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To clarify the effects of sex hormones on the expression of atrial natriuretic peptide (ANP), ovariectomized and intact female rats were subcutaneously injected with estradiol, progesterone, a mixture of them or olive oil solvent; castrated and untouched male rats were subcutaneously injected with estradiol, testosterone or olive oil, once a day for 7 days. The relative rANP-mRNA contents of rat atrial were measured by molecular hybridization. rANP-cDNA was labeled with 32 P as a probe.The results revealed that estradiol and progesterone increased ANP gene expression. Furthermore their effects were associated with administration dose of these hormones and it was shown that they are probably coordinated. The physiological amounts of estradiol and progesterone may maintain suitable levels of rANP-mRNA and androgen may also increase the ANP gene expression in vivo. These experiments suggested that female sex hormone may have a dual purpose in fluid balance. Östradiol, Progesteron und Testosteron beeinflussen die Genexpression in vivo von atrial-natriuretischem Peptid bei der RatteZusammenfassung: Um die Wirkung von Sexualhormonen auf die Genexpression von atrial-natriuretischem Peptid (NAP) zu untersuchen, erhielten ovarektomierte und intakte weibliche Ratten 7 Tage lang täglich subkutane Injektionen von Östradiol, Progesteron, einer Mischung beider oder nur das Lösungs-mittel Olivenöl. Kastrierte und intakte Rattenmänn-chen erhielten ebenso Östradiol, Testosteron oder Olivenöl. Der relative Gehalt an rANP-mRNA in den Atria wurde durch molekulare Hybridisierung gemessen. rANP-cDNAwurde mit 32 P markiert.Die Ergebnisse zeigen, daß Östradiol und Progesteron die ANP-Genexpression in Abhängigkeit von der Dosis verstärken. Physiologische Mengen dieser Hormone können einen geeigneten rANP-mRNA-Spiegel aufrechterhalten. Auch Testosteron erhöht die ANP-Expression in vivo. Die Experimente zeigen, daß weibliche Sexualhormone im Flüssigkeits-Gleichge wicht eine doppelte Aufgabe haben können.

The user interaction with the mobile device plays an important role in user habit understanding. In this paper, we propose to mine the associations between user interactions and contexts captured by mobile devices, or behavior patterns for short, from context logs to characterize the habits of mobile users. The extensive experiments on the collected real life data clearly validate the ability of our approach for mining effective behavior patterns.

Summary Deformation is the most intuitive reflection of comprehensive behavior of concrete dams; it is of great significance to predict and interpret the deformation observation data for dam health monitoring. The world's highest concrete dam, Jinping I arch dam in China, was discussed in this paper. Aiming at its annually measured continuous growth phenomenon of dam body deformation towards the downstream direction when reservoir keeps stable at the normal water level of 1,880.0 m, influences of cement hydration heat‐induced temperature rise effect, valley contraction, and dam material creep on deformation behavior of this dam were estimated by finite element method (FEM) and the measured data. Combined with the results of the hydraulic, seasonal, and time (HST) model, the abnormal deformation behavior was detected to be jointly caused by the hysteretic hydraulic deformation and the ambient temperature drop effect. Subsequently, to solve the deficiency that the traditional HST model cannot reasonably explain this measured deformation behavior, a hysteretic hydraulic component was introduced into the HST model, and a special hydraulic, hysteretic, seasonal, and time (HHST) model was proposed. Based on the numerical simulation of viscoelastic FEM and the constrained least square method, the newly added component was represented by a continuous piecewise fitting function, with model factors of previous relative water depth and cumulative days of the current water level stage. HHST model results of Jinping I arch dam show that the measured abnormal displacement increment of dam body is 70% caused by the ambient temperature drop effect and 30% caused by the viscoelastic hysteretic hydraulic deformation.

Mobile context modeling is a process of recognizing and reasoning about contexts and situations in a mobile environment, which is critical for the success of context-aware mobile services. While there are prior works on mobile context modeling, the use of unsupervised learning techniques for mobile context modeling is still under-explored. Indeed, unsupervised techniques have the ability to learn personalized contexts, which are difficult to be predefined. To that end, in this paper, we propose an unsupervised approach to modeling personalized contexts of mobile users. Along this line, we first segment the raw context data sequences of mobile users into context sessions where a context session contains a group of adjacent context records which are mutually similar and usually reflect the similar contexts. Then, we exploit two methods for mining personalized contexts from context sessions. The first method is to cluster context sessions and then to extract the frequent contextual feature-value pairs from context session clusters as contexts. The second method leverages topic models to learn personalized contexts in the form of probabilistic distributions of raw context data from the context sessions. Finally, experimental results on real-world data show that the proposed approach is efficient and effective for mining personalized contexts of mobile users.

Program control dependence has substantial impact on applications such as dynamic information flow tracking and data lineage tracing (a technique tracking the set of inputs that affects individual outputs). Without considering control dependence, information can leak via implicit channels without being tracked; important inputs may be absent from output lineage. However, considering control dependence may lead to a large volume of false alarms in information flow tracking or undesirably large lineage sets. We identify a special type of control dependence called strict control dependence (SCD). The nature of SCDs highly resembles that of data dependences, reflecting strong correlations between statements and hence should be considered the same way as data dependences in various applications. We formally define the semantics. We also describe a cost-effective design that allows tracing only strict control dependence. Our empirical evaluation shows that the proposed technique has very low overhead and it greatly improves the effectiveness of lineage tracing and taint analysis.

Collaborative Filtering (CF) is a popular way to build recommender systems and has been successfully employed in many applications. Generally, two kinds of approaches to CF, the local neighborhood methods and the global matrix factorization models, have been widely studied. Though some previous researches target on combining the complementary advantages of both approaches, the performance is still limited due to the extreme sparsity of the rating data. Therefore, it is necessary to consider more information for better reflecting user preference and item content. To that end, in this paper, by leveraging the extra tagging data, we propose a novel unified two-stage recommendation framework, named Neighborhood-aware Probabilistic Matrix Factorization(NHPMF). Specifically, we first use the tagging data to select neighbors of each user and each item, then add unique Gaussian distributions on each user's (item's) latent feature vector in the matrix factorization to ensure similar users (items) will have similar latent features. Since the proposed method can effectively explores the external data source (i.e., tagging data) in a unified probabilistic model, it leads to more accurate recommendations. Extensive experimental results on two real world datasets demonstrate that our NHPMF model outperforms the state-of-the-art methods.

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