The influence of ovarian fluid on the sperm physiology of Rutilus kutum

Motility parameters of the spermatozoa in most fish species spawning in fresh water like Rutilus kutum lasts for a short time after activation. Ovarian fluid significantly influenced sperm motility (motility duration period) and percent motility (progressive forward motile sperm). Both of these variables generally increased as the concentration of ovarian fluid increased from 33% to 50%, respectively. It is concluded that ovarian fluid enhances sperm movement in this species at appropriate level and thus has the potential to influence fertilization capacity

Walls-in-one : usage and temporal patterns in a social media aggregator

The continual launches of new online social media that meet the most varied people\u2019s needs are resulting in a simultaneous adoption of different social platforms. As a consequence people are pushed to handle their identity across multiple platforms. However, due the to specialization of the services, people\u2019s identity and behavior are often partial, incomplete and scattered in different \u201cplaces\u201d. To overcome this identity fragmentation and to give an all-around picture of people\u2019s online behavior, in this paper we perform a multidimensional analysis of users across multiple social media sites. Our study relies on a new rich dataset collecting information about how and when users post their favorite contents, about their centrality on different social media and about the choice of their username. Specifically we gathered the posting activities and social sites usage from Alternion, a social media aggregator. The analysis of social media usage shows that Alternion data reflect the novel trend of today\u2019s users of branching out into different social platforms. However the novelty is the multidimensional and longitudinal nature of the dataset. Having at our disposal users\u2019 degree in five different social networks, we performed a rank correlation analysis on users\u2019 degree centrality and we find that the degrees of a given user are scarcely correlated. This is suggesting that the individuals\u2019 importance changes from medium to medium. The longitudinal nature of the dataset has been exploited to investigate the posting activity. We find a slightly positive correlation on how often users publish on different social media and we confirm the burstiness of the posting activities extending it to multidimensional time-series. Finally we show that users tend to use similar usernames to keep their identifiability across social sites

Inhibition of Mesothelin as a Novel Strategy for Targeting Cancer Cells

Mesothelin, a differentiation antigen present in a series of malignancies such as mesothelioma, ovarian, lung and pancreatic cancer, has been studied as a marker for diagnosis and a target for immunotherapy. We, however, were interested in evaluating the effects of direct targeting of Mesothelin on the viability of cancer cells as the first step towards developing a novel therapeutic strategy. We report here that gene specific silencing for Mesothelin by distinct methods (siRNA and microRNA) decreased viability of cancer cells from different origins such as mesothelioma (H2373), ovarian cancer (Skov3 and Ovcar-5) and pancreatic cancer (Miapaca2 and Panc-1). Additionally, the invasiveness of cancer cells was also significantly decreased upon such treatment. We then investigated pro-oncogenic signaling characteristics of cells upon mesothelin-silencing which revealed a significant decrease in phospho-ERK1 and PI3K/AKT activity. The molecular mechanism of reduced invasiveness was connected to the reduced expression of β-Catenin, an important marker of EMT (epithelial-mesenchymal transition). Ero1, a protein involved in clearing unfolded proteins and a member of the ER-Stress (endoplasmic reticulum-stress) pathway was also markedly reduced. Furthermore, Mesothelin silencing caused a significant increase in fraction of cancer cells in S-phase. In next step, treatment of ovarian cancer cells (OVca429) with a lentivirus expressing anti-mesothelin microRNA resulted in significant loss of viability, invasiveness, and morphological alterations. Therefore, we propose the inhibition of Mesothelin as a potential novel strategy for targeting human malignancies

Adsorption of tetracycline antibiotic onto modified zeolite: Experimental investigation and modeling

Artificial Neural Networks (ANNs) model and Adaptive Neuro-Fuzzy Inference System (ANFIS) were used to estimate and predict the removal efficiency of tetracycline (TC) using the adsorption process from aqueous solutions. The obtained results demonstrated that the optimum condition for removal efficiency of TC were 1.5 g L�1 modified zeolite (MZ), pH of 8.0, initial TC concentration of 10.0 mg L�1, and reaction time of 60 min. Among the different back-propagation algorithms, the Marquardt�Levenberg learning algorithm was selected for ANN Model. The log sigmoid transfer function (log sig) at the hidden layer with ten neurons in the first layer and a linear transfer function were used for prediction of the removal efficiency. Accordingly, a correlation coefficient, mean square error, and absolute error percentage of 0.9331, 0.0017, and 0.56 were obtained for the total dataset, respectively. The results revealed that the ANN has great performance in predicting the removal efficiency of TC. � ANNs used to estimate and predict tetracycline antibiotic removal using the adsorption process from aqueous solutions. � The model's predictive performance evaluated by MSE, MAPE, and R2. © 2020 The Author(s

Following people's behavior across social media

To face the new challenge of giving an all-around picture of people's online behavior, in this paper we perform a multidimensional analysis of users across multiple social media sites. Our study relies on a new rich dataset collecting information about how users post their favorite contents and about their centrality on different social media. Specifically posting activities and social sites usage have been gathered from the social media aggregator Alternion. The analysis of social media usage shows that Alternion data capture the typical trend of today's users. However the novelty is the multidimensional and longitudinal nature of the dataset. In fact by performing a rank correlation analysis on the degree in the different social sites, we find that the degrees of a given user are scarcely correlated. This is suggesting that the individuals' importance changes from medium to medium.We also investigate the posting activities finding a slightly positive correlation on how often users publish on different social media. Finally we show that users tend to use similar usernames to keep their identifiability across social sites

User identification across online social networks in practice : Pitfalls and solutions

To take advantage of the full range of services that online social networks (OSNs) offer, people commonly open several accounts on diverse OSNs where they leave lots of different types of profile information. The integration of these pieces of information from various sources can be achieved by identifying individuals across social networks. In this article, we address the problem of user identification by treating it as a classification task. Relying on common public attributes available through the official application programming interface (API) of social networks, we propose different methods for building negative instances that go beyond usual random selection so as to investigate the effectiveness of each method in training the classifier. Two test sets with different levels of discrimination are set up to evaluate the robustness of our different classifiers. The effectiveness of the approach is measured in real conditions by matching profiles gathered from Google+, Facebook and Twitter