeper to transformation and tumorigenesis of primary cells.

The ridge polynomial neural network is a special type of higher-order neural networks. It not only provides a more efficient and regular architecture compared to ordinary higher-order feedforward networks, but also maintains the fast learning property and powerful nonlinear mapping capability while avoiding the combinatorial increase in the number of required weights. In this paper, a monotonicity theorem and two convergence theorems of the asynchronous gradient method for training the ridge polynomial neural network are proved. They are important to choosing appropriate learning rate and initial weights to perform effective training. To illustrate the theoretical finding, numerical experiments are carried out for 4-dimensional parity problem and function approximation problem. It is shown that the experimental results are in agreement with the proposed theorems.

In this study, experiments were conducted to investigate the biosorption effect of the cyanobacteria Synechocystis sp. PCC 7806 on trivalent chromium (Cr (III)) and the fate of extracellular polymeric substances (EPS) during the biosorption process. Results indicated that Synechocystis sp. PCC 7806 could effectively adsorb Cr (III) but this adsorption capacity was inhibited by high amount Cr (III). The biosorption process decreased the growth rate of Synechocystis sp. PCC 7806. Furthermore, the characteristics of Synechocystis sp. PCC 7806 EPS were evaluated by parallel factor analysis (PARAFAC) combined with fluorescence excitation–emission matrices (EEMs). The results showed that there were four main components of EPS in Synechocystis sp. PCC 7806. It indicated that the fluorescent characterization of EPS was obvious affected by adsorption process of Cr (III). The tryptophan-like substances, amino acid substances and fulvic acids-like substances largely participated in the binding and biosorption of Cr (III).

SummaryHuman pluripotent stem cell (hPSC) differentiation typically yields heterogeneous populations. Knowledge of signals controlling embryonic lineage bifurcations could efficiently yield desired cell types through exclusion of alternate fates. Therefore, we revisited signals driving induction and anterior-posterior patterning of definitive endoderm to generate a coherent roadmap for endoderm differentiation. With striking temporal dynamics, BMP and Wnt initially specified anterior primitive streak (progenitor to endoderm), yet, 24 hr later, suppressed endoderm and induced mesoderm. At lineage bifurcations, cross-repressive signals separated mutually exclusive fates; TGF-β and BMP/MAPK respectively induced pancreas versus liver from endoderm by suppressing the alternate lineage. We systematically blockaded alternate fates throughout multiple consecutive bifurcations, thereby efficiently differentiating multiple hPSC lines exclusively into endoderm and its derivatives. Comprehensive transcriptional and chromatin mapping of highly pure endodermal populations revealed that endodermal enhancers existed in a surprising diversity of “pre-enhancer” states before activation, reflecting the establishment of a permissive chromatin landscape as a prelude to differentiation.

In this paper, a penalty term is added to the conventional error function to improve the generalization of the Ridge Polynomial neural network. In order to choose appropriate learning parameters, we propose a monotonicity theorem and two convergence theorems including a weak convergence and a strong convergence for the synchronous gradient method with penalty for the neural network. The experimental results of the function approximation problem illustrate the above theoretical results are valid.

Many clustering approaches have been developed for biological data analysis, however, the application of traditional clustering algorithms for RNA structure data analysis is still a challenging issue. This arises from the existence of complex secondary structures while clustering. One of the most critical issues of cluster analysis is the development of appropriate distance measures in high dimensional space. The traditional distance measures focus on scale issues, but ignores the correlation between two values. This article develops a novel interval-based distance (Hausdorff) measure for computing the similarity between characterized structures. Three relationships including perfect match, partially overlapped and non-overlapped are considered. Finally, we demonstrate the methods by analyzing a data set of RNA secondary structures from the Rfam database.

This paper presents a formal framework for multiple data source (MDS) discovery. A measure is first proposed for estimating the consistency, inconsistency and uncertainty between data sources using possibilistic minimal model. Then, two metrics are defined for measuring the support and confidence of a set of formulae (itemsets) in terms of the degree of consistency of the items. The consistency measure, in conjunction with support-confidence framework in data mining, assists in identifying interesting knowledge from MDSs. Finally, the impact of consistency among knowledge bases is considered to determine the knowledge base from which a set of formulae is most likely identified as a pattern of interest. A major advantage of this framework is that the mining algorithm supports the reasoning about the knowledge from possibilistic data-sources. We evaluate the proposed approach with both examples and experiment, and demonstrate that our method is useful and efficient in identifying interesting patterns from multiple databases.

