Evaluation of Data Storage in HathiTrust Research Center Using Cassandra

As digital data sources grow in number and size, they pose an opportunity for computational investigation by means of text mining, NLP, and other text analysis techniques. The HathiTrust Re-search Center (HTRC) was recently established to provision for automated analytical techniques on the over 11 million digitized volumes (books) of the HathiTrust digital repository. The HTRC data store that hosts and provisions access to HathiTrust volumes needs to be efficient, fault-tolerant and large-scale. In this paper, we propose three schema designs of Cassandra NoSQL store to represent HathiTrust corpus and perform extensive performance evaluation using simulated workloads. The experimental results demonstrate that encapsulating the whole volume within a single row with regular columns delivers the best overall performance

The happiness paradox: your friends are happier than you

Most individuals in social networks experience a so-called Friendship Paradox: they are less popular than their friends on average. This effect may explain recent findings that widespread social network media use leads to reduced happiness. However the relation between popularity and happiness is poorly understood. A Friendship paradox does not necessarily imply a Happiness paradox where most individuals are less happy than their friends. Here we report the first direct observation of a significant Happiness Paradox in a large-scale online social network of $39,110$ Twitter users. Our results reveal that popular individuals are indeed happier and that a majority of individuals experience a significant Happiness paradox. The magnitude of the latter effect is shaped by complex interactions between individual popularity, happiness, and the fact that users cluster assortatively by level of happiness. Our results indicate that the topology of online social networks and the distribution of happiness in some populations can cause widespread psycho-social effects that affect the well-being of billions of individuals.Comment: 15 pages, 3 figures, 2 table

TextRWeb: Large-Scale Text Analytics with R on the Web

As digital data sources grow in number and size, they pose an opportunity for computational investigation by means of text mining, NLP, and other text analysis techniques. R is a popular and powerful text analytics tool; however, it needs to run in parallel and re- quires special handling to protect copyrighted content against full access (consumption). The HathiTrust Research Center (HTRC) currently has 11 million volumes (books) where 7 million volumes are copyrighted. In this paper we propose HTRC TextRWeb, an interactive R software environment which employs complexity hiding interfaces and automatic code generation to allow large-scale text analytics in a non-consumptive means. For our principal test case of copyrighted data in HathiTrust Digital Library, TextRWeb permits us to code, edit, and submit text analytics methods empowered by a family of interactive web user interfaces. All these methods combine to reveal a new interactive paradigm for large-scale text analytics on the web

Happiness is assortative in online social networks

Social networks tend to disproportionally favor connections between individuals with either similar or dissimilar characteristics. This propensity, referred to as assortative mixing or homophily, is expressed as the correlation between attribute values of nearest neighbour vertices in a graph. Recent results indicate that beyond demographic features such as age, sex and race, even psychological states such as "loneliness" can be assortative in a social network. In spite of the increasing societal importance of online social networks it is unknown whether assortative mixing of psychological states takes place in situations where social ties are mediated solely by online networking services in the absence of physical contact. Here, we show that general happiness or Subjective Well-Being (SWB) of Twitter users, as measured from a 6 month record of their individual tweets, is indeed assortative across the Twitter social network. To our knowledge this is the first result that shows assortative mixing in online networks at the level of SWB. Our results imply that online social networks may be equally subject to the social mechanisms that cause assortative mixing in real social networks and that such assortative mixing takes place at the level of SWB. Given the increasing prevalence of online social networks, their propensity to connect users with similar levels of SWB may be an important instrument in better understanding how both positive and negative sentiments spread through online social ties. Future research may focus on how event-specific mood states can propagate and influence user behavior in "real life".Comment: 17 pages, 9 figure

Proposal for Persistent & Unique Entity Identifiers

This proposal argues for the establishment of persistent and unique identifiers for page level content. The page is a key conceptual entity within the HathiTrust Research Center (HTRC) framework. Volumes are composed of pages and pages are the size of the portions of data that the HTRC’s analytics modules consume and execute algorithms across. The need for infrastructure that supports persistent and unique identity for is best described by seven use cases: 1. Persistent Citability: Scholars engaging in the analysis of HTRC resources have a clear need to cite those resources in a persistent manner independent of those resources’ relative positions within other entities. 2. Point-in-time Citability: Scholars engaging in the analysis of HTRC resources have a clear need to cite resources in an unambiguous way that is persistent with respect to time. 3. Reproducibility: Scholars need methods by which the resources that they cite can be shared so that their work conforms to the norms of peer-review and reproducibility of results. 4. Supporting “non-consumptive” Usage: Anonymizing page-level content by disassociating it from the volumes that it is conceptually a part of increases the difficulty of leveraging HTRC analytics modules for the direct reproduction of HathiTrust (HT) content. 5. Improved Granularity: Since many features that scholars are interested in exist at the conceptual level of a page rather than at the level of a volume, unique page-level entities expand the types of methods by which worksets can be gathered and by which analytics modules can be constructed. 6. Expanded Workset Membership: In the near future we would like to empower scholars with options for creating worksets from arbitrary resources at arbitrary levels of granularity, including constructing worksets from collections of arbitrary pages. 7. Supporting Graph Representations: Unique identifiers for page-level content facilitate the creation of more conceptually accurate and functional graph representations of the HT corpus. There several waysOpe

The happiness paradox : your friends are happier than you

Most individuals in social networks experience a so-called Friendship Paradox: they are less popular than their friends on average. This effect may explain recent findings that widespread social network media use leads to reduced happiness. However the relation between popularity and happiness is poorly understood. A Friendship paradox does not necessarily imply a Happiness paradox where most individuals are less happy than their friends. Here we report the first direct observation of a significant Happiness Paradox in a large-scale online social network of 39,110 Twitter users. Our results reveal that popular individuals are indeed happier and that a majority of individuals experience a significant Happiness paradox. The magnitude of the latter effect is shaped by complex interactions between individual popularity, happiness, and the fact that users strongly cluster by similar level of happiness. Our results indicate that the topology of online social networks, combined with how happiness is distributed in some populations, may be associated with significant psycho-social effects.</p

Periploca forrestii Saponin Ameliorates Murine CFA-Induced Arthritis by Suppressing Cytokine Production

Periploca forrestii Schltr. has been used as a Chinese folk medicine due to its versatile pharmacological effects such as promoting wounds and rheumatoid arthritis. However, the antiarthritic activity of Periploca forrestii saponin (PFS) and its active compound Periplocin has still not been demonstrated. Here, we evaluated the antiarthritic effects of PFS in adjuvant-induced arthritis (AIA) rats by intragastric administration at a dose of 50 mg/kg. The anti-inflammatory activities of Periplocin were also examined in LPS-induced AIA splenocytes and synoviocytes. PFS significantly ameliorated joint swelling; inhibited bone erosion in joints; lowered levels of IL-6 and TGF-β1 in AIA rat splenocyte; and reduced joint protein expression levels of phospho-STAT3 and IKKα. Using LPS-induced AIA splenocytes, we demonstrate that Periplocin suppressed the key proinflammatory cytokines levels of IL-6, IFN-γ, TGF-β1, and IL-13 and IL-22 and transcription factor levels of T-bet, GATA3, and C-Jun genes. Periplocin also suppressed LPS-induced cytokine secretion from synoviocytes. Our study highlights the antiarthritic activity of PFS and its derived Periplocin and the underlying mechanisms. These results provide a strong rationale for further testing and validation of the use of Periploca forrestii Schltr. as an alternative modality for the treatment of RA