An off-line filtering ditch–pond system was designed and constructed to control the small point and runoff pollution at the Wuhan City Zoo, Hubei Province, China. The quantity and quality of wastewater discharge and runoff from 16 rainfall events were measured to test the effectiveness of the off-line treatment train. The results showed that the water quality was improved and high retention rates for water and pollutants were also achieved by the off-line treatment train. In the outflows, the event mean concentrations (EMCs) of TSS, COD, TN, TDN, TP and TDP were reduced by 75%, 50%, 50%, 57%, 74% and 80% compared to the inflows. In 2005, the annual inflow volume in the catchment was 6783 m 3 and the water retention rate was 80.1%. The retention rates in the annual loads of TSS, COD, TN and TP came to 86.4%, 85.5%, 83.9% and 82.9%, respectively. Therefore, the off-line filtering ditch–pond system was shown to be an effective and economical measure to control diffuse pollution. It would be worthwhile to extend the off-line treatment train to regions with limited land resources, especially in urban areas.

Trace amounts (≤10−6 mol fraction) of lanthanum were introduced into five, premixed, fuel-rich, H2–O2–N2 flames at atmospheric pressure in the temperature range 1820–2400 K. Aqueous salt solutions of the metal were sprayed into the premixed flame gas as an aerosol using an atomizer technique. The concentrations of the major neutral species present in flames, believed to be LaO and OLaOH, are linked by the balanced reaction LaO + H2O = OLaOH + H having an equilibrium constant K = 0.1859 exp(−11 464/T), based on a crude estimate of the bond dissociation energy for lanthanum oxide-hydroxide D00(OLa–OH) = 408 ± 40 kJ mol−1. Metallic ions were observed by sampling the flames through a nozzle into a mass spectrometer and, for kinetics purposes, were measured as profiles of ion concentration versus distance (i.e. time) along the flame axis. The ions detected can be represented by an oxide ion series LaO+.nH2O (n = 0−3 or more); the genuine flame ions are thought to be LaO+ (n = 0) and La(OH)2+ (n = 1), equivalent to protonated OLaOH. The two ions are linked by the fast balanced reaction LaO+ + H2O + M = La(OH)2+ + M, where M is a third body. The major ion production processes appear to be the chem-ionization reaction OLaOH + H ⇄ La(OH)2+ + e−, and thermal (collisional) ionization LaO + M ⇄ LaO+ + e− + M because the ionization energy IE00(LaO) = 4.90 eV is low. Ion loss processes involve dissociative electron–ion recombination. When electron-ion recombination of LaO+.nH2O was made dominant by the trace addition of potassium, values of the global recombination coefficient are given by (1718 ± 515)T−3.0±0.2 cm3 molecule−1 s−1. The rate constant for thermal (collisional) ionization of LaO was found to be 8.0 × 10−10 T1/2 exp(−IE00/RT) cm3 molecule−1 s−1. The pre-exponential factor or cross section is only 8% of that determined for the alkali metals. The rate constant for the chemi-ionization of OLaOH is given by (5.6 ± 1.7) × 10−12 exp(−17 350/T) cm3 molecule−1 s−1. Although no direct evidence was found for particle formation in these flames, both rate constants for ion production would be lower limits if appreciable lanthanum were present as solid particles.

Molecular orbital calculations were carried out for the neutral and ionic species that occur in the gas-phase chemistry of magnesium in the presence of oxygen and hydrogen including Mg 1 , MgO, MgOH, MgOH 1 , MgOH2 , Mg(OH)2, HOMgOH2 , and the hydrate structure MgOH 1 ...( OH 2). Standard enthalpies of formation for these species were obtained from single-point calculations at the QCISD(T)(full)/6-31111G(2df,p) and CCSD(T)(full)/6-31111G(2df,p) levels of theory using geometrical parameters obtained from MP2(full)/6-31111G(d,p) optimizations. These DHf values provide a recommended and self-consistent set with uncertainties as small as 612.6 kJ mol 21 (63 kcal mol 21 ) for deriving thermodynamic properties. The properties of interest include the proton affinities PA298 of MgO, MgOH, and Mg(OH)2, ionization energy IE0 of MgOH, bond dissociation energies D0 of Mg‐O, Mg‐OH, MgO‐H, HOMg‐OH, Mg‐OH 1 , H2O‐Mg 1 , and H2O‐MgOH 1 ; the latter two are hydration energies. Values in the literature, both experimental and theoretical, for many of these quantities show considerable scatter and a detailed comparison is made. Magnesium ions in fuel-rich, H2‐O2‐N2 flames at atmospheric pressure in the temperature range 1820 ‐2400 K were investigated experimentally by sampling the flames doped with magnesium through a nozzle into a mass spectrometer. It was shown that the interconversion of Mg 1 and MgOH 1 proceeds by way of the three-body reaction of Mg 1 with OH, in support of Sugden’s criterion that this occurs in flames if the weak HO‐Mg 1 bond has a dissociation energy less than 335 kJ mol 21 . The proton affinity PA 298 (MgO) was measured to be 1056 6 29 kJ mol 21 (252 6 7 kcal mol 21 ) along with approximate values for PA298 (MgOH) 5 919 kJ mol 21 (220 kcal mol 21 ) and PA298 [Mg(OH)2] 5 878 kJ mol 21 (210 kcal mol 21 ). Values were estimated for the electron‐ion recombination coefficient for MgH mOn molecular ions of 2 3 10 27 cm 3 molecule 21 s 21 , and for Mg 1 of 4 3 10 224 T 21 cm 6 molecule 22 s 21 similar to those for alkali metal ions. Finally, small rate coefficients for the chemi-ionization of magnesium via the reactions of Mg 1 OH and MgO 1 H to give MgOH 1 were estimated to be 4.832 3 10 29 exp(255 700/T) and 3.341 3 10 29 exp(232 970/T )c m 3 molecule 21 s 21 , respectively. (Int J Mass Spectrom 184 (1999) 153‐173) © 1999 Elsevier Science B.V.

A small amount (≤ 10 −6 mol fraction) of four alkaline earth metals, tin and yttrium were introduced into five, premixed, fuel-rich, H 2 –O 2 –N 2 flames at atmospheric pressure in the temperature range 1820–2400 K. Aqueous salt solutions of the metals were sprayed into the premixed flame gas as an aerosol using an atomizer technique. Ions in a flame were observed by sampling flame gas through a nozzle into a mass spectrometer. The concentrations of the major neutral metallic species present in the flame were calculated from thermodynamic data currently available. The principal metallic ions observed were AOH + (A = Mg, Ca, Sr, Ba, Sn) and A(OH) 2 + (A = Y), formed initially by proton transfer to AO and OAOH from H 3 O + , a natural flame ion. Except for Mg, the ions were also produced by chemi-ionization processes. By adjusting the concentration(s) of the salt solution in the atomizer, it was found that a pair of ions could be brought into equilibrium within the time scale of the flame; the pairs included H 3 O + with a metal ion or two metallic ions. Because water is a major product of combustion, a very large difference in proton affinity PA 0 (AO) − PA 0 (H 2 O) ≤ 490 kJ mol −1 (117 kcal mol −1 ) could be attempted for the proton transfer equilibrium. Using PA 0 (H 2 O) = 691.0 kJ mol −1 (165.2 kcal mol −1 ) as a reference base to anchor the proton affinity scale, ion ratio measurements led to proton affinity PA 0 values of 766, 912, 1004, 1184, 1201, and 1222 kJ mol −1 (183, 218, 240, 283, 287, and 292 kcal mol −1 ) corrected to 298 K for OYOH, SnO, MgO, CaO, SrO, and BaO, respectively; of these, only the value for OYOH has not been reported previously. If it is assumed that the neutral thermodynamic data are correct (although some appear to be in error), the uncertainties in the PA results reported here are ± 21 kJ mol −1 (5 kcal mol −1 ). The realization that these equilibria can be achieved in flames provides a new approach to consolidate and build the high end of the proton affinity ladder, primarily of metallic species which are not accessible at lower temperatures.

P0482 IMPROVING NATURAL KILLER CELLS CYTOTOXICTY AND HARNESSING HEPATOCYTE CANCER PROGRESSION BY MIR-486-5P A.A. Bassiouni, M.A. Rahmoon, R.A. Youness, A.I. Gomaa, I. Waked, G. Esmat, H.M. El Tayebi, A.I. Abdelaziz. The Molecular Pathology Research Group, Department of Pharmacology and Toxicology, Faculty of Pharmacy and Biotechnology, German University in Cairo, Cairo, Hepatology, National Liver Instiute, Menoufiya University, Menoufiya, Endemic Medicine and Hepatology Department, Faculty of Medicine, Cairo University, Cairo, Egypt E-mail: aya.bassiouni@gmail.